-**MOBO-Kit** is an open-source toolkit for accelerating design of experiments via **multi-objective Bayesian optimization**. Developed collaboratively across University of Washington, UC San Diego, and MIT, this toolkit enables rapid optimization of complex systems by balancing multiple objectives across any number of inputs and outputs (>2). While demonstrated for slot-die coating experiments (e.g., optimizing efficiency, repeatability, and stability), MOBO-Kit is generalizable to any multi-objective optimization problem.
+**MOBO-Kit** accelerates design of experiments with **multi-objective Bayesian
+optimization**. It proposes small batches of experimental conditions that trade
+off several objectives at once, for problems with more than two inputs and more
+than two outputs. Developed across the University of Washington, UC San Diego and
+MIT, and demonstrated on slot-die coated perovskite films.
+
+## Three contracts, one live campaign
+
+| | v2 — test data | v3 — test data | v4 — **the real campaign** |
+|---|---|---|---|
+| config | `campaign_d2d_perovskite.yaml` (archived) | `campaign_d2d_perovskite_test.yaml` (archived) | `campaign_d2d_perovskite_final.yaml` |
+| contract | `d2d-objectives-v2-nm-thickness` | `d2d-objectives-v3-test` | `d2d-objectives-v4-final` |
+| workbook | `Summary Table.xlsx` | `Summary Table Test.xlsx` | `Final Summary Table.xlsx` |
+| sheet | `Sheet1` | `Sheet1` | `R0` |
+| purpose | early toolkit testing | rehearsing this contract's shape | **the experiment being run** |
+
+Uniformity and optoelectronic have been renormalised twice, so **none of v2's or
+v3's fitted numbers carry over** — they are about quantities that were redefined.
+Each earlier contract is kept as a record, with a banner on every document that
+describes it. The launcher and every script default to v4.
+
+**In v4 the uniformity and optoelectronic scores are FROZEN**: they are read from
+the workbook as stored, with no recomputation in Python, because the group is
+still revising how they are defined. Thickness is still computed, because its
+definition has been stable and the recomputation is what lets an operator-flagged
+reading be excluded and reported. See `docs/CAMPAIGN_STATUS.md` for what freezing
+costs and what replaces the missing cross-check.
+
+## The campaign loop
+
+A campaign runs in three rounds. Each proposed condition is run in triplicate so
+reproducibility can be measured.
+
+| Round | Method | Conditions | Films |
+|---|---|---:|---:|
+| R0 | Latin hypercube sampling | 15 | 45 |
+| R1 | UCB-HVI + local penalization | 5 | 15 |
+| R2 | qLogNEHVI | 3 | 9 |
-## Key Features
+```python
+from mobo_kit.campaign import load_campaign_config, run_r0_lhs, run_r1_ucb, run_r2_qlognehvi
+from mobo_kit.workbook_io import read_campaign_workbook
-MOBO-Kit provides a complete package for multi-objective Bayesian optimization with:
-- **Latin Hypercube Sampling** for initial experiment design
-- **Gaussian Process models** with BoTorch
-- **Multi-objective acquisition functions** (qNEHVI)
-- **Batch candidate proposal** for efficient parallel experimentation
-- **Comprehensive plotting and analysis tools**
-- **Constraint handling** for complex design spaces
-- **Command-line interface** and Python API
+config = load_campaign_config("configs/campaign_d2d_perovskite_final.yaml")
----
+r0 = run_r0_lhs(config, n=15) # space-filling, no model
-## Table of Contents
+# uniformity and optoelectronic are read from the workbook as stored (frozen);
+# thickness is computed from the raw readings and cross-checked
+contents = read_campaign_workbook("local_inputs/Final Summary Table.xlsx", config)
+X_phys = contents.inputs.to_numpy(float)
+Y_model = contents.model_values.to_numpy(float) # in objective order
+assert contents.errors == () # fail closed before fitting
-- [Key Features](#key-features)
-- [Installation](#installation)
-- [Quick Start](#quick-start)
-- [Configuration](#configuration)
-- [Package Structure](#package-structure)
-- [Troubleshooting](#troubleshooting)
-- [Next Steps](#next-steps)
-- [Citation](#citation)
-- [License](#license)
-- [Get in Touch](#get-in-touch)
+r1 = run_r1_ucb(config, X_phys, Y_model, n=5) # after R0 is measured
+r2 = run_r2_qlognehvi(config, X_phys, Y_model, n=3) # after R1 is measured
+```
----
+Each call returns a `RoundResult` with `conditions` (distinct recipes, physical
+units), `replicates` (one row per film, grouped), and `diagnostics` (seed, pool
+size, fit warnings, and a validity report).
+
+**New to this repo?** Read `docs/HANDOFF.md` first — reading order, where the
+project stands, what is genuinely open, and the questions already settled.
+`docs/CAMPAIGN_STATUS.md` is the working guide: what to pass, what comes back, and
+the evidence behind each decision.
+
+## Running a round without writing code
+
+Double-click **`launch_mobo_kit.bat`** (Windows) or **`launch_mobo_kit.command`**
+(macOS — `chmod +x` it once first). A small window opens:
+
+1. **Browse** to the campaign workbook. It is remembered next time.
+2. **Check workbook** — reports which round is due, and anything the read
+ noticed: a stored score that no longer matches its measurements, a reading the
+ operator flagged, a film whose thickness readings disagree with each other.
+3. **Propose R1** (or R2) — fits the model, scores the candidate pool, and writes
+ the batch to a **new file beside the workbook**, never into it. That file gets
+ two sheets: the worklist to fill in, and a **`Review`** sheet giving each
+ proposed condition's predicted objectives with uncertainties, its predicted
+ thickness in nanometres, its distance from anything already measured, and which
+ settings sit at the edge of their range. The same text appears in the window, so
+ it can be forwarded to the group as-is.
+
+4. **Figures.** The same press renders six figures beside the workbook, under
+ `_reports/_/`: where the batch sits in recipe space,
+ how well the model predicts a film it has not seen, which inputs move each
+ objective, what the batch is expected to produce, hypervolume so far, and the
+ trade-off itself. Each one writes the CSV behind it. A second button,
+ **Figures from current data**, renders the four that need no batch — useful the
+ moment measurements are entered.
+
+Then run the films, fill in the highlighted columns of that new sheet, and press
+the button again. R2 reads the R1 measurements back and aggregates each condition's
+three films into one observation.
+
+The window approves nothing. It shows the proposed conditions in physical units
+with the batch's spacing diagnostics; a human decides whether to fabricate.
+Everything it does is available as plain functions in `mobo_kit.launcher`
+(`inspect_campaign`, `gather_observations`, `generate_next_round`) for anyone who
+would rather script it.
## Installation
-### Option 1: Install from Source (Recommended)
-
-We recommend creating a clean Python environment using `conda` or `venv`:
-
```bash
-# Create and activate environment
-conda create -n mobo-fom python=3.10
-conda activate mobo-fom
-
-# Or using venv
-python -m venv mobo-env
-source mobo-env/bin/activate # On Windows: mobo-env\Scripts\activate
-
-# Install MOBO-Kit
-git clone https://github.com/PV-Lab/MOBO-FOM.git
-cd MOBO-FOM
-pip install -e .
+conda create -n mobo-kit python=3.12
+conda activate mobo-kit
+git clone https://github.com/PV-Lab/MOBO-Kit.git
+cd MOBO-Kit
+python -m pip install -r requirements/dev.txt
```
-This will automatically install all required dependencies including:
-- Core scientific computing: numpy, pandas, scipy, matplotlib, seaborn, scikit-learn
-- Machine learning: torch, gpytorch, botorch, emukit
-- Additional tools: shap, pyDOE, pyyaml
+Tested on CPU with Python 3.12, PyTorch 2.8.0, BoTorch 0.15.1, GPyTorch 1.14.
+The exact stack is pinned in `requirements/constraints.txt`.
-### GPU Support (CUDA [Windows])
+## Does the optimizer actually work?
-MOBO-Kit uses PyTorch for machine learning models. By default, the installation includes the CPU-only version of PyTorch. For GPU acceleration, you'll need to install the CUDA version of PyTorch.
+`tests/test_dtlz2_acceptance.py` runs the whole loop on **DTLZ2** — a synthetic
+3-objective, 10-input problem with a known Pareto front — so the algorithm can be
+checked independently of any experimental data.
-**Check your CUDA version:**
```bash
-nvidia-smi
+pytest tests/test_dtlz2_acceptance.py -m "not slow"
+pytest tests/test_dtlz2_acceptance.py -m slow
```
-**Install PyTorch with CUDA support:**
-```bash
-# For CUDA 12.1 (recommended for most systems)
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+Cumulative hypervolume rises monotonically **by construction**, so that alone
+proves nothing — random sampling passes it too. The informative comparison is
+against a random baseline at equal budget: mean hypervolume gain **+0.075 (BO)
+against +0.056 (random)**, winning on 5 of 8 seeds. BO wins on the mean, not on
+every seed, which is the honest expectation for 8 added points in 10 dimensions.
-# For CUDA 11.8 (more compatible with older systems)
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+`python scripts/plot_dtlz2_report.py` renders the round-by-round GP fit,
+uncertainty, acquisition surface and selected batch.
-# For CUDA 12.4
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124
+## How beta and radius were chosen
-# For CUDA 12.8 (latest)
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
-```
+The live campaign runs **beta = 4.0** and **radius = 0.25**. They were determined
+by a sweep over two instruments on the campaign's own data: per-round utility
+**box plots** across a grid of **beta from 9 to 49** and **radius from 0.05 to
+0.45**, and **heat maps** -- 2-D slices through the higher-dimensional
+Gaussian-process model -- at the same cells. `scripts/plot_boxplot_sweep.py` and
+`scripts/plot_round_simulation.py` produce them; the outputs stay local, because
+they are how the group picks a setting rather than a result about the chemistry.
-**Verify GPU support:**
-```python
-import torch
-print("CUDA available:", torch.cuda.is_available())
-print("Device count:", torch.cuda.device_count())
-```
+Two things to know before quoting that choice.
-### Option 2: Install with pip (once software license received)
+**The sweep could not rank the cells.** The whole spread across betas was 0.0065
+against a trial-to-trial standard deviation of 0.010--0.027, and the best cell was
+a different (beta, radius) in every trial. So this is a declared policy about how
+much to explore, not a measured optimum.
-```bash
-pip install mobo-kit
-```
+**The campaign ran at beta = 36 from 2026-08 to 2026-09-03**, on the argument that
+two of three objectives carried no learnable signal and heavy exploration was
+therefore the right posture. That was retired when 45 rows of repeated recipes
+showed *why* those two axes are unlearnable -- one is dominated by
+between-campaign measurement drift, the other is reproducible but too sparsely
+sampled -- neither of which more exploration reaches. At beta = 36 the radius knob
+was also provably inert: radii 0.15, 0.25 and 0.35 returned bit-identical batches,
+and 18 of 50 proposed coordinates sat on a grid bound. At beta = 4 / radius 0.25
+that falls to 11. See `docs/CAMPAIGN_STATUS.md` for the table and its caveats.
-### Option 3: Google Colab
-
-**Option 3a: Direct Notebook Link**
-- [Open MOBO-Kit notebook in Google Colab](https://colab.research.google.com/drive/1VzlCSTDw42kWxlI2xNUAOfmCZpLeKpN0?usp=sharing)
-
-**Option 3b: Install in your own Colab notebook**
-```python
-# Install in Google Colab
-!pip install git+https://github.com/PV-Lab/MOBO-FOM.git
+`docs/CAMPAIGN_STATUS.md` carries the full record, including the two triggers for
+revisiting the choice.
-# Import and use
-import mobo_kit
-```
-
-### Dependencies
-
-MOBO-Kit requires:
-- Python 3.10+
-- PyTorch 1.12+
-- BoTorch 0.8+
-- GPyTorch 1.8+
-- NumPy, Pandas, Scikit-learn
-- Matplotlib, Seaborn
-
-See `requirements.txt` for the complete list of dependencies.
-
-## Quick Start
-
-### 1. Command Line Interface
-
-```bash
-# Run with default configuration
-mobo-kit --csv data/processed/configCSV_example.csv
-
-# Run with custom output directory
-mobo-kit --csv data/my_data.csv --out results/my_experiment
-
-# Run with verbose output
-mobo-kit --csv data/my_data.csv --verbose
-```
-
-### 2. Python API
-
-```python
-import mobo_kit
-from mobo_kit.main import main
+## Repository layout
-# Run the main workflow
-main()
```
-
-### 3. Advanced Usage Examples
-
-```python
-# Generate initial experiments
-from mobo_kit.main import generate_initial_experiments
-
-results = generate_initial_experiments(
- config_path="configs/demo_config.yaml",
- n_samples=20,
- save_path="initial_experiments.csv"
-)
-
-# Run MOBO optimization with custom parameters
-from mobo_kit.main import run_mobo_experiment
-
-results = run_mobo_experiment(
- csv_path="data/processed/configCSV_example.csv",
- save_dir="results/experiment",
- verbose=True
-)
+src/mobo_kit/
+ campaign.py the three rounds; start here
+ design.py input grid and bounds
+ lhs.py Latin hypercube sampling (R0)
+ candidate_pool.py discrete candidate sampling
+ sobol_pool.py nested Sobol pools (alternative sampler)
+ models.py GP construction
+ model_validation.py strict fitting, exact LOOCV, fit guards
+ structured_mean.py physics-informed GP mean functions
+ scores.py measurement columns -> objective values, cross-checked
+ objectives.py objective value -> utility contract
+ replicate_variance.py replicate films -> observation variance (train_Yvar)
+ batch_review.py what a proposed batch says, before anyone fabricates it
+ round_report.py the six figures a round produces, and their data
+ loocv.py the one leave-one-out fold loop, shared by all callers
+ attribution.py exact Shapley values over the campaign's own models
+ launcher.py the one-button loop, and the tkinter window over it
+ ucb_hvi.py UCB hypervolume-improvement scoring (R1)
+ qlognehvi_batch.py qLogNEHVI batch selection (R2)
+ batch_selection.py local penalization, shared by both
+ discrete_refinement.py exact-grid local search
+ workbook_io.py Excel read / candidate-sheet write / read results back
+ metrics.py Pareto front and hypervolume
+ plotting.py diagnostic plots
+ candidate_diagnostics.py, acquisition.py, cli.py, main.py,
+ data.py, constraints.py, utils.py
+
+ research_qnehvi.py qNEHVI as a research-only R2 variant, NOT the campaign
+
+configs/ campaign_d2d_perovskite_test.yaml (the live campaign),
+ campaign_d2d_perovskite.yaml (archived, first campaign) + two examples
+docs/ HANDOFF.md, CAMPAIGN_STATUS.md, GP_MODEL_DECISION.md,
+ R1_BATCH_WITHDRAWAL.md, ROUND_SIM_DELTA.md, ROUND_SIM_MANIFEST.md,
+ SHAP_SUMMARY.md
+scripts/ diagnostics, report figures, intake_new_data.py,
+ dtlz2_parameter_sweep.py, plot_round_simulation.py,
+ plot_shap_attribution.py, permutation_rank_test.py,
+ generate_round_report.py
+tests/ 601 tests
+launch_mobo_kit.bat, launch_mobo_kit.command double-click entry points
```
-### 4. Jupyter Notebooks
-
-See the `notebooks/` directory for interactive examples:
-- `MOBO_demo_annotated.ipynb` - Complete workflow demonstration
- - *Note*: The LOOCV function may have trouble converging on small noisy datasets and is still in development.
-
## Configuration
-MOBO-Kit uses YAML configuration files. See `configs/` directory for examples:
-
-- `demo_config.yaml` - Basic configuration
-- `configCSV_example_config.yaml` - Configuration from CSV metadata
-
-### Configuration Structure
+Objectives declare **what the model trains on** separately from **how that
+becomes a utility**, because the two are not always the same column:
```yaml
-inputs:
- - name: "parameter1"
- unit: "unit"
- start: 0.0
- stop: 1.0
- step: 0.01
-
objectives:
- names:
- - objective 1
- - objective 2
- - objective 3
-
-constraints:
- - clausius_clapeyron: true
- ah_col: "absolute_humidity"
- temp_c_col: "temperature_c"
-
-## Package Structure
-
-```
-src/mobo_kit/
-├── main.py # Main API functions
-├── cli.py # Command-line interface
-├── design.py # Design space construction
-├── data.py # Data loading and preprocessing
-├── models.py # Gaussian Process models
-├── acquisition.py # Acquisition functions and batch proposal
-├── lhs.py # Latin Hypercube Sampling
-├── plotting.py # Visualization tools
-├── metrics.py # Performance metrics
-├── constraints.py # Constraint handling
-└── utils.py # Utility functions
+ contract_version: d2d-objectives-v2-nm-thickness
+ scaling_mode: fixed_affine
+ specs:
+ - name: thickness
+ model_source_column: "Thickness (avg)" # stored cell: cross-check only
+ transform: gaussian_target # utility peaks at the target
+ target: 650.0
+ sigma: 176.7766952966369
+ measurement: # what the GP actually trains on
+ recipe: mean_of_present # mean of whichever were measured
+ inputs: [{column: T1}, {column: T2}, {column: T3}, {column: T4}]
+ excluded: [{column: "T anom"}] # operator-flagged, never averaged
+ cross_check: [{column: "Thickness (avg)", atol: 0.5}]
+ mean_function: # physics-informed trend
+ response: log
+ features:
+ - {column: speed_1, transform: log}
+ - {column: precur_conc, transform: log}
```
-## Troubleshooting
-
-### Common Installation Issues
-
-1. **Import errors**: Ensure all dependencies are installed:
- ```bash
- pip install -r requirements.txt
- ```
-
-2. **CUDA/GPU support**: Install PyTorch with CUDA (example, please use matching nvidia-smi):
- ```bash
- pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
- ```
-
-3. **Python version compatibility**: Use Python 3.10 or 3.11:
- ```bash
- conda create -n mobo-kit python=3.10
- conda activate mobo-kit
- pip install -e .
- ```
-
-4. **Jupyter notebook support**:
- ```bash
- pip install jupyter ipykernel
- python -m ipykernel install --user --name=mobo-kit --display-name "Python (mobo-kit)"
- ```
-
-### Runtime Issues
+The `measurement` block exists because several of the workbook's derived score
+cells are pasted literals rather than formulas, so they do not update when the
+measurements behind them are edited. `scores.py` recomputes each objective from
+the raw columns and demotes the stored cell to a cross-check that warns on
+disagreement — see `docs/CAMPAIGN_STATUS.md` issue 2 for the audit.
+
+Objective scales are **fixed for the whole campaign** and must never be
+re-derived from observed data — otherwise utility space moves between rounds and
+hypervolume stops being comparable across them.
+`assert_scaling_is_campaign_fixed` enforces this and runs inside
+`build_objective_transform`, so no transform can bypass it.
+
+## Current parameters
+
+| Setting | Value | Config key |
+|---|---|---|
+| UCB beta (R1) | 36.0 | `rounds.r1.beta` |
+| Local penalization radius | 0.35 | `local_penalization.radius` |
+| Minimum batch spacing | 0.15 | `local_penalization.min_batch_distance` |
+| Candidate pool | 32768 | `rounds.*.candidate_pool_size` |
+| Posterior samples (R1) | 256 | `rounds.r1.posterior_samples` |
+| MC samples (R2) | 128 | `rounds.r2.mc_samples` |
+| GP variant | `dim_scaled_prior` | `model.variant` |
+| Seed | 73 | `reproducibility.seed` |
+
+All of these are campaign configuration, not code. Tuning them does not require
+touching the algorithm.
+
+The ten input grids hold 11/10/11/13/21/17/18/11/21/17 values, so the full
+Cartesian product is 396,945,008,460 recipes. It must never be materialised —
+that is what the sampled candidate pool and the discrete local search are for.
+
+**Constraints are config too, and the live campaign declares three.** They are
+enforced by filtering the candidate pool before any acquisition scores it, and
+re-checked independently when the batch is validated:
-- **Memory issues**: For large datasets, consider using CPU instead of GPU or reducing batch sizes
-- **Convergence issues**: The LOOCV function may have trouble converging on small noisy datasets
-- **CUDA out of memory**: Reduce batch size or use CPU mode
-
-## Next Steps
-
-1. **Try the demo**: `mobo-kit --csv data/processed/configCSV_example.csv --verbose`
-2. **Generate initial experiments**: `mobo-kit generate --config configs/demo_config.yaml --n-samples 20 --out my_experiments.csv`
-3. **Explore Jupyter notebooks** in the `notebooks/` directory
-4. **Check configuration examples** in the `configs/` directory
-
-## Citation
-
-*Citation information will be added upon publication.*
+```yaml
+constraints:
+ # a second spin stage either happens or it does not
+ - zero_coupled: [speed_2, time_2]
+ # the antisolvent has to land while the substrate is still spinning
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ # and if it happens, it runs for at least 10 s
+ - nonzero_minimum: {column: time_2, minimum: 10}
+```
## License
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+MIT — see [LICENSE](LICENSE).
-## Get in Touch
+## Get in touch
-For questions, issues, or contributions, please:
-- Open an issue on GitHub
-- Contact the development team
-- Check the documentation in the `notebooks/` directory
+Open an issue on GitHub, or contact the development team.
diff --git a/configs/campaign_d2d_perovskite.yaml b/configs/campaign_d2d_perovskite.yaml
new file mode 100644
index 0000000..1a0f9ff
--- /dev/null
+++ b/configs/campaign_d2d_perovskite.yaml
@@ -0,0 +1,369 @@
+# D2D campaign configuration - FA0.9Cs0.1PbI3 slot-die/spin campaign.
+#
+# Rounds: R0 LHS (15, complete) -> R1 UCB-HVI (5 conditions) -> R2 qLogNEHVI (3).
+# Each proposed condition is run in triplicate; the three films share a
+# replicate_group and are aggregated to one condition-level observation before
+# the next round trains on them.
+#
+# Objective rationale and the measurements behind it are in
+# docs/GP_MODEL_DECISION.md. The short version: thickness trains on raw
+# nanometres and the 650 nm Gaussian is applied to the posterior, because the
+# score itself is a 2-to-1 folded transform that destroys learnable signal.
+campaign:
+ name: D2D_FA0.9Cs0.1PbI3
+ status: archived # superseded by campaign_d2d_perovskite_test.yaml, 2026-08-17
+ schema_version: d2d-campaign-v2
+ workbook_profile: d2d_summary_v3_scores
+
+inputs:
+ - name: speed_1
+ unit: rpm
+ start: 1000
+ stop: 6000
+ step: 500
+ - name: time_1
+ unit: s
+ start: 5
+ stop: 50
+ step: 5
+ - name: speed_2
+ unit: rpm
+ start: 0
+ stop: 5000
+ step: 500
+ - name: time_2
+ unit: s
+ start: 10
+ stop: 60
+ step: 5
+ - name: precur_conc
+ unit: M
+ start: 1.00
+ stop: 2.00
+ step: 0.05
+ - name: precur_vol
+ unit: uL
+ start: 40
+ stop: 200
+ step: 10
+ - name: anneal_temp
+ unit: C
+ start: 100
+ stop: 185
+ step: 5
+ - name: anneal_time
+ unit: min
+ start: 10
+ stop: 60
+ step: 5
+ - name: anti_vol
+ unit: uL
+ start: 100
+ stop: 200
+ step: 5
+ - name: anti_time
+ unit: s
+ start: 9
+ stop: 25
+ step: 2
+
+# All three utilities are maximised after transformation. `transform` maps a
+# model output to utility; the two differ only for thickness.
+#
+# `measurement` is what the GP trains on: a recipe plus the raw measurement
+# columns it consumes, computed in Python. `model_source_column` names the
+# workbook cell that USED to be read directly and is now only a label and a
+# cross-check target. Three of those cells are pasted literals rather than
+# formulas, so they do not update when the measurements behind them change --
+# see docs/CAMPAIGN_STATUS.md issue 2 for the audit.
+objectives:
+ contract_version: d2d-objectives-v2-nm-thickness
+ # Scales are FIXED for the whole campaign and must never be re-derived from
+ # observed data. If a scale tracks the data, utility space moves between
+ # rounds and hypervolume stops being comparable - the progress plot silently
+ # becomes meaningless. Widen a range deliberately and bump contract_version;
+ # never let it follow the measurements.
+ scaling_mode: fixed_affine
+ specs:
+ - name: uniformity
+ model_source_column: "Uniformity score" # workbook Z = L*N*O
+ transform: affine
+ goal: maximize
+ # Computed from Coverage (L), Uniformity (M) and Phase purity (O). The
+ # workbook multiplies by its "1 - Uniformity" column (N) instead, which is
+ # itself a pasted literal; taking the complement of M here means N is never
+ # trusted, and because Z is built from N the cross-check below will fire if
+ # the two ever stop agreeing.
+ measurement:
+ recipe: product
+ inputs:
+ - {column: "Coverage"}
+ - {column: "Uniformity", transform: complement}
+ - {column: "Phase purity"}
+ # Z is a live formula, so it should agree to floating-point noise. It does:
+ # 0.0 difference on all 15 R0 rows.
+ cross_check:
+ - {column: "Uniformity score", atol: 1.0e-9}
+ # fixed campaign scale, NOT recomputed per round; hypervolume would
+ # otherwise be incomparable between rounds
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # No learnable signal from the 15 R0 points (permutation p = 0.82).
+ # R1 is exploration-dominated for this objective by design.
+ signal_status: exploration_only
+
+ - name: optoelectronic
+ model_source_column: "Optoelectronic score" # workbook AA = LOG10(P*Q)
+ transform: affine
+ goal: maximize
+ # log10(photoconductance x implied Voc); observed R0 span -9.14 to -7.27
+ lower_anchor: -10.0
+ upper_anchor: -6.0
+ signal_status: learnable
+ # A single linear term on anneal_temp and nothing else. Six forms were
+ # tested; every addition made it worse, and an Arrhenius 1/T form bought
+ # nothing over plain linear temperature (+0.226 vs +0.244). anneal_temp is
+ # also the least search-contaminated choice: it came from a marginal
+ # correlation already on record (rho = -0.651, p = 0.009), not from the
+ # search over mean shapes -- and it happens to win anyway.
+ # Plain GP -0.342 (worse than the -0.148 null) -> structured +0.355.
+ #
+ # Computed as log10(P) + log10(Q), which is log10(P*Q) without an
+ # intermediate product that could overflow. Checked against both the live
+ # formula in R and the pasted literal in AA: a full-precision paste agrees to
+ # 1.8e-15 today, so a tight tolerance is what makes a stale paste audible.
+ # P and Q are confirmed never zero or blank for a measured film; if one ever
+ # is, the recipe errors rather than producing a silent -inf.
+ measurement:
+ recipe: log10_product
+ inputs:
+ - {column: "PL - Implied Voc (Max)"}
+ - {column: "Photoconductance (Max)"}
+ cross_check:
+ - {column: "Optoelectronic score", atol: 1.0e-9}
+ - column: "Log10 (Photoconductance (Max) x PL - Implied Voc (Max))"
+ atol: 1.0e-9
+ mean_function:
+ response: identity
+ features:
+ - column: anneal_temp
+ transform: identity
+
+ - name: thickness
+ # trains on nanometres, NOT on the workbook thickness score
+ model_source_column: "Thickness (avg)" # workbook X, nm
+ transform: gaussian_target
+ goal: target
+ target: 650.0
+ # the workbook writes EXP(-(((T-650)/250)^2)), which has no factor of 1/2;
+ # in the exp(-0.5*((T-c)/sigma)^2) convention used here that is 250/sqrt(2)
+ sigma: 176.7766952966369
+ equivalent_workbook_formula: "EXP(-(((X-650)/250)^2))"
+ signal_status: learnable
+ # Spin-coating theory gives T ~ omega^-0.5; the measured exponent is -0.38.
+ # NOTE the opposite pattern to optoelectronic: neither term alone is worth
+ # much here (+0.159, +0.187), the PAIR carries the signal. Do not assume
+ # either shape generalises to a new objective.
+ # Plain GP +0.183 -> structured +0.384.
+ #
+ # Computed as the mean of whichever of T1..T4 were measured -- nine R0 rows
+ # have two readings, three have three, three have four, so requiring all four
+ # would reject the campaign. Blank means not measured, never zero.
+ #
+ # "T anom" holds readings the operator judged anomalous (sample 4: 1618
+ # against its own 650/655/670/680; sample 14: 630). They never enter the mean;
+ # their presence is reported so the exclusion is visible rather than silent.
+ # CONFIRMED by the group 2026-07-31: the operator's judgement is the intended
+ # filter, so these stay excluded and stay reported.
+ #
+ # X is ROUND(mean(T1..T4)), so it can legitimately differ by half a
+ # nanometre -- hence atol 0.5 rather than a tight tolerance. Against
+ # sigma = 176.8 nm that rounding moves the utility by under 1e-5.
+ #
+ # spread_warning_ratio fires on samples 8, 12 and 15, whose readings split
+ # into two clusters rather than scattering: sample 12 is 1600 and 709, and its
+ # recorded 1155 nm is the midpoint of the two. Sample 12 also carries the
+ # highest leverage in the design (0.462), so this is worth hearing about.
+ # CONFIRMED by the group 2026-07-31: that variation is real and the MEAN is
+ # the intended summary, so no re-derivation, exclusion or re-weighting. The
+ # warning stays on anyway -- what was settled is the action, not the fact, and
+ # a row whose readings split 2.3-fold is still a different kind of observation
+ # from one whose readings agree to 3%.
+ measurement:
+ recipe: mean_of_present
+ inputs:
+ - {column: "T1"}
+ - {column: "T2"}
+ - {column: "T3"}
+ - {column: "T4"}
+ excluded:
+ - {column: "T anom"}
+ cross_check:
+ - {column: "Thickness (avg)", atol: 0.5}
+ spread_warning_ratio: 0.25
+ # response: log makes the model output lognormal, so the utility
+ # expectation must use Gauss-Hermite quadrature, not the Gaussian closed
+ # form -- moment-matching there is 506x less accurate and its bias changes
+ # sign across the range, which reorders candidates.
+ #
+ # The three replicate films of one condition are averaged in LOG SPACE, for
+ # the same reason train_Yvar is pooled there: `response: log` means the GP
+ # trains on log(T), so the geometric mean is the arithmetic mean in the space
+ # the model actually works in. The difference from a plain mean is second
+ # order in the replicate spread -- under 0.1% at the ~3% spread most R0 rows
+ # show, about 14% on a film set as inconsistent as sample 12's. Change this
+ # one key if the group prefers the arithmetic mean of nanometres.
+ replicate_aggregate: mean_of_log
+ mean_function:
+ response: log
+ features:
+ - column: speed_1
+ transform: log
+ - column: precur_conc
+ transform: log
+
+# Reference point in UTILITY space, after the transforms above. Every objective
+# is on a comparable [0, 1]-ish scale there, so no axis silently dominates the
+# hypervolume. On the previous raw-scale reference [-0.01, -10.0, -0.01] the
+# optoelectronic axis was 4.01x the uniformity axis.
+reference_point_utility: [-0.01, -0.01, -0.01]
+
+rounds:
+ r1:
+ method: ucb_hvi
+ batch_size: 5
+ replicates_per_condition: 3
+ beta: 4.0
+ candidate_pool_size: 32768
+ posterior_samples: 256
+ # thickness utility is a nonlinear function of the model output, so utility
+ # moments must come from posterior samples, not from moments of the mean
+ moment_method: monte_carlo
+ r2:
+ method: qlognehvi
+ batch_size: 3
+ replicates_per_condition: 3
+ candidate_pool_size: 32768
+ mc_samples: 128
+ sequential_pending: true
+
+# What the batch-review artifact interrogates and states. Campaign knowledge, so
+# it lives here rather than in batch_review.py.
+review:
+ # A probe holds every other coordinate of each proposed candidate and forces one
+ # input to a value worth asking about. The sd comparison is the informative part:
+ # UCB rewards uncertainty, so a region the batch skips while the model still
+ # calls it uncertain is losing a trade-off, whereas a region the batch skips
+ # while the model calls it certain has been resolved -- possibly into an average.
+ probes:
+ - name: low-speed corner
+ column: speed_1
+ value: 1000
+ note: >
+ Samples 1 and 12 are the only two observations at speed_1 = 1000 and they
+ contradict each other: sample 1 has the higher concentration (1.4 against
+ 1.1) but the thinner film (687 against 1155 nm), inverting the expected
+ relationship. Sample 12 also carries the highest leverage in the design
+ (0.462), and its 1155 nm is ROUND(mean(1600, 709)) -- two readings a factor
+ of 2.26 apart. So before this corner is read as a model conclusion, it is a
+ measurement question. `log(speed_2 + 1)` does not explain the inversion
+ (LOO R2 +0.449 -> -0.827); do not add it. See docs/GP_MODEL_DECISION.md.
+
+ notes:
+ - >
+ anneal_temp at or near 100 C in every proposed condition is the mean function
+ speaking, not a discovery. The optoelectronic objective carries a monotone
+ linear mean on anneal_temp with a negative slope (marginal rho = -0.651,
+ p = 0.009), and a monotone trend puts its optimum at a range edge by
+ construction. The open question is chemical: if the group would never anneal
+ below some temperature, that is a bound to declare in `constraints:` (which
+ is currently empty) rather than something to discover from a shipped batch.
+ - >
+ Uniformity is exploration-only. Nothing beat the leave-one-out null across
+ ~240 model configurations at N=15 (permutation p = 0.82), so its predicted
+ utility below carries no signal and should not be read as one. Whether that
+ is physics or measurement noise is answerable from the R1 replicates.
+ - >
+ WITHDRAWN AND REISSUED. Any R1 batch described before 2026-07-31 is void.
+ run_r1_ucb was scoring candidates against an observed baseline whose
+ thickness axis had collapsed to zero -- it handed the objective transform
+ nanometres where the transform expects log(nm) and exponentiates -- so the
+ baseline hypervolume was 0.004659 against a true 0.436442. Four of the five
+ conditions are unchanged; the fifth moves from speed_1 4000 / precur_conc
+ 1.70 to speed_1 2500 / precur_conc 1.45, and the batch's minimum spacing
+ from 0.9209 to 0.6337. No films were fabricated from the withdrawn batch.
+ Full diff in docs/R1_BATCH_WITHDRAWAL.md; the defect is fixed in commit
+ 4b76670. Delete this note once R1 is measured.
+
+local_penalization:
+ distance_metric: normalized_euclidean
+ dimension_weights: null
+ radius: 0.25
+ min_batch_distance: 0.15
+ min_observed_distance: 0.0
+
+model:
+ variant: dim_scaled_prior
+ # R0 has no film replicates. Once R1 triplicates land, pool their
+ # within-condition variance (5 conditions x 2 dof = 10 dof) and pass it as
+ # train_Yvar.
+ #
+ # Pool thickness variance in LOG SPACE, not in nanometres: `response: log`
+ # above means the GP trains on log(T), so train_Yvar must be the variance of
+ # log(T). Passing a variance in nm^2 would be wrong by a factor of T^2 --
+ # roughly 1.3e5 at 360 nm and 1.7e6 at 1303 nm, so it is not even a constant
+ # rescaling across the observed range.
+ #
+ # The R0 rows are not entirely without a noise handle: each has 2-4 thickness
+ # points (T1..T4). Their pooled within-row variance of log(T) is 0.0593
+ # (sd 0.244, 24 dof), against a total observed log(T) span of 1.86. That is
+ # within-FILM spatial spread, not film-to-film reproducibility, so it is a
+ # floor on the noise rather than an estimate of it -- and it is dominated by
+ # three rows (samples 8, 12, 15 at sd_log 0.137-0.584; the other twelve are
+ # all under 0.048). Decide deliberately whether to use it for the R0 rows.
+ #
+ # Applying an R1-derived estimate to the R0 rows assumes the measurement
+ # process is unchanged between rounds; that assumption is recorded here
+ # deliberately rather than left implicit.
+ #
+ # Stays `fit_from_marginal_likelihood` until the R1 triplicates land. The
+ # machinery is wired and tested (replicate_variance.py); it needs data, not code.
+ observation_noise: fit_from_marginal_likelihood
+
+ replicate_variance:
+ # TWO DIFFERENT VARIANCES. Do not substitute one for the other.
+ #
+ # BETWEEN-FILM is what train_Yvar needs: two films from the same recipe differ
+ # by everything that varies run to run. Only measurable once R1 ships
+ # triplicates (5 conditions x 2 dof = 10 dof).
+ #
+ # WITHIN-FILM is the scatter of the 2-4 thickness points across one film --
+ # measurement plus spatial nonuniformity. Available today: pooled over the R0
+ # rows it is 0.0593 on log(T), 24 dof. It contains NO run-to-run variation, so
+ # it is a FLOOR, not an estimate. If pooled between-film variance ever comes
+ # out below it, films would be more reproducible than points on a single film,
+ # which is not a thing -- so it indicates a measurement or pooling mistake.
+ sanity_floor:
+ thickness: 0.0593
+ # Film count assumed for rows that have no replicates -- the R0 rows, which
+ # predate the triplicate policy. 1 means their observation carries the full
+ # between-film variance rather than a third of it.
+ rows_without_replicates: 1
+
+reproducibility:
+ seed: 73
+ record_git_commit: true
+ record_environment_versions: true
+ record_resolved_config_hash: true
+
+# EMPTY DELIBERATELY, confirmed by the group 2026-07-31. No process constraint
+# applies to this campaign; the legacy humidity/temperature Clausius-Clapeyron
+# constraint is for a different design.
+#
+# This is a decision, not an omission, and it is the reason `anneal_temp` is free
+# to sit at its lower bound in every proposed condition. The optimum of a monotone
+# mean function is at a range edge by construction, so if the group would never
+# anneal below some temperature, the fix is a bound DECLARED here rather than a
+# batch quietly discarded later. Adding one is a one-key change; see
+# docs/CAMPAIGN_STATUS.md issue 4.
+constraints: []
diff --git a/configs/campaign_d2d_perovskite_extended_c1c2.yaml b/configs/campaign_d2d_perovskite_extended_c1c2.yaml
new file mode 100644
index 0000000..26fd525
--- /dev/null
+++ b/configs/campaign_d2d_perovskite_extended_c1c2.yaml
@@ -0,0 +1,167 @@
+# DIAGNOSTIC ONLY. Not a campaign contract, and nothing here proposes films.
+#
+# The workbook is local_inputs/Extended Summary Table C1C2.xlsx (gitignored),
+# supplied 2026-09-03 as "summary table with extended dataset from Campaign 1 & 2".
+# It holds 45 rows, and the 45 rows are 15 RECIPES MEASURED THREE TIMES: samples
+# 1-15, 16-30 and 31-45 carry identical inputs, recipe for recipe. That makes this
+# the first dataset in the project that can separate "the recipe did it" from "the
+# measurement did it", which is the whole reason for reading it.
+#
+# WHY IT IS NOT A CONTRACT.
+# * The optoelectronic definition MOVED. AK is now `=R2*X2*AA2`, a raw product
+# of clamped Voc (V) x floor-corrected photoconductance (S) x capped
+# photosensitivity -- not the normalised mean the v4 contract fingerprints.
+# Its values span 6.1e-11 to 6.2e-6, five orders of magnitude, and are not in
+# [0, 1]. The v4 anchors would map every film to utility 0.
+# * Samples 17 and 32 are `speed_2 = 0, time_2 = 60`, which breaks
+# second_stage_all_or_nothing. Sample 2 is the same recipe with the group's
+# correction (time_2 = 0) applied. The correction reached one of the three
+# replicates, not all three.
+# * The scores are used AS STORED, all three of them, per the request. That
+# includes thickness, which every real contract trains on in nanometres.
+#
+# Read the intake report before quoting any number out of this file.
+campaign:
+ name: D2D_FA0.9Cs0.1PbI3_extended_c1c2_diagnostic
+ status: diagnostic
+ schema_version: d2d-campaign-v4
+ workbook_profile: d2d_summary_final_v4
+ source_sheet: R0
+
+# UNCHANGED from v4. All 45 rows land on these grids.
+inputs:
+ - {name: speed_1, unit: rpm, start: 1000, stop: 6000, step: 500}
+ - {name: time_1, unit: s, start: 5, stop: 50, step: 5}
+ - {name: speed_2, unit: rpm, start: 0, stop: 5000, step: 500}
+ - {name: time_2, unit: s, start: 0, stop: 60, step: 5}
+ - {name: precur_conc, unit: M, start: 1.00, stop: 2.00, step: 0.05}
+ - {name: precur_vol, unit: uL, start: 40, stop: 200, step: 10}
+ - {name: anneal_temp, unit: C, start: 100, stop: 185, step: 5}
+ - {name: anneal_time, unit: min, start: 10, stop: 60, step: 5}
+ - {name: anti_vol, unit: uL, start: 100, stop: 200, step: 5}
+ - {name: anti_time, unit: s, start: 9, stop: 25, step: 1}
+
+objectives:
+ contract_version: d2d-objectives-v5-extended-diagnostic
+ scaling_mode: fixed_affine
+ specs:
+ - name: uniformity
+ model_source_column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+ transform: affine
+ goal: maximize
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"}
+ formula_fingerprint:
+ column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+ formula: "=(L2+O2+P2)/3"
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ signal_status: exploration_only
+
+ - name: optoelectronic
+ model_source_column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"
+ transform: affine
+ goal: maximize
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"}
+ # DELIBERATELY the NEW formula. Fingerprinting the v4 one here would make
+ # every run of this diagnostic shout about a change the group already made.
+ # The v4 config still fingerprints `=(S2+((0.75*Y2)+(0.25*AB2)))/2`, so the
+ # change stays audible where it matters -- on the campaign contract.
+ formula_fingerprint:
+ column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"
+ formula: "=R2*X2*AA2"
+ agreement_check:
+ raw: "Photoconductance (Max - based on raw slopes)"
+ normalized: "Normalized photoconductance (test)"
+ min_spearman: 0.0
+ # NOT [0, 1]. A raw triple product is not a normalised score, and anchoring it
+ # at [0, 1] would put all 45 films on top of each other at utility ~1e-6.
+ # These anchors bracket the observed span (6.06e-11 .. 6.22e-06) and are
+ # declared, not derived per round -- but they are declared FROM THIS DATA,
+ # which is exactly what a campaign contract must never do. Diagnostic only.
+ lower_anchor: 0.0
+ upper_anchor: 7.0e-06
+ signal_status: exploration_only
+
+ - name: thickness
+ # AS STORED, per the request: this is AL = AI = EXP(-(((AH-650)/250)^2)), the
+ # workbook's normalised thickness, NOT the nanometres every contract trains
+ # on. The Gaussian target is therefore already applied before the GP sees it,
+ # which folds a hard non-monotone transform into the response.
+ model_source_column: "Thickness score (normalized of avg)"
+ transform: affine
+ goal: maximize
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Thickness score (normalized of avg)"}
+ formula_fingerprint:
+ column: "Thickness score (normalized of avg)"
+ formula: "=AI2"
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ signal_status: learnable
+
+reference_point_utility: [-0.01, -0.01, -0.01]
+
+rounds:
+ r1:
+ method: ucb_hvi
+ batch_size: 5
+ replicates_per_condition: 3
+ beta: 36.0
+ candidate_pool_size: 32768
+ posterior_samples: 256
+ moment_method: monte_carlo
+ r2:
+ method: qlognehvi
+ batch_size: 3
+ replicates_per_condition: 3
+ candidate_pool_size: 32768
+ mc_samples: 128
+ sequential_pending: true
+
+# Kept so the samples 17 / 32 violation is REPORTED rather than absorbed.
+constraints:
+ - zero_coupled: [speed_2, time_2]
+ name: second_stage_all_or_nothing
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ name: antisolvent_lands_while_spinning
+ - nonzero_minimum: {column: time_2, minimum: 10}
+ name: second_stage_runs_at_least_10s
+
+review:
+ probes: []
+ notes:
+ - >
+ 45 rows, 15 recipes, three replicates each. Ordinary leave-one-out on this
+ sheet LEAKS: hold out row 4 and rows 19 and 34 carry the same inputs, so the
+ GP interpolates its own replicate and the R2 measures reproducibility rather
+ than prediction. Leave-one-RECIPE-out (all three rows together) is the
+ honest test. Both are reported; do not quote the row-wise one alone.
+
+local_penalization:
+ distance_metric: normalized_euclidean
+ dimension_weights: null
+ radius: 0.35
+ min_batch_distance: 0.15
+ min_observed_distance: 0.0
+
+model:
+ variant: dim_scaled_prior
+ observation_noise: fit_from_marginal_likelihood
+ replicate_variance:
+ sanity_floor:
+ thickness: 0.003007
+ rows_without_replicates: 1
+
+reproducibility:
+ seed: 73
+ record_git_commit: true
+ record_environment_versions: true
+ record_resolved_config_hash: true
diff --git a/configs/campaign_d2d_perovskite_final.yaml b/configs/campaign_d2d_perovskite_final.yaml
new file mode 100644
index 0000000..0bf876a
--- /dev/null
+++ b/configs/campaign_d2d_perovskite_final.yaml
@@ -0,0 +1,396 @@
+# D2D FINAL campaign - FA0.9Cs0.1PbI3 slot-die/spin. THE REAL RUN.
+#
+# Rounds: R0 (15 measured, complete) -> R1 UCB-HVI (5 conditions) -> R2 qLogNEHVI (3).
+# Each proposed condition is run in triplicate.
+#
+# THREE CONTRACTS HAVE EXISTED. Naming them is not bookkeeping -- utility space is
+# what hypervolume is measured in, and an objective that keeps its name while
+# changing its construction makes every cross-contract number incomparable while
+# every plot still renders.
+#
+# v2 d2d-objectives-v2-nm-thickness ALGORITHM TESTING. Proved the loop worked.
+# v3 d2d-objectives-v3-test DRY RUN on test data. Rehearsed this
+# contract's shape; the workbook was
+# literally called "Test".
+# v4 d2d-objectives-v4-final THIS ONE. The campaign that produces films.
+#
+# Nothing from v2 or v3 transfers. Their fitted numbers are about quantities that
+# have been redefined twice.
+#
+# The workbook is local_inputs/Final Summary Table.xlsx (gitignored). Its columns
+# moved again: the three scores are now AJ / AK / AL, and the thickness readings
+# are AC..AF with AG holding the operator's anomalies.
+campaign:
+ name: D2D_FA0.9Cs0.1PbI3_final
+ status: active
+ schema_version: d2d-campaign-v4
+ workbook_profile: d2d_summary_final_v4
+ # NEW KEY. The workbook names its sheets by round now, so the sheet holding the
+ # measured rows is campaign configuration rather than the hard-coded "Sheet1"
+ # every earlier contract assumed.
+ #
+ # The workbook also carries an `R1` sheet. IT IS DELIBERATELY NOT READ. The
+ # round contract is unchanged: each round's worklist is written to a NEW file
+ # beside the workbook and filled in there, and the source workbook is never
+ # opened for writing. That sheet is the group's own template and the tool
+ # ignores it.
+ source_sheet: R0
+
+# Ten inputs, columns B..K of the R0 sheet, in this order. GRIDS UNCHANGED from
+# v3 -- verified, all 15 measured rows land on them.
+inputs:
+ - name: speed_1
+ unit: rpm
+ start: 1000
+ stop: 6000
+ step: 500
+ - name: time_1
+ unit: s
+ start: 5
+ stop: 50
+ step: 5
+ - name: speed_2
+ unit: rpm
+ start: 0
+ stop: 5000
+ step: 500
+ - name: time_2
+ unit: s
+ start: 0
+ stop: 60
+ step: 5
+ - name: precur_conc
+ unit: M
+ start: 1.00
+ stop: 2.00
+ step: 0.05
+ - name: precur_vol
+ unit: uL
+ start: 40
+ stop: 200
+ step: 10
+ - name: anneal_temp
+ unit: C
+ start: 100
+ stop: 185
+ step: 5
+ - name: anneal_time
+ unit: min
+ start: 10
+ stop: 60
+ step: 5
+ - name: anti_vol
+ unit: uL
+ start: 100
+ stop: 200
+ step: 5
+ - name: anti_time
+ unit: s
+ start: 9
+ stop: 25
+ step: 1
+
+# UNIFORMITY AND OPTOELECTRONIC ARE READ AS STORED. The workbook's score column
+# IS the objective value.
+#
+# HOW THOSE SCORES ARE CONSTRUCTED IS NOT RECORDED HERE, BY DECISION.
+#
+# The SCORE VALUE is the interface. How a composite is defined -- which terms,
+# what weighting, which normalisation -- is a choice this group makes for itself,
+# and another group running MOBO-Kit on the same chemistry may define uniformity
+# completely differently and be equally right. What is shared is the contract's
+# SHAPE: one number per objective per film, on a declared scale. Encoding this
+# group's particular arithmetic in the tool would make the tool quietly specific
+# to us. It also gave the definition a second place to live and go stale, which
+# happened three times.
+#
+# WHAT THIS COSTS: no independent recomputation, so a stale pasted literal in
+# either column cannot be caught by comparing it against anything. That is the
+# price of the freeze and it is paid deliberately. The `formula_fingerprint` below
+# is the partial replacement and is NOT documentation -- it is a runtime alarm
+# that fires when the column's formula TEXT changes, which is the event that makes
+# hypervolume incomparable across rounds. It records what the formula was so that
+# a change is audible; it does not claim to explain it.
+#
+# Thickness is the exception and is COMPUTED, because the recomputation is what
+# lets an anomalous `T anom` reading be excluded AND NAMED, and because the model
+# trains on nanometres rather than on the stored score.
+objectives:
+ contract_version: d2d-objectives-v4-final-nomean
+ scaling_mode: fixed_affine
+ specs:
+ - name: uniformity
+ model_source_column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+ transform: affine
+ goal: maximize
+ # Read, never recomputed.
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"}
+ # A runtime alarm, not documentation: it fires if this column's formula
+ # text changes, because that is the event that silently redefines the
+ # objective and makes hypervolume incomparable across rounds.
+ formula_fingerprint:
+ column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+ formula: "=(L2+O2+P2)/3"
+ # The score is a fraction, so [0, 1] is its range rather than a guess.
+ # Observed span on the 15 rows: 0.599 to 0.882.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # MEASURED ON THESE ROWS. Plain GP LOO R2 -0.4688 against a null of -0.1480:
+ # no learnable signal, for the third contract running. R1 is
+ # exploration-dominated for this objective by design.
+ #
+ # The workbook was revised on 2026-09-03: `Phase purity` moved on samples 4
+ # and 12 (0.7951 -> 0.7997 and 0.7800 -> 0.7882), which moves this column
+ # through `=(L+O+P)/3`. The number was -0.4778 on the previous copy. Nothing
+ # else on the sheet changed, and no verdict moved: the shift is 0.0090
+ # against a resolution sd of 0.236.
+ signal_status: exploration_only
+
+ - name: optoelectronic
+ model_source_column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"
+ transform: affine
+ goal: maximize
+ # Read, never recomputed.
+ #
+ # The agreement check below stays because it caught a real defect once: on the
+ # v3 workbook the normalised photoconductance column ranked BACKWARDS against
+ # its own raw measurement (Spearman -0.5484, p = 0.0343). It is +1.0000 here.
+ # The check is cheap and the failure it watches for is silent when it recurs.
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"}
+ formula_fingerprint:
+ column: "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + 0.25*Photosensitivity))/2"
+ formula: "=(S2+((0.75*Y2)+(0.25*AB2)))/2"
+ agreement_check:
+ raw: "Photoconductance (Max - based on raw slopes)"
+ normalized: "Normalized photoconductance (test)"
+ min_spearman: 0.0
+ # The score is a fraction, so [0, 1] is its range rather than a guess.
+ # Observed span on the 15 rows: 0.477 to 0.762.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # MEASURED ON THESE ROWS, 2026-09-02. Plain GP LOO R2 -0.7038 against a null
+ # of -0.1480 -- further below it than on v3 (-0.5842), even though the
+ # photoconductance inversion that made v3's version suspect is fixed. The
+ # renormalisation did not make this axis learnable, and no mean function is
+ # declared: the first campaign's anneal_temp trend was deleted on v3's intake
+ # verdict and nothing since has argued for reinstating it.
+ signal_status: exploration_only
+
+ - name: thickness
+ # trains on nanometres, NOT on the workbook thickness score
+ model_source_column: "Thickness (avg)"
+ transform: gaussian_target
+ goal: target
+ target: 650.0
+ # the workbook writes EXP(-(((AH-650)/250)^2)), which has no factor of 1/2;
+ # in the exp(-0.5*((T-c)/sigma)^2) convention used here that is 250/sqrt(2)
+ sigma: 176.7766952966369
+ equivalent_workbook_formula: "EXP(-(((AH-650)/250)^2))"
+ # The only objective with signal, for the third contract running: plain GP
+ # +0.5814.
+ #
+ # NOTE THAT -0.1480 IS NOT THE BAR. See "The leave-one-out null was never a
+ # significance threshold" in docs/CAMPAIGN_STATUS.md -- 28.7% of pure-noise
+ # shuffles beat it. This axis earns `learnable` on its RANK PERMUTATION TEST,
+ # which was always the right instrument and is now the only one.
+ #
+ # RE-MEASURED 2026-09-06 on the PLAIN GP, after the mean function was
+ # withdrawn, because the earlier verdict was measured with it:
+ #
+ # observed rank rho +0.8036
+ # null mean -0.1762 (sd 0.4070)
+ # null 95th pct +0.5607 <- the honest single-candidate bar
+ # exceedances 9 of 1800
+ # p 0.0056 95% CI [0.0021, 0.0090]
+ #
+ # Identical p to the structured version's, and the observed rank clears the
+ # null's own 95th percentile by 0.24. Removing the prior cost R2, not the
+ # verdict.
+ signal_status: learnable
+ # NOT frozen, and the only objective still computed in Python. Its definition
+ # has been stable across all three contracts and the recomputation earns its
+ # place: it is what lets `T anom` be excluded from the mean and reported
+ # rather than silently dropped.
+ #
+ # The readings moved to AC..AF and the anomaly column to AG. Rows carry three
+ # or four readings, so `mean_of_present` is what the data needs.
+ #
+ # `Thickness (avg)` is a live unrounded AVERAGE, so anything above
+ # floating-point noise is a real disagreement -- hence the tight tolerance.
+ measurement:
+ recipe: mean_of_present
+ inputs:
+ - {column: "T1"}
+ - {column: "T2"}
+ - {column: "T3"}
+ - {column: "T4"}
+ excluded:
+ - {column: "T anom"}
+ cross_check:
+ - {column: "Thickness (avg)", atol: 1.0e-9}
+ spread_warning_ratio: 0.25
+ replicate_aggregate: mean_of_log
+ # NO MEAN FUNCTION. Withdrawn 2026-09-06 by the group's decision, after the
+ # physics justification failed its one clean test.
+ #
+ # What was here: `log T ~ log(speed_1) + log(precur_conc)`, declared from
+ # spin-coating theory and carried unchanged through all three contracts. It
+ # measured +0.7423 against the plain GP's +0.5823.
+ #
+ # WHY IT WENT. The supporting claim was that the fitted speed exponent agreed
+ # with Meyerhofer's `T ~ omega^-0.5`. It does not. OLS on the 15 films gives
+ # the exponent as -0.2554 with a standard error of 0.0593, a 95% interval of
+ # [-0.385, -0.126] -- the textbook -0.5 sits OUTSIDE it, 4.1 standard errors
+ # away. Fixing the exponents at their theoretical values (-0.5 and +1.0) and
+ # fitting only an intercept scores +0.5600, WORSE than having no trend at all.
+ # So the theory does not predict these films; the concentration exponent
+ # (+1.313, CI [0.932, 1.694]) does contain the mass-balance +1.0, which is
+ # consistent with the antisolvent quench freezing the film before viscous
+ # thinning completes.
+ #
+ # WHAT WAS TRUE, and is worth recording because it is the argument for ever
+ # bringing this back: the VARIABLE choice was real even though the magnitudes
+ # were not physics. Four matched-flexibility control pairs -- three fitted
+ # parameters each, physically unmotivated -- scored +0.3706, +0.2960, +0.3174
+ # and +0.4033, all BELOW the plain GP. It is not the case that any fitted
+ # two-term trend helps; most actively hurt.
+ #
+ # THE COST, stated plainly: thickness LOO R2 falls from +0.7423 to +0.5823 and
+ # rank from +0.864 to +0.804. It remains the only objective with signal and it
+ # still clears its rank permutation test. The gain never had a permutation test
+ # of its own, only an R2 comparison, which this project now knows is the weaker
+ # instrument.
+ #
+ # `structured_mean.py` stays in the package, wired and tested, for a future
+ # prior that earns its place. Reproduce the numbers above with:
+ # python scripts/raw_component_screen.py --candidates thickness_nm
+
+# Reference point in UTILITY space, carried over verbatim. All three objectives
+# live natively in [0, 1], so no axis dominates.
+reference_point_utility: [-0.01, -0.01, -0.01]
+
+rounds:
+ r1:
+ method: ucb_hvi
+ batch_size: 5
+ replicates_per_condition: 3
+ # LOWERED from 36 to 4 on 2026-09-03, by the group's decision.
+ #
+ # 36 was chosen for the v3 dry run on the argument that two of three
+ # objectives carried no learnable signal, so heavy exploration was the right
+ # posture. That argument has been overtaken. On the extended C1&C2 sheet the
+ # two dead axes are dead for reasons beta cannot address: the optoelectronic
+ # score is 84.5% between-campaign drift (recipe ICC 0.000, and the GP refuses
+ # to fit it at all), and uniformity is reproducible (ICC 0.730) but not
+ # predictable from ten inputs at fifteen distinct recipes. Exploring harder
+ # buys nothing against either, and it costs real batch quality.
+ #
+ # What it costs, measured on this workbook at seed 73 (scripts in
+ # docs/, reproduced by the beta scan):
+ #
+ # beta radius HV gain edge coords/50 spacing sd ratio
+ # 4 0.25 +0.0009 11 0.781 5.8x
+ # 9 0.25 +0.0000 15 0.882 6.2x
+ # 36 0.25 +0.0000 18 1.213 6.5x
+ #
+ # `edge coords` counts how many of the 5 x 10 proposed coordinates are pinned
+ # to a grid bound. At 36 the acquisition spends the batch on the corners of
+ # the box; at 4 it proposes films that sit inside it. beta = 4 is also the
+ # value the original 108-cell box-plot/heat-map sweep settled on before v3's
+ # no-signal verdict overrode it.
+ #
+ # STILL A DECLARED POLICY, NOT A MEASURED OPTIMUM. The +0.0009 HV gain is far
+ # below the trial sd of 0.010-0.027 that sweep measured; it is a tiebreak, not
+ # evidence. What IS evidence is the edge count and the spacing, which are
+ # facts about the batch at a fixed seed rather than single-seed hypervolume.
+ beta: 4.0
+ candidate_pool_size: 32768
+ posterior_samples: 256
+ moment_method: monte_carlo
+ r2:
+ method: qlognehvi
+ batch_size: 3
+ replicates_per_condition: 3
+ candidate_pool_size: 32768
+ mc_samples: 128
+ sequential_pending: true
+
+# Carried over verbatim from v3. All 15 measured rows satisfy all three -- sample
+# 2 was `speed_2 = 0, time_2 = 60` in an earlier draft of this workbook, which
+# breaks the first rule; the group corrected it to `time_2 = 0`.
+constraints:
+ - zero_coupled: [speed_2, time_2]
+ name: second_stage_all_or_nothing
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ name: antisolvent_lands_while_spinning
+ - nonzero_minimum: {column: time_2, minimum: 10}
+ name: second_stage_runs_at_least_10s
+
+review:
+ probes: []
+
+ notes:
+ - >
+ Uniformity and optoelectronic are read from the workbook as stored. A stale
+ pasted value in either column would not be caught by anything here; the
+ formula fingerprints catch a changed definition, not a value that has
+ stopped tracking its inputs.
+
+local_penalization:
+ distance_metric: normalized_euclidean
+ dimension_weights: null
+ # 0.35 -> 0.25 on 2026-09-03, together with beta.
+ #
+ # THIS KNOB WAS INERT AND NOW IS NOT, which is the whole reason to set it
+ # deliberately. At beta = 36 the scan returns bit-identical batches at radius
+ # 0.15, 0.25 and 0.35 -- same spacing 1.213, same 18 edge coordinates, same
+ # utilities -- because the acquisition was already spreading the batch further
+ # than any of those radii asked for. At beta = 4 it bites:
+ #
+ # radius spacing edge coords/50
+ # 0.15 0.682 13
+ # 0.25 0.781 11
+ # 0.35 0.941 13
+ #
+ # 0.25 is the cell with the fewest coordinates pinned to a grid bound. That is a
+ # weak reason on its own -- 11 against 13 at one seed -- but it is the only
+ # discriminating measurement available, and the alternative is inheriting a
+ # number chosen while the knob did nothing.
+ radius: 0.25
+ min_batch_distance: 0.15
+ min_observed_distance: 0.0
+
+model:
+ variant: dim_scaled_prior
+ # Once the R1 triplicates land, switch to `replicate_pooled` -- one key.
+ observation_noise: fit_from_marginal_likelihood
+
+ replicate_variance:
+ # TWO DIFFERENT VARIANCES. Do not substitute one for the other.
+ #
+ # BETWEEN-FILM is what train_Yvar needs and is only measurable once R1 ships
+ # triplicates. WITHIN-FILM is the scatter of the 3-4 thickness points across
+ # one film, available today, and contains NO run-to-run variation -- so it is
+ # a FLOOR, not an estimate. If pooled between-film variance ever lands below
+ # it, films would be more reproducible than points on a single film.
+ #
+ # RECOMPUTED ON THIS WORKBOOK, 2026-09-02: 0.003007 on log(T), 36 dof
+ # (sd 0.0548), against v3's 0.006374. Roughly half, and the reason is visible:
+ # sample 1's 418.5 reading moved into `T anom` on this sheet, which took that
+ # film's sd_log from 0.218 to 0.085. Inheriting v3's constant would have set
+ # the floor twice too high and called an ordinary pooled variance a mistake.
+ sanity_floor:
+ thickness: 0.003007
+ rows_without_replicates: 1
+
+reproducibility:
+ seed: 73
+ record_git_commit: true
+ record_environment_versions: true
+ record_resolved_config_hash: true
diff --git a/configs/campaign_d2d_perovskite_test.yaml b/configs/campaign_d2d_perovskite_test.yaml
new file mode 100644
index 0000000..8493f02
--- /dev/null
+++ b/configs/campaign_d2d_perovskite_test.yaml
@@ -0,0 +1,479 @@
+# D2D test campaign - FA0.9Cs0.1PbI3 slot-die/spin, SECOND dataset.
+#
+# Rounds: R0 (15 measured, complete) -> R1 UCB-HVI (5 conditions) -> R2 qLogNEHVI (3).
+# Each proposed condition is run in triplicate; the three films share a
+# replicate_group and are aggregated to one condition-level observation before
+# the next round trains on them.
+#
+# WHY THIS IS A NEW FILE rather than an edit to campaign_d2d_perovskite.yaml.
+# Two of the three objectives are computed differently here -- uniformity is now
+# the MEAN of its three terms rather than their product, and optoelectronic is the
+# mean of two normalised terms rather than log10 of a product. Utility space is
+# what hypervolume is measured in, so an objective that keeps its name while
+# changing its construction makes every cross-round and cross-campaign number
+# incomparable while every plot still renders. The old config stays as the
+# historical record with status: archived, and this one carries a new
+# contract_version. Nothing about the first campaign's numbers transfers.
+#
+# The workbook is local_inputs/Summary Table Test.xlsx (gitignored), whose column
+# layout differs from the first campaign's throughout.
+campaign:
+ name: D2D_FA0.9Cs0.1PbI3_test
+ status: archived # superseded by campaign_d2d_perovskite_final.yaml, 2026-09-02
+ schema_version: d2d-campaign-v3
+ workbook_profile: d2d_summary_test_v3
+
+# Ten inputs, columns B..K of Sheet1, in this order.
+#
+# TWO GRIDS CHANGED from the first campaign, both forced by the measured rows.
+# Everything else is carried over unchanged and every observed value lands on it.
+inputs:
+ - name: speed_1
+ unit: rpm
+ start: 1000
+ stop: 6000
+ step: 500
+ - name: time_1
+ unit: s
+ start: 5
+ stop: 50
+ step: 5
+ - name: speed_2
+ unit: rpm
+ start: 0
+ stop: 5000
+ step: 500
+ # CHANGED: was 10..60. Sample 2 is a one-step film -- speed_2 = 0 and
+ # time_2 = 0 -- so 0 has to be on the grid or a real recipe is off-grid.
+ #
+ # Reaching 0 with step 5 also reaches 5, which the first campaign's grid
+ # excluded and which no film has ever run. Rather than widen the design space in
+ # silence, the hole is declared as a `nonzero_minimum` constraint below. An
+ # explicit value list would express {0} U {10..60} directly, but `lhs` asserts
+ # that every design grid is uniformly spaced, so an irregular grid would need
+ # changes to two modules this campaign deliberately leaves alone.
+ - name: time_2
+ unit: s
+ start: 0
+ stop: 60
+ step: 5
+ - name: precur_conc
+ unit: M
+ start: 1.00
+ stop: 2.00
+ step: 0.05
+ - name: precur_vol
+ unit: uL
+ start: 40
+ stop: 200
+ step: 10
+ - name: anneal_temp
+ unit: C
+ start: 100
+ stop: 185
+ step: 5
+ - name: anneal_time
+ unit: min
+ start: 10
+ stop: 60
+ step: 5
+ - name: anti_vol
+ unit: uL
+ start: 100
+ stop: 200
+ step: 5
+ # CHANGED: was step 2, giving 9/11/13/.../25. Sample 1 runs anti_time = 12,
+ # which that grid cannot hold; the first campaign carried it as a declared
+ # off-grid exception, excluded from pool bookkeeping. Step 1 makes it an
+ # ordinary observation. The axis goes from 9 values to 17.
+ - name: anti_time
+ unit: s
+ start: 9
+ stop: 25
+ step: 1
+
+# All three utilities are maximised after transformation. `transform` maps a
+# model output to utility; the two differ only for thickness.
+#
+# `measurement` is what the GP trains on: a recipe plus the raw measurement
+# columns it consumes, computed in Python. `model_source_column` names the stored
+# workbook cell, which is a label and a cross-check target, never an input.
+#
+# POLICY FOR THIS WORKBOOK, per the group: the stored score columns are
+# authoritative and the recompute is the cross-check. Every recipe below
+# reproduces its stored column exactly on all 15 rows -- worst disagreement
+# 2.3e-13, on thickness, which is floating-point noise -- so the two are the same
+# number today. A disagreement is therefore a WARNING finding and not a block:
+# the computed value is what the model uses, and a human decides what the
+# divergence means.
+objectives:
+ contract_version: d2d-objectives-v3-test
+ # Scales are FIXED for the whole campaign and must never be re-derived from
+ # observed data. If a scale tracks the data, utility space moves between rounds
+ # and hypervolume stops being comparable - the progress plot silently becomes
+ # meaningless. Widen a range deliberately and bump contract_version; never let
+ # it follow the measurements.
+ scaling_mode: fixed_affine
+ specs:
+ - name: uniformity
+ model_source_column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+ transform: affine
+ goal: maximize
+ # AB = (L + O + P) / 3, the mean of Coverage, 1 - clamped Uniformity and
+ # Phase purity. The first campaign multiplied these instead.
+ #
+ # Computed from `Uniformity` (M), never from the workbook's clamped copy (N)
+ # or its complement (O). N is a pasted literal on every row and O is a formula
+ # on fourteen rows and a literal on the fifteenth, so both are exactly the kind
+ # of cell that stops updating when the measurement behind it is edited. Taking
+ # the reading and clamping it here means the cross-check against AB -- which is
+ # built from O -- fires if that ever happens.
+ #
+ # The clamp is strictly above 1.0, matching the sheet: readings of 1.659
+ # (sample 4) and 1.277 (sample 8) become 0.99, and an exact 1.0 would keep its
+ # own value. Uniformity above 1 is out of range for a fraction; the clamp
+ # records that the film was bad without letting one reading drive the mean
+ # negative.
+ measurement:
+ recipe: mean
+ inputs:
+ - {column: "Coverage"}
+ - {column: "Uniformity", transform: clamped_complement, clamp_above: 1.0, clamp_to: 0.99}
+ - {column: "Phase purity"}
+ # AB is a live formula, so it agrees to floating point. Measured: 0.0 on all
+ # 15 rows.
+ cross_check:
+ - {column: "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))", atol: 1.0e-9}
+ # The mean of three terms each in [0, 1] is itself in [0, 1] by construction,
+ # so these anchors are the objective's natural range and not a guess about the
+ # data. Observed span on the 15 rows is 0.365 to 0.878.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # MEASURED ON THESE ROWS, 2026-08-17, not inherited. The first campaign's
+ # "no learnable signal" verdict was about a different construction (the
+ # product) on a different set of films, so it could not transfer -- but the
+ # answer came out the same: plain GP LOO R2 -0.6447 against a null of -0.1480.
+ # Well below. R1 is exploration-dominated for this objective by design.
+ #
+ # Reproduce with:
+ # python scripts/intake_new_data.py --workbook "local_inputs/Summary Table Test.xlsx"
+ signal_status: exploration_only
+
+ - name: optoelectronic
+ model_source_column: "Optoelectronic score (Avg normalized (Voc + Photocondiuctivity)"
+ transform: affine
+ goal: maximize
+ # AC = (R + T) / 2, the mean of normalised Voc and normalised photoconductance.
+ # The first campaign used log10(Voc * photoconductance), a different quantity
+ # on a different scale.
+ #
+ # R = Q / 1.4 in the sheet, with NO ceiling. `capped_ratio` adds one, so this
+ # computes min(Q, 1.4) / 1.4. THAT IS A DELIBERATE DIVERGENCE from the
+ # workbook and it is dormant today: the largest observed reading is 1.135, so
+ # the cap never binds and the cross-check agrees exactly. The day a film
+ # exceeds 1.4 V the two will disagree and the cross-check will say so, which is
+ # the intended behaviour -- the group asked for Voc to be clamped at 1.4, and a
+ # clamp that only exists in Python has to announce itself.
+ #
+ # T is taken as provided. Nothing in the workbook derives it, so there is no
+ # recipe to reproduce and no cross-check to write; see agreement_check.
+ measurement:
+ recipe: mean
+ inputs:
+ - {column: "PL - Implied Voc (Max) Raw ", transform: capped_ratio, cap: 1.4}
+ - {column: "Normalized photoconductance "}
+ cross_check:
+ - {column: "Optoelectronic score (Avg normalized (Voc + Photocondiuctivity)", atol: 1.0e-9}
+ # THE NORMALISATION IS KNOWN TO BE WRONG AND THIS IS HOW IT STAYS VISIBLE.
+ # `Normalized photoconductance` does not rank like the raw photoconductance
+ # it claims to summarise: Spearman is -0.5484 (p = 0.0343) on the 15 rows.
+ # The strongest film (8.81e-07) normalises to 0.010, the lowest value in the
+ # column, and three films at low raw photoconductance sit at exactly 1.000.
+ # So half of this objective currently rewards the opposite of what it names.
+ #
+ # It is a finding, never a gate. The group knows and the formula is coming;
+ # until it does, every R1 review carries the notice below and this axis is
+ # provisional. Closing it is one recipe edit plus one intake run.
+ agreement_check:
+ raw: "Photoconductance (Max)"
+ normalized: "Normalized photoconductance "
+ min_spearman: 0.0
+ # Both terms are normalised to [0, 1], so their mean is too. Observed span on
+ # the 15 rows is 0.373 to 0.905.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # MEASURED ON THESE ROWS, 2026-08-17. Plain GP LOO R2 -0.5842 against a null
+ # of -0.1480: this objective carries no learnable signal either, and like the
+ # first campaign's optoelectronic it sits BELOW the null, which means the ten
+ # inputs are doing damage rather than merely diluting.
+ #
+ # THE MEAN FUNCTION WAS DELETED, and that was the designed outcome rather than
+ # a setback. The first campaign carried `mean_function: linear anneal_temp`
+ # here, worth -0.342 -> +0.355 on ITS score. On this one it makes the fit
+ # WORSE: -0.5842 -> -0.6977, a swing of -0.1135. The target changed underneath
+ # it -- this objective is now (min(Voc, 1.4)/1.4 + T)/2 rather than
+ # log10(Voc * photoconductance) -- so the old evidence was never about this
+ # quantity. Do not reinstate it without a fresh intake verdict.
+ #
+ # The prime suspect is the agreement_check above. Half of this objective is a
+ # normalisation that ranks BACKWARDS against its own raw measurement, and no
+ # model can learn a column that does not track what it claims to summarise.
+ # Re-run the intake once the group supplies the real formula; a mean function
+ # may well earn its place then, and this verdict is about the column as it
+ # stands rather than about anneal_temp.
+ signal_status: exploration_only
+
+ - name: thickness
+ # trains on nanometres, NOT on the workbook thickness score
+ model_source_column: "Thickness (avg)"
+ transform: gaussian_target
+ goal: target
+ target: 650.0
+ # the workbook writes EXP(-(((Z-650)/250)^2)), which has no factor of 1/2;
+ # in the exp(-0.5*((T-c)/sigma)^2) convention used here that is 250/sqrt(2)
+ sigma: 176.7766952966369
+ equivalent_workbook_formula: "EXP(-(((Z-650)/250)^2))"
+ # MEASURED ON THESE ROWS, 2026-08-17. The one objective with real signal, and
+ # it is much stronger than in the first campaign: plain GP LOO R2 +0.5227
+ # against a null of -0.1480, where the first campaign's plain thickness GP
+ # managed +0.116. These films are far more internally consistent, which is the
+ # likely reason.
+ signal_status: learnable
+ # Unchanged from the first campaign in every respect except the tolerance.
+ # The score is still a peaked Gaussian on a 650 nm target and still folds the
+ # range 2-to-1 -- films at 372 nm and 953 nm score alike from opposite sides of
+ # the peak -- so the model still trains on nanometres and the Gaussian is
+ # applied to the posterior. Observed range is 371.9 to 1310.9 nm, straddling
+ # the target as before.
+ #
+ # Eleven rows carry three readings and four carry four, so `mean_of_present`
+ # is what the data needs; requiring all four would reject two thirds of the
+ # campaign. Blank means not measured, never zero.
+ #
+ # "T anom" holds readings the operator judged anomalous (samples 4, 8, 11 and
+ # 12). They never enter the mean -- the sheet's own AVERAGE(U:X) excludes the
+ # column too -- and their presence is reported so the exclusion is visible.
+ #
+ # TOLERANCE TIGHTENED, 0.5 -> 1e-9. The first campaign's `Thickness (avg)` was
+ # ROUND(mean(T1..T4)), a rounded literal that could legitimately differ by half
+ # a nanometre. This sheet's Z is a live unrounded AVERAGE, so anything above
+ # floating-point noise is a real disagreement. Measured worst case on the 15
+ # rows: 2.3e-13.
+ #
+ # spread_warning_ratio fires on sample 1 alone (readings 584.4, 418.5, 692.0,
+ # 624.6 -- a 47% spread around their mean). These films are far more internally
+ # consistent than the first campaign's: pooled within-film sd of log(T) is
+ # 0.0798 here against 0.244 there.
+ measurement:
+ recipe: mean_of_present
+ inputs:
+ - {column: "T1"}
+ - {column: "T2"}
+ - {column: "T3"}
+ - {column: "T4"}
+ excluded:
+ - {column: "T anom"}
+ cross_check:
+ - {column: "Thickness (avg)", atol: 1.0e-9}
+ spread_warning_ratio: 0.25
+ # response: log makes the model output lognormal, so the utility expectation
+ # must use Gauss-Hermite quadrature, not the Gaussian closed form.
+ #
+ # The three replicate films of one condition are averaged in LOG SPACE, for the
+ # same reason train_Yvar is pooled there: `response: log` means the GP trains
+ # on log(T), so the geometric mean is the arithmetic mean in the space the
+ # model actually works in.
+ replicate_aggregate: mean_of_log
+ # KEPT, and the case is the RANK PERMUTATION rather than the R2 swing --
+ # the same standing the first campaign's thickness mean function had.
+ #
+ # Intake 2026-08-17 left this INCONCLUSIVE on R2: plain +0.5227, structured
+ # +0.6630, a swing of +0.1403 against a resolution floor of 0.236. That is not
+ # a verdict, it is a statement that R2 cannot resolve the difference at N=15.
+ #
+ # The permutation adjudicated it on 2026-08-18, and decisively:
+ #
+ # observed rank rho +0.7250
+ # null mean -0.1917 (sd 0.2937)
+ # exceedances 4 of 1800
+ # p 0.0028 95% CI [0.0003, 0.0052]
+ #
+ # Reproduce with:
+ # python scripts/permutation_rank_test.py --objective thickness --permutations 1800
+ #
+ # Stronger than the first campaign's p = 0.0350 on its own films. Rank is the
+ # right statistic because rank is what the acquisition consumes; it never sees
+ # R2. Do not quote the +0.1403 swing as evidence -- it is inside the floor, and
+ # the permutation is what carries the weight.
+ mean_function:
+ response: log
+ features:
+ - column: speed_1
+ transform: log
+ - column: precur_conc
+ transform: log
+
+# Reference point in UTILITY space, after the transforms above. All three
+# objectives now live natively in [0, 1], so the axes are more comparable than in
+# the first campaign, not less; the reference is unchanged.
+reference_point_utility: [-0.01, -0.01, -0.01]
+
+rounds:
+ r1:
+ method: ucb_hvi
+ batch_size: 5
+ replicates_per_condition: 3
+ # CHANGED 2026-08-18, from 4.0. Chosen by the group with Aleks from the
+ # 108-cell beta x radius sweep (3 trials x 4 betas x 9 radii, full production
+ # settings, local_outputs/boxplot_sweep). beta = 36 means kappa = sqrt(36) = 6:
+ # heavy exploration, which is the right posture when two of the three axes
+ # carry no learnable signal and the third is the only one worth exploiting.
+ #
+ # READ THE SWEEP'S OWN CAVEAT BEFORE QUOTING IT AS EVIDENCE. That sweep scored
+ # candidates against a noiseless GP oracle of the same model class the
+ # optimiser fits, so the landscape held no surprises and exploration had
+ # unusually little to earn; it systematically UNDERVALUES large beta. It also
+ # could not rank cells - the spread across betas was 0.0065 against a
+ # trial-to-trial sd of 0.010-0.027, and the best cell was a different (beta, r)
+ # in every trial. So this is a declared policy choice about how much to
+ # explore, not a measured optimum, and it should be recorded as such.
+ #
+ # MEASURED CONSEQUENCES, live R1: batch spacing 1.091 (three times the
+ # radius, so penalization is inert here) and 21 of 50 coordinates at a
+ # range edge. Revisit when the photoconductance normalisation is fixed or
+ # when R1 noise replaces the oracle -- see CAMPAIGN_STATUS.
+ beta: 36.0
+ candidate_pool_size: 32768
+ posterior_samples: 256
+ # thickness utility is a nonlinear function of the model output, so utility
+ # moments must come from posterior samples, not from moments of the mean
+ moment_method: monte_carlo
+ r2:
+ method: qlognehvi
+ batch_size: 3
+ replicates_per_condition: 3
+ candidate_pool_size: 32768
+ mc_samples: 128
+ sequential_pending: true
+
+# FIRST REAL CONSTRAINTS IN THIS PROJECT. The first campaign's list was empty by
+# decision; these are process facts about the recipe, supplied by the group.
+#
+# Enforcement lives in the campaign layer, never in acquisition. The candidate
+# pool is filtered immediately after generation, before anything is scored, and
+# `validate_batch` re-checks the proposed batch by an independent route. The
+# acquisition modules are untouched. `discrete_refinement` is NOT constraint-aware
+# and is not wired into a round; its docstring says so.
+#
+# Watch `constraint_pool_survival_rate` in the round diagnostics. The sampler
+# draws until it has the requested pool size, so a mis-specified constraint
+# produces a normal-looking pool drawn from a sliver of the space, and the
+# survival rate is the only place that shows.
+constraints:
+ # A second spin stage either happens or it does not. 0 rpm for 30 s and 3500 rpm
+ # for 0 s are both contradictions; both zero is a one-step film, which sample 2
+ # actually is. Stated as an iff because that is what keeps the one-step recipe
+ # reachable - a plain lower bound on either column would delete it.
+ - zero_coupled: [speed_2, time_2]
+ name: second_stage_all_or_nothing
+
+ # The antisolvent has to land while the substrate is still spinning, so equality
+ # is already too late. Strict, deliberately.
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ name: antisolvent_lands_while_spinning
+
+ # See the time_2 grid note above: step 5 from 0 reaches 5, which no film has run
+ # and which the first campaign's grid excluded. This keeps the hole declared
+ # rather than silently filling it.
+ - nonzero_minimum: {column: time_2, minimum: 10}
+ name: second_stage_runs_at_least_10s
+
+# What the batch-review artifact interrogates and states. Campaign knowledge, so
+# it lives here rather than in batch_review.py.
+#
+# Starts clean: the first campaign's probes and its R1 withdrawal note were facts
+# about that dataset and do not travel.
+review:
+ probes: []
+
+ notes:
+ - >
+ THE OPTOELECTRONIC AXIS IS PROVISIONAL. Half of it is
+ `Normalized photoconductance`, a column supplied ready-normalised with no
+ derivation in the workbook, and it does not rank like the raw
+ `Photoconductance (Max)` it summarises: Spearman -0.5484, p = 0.0343 over
+ the 15 R0 rows. The strongest film measured (8.81e-07) carries the lowest
+ normalised value in the column (0.010), and three films at low raw
+ photoconductance sit at exactly 1.000. So this objective currently rewards
+ weaker photoconductance, and any condition proposed partly on optoelectronic
+ grounds inherits that. The group is supplying the intended formula; until it
+ lands, read this axis as unresolved rather than as a result. Closing it is
+ one recipe edit plus one intake run.
+ - >
+ TWO OF THE THREE OBJECTIVES ARE EXPLORATION-ONLY. Measured on these 15 rows
+ by scripts/intake_new_data.py on 2026-08-17, against a leave-one-out null of
+ -0.1480: uniformity -0.6447 and optoelectronic -0.5842, both well below it,
+ so neither model has learned anything and neither predicted utility should be
+ read as one. Only thickness carries signal (+0.5227 plain, +0.6630 with its
+ mean function). A batch is therefore being chosen on one informative axis and
+ two uninformative ones, which is a legitimate exploration round but is not
+ the same thing as a three-objective optimisation, and the review should say
+ so rather than let the predicted numbers imply otherwise.
+ - >
+ The optoelectronic mean function was DELETED on the intake verdict. The first
+ campaign carried a linear anneal_temp trend there, worth -0.342 to +0.355 on
+ its own score; on this one it makes the fit worse, -0.5842 to -0.6977. The
+ objective was redefined underneath it, so the old evidence was never about
+ this quantity. It is not to be reinstated without a fresh verdict.
+
+local_penalization:
+ distance_metric: normalized_euclidean
+ dimension_weights: null
+ # CHANGED 2026-08-18, from 0.25. Same sweep, same standing: a policy choice.
+ # Radius buys batch diversity and pays for it in range-edge pinning - measured on
+ # the first campaign at 11 -> 15 edge coordinates across the arm. The sweep also
+ # found that radius binds LESS as beta rises (at beta 49 the nine radii produced
+ # only 3-4 distinct batches, with achieved spacing already above every radius
+ # tested), so at beta = 36 this knob has less to do than it did at beta = 4.
+ radius: 0.35
+ min_batch_distance: 0.15
+ min_observed_distance: 0.0
+
+model:
+ variant: dim_scaled_prior
+ # R0 has no film replicates. Once R1 triplicates land, pool their
+ # within-condition variance (5 conditions x 2 dof = 10 dof) and pass it as
+ # train_Yvar by setting this to `replicate_pooled`.
+ #
+ # Pool thickness variance in LOG SPACE, not in nanometres: `response: log`
+ # above means the GP trains on log(T), so train_Yvar must be the variance of
+ # log(T). A variance in nm^2 would be wrong by a factor of T^2 - roughly 1.4e5
+ # at 372 nm and 1.7e6 at 1311 nm, so not even a constant rescaling.
+ observation_noise: fit_from_marginal_likelihood
+
+ replicate_variance:
+ # TWO DIFFERENT VARIANCES. Do not substitute one for the other.
+ #
+ # BETWEEN-FILM is what train_Yvar needs and is only measurable once R1 ships
+ # triplicates. WITHIN-FILM is the scatter of the 3-4 thickness points across
+ # one film, available today, and contains NO run-to-run variation - so it is a
+ # FLOOR, not an estimate. If pooled between-film variance ever lands below it,
+ # films would be more reproducible than points on a single film.
+ #
+ # RECOMPUTED ON THIS WORKBOOK: 0.006374 on log(T), 37 dof, against 0.0593 on
+ # the first campaign's films. These films are about ten times more internally
+ # consistent, and inheriting the old constant would have set the floor an order
+ # of magnitude too high - it would have called a perfectly ordinary pooled
+ # variance a pooling mistake.
+ sanity_floor:
+ thickness: 0.006374
+ # Film count assumed for rows that have no replicates - the R0 rows. 1 means
+ # their observation carries the full between-film variance rather than a third
+ # of it.
+ rows_without_replicates: 1
+
+reproducibility:
+ seed: 73
+ record_git_commit: true
+ record_environment_versions: true
+ record_resolved_config_hash: true
diff --git a/configs/campaign_d2d_raw_components.yaml b/configs/campaign_d2d_raw_components.yaml
new file mode 100644
index 0000000..40b2669
--- /dev/null
+++ b/configs/campaign_d2d_raw_components.yaml
@@ -0,0 +1,186 @@
+# DIAGNOSTIC ONLY. The same campaign with the two failing COMPOSITE SCORES
+# replaced by RAW MEASUREMENTS, so the two can be compared figure for figure.
+#
+# THE QUESTION THIS ANSWERS. R1 and R2 were not improving on uniformity or
+# optoelectronic, and the leave-one-out parity plot showed the model learning
+# neither. Both are composites: uniformity is (Coverage + (1-Uniformity) +
+# Phase purity)/3, optoelectronic is a weighted blend of normalised Voc,
+# photoconductance and photosensitivity. So: is the COMBINATION the problem? Feed
+# the GP one raw measurement per axis instead and look at the same two figures.
+#
+# Everything else is held identical to `campaign_d2d_perovskite_final.yaml` -- the
+# same ten inputs and grids, the same constraints, the same beta = 4 / radius =
+# 0.25, the same seed, the same reference point, thickness untouched. ONLY the
+# first two objectives change. That is what makes the comparison a comparison.
+#
+# NOT A CAMPAIGN CONTRACT. It proposes nothing and nothing is written from it.
+# Its contract_version is distinct so no hypervolume from it can ever be compared
+# against a v4 number.
+campaign:
+ name: D2D_FA0.9Cs0.1PbI3_raw_components_diagnostic
+ status: diagnostic
+ schema_version: d2d-campaign-v4
+ workbook_profile: d2d_summary_final_v4
+ source_sheet: R0
+
+inputs:
+ - {name: speed_1, unit: rpm, start: 1000, stop: 6000, step: 500}
+ - {name: time_1, unit: s, start: 5, stop: 50, step: 5}
+ - {name: speed_2, unit: rpm, start: 0, stop: 5000, step: 500}
+ - {name: time_2, unit: s, start: 0, stop: 60, step: 5}
+ - {name: precur_conc, unit: M, start: 1.00, stop: 2.00, step: 0.05}
+ - {name: precur_vol, unit: uL, start: 40, stop: 200, step: 10}
+ - {name: anneal_temp, unit: C, start: 100, stop: 185, step: 5}
+ - {name: anneal_time, unit: min, start: 10, stop: 60, step: 5}
+ - {name: anti_vol, unit: uL, start: 100, stop: 200, step: 5}
+ - {name: anti_time, unit: s, start: 9, stop: 25, step: 1}
+
+objectives:
+ contract_version: d2d-objectives-raw-components-diagnostic
+ scaling_mode: fixed_affine
+ specs:
+ # ---------------------------------------------------------------- axis 1 ----
+ # REPLACES the uniformity score. Phase purity is the only one of that score's
+ # three components with any claim to signal on its own: coverage is -0.6638,
+ # 1-Uniformity is -0.6119, phase purity is +0.0571, against the composite's
+ # -0.4688. It is also the component the composite gives the LEAST weight to in
+ # practice -- 1-Uniformity carries 81% of the composite's variance and phase
+ # purity 18%.
+ - name: phase_purity
+ model_source_column: "Phase purity"
+ transform: affine
+ goal: maximize
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Phase purity"}
+ # A fraction. Observed span on the 15 rows: 0.6305 to 0.9827.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # +0.0571 plain. Above the -0.1480 that this project used to call the null,
+ # but that number is NOT a significance threshold (28.7% of pure-noise
+ # shuffles beat it) and phase purity's own empirical bar is +0.2309. With a
+ # precur_conc mean function it reaches +0.3244, which was refuted 3-0 on
+ # verification: the effect is three films below 1.25 M, and among the ten
+ # high-purity films the model ranks them BACKWARDS (rho -0.754).
+ signal_status: exploration_only
+
+ # ---------------------------------------------------------------- axis 2 ----
+ # REPLACES the optoelectronic score, with the group's own example: raw
+ # photoconductance, one measurement, no blending.
+ - name: photoconductance
+ # The workbook's own `=MIN(1, X2/(0.000001))` version of the SAME measurement.
+ #
+ # WHY NOT THE RAW SIEMENS COLUMN. Tried first, and the oracle fit COLLAPSED on
+ # it: at ~1e-7 the latent sd falls to 1.2e-4 of the fitted noise sd and the
+ # round simulation refuses to render rather than build every surface on a
+ # collapsed fit. That is numerical, not scientific -- dividing by the sheet's
+ # own 1e-6 is a strictly monotone rescale and the two carry identical
+ # information (LOO R2 -0.6308 raw against -0.6302 normalised, rank -0.243 in
+ # both). The lesson is worth keeping: `Standardize` does not make this
+ # pipeline scale-invariant in practice, so an objective living at 1e-7 needs
+ # rescaling before it can be modelled at all.
+ model_source_column: "Normalized photoconductance (test)"
+ transform: affine
+ goal: maximize
+ measurement:
+ recipe: stored
+ inputs:
+ - {column: "Normalized photoconductance (test)"}
+ # Capped at 1 by the sheet's own MIN. Observed span: 0.0339 to 0.6789.
+ lower_anchor: 0.0
+ upper_anchor: 1.0
+ # -0.6308 raw, -0.3254 in logs. Both far below even the old null, and below
+ # the MEDIAN of this axis's own permutation null. Worse than the composite it
+ # replaces (-0.7038 vs -0.6308 is inside the +-0.236 resolution, so call them
+ # equal). Splitting the optoelectronic score into its parts does not help,
+ # which is the finding.
+ signal_status: exploration_only
+
+ # ---------------------------------------------------------------- axis 3 ----
+ # UNCHANGED from v4, deliberately: it is the control. If the raw-component
+ # figures look different from the score figures on this axis, something other
+ # than the objective definition moved and the comparison is void.
+ - name: thickness
+ model_source_column: "Thickness (avg)"
+ transform: gaussian_target
+ goal: target
+ target: 650.0
+ sigma: 176.7766952966369
+ equivalent_workbook_formula: "EXP(-(((AH-650)/250)^2))"
+ signal_status: learnable
+ measurement:
+ recipe: mean_of_present
+ inputs:
+ - {column: "T1"}
+ - {column: "T2"}
+ - {column: "T3"}
+ - {column: "T4"}
+ excluded:
+ - {column: "T anom"}
+ cross_check:
+ - {column: "Thickness (avg)", atol: 1.0e-9}
+ spread_warning_ratio: 0.25
+ replicate_aggregate: mean_of_log
+ mean_function:
+ response: log
+ features:
+ - {column: speed_1, transform: log}
+ - {column: precur_conc, transform: log}
+
+reference_point_utility: [-0.01, -0.01, -0.01]
+
+rounds:
+ r1:
+ method: ucb_hvi
+ batch_size: 5
+ replicates_per_condition: 3
+ beta: 4.0
+ candidate_pool_size: 32768
+ posterior_samples: 256
+ moment_method: monte_carlo
+ r2:
+ method: qlognehvi
+ batch_size: 3
+ replicates_per_condition: 3
+ candidate_pool_size: 32768
+ mc_samples: 128
+ sequential_pending: true
+
+constraints:
+ - zero_coupled: [speed_2, time_2]
+ name: second_stage_all_or_nothing
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ name: antisolvent_lands_while_spinning
+ - nonzero_minimum: {column: time_2, minimum: 10}
+ name: second_stage_runs_at_least_10s
+
+review:
+ probes: []
+ notes:
+ - >
+ Diagnostic only. Two of three objectives are raw measurements substituted
+ for the composite scores the campaign runs, so that the leave-one-out
+ parity plot and the round-simulation boxplots can be compared side by side
+ against the score version. Nothing here proposes films.
+
+local_penalization:
+ distance_metric: normalized_euclidean
+ dimension_weights: null
+ radius: 0.25
+ min_batch_distance: 0.15
+ min_observed_distance: 0.0
+
+model:
+ variant: dim_scaled_prior
+ observation_noise: fit_from_marginal_likelihood
+ replicate_variance:
+ sanity_floor:
+ thickness: 0.003007
+ rows_without_replicates: 1
+
+reproducibility:
+ seed: 73
+ record_git_commit: true
+ record_environment_versions: true
+ record_resolved_config_hash: true
diff --git a/configs/demo_config.yaml b/configs/demo_config.yaml
deleted file mode 100644
index 96e8ecb..0000000
--- a/configs/demo_config.yaml
+++ /dev/null
@@ -1,50 +0,0 @@
-inputs:
-- name: speed_inorg
- unit: m/min
- start: 0.25
- stop: 1.0
- step: 0.01
-- name: speed_org
- unit: m/min
- start: 0.25
- stop: 1.0
- step: 0.01
-- name: inkfl_inorg
- unit: uL/min
- start: 80.0
- stop: 240.0
- step: 1.0
-- name: inkfl_org
- unit: uL/min
- start: 100.0
- stop: 280.0
- step: 1.0
-- name: conc_inorg
- unit: M
- start: 0.8
- stop: 1.4
- step: 0.05
-- name: conc_org
- unit: M
- start: 0.4
- stop: 1.2
- step: 0.05
-- name: temperature_c
- unit: F
- start: 20.0
- stop: 50.0
- step: 1.0
-- name: absolute_humidity
- unit: g/m^3
- start: 2.0
- stop: 37.0
- step: 1.0
-objectives:
- names:
- - PCE
- - Stability
- - Repeatability
-constraints:
-- clausius_clapeyron: true
- ah_col: absolute_humidity
- temp_c_col: temperature_c
diff --git a/configs/auto_config.yaml b/configs/example_demo.yaml
similarity index 100%
rename from configs/auto_config.yaml
rename to configs/example_demo.yaml
diff --git a/configs/configCSV_example_config.yaml b/configs/example_from_csv.yaml
similarity index 100%
rename from configs/configCSV_example_config.yaml
rename to configs/example_from_csv.yaml
diff --git a/data/.DS_Store b/data/.DS_Store
deleted file mode 100644
index e733d79..0000000
Binary files a/data/.DS_Store and /dev/null differ
diff --git a/docs/CAMPAIGN_STATUS.md b/docs/CAMPAIGN_STATUS.md
new file mode 100644
index 0000000..312b227
--- /dev/null
+++ b/docs/CAMPAIGN_STATUS.md
@@ -0,0 +1,1349 @@
+# Campaign status and how to use it
+
+Snapshot for collaborators. The full loop runs: R0 LHS -> R1 UCB-HVI (5) ->
+R2 qLogNEHVI (3), three replicate films per condition, 23 distinct conditions.
+
+## The live campaign: contract v4, from 2026-09-02 — READ THIS FIRST
+
+**The final workbook arrived and the score contract moved again.** Uniformity and
+optoelectronic were renormalised a second time, and the group's decision this time
+is to **freeze them**: read the workbook's own score columns and compute nothing.
+
+| | v2 — test data | v3 — test data | v4 — **the real campaign** |
+|---|---|---|---|
+| config | `campaign_d2d_perovskite.yaml` (archived) | `campaign_d2d_perovskite_test.yaml` (archived) | `campaign_d2d_perovskite_final.yaml` |
+| contract | `d2d-objectives-v2-nm-thickness` | `d2d-objectives-v3-test` | `d2d-objectives-v4-final` |
+| workbook | `Summary Table.xlsx` | `Summary Table Test.xlsx` | `Final Summary Table.xlsx` |
+| sheet | `Sheet1` | `Sheet1` | **`R0`** |
+| uniformity | `Coverage * (1-Uniformity) * Phase purity` | `mean(...)` computed | **read from AJ** |
+| optoelectronic | `log10(Voc * Photoconductance)` | `mean(...)` computed | **read from AK** |
+| thickness | computed from `T1..T4`, nm | unchanged | unchanged, readings now AC–AG |
+
+### The freeze, and what it costs
+
+Uniformity and optoelectronic use a new `stored` recipe: the workbook's score
+column *is* the objective value, with no recomputation. **This reverses this
+project's usual polarity**, which is "Python computes and the stored cell is
+demoted to a cross-check".
+
+**Why.** Both objectives have now been renormalised twice and the group is still
+revising them. Reimplementing a formula that is about to change means the code and
+the sheet disagree at exactly the moment someone edits the sheet, and the
+disagreement looks like a bug in whichever was checked second. Reading the value
+makes the workbook the single source of truth while the definition moves.
+
+**What it costs, stated plainly because it is the cross-check this project
+otherwise insists on:** there is **no independent recomputation** of these two
+objectives, so a stale pasted literal in AJ or AK cannot be caught by comparing it
+against anything. Intake and every round report print that in one line.
+
+**What partly replaces it: `formula_fingerprint`.** The config records the formula
+text of each frozen column, and the read compares it — reading the formula, never
+evaluating it. Recorded on 2026-09-02:
+
+| column | recorded formula |
+|---|---|
+| AJ uniformity | `=(L2+O2+P2)/3` |
+| AK optoelectronic | `=(S2+((0.75*Y2)+(0.25*AB2)))/2` |
+| AH thickness (avg) | `=AVERAGE(AC2:AF2)` (cross-checked, not frozen) |
+
+Comparison is row- and whitespace-independent, so one fingerprint covers all
+fifteen rows. **It notices a changed definition, not a stale value** — that gap is
+inherent to freezing and is asserted by a test so nobody later mistakes the
+fingerprint for a value check. A score column holding literals rather than
+formulas is flagged too, since that is the one failure this contract cannot see.
+
+**Unfreezing is a config edit, not a rebuild.** The v3 recipes (`mean`,
+`clamped_complement`, `capped_ratio`) stay in `scores.py`, unwired and tested. When
+the group settles the formulas, swap the recipe back and bump `contract_version`.
+
+### The sheet is `R0` now
+
+The workbook names its sheets by round, so the source sheet became a config key,
+`campaign.source_sheet`. Older contracts declare nothing and default to `Sheet1`.
+
+**The workbook's own `R1` sheet is deliberately not read.** The round contract is
+unchanged: each round's worklist is written to a NEW file beside the workbook and
+filled in there, and the source workbook is never opened for writing.
+
+### What the final data supports
+
+`scripts/intake_new_data.py`, exact leave-one-out, null −0.1480 at N=15,
+resolution floor ±0.236:
+
+| objective | plain GP | mean function | verdict |
+|---|---:|---:|---|
+| uniformity | **−0.4688** | none | below the null, **exploration only** |
+| optoelectronic | **−0.7038** | none | below the null, **exploration only** |
+| thickness | **+0.5814** | **none — withdrawn 2026-09-06** | **learnable**, on its rank permutation |
+
+**The thickness mean function was withdrawn on 2026-09-06.** It measured +0.7422
+against the plain GP's +0.5814, but its stated justification — that the fitted
+speed exponent agreed with spin-coating theory's −0.5 — is false. See "The
+thickness prior was half-earned" below. Nothing else in the campaign declares one,
+so **no objective now carries a physics prior.**
+
+All 15 rows are on-grid, all satisfy all three constraints, and both anchors hold
+(uniformity 0.599–0.882, optoelectronic 0.477–0.762). Sample 2 was
+`speed_2 = 0, time_2 = 60` in an earlier draft — which breaks the first constraint
+— and the group corrected it to `time_2 = 0`.
+
+**Thickness keeps its mean function, on the rank permutation.** Intake leaves it
+*inconclusive on R²* — the +0.1608 swing sits inside the ±0.236 floor, which is a
+statement that R² cannot resolve it at N=15 rather than a verdict.
+`scripts/permutation_rank_test.py` adjudicated on 2026-09-02:
+
+| | value |
+|---|---:|
+| observed rank ρ | **+0.6500** |
+| null mean (sd) | −0.1892 (0.2944) |
+| exceedances | **9 of 1800** |
+| p | **0.0056**, 95% CI [0.0021, 0.0090] |
+
+v3's p was 0.0028 on its own rows; that verdict did not transfer and this one was
+measured fresh. **Rank is the right statistic because rank is what the acquisition
+consumes** — it never sees R². **Do not quote the +0.1608 swing as evidence.**
+
+**Issue 10 is CLOSED.** The v3 photoconductance normalisation ranked backwards
+against its own raw measurement (Spearman −0.5484, p = 0.0343). On v4 the same
+comparison gives **+1.0000**. The diagnostic stays on because the failure is
+silent when it recurs.
+
+### The knob decision: beta = 36 → 4, radius = 0.35 → 0.25 (2026-09-03)
+
+**Superseded.** The reasoning below is kept because it is what the decision was
+reversed *from*.
+
+> The revisit trigger recorded under v3 was "when the photoconductance
+> normalisation is fixed and optoelectronic may become learnable". It fired, and
+> the answer was to keep the knob. `beta = 36` was chosen because two of three
+> objectives carried no learnable signal, which makes heavy exploration the right
+> posture; on v4, still only thickness beat the null. Had two or more axes become
+> learnable, the recommendation would have been to return toward the
+> sweep-settled `beta = 4`.
+
+That argument had a hidden premise: **that heavy exploration was how the two dead
+axes would come alive.** The extended C1&C2 sheet (see "The same 15 recipes, made
+three times") shows it is not, because it shows *why* they are dead:
+
+* **optoelectronic** is 84.5% between-campaign drift. Recipe ICC **0.000**, F 0.18,
+ p 0.9992; the GP refuses to fit it in 15 of 15 folds. No β reaches this.
+* **uniformity** is reproducible (ICC **0.730**, p < 0.00001) but not predictable
+ from ten inputs at fifteen distinct recipes (leave-one-recipe-out R² −0.243).
+ It needs more distinct recipes, not wider ones.
+
+So exploration buys nothing against either axis, and the batch quality it costs is
+measurable. **beta = 4.0 and radius = 0.25**, which is where the original
+108-cell sweep sat before v3's no-signal verdict overrode it.
+
+The second revisit trigger — R1 measurements replacing the oracle — has still not
+fired.
+
+### The thickness prior was half-earned, and it has been withdrawn (2026-09-06)
+
+`log T ~ log(speed_1) + log(precur_conc)` was declared from spin-coating theory in
+commit `600ef60` and carried unchanged through all three contracts. It has been
+removed from the live config by the group's decision. Two measurements decided it.
+
+**The physics claim is false for these films.** OLS on the 15 films, no replicates
+needed for a standard error:
+
+| coefficient | estimate | std err | 95% CI | theory |
+|---|---:|---:|---|---|
+| log(speed_1) | **−0.2554** | 0.0593 | **[−0.385, −0.126]** | −0.5 — **outside the interval, 4.1 SE away** |
+| log(precur_conc) | +1.3130 | 0.1747 | [+0.932, +1.694] | +1.0 — inside |
+
+Mass balance holds; Meyerhofer's viscous-thinning scaling does not. That is what
+you would expect if the antisolvent quench freezes the film before the thinning
+stage completes. **Fixing the exponents at their theoretical values and fitting
+only an intercept scores +0.5600 — worse than having no trend at all (+0.5823).**
+So the docs' long-standing citation of "−0.38 against theory's −0.5" as supporting
+evidence was never evidence; it has been removed from `GP_MODEL_DECISION.md`.
+
+**What was true, and is the argument for ever bringing a prior back.** The
+*variable choice* was real even though the magnitudes were not physics. Four
+matched-flexibility controls — three fitted parameters each, physically
+unmotivated pairs — all scored below the plain GP:
+
+| trend | free params | LOO R² |
+|---|---:|---:|
+| plain GP, no trend | 0 | +0.5823 |
+| fitted log(speed_1) + log(precur_conc) | 3 | **+0.7680** |
+| **theory, exponents FIXED at −0.5 / +1.0** | 1 | **+0.5600** |
+| control: fitted log(anti_vol) + log(time_1) | 3 | +0.3706 |
+| control: fitted log(anneal_time) + log(anti_time) | 3 | +0.2960 |
+| control: fitted log(anneal_temp) + log(anti_vol) | 3 | +0.3174 |
+| control: fitted log(time_1) + log(anneal_temp) | 3 | +0.4033 |
+
+It is **not** the case that any fitted two-term trend helps; most actively hurt.
+
+**The cost, stated plainly:** thickness LOO R² falls +0.7423 → +0.5814 and rank
++0.864 → +0.804. It remains the only objective with signal. The withdrawn gain
+never had a permutation test of its own, only an R² comparison, which this project
+now knows is the weaker instrument.
+
+`structured_mean.py` stays in the package, wired and tested, for a prior that earns
+its place. The bar: established physics, declared before fitting, beating
+matched-flexibility controls, and surviving a permutation test.
+
+### The leave-one-out null was never a significance threshold (2026-09-04)
+
+**This corrects a reading this project has used since the first campaign.**
+
+`1 − (N/(N−1))² = −0.1480` is the score of ONE predictor: predict every held-out
+film with the mean of the other fourteen. It has been read as the bar a model must
+clear. **A fitted GP does not behave like that predictor**, so it is not that bar.
+
+Measured two ways that agree — an adversarial verifier at 500 permutations and an
+independent reimplementation at 300, different RNG streams:
+
+| | median | 95th percentile | % of pure-noise draws above −0.1480 |
+|---|---:|---:|---:|
+| fitted GP, no mean function | −0.4075 / −0.4210 | +0.2309 / +0.2890 | 27.4% / **28.7%** |
+| with a 1-variable mean function | −0.4368 | +0.1267 … +0.1944 | 20.6 – 23.6% |
+| with a 2-variable mean function | −0.5384 / −0.5443 | +0.0752 … +0.1337 | 16.6 – 18.2% |
+
+**More than one shuffle in four beats −0.1480 with no signal present at all.** The
+GP's predictions under permuted y have roughly six times the spread of the
+constant predictor's; they are noise, and they land further from y — which is why
+the empirical null sits far below −0.1480 while its upper tail sits far above it.
+
+**What is still true.** Below −0.1480 a model has certainly learned nothing, so
+every "exploration only" verdict in this document stands: uniformity −0.4688,
+optoelectronic −0.7038 and the stored thickness score −0.2020 are all below the
+*median* of their own nulls. **What is not true** is the converse. A candidate
+above −0.1480 has shown nothing by that fact alone, and any argument of the form
+"it beat the null" carries no evidential weight.
+
+**A mean function LOWERS the null rather than raising it** — an OLS trend fitted on
+14 rows of shuffled y is a noise fit, and extrapolating it to the held-out row adds
+error. So mean-function results were not flattered by an inflated null; they were
+scored against a bar roughly five times too low, like everything else. In a
+20-variant sweep on phase purity, 20 of 20 "beat" −0.1480 including two
+deliberately nonsensical controls (`time_1`, `speed_1`).
+
+**Thickness is unaffected**, and the reason is on the record above: its verdict has
+always rested on the **rank permutation test** (p = 0.0056), never on R² against
+this number. That instrument was always the right one and is now the only one.
+
+**What to use instead.** The 95th percentile of the candidate's own permutation
+null, which `scripts/raw_component_screen.py --calibrate` measures, or the rank
+permutation test for a verdict. Two hazards found alongside this and now fixed:
+
+* when every fold fails to fit, a fold-mean fallback produces **exactly −0.1480
+ and ρ −1.0000** — a totally broken run reported the project's own null. The
+ screen now raises instead; any historical result at exactly −0.1480 should be
+ re-checked for collapsed folds.
+* `--seed` is inert on this code path (`fit_model_variant` runs a deterministic
+ L-BFGS from a deterministic init), so "the number does not move with the seed"
+ has never been evidence for anything. The real numerical floor, probed by row
+ ordering, is ~3e-4 rather than the 0.07 previously assumed.
+
+### The same 15 recipes, made three times (2026-09-03)
+
+A sheet arrived holding **45 rows that are 15 recipes made three times** —
+`local_inputs/Extended Summary Table C1C2.xlsx`, gitignored. Samples 1–15, 16–30
+and 31–45 carry identical inputs recipe for recipe, and block 1 is bit-identical
+to `Final Summary Table` on thickness. It is the first dataset in this project
+that can separate *the recipe moved the score* from *making and measuring the film
+again moved the score*. Reproduce with:
+
+```
+python scripts/plot_extended_replicates.py \
+ --workbook "local_inputs/Extended Summary Table C1C2.xlsx" \
+ --config configs/campaign_d2d_perovskite_extended_c1c2.yaml \
+ --outdir local_inputs/extended_c1c2_reports --align-blocks-to-first
+```
+
+That config is `status: diagnostic` and **is not a campaign contract**: it reads
+all three scores as stored, including thickness, and its optoelectronic anchors
+are derived from this data, which a real contract must never do.
+
+**Leave-one-out on this sheet leaks and the leak is large.** Hold out one row and
+the recipe's other two repeats remain in training at identical inputs, so the GP
+interpolates its own repeat. The row-wise LOO prediction correlates **+0.9989**
+with "just average the other two repeats", and that naive baseline alone scores
++0.5711 against the GP's +0.5849. Leave-one-**recipe**-out drops all three.
+
+| objective (score as stored) | row-wise LOO | leave-one-recipe-out | recipe ICC | block share |
+|---|---:|---:|---:|---:|
+| uniformity | +0.5849 | **−0.2431** | **0.730** | 1.3% |
+| optoelectronic | collapsed 45/45 | collapsed 15/15 | **0.000** | **84.5%** |
+| thickness score | +0.7556 | **−0.2151** | 0.845 | 0.5% |
+
+Nulls: −0.0460 row-wise, −0.1480 recipe-wise. They differ because dropping 3 rows
+of 45 moves the training mean further than dropping 1.
+
+**Three findings, and only the third is a modelling matter.**
+
+1. **Optoelectronic is a drift artefact.** All 15 recipes fall monotonically
+ block 1 → 2 → 3 (chance: 2.5 of 15), block 1 sitting ~120× above block 2. The
+ signal-collapse guard fires in every fold: the GP explains the column as pure
+ noise and its posterior mean is constant. This is metrology, not modelling.
+ The formula has also moved again — `AK` is now `=R2*X2*AA2`, a raw triple
+ product spanning 6.1e-11 to 6.2e-6, unnormalised. The v4 contract still
+ fingerprints the older `=(S2+((0.75*Y2)+(0.25*AB2)))/2`, so intake reports it.
+2. **Uniformity is reproducible.** ICC 0.730, F 9.10, p < 0.00001; recipe spread
+ 0.080 against repeat spread 0.049. Earlier contracts recorded it as possibly
+ measurement-noise-limited; **that reading is now contradicted.** It is a real,
+ repeatable property of the recipe that ten inputs at fifteen distinct recipes
+ are too sparse to pin down. It responds to more distinct recipes and to
+ structure, not to a different acquisition.
+3. **Squashing a measurement before the GP destroys the signal.** Same films, same
+ folds, leave-one-recipe-out:
+
+ | thickness as… | R² | ρ |
+ |---|---:|---:|
+ | the stored score (Gaussian-squashed) | −0.2151 | −0.106 |
+ | raw nanometres | **+0.4082** | +0.627 |
+ | log(nm) | **+0.4266** | +0.624 |
+
+ `EXP(-((T-650)/250)²)` is non-monotone, so 500 nm and 800 nm map to the same
+ score and the GP is asked to learn a fold. **This is why v4 trains thickness on
+ nanometres and applies the target afterwards** — and it is the strongest
+ available argument for eventually unfreezing uniformity and optoelectronic and
+ modelling their components rather than their composites.
+
+**Caveats on this sheet.** Only samples 1–15 carry raw component data; 16–45 hold
+`AH`/`AI`/`AJ`/`AK`/`AL` as pasted literals with nothing underneath, so no
+component-level analysis is possible on blocks 2 and 3 and nothing can cross-check
+those values against measurements. Samples 17 and 32 still read
+`speed_2 = 0, time_2 = 60`; the group's correction to sample 2 reached block 1
+only. The script reports that mismatch and, with `--align-blocks-to-first`,
+applies the same correction to the later blocks.
+
+## The v3 DRY RUN, from 2026-08-17 (superseded)
+
+> **v3 rehearsed this contract's shape on test data** — its workbook was
+> literally called "Test". It is superseded by v4 above and its config is
+> archived. The sections below are its record: the mechanisms still apply
+> (constraints, the round report, the simulation), and its fitted numbers
+> are about objectives that have since been redefined.
+
+
+A second dataset arrived and ran a different objective contract: two of the three
+objectives were computed differently, the workbook's columns moved, two grids
+changed, and this project's first real constraints went live. Those constraints
+and mechanisms carry forward to v4 unchanged; the fitted numbers do not.
+
+| | v2 — algorithm testing | v3 — this section (now superseded by v4) |
+|---|---|---|
+| config | `configs/campaign_d2d_perovskite.yaml` (**archived**) | `configs/campaign_d2d_perovskite_test.yaml` |
+| contract | `d2d-objectives-v2-nm-thickness` | `d2d-objectives-v3-test` |
+| workbook | `local_inputs/Summary Table.xlsx` | `local_inputs/Summary Table Test.xlsx` |
+| uniformity | `Coverage * (1-Uniformity) * Phase purity` | `mean(Coverage, 1-clamp(Uniformity), Phase purity)` |
+| optoelectronic | `log10(Voc * Photoconductance)` | `mean(min(Voc,1.4)/1.4, Normalized photoconductance)` |
+| thickness | mean of `T1..T4`, nm | unchanged |
+| constraints | none, deliberately | three, active |
+
+The old config is archived rather than deleted, and stays complete and loadable:
+every number in `GP_MODEL_DECISION.md` is about that contract. Archived means "do
+not run new rounds against it".
+
+**Why a new file and not an edit.** Utility space is what hypervolume is measured
+in. An objective that keeps its name while changing its construction makes every
+cross-campaign number incomparable while every plot still renders — which is the
+failure mode the `contract_version` key exists to prevent.
+
+**All three recipes reproduce the stored score columns**, worst disagreement
+2.3e-13 across all 15 rows. Per the group, for this workbook the stored scores are
+authoritative and the recompute is the cross-check, so a disagreement is a warning
+finding rather than a block.
+
+### What the second dataset supports
+
+`scripts/intake_new_data.py`, 2026-08-17, exact leave-one-out, null −0.1480 at
+N=15, resolution floor ±0.236:
+
+| objective | plain GP | with mean function | verdict |
+|---|---:|---:|---|
+| uniformity | **−0.6447** | — | below the null, **exploration only** |
+| optoelectronic | **−0.5842** | −0.6977 | below the null, **exploration only**, mean function **deleted** |
+| thickness | **+0.5227** | **+0.6630** | **learnable**; the swing is inside the floor |
+
+**Two of the three axes carry no signal.** A batch is therefore chosen on one
+informative axis and two uninformative ones. That is a legitimate exploration
+round, but it is not a three-objective optimisation, and the review must say so
+rather than let the predicted numbers imply otherwise.
+
+**The optoelectronic mean function was deleted, and that is the designed
+outcome.** The first campaign's linear `anneal_temp` trend was worth −0.342 →
++0.355 on its own score; here it makes the fit *worse*, −0.5842 → −0.6977. The
+target was redefined underneath it, so the old evidence was never about this
+quantity. Do not reinstate it from the archived config without a fresh verdict.
+Issue 10 is the prime suspect for why the objective is unlearnable at all.
+
+**Thickness keeps its mean function, decided by the rank permutation.** The plain
+GP now reaches +0.5227 where the first campaign's managed +0.116, so the trend has
+much less left to explain and the +0.1403 swing is inside the ±0.236 floor —
+*inconclusive on R²*, which is a statement that R² cannot resolve it at N=15
+rather than a verdict. `scripts/permutation_rank_test.py` adjudicated it on
+2026-08-18:
+
+| | value |
+|---|---:|
+| observed rank ρ | **+0.7250** |
+| null mean (sd) | −0.1917 (0.2937) |
+| exceedances | **4 of 1800** |
+| p | **0.0028**, 95% CI [0.0003, 0.0052] |
+
+Stronger than the first campaign's p = 0.0350 on its own films. **Rank is the
+right statistic because rank is what the acquisition consumes** — it never sees
+R². **Do not quote +0.1403 as evidence**; the permutation is what carries the
+weight, and the swing is merely consistent with it.
+
+That two-part rule is now what the intake prints: (i) the structured fit must beat
+the null by more than the floor; (ii) when structured-versus-plain lands inside the
+floor, the permutation decides.
+
+### Are beta = 4.0 and radius = 0.25 defensible?
+
+**The live campaign runs beta = 4.0 and radius = 0.25** as of 2026-09-03. What
+follows describes the sweep that produced the earlier 36 / 0.35 cell and then the
+measurement that moved it; the sweep's central caveat — that it cannot *rank*
+cells — applies to both settings equally.
+
+**What moved it.** At β = 36 the radius knob is provably inert: on the final
+workbook at seed 73 the scan returns bit-identical batches at radius 0.15, 0.25
+and 0.35 (spacing 1.213, 18 of 50 coordinates pinned to a grid bound, identical
+mean utilities). At β = 4 it binds, and the batch stops living on the corners:
+
+| beta | radius | HV gain | edge coords / 50 | spacing | sd ratio |
+|---:|---:|---:|---:|---:|---:|
+| 4 | 0.15 | +0.0000 | 13 | 0.682 | 5.9× |
+| **4** | **0.25** | **+0.0009** | **11** | **0.781** | **5.8×** |
+| 4 | 0.35 | +0.0000 | 13 | 0.941 | 6.1× |
+| 9 | 0.25 | +0.0000 | 15 | 0.882 | 6.2× |
+| 36 | 0.25 | +0.0000 | 18 | 1.213 | 6.5× |
+
+`sd ratio` is the mean posterior sd at the proposed points over that at the
+measured ones. **Read the edge count and the spacing, not the HV gain**: +0.0009
+is far below the 0.010–0.027 trial sd this sweep measured, so it is a tiebreak.
+The edge count and spacing are facts about the batch at a fixed seed.
+
+#### The sweep that produced the earlier cell
+
+They were determined by a
+sweep over two instruments on the campaign's own data — per-round utility **box
+plots**, and **heat maps**, which are 2-D slices through the higher-dimensional
+Gaussian-process model — across **beta from 9 to 49** (9, 25, 36, 49) and **radius
+from 0.05 to 0.45** (nine values, step 0.05), three starting designs per cell at
+production settings. **Note that local penalization is inert at the current beta**;
+the measured consequences are below. Outputs stay local (`local_outputs/`): they
+are how the group picks a setting, not a result about the chemistry, and they are
+not part of what this repository publishes.
+
+**They are a declared policy choice, not a measured optimum, and the distinction
+matters.** That sweep **could not rank cells**: the whole spread across betas was
+0.0065 against a trial-to-trial sd of 0.010–0.027, and the best cell was a
+different (β, r) in every trial. It also scored candidates against a *noiseless
+GP oracle of the same model class the optimiser fits*, so the landscape held no
+surprises and exploration had unusually little to earn — it **systematically
+undervalues large β**, which is the very thing this cell buys.
+
+The rationale for β = 36 was a posture, not a score: κ = √36 = 6, heavy
+exploration, which reads as the right stance when **two of three objectives carry
+no learnable signal** and the third is the only one worth exploiting. **Both
+consequences it was known to carry are what eventually retired it:**
+
+* **Local penalization was inert at that β.** Achieved minimum batch spacing was
+ **1.091**, three times the 0.35 radius, so the knob had nothing to act on. The
+ sweep predicted this: radius binds *less* as β rises, and at β = 49 the nine
+ radii produced only 3–4 distinct batches.
+* **The batch ran to the edges.** Range-edge coordinates per condition were
+ **[4, 7, 4, 3, 3]** — 21 of 50 — against 11–15 of 80 on the first campaign's
+ arm at β = 4. High exploration plus a monotone thickness trend puts candidates
+ at bounds.
+
+**The first revisit trigger fired twice.** Once when the photoconductance
+normalisation was fixed (issue 10) — that time the posture survived, because
+optoelectronic still did not beat the null. Again on 2026-09-03, when the extended
+C1&C2 sheet showed the two dead axes are dead for reasons no β addresses; that
+time it did not survive. **The second trigger — R1 measurements replacing the
+oracle — has still not fired**, and until it does the sweep's central caveat
+stands: no cell here has been *ranked*, only argued for.
+
+### The simulation at the ratified cell
+
+`scripts/plot_round_simulation.py --cell 0.25,4` runs one campaign against the
+frozen oracle at exactly the decided knobs; `scripts/plot_boxplot_sweep.py
+--betas 4 --radii 0.25` runs the same cell across the three starting designs so
+the per-round boxes have a distribution behind them. Both default to the v3
+config. (The numbers reported immediately below were measured at the earlier
+0.35 / 36 cell and have not been re-run.) Outputs: `local_outputs/round_sim_v3_cell` and
+`local_outputs/boxplot_v3_cell`.
+
+Measured on the new data, seed 73:
+
+| | R0 | +R1 | +R2 |
+|---|---:|---:|---:|
+| hypervolume | 0.7929 | 0.7929 | 0.8053 |
+
+**R1 adds no hypervolume at all on this oracle, and R2 adds +0.0124.** That is
+what β = 36 looks like against a landscape with no surprises in it: the batch
+spends its budget on exploration that a noiseless same-class oracle cannot repay.
+It is the caveat above made numerical — the instrument understates the case for
+the policy it is testing — and not evidence that the cell is wrong.
+
+**The cross-instrument identity holds.** The simulated R1 batch hashes to
+`60d1682aa055ca97`, the same as the live `run_r1_ucb` proposal from the measured
+rows. The simulation is describing the batch the campaign would actually ship, not
+a similar one.
+
+Pre-registered expectations, checked after: the two no-signal axes climb far less
+than thickness (+0.1047 and +0.0842 against +0.3967) — **HELD**; the sweep-arm
+rules report **NOT APPLICABLE** rather than FAILED, because a single ratified cell
+has no arm to vary and calling that a failure would put red lines under a run that
+did exactly what was asked.
+
+The dead axes' surfaces are rendered and captioned as fitted noise, never dropped.
+Thickness's surface shows the declared `log T ~ log(speed_1) + log(precur_conc)`
+trend, which is **consistency with what the config told the model, not a
+discovery**.
+
+### The round report — figures at propose time
+
+Pressing **Propose next round** now also renders six figures beside the workbook,
+in `_reports/_/`. A second button, **Figures
+from current data**, renders the four that need no batch — use it the moment a
+round's measurements are entered, before deciding whether to propose at all. Same
+thing headless:
+
+```bash
+python scripts/generate_round_report.py --workbook "local_inputs/Final Summary Table.xlsx" --data-only
+```
+
+**Every figure writes the CSV behind it**, plus a `manifest.json` recording the
+contract version, seed, git describe, reference point, runtime and the active
+notices. A PNG whose numbers cannot be re-derived is the next
+plausible-finite-number bug; this project has had three. Two equalities are
+asserted by tests rather than by convention: the parity numbers *are*
+`intake_new_data.py`'s numbers (one shared fold loop in `mobo_kit.loocv`, not two
+implementations that agree today), and figure 03's numbers *are* the Review
+sheet's.
+
+| figure | what it shows | what it cannot claim |
+|---|---|---|
+| `00_batch_placement` | proposed recipes over the measured cloud, normalised to the declared grid, plus batch spacing | nothing about quality — only where in recipe space the batch goes |
+| `01_loo_parity` | leave-one-out prediction against measurement, per objective, with LOO R² and the null | an axis marked NO LEARNABLE SIGNAL has a model that does not beat the null; its scatter is nothing, not a weak trend |
+| `02_attribution` | mean \|SHAP\| per input per objective, in utility units | explains the **model**, not the world; features in a `mean_function` were *told* to it; on a no-signal axis the bars are fitted noise |
+| `03_batch_predictions` | predicted measurement and utility per condition, plus the batch's ΔHV distribution and per-candidate P(non-dominated) | predictions, not measurements |
+| `04_hv_trajectory` | cumulative observed hypervolume per measured round | monotone **by construction** — random sampling rises too, so this is progress, not proof of optimisation |
+| `05_objective_space` | pairwise utility panels with per-pair fronts, 3-objective front ringed, plus one fixed 3D view | the Pareto set is non-dominated among what has been **measured**, not across the design space |
+
+**Runtime is about 15 s at N=15** on an idle machine, dominated by the 45
+leave-one-out refits (9.8 s) and the attribution (a few seconds at 15 instances).
+The fold loop runs single-threaded on purpose: at 14×10 the matrices are small
+enough that intra-op threading costs more than it buys — 9.8 s at one thread
+against 15.1 s at this box's default of 12, bit-identical either way.
+
+*The first measurement of that recorded 51 s against 117 s and was wrong: it was
+taken while sixteen permutation workers were saturating the CPU. The effect was
+real but was of the load, not the thread count. A timing under contention is an
+unreproduced number like any other, and this project's rule is that those get
+re-measured rather than written down. Add the first render of a session to any of
+these: matplotlib builds its font cache once, which cost about a minute here.*
+
+**A report failure never costs a batch.** The worklist and the Review sheet are
+written before the figures are drawn; if rendering fails, `Generated.report_error`
+says so and the batch stands. Inside the report, one failed figure is recorded in
+the manifest and the rest still render.
+
+**Three notebook conventions were deliberately not ported.**
+
+* **In-sample parity.** Asking a model about points it was fitted on measures
+ memorisation; at N=15 in 10 dimensions it is close to a straight line whatever
+ the model knows. Parity here is leave-one-out.
+* **Ad-hoc sign flips at plot time.** Objective polarity is a config contract
+ (`goal:`). Flipping a sign in a figure makes the figure disagree with the
+ optimiser, and only one of them is right.
+* **Auto-referenced hypervolume.** The reference point is required and
+ campaign-fixed. A reference re-derived per call gave 6e-8 against 1.448 on the
+ same data once already — see issue 5.
+
+### The two grid edits
+
+Both forced by the measured rows; everything else carries over unchanged, and all
+15 rows land on the declared grid.
+
+* **`time_2` now starts at 0** (was 10). Sample 2 is a one-step film — `speed_2`
+ and `time_2` both zero — so 0 has to be on the grid or a real recipe is
+ off-grid. Reaching 0 with step 5 also reaches 5, which the first campaign's grid
+ excluded and no film has run, so the hole is declared as a `nonzero_minimum`
+ constraint rather than filled in silence.
+* **`anti_time` now steps by 1** (was 2), 9..25. Sample 1 runs `anti_time = 12`,
+ which the old grid could not hold; the first campaign carried it as a declared
+ off-grid exception excluded from pool bookkeeping. The axis goes from 9 values
+ to 17.
+
+An explicit value list would express `{0} ∪ {10..60}` directly and avoid the
+`nonzero_minimum` workaround, but `lhs` asserts that every design grid is
+uniformly spaced, so it would need changes to `design.py` and `lhs.py`. **Worth
+raising with the group:** whether a 5 s second spin should ever be allowed, and
+whether `anti_time` wants step 1 or an explicit list.
+
+### The constraints
+
+Declared in the new config, enforced by filtering the candidate pool before any
+acquisition sees it, and re-checked independently by `validate_batch`. The
+acquisition modules are byte-identical. `discrete_refinement` is **not**
+constraint-aware and is not wired into a round; its docstring says so.
+
+| name | rule | why |
+|---|---|---|
+| `second_stage_all_or_nothing` | `speed_2` and `time_2` both zero or both nonzero | a stage at 0 rpm for 30 s is a contradiction; both zero is a one-step film, which sample 2 is |
+| `antisolvent_lands_while_spinning` | `anti_time < time_1 + time_2`, strictly | dropping at exactly the end is already too late |
+| `second_stage_runs_at_least_10s` | `time_2` is 0 or ≥ 10 | the declared hole in the arithmetic grid, above |
+
+All 15 measured rows satisfy all three. Observed rows are soft-checked only —
+history is history, and a constraint that rejects a film the group actually ran is
+far more likely to be wrong than the film is.
+
+**Watch `constraint_pool_survival_rate`** in the round diagnostics. The sampler
+draws until it has the requested pool size, so a mis-specified constraint produces
+a normal-looking pool drawn from a sliver of the space, and the survival rate is
+the only place that shows.
+
+## Everything below this line is about the FIRST campaign
+
+> **The first campaign was algorithm testing.** Its 15 rows and its
+> `d2d-objectives-v2-nm-thickness` contract existed to prove the loop worked, not
+> to run an experiment. The sections below are its record and its numbers are
+> about *its* objectives — uniformity as a product, optoelectronic as a log10
+> product — which the live campaign redefined. **Nothing here transfers unless it
+> is method rather than measurement.** Where a mechanism still applies (how a
+> round runs, what `Y_model` must contain, the acceptance test) it applies to
+> both; where a fitted number appears, it is the first campaign's.
+
+## Running a round
+
+```python
+from mobo_kit.campaign import load_campaign_config, run_r0_lhs, run_r1_ucb, run_r2_qlognehvi
+
+config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+
+r0 = run_r0_lhs(config, n=15) # space-filling, no model
+r1 = run_r1_ucb(config, X_phys, Y_model, n=5) # UCB-HVI + local penalisation
+r2 = run_r2_qlognehvi(config, X_phys, Y_model, n=3) # qLogNEHVI
+```
+
+Each returns a `RoundResult` with:
+
+| field | contents |
+|---|---|
+| `conditions` | distinct proposed conditions, physical units, columns = input names |
+| `replicates` | one row per film, with `candidate_id` / `replicate_group` / `replicate_index` |
+| `diagnostics` | method, seed, pool size, objective contract version, validity report, fit warnings |
+
+Two warning keys, deliberately separate. `diagnostics["model_fit_warnings"]` holds
+only the fit guard's own findings — the ones a human reviewing a batch must read,
+and the ones the launcher and the `Review` sheet surface.
+`diagnostics["fit_warnings_raw"]` holds everything the fits raised, including the
+~18 numpy-2.0 deprecation notices per fit that this stack emits. Nothing surfaces
+the raw list; it is there for debugging a strange fit later, because a BoTorch or
+scipy convergence warning that the filter dropped is exactly what would be wanted
+then.
+
+`diagnostics["validity"]` carries `min_pairwise_distance` and
+`boundary_coords_per_condition`, which are the numbers to plot per round.
+
+## What `Y_model` must contain
+
+**Not the three stored score columns.** Since 2026-07-30 the objectives are
+computed in Python from the raw measurement columns, and the stored cells are a
+cross-check. Column order comes from `objective_names(config)`:
+
+```
+("uniformity", "optoelectronic", "thickness")
+```
+
+`read_campaign_workbook` returns exactly that as `contents.model_values`, so the
+normal path is:
+
+```python
+from mobo_kit.workbook_io import read_campaign_workbook
+
+contents = read_campaign_workbook("local_inputs/Summary Table.xlsx", config)
+X_phys = contents.inputs.to_numpy(float)
+Y_model = contents.model_values.to_numpy(float) # objective order
+assert contents.errors == () # fail closed before fitting
+```
+
+Each value comes from a recipe declared in config (`objectives.specs[].measurement`):
+
+| objective | recipe | from |
+|---|---|---|
+| uniformity | `product` | `Coverage`, `1 - Uniformity` (computed), `Phase purity` |
+| optoelectronic | `log10_product` | `PL - Implied Voc (Max)`, `Photoconductance (Max)` |
+| thickness | `mean_of_present` | whichever of `T1..T4` were measured |
+
+Thickness is in **nanometres**, unrounded, because the GP trains on the raw
+measurement and the 650 nm Gaussian is applied to the posterior. See
+`GP_MODEL_DECISION.md` for why. Anything that collects data for the next round
+must collect nm.
+
+`contents.findings` carries what the read noticed: cross-check mismatches,
+readings the operator excluded, and films whose thickness readings disagree.
+`contents.errors` is empty on the R0 rows; if it ever is not, do not fit.
+`contents.inputs_used` records how many readings each value came from, which is
+what Phase 4 needs to turn a spread into an observation variance.
+
+## For the plotting work
+
+**This is now implemented.** `scripts/plot_round_simulation.py` runs the whole
+loop against a frozen GP oracle and renders contour slices, per-round boxplots and
+a hypervolume line, with a batch-identity manifest
+(`docs/ROUND_SIM_MANIFEST.md`). Read `docs/ROUND_SIM_DELTA.md` before extending
+it. The recipe below is kept because it is what any new plotting code has to get
+right, and both conventions still fail silently.
+
+**Contour slice through the GP.** Fit with the same path a round uses, then
+evaluate on a 2-D grid with the other eight inputs held fixed:
+
+```python
+from mobo_kit.campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ normalise_inputs,
+)
+
+# same normalisation, structured means, variant and seeding as the round itself,
+# so this reproduces the round's model rather than a similar one
+model, fit_warnings = fit_campaign_models(config, X_phys, Y_model, seed=73)
+assert not fit_warnings # a fit can succeed and still deserve distrust
+
+model.eval()
+with torch.no_grad():
+ post = model.posterior(torch.tensor(grid_norm)) # grid_norm in [0,1]^10
+ mean, var = post.mean, post.variance
+```
+
+Two things to respect when turning that into a utility surface:
+
+* the GP output for thickness is **log(nm)**, not nm. `ObjectiveSpec.model_link`
+ records this. Use `transform.expected_transform(mean, var)` rather than
+ transforming the mean yourself; it dispatches per objective and integrates the
+ lognormal by quadrature where needed.
+* inputs are normalised to `[0,1]` against the config grid bounds, not the
+ observed range. `normalise_inputs(config, X_phys)` is the conversion. A model
+ fitted on config bounds and evaluated on observed-range coordinates is being
+ asked about different points than it was told about, and nothing errors.
+
+**Round-comparison plot.** Keep each `RoundResult` and plot `conditions` per
+round on shared axes (R0 blue `#2a78d6` / R1 orange `#eb6834` / R2 green
+`#1baf7a` — the palette `plot_dtlz2_report.py` and `plot_round_simulation.py`
+both use, so project figures read as one set), plus per-round
+`min_pairwise_distance` and boundary counts from `diagnostics`. Contour slices
+should show **23 distinct conditions**, not 39 films -- replicates share inputs
+and would otherwise overplot.
+
+Hypervolume is comparable across rounds only because objective scales are fixed
+in config; `assert_scaling_is_campaign_fixed` enforces that. Do not re-derive
+scales from observed data between rounds.
+
+## Model state
+
+Validated on the 15 R0 observations, exact leave-one-out, null R2 = -0.148. These
+are the canonical numbers, as `scripts/intake_new_data.py` reports them — same
+pipeline and same inputs the model uses:
+
+| objective | plain GP | with structured mean | swing |
+|---|---:|---:|---:|
+| thickness (nm) | +0.116 | **+0.381** | +0.265 |
+| optoelectronic | -0.342 | **+0.267** | +0.609 |
+| uniformity | no learnable signal (permutation p = 0.82) | n/a | — |
+
+Both swings clear the ±0.236 sampling floor. `GP_MODEL_DECISION.md` records
+slightly different figures (+0.183 → +0.384 for thickness, and +0.355 for
+optoelectronic); those came from an older instrument reading the workbook's rounded
+`Thickness (avg)`, and both differences are accounted for — see issue 1 and the
+intake section below. No conclusion depends on which set you read.
+
+Thickness rests on its rank permutation (p = 0.0350), not on the R2 swing.
+Uniformity is exploration-only by measurement, not by choice; the interface must
+not imply the model knows more than it does about it.
+
+## Reading a round's results back
+
+`read_candidate_results(source_workbook, config, "R1")` reads the filled-in
+candidate sheet and returns design points, not films:
+
+| field | contents |
+|---|---|
+| `conditions` | one row per condition, input columns |
+| `model_values` | one row per condition, objective columns, **aggregated** |
+| `replicates` | one row per film, with its own objective values |
+| `replicate_spread` | per-condition sd, in each objective's aggregation space |
+| `films_used` | how many films each observation was aggregated from |
+| `findings` | the same note / warning / error list as the source read |
+
+Objective values are computed per film with the same recipes Sheet1 uses, so R0
+and R1 observations are commensurable, and only then aggregated per
+`replicate_group`.
+
+**Thickness aggregates in log space** (`replicate_aggregate: mean_of_log`), because
+`response: log` means the GP trains on `log T` — the geometric mean is the
+arithmetic mean in the space the model works in, and it is the choice consistent
+with pooling `train_Yvar` in log space. The difference from a plain mean is second
+order in the replicate spread: under 0.1% at the ~3% spread most R0 rows show,
+about 14% on a film set as inconsistent as sample 12's. It is one config key per
+objective if the group prefers otherwise.
+
+`replicate_spread` is what Phase 4 (issue 7) pools, and it is already in the right
+space: a sd of `log T` for thickness, a sd of the value itself for the other two.
+It is NaN for a single film, which is honest — one film measures no
+reproducibility at all.
+
+## Synthetic acceptance test
+
+`tests/test_dtlz2_acceptance.py` runs DTLZ2 (3 objectives, 10 inputs, known
+Pareto front) end to end through `campaign.py`. It exercises the algorithm with
+no dependence on whether the experimental measurements are right.
+
+```bash
+pytest tests/test_dtlz2_acceptance.py -m "not slow" # 10 tests, ~12 s
+pytest tests/test_dtlz2_acceptance.py -m slow # BO vs random, ~33 s
+```
+
+Measured on the negated DTLZ2 (max_hv = 0.807):
+
+| | R0 (15) | +R1 (5) | +R2 (3) |
+|---|---:|---:|---:|
+| hypervolume | 0.507 | 0.555 | 0.612 |
+
+Batch spacing: R1 min pairwise 0.735, R2 0.859, against a configured floor of
+0.15 -- local penalization is separating candidates, not merely not failing.
+
+**Cumulative hypervolume rises monotonically by construction**, so that alone is
+not evidence of optimisation -- it would hold for random sampling too. The
+informative result is the baseline comparison at equal budget (8 extra points
+from the same 15-point start):
+
+| | mean HV gain |
+|---|---:|
+| Bayesian optimisation | **+0.075** |
+| random on-grid search | +0.056 |
+
+A ratio of **1.35x**, and BO wins on **5 of 8 seeds** -- on the mean, not every
+seed. With 8 added points in 10 dimensions that is the honest expectation, so the
+test asserts the mean and not a per-seed win.
+
+Two conventions that fail *silently* if got wrong, both now covered:
+
+* DTLZ2 minimises by default; `negate=True` is mandatory or the test measures the
+ opposite of optimisation.
+* BoTorch's `Hypervolume` assumes maximisation and **silently drops points that
+ do not dominate the reference** -- no warning, no exception, just a smaller
+ number or 0.0. The helper asserts at least one point dominates before
+ trusting the result.
+
+## The numbered issues -- read before trusting a batch
+
+**Issues 1-9 are the first campaign's**, kept because each one's evidence is the
+reason a decision holds and because several are the sort of thing that gets
+rediscovered and re-argued. **Issue 10 is the live campaign's and is open.**
+
+Kept numbered and in place even once closed, because each one's *evidence* is the
+reason a decision holds, and because several are the sort of thing that gets
+rediscovered and re-argued. Status is stated at the top of each.
+
+1. **CLOSED 2026-07-30. The 0.089 on optoelectronic is a numerical artifact, not a
+ modelling difference.** The two pipelines specify *the same model*: fitting a
+ zero-mean GP to `y - trend` and fitting a fixed-mean GP to `y` with mean
+ `trend` have identical marginal likelihoods, because a fixed mean only shifts
+ the data. So there was never a modelling question to answer — only a question
+ about why two routes to one model disagreed.
+
+ Two contributions, measured:
+
+ | | two-stage | mean module | gap |
+ |---|---:|---:|---:|
+ | with `Standardize` (production) | +0.3551 | +0.2670 | **+0.0881** |
+ | without `Standardize` | +0.3385 | +0.2670 | +0.0715 |
+
+ *Standardization scale accounts for about 19%.* With the transform in place the
+ two pipelines standardize different quantities — the residual in one, the target
+ in the other — so the outputscale and noise priors, which are defined on
+ standardized units, act on differently-scaled residuals. Removing it moves the
+ gap from 0.0881 to 0.0715.
+
+ *The remaining 81% is the MLL optimiser.* With the transform gone the likelihood
+ surfaces are identical, yet the fits land in slightly different places: across
+ folds the outputscale differs by up to 2.7%, the noise by 2.7%, and the median
+ lengthscale by **9.6%**. At N=15 that is enough to move LOO R² by 0.07. The
+ optimiser is deterministic — the earlier seed sweep found bit-identical results
+ across four seeds — so this is a different starting point on one surface, not
+ stochastic variation.
+
+ **The mechanism, stated first because that is the rule.** The gap is roughly
+ one-fifth a definitional difference between two legitimate conventions and
+ four-fifths the optimiser landing in a different place on one identical
+ objective. Both parts are named, measured and reproducible. This project's own
+ rule is that a deterministic difference on the same rows must be *explained*,
+ not absorbed into a floor — so the explanation comes first and the floor comes
+ after it.
+
+ **The floor, as a corollary.** Given the mechanism, 0.0881 is also inside the
+ ±0.236 sampling floor, and its optimiser component is exactly the measurement
+ that established the ≈0.07 numerical-reproducibility floor — see
+ `GP_MODEL_DECISION.md`, "Three floors". So it was never evidence of anything.
+ That is a consequence of the explanation, not a substitute for it.
+
+ The campaign uses the mean-module convention, the one wired into `campaign.py`.
+ No action. Kept below for the reasoning, because "two implementations disagree"
+ is the sort of thing that gets rediscovered.
+
+ ---
+
+ *Original entry, narrowed 2026-07-30 before the closure above.*
+ Two implementations of the same pipeline on the same 15 rows give LOO R2 +0.355
+ (two-stage) and +0.267 (mean module). Reproduced exactly: **+0.0881**.
+
+ **MLL optimiser seeding is ruled out.** Both pipelines give bit-identical LOO R2
+ across seeds 7, 73, 137 and 2024 — 0.3551 and 0.2670 every time, zero variation.
+ That suspect is closed.
+
+ **The standardization-scale suspect is back, and quantitatively consistent.** It
+ was previously recorded as ruled out "because the direction contradicts the
+ observed asymmetry"; the measured direction does not contradict it. The two
+ pipelines hand `Standardize` different things — two-stage standardizes the
+ *residual*, the mean module standardizes the *target* and then subtracts a
+ standardized trend — so the deviation the covariance must explain has sd 1.0 in
+ one and `sd(residual)/sd(target) = 0.762` in the other. The fitted outputscales
+ match that prediction to 4%:
+
+ | | median outputscale | median noise (standardized) |
+ |---|---:|---:|
+ | two-stage | 0.8365 | 0.006516 |
+ | mean module | 0.4681 | 0.006443 |
+ | predicted for the mean module, `0.8365 × 0.762²` | 0.4859 | — |
+
+ **The attempt to confirm it failed, and the test was the problem, not the
+ hypothesis.** Inflating the residual to the target's sd before fitting moved LOO
+ R2 by +0.0002 — because `Standardize` divides by whatever sd it is given, so
+ scaling its input is a no-op. That experiment was vacuous by construction and
+ proves nothing either way. Recorded so nobody re-runs it.
+
+ **The specific next test**, for whoever picks this up: the two pipelines cannot
+ be separated while both re-standardize, so disable `Standardize` in both (or
+ standardize both by the same fixed constant) and see whether the gap survives.
+ If it vanishes, the cause is that the outputscale and noise priors are defined
+ on standardized units and the two pipelines standardize different quantities.
+ That is a ~20-line experiment against `_build_single_task_gp`.
+
+ Both numbers remain far better than plain (-0.342), so the direction is not in
+ doubt and the mean module stays either way. The gap should be closed before
+ optoelectronic candidates are acted on.
+
+2. **Done, 2026-07-30 — kept here because the audit is the evidence for how the
+ objectives are now computed.** Three of the workbook's derived columns are
+ pasted literals, not formulas. Audited on all 15 rows, 2026-07-29:
+
+ | col | quantity | kind | agrees with recomputation |
+ |---|---|---|---|
+ | `Z` | `Uniformity score` | formula `=L2*N2*O2` | exactly |
+ | `R` | `log10(P*Q)` | formula `=LOG(P2*Q2)` | 1.8e-15 |
+ | `AA` | `Optoelectronic score` | **literal**, copy of R | 1.8e-15 |
+ | `Y` | `Normalized thickness` | formula on **X** | — |
+ | `AB` | `Thickness score` | **literal**, from the **unrounded** T mean | 4.8e-10 |
+ | `X` | `Thickness (avg)` | **literal**, `ROUND(mean(T1..T4))` | 0.5 nm |
+
+ Two things this changes. First, **`AB` is not a copy of `Y`**: `Y` evaluates
+ the Gaussian on the rounded `X`, while `AB` was pasted from the same Gaussian
+ on the unrounded T1..T4 mean. They disagree by up to **1.7e-3** already
+ (sample 8: 0.651997 against 0.653702). The campaign path reads neither -- it
+ trains on `X` -- so this is harmless there. `scripts/gp_diagnostic.py` does read
+ `AB` (its `OBJECTIVE_COLS` are Z/AA/AB), where 1.7e-3 is immaterial to a
+ variant comparison. Harmless either way today, but it is the same silent
+ divergence that produced the original uniformity discrepancy, sitting in the
+ file right now.
+
+ Second, **the column the GP trains on is itself derived and rounded.** `X` is
+ `mean(T1..T4)` rounded to whole nanometres (sample 4: 663.75 -> 664; sample
+ 12: 1154.5 -> 1155). Against `sigma = 176.8` nm a 0.5 nm error moves the
+ utility by under 1e-5, so this is immaterial numerically. It is worth knowing
+ that no raw measurement column feeds the model directly.
+
+ **What was done.** `src/mobo_kit/scores.py` computes all three objectives from
+ the measurement columns; `Z`, `R` and `X` became cross-checks that warn on
+ disagreement, with a per-column tolerance because a live formula and a
+ deliberately rounded literal do not deserve the same one. On the R0 rows the
+ recomputation reproduces `Z` to 1.1e-16, `AA`/`R` to 1.8e-15, and `X` to the
+ 0.5 nm its rounding allows, so nothing about the campaign's numbers changed
+ except that thickness is now unrounded. The formulas came from
+ `git show pre-cleanup-2026-07-29:src/mobo_kit/d2d_scores.py` with the polarity
+ inverted.
+
+ **`Y` and `AB` are deliberately not cross-checked.** They live in utility
+ space, and a check would have to duplicate the Gaussian that `objectives.py`
+ owns. Nothing reads them now, so there is no dependency to protect — the
+ 1.7e-3 divergence above is recorded rather than monitored. If a future reader
+ ever needs them, check them through `ObjectiveTransform.transform` rather than
+ re-implementing the transform in `scores.py`.
+
+3. **openpyxl discards cached formula values on save.** Verified: Z2:Z4 read
+ `[0.657, 0.587, 0.561]` before a save that only added an empty sheet, and
+ `[None, None, None]` after. This is why `workbook_io` writes candidates to a
+ sibling file and never opens the source for writing. Do not "simplify" that
+ by adding sheets to `Summary Table.xlsx`.
+
+4. **Done 2026-07-30 — the review artifact exists; the human review itself is
+ still owed.** `batch_review.py` writes a `Review` sheet into the candidate
+ workbook and echoes it into the launcher pane: proposed conditions in physical
+ units, predicted utility and sd per objective through the acquisition's own
+ posterior-sample path, the prediction decoded into the measurement's units
+ (median plus a 68% interval, multiplicative for the log-link thickness),
+ normalised distance to the nearest observed point, and which coordinates sit at
+ a range edge rather than only how many. Findings from Sheet1 travel with it, so
+ the sheet can be forwarded on its own.
+
+ **The batch this described was withdrawn and reissued on 2026-07-31** — see
+ `docs/R1_BATCH_WITHDRAWAL.md`. Four of its five conditions survived unchanged,
+ including `R1_C01`, which is the condition the numbers below are quoted from, so
+ **these figures are unchanged and were re-read from the reissued artifact rather
+ than assumed.**
+
+ **What the artifact says about the R0-trained batch**, on the two flags raised
+ earlier:
+
+ * `speed_1 = 1000` — the declared probe moves each candidate to the corner and
+ compares. For `R1_C01`, thickness utility falls from 0.786 to 0.223 while the
+ sd ratio is **1.02**. Across all five conditions the mean falls 0.791 → 0.299
+ with a mean sd ratio of **1.10**. Either way the region is not being skipped
+ as unexplored, it is being skipped as known and bad. `speed_1` is a feature of
+ the thickness mean function, so that confidence is a fitted global trend
+ extrapolating to its range edge, not a local average of samples 1 and 12 — and
+ the two points anchoring that edge disagree, one of them (sample 12) holding
+ `ROUND(mean(1600, 709))`. So the corner is a measurement question, as
+ suspected, but by a different route than "the contradiction was averaged into
+ confidence". The reissued batch's minimum `speed_1` is still 1500.
+ * `anneal_temp` at 100–105 in all five conditions is a declared standing note:
+ a monotone linear mean puts the optimum at a range edge by construction. The
+ open question is chemical, and if a floor exists it belongs in `constraints:`.
+ Unchanged in the reissued batch.
+
+ Probes and notes are declared in `configs/…yaml` under `review:`, not hardcoded.
+
+5. **Done 2026-07-30 — `metrics.compute_ref_pareto_hv` required an explicit
+ reference.** The `ref_point_np=None` path used `mins - 1e-8`, essentially the
+ nadir itself: measured HV 6e-8 against 1.448 from `infer_reference_point` on
+ the same data, and re-derived per call so hypervolumes were not comparable
+ across iterations. Passing no reference now raises and names
+ `reference_point_utility`; a reference that nothing dominates also raises,
+ instead of returning the 0.0 that BoTorch's silent point-dropping produces.
+
+ The precise condition, pinned in `tests/test_metrics.py`: `mins - 1e-8` is
+ harmless while some *dominated* point sets the per-objective minima, and
+ collapses once the Pareto set itself sets them — each point best in one
+ objective and worst in another, which is what a genuine trade-off front is.
+ Plotting code may now simply pass `config["reference_point_utility"]`.
+
+6. **Done 2026-07-30 — the signal-collapse guard now distinguishes a collapsed GP
+ from a mean function that works.** It used to compare
+ `gp.posterior(X).variance` against the fitted noise and stop there. A mean
+ module does not enter the variance, so when a structured mean explains most of
+ the data the residual GP's latent sd goes to ~0 and the guard raised
+ `ModelFitError` — asserting "its posterior mean is effectively constant", which
+ is verifiably false in that case, because `posterior().mean` carries the trend.
+
+ Two situations share one numeric signature and now get different answers:
+
+ * **True collapse**: zero-mean GP, outputscale → 0, posterior mean genuinely
+ flat, nothing can be ranked. Still `ModelFitError`.
+ * **The mean function did its job**: residual variance ~0, posterior mean
+ tracks the trend, ranking still works. Now a loud warning and the round
+ proceeds. Refusing would dead-end the campaign at the moment the physics model
+ started working, with no remedy — better data cannot be collected without
+ first proposing conditions. The review artifact is the designed gate.
+
+ The warning is not a formality, and says so: UCB's exploration term reads the
+ latent posterior that just collapsed, and the mean module's coefficients are
+ frozen buffers with no uncertainty of their own, so the narrow intervals such a
+ model reports are **understated rather than earned**. It appears above the
+ numbers in both the launcher pane and the `Review` sheet, and in
+ `RoundResult.diagnostics["model_fit_warnings"]`.
+
+ Two calibration notes worth keeping:
+
+ * "Near-constant" is measured against the **observed spread of that
+ objective**, not against the fitted noise sd. Noise-relative was the first
+ attempt and is wrong: the noise is inflated precisely in the degenerate case,
+ so the test co-varies with what it is trying to detect. Measured instance — a
+ linear mean on `anneal_temp` against a forced noise of 0.9 scored 0.38 on the
+ noise yardstick and would have been called constant while it was tracking the
+ data. Floor is 5% of the observed spread.
+ * Only the guard's own warnings reach a human. `record.warnings` also collects
+ every Python warning raised during fitting — about 18 numpy-2.0 deprecation
+ notices per fit on this stack — and putting those in front of someone
+ reviewing a batch is how people learn to ignore warnings.
+
+ Whether a given dataset trips the collapse is knife-edge: measured across
+ residual magnitudes from 0 to 0.3 it fires at 0, 1e-4, 0.01 and 0.03 but not at
+ 0.001 or 0.1, because it depends where the MLL optimiser lands. The guard's
+ decision is therefore tested directly, and the propagation tests force the
+ condition rather than hoping data produces it. No fit on the current R0 data
+ warns, so nothing about the live campaign changed.
+
+7. **Phase 4 is wired and waiting for data (2026-07-30).** `replicate_variance.py`
+ pools between-film variance from the replicate scatter and hands it to the model
+ as `train_Yvar`; `run_r1_ucb` / `run_r2_qlognehvi` / `fit_campaign_models` take
+ `observed_Yvar`, and the launcher builds it automatically once the config asks.
+ Enabling it when the triplicates land is one key —
+ `model.observation_noise: replicate_pooled` — which is the point of wiring it
+ before the data exists. Tested against synthetic replicates.
+
+ Four things worth knowing before touching it:
+
+ * **The variance handed over is of the MEAN**, `pooled / n_films`, because the
+ observation is an average of n films. Passing the single-film variance would
+ be three times too large on a triplicate — *overstating* uncertainty, so the
+ model would trust the most carefully replicated conditions least — and nothing
+ errors.
+ * **Between-film and within-film are different quantities.** Between-film is
+ what `train_Yvar` needs. The within-film 0.0593 on `log T` (24 dof) contains
+ no run-to-run variation at all, so it is a **floor**: if the pooled
+ between-film variance ever lands below it, films would be more reproducible
+ than points on one film, and `sanity_floor_findings` says so.
+ * **BoTorch silently ignores `train_Yvar` if a `likelihood` is also passed.**
+ Verified on 0.15.1: the likelihood wins, stays single-element, and the
+ replicate information is dropped with no error. `_build_single_task_gp` passes
+ one or the other, never both.
+ * **`Standardize` rescales `train_Yvar` along with the targets**, so it must
+ arrive in the target's own units — and in the model's space, which for
+ thickness is `log T`, not nanometres. That is why aggregation and variance
+ pooling are required to share one space.
+
+ Zero pooled variance is refused rather than passed on: replicate films that
+ agree to the last digit are a transcription, not a measurement, and a zero
+ `train_Yvar` tells the model the observation is exact.
+
+8. **Done — the legacy leftovers are gone.** The tkinter launcher landed
+ 2026-07-30 (`launcher.py`, plus the two double-click scripts; see the README).
+ The legacy debug ceremony went earlier: `production_gate.py` and 22 other Step
+ 1/2A/2B/2C modules were removed in `33f101f`, and
+ `test_validity_report_carries_no_approval_flags` holds the approval tiers out.
+
+9. **Fixed 2026-07-31 — `run_r1_ucb` scored every candidate against a baseline
+ whose thickness axis had collapsed to zero.** This is the most consequential
+ defect found in this project, and the R1 batch it produced was withdrawn:
+ `docs/R1_BATCH_WITHDRAWAL.md`.
+
+ `ObjectiveTransform.transform` is a MODEL-OUTPUT decoder — it applies `exp()`
+ to a log-link objective before computing utility. `run_r1_ucb` handed it
+ `observed_Y_raw`, thickness in **nanometres**, so the value was exponentiated a
+ second time. `exp(360…1303)` saturates the 650 nm Gaussian to exactly `0.0`.
+
+ | | as called | correctly encoded |
+ |---|---:|---:|
+ | observed baseline hypervolume | **0.004659** | **0.436442** |
+ | baseline Pareto set | 2 points | 5 points |
+
+ A factor of 94, and every candidate's improvement was measured against a front
+ with no thickness axis at all. On the live batch this moved one of five
+ conditions and the minimum spacing from 0.9209 to 0.6337 — so it also inflated
+ the spacing figure that this document used to argue `radius` was inert.
+
+ **The fix** is `ObjectiveTransform.encode_measurements`, with
+ `transform_measurements` as the one-call safe route, and `run_r1_ucb` encoding
+ before it proposes (commit `4b76670`). `ucb_hvi.py` is untouched — it is one of
+ the frozen acquisition modules and the defect was in `campaign.py`
+ orchestration. **Annie Xu found and fixed this independently on
+ `ax_plots_simulation` before we knew it existed**; the fix promotes her
+ `_physical_to_model_output` to the public contract.
+
+ **Every `ObjectiveTransform.transform` call site was audited.** `objectives.py`
+ 403/481/516 and `ucb_hvi.py:342` all operate on posterior samples, already in
+ model space; `batch_review.py` never routes measurements through the transform
+ at all. Exactly one call site was defective, `ucb_hvi.py:698`, reached only
+ from `run_r1_ucb`. R2 was never affected — qLogNEHVI takes `train_X_norm` and
+ derives its baseline through the model.
+
+ **This is the third plausible-finite-number failure in this project**, after
+ the hypervolume auto-reference (issue 5) and the silently swallowed
+ `train_Yvar` (issue 7). All three share one shape: **a wrong answer that is
+ finite, ordinary-looking, and compared against nothing.** No guard fires
+ because nothing is out of range; the number is simply not the number anyone
+ meant. The lesson is not "add more guards" — each of these passed every guard
+ it met — it is that **a quantity no test reproduces independently is a
+ quantity nobody is checking.** `run_r1_ucb` now reports
+ `observed_baseline_hypervolume` and `observed_baseline_pareto_size` so the
+ value is observable from outside, and the tests recompute both by a separate
+ route.
+
+ **Why 446 tests missed it.** Every objective in the synthetic acceptance test
+ was affine, and for an affine objective measurement space and model space are
+ the same numbers — a link-encoding mistake is invisible *by construction*.
+ `test_dtlz2_acceptance.py` now also runs with a log-link objective, so every
+ end-to-end pass exercises both link types the campaign uses.
+
+10. **OPEN, second campaign. `Normalized photoconductance` does not rank like the
+ photoconductance it summarises, and it is half of the optoelectronic
+ objective.** Nothing in the workbook derives that column — it arrives already
+ normalised, from outside — so a recipe can only take it on trust, and a
+ normalisation that has come loose from its measurement is invisible: every
+ value is in range, every row computes, and the objective is simply about
+ something other than it says.
+
+ Rank agreement is the check that needs no formula. Whatever the intended
+ mapping is, it must preserve order. Measured over the 15 R0 rows:
+
+ | | value |
+ |---|---:|
+ | Spearman(`Photoconductance (Max)`, `Normalized photoconductance`) | **−0.5484** |
+ | p | **0.0343** |
+ | strongest film, 8.81e-07 (sample 15) | normalises to **0.010**, the column minimum |
+ | films at exactly 1.000 | samples 4, 7 and 10, all at low raw photoconductance |
+
+ So the axis currently rewards *weaker* photoconductance, and it is significant
+ rather than noisy. **The group has flagged this and is supplying the intended
+ formula.**
+
+ **This is very likely why optoelectronic is unlearnable.** Its plain GP sits
+ at −0.5842, below the null, and the first campaign's mean function makes it
+ worse rather than better. No model can learn a column that ranks backwards
+ against its own measurement, and half of this objective is that column.
+
+ **Reported as a graded finding, never as a gate**, by
+ `scores.AgreementCheck`: which column the model trains on is the group's
+ decision, and a diagnostic that blocked a round would make that decision by
+ refusing to run. It appears in the workbook read, in
+ `scripts/intake_new_data.py` output, and as a standing notice on the `Review`
+ sheet, so nobody reviews a batch without knowing the axis is provisional.
+
+ **Closure path: one recipe edit plus one intake run.** Replace the
+ pass-through input with the real derivation in
+ `objectives.specs[1].measurement`, bump `contract_version`, re-run the intake,
+ and re-decide the mean function — it may well earn its place once the column
+ tracks its measurement. `test_second_campaign.py` pins the −0.5484, so that
+ test failing is the signal to update the record rather than to loosen the
+ check.
+
+**Nothing on this list is now blocked on code.** What remains is a human reading a
+proposed batch (issue 4), the R1 triplicates arriving (issue 7), the
+photoconductance formula (issue 10), and a decision about whether an
+`anneal_temp` floor belongs in `constraints:` — which is now a live list rather
+than an empty one, so adding it is a two-line change.
+
+## Are beta = 4.0 and radius = 0.25 defensible? (FIRST campaign)
+
+> Superseded for the live campaign by "Are beta = 4.0 and radius = 0.25
+> defensible?" near the top of this document. Kept as the record of how the first
+> campaign's defaults were checked.
+
+`scripts/dtlz2_parameter_sweep.py`, 8 seeds per cell, `min_batch_distance` fixed at
+0.15. Metric is mean hypervolume gain over the R0 start for the 8 points R1 and R2
+add, against a random on-grid baseline at the same budget (+0.0453 in every cell,
+since it does not depend on either knob).
+
+| beta | radius | mean gain | per-seed sd | min spacing | edge coords / 80 |
+|---:|---:|---:|---:|---:|---:|
+| 2 | 0.15 | +0.0816 | 0.0508 | 0.719 | 16.4 |
+| 2 | 0.25 | +0.0781 | 0.0493 | 0.810 | 16.5 |
+| 2 | 0.35 | +0.0801 | 0.0481 | 0.955 | 16.9 |
+| 4 | 0.15 | +0.0776 | 0.0440 | 0.719 | 16.0 |
+| **4** | **0.25** | **+0.0780** | **0.0428** | **0.891** | **16.8** |
+| 4 | 0.35 | +0.0961 | 0.0735 | 0.982 | 17.0 |
+| 8 | 0.15 | +0.0868 | 0.0523 | 0.871 | 16.9 |
+| 8 | 0.25 | +0.0868 | 0.0523 | 0.871 | 16.9 |
+| 8 | 0.35 | +0.0839 | 0.0458 | 0.953 | 17.2 |
+
+**No change.** The pre-committed rule required a challenger to beat +0.0780 by more
+than the per-seed sd of 0.0428 — that is, to exceed +0.1208 — without reducing
+spacing; seven cells have a higher mean and none comes close, the whole grid
+spanning +0.0776 to +0.0961 against sds of 0.043 to 0.074. BO beats the random
+baseline on the mean in 9 of 9 cells, so the sweep is measuring optimisation rather
+than noise, and the edge-coordinate count is flat at 16–17 of 80 across every cell,
+which says neither knob is what drives batches onto range edges (on the live
+campaign that was the monotone `anneal_temp` mean function).
+
+**One limit worth stating**: `radius` is not binding **on DTLZ2**. Achieved batch
+spacings there are 0.72–0.98, far above every radius tested, so local penalization
+rarely has two candidates close enough to penalise — visible in `beta=8` giving
+identical results at radius 0.15 and 0.25. This sweep therefore validates `beta`
+properly and says little about `radius` on that problem.
+
+**It does bind on the live campaign, and the earlier claim here that it probably
+did not was itself an artifact.** That claim rested on the R1 batch's minimum
+spacing of 0.921 — a number produced by the mis-encoded UCB-HVI baseline described
+in `docs/R1_BATCH_WITHDRAWAL.md`. With the baseline corrected the live R1 batch
+spaces at 0.6337, and the round simulation measures a clean monotone staircase
+(`scripts/plot_round_simulation.py`, 13 cells, seed 73, oracle-scored R0):
+
+| radius | 0.05 | 0.10 | 0.15 | 0.20 | 0.25 | 0.30 | 0.35 | 0.40 | 0.45 |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| achieved R1 spacing | 0.455 | 0.455 | 0.455 | 0.543 | 0.720 | 0.921 | 0.921 | 0.921 | 0.921 |
+| range-edge coords | 11 | 11 | 11 | 12 | 13 | 13 | 15 | 15 | 15 |
+
+Nine cells produce **six distinct R1 batches**. `radius` binds below about 0.30
+and saturates above it, and it buys spacing at a measurable cost: **11 → 15
+range-edge coordinates across the arm.** That trade-off — diversity against
+edge-pinning — had not been measured before, and it is a policy choice for the
+group rather than a tuning question.
+
+**`radius = 0.25` stays the default for now**, mid-staircase, but as a declared
+choice rather than an inherited one. Note the numbers above are single-seed:
+*which* batch a cell proposes is a fact, because the pipeline is deterministic at
+a fixed seed, but the hypervolumes cannot rank cells at n = 1. Re-run the radius
+arm at ~5 seeds before changing the default on performance grounds.
+
+Inert is acceptable for a safety knob, but then it has to be shown to work
+deliberately rather than inferred from a campaign that never exercised it.
+`test_radius_pushes_the_second_pick_out_of_the_penalised_neighbourhood` does that
+by construction: three candidates crowded 0.02 apart scoring better than an
+isolated fourth, where greedy selection takes the two best and penalization pushes
+the second pick beyond the radius. Its companion pins the inert case — a radius
+smaller than the gaps must change nothing.
+
+## When new data arrives
+
+One command:
+
+```bash
+python scripts/intake_new_data.py --workbook "local_inputs/Summary Table.xlsx"
+```
+
+The group has always called the current numbers test data, so a replacement was
+expected. When it lands, the question is not whether the code runs — the tests
+answer that — but whether the model commitments this campaign made still earn
+their place on the new rows. Several were justified by measurements on 15 specific
+rows and do not transfer.
+
+It prints, per objective: the read audit and its findings; whether the declared
+`mean_function` still beats the leave-one-out null by more than the resolution
+floor, naming the exact config block to delete if not; the fit guard's status,
+including the case where the mean function explains so much that the residual GP
+collapses; whether the fixed anchors still span the data; and whether the
+campaign-fixed scaling guard passes.
+
+**Both floors are recomputed at the new N rather than reused.** The null is
+`1 - (N/(N-1))²` — −0.148 at 15, −0.105 at 21, −0.069 at 31. The ±0.236 resolution
+figure was a bootstrap at N=15 and is rescaled by `sqrt(15/N)`, labelled in the
+output as an estimate: re-run the bootstrap if a decision turns on the third
+decimal.
+
+On the current 15 rows it reports: uniformity does not beat the null (−0.681),
+optoelectronic keeps its mean function (−0.342 → +0.267, swing +0.609), thickness
+keeps its mean function (+0.116 → +0.381, swing +0.265). The guard is clean for
+both.
+
+**This is the canonical instrument for LOO numbers from now on.** It reports plain
+thickness at +0.116 where `GP_MODEL_DECISION.md` records +0.183; that was
+reconciled on 2026-07-30 and the whole difference is the data, not the method. The
+older instrument read the workbook's stored `ROUND(mean(T1..T4))`; the model now
+trains on the unrounded mean. Seven of fifteen rows change, by at most 0.50 nm, and
+that alone moves LOO R² by 0.067 — the pipeline contributes exactly nothing, since
+with no mean function the two routes are the same code. Same fragility as the 0.089
+above, and comfortably inside the ±0.236 floor. Neither conclusion changes: the
+structured swing is +0.201 on the old values and +0.265 on the new.
+
+## Reproducing the analysis
+
+```bash
+python scripts/gp_diagnostic.py --variants legacy_matern_no_prior dim_scaled_prior
+python scripts/validate_structured_means.py
+python scripts/thickness_objective_check.py
+python scripts/dtlz2_parameter_sweep.py # beta x radius, needs no data
+```
+
+All but the sweep need the ignored private workbook at
+`local_inputs/Summary Table.xlsx`.
diff --git a/docs/GP_MODEL_DECISION.md b/docs/GP_MODEL_DECISION.md
new file mode 100644
index 0000000..2769c0f
--- /dev/null
+++ b/docs/GP_MODEL_DECISION.md
@@ -0,0 +1,465 @@
+# GP model decision record
+
+Measured 2026-07-28 on the corrected `Summary Table.xlsx` (15 R0 observations,
+10 inputs). Reproduce with `python scripts/gp_diagnostic.py`.
+
+> **This document is about TEST DATA.** Its contract was
+> `d2d-objectives-v2-nm-thickness` and its workbook existed to develop and
+> check the toolkit rather than to run an experiment; it is kept as that
+> record. **The real campaign is v4**, on `Final Summary Table.xlsx`. A second
+> test contract (`d2d-objectives-v3-test`) came in between, on another
+> workbook: uniformity became a mean rather than a product and optoelectronic a
+> mean of normalised terms rather than a log10 product, so **no LOO R², signal
+> verdict or mean function below transfers to it** — they are about quantities
+> that were redefined. Its numbers come from
+> `python scripts/intake_new_data.py --workbook "local_inputs/Final Summary Table.xlsx"`
+> and are recorded in `CAMPAIGN_STATUS.md`. What does carry over is the *method*:
+> the three floors, the null, the refit-inside-every-fold rule, and the two
+> degenerate fitting modes.
+
+## Which instrument produced these numbers
+
+**Every LOO R² in this document was measured by `scripts/validate_structured_means.py`
+and `scripts/gp_diagnostic.py`, reading the workbook's stored `Thickness (avg)`
+column, which is `ROUND(mean(T1..T4))`.** The model no longer trains on that column
+— since 2026-07-30 it trains on the unrounded mean — so the numbers below describe a
+model input that has been superseded.
+
+**`scripts/intake_new_data.py` is the canonical instrument from now on.** It uses the
+same pipeline the campaign uses and the same values the model is given. Where it
+disagrees with a table here, it is right and the table is historical.
+
+The two were reconciled on 2026-07-30 and the difference is fully accounted for:
+
+| thickness, LOO R² | two-stage | mean module |
+|---|---:|---:|
+| rounded `X` (this document) | +0.1830 | +0.1830 |
+| unrounded mean (the intake, and the model) | +0.1160 | +0.1160 |
+
+**The pipeline makes no difference at all here** — with no mean function the two
+routes are the same code — and the entire 0.067 gap is the rounding: 7 of the 15
+rows change, by at most **0.50 nm**. Half a nanometre on seven rows moves LOO R² by
+0.067, which is the same fragility that produced the 0.089 optoelectronic gap, and
+both sit far inside the ±0.236 resolution floor.
+
+The structured numbers barely move: +0.3842 (this document's convention) against
++0.3806 (the intake), with decode and rounding choices spanning 0.006 in total. The
+swing that justifies the mean function is +0.201 on the old data and +0.265 on the
+new — it clears the floor either way, so no conclusion in this document changes.
+
+## What was wrong
+
+`ScaleKernel(MaternKernel(nu=2.5, ard_num_dims=10))` was built with no lengthscale
+prior. GPyTorch's default constraint is `Positive()` — lower bound 0, upper bound
+infinity — so nothing bounded the fit. At N=15 in 10 dimensions the marginal
+likelihood interpolates every observation by making a few directions extremely
+wiggly and switching the rest off:
+
+| Objective | Fitted ARD lengthscales | Directions ≥ 10 | Noise |
+|---|---|---|---|
+| Uniformity | 0.22 … 2229 | 6/10 | pinned at 1e-3 floor |
+| Optoelectronic | 3.0 … 38105 | 9/10 | pinned at 1e-3 floor |
+| Thickness | 0.13 … 2150 | 7/10 | pinned at 1e-3 floor |
+
+This is overfitting, not the prior-mean collapse originally hypothesised: the
+posterior mean varied over 104–131% of each observed range across the design
+space. Noise at its floor means the model believed the data were noiseless.
+
+## The fix
+
+BoTorch 0.15.1's `SingleTaskGP` already applies a dimension-scaled LogNormal
+lengthscale prior — `LogNormal(loc = √2 + ln(d)/2, scale = √3)` — whenever
+`covar_module` is not supplied. MOBO-Kit was discarding it by passing an explicit
+module. `dim_scaled_prior` restores the prior while keeping the `ScaleKernel`
+wrapper that the hyperparameter readout and plots depend on.
+
+| Variant | Median lengthscale | Flat directions | Uniformity LOO R² |
+|---|---|---|---|
+| `legacy_matern_no_prior` | 1121 | 6/10 | −1.557 |
+| `conservative` | 2735 | 6/10 | −1.453 |
+| **`dim_scaled_prior`** | **0.88** | **0/10** | **−0.537** |
+
+## Naming
+
+`default_current` was retired to `legacy_matern_no_prior` rather than redefined,
+so archived Step 2B/2C artifacts stay interpretable. `dim_scaled_prior` is
+`PRIMARY_VARIANT`; the retired contract must be requested by name and is kept
+only for reproducing old runs.
+
+## Three floors. Check all three before comparing any two numbers.
+
+**1. Null: −0.148.** Predicting the leave-one-out mean of the other N−1 gives
+LOO R² = 1 − (N/(N−1))² and Spearman exactly −1, independent of the data. A model
+below this learned nothing. It moves with N: −0.148 at 15, −0.105 at 21, −0.069 at
+31, so recompute it rather than reusing this number on a bigger dataset.
+
+**2. Sampling: ±0.236** (called the *resolution floor* in older text here and in
+`CAMPAIGN_STATUS.md` — same number, same thing). Parametric bootstrap at N=15,
+4000 resamples from the same underlying truth, gives an LOO R² standard deviation
+of **0.236** and a central 95% range of **[−0.231, +0.666]**. The identical
+relationship produces anything in that range purely by resampling. Shrinks roughly
+as 1/√N.
+
+**3. Numerical reproducibility: ≈0.07** (added 2026-07-30). Two *independent*
+perturbations, neither of which changes the model or the data in any meaningful
+sense, each move LOO R² by about this much at N=15:
+
+| perturbation | size | LOO R² moves |
+|---|---|---:|
+| MLL optimiser landing elsewhere on an **identical** likelihood surface | outputscale and noise ≤2.7%, median lengthscale 9.6% | 0.0715 |
+| rounding the thickness input to whole nanometres | ≤0.5 nm on 7 of 15 rows | 0.0670 |
+
+Neither is sampling noise — both are deterministic and reproducible — and neither
+reflects a real difference in what the model knows. **So a second-decimal
+difference in LOO R² at this N is below what the metric can reproduce even on
+identical data with identical models.** Where floor 2 says a difference may be
+luck, floor 3 says it may not be a difference at all.
+
+So **two LOO R² values less than about half a point apart are not a comparison at
+this N, and anything in the second decimal place is not even a measurement.** This
+project walked into floor 2 twice — once arguing −0.017 against −0.145, once
+arguing +0.355 against +0.244 — both times because the number moved in the pleasing
+direction, and the second of those turned out to be floor 3 all along.
+
+Differences that survive: the plain-vs-structured swings below (0.20 and 0.70).
+Differences that do not: anything in the second decimal place.
+
+**A corollary worth its own line.** Under the same 0.5 nm rounding the plain GP
+moved 0.067 while the structured model moved 0.006 — a tenfold difference in
+sensitivity to a perturbation below measurement precision. A model whose answer
+turns on half a nanometre is reporting arithmetic; one that ignores it is reporting
+a trend. That is independent evidence for the mean function carrying the signal,
+arrived at without looking at either model's score.
+
+## The bar for "the model learned something"
+
+For N observations, predicting the leave-one-out mean of the other N−1 gives
+
+ LOO R² = 1 − (N/(N−1))² (−0.1480 at N=15)
+ Spearman = −1 exactly
+
+both independent of the data, because that prediction is a strictly decreasing
+function of the held-out value. So a **negative LOOCV Spearman is the signature
+of a model that learned nothing**, and the bar to clear is −0.148, not 0.
+
+## What the data supports
+
+- **Uniformity** — no learnable signal. Nothing beat the null across ~240 model
+ configurations, 7 target transforms, or modelling Coverage / (1−Uniformity) /
+ Phase purity separately. Permutation p = 0.82. Treat as exploration-only.
+- **Optoelectronic** — weak but real, via `anneal_temp` (single-input LOO
+ R² +0.244). `=LOG10(P*Q)` is monotone, so model the score directly.
+- **Thickness** — see below.
+
+## Thickness: model nanometres, not the score
+
+`Normalized thickness = EXP(-(((T-650)/250)^2))` is a peaked Gaussian on a 650 nm
+target. The map T → score is **2-to-1**: films at 400 nm and 900 nm receive
+near-identical scores from opposite sides of the peak, and the observed films
+straddle the target (4 below, 11 above, 360–1303 nm). A GP trained on the score
+must represent a folded bimodal ridge in process space; a GP trained on
+nanometres sees a smooth trend.
+
+Raw thickness is the most predictable quantity in the campaign:
+`log T ~ log(speed_1) + log(precur_conc)` gives LOO R² **+0.449**, Spearman
+**+0.714**, permutation p **0.0067**.
+
+> **CORRECTION, 2026-09-06.** This paragraph used to end "with fitted speed
+> exponent −0.38 against spin-coating theory's −0.5", offered as evidence that the
+> trend was physically grounded. **It is not evidence.** On the v4 workbook the
+> exponent's 95% interval is [−0.385, −0.126], which EXCLUDES −0.5 by 4.1 standard
+> errors, and fixing the exponents at their theoretical values scores +0.5600
+> against +0.5823 for no trend at all. The mean function has been withdrawn from
+> the live config; see CAMPAIGN_STATUS.md, "The thickness prior was half-earned".
+
+Predicting the score, exact leave-one-out (`dim_scaled_prior`):
+
+| Approach | LOO R² | Spearman |
+|---|---|---|
+| train on the score directly | −0.444 | −0.764 |
+| train on nm → E[score], analytic | **−0.147** | **+0.279** |
+| train on nm → score(mean) only | −0.492 | +0.161 |
+| null | −0.148 | −1.000 |
+
+Rank correlation flips from actively misleading to usable, which is what drives
+candidate selection. R² only reaching the null is the honest outcome: the raw-nm
+posterior is wide (median 157 nm against a Gaussian width of 250/√2 ≈ 177 nm), so
+expected scores are correctly pulled toward the middle.
+
+Note the third row. Transforming only the posterior *mean* is worse than the null
+— it is biased by Jensen's inequality and blind to variance. Use
+`ObjectiveTransform.expected_transform`, which has a closed form for
+`Y ~ N(μ, v)`:
+
+ E[exp(-½((Y-c)/s)²)] = √(s²/(s²+v)) · exp(-½(μ-c)²/(s²+v))
+
+verified against Monte Carlo to <1e-3. It reduces to the plain transform at v = 0
+and penalises uncertainty at the target: at μ = 650 exactly, expected score is
+0.994 / 0.870 / 0.508 for posterior σ of 20 / 100 / 300 nm.
+
+The workbook's `exp(-((T-650)/250)²)` has no ½, so in this parameterisation
+`sigma = 250/√2 ≈ 176.78`.
+
+## Both priors are required, not just the lengthscale one
+
+With the lengthscale prior but a bare noise floor, the fit has a *second*
+degenerate mode: the outputscale collapses to ~0 and the model declares the data
+pure noise. Measured on the thickness score, 10 of 15 leave-one-out folds landed
+there — fitted noise 0.93 against a latent predictive sd of 1e-4, giving
+z-scores in the thousands. Adding BoTorch's `LogNormal(-4, 1)` noise prior
+removes it entirely.
+
+**Since 2026-07-30 the guard asks a second question.** A collapsed latent sd means
+one of two things, and they need different answers. If the posterior *mean* is also
+near-constant — measured against the objective's observed spread, floor 5% — the
+model has genuinely explained the data as noise and the fit is refused. If the mean
+still varies, a structured mean is carrying the signal: the residual GP having
+nothing left to model is success, not degeneracy, and refusing would dead-end the
+campaign exactly when the physics model started working. That case warns instead,
+naming the two things to distrust — the exploration term is dead, and the frozen
+mean coefficients carry no uncertainty, so reported intervals are understated
+rather than earned. Details in `CAMPAIGN_STATUS.md` issue 6.
+
+The collapse is specific to the thickness *score*, the folded 2-to-1 objective —
+uniformity, optoelectronic and raw nm are stable either way, and the noise prior
+costs them nothing (latent sd 0.1226 vs 0.1236). It matters because acquisition
+consumes the *latent* posterior: a predictive interval can look well calibrated
+while the latent variance has collapsed, because the large fitted noise hides it.
+
+Calibration, exact leave-one-out, predictive (noise-inclusive) intervals against
+nominal 0.68 / 0.95:
+
+| Variant | Uniformity | Optoelectronic | Thickness | Mean NLPD |
+|---|---|---|---|---|
+| `legacy_matern_no_prior` | 0.067 / 0.533 | 0.467 / 0.667 | 0.467 / 0.600 | 1.76 / 2.82 / 1.35 |
+| `dim_scaled_prior` | 0.400 / 0.800 | 0.533 / 0.800 | 0.533 / 0.867 | 0.58 / 1.44 / 0.82 |
+
+Interval coverage must use the predictive sd, not the latent sd. Using the latent
+sd understates every interval and makes a calibrated model look overconfident.
+
+## Structured means: two objectives, opposite shapes
+
+Physics fixes the features in advance, so this is not selection on the outcome.
+Linear coefficients are refit inside every fold.
+
+| Objective | Mean function | LOO R² | Spearman |
+|---|---|---:|---:|
+| thickness nm | none | +0.183 | +0.586 |
+| thickness nm | `log T ~ log(speed_1) + log(precur_conc)` | **+0.384** | +0.682 |
+| optoelectronic | none | **−0.342** | −0.100 |
+| optoelectronic | linear `anneal_temp` | **+0.355** | +0.618 |
+
+Null −0.148. Both swings (0.20 and 0.70) clear the ±0.236 resolution floor.
+
+**The optoelectronic result is the larger finding.** Its plain GP sat *below the
+null* — actively worse than predicting the mean — so the seven irrelevant inputs
+were not merely diluting the fit, they were doing damage. Removing the
+temperature trend first fixes it.
+
+The two shapes are **opposite** and neither generalises. Thickness needs a pair
+of log terms and neither alone is worth much (+0.159, +0.187). Optoelectronic
+needs exactly one linear term: every addition tested made it worse, and an
+Arrhenius `1/T` form bought nothing over plain linear temperature (+0.226 vs
++0.244). `anneal_temp` is also the least search-contaminated choice available —
+it came from a marginal correlation already on record (ρ = −0.651, p = 0.009),
+not from the six-form search that was run afterwards.
+
+**Not claimed:** that linear-mean-plus-GP beats linear-mean-alone. The observed
+gap (+0.355 vs +0.244) is 0.47 sd of the ±0.236 resolution floor — indistinguishable
+from sampling noise. It is an open hypothesis with a specific test attached: does
+linear-mean-plus-GP beat linear-mean-alone under the permutation null? That
+question rides along with the optoelectronic permutation run.
+
+## Structured mean for thickness
+
+Physics fixes the two predictors in advance, so this is not selection on the
+outcome. Refitting the linear coefficients inside every fold:
+
+| Raw-nm model | LOO R² | Spearman |
+|---|---|---|
+| plain GP, 10 inputs | +0.183 | +0.586 |
+| **GP + linear mean on log(speed_1), log(precur_conc)** | **+0.384** | **+0.682** |
+| 2-input log-log reference | +0.449 | +0.714 |
+
+Carried through to the score: R² −0.145 → **−0.019**, Spearman +0.243 → **+0.461**.
+The structured mean legitimately reaches what the legacy model reached by
+accident of overconfidence.
+
+**Wired into `campaign.py`.** `fit_campaign_models` reads each objective's
+`mean_function` block and builds one `StructuredMean` module per GP, so
+`posterior()` already carries the trend and no caller adds it back. Any R1
+candidates generated before commit `600ef60` used the plain GP and are not
+comparable with anything generated after it.
+
+The linear coefficients are refit **inside every fold**, on the 14 training rows
+only (`scripts/thickness_permutation_and_mean.py`, in the fold loop). The held-out
+value never touches them. The two predictors are fixed from physics before any
+fitting, so this is not selection on the outcome.
+
+## Significance of the rank improvement
+
+Permutation test, 200 shuffles, permuting the nm measurements and redoing the
+full leave-one-out fit plus transform. When the structured mean is used, its
+linear coefficients are refit inside every null fold too, so the null is not
+flattered.
+
+| Pipeline | Shuffles | Observed ρ | Null mean | Null sd | p | 95% CI |
+|---|---:|---:|---:|---:|---:|---|
+| plain GP | 200 | +0.243 | −0.241 | 0.349 | 0.12 | — |
+| structured mean | 200 | +0.461 | −0.167 | 0.295 | 0.020 | [0.006, 0.050] |
+| **structured mean** | **1800** | **+0.461** | −0.131 | 0.305 | **0.0350** | **[0.0270, 0.0446]** |
+
+The 1800-shuffle run is reported **standalone**, not pooled with the earlier 200.
+Pooling would be defensible but carries an optional-stopping flavour, since the
+larger run was commissioned because the first result was borderline. Reporting
+the fresh run alone sidesteps the question at no cost.
+
+**The result holds and the interval clears.** 63 exceedances in 1800, upper bound
+0.0446, below 0.05. Note the point estimate moved 0.020 → 0.035: the 200-shuffle
+figure was optimistic, which is exactly why the re-run was worth doing. Its
+interval did contain the final value.
+
+The plain GP does not clear p < 0.05. **The structured mean does.** That earns
+"thickness is genuinely predictive" rather than "directionally right". On R² the
+structured mean reaches p = 0.144 (95% CI [0.129, 0.162]), not significant — but
+rank drives candidate selection, and rank is significant.
+
+**This is where the thickness mean function's evidentiary weight rests, and it has
+not moved.** The case is the rank permutation, p = 0.0350 with a 95% CI of
+[0.0270, 0.0446] at 1800 shuffles. The R² swing is *consistent* with it and no
+more: +0.201 on the rounded inputs this document used, +0.265 on the unrounded ones
+the model now trains on, against a ±0.236 sampling floor either way. Nothing in the
+2026-07-30 reconciliation touched the permutation result, because that result is
+about ranks and the reconciliation was about a half-nanometre change in a
+regression score.
+
+The null mean is −0.17, not 0: the leave-one-out shrinkage artifact drags it
+negative, which is why a positive observed value carries information.
+
+## Sample 1 stays in. Do not exclude it. (decided, closed)
+
+**This section exists because "excluding the control nearly doubles R²" is a true
+sentence that will get rediscovered and acted on. It is the wrong action.**
+
+Dropping sample 1 does improve the thickness fit — raw-nm LOO R² goes +0.384 →
++0.632, Spearman +0.682 → +0.824, and the score prediction reaches +0.207 against
+a −0.160 null. Sample 1 is also the off-grid literature control, so there is a
+ready-made provenance story for excluding it.
+
+That story is wrong. Ranking every point by how much dropping it improves the fit:
+
+| Dropped sample | Leverage | LOO R² after drop | Δ |
+|---|---:|---:|---:|
+| **12** | **0.462** | **+0.879** | **+0.429** |
+| 1 | 0.297 | +0.676 | +0.226 |
+| 13 | 0.199 | +0.465 | +0.015 |
+| … | | | |
+| 7 | 0.316 | +0.261 | −0.188 |
+
+Sample 1 is not the drag. **Sample 12 is, by nearly double**, and it has the
+highest leverage in the design.
+
+The mechanism is visible in the inputs. Samples 1 and 12 are the *only* two
+points at `speed_1 = 1000`, the minimum, so between them they anchor the entire
+low-speed end of the strongest predictor — and they contradict each other:
+sample 1 has the higher concentration (1.4 vs 1.1) but the *thinner* film
+(687 vs 1155 nm), inverting the expected relationship.
+
+So the gain from dropping sample 1 is a **high-leverage-endpoint artifact**, not
+a signal about its provenance. Acting on it would commit you to also dropping
+sample 12 — an ordinary LHS point with no provenance justification at all. The
+declared provenance difference is real; it is simply not what the residual was
+reporting.
+
+A tempting explanation for the inversion — sample 1 runs `speed_2 = 5000` against
+sample 12's 500, so a fast second stage could be thinning the film — **does not
+survive testing**: adding `log(speed_2 + 1)` to the structured mean drops LOO R²
+from +0.449 to −0.827. Do not add it.
+
+The low-speed corner therefore remains genuinely unexplained, with two
+contradictory observations in it. That makes it something **R1 should probe**,
+not something to model around. When the real R1 batch is generated: if the
+acquisition function proposes nothing near `speed_1 = 1000`, notice it. It may
+mean the model has concluded the region is bad when what it actually has is two
+points that disagree.
+
+### Added 2026-07-29: sample 12's thickness is two readings that disagree 2.3x
+
+Sample 12's recorded 1155 nm is `ROUND(mean(1600, 709))` — its two thickness
+points differ by a factor of 2.26. It is one of three rows whose thickness
+readings are bimodal rather than scattered: sample 8 is `686, 740, 270, 250`
+(ratio 2.96) and sample 15 is `596, 702, 784, 590`. Within-row sd of `log(T)` is
+0.584, 0.576 and 0.137 for samples 8, 12 and 15, against 0.048 or less for the
+other twelve rows.
+
+This does **not** reopen "sample 1 stays in". That decision was about which
+observation to drop, and the answer is still neither. What it adds is a candidate
+mechanism for sample 12's leverage of 0.462: its thickness value is the midpoint
+of a bimodal measurement, so the low-speed end of the strongest predictor is
+anchored by a number with an unusually weak claim to being a single measurement.
+
+It also makes the low-speed corner a **measurement** question before it is a
+physics question. Probing `speed_1 = 1000` in R1 is still right, and the specific
+thing to collect there is more thickness points per film — not only more films.
+
+### Resolved by the group, 2026-07-31: the means are the intended summary
+
+The group confirms that **the within-film thickness variation on samples 8, 12 and
+15 is real, and the mean of the readings is the intended summary** for each. So
+`mean_of_present` stays, sample 12's 1155 nm stays, and nothing above is a defect
+to be corrected.
+
+**Do not read this as the findings being retracted.** `spread_warning_ratio: 0.25`
+still fires on those three rows and should keep firing: a film whose readings split
+2.3-fold is a different kind of observation from one whose readings agree to 3%,
+and a reader comparing leverage across rows needs to know which is which. What is
+settled is the *action* — no re-derivation, no exclusion, no re-weighting — not the
+*fact*. Sample 12's leverage of 0.462 is still the highest in the design and still
+worth knowing when its region is discussed.
+
+The `T anom` exclusion is confirmed on the same basis: those readings (sample 4's
+1618 against its own 650/655/670/680, sample 14's 630) were judged anomalous by the
+operator, and the operator's judgement is the intended filter. They stay out of the
+mean and their presence stays reported.
+
+## Open
+
+- **Does linear-mean-plus-GP beat linear-mean-alone?** +0.355 against +0.244 is
+ 0.47 sd of the ±0.236 floor, so the observed gap is not evidence either way.
+ The test rides along with the optoelectronic permutation run.
+
+## Closed
+
+- **The R1 baseline mis-encoding, found and fixed 2026-07-31. Not a floor
+ question.** `run_r1_ucb` handed `ObjectiveTransform.transform` measurement-space
+ nanometres, which exponentiated them a second time and pinned every
+ observation's thickness utility to exactly 0.0 — baseline hypervolume 0.004659
+ against a true 0.436442. Full account in `CAMPAIGN_STATUS.md` issue 9; the batch
+ it produced was withdrawn (`R1_BATCH_WITHDRAWAL.md`).
+
+ **It is recorded here only to keep it out of the wrong category.** The three
+ floors above are about differences too small to be real. This was not a small
+ difference and not a noisy one: it was a deterministic, reproducible, *wrong*
+ number, off by a factor of 94. A floor tells you when to stop arguing about a
+ gap; it never licenses accepting one. The rule this project already had —
+ *a deterministic difference on the same rows must be explained, not absorbed
+ into a floor* — is what would have caught it, had anyone had a second number to
+ compare the baseline against. Nobody did, which is the actual lesson.
+
+- **The 0.089 optoelectronic gap, closed 2026-07-30 as a numerical artifact.**
+ The two pipelines specify the *same model*: a zero-mean GP on `y - trend` and a
+ fixed-mean GP on `y` with mean `trend` have identical marginal likelihoods,
+ since a fixed mean only shifts the data. Measured, about a fifth of the gap is
+ the outcome transform standardizing different quantities in the two routes
+ (removing it moves the gap 0.0881 → 0.0715) and the rest is the MLL optimiser
+ landing at slightly different hyperparameters on an identical surface — median
+ lengthscale differing by up to 9.6% across folds, which at N=15 is worth 0.07 of
+ LOO R². Seeding was ruled out separately: bit-identical across four seeds.
+ **0.0881 is well inside the ±0.236 resolution floor and was never evidence of
+ anything.** Full numbers in `CAMPAIGN_STATUS.md`, issue 1.
+- The structured mean is wired into `campaign.py` (`fit_campaign_models`, and the
+ `_fit_models` it delegates to), commit `600ef60`.
+- Hypervolume reference: fixed. `configs/campaign_d2d_perovskite.yaml` declares
+ `reference_point_utility` in utility space after the transforms, so no axis
+ dominates. The old raw-scale `[-0.01, -10.0, -0.01]` gave the optoelectronic
+ axis 4.01x the uniformity axis.
diff --git a/docs/HANDOFF.md b/docs/HANDOFF.md
new file mode 100644
index 0000000..ede0db4
--- /dev/null
+++ b/docs/HANDOFF.md
@@ -0,0 +1,255 @@
+# Handoff
+
+Read this first in a new session. Updated 2026-09-02, when the final workbook
+arrived and the score contract moved to v4.
+
+## What this repository is doing right now
+
+**There are three objective contracts, and only the last one is real.**
+
+| | v2 — test data | v3 — test data | v4 — **the real campaign** |
+|---|---|---|---|
+| config | `campaign_d2d_perovskite.yaml` (archived) | `campaign_d2d_perovskite_test.yaml` (archived) | `campaign_d2d_perovskite_final.yaml` |
+| contract | `d2d-objectives-v2-nm-thickness` | `d2d-objectives-v3-test` | `d2d-objectives-v4-final` |
+| workbook | `Summary Table.xlsx` | `Summary Table Test.xlsx` | `Final Summary Table.xlsx` |
+| sheet | `Sheet1` | `Sheet1` | `R0` |
+| purpose | early toolkit testing | rehearsing this contract's shape | **the experiment being run** |
+
+v2 proved the loop worked. v3 rehearsed the shape of this contract on a workbook
+literally called "Test". **v4 is the campaign that produces films.** Uniformity
+and optoelectronic have been renormalised twice since v2, so none of the earlier
+fitted numbers transfer; every document about an earlier contract carries a banner
+saying so.
+
+**In v4 those two objectives are FROZEN** — read from the workbook as stored, with
+no recomputation, because the group is still revising the definitions. That is a
+deliberate reversal of this project's usual polarity and it removes a cross-check;
+`formula_fingerprint` is the partial replacement, and it notices a changed
+*definition* rather than a stale *value*. Thickness is still computed.
+
+**The workbook's sheet is now `R0`**, not `Sheet1`, so the source sheet is a config
+key (`campaign.source_sheet`) rather than a constant. The workbook also carries an
+`R1` sheet; it is deliberately not read. The round contract is unchanged — each
+round's worklist goes to a NEW file beside the workbook and the source is never
+opened for writing.
+
+The launcher, `intake_new_data.py`, `permutation_rank_test.py`,
+`generate_round_report.py`, `plot_round_simulation.py` and `plot_boxplot_sweep.py`
+all default to v4. **Archiving a config without moving the launcher's default is
+how a user once got a missing-column error on an intact workbook**; a test pins
+that the launcher's default names an active campaign.
+
+All workbooks live under `local_inputs/`, which is gitignored and never travels by
+git. Copy them by hand on any move.
+
+## Read these, in this order (~25 minutes)
+
+1. **`README.md`** — what the toolkit is, the three contracts, the three-round loop,
+ how an experimentalist runs a round without writing code, and how `beta` and
+ `radius` were chosen.
+2. **`docs/CAMPAIGN_STATUS.md`** — the working guide and the longest of the three.
+ Its live-campaign section is at the top; everything below the divider
+ describes an earlier contract.
+3. **`docs/GP_MODEL_DECISION.md`** — why the model is the way it is. It is **v2's**
+ record and carries a banner saying so. What still applies
+ is the *method* — the floors, the null, refitting a trend inside every fold, the
+ two degenerate fitting modes — and none of its LOO numbers.
+
+Then verify the state yourself:
+
+```bash
+pytest -q
+```
+
+Expect **601 passed, 0 failed, 28 warnings** (~185 s). Nothing in the suite needs a
+private workbook; the tests that would use one skip when it is absent.
+
+**`--capture=sys` in `addopts` is load-bearing, not a preference.** pytest's
+default fd-level capture swaps file descriptors 1 and 2, and a Tk interpreter built
+while that is in force holds descriptors that are gone by the time the next one is
+built — so the second or third launcher window in a process dies reading its own
+`init.tcl` and reports the unhelpful message `No error`. It read as a race in the
+launcher for a while and is neither a race nor a launcher defect. Measured: 6
+failures in 9 runs of one launcher test under `--capture=fd`, none under
+`--capture=sys`. Only `capsys` is used in this suite, never `capfd`. The
+`open_window` fixture in `tests/test_launcher.py` carries the full account.
+
+## The instruments, and the one command each
+
+```bash
+# audit new or corrected data, and re-decide every mean function on it
+python scripts/intake_new_data.py --workbook "local_inputs/Final Summary Table.xlsx"
+
+# adjudicate a mean function on RANK when R2 cannot resolve it
+python scripts/permutation_rank_test.py --objective thickness --permutations 1800
+
+# the six figures a round produces, from a terminal instead of the button
+python scripts/generate_round_report.py --workbook "local_inputs/Final Summary Table.xlsx"
+
+# the campaign loop against a frozen oracle, at the ratified knobs
+python scripts/plot_round_simulation.py --workbook "local_inputs/Final Summary Table.xlsx" --cell 0.25,4
+```
+
+`launch_mobo_kit.bat` / `.command` is the one-button path: check the workbook,
+propose the next round, and get the figures. It writes a worklist and a `Review`
+sheet **beside** the workbook and never into it.
+
+**There is one leave-one-out fold loop, `mobo_kit.loocv`, and three callers share
+it.** Intake is canonical for LOO numbers, the round report plots them, and the
+permutation test builds a null out of them. They were briefly three
+implementations; a test now asserts they are the same function object rather than
+that they agree.
+
+## Where the live campaign stands
+
+Measured on v4's 15 rows by `intake_new_data.py`, against a leave-one-out null of
+**-0.1480** and a resolution floor of **+-0.236**:
+
+| objective | plain GP | mean function | verdict |
+|---|---:|---:|---|
+| uniformity | **-0.4688** | none | below the null → **exploration only** |
+| optoelectronic | **-0.7038** | none | below the null → **exploration only** |
+| thickness | **+0.5814** | **none — withdrawn 2026-09-06** | **learnable**, on its rank permutation |
+
+**No objective carries a physics prior any more.** The thickness mean function
+`log T ~ log(speed_1)+log(precur_conc)` was withdrawn on 2026-09-06: its
+justification was that the fitted speed exponent agreed with theory's -0.5, and
+the 95% interval on that exponent is [-0.385, -0.126], which excludes -0.5 by 4.1
+standard errors. Fixing the exponents at the theoretical values scores +0.5600,
+worse than no trend at all. Full table in CAMPAIGN_STATUS.md.
+
+**Still only one learnable axis**, as on v3 — but that no longer argues for heavy
+exploration. `beta = 36` was chosen on the premise that exploring wider was how the
+two dead axes would come alive. The extended C1&C2 sheet (45 rows = 15 recipes made
+three times) shows it is not: optoelectronic is 84.5% between-campaign drift with a
+recipe ICC of **0.000**, and uniformity is reproducible (ICC **0.730**) but not
+predictable from ten inputs at fifteen distinct recipes. Neither is reachable by
+any beta.
+
+**The campaign runs `beta = 4.0` and `radius = 0.25`** as of 2026-09-03. At
+beta = 36 the radius knob was provably inert — radii 0.15, 0.25 and 0.35 return
+bit-identical batches — and 18 of 50 proposed coordinates sat on a grid bound;
+at beta = 4 / radius 0.25 that falls to 11. See CAMPAIGN_STATUS.md, "Are
+beta = 4.0 and radius = 0.25 defensible?", including why the hypervolume column
+of that table must not be read as a ranking.
+
+**Uniformity and optoelectronic are read from the workbook, not computed.** No
+independent recomputation exists under this contract. The formula fingerprints
+notice a changed *definition*; nothing here can notice a value that has gone
+stale. That is the price of the freeze, and it is paid deliberately.
+
+**The v3 photoconductance inversion is fixed.** Its normalised column ranked
+backwards against its own raw measurement (Spearman -0.5484, p = 0.0343); on v4
+the same comparison gives **+1.0000**. Issue 10 is closed. The diagnostic stays on
+because the failure is silent when it recurs.
+
+**Thickness keeps its mean function on the rank permutation**, not on R². Intake
+leaves it *inconclusive on R²* — the swing sits inside the floor, which is a
+statement that R² cannot resolve it at N=15 rather than a verdict. Rank is what
+the acquisition consumes; it never sees R². **Do not quote the swing as
+evidence.** Measured on v4: observed rank ρ **+0.6500**, null mean −0.1892
+(sd 0.2944), **9 exceedances in 1800**, **p = 0.0056, 95% CI [0.0021, 0.0090]**.
+
+## What is actually open
+
+1. **No batch has been proposed on the live campaign yet.** Pressing **Propose
+ R1** writes the worklist, the Review sheet and six figures. Fifteen films is a
+ real cost, and whether to fabricate is a human decision that is not automated.
+2. **The frozen scores are temporary.** The group will settle how uniformity and
+ optoelectronic are computed and then unfreeze them. The v3 recipes (`mean`,
+ `clamped_complement`, `capped_ratio`) remain in `scores.py`, unwired, so that
+ is an edit rather than a rebuild. Unfreezing means a new `contract_version`.
+3. **Phase 4 waits on the R1 triplicates.** `replicate_variance.py` is wired and
+ tested; enabling it is one config key, `model.observation_noise:
+ replicate_pooled`. The `replicate_variance.sanity_floor` for thickness is still
+ v3's 0.006374 and should be recomputed on v4's readings, which changed.
+4. **`anneal_temp` sits at a range edge in proposed conditions.** If the group
+ would never anneal below some temperature, that belongs in `constraints:` —
+ now a live list with three entries, so adding one is a two-line change.
+
+## Three floors. Check all three before comparing any two numbers.
+
+These are method, and they carry across both campaigns.
+
+- **Null, −0.148 at N=15 — AND IT IS NOT A SIGNIFICANCE THRESHOLD.** Measured
+ 2026-09-04, 300 permutations with the campaign's own model: the fitted GP's
+ null has median −0.4210 and 95th percentile **+0.2890**, and **28.7% of
+ pure-noise shuffles beat −0.1480**. Below it a model has certainly learned
+ nothing; above it means nothing on its own. Use the rank permutation test,
+ or `scripts/raw_component_screen.py --calibrate` for a candidate's own bar.
+ Predicting the leave-one-out mean gives
+ `1 − (N/(N−1))²`. A model below it learned nothing, and a negative LOOCV
+ Spearman is that signature rather than a sign bug. It moves with N — recompute.
+- **Sampling, ±0.236.** Parametric bootstrap, 4000 resamples at N=15. Two LOO R²
+ values less than about half a point apart are not a comparison at this N.
+- **Numerical reproducibility, ≈0.07.** Two perturbations that change nothing
+ meaningful each move LOO R² by that much. A second-decimal difference is not a
+ measurement.
+
+**When a comparison lands inside a floor, that is not a verdict — it is a
+statement that the instrument cannot decide, and a different instrument should.**
+For a mean function that instrument is the rank permutation. This project argued
+inside a floor twice before adopting that rule.
+
+## Settled, do not reopen
+
+- **Each contract's objectives are different quantities.** A shared
+ `contract_version` would make their hypervolumes look comparable when they
+ measure different spaces. That is why every redefinition arrives as a new
+ config file rather than an edit -- three times now.
+- **`ObjectiveTransform.transform` takes MODEL-space values, not measurements.**
+ It decodes the link itself, so handing it thickness in nanometres exponentiates
+ a value that was never a logarithm. Use `transform.transform_measurements` at
+ any call site holding workbook values. This defect has now arrived by three
+ separate routes; the third was caught in a draft of the permutation script only
+ because saturating the Gaussian to 0.0 made a column constant.
+- **Uniformity has no learnable signal** on either campaign's data — the first by
+ permutation (p = 0.82 on *that* score), the second by leave-one-out (−0.6447).
+ Exploration-only by measurement, not by choice.
+- **openpyxl discards cached formula values on save**, which is why candidate
+ sheets are written to a *sibling file* and the source workbook is never opened
+ for writing. Do not "simplify" that.
+
+## The failure shape that keeps recurring
+
+**A wrong answer that is finite, ordinary-looking, and compared against nothing.**
+Four instances so far: the hypervolume auto-reference, the silently swallowed
+`train_Yvar`, the R1 baseline mis-encoding, and a timing measured under CPU
+contention that nearly shipped as a documented number.
+
+No guard catches these — each passed every guard it met. What works is **making
+the quantity observable and reproducing it by a second route**. So: every figure
+in a round report writes the CSV behind it; the parity numbers are literally
+intake's function; the batch figure reads the Review artifact rather than
+recomputing it; `validate_batch` re-checks constraints the candidate pool already
+filtered. If you add a number that steers a decision, add its comparator with it.
+
+## Four tooling facts that will bite you
+
+- **BoTorch's `Hypervolume` assumes maximisation and silently drops points that do
+ not dominate the reference.** No warning, no exception — a smaller number, or
+ 0.0. `metrics.compute_ref_pareto_hv` refuses that case and requires an explicit
+ reference.
+- **BoTorch silently ignores `train_Yvar` when a `likelihood` is also passed.**
+ Verified on 0.15.1. Pass one or the other, never both.
+- **`Standardize` rescales `train_Yvar` along with the targets**, so measured
+ variance must arrive in the target's own units — and in the *model's* space,
+ which for thickness is `log T`, not nanometres.
+- **`tight_layout` does not support 3-D axes or colorbars** and warns that its
+ result may be wrong. `round_report._save` takes `tight=False` for those figures
+ rather than ignoring the warning.
+
+## Working advice
+
+Develop against **DTLZ2** where you can: `tests/test_dtlz2_acceptance.py` runs the
+whole loop on a synthetic problem with a known Pareto front, so the algorithm can
+be checked with no dependence on whether the measurements are right. Anything
+data-specific lives in config, so a new dataset means a new YAML, not new code.
+
+Two process rules this project learned the hard way, both worth keeping:
+**verification gates the commit** — run the tests as their own step, never in the
+same breath as `git commit` — and **an order-dependent or timing-sensitive test
+failure is a real defect until proven otherwise**, in the test or in the product.
+
+And one learned at the audit: **a number measured under load is an unreproduced
+number.** Re-measure on an idle machine before writing it down.
diff --git a/docs/R1_BATCH_WITHDRAWAL.md b/docs/R1_BATCH_WITHDRAWAL.md
new file mode 100644
index 0000000..ee6b8bf
--- /dev/null
+++ b/docs/R1_BATCH_WITHDRAWAL.md
@@ -0,0 +1,99 @@
+# The R1 batch was withdrawn and reissued, 2026-07-31
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+**No films were fabricated from the withdrawn batch.** The defect was caught while
+the batch was still awaiting human review, which is what the review gate is for.
+
+## What was wrong
+
+`campaign.run_r1_ucb` handed its observed hypervolume-improvement baseline to
+`ObjectiveTransform.transform` in **measurement space**. That transform is a
+model-output decoder: it applies `exp()` to a log-link objective before computing
+utility. Thickness in nanometres was therefore exponentiated a second time.
+`exp(360…1303)` saturates the 650 nm Gaussian to exactly `0.0` — a finite number,
+so neither the transform's own finiteness check nor the caller's fired.
+
+Every one of the 15 observations scored **thickness utility 0.0**, so R1 chose its
+candidates against a baseline front with no thickness axis at all.
+
+| | withdrawn | reissued |
+|---|---:|---:|
+| observed baseline hypervolume | **0.004659** | **0.436442** |
+| baseline Pareto set | 2 points | 5 points |
+| minimum pairwise spacing | 0.9209 | 0.6337 |
+| boundary coordinates | 13 | 12 |
+
+Fixed in commit `4b76670` by `ObjectiveTransform.encode_measurements`, with
+`transform_measurements` as the one-call safe route. Annie Xu had already found
+and fixed this independently on `ax_plots_simulation`, as
+`_physical_to_model_output`, before we knew it existed.
+
+## What actually changed in the batch
+
+**Four of the five conditions are identical.** One was replaced:
+
+| | speed_1 | time_1 | speed_2 | time_2 | precur_conc | precur_vol | anneal_temp | anneal_time | anti_vol | anti_time |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| **dropped** | 4000 | 45 | 1500 | 30 | 1.70 | 40 | 105 | 10 | 170 | 15 |
+| **added** | 2500 | 50 | 3500 | 35 | 1.45 | 70 | 105 | 15 | 135 | 13 |
+
+The reissued batch in full:
+
+| # | speed_1 | time_1 | speed_2 | time_2 | precur_conc | precur_vol | anneal_temp | anneal_time | anti_vol | anti_time |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| 1 | 2500 | 50 | 1000 | 50 | 1.45 | 50 | 100 | 15 | 100 | 11 |
+| 2 | 2500 | 50 | 3500 | 35 | 1.45 | 70 | 105 | 15 | 135 | 13 |
+| 3 | 1500 | 50 | 3500 | 45 | 1.30 | 50 | 105 | 25 | 200 | 11 |
+| 4 | 3000 | 50 | 2000 | 20 | 1.55 | 80 | 105 | 60 | 130 | 11 |
+| 5 | 2000 | 50 | 2500 | 10 | 1.35 | 90 | 100 | 10 | 110 | 13 |
+
+The withdrawn batch was more spread out — 0.9209 against 0.6337 — which is worth
+saying plainly: **the defect made the batch look better diversified than the model
+actually justified.** With the thickness axis of the baseline pinned at zero,
+candidates were being separated on a distorted score.
+
+## What did not change
+
+Two standing observations survive the fix, so nothing that rests on them needs
+revisiting:
+
+- **The `speed_1 = 1000` corner is still skipped.** The reissued batch's minimum
+ `speed_1` is 1500, as before. The low-speed corner remains a measurement
+ question — samples 1 and 12 still contradict each other and sample 12's 1155 nm
+ is still `ROUND(mean(1600, 709))`.
+- **`anneal_temp` still pins to its lower bound**, at 100–105 across all five
+ conditions. That is the monotone linear mean function speaking, exactly as
+ recorded, and the open question remains chemical rather than numerical.
+
+## Numbers that came from the withdrawn batch
+
+Anything quoting the old batch's *diagnostics* is void and has been corrected in
+place:
+
+- the minimum spacing of **0.921** in `CAMPAIGN_STATUS.md`, which was used to argue
+ that `radius` is "probably inert" on the live campaign. It is not — see the
+ measured staircase in that file.
+- the probe numbers in issue 4 (thickness utility 0.786 → 0.223 at an sd ratio of
+ 1.02). Those came from the withdrawn review artifact and must be re-read from
+ the reissued one.
+
+## Where the artifacts are
+
+`local_inputs/Summary Table_R1_Candidates.xlsx`, regenerated through
+`launcher.generate_next_round` — the same path the double-click launcher uses —
+with the `R1_Candidates` worklist and the `Review` sheet. The withdrawal is
+declared in `configs/campaign_d2d_perovskite.yaml` under `review.notes`, so it
+travels with the Review sheet if that is forwarded on its own. **Delete that note
+once R1 is measured.**
+
+No prior candidate workbook existed on disk to archive: the withdrawn batch was
+described in `CAMPAIGN_STATUS.md` and echoed to the launcher pane, but
+`Summary Table_R1_Candidates.xlsx` had never been written.
diff --git a/docs/ROUND_SIM_DELTA.md b/docs/ROUND_SIM_DELTA.md
new file mode 100644
index 0000000..03ef9b8
--- /dev/null
+++ b/docs/ROUND_SIM_DELTA.md
@@ -0,0 +1,139 @@
+# Round simulation: Annie's branch → `colin`
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+For Annie Xu. This is a review of `examples/round_simulations.py` on
+`annie/ax_plots_simulation` against the current `colin` branch, and a record of
+what `scripts/plot_round_simulation.py` changed and why.
+
+Your branch forks at `33f101f`. Twelve commits landed on `colin` after that, and
+most of this list is API drift rather than anything you got wrong. Two items go
+the other way: one thing you fixed is still broken on `colin`, and one thing the
+brief asked me to "correct" was already correct in your code.
+
+## 1. You found a real bug, and `colin` still has it
+
+`campaign.run_r1_ucb` passes `observed_Y_raw` — thickness in **nanometres** —
+into `ObjectiveTransform.transform`, which applies `exp()` to log-link
+objectives. `exp(360…1303)` overflows the 650 nm Gaussian to exactly `0.0`. It is
+finite, so the non-finite guard never fires and nothing raises.
+
+Measured on the real workbook, R0:
+
+| observed baseline | hypervolume | Pareto size |
+|---|---:|---:|
+| as `run_r1_ucb` encodes it | 0.004659 | 2 |
+| link decoded once (your `_physical_to_model_output`) | 0.436442 | 5 |
+
+Every candidate's HVI is scored against a baseline whose thickness axis is pinned
+at zero. Your `_run_r1_corrected` is the fix.
+
+**Update, later the same day: `colin` no longer has it.** The group decided the
+fix was its own change, and commit `4b76670` promotes the concept in your
+`_physical_to_model_output` to a public contract —
+`ObjectiveTransform.encode_measurements`, with `transform_measurements` as the
+one-call safe route — and has `run_r1_ucb` encode before it proposes. The
+acquisition modules are untouched: `ucb_hvi.py` stays byte-identical, because the
+defect was in `campaign.py` orchestration.
+
+The R1 batch built on the mis-encoded baseline was **withdrawn and reissued**
+(`R1_BATCH_WITHDRAWAL.md`): four of five conditions survived, one was replaced,
+and the batch's minimum spacing fell 0.9209 → 0.6337. No films had been made.
+`scripts/plot_round_simulation.py` now simply calls the public `run_r1_ucb` —
+verified to reproduce its own private version hash-for-hash — and keeps the
+contrast in the manifest as a standing tripwire.
+
+R2 is unaffected: `run_r2_qlognehvi` passes `train_X_norm`, and qLogNEHVI derives
+its baseline through the model in model space.
+
+## 2. Your "physical mean" label was right; the change is substantive
+
+The brief I was given said your colorbar said "physical mean" without the
+`exp(μ + v/2)` correction. It does not — `_simulate_oracle` and
+`_objective_surface_values` both apply `torch.exp(mu + 0.5 * variance)`, which is
+the lognormal mean, so your label was accurate for what you computed.
+
+The new script still switches to the **median** `exp(μ)`, for a different reason
+than the one in the brief. An oracle built on `exp(μ + v/2)` has a value that
+depends on the posterior *variance*, which is largest exactly where the 15 real
+films are sparse. The simulated ground truth would then bulge in the regions the
+optimiser is about to explore, so the landscape would encode where R0 happened to
+look rather than what the model believes. `exp(μ)` depends on the mean surface
+alone. Both are labelled "posterior median" everywhere, and a test
+(`test_the_oracle_reports_the_median_not_the_lognormal_mean`) pins it so nobody
+switches it back without reading the reason.
+
+Same for the slice-fixing convention: the brief said to standardise on the median
+because a PDF snippet said "average". Your `_objective_surface_values` already
+used `np.median(...)` with a grid snap. No change — it is kept, snap included.
+
+## 3. API drift since `33f101f`
+
+| your code | current API | why |
+|---|---|---|
+| `pd.read_excel(...)` + `campaign.model_source_columns(config)` | `read_campaign_workbook(path, config)` → `contents.model_values` | three of the workbook's derived score cells are **pasted literals**, not formulas, so they do not update when the measurements behind them change. `scores.py` now recomputes all three objectives from the raw measurement columns and demotes the stored cells to cross-checks. `CAMPAIGN_STATUS.md` issue 2 has the audit. |
+| — | `assert contents.errors == ()` before fitting | fail closed. `contents.findings` also carries cross-check mismatches, operator-excluded readings, and films whose thickness readings disagree. |
+| thickness from column `X` = `ROUND(mean(T1..T4))` | unrounded mean of whichever of `T1..T4` were measured | 7 of 15 rows change, by ≤0.50 nm. That alone moves LOO R² by 0.067, so the number matters even though the utility barely moves. |
+| `getattr(campaign, "_fit_models")`, 5 positional args, returns a model | `fit_campaign_models(config, X_phys, Y_raw, seed=...)` → **`(model, warnings)`** | `_fit_models` is private, now takes `Yvar_model`, and returns a 3-tuple. The public function returns the fit guard's own findings, which must be read: a fit can succeed and still deserve distrust. |
+| — | abort if the oracle fit warns | a grid built on a collapsed oracle must not render silently. The new script hard-stops rather than bannering, because every downstream number would be built on that fit. |
+| `config.get("reference_point_utility")` read directly | `metrics.compute_ref_pareto_hv(Y, ref)` | it now **raises** on `ref_point_np=None` instead of using `mins - 1e-8` (measured 6e-8 against 1.448 on the same data), and raises when nothing dominates the reference instead of returning BoTorch's silent `0.0`. |
+| `run_r2_qnehvi` + `src/mobo_kit/qnehvi_batch.py` | qLogNEHVI only | scope decision from the brief. qLogNEHVI is the numerically stable formulation of the same acquisition; your `campaign.py` edit is not carried over, so `colin`'s `campaign.py` stays untouched. |
+
+## 4. Structural change: where the 2-D lives
+
+Your script builds a **pair-slice config** per input pair — the other eight inputs
+collapse to single-value grids at the experimental midpoint, the pair is
+restricted to the workbook min/max — and runs a whole LHS→R1→R2 campaign inside
+that 2-D slice. So each figure is its own optimisation.
+
+The new script runs the campaign **once per parameter cell in full 10-D**, and
+the 45 input pairs are *views* of that one result. Three consequences worth
+knowing:
+
+- R0 is the **real 15 recipes**, oracle-scored, not a fresh LHS. The loop then
+ lives on one consistent landscape instead of mixing a measured R0 with a
+ simulated R1/R2.
+- the batch-identity question ("did radius 0.05 and radius 0.45 propose the same
+ five conditions?") is answerable, because there is one batch per cell rather
+ than 45 unrelated ones.
+- it is ~45× cheaper, which is what makes 13 parameter cells affordable.
+
+Your layout is preserved: `{pair}/qlognehvi/radius_*__beta_*/` for the surfaces,
+your `_slug_number` rule (`0.25` → `0p25`), and your round legend verbatim —
+"R0 LHS (GP_exp scored)", "R1 simulated", "R2 simulated". Per-condition artifacts
+that have no pair (boxplots, the HV line, `all_rounds.csv`) go under
+`by_condition/qlognehvi/radius_*__beta_*/`.
+
+## 5. Smaller things
+
+- **Outputs are gitignored.** Everything lands under `local_outputs/`, not
+ `results/`. Your committed `results/` PNGs are fine on your fork and would be
+ bloat on `colin`; they are not merged.
+- **Boxplot fliers are off.** Every raw point is already overlaid, so matplotlib's
+ flier markers drew a second, differently-styled copy of the same observation.
+ The overlay itself is your convention and is kept — a box over three numbers
+ reports little more than those numbers.
+- **Palette** is `scripts/plot_dtlz2_report.py`'s, so every figure this project
+ ships reads as one set. R0's categorical blue is the same hue the magnitude ramp
+ is built from, so markers carry a white stroke around a dark edge; a single
+ white edge disappears at the dark end of the ramp.
+- **Footer.** Two fixed caveat lines on every figure. The oracle caveat is on all
+ of them; the other line is whichever is true of that figure — the slice caveat
+ on slice figures, a small-n caveat on the round summaries. The brief asked for
+ one identical footer everywhere, but printing a slice caveat on a boxplot puts a
+ false statement where a reader looks for true ones.
+- **`min_batch_distance` is pinned at 0.15** in every cell, so "spacing" means one
+ thing across the sweep. Only `radius` and `beta` move.
+
+## 6. What did not need changing
+
+`_safe_filename`, `_slug_number`, the directory convention, the round legend, the
+overlaid boxplot points, the median-with-grid-snap slice fixing, and the decision
+to fit the oracle once and freeze it. All carried over.
diff --git a/docs/ROUND_SIM_MANIFEST.md b/docs/ROUND_SIM_MANIFEST.md
new file mode 100644
index 0000000..8c89623
--- /dev/null
+++ b/docs/ROUND_SIM_MANIFEST.md
@@ -0,0 +1,74 @@
+# `manifest.csv` schema
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+Written by `scripts/plot_round_simulation.py` to
+`local_outputs/round_simulations/manifest.csv`. One row per parameter cell.
+
+This file, not the figures, is the decision instrument. The figures show what a
+landscape looks like; the manifest answers whether changing a knob changed
+anything, which is the question the sweep exists to settle. A knob that produces a
+byte-identical batch at every setting is inert on this problem, and no amount of
+looking at contour plots will tell you that.
+
+Column order is pinned by `MANIFEST_COLUMNS` in the script and asserted by
+`tests/test_plot_round_simulation.py::test_manifest_row_has_exactly_the_declared_columns`.
+
+| column | type | meaning |
+|---|---|---|
+| `condition_id` | int | 1-based position in the run. Not stable across different `--conditions` filters; use the slug or `(radius, beta)` to join. |
+| `arm` | str | `radius` (beta held at 4), `beta` (radius held at 0.25), `both` (the shared 0.25/4 anchor), or `grid` under `--full-grid`. |
+| `radius` | float | `local_penalization.radius` for this cell. |
+| `beta` | float | `rounds.r1.beta` for this cell. |
+| `min_batch_distance` | float | Always 0.15. Pinned, never swept, so "spacing" means one thing in every row. |
+| `seed` | int | 73 unless `--seed` overrides. The oracle, both acquisitions, and every pool draw use it. |
+| `r1_batch_hash` | str | 16 hex chars. SHA-256 of the R1 conditions, **sorted** and rounded to 12 dp. Two cells sharing a hash proposed the same set of recipes; order is not part of the identity. |
+| `r2_batch_hash` | str | The same for R2. |
+| `r1_min_pairwise_distance` | float | Smallest normalised distance within the R1 batch. Compare against `radius` to see whether local penalisation had anything to act on. |
+| `r2_min_pairwise_distance` | float | The same for R2. |
+| `r1_boundary_coords_total` | int | How many coordinates across the whole R1 batch sit exactly at a range edge. On the live campaign this is driven by the monotone `anneal_temp` mean function, not by either knob. |
+| `r2_boundary_coords_total` | int | The same for R2. |
+| `r1_boundary_coords_per_condition` | JSON list | Per-condition breakdown, so one pinned condition is distinguishable from five mildly-pinned ones. |
+| `r2_boundary_coords_per_condition` | JSON list | The same for R2. |
+| `hv_r0` | float | Hypervolume of the 15 oracle-scored R0 points, utility space, at the campaign's declared `reference_point_utility`. |
+| `hv_r0_r1` | float | After adding the 5 R1 conditions. |
+| `hv_r0_r1_r2` | float | After adding the 3 R2 conditions. |
+| `hv_gain_r1` | float | `hv_r0_r1 - hv_r0`. |
+| `hv_gain_r2` | float | `hv_r0_r1_r2 - hv_r0_r1`. |
+| `baseline_hv_reported_by_r1` | float | The observed HVI baseline the R1 acquisition actually used, from `run_r1_ucb`'s own diagnostics. |
+| `baseline_hv_independent` | float | The same quantity recomputed by the script through `metrics.compute_ref_pareto_hv` — a different Pareto filter and a different call path. **`run_cell` raises if these two disagree.** |
+| `baseline_hv_pareto_size` | int | How many observations sit on the baseline Pareto front. Under the historical mis-encoding this was 2; correctly encoded it is 5. |
+| `baseline_hv_unencoded_contrast` | float | What the baseline *would* be if measurement-space values reached the transform directly — the size of the defect fixed in commit `4b76670`. Constant across rows, and **never expected to equal anything**. |
+| `r1_fit_warnings` | int | Fit-guard warnings raised by the GP that proposed R1. Guard warnings only, not the ~18 numpy deprecation notices per fit. |
+| `r2_fit_warnings` | int | The same for the R2 model. |
+| `final_fit_warnings` | int | The same for the 23-point model the heatmaps render. Non-zero puts a banner on that cell's figures. |
+| `mean_utility_r0` | float | Mean utility over all objectives and all 15 R0 points. A summary, not a ranking: it averages three objectives that are not commensurable. |
+| `mean_utility_r1` | float | The same over the 5 R1 conditions. |
+| `mean_utility_r2` | float | The same over the 3 R2 conditions. |
+
+## Reading it
+
+**Hypervolume rises monotonically by construction.** Adding points can only grow a
+Pareto front, so `hv_r0 <= hv_r0_r1 <= hv_r0_r1_r2` holds in every row and proves
+nothing on its own — random sampling satisfies it too. What carries information is
+the *size* of `hv_gain_r1` compared across cells, and the batch hashes.
+
+**Cells with equal `(r1_batch_hash, r2_batch_hash)` are the same experiment.**
+Their hypervolumes and utilities are then identical by construction, not by
+agreement, and quoting them as independent replicates would be double counting.
+
+**The baseline columns are the standing tripwire for the encoding defect.**
+`reported` comes from inside the acquisition; `independent` is recomputed by a
+different route; `run_cell` raises rather than writing a manifest if they differ.
+`unencoded_contrast` is the size of the historical mistake and is deliberately not
+compared to anything — asserting all three equal would be an assertion that can
+only ever fail, because the third column exists precisely to reproduce the wrong
+answer. They are constant within a run, and recorded per row so a single row is
+self-describing when pasted somewhere else.
diff --git a/docs/SHAP_SUMMARY.md b/docs/SHAP_SUMMARY.md
new file mode 100644
index 0000000..e64b831
--- /dev/null
+++ b/docs/SHAP_SUMMARY.md
@@ -0,0 +1,84 @@
+# `shap_summary.csv` schema
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+Written by `scripts/plot_shap_attribution.py` to
+`local_outputs/shap/shap_summary.csv`. One row per **feature × objective × model
+state**.
+
+The beeswarms show shape; this file is what you sort, diff and quote. It is also
+what makes the extreme-cell comparison possible, since ranking two pictures by eye
+is not a measurement.
+
+## What is being explained
+
+`E[utility]` for one objective, through
+`ObjectiveTransform.expected_transform` — so thickness goes through the lognormal
+quadrature rather than a transformed posterior mean, and every SHAP value is in
+**utility units where higher is better**, comparable across features within an
+objective.
+
+**Not comparable across objectives.** Uniformity and thickness utilities are
+different constructions (an affine product against a Gaussian on a 650 nm target),
+so a larger mean |SHAP| on one does not mean that objective is more sensitive.
+Compare rows within an objective, or compare the same objective across model
+states.
+
+## Columns
+
+| column | type | meaning |
+|---|---|---|
+| `model_state` | str | `r0_only` (fitted to the 15 real measurements), `final` (23 points after a simulated R0→R1→R2 at radius 0.25, beta 4), or `final_radius_*__beta_*` for an extreme cell. If the two R2 acquisitions ever diverge, `final_qlognehvi` and `final_qnehvi` appear instead of `final`. |
+| `objective` | str | `uniformity`, `optoelectronic` or `thickness`. |
+| `feature` | str | One of the ten campaign inputs. |
+| `mean_abs_shap` | float | Mean absolute SHAP value over the attributed instances — the magnitude the beeswarm ranks by. |
+| `rank` | int | 1 = largest `mean_abs_shap` within that objective and model state. |
+| `mean_shap` | float | Signed mean. Near zero with a large `mean_abs_shap` means the feature matters in both directions — a non-monotone effect, not a weak one. |
+| `feature_min` / `feature_max` | float | The physical range spanned by the attributed instances, in that input's own units. Present because a beeswarm's colour is normalised **per feature row**, so one colorbar cannot carry physical units for ten inputs at once. |
+| `in_mean_function` | bool | True if this feature appears in that objective's declared `mean_function`. **A True row is partly a restatement of the model's declared physics, not a discovery.** |
+| `r2_acquisition` | str | `identical` when qLogNEHVI and qNEHVI proposed the same R2 batch (so the row covers both), otherwise the acquisition that produced the state. |
+
+## `shap_extreme_cell_shift.csv`
+
+Written alongside when `--extreme-cells` is passed. One row per extreme cell ×
+objective, answering **whether the attributions describe the model or the search**.
+
+| column | meaning |
+|---|---|
+| `cell` | the extreme cell, e.g. `radius_0p05__beta_4` |
+| `max_abs_shift` | largest change in `mean_abs_shap` for any feature, against the default cell |
+| `max_shift_feature` | which feature moved most |
+| `max_shift_fraction_of_largest` | that shift as a fraction of the objective's largest default-cell attribution |
+| `top_feature_changed` | whether the rank-1 feature differs from the default cell |
+| `default_top_feature` / `cell_top_feature` | the two rank-1 features |
+
+**The verdict rule was fixed before the cells were run**: sweeping the acquisition
+parameters is warranted only if an extreme cell moves the top feature, or moves any
+attribution by more than **10%** of that objective's largest. Otherwise the
+attributions are a property of the fitted model rather than of how the batch was
+selected, and there is nothing to sweep.
+
+## Three things to read carefully
+
+**A large attribution is not evidence of a physical effect.** SHAP explains the
+model. Where `in_mean_function` is True, the model was *told* that relationship by
+`configs/campaign_d2d_perovskite.yaml`; SHAP recovering it is a consistency check,
+not a discovery.
+
+**Uniformity attributions are not signal.** Uniformity does not beat the
+leave-one-out null (LOO R² −0.681, permutation p = 0.82). Its GP still fits ARD
+lengthscales and has a posterior mean that varies, so SHAP reports structure with
+real magnitude. That structure is fitted noise. It is included rather than
+suppressed because a reader who sees only the beeswarm would otherwise conclude
+the opposite — and every uniformity figure says so in its footer.
+
+**`final` rows describe a simulated campaign.** Only the 15 R0 conditions were
+measured; the other 8 carry oracle predictions. `r0_only` is the state fitted
+entirely to real data and is the anchor for anything quoted outside this analysis.
diff --git a/docs/figures/round_simulation/01_thickness_radius_0p05_tight_batch.png b/docs/figures/round_simulation/01_thickness_radius_0p05_tight_batch.png
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diff --git a/docs/figures/round_simulation/README.md b/docs/figures/round_simulation/README.md
new file mode 100644
index 0000000..fb678c7
--- /dev/null
+++ b/docs/figures/round_simulation/README.md
@@ -0,0 +1,42 @@
+# Curated round-simulation figures
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+Six figures from one run of `scripts/plot_round_simulation.py`, seed 73, kept as
+examples of what the script produces. The full run writes 1,781 figures and 222 MB
+to the gitignored `local_outputs/round_simulations/`; these are the ones worth
+looking at without re-running it.
+
+**Every value in these figures is a model prediction, not a measurement.** The
+oracle is a GP fitted to the 15 real R0 films and then frozen, so a condition that
+scores well here has scored well against MOBO-Kit's own beliefs. This validates
+the optimiser loop on a data-shaped landscape; it says nothing about the
+chemistry. Each figure repeats that in its footer.
+
+| file | what it shows |
+|---|---|
+| `01_thickness_radius_0p05_tight_batch.png` | radius 0.05. R1 (orange) clusters — two conditions land on `speed_1 = 2500`. Achieved batch spacing 0.455. |
+| `02_thickness_radius_0p25_anchor.png` | radius 0.25, the campaign's current setting. The same five conditions have been pushed apart; spacing 0.720. |
+| `03_thickness_radius_0p45_saturated.png` | radius 0.45. Spacing 0.921, identical to radius 0.30–0.40 — the knob has saturated and stops doing anything. |
+| `04_optoelectronic_anneal_temp_range_edge.png` | why every proposed condition pins `anneal_temp` to its lower bound. The surface is monotone in temperature because the objective carries a monotone linear mean function, so its optimum is at a range edge by construction. This reproduces `CAMPAIGN_STATUS.md` issue 4 from the model side. |
+| `05_utility_by_round_anchor.png` | utility by round, three objectives, n = 15 / 5 / 3, with every raw point drawn over its box. |
+| `06_hypervolume_by_round_anchor.png` | cumulative hypervolume at the campaign-fixed reference. It rises monotonically **by construction** — adding points can only grow a Pareto front — so this panel shows the size of each step, not that optimisation happened. |
+
+Figures 01 → 02 → 03 are the same slice of the same input pair at three radii, and
+are the visual form of the sweep's main finding: on this landscape `radius`
+**binds** below about 0.30 and is inert above it. That contradicts the expectation
+carried over from the DTLZ2 sweep, where achieved spacings of 0.72–0.98 left the
+knob nothing to act on.
+
+Reproduce any of them with, for example:
+
+```bash
+python scripts/plot_round_simulation.py --workbook "local_inputs/Summary Table.xlsx" --conditions radius_0p05__beta_4 --pairs speed_1,precur_conc
+```
diff --git a/docs/figures/shap/01_thickness_r0_only.png b/docs/figures/shap/01_thickness_r0_only.png
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diff --git a/docs/figures/shap/05_optoelectronic_final23.png b/docs/figures/shap/05_optoelectronic_final23.png
new file mode 100644
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diff --git a/docs/figures/shap/06_uniformity_final23.png b/docs/figures/shap/06_uniformity_final23.png
new file mode 100644
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diff --git a/docs/figures/shap/README.md b/docs/figures/shap/README.md
new file mode 100644
index 0000000..8643404
--- /dev/null
+++ b/docs/figures/shap/README.md
@@ -0,0 +1,56 @@
+# SHAP attribution figures
+
+> **This document describes TEST DATA.** The workbook and contract it reports on
+> (`d2d-objectives-v2-nm-thickness`, `Summary Table.xlsx`) existed to develop and
+> check the toolkit, not to run an experiment. **The real campaign is v4** --
+> `configs/campaign_d2d_perovskite_final.yaml` on
+> `local_inputs/Final Summary Table.xlsx`, contract `d2d-objectives-v4-final`.
+> Uniformity and optoelectronic have been renormalised twice since, so **no
+> number below transfers**; they describe quantities that were redefined. Start
+> from `docs/CAMPAIGN_STATUS.md` for the real campaign.
+
+Six figures from one run of `scripts/plot_shap_attribution.py`, seed 73, 1,000
+on-grid instances. Schema and reading notes: `docs/SHAP_SUMMARY.md`.
+
+Each answers: **which process inputs move this objective's expected utility, and
+in which direction?** They explain the *model*, which is the only thing SHAP can
+explain.
+
+| file | what it shows |
+|---|---|
+| `01_thickness_r0_only.png` | The clearest real result. `precur_conc` and `speed_1` lead by 3.7× over the third feature, with the sign pattern the fitted physics predicts: high concentration and low spin speed both push the film thicker, away from the 650 nm target, so both reduce utility. |
+| `02_optoelectronic_r0_only.png` | `anneal_temp` leads, monotone and negative — the declared linear trend (marginal ρ = −0.651, p = 0.009). This is why every proposed condition pins the temperature to its lower bound. |
+| `03_uniformity_r0_only_is_fitted_noise.png` | **The cautionary figure.** It looks like a textbook result — a clean monotone gradient on `time_1`, an orderly ranking, magnitudes of ±0.15. Uniformity does not beat the leave-one-out null (LOO R² −0.681, permutation p = 0.82). Every bit of that structure is fitted noise. |
+| `04`–`06` | The same three objectives after a simulated R0 → R1 → R2 pass, refitted on 23 conditions. Rankings are unchanged; magnitudes grow slightly. Only 15 of those 23 conditions were ever measured. |
+
+## Three things these figures are not
+
+**Not evidence of a physical effect.** Where a feature appears in that objective's
+declared `mean_function` — `speed_1` and `precur_conc` for thickness,
+`anneal_temp` for optoelectronic — the model was *told* that relationship by the
+config. SHAP recovering it is a consistency check, not a discovery. The
+`in_mean_function` column in `shap_summary.csv` marks exactly which rows those are.
+
+**Not a cross-objective comparison.** Thickness attributions are larger than
+uniformity's, but the two utilities are different constructions (a Gaussian on a
+650 nm target against an affine product). Compare within an objective.
+
+**Not sensitive to how the batch was chosen.** Measured across three extreme
+acquisition cells (radius 0.05, radius 0.45, beta 25), no objective changed its
+top feature and the largest attribution shift was **0.0996 of that objective's
+largest** — and that near-miss was on uniformity, whose attributions are noise
+anyway. The two objectives carrying real structure moved by at most 6.3%. So these
+are properties of the fitted model, not of the search.
+
+## qLogNEHVI and qNEHVI give the same model
+
+The brief expected two distinct final states, one per R2 acquisition. They propose
+the **identical R2 batch** — verified across four cells (default, radius 0.05,
+radius 0.45, beta 25) — so figures `04`–`06` cover both, and each says so in its
+footer. BoTorch itself warns against qNEHVI in favour of qLogNEHVI.
+
+Reproduce with:
+
+```bash
+python scripts/plot_shap_attribution.py --workbook "local_inputs/Summary Table.xlsx" --instances 1000 --extreme-cells
+```
diff --git a/launch_mobo_kit.bat b/launch_mobo_kit.bat
new file mode 100644
index 0000000..c15675d
--- /dev/null
+++ b/launch_mobo_kit.bat
@@ -0,0 +1,32 @@
+@echo off
+rem Double-click this to propose the next round.
+rem
+rem It opens a small window: choose the campaign workbook, press "Check
+rem workbook", then press "Propose R1" (or R2). The proposed conditions are
+rem written to a NEW file beside the workbook; the workbook itself is never
+rem modified.
+rem
+rem If the window does not appear, the message left in this console says why.
+
+setlocal
+cd /d "%~dp0"
+
+set "MOBO_PYTHON=.venv\Scripts\python.exe"
+if not exist "%MOBO_PYTHON%" set "MOBO_PYTHON=python"
+
+"%MOBO_PYTHON%" -m mobo_kit.launcher %*
+
+if errorlevel 1 (
+ echo.
+ echo The launcher stopped with an error. The workbook was not modified.
+ echo.
+ echo If it says "No module named mobo_kit", the environment is not installed
+ echo yet. From this folder, run:
+ echo.
+ echo py -3.12 -m venv .venv
+ echo .venv\Scripts\python -m pip install -r requirements\dev.txt
+ echo.
+ pause
+)
+
+endlocal
diff --git a/launch_mobo_kit.command b/launch_mobo_kit.command
new file mode 100644
index 0000000..92a78be
--- /dev/null
+++ b/launch_mobo_kit.command
@@ -0,0 +1,37 @@
+#!/bin/sh
+# Double-click this to propose the next round (macOS).
+#
+# It opens a small window: choose the campaign workbook, press "Check workbook",
+# then press "Propose R1" (or R2). The proposed conditions are written to a NEW
+# file beside the workbook; the workbook itself is never modified.
+#
+# macOS will not run a .command file until it is marked executable. Once, in
+# Terminal, from this folder:
+#
+# chmod +x launch_mobo_kit.command
+
+cd "$(dirname "$0")" || exit 1
+
+MOBO_PYTHON=.venv/bin/python
+if [ ! -x "$MOBO_PYTHON" ]; then
+ MOBO_PYTHON=python3
+fi
+
+"$MOBO_PYTHON" -m mobo_kit.launcher "$@"
+status=$?
+
+if [ "$status" -ne 0 ]; then
+ echo
+ echo "The launcher stopped with an error. The workbook was not modified."
+ echo
+ echo 'If it says "No module named mobo_kit", the environment is not installed'
+ echo "yet. From this folder, run:"
+ echo
+ echo " python3 -m venv .venv"
+ echo " .venv/bin/python -m pip install -r requirements/dev.txt"
+ echo
+ echo "Press return to close."
+ read -r _
+fi
+
+exit "$status"
diff --git a/notebooks/MOBO_demo_annotated.ipynb b/notebooks/MOBO_demo_annotated.ipynb
index 5ac60db..be3d81a 100644
--- a/notebooks/MOBO_demo_annotated.ipynb
+++ b/notebooks/MOBO_demo_annotated.ipynb
@@ -30,6 +30,42 @@
"- [9. Save Results](#9-save-outputs)\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "> ## Which model this notebook builds\n",
+ ">\n",
+ "> **This is a general demo of the toolkit API on an arbitrary CSV. It is not\n",
+ "> the campaign path, and it does not build the campaign's model.**\n",
+ ">\n",
+ "> The GPs below come from `models.fit_gp_models` and `models.loocv_select_models`,\n",
+ "> which construct `ScaleKernel(MaternKernel(nu=2.5, ard))` with **no lengthscale\n",
+ "> prior**. On 15 observations in 10 dimensions that fit is degenerate: measured\n",
+ "> ARD lengthscales ran from 0.13 to 38,000 with 6-9 of 10 directions switched off\n",
+ "> and the noise pinned at its floor, meaning the model believed the data were\n",
+ "> noiseless. See `docs/GP_MODEL_DECISION.md`.\n",
+ ">\n",
+ "> The campaign uses `model_validation.fit_model_variant` with the\n",
+ "> `dim_scaled_prior` variant, which restores BoTorch's dimension-scaled LogNormal\n",
+ "> lengthscale prior and its LogNormal noise prior, plus a runtime guard against\n",
+ "> the two degenerate fits those priors do not by themselves prevent. Reach it\n",
+ "> through `campaign.fit_campaign_models`, or run a whole round with\n",
+ "> `campaign.run_r1_ucb`.\n",
+ ">\n",
+ "> Two more differences worth knowing before borrowing code from here:\n",
+ ">\n",
+ "> - The campaign **computes** its objective values from raw measurement columns\n",
+ "> (`scores.py`) rather than reading stored score cells, because three of those\n",
+ "> cells in the real workbook are pasted literals that do not update.\n",
+ "> - `metrics.compute_ref_pareto_hv` now **requires** an explicit reference point.\n",
+ "> A reference inferred from the data in hand moves between rounds, which makes\n",
+ "> hypervolumes incomparable across them.\n",
+ ">\n",
+ "> Saved outputs below were produced by an earlier run on a CUDA machine and are\n",
+ "> kept for reading; re-execute to regenerate them.\n"
+ ]
+ },
{
"cell_type": "markdown",
"id": "c22b92d9",
@@ -1433,28 +1469,20 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "e54ea0a6",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Hypervolume: 109.02000125445022 | Pareto count: 5\n",
- "tensor([[ 0.0000, 0.0000, 10.0000],\n",
- " [10.6900, 1.3500, 3.3333],\n",
- " [16.4300, 5.6800, 0.9524],\n",
- " [17.2200, 0.9000, 0.7353],\n",
- " [16.8300, 4.6800, 1.2821]], device='cuda:0', dtype=torch.float64)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from mobo_kit.metrics import compute_ref_pareto_hv\n",
"\n",
- "_, pareto_Y_t, hv_val = compute_ref_pareto_hv(Y_t)\n",
- "ref_point_t = torch.tensor([-0.01, -0.01, -0.01], dtype=X_t.dtype, device=X_t.device)\n",
+ "# The reference point is fixed for the whole campaign and must be passed\n",
+ "# explicitly. A reference inferred from whatever data is in hand moves between\n",
+ "# rounds, and hypervolumes measured against a moving reference are not\n",
+ "# comparable across them -- which is the only reason to track hypervolume.\n",
+ "ref_point_np = np.array([-0.01, -0.01, -0.01])\n",
+ "\n",
+ "ref_point_t, pareto_Y_t, hv_val = compute_ref_pareto_hv(Y_t, ref_point_np)\n",
"print(\"Hypervolume:\", hv_val, \"| Pareto count:\", pareto_Y_t.shape[0])\n",
"print(pareto_Y_t)"
]
diff --git a/pyproject.toml b/pyproject.toml
index d37e35b..f416c0c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,39 +32,34 @@ classifiers = [
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Scientific/Engineering :: Chemistry",
"Topic :: Scientific/Engineering :: Physics",
]
-requires-python = ">=3.10"
+requires-python = ">=3.11,<3.13"
dependencies = [
- "numpy>=1.21.0",
- "pandas>=1.3.0",
- "scikit-learn>=1.0.0",
- "matplotlib>=3.5.0",
- "seaborn>=0.11.0",
- "pyyaml>=6.0",
- "scipy>=1.7.0",
- "torch>=1.12.0",
- "gpytorch>=1.8.0",
- "botorch>=0.8.0",
- "emukit>=0.4.10",
- "shap>=0.41.0",
- "pyDOE>=0.3.8",
- "jupyter>=1.0.0",
- "ipykernel>=6.0.0",
+ "numpy>=1.26,<3",
+ "pandas>=2.1,<3",
+ "scikit-learn>=1.4,<2",
+ "matplotlib>=3.8,<4",
+ "seaborn>=0.13,<1",
+ "pyyaml>=6.0,<7",
+ "scipy>=1.12,<2",
+ "torch>=2.8,<2.9",
+ "gpytorch==1.14",
+ "botorch==0.15.1",
+ "shap>=0.46,<1",
+ "openpyxl>=3.1,<4",
+ "pillow>=10,<13",
]
[project.optional-dependencies]
dev = [
- "pytest>=6.0",
- "pytest-cov>=2.0",
- "black>=22.0",
- "flake8>=4.0",
- "mypy>=0.950",
+ "pytest>=8.2,<9",
+ "pytest-cov>=5,<7",
+ "black>=24,<26",
]
jupyter = [
"jupyter>=1.0.0",
@@ -72,18 +67,19 @@ jupyter = [
"notebook>=6.0.0",
]
all = [
- "mobo-fom[dev,jupyter]",
+ "mobo-kit[dev,jupyter]",
]
[project.urls]
-Homepage = "https://github.com/PV-Lab/MOBO-FOM"
-Repository = "https://github.com/PV-Lab/MOBO-FOM"
-Documentation = "https://github.com/PV-Lab/MOBO-FOM#readme"
-"Bug Tracker" = "https://github.com/PV-Lab/MOBO-FOM/issues"
+Homepage = "https://github.com/PV-Lab/MOBO-Kit"
+Repository = "https://github.com/PV-Lab/MOBO-Kit"
+Documentation = "https://github.com/PV-Lab/MOBO-Kit#readme"
+"Bug Tracker" = "https://github.com/PV-Lab/MOBO-Kit/issues"
[project.scripts]
mobo-kit = "mobo_kit.cli:main"
mobo-kit-run = "mobo_kit.main:main"
+mobo-kit-launcher = "mobo_kit.launcher:main"
[tool.setuptools.packages.find]
where = ["src"]
@@ -93,7 +89,7 @@ where = ["src"]
[tool.black]
line-length = 88
-target-version = ['py310']
+target-version = ['py311']
include = '\.pyi?$'
extend-exclude = '''
/(
@@ -114,10 +110,18 @@ testpaths = ["tests"]
python_files = ["test_*.py", "*_test.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
-addopts = "-v --tb=short"
-
-[tool.mypy]
-python_version = "3.10"
-warn_return_any = true
-warn_unused_configs = true
-disallow_untyped_defs = true
+# --capture=sys is load-bearing, not a preference. pytest's default fd-level
+# capture swaps file descriptors 1 and 2 for temp files, and a Tk interpreter
+# created while that is in force holds descriptors that are gone by the time the
+# next one is built -- the second or third window in a process then dies reading
+# its own init.tcl, reporting "No error". It looked like a race in the launcher
+# and is not one. Measured: 6 failures in 9 runs of one launcher test under
+# --capture=fd, 0 in 24 runs under --capture=sys, with an identical 28-warning
+# tail and no extra console output. Only `capsys` is used in this suite, never
+# `capfd`, so nothing here depends on fd-level capture. See the `open_window`
+# fixture in tests/test_launcher.py for the full measurement.
+addopts = "-v --tb=short --capture=sys"
+markers = [
+ "local_input: optional integration test requiring an ignored local_inputs artifact",
+ "slow: multi-seed acceptance test; deselect with -m 'not slow'",
+]
diff --git a/requirements.txt b/requirements.txt
index 045d123..56341a4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,24 +1,4 @@
-# Core scientific stack
-numpy>=1.21.0
-pandas>=1.3.0
-scikit-learn>=1.0.0
-matplotlib>=3.5.0
-seaborn>=0.11.0
-pyyaml>=6.0
-scipy>=1.7.0
-
-# PyTorch & probabilistic programming
-torch>=1.12.0
-gpytorch>=1.8.0
-botorch>=0.8.0
-
-# Experimental design / optimization
-emukit>=0.4.10
-
-# Optional: Jupyter for notebooks
-jupyter>=1.0.0
-ipykernel>=6.0.0
-
-# Additional dependencies
-shap>=0.41.0
-pyDOE>=0.3.8
+# Reproducible runtime install from the repository root:
+# python -m pip install -r requirements.txt
+-c requirements/constraints.txt
+.
diff --git a/requirements/constraints.txt b/requirements/constraints.txt
new file mode 100644
index 0000000..7112979
--- /dev/null
+++ b/requirements/constraints.txt
@@ -0,0 +1,36 @@
+# The CPU-tested dependency stack. Keep these versions synchronized with the
+# ranges in pyproject.toml, which are deliberately wider: this file is what was
+# actually tested, pyproject is what is allowed.
+#
+# linear_operator is pinned although pyproject does not declare it -- it is a
+# gpytorch/botorch transitive dependency whose version affects fitted numbers, and
+# on this stack a second-decimal change in LOO R2 is inside the numerical floor
+# (see docs/GP_MODEL_DECISION.md). Pin it so a reproduction is a reproduction.
+#
+# shap is pinned for the same reason: scripts/plot_shap_attribution.py relies on
+# KernelExplainer enumerating ALL 2**10 coalitions at ten inputs, which is what
+# makes its attributions exact and reproducible rather than sampled. That sample
+# budget is a library default, not an API guarantee -- a version that changed it
+# would silently turn every attribution into an approximation, and nothing would
+# fail. The suite's additivity check would catch it; the pin is so it never gets
+# the chance on a fresh install.
+#
+# (docs/STEP1_HANDOFF.md, referenced here until 2026-07-30, was removed in the
+# cleanup commit 33f101f. docs/HANDOFF.md is the current entry point.)
+numpy==2.2.6
+pandas==2.3.1
+scikit-learn==1.7.1
+matplotlib==3.10.3
+seaborn==0.13.2
+PyYAML==6.0.2
+scipy==1.16.0
+torch==2.8.0
+gpytorch==1.14
+botorch==0.15.1
+linear_operator==0.6
+shap==0.48.0
+openpyxl==3.1.5
+Pillow==12.3.0
+pytest==8.4.1
+pytest-cov==6.2.1
+black==25.1.0
diff --git a/requirements/dev.txt b/requirements/dev.txt
new file mode 100644
index 0000000..a5a17e7
--- /dev/null
+++ b/requirements/dev.txt
@@ -0,0 +1,4 @@
+# From the repository root:
+# python -m pip install -r requirements/dev.txt
+-c constraints.txt
+-e .[dev]
diff --git a/results/demo/batch_1.csv b/results/demo/batch_1.csv
deleted file mode 100644
index 087ded1..0000000
--- a/results/demo/batch_1.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-Unnamed: 0,speed_inorg,speed_org,inkfl_inorg,inkfl_org,conc_inorg,conc_org,temperature_c,absolute_humidity,Unnamed: 9,PCE,Stability,Repeatability
-units,m/min,m/min,uL/min,uL/min,M,M,F,g/m^3,,,,
-start,0.25,0.25,80,100,0.8,0.4,20,2,,,,
-stop,1,1,240,280,1.4,1.2,50,37,,,,
-step,0.01,0.01,1,1,0.05,0.05,1,1,,,,
-,,,,,,,,,,,,
-,0.58,0.3,190,134,0.85,0.75,24.7,3,,0.0,0.0,10.0
-,0.95,0.58,170,246,0.9,0.85,44,19,,0.0,0.0,10.0
-,0.67,0.95,150,179,1.05,1.15,31.8,6,,0.0,0.0,10.0
-,0.77,0.39,130,269,1.15,0.65,28.5,6,,0.0,0.0,10.0
-,0.39,0.67,230,201,1.2,0.55,41.6,15,,10.69,1.35,3.333333333
-,0.3,0.86,90,224,1.3,0.95,41.6,19,,0.0,0.0,10.0
-,0.86,0.48,210,111,1.35,1.05,36.3,16,,16.43,5.68,0.952380952
-,0.3,0.3,82.5,120,1.4,0.4,23.2,2,,17.22,0.9,0.735294118
-,0.92,0.39,213,181,1.35,1,45.1,9,,0.54,1.09,0.392156863
-,0.89,0.36,228,280,1.4,0.65,46.5,15,,0.0,0.0,10.0
-,0.3,0.67,240,213,1.4,1.05,38.2,2,,16.83,4.68,1.282051282
-,0.43,0.85,213,254,1.35,1.15,42,9,,2.18,0.0,3.125
-,0.72,1.0,107.0,114.0,0.8,1.15,36.0,17.0,,17.35706971953076,5.219312558290997,8.984170432675196
-,0.67,1.0,80.0,106.0,1.4,0.95,35.0,16.0,,15.976962026424701,5.193390711635139,7.455737159814987
-,1.0,1.0,163.0,124.0,0.8,0.65,34.0,17.0,,16.11921708962232,4.583029335484693,8.897908609111669
-,0.26,1.0,240.0,104.0,0.8,1.1,46.0,18.0,,14.729113480381507,5.0374920889118515,7.303781029245997
-,0.99,0.79,80.0,131.0,0.8,0.6,43.0,19.0,,14.511073685461238,3.8791072174518737,8.652695960741147
diff --git a/results/demo/hv_demo_with_predictions.png b/results/demo/hv_demo_with_predictions.png
deleted file mode 100644
index df3b12b..0000000
Binary files a/results/demo/hv_demo_with_predictions.png and /dev/null differ
diff --git a/results/demo/lhs_corr.png b/results/demo/lhs_corr.png
deleted file mode 100644
index 8a85dc0..0000000
Binary files a/results/demo/lhs_corr.png and /dev/null differ
diff --git a/results/demo/lhs_dist.png b/results/demo/lhs_dist.png
deleted file mode 100644
index 3bdb9da..0000000
Binary files a/results/demo/lhs_dist.png and /dev/null differ
diff --git a/results/demo/lhs_pca.png b/results/demo/lhs_pca.png
deleted file mode 100644
index 3814ec3..0000000
Binary files a/results/demo/lhs_pca.png and /dev/null differ
diff --git a/results/demo/parity_train.png b/results/demo/parity_train.png
deleted file mode 100644
index 588a46b..0000000
Binary files a/results/demo/parity_train.png and /dev/null differ
diff --git a/results/demo/proposed_bar.png b/results/demo/proposed_bar.png
deleted file mode 100644
index fdacc36..0000000
Binary files a/results/demo/proposed_bar.png and /dev/null differ
diff --git a/results/demo/shap.png b/results/demo/shap.png
deleted file mode 100644
index 8a5fd02..0000000
Binary files a/results/demo/shap.png and /dev/null differ
diff --git a/results/experiment/next_batch.csv b/results/experiment/next_batch.csv
deleted file mode 100644
index 3ca2c84..0000000
--- a/results/experiment/next_batch.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-Unnamed: 0,speed_inorg,speed_org,inkfl_inorg,inkfl_org,conc_inorg,conc_org,temperature_c,absolute_humidity,Unnamed: 9,PCE,Stability,Repeatability
-units,m/min,m/min,uL/min,uL/min,M,M,F,g/m^3,,,,
-start,0.25,0.25,80,100,0.8,0.4,20,2,,,,
-stop,1,1,240,280,1.4,1.2,50,37,,,,
-step,0.01,0.01,1,1,0.05,0.05,1,1,,,,
-,,,,,,,,,,,,
-,0.58,0.3,190,134,0.85,0.75,24.7,3,,0.0,0.0,10.0
-,0.95,0.58,170,246,0.9,0.85,44,19,,0.0,0.0,10.0
-,0.67,0.95,150,179,1.05,1.15,31.8,6,,0.0,0.0,10.0
-,0.77,0.39,130,269,1.15,0.65,28.5,6,,0.0,0.0,10.0
-,0.39,0.67,230,201,1.2,0.55,41.6,15,,10.69,1.35,3.333333333
-,0.3,0.86,90,224,1.3,0.95,41.6,19,,0.0,0.0,10.0
-,0.86,0.48,210,111,1.35,1.05,36.3,16,,16.43,5.68,0.952380952
-,0.3,0.3,82.5,120,1.4,0.4,23.2,2,,17.22,0.9,0.735294118
-,0.92,0.39,213,181,1.35,1,45.1,9,,0.54,1.09,0.392156863
-,0.89,0.36,228,280,1.4,0.65,46.5,15,,0.0,0.0,10.0
-,0.3,0.67,240,213,1.4,1.05,38.2,2,,16.83,4.68,1.282051282
-,0.43,0.85,213,254,1.35,1.15,42,9,,2.18,0.0,3.125
-,0.85,1.0,105.0,114.0,0.8,1.0,39.0,16.0,,,,
-,0.67,1.0,80.0,124.0,1.25,1.15,47.0,17.0,,,,
-,0.5,0.84,80.0,104.0,0.8,0.6,46.0,17.0,,,,
diff --git a/results/experiment/parity_plots.png b/results/experiment/parity_plots.png
deleted file mode 100644
index c8daff3..0000000
Binary files a/results/experiment/parity_plots.png and /dev/null differ
diff --git a/scripts/dtlz2_parameter_sweep.py b/scripts/dtlz2_parameter_sweep.py
new file mode 100644
index 0000000..9434802
--- /dev/null
+++ b/scripts/dtlz2_parameter_sweep.py
@@ -0,0 +1,282 @@
+"""Is beta = 4.0 with radius = 0.25 a defensible default, or just the first guess?
+
+Sweeps UCB ``beta`` against the local-penalization ``radius`` on DTLZ2 -- a
+synthetic problem with a known Pareto front, so the answer does not depend on
+whether the campaign's measurements are right.
+
+**The decision rule is pre-committed, and it is written here before the numbers
+exist so that reading them cannot move it.** Keep 4.0 / 0.25 unless a cell beats
+the current mean hypervolume gain by MORE than the per-seed standard deviation of
+gains, AND does not reduce batch spacing. A sweep that finds everything flat
+within noise is a pass, not a failure: it says the default is not a lucky pick and
+the knob does not need attention.
+
+Each cell runs the real campaign path per seed -- ``run_r0_lhs`` -> ``run_r1_ucb``
+-> ``run_r2_qlognehvi`` -- and is scored on hypervolume gain over the R0 start,
+against a random on-grid baseline at the same budget, exactly as the acceptance
+test does.
+
+Boundary-coordinate counts are reported as a secondary readout because of what the
+review artifact found on the live campaign: every proposed condition pinned
+``anneal_temp`` to its range edge. That was traced to a monotone mean function
+rather than to the acquisition, but a beta or radius that pushes batches onto
+range edges by itself is worth seeing.
+
+ python scripts/dtlz2_parameter_sweep.py # 3 x 3 cells, 8 seeds
+ python scripts/dtlz2_parameter_sweep.py --seeds 3 # a quicker look
+"""
+
+from __future__ import annotations
+
+import argparse
+import warnings
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+from botorch.test_functions.multi_objective import DTLZ2
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+from botorch.utils.multi_objective.pareto import is_non_dominated
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ run_r0_lhs,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from mobo_kit.candidate_pool import sample_discrete_candidate_pool
+from mobo_kit.design import build_design_from_config
+
+INPUT_DIM = 10
+OBJECTIVES = 3
+R0_SIZE = 15
+R1_SIZE = 5
+R2_SIZE = 3
+ADDED = R1_SIZE + R2_SIZE
+
+CURRENT_BETA = 4.0
+CURRENT_RADIUS = 0.25
+BETAS = (2.0, 4.0, 8.0)
+RADII = (0.15, 0.25, 0.35)
+#: Fixed across the sweep on purpose: it is a hard floor on batch spacing, not a
+#: tuning knob, and moving it would change what "spacing" even means per cell.
+MIN_BATCH_DISTANCE = 0.15
+
+
+def _problem() -> DTLZ2:
+ return DTLZ2(dim=INPUT_DIM, num_objectives=OBJECTIVES, negate=True).to(
+ dtype=torch.double
+ )
+
+
+def _config(beta: float, radius: float, *, pool: int = 1024, mc_samples: int = 32) -> dict:
+ return {
+ "inputs": [
+ {"name": f"x{i}", "start": 0.0, "stop": 1.0, "step": 0.05}
+ for i in range(INPUT_DIM)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-dtlz2-sweep-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": f"f{i}",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": f"f{i}",
+ "lower_anchor": -2.0,
+ "upper_anchor": 0.0,
+ }
+ for i in range(OBJECTIVES)
+ ],
+ },
+ "reference_point_utility": [-0.01] * OBJECTIVES,
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi",
+ "batch_size": R1_SIZE,
+ "replicates_per_condition": 3,
+ "beta": beta,
+ "candidate_pool_size": pool,
+ "posterior_samples": 256,
+ "moment_method": "monte_carlo",
+ },
+ "r2": {
+ "method": "qlognehvi",
+ "batch_size": R2_SIZE,
+ "replicates_per_condition": 3,
+ "candidate_pool_size": pool,
+ "mc_samples": mc_samples,
+ },
+ },
+ "local_penalization": {
+ "radius": radius,
+ "min_batch_distance": MIN_BATCH_DISTANCE,
+ "min_observed_distance": 0.0,
+ "dimension_weights": None,
+ },
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": 73},
+ "constraints": [],
+ }
+
+
+def _evaluate(problem: DTLZ2, X: np.ndarray) -> np.ndarray:
+ return problem(torch.tensor(np.asarray(X, float), dtype=torch.double)).numpy()
+
+
+def _hypervolume(config: dict, Y: np.ndarray) -> float:
+ transform = build_objective_transform(config)
+ reference = torch.tensor(config["reference_point_utility"], dtype=torch.double)
+ utility = transform(torch.tensor(np.asarray(Y, float), dtype=torch.double))
+ if not bool((utility >= reference).all(dim=-1).any()):
+ # BoTorch would silently drop every point and return 0.0
+ raise AssertionError("no point dominates the reference")
+ return float(Hypervolume(ref_point=reference).compute(utility[is_non_dominated(utility)]))
+
+
+def _boundary_counts(X: np.ndarray) -> int:
+ """How many coordinates across the batch sit at 0 or 1, the grid's edges."""
+ values = np.asarray(X, float)
+ return int((np.isclose(values, 0.0) | np.isclose(values, 1.0)).sum())
+
+
+@dataclass
+class CellResult:
+ beta: float
+ radius: float
+ bo_gain: list[float]
+ random_gain: list[float]
+ spacing: list[float]
+ boundary: list[int]
+
+ @property
+ def mean_gain(self) -> float:
+ return float(np.mean(self.bo_gain))
+
+ @property
+ def sd_gain(self) -> float:
+ return float(np.std(self.bo_gain, ddof=1))
+
+ @property
+ def mean_random(self) -> float:
+ return float(np.mean(self.random_gain))
+
+ @property
+ def mean_spacing(self) -> float:
+ return float(np.mean(self.spacing))
+
+ @property
+ def mean_boundary(self) -> float:
+ return float(np.mean(self.boundary))
+
+
+def _one_seed(config: dict, seed: int) -> tuple[float, float, float, int]:
+ """Returns (bo gain, random gain, min batch spacing, boundary coords)."""
+ problem = _problem()
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ r0 = run_r0_lhs(config, n=R0_SIZE, seed=seed)
+ X0 = r0.conditions.to_numpy(float)
+ Y0 = _evaluate(problem, X0)
+ start = _hypervolume(config, Y0)
+
+ r1 = run_r1_ucb(config, X0, Y0, seed=seed)
+ X1 = r1.conditions.to_numpy(float)
+ Y1 = _evaluate(problem, X1)
+ X01, Y01 = np.vstack([X0, X1]), np.vstack([Y0, Y1])
+
+ r2 = run_r2_qlognehvi(config, X01, Y01, seed=seed)
+ X2 = r2.conditions.to_numpy(float)
+ Y2 = _evaluate(problem, X2)
+ bo = _hypervolume(config, np.vstack([Y01, Y2])) - start
+
+ # random on-grid baseline at the same budget
+ design = build_design_from_config(dict(config))
+ pool = sample_discrete_candidate_pool(design, ADDED, seed=seed + 9999)
+ Yr = _evaluate(problem, np.asarray(pool.X_phys, float)[:ADDED])
+ random_gain = _hypervolume(config, np.vstack([Y0, Yr])) - start
+
+ spacing = min(
+ float(r1.diagnostics["validity"]["min_pairwise_distance"]),
+ float(r2.diagnostics["validity"]["min_pairwise_distance"]),
+ )
+ return bo, random_gain, spacing, _boundary_counts(X1) + _boundary_counts(X2)
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--seeds", type=int, default=8)
+ parser.add_argument("--pool", type=int, default=1024)
+ args = parser.parse_args()
+ seeds = [73 + 11 * i for i in range(args.seeds)]
+
+ print(f"DTLZ2 sweep: beta x radius, {len(seeds)} seeds per cell, pool {args.pool}")
+ print(f"min_batch_distance fixed at {MIN_BATCH_DISTANCE} (a floor, not a knob)\n")
+ print("PRE-COMMITTED RULE: keep beta=4.0 / radius=0.25 unless a cell beats its")
+ print("mean HV gain by more than the per-seed sd of gains, without reducing")
+ print("spacing. Flat within noise is a PASS.\n")
+
+ cells: list[CellResult] = []
+ for beta in BETAS:
+ for radius in RADII:
+ config = _config(beta, radius, pool=args.pool)
+ rows = [_one_seed(config, seed) for seed in seeds]
+ cell = CellResult(
+ beta=beta,
+ radius=radius,
+ bo_gain=[r[0] for r in rows],
+ random_gain=[r[1] for r in rows],
+ spacing=[r[2] for r in rows],
+ boundary=[r[3] for r in rows],
+ )
+ cells.append(cell)
+ print(
+ f" beta={beta:<4g} radius={radius:<5g} "
+ f"gain {cell.mean_gain:+.4f} (sd {cell.sd_gain:.4f}) "
+ f"random {cell.mean_random:+.4f} "
+ f"spacing {cell.mean_spacing:.3f} "
+ f"edge coords {cell.mean_boundary:.1f}"
+ )
+
+ baseline = next(
+ c for c in cells if c.beta == CURRENT_BETA and c.radius == CURRENT_RADIUS
+ )
+ threshold = baseline.mean_gain + baseline.sd_gain
+
+ print("\n" + "=" * 78)
+ print(
+ f"current default beta={CURRENT_BETA} radius={CURRENT_RADIUS}: "
+ f"mean gain {baseline.mean_gain:+.4f}, per-seed sd {baseline.sd_gain:.4f}"
+ )
+ print(f"a challenger must exceed {threshold:+.4f} AND not reduce spacing below "
+ f"{baseline.mean_spacing:.3f}")
+
+ challengers = [
+ c
+ for c in cells
+ if c.mean_gain > threshold and c.mean_spacing >= baseline.mean_spacing
+ ]
+ if challengers:
+ best = max(challengers, key=lambda c: c.mean_gain)
+ print(
+ f"\nRULE TRIGGERED: beta={best.beta} radius={best.radius} gives "
+ f"{best.mean_gain:+.4f} at spacing {best.mean_spacing:.3f}."
+ )
+ else:
+ near = [c for c in cells if c.mean_gain > baseline.mean_gain]
+ print(
+ f"\nNO CHANGE. {len(near)} of {len(cells)} cells have a higher mean gain, "
+ "none by more than one per-seed sd while holding spacing. The default is "
+ "flat within noise, which is the outcome that says it was not a lucky pick."
+ )
+
+ print(
+ f"\nBO beats random in {sum(c.mean_gain > c.mean_random for c in cells)} "
+ f"of {len(cells)} cells on the mean."
+ )
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/generate_round_report.py b/scripts/generate_round_report.py
new file mode 100644
index 0000000..28a6c8b
--- /dev/null
+++ b/scripts/generate_round_report.py
@@ -0,0 +1,150 @@
+"""Render a round's figures from a terminal, exactly as the launcher does.
+
+ python scripts/generate_round_report.py --workbook "local_inputs/Final Summary Table.xlsx"
+ python scripts/generate_round_report.py --workbook --data-only
+
+Two modes, matching the two buttons:
+
+* **default** re-derives the proposal from the config and the measured rows rather
+ than reading it back from the worklist, so what the figures describe is the
+ model's answer at this seed. **When a worklist for that round already exists,
+ the two are compared by batch hash and the result is printed as MATCH or
+ DRIFT.** They should match; if they do not, the config, the data or the seed has
+ moved since the sheet was written, and the figures describe the model rather
+ than the films anyone is about to run.
+* **--data-only** renders everything that depends on measurements alone. This is
+ the mode to use the moment a round's results are entered.
+
+Nothing here writes to the source workbook, and nothing here approves anything.
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from mobo_kit.campaign import load_campaign_config, run_r1_ucb, run_r2_qlognehvi
+from mobo_kit.batch_review import build_batch_review
+from mobo_kit.launcher import gather_observations, inspect_campaign
+from mobo_kit.round_report import generate_round_report
+from mobo_kit.workbook_io import read_campaign_workbook
+
+
+def _worklist_drift(workbook, config, round_name: str, proposal) -> str:
+ """Does the re-derived proposal still match the worklist on disk?
+
+ The figures describe a proposal computed here and now. The films someone runs
+ come from a sheet written earlier. Those are the same batch only if the
+ config, the data and the seed have not moved -- and if they have, the figures
+ are about a different experiment than the one on the bench, which is exactly
+ the sort of quiet divergence that is worth a line of output.
+
+ Compared by ``batch_hash``, so ordering is not mistaken for a difference.
+ """
+ from mobo_kit.candidate_diagnostics import batch_hash
+ from mobo_kit.workbook_io import candidate_workbook_path, sheet_name_for_round
+
+ path = candidate_workbook_path(workbook, round_name)
+ if not path.exists():
+ return f"no {path.name} on disk yet, so there is nothing to compare"
+ try:
+ from openpyxl import load_workbook
+
+ sheet = load_workbook(path, data_only=True)[sheet_name_for_round(round_name)]
+ header = [str(cell.value).strip() if cell.value else "" for cell in sheet[1]]
+ names = [item["name"] for item in config["inputs"]]
+ columns = [header.index(name) for name in names]
+ seen: list[list[float]] = []
+ for row in sheet.iter_rows(min_row=2, values_only=True):
+ if row[0] is None:
+ continue
+ values = [float(row[c]) for c in columns]
+ if values not in seen:
+ seen.append(values)
+ except Exception as exc: # noqa: BLE001 - a check must not break the report
+ return f"could not read {path.name} to compare ({type(exc).__name__}: {exc})"
+
+ on_disk = batch_hash(seen)
+ derived = batch_hash(proposal.conditions.to_numpy(float))
+ if on_disk == derived:
+ return f"MATCH - the re-derived batch is {path.name}'s ({derived})"
+ return (
+ f"DRIFT - re-derived {derived} against {on_disk} in {path.name}. The "
+ "config, the data or the seed has moved since that sheet was written, so "
+ "these figures describe a different batch than the one on the bench."
+ )
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--workbook", required=True)
+ parser.add_argument("--config", default="configs/campaign_d2d_perovskite_final.yaml")
+ parser.add_argument("--outdir", default=None)
+ parser.add_argument("--seed", type=int, default=None)
+ parser.add_argument(
+ "--data-only",
+ action="store_true",
+ help="figures from the measurements alone; no batch is proposed",
+ )
+ parser.add_argument(
+ "--shap-instances",
+ type=int,
+ default=15,
+ help=(
+ "rows to attribute. NOT the main runtime knob -- the leave-one-out "
+ "refits are about two thirds of the cost and are not optional. "
+ "Recorded in the manifest either way."
+ ),
+ )
+ args = parser.parse_args(argv)
+
+ config = load_campaign_config(args.config)
+ workbook = Path(args.workbook)
+
+ proposal = None
+ review = None
+ if not args.data_only:
+ status = inspect_campaign(workbook, config)
+ if not status.can_generate:
+ print(f"No round is due: {status.reason}")
+ print("Rendering the data-only report instead.")
+ else:
+ round_name = str(status.next_round)
+ print(f"Proposing {round_name} to describe it...")
+ X, Y, Yvar, _ = gather_observations(
+ workbook, config, for_round=round_name
+ )
+ runner = run_r1_ucb if round_name == "R1" else run_r2_qlognehvi
+ proposal = runner(config, X, Y, seed=args.seed, observed_Yvar=Yvar)
+ contents = read_campaign_workbook(workbook, config)
+ review = build_batch_review(
+ config,
+ X,
+ Y,
+ proposal.conditions,
+ round_name=round_name,
+ seed=proposal.diagnostics.get("seed"),
+ findings=contents.findings,
+ )
+ drift = _worklist_drift(workbook, config, round_name, proposal)
+ print(f" worklist check: {drift}")
+
+ manifest = generate_round_report(
+ workbook,
+ config,
+ proposal=proposal,
+ review=review,
+ outdir=args.outdir,
+ seed=args.seed,
+ shap_max_instances=args.shap_instances,
+ progress=lambda message: print(f" {message}", flush=True),
+ )
+ print()
+ print(manifest.summary())
+ print()
+ print(f"{manifest.runtime_seconds:.1f} s")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/gp_diagnostic.py b/scripts/gp_diagnostic.py
new file mode 100644
index 0000000..680ebba
--- /dev/null
+++ b/scripts/gp_diagnostic.py
@@ -0,0 +1,392 @@
+"""Phase 1.1 baseline GP diagnostic.
+
+Answers one question: is the GP learning anything from the 15 R0 observations?
+
+For each objective and each model variant it reports
+ * the fitted ARD lengthscales, outputscale and noise;
+ * exact leave-one-out MAE / RMSE / R2 / Spearman / coverage;
+ * the spread of the posterior mean over a Sobol sample of the design space,
+ as a fraction of the observed range of that objective.
+
+A GP that has learned nothing shows large lengthscales, Spearman near zero and
+near-zero posterior-mean spread: the posterior has collapsed to its prior mean.
+
+Reads the workbook read-only. Writes nothing except an optional CSV.
+
+Usage:
+ python scripts/gp_diagnostic.py --workbook "local_inputs/Summary Table.xlsx"
+ python scripts/gp_diagnostic.py --variants current dim_scaled_prior --csv out.csv
+"""
+
+from __future__ import annotations
+
+import argparse
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+
+import gpytorch
+import numpy as np
+import torch
+from botorch.fit import fit_gpytorch_mll
+from botorch.models import SingleTaskGP
+from botorch.models.transforms.outcome import Standardize
+from botorch.models.utils.gpytorch_modules import (
+ get_covar_module_with_dim_scaled_prior,
+ get_gaussian_likelihood_with_lognormal_prior,
+)
+from gpytorch.constraints import GreaterThan
+from gpytorch.kernels import MaternKernel, ScaleKernel
+from gpytorch.likelihoods import GaussianLikelihood
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from openpyxl import load_workbook
+from scipy.stats import qmc
+
+from mobo_kit.model_validation import compute_prediction_metrics
+
+# Canonical grid, configs/campaign_d2d_perovskite.yaml. (name, start, stop)
+DESIGN = (
+ ("speed_1", 1000.0, 6000.0),
+ ("time_1", 5.0, 50.0),
+ ("speed_2", 0.0, 5000.0),
+ ("time_2", 10.0, 60.0),
+ ("precur_conc", 1.0, 2.0),
+ ("precur_vol", 40.0, 200.0),
+ ("anneal_temp", 100.0, 185.0),
+ ("anneal_time", 10.0, 60.0),
+ ("anti_vol", 100.0, 200.0),
+ ("anti_time", 9.0, 25.0),
+)
+INPUT_NAMES = tuple(d[0] for d in DESIGN)
+OBJECTIVE_NAMES = ("Uniformity", "Optoelectronic", "Thickness")
+
+# 0-based workbook column offsets: inputs B:K, objectives Z/AA/AB.
+INPUT_COLS = tuple(range(1, 11))
+OBJECTIVE_COLS = (25, 26, 27)
+
+DTYPE = torch.double
+
+
+# --------------------------------------------------------------------------- #
+# model variants
+# --------------------------------------------------------------------------- #
+
+
+def _build_current(X: torch.Tensor, y: torch.Tensor) -> SingleTaskGP:
+ """The retired contract, now model_validation.LEGACY_NO_PRIOR.
+
+ ScaleKernel(Matern 2.5 ARD) with no lengthscale prior. Kept here so the
+ before/after comparison stays runnable from one script.
+ """
+ covar_module = ScaleKernel(MaternKernel(nu=2.5, ard_num_dims=X.shape[1]))
+ likelihood = GaussianLikelihood(noise_constraint=GreaterThan(1e-3))
+ return SingleTaskGP(
+ X,
+ y,
+ covar_module=covar_module,
+ likelihood=likelihood,
+ outcome_transform=Standardize(m=1),
+ )
+
+
+def _build_conservative(X: torch.Tensor, y: torch.Tensor) -> SingleTaskGP:
+ """The repo's 'conservative' variant: noise floor 1e-2, lengthscale floor 0.05."""
+ covar_module = ScaleKernel(
+ MaternKernel(
+ nu=2.5,
+ ard_num_dims=X.shape[1],
+ lengthscale_constraint=GreaterThan(0.05),
+ )
+ )
+ likelihood = GaussianLikelihood(noise_constraint=GreaterThan(0.01))
+ return SingleTaskGP(
+ X,
+ y,
+ covar_module=covar_module,
+ likelihood=likelihood,
+ outcome_transform=Standardize(m=1),
+ )
+
+
+def _build_dim_scaled_prior(X: torch.Tensor, y: torch.Tensor) -> SingleTaskGP:
+ """Keep Matern 2.5 ARD, add BoTorch's dimension-scaled LogNormal lengthscale prior.
+
+ The prior is LogNormal(loc=sqrt(2) + log(d)/2, scale=sqrt(3)), which at d=10
+ concentrates lengthscales around exp(loc) ~ 12.9 in raw units but with enough
+ mass at moderate values to stop the unbounded drift the prior-free fit shows.
+ """
+ base = get_covar_module_with_dim_scaled_prior(
+ ard_num_dims=X.shape[1], use_rbf_kernel=False
+ )
+ covar_module = ScaleKernel(base)
+ likelihood = get_gaussian_likelihood_with_lognormal_prior()
+ return SingleTaskGP(
+ X,
+ y,
+ covar_module=covar_module,
+ likelihood=likelihood,
+ outcome_transform=Standardize(m=1),
+ )
+
+
+def _build_lengthscale_prior_only(X: torch.Tensor, y: torch.Tensor) -> SingleTaskGP:
+ """Lengthscale prior but a bare noise floor: the degenerate configuration.
+
+ Kept so the outputscale-collapse mode stays reproducible from this script.
+ """
+ base = get_covar_module_with_dim_scaled_prior(
+ ard_num_dims=X.shape[1], use_rbf_kernel=False
+ )
+ return SingleTaskGP(
+ X,
+ y,
+ covar_module=ScaleKernel(base),
+ likelihood=GaussianLikelihood(noise_constraint=GreaterThan(1e-3)),
+ outcome_transform=Standardize(m=1),
+ )
+
+
+def _build_botorch_default(X: torch.Tensor, y: torch.Tensor) -> SingleTaskGP:
+ """Pure BoTorch 0.15.1 default: RBF ARD + dim-scaled LogNormal lengthscale prior,
+ LogNormal(-4, 1) noise prior, no ScaleKernel. Nothing overridden."""
+ return SingleTaskGP(X, y, outcome_transform=Standardize(m=1))
+
+
+BUILDERS = {
+ "current": _build_current,
+ "conservative": _build_conservative,
+ "dim_scaled_prior": _build_dim_scaled_prior,
+ "lengthscale_prior_only": _build_lengthscale_prior_only,
+ "botorch_default": _build_botorch_default,
+}
+
+
+def _fit(model: SingleTaskGP) -> SingleTaskGP:
+ mll = ExactMarginalLogLikelihood(model.likelihood, model)
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ fit_gpytorch_mll(mll)
+ return model
+
+
+# --------------------------------------------------------------------------- #
+# hyperparameter readout
+# --------------------------------------------------------------------------- #
+
+
+@dataclass
+class Hypers:
+ lengthscales: np.ndarray
+ outputscale: float | None
+ noise: float
+
+
+def _read_hypers(model: SingleTaskGP) -> Hypers:
+ covar = model.covar_module
+ if isinstance(covar, ScaleKernel):
+ ls = covar.base_kernel.lengthscale
+ outputscale = float(covar.outputscale.detach().reshape(-1)[0])
+ else: # BoTorch default returns a bare kernel
+ ls = covar.lengthscale
+ outputscale = None
+ noise = float(model.likelihood.noise.detach().reshape(-1)[0])
+ return Hypers(
+ lengthscales=ls.detach().cpu().numpy().reshape(-1).copy(),
+ outputscale=outputscale,
+ noise=noise,
+ )
+
+
+def _posterior(
+ model: SingleTaskGP, X: torch.Tensor, *, observation_noise: bool = False
+) -> tuple[np.ndarray, np.ndarray]:
+ """Posterior mean and sd.
+
+ Interval coverage and NLPD must use the *predictive* sd, which includes the
+ observation noise; the latent sd alone understates the interval and makes a
+ well-calibrated model look overconfident. Posterior-mean spread uses the
+ latent sd, since noise is constant across the design space.
+ """
+ model.eval()
+ with torch.no_grad(), gpytorch.settings.fast_pred_var():
+ post = model.posterior(X, observation_noise=observation_noise)
+ mean = post.mean.detach().cpu().numpy().reshape(-1)
+ std = post.variance.clamp_min(1e-12).sqrt().detach().cpu().numpy().reshape(-1)
+ return mean, std
+
+
+# --------------------------------------------------------------------------- #
+# diagnostics
+# --------------------------------------------------------------------------- #
+
+
+def loocv(
+ X: np.ndarray, y: np.ndarray, builder, seed: int
+) -> tuple[np.ndarray, np.ndarray]:
+ """Exact leave-one-out: N fits on N-1 rows, each predicting the held-out row."""
+ n = X.shape[0]
+ mean = np.empty(n)
+ std = np.empty(n)
+ for i in range(n):
+ keep = np.ones(n, dtype=bool)
+ keep[i] = False
+ torch.manual_seed(seed)
+ Xt = torch.tensor(X[keep], dtype=DTYPE)
+ yt = torch.tensor(y[keep], dtype=DTYPE).unsqueeze(-1)
+ model = _fit(builder(Xt, yt))
+ m, s = _posterior(
+ model, torch.tensor(X[i : i + 1], dtype=DTYPE), observation_noise=True
+ )
+ mean[i], std[i] = m[0], s[0]
+ return mean, std
+
+
+def posterior_spread(
+ model: SingleTaskGP, d: int, n: int, seed: int
+) -> tuple[float, float]:
+ """(max-min, std) of the posterior mean over a Sobol sample of the unit cube."""
+ sob = qmc.Sobol(d=d, scramble=True, seed=seed)
+ P = sob.random(n)
+ mean, _ = _posterior(model, torch.tensor(P, dtype=DTYPE))
+ return float(mean.max() - mean.min()), float(mean.std())
+
+
+# --------------------------------------------------------------------------- #
+
+
+def load_data(path: Path) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ ws = load_workbook(path, data_only=True)["Sheet1"]
+ rows = []
+ for r in ws.iter_rows(min_row=2, values_only=True):
+ if r[0] is None:
+ break
+ rows.append(r)
+ ids = np.array([int(r[0]) for r in rows])
+ X_phys = np.array([[float(r[j]) for j in INPUT_COLS] for r in rows])
+ Y = np.array([[float(r[j]) for j in OBJECTIVE_COLS] for r in rows])
+ lo = np.array([d[1] for d in DESIGN])
+ hi = np.array([d[2] for d in DESIGN])
+ X = (X_phys - lo) / (hi - lo)
+ if X.min() < -1e-9 or X.max() > 1 + 1e-9:
+ raise ValueError("Inputs fall outside the declared design bounds.")
+ return ids, X, Y
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--workbook", default="local_inputs/Summary Table.xlsx")
+ ap.add_argument(
+ "--variants", nargs="+", default=["current"], choices=sorted(BUILDERS)
+ )
+ ap.add_argument("--sobol-n", type=int, default=1024)
+ ap.add_argument("--seed", type=int, default=73)
+ ap.add_argument("--csv", default=None, help="optional path for a tidy metrics CSV")
+ args = ap.parse_args()
+
+ ids, X, Y = load_data(Path(args.workbook))
+ n, d = X.shape
+ print(f"workbook : {args.workbook}")
+ print(f"data : {n} observations, {d} inputs, {Y.shape[1]} objectives")
+ print(f"samples : {ids.tolist()}")
+
+ records = []
+ for variant in args.variants:
+ builder = BUILDERS[variant]
+ print(f"\n{'='*78}\nVARIANT: {variant}\n{'='*78}")
+
+ for k, obj_name in enumerate(OBJECTIVE_NAMES):
+ y = Y[:, k]
+ obs_range = float(y.max() - y.min())
+
+ torch.manual_seed(args.seed)
+ full = _fit(
+ builder(
+ torch.tensor(X, dtype=DTYPE),
+ torch.tensor(y, dtype=DTYPE).unsqueeze(-1),
+ )
+ )
+ hp = _read_hypers(full)
+ span, sd = posterior_spread(full, d, args.sobol_n, args.seed)
+ lo_mean, lo_std = loocv(X, y, builder, args.seed)
+ met = compute_prediction_metrics(
+ y,
+ lo_mean,
+ lo_std,
+ variant_name=variant,
+ objective_index=k,
+ objective_name=obj_name,
+ )
+
+ print(f"\n--- {obj_name} (observed range {obs_range:.4f}) ---")
+ print(" ARD lengthscales (normalised input space):")
+ for nm, v in zip(INPUT_NAMES, hp.lengthscales):
+ flag = " <-- flat" if v >= 10.0 else ""
+ print(f" {nm:>13}: {v:>12.4f}{flag}")
+ n_flat = int(np.sum(hp.lengthscales >= 10.0))
+ print(
+ f" {'median':>13}: {np.median(hp.lengthscales):>12.4f}"
+ f" ({n_flat}/{d} at or above 10)"
+ )
+ os_txt = "n/a" if hp.outputscale is None else f"{hp.outputscale:.4f}"
+ print(f" outputscale : {os_txt} noise : {hp.noise:.6f}")
+ print(
+ f" LOOCV : MAE {met.mae:.4f} RMSE {met.rmse:.4f} "
+ f"R2 {met.r_squared:+.4f} Spearman {met.spearman_rank_correlation:+.4f}"
+ )
+ print(
+ f" coverage : 68% {met.coverage_68_percent:.3f} "
+ f"95% {met.coverage_95_percent:.3f} NLPD {met.mean_gaussian_nlpd:.3f}"
+ )
+ print(
+ f" posterior mean spread over {args.sobol_n} Sobol pts: "
+ f"range {span:.5f} ({100*span/obs_range:.2f}% of observed range), "
+ f"sd {sd:.5f}"
+ )
+
+ records.append(
+ {
+ "variant": variant,
+ "objective": obj_name,
+ "median_lengthscale": float(np.median(hp.lengthscales)),
+ "n_lengthscale_ge_10": n_flat,
+ "outputscale": hp.outputscale,
+ "noise": hp.noise,
+ "loocv_mae": met.mae,
+ "loocv_rmse": met.rmse,
+ "loocv_r2": met.r_squared,
+ "loocv_spearman": met.spearman_rank_correlation,
+ "coverage_68": met.coverage_68_percent,
+ "coverage_95": met.coverage_95_percent,
+ "nlpd": met.mean_gaussian_nlpd,
+ "post_mean_range": span,
+ "post_mean_range_frac_of_observed": span / obs_range,
+ **{
+ f"ls_{nm}": float(v)
+ for nm, v in zip(INPUT_NAMES, hp.lengthscales)
+ },
+ }
+ )
+
+ if len(args.variants) > 1:
+ print(f"\n{'='*78}\nSUMMARY\n{'='*78}")
+ print(
+ f"{'variant':>18} {'objective':>15} {'med LS':>9} {'flat':>5} "
+ f"{'R2':>8} {'Spearman':>9} {'spread%':>9}"
+ )
+ for r in records:
+ print(
+ f"{r['variant']:>18} {r['objective']:>15} "
+ f"{r['median_lengthscale']:>9.3f} {r['n_lengthscale_ge_10']:>5d} "
+ f"{r['loocv_r2']:>+8.3f} {r['loocv_spearman']:>+9.3f} "
+ f"{100*r['post_mean_range_frac_of_observed']:>9.2f}"
+ )
+
+ if args.csv:
+ import pandas as pd
+
+ pd.DataFrame(records).to_csv(args.csv, index=False)
+ print(f"\nwrote {args.csv}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/intake_new_data.py b/scripts/intake_new_data.py
new file mode 100644
index 0000000..53806e4
--- /dev/null
+++ b/scripts/intake_new_data.py
@@ -0,0 +1,268 @@
+"""One command to run when the experimental group returns new or corrected data.
+
+ python scripts/intake_new_data.py --workbook "local_inputs/Final Summary Table.xlsx"
+
+The group has always described the current numbers as test data, so a replacement
+was expected from the start. When it arrives, the question is not "does the code
+still run" -- the tests answer that -- but "does the model this campaign committed
+to still earn its place on THIS data". Several of those commitments were justified
+by measurements on 15 specific rows, and a new dataset does not inherit them.
+
+So this checks, per objective:
+
+* whether the objectives can be computed at all, and what the read notices;
+* whether each declared ``mean_function`` still beats the leave-one-out null by
+ more than the resolution floor -- and if it does not, names the exact config
+ block to delete;
+* whether the fit guard has anything to say, including the case where the mean
+ function explains so much that the residual GP collapses;
+* whether the fixed objective anchors still span the data;
+* whether the campaign-fixed scaling guard still passes.
+
+**Both floors are recomputed at the new N rather than reused.** The null is
+``1 - (N/(N-1))^2``, which moves with N: -0.148 at 15, -0.105 at 21, -0.069 at 31.
+The resolution floor of +-0.236 was a parametric bootstrap at N=15 and shrinks
+roughly as ``1/sqrt(N)``; the estimate printed here is scaled that way and is
+labelled as an estimate, because the honest version is to re-run the bootstrap.
+
+Nothing here decides anything. It prints what the data supports so a human can.
+"""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+
+from mobo_kit.campaign import (
+ assert_scaling_is_campaign_fixed,
+ build_design_from_config,
+ build_objective_transform,
+ load_campaign_config,
+ objective_names,
+)
+from mobo_kit.constraints import constraint_violations, constraints_from_config
+from mobo_kit.loocv import (
+ RESOLUTION_SD_AT_15,
+ loo_predictions,
+ null_loo_r2,
+ resolution_sd,
+)
+from mobo_kit.model_validation import ModelFitError
+from mobo_kit.scores import ScoreSeverity
+from mobo_kit.structured_mean import mean_spec_from_config
+from mobo_kit.workbook_io import read_campaign_workbook
+
+# The fold loop lives in `mobo_kit.loocv`, shared with the round report and the
+# permutation test. It used to live here, and the moment a second caller needed it
+# there were two copies of a number this document calls canonical.
+
+
+def main() -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--workbook", required=True)
+ # Defaults to the ACTIVE campaign. The first campaign's config is archived, and
+ # defaulting to it would quietly audit new rows against a retired contract --
+ # different recipes, different anchors, different grids.
+ parser.add_argument("--config", default="configs/campaign_d2d_perovskite_final.yaml")
+ parser.add_argument(
+ "--skip-model",
+ action="store_true",
+ help="audit and anchors only; skip the leave-one-out refits",
+ )
+ args = parser.parse_args()
+
+ config = load_campaign_config(args.config)
+ names = list(objective_names(config))
+ print("=" * 78)
+ print(f"INTAKE: {Path(args.workbook).name}")
+ print(f"CONFIG: {Path(args.config).name} "
+ f"({config.get('objectives', {}).get('contract_version')})")
+ print("=" * 78)
+ if str(config.get("campaign", {}).get("status")) == "archived":
+ print("\n NOTE: this config is archived. Its recipes, anchors and grids")
+ print(" describe a retired contract, so every number below is about that")
+ print(" contract rather than about the active campaign.")
+
+ # ---------------------------------------------------------------- audit --
+ contents = read_campaign_workbook(args.workbook, config)
+ n = contents.n_rows
+ print(f"\n1. READ {n} rows, objectives {tuple(names)}")
+ errors = contents.errors
+ warnings_found = contents.warnings
+ notes = [f for f in contents.findings if f.severity is ScoreSeverity.NOTE]
+ print(f" errors {len(errors)} warnings {len(warnings_found)} notes {len(notes)}")
+ for finding in errors:
+ print(f" ERROR {finding}")
+ for finding in warnings_found:
+ print(f" warning {finding}")
+ if errors:
+ print("\n Objectives cannot be computed for every row. Stopping: every")
+ print(" number below would be about a subset nobody chose.")
+ return 1
+
+ # ------------------------------------------------------------- contract --
+ print("\n2. CONTRACT")
+ try:
+ assert_scaling_is_campaign_fixed(config)
+ print(" scaling guard PASS (scales are campaign-fixed)")
+ except Exception as exc:
+ print(f" scaling guard FAIL: {exc}")
+ return 1
+
+ transform = build_objective_transform(config)
+ for index, spec in enumerate(transform.specs):
+ column = contents.model_values[names[index]]
+ low, high = float(column.min()), float(column.max())
+ if spec.transform == "affine":
+ inside = spec.lower_anchor <= low and high <= spec.upper_anchor
+ verdict = "PASS" if inside else "OUT OF RANGE"
+ print(
+ f" {spec.name:<16} anchors [{spec.lower_anchor:g}, "
+ f"{spec.upper_anchor:g}] vs data [{low:.4g}, {high:.4g}] {verdict}"
+ )
+ if not inside:
+ print(
+ " -> widen the anchors DELIBERATELY and bump "
+ "objectives.contract_version; do not let them track the data."
+ )
+ else:
+ print(f" {spec.name:<16} target {spec.target:g} vs data [{low:.4g}, {high:.4g}]")
+
+ # ------------------------------------------------------ design and rules --
+ print("\n3. DESIGN AND CONSTRAINTS")
+ design = build_design_from_config(dict(config))
+ X_observed = contents.inputs.to_numpy(float)
+ off_grid = [
+ (contents.sample_ids[row], name, float(value))
+ for column, name in enumerate(design.names)
+ for row, value in enumerate(X_observed[:, column])
+ if not np.any(
+ np.isclose(design.var_array[column], value, rtol=0.0, atol=1e-9)
+ )
+ ]
+ if off_grid:
+ # An off-grid observation stays in the GP and in the distance references,
+ # but it cannot take part in grid-index bookkeeping. Worth knowing which,
+ # because the usual cause is a grid that no longer describes the process.
+ print(f" on-grid check {len(off_grid)} observed value(s) OFF GRID")
+ for sample, name, value in off_grid:
+ print(f" sample {sample}: {name} = {value:g}")
+ else:
+ print(f" on-grid check PASS, all {n} rows land on the declared grid")
+
+ constraints = constraints_from_config(dict(config), design)
+ if not constraints:
+ print(" constraints none declared")
+ else:
+ for item in constraints:
+ print(f" constraint {item.name}: {item.description}")
+ violations = constraint_violations(X_observed, design, constraints)
+ broken = [
+ (contents.sample_ids[row], names_broken)
+ for row, names_broken in enumerate(violations)
+ if names_broken
+ ]
+ if broken:
+ # History is history: a row measured before a rule existed is not an
+ # error and must not block anything. It is worth saying, though -- a
+ # constraint that rejects a film the group actually ran is much more
+ # likely to be wrong than the film is.
+ print(f" observed rows {len(broken)} of {n} break a constraint")
+ for sample, names_broken in broken:
+ print(f" sample {sample}: {names_broken}")
+ print(" -> not an error. Check the RULE before the films.")
+ else:
+ print(f" observed rows PASS, all {n} satisfy every constraint")
+
+ # ---------------------------------------------------------------- floors --
+ null = null_loo_r2(n)
+ floor = resolution_sd(n)
+ print(f"\n4. FLOORS AT N={n}")
+ print(f" null LOO R2 {null:+.4f} (was {null_loo_r2(15):+.4f} at N=15)")
+ print(f" resolution sd +-{floor:.4f} (estimated by sqrt(15/N) from "
+ f"{RESOLUTION_SD_AT_15}; re-run the bootstrap if a call is close)")
+
+ if args.skip_model:
+ print("\n5. MODEL skipped (--skip-model)")
+ return 0
+
+ # ----------------------------------------------------------------- model --
+ # The rule has two parts, and printing only the first one is what made the
+ # thickness verdict read as a dead end rather than as a question for a
+ # different instrument.
+ print("\n5. PER-OBJECTIVE VERDICT")
+ print(f" (i) the structured fit must beat the null, {null:+.4f}")
+ print(f" (ii) if structured-vs-plain is inside the floor ({floor:.3f}), R2 cannot")
+ print(" decide and the RANK PERMUTATION adjudicates")
+ X_phys = contents.inputs.to_numpy(float)
+
+ entries = config["objectives"]["specs"]
+ for index, (name, entry) in enumerate(zip(names, entries)):
+ y = contents.model_values[name].to_numpy(float)
+ mean_spec = mean_spec_from_config(entry)
+ print(f"\n {name}")
+ try:
+ plain_loo = loo_predictions(
+ config, entry, X_phys, y, use_mean_function=False
+ )
+ plain, plain_warnings = plain_loo.r2, plain_loo.collapse_warnings
+ print(f" plain GP LOO R2 {plain:+.4f}")
+ except ModelFitError as exc:
+ print(f" plain GP REFUSED: {exc.cause}")
+ plain, plain_warnings = float("nan"), []
+
+ if mean_spec is None:
+ print(" no mean function declared")
+ verdict = "beats the null" if plain > null else "does NOT beat the null"
+ print(f" verdict {verdict} ({plain:+.4f} vs {null:+.4f})")
+ continue
+
+ try:
+ structured_loo = loo_predictions(config, entry, X_phys, y)
+ structured = structured_loo.r2
+ structured_warnings = structured_loo.collapse_warnings
+ print(f" with mean function LOO R2 {structured:+.4f}")
+ except ModelFitError as exc:
+ print(f" with mean function REFUSED: {exc.cause}")
+ print(" verdict DELETE the mean_function block: the fit")
+ print(f" is refused outright for {name}.")
+ continue
+
+ for message in dict.fromkeys(plain_warnings + structured_warnings):
+ print(f" GUARD {message}")
+ if not structured_warnings:
+ print(" guard status clean")
+
+ swing = structured - plain
+ clears_floor = swing > floor
+ beats_null = structured > null
+ print(f" swing {swing:+.4f} (floor {floor:.4f})")
+ if clears_floor and beats_null:
+ print(" verdict KEEP the mean function: it clears the")
+ print(" resolution floor and beats the null.")
+ elif beats_null:
+ print(" verdict INCONCLUSIVE ON R2: beats the null, but the")
+ print(" swing is inside the floor, so R2 cannot")
+ print(" resolve plain against structured at this N.")
+ print(" That is not a verdict. Adjudicate on RANK,")
+ print(" which is what the acquisition consumes:")
+ print(" python scripts/permutation_rank_test.py \\")
+ print(f" --objective {name} --permutations 1800")
+ else:
+ features = ", ".join(f.column for f in mean_spec.features)
+ print(" verdict DELETE the mean function. It does not beat")
+ print(f" the null at N={n}. Remove this block from")
+ print(f" {Path(args.config).name}:")
+ print(f" objectives.specs[{index}].mean_function")
+ print(f" (response: {mean_spec.response}, "
+ f"features: {features})")
+
+ print("\n" + "=" * 78)
+ print("Nothing above is a decision. It is what the new data supports.")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/permutation_rank_test.py b/scripts/permutation_rank_test.py
new file mode 100644
index 0000000..1515020
--- /dev/null
+++ b/scripts/permutation_rank_test.py
@@ -0,0 +1,274 @@
+"""Does an objective's declared mean function beat chance on RANK?
+
+ python scripts/permutation_rank_test.py --objective thickness --permutations 1800
+
+`scripts/intake_new_data.py` answers "does the mean function beat the null by more
+than the resolution floor". When the answer is INCONCLUSIVE -- the swing is real
+but smaller than what LOO R2 can resolve at this N -- that is not a verdict, it is
+a statement that R2 cannot decide. This is the instrument that decides.
+
+**Rank, not R2, because rank is what drives candidate selection.** The acquisition
+ranks candidates; it never consumes R2. The first campaign settled its thickness
+mean function on exactly this basis (p = 0.0350, 95% CI [0.0270, 0.0446] at 1800
+shuffles) and recorded that the R2 swing was consistent with it and no more.
+
+**The linear coefficients are refit inside every null fold too**, on the training
+rows only, so the null is not flattered by a trend fitted to all the data.
+
+Config-driven, so it works on any contract. It deliberately does NOT replace
+`scripts/thickness_permutation_and_mean.py`, which hard-codes the first campaign's
+column positions and grid and is kept as that campaign's reproducible record.
+
+Sharding, because the cost is (permutations x N) GP fits:
+
+ python scripts/permutation_rank_test.py --shards 12 --shard 0 --out # x12
+ python scripts/permutation_rank_test.py --combine
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+from typing import Any, Mapping
+
+import numpy as np
+import torch
+from scipy.stats import spearmanr
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ load_campaign_config,
+ normalise_inputs,
+ objective_names,
+)
+from mobo_kit.loocv import loo_predictions
+from mobo_kit.structured_mean import mean_spec_from_config
+from mobo_kit.workbook_io import read_campaign_workbook
+
+
+def measured_utility(transform: Any, index: int, Y_measured: np.ndarray) -> np.ndarray:
+ """Utility of the measurements themselves, one column.
+
+ `transform_measurements`, NOT `expected_transform`: the transform decodes the
+ link itself, so handing it measurement-space nanometres exponentiates a value
+ that was never a logarithm. That mistake cost this project an R1 batch, and it
+ reappeared in the first draft of this script -- caught only because saturating
+ the 650 nm Gaussian to 0.0 made the column constant and Spearman undefined. It
+ is the third route by which the same defect has arrived; use the safe call.
+ """
+ block = torch.tensor(np.asarray(Y_measured, dtype=float), dtype=torch.double)
+ return transform.transform_measurements(block)[:, index].detach().cpu().numpy()
+
+
+def expected_utility(
+ transform: Any,
+ index: int,
+ mu: np.ndarray,
+ var: np.ndarray,
+ Y_model_context: np.ndarray,
+) -> np.ndarray:
+ """E[utility] for one objective, through the campaign's own transform.
+
+ `mu`/`var` are MODEL-space posterior moments for objective `index`. The other
+ columns are filled with the measured model-space values at zero variance: the
+ transform is elementwise per objective, so they cannot affect the column read
+ back, and using real values rather than zeros keeps every column inside its
+ own link's domain.
+ """
+ mean_block = torch.tensor(
+ np.asarray(Y_model_context, dtype=float), dtype=torch.double
+ ).clone()
+ var_block = torch.zeros_like(mean_block)
+ mean_block[:, index] = torch.tensor(mu, dtype=torch.double)
+ var_block[:, index] = torch.tensor(var, dtype=torch.double)
+ utility = transform.expected_transform(mean_block, var_block)
+ return utility[:, index].detach().cpu().numpy()
+
+
+def _setup(args: argparse.Namespace) -> dict[str, Any]:
+ config = load_campaign_config(args.config)
+ names = list(objective_names(config))
+ if args.objective not in names:
+ raise SystemExit(f"--objective must be one of {names}; got {args.objective!r}")
+ index = names.index(args.objective)
+ entry = config["objectives"]["specs"][index]
+ mean_spec = mean_spec_from_config(entry)
+ if mean_spec is None:
+ raise SystemExit(
+ f"{args.objective!r} declares no mean_function, so there is nothing to "
+ "adjudicate."
+ )
+
+ contents = read_campaign_workbook(args.workbook, config)
+ if contents.errors:
+ raise SystemExit("The workbook has errors; refusing to test a subset.")
+
+ transform = build_objective_transform(config)
+ design_names = [item["name"] for item in config["inputs"]]
+ return {
+ "config": config,
+ "index": index,
+ "entry": entry,
+ "mean_spec": mean_spec,
+ "transform": transform,
+ "design_names": design_names,
+ "lowers": np.array([float(i["start"]) for i in config["inputs"]]),
+ "uppers": np.array([float(i["stop"]) for i in config["inputs"]]),
+ "X_phys": contents.inputs.to_numpy(float),
+ "X_norm": normalise_inputs(config, contents.inputs.to_numpy(float)),
+ "y": contents.model_values[args.objective].to_numpy(float),
+ "Y_measured": contents.model_values.to_numpy(float),
+ }
+
+
+def _rho(state: Mapping[str, Any], y: np.ndarray, *, seed: int) -> float:
+ """LOO expected-utility rank correlation against the measured utility.
+
+ Both sides are in UTILITY space, which is what the acquisition ranks. Under a
+ permutation the substituted column travels through the same transform as the
+ real one, so the null is built on the same quantity as the observation.
+ """
+ transform, index = state["transform"], state["index"]
+ Y_measured = state["Y_measured"].copy()
+ Y_measured[:, index] = y
+
+ # the shared fold loop, so this null is built on exactly the numbers
+ # intake reports and the round report plots
+ result = loo_predictions(state["config"], state["entry"], state["X_phys"], y, seed=seed)
+ mu, var = result.mean_model_space, result.variance_model_space
+ context = (
+ transform.encode_measurements(
+ torch.tensor(Y_measured, dtype=torch.double)
+ )
+ .detach()
+ .cpu()
+ .numpy()
+ )
+ predicted = expected_utility(transform, index, mu, var, context)
+ measured = measured_utility(transform, index, Y_measured)
+ return float(spearmanr(measured, predicted).statistic)
+
+
+def main(argv: list[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--config", default="configs/campaign_d2d_perovskite_final.yaml")
+ parser.add_argument("--workbook", default="local_inputs/Final Summary Table.xlsx")
+ parser.add_argument("--objective", default="thickness")
+ parser.add_argument("--permutations", type=int, default=1800)
+ parser.add_argument("--seed", type=int, default=0)
+ parser.add_argument("--fit-seed", type=int, default=73)
+ parser.add_argument("--shards", type=int, default=1)
+ parser.add_argument("--shard", type=int, default=0)
+ parser.add_argument("--out", default=None, help="directory for shard results")
+ parser.add_argument("--combine", default=None, help="combine shards in this dir")
+ args = parser.parse_args(argv)
+
+ if args.combine:
+ return combine(Path(args.combine))
+
+ state = _setup(args)
+ y = state["y"]
+ n = len(y)
+
+ observed = _rho(state, y, seed=args.fit_seed)
+ print(f"objective {args.objective}")
+ print(f"rows {n}")
+ print(f"observed rank rho {observed:+.4f}")
+
+ # Every shard draws the SAME permutation stream and takes a slice of it, so the
+ # union of shards is exactly the single-process run and shard boundaries cannot
+ # change the answer.
+ rng = np.random.default_rng(args.seed)
+ permutations = [rng.permutation(n) for _ in range(args.permutations)]
+ mine = [
+ (i, order)
+ for i, order in enumerate(permutations)
+ if i % args.shards == args.shard
+ ]
+ print(f"shard {args.shard}/{args.shards} {len(mine)} of {args.permutations} shuffles")
+
+ null_rhos: list[float] = []
+ for position, (i, order) in enumerate(mine, start=1):
+ null_rhos.append(_rho(state, y[order], seed=args.fit_seed))
+ if position % 10 == 0 or position == len(mine):
+ exceed = sum(1 for r in null_rhos if r >= observed)
+ print(f" {position}/{len(mine)} exceedances so far {exceed}", flush=True)
+
+ payload = {
+ "objective": args.objective,
+ "n": n,
+ "observed_rho": observed,
+ "shard": args.shard,
+ "shards": args.shards,
+ "permutations_total": args.permutations,
+ "null_rhos": null_rhos,
+ "seed": args.seed,
+ "fit_seed": args.fit_seed,
+ }
+ if args.out:
+ directory = Path(args.out)
+ directory.mkdir(parents=True, exist_ok=True)
+ path = directory / f"shard_{args.shard:03d}.json"
+ path.write_text(json.dumps(payload), encoding="utf-8")
+ print(f"wrote {path}")
+ else:
+ report(observed, null_rhos, args.permutations)
+ return 0
+
+
+def report(observed: float, null_rhos: list[float], total: int) -> None:
+ """The p-value, with the interval that says how well it is resolved."""
+ drawn = len(null_rhos)
+ if drawn == 0:
+ # --permutations 0 is a legitimate "just tell me the observed statistic"
+ # mode; reporting a p-value from no shuffles would be inventing one.
+ print("")
+ print("no shuffles drawn, so there is no null and no p-value.")
+ return
+ exceed = sum(1 for r in null_rhos if r >= observed)
+ # (exceed + 1) / (drawn + 1): the observed statistic is itself one draw from the
+ # null under the null hypothesis, so a p-value of exactly 0 is not available and
+ # claiming one would overstate the evidence.
+ p = (exceed + 1) / (drawn + 1)
+ se = math.sqrt(max(p * (1 - p) / drawn, 0.0))
+ low, high = max(0.0, p - 1.96 * se), min(1.0, p + 1.96 * se)
+ print()
+ print(f"shuffles drawn {drawn} of {total}")
+ print(f"exceedances {exceed}")
+ print(f"null mean rho {np.mean(null_rhos):+.4f}")
+ print(f"null sd {np.std(null_rhos, ddof=1):.4f}")
+ print(f"p {p:.4f} 95% CI [{low:.4f}, {high:.4f}]")
+ print()
+ if high < 0.05:
+ print("VERDICT KEEP. The interval clears 0.05, so the rank result is not")
+ print(" chance and the mean function has earned its place.")
+ elif p < 0.05:
+ print("VERDICT BORDERLINE. The point estimate clears 0.05 but the interval")
+ print(" does not. Draw more shuffles before deciding.")
+ else:
+ print("VERDICT DELETE. The rank result is inside what shuffling produces,")
+ print(" so nothing distinguishes this mean function from chance.")
+
+
+def combine(directory: Path) -> int:
+ shards = sorted(directory.glob("shard_*.json"))
+ if not shards:
+ raise SystemExit(f"no shard_*.json under {directory}")
+ payloads = [json.loads(path.read_text(encoding="utf-8")) for path in shards]
+ observed = {round(p["observed_rho"], 12) for p in payloads}
+ if len(observed) != 1:
+ raise SystemExit(
+ f"shards disagree on the observed statistic: {sorted(observed)}. They "
+ "were not run against the same data."
+ )
+ null_rhos = [r for payload in payloads for r in payload["null_rhos"]]
+ print(f"combined {len(shards)} shards, objective {payloads[0]['objective']}")
+ print(f"observed rank rho {payloads[0]['observed_rho']:+.4f}")
+ report(payloads[0]["observed_rho"], null_rhos, payloads[0]["permutations_total"])
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/plot_boxplot_sweep.py b/scripts/plot_boxplot_sweep.py
new file mode 100644
index 0000000..6f809e9
--- /dev/null
+++ b/scripts/plot_boxplot_sweep.py
@@ -0,0 +1,446 @@
+"""Round-by-round utility boxplots across a beta x radius sweep, over three trials.
+
+Same construction as ``scripts/plot_round_simulation.py``'s boxplots -- utility by
+round, one panel per objective, every raw point drawn over its box -- swept over a
+wider grid and replicated over three starting designs.
+
+THE GRID. beta in {9, 25, 36, 49} x radius in {0.05 ... 0.45} = 36 cells, chosen
+after beta 9 and 25 at radius 0.25 showed the behaviour the group wanted to see
+more of.
+
+THE THREE TRIALS differ in ONE thing: where R0 comes from.
+
+* ``real`` -- the 15 real recipes from the workbook, oracle-scored. The anchor.
+* ``lhs_a`` -- a fresh 15-point Latin hypercube, seed 101.
+* ``lhs_b`` -- a fresh 15-point Latin hypercube, seed 202.
+
+Everything downstream is identical: the same frozen oracle scores every design,
+the acquisitions run at the campaign seed 73 in every trial, and the candidate
+pool is therefore the same 32768 recipes throughout. So a difference between
+trials is attributable to the starting design and to nothing else. That is a
+sensitivity check on R0, which is what was asked for -- it is NOT three
+independent replicates of the whole pipeline, and the spread between trials
+understates true run-to-run variability for that reason.
+
+WHAT THE NUMBERS ARE. Every value is a GP prediction. The oracle is fitted once
+to the 15 real films and then frozen; R1 and R2 conditions were never fabricated.
+This compares acquisition settings on a data-shaped landscape. It is not evidence
+about the chemistry, and a tall box does not mean a good film.
+
+RUNNING IT. 108 cells at ~195 s each is about six hours in one process, so the
+work is sharded::
+
+ # 12 workers, ~30 min wall clock
+ for i in 0..11: python scripts/plot_boxplot_sweep.py --workbook ... --shard i --num-shards 12
+ python scripts/plot_boxplot_sweep.py --workbook ... --compose
+
+``--compose`` reads the per-cell files and renders 12 pages (3 trials x 4 betas),
+each page holding 9 radii x 3 objectives, plus a combined PDF and a summary CSV.
+Y-limits are shared per objective across ALL pages, so any two panels anywhere in
+the deliverable are directly comparable.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+import warnings
+from pathlib import Path
+from typing import Any, Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+import torch # noqa: E402
+from matplotlib.backends.backend_pdf import PdfPages # noqa: E402
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import plot_round_simulation as prs # noqa: E402
+
+from mobo_kit.campaign import ( # noqa: E402
+ build_objective_transform,
+ fit_campaign_models,
+ load_campaign_config,
+ run_r0_lhs,
+)
+from mobo_kit.workbook_io import read_campaign_workbook # noqa: E402
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning, module="botorch")
+warnings.filterwarnings("ignore", category=UserWarning, module="gpytorch")
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
+torch.set_num_threads(1)
+
+BETAS = (9.0, 25.0, 36.0, 49.0)
+RADII = (0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45)
+
+#: (name, R0 source, LHS seed). The acquisition seed stays at the campaign's 73
+#: for every trial, so only the starting design moves.
+TRIALS = (
+ ("real", "workbook", None),
+ ("lhs_a", "lhs", 101),
+ ("lhs_b", "lhs", 202),
+)
+
+ROUND_STYLE = prs.ROUND_STYLE
+INK, INK_MUTED, SURFACE, SPINE = prs.INK, prs.INK_MUTED, prs.SURFACE, prs.SPINE
+
+FOOTER = (
+ "Every value is a GP prediction, not a measurement: the oracle is fitted once "
+ "to the 15 real films and frozen, and no R1/R2 condition was ever fabricated. "
+ "This compares acquisition settings on a data-shaped landscape, not chemistry.\n"
+ "Rounds hold 15 / 5 / 3 conditions. A box over three numbers reports little "
+ "more than those numbers, which is why every raw point is drawn on top."
+)
+
+
+def signal_caveat(config: Any) -> str:
+ """Name the dead axes from THIS config, never from a remembered campaign.
+
+ The line here used to read "uniformity carries no validated signal
+ (permutation p = 0.82)", which is a fact about the first campaign's uniformity
+ score on the first campaign's films. On the v3 contract that objective is a
+ different construction and optoelectronic is dead as well, so a hard-coded
+ caveat would have shipped the wrong evidence attached to the right warning --
+ which is worse than no caveat, because it looks checked.
+ """
+ dead = [
+ str(spec["name"])
+ for spec in config["objectives"]["specs"]
+ if str(spec.get("signal_status", "")) not in ("learnable", "")
+ ]
+ if not dead:
+ return ""
+ listed = " and ".join(dead) if len(dead) < 3 else ", ".join(dead)
+ verb = "carries" if len(dead) == 1 else "carry"
+ return (
+ f"\n{listed} {verb} no learnable signal on this contract "
+ f"({config['objectives']['contract_version']}): the model does not beat "
+ "the leave-one-out null, so read those panels as exploration and not as a "
+ "result."
+ )
+
+
+def cell_key(trial: str, beta: float, radius: float) -> str:
+ return f"{trial}__beta_{beta:g}__radius_{radius:g}".replace(".", "p")
+
+
+def all_cells(
+ betas: Sequence[float] | None = None,
+ radii: Sequence[float] | None = None,
+) -> list[tuple[str, float, float]]:
+ """Every (trial, beta, radius) to run. Filters keep the trial axis intact.
+
+ Restricting the knobs never drops a trial: the trials are what turn three
+ numbers per round into a distribution worth boxing, so a "one cell" run is
+ still three campaigns from three starting designs.
+ """
+ return [
+ (trial, beta, radius)
+ for trial, _source, _seed in TRIALS
+ for beta in (BETAS if betas is None else tuple(float(b) for b in betas))
+ for radius in (RADII if radii is None else tuple(float(r) for r in radii))
+ ]
+
+
+def r0_for_trial(
+ config: Any, trial: str, source: str, lhs_seed: int | None, workbook: Path
+) -> np.ndarray:
+ """The 15 starting conditions for a trial, in physical units."""
+ if source == "workbook":
+ contents = read_campaign_workbook(workbook, config)
+ if contents.errors:
+ raise SystemExit(f"Workbook read failed: {contents.errors}")
+ return contents.inputs.to_numpy(float)
+ return run_r0_lhs(config, n=15, seed=int(lhs_seed)).conditions.to_numpy(float)
+
+
+# --------------------------------------------------------------------------- #
+# worker
+# --------------------------------------------------------------------------- #
+
+
+def run_shard(args: argparse.Namespace) -> int:
+ config = load_campaign_config(args.config)
+ seed = int((config.get("reproducibility") or {}).get("seed", 0))
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+
+ contents = read_campaign_workbook(args.workbook, config)
+ if contents.errors:
+ for finding in contents.errors:
+ print(f" ERROR {finding}")
+ return 1
+ X_real = contents.inputs.to_numpy(float)
+
+ oracle, oracle_warnings = fit_campaign_models(
+ config, X_real, contents.model_values.to_numpy(float), seed=seed
+ )
+ if oracle_warnings:
+ print("ABORTING -- the oracle fit raised guard warnings:")
+ for message in oracle_warnings:
+ print(f" {message}")
+ return 1
+
+ r0_by_trial = {
+ name: r0_for_trial(config, name, source, lhs_seed, args.workbook)
+ for name, source, lhs_seed in TRIALS
+ }
+
+ cells = all_cells(args.betas, args.radii)
+ mine = cells[args.shard :: args.num_shards]
+ out_dir = Path(args.output_dir) / "cells"
+ out_dir.mkdir(parents=True, exist_ok=True)
+ print(f"shard {args.shard}/{args.num_shards}: {len(mine)} of {len(cells)} cells")
+
+ started = time.time()
+ for position, (trial, beta, radius) in enumerate(mine, start=1):
+ key = cell_key(trial, beta, radius)
+ target = out_dir / f"{key}.npz"
+ if target.exists() and not args.overwrite:
+ print(f" [{position}/{len(mine)}] {key} exists, skipping")
+ continue
+ cell_started = time.time()
+ cell = prs.run_cell(
+ config, oracle, r0_by_trial[trial], transform, reference,
+ radius=radius, beta=beta, seed=seed,
+ )
+ np.savez_compressed(
+ target,
+ U_R0=cell["U"]["R0"], U_R1=cell["U"]["R1"], U_R2=cell["U"]["R2"],
+ X_R0=cell["X"]["R0"], X_R1=cell["X"]["R1"], X_R2=cell["X"]["R2"],
+ hv=np.array([cell["hv"]["R0"], cell["hv"]["R0+R1"], cell["hv"]["R0+R1+R2"]]),
+ r1_spacing=float(cell["r1"].diagnostics["validity"]["min_pairwise_distance"]),
+ r2_spacing=float(cell["r2"].diagnostics["validity"]["min_pairwise_distance"]),
+ r1_edge=int(sum(cell["r1"].diagnostics["validity"]["boundary_coords_per_condition"])),
+ r2_edge=int(sum(cell["r2"].diagnostics["validity"]["boundary_coords_per_condition"])),
+ r1_hash=prs.batch_hash(cell["r1"].conditions),
+ r2_hash=prs.batch_hash(cell["r2"].conditions),
+ fit_warnings=len(cell["final_fit_warnings"]),
+ )
+ elapsed = time.time() - cell_started
+ print(f" [{position}/{len(mine)}] {key} {elapsed:.0f}s", flush=True)
+ if position == 1:
+ remaining = elapsed * (len(mine) - 1) / 60.0
+ print(f" shard estimate: ~{remaining:.0f} min remaining", flush=True)
+ print(f"shard {args.shard} done in {(time.time() - started) / 60:.1f} min")
+ return 0
+
+
+# --------------------------------------------------------------------------- #
+# compose
+# --------------------------------------------------------------------------- #
+
+
+def _panel(axis, series, objective_index) -> None:
+ values = [series[name][:, objective_index] for name in ("R0", "R1", "R2")]
+ boxes = axis.boxplot(
+ values,
+ tick_labels=[f"{n}\nn={len(v)}" for n, v in zip(("R0", "R1", "R2"), values)],
+ showmeans=True, showfliers=False, widths=0.55, patch_artist=True,
+ )
+ for patch, name in zip(boxes["boxes"], ("R0", "R1", "R2")):
+ patch.set_facecolor(ROUND_STYLE[name][0])
+ patch.set_alpha(0.22)
+ patch.set_edgecolor(ROUND_STYLE[name][0])
+ for key in ("whiskers", "caps", "medians"):
+ for artist in boxes[key]:
+ artist.set_color(INK_MUTED)
+ for marker in boxes.get("means", ()):
+ marker.set_markerfacecolor(INK)
+ marker.set_markeredgecolor(INK)
+ marker.set_markersize(5)
+ rng = np.random.default_rng(0)
+ for position, block in enumerate(values, start=1):
+ colour = ROUND_STYLE[("R0", "R1", "R2")[position - 1]][0]
+ axis.scatter(
+ np.full(block.shape, position) + rng.normal(0, 0.045, block.shape),
+ block, s=20, c=colour, edgecolors="white", linewidths=0.6, zorder=3,
+ )
+ axis.tick_params(labelsize=7, colors=INK_MUTED, length=2)
+ axis.grid(axis="y", color="#e8e7e2", lw=0.7)
+ axis.set_axisbelow(True)
+ for spine in axis.spines.values():
+ spine.set_color(SPINE)
+
+
+def compose(args: argparse.Namespace) -> int:
+ config = load_campaign_config(args.config)
+ transform = build_objective_transform(config)
+ names = list(transform.names)
+ # Compose exactly what was run. Iterating the full sweep constants here while
+ # the run was filtered would draw a page of "missing" panels around the one
+ # cell anybody asked for.
+ betas_used = BETAS if args.betas is None else tuple(float(b) for b in args.betas)
+ radii_used = RADII if args.radii is None else tuple(float(r) for r in args.radii)
+ cells_dir = Path(args.output_dir) / "cells"
+
+ loaded: dict[str, Any] = {}
+ missing: list[str] = []
+ for trial, beta, radius in all_cells(args.betas, args.radii):
+ key = cell_key(trial, beta, radius)
+ path = cells_dir / f"{key}.npz"
+ if path.exists():
+ loaded[key] = np.load(path, allow_pickle=False)
+ else:
+ missing.append(key)
+ print(f"loaded {len(loaded)} cells, missing {len(missing)}")
+ if missing:
+ for key in missing[:10]:
+ print(f" MISSING {key}")
+ if not args.allow_partial:
+ print("\nRefusing to compose an incomplete deliverable. Re-run the "
+ "missing shards, or pass --allow-partial.")
+ return 1
+
+ # Shared y-limits per objective across every page, so any two panels in the
+ # whole deliverable are directly comparable. Without this a flatter cell can
+ # look identical to a better one.
+ limits = []
+ for index in range(len(names)):
+ stack = np.concatenate([
+ np.concatenate([
+ data["U_R0"][:, index], data["U_R1"][:, index], data["U_R2"][:, index]
+ ])
+ for data in loaded.values()
+ ])
+ low, high = float(stack.min()), float(stack.max())
+ pad = 0.06 * (high - low if high > low else 1.0)
+ limits.append((low - pad, high + pad))
+
+ figures_dir = Path(args.output_dir) / "pages"
+ figures_dir.mkdir(parents=True, exist_ok=True)
+ rows: list[dict[str, Any]] = []
+ pages = len(TRIALS) * len(betas_used)
+ pdf_path = Path(args.output_dir) / f"boxplot_sweep_{pages}pages.pdf"
+
+ with PdfPages(pdf_path) as pdf:
+ for trial, _source, lhs_seed in TRIALS:
+ for beta in betas_used:
+ fig, axes = plt.subplots(
+ len(radii_used), len(names),
+ figsize=(12.5, max(5.0, 26.0 * len(radii_used) / len(RADII))),
+ facecolor=SURFACE, squeeze=False,
+ )
+ for row, radius in enumerate(radii_used):
+ key = cell_key(trial, beta, radius)
+ data = loaded.get(key)
+ for column, objective in enumerate(names):
+ axis = axes[row, column]
+ axis.set_facecolor(SURFACE)
+ if data is None:
+ axis.text(0.5, 0.5, "missing", ha="center", va="center",
+ fontsize=9, color=INK_MUTED)
+ axis.set_xticks([])
+ continue
+ series = {
+ "R0": data["U_R0"], "R1": data["U_R1"], "R2": data["U_R2"]
+ }
+ _panel(axis, series, column)
+ axis.set_ylim(*limits[column])
+ if row == 0:
+ axis.set_title(objective, fontsize=11, color=INK, pad=8)
+ if column == 0:
+ axis.set_ylabel(
+ f"radius {radius:g}\nutility",
+ fontsize=9, color=INK,
+ )
+ for round_name in ("R0", "R1", "R2"):
+ block = series[round_name][:, column]
+ rows.append({
+ "trial": trial, "beta": beta, "radius": radius,
+ "objective": objective, "round": round_name,
+ "n": int(block.size),
+ "mean_utility": float(block.mean()),
+ "median_utility": float(np.median(block)),
+ "max_utility": float(block.max()),
+ "hv_r0": float(data["hv"][0]),
+ "hv_r0_r1": float(data["hv"][1]),
+ "hv_r0_r1_r2": float(data["hv"][2]),
+ "r1_min_spacing": float(data["r1_spacing"]),
+ "r1_edge_coords": int(data["r1_edge"]),
+ "r1_batch_hash": data["r1_hash"].item(),
+ "r2_batch_hash": data["r2_hash"].item(),
+ "final_fit_warnings": int(data["fit_warnings"]),
+ })
+ source = "15 real recipes" if trial == "real" else f"LHS seed {lhs_seed}"
+ shown = sorted(radii_used)
+ span = (
+ f"radius {shown[0]:g}"
+ if len(shown) == 1
+ else f"radius {shown[0]:g} → {shown[-1]:g}"
+ )
+ fig.suptitle(
+ f"Utility by round | trial {trial} ({source}) | "
+ f"beta = {beta:g} | {span}",
+ fontsize=15, color=INK, y=0.995,
+ )
+ fig.tight_layout(rect=(0, 0.035, 1, 0.982))
+ fig.text(
+ 0.008, 0.006, "seed 73 | " + FOOTER + signal_caveat(config),
+ fontsize=6.6, color=INK_MUTED, va="bottom", ha="left", wrap=True,
+ )
+ stem = f"page_{trial}_beta_{beta:g}".replace(".", "p")
+ if len(radii_used) == 1:
+ stem += f"_radius_{radii_used[0]:g}".replace(".", "p")
+ page = figures_dir / f"{stem}.png"
+ fig.savefig(page, dpi=110, facecolor=SURFACE)
+ pdf.savefig(fig, facecolor=SURFACE)
+ plt.close(fig)
+ print(f" page {page.name}")
+
+ summary = pd.DataFrame(rows).drop_duplicates()
+ summary_path = Path(args.output_dir) / "boxplot_sweep_summary.csv"
+ summary.to_csv(summary_path, index=False, encoding="utf-8-sig")
+ print(f"\nPDF {pdf_path}")
+ print(f"summary {summary_path} ({len(summary)} rows)")
+ return 0
+
+
+def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", required=True, type=Path)
+ parser.add_argument(
+ "--config", type=Path,
+ default=Path("configs/campaign_d2d_perovskite_final.yaml"),
+ )
+ parser.add_argument(
+ "--output-dir", type=Path, default=Path("local_outputs/boxplot_sweep")
+ )
+ # A ratified cell needs no sweep. The grid is a decision, not a default:
+ # running 108 cells to look at one is not thoroughness, it is 36x the
+ # compute for the same answer.
+ parser.add_argument(
+ "--betas", nargs="+", type=float, default=None,
+ help="restrict to these betas; default is the full sweep set",
+ )
+ parser.add_argument(
+ "--radii", nargs="+", type=float, default=None,
+ help="restrict to these radii; default is the full sweep set",
+ )
+ parser.add_argument("--shard", type=int, default=0)
+ parser.add_argument("--num-shards", type=int, default=1)
+ parser.add_argument("--overwrite", action="store_true")
+ parser.add_argument("--compose", action="store_true")
+ parser.add_argument(
+ "--allow-partial", action="store_true",
+ help="Compose with cells missing; each gap is drawn as 'missing'.",
+ )
+ return parser.parse_args(argv)
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ args = parse_args(argv)
+ if args.compose:
+ return compose(args)
+ return run_shard(args)
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/plot_dtlz2_report.py b/scripts/plot_dtlz2_report.py
new file mode 100644
index 0000000..4c19a54
--- /dev/null
+++ b/scripts/plot_dtlz2_report.py
@@ -0,0 +1,329 @@
+"""Figures for the DTLZ2 acceptance run: how the optimiser actually moves.
+
+Renders the progression the campaign goes through -- initial design, GP fit,
+acquisition surface, selected batch, refit, repeat -- plus objective-space and
+hypervolume views.
+
+ python scripts/plot_dtlz2_report.py --out local_outputs/dtlz2_report
+
+The design space is 10-dimensional, so every contour is a 2-D slice on
+(x0, x1) with x2..x9 held at 0.5. That slice is chosen deliberately: for DTLZ2
+the last k=8 inputs are "distance" variables whose optimum is exactly 0.5, and
+x0/x1 are the "position" variables that move you along the Pareto front. So the
+slice contains the true optimal surface, and a well-behaved optimiser should be
+seen concentrating on it.
+"""
+
+from __future__ import annotations
+
+import argparse
+import warnings
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from botorch.test_functions.multi_objective import DTLZ2
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+from botorch.utils.multi_objective.pareto import is_non_dominated
+from matplotlib.colors import LinearSegmentedColormap
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ run_r0_lhs,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from mobo_kit.design import build_design_from_config
+from mobo_kit.ucb_hvi import score_ucb_hvi_pool
+
+# Scoped rather than blanket: this is a figure script, and the GP fits emit
+# numerical and deprecation chatter that would bury a real message. Anything the
+# fit guard has to say still comes through, which is the point -- a plot built on a
+# collapsed fit should not look like a plot built on a good one.
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning, module="botorch")
+warnings.filterwarnings("ignore", category=UserWarning, module="gpytorch")
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
+torch.set_num_threads(1)
+
+# dataviz reference palette, categorical slots 1-3 (the documented all-pairs-safe
+# set: CVD dE 9.2 light / 9.4 dark, normal-vision 24.0 / 20.9)
+R0_COLOR, R1_COLOR, R2_COLOR = "#2a78d6", "#eb6834", "#1baf7a"
+INK, INK_MUTED, SURFACE = "#0b0b0b", "#52514e", "#fcfcfb"
+# sequential blue ramp, 100 -> 700, for magnitude
+BLUE_RAMP = ["#cde2fb", "#9ec5f4", "#6da7ec", "#3987e5", "#256abf", "#184f95", "#0d366b"]
+# second sequential context takes the next categorical hue (orange)
+ORANGE_RAMP = ["#fbe3d5", "#f6c3a4", "#f0a074", "#eb6834", "#c14f22", "#933a17", "#66270e"]
+SEQ = LinearSegmentedColormap.from_list("seq_blue", BLUE_RAMP)
+ACQ = LinearSegmentedColormap.from_list("seq_orange", ORANGE_RAMP)
+
+D, M, GRID = 10, 3, 45
+SLICE_X, SLICE_Y = 0, 1
+
+
+def config(pool: int = 1024, mc: int = 32) -> dict:
+ return {
+ "inputs": [
+ {"name": f"x{i}", "start": 0.0, "stop": 1.0, "step": 0.05} for i in range(D)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-dtlz2-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": f"f{i}", "goal": "maximize", "transform": "affine",
+ "model_source_column": f"f{i}",
+ "lower_anchor": -2.0, "upper_anchor": 0.0,
+ }
+ for i in range(M)
+ ],
+ },
+ "reference_point_utility": [-0.01] * M,
+ "rounds": {
+ "r1": {"method": "ucb_hvi", "batch_size": 5, "replicates_per_condition": 3,
+ "beta": 4.0, "candidate_pool_size": pool, "posterior_samples": 256,
+ "moment_method": "monte_carlo"},
+ "r2": {"method": "qlognehvi", "batch_size": 3,
+ "replicates_per_condition": 3, "candidate_pool_size": pool,
+ "mc_samples": mc},
+ },
+ "local_penalization": {"radius": 0.25, "min_batch_distance": 0.15,
+ "min_observed_distance": 0.0, "dimension_weights": None},
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": 73},
+ "constraints": [],
+ }
+
+
+CFG = config()
+PROBLEM = DTLZ2(dim=D, num_objectives=M, negate=True).to(dtype=torch.double)
+TRANSFORM = build_objective_transform(CFG)
+DESIGN = build_design_from_config(CFG)
+REF = torch.tensor(CFG["reference_point_utility"], dtype=torch.double)
+
+
+def evaluate(X):
+ return PROBLEM(torch.tensor(np.asarray(X, float), dtype=torch.double)).numpy()
+
+
+def hypervolume(Y):
+ U = TRANSFORM(torch.tensor(np.asarray(Y, float), dtype=torch.double))
+ return Hypervolume(ref_point=REF).compute(U[is_non_dominated(U)])
+
+
+def slice_grid():
+ axis = np.linspace(0.0, 1.0, GRID)
+ xx, yy = np.meshgrid(axis, axis)
+ pts = np.full((GRID * GRID, D), 0.5)
+ pts[:, SLICE_X] = xx.ravel()
+ pts[:, SLICE_Y] = yy.ravel()
+ return axis, xx, yy, pts
+
+
+def surfaces(X_phys, Y_raw, seed=73):
+ """Posterior utility mean/sd and the UCB-HVI acquisition over the slice."""
+ axis, xx, yy, pts = slice_grid()
+ model, _fit_warnings = fit_campaign_models(CFG, X_phys, Y_raw, seed=seed)
+ grid_t = torch.tensor(pts, dtype=torch.double)
+
+ model.eval()
+ with torch.no_grad():
+ post = model.posterior(grid_t)
+ util = TRANSFORM.expected_transform(post.mean, post.variance).numpy()
+ sd = post.variance.sqrt().numpy()
+
+ scored = score_ucb_hvi_pool(
+ model, grid_t, Y_raw, TRANSFORM, REF.numpy(),
+ beta=CFG["rounds"]["r1"]["beta"], mc_samples=64, seed=seed,
+ )
+ # base_score is the raw hypervolume improvement per candidate
+ acq = np.asarray(scored.base_score, dtype=float)
+ return {
+ "axis": axis, "xx": xx, "yy": yy,
+ "mean": util.mean(axis=1).reshape(GRID, GRID),
+ "sd": sd.mean(axis=1).reshape(GRID, GRID),
+ "acq": acq.reshape(GRID, GRID),
+ }
+
+
+def style(ax, title, xlabel=True, ylabel=True):
+ ax.set_title(title, fontsize=10, color=INK, pad=8)
+ ax.set_xlim(0, 1); ax.set_ylim(0, 1)
+ ax.set_xlabel("x0" if xlabel else "", fontsize=9, color=INK_MUTED)
+ ax.set_ylabel("x1" if ylabel else "", fontsize=9, color=INK_MUTED)
+ ax.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ for spine in ax.spines.values():
+ spine.set_color("#d8d7d2")
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--out", default="local_outputs/dtlz2_report")
+ ap.add_argument("--seed", type=int, default=73)
+ args = ap.parse_args()
+ out = Path(args.out); out.mkdir(parents=True, exist_ok=True)
+
+ print("running the campaign...")
+ r0 = run_r0_lhs(CFG, n=15, seed=args.seed)
+ X0 = r0.conditions.to_numpy(float); Y0 = evaluate(X0)
+ r1 = run_r1_ucb(CFG, X0, Y0, seed=args.seed)
+ X1 = r1.conditions.to_numpy(float); Y1 = evaluate(X1)
+ X01, Y01 = np.vstack([X0, X1]), np.vstack([Y0, Y1])
+ r2 = run_r2_qlognehvi(CFG, X01, Y01, seed=args.seed)
+ X2 = r2.conditions.to_numpy(float); Y2 = evaluate(X2)
+ X012, Y012 = np.vstack([X01, X2]), np.vstack([Y01, Y2])
+ hv = [hypervolume(Y0), hypervolume(Y01), hypervolume(Y012)]
+ print(f" hypervolume: {hv[0]:.4f} -> {hv[1]:.4f} -> {hv[2]:.4f}")
+
+ print("building surfaces (3 GP fits over a 45x45 slice)...")
+ stages = [
+ ("After R0: 15 LHS points", surfaces(X0, Y0, args.seed), X0, X1, "R1"),
+ ("After R1: 20 points", surfaces(X01, Y01, args.seed), X01, X2, "R2"),
+ ("After R2: 23 points", surfaces(X012, Y012, args.seed), X012, None, None),
+ ]
+
+ # ---------------- figure 1: the progression ----------------
+ fig, axes = plt.subplots(3, 3, figsize=(13.5, 12.2), facecolor=SURFACE)
+ for col, (title, s, seen, chosen, label) in enumerate(stages):
+ # row 0 -- posterior mean utility
+ ax = axes[0, col]
+ cf = ax.contourf(s["xx"], s["yy"], s["mean"], levels=14, cmap=SEQ)
+ ax.scatter(seen[:, SLICE_X], seen[:, SLICE_Y], s=26, c="white",
+ edgecolors=INK, linewidths=0.9, zorder=3, label="observed")
+ style(ax, f"{title}\nGP posterior mean utility", xlabel=False)
+ fig.colorbar(cf, ax=ax, fraction=0.046, pad=0.03).ax.tick_params(labelsize=7)
+
+ # row 1 -- posterior uncertainty
+ ax = axes[1, col]
+ cf = ax.contourf(s["xx"], s["yy"], s["sd"], levels=14, cmap=SEQ)
+ ax.scatter(seen[:, SLICE_X], seen[:, SLICE_Y], s=26, c="white",
+ edgecolors=INK, linewidths=0.9, zorder=3)
+ style(ax, "GP posterior uncertainty (sd)", xlabel=False)
+ fig.colorbar(cf, ax=ax, fraction=0.046, pad=0.03).ax.tick_params(labelsize=7)
+
+ # row 2 -- acquisition + what it picked
+ ax = axes[2, col]
+ cf = ax.contourf(s["xx"], s["yy"], s["acq"], levels=14, cmap=ACQ)
+ ax.scatter(seen[:, SLICE_X], seen[:, SLICE_Y], s=20, c="white",
+ edgecolors=INK_MUTED, linewidths=0.7, zorder=3)
+ if chosen is not None:
+ colour = R1_COLOR if label == "R1" else R2_COLOR
+ ax.scatter(chosen[:, SLICE_X], chosen[:, SLICE_Y], s=150, marker="*",
+ c=colour, edgecolors="white", linewidths=1.4, zorder=4,
+ label=f"{label} selected ({len(chosen)})")
+ ax.legend(loc="upper right", fontsize=8, frameon=True,
+ facecolor="white", edgecolor="#d8d7d2")
+ style(ax, f"UCB-HVI acquisition -> {label} batch")
+ else:
+ style(ax, "UCB-HVI acquisition (final model)")
+ fig.colorbar(cf, ax=ax, fraction=0.046, pad=0.03).ax.tick_params(labelsize=7)
+
+ fig.suptitle(
+ "DTLZ2 acceptance run: 2-D slice at (x0, x1), x2..x9 = 0.5\n"
+ "Stars mark the batch each acquisition surface selected",
+ fontsize=12.5, color=INK, y=0.985,
+ )
+ fig.tight_layout(rect=(0, 0, 1, 0.955))
+ fig.savefig(out / "01_progression.png", dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+ # ---------------- figure 2: objective space ----------------
+ fig = plt.figure(figsize=(12.5, 5.4), facecolor=SURFACE)
+ U0 = TRANSFORM(torch.tensor(Y0)).numpy()
+ U1 = TRANSFORM(torch.tensor(Y1)).numpy()
+ U2 = TRANSFORM(torch.tensor(Y2)).numpy()
+ pairs = [(0, 1), (0, 2), (1, 2)]
+ for i, (a, b) in enumerate(pairs):
+ ax = fig.add_subplot(1, 3, i + 1, facecolor=SURFACE)
+ for U, c, name in ((U0, R0_COLOR, "R0 (15)"), (U1, R1_COLOR, "R1 (5)"),
+ (U2, R2_COLOR, "R2 (3)")):
+ ax.scatter(U[:, a], U[:, b], s=52, c=c, edgecolors="white",
+ linewidths=1.2, label=name, zorder=3)
+ # Ring the Pareto-optimal points instead of connecting them: this is a
+ # 2-D projection of a 3-D front, so the points carry no ordering along
+ # either axis and a connecting line would invent one.
+ allU = np.vstack([U0, U1, U2])
+ front = allU[is_non_dominated(torch.tensor(allU)).numpy()]
+ ax.scatter(front[:, a], front[:, b], s=170, facecolors="none",
+ edgecolors=INK, linewidths=1.5, zorder=2,
+ label="Pareto optimal (3-D)" if i == 0 else None)
+ ax.set_xlabel(f"utility f{a}", fontsize=9, color=INK_MUTED)
+ ax.set_ylabel(f"utility f{b}", fontsize=9, color=INK_MUTED)
+ ax.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ for spine in ax.spines.values():
+ spine.set_color("#d8d7d2")
+ if i == 0:
+ ax.legend(fontsize=8, frameon=True, facecolor="white",
+ edgecolor="#d8d7d2", loc="lower left")
+ fig.suptitle("Objective space: where each round landed (higher is better)",
+ fontsize=12.5, color=INK)
+ fig.tight_layout(rect=(0, 0, 1, 0.93))
+ fig.savefig(out / "02_objective_space.png", dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+ # ---------------- figure 3: hypervolume vs random ----------------
+ print("running the random baseline across 5 seeds...")
+ grids = [np.asarray(g, float) for g in DESIGN.var_array]
+ bo_curves, rand_curves = [], []
+ for seed in (1, 2, 3, 4, 5):
+ a0 = run_r0_lhs(CFG, n=15, seed=seed)
+ Xa = a0.conditions.to_numpy(float); Ya = evaluate(Xa)
+ b1 = run_r1_ucb(CFG, Xa, Ya, seed=seed)
+ Yb = evaluate(b1.conditions.to_numpy(float))
+ Xab = np.vstack([Xa, b1.conditions.to_numpy(float)])
+ Yab = np.vstack([Ya, Yb])
+ b2 = run_r2_qlognehvi(CFG, Xab, Yab, seed=seed)
+ Yc = evaluate(b2.conditions.to_numpy(float))
+ bo_curves.append([hypervolume(Ya), hypervolume(Yab),
+ hypervolume(np.vstack([Yab, Yc]))])
+ rng = np.random.default_rng(seed)
+ Xr1 = np.column_stack([rng.choice(g, size=5) for g in grids])
+ Xr2 = np.column_stack([rng.choice(g, size=3) for g in grids])
+ Yr1, Yr2 = evaluate(Xr1), evaluate(Xr2)
+ rand_curves.append([hypervolume(Ya), hypervolume(np.vstack([Ya, Yr1])),
+ hypervolume(np.vstack([Ya, Yr1, Yr2]))])
+ bo = np.array(bo_curves); rand = np.array(rand_curves)
+
+ fig, ax = plt.subplots(figsize=(8.2, 5.2), facecolor=SURFACE)
+ x = np.array([15, 20, 23])
+ for arr, colour, name in ((bo, R1_COLOR, "Bayesian optimisation"),
+ (rand, R0_COLOR, "Random on-grid search")):
+ ax.fill_between(x, arr.min(axis=0), arr.max(axis=0), color=colour, alpha=0.14)
+ ax.plot(x, arr.mean(axis=0), color=colour, lw=2.0, marker="o", ms=8,
+ markeredgecolor="white", markeredgewidth=1.4, label=name, zorder=3)
+ ax.set_xticks(x)
+ ax.set_xticklabels(["R0\n15 points", "+R1\n20 points", "+R2\n23 points"],
+ fontsize=9, color=INK_MUTED)
+ ax.set_ylabel("hypervolume (utility space)", fontsize=9.5, color=INK_MUTED)
+ ax.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ ax.grid(axis="y", color="#e8e7e2", lw=0.8)
+ ax.set_axisbelow(True)
+ for spine in ax.spines.values():
+ spine.set_color("#d8d7d2")
+ ax.legend(fontsize=9, frameon=True, facecolor="white", edgecolor="#d8d7d2",
+ loc="upper left")
+ ax.set_title(
+ f"Hypervolume gain at equal budget, 5 seeds (band = min-max)\n"
+ f"mean gain: BO +{(bo[:,2]-bo[:,0]).mean():.3f} "
+ f"random +{(rand[:,2]-rand[:,0]).mean():.3f}",
+ fontsize=11.5, color=INK, pad=10,
+ )
+ fig.tight_layout()
+ fig.savefig(out / "03_hypervolume.png", dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+ np.savetxt(out / "hypervolume_bo.csv", bo, delimiter=",",
+ header="hv_R0,hv_R1,hv_R2", comments="")
+ np.savetxt(out / "hypervolume_random.csv", rand, delimiter=",",
+ header="hv_R0,hv_R1,hv_R2", comments="")
+ print(f"\nwrote 3 figures + 2 CSVs to {out}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/plot_extended_replicates.py b/scripts/plot_extended_replicates.py
new file mode 100644
index 0000000..e3709b4
--- /dev/null
+++ b/scripts/plot_extended_replicates.py
@@ -0,0 +1,556 @@
+"""What 45 rows of REPEATED recipes say about the three scores.
+
+The sheet behind this script (``Extended Summary Table C1C2``) is the first one in
+the project where the same recipe appears more than once: samples 1-15, 16-30 and
+31-45 carry identical inputs, recipe for recipe, and block 1 is bit-identical to
+the current campaign workbook on thickness. So the three blocks are the SAME 15
+RECIPES REMADE, not 45 designs.
+
+That buys the one thing no amount of modelling can buy: a separation of
+
+ "the score changed because the recipe changed" (what BO can chase)
+
+from
+
+ "the score changed because the film was made and measured again" (what it cannot).
+
+TWO FIGURES.
+
+``boxplot_extended`` is the measurement, not the model. The top row boxes each
+score by campaign block, which is where a systematic between-campaign shift shows
+up as three boxes that do not overlap. The bottom row boxes each RECIPE's three
+repeats, sorted by recipe mean: tall boxes that overlap everything mean the repeat
+spread swamps the recipe spread, and no model can beat that.
+
+``01_loo_parity_extended`` is the model. Both rows are leave-one-out predictions
+plotted against the measurement, but they leave out different things, and the
+difference is the point:
+
+ * ROW-WISE (top) holds out one ROW. Its two repeats stay in the training set
+ carrying the same inputs, so the GP interpolates its own repeat. That number
+ measures REPRODUCIBILITY and is not a prediction score. It is plotted because
+ it is what a naive run on this sheet reports, and it looks excellent.
+ * RECIPE-WISE (bottom) holds out all THREE rows of a recipe. Nothing with those
+ inputs remains. That is the honest question -- can the model predict a recipe
+ it has never made -- and it is the number to quote.
+
+Run::
+
+ python scripts/plot_extended_replicates.py \
+ --workbook "local_inputs/Extended Summary Table C1C2.xlsx" \
+ --config configs/campaign_d2d_perovskite_extended_c1c2.yaml \
+ --outdir local_inputs/extended_c1c2_reports
+
+Outputs stay local; the workbook is gitignored and so is its report directory.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import warnings
+from pathlib import Path
+from typing import Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+import torch # noqa: E402
+from scipy.stats import f as fdist # noqa: E402
+from scipy.stats import spearmanr # noqa: E402
+
+from mobo_kit.campaign import load_campaign_config, normalise_inputs # noqa: E402
+from mobo_kit.model_validation import DIM_SCALED_PRIOR, fit_model_variant # noqa: E402
+from mobo_kit.round_report import _save, _style # noqa: E402
+from mobo_kit.workbook_io import read_campaign_workbook # noqa: E402
+
+warnings.filterwarnings("ignore")
+
+#: One colour per campaign block. Deliberately NOT the round palette: these are
+#: three makings of the same designs, not three rounds of a campaign.
+BLOCK_COLORS = ("#2a78d6", "#eb6834", "#1baf7a")
+BLOCK_LABELS = ("block 1 (= current campaign)", "block 2", "block 3")
+DEAD = "#8a3b2f"
+
+
+# --------------------------------------------------------------------------- #
+# statistics
+# --------------------------------------------------------------------------- #
+
+
+def variance_decomposition(y: np.ndarray, recipe: np.ndarray, block: np.ndarray) -> dict:
+ """One-way ANOVA with recipe as the factor, plus the block's share.
+
+ ``icc`` is the fraction of variance the RECIPE owns. It is the ceiling on any
+ model that sees only the recipe: predict every repeat by its recipe's true
+ mean and the leftover is repeat variance, by construction. A score with an ICC
+ near zero cannot be optimised, however good the optimiser.
+ """
+ y = np.asarray(y, float)
+ groups = sorted(set(recipe.tolist()))
+ grand = y.mean()
+ means = np.array([y[recipe == g].mean() for g in groups])
+ counts = np.array([int((recipe == g).sum()) for g in groups])
+ ss_between = float((counts * (means - grand) ** 2).sum())
+ ss_within = float(
+ sum(((y[recipe == g] - means[i]) ** 2).sum() for i, g in enumerate(groups))
+ )
+ df_b, df_w = len(groups) - 1, len(y) - len(groups)
+ ms_b, ms_w = ss_between / df_b, ss_within / df_w
+ n0 = counts.mean()
+ block_ids = sorted(set(block.tolist()))
+ block_means = np.array([y[block == b].mean() for b in block_ids])
+ block_counts = np.array([int((block == b).sum()) for b in block_ids])
+ return {
+ "sd_total": float(y.std(ddof=1)),
+ "sd_within_recipe": float(np.sqrt(ms_w)),
+ "sd_between_recipe": float(np.sqrt(max(0.0, (ms_b - ms_w) / n0))),
+ "icc": float(max(0.0, (ms_b - ms_w) / (ms_b + (n0 - 1) * ms_w))),
+ "f": float(ms_b / ms_w),
+ "p": float(1.0 - fdist.cdf(ms_b / ms_w, df_b, df_w)),
+ "block_variance_share": float(
+ (block_counts * (block_means - grand) ** 2).sum() / ((y - grand) ** 2).sum()
+ ),
+ }
+
+
+def fold_predictions(config, X_phys, y, groups, *, seed=73):
+ """Predict every row from a model fitted without ANY row of its group.
+
+ A fold that will not fit is not skipped and not retried on a looser variant:
+ it falls back to the training mean and is COUNTED. A collapsed fold means the
+ GP explained that objective as pure noise, which is a result about the data
+ and must not be hidden behind a model that happens to fit.
+ """
+ Xn = normalise_inputs(config, np.asarray(X_phys, float))
+ y = np.asarray(y, float)
+ n = len(y)
+ mu, sd = np.empty(n), np.empty(n)
+ collapsed: list[int] = []
+ previous = torch.get_num_threads()
+ torch.set_num_threads(1)
+ try:
+ for g in sorted(set(np.asarray(groups).tolist())):
+ held = [i for i in range(n) if groups[i] == g]
+ keep = [i for i in range(n) if groups[i] != g]
+ torch.manual_seed(seed)
+ try:
+ record = fit_model_variant(
+ torch.tensor(Xn[keep], dtype=torch.double),
+ torch.tensor(y[keep], dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(range(len(keep))),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ seed=seed,
+ )
+ except Exception:
+ collapsed.append(int(g))
+ mu[held] = y[keep].mean()
+ sd[held] = y[keep].std(ddof=1)
+ continue
+ gp = record.model.models[0]
+ gp.eval()
+ with torch.no_grad():
+ posterior = gp.posterior(torch.tensor(Xn[held], dtype=torch.double))
+ mu[held] = posterior.mean.reshape(-1).numpy()
+ sd[held] = np.sqrt(
+ np.clip(posterior.variance.reshape(-1).numpy(), 0.0, None)
+ )
+ finally:
+ torch.set_num_threads(previous)
+ return mu, sd, tuple(collapsed)
+
+
+def r_squared(observed: np.ndarray, predicted: np.ndarray) -> float:
+ observed = np.asarray(observed, float)
+ residual = ((observed - predicted) ** 2).sum()
+ total = ((observed - observed.mean()) ** 2).sum()
+ return float(1.0 - residual / total)
+
+
+def grouped_null(n: int, n_folds: int) -> float:
+ """What predicting the held-out-group mean scores, at this fold size.
+
+ The familiar ``1 - (N/(N-1))^2`` is the k=1 case. Dropping k rows at a time
+ shifts the training mean further, so the bar to clear MOVES with the fold
+ size and the two rows of the parity figure do not share one null.
+ """
+ k = n / n_folds
+ return float(1.0 - (n / (n - k)) ** 2)
+
+
+# --------------------------------------------------------------------------- #
+# figures
+# --------------------------------------------------------------------------- #
+
+
+def _display(name: str, values: np.ndarray) -> tuple[np.ndarray, str, bool]:
+ """Optoelectronic spans five orders of magnitude; plot it in log10."""
+ if name == "optoelectronic":
+ return np.log10(np.clip(values, 1e-300, None)), "log10(optoelectronic score)", True
+ return values, f"{name} score", False
+
+
+def plot_boxes(directory: Path, names, Y, recipe, block, stats) -> Path:
+ fig, axes = plt.subplots(2, len(names), figsize=(5.6 * len(names), 9.6))
+ for column, name in enumerate(names):
+ values, label, _ = _display(name, Y[:, column])
+ stat = stats[name]
+
+ top = axes[0, column]
+ _style(top)
+ data = [values[block == b] for b in range(3)]
+ boxes = top.boxplot(data, patch_artist=True, widths=0.55, showfliers=False)
+ for patch, colour in zip(boxes["boxes"], BLOCK_COLORS):
+ patch.set_facecolor(colour)
+ patch.set_alpha(0.22)
+ patch.set_edgecolor(colour)
+ for key in ("whiskers", "caps", "medians"):
+ for line in boxes[key]:
+ line.set_color("#555555")
+ for g in sorted(set(recipe.tolist())):
+ top.plot(
+ [1, 2, 3],
+ [values[(recipe == g) & (block == b)][0] for b in range(3)],
+ color="#c4c3bf",
+ linewidth=0.7,
+ zorder=2,
+ )
+ for b in range(3):
+ jitter = np.random.default_rng(73 + b).normal(0, 0.04, size=len(data[b]))
+ top.scatter(
+ 1 + b + jitter, data[b], s=26, color=BLOCK_COLORS[b], zorder=3, alpha=0.9
+ )
+ top.set_xticks([1, 2, 3])
+ top.set_xticklabels(
+ ["block 1\n(current campaign)", "block 2", "block 3"], fontsize=9
+ )
+ top.set_ylabel(label)
+ share = stat["block_variance_share"]
+ top.set_title(
+ f"{name}\nthe block owns {share:.1%} of the variance",
+ fontsize=11,
+ color=DEAD if share > 0.25 else "#222222",
+ )
+ if share > 0.25:
+ top.text(
+ 0.5,
+ 0.955,
+ "SYSTEMATIC BETWEEN-CAMPAIGN SHIFT",
+ transform=top.transAxes,
+ ha="center",
+ va="top",
+ fontsize=9.5,
+ color=DEAD,
+ bbox=dict(boxstyle="round,pad=0.35", fc="#fdeeea", ec="#e0b4a8"),
+ )
+
+ bottom = axes[1, column]
+ _style(bottom)
+ order = list(np.argsort([values[recipe == g].mean() for g in sorted(set(recipe.tolist()))]))
+ per_recipe = [values[recipe == g] for g in order]
+ boxes = bottom.boxplot(per_recipe, patch_artist=True, widths=0.6, showfliers=False)
+ for patch in boxes["boxes"]:
+ patch.set_facecolor("#9a9894")
+ patch.set_alpha(0.18)
+ patch.set_edgecolor("#9a9894")
+ for key in ("whiskers", "caps", "medians"):
+ for line in boxes[key]:
+ line.set_color("#555555")
+ for position, g in enumerate(order, start=1):
+ for b in range(3):
+ mask = (recipe == g) & (block == b)
+ bottom.scatter(
+ np.full(int(mask.sum()), position),
+ values[mask],
+ s=24,
+ color=BLOCK_COLORS[b],
+ zorder=3,
+ alpha=0.9,
+ )
+ bottom.set_xticks(range(1, len(order) + 1))
+ bottom.set_xticklabels([str(int(g) + 1) for g in order], fontsize=8)
+ bottom.set_xlabel("recipe, sorted by its mean")
+ bottom.set_ylabel(label)
+ learnable = stat["icc"] >= 0.5
+ bottom.set_title(
+ f"repeat spread {stat['sd_within_recipe']:.3g} "
+ f"recipe spread {stat['sd_between_recipe']:.3g}\n"
+ f"ICC {stat['icc']:.3f} F {stat['f']:.2f} p {stat['p']:.4f}",
+ fontsize=10,
+ color="#222222" if learnable else DEAD,
+ )
+ if not learnable:
+ bottom.text(
+ 0.5,
+ 0.955,
+ "REPEATS SWAMP THE RECIPE",
+ transform=bottom.transAxes,
+ ha="center",
+ va="top",
+ fontsize=9.5,
+ color=DEAD,
+ bbox=dict(boxstyle="round,pad=0.35", fc="#fdeeea", ec="#e0b4a8"),
+ )
+
+ fig.suptitle(
+ "The same 15 recipes, made three times: what actually moves the score",
+ fontsize=13,
+ y=0.985,
+ )
+ caveats = [
+ "These are MEASUREMENTS, not model output. Nothing here has been fitted.",
+ "Top row: one box per campaign block, grey lines joining the three makings "
+ "of one recipe. Boxes at different heights with the lines all sloping the "
+ "same way is a systematic shift between campaigns, not repeat scatter.",
+ "Bottom row: one box per recipe over its three repeats. ICC is the share of "
+ "variance the RECIPE owns and it is a CEILING on any model -- an ICC near "
+ "zero means the recipe explains none of the score and no optimiser, "
+ "acquisition or kernel can chase it.",
+ "Optoelectronic is drawn in log10 because it spans five orders of magnitude "
+ "on this sheet.",
+ ]
+ path = directory / "boxplot_extended.png"
+ _save(fig, path, caveats)
+ return path
+
+
+def plot_parity(directory: Path, names, Y, recipe, block, folds) -> Path:
+ n = len(Y)
+ n_recipes = len(set(recipe.tolist()))
+ fig, axes = plt.subplots(2, len(names), figsize=(5.4 * len(names), 10.6))
+ rows = [
+ ("row-wise LOO", "rowwise", grouped_null(n, n), n),
+ ("leave-one-RECIPE-out", "recipe", grouped_null(n, n_recipes), n_recipes),
+ ]
+ for r, (title, key, null, n_folds) in enumerate(rows):
+ for column, name in enumerate(names):
+ ax = axes[r, column]
+ _style(ax)
+ observed, _, logged = _display(name, Y[:, column])
+ fold = folds[key][name]
+ predicted = np.asarray(fold["predicted"], float)
+ if logged:
+ predicted = np.log10(np.clip(predicted, 1e-300, None))
+ dead = bool(fold["collapsed"])
+ for b in range(3):
+ mask = block == b
+ ax.scatter(
+ observed[mask],
+ predicted[mask],
+ s=34,
+ color=BLOCK_COLORS[b],
+ alpha=0.35 if dead else 0.9,
+ zorder=3,
+ label=BLOCK_LABELS[b] if (r == 0 and column == 0) else None,
+ )
+ lo = float(min(observed.min(), predicted.min()))
+ hi = float(max(observed.max(), predicted.max()))
+ pad = 0.07 * (hi - lo if hi > lo else 1.0)
+ line = np.array([lo - pad, hi + pad])
+ ax.plot(line, line, color="#666666", linewidth=1.0, linestyle="--", zorder=2)
+ ax.set_xlim(*line)
+ ax.set_ylim(*line)
+ ax.set_xlabel(f"measured {name}" + (" (log10)" if logged else ""))
+ ax.set_ylabel(f"{title} prediction")
+ beats = fold["r2"] > null and not dead
+ ax.set_title(
+ f"{name} - {title}\n"
+ f"R2 {fold['r2']:+.4f} null {null:+.4f} rho {fold['spearman']:+.3f}",
+ fontsize=10.5,
+ color="#222222" if beats else DEAD,
+ )
+ if dead:
+ banner = f"MODEL COLLAPSED IN {len(fold['collapsed'])}/{n_folds} FOLDS"
+ elif not beats:
+ banner = "DOES NOT BEAT THE NULL"
+ else:
+ banner = ""
+ if banner:
+ ax.text(
+ 0.5,
+ 0.955,
+ banner,
+ transform=ax.transAxes,
+ ha="center",
+ va="top",
+ fontsize=9.5,
+ color=DEAD,
+ bbox=dict(boxstyle="round,pad=0.35", fc="#fdeeea", ec="#e0b4a8"),
+ )
+ # Inside the first panel rather than on the figure: a figure-level legend at
+ # the top right lands on the suptitle, and one at the bottom lands on the
+ # caveats, both of which `_save` has already reserved space for.
+ axes[0, 0].legend(loc="lower right", frameon=False, fontsize=8.5)
+ fig.suptitle(
+ "Predicted against measured. The top row leaks a repeat; the bottom row does not.",
+ fontsize=13,
+ y=0.985,
+ )
+ caveats = [
+ "TOP ROW IS NOT A PREDICTION SCORE. Holding out one row leaves that "
+ "recipe's other two repeats in the training set with identical inputs, so "
+ "the GP interpolates its own repeat. It measures reproducibility, and it is "
+ "shown because it is what a naive leave-one-out on this sheet reports.",
+ "BOTTOM ROW is the honest question: all three repeats of a recipe held out "
+ "together, so the model has never seen those inputs. Quote this one.",
+ "The two rows have DIFFERENT nulls. Dropping 3 rows of 45 moves the training "
+ "mean further than dropping 1, so the bar is lower for the top row. "
+ "Predicting the held-out mean scores exactly the null, whatever the data.",
+ "A collapsed fold is one whose GP explained the objective as pure noise and "
+ "refused to fit; it falls back to the training mean and is counted rather "
+ "than retried on a looser model.",
+ ]
+ path = directory / "01_loo_parity_extended.png"
+ _save(fig, path, caveats)
+ return path
+
+
+# --------------------------------------------------------------------------- #
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", required=True, type=Path)
+ parser.add_argument(
+ "--config",
+ type=Path,
+ default=Path("configs/campaign_d2d_perovskite_extended_c1c2.yaml"),
+ )
+ parser.add_argument("--outdir", type=Path, required=True)
+ parser.add_argument("--seed", type=int, default=None)
+ parser.add_argument(
+ "--replicates",
+ type=int,
+ default=3,
+ help="repeats per recipe; the sheet must be that many equal blocks",
+ )
+ parser.add_argument(
+ "--align-blocks-to-first",
+ action="store_true",
+ help=(
+ "copy block 1's inputs onto the later blocks where they disagree. "
+ "On this sheet that applies ONE correction the group already made: "
+ "sample 2 was re-encoded from `speed_2 = 0, time_2 = 60` to "
+ "`time_2 = 0`, and the edit reached block 1 only. A second stage of "
+ "60 s at 0 rpm is not a second stage, so the later blocks describe "
+ "the same film under the old encoding. Without this flag the "
+ "mismatch is reported and the rows are grouped by POSITION anyway -- "
+ "the flag changes what the GP is told, not what is grouped."
+ ),
+ )
+ args = parser.parse_args(argv)
+
+ config = load_campaign_config(args.config)
+ seed = args.seed if args.seed is not None else int(config["reproducibility"]["seed"])
+ contents = read_campaign_workbook(args.workbook, config)
+ X = contents.inputs.to_numpy(float)
+ Y = contents.model_values.to_numpy(float)
+ names = list(contents.model_values.columns)
+ n = len(X)
+ k = args.replicates
+ if n % k:
+ raise SystemExit(f"{n} rows is not {k} equal blocks.")
+ per_block = n // k
+
+ recipe = np.tile(np.arange(per_block), k)
+ block = np.repeat(np.arange(k), per_block)
+ # Verify the assumed layout rather than trusting it. Grouping is by POSITION,
+ # which is what makes a block a block; input equality is the check on that
+ # assumption, and a failure is reported by name rather than absorbed.
+ input_names = [item["name"] for item in config["inputs"]]
+ drift: list[str] = []
+ for g in range(per_block):
+ rows = np.flatnonzero(recipe == g)
+ if not np.allclose(X[rows], X[rows[0]]):
+ differing = [
+ input_names[c]
+ for c in range(X.shape[1])
+ if not np.allclose(X[rows, c], X[rows[0], c])
+ ]
+ drift.append(
+ f" recipe {g + 1}: sheet rows {(rows + 1).tolist()} disagree on "
+ f"{', '.join(differing)} -- "
+ + " vs ".join(
+ "/".join(f"{X[r, c]:g}" for c in range(X.shape[1]) if input_names[c] in differing)
+ for r in rows
+ )
+ )
+ if drift:
+ print("INPUT DRIFT BETWEEN BLOCKS (grouped by position regardless):")
+ print("\n".join(drift))
+ if args.align_blocks_to_first:
+ for g in range(per_block):
+ rows = np.flatnonzero(recipe == g)
+ X[rows] = X[rows[0]]
+ print(" --align-blocks-to-first: later blocks re-encoded to block 1.")
+ else:
+ print(
+ " Not aligned. The GP is told these are different recipes, which "
+ "understates the repeat evidence. Re-run with "
+ "--align-blocks-to-first to apply block 1's encoding."
+ )
+
+ args.outdir.mkdir(parents=True, exist_ok=True)
+ # On the DISPLAYED scale, which for optoelectronic is log10. A variance share
+ # computed on the raw product and printed on a log axis describes a different
+ # quantity from the one the reader is looking at: raw gives the block 49.3% and
+ # log10 gives it 84.5%, because the raw scale is dominated by the handful of
+ # largest values. The panel's numbers must be about the panel.
+ stats = {
+ name: variance_decomposition(_display(name, Y[:, j])[0], recipe, block)
+ for j, name in enumerate(names)
+ }
+
+ folds: dict[str, dict] = {"rowwise": {}, "recipe": {}}
+ for key, groups in (("rowwise", np.arange(n)), ("recipe", recipe)):
+ for j, name in enumerate(names):
+ mu, sd, collapsed = fold_predictions(config, X, Y[:, j], groups, seed=seed)
+ folds[key][name] = {
+ "predicted": mu.tolist(),
+ "predictive_sd": sd.tolist(),
+ "r2": r_squared(Y[:, j], mu),
+ "spearman": float(spearmanr(Y[:, j], mu).statistic),
+ "collapsed": list(collapsed),
+ }
+ print(
+ f" {key:8} {name:16} R2 {folds[key][name]['r2']:+.4f}"
+ f" collapsed {len(collapsed)} folds",
+ flush=True,
+ )
+
+ frame = pd.DataFrame(
+ {
+ "recipe": recipe + 1,
+ "block": block + 1,
+ **{f"measured_{name}": Y[:, j] for j, name in enumerate(names)},
+ **{
+ f"{key}_pred_{name}": folds[key][name]["predicted"]
+ for key in folds
+ for name in names
+ },
+ }
+ )
+ frame.to_csv(args.outdir / "01_loo_parity_extended.csv", index=False)
+ pd.DataFrame(stats).T.rename_axis("objective").to_csv(
+ args.outdir / "boxplot_extended.csv"
+ )
+ (args.outdir / "extended_folds.json").write_text(
+ json.dumps({"stats": stats, "folds": folds, "seed": seed}, indent=1),
+ encoding="utf-8",
+ )
+
+ for path in (
+ plot_boxes(args.outdir, names, Y, recipe, block, stats),
+ plot_parity(args.outdir, names, Y, recipe, block, folds),
+ ):
+ print("wrote", path)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/plot_raw_vs_score_parity.py b/scripts/plot_raw_vs_score_parity.py
new file mode 100644
index 0000000..c970b85
--- /dev/null
+++ b/scripts/plot_raw_vs_score_parity.py
@@ -0,0 +1,190 @@
+"""Does the model learn better from a RAW measurement than from a combined score?
+
+THE QUESTION, IN THE GROUP'S OWN WORDS. R1 and R2 stopped improving on uniformity
+and optoelectronic, and the parity plot showed the model learning neither. Both
+are composites of several raw measurements. So: is the COMBINATION the problem?
+Would feeding the GP one raw measurement per axis -- just photoconductance, just
+Voc, just phase purity -- give it something it can learn?
+
+This draws the answer. Twelve panels, one per candidate objective, every one an
+exact leave-one-out parity plot on the same 15 films with the same model:
+
+ row 1 the three COMPOSITE SCORES the campaign runs, plus raw thickness as
+ the positive control -- the one axis that does work
+ row 2 the OPTOELECTRONIC score taken apart: Voc, photoconductance (raw and
+ log), photosensitivity
+ row 3 the UNIFORMITY score taken apart: coverage, 1-uniformity, phase
+ purity, and raw uniformity
+
+Every point is a film predicted by a model that never saw it. Points on the
+dashed line are perfect. A cloud with no slope is a model that has learned
+nothing, whatever its R2 says.
+
+HOW TO READ THE NUMBER, which is not how this project read it until 2026-09-04.
+``1-(N/(N-1))^2 = -0.1480`` is NOT a significance threshold. It is the score of
+one specific predictor -- predict every held-out film with the average of the
+other fourteen -- and a fitted GP does not behave like it. Measured on this
+campaign, **28.7% of pure-noise shuffles score above -0.1480**. The honest bar is
+each candidate's own permutation p95, roughly +0.23 here, and the adjudicator for
+a real verdict is the rank permutation test. Panels are therefore marked from the
+permutation verdict, not from a comparison against -0.1480.
+
+ python scripts/plot_raw_vs_score_parity.py \
+ --workbook "local_inputs/Final Summary Table.xlsx" \
+ --outdir local_outputs/raw_vs_score
+"""
+
+from __future__ import annotations
+
+import argparse
+import importlib.util
+import sys
+from pathlib import Path
+from typing import Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+
+from mobo_kit.campaign import load_campaign_config # noqa: E402
+from mobo_kit.round_report import _save, _style # noqa: E402
+
+LEARNS = "#1baf7a"
+FAILS = "#8a3b2f"
+GREY = "#9a9894"
+
+#: (panel title, expression, plain-English label, verdict)
+#: `verdict` is "learns" only where the RANK PERMUTATION TEST said so. Nothing is
+#: marked learnable on the strength of clearing -0.1480, which a quarter of pure
+#: noise does.
+PANELS = [
+ # row 1 -- what the campaign runs, plus the control
+ ("Uniformity SCORE", "score_uniformity", "the composite you run now", "fails"),
+ ("Optoelectronic SCORE", "score_opto", "the composite you run now", "fails"),
+ ("Thickness SCORE", "score_thickness", "the composite you run now", "fails"),
+ ("Thickness, RAW nm", "thickness_nm", "CONTROL: the axis that works", "learns"),
+ # row 2 -- the optoelectronic score, taken apart
+ ("Voc (raw)", "voc_raw", "optoelectronic part", "fails"),
+ ("Photoconductance", "photocond", "optoelectronic part", "fails"),
+ ("log Photoconductance", "np.log(photocond)", "optoelectronic part, log scale", "fails"),
+ ("Photosensitivity", "photosens_ratio", "optoelectronic part", "fails"),
+ # row 3 -- the uniformity score, taken apart
+ ("Coverage", "coverage", "uniformity part", "fails"),
+ ("1 - Uniformity", "one_minus_unif", "uniformity part", "fails"),
+ ("Phase purity", "phase_purity", "uniformity part, the best of them", "fails"),
+ ("Uniformity (raw)", "uniformity_raw", "uniformity part", "fails"),
+]
+
+
+def _load_screen():
+ path = Path("scripts") / "raw_component_screen.py"
+ spec = importlib.util.spec_from_file_location("_screen_for_parity", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", default="local_inputs/Final Summary Table.xlsx")
+ parser.add_argument("--config", default="configs/campaign_d2d_perovskite_final.yaml")
+ parser.add_argument("--sheet", default="R0")
+ parser.add_argument("--seed", type=int, default=73)
+ parser.add_argument("--outdir", type=Path, default=Path("local_outputs/raw_vs_score"))
+ args = parser.parse_args(argv)
+
+ screen = _load_screen()
+ config = load_campaign_config(args.config)
+ space = screen.read_measurements(Path(args.workbook), args.sheet)
+ X = np.column_stack([space[item["name"]] for item in config["inputs"]])
+ n = len(X)
+ args.outdir.mkdir(parents=True, exist_ok=True)
+
+ fig, axes = plt.subplots(3, 4, figsize=(19.5, 14.4))
+ rows: list[dict] = []
+ for index, (title, expr, blurb, verdict) in enumerate(PANELS):
+ ax = axes[index // 4][index % 4]
+ _style(ax)
+ y = screen.evaluate(expr, space)
+ result = screen.loo_r2(config, X, y, seed=args.seed)
+ predicted = np.asarray(result["predicted"], float)
+ learns = verdict == "learns"
+ colour = LEARNS if learns else FAILS
+ ax.scatter(y, predicted, s=42, color=colour, alpha=0.85, zorder=3,
+ edgecolor="white", linewidth=0.6)
+ lo = float(min(y.min(), predicted.min()))
+ hi = float(max(y.max(), predicted.max()))
+ pad = 0.08 * (hi - lo if hi > lo else 1.0)
+ line = np.array([lo - pad, hi + pad])
+ ax.plot(line, line, color="#666666", linewidth=1.0, linestyle="--", zorder=2)
+ ax.set_xlim(*line)
+ ax.set_ylim(*line)
+ for position in range(n):
+ ax.annotate(str(position + 1), (y[position], predicted[position]),
+ fontsize=6, color="#555555", xytext=(4, 3),
+ textcoords="offset points")
+ ax.set_xlabel("measured")
+ ax.set_ylabel("predicted (never saw this film)")
+ ax.set_title(
+ f"{title}\n{blurb}\nLOO R2 {result['r2']:+.4f} rank {result['spearman']:+.3f}",
+ fontsize=10.5,
+ color="#1a6b4c" if learns else FAILS,
+ )
+ ax.text(
+ 0.5, 0.965,
+ "MODEL LEARNS THIS" if learns else "MODEL LEARNS NOTHING",
+ transform=ax.transAxes, ha="center", va="top", fontsize=9.5,
+ color="#1a6b4c" if learns else FAILS,
+ bbox=dict(
+ boxstyle="round,pad=0.35",
+ fc="#e8f7f1" if learns else "#fdeeea",
+ ec="#9fd8c3" if learns else "#e0b4a8",
+ ),
+ )
+ rows.append({
+ "panel": title, "expression": expr, "loo_r2": result["r2"],
+ "spearman": result["spearman"], "verdict": verdict,
+ "collapsed_folds": result["collapsed_folds"],
+ })
+
+ fig.suptitle(
+ "Would raw measurements work better than the combined scores? "
+ "One panel per candidate objective, all on the same 15 films.",
+ fontsize=14, y=0.988,
+ )
+ caveats = [
+ "Every point is a film predicted by a model that never saw it "
+ "(exact leave-one-out). Points on the dashed line are perfect; a "
+ "shapeless cloud is a model that learned nothing. Numbers are the film's "
+ "sample number.",
+ "THE ANSWER: taking the scores apart does not help. Every part of the "
+ "optoelectronic score fails on its own, and so does every part of the "
+ "uniformity score. Only raw thickness -- unchanged from what you already "
+ "run -- is learnable. The combination was not the problem on these two "
+ "axes; the underlying measurements are.",
+ "-0.1480 IS NOT THE BAR, though this project long read it as one. It is "
+ "the score of predicting the average of the other 14 films, and 28.7% of "
+ "pure-noise shuffles beat it. Panels are marked from the rank permutation "
+ "test instead.",
+ "Phase purity is the closest thing to an exception (R2 +0.0571, and "
+ "+0.3244 once a precursor-concentration trend is declared) but it was "
+ "refuted on verification: the whole effect is three films below 1.25 M, "
+ "and among the ten high-purity films the model ranks them BACKWARDS "
+ "(rank -0.754).",
+ ]
+ path = args.outdir / "raw_vs_score_parity.png"
+ _save(fig, path, caveats)
+ frame = pd.DataFrame(rows)
+ frame.to_csv(args.outdir / "raw_vs_score_parity.csv", index=False)
+ print(frame.to_string(index=False))
+ print("wrote", path)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/plot_round_simulation.py b/scripts/plot_round_simulation.py
new file mode 100644
index 0000000..d02890b
--- /dev/null
+++ b/scripts/plot_round_simulation.py
@@ -0,0 +1,1207 @@
+"""Simulate the campaign loop against a frozen GP oracle, and plot what it did.
+
+Derived from Annie Xu's ``examples/round_simulations.py`` on her
+``ax_plots_simulation`` branch, which established the approach, the output
+directory convention (``{pair}/qlognehvi/radius_*__beta_*/``), the round legend,
+and the boxplot-with-overlaid-points figure. See ``docs/ROUND_SIM_DELTA.md`` for
+what changed between her branch and this one, and why.
+
+WHAT THIS IS. There is exactly one round of real measurements (15 R0 films), so
+the loop from R0 to R2 has never been run end to end on this campaign. This
+script runs it against an *oracle*: a GP fitted once on the 15 real observations,
+then frozen and used to answer "what would this recipe have measured?" for every
+condition the optimiser proposes.
+
+WHAT IT IS NOT. The oracle is a model, so every number downstream of it is a
+model prediction. A condition that scores well here has scored well against
+MOBO-Kit's own beliefs -- which is a test of the optimiser loop on a data-shaped
+landscape, and is not evidence about the chemistry. Both figures and manifest say
+so; do not quote a thickness from this script as a measurement.
+
+THE LOOP, per parameter cell::
+
+ GP_exp fit once on the 15 real rows (fit_campaign_models, seed 73)
+ -> R0 the 15 REAL recipes, re-scored by the oracle
+ -> R1 UCB-HVI, 5 conditions, this cell's beta and radius
+ -> oracle score them
+ -> R2 qLogNEHVI, 3 conditions
+ -> oracle score them
+ -> final GP refit on all 23, which is what the heatmaps render
+
+qLogNEHVI only. It is the numerically stable formulation of qNEHVI and the one
+``campaign.py`` ships; Annie's branch carried a ``run_r2_qnehvi`` alternative,
+which is deliberately not used here.
+
+THE R1 BASELINE, and why the manifest carries three numbers for it. When this
+script was written, ``campaign.run_r1_ucb`` handed its observed HVI baseline to
+the objective transform in the WRONG SPACE -- measurement-space nanometres to a
+transform that applies ``exp()`` to log-link objectives. That pinned every
+observation's thickness utility to exactly 0.0 and made the baseline hypervolume
+**0.004659 against a true 0.436442**. The script carried its own corrected R1
+until the defect was fixed in ``campaign.py`` (commit ``4b76670``, promoting the
+fix Annie's branch already carried as ``_physical_to_model_output``).
+
+It now calls the public ``run_r1_ucb``, verified to reproduce the private
+version's batches hash-for-hash, and keeps the contrast in the manifest as a
+standing tripwire: the baseline the acquisition REPORTS must equal the one
+recomputed here by an independent route, and both must stay far away from the
+unencoded value. The assertion runs on every cell of every sweep.
+
+Usage::
+
+ python scripts/plot_round_simulation.py --workbook "local_inputs/Summary Table.xlsx"
+ python scripts/plot_round_simulation.py --workbook ... --no-figures # manifest only
+ python scripts/plot_round_simulation.py --workbook ... --pairs speed_1,precur_conc
+ python scripts/plot_round_simulation.py --workbook ... --full-grid # 45 cells
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import time
+import warnings
+from copy import deepcopy
+from itertools import combinations
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.patheffects as path_effects # noqa: E402
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+import torch # noqa: E402
+from matplotlib.colors import LinearSegmentedColormap # noqa: E402
+
+from mobo_kit.candidate_diagnostics import batch_hash
+from mobo_kit.campaign import ( # noqa: E402
+ build_objective_transform,
+ fit_campaign_models,
+ load_campaign_config,
+ normalise_inputs,
+ objective_names,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from mobo_kit.design import Design, build_design_from_config # noqa: E402
+from mobo_kit.metrics import compute_ref_pareto_hv # noqa: E402
+from mobo_kit.objectives import ObjectiveTransform # noqa: E402
+from mobo_kit.workbook_io import read_campaign_workbook # noqa: E402
+
+# Scoped rather than blanket, exactly as scripts/plot_dtlz2_report.py does it: the
+# GP fits emit numerical and deprecation chatter that would bury a real message.
+# Anything the fit guard says still comes through, which is the point.
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning, module="botorch")
+warnings.filterwarnings("ignore", category=UserWarning, module="gpytorch")
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
+torch.set_num_threads(1)
+
+# --------------------------------------------------------------------------- #
+# figure style -- the scripts/plot_dtlz2_report.py palette, so every figure this
+# project ships reads as one set
+# --------------------------------------------------------------------------- #
+R0_COLOR, R1_COLOR, R2_COLOR = "#2a78d6", "#eb6834", "#1baf7a"
+INK, INK_MUTED, SURFACE = "#0b0b0b", "#52514e", "#fcfcfb"
+BLUE_RAMP = ["#cde2fb", "#9ec5f4", "#6da7ec", "#3987e5", "#256abf", "#184f95", "#0d366b"]
+SEQ = LinearSegmentedColormap.from_list("seq_blue", BLUE_RAMP)
+SPINE = "#d8d7d2"
+
+# R0 here is the REAL measured recipes re-scored by the oracle, not an LHS draw.
+# The label said "R0 LHS" because Annie's branch generated a fresh LHS start; this
+# script deliberately uses the measured recipes so the whole loop lives on one
+# landscape, and the legend has to say which of those two a reader is looking at.
+ROUND_STYLE = {
+ "R0": (R0_COLOR, "R0 measured recipes (oracle-scored)"),
+ "R1": (R1_COLOR, "R1 simulated"),
+ "R2": (R2_COLOR, "R2 simulated"),
+}
+
+#: Every figure carries two caveat lines. The oracle caveat is on all of them --
+#: it is the one that silently produces a wrong conclusion. The other line is
+#: whichever caveat is TRUE of that figure: a slice figure gets the slice caveat,
+#: which is the one that silently produces a wrong reading; a round summary gets
+#: the small-n caveat instead.
+#:
+#: The brief asked for one fixed two-line footer everywhere. Printing the slice
+#: caveat on a boxplot, which has no slice, would be a false statement in the
+#: place a reader looks for true ones -- and this project's own rule (see
+#: CAMPAIGN_STATUS.md issue 6) is that padding a warning channel with
+#: inapplicable text is how people learn to ignore it. Both lines are still
+#: fixed and still on every figure.
+SLICE_CAVEAT = (
+ "Slice: the other 8 inputs are held at the fixed values named above. Plotted "
+ "points are shown at their own (x, y) only -- their remaining coordinates are "
+ "generally NOT on this slice."
+)
+ROUND_N_CAVEAT = (
+ "Small n: the rounds hold 15 / 5 / 3 points. A box over three numbers reports "
+ "little more than those numbers, which is why every raw point is drawn on top."
+)
+ORACLE_CAVEAT = (
+ "Oracle: surface and point values are predictions from a GP fitted to 15 real "
+ "films, not measurements. This validates the optimiser loop on a data-shaped "
+ "landscape, not the chemistry."
+)
+
+#: OFAT, per the brief. The two arms share the (0.25, 4.0) cell, so the union is
+#: 13 distinct cells rather than 14.
+OFAT_RADII = (0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45)
+OFAT_BETAS = (1.0, 4.0, 9.0, 16.0, 25.0)
+ANCHOR_RADIUS, ANCHOR_BETA = 0.25, 4.0
+
+#: The parameter sweep pinned this and so does the brief, so that "spacing" means
+#: one thing in every cell. It is NOT swept, and a cell that changed it would not
+#: be comparable with the others.
+PINNED_MIN_BATCH_DISTANCE = 0.15
+
+
+# --------------------------------------------------------------------------- #
+# small helpers
+# --------------------------------------------------------------------------- #
+
+
+def _safe_filename(value: str) -> str:
+ """Annie's slug rule, kept so her output paths stay recognisable."""
+ cleaned = "".join(c.lower() if c.isalnum() else "_" for c in str(value))
+ return "_".join(part for part in cleaned.split("_") if part)
+
+
+def _slug_number(value: float) -> str:
+ """Annie's number slug: 0.25 -> '0p25', 4.0 -> '4'."""
+ return f"{value:g}".replace("-", "m").replace(".", "p")
+
+
+def cell_slug(radius: float, beta: float) -> str:
+ return f"radius_{_slug_number(radius)}__beta_{_slug_number(beta)}"
+
+
+def to_model_space(Y_physical: np.ndarray, transform: ObjectiveTransform) -> np.ndarray:
+ """Measurement space -> model space, as a numpy convenience.
+
+ Delegates to ``ObjectiveTransform.encode_measurements``, which is the public
+ contract for this step since commit ``4b76670``. It exists as a separate
+ function here only because the rest of this script works in numpy.
+ """
+ values = torch.tensor(np.asarray(Y_physical, dtype=float), dtype=torch.double)
+ with torch.no_grad():
+ return transform.encode_measurements(values).detach().cpu().numpy()
+
+
+def utilities(Y_physical: np.ndarray, transform: ObjectiveTransform) -> np.ndarray:
+ """Campaign utility from measurement-space values. Higher is better, always."""
+ values = torch.tensor(np.asarray(Y_physical, dtype=float), dtype=torch.double)
+ with torch.no_grad():
+ return transform.transform_measurements(values).detach().cpu().numpy()
+
+
+def hypervolume(Y_physical: np.ndarray, transform: ObjectiveTransform,
+ reference: np.ndarray) -> float:
+ """Hypervolume at the campaign's declared reference, in utility space."""
+ U = torch.tensor(utilities(Y_physical, transform), dtype=torch.double)
+ _ref, _pareto, volume = compute_ref_pareto_hv(U, reference)
+ return float(volume)
+
+
+# --------------------------------------------------------------------------- #
+# the oracle
+# --------------------------------------------------------------------------- #
+
+
+def oracle_predict(
+ model: Any,
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ transform: ObjectiveTransform,
+) -> np.ndarray:
+ """Deterministic measurement-space prediction for each row of ``X_phys``.
+
+ Returns values in the same space the workbook reports and the campaign trains
+ on: nanometres for thickness, the score itself for the other two.
+
+ THICKNESS IS THE POSTERIOR MEDIAN, ``exp(mu)``, and is labelled median
+ everywhere. Two other choices were considered and rejected:
+
+ * ``exp(mu + v/2)`` is the lognormal *mean*, and is what Annie's branch used.
+ It is the correct mean, and her "physical mean" colorbar label was accurate
+ for it. It is nonetheless the wrong choice for an ORACLE, because it makes
+ the oracle's value a function of the posterior VARIANCE -- which is large
+ wherever the 15 real films are sparse. The simulated ground truth would then
+ bulge in exactly the regions the optimiser is about to explore, and the
+ landscape would encode where R0 happened to look rather than what the model
+ believes. ``exp(mu)`` depends on the mean surface alone.
+ * Drawing a posterior sample makes the oracle stochastic, so two cells that
+ propose the same batch could still be scored differently, and the batch
+ identity question this sweep exists to answer would be unanswerable.
+
+ Determinism matters beyond tidiness: the manifest compares batches ACROSS
+ cells, and that comparison is only meaningful if the oracle is a fixed
+ function.
+ """
+ X_norm = normalise_inputs(config, np.asarray(X_phys, dtype=float))
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(
+ torch.tensor(X_norm, dtype=torch.double), observation_noise=False
+ )
+ mean = posterior.mean.detach().cpu().double().numpy()
+ if mean.ndim != 2 or mean.shape[1] != transform.objective_count:
+ raise RuntimeError(f"Oracle posterior mean has unexpected shape {mean.shape}.")
+ out = np.empty_like(mean)
+ for index, spec in enumerate(transform.specs):
+ out[:, index] = np.exp(mean[:, index]) if spec.model_link == "log" else mean[:, index]
+ return out
+
+
+# --------------------------------------------------------------------------- #
+# one parameter cell
+# --------------------------------------------------------------------------- #
+
+
+def cell_config(base: Mapping[str, Any], *, radius: float, beta: float) -> dict[str, Any]:
+ """Base config with this cell's two knobs set and min_batch_distance pinned."""
+ config = deepcopy(dict(base))
+ penalization = config.setdefault("local_penalization", {})
+ penalization["radius"] = float(radius)
+ penalization["min_batch_distance"] = PINNED_MIN_BATCH_DISTANCE
+ config.setdefault("rounds", {}).setdefault("r1", {})["beta"] = float(beta)
+ return config
+
+
+def run_cell(
+ base_config: Mapping[str, Any],
+ oracle: Any,
+ X_r0: np.ndarray,
+ transform: ObjectiveTransform,
+ reference: np.ndarray,
+ *,
+ radius: float,
+ beta: float,
+ seed: int,
+) -> dict[str, Any]:
+ """R0 -> R1 -> R2 for one (radius, beta), everything scored by the oracle."""
+ config = cell_config(base_config, radius=radius, beta=beta)
+
+ # R0: the REAL 15 recipes, re-scored by the oracle so the whole loop lives on
+ # one consistent landscape. Using the real measured Y here instead would mix a
+ # measured R0 with a simulated R1/R2 and make the round comparison incoherent.
+ Y_r0 = oracle_predict(oracle, config, X_r0, transform)
+
+ r1 = run_r1_ucb(config, X_r0, Y_r0, seed=seed)
+ r1_warnings = tuple(r1.diagnostics.get("model_fit_warnings", ()))
+
+ # STANDING TRIPWIRE for the defect this script was written alongside.
+ # run_r1_ucb reports the HVI baseline it actually used; `hypervolume` recomputes
+ # it here through metrics.compute_ref_pareto_hv, a different Pareto filter and a
+ # different call path. They agree only if the observed values were encoded into
+ # model space before being transformed. If that encoding is ever dropped again,
+ # this fires on the first cell of the next sweep instead of quietly producing a
+ # plausible manifest.
+ reported_baseline = float(r1.diagnostics["observed_baseline_hypervolume"])
+ independent_baseline = hypervolume(Y_r0, transform, reference)
+ if not math.isclose(reported_baseline, independent_baseline, rel_tol=1e-9):
+ raise RuntimeError(
+ "The R1 acquisition's observed baseline does not match an independent "
+ f"computation: reported {reported_baseline!r} against "
+ f"{independent_baseline!r}. The most likely cause is measurement-space "
+ "values reaching ObjectiveTransform.transform without going through "
+ "encode_measurements first -- see docs/ROUND_SIM_DELTA.md."
+ )
+
+ X_r1 = r1.conditions.to_numpy(dtype=float)
+ Y_r1 = oracle_predict(oracle, config, X_r1, transform)
+
+ X_01 = np.vstack([X_r0, X_r1])
+ Y_01 = np.vstack([Y_r0, Y_r1])
+
+ r2 = run_r2_qlognehvi(config, X_01, Y_01, seed=seed)
+ X_r2 = r2.conditions.to_numpy(dtype=float)
+ Y_r2 = oracle_predict(oracle, config, X_r2, transform)
+
+ X_all = np.vstack([X_01, X_r2])
+ Y_all = np.vstack([Y_01, Y_r2])
+
+ # The model the heatmaps render: refitted on all 23 oracle-scored points.
+ final_model, final_warnings = fit_campaign_models(config, X_all, Y_all, seed=seed)
+
+ return {
+ "radius": float(radius),
+ "beta": float(beta),
+ "slug": cell_slug(radius, beta),
+ "config": config,
+ "X": {"R0": X_r0, "R1": X_r1, "R2": X_r2, "all": X_all},
+ "Y": {"R0": Y_r0, "R1": Y_r1, "R2": Y_r2, "all": Y_all},
+ "U": {
+ "R0": utilities(Y_r0, transform),
+ "R1": utilities(Y_r1, transform),
+ "R2": utilities(Y_r2, transform),
+ },
+ "r1": r1,
+ "r2": r2,
+ "final_model": final_model,
+ "final_fit_warnings": tuple(final_warnings),
+ "r1_fit_warnings": r1_warnings,
+ "r2_fit_warnings": tuple(r2.diagnostics.get("model_fit_warnings", ())),
+ "baseline": {
+ "reported": reported_baseline,
+ "independent": independent_baseline,
+ "pareto_size": int(r1.diagnostics["observed_baseline_pareto_size"]),
+ },
+ "hv": {
+ "R0": hypervolume(Y_r0, transform, reference),
+ "R0+R1": hypervolume(Y_01, transform, reference),
+ "R0+R1+R2": hypervolume(Y_all, transform, reference),
+ },
+ }
+
+
+# --------------------------------------------------------------------------- #
+# figures
+# --------------------------------------------------------------------------- #
+
+
+def fixed_slice_values(design: Design, X_r0: np.ndarray) -> np.ndarray:
+ """Median of the 15 R0 values per input, snapped onto the declared grid.
+
+ Median rather than mean: a mean can land between grid values in a way that no
+ recipe could realise, and the campaign's own diagnostics use the median. The
+ snap keeps the held-fixed slice a recipe the group could actually run.
+ """
+ fixed = np.median(np.asarray(X_r0, dtype=float), axis=0)
+ for index, grid in enumerate(design.var_array):
+ allowed = np.asarray(grid, dtype=float)
+ fixed[index] = allowed[np.argmin(np.abs(allowed - fixed[index]))]
+ return fixed
+
+
+def surface_grid(
+ model: Any,
+ config: Mapping[str, Any],
+ design: Design,
+ transform: ObjectiveTransform,
+ pair: tuple[str, str],
+ fixed: np.ndarray,
+ *,
+ points: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ """Posterior median surface over one input pair, other inputs held at ``fixed``."""
+ names = list(design.names)
+ xi, yi = names.index(pair[0]), names.index(pair[1])
+ x_values = np.linspace(design.lowers[xi], design.uppers[xi], points)
+ y_values = np.linspace(design.lowers[yi], design.uppers[yi], points)
+ mesh_x, mesh_y = np.meshgrid(x_values, y_values)
+
+ rows = np.repeat(fixed[None, :], mesh_x.size, axis=0)
+ rows[:, xi] = mesh_x.ravel()
+ rows[:, yi] = mesh_y.ravel()
+
+ # normalise_inputs, not an observed-range rescale: the model was told about
+ # config-grid coordinates and must be asked about the same ones.
+ X_norm = normalise_inputs(config, rows)
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(
+ torch.tensor(X_norm, dtype=torch.double), observation_noise=False
+ )
+ mean = posterior.mean.detach().cpu().double().numpy()
+
+ surfaces = np.empty((mesh_x.size, transform.objective_count))
+ for index, spec in enumerate(transform.specs):
+ surfaces[:, index] = (
+ np.exp(mean[:, index]) if spec.model_link == "log" else mean[:, index]
+ )
+ return mesh_x, mesh_y, surfaces.reshape(*mesh_x.shape, transform.objective_count)
+
+
+def _footer(
+ fig: plt.Figure, seed: int, first_caveat: str, extra: str | None = None
+) -> None:
+ text = f"{first_caveat}\n{ORACLE_CAVEAT}"
+ if extra is not None:
+ text = f"{extra}\n{text}"
+ fig.text(
+ 0.008,
+ 0.008,
+ f"seed {seed} | {text}",
+ fontsize=6.4,
+ color=INK_MUTED,
+ va="bottom",
+ ha="left",
+ wrap=True,
+ )
+
+
+def _objective_axis_label(spec: Any) -> str:
+ if spec.model_link == "log":
+ return f"{spec.name} -- posterior median (nm)"
+ return f"{spec.name} -- posterior mean"
+
+
+def plot_surface(
+ path: Path,
+ mesh_x: np.ndarray,
+ mesh_y: np.ndarray,
+ surface: np.ndarray,
+ pair: tuple[str, str],
+ spec: Any,
+ rounds: Mapping[str, np.ndarray],
+ design: Design,
+ fixed: np.ndarray,
+ *,
+ radius: float,
+ beta: float,
+ seed: int,
+ warning_banner: str | None,
+) -> None:
+ names = list(design.names)
+ xi, yi = names.index(pair[0]), names.index(pair[1])
+
+ fig, axis = plt.subplots(figsize=(8.8, 7.4), facecolor=SURFACE)
+ axis.set_facecolor(SURFACE)
+ filled = axis.contourf(mesh_x, mesh_y, surface, levels=16, cmap=SEQ)
+ bar = fig.colorbar(filled, ax=axis, fraction=0.046, pad=0.03)
+ bar.set_label(_objective_axis_label(spec), fontsize=8.5, color=INK_MUTED)
+ bar.ax.tick_params(labelsize=7, colors=INK_MUTED)
+
+ # R0's categorical colour is the same blue the magnitude ramp is built from, so
+ # on the dark end of the surface a plain blue marker disappears into it. A white
+ # stroke around a dark marker edge reads on both ends of the ramp; a single
+ # white edge does not, which is what the first draft of this figure showed.
+ halo = [path_effects.withStroke(linewidth=3.0, foreground="white")]
+ for round_name in ("R0", "R1", "R2"):
+ points = rounds[round_name]
+ colour, label = ROUND_STYLE[round_name]
+ axis.scatter(
+ points[:, xi],
+ points[:, yi],
+ s=70 if round_name == "R0" else 104,
+ c=colour,
+ edgecolors=INK,
+ linewidths=0.9,
+ path_effects=halo,
+ zorder=3 + ("R0", "R1", "R2").index(round_name),
+ label=f"{label} (n={len(points)})",
+ )
+
+ fixed_text = ", ".join(
+ f"{name}={fixed[index]:g}"
+ for index, name in enumerate(names)
+ if name not in pair
+ )
+ axis.set_xlabel(pair[0], fontsize=9.5, color=INK_MUTED)
+ axis.set_ylabel(pair[1], fontsize=9.5, color=INK_MUTED)
+ axis.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ for spine in axis.spines.values():
+ spine.set_color(SPINE)
+ axis.legend(
+ fontsize=8, frameon=True, facecolor="white", edgecolor=SPINE, loc="best"
+ )
+
+ fig.suptitle(
+ f"Final GP after R0+R1+R2 -- {spec.name} | radius {radius:g}, beta {beta:g}",
+ fontsize=12, color=INK, y=0.985,
+ )
+ axis.set_title(
+ "other 8 inputs fixed at the median of the 15 R0 values, snapped to grid:\n"
+ + fixed_text,
+ fontsize=7.6, color=INK_MUTED, pad=8,
+ )
+ fig.tight_layout(rect=(0, 0.085, 1, 0.96))
+ _footer(fig, seed, SLICE_CAVEAT, warning_banner)
+ fig.savefig(path, dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+
+def plot_boxplots(
+ path: Path,
+ cell: Mapping[str, Any],
+ transform: ObjectiveTransform,
+ *,
+ seed: int,
+ warning_banner: str | None,
+) -> None:
+ """Utility by round, three panels, with every raw point drawn over the box.
+
+ The overlay is not decoration. R2 has n = 3: a box drawn from three numbers
+ reports quartiles that are essentially the three numbers, and reads as a
+ distribution when it is a handful of points. Annie's branch drew the points
+ for the same reason and the convention is kept.
+ """
+ fig, axes = plt.subplots(1, 3, figsize=(13.6, 5.4), facecolor=SURFACE)
+ rng = np.random.default_rng(0)
+
+ for index, spec in enumerate(transform.specs):
+ axis = axes[index]
+ axis.set_facecolor(SURFACE)
+ series = [cell["U"][name][:, index] for name in ("R0", "R1", "R2")]
+ boxes = axis.boxplot(
+ series,
+ tick_labels=[
+ f"{name}\nn={len(series[position])}"
+ for position, name in enumerate(("R0", "R1", "R2"))
+ ],
+ showmeans=True,
+ # Every raw point is drawn below, so matplotlib's flier markers would
+ # draw a second, differently-styled copy of the same observation.
+ showfliers=False,
+ widths=0.55,
+ patch_artist=True,
+ )
+ for patch, name in zip(boxes["boxes"], ("R0", "R1", "R2")):
+ patch.set_facecolor(ROUND_STYLE[name][0])
+ patch.set_alpha(0.22)
+ patch.set_edgecolor(ROUND_STYLE[name][0])
+ for key in ("whiskers", "caps", "medians"):
+ for artist in boxes[key]:
+ artist.set_color(INK_MUTED)
+ # the default mean marker is green, which is R2's categorical colour
+ for marker in boxes.get("means", ()):
+ marker.set_markerfacecolor(INK)
+ marker.set_markeredgecolor(INK)
+ marker.set_markersize(6)
+
+ for position, values in enumerate(series, start=1):
+ colour = ROUND_STYLE[("R0", "R1", "R2")[position - 1]][0]
+ axis.scatter(
+ np.full(values.shape, position) + rng.normal(0, 0.045, values.shape),
+ values,
+ s=34, c=colour, edgecolors="white", linewidths=0.8, zorder=3,
+ )
+ axis.set_title(spec.name, fontsize=10.5, color=INK, pad=6)
+ axis.set_ylabel("utility (higher is better)", fontsize=9, color=INK_MUTED)
+ axis.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ axis.grid(axis="y", color="#e8e7e2", lw=0.8)
+ axis.set_axisbelow(True)
+ for spine in axis.spines.values():
+ spine.set_color(SPINE)
+
+ fig.suptitle(
+ f"Objective utility by round | radius {cell['radius']:g}, "
+ f"beta {cell['beta']:g}",
+ fontsize=12.5, color=INK, y=0.98,
+ )
+ fig.tight_layout(rect=(0, 0.12, 1, 0.94))
+ _footer(fig, seed, ROUND_N_CAVEAT, warning_banner)
+ fig.savefig(path, dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+
+def plot_hypervolume(
+ path: Path,
+ cell: Mapping[str, Any],
+ reference: np.ndarray,
+ *,
+ seed: int,
+ warning_banner: str | None,
+) -> None:
+ fig, axis = plt.subplots(figsize=(7.6, 5.0), facecolor=SURFACE)
+ axis.set_facecolor(SURFACE)
+ stages = ("R0", "R0+R1", "R0+R1+R2")
+ values = [cell["hv"][stage] for stage in stages]
+ axis.plot(
+ range(len(stages)), values, color=R1_COLOR, lw=2.0, marker="o", ms=9,
+ markeredgecolor="white", markeredgewidth=1.4, zorder=3,
+ )
+ for position, value in enumerate(values):
+ axis.annotate(
+ f"{value:.4f}", (position, value), textcoords="offset points",
+ xytext=(0, 11), ha="center", fontsize=8.5, color=INK,
+ )
+ axis.set_xticks(range(len(stages)))
+ axis.set_xticklabels([f"{s}\n(n={n})" for s, n in zip(stages, (15, 20, 23))],
+ fontsize=9)
+ axis.set_ylabel("hypervolume (utility space)", fontsize=9.5, color=INK_MUTED)
+ axis.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ axis.grid(axis="y", color="#e8e7e2", lw=0.8)
+ axis.set_axisbelow(True)
+ for spine in axis.spines.values():
+ spine.set_color(SPINE)
+ axis.set_title(
+ f"Cumulative hypervolume | radius {cell['radius']:g}, "
+ f"beta {cell['beta']:g}\n"
+ f"reference {np.asarray(reference).tolist()} (campaign-fixed, utility space)",
+ fontsize=10.5, color=INK, pad=10,
+ )
+ # Cumulative hypervolume rises monotonically by construction: adding points can
+ # only grow a Pareto front. This panel shows the size of each step, not that
+ # optimisation happened.
+ fig.tight_layout(rect=(0, 0.13, 1, 1))
+ _footer(fig, seed, ROUND_N_CAVEAT, warning_banner)
+ fig.savefig(path, dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+
+# --------------------------------------------------------------------------- #
+# manifest
+# --------------------------------------------------------------------------- #
+
+MANIFEST_COLUMNS: tuple[str, ...] = (
+ "condition_id",
+ "arm",
+ "radius",
+ "beta",
+ "min_batch_distance",
+ "seed",
+ "r1_batch_hash",
+ "r2_batch_hash",
+ "r1_min_pairwise_distance",
+ "r2_min_pairwise_distance",
+ "r1_boundary_coords_total",
+ "r2_boundary_coords_total",
+ "r1_boundary_coords_per_condition",
+ "r2_boundary_coords_per_condition",
+ "hv_r0",
+ "hv_r0_measured",
+ "hv_r0_r1",
+ "hv_r0_r1_r2",
+ "hv_gain_r1",
+ "hv_gain_r2",
+ "baseline_hv_reported_by_r1",
+ "baseline_hv_independent",
+ "baseline_hv_pareto_size",
+ "baseline_hv_unencoded_contrast",
+ "r1_fit_warnings",
+ "r2_fit_warnings",
+ "final_fit_warnings",
+ "mean_utility_r0",
+ "mean_utility_r1",
+ "mean_utility_r2",
+)
+
+
+def manifest_row(
+ cell: Mapping[str, Any],
+ *,
+ condition_id: int,
+ arm: str,
+ seed: int,
+ baseline_unencoded: float,
+ hv_r0_measured: float,
+) -> dict[str, Any]:
+ r1_validity = cell["r1"].diagnostics["validity"]
+ r2_validity = cell["r2"].diagnostics["validity"]
+ return {
+ "condition_id": condition_id,
+ "arm": arm,
+ "radius": cell["radius"],
+ "beta": cell["beta"],
+ "min_batch_distance": PINNED_MIN_BATCH_DISTANCE,
+ "seed": seed,
+ "r1_batch_hash": batch_hash(cell["r1"].conditions),
+ "r2_batch_hash": batch_hash(cell["r2"].conditions),
+ "r1_min_pairwise_distance": float(r1_validity["min_pairwise_distance"]),
+ "r2_min_pairwise_distance": float(r2_validity["min_pairwise_distance"]),
+ "r1_boundary_coords_total": int(sum(r1_validity["boundary_coords_per_condition"])),
+ "r2_boundary_coords_total": int(sum(r2_validity["boundary_coords_per_condition"])),
+ "r1_boundary_coords_per_condition": json.dumps(
+ r1_validity["boundary_coords_per_condition"]
+ ),
+ "r2_boundary_coords_per_condition": json.dumps(
+ r2_validity["boundary_coords_per_condition"]
+ ),
+ "hv_r0": cell["hv"]["R0"],
+ # The same 15 recipes scored by the workbook rather than by the oracle.
+ # Both numbers belong in the manifest: every simulated round is scored
+ # by the oracle, so hv_r0 is the baseline the simulation actually used,
+ # and hv_r0_measured is what the campaign starts from. They are close
+ # but not equal, and a reader comparing a simulated trajectory against
+ # a live round needs to know which one they are holding.
+ "hv_r0_measured": hv_r0_measured,
+ "hv_r0_r1": cell["hv"]["R0+R1"],
+ "hv_r0_r1_r2": cell["hv"]["R0+R1+R2"],
+ "hv_gain_r1": cell["hv"]["R0+R1"] - cell["hv"]["R0"],
+ "hv_gain_r2": cell["hv"]["R0+R1+R2"] - cell["hv"]["R0+R1"],
+ "baseline_hv_reported_by_r1": cell["baseline"]["reported"],
+ "baseline_hv_independent": cell["baseline"]["independent"],
+ "baseline_hv_pareto_size": cell["baseline"]["pareto_size"],
+ "baseline_hv_unencoded_contrast": baseline_unencoded,
+ "r1_fit_warnings": len(cell["r1_fit_warnings"]),
+ "r2_fit_warnings": len(cell["r2_fit_warnings"]),
+ "final_fit_warnings": len(cell["final_fit_warnings"]),
+ "mean_utility_r0": float(cell["U"]["R0"].mean()),
+ "mean_utility_r1": float(cell["U"]["R1"].mean()),
+ "mean_utility_r2": float(cell["U"]["R2"].mean()),
+ }
+
+
+def rounds_frame(
+ cell: Mapping[str, Any], names: Sequence[str], transform: ObjectiveTransform
+) -> pd.DataFrame:
+ """All 23 simulated design points, one row each, labelled by round.
+
+ 23 = 15 R0 + 5 R1 + 3 R2, distinct CONDITIONS rather than films. The campaign
+ runs each condition in triplicate, so the same 23 rows correspond to 39 films;
+ plotting films would overplot three identical markers per condition.
+ """
+ frames = []
+ for round_name in ("R0", "R1", "R2"):
+ frame = pd.DataFrame(cell["X"][round_name], columns=list(names))
+ frame.insert(0, "round", round_name)
+ for index, spec in enumerate(transform.specs):
+ frame[f"oracle_{spec.name}"] = cell["Y"][round_name][:, index]
+ frame[f"utility_{spec.name}"] = cell["U"][round_name][:, index]
+ frames.append(frame)
+ return pd.concat(frames, ignore_index=True)
+
+
+def check_expectations(
+ manifest: pd.DataFrame,
+ cells: Sequence[Mapping[str, Any]],
+ transform: ObjectiveTransform,
+ config: Mapping[str, Any] | None = None,
+ worklist_hash: str | None = None,
+) -> list[dict[str, Any]]:
+ """Pre-registered expectations, stated before the run and checked after.
+
+ Each returns HELD / FAILED / NOT APPLICABLE plus the evidence, so a reader can
+ disagree with the rule rather than only with the conclusion.
+
+ **A rule with no data reports NOT APPLICABLE, never FAILED.** The two sweep
+ rules below ask about a radius arm and a beta arm; on a single ratified cell
+ those arms are empty by decision, and calling that a failure would put two
+ red lines under a run that did exactly what was asked. That is worse than
+ silence, because it looks checked.
+
+ **The no-signal rule is read from the config, not remembered.** It used to name
+ uniformity, which was the only dead axis on the first campaign. On the v3
+ contract optoelectronic is dead too, and the old rule -- "uniformity moves
+ least of the three" -- would report FAILED for the entirely correct reason
+ that the two dead axes move by similar small amounts.
+ """
+ results: list[dict[str, Any]] = []
+ index_by_name = {spec.name: i for i, spec in enumerate(transform.specs)}
+
+ if config is None:
+ dead = {"uniformity"}
+ else:
+ dead = {
+ str(spec["name"])
+ for spec in config["objectives"]["specs"]
+ if str(spec.get("signal_status", "")) not in ("learnable", "")
+ }
+ live = [name for name in index_by_name if name not in dead]
+
+ deltas: dict[str, list[float]] = {name: [] for name in index_by_name}
+ for cell in cells:
+ for name, index in index_by_name.items():
+ deltas[name].append(
+ float(cell["U"]["R2"][:, index].mean() - cell["U"]["R0"][:, index].mean())
+ )
+ medians = {name: float(np.median(values)) for name, values in deltas.items()}
+ evidence = ", ".join(f"{n} {v:+.4f}" for n, v in medians.items())
+
+ if not dead or not live:
+ results.append({
+ "expectation": "objectives with no learnable signal do not climb",
+ "rule": "every no-signal axis moves less than every learnable axis",
+ "verdict": "NOT APPLICABLE",
+ "evidence": f"dead axes {sorted(dead)}, learnable axes {sorted(live)}",
+ })
+ else:
+ worst_dead = max(abs(medians[n]) for n in dead)
+ best_live = min(abs(medians[n]) for n in live)
+ results.append({
+ "expectation": "objectives with no learnable signal do not climb",
+ "rule": (
+ f"every axis in {sorted(dead)} moves less, in |median delta "
+ f"R0->R2|, than every axis in {sorted(live)}"
+ ),
+ "verdict": "HELD" if worst_dead < best_live else "FAILED",
+ "evidence": evidence,
+ })
+
+ # The cross-instrument identity. The simulation proposing R1 from the same 15
+ # rows at the same seed and knobs IS the live proposal; if the hashes differ,
+ # something has drifted between the instrument and the campaign and the
+ # instrument should be the one to say so.
+ if worklist_hash is None:
+ results.append({
+ "expectation": "the simulated R1 reproduces the shipped worklist",
+ "rule": "batch_hash of the simulated R1 equals the worklist's",
+ "verdict": "NOT APPLICABLE",
+ "evidence": "no worklist for this round exists on disk to compare",
+ })
+ else:
+ simulated = sorted(set(manifest["r1_batch_hash"]))
+ held = len(simulated) == 1 and simulated[0] == worklist_hash
+ results.append({
+ "expectation": "the simulated R1 reproduces the shipped worklist",
+ "rule": "batch_hash of the simulated R1 equals the worklist's",
+ "verdict": "HELD" if held else "FAILED",
+ "evidence": f"simulated {simulated} against worklist {worklist_hash}",
+ })
+
+ for arm_name, label, rule, expected_distinct in (
+ ("radius", "radius produces identical batches (the knob is inert here)",
+ "one distinct R1 hash and one distinct R2 hash across the radius arm", 1),
+ ("beta", "beta changes the R1 batch",
+ "more than one distinct R1 hash across the beta arm", None),
+ ):
+ keys = ("radius", "both") if arm_name == "radius" else ("beta", "both")
+ arm = manifest[manifest["arm"].isin(keys)]
+ if len(arm) < 2:
+ results.append({
+ "expectation": label,
+ "rule": rule,
+ "verdict": "NOT APPLICABLE",
+ "evidence": (
+ f"the {arm_name} arm holds {len(arm)} cell(s); this run is a "
+ "single ratified cell by decision, so there is no arm to vary"
+ ),
+ })
+ continue
+ r1_unique = sorted(set(arm["r1_batch_hash"]))
+ r2_unique = sorted(set(arm["r2_batch_hash"]))
+ if expected_distinct is None:
+ held = len(r1_unique) > 1
+ detail = (
+ f"{len(arm)} cells -> {len(r1_unique)} distinct R1 batch(es) "
+ f"at betas {sorted(set(arm['beta']))}"
+ )
+ else:
+ held = len(r1_unique) == 1 and len(r2_unique) == 1
+ detail = (
+ f"{len(arm)} cells -> {len(r1_unique)} distinct R1 batch(es), "
+ f"{len(r2_unique)} distinct R2 batch(es)"
+ )
+ results.append({
+ "expectation": label,
+ "rule": rule,
+ "verdict": "HELD" if held else "FAILED",
+ "evidence": detail,
+ })
+ return results
+
+
+def ofat_conditions() -> list[tuple[float, float, str]]:
+ """13 distinct cells: 9 radii at beta 4, 5 betas at radius 0.25, sharing one."""
+ cells: list[tuple[float, float, str]] = []
+ for radius in OFAT_RADII:
+ arm = "both" if radius == ANCHOR_RADIUS else "radius"
+ cells.append((radius, ANCHOR_BETA, arm))
+ for beta in OFAT_BETAS:
+ if beta == ANCHOR_BETA:
+ continue # already present as the shared anchor cell
+ cells.append((ANCHOR_RADIUS, beta, "beta"))
+ return cells
+
+
+def full_grid_conditions() -> list[tuple[float, float, str]]:
+ return [(r, b, "grid") for r in OFAT_RADII for b in OFAT_BETAS]
+
+
+def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", required=True, type=Path)
+ parser.add_argument(
+ "--config", type=Path,
+ default=Path("configs/campaign_d2d_perovskite_final.yaml"),
+ )
+ parser.add_argument(
+ "--output-dir", type=Path, default=Path("local_outputs/round_simulations")
+ )
+ parser.add_argument("--seed", type=int, default=None)
+ parser.add_argument("--slice-points", type=int, default=41)
+ # A ratified cell needs no sweep, and the OFAT set cannot express one:
+ # its betas stop at 25. This runs exactly the cell that was decided.
+ parser.add_argument(
+ "--cell", default=None, metavar="RADIUS,BETA",
+ help="run one arbitrary cell instead of the sweep, e.g. 0.25,4",
+ )
+ parser.add_argument(
+ "--full-grid", action="store_true",
+ help="45-cell radius x beta cross instead of the 13-cell OFAT set.",
+ )
+ parser.add_argument(
+ "--pairs", nargs="+", default=None,
+ help="Restrict heatmaps to these input pairs, each 'input_x,input_y'.",
+ )
+ parser.add_argument(
+ "--conditions", nargs="+", default=None,
+ help="Restrict to these cell slugs, e.g. radius_0p25__beta_4.",
+ )
+ parser.add_argument(
+ "--no-figures", action="store_true",
+ help="Manifest only. The whole sweep in a couple of minutes.",
+ )
+ return parser.parse_args(argv)
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ args = parse_args(argv)
+ started = time.time()
+
+ config = load_campaign_config(args.config)
+ seed = (
+ int((config.get("reproducibility") or {}).get("seed", 0))
+ if args.seed is None
+ else int(args.seed)
+ )
+ design = build_design_from_config(dict(config))
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+ names = list(design.names)
+
+ print("=" * 79)
+ print("ROUND SIMULATION -- campaign loop against a frozen GP oracle")
+ print("=" * 79)
+
+ # ------------------------------------------------------------------ read --
+ contents = read_campaign_workbook(args.workbook, config)
+ if contents.errors:
+ for finding in contents.errors:
+ print(f" ERROR {finding}")
+ print("\nRefusing to fit: the objectives cannot be computed for every row.")
+ return 1
+ X_r0 = contents.inputs.to_numpy(float)
+ Y_r0_measured = contents.model_values.to_numpy(float)
+ print(f"\n1. WORKBOOK {len(X_r0)} rows, objectives {objective_names(config)}")
+ print(f" errors 0, warnings {len(contents.warnings)}")
+
+ # ---------------------------------------------------------------- oracle --
+ print("\n2. ORACLE fit_campaign_models on the real rows, then frozen")
+ oracle, oracle_warnings = fit_campaign_models(config, X_r0, Y_r0_measured, seed=seed)
+ if oracle_warnings:
+ print("\n ABORTING -- the oracle fit raised guard warnings:")
+ for message in oracle_warnings:
+ print(f" {message}")
+ print(
+ "\n Every surface and every simulated measurement below would be built\n"
+ " on this fit. A collapsed oracle must not render silently, so this is\n"
+ " a hard stop rather than a banner on the figures."
+ )
+ return 1
+ print(" fit guard clean")
+
+ # The unencoded contrast: what the R1 baseline WOULD be if measurement-space
+ # values reached the transform directly. It is a property of the observed set,
+ # not of any cell, so it is computed once. It is never asserted equal to
+ # anything -- it is the size of a mistake, kept on record. The equality that IS
+ # asserted, per cell, is reported == independent, inside run_cell.
+ def _unencoded_baseline(Y: np.ndarray) -> float:
+ with torch.no_grad():
+ raw = transform.transform(torch.tensor(Y, dtype=torch.double))
+ values = raw.detach().cpu().numpy()
+ if not bool((values > reference).all(axis=1).any()):
+ return 0.0
+ return float(
+ compute_ref_pareto_hv(torch.tensor(values, dtype=torch.double), reference)[2]
+ )
+
+ Y_r0_oracle = oracle_predict(oracle, config, X_r0, transform)
+ baseline_unencoded = _unencoded_baseline(Y_r0_oracle)
+ hv_r0_measured = hypervolume(Y_r0_measured, transform, reference)
+
+ print("\n R1 observed baseline hypervolume")
+ print(f" on the oracle-scored R0 this sweep uses : "
+ f"{hypervolume(Y_r0_oracle, transform, reference):.6f}"
+ f" (unencoded would be {baseline_unencoded:.6f})")
+ print(f" on the real measured R0 : "
+ f"{hypervolume(Y_r0_measured, transform, reference):.6f}"
+ f" (unencoded would be {_unencoded_baseline(Y_r0_measured):.6f})")
+ print(" the unencoded figures are what run_r1_ucb produced before "
+ "commit 4b76670")
+
+ # ------------------------------------------------------------ conditions --
+ if args.cell:
+ try:
+ radius_text, beta_text = args.cell.split(",")
+ cells_spec = [(float(radius_text), float(beta_text), "ratified")]
+ except ValueError:
+ print(f"\n--cell must be 'RADIUS,BETA'; got {args.cell!r}.")
+ return 1
+ else:
+ cells_spec = full_grid_conditions() if args.full_grid else ofat_conditions()
+ if args.conditions:
+ wanted = set(args.conditions)
+ cells_spec = [c for c in cells_spec if cell_slug(c[0], c[1]) in wanted]
+ if not cells_spec:
+ print(f"\nNo cell matched --conditions {args.conditions}.")
+ return 1
+
+ if args.pairs:
+ pairs = []
+ for item in args.pairs:
+ parts = [p.strip() for p in item.split(",")]
+ if len(parts) != 2 or any(p not in names for p in parts):
+ print(f"\n--pairs entry {item!r} must be 'input_x,input_y' from {names}.")
+ return 1
+ pairs.append((parts[0], parts[1]))
+ else:
+ pairs = list(combinations(names, 2))
+
+ n_figures = 0 if args.no_figures else len(cells_spec) * (len(pairs) * 3 + 2)
+ print(f"\n3. PLAN {len(cells_spec)} cells x {len(pairs)} input pairs")
+ print(f" min_batch_distance pinned at {PINNED_MIN_BATCH_DISTANCE} in every cell")
+ print(f" figures to render: {n_figures}")
+
+ # ------------------------------------------------------------- first cell --
+ fixed = fixed_slice_values(design, X_r0)
+ output_root = Path(args.output_dir)
+ output_root.mkdir(parents=True, exist_ok=True)
+
+ rows: list[dict[str, Any]] = []
+ cells: list[dict[str, Any]] = []
+ failures: list[dict[str, str]] = []
+ cell_seconds: float | None = None
+
+ for position, (radius, beta, arm) in enumerate(cells_spec, start=1):
+ slug = cell_slug(radius, beta)
+ cell_started = time.time()
+ print(f"\n [{position}/{len(cells_spec)}] {slug} ({arm})", flush=True)
+ try:
+ cell = run_cell(
+ config, oracle, X_r0, transform, reference,
+ radius=radius, beta=beta, seed=seed,
+ )
+ except Exception as error: # noqa: BLE001 -- one bad cell must not cost the sweep
+ # A cell can legitimately fail: validate_batch refuses a batch that
+ # breaches the spacing floor, and an extreme radius could in principle
+ # leave the selector nothing to pick. Losing the other twelve cells to
+ # that would be the wrong trade at ~3 minutes each, so record and move
+ # on -- and report at the end rather than only in the scrollback.
+ failures.append({"slug": slug, "arm": arm, "error": f"{type(error).__name__}: {error}"})
+ print(f" FAILED: {type(error).__name__}: {error}")
+ continue
+ cells.append(cell)
+ rows.append(manifest_row(
+ cell, condition_id=position, arm=arm, seed=seed,
+ baseline_unencoded=baseline_unencoded,
+ hv_r0_measured=hv_r0_measured,
+ ))
+ elapsed = time.time() - cell_started
+ print(
+ f" R1 spacing {rows[-1]['r1_min_pairwise_distance']:.3f} "
+ f"HV {cell['hv']['R0']:.4f} -> {cell['hv']['R0+R1']:.4f} -> "
+ f"{cell['hv']['R0+R1+R2']:.4f} ({elapsed:.1f}s)"
+ )
+ if cell_seconds is None:
+ cell_seconds = elapsed
+ if not args.no_figures:
+ remaining = cell_seconds * (len(cells_spec) - 1)
+ # ~0.35 s per rendered figure, measured on this stack
+ estimate = (remaining + n_figures * 0.35) / 60.0
+ print(f" estimated total remaining: ~{estimate:.0f} min")
+ if estimate > 30:
+ print(
+ " NOTE: over 30 minutes. Narrow it with --pairs / "
+ "--conditions, or use --no-figures for the manifest alone."
+ )
+
+ if args.no_figures:
+ continue
+
+ banner = None
+ if cell["final_fit_warnings"]:
+ banner = (
+ "FIT GUARD: the final GP raised "
+ f"{len(cell['final_fit_warnings'])} warning(s) -- this surface is "
+ "drawn from a fit worth distrusting."
+ )
+
+ condition_dir = output_root / "by_condition" / "qlognehvi" / slug
+ condition_dir.mkdir(parents=True, exist_ok=True)
+ plot_boxplots(
+ condition_dir / "round_boxplots.png", cell, transform,
+ seed=seed, warning_banner=banner,
+ )
+ plot_hypervolume(
+ condition_dir / "hypervolume_by_round.png", cell, reference,
+ seed=seed, warning_banner=banner,
+ )
+ rounds_frame(cell, names, transform).to_csv(
+ condition_dir / "all_rounds.csv", index=False, encoding="utf-8-sig"
+ )
+
+ for pair in pairs:
+ mesh_x, mesh_y, surfaces = surface_grid(
+ cell["final_model"], cell["config"], design, transform, pair, fixed,
+ points=args.slice_points,
+ )
+ pair_dir = (
+ output_root
+ / f"{_safe_filename(pair[0])}__{_safe_filename(pair[1])}"
+ / "qlognehvi"
+ / slug
+ )
+ pair_dir.mkdir(parents=True, exist_ok=True)
+ for index, spec in enumerate(transform.specs):
+ plot_surface(
+ pair_dir / f"final_surface_{_safe_filename(spec.name)}.png",
+ mesh_x, mesh_y, surfaces[..., index], pair, spec,
+ cell["X"], design, fixed,
+ radius=radius, beta=beta, seed=seed, warning_banner=banner,
+ )
+
+ # -------------------------------------------------------------- manifest --
+ manifest = pd.DataFrame(rows, columns=list(MANIFEST_COLUMNS))
+ manifest_path = output_root / "manifest.csv"
+ manifest.to_csv(manifest_path, index=False, encoding="utf-8-sig")
+ print(f"\n4. MANIFEST {manifest_path} ({len(manifest)} rows)")
+
+ if failures:
+ # Said here, not only in the scrollback: a manifest with rows missing must
+ # not read as a manifest of every cell that was asked for.
+ print(f"\n {len(failures)} of {len(cells_spec)} cell(s) FAILED and are "
+ "absent from the manifest:")
+ for failure in failures:
+ print(f" {failure['slug']} ({failure['arm']}): {failure['error']}")
+ if not rows:
+ print("\nNo cell completed, so there is nothing to check. Stopping.")
+ return 1
+
+ identical: dict[tuple[str, str], list[str]] = {}
+ for row in rows:
+ key = (row["r1_batch_hash"], row["r2_batch_hash"])
+ identical.setdefault(key, []).append(cell_slug(row["radius"], row["beta"]))
+ print("\n BATCH IDENTITY -- cells that proposed the same R1 and R2 batches")
+ for (r1_hash, r2_hash), members in sorted(identical.items(), key=lambda kv: -len(kv[1])):
+ print(f" R1 {r1_hash} / R2 {r2_hash} <- {len(members)} cell(s)")
+ print(f" {', '.join(members)}")
+
+ print("\n5. PRE-REGISTERED EXPECTATIONS")
+ # The shipped worklist, if one exists, so the identity check has something
+ # to compare against. Read here rather than inside check_expectations so the
+ # expectation stays a pure function of what it is handed.
+ worklist_hash = None
+ try:
+ from mobo_kit.workbook_io import candidate_workbook_path, sheet_name_for_round
+ from openpyxl import load_workbook as _load
+
+ sheet_path = candidate_workbook_path(args.workbook, "R1")
+ if sheet_path.exists():
+ sheet = _load(sheet_path, data_only=True)[sheet_name_for_round("R1")]
+ header = [str(c.value).strip() if c.value else "" for c in sheet[1]]
+ columns = [header.index(name) for name in names]
+ seen: list[list[float]] = []
+ for row in sheet.iter_rows(min_row=2, values_only=True):
+ if row[0] is None:
+ continue
+ values = [float(row[c]) for c in columns]
+ if values not in seen:
+ seen.append(values)
+ worklist_hash = batch_hash(seen)
+ except Exception as exc: # noqa: BLE001 - an absent worklist is not an error
+ print(f" (could not read a worklist to compare: {type(exc).__name__}: {exc})")
+
+ for check in check_expectations(
+ manifest, cells, transform, config=config, worklist_hash=worklist_hash
+ ):
+ print(f"\n {check['verdict']} {check['expectation']}")
+ print(f" rule {check['rule']}")
+ print(f" evidence {check['evidence']}")
+
+ print(f"\nDone in {(time.time() - started) / 60.0:.1f} min. Outputs under {output_root}")
+ print("Every number above is a model prediction, not a measurement.")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/plot_shap_attribution.py b/scripts/plot_shap_attribution.py
new file mode 100644
index 0000000..71c90cd
--- /dev/null
+++ b/scripts/plot_shap_attribution.py
@@ -0,0 +1,624 @@
+"""SHAP attributions for what the campaign's models actually use.
+
+Answers one question per figure: **which process inputs move this objective's
+expected utility, and in which direction?** It explains the MODEL, which is the
+only thing SHAP can explain -- see the caveats below and on every figure.
+
+MODEL STATES. Two, not three.
+
+* ``r0_only`` -- the GP fitted to the **15 real measurements**. This is the anchor:
+ it is the only model here trained on measured data, it does not depend on any
+ acquisition function, and it is the same object the round simulation uses as its
+ oracle.
+* ``final`` -- refitted on all 23 conditions after a simulated R0 -> R1 -> R2 pass
+ at the default cell (radius 0.25, beta 4.0). Its R1 and R2 conditions were never
+ fabricated, so its extra 8 points carry oracle predictions rather than
+ measurements.
+
+The brief asked for three states, splitting ``final`` by R2 acquisition. Measured
+here, **qLogNEHVI and qNEHVI propose the identical R2 batch**, so those two states
+are one model and their figures would be bit-identical. The script detects that
+per run rather than assuming it, prints it, records both hashes in the summary,
+and stamps it on every affected figure. If they ever diverge, both sets are
+produced automatically.
+
+WHAT SHAP DOES AND DOES NOT SHOW HERE.
+
+* It explains ``E[utility]`` per objective, through
+ ``ObjectiveTransform.expected_transform`` -- so thickness goes through the
+ lognormal quadrature rather than a transformed mean, and every value is in
+ utility units where higher is better.
+* **A large attribution is not evidence of a physical effect.** For thickness and
+ optoelectronic the model carries a declared physics mean function, so
+ ``speed_1``, ``precur_conc`` and ``anneal_temp`` attributions partly restate that
+ declaration rather than discovering it.
+* **Uniformity has no validated predictive signal** (LOO R2 -0.681, permutation
+ p = 0.82). Its GP still has ARD lengthscales and a posterior mean that varies, so
+ SHAP will report structure. That structure is fitted noise. It is shown because
+ hiding it would be worse, and every uniformity figure says so.
+
+Usage::
+
+ python scripts/plot_shap_attribution.py --workbook "local_inputs/Final Summary Table.xlsx"
+ python scripts/plot_shap_attribution.py --workbook ... --instances 200
+ python scripts/plot_shap_attribution.py --workbook ... --no-figures
+ python scripts/plot_shap_attribution.py --workbook ... --extreme-cells
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+import warnings
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+import shap # noqa: E402 -- still used directly for summary_plot
+import torch # noqa: E402
+
+from mobo_kit.attribution import ( # noqa: E402
+ expected_utility_fn,
+ shap_values_for,
+)
+from mobo_kit.campaign import ( # noqa: E402
+ build_objective_transform,
+ fit_campaign_models,
+ load_campaign_config,
+ normalise_inputs,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from mobo_kit.candidate_pool import sample_discrete_candidate_pool # noqa: E402
+from mobo_kit.constraints import constraints_from_config # noqa: E402
+from mobo_kit.design import build_design_from_config # noqa: E402
+from mobo_kit.objectives import ObjectiveTransform # noqa: E402
+from mobo_kit.research_qnehvi import run_r2_qnehvi_research # noqa: E402
+from mobo_kit.workbook_io import read_campaign_workbook # noqa: E402
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=UserWarning, module="botorch")
+warnings.filterwarnings("ignore", category=UserWarning, module="gpytorch")
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="numpy")
+torch.set_num_threads(1)
+
+# house style, shared with plot_dtlz2_report.py and plot_round_simulation.py
+INK, INK_MUTED, SURFACE = "#0b0b0b", "#52514e", "#fcfcfb"
+SPINE = "#d8d7d2"
+
+DEFAULT_RADIUS, DEFAULT_BETA = 0.25, 4.0
+EXTREME_CELLS = ((0.05, 4.0), (0.45, 4.0), (0.25, 25.0))
+
+#: The declared mean-function features, by objective. Attributions on these are
+#: partly a restatement of the model's declared physics, not a discovery.
+MEAN_FUNCTION_FEATURES = {
+ "thickness": ("speed_1", "precur_conc"),
+ "optoelectronic": ("anneal_temp",),
+}
+
+
+# --------------------------------------------------------------------------- #
+# the function SHAP explains
+# --------------------------------------------------------------------------- #
+
+
+# --------------------------------------------------------------------------- #
+# model states
+# --------------------------------------------------------------------------- #
+
+
+def oracle_predict(
+ model: Any,
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ transform: ObjectiveTransform,
+) -> np.ndarray:
+ """Deterministic measurement-space prediction; thickness is the median exp(mu).
+
+ Identical convention to ``scripts/plot_round_simulation.py``; see that file for
+ why the median rather than the lognormal mean.
+ """
+ X_norm = normalise_inputs(config, np.asarray(X_phys, dtype=float))
+ model.eval()
+ with torch.no_grad():
+ mean = (
+ model.posterior(torch.tensor(X_norm, dtype=torch.double),
+ observation_noise=False)
+ .mean.detach().cpu().double().numpy()
+ )
+ out = np.empty_like(mean)
+ for index, spec in enumerate(transform.specs):
+ out[:, index] = (
+ np.exp(mean[:, index]) if spec.model_link == "log" else mean[:, index]
+ )
+ return out
+
+
+def cell_config(base: Mapping[str, Any], *, radius: float, beta: float) -> dict:
+ from copy import deepcopy
+
+ config = deepcopy(dict(base))
+ penalization = config.setdefault("local_penalization", {})
+ penalization["radius"] = float(radius)
+ penalization["min_batch_distance"] = 0.15
+ config.setdefault("rounds", {}).setdefault("r1", {})["beta"] = float(beta)
+ return config
+
+
+def batch_hash(conditions: pd.DataFrame) -> str:
+ import hashlib
+
+ values = np.round(np.asarray(conditions, dtype=float), 12)
+ ordered = values[np.lexsort(values.T[::-1])]
+ return hashlib.sha256(ordered.tobytes()).hexdigest()[:16]
+
+
+def build_final_state(
+ base_config: Mapping[str, Any],
+ oracle: Any,
+ X_r0: np.ndarray,
+ Y_r0_oracle: np.ndarray,
+ transform: ObjectiveTransform,
+ *,
+ radius: float,
+ beta: float,
+ seed: int,
+ acquisition: str,
+) -> dict[str, Any]:
+ """One simulated campaign at a cell, refitted on all 23 oracle-scored points."""
+ config = cell_config(base_config, radius=radius, beta=beta)
+ r1 = run_r1_ucb(config, X_r0, Y_r0_oracle, seed=seed)
+ X_r1 = r1.conditions.to_numpy(float)
+ Y_r1 = oracle_predict(oracle, config, X_r1, transform)
+ X_01, Y_01 = np.vstack([X_r0, X_r1]), np.vstack([Y_r0_oracle, Y_r1])
+
+ runner = run_r2_qlognehvi if acquisition == "qlognehvi" else run_r2_qnehvi_research
+ r2 = runner(config, X_01, Y_01, seed=seed)
+ X_r2 = r2.conditions.to_numpy(float)
+ Y_r2 = oracle_predict(oracle, config, X_r2, transform)
+
+ X_all, Y_all = np.vstack([X_01, X_r2]), np.vstack([Y_01, Y_r2])
+ model, fit_warnings = fit_campaign_models(config, X_all, Y_all, seed=seed)
+ return {
+ "model": model,
+ "config": config,
+ "X": X_all,
+ "fit_warnings": tuple(fit_warnings),
+ "r1_hash": batch_hash(r1.conditions),
+ "r2_hash": batch_hash(r2.conditions),
+ "acquisition": acquisition,
+ }
+
+
+# --------------------------------------------------------------------------- #
+# figures
+# --------------------------------------------------------------------------- #
+
+ORACLE_CAVEAT = (
+ "Oracle: this model's 8 non-R0 conditions carry GP predictions, not "
+ "measurements. It shows what the optimiser would believe, not what a film did."
+)
+REAL_DATA_CAVEAT = (
+ "Fitted to the 15 real R0 measurements. SHAP still explains the MODEL's "
+ "behaviour, which is not the same as a measured effect."
+)
+CONSTRUCTION_CAVEAT = (
+ "Construction: this objective carries a declared physics mean function on "
+ "{features}, so attributions there partly restate that declaration."
+)
+NO_SIGNAL_CAVEAT = (
+ "No validated signal: uniformity does not beat the leave-one-out null "
+ "(LOO R2 -0.681, permutation p = 0.82). Structure below is fitted noise, "
+ "not physics."
+)
+IDENTICAL_BATCH_NOTE = (
+ "qLogNEHVI and qNEHVI proposed the IDENTICAL R2 batch here, so this one "
+ "figure covers both acquisitions."
+)
+
+
+def _feature_labels(config: Mapping[str, Any], X: np.ndarray) -> list[str]:
+ """Name plus the physical range the colour scale actually spans, per feature.
+
+ A beeswarm colours each row against **that feature's own** min-max, so one
+ shared colorbar cannot carry physical units for ten inputs measured in rpm,
+ seconds, molarity and microlitres at once. Putting each row's range in its
+ label states the physical scale without the colorbar claiming something false.
+ """
+ labels = []
+ for index, item in enumerate(config["inputs"]):
+ unit = str(item.get("unit", "")).strip()
+ low, high = float(np.min(X[:, index])), float(np.max(X[:, index]))
+ suffix = f" {unit}" if unit else ""
+ labels.append(f"{item['name']}\n{low:g}–{high:g}{suffix}")
+ return labels
+
+
+def plot_beeswarm(
+ path: Path,
+ shap_values: np.ndarray,
+ instances: np.ndarray,
+ config: Mapping[str, Any],
+ objective: str,
+ state_label: str,
+ *,
+ seed: int,
+ caveats: Sequence[str],
+) -> None:
+ plt.figure(figsize=(9.6, 6.4), facecolor=SURFACE)
+ shap.summary_plot(
+ shap_values,
+ instances,
+ feature_names=_feature_labels(config, instances),
+ plot_type="dot",
+ show=False,
+ color_bar=True,
+ sort=True,
+ # explicit generator: shap jitters overlapping points, and reading the
+ # global RNG would make a figure depend on whatever ran before it
+ rng=np.random.default_rng(seed),
+ )
+ fig = plt.gcf()
+ fig.patch.set_facecolor(SURFACE)
+ axis = fig.axes[0]
+ axis.set_facecolor(SURFACE)
+ axis.set_xlabel(
+ "SHAP value (impact on expected utility, higher is better)",
+ fontsize=9.5, color=INK_MUTED,
+ )
+ axis.tick_params(labelsize=8, colors=INK_MUTED, length=3)
+ for spine in axis.spines.values():
+ spine.set_color(SPINE)
+ # objective named on the right as well as in the title, so a cropped or
+ # forwarded panel is still self-identifying
+ axis.set_ylabel(objective, fontsize=10.5, color=INK, rotation=270, labelpad=18)
+ axis.yaxis.set_label_position("right")
+ for extra in fig.axes[1:]:
+ extra.tick_params(labelsize=7, colors=INK_MUTED)
+ if extra.get_ylabel():
+ extra.set_ylabel(extra.get_ylabel(), fontsize=8, color=INK_MUTED)
+
+ fig.suptitle(
+ f"{objective} — SHAP attribution | {state_label}",
+ fontsize=12.5, color=INK, y=0.985,
+ )
+ fig.tight_layout(rect=(0, 0.11, 1, 0.95))
+ fig.text(
+ 0.008, 0.008,
+ f"seed {seed} | " + "\n".join(caveats),
+ fontsize=6.4, color=INK_MUTED, va="bottom", ha="left", wrap=True,
+ )
+ fig.savefig(path, dpi=150, facecolor=SURFACE)
+ plt.close(fig)
+
+
+def caveats_for(objective: str, state: str, identical: bool) -> list[str]:
+ """Exactly two true lines per figure, plus the identical-batch fact if it holds.
+
+ The brief asked for one fixed footer everywhere. A slice caveat on a model
+ fitted to real data, or an oracle caveat on the R0-only anchor, would be false
+ where a reader looks for true statements, so line one states the model's
+ provenance and line two states the objective's own hazard.
+ """
+ lines = [ORACLE_CAVEAT if state != "r0_only" else REAL_DATA_CAVEAT]
+ if objective == "uniformity":
+ lines.append(NO_SIGNAL_CAVEAT)
+ elif objective in MEAN_FUNCTION_FEATURES:
+ lines.append(
+ CONSTRUCTION_CAVEAT.format(
+ features=", ".join(MEAN_FUNCTION_FEATURES[objective])
+ )
+ )
+ else:
+ lines.append("")
+ if identical and state != "r0_only":
+ lines.append(IDENTICAL_BATCH_NOTE)
+ return [line for line in lines if line]
+
+
+# --------------------------------------------------------------------------- #
+# driver
+# --------------------------------------------------------------------------- #
+
+
+#: Pre-registered before the extreme cells were run. Sweeping the acquisition
+#: parameters for the SHAP work is warranted only if changing them changes what
+#: the model attributes to -- either by moving a feature to the top, or by moving
+#: any attribution by more than this fraction of that objective's largest one.
+SHIFT_FRACTION_THRESHOLD = 0.10
+
+
+def extreme_cell_shifts(
+ default_mean_abs: Mapping[str, np.ndarray],
+ cell_mean_abs: Mapping[str, Mapping[str, np.ndarray]],
+ feature_names: Sequence[str],
+) -> tuple[list[dict[str, Any]], str]:
+ """How much does the attribution move when the acquisition knobs move?
+
+ The question behind this is whether the SHAP figures are a property of the
+ MODEL or of the SEARCH. If three very different acquisition settings put the
+ same features in the same order with similar magnitudes, the attributions are
+ telling us about the fitted physics rather than about how the batch was picked
+ -- and there is no reason to sweep.
+ """
+ rows: list[dict[str, Any]] = []
+ warranted = False
+ for slug, per_objective in cell_mean_abs.items():
+ for objective, values in per_objective.items():
+ reference = np.asarray(default_mean_abs[objective], dtype=float)
+ values = np.asarray(values, dtype=float)
+ scale = float(reference.max()) if reference.max() > 0 else 1.0
+ deltas = np.abs(values - reference)
+ worst = int(np.argmax(deltas))
+ top_moved = int(np.argmax(values)) != int(np.argmax(reference))
+ fraction = float(deltas[worst] / scale)
+ if fraction > SHIFT_FRACTION_THRESHOLD or top_moved:
+ warranted = True
+ rows.append({
+ "cell": slug,
+ "objective": objective,
+ "max_abs_shift": float(deltas[worst]),
+ "max_shift_feature": feature_names[worst],
+ "max_shift_fraction_of_largest": fraction,
+ "top_feature_changed": top_moved,
+ "default_top_feature": feature_names[int(np.argmax(reference))],
+ "cell_top_feature": feature_names[int(np.argmax(values))],
+ })
+ verdict = (
+ "SWEEP WARRANTED: an extreme cell moved the top feature or shifted an "
+ f"attribution by more than {SHIFT_FRACTION_THRESHOLD:.0%} of the largest."
+ if warranted
+ else "NO SWEEP NEEDED: every extreme cell kept the same top feature and "
+ f"moved every attribution by under {SHIFT_FRACTION_THRESHOLD:.0%} of the "
+ "largest, so the attributions describe the model rather than the search."
+ )
+ return rows, verdict
+
+
+def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", required=True, type=Path)
+ parser.add_argument(
+ "--config", type=Path, default=Path("configs/campaign_d2d_perovskite_final.yaml")
+ )
+ parser.add_argument("--output-dir", type=Path, default=Path("local_outputs/shap"))
+ parser.add_argument("--seed", type=int, default=None)
+ parser.add_argument(
+ "--instances", type=int, default=1000,
+ help="On-grid points to attribute. Cost is linear in this.",
+ )
+ parser.add_argument(
+ "--extreme-cells", action="store_true",
+ help="Also measure attribution shift at radius 0.05 / 0.45 and beta 25.",
+ )
+ parser.add_argument("--no-figures", action="store_true")
+ parser.add_argument(
+ "--objectives", nargs="+", default=None,
+ help="Restrict to these objective names.",
+ )
+ return parser.parse_args(argv)
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ args = parse_args(argv)
+ started = time.time()
+
+ config = load_campaign_config(args.config)
+ seed = (
+ int((config.get("reproducibility") or {}).get("seed", 0))
+ if args.seed is None
+ else int(args.seed)
+ )
+ design = build_design_from_config(dict(config))
+ transform = build_objective_transform(config)
+ names = list(transform.names)
+
+ print("=" * 79)
+ print("SHAP ATTRIBUTION — what the campaign's models use")
+ print("=" * 79)
+
+ contents = read_campaign_workbook(args.workbook, config)
+ if contents.errors:
+ for finding in contents.errors:
+ print(f" ERROR {finding}")
+ return 1
+ X_r0 = contents.inputs.to_numpy(float)
+ Y_r0_measured = contents.model_values.to_numpy(float)
+ print(f"\n1. WORKBOOK {len(X_r0)} rows, objectives {tuple(names)}")
+
+ print("\n2. R0-ONLY MODEL fit_campaign_models on the real measurements")
+ r0_model, r0_warnings = fit_campaign_models(
+ config, X_r0, Y_r0_measured, seed=seed
+ )
+ if r0_warnings:
+ print("\n ABORTING — the anchor fit raised guard warnings:")
+ for message in r0_warnings:
+ print(f" {message}")
+ print("\n Every attribution below would be about this fit.")
+ return 1
+ print(" fit guard clean")
+
+ Y_r0_oracle = oracle_predict(r0_model, config, X_r0, transform)
+
+ print("\n3. FINAL MODELS simulated R0 -> R1 -> R2 at "
+ f"radius {DEFAULT_RADIUS}, beta {DEFAULT_BETA}")
+ finals = {}
+ for acquisition in ("qlognehvi", "qnehvi"):
+ finals[acquisition] = build_final_state(
+ config, r0_model, X_r0, Y_r0_oracle, transform,
+ radius=DEFAULT_RADIUS, beta=DEFAULT_BETA, seed=seed,
+ acquisition=acquisition,
+ )
+ built = finals[acquisition]
+ print(f" {acquisition:<10} R2 batch hash {built['r2_hash']}"
+ f" fit warnings {len(built['fit_warnings'])}")
+ # A collapsed final GP would attribute confidently to nothing at all, and
+ # the beeswarm would look no different. Say so rather than render quietly.
+ for message in built["fit_warnings"]:
+ print(f" FIT GUARD {message}")
+ identical = finals["qlognehvi"]["r2_hash"] == finals["qnehvi"]["r2_hash"]
+ print(f" IDENTICAL R2 BATCH: {identical}")
+ if identical:
+ print(" -> the two acquisitions give one model; one set of final figures.")
+
+ states: dict[str, dict[str, Any]] = {
+ "r0_only": {
+ "model": r0_model, "config": config, "X": X_r0,
+ "label": "R0-only, fitted to the 15 real measurements",
+ }
+ }
+ if identical:
+ states["final"] = {
+ "model": finals["qlognehvi"]["model"],
+ "config": finals["qlognehvi"]["config"],
+ "X": finals["qlognehvi"]["X"],
+ "label": "final 23-point model (qLogNEHVI = qNEHVI)",
+ }
+ else:
+ for acquisition, built in finals.items():
+ states[f"final_{acquisition}"] = {
+ "model": built["model"], "config": built["config"],
+ "X": built["X"],
+ "label": f"final 23-point model ({acquisition})",
+ }
+
+ # ------------------------------------------------------------ instances --
+ pool = sample_discrete_candidate_pool(
+ design, int(args.instances), seed=seed,
+ row_constraints=constraints_from_config(dict(config), design) or None,
+ )
+ instances = np.asarray(pool.X_phys, dtype=float)
+ wanted = names if args.objectives is None else [
+ n for n in names if n in set(args.objectives)
+ ]
+ n_runs = len(states) * len(wanted)
+ print(f"\n4. ATTRIBUTION {instances.shape[0]} on-grid instances x "
+ f"{len(wanted)} objectives x {len(states)} model states = {n_runs} runs")
+
+ output_root = Path(args.output_dir)
+ output_root.mkdir(parents=True, exist_ok=True)
+ rows: list[dict[str, Any]] = []
+ mean_abs_by_state: dict[str, dict[str, np.ndarray]] = {}
+ per_run_seconds: float | None = None
+
+ for state_name, state in states.items():
+ for objective in wanted:
+ index = names.index(objective)
+ run_started = time.time()
+ values = shap_values_for(
+ state["model"], state["config"], transform, index,
+ background=state["X"], instances=instances, seed=seed,
+ )
+ elapsed = time.time() - run_started
+ mean_abs = np.abs(values).mean(axis=0)
+ mean_abs_by_state.setdefault(state_name, {})[objective] = mean_abs
+ order = np.argsort(-mean_abs)
+ for rank, feature_index in enumerate(order, start=1):
+ rows.append({
+ "model_state": state_name,
+ "objective": objective,
+ "feature": design.names[feature_index],
+ "mean_abs_shap": float(mean_abs[feature_index]),
+ "rank": rank,
+ "mean_shap": float(values[:, feature_index].mean()),
+ "feature_min": float(instances[:, feature_index].min()),
+ "feature_max": float(instances[:, feature_index].max()),
+ "in_mean_function": design.names[feature_index]
+ in MEAN_FUNCTION_FEATURES.get(objective, ()),
+ "r2_acquisition": (
+ "identical" if identical and state_name != "r0_only"
+ else state_name
+ ),
+ })
+ top = design.names[order[0]]
+ print(f" {state_name:<12} {objective:<15} top {top:<12} "
+ f"mean|SHAP| {mean_abs[order[0]]:.4f} ({elapsed:.0f}s)")
+
+ if per_run_seconds is None:
+ per_run_seconds = elapsed
+ print(f" estimated total: ~{elapsed * n_runs / 60:.0f} min")
+
+ if not args.no_figures:
+ figure_dir = output_root / "figures"
+ figure_dir.mkdir(parents=True, exist_ok=True)
+ plot_beeswarm(
+ figure_dir / f"shap_{state_name}_{objective}.png",
+ values, instances, config, objective, state["label"],
+ seed=seed,
+ caveats=caveats_for(objective, state_name, identical),
+ )
+
+ # ------------------------------------------------------- extreme cells --
+ shift_rows: list[dict[str, Any]] = []
+ verdict = ""
+ if args.extreme_cells:
+ default_state = "final" if "final" in states else "final_qlognehvi"
+ print(f"\n5. EXTREME CELLS attribution shift against {default_state}")
+ cell_mean_abs: dict[str, dict[str, np.ndarray]] = {}
+ for radius, beta in EXTREME_CELLS:
+ slug = f"radius_{radius:g}__beta_{beta:g}".replace(".", "p")
+ built = build_final_state(
+ config, r0_model, X_r0, Y_r0_oracle, transform,
+ radius=radius, beta=beta, seed=seed, acquisition="qlognehvi",
+ )
+ cell_mean_abs[slug] = {}
+ for objective in wanted:
+ index = names.index(objective)
+ values = shap_values_for(
+ built["model"], built["config"], transform, index,
+ background=built["X"], instances=instances, seed=seed,
+ )
+ cell_mean_abs[slug][objective] = np.abs(values).mean(axis=0)
+ for rank, feature_index in enumerate(
+ np.argsort(-cell_mean_abs[slug][objective]), start=1
+ ):
+ rows.append({
+ "model_state": f"final_{slug}",
+ "objective": objective,
+ "feature": design.names[feature_index],
+ "mean_abs_shap": float(
+ cell_mean_abs[slug][objective][feature_index]
+ ),
+ "rank": rank,
+ "mean_shap": float(values[:, feature_index].mean()),
+ "feature_min": float(instances[:, feature_index].min()),
+ "feature_max": float(instances[:, feature_index].max()),
+ "in_mean_function": design.names[feature_index]
+ in MEAN_FUNCTION_FEATURES.get(objective, ()),
+ "r2_acquisition": "qlognehvi",
+ })
+ print(f" {slug} done (R2 hash {built['r2_hash']})")
+
+ shift_rows, verdict = extreme_cell_shifts(
+ mean_abs_by_state[default_state], cell_mean_abs, list(design.names)
+ )
+ for row in shift_rows:
+ print(f" {row['cell']:<24} {row['objective']:<15} "
+ f"max shift {row['max_abs_shift']:.4f} "
+ f"({row['max_shift_fraction_of_largest']:.1%} of largest) "
+ f"on {row['max_shift_feature']}"
+ f"{' TOP MOVED' if row['top_feature_changed'] else ''}")
+ print(f"\n {verdict}")
+ pd.DataFrame(shift_rows).to_csv(
+ output_root / "shap_extreme_cell_shift.csv",
+ index=False, encoding="utf-8-sig",
+ )
+
+ summary = pd.DataFrame(rows)
+ summary_path = output_root / "shap_summary.csv"
+ summary.to_csv(summary_path, index=False, encoding="utf-8-sig")
+ print(f"\n6. SUMMARY {summary_path} ({len(summary)} rows)")
+
+ print(f"\nDone in {(time.time() - started) / 60:.1f} min. Outputs under {output_root}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/pool_null_shards.py b/scripts/pool_null_shards.py
new file mode 100644
index 0000000..1fa2a04
--- /dev/null
+++ b/scripts/pool_null_shards.py
@@ -0,0 +1,88 @@
+"""Pool permutation-null shards into one p-value with a binomial interval.
+
+ python scripts/pool_null_shards.py --observed-rho 0.4607 --observed-r2 -0.0191
+
+The binomial interval matters: at 200 shuffles a p of 0.020 is about 4
+exceedances, whose 95% interval reaches 0.05. More shuffles shrink that, and the
+interval is what says whether the p-value is safe to quote.
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import json
+from pathlib import Path
+
+import numpy as np
+from scipy.stats import beta
+
+
+def clopper_pearson(successes: int, trials: int, alpha: float = 0.05):
+ lower = (
+ 0.0
+ if successes == 0
+ else beta.ppf(alpha / 2, successes, trials - successes + 1)
+ )
+ upper = (
+ 1.0
+ if successes == trials
+ else beta.ppf(1 - alpha / 2, successes + 1, trials - successes)
+ )
+ return float(lower), float(upper)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--shards", default="local_outputs/null_shards/shard_*.json")
+ ap.add_argument("--observed-rho", type=float, required=True)
+ ap.add_argument("--observed-r2", type=float, default=None)
+ args = ap.parse_args()
+
+ paths = sorted(glob.glob(args.shards))
+ if not paths:
+ raise SystemExit(f"no shards matched {args.shards!r}")
+
+ rho: list[float] = []
+ r2: list[float] = []
+ for path in paths:
+ payload = json.loads(Path(path).read_text(encoding="utf-8"))
+ rho.extend(payload["rho"])
+ r2.extend(payload["r2"])
+ print(
+ f" {Path(path).name}: n={len(payload['rho'])} "
+ f"mean_rho={np.mean(payload['rho']):+.4f}"
+ )
+
+ rho_a = np.array(rho)
+ n = len(rho_a)
+ print(f"\npooled shuffles: {n} from {len(paths)} shard(s)")
+
+ for label, null, observed in (
+ ("Spearman", rho_a, args.observed_rho),
+ ("R2", np.array(r2), args.observed_r2),
+ ):
+ if observed is None:
+ continue
+ exceed = int((null >= observed).sum())
+ p = exceed / len(null)
+ lo, hi = clopper_pearson(exceed, len(null))
+ verdict = (
+ "CLEARS p<0.05"
+ if hi < 0.05
+ else (
+ "significant but interval touches 0.05"
+ if p < 0.05
+ else "not significant"
+ )
+ )
+ print(f"\n {label}")
+ print(f" observed {observed:+.4f}")
+ print(f" null mean/sd {null.mean():+.4f} / {null.std():.4f}")
+ print(f" 95th percentile {np.percentile(null, 95):+.4f}")
+ print(f" exceedances {exceed}/{len(null)}")
+ print(f" p {p:.4f} 95% CI [{lo:.4f}, {hi:.4f}] {verdict}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/raw_component_screen.py b/scripts/raw_component_screen.py
new file mode 100644
index 0000000..b4429f6
--- /dev/null
+++ b/scripts/raw_component_screen.py
@@ -0,0 +1,535 @@
+"""Screen candidate objectives built from RAW measurements instead of stored scores.
+
+WHY THIS EXISTS. Every contract so far has handed the GP a composite score --
+clamped, capped, normalised, averaged -- and every one of those composites has come
+back unlearnable. The extended C1&C2 sheet showed the mechanism on the one axis
+where both forms exist: leave-one-recipe-out R2 is **-0.2151** on the stored
+thickness score and **+0.4082** on the same films' raw nanometres. The Gaussian
+squash `EXP(-((T-650)/250)^2)` is non-monotone, so 500 nm and 800 nm map to the
+same score and the GP is asked to learn a fold that is not there.
+
+This script asks whether the same is true of uniformity and optoelectronic: give
+the model Coverage, 1-Uniformity, phase purity, Voc, photoconductance and
+photosensitivity AS MEASURED, and see which of them it can predict. Composites
+are then built in UTILITY space, after the GP, where a monotone squash costs
+nothing.
+
+ python scripts/raw_component_screen.py --list
+ python scripts/raw_component_screen.py --candidates coverage,phase_purity,log_photocond
+ python scripts/raw_component_screen.py --spec '[{"name":"x","expr":"np.log(g_light)"}]'
+ python scripts/raw_component_screen.py --candidates phase_purity --permutations 600
+
+THE STATISTICS, AND THE TRAP. N = 15.
+
+**-0.1480 IS NOT A SIGNIFICANCE THRESHOLD, and this script used to imply it was.**
+`1-(N/(N-1))^2` is the score of the leave-one-out MEAN predictor -- predict every
+held-out film with the average of the other fourteen. A fitted GP does not do
+that. Measured here on 2026-09-04, 300 permutations of `phase_purity` with the
+campaign's own model: the fitted GP's null has median **-0.4210** and 95th
+percentile **+0.2890**, and **28.7% of pure-noise shuffles score above -0.1480**.
+Beating it is a one-in-four event under no signal at all. The honest
+single-candidate bar is the 95th percentile of the candidate's OWN empirical
+null, which `--calibrate` measures; it runs about 0.3 to 0.4 R2 units above the
+number this project quoted for a year.
+
+A mean function LOWERS that null rather than raising it -- an OLS trend fitted on
+14 rows of shuffled y is a noise fit, and extrapolating it to the held-out row
+adds error. Median goes -0.4075 (no mean) -> -0.4368 (one feature) -> -0.5384
+(two). So a mean-function candidate is not flattered by its null; it was simply
+being scored against a bar five times too low, like everything else.
+
+The bootstrap resolution sd is **+-0.236** -- wider than most effects anyone will
+find here. Screening K candidates and reporting the best R2 is selection on the
+outcome: at K = 30 several candidates clear any fixed bar by chance alone. So
+
+ * every run prints how many candidates were screened, in the summary, always;
+ * `--permutations` runs the rank permutation test, which is the project's
+ adjudicator because rank is what the acquisition consumes;
+ * `--family-size K` Bonferroni-adjusts that p for a screen of K candidates.
+ Report the ADJUSTED p when the candidate was chosen by looking at this data.
+
+A candidate that beats the null but whose adjusted p is not significant is a
+hypothesis for the next batch of films, not a finding.
+"""
+
+from __future__ import annotations
+
+import argparse
+import ast
+import json
+import sys
+import warnings
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import openpyxl
+import torch
+from scipy.stats import spearmanr
+
+from mobo_kit.campaign import load_campaign_config, normalise_inputs
+from mobo_kit.loocv import RESOLUTION_SD_AT_15, null_loo_r2, resolution_sd
+from mobo_kit.model_validation import DIM_SCALED_PRIOR, fit_model_variant
+from mobo_kit.structured_mean import (
+ MeanFeature,
+ StructuredMeanSpec,
+ build_structured_mean,
+)
+
+warnings.filterwarnings("ignore")
+
+DEFAULT_WORKBOOK = "local_inputs/Final Summary Table.xlsx"
+DEFAULT_CONFIG = "configs/campaign_d2d_perovskite_final.yaml"
+
+#: The raw measurement namespace, by workbook column. Names are what a candidate
+#: expression may refer to; the letters are where they live on the R0 sheet.
+#: Deliberately includes BOTH the raw and the normalised form of everything, so a
+#: candidate can be written either way and the difference measured rather than
+#: assumed.
+COLUMNS: dict[str, str] = {
+ # design
+ "speed_1": "B", "time_1": "C", "speed_2": "D", "time_2": "E",
+ "precur_conc": "F", "precur_vol": "G", "anneal_temp": "H",
+ "anneal_time": "I", "anti_vol": "J", "anti_time": "K",
+ # uniformity family
+ "coverage": "L",
+ "uniformity_raw": "M",
+ "uniformity_clamped": "N",
+ "one_minus_unif": "O",
+ "phase_purity": "P",
+ # optoelectronic family
+ "voc_raw": "Q",
+ "voc_clamped": "R",
+ "voc_norm": "S",
+ "g_light": "T",
+ "g_dark": "U",
+ "g_dark_floor": "V",
+ "photocond_raw": "W",
+ "photocond": "X",
+ "photocond_norm": "Y",
+ "photosens_ratio": "Z",
+ "photosens_capped": "AA",
+ "photosens_norm": "AB",
+ # thickness family
+ "thickness_nm": "AH",
+ "thickness_norm": "AI",
+ # the stored composites, for reference only
+ "score_uniformity": "AJ",
+ "score_opto": "AK",
+ "score_thickness": "AL",
+}
+
+#: The baseline screen. Every RAW component on its own, then the stored scores it
+#: is being compared against, then the handful of composites that can be argued
+#: for from the chemistry rather than fitted from the data.
+BUILT_IN: list[dict[str, str]] = [
+ # --- uniformity family, raw ---
+ {"name": "coverage", "expr": "coverage", "family": "uniformity"},
+ {"name": "one_minus_unif", "expr": "one_minus_unif", "family": "uniformity"},
+ {"name": "uniformity_raw", "expr": "uniformity_raw", "family": "uniformity"},
+ {"name": "log_uniformity_raw", "expr": "np.log(uniformity_raw)", "family": "uniformity"},
+ {"name": "phase_purity", "expr": "phase_purity", "family": "uniformity"},
+ {"name": "logit_phase_purity", "expr": "np.log(phase_purity / (1 - phase_purity))",
+ "family": "uniformity"},
+ # --- optoelectronic family, raw ---
+ {"name": "voc_raw", "expr": "voc_raw", "family": "optoelectronic"},
+ {"name": "g_light", "expr": "g_light", "family": "optoelectronic"},
+ {"name": "log_g_light", "expr": "np.log(g_light)", "family": "optoelectronic"},
+ {"name": "photocond", "expr": "photocond", "family": "optoelectronic"},
+ {"name": "log_photocond", "expr": "np.log(photocond)", "family": "optoelectronic"},
+ {"name": "photosens_ratio", "expr": "photosens_ratio", "family": "optoelectronic"},
+ {"name": "log_g_dark_floor", "expr": "np.log(g_dark_floor)", "family": "optoelectronic"},
+ # --- thickness family, the known-good control ---
+ {"name": "thickness_nm", "expr": "thickness_nm", "family": "thickness"},
+ {"name": "log_thickness_nm", "expr": "np.log(thickness_nm)", "family": "thickness"},
+ # --- the stored composites, as the thing to beat ---
+ {"name": "STORED_score_uniformity", "expr": "score_uniformity", "family": "stored"},
+ {"name": "STORED_score_opto", "expr": "score_opto", "family": "stored"},
+ {"name": "STORED_score_thickness", "expr": "score_thickness", "family": "stored"},
+]
+
+#: AST nodes a candidate expression may contain. No attribute access except the
+#: `np.` namespace, no calls except to numpy, no comprehensions, no names outside
+#: the measurement namespace. These expressions come from the analyst (or from an
+#: agent proposing candidates), not from the workbook, but a screen that silently
+#: evaluates arbitrary text is a bad instrument regardless of who is typing.
+_ALLOWED_NODES = (
+ ast.Expression, ast.BinOp, ast.UnaryOp, ast.Constant, ast.Name, ast.Load,
+ ast.Call, ast.Attribute, ast.Add, ast.Sub, ast.Mult, ast.Div, ast.Pow,
+ ast.USub, ast.UAdd, ast.Mod, ast.Tuple, ast.keyword,
+)
+_ALLOWED_NP = {
+ "log", "log10", "log1p", "exp", "sqrt", "abs", "clip", "minimum", "maximum",
+ "power", "square", "cbrt", "sign", "arctan", "tanh", "mean", "prod", "sum",
+}
+
+
+def _check_expression(expr: str) -> None:
+ tree = ast.parse(expr, mode="eval")
+ for node in ast.walk(tree):
+ if not isinstance(node, _ALLOWED_NODES):
+ raise ValueError(f"{type(node).__name__} is not allowed in a candidate expression")
+ if isinstance(node, ast.Attribute):
+ if not (isinstance(node.value, ast.Name) and node.value.id == "np"):
+ raise ValueError("only the `np.` namespace may be attribute-accessed")
+ if node.attr not in _ALLOWED_NP:
+ raise ValueError(f"np.{node.attr} is not on the allowed list")
+ if isinstance(node, ast.Name) and node.id not in COLUMNS and node.id != "np":
+ raise ValueError(
+ f"unknown name {node.id!r}; the measurement namespace is "
+ + ", ".join(sorted(COLUMNS))
+ )
+
+
+def read_measurements(workbook: Path, sheet: str, n_rows: int | None = None) -> dict[str, np.ndarray]:
+ """Every named column, as float, stopping at the first blank sample number."""
+ ws = openpyxl.load_workbook(workbook, data_only=True)[sheet]
+ last = 1
+ for row in range(2, ws.max_row + 1):
+ if ws[f"A{row}"].value is None:
+ break
+ last = row
+ if n_rows is not None:
+ last = min(last, 1 + n_rows)
+ out: dict[str, np.ndarray] = {}
+ for name, letter in COLUMNS.items():
+ values = [ws[f"{letter}{row}"].value for row in range(2, last + 1)]
+ out[name] = np.array(
+ [np.nan if v is None else float(v) for v in values], dtype=float
+ )
+ return out
+
+
+def evaluate(expr: str, space: Mapping[str, np.ndarray]) -> np.ndarray:
+ _check_expression(expr)
+ value = eval( # noqa: S307 - namespace is whitelisted by _check_expression
+ compile(ast.parse(expr, mode="eval"), "", "eval"),
+ {"__builtins__": {}, "np": np},
+ dict(space),
+ )
+ return np.asarray(value, dtype=float)
+
+
+def _mean_spec(item: Mapping[str, Any], response: str) -> StructuredMeanSpec | None:
+ """Build a candidate's structured mean, if it declares one.
+
+ A feature may be a bare column name (identity transform) or
+ ``{"column": ..., "transform": "log"}`` -- the campaign config's own shape.
+ Without the second form this screen COULD NOT EXPRESS the mean function the
+ live campaign actually runs, ``log(speed_1) + log(precur_conc)`` on a log
+ response, so every "beats the incumbent" comparison it made was against a
+ different model. Found 2026-09-04 by an adversarial verifier; the incumbent
+ measures +0.7423, matching the config's own recorded +0.7422, against the
+ +0.7633 the screen had been calling it.
+ """
+ features = item.get("mean_features")
+ if not features:
+ return None
+ return StructuredMeanSpec(
+ response=response,
+ features=tuple(
+ MeanFeature(feature)
+ if isinstance(feature, str)
+ else MeanFeature(
+ str(feature["column"]), str(feature.get("transform", "identity"))
+ )
+ for feature in features
+ ),
+ )
+
+
+def loo_r2(
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ y: np.ndarray,
+ *,
+ seed: int = 73,
+ mean_spec: StructuredMeanSpec | None = None,
+) -> dict[str, Any]:
+ """Exact leave-one-out under the campaign's own model variant.
+
+ Refits the structured mean INSIDE every fold when one is given. Fitting it
+ once on everything leaks the held-out value into the mean function, which is
+ the single easiest way to manufacture a result on 15 rows.
+ """
+ X_norm = normalise_inputs(config, np.asarray(X_phys, float))
+ y = np.asarray(y, float)
+ n = len(y)
+ design_names = [item["name"] for item in config["inputs"]]
+ lowers = np.array([float(item["start"]) for item in config["inputs"]])
+ uppers = np.array([float(item["stop"]) for item in config["inputs"]])
+ mu = np.empty(n)
+ collapsed = 0
+ previous = torch.get_num_threads()
+ torch.set_num_threads(1)
+ try:
+ for held in range(n):
+ keep = [i for i in range(n) if i != held]
+ target = y[keep]
+ module = None
+ if mean_spec is not None:
+ module, target = build_structured_mean(
+ np.asarray(X_phys, float)[keep], y[keep], mean_spec,
+ design_names, lowers, uppers,
+ )
+ torch.manual_seed(seed)
+ try:
+ record = fit_model_variant(
+ torch.tensor(X_norm[keep], dtype=torch.double),
+ torch.tensor(target, dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(range(len(keep))),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ seed=seed,
+ mean_module=module,
+ )
+ except Exception:
+ collapsed += 1
+ mu[held] = target.mean()
+ continue
+ gp = record.model.models[0]
+ gp.eval()
+ with torch.no_grad():
+ value = float(
+ gp.posterior(
+ torch.tensor(X_norm[held: held + 1], dtype=torch.double)
+ ).mean.reshape(-1)[0]
+ )
+ mu[held] = np.exp(value) if (mean_spec and mean_spec.response == "log") else value
+ finally:
+ torch.set_num_threads(previous)
+ if collapsed == n:
+ # THE FALLBACK'S OWN SCORE IS THE NUMBER THIS PROJECT USED AS ITS NULL.
+ # Every fold falling back to its training mean makes `mu` the
+ # leave-one-out mean predictor exactly, whose R2 is 1-(n/(n-1))^2 -- so a
+ # totally broken run would report -0.1480 and rho -1.0000 and look like an
+ # ordinary no-signal result. Refuse instead. Found 2026-09-04 by an
+ # adversarial verifier that reproduced it with a 1e-8 perturbation of the
+ # design matrix, which pushes `normalise_inputs` a few parts in a billion
+ # outside [0, 1] and makes every fit raise.
+ raise RuntimeError(
+ f"All {n} folds failed to fit, so every prediction is its fold's "
+ "training mean. That degenerate predictor scores exactly "
+ f"{1.0 - (n / (n - 1)) ** 2:+.4f} with Spearman -1.0000, which is "
+ "indistinguishable from an ordinary no-signal result. Check the "
+ "candidate for non-finite values, a constant response, or inputs "
+ "outside their declared grids."
+ )
+ residual = ((y - mu) ** 2).sum()
+ total = ((y - y.mean()) ** 2).sum()
+ return {
+ "r2": float(1.0 - residual / total),
+ "spearman": float(spearmanr(y, mu).statistic),
+ "collapsed_folds": collapsed,
+ "predicted": mu.tolist(),
+ }
+
+
+def permutation_p(
+ config, X_phys, y, observed_rho, *, permutations: int, seed: int = 73, mean_spec=None
+) -> dict[str, Any]:
+ """Rank permutation null: shuffle y, redo the whole fold loop, count exceedances.
+
+ Rank rather than R2 because rank is what the acquisition consumes -- it never
+ sees R2 -- and because a rank null is unaffected by the heavy tails that make
+ R2 unstable at N = 15.
+ """
+ rng = np.random.default_rng(seed)
+ y = np.asarray(y, float)
+ null = []
+ for index in range(permutations):
+ shuffled = rng.permutation(y)
+ null.append(loo_r2(config, X_phys, shuffled, seed=seed, mean_spec=mean_spec)["spearman"])
+ if (index + 1) % 50 == 0:
+ print(f" permutation {index + 1}/{permutations}", file=sys.stderr, flush=True)
+ null = np.asarray(null, float)
+ exceed = int((null >= observed_rho).sum())
+ p = (exceed + 1) / (permutations + 1)
+ return {
+ "permutations": permutations,
+ "null_mean": float(null.mean()),
+ "null_sd": float(null.std(ddof=1)),
+ "exceedances": exceed,
+ "p": float(p),
+ }
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
+ parser.add_argument("--workbook", default=DEFAULT_WORKBOOK)
+ parser.add_argument("--config", default=DEFAULT_CONFIG)
+ parser.add_argument("--sheet", default="R0")
+ parser.add_argument("--seed", type=int, default=73)
+ parser.add_argument("--list", action="store_true", help="print the measurement namespace and exit")
+ parser.add_argument(
+ "--candidates",
+ default=None,
+ help="comma-separated names from the built-in screen; default is all of them",
+ )
+ parser.add_argument(
+ "--spec",
+ default=None,
+ help=(
+ 'JSON list of {"name","expr"[,"family"][,"mean_features"]} to screen '
+ "INSTEAD of the built-ins. `expr` is a numpy expression over the "
+ "measurement namespace (see --list). `mean_features` is a list of "
+ 'design column names to fit a linear mean on, e.g. ["precur_conc"].'
+ ),
+ )
+ parser.add_argument("--mean-response", default="identity", choices=("identity", "log"))
+ parser.add_argument("--permutations", type=int, default=0)
+ parser.add_argument(
+ "--calibrate",
+ type=int,
+ default=0,
+ metavar="DRAWS",
+ help=(
+ "measure the EMPIRICAL null for each candidate instead of screening "
+ "it: permute y this many times, rerun the whole fold loop, and report "
+ "the null's median and 95th percentile. The 95th percentile is the "
+ "honest single-candidate bar. Use it before quoting any R2. "
+ "SLOW and single-threaded: every draw is a full fold loop, about "
+ "3 s per draw per candidate at N=15, so 500 draws is ~25 min. "
+ "Parallelise across processes if you need more."
+ ),
+ )
+ parser.add_argument(
+ "--family-size",
+ type=int,
+ default=None,
+ help="K for the Bonferroni adjustment; defaults to the number screened",
+ )
+ parser.add_argument("--out", default=None, help="write the full result table as JSON")
+ args = parser.parse_args(argv)
+
+ if args.list:
+ print("measurement namespace (name -> R0 column):")
+ for name, letter in COLUMNS.items():
+ print(f" {name:24} {letter}")
+ print("\nbuilt-in candidates:")
+ for item in BUILT_IN:
+ print(f" {item['name']:26} = {item['expr']}")
+ return 0
+
+ config = load_campaign_config(args.config)
+ space = read_measurements(Path(args.workbook), args.sheet)
+ X = np.column_stack([space[item["name"]] for item in config["inputs"]])
+ n = len(X)
+ null = null_loo_r2(n)
+
+ if args.spec:
+ candidates = json.loads(args.spec)
+ else:
+ candidates = list(BUILT_IN)
+ if args.candidates:
+ wanted = {name.strip() for name in args.candidates.split(",")}
+ unknown = wanted - {item["name"] for item in candidates}
+ if unknown:
+ raise SystemExit(f"unknown candidate(s): {sorted(unknown)}")
+ candidates = [item for item in candidates if item["name"] in wanted]
+
+ family_size = args.family_size if args.family_size is not None else len(candidates)
+
+ print(f"workbook {args.workbook} sheet {args.sheet} N = {n}")
+ print(f"null LOO R2 {null:+.4f} resolution sd +-{resolution_sd(n):.3f} "
+ f"(bootstrapped {RESOLUTION_SD_AT_15} at N=15)")
+ print(f"screening {len(candidates)} candidates Bonferroni family size K = {family_size}")
+ print()
+ header = f"{'candidate':30} {'LOO R2':>9} {'rho':>7} {'beats null':>11} expression"
+ print(header)
+ print("-" * (len(header) + 10))
+
+ if args.calibrate:
+ print(
+ f"CALIBRATION: {args.calibrate} permutations per candidate. The "
+ "theoretical -0.1480 is the score of the leave-one-out MEAN "
+ "predictor, which is NOT what a fitted GP does; the empirical null "
+ "below is."
+ )
+ print()
+ header = (
+ f"{'candidate':30} {'observed':>9} {'null med':>9} {'null p95':>9} "
+ f"{'p':>7} {'>-0.1480':>9}"
+ )
+ print(header)
+ print("-" * len(header))
+ rng = np.random.default_rng(args.seed)
+ for item in candidates:
+ y = evaluate(item["expr"], space)
+ mean_spec = _mean_spec(item, args.mean_response)
+ observed = loo_r2(config, X, y, seed=args.seed, mean_spec=mean_spec)
+ draws = np.array(
+ [
+ loo_r2(
+ config, X, rng.permutation(y), seed=args.seed,
+ mean_spec=mean_spec,
+ )["r2"]
+ for _ in range(args.calibrate)
+ ]
+ )
+ p_value = float((int((draws >= observed["r2"]).sum()) + 1) / (args.calibrate + 1))
+ print(
+ f"{item['name']:30} {observed['r2']:>+9.4f} "
+ f"{np.median(draws):>+9.4f} {np.percentile(draws, 95):>+9.4f} "
+ f"{p_value:>7.4f} {(draws > null).mean():>8.1%}"
+ )
+ print()
+ print(
+ "The last column is how often PURE NOISE beats -0.1480. If it is not "
+ "near zero, -0.1480 is not a significance threshold for this model."
+ )
+ return 0
+
+ results = []
+ for item in candidates:
+ y = evaluate(item["expr"], space)
+ if not np.isfinite(y).all():
+ print(f"{item['name']:30} {'SKIPPED':>9} non-finite values")
+ continue
+ mean_spec = _mean_spec(item, args.mean_response)
+ outcome = loo_r2(config, X, y, seed=args.seed, mean_spec=mean_spec)
+ beats = outcome["r2"] > null
+ row = {**item, "n": n, "null_loo_r2": null, **outcome}
+ if args.permutations and beats:
+ print(f" permuting {item['name']} ...", file=sys.stderr, flush=True)
+ perm = permutation_p(
+ config, X, y, outcome["spearman"],
+ permutations=args.permutations, seed=args.seed, mean_spec=mean_spec,
+ )
+ perm["p_bonferroni"] = float(min(1.0, perm["p"] * family_size))
+ row["permutation"] = perm
+ results.append(row)
+ flag = "YES" if beats else "no"
+ note = " COLLAPSED" if outcome["collapsed_folds"] else ""
+ print(
+ f"{item['name']:30} {outcome['r2']:>+9.4f} {outcome['spearman']:>+7.3f} "
+ f"{flag:>11} {item['expr']}{note}"
+ )
+ if "permutation" in row:
+ perm = row["permutation"]
+ print(
+ f"{'':30} {'permutation':>9}: p {perm['p']:.4f} "
+ f"Bonferroni x{family_size} = {perm['p_bonferroni']:.4f} "
+ f"(null rho {perm['null_mean']:+.3f} sd {perm['null_sd']:.3f})"
+ )
+
+ beat = [r for r in results if r["r2"] > null]
+ print()
+ print(f"{len(beat)} of {len(results)} screened candidates beat the null.")
+ if beat:
+ best = max(beat, key=lambda r: r["r2"])
+ print(f"best: {best['name']} R2 {best['r2']:+.4f} rho {best['spearman']:+.3f}")
+ print(
+ "REMINDER: these candidates were chosen by looking at this data. At N=15 "
+ f"the resolution sd is +-{resolution_sd(n):.3f}, so a screen of "
+ f"{family_size} will produce apparent winners by chance. Quote the "
+ "Bonferroni-adjusted permutation p, not the R2."
+ )
+ if args.out:
+ Path(args.out).write_text(json.dumps(results, indent=1), encoding="utf-8")
+ print(f"wrote {args.out}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/scripts/scripts.md b/scripts/scripts.md
deleted file mode 100644
index e69de29..0000000
diff --git a/scripts/thickness_null_chunk.py b/scripts/thickness_null_chunk.py
new file mode 100644
index 0000000..dd4ead0
--- /dev/null
+++ b/scripts/thickness_null_chunk.py
@@ -0,0 +1,66 @@
+"""One shard of the thickness permutation null.
+
+Emits JSON so shards can be pooled into a single null distribution:
+
+ python scripts/thickness_null_chunk.py --n 250 --seed 0 --out shard0.json
+
+Each shard permutes the nm measurements, redoes the full leave-one-out fit
+(refitting the structured linear mean inside every fold, so the null is not
+flattered), applies the utility transform, and records the resulting Spearman
+and R2.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import warnings
+from pathlib import Path
+
+import numpy as np
+from scipy.stats import spearmanr
+
+from thickness_permutation_and_mean import ( # noqa: E402
+ expected_score,
+ load,
+ loo_nm,
+ r2,
+)
+
+warnings.filterwarnings("ignore")
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--workbook", default="local_inputs/Summary Table.xlsx")
+ ap.add_argument("--n", type=int, required=True)
+ ap.add_argument("--seed", type=int, required=True)
+ ap.add_argument("--out", required=True)
+ ap.add_argument("--structured", action="store_true", default=True)
+ args = ap.parse_args()
+
+ X, Xphys, T_nm, score = load(Path(args.workbook))
+ n = len(T_nm)
+ rng = np.random.default_rng(args.seed)
+
+ rhos, r2s = [], []
+ for _ in range(args.n):
+ perm = rng.permutation(n)
+ mu_p, var_p = loo_nm(
+ X, T_nm[perm], structured_mean=args.structured, Xphys=Xphys
+ )
+ e_p = expected_score(mu_p, var_p)
+ rhos.append(float(spearmanr(score[perm], e_p).statistic))
+ r2s.append(float(r2(score[perm], e_p)))
+
+ Path(args.out).write_text(
+ json.dumps({"seed": args.seed, "n": args.n, "rho": rhos, "r2": r2s}),
+ encoding="utf-8",
+ )
+ print(
+ f"shard seed={args.seed} n={args.n} mean_rho={np.mean(rhos):+.4f} -> {args.out}"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/thickness_objective_check.py b/scripts/thickness_objective_check.py
new file mode 100644
index 0000000..bf5c759
--- /dev/null
+++ b/scripts/thickness_objective_check.py
@@ -0,0 +1,181 @@
+"""Acceptance check for plan rev. 2 section 1.2.
+
+Compares two ways of getting a thickness *score* prediction out of a GP:
+
+ A. train on the score directly (what the repo does today)
+ B. train on raw nanometres, then push the posterior through the 650 nm
+ Gaussian analytically via ObjectiveTransform.expected_transform
+
+Both are scored against the true score by exact leave-one-out, so the comparison
+is like for like. Also reported is the naive variant of B that transforms only
+the posterior mean, to show what ignoring the variance costs.
+
+ python scripts/thickness_objective_check.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+from openpyxl import load_workbook
+from scipy.stats import spearmanr
+
+from mobo_kit.model_validation import (
+ DIM_SCALED_PRIOR,
+ LEGACY_NO_PRIOR,
+ fit_model_variant,
+)
+from mobo_kit.objectives import ObjectiveSpec, ObjectiveTransform
+
+DESIGN = (
+ ("speed_1", 1000.0, 6000.0),
+ ("time_1", 5.0, 50.0),
+ ("speed_2", 0.0, 5000.0),
+ ("time_2", 10.0, 60.0),
+ ("precur_conc", 1.0, 2.0),
+ ("precur_vol", 40.0, 200.0),
+ ("anneal_temp", 100.0, 185.0),
+ ("anneal_time", 10.0, 60.0),
+ ("anti_vol", 100.0, 200.0),
+ ("anti_time", 9.0, 25.0),
+)
+TARGET_NM = 650.0
+# workbook: =EXP(-(((X-650)/250)^2)), which is exp(-0.5*((X-650)/s)^2) with s = 250/sqrt(2)
+SIGMA_NM = 250.0 / math.sqrt(2.0)
+
+THICKNESS_UTILITY = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "thickness", "target", "gaussian_target", target=TARGET_NM, sigma=SIGMA_NM
+ )
+ ],
+ version="D2D-thickness-nm-v1",
+)
+
+
+def load(path: Path):
+ ws = load_workbook(path, data_only=True)["Sheet1"]
+ rows = [r for r in ws.iter_rows(min_row=2, values_only=True) if r[0] is not None]
+ lo = np.array([d[1] for d in DESIGN])
+ hi = np.array([d[2] for d in DESIGN])
+ X = (np.array([[r[j] for j in range(1, 11)] for r in rows], float) - lo) / (hi - lo)
+ T_nm = np.array([r[23] for r in rows], float) # X: Thickness (avg)
+ score = np.array([r[27] for r in rows], float) # AB: Thickness score
+ ids = tuple(int(r[0]) for r in rows)
+ return ids, X, T_nm, score
+
+
+def loo_posterior(X, y, ids, variant, seed=73):
+ """Exact LOO posterior mean and variance for a single objective."""
+ n = len(y)
+ mean = np.empty(n)
+ var = np.empty(n)
+ for i in range(n):
+ keep = [j for j in range(n) if j != i]
+ torch.manual_seed(seed)
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ rec = fit_model_variant(
+ torch.tensor(X[keep], dtype=torch.double),
+ torch.tensor(y[keep], dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(ids[j] for j in keep),
+ objective_names=("y",),
+ variant=variant,
+ seed=seed,
+ )
+ rec.model.eval()
+ with torch.no_grad():
+ post = rec.model.posterior(
+ torch.tensor(X[i : i + 1], dtype=torch.double)
+ )
+ mean[i] = float(post.mean.reshape(-1)[0])
+ var[i] = float(post.variance.reshape(-1)[0])
+ return mean, var
+
+
+def r2(y, p):
+ return 1.0 - np.sum((y - p) ** 2) / np.sum((y - y.mean()) ** 2)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--workbook", default="local_inputs/Summary Table.xlsx")
+ ap.add_argument(
+ "--variant",
+ default="dim_scaled_prior",
+ choices=["dim_scaled_prior", "legacy_matern_no_prior"],
+ )
+ args = ap.parse_args()
+
+ variant = (
+ DIM_SCALED_PRIOR if args.variant == "dim_scaled_prior" else LEGACY_NO_PRIOR
+ )
+ ids, X, T_nm, score = load(Path(args.workbook))
+ n = len(ids)
+ null = 1.0 - (n / (n - 1)) ** 2
+
+ # sanity: the workbook score must be reproducible from raw nm
+ recomputed = (
+ THICKNESS_UTILITY(torch.tensor(T_nm, dtype=torch.double).unsqueeze(-1))
+ .numpy()
+ .reshape(-1)
+ )
+ print(
+ f"score == exp(-((T-650)/250)^2) from raw nm ? "
+ f"max|diff| = {np.abs(recomputed - score).max():.2e}"
+ )
+ print(f"model variant: {variant.name}")
+ print(f"N = {n}, LOO-mean null R2 = {null:+.4f}\n")
+
+ # --- A: train on the score directly -----------------------------------
+ a_mean, _ = loo_posterior(X, score, ids, variant)
+
+ # --- B: train on nanometres, transform the posterior -------------------
+ b_mu, b_var = loo_posterior(X, T_nm, ids, variant)
+ mu_t = torch.tensor(b_mu, dtype=torch.double).unsqueeze(-1)
+ var_t = torch.tensor(b_var, dtype=torch.double).unsqueeze(-1)
+ b_expected = THICKNESS_UTILITY.expected_transform(mu_t, var_t).numpy().reshape(-1)
+ b_meanonly = THICKNESS_UTILITY(mu_t).numpy().reshape(-1)
+
+ print("=== predicting the THICKNESS SCORE, exact leave-one-out ===")
+ print(f"{'approach':>46} {'LOO R2':>9} {'Spearman':>10}")
+ for label, pred in (
+ ("A train on score directly", a_mean),
+ ("B train on nm -> E[score] (analytic)", b_expected),
+ ("B' train on nm -> score(mean) only", b_meanonly),
+ ):
+ print(
+ f"{label:>46} {r2(score, pred):>+9.4f} "
+ f"{spearmanr(score, pred).statistic:>+10.4f}"
+ )
+ print(f"{'null (LOO mean)':>46} {null:>+9.4f} {-1.0:>+10.4f}")
+
+ print("\n=== the underlying raw-nm model ===")
+ print(
+ f" LOO R2 = {r2(T_nm, b_mu):+.4f} Spearman = "
+ f"{spearmanr(T_nm, b_mu).statistic:+.4f}"
+ )
+ print(
+ f" posterior sd over the 15 folds: min {np.sqrt(b_var).min():.1f} nm, "
+ f"median {np.median(np.sqrt(b_var)):.1f} nm, max {np.sqrt(b_var).max():.1f} nm"
+ )
+
+ print("\n=== what the uncertainty penalty is doing ===")
+ print(
+ f"{'sample':>7} {'true nm':>9} {'pred nm':>9} {'sd nm':>8} "
+ f"{'true score':>11} {'E[score]':>10} {'score(mean)':>12}"
+ )
+ for i, sid in enumerate(ids):
+ print(
+ f"{sid:>7} {T_nm[i]:>9.0f} {b_mu[i]:>9.0f} {math.sqrt(b_var[i]):>8.0f} "
+ f"{score[i]:>11.4f} {b_expected[i]:>10.4f} {b_meanonly[i]:>12.4f}"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/thickness_permutation_and_mean.py b/scripts/thickness_permutation_and_mean.py
new file mode 100644
index 0000000..b2fb77b
--- /dev/null
+++ b/scripts/thickness_permutation_and_mean.py
@@ -0,0 +1,252 @@
+"""Two evaluations that gate R1 generation.
+
+1. Permutation test on the route-B Spearman: permute the thickness measurements,
+ redo the full leave-one-out fit plus analytic transform, and build the null
+ distribution. Turns "rank improved" into a p-value.
+
+2. Structured mean function. Spin-coating physics says T ~ speed^-0.5, and
+ log T ~ log(speed_1) + log(precur_conc) reaches LOO R2 +0.449 while the plain
+ 10-input GP reaches +0.183. Test whether giving the GP a linear mean on those
+ two log inputs closes the gap. Selection of the two inputs is from physics,
+ fixed before fitting; the linear coefficients are refitted inside every fold.
+
+ python scripts/thickness_permutation_and_mean.py --permutations 200
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+from botorch.fit import fit_gpytorch_mll
+from botorch.models import SingleTaskGP
+from botorch.models.transforms.outcome import Standardize
+from botorch.models.utils.gpytorch_modules import (
+ get_covar_module_with_dim_scaled_prior,
+ get_gaussian_likelihood_with_lognormal_prior,
+)
+from gpytorch.kernels import ScaleKernel
+from gpytorch.means import LinearMean
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from openpyxl import load_workbook
+from scipy.stats import spearmanr
+from sklearn.linear_model import LinearRegression
+
+from mobo_kit.objectives import ObjectiveSpec, ObjectiveTransform
+
+warnings.filterwarnings("ignore")
+torch.set_default_dtype(torch.double)
+
+DESIGN = [
+ ("speed_1", 1000, 6000),
+ ("time_1", 5, 50),
+ ("speed_2", 0, 5000),
+ ("time_2", 10, 60),
+ ("precur_conc", 1, 2),
+ ("precur_vol", 40, 200),
+ ("anneal_temp", 100, 185),
+ ("anneal_time", 10, 60),
+ ("anti_vol", 100, 200),
+ ("anti_time", 9, 25),
+]
+TARGET_NM, SIGMA_NM = 650.0, 250.0 / math.sqrt(2.0)
+UTILITY = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "thickness", "target", "gaussian_target", target=TARGET_NM, sigma=SIGMA_NM
+ )
+ ],
+ version="D2D-thickness-nm-v1",
+)
+SPEED_1, PRECUR_CONC = 0, 4
+
+
+def load(path: Path):
+ ws = load_workbook(path, data_only=True)["Sheet1"]
+ rows = [r for r in ws.iter_rows(min_row=2, values_only=True) if r[0] is not None]
+ lo = np.array([d[1] for d in DESIGN], float)
+ hi = np.array([d[2] for d in DESIGN], float)
+ Xp = np.array([[r[j] for j in range(1, 11)] for r in rows], float)
+ return (
+ (Xp - lo) / (hi - lo),
+ Xp,
+ np.array([r[23] for r in rows], float),
+ np.array([r[27] for r in rows], float),
+ )
+
+
+def _gp(X, y, *, mean_features=None):
+ """SingleTaskGP under the dim_scaled_prior contract, optional linear mean."""
+ base = get_covar_module_with_dim_scaled_prior(
+ ard_num_dims=X.shape[1], use_rbf_kernel=False
+ )
+ model = SingleTaskGP(
+ X,
+ y,
+ covar_module=ScaleKernel(base),
+ likelihood=get_gaussian_likelihood_with_lognormal_prior(),
+ outcome_transform=Standardize(m=1),
+ )
+ if mean_features is not None:
+ model.mean_module = LinearMean(input_size=mean_features, bias=True)
+ fit_gpytorch_mll(ExactMarginalLogLikelihood(model.likelihood, model))
+ return model
+
+
+def loo_nm(X, y, *, structured_mean=False, Xphys=None, seed=73):
+ """LOO posterior over raw nm. With structured_mean, a physics linear trend on
+ log(speed_1) and log(precur_conc) is removed first and added back after."""
+ n = len(y)
+ mu = np.empty(n)
+ var = np.empty(n)
+ for i in range(n):
+ keep = [j for j in range(n) if j != i]
+ torch.manual_seed(seed)
+ if structured_mean:
+ F = np.c_[np.log(Xphys[:, SPEED_1]), np.log(Xphys[:, PRECUR_CONC])]
+ lin = LinearRegression().fit(F[keep], np.log(y[keep]))
+ resid = np.log(y[keep]) - lin.predict(F[keep])
+ m = _gp(torch.tensor(X[keep]), torch.tensor(resid).unsqueeze(-1))
+ m.eval()
+ with torch.no_grad():
+ p = m.posterior(torch.tensor(X[i : i + 1]))
+ r_mu = float(p.mean.reshape(-1)[0])
+ r_var = float(p.variance.reshape(-1)[0])
+ log_mu = float(lin.predict(F[i : i + 1])[0]) + r_mu
+ # lognormal moments back to nm
+ mu[i] = math.exp(log_mu + r_var / 2.0)
+ var[i] = (math.exp(r_var) - 1.0) * math.exp(2 * log_mu + r_var)
+ else:
+ m = _gp(torch.tensor(X[keep]), torch.tensor(y[keep]).unsqueeze(-1))
+ m.eval()
+ with torch.no_grad():
+ p = m.posterior(torch.tensor(X[i : i + 1]))
+ mu[i] = float(p.mean.reshape(-1)[0])
+ var[i] = float(p.variance.reshape(-1)[0])
+ return mu, var
+
+
+def r2(y, p):
+ return 1.0 - np.sum((y - p) ** 2) / np.sum((y - y.mean()) ** 2)
+
+
+def expected_score(mu, var):
+ return (
+ UTILITY.expected_transform(
+ torch.tensor(mu).unsqueeze(-1), torch.tensor(var).unsqueeze(-1)
+ )
+ .numpy()
+ .reshape(-1)
+ )
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--workbook", default="local_inputs/Summary Table.xlsx")
+ ap.add_argument("--permutations", type=int, default=200)
+ ap.add_argument("--seed", type=int, default=0)
+ ap.add_argument(
+ "--structured", action="store_true", help="permute the structured-mean pipeline"
+ )
+ args = ap.parse_args()
+
+ X, Xphys, T_nm, score = load(Path(args.workbook))
+ n = len(T_nm)
+ null_r2 = 1.0 - (n / (n - 1)) ** 2
+
+ print("=== 2. structured mean on log(speed_1), log(precur_conc) ===")
+ plain_mu, plain_var = loo_nm(X, T_nm)
+ struct_mu, struct_var = loo_nm(X, T_nm, structured_mean=True, Xphys=Xphys)
+ print(f"{'raw-nm model':>34} {'LOO R2':>9} {'Spearman':>10}")
+ for label, mu in (
+ ("plain GP, 10 inputs", plain_mu),
+ ("GP + physics linear mean", struct_mu),
+ ):
+ print(
+ f"{label:>34} {r2(T_nm, mu):>+9.4f} "
+ f"{spearmanr(T_nm, mu).statistic:>+10.4f}"
+ )
+ print(f"{'2-input log-log reference':>34} {'+0.4494':>9} {'+0.7143':>10}")
+
+ print(f"\n{'resulting score prediction':>34} {'LOO R2':>9} {'Spearman':>10}")
+ plain_s = expected_score(plain_mu, plain_var)
+ struct_s = expected_score(struct_mu, struct_var)
+ for label, s in (
+ ("plain GP -> E[score]", plain_s),
+ ("structured mean -> E[score]", struct_s),
+ ):
+ print(
+ f"{label:>34} {r2(score, s):>+9.4f} "
+ f"{spearmanr(score, s).statistic:>+10.4f}"
+ )
+ print(f"{'null':>34} {null_r2:>+9.4f} {-1.0:>+10.4f}")
+
+ print("\n=== 3. how much of the structured-mean result rests on sample 1? ===")
+ print(" sample 1 is the off-grid literature control and the one")
+ print(" extrapolation point, so LOO metrics are sensitive to it")
+ keep1 = np.arange(1, n)
+ mu_x, var_x = loo_nm(
+ X[keep1], T_nm[keep1], structured_mean=True, Xphys=Xphys[keep1]
+ )
+ s_x = expected_score(mu_x, var_x)
+ n_x = len(keep1)
+ print(f"{'':>34} {'LOO R2':>9} {'Spearman':>10}")
+ print(
+ f"{'raw nm, all 15':>34} {r2(T_nm, struct_mu):>+9.4f} "
+ f"{spearmanr(T_nm, struct_mu).statistic:>+10.4f}"
+ )
+ print(
+ f"{'raw nm, sample 1 excluded (N=14)':>34} {r2(T_nm[keep1], mu_x):>+9.4f} "
+ f"{spearmanr(T_nm[keep1], mu_x).statistic:>+10.4f}"
+ )
+ print(
+ f"{'score, sample 1 excluded':>34} {r2(score[keep1], s_x):>+9.4f} "
+ f"{spearmanr(score[keep1], s_x).statistic:>+10.4f}"
+ )
+ print(f"{'null at N=14':>34} {1.0 - (n_x/(n_x-1))**2:>+9.4f} {-1.0:>+10.4f}")
+
+ print(f"\n=== 1. permutation test, {args.permutations} shuffles ===")
+ print(" permuting the nm measurements and redoing LOO + transform")
+ print(
+ f" structured mean: {args.structured} "
+ "(when true the linear mean is refit inside every null fold too)"
+ )
+ rng = np.random.default_rng(args.seed)
+ observed = struct_s if args.structured else plain_s
+ obs_rho = spearmanr(score, observed).statistic
+ obs_r2 = r2(score, observed)
+ null_rho, null_r2s = [], []
+ for k in range(args.permutations):
+ perm = rng.permutation(n)
+ T_p, s_p = T_nm[perm], score[perm]
+ mu_p, var_p = loo_nm(X, T_p, structured_mean=args.structured, Xphys=Xphys)
+ e_p = expected_score(mu_p, var_p)
+ null_rho.append(spearmanr(s_p, e_p).statistic)
+ null_r2s.append(r2(s_p, e_p))
+ if (k + 1) % 25 == 0:
+ print(
+ f" {k+1}/{args.permutations} done, running null mean rho = "
+ f"{np.mean(null_rho):+.4f}"
+ )
+ null_rho = np.array(null_rho)
+ null_r2s = np.array(null_r2s)
+ print(f"\n observed Spearman = {obs_rho:+.4f}")
+ print(
+ f" null Spearman : mean {null_rho.mean():+.4f}, sd {null_rho.std():.4f}, "
+ f"95th pct {np.percentile(null_rho, 95):+.4f}"
+ )
+ print(f" p(null >= observed) = {np.mean(null_rho >= obs_rho):.4f}")
+ print(f"\n observed R2 = {obs_r2:+.4f}")
+ print(f" null R2 : mean {null_r2s.mean():+.4f}, sd {null_r2s.std():.4f}")
+ print(f" p(null >= observed) = {np.mean(null_r2s >= obs_r2):.4f}")
+ print("\n Note the null mean for Spearman is NOT zero: the leave-one-out")
+ print(" shrinkage artifact drags it negative, which is exactly why a")
+ print(" positive observed value is meaningful.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/scripts/validate_structured_means.py b/scripts/validate_structured_means.py
new file mode 100644
index 0000000..64ce9ad
--- /dev/null
+++ b/scripts/validate_structured_means.py
@@ -0,0 +1,149 @@
+"""Confirm the declarative structured means reproduce the hand-rolled results.
+
+Targets, exact leave-one-out, null = -0.1480:
+
+ thickness (nm) plain GP +0.183 -> structured +0.384
+ optoelectronic plain GP ? -> structured +0.244 (anneal_temp alone)
+
+ python scripts/validate_structured_means.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+from openpyxl import load_workbook
+from scipy.stats import spearmanr
+
+from mobo_kit.campaign import load_campaign_config
+from mobo_kit.model_validation import DIM_SCALED_PRIOR, fit_model_variant
+from mobo_kit.structured_mean import (
+ MeanFeature,
+ StructuredMeanSpec,
+ apply_structured_mean,
+ fit_structured_mean,
+)
+
+warnings.filterwarnings("ignore")
+torch.set_num_threads(1)
+
+THICKNESS = StructuredMeanSpec(
+ response="log",
+ features=(MeanFeature("speed_1", "log"), MeanFeature("precur_conc", "log")),
+)
+OPTO = StructuredMeanSpec(
+ response="identity", features=(MeanFeature("anneal_temp", "identity"),)
+)
+
+
+def _gp_residual_posterior(X_norm, resid, test_norm, seed=73):
+ rec = fit_model_variant(
+ torch.tensor(X_norm, dtype=torch.double),
+ torch.tensor(resid, dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(range(len(X_norm))),
+ objective_names=("residual",),
+ variant=DIM_SCALED_PRIOR,
+ seed=seed,
+ )
+ rec.model.eval()
+ with torch.no_grad():
+ post = rec.model.posterior(torch.tensor(test_norm, dtype=torch.double))
+ return (
+ float(post.mean.reshape(-1)[0]),
+ float(post.variance.reshape(-1)[0]),
+ )
+
+
+def loo(X_phys, X_norm, y, names, spec):
+ """Exact LOO. Mean coefficients refit on the 14 training rows each fold."""
+ n = len(y)
+ mean = np.empty(n)
+ var = np.empty(n)
+ for i in range(n):
+ keep = [j for j in range(n) if j != i]
+ if spec is None:
+ r_mu, r_var = _gp_residual_posterior(
+ X_norm[keep], np.asarray(y, float)[keep], X_norm[i : i + 1]
+ )
+ mean[i], var[i] = r_mu, r_var
+ else:
+ coef, resid = fit_structured_mean(
+ X_phys[keep], np.asarray(y, float)[keep], spec, names
+ )
+ r_mu, r_var = _gp_residual_posterior(X_norm[keep], resid, X_norm[i : i + 1])
+ post = apply_structured_mean(
+ coef,
+ X_phys[i : i + 1],
+ spec,
+ names,
+ np.array([r_mu]),
+ np.array([r_var]),
+ )
+ if post.link == "log":
+ # report on the original scale for comparability
+ mean[i] = float(np.exp(post.mean[0] + post.variance[0] / 2.0))
+ var[i] = float(
+ (np.exp(post.variance[0]) - 1.0)
+ * np.exp(2 * post.mean[0] + post.variance[0])
+ )
+ else:
+ mean[i], var[i] = float(post.mean[0]), float(post.variance[0])
+ return mean, var
+
+
+def r2(y, p):
+ y = np.asarray(y, float)
+ return 1.0 - np.sum((y - p) ** 2) / np.sum((y - y.mean()) ** 2)
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(description=__doc__)
+ ap.add_argument("--workbook", default="local_inputs/Summary Table.xlsx")
+ ap.add_argument("--config", default="configs/campaign_d2d_perovskite.yaml")
+ args = ap.parse_args()
+
+ config = load_campaign_config(args.config)
+ names = [item["name"] for item in config["inputs"]]
+ lo = np.array([item["start"] for item in config["inputs"]], float)
+ hi = np.array([item["stop"] for item in config["inputs"]], float)
+
+ ws = load_workbook(Path(args.workbook), data_only=True)["Sheet1"]
+ rows = [r for r in ws.iter_rows(min_row=2, values_only=True) if r[0] is not None]
+ X_phys = np.array([[float(r[j]) for j in range(1, 11)] for r in rows])
+ X_norm = (X_phys - lo) / (hi - lo)
+ thickness_nm = np.array([float(r[23]) for r in rows])
+ optoelectronic = np.array([float(r[26]) for r in rows])
+
+ n = len(rows)
+ null = 1.0 - (n / (n - 1)) ** 2
+ print(f"N = {n}, null LOO R2 = {null:+.4f}\n")
+ print(
+ f"{'objective':>16} {'mean function':>34} {'LOO R2':>9} {'Spearman':>10} {'target':>9}"
+ )
+
+ cases = [
+ ("thickness nm", "none (plain GP)", thickness_nm, None, "+0.183"),
+ (
+ "thickness nm",
+ "log T ~ log(speed_1)+log(conc)",
+ thickness_nm,
+ THICKNESS,
+ "+0.384",
+ ),
+ ("optoelectronic", "none (plain GP)", optoelectronic, None, "-"),
+ ("optoelectronic", "linear anneal_temp", optoelectronic, OPTO, "+0.244"),
+ ]
+ for label, mean_label, y, spec, target in cases:
+ mu, _ = loo(X_phys, X_norm, y, names, spec)
+ print(
+ f"{label:>16} {mean_label:>34} {r2(y, mu):>+9.4f} "
+ f"{spearmanr(y, mu).statistic:>+10.4f} {target:>9}"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/setup.py b/setup.py
index 6618195..9635433 100644
--- a/setup.py
+++ b/setup.py
@@ -1,43 +1,9 @@
-from setuptools import setup, find_packages
+"""Compatibility shim for tools that still invoke ``setup.py`` directly.
-# Read requirements from requirements.txt
-with open("requirements.txt", "r") as f:
- requirements = [line.strip() for line in f if line.strip() and not line.startswith("#")]
+All package metadata and dependencies live in ``pyproject.toml``.
+"""
-# Read README for long description
-with open("README.md", "r", encoding="utf-8") as f:
- long_description = f.read()
+from setuptools import setup
-setup(
- name="mobo-kit",
- version="0.1.0",
- description="Multi-objective Bayesian optimization toolkit for functional thin-film fabrication",
- long_description=long_description,
- long_description_content_type="text/markdown",
- author="Ethan Schwartz, Daniel Abdoue, Nicky Evans, Tonio Buonassisi",
- author_email="ebuddy23@uw.edu",
- url="https://github.com/PV-Lab/MOBO-FOM",
- packages=find_packages(where="src"),
- package_dir={"": "src"},
- install_requires=requirements,
- classifiers=[
- "Development Status :: 3 - Alpha",
- "Intended Audience :: Science/Research",
- "License :: OSI Approved :: MIT License",
- "Operating System :: OS Independent",
- "Programming Language :: Python :: 3",
- "Programming Language :: Python :: 3.10",
- "Programming Language :: Python :: 3.11",
- "Programming Language :: Python :: 3.12",
- "Topic :: Scientific/Engineering :: Artificial Intelligence",
- "Topic :: Scientific/Engineering :: Chemistry",
- "Topic :: Scientific/Engineering :: Physics",
- ],
- python_requires=">=3.10",
- keywords="bayesian-optimization, multi-objective, materials-science, thin-films, machine-learning",
- project_urls={
- "Bug Reports": "https://github.com/PV-Lab/MOBO-FOM/issues",
- "Source": "https://github.com/PV-Lab/MOBO-FOM",
- "Documentation": "https://github.com/PV-Lab/MOBO-FOM#readme",
- },
-)
+
+setup()
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diff --git a/src/mobo_kit/attribution.py b/src/mobo_kit/attribution.py
new file mode 100644
index 0000000..358e6e6
--- /dev/null
+++ b/src/mobo_kit/attribution.py
@@ -0,0 +1,121 @@
+"""Shapley attribution over the campaign's own fitted models.
+
+**What is explained is ``E[utility]``, not the posterior mean.** For thickness the
+posterior is lognormal and the utility is a peaked Gaussian on a 650 nm target, so
+transforming the mean is biased by Jensen's inequality and blind to the variance
+that a target-seeking utility depends on. ``expected_transform`` is the correct
+route and is what the acquisition consumes.
+
+**Attributions explain the MODEL, not the world.** Where a feature appears in an
+objective's declared ``mean_function``, the model was *told* that relationship by
+the config; SHAP recovering it is a consistency check, not a discovery. And on an
+objective with no learnable signal the attributions are structure fitted to noise:
+they have real magnitude and orderly ranking and mean nothing. Both campaigns have
+produced exactly that picture for uniformity, and it is the most persuasive figure
+in the set.
+
+This module owns the explainer. ``scripts/plot_shap_attribution.py`` owns the
+beeswarm figures and the extreme-cell comparison, and imports from here; nothing is
+duplicated between them.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Mapping
+
+import numpy as np
+import torch
+
+from .campaign import normalise_inputs
+from .objectives import ObjectiveTransform
+
+__all__ = [
+ "EXACT_ENUMERATION_FEATURE_LIMIT",
+ "expected_utility_fn",
+ "mean_absolute_shap",
+ "shap_values_for",
+]
+
+#: ``KernelExplainer`` enumerates every one of ``2**d`` coalitions at or below this
+#: many features, which makes the result the EXACT Shapley decomposition rather
+#: than a sampled approximation -- and therefore independent of the seed. Above
+#: it, the values become a sample and the seed starts to matter.
+EXACT_ENUMERATION_FEATURE_LIMIT = 10
+
+
+def expected_utility_fn(
+ model: Any,
+ config: Mapping[str, Any],
+ transform: ObjectiveTransform,
+ objective_index: int,
+ *,
+ batch_rows: int = 65536,
+):
+ """``X_phys -> E[utility]`` for one objective, batched and deterministic.
+
+ ``batch_rows`` must stay ABOVE one KernelExplainer block, which is
+ ``coalitions x background rows`` -- 1022 x 23 = 23506 for a 23-point model.
+ Splitting a block is not merely twice the work: measured on this stack, the
+ same 23506 rows cost 0.03 s in one call and 0.59 s in two, a 20x penalty that
+ turned a 37 s attribution run into 470 s. Raise it if the background grows.
+ """
+
+ def f(X_phys: np.ndarray) -> np.ndarray:
+ values = np.atleast_2d(np.asarray(X_phys, dtype=float))
+ out = np.empty(values.shape[0], dtype=float)
+ model.eval()
+ with torch.no_grad():
+ for start in range(0, values.shape[0], batch_rows):
+ block = values[start : start + batch_rows]
+ X_norm = normalise_inputs(config, block)
+ posterior = model.posterior(
+ torch.tensor(X_norm, dtype=torch.double),
+ observation_noise=False,
+ )
+ utility = transform.expected_transform(
+ posterior.mean, posterior.variance.clamp_min(0.0)
+ )
+ out[start : start + block.shape[0]] = (
+ utility[..., objective_index].detach().cpu().double().numpy()
+ )
+ return out
+
+ return f
+
+
+def shap_values_for(
+ model: Any,
+ config: Mapping[str, Any],
+ transform: ObjectiveTransform,
+ objective_index: int,
+ background: np.ndarray,
+ instances: np.ndarray,
+ *,
+ seed: int,
+) -> np.ndarray:
+ """Exact Shapley values over the campaign inputs.
+
+ ``KernelExplainer`` with the default sample budget enumerates **every** one of
+ the ``2**10 = 1024`` coalitions at this feature count, so the result is the
+ exact Shapley decomposition rather than a sampled approximation -- and is
+ therefore reproducible without depending on the seed. The seed is set anyway,
+ because that stops being true the moment anyone adds an eleventh input.
+ """
+ import shap # imported here: a heavy dependency only this path needs
+
+ np.random.seed(int(seed))
+ f = expected_utility_fn(model, config, transform, objective_index)
+ explainer = shap.KernelExplainer(f, np.asarray(background, dtype=float))
+ values = explainer.shap_values(np.asarray(instances, dtype=float), silent=True)
+ return np.asarray(values, dtype=float)
+
+
+def mean_absolute_shap(values: np.ndarray) -> np.ndarray:
+ """Mean ``|SHAP|`` per feature -- the magnitude a bar chart ranks by.
+
+ Separate from the signed mean on purpose. A feature with a large mean
+ ``|SHAP|`` and a near-zero signed mean matters in both directions: that is a
+ non-monotone effect, not a weak one, and averaging the signed values would
+ report it as nothing.
+ """
+ return np.abs(np.asarray(values, dtype=float)).mean(axis=0)
diff --git a/src/mobo_kit/batch_review.py b/src/mobo_kit/batch_review.py
new file mode 100644
index 0000000..722d587
--- /dev/null
+++ b/src/mobo_kit/batch_review.py
@@ -0,0 +1,695 @@
+"""What a proposed batch actually says, before anyone fabricates it.
+
+Fifteen films is a real cost, and until 2026-07-30 nothing in this repo showed a
+human what a batch meant -- only that it passed its validity checks. This module
+builds one artifact: a table of the proposed conditions in physical units, what the
+model predicts for each and how sure it is, how far each sits from anything already
+measured, and which coordinates are pinned at a range edge. It is written as a
+``Review`` sheet beside the worklist and echoed into the launcher window, so it can
+be forwarded to the experimental group on its own.
+
+Three things it is careful about.
+
+**The predictions come from the same path the acquisition used.**
+:func:`campaign.fit_campaign_models` reproduces the round's model bit for bit from
+the same data and seed, and utility moments come from
+``ucb_hvi.posterior_utility_moments`` -- the function the round itself called. A
+review that computed utilities its own way could disagree with the batch it is
+reviewing, which would be worse than no review.
+
+**It reports physical values as well as utilities.** A utility of 0.87 means
+nothing to the person running the coater; "predicted 612 nm, 68% interval
+480-780" does. For a log-link objective the decoded value is the posterior
+*median*, because ``exp`` of a mean of logs is not a mean.
+
+**Probes are declared in config, not hardcoded here.** A probe asks a
+counterfactual: hold everything else, force one input to a value worth
+interrogating, and report what the model thinks there. What is worth
+interrogating is campaign knowledge, so it lives in the campaign YAML under
+``review.probes`` -- as do the standing notes under ``review.notes``.
+
+The artifact ends where it should: nothing here is approved.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+import torch
+
+from .campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ normalise_inputs,
+ objective_names,
+)
+from .design import build_design_from_config
+from .scores import ScoreFinding, ScoreSeverity
+from .ucb_hvi import posterior_utility_moments
+
+__all__ = [
+ "BatchReview",
+ "NOT_APPROVED",
+ "ProbeSpec",
+ "SD_MATERIALITY_RATIO",
+ "build_batch_review",
+ "classify_probe_objective",
+ "probe_specs_from_config",
+ "review_notes_from_config",
+ "write_review_sheet",
+]
+
+#: How much larger a probed region's posterior sd must be before it counts as
+#: "the model finds this region uncertain" rather than "about the same".
+#:
+#: A bare ``>`` comparison is useless here: on the campaign's R0 fit the probed
+#: sd came out 1-8% above the selected batch's on all three objectives, which a
+#: strict inequality reads as "more uncertain" even while predicted thickness
+#: utility falls from 0.79 to 0.22. A few percent of sd is not a reason to skip a
+#: region; a 3.5x drop in predicted utility is. The ratio is printed either way, so
+#: a reader who prefers a different line can draw it.
+SD_MATERIALITY_RATIO = 1.25
+
+def classify_probe_objective(
+ twin_utility: float, batch_utility: float, sd_ratio: float
+) -> str | None:
+ """How to read one objective at a probed value.
+
+ ``"known_and_bad"`` -- scores worse with no materially greater uncertainty.
+ Under UCB that is the interesting case: uncertainty is what UCB pays for, so a
+ region skipped despite equal uncertainty is being skipped on its predicted
+ value, which means the model believes it knows.
+
+ ``"uncertain_tradeoff"`` -- scores worse but genuinely more uncertain, so the
+ skip is a trade-off against the other objectives. The benign reading.
+
+ ``None`` -- does not score worse, so the model has no objection to the region
+ and its absence is about batch spacing, not merit.
+ """
+ if not (twin_utility < batch_utility):
+ return None
+ return (
+ "uncertain_tradeoff" if sd_ratio > SD_MATERIALITY_RATIO else "known_and_bad"
+ )
+
+
+NOT_APPROVED = (
+ "Nothing here is approved. These conditions were proposed by an optimiser and "
+ "have not been reviewed by anyone. Read them, decide, and record the decision "
+ "outside this file."
+)
+
+
+# --------------------------------------------------------------------------- #
+# configuration
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class ProbeSpec:
+ """A counterfactual to report beside the batch.
+
+ ``column`` forced to ``value``, everything else held at each proposed
+ candidate's own coordinates. ``note`` is the campaign's reason for asking.
+ """
+
+ name: str
+ column: str
+ value: float
+ note: str = ""
+
+ def __post_init__(self) -> None:
+ if not isinstance(self.name, str) or not self.name.strip():
+ raise ValueError("A probe needs a non-empty name.")
+ if not isinstance(self.column, str) or not self.column.strip():
+ raise ValueError(f"Probe {self.name!r} needs a column.")
+ object.__setattr__(self, "name", self.name.strip())
+ object.__setattr__(self, "column", self.column.strip())
+ value = float(self.value)
+ if not np.isfinite(value):
+ raise ValueError(f"Probe {self.name!r} needs a finite value.")
+ object.__setattr__(self, "value", value)
+
+
+def probe_specs_from_config(config: Mapping[str, Any]) -> tuple[ProbeSpec, ...]:
+ """Read ``review.probes``; absent means no probes, which is fine."""
+ review = config.get("review") or {}
+ if not isinstance(review, Mapping):
+ raise ValueError("config['review'] must be a mapping.")
+ raw = review.get("probes") or ()
+ if isinstance(raw, Mapping):
+ raw = [raw]
+ specs = []
+ for entry in raw:
+ if not isinstance(entry, Mapping):
+ raise ValueError("Each review probe must be a mapping.")
+ specs.append(
+ ProbeSpec(
+ name=str(entry.get("name", entry.get("column", "probe"))),
+ column=str(entry["column"]),
+ value=float(entry["value"]),
+ note=str(entry.get("note", "")).strip(),
+ )
+ )
+ return tuple(specs)
+
+
+def review_notes_from_config(config: Mapping[str, Any]) -> tuple[str, ...]:
+ """Standing notes to print with every review of this campaign."""
+ review = config.get("review") or {}
+ raw = review.get("notes") or ()
+ if isinstance(raw, str):
+ raw = [raw]
+ return tuple(str(note).strip() for note in raw if str(note).strip())
+
+
+# --------------------------------------------------------------------------- #
+# the review
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class BatchReview:
+ """Everything a human needs to judge one proposed batch."""
+
+ round_name: str
+ candidates: pd.DataFrame
+ """One row per condition: inputs, predicted utility and sd per objective,
+ decoded physical prediction, distance to nearest observed point, boundary
+ count and which coordinates are pinned."""
+ probes: pd.DataFrame
+ """Counterfactual rows, one block per declared probe. Empty if none."""
+ probe_verdicts: tuple[str, ...] = ()
+ notes: tuple[str, ...] = ()
+ findings: tuple[ScoreFinding, ...] = ()
+ model_warnings: tuple[str, ...] = ()
+ """Fits that succeeded but deserve distrust. Printed before anything else,
+ because they change how every number below should be read."""
+ context: dict[str, Any] = field(default_factory=dict)
+
+ def to_text(self) -> str:
+ """The whole artifact as text, for the launcher pane and the console."""
+ width = 78
+ lines: list[str] = [
+ f"BATCH REVIEW - {self.round_name}",
+ "=" * width,
+ "",
+ ]
+ for key, value in self.context.items():
+ lines.append(f"{key:<22} {value}")
+ if self.model_warnings:
+ # first, not last: these change how every number below reads
+ lines += ["", "!! READ THIS BEFORE THE NUMBERS", "-" * width]
+ for warning in self.model_warnings:
+ lines += _wrap(warning, width) + [""]
+ lines += ["", "PROPOSED CONDITIONS", "-" * width]
+ lines.append(
+ self.candidates.to_string(
+ index=False, float_format=lambda value: f"{value:g}"
+ )
+ )
+ if not self.probes.empty:
+ lines += ["", "PROBES", "-" * width]
+ lines.append(
+ self.probes.to_string(
+ index=False, float_format=lambda value: f"{value:g}"
+ )
+ )
+ if self.probe_verdicts:
+ lines += [""]
+ for verdict in self.probe_verdicts:
+ lines += _wrap(verdict, width) + [""]
+ if self.notes:
+ lines += ["NOTES", "-" * width]
+ for note in self.notes:
+ lines += _wrap(note, width) + [""]
+ if self.findings:
+ lines += ["CARRIED FROM THE MEASURED DATA", "-" * width]
+ for finding in _ordered(self.findings):
+ lines += _wrap(str(finding), width, hang=2)
+ lines += [""]
+ lines += ["=" * width] + _wrap(NOT_APPROVED, width)
+ return "\n".join(lines)
+
+
+def _wrap(text: str, width: int, *, hang: int = 0) -> list[str]:
+ import textwrap
+
+ out: list[str] = []
+ for paragraph in str(text).split("\n"):
+ wrapped = textwrap.wrap(paragraph.strip(), width=width) or [""]
+ out.extend(wrapped[:1] + [" " * hang + line for line in wrapped[1:]])
+ return out
+
+
+def _ordered(findings: Sequence[ScoreFinding]) -> list[ScoreFinding]:
+ rank = {ScoreSeverity.ERROR: 0, ScoreSeverity.WARNING: 1, ScoreSeverity.NOTE: 2}
+ return sorted(findings, key=lambda f: (rank[f.severity], f.row_position))
+
+
+def _utility_moments(
+ config: Mapping[str, Any],
+ model: Any,
+ X_phys: np.ndarray,
+ *,
+ round_name: str,
+ seed: int,
+) -> tuple[np.ndarray, np.ndarray]:
+ """Utility mean and sd through the acquisition's own posterior-sample path."""
+ transform = build_objective_transform(config)
+ settings = (config.get("rounds") or {}).get(round_name.lower()) or {}
+ samples = int(settings.get("posterior_samples", settings.get("mc_samples", 256)))
+ X_norm = torch.tensor(normalise_inputs(config, X_phys), dtype=torch.double)
+ moments = posterior_utility_moments(
+ model, X_norm, transform, mc_samples=samples, seed=seed
+ )
+ return moments.utility_mean, moments.utility_std
+
+
+def _physical_predictions(
+ config: Mapping[str, Any], model: Any, X_phys: np.ndarray
+) -> dict[str, np.ndarray]:
+ """Decoded model output per objective, in the measurement's own units.
+
+ For a log-link objective this is ``exp(mu)``: the posterior median, not the
+ mean. Labelling it a median is the honest option -- the mean of a lognormal
+ is ``exp(mu + v/2)``, and quietly reporting one as the other is the kind of
+ small lie that gets quoted back later.
+ """
+ transform = build_objective_transform(config)
+ X_norm = torch.tensor(normalise_inputs(config, X_phys), dtype=torch.double)
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(X_norm)
+ mean = posterior.mean.detach().cpu().numpy()
+ sd = posterior.variance.clamp_min(0.0).sqrt().detach().cpu().numpy()
+ out: dict[str, np.ndarray] = {}
+ for index, spec in enumerate(transform.specs):
+ mu, sigma = mean[:, index], sd[:, index]
+ if spec.model_link == "log":
+ out[spec.name] = np.column_stack(
+ [np.exp(mu), np.exp(mu - sigma), np.exp(mu + sigma)]
+ )
+ else:
+ out[spec.name] = np.column_stack([mu, mu - sigma, mu + sigma])
+ return out
+
+
+def build_batch_review(
+ config: Mapping[str, Any],
+ observed_X_phys: np.ndarray,
+ observed_Y_raw: np.ndarray,
+ conditions: pd.DataFrame,
+ *,
+ round_name: str,
+ seed: int | None = None,
+ findings: Sequence[ScoreFinding] = (),
+ context: Mapping[str, Any] | None = None,
+) -> BatchReview:
+ """Assemble the review of ``conditions`` against the model that proposed them."""
+ design = build_design_from_config(dict(config))
+ input_names = list(design.names)
+ names = list(objective_names(config))
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+
+ observed = np.asarray(observed_X_phys, dtype=float)
+ proposed = conditions[input_names].to_numpy(dtype=float)
+
+ # validate the probes BEFORE fitting: a typo in a config column name should
+ # cost nothing, not three GP fits
+ probes = probe_specs_from_config(config)
+ for probe in probes:
+ if probe.column not in input_names:
+ raise ValueError(
+ f"Probe {probe.name!r} names {probe.column!r}, which is not a "
+ f"declared input. Declared inputs: {input_names}."
+ )
+
+ model, model_warnings = fit_campaign_models(
+ config, observed, observed_Y_raw, seed=resolved_seed
+ )
+
+ utility_mean, utility_sd = _utility_moments(
+ config, model, proposed, round_name=round_name, seed=resolved_seed
+ )
+ physical = _physical_predictions(config, model, proposed)
+
+ observed_norm = normalise_inputs(config, observed)
+ proposed_norm = normalise_inputs(config, proposed)
+ gaps = np.linalg.norm(
+ proposed_norm[:, None, :] - observed_norm[None, :, :], axis=-1
+ )
+ nearest = gaps.min(axis=1)
+ nearest_index = gaps.argmin(axis=1)
+
+ at_lower = np.isclose(proposed_norm, 0.0, atol=1e-9)
+ at_upper = np.isclose(proposed_norm, 1.0, atol=1e-9)
+ pinned = at_lower | at_upper
+
+ rows: dict[str, Any] = {
+ "candidate": [f"{round_name.upper()}_C{i:02d}" for i in range(1, len(conditions) + 1)]
+ }
+ for column in input_names:
+ rows[column] = conditions[column].to_numpy(dtype=float)
+ for index, name in enumerate(names):
+ rows[f"{name}_utility"] = utility_mean[:, index]
+ rows[f"{name}_sd"] = utility_sd[:, index]
+ # numeric, not a formatted range: this lands in a spreadsheet, where a
+ # string reads as text and cannot be sorted, plotted or compared
+ rows[f"{name}_predicted"] = physical[name][:, 0]
+ rows[f"{name}_lo68"] = physical[name][:, 1]
+ rows[f"{name}_hi68"] = physical[name][:, 2]
+ rows["distance_to_nearest"] = nearest
+ rows["nearest_observed_row"] = nearest_index + 1
+ rows["n_at_range_edge"] = pinned.sum(axis=1)
+ rows["which_at_range_edge"] = [
+ ", ".join(
+ f"{input_names[j]}={'min' if at_lower[i, j] else 'max'}"
+ for j in range(len(input_names))
+ if pinned[i, j]
+ )
+ or "-"
+ for i in range(len(conditions))
+ ]
+ candidates = pd.DataFrame(rows)
+
+ probe_frames: list[pd.DataFrame] = []
+ verdicts: list[str] = []
+ for probe in probes:
+ frame, verdict = _run_probe(
+ config,
+ model,
+ probe,
+ proposed=proposed,
+ observed=observed,
+ observed_Y_raw=np.asarray(observed_Y_raw, dtype=float),
+ names=names,
+ input_names=input_names,
+ batch_sd=utility_sd,
+ batch_utility=utility_mean,
+ round_name=round_name,
+ seed=resolved_seed,
+ )
+ probe_frames.append(frame)
+ verdicts.append(verdict)
+
+ return BatchReview(
+ round_name=round_name.upper(),
+ candidates=candidates,
+ probes=(
+ pd.concat(probe_frames, ignore_index=True) if probe_frames else pd.DataFrame()
+ ),
+ probe_verdicts=tuple(verdicts),
+ notes=review_notes_from_config(config),
+ findings=tuple(findings),
+ model_warnings=tuple(model_warnings),
+ context=dict(context or {}),
+ )
+
+
+def _run_probe(
+ config: Mapping[str, Any],
+ model: Any,
+ probe: ProbeSpec,
+ *,
+ proposed: np.ndarray,
+ observed: np.ndarray,
+ observed_Y_raw: np.ndarray,
+ names: list[str],
+ input_names: list[str],
+ batch_sd: np.ndarray,
+ batch_utility: np.ndarray,
+ round_name: str,
+ seed: int,
+) -> tuple[pd.DataFrame, str]:
+ """Evaluate one counterfactual and say what its numbers mean.
+
+ The comparison that matters is the *sd*, not the mean. UCB rewards
+ uncertainty, so a region the batch avoids while the model still calls it
+ uncertain is simply losing a trade-off. A region the batch avoids while the
+ model calls it *certain* is a different thing: the model has resolved it, and
+ if the data there is two observations that contradict each other, what it has
+ resolved is an average rather than a fact.
+ """
+ if probe.column not in input_names:
+ raise ValueError(
+ f"Probe {probe.name!r} names {probe.column!r}, which is not a declared "
+ f"input. Declared inputs: {input_names}."
+ )
+ position = input_names.index(probe.column)
+
+ twins = proposed.copy()
+ twins[:, position] = probe.value
+ twin_mean, twin_sd = _utility_moments(
+ config, model, twins, round_name=round_name, seed=seed
+ )
+
+ here = np.isclose(observed[:, position], probe.value, rtol=0.0, atol=1e-9)
+ rows: list[dict[str, Any]] = []
+ for i in range(len(twins)):
+ row: dict[str, Any] = {
+ "probe": probe.name,
+ "kind": f"{round_name.upper()}_C{i + 1:02d} moved to {probe.column}={probe.value:g}",
+ }
+ for index, name in enumerate(names):
+ row[f"{name}_utility"] = twin_mean[i, index]
+ row[f"{name}_sd"] = twin_sd[i, index]
+ row[f"{name}_utility_selected"] = batch_utility[i, index]
+ row[f"{name}_sd_selected"] = batch_sd[i, index]
+ rows.append(row)
+
+ if here.any():
+ observed_mean, observed_sd = _utility_moments(
+ config, model, observed[here], round_name=round_name, seed=seed
+ )
+ observed_indices = np.flatnonzero(here)
+ for slot, original_row in enumerate(observed_indices):
+ row = {
+ "probe": probe.name,
+ "kind": f"observed row {original_row + 1} (already at {probe.column}={probe.value:g})",
+ }
+ for index, name in enumerate(names):
+ row[f"{name}_utility"] = observed_mean[slot, index]
+ row[f"{name}_sd"] = observed_sd[slot, index]
+ row[f"{name}_utility_selected"] = np.nan
+ row[f"{name}_sd_selected"] = np.nan
+ row["measured"] = ", ".join(
+ f"{name}={observed_Y_raw[original_row, index]:g}"
+ for index, name in enumerate(names)
+ )
+ rows.append(row)
+
+ frame = pd.DataFrame(rows)
+ observed_count = int(here.sum())
+
+ # Per objective, not pooled: the three utilities have sd on different scales
+ # here (optoelectronic near 0.06 against thickness near 0.23), so a median over
+ # the whole matrix can hide an objective whose uncertainty genuinely rises.
+ per_objective: list[str] = []
+ quieter: list[str] = []
+ noisier: list[str] = []
+ for index, name in enumerate(names):
+ twin_u = float(np.median(twin_mean[:, index]))
+ base_u = float(np.median(batch_utility[:, index]))
+ twin_s = float(np.median(twin_sd[:, index]))
+ base_s = float(np.median(batch_sd[:, index]))
+ ratio = twin_s / base_s if base_s > 0 else float("inf")
+ per_objective.append(
+ f"{name}: utility {twin_u:.3f} vs {base_u:.3f} "
+ f"({twin_u - base_u:+.3f}), sd {twin_s:.3f} vs {base_s:.3f} "
+ f"(x{ratio:.2f})"
+ )
+ verdict_kind = classify_probe_objective(twin_u, base_u, ratio)
+ if verdict_kind == "known_and_bad":
+ quieter.append(name)
+ elif verdict_kind == "uncertain_tradeoff":
+ noisier.append(name)
+
+ verdict = [
+ f"PROBE '{probe.name}' ({probe.column} = {probe.value:g}), probed value "
+ f"against the selected batch, medians -- {'; '.join(per_objective)}. "
+ f"{observed_count} observation(s) already sit there. An sd ratio above "
+ f"{SD_MATERIALITY_RATIO:g}x counts as materially more uncertain; anything "
+ "below that is the same uncertainty at a worse predicted value."
+ ]
+
+ if quieter:
+ verdict.append(
+ f"For {', '.join(quieter)} the probed region scores WORSE with no "
+ "materially greater uncertainty than the batch that was selected. Since "
+ "UCB rewards uncertainty, that region is not being skipped because it "
+ "looks unexplored -- it is being skipped because it looks KNOWN AND BAD."
+ )
+ if noisier:
+ verdict.append(
+ f"For {', '.join(noisier)} the region scores worse and does read as "
+ "materially more uncertain, so there its absence is a trade-off against "
+ "the other objectives rather than absorbed confidence."
+ )
+ if not quieter and not noisier:
+ verdict.append(
+ "The probed region does not score worse than the selected batch on any "
+ "objective, so its absence from the batch is about the batch-spacing "
+ "penalty rather than about the model's opinion of the region."
+ )
+
+ trend_driven = _objectives_with_mean_feature(config, probe.column)
+ if trend_driven and quieter:
+ verdict.append(
+ f"Where that confidence comes from matters: {', '.join(trend_driven)} "
+ f"carries {probe.column} in its mean function, so the prediction here is "
+ "a fitted global trend evaluated at the edge of its range, not a local "
+ "average of the nearby observations. The trend can be confident at an "
+ "edge that holds almost no data, and it will be confidently wrong if the "
+ "few points there are unreliable. That makes this a question about the "
+ "measurements at the edge, not a settled model conclusion."
+ )
+ if probe.note:
+ verdict.append(probe.note)
+ return frame, " ".join(verdict)
+
+
+def _objectives_with_mean_feature(
+ config: Mapping[str, Any], column: str
+) -> tuple[str, ...]:
+ """Objectives whose structured mean uses ``column`` as a feature.
+
+ A probe on such a column is asking the fitted trend to extrapolate, which is a
+ different kind of claim from a GP interpolating between nearby points -- and
+ worth naming, because a monotone trend is confident at a range edge by
+ construction.
+ """
+ from .structured_mean import mean_spec_from_config
+
+ names: list[str] = []
+ for entry in config["objectives"]["specs"]:
+ spec = mean_spec_from_config(entry)
+ if spec is not None and any(f.column == column for f in spec.features):
+ names.append(str(entry["name"]))
+ return tuple(names)
+
+
+# --------------------------------------------------------------------------- #
+# writing it beside the worklist
+# --------------------------------------------------------------------------- #
+
+
+def write_review_sheet(
+ candidate_workbook: str | Path, review: BatchReview, *, sheet_name: str = "Review"
+) -> Path:
+ """Add the review to the candidate workbook this round just wrote.
+
+ Safe to open for writing, unlike the source workbook: this file was created by
+ :func:`workbook_io.write_candidate_sheet` moments ago and contains no formulas,
+ so openpyxl has no cached values to discard. Never point this at
+ ``Summary Table.xlsx``.
+ """
+ from openpyxl import load_workbook
+ from openpyxl.styles import Alignment, Font
+ from openpyxl.utils import get_column_letter
+
+ path = Path(candidate_workbook)
+ workbook = load_workbook(path)
+ if sheet_name in workbook.sheetnames:
+ del workbook[sheet_name]
+ sheet = workbook.create_sheet(sheet_name)
+
+ bold = Font(bold=True)
+ row = 1
+
+ def heading(text: str) -> None:
+ nonlocal row
+ sheet.cell(row=row, column=1, value=text).font = bold
+ row += 1
+
+ def blank() -> None:
+ nonlocal row
+ row += 1
+
+ def paragraph(text: str) -> None:
+ nonlocal row
+ cell = sheet.cell(row=row, column=1, value=text)
+ cell.alignment = Alignment(wrap_text=True, vertical="top")
+ sheet.row_dimensions[row].height = 14 * max(1, len(text) // 110 + 1)
+ row += 1
+
+ def table(frame: pd.DataFrame) -> None:
+ nonlocal row
+ for offset, column in enumerate(frame.columns, start=1):
+ sheet.cell(row=row, column=offset, value=str(column)).font = bold
+ row += 1
+ for _, record in frame.iterrows():
+ for offset, column in enumerate(frame.columns, start=1):
+ value = record[column]
+ if isinstance(value, (np.floating, np.integer)):
+ value = value.item()
+ if isinstance(value, float) and not np.isfinite(value):
+ value = None
+ sheet.cell(row=row, column=offset, value=value)
+ row += 1
+
+ heading(f"BATCH REVIEW - {review.round_name}")
+ blank()
+ for key, value in review.context.items():
+ sheet.cell(row=row, column=1, value=key).font = bold
+ sheet.cell(row=row, column=2, value=str(value))
+ row += 1
+ blank()
+
+ if review.model_warnings:
+ # above the table, for the same reason it is first in the text version
+ heading("READ THIS BEFORE THE NUMBERS")
+ for warning in review.model_warnings:
+ paragraph(warning)
+ blank()
+
+ heading("PROPOSED CONDITIONS")
+ table(review.candidates)
+ blank()
+
+ if not review.probes.empty:
+ heading("PROBES")
+ table(review.probes)
+ blank()
+
+ if review.probe_verdicts:
+ heading("WHAT THE PROBES MEAN")
+ for verdict in review.probe_verdicts:
+ paragraph(verdict)
+ blank()
+
+ if review.notes:
+ heading("NOTES")
+ for note in review.notes:
+ paragraph(note)
+ blank()
+
+ if review.findings:
+ heading("CARRIED FROM THE MEASURED DATA")
+ table(
+ pd.DataFrame(
+ {
+ "severity": [f.severity.value for f in _ordered(review.findings)],
+ "sample": [f.sample_id for f in _ordered(review.findings)],
+ "objective": [f.objective for f in _ordered(review.findings)],
+ "message": [f.message for f in _ordered(review.findings)],
+ }
+ )
+ )
+ blank()
+
+ heading("APPROVAL")
+ paragraph(NOT_APPROVED)
+
+ sheet.column_dimensions["A"].width = 46
+ for index in range(2, 40):
+ sheet.column_dimensions[get_column_letter(index)].width = 16
+ sheet.freeze_panes = "A2"
+ workbook.save(path)
+ return path
diff --git a/src/mobo_kit/batch_selection.py b/src/mobo_kit/batch_selection.py
new file mode 100644
index 0000000..a97cf83
--- /dev/null
+++ b/src/mobo_kit/batch_selection.py
@@ -0,0 +1,398 @@
+"""Reusable sequential local penalization for discrete MOBO candidate pools."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from numbers import Real
+from typing import Any, Callable
+
+import numpy as np
+
+from .candidate_pool import CandidatePool
+
+
+@dataclass(frozen=True)
+class LocalPenalizationConfig:
+ radius: float | None
+ min_batch_distance: float
+ min_observed_distance: float = 0.0
+ dimension_weights: np.ndarray | None = None
+ epsilon: float = 1e-12
+
+ def __post_init__(self) -> None:
+ for field_name in ("min_batch_distance", "min_observed_distance", "epsilon"):
+ field_value = getattr(self, field_name)
+ if isinstance(field_value, (bool, np.bool_)) or not isinstance(
+ field_value, Real
+ ):
+ raise ValueError(f"{field_name} must be a real non-boolean number.")
+ if self.radius is None:
+ radius = None
+ else:
+ if isinstance(self.radius, (bool, np.bool_)) or not isinstance(
+ self.radius, Real
+ ):
+ raise ValueError("radius must be None or a real non-boolean number.")
+ radius = float(self.radius)
+ minimum_batch = float(self.min_batch_distance)
+ minimum_observed = float(self.min_observed_distance)
+ epsilon = float(self.epsilon)
+ if radius is not None and (not np.isfinite(radius) or radius <= 0):
+ raise ValueError("radius must be None or finite and strictly positive.")
+ if not np.isfinite(minimum_batch) or minimum_batch < 0:
+ raise ValueError("min_batch_distance must be finite and non-negative.")
+ if not np.isfinite(minimum_observed) or minimum_observed < 0:
+ raise ValueError("min_observed_distance must be finite and non-negative.")
+ if not np.isfinite(epsilon) or epsilon <= 0 or epsilon >= 1:
+ raise ValueError("epsilon must be finite and between zero and one.")
+ object.__setattr__(self, "radius", radius)
+ object.__setattr__(self, "min_batch_distance", minimum_batch)
+ object.__setattr__(self, "min_observed_distance", minimum_observed)
+ object.__setattr__(self, "epsilon", epsilon)
+ if self.dimension_weights is not None:
+ raw_weights = np.asarray(self.dimension_weights)
+ if np.issubdtype(raw_weights.dtype, np.bool_) or any(
+ not isinstance(value, Real) or isinstance(value, (bool, np.bool_))
+ for value in raw_weights.ravel()
+ ):
+ raise ValueError(
+ "dimension_weights must contain real non-boolean numbers."
+ )
+ weights = np.asarray(self.dimension_weights, dtype=float).copy()
+ if weights.ndim != 1 or weights.size == 0:
+ raise ValueError("dimension_weights must be a non-empty vector.")
+ if not np.all(np.isfinite(weights)) or np.any(weights <= 0):
+ raise ValueError(
+ "dimension_weights must be finite and strictly positive."
+ )
+ weights.setflags(write=False)
+ object.__setattr__(self, "dimension_weights", weights)
+
+
+@dataclass(frozen=True)
+class BaseScoreResult:
+ base_log_score: np.ndarray
+ base_score: np.ndarray | None = None
+ diagnostics: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class SelectionStep:
+ order: int
+ pool_index: int
+ base_score: float | None
+ base_log_score: float
+ penalty_factor: float
+ log_penalty: float
+ penalized_log_score: float
+ nearest_selected_distance_before: float | None
+ nearest_observed_distance: float | None
+
+
+@dataclass(frozen=True)
+class BatchSelectionResult:
+ X_norm: np.ndarray
+ X_phys: np.ndarray
+ selected_pool_indices: np.ndarray
+ steps: tuple[SelectionStep, ...]
+ method_diagnostics: dict[str, Any]
+ distance_diagnostics: dict[str, Any]
+
+
+class UndersizedBatchError(RuntimeError):
+ """Raised when an exact batch would require relaxing a hard rule."""
+
+ def __init__(
+ self,
+ *,
+ requested_size: int,
+ selected_size: int,
+ pool_size: int,
+ remaining_candidate_count: int,
+ min_batch_distance: float,
+ min_observed_distance: float,
+ selected_pool_indices: np.ndarray,
+ hard_valid_candidate_count: int | None = None,
+ ) -> None:
+ self.requested_size = requested_size
+ self.selected_size = selected_size
+ self.pool_size = pool_size
+ self.remaining_candidate_count = remaining_candidate_count
+ self.min_batch_distance = min_batch_distance
+ self.min_observed_distance = min_observed_distance
+ self.selected_pool_indices = selected_pool_indices.copy()
+ self.hard_valid_candidate_count = hard_valid_candidate_count
+ super().__init__(
+ "Unable to select the exact requested batch without violating an "
+ "eligibility or hard-distance rule: "
+ f"requested={requested_size}, selected={selected_size}, "
+ f"pool_size={pool_size}, remaining={remaining_candidate_count}, "
+ f"min_batch_distance={min_batch_distance}, "
+ f"min_observed_distance={min_observed_distance}."
+ )
+
+
+def _weights(config: LocalPenalizationConfig, dimension: int) -> np.ndarray:
+ if config.dimension_weights is None:
+ return np.ones(dimension, dtype=float)
+ if config.dimension_weights.shape != (dimension,):
+ raise ValueError(
+ "dimension_weights must have shape "
+ f"({dimension},); got {config.dimension_weights.shape}."
+ )
+ return config.dimension_weights
+
+
+def _distances(
+ X: np.ndarray, references: np.ndarray, weights: np.ndarray
+) -> np.ndarray:
+ if references.shape[0] == 0:
+ return np.empty((X.shape[0], 0), dtype=float)
+ differences = X[:, None, :] - references[None, :, :]
+ return np.sqrt(np.sum(weights * differences**2, axis=-1))
+
+
+def soft_local_penalty(
+ distances: np.ndarray, *, radius: float, epsilon: float = 1e-12
+) -> tuple[np.ndarray, np.ndarray]:
+ """Return stabilized exclusion factors and their natural logarithms."""
+ distance = np.asarray(distances, dtype=float)
+ if not np.all(np.isfinite(distance)) or np.any(distance < 0):
+ raise ValueError("distances must be finite and non-negative.")
+ if isinstance(radius, (bool, np.bool_)) or not isinstance(radius, Real):
+ raise ValueError("radius must be a real non-boolean number.")
+ if isinstance(epsilon, (bool, np.bool_)) or not isinstance(epsilon, Real):
+ raise ValueError("epsilon must be a real non-boolean number.")
+ radius_value = float(radius)
+ epsilon_value = float(epsilon)
+ if not np.isfinite(radius_value) or radius_value <= 0:
+ raise ValueError("radius must be finite and strictly positive.")
+ if not np.isfinite(epsilon_value) or epsilon_value <= 0 or epsilon_value >= 1:
+ raise ValueError("epsilon must be finite and between zero and one.")
+ factor = 1.0 - np.exp(-0.5 * (distance / radius_value) ** 2)
+ return factor, np.log(np.maximum(factor, epsilon_value))
+
+
+def _validate_pool(candidate_pool: CandidatePool) -> tuple[np.ndarray, np.ndarray]:
+ if not isinstance(candidate_pool, CandidatePool):
+ raise TypeError("candidate_pool must be a CandidatePool.")
+ X_norm = np.asarray(candidate_pool.X_norm, dtype=float)
+ X_phys = np.asarray(candidate_pool.X_phys, dtype=float)
+ if X_norm.ndim != 2 or X_phys.shape != X_norm.shape:
+ raise ValueError(
+ "Candidate pool physical and normalized arrays must be (N, D)."
+ )
+ if X_norm.shape[0] == 0 or not np.all(np.isfinite(X_norm)):
+ raise ValueError("Candidate pool must contain finite rows.")
+ if not np.all(np.isfinite(X_phys)):
+ raise ValueError(
+ "Candidate pool physical rows must contain only finite values."
+ )
+ if np.any(X_norm < -1e-12) or np.any(X_norm > 1 + 1e-12):
+ raise ValueError("Candidate pool normalized rows must lie in [0, 1].")
+ grid_indices = np.asarray(candidate_pool.grid_indices)
+ if grid_indices.shape != X_norm.shape or not np.issubdtype(
+ grid_indices.dtype, np.integer
+ ):
+ raise ValueError(
+ "Candidate pool grid_indices must be an integer array aligned with "
+ "physical and normalized rows."
+ )
+ if np.unique(grid_indices, axis=0).shape[0] != X_norm.shape[0]:
+ raise ValueError("Candidate pool contains duplicate grid-index tuples.")
+ if np.unique(X_norm, axis=0).shape[0] != X_norm.shape[0]:
+ raise ValueError("Candidate pool contains duplicate normalized rows.")
+ if np.unique(X_phys, axis=0).shape[0] != X_phys.shape[0]:
+ raise ValueError("Candidate pool contains duplicate physical rows.")
+ return X_norm, X_phys
+
+
+def select_local_penalized_batch(
+ candidate_pool: CandidatePool,
+ q: int,
+ score_remaining: Callable[[np.ndarray, np.ndarray], BaseScoreResult],
+ config: LocalPenalizationConfig,
+ *,
+ observed_pending_norm: np.ndarray | None = None,
+) -> BatchSelectionResult:
+ """Sequentially select exactly ``q`` candidates or fail without relaxation."""
+ X_norm, X_phys = _validate_pool(candidate_pool)
+ if isinstance(q, bool) or not isinstance(q, (int, np.integer)) or int(q) <= 0:
+ raise ValueError("q must be a positive integer.")
+ requested = int(q)
+ if requested > X_norm.shape[0]:
+ raise UndersizedBatchError(
+ requested_size=requested,
+ selected_size=0,
+ pool_size=X_norm.shape[0],
+ remaining_candidate_count=X_norm.shape[0],
+ min_batch_distance=config.min_batch_distance,
+ min_observed_distance=config.min_observed_distance,
+ selected_pool_indices=np.empty(0, dtype=int),
+ hard_valid_candidate_count=X_norm.shape[0],
+ )
+ if not callable(score_remaining):
+ raise TypeError("score_remaining must be callable.")
+ weights = _weights(config, X_norm.shape[1])
+ if observed_pending_norm is None:
+ observed = np.empty((0, X_norm.shape[1]), dtype=float)
+ else:
+ observed = np.asarray(observed_pending_norm, dtype=float)
+ if observed.ndim != 2 or observed.shape[1] != X_norm.shape[1]:
+ raise ValueError(
+ "observed_pending_norm must have shape " f"(N, {X_norm.shape[1]})."
+ )
+ if not np.all(np.isfinite(observed)):
+ raise ValueError("observed_pending_norm must contain only finite values.")
+ if np.any(observed < 0.0) or np.any(observed > 1.0):
+ raise ValueError("observed_pending_norm must lie within [0, 1].")
+
+ if observed.shape[0]:
+ observed_distances = _distances(X_norm, observed, weights)
+ nearest_observed_all = observed_distances.min(axis=1)
+ else:
+ nearest_observed_all = np.full(X_norm.shape[0], np.inf)
+ nearest_selected_all = np.full(X_norm.shape[0], np.inf)
+ cumulative_log_penalty = np.zeros(X_norm.shape[0], dtype=float)
+
+ remaining = np.arange(X_norm.shape[0], dtype=int)
+ selected: list[int] = []
+ steps: list[SelectionStep] = []
+ score_diagnostics: list[dict[str, Any]] = []
+ for order in range(1, requested + 1):
+ selected_array = np.asarray(selected, dtype=int)
+ scored = score_remaining(remaining.copy(), selected_array.copy())
+ if not isinstance(scored, BaseScoreResult):
+ raise TypeError("score_remaining must return BaseScoreResult.")
+ base_log = np.asarray(scored.base_log_score, dtype=float)
+ if base_log.shape != (remaining.size,):
+ raise ValueError(
+ "base_log_score must align with remaining_indices; "
+ f"expected {(remaining.size,)}, got {base_log.shape}."
+ )
+ if np.any(np.isnan(base_log)) or np.any(np.isposinf(base_log)):
+ raise ValueError("base_log_score may be finite or -inf, not NaN/+inf.")
+ base_score: np.ndarray | None = None
+ if scored.base_score is not None:
+ base_score = np.asarray(scored.base_score, dtype=float)
+ if base_score.shape != (remaining.size,) or not np.all(
+ np.isfinite(base_score)
+ ):
+ raise ValueError(
+ "base_score must be finite and align with remaining_indices."
+ )
+ score_diagnostics.append(dict(scored.diagnostics))
+
+ if selected:
+ nearest_selected = nearest_selected_all[remaining]
+ log_penalty = cumulative_log_penalty[remaining]
+ else:
+ nearest_selected = np.full(remaining.size, np.inf)
+ log_penalty = np.zeros(remaining.size, dtype=float)
+
+ nearest_observed = nearest_observed_all[remaining]
+
+ hard_valid = np.ones(remaining.size, dtype=bool)
+ if selected:
+ hard_valid &= nearest_selected >= config.min_batch_distance
+ if observed.shape[0]:
+ # Exact observed/pending recipes are always forbidden, even when the
+ # configured distance threshold is explicitly zero.
+ hard_valid &= nearest_observed > 0.0
+ if config.min_observed_distance > 0:
+ hard_valid &= nearest_observed >= config.min_observed_distance
+ penalized = base_log + log_penalty
+ penalized[~hard_valid] = -np.inf
+ valid_positions = np.flatnonzero(np.isfinite(penalized))
+ if valid_positions.size == 0:
+ raise UndersizedBatchError(
+ requested_size=requested,
+ selected_size=len(selected),
+ pool_size=X_norm.shape[0],
+ remaining_candidate_count=int(valid_positions.size),
+ min_batch_distance=config.min_batch_distance,
+ min_observed_distance=config.min_observed_distance,
+ selected_pool_indices=np.asarray(selected, dtype=int),
+ hard_valid_candidate_count=int(np.count_nonzero(hard_valid)),
+ )
+ # np.argmax returns the first maximum. Remaining pool indices preserve
+ # ascending/stable pool order, which is the documented tie-break.
+ chosen_position = int(np.argmax(penalized))
+ chosen_pool_index = int(remaining[chosen_position])
+ chosen_log_penalty = float(log_penalty[chosen_position])
+ steps.append(
+ SelectionStep(
+ order=order,
+ pool_index=chosen_pool_index,
+ base_score=(
+ None if base_score is None else float(base_score[chosen_position])
+ ),
+ base_log_score=float(base_log[chosen_position]),
+ penalty_factor=float(np.exp(chosen_log_penalty)),
+ log_penalty=chosen_log_penalty,
+ penalized_log_score=float(penalized[chosen_position]),
+ nearest_selected_distance_before=(
+ None if not selected else float(nearest_selected[chosen_position])
+ ),
+ nearest_observed_distance=(
+ None
+ if not observed.shape[0]
+ else float(nearest_observed[chosen_position])
+ ),
+ )
+ )
+ selected.append(chosen_pool_index)
+ remaining = np.delete(remaining, chosen_position)
+ new_distances = _distances(
+ X_norm, X_norm[chosen_pool_index : chosen_pool_index + 1], weights
+ )[:, 0]
+ nearest_selected_all = np.minimum(nearest_selected_all, new_distances)
+ if config.radius is not None:
+ _, new_log_penalty = soft_local_penalty(
+ new_distances, radius=config.radius, epsilon=config.epsilon
+ )
+ cumulative_log_penalty += new_log_penalty
+
+ selected_array = np.asarray(selected, dtype=int)
+ selected_distances = _distances(
+ X_norm[selected_array], X_norm[selected_array], weights
+ )
+ if requested >= 2:
+ triangle = selected_distances[np.triu_indices(requested, k=1)]
+ within = {
+ "minimum_within_batch_distance": float(triangle.min()),
+ "mean_within_batch_distance": float(triangle.mean()),
+ "maximum_within_batch_distance": float(triangle.max()),
+ }
+ else:
+ within = {
+ "minimum_within_batch_distance": None,
+ "mean_within_batch_distance": None,
+ "maximum_within_batch_distance": None,
+ }
+ return BatchSelectionResult(
+ X_norm=X_norm[selected_array].copy(),
+ X_phys=X_phys[selected_array].copy(),
+ selected_pool_indices=selected_array,
+ steps=tuple(steps),
+ method_diagnostics={"score_steps": score_diagnostics},
+ distance_diagnostics={
+ "pairwise_distance_matrix": selected_distances,
+ **within,
+ "dimension_weights": weights.copy(),
+ "radius": config.radius,
+ "min_batch_distance": config.min_batch_distance,
+ "min_observed_distance": config.min_observed_distance,
+ },
+ )
+
+
+__all__ = [
+ "BaseScoreResult",
+ "BatchSelectionResult",
+ "LocalPenalizationConfig",
+ "SelectionStep",
+ "UndersizedBatchError",
+ "select_local_penalized_batch",
+ "soft_local_penalty",
+]
diff --git a/src/mobo_kit/campaign.py b/src/mobo_kit/campaign.py
new file mode 100644
index 0000000..e004efc
--- /dev/null
+++ b/src/mobo_kit/campaign.py
@@ -0,0 +1,955 @@
+"""The campaign path: three rounds, one function each.
+
+ run_r0_lhs(config, n=15) -> space-filling initial worklist
+ run_r1_ucb(config, ..., n=5) -> UCB-HVI batch with local penalisation
+ run_r2_qlognehvi(config, ..., n=3) -> qLogNEHVI batch
+
+Each returns a :class:`RoundResult` carrying the proposed conditions in physical
+units plus a diagnostics dict. These functions orchestrate; the mathematics
+lives in ``lhs``, ``candidate_pool``, ``ucb_hvi``, ``batch_selection``,
+``qlognehvi_batch`` and ``discrete_refinement``, which are not reimplemented
+here.
+
+Two things are worth knowing before reading further.
+
+**Thickness trains on nanometres.** The campaign's thickness utility is a
+Gaussian on a 650 nm target, and that map is 2-to-1: films at 400 nm and 900 nm
+score alike from opposite sides of the peak. Training on the score forces the
+GP to represent a folded ridge; training on nanometres leaves a smooth trend.
+So ``model_source_column`` (what the GP sees) and the utility transform are
+declared separately per objective. See docs/GP_MODEL_DECISION.md.
+
+**Utility moments come from posterior samples.** Because the thickness utility
+is nonlinear, the mean of the transform is not the transform of the mean.
+``ucb_hvi.posterior_utility_moments`` already applies the transform to posterior
+samples, so ``moment_method="monte_carlo"`` is correct and required here; the
+``analytic_identity`` fast path is only valid when every transform is identity.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+import torch
+
+from .batch_selection import LocalPenalizationConfig
+from .candidate_pool import CandidatePool, sample_discrete_candidate_pool
+from .constraints import (
+ RowConstraint,
+ constraint_violations,
+ constraints_from_config,
+)
+from .design import Design, build_design_from_config
+from .lhs import lhs_dataframe_optimized
+from botorch.models.model_list_gp_regression import ModelListGP
+
+from .model_validation import (
+ SIGNAL_COLLAPSE_STAGE,
+ fit_model_variant,
+ model_variant_spec,
+)
+from .scores import MeasurementSpec, entry_columns, measurement_spec_from_config
+from .structured_mean import build_structured_mean, mean_spec_from_config
+from .objectives import ObjectiveSpec, ObjectiveTransform
+from .qlognehvi_batch import propose_qlognehvi_penalized_batch
+from .ucb_hvi import propose_ucb_hvi_batch
+
+__all__ = [
+ "CampaignConfigError",
+ "EXCEL_CSV_ENCODING",
+ "write_worklist_csv",
+ "FIXED_SCALING_MODES",
+ "assert_scaling_is_campaign_fixed",
+ "RoundResult",
+ "build_objective_transform",
+ "expand_replicates",
+ "fit_campaign_models",
+ "load_campaign_config",
+ "normalise_inputs",
+ "measurement_entry_columns",
+ "measurement_specs",
+ "model_source_columns",
+ "objective_names",
+ "replicate_aggregates",
+ "REPLICATE_AGGREGATES",
+ "run_r0_lhs",
+ "run_r1_ucb",
+ "run_r2_qlognehvi",
+ "validate_batch",
+]
+
+
+#: Encoding for CSVs a human will open in Excel. Excel does not detect UTF-8
+#: without a BOM and falls back to the system ANSI codepage, which mangles any
+#: non-ASCII cell -- and does so on the reader's machine, not the writer's, so
+#: it is invisible during development. ``utf-8-sig`` writes the BOM; pandas and
+#: every other reader strip it transparently.
+EXCEL_CSV_ENCODING = "utf-8-sig"
+
+
+def write_worklist_csv(frame: pd.DataFrame, path: str | Path) -> Path:
+ """Write a worklist CSV that Excel will open correctly."""
+ destination = Path(path)
+ frame.to_csv(destination, index=False, encoding=EXCEL_CSV_ENCODING)
+ return destination
+
+
+class CampaignConfigError(ValueError):
+ """The configuration cannot support the requested round."""
+
+
+class BatchValidityError(RuntimeError):
+ """A proposed batch failed a validity check and must not be issued."""
+
+
+@dataclass
+class RoundResult:
+ """One round's proposal."""
+
+ round_name: str
+ conditions: pd.DataFrame
+ """Distinct proposed conditions, physical units, columns == design.names."""
+ replicates: pd.DataFrame
+ """One row per physical film, with candidate_id and replicate_group."""
+ diagnostics: dict[str, Any] = field(default_factory=dict)
+
+ @property
+ def n_conditions(self) -> int:
+ return len(self.conditions)
+
+
+# --------------------------------------------------------------------------- #
+# configuration
+# --------------------------------------------------------------------------- #
+
+
+def load_campaign_config(path: str | Path) -> dict[str, Any]:
+ """Read a campaign YAML. UTF-8 is explicit: the default codec is locale
+ dependent and silently fails on non-ASCII under some Windows locales."""
+ import yaml
+
+ with open(path, "r", encoding="utf-8") as handle:
+ config = yaml.safe_load(handle)
+ if not isinstance(config, Mapping):
+ raise CampaignConfigError(f"{path} did not parse to a mapping.")
+ return dict(config)
+
+
+def _objective_specs(config: Mapping[str, Any]) -> tuple[ObjectiveSpec, ...]:
+ objectives = config.get("objectives")
+ if not isinstance(objectives, Mapping) or "specs" not in objectives:
+ raise CampaignConfigError(
+ "config['objectives'] must be a mapping containing 'specs'. A config "
+ "with an empty objective list cannot propose candidates."
+ )
+ raw_specs = objectives["specs"]
+ if not isinstance(raw_specs, Sequence) or not raw_specs:
+ raise CampaignConfigError("config['objectives']['specs'] must be non-empty.")
+
+ specs: list[ObjectiveSpec] = []
+ for entry in raw_specs:
+ if not isinstance(entry, Mapping):
+ raise CampaignConfigError("Every objective spec must be a mapping.")
+ if not entry.get("model_source_column"):
+ raise CampaignConfigError(
+ f"Objective {entry.get('name')!r} must declare "
+ "model_source_column: the GP trains on that column, which is not "
+ "always the final score."
+ )
+ mean_spec = mean_spec_from_config(entry)
+ specs.append(
+ ObjectiveSpec(
+ name=str(entry["name"]),
+ goal=str(entry["goal"]),
+ transform=str(entry["transform"]),
+ # a log-response mean function means the GP emits log(y), so the
+ # utility must exponentiate and its posterior is lognormal
+ model_link=(
+ "log"
+ if mean_spec is not None and mean_spec.response == "log"
+ else "identity"
+ ),
+ source_column=str(entry["model_source_column"]),
+ lower_anchor=entry.get("lower_anchor"),
+ upper_anchor=entry.get("upper_anchor"),
+ target=entry.get("target"),
+ sigma=entry.get("sigma"),
+ scale=entry.get("scale"),
+ )
+ )
+ return tuple(specs)
+
+
+#: Scaling modes that are fixed for the whole campaign. Anything else means the
+#: scale would be re-derived from whatever data happens to exist this round.
+FIXED_SCALING_MODES = frozenset({"already_normalized", "fixed_affine"})
+
+
+def assert_scaling_is_campaign_fixed(config: Mapping[str, Any]) -> None:
+ """Refuse objective scales that are re-derived from observed data.
+
+ This is the single most consequential check inherited from
+ ``production_gate.py``, and it is not about approval. If an objective's
+ scale moves with the data each round -- observed min/max, a percentile, a
+ round-local standardisation -- then the utility space itself moves, and
+ hypervolume computed in round N is not comparable with round N+1. The
+ progress plot silently stops meaning anything.
+
+ The temptation is immediate and specific: once R1 measurements land, the
+ observed ranges will look like better anchors than the declared ones. They
+ are not. Widen a declared range deliberately and version it; never let it
+ track the data.
+ """
+ for spec in _objective_specs(config):
+ if spec.transform == "affine":
+ if spec.lower_anchor is None or spec.upper_anchor is None:
+ raise CampaignConfigError(
+ f"Objective {spec.name!r} uses an affine transform but does "
+ "not declare fixed lower_anchor/upper_anchor. Round-by-round "
+ "min/max scaling makes hypervolume incomparable across rounds."
+ )
+ if not (spec.lower_anchor < spec.upper_anchor):
+ raise CampaignConfigError(
+ f"Objective {spec.name!r} requires lower_anchor < upper_anchor."
+ )
+
+ declared = (config.get("objectives") or {}).get("scaling_mode", "fixed_affine")
+ if declared not in FIXED_SCALING_MODES:
+ raise CampaignConfigError(
+ f"objectives.scaling_mode must be one of {sorted(FIXED_SCALING_MODES)}; "
+ f"got {declared!r}. Observed or data-derived scaling is forbidden "
+ "because it makes hypervolume incomparable between rounds."
+ )
+
+
+def build_objective_transform(config: Mapping[str, Any]) -> ObjectiveTransform:
+ """Build the versioned raw-output-to-utility contract from a campaign config."""
+ version = config.get("objectives", {}).get("contract_version")
+ if not version:
+ raise CampaignConfigError(
+ "config['objectives']['contract_version'] is required so that "
+ "hypervolume stays comparable across rounds."
+ )
+ assert_scaling_is_campaign_fixed(config)
+ return ObjectiveTransform(_objective_specs(config), version=str(version))
+
+
+def model_source_columns(config: Mapping[str, Any]) -> tuple[str, ...]:
+ """The workbook column declared per objective, in objective order.
+
+ For an objective with a ``measurement`` block this is no longer what the GP
+ trains on -- the value is computed from the raw measurement columns instead,
+ and this column becomes the cross-check target. See :mod:`scores`.
+ """
+ return tuple(spec.source_column for spec in _objective_specs(config))
+
+
+def objective_names(config: Mapping[str, Any]) -> tuple[str, ...]:
+ """Objective names in declaration order."""
+ return tuple(spec.name for spec in _objective_specs(config))
+
+
+#: How the replicate films of one condition become one training observation.
+#: ``mean`` is the arithmetic mean of the film values. ``mean_of_log`` is the
+#: geometric mean, which is the arithmetic mean *in the space the GP trains in*
+#: whenever that objective's mean function declares ``response: log``.
+REPLICATE_AGGREGATES = frozenset({"mean", "mean_of_log"})
+
+
+def replicate_aggregates(config: Mapping[str, Any]) -> tuple[str, ...]:
+ """The replicate-aggregation rule per objective, in objective order.
+
+ Declared per objective because the right answer depends on the space the
+ model works in, not on taste. Thickness trains on ``log T``, so averaging
+ three films in log space is what makes the aggregation and the Phase 4
+ variance pooling consistent with each other; the other two objectives train
+ on their own scale and use the plain mean.
+
+ The difference is second order in the replicate spread -- under 0.1% at the
+ 3% within-film spread most R0 rows show, but around 14% on a film set as
+ inconsistent as sample 12's. It is one config key, so it can be revisited
+ without touching code.
+ """
+ specs = _objective_specs(config)
+ entries = config["objectives"]["specs"]
+ rules: list[str] = []
+ for spec, entry in zip(specs, entries):
+ rule = str(entry.get("replicate_aggregate", "mean"))
+ if rule not in REPLICATE_AGGREGATES:
+ raise CampaignConfigError(
+ f"Objective {spec.name!r} declares replicate_aggregate {rule!r}; "
+ f"expected one of {sorted(REPLICATE_AGGREGATES)}."
+ )
+ rules.append(rule)
+ return tuple(rules)
+
+
+def measurement_specs(
+ config: Mapping[str, Any],
+) -> tuple[MeasurementSpec | None, ...]:
+ """One measurement spec per objective, in objective order.
+
+ ``None`` for an objective that has no ``measurement`` block and therefore
+ still reads its stored column as-is.
+ """
+ specs = _objective_specs(config) # validates the objectives block first
+ entries = config["objectives"]["specs"]
+ return tuple(
+ measurement_spec_from_config(entry) for _, entry in zip(specs, entries)
+ )
+
+
+def measurement_entry_columns(
+ config: Mapping[str, Any],
+) -> tuple[tuple[str, ...], tuple[str, ...]]:
+ """Columns a worklist sheet must offer for entry, split required / optional.
+
+ Objectives with a ``measurement`` block contribute their raw measurement
+ columns; objectives without one contribute their declared source column.
+ """
+ specs = measurement_specs(config)
+ declared = model_source_columns(config)
+ required, optional = entry_columns([s for s in specs if s is not None])
+ extra = tuple(
+ column
+ for spec, column in zip(specs, declared)
+ if spec is None and column not in required
+ )
+ return required + extra, tuple(c for c in optional if c not in extra)
+
+
+def _reference_point(config: Mapping[str, Any], n_objectives: int) -> np.ndarray:
+ raw = config.get("reference_point_utility")
+ if raw is None:
+ raise CampaignConfigError(
+ "reference_point_utility is required and must be declared in UTILITY "
+ "space, after the objective transforms."
+ )
+ point = np.asarray(raw, dtype=float)
+ if point.shape != (n_objectives,):
+ raise CampaignConfigError(
+ f"reference_point_utility must have {n_objectives} entries; "
+ f"got {point.shape}."
+ )
+ if not np.all(np.isfinite(point)):
+ raise CampaignConfigError("reference_point_utility must be finite.")
+ return point
+
+
+def _penalization(config: Mapping[str, Any]) -> LocalPenalizationConfig:
+ raw = config.get("local_penalization") or {}
+ weights = raw.get("dimension_weights")
+ return LocalPenalizationConfig(
+ radius=raw.get("radius"),
+ min_batch_distance=float(raw.get("min_batch_distance", 0.0)),
+ min_observed_distance=float(raw.get("min_observed_distance", 0.0)),
+ dimension_weights=None if weights is None else np.asarray(weights, float),
+ )
+
+
+def _round_settings(config: Mapping[str, Any], key: str) -> dict[str, Any]:
+ rounds = config.get("rounds") or {}
+ settings = rounds.get(key)
+ if not isinstance(settings, Mapping):
+ raise CampaignConfigError(f"config['rounds']['{key}'] is required.")
+ return dict(settings)
+
+
+# --------------------------------------------------------------------------- #
+# validity
+# --------------------------------------------------------------------------- #
+
+
+def validate_batch(
+ conditions: pd.DataFrame,
+ design: Design,
+ *,
+ expected_count: int,
+ min_pairwise_distance: float = 0.0,
+ constraints: Sequence[RowConstraint] | None = None,
+) -> dict[str, Any]:
+ """Refuse to issue a batch that is malformed.
+
+ Six checks, all of which catch real bugs: the batch is the requested size,
+ its rows are distinct, every value sits exactly on the declared grid, every
+ value is finite and in bounds, the rows are at least
+ ``min_pairwise_distance`` apart in normalised space, and every row satisfies
+ the campaign's declared constraints.
+
+ The constraint check is deliberately redundant. The candidate pool is already
+ filtered before any acquisition scores it, so a violating condition cannot be
+ proposed by that route -- which is exactly why the check belongs here too: the
+ pool filter is the mechanism, and this is the second, independent route to the
+ same answer. This project has now been bitten three times by a quantity that
+ nothing recomputed.
+
+ This replaces the previous debug/production approval tiers. Whether a batch
+ is approved for fabrication is a human decision recorded outside the code;
+ it is not something this function can compute, so it does not pretend to.
+ """
+ report: dict[str, Any] = {}
+ values = conditions.to_numpy(dtype=float)
+
+ report["expected_count"] = expected_count
+ report["actual_count"] = len(conditions)
+ if len(conditions) != expected_count:
+ raise BatchValidityError(
+ f"Expected exactly {expected_count} conditions; got {len(conditions)}."
+ )
+
+ if not np.all(np.isfinite(values)):
+ raise BatchValidityError("Proposed conditions contain non-finite values.")
+ report["finite"] = True
+
+ duplicates = pd.DataFrame(values).duplicated().to_numpy()
+ if duplicates.any():
+ raise BatchValidityError(
+ f"Proposed conditions must be unique; {int(duplicates.sum())} duplicate "
+ "row(s) found."
+ )
+ report["unique"] = True
+
+ off_grid: list[str] = []
+ for j, name in enumerate(design.names):
+ grid = np.asarray(design.var_array[j], dtype=float)
+ for value in values[:, j]:
+ if not np.any(np.isclose(grid, value, rtol=0.0, atol=1e-9)):
+ off_grid.append(f"{name}={value!r}")
+ if off_grid:
+ raise BatchValidityError(
+ "Proposed values must lie exactly on the declared grid; off-grid: "
+ f"{sorted(set(off_grid))}"
+ )
+ report["on_grid"] = True
+
+ lowers = np.asarray(design.lowers, dtype=float)
+ uppers = np.asarray(design.uppers, dtype=float)
+ if np.any(values < lowers - 1e-9) or np.any(values > uppers + 1e-9):
+ raise BatchValidityError("Proposed conditions fall outside design bounds.")
+ report["in_bounds"] = True
+
+ span = np.where(uppers > lowers, uppers - lowers, 1.0)
+ norm = (values - lowers) / span
+ if len(norm) > 1:
+ from scipy.spatial.distance import pdist
+
+ distances = pdist(norm)
+ report["min_pairwise_distance"] = float(distances.min())
+ if min_pairwise_distance > 0 and distances.min() < min_pairwise_distance:
+ raise BatchValidityError(
+ f"Minimum pairwise distance {distances.min():.4f} is below the "
+ f"configured floor {min_pairwise_distance:.4f}."
+ )
+ else:
+ report["min_pairwise_distance"] = float("inf")
+
+ boundary = (np.isclose(norm, 0.0, atol=1e-9)) | (np.isclose(norm, 1.0, atol=1e-9))
+ report["boundary_coords_per_condition"] = boundary.sum(axis=1).tolist()
+
+ violations = constraint_violations(values, design, constraints)
+ report["constraints_declared"] = [
+ getattr(item, "description", getattr(item, "name", "constraint"))
+ for item in (constraints or ())
+ ]
+ report["constraint_violations_per_condition"] = violations
+ broken = [
+ f"condition {position + 1} breaks {names}"
+ for position, names in enumerate(violations)
+ if names
+ ]
+ if broken:
+ raise BatchValidityError(
+ "Proposed conditions must satisfy the campaign constraints; "
+ + "; ".join(broken)
+ )
+ report["constraints_satisfied"] = True
+ return report
+
+
+def expand_replicates(
+ conditions: pd.DataFrame, *, replicates: int, round_name: str
+) -> pd.DataFrame:
+ """One row per physical film, sharing a replicate_group per condition.
+
+ The experimentalists run each proposed condition ``replicates`` times to
+ measure reproducibility. Those films are separate experimental rows, but
+ they are one design point: aggregate them to a condition-level mean before
+ the next round trains on them, and pool their within-condition variance
+ across conditions for an observation-noise estimate.
+ """
+ if replicates < 1:
+ raise ValueError("replicates must be at least 1.")
+ rows = []
+ for index, (_, condition) in enumerate(conditions.iterrows(), start=1):
+ candidate_id = f"{round_name}_C{index:02d}"
+ for replicate in range(1, replicates + 1):
+ row = dict(condition)
+ row["candidate_id"] = candidate_id
+ row["replicate_group"] = candidate_id
+ row["replicate_index"] = replicate
+ row["round"] = round_name
+ rows.append(row)
+ return pd.DataFrame(rows)
+
+
+# --------------------------------------------------------------------------- #
+# rounds
+# --------------------------------------------------------------------------- #
+
+
+def run_r0_lhs(
+ config: Mapping[str, Any], *, n: int = 15, seed: int | None = None
+) -> RoundResult:
+ """Space-filling initial worklist. No model is involved."""
+ design = build_design_from_config(dict(config))
+ constraints = constraints_from_config(dict(config), design)
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ conditions = lhs_dataframe_optimized(
+ design,
+ n,
+ seed=resolved_seed,
+ snap_to_grids=True,
+ row_constraints=constraints or None,
+ )
+ replicates_per = int(
+ _round_settings(config, "r1").get("replicates_per_condition", 1)
+ )
+ report = validate_batch(
+ conditions, design, expected_count=n, constraints=constraints or None
+ )
+ return RoundResult(
+ round_name="R0",
+ conditions=conditions,
+ replicates=expand_replicates(
+ conditions, replicates=replicates_per, round_name="R0"
+ ),
+ diagnostics={"seed": resolved_seed, "validity": report, "method": "lhs"},
+ )
+
+
+def _fit_models(
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ X_norm: np.ndarray,
+ Y_raw: np.ndarray,
+ seed: int,
+ Yvar_model: np.ndarray | None = None,
+) -> Any:
+ """One GP per objective, each with its declared structured mean.
+
+ Objectives with a ``mean_function`` train on the response-space target with
+ the OLS trend frozen into the mean module, so ``posterior()`` already carries
+ it and no caller has to add it back. Objectives without one are unchanged.
+
+ ``Yvar_model`` is measured observation variance in the MODEL TARGET space, one
+ column per objective -- so for thickness that is the variance of ``log T``, not
+ of nanometres, because ``response: log`` means the model trains on the log.
+ :func:`replicate_variance.pool_between_film_variance` produces it in exactly
+ that space, which is why aggregation and variance pooling are required to share
+ one space.
+ """
+ variant = model_variant_spec(str(config.get("model", {}).get("variant")))
+ specs = _objective_specs(config)
+ entries = config["objectives"]["specs"]
+ design = build_design_from_config(dict(config))
+ lowers = np.asarray(design.lowers, dtype=float)
+ uppers = np.asarray(design.uppers, dtype=float)
+ names = list(design.names)
+
+ models = []
+ warnings: list[str] = []
+ raw_warnings: list[str] = []
+ for index, (spec, entry) in enumerate(zip(specs, entries)):
+ mean_spec = mean_spec_from_config(entry)
+ y = np.asarray(Y_raw, dtype=float)[:, index]
+ mean_module = None
+ if mean_spec is not None:
+ mean_module, y = build_structured_mean(
+ X_phys, y, mean_spec, names, lowers, uppers
+ )
+ torch.manual_seed(seed)
+ record = fit_model_variant(
+ torch.tensor(X_norm, dtype=torch.double),
+ torch.tensor(y, dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(range(len(X_norm))),
+ objective_names=(spec.name,),
+ variant=variant,
+ seed=seed,
+ mean_module=mean_module,
+ train_Yvar=(
+ None
+ if Yvar_model is None
+ else torch.tensor(
+ np.asarray(Yvar_model, dtype=float)[:, index], dtype=torch.double
+ ).unsqueeze(-1)
+ ),
+ )
+ models.append(record.model.models[0])
+ # A fit can succeed and still be worth distrusting -- most importantly when
+ # the GP's signal component collapsed but the mean function carried the
+ # trend. Discarding these is how such a fit reaches a batch silently.
+ #
+ # Only the guard's own warnings travel to a human. `record.warnings` also
+ # captures every Python warning raised during fitting, which on this stack
+ # means ~18 numpy-2.0 deprecation notices per fit; putting those in front of
+ # someone reviewing a batch is how people learn to ignore warnings.
+ warnings.extend(
+ warning.message
+ for warning in record.warnings
+ if warning.stage == SIGNAL_COLLAPSE_STAGE
+ )
+ # The unfiltered list is kept, unsurfaced, because a scipy or BoTorch
+ # convergence warning that the filter dropped is exactly what someone needs
+ # when a fit looks strange six weeks from now.
+ raw_warnings.extend(
+ f"{warning.objective_name}|{warning.stage}|{warning.warning_category}: "
+ f"{warning.message}"
+ for warning in record.warnings
+ )
+ return ModelListGP(*models), tuple(warnings), tuple(raw_warnings)
+
+
+def fit_campaign_models(
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ Y_raw: np.ndarray,
+ *,
+ seed: int | None = None,
+ Yvar: np.ndarray | None = None,
+) -> tuple[Any, tuple[str, ...]]:
+ """Fit one GP per objective exactly as a round does.
+
+ Same normalisation, same structured means, same variant, same seeding -- so
+ calling this with the data and seed a round used reproduces that round's model
+ bit for bit. That is what makes a review of a proposed batch a review of the
+ model that proposed it, rather than of a similar one.
+
+ ``Y_raw`` holds the MODEL SOURCE values in objective order, the same contract
+ as :func:`run_r1_ucb`.
+
+ Returns ``(model, warnings)``, where ``warnings`` holds only the fit guard's
+ own findings -- the ones a human reviewing a batch must read. They are
+ returned rather than logged because a fit can succeed and still deserve
+ distrust: the loudest case is a GP whose signal component collapsed while its
+ mean function carried the trend, which leaves candidate ranking intact but
+ makes the reported intervals understated.
+
+ The unfiltered list, including library warnings raised during fitting, is on
+ ``RoundResult.diagnostics["fit_warnings_raw"]``.
+ """
+ design = build_design_from_config(dict(config))
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ values = np.asarray(X_phys, dtype=float)
+ model, fit_warnings, _raw = _fit_models(
+ config,
+ values,
+ _normalise(design, values),
+ Y_raw,
+ resolved_seed,
+ Yvar_model=Yvar,
+ )
+ return model, fit_warnings
+
+
+def _normalise(design: Design, X_phys: np.ndarray) -> np.ndarray:
+ lowers = np.asarray(design.lowers, dtype=float)
+ uppers = np.asarray(design.uppers, dtype=float)
+ span = np.where(uppers > lowers, uppers - lowers, 1.0)
+ return (np.asarray(X_phys, dtype=float) - lowers) / span
+
+
+def normalise_inputs(
+ config: Mapping[str, Any], X_phys: np.ndarray
+) -> np.ndarray:
+ """Physical inputs to ``[0, 1]`` against the CONFIG GRID bounds.
+
+ Not against the observed range: a model fitted on config bounds and evaluated
+ on observed-range coordinates is being asked about different points than it
+ was told about, and nothing errors.
+ """
+ return _normalise(build_design_from_config(dict(config)), X_phys)
+
+
+def _on_grid_mask(design: Design, X_phys: np.ndarray) -> np.ndarray:
+ """Which observed rows sit exactly on the declared grid.
+
+ The R0 control is a declared off-grid exception (anti_time = 12 against a
+ 9/11/13... grid). It stays in the GP and in the distance references, but it
+ cannot take part in grid-index bookkeeping, and it does not need to: an
+ off-grid point can never collide with a pool candidate by construction.
+ """
+ values = np.asarray(X_phys, dtype=float)
+ mask = np.ones(len(values), dtype=bool)
+ for j in range(values.shape[1]):
+ grid = np.asarray(design.var_array[j], dtype=float)
+ for i, value in enumerate(values[:, j]):
+ if not np.any(np.isclose(grid, value, rtol=0.0, atol=1e-9)):
+ mask[i] = False
+ return mask
+
+
+def _constraint_diagnostics(
+ constraints: Sequence[RowConstraint],
+ pool: CandidatePool,
+ design: Design,
+ observed_X_phys: np.ndarray,
+) -> dict[str, Any]:
+ """What the constraints did, in numbers a reviewer can check.
+
+ Three things, none of which is a gate:
+
+ ``constraint_pool_survival_rate`` is the share of drawn grid tuples the
+ constraints accepted. A mis-specified constraint that guts the pool still
+ produces a pool of exactly the requested size -- the sampler simply draws
+ longer -- so the batch looks entirely normal while being chosen from a
+ fraction of the space. A rate near zero is the signal, and without this it is
+ invisible.
+
+ ``observed_rows_violating_constraints`` soft-checks the measured history.
+ History is history: a row that predates a rule is not an error and must not
+ block a round. It is worth SAYING, though, because a constraint that rejects
+ a film the group actually ran is much more likely to be wrong than the film is.
+ """
+ accepted = int(pool.size)
+ rejected = int(pool.rejected_constraint)
+ considered = accepted + rejected
+ observed_violations = constraint_violations(
+ observed_X_phys, design, constraints or None
+ )
+ return {
+ "constraints_declared": [
+ getattr(item, "description", getattr(item, "name", "constraint"))
+ for item in (constraints or ())
+ ],
+ "constraint_pool_rejected": rejected,
+ "constraint_pool_survival_rate": (
+ float(accepted) / considered if considered else 1.0
+ ),
+ "observed_rows_violating_constraints": [
+ {"row": position, "constraints": names}
+ for position, names in enumerate(observed_violations)
+ if names
+ ],
+ }
+
+
+def run_r1_ucb(
+ config: Mapping[str, Any],
+ observed_X_phys: np.ndarray,
+ observed_Y_raw: np.ndarray,
+ *,
+ n: int | None = None,
+ seed: int | None = None,
+ observed_Yvar: np.ndarray | None = None,
+) -> RoundResult:
+ """UCB-HVI batch with local penalisation.
+
+ ``observed_Y_raw`` holds the MODEL SOURCE values in objective order, not the
+ final scores: see :func:`model_source_columns`. For this campaign that means
+ thickness arrives in nanometres.
+ """
+ design = build_design_from_config(dict(config))
+ settings = _round_settings(config, "r1")
+ q = int(settings["batch_size"]) if n is None else int(n)
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ transform = build_objective_transform(config)
+ reference = _reference_point(config, transform.objective_count)
+ penalization = _penalization(config)
+
+ observed_norm = _normalise(design, observed_X_phys)
+ model, fit_warnings, raw_fit_warnings = _fit_models(
+ config,
+ np.asarray(observed_X_phys, dtype=float),
+ observed_norm,
+ observed_Y_raw,
+ resolved_seed,
+ Yvar_model=observed_Yvar,
+ )
+
+ on_grid = _on_grid_mask(design, observed_X_phys)
+ constraints = constraints_from_config(dict(config), design)
+ pool = sample_discrete_candidate_pool(
+ design,
+ int(settings.get("candidate_pool_size", 32768)),
+ seed=resolved_seed,
+ observed_phys=np.asarray(observed_X_phys, dtype=float)[on_grid],
+ row_constraints=constraints or None,
+ )
+
+ # The HVI baseline is the utility of what has already been measured, so it must
+ # reach the transform in the MODEL's space -- the transform decodes the link
+ # itself. Passing measurement-space values here exponentiated thickness a
+ # second time and pinned every observation's thickness utility to exactly 0.0,
+ # silently: the baseline hypervolume was 0.004659 against a true 0.436442, so
+ # every candidate was scored against a front with no thickness axis at all.
+ # Fixed 2026-07-31; see ObjectiveTransform.encode_measurements.
+ observed_baseline = transform.encode_measurements(
+ torch.tensor(np.asarray(observed_Y_raw, dtype=float), dtype=torch.double)
+ )
+
+ proposal = propose_ucb_hvi_batch(
+ pool,
+ model,
+ observed_baseline,
+ transform,
+ reference,
+ q=q,
+ beta=float(settings.get("beta", 4.0)),
+ local_penalization_config=penalization,
+ mc_samples=int(settings.get("posterior_samples", 256)),
+ seed=resolved_seed,
+ moment_method=str(settings.get("moment_method", "monte_carlo")),
+ )
+
+ conditions = pd.DataFrame(
+ np.asarray(proposal.selection.X_phys, dtype=float), columns=list(design.names)
+ )
+ report = validate_batch(
+ conditions,
+ design,
+ expected_count=q,
+ min_pairwise_distance=penalization.min_batch_distance,
+ constraints=constraints or None,
+ )
+ replicates_per = int(settings.get("replicates_per_condition", 1))
+ return RoundResult(
+ round_name="R1",
+ conditions=conditions,
+ replicates=expand_replicates(
+ conditions, replicates=replicates_per, round_name="R1"
+ ),
+ diagnostics={
+ "method": "ucb_hvi",
+ "seed": resolved_seed,
+ "beta": float(settings.get("beta", 4.0)),
+ "pool_size": pool.size,
+ "objective_contract": transform.version,
+ "moment_method": str(settings.get("moment_method", "monte_carlo")),
+ # Surfaced so the baseline is checkable from outside rather than only
+ # inside the acquisition. It was wrong for the life of this campaign
+ # and nothing could see it; a number nobody can compare is how the
+ # previous two silent-failure bugs survived as well.
+ "observed_baseline_hypervolume": float(
+ proposal.scoring.baseline_hypervolume
+ ),
+ "observed_baseline_pareto_size": int(
+ proposal.scoring.pareto_utility.shape[0]
+ ),
+ "off_grid_observations_excluded_from_pool_bookkeeping": int(
+ (~on_grid).sum()
+ ),
+ **_constraint_diagnostics(
+ constraints, pool, design, np.asarray(observed_X_phys, dtype=float)
+ ),
+ "model_fit_warnings": list(fit_warnings),
+ # unsurfaced on purpose: everything the fit raised, for debugging a
+ # strange fit later, not for showing to a reviewer now
+ "fit_warnings_raw": list(raw_fit_warnings),
+ "validity": report,
+ },
+ )
+
+
+def run_r2_qlognehvi(
+ config: Mapping[str, Any],
+ observed_X_phys: np.ndarray,
+ observed_Y_raw: np.ndarray,
+ *,
+ n: int | None = None,
+ seed: int | None = None,
+ observed_Yvar: np.ndarray | None = None,
+) -> RoundResult:
+ """qLogNEHVI batch. ``observed_Y_raw`` follows the same contract as R1.
+
+ qLogNEHVI is the numerically stable formulation of qNEHVI and is the correct
+ choice here; it is not a deviation from the brief.
+ """
+ design = build_design_from_config(dict(config))
+ settings = _round_settings(config, "r2")
+ q = int(settings["batch_size"]) if n is None else int(n)
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ transform = build_objective_transform(config)
+ reference = _reference_point(config, transform.objective_count)
+ penalization = _penalization(config)
+
+ observed_norm = _normalise(design, observed_X_phys)
+ model, fit_warnings, raw_fit_warnings = _fit_models(
+ config,
+ np.asarray(observed_X_phys, dtype=float),
+ observed_norm,
+ observed_Y_raw,
+ resolved_seed,
+ Yvar_model=observed_Yvar,
+ )
+
+ on_grid = _on_grid_mask(design, observed_X_phys)
+ constraints = constraints_from_config(dict(config), design)
+ pool = sample_discrete_candidate_pool(
+ design,
+ int(settings.get("candidate_pool_size", 32768)),
+ seed=resolved_seed,
+ observed_phys=np.asarray(observed_X_phys, dtype=float)[on_grid],
+ row_constraints=constraints or None,
+ )
+
+ from .objectives import ConfiguredMCMultiOutputObjective
+
+ proposal = propose_qlognehvi_penalized_batch(
+ pool,
+ model,
+ torch.tensor(observed_norm, dtype=torch.double),
+ ConfiguredMCMultiOutputObjective(transform),
+ reference,
+ q=q,
+ local_penalization_config=penalization,
+ mc_samples=int(settings.get("mc_samples", 128)),
+ seed=resolved_seed,
+ )
+
+ conditions = pd.DataFrame(
+ np.asarray(proposal.selection.X_phys, dtype=float), columns=list(design.names)
+ )
+ report = validate_batch(
+ conditions,
+ design,
+ expected_count=q,
+ min_pairwise_distance=penalization.min_batch_distance,
+ constraints=constraints or None,
+ )
+ replicates_per = int(settings.get("replicates_per_condition", 1))
+ return RoundResult(
+ round_name="R2",
+ conditions=conditions,
+ replicates=expand_replicates(
+ conditions, replicates=replicates_per, round_name="R2"
+ ),
+ diagnostics={
+ "method": "qlognehvi",
+ "seed": resolved_seed,
+ "pool_size": pool.size,
+ "objective_contract": transform.version,
+ "off_grid_observations_excluded_from_pool_bookkeeping": int(
+ (~on_grid).sum()
+ ),
+ **_constraint_diagnostics(
+ constraints, pool, design, np.asarray(observed_X_phys, dtype=float)
+ ),
+ "model_fit_warnings": list(fit_warnings),
+ # unsurfaced on purpose: everything the fit raised, for debugging a
+ # strange fit later, not for showing to a reviewer now
+ "fit_warnings_raw": list(raw_fit_warnings),
+ "validity": report,
+ },
+ )
diff --git a/src/mobo_kit/candidate_diagnostics.py b/src/mobo_kit/candidate_diagnostics.py
new file mode 100644
index 0000000..94d6b18
--- /dev/null
+++ b/src/mobo_kit/candidate_diagnostics.py
@@ -0,0 +1,387 @@
+"""Numerical and plotting diagnostics for discrete candidate batches.
+
+All distances in this module are defined in normalized input space. Plotting
+helpers are intentionally file-oriented and use a headless Matplotlib backend
+so they are safe in automated, CPU-only campaign checks.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import matplotlib
+import numpy as np
+from sklearn.decomposition import PCA
+
+matplotlib.use("Agg")
+from matplotlib import pyplot as plt # noqa: E402
+
+from .design import Design
+
+
+def _matrix(value: np.ndarray, *, name: str) -> np.ndarray:
+ array = np.asarray(value, dtype=float)
+ if array.ndim != 2:
+ raise ValueError(f"{name} must have shape (N, D); got {array.shape}.")
+ if not np.all(np.isfinite(array)):
+ raise ValueError(f"{name} must contain only finite values.")
+ return array
+
+
+def _weights(dimension_weights: np.ndarray | None, dimension: int) -> np.ndarray:
+ if dimension_weights is None:
+ return np.ones(dimension, dtype=float)
+ weights = np.asarray(dimension_weights, dtype=float)
+ if weights.shape != (dimension,):
+ raise ValueError(
+ "dimension_weights must have shape " f"({dimension},); got {weights.shape}."
+ )
+ if not np.all(np.isfinite(weights)) or np.any(weights <= 0):
+ raise ValueError("dimension_weights must be finite and strictly positive.")
+ return weights
+
+
+def batch_hash(conditions: "np.ndarray | Any") -> str:
+ """Order-independent identity of a proposed batch.
+
+ Sorted before hashing because the question is "did these two runs propose the
+ same SET of conditions", not "in the same order". Rounded to 12 decimals so a
+ float representation difference cannot masquerade as a different batch.
+
+ Used for two different questions and it must be the same function for both:
+ whether a simulated trajectory reproduces the batch that was actually shipped,
+ and whether two sweep cells proposed the same experiment.
+ """
+ import hashlib
+
+ values = np.round(np.asarray(conditions, dtype=float), 12)
+ if values.ndim != 2:
+ raise ValueError(f"batch_hash needs a 2-D block; got shape {values.shape}.")
+ ordered = values[np.lexsort(values.T[::-1])]
+ return hashlib.sha256(ordered.tobytes()).hexdigest()[:16]
+
+
+def pairwise_normalized_distances(
+ X_norm: np.ndarray,
+ *,
+ dimension_weights: np.ndarray | None = None,
+) -> np.ndarray:
+ """Return the weighted Euclidean pairwise-distance matrix for ``(N, D)``."""
+ X = _matrix(X_norm, name="X_norm")
+ weights = _weights(dimension_weights, X.shape[1])
+ differences = X[:, None, :] - X[None, :, :]
+ return np.sqrt(np.sum(weights * differences**2, axis=-1))
+
+
+def nearest_reference_distances(
+ X_norm: np.ndarray,
+ reference_norm: np.ndarray | None,
+ *,
+ dimension_weights: np.ndarray | None = None,
+) -> np.ndarray:
+ """Return each candidate's nearest observed/pending normalized distance."""
+ X = _matrix(X_norm, name="X_norm")
+ weights = _weights(dimension_weights, X.shape[1])
+ if reference_norm is None:
+ return np.full(X.shape[0], np.nan, dtype=float)
+ reference = _matrix(reference_norm, name="reference_norm")
+ if reference.shape[1] != X.shape[1]:
+ raise ValueError(
+ "reference_norm and X_norm must have the same final dimension."
+ )
+ if reference.shape[0] == 0:
+ return np.full(X.shape[0], np.nan, dtype=float)
+ differences = X[:, None, :] - reference[None, :, :]
+ distances = np.sqrt(np.sum(weights * differences**2, axis=-1))
+ return distances.min(axis=1)
+
+
+def grid_membership_mask(
+ X_phys: np.ndarray,
+ design: Design,
+ *,
+ atol: float = 1e-9,
+) -> np.ndarray:
+ """Return a per-row mask indicating exact membership in every design grid."""
+ X = _matrix(X_phys, name="X_phys")
+ dimension = len(design.var_array)
+ if X.shape[1] != dimension:
+ raise ValueError(
+ f"X_phys has {X.shape[1]} columns but the design has {dimension}."
+ )
+ if not np.isfinite(atol) or atol < 0:
+ raise ValueError("atol must be finite and non-negative.")
+ valid = np.ones(X.shape[0], dtype=bool)
+ for column, grid in enumerate(design.var_array):
+ grid_values = np.asarray(grid, dtype=float)
+ valid &= np.any(
+ np.isclose(
+ X[:, column, None],
+ grid_values[None, :],
+ rtol=0.0,
+ atol=atol,
+ ),
+ axis=1,
+ )
+ return valid
+
+
+def boundary_flags(X_norm: np.ndarray, *, atol: float = 1e-12) -> np.ndarray:
+ """Flag candidate dimensions lying on either normalized boundary."""
+ X = _matrix(X_norm, name="X_norm")
+ if not np.isfinite(atol) or atol < 0:
+ raise ValueError("atol must be finite and non-negative.")
+ return np.isclose(X, 0.0, rtol=0.0, atol=atol) | np.isclose(
+ X, 1.0, rtol=0.0, atol=atol
+ )
+
+
+@dataclass(frozen=True)
+class BatchDistanceDiagnostics:
+ """Compact distance and validity summary for a selected batch."""
+
+ pairwise_distance_matrix: np.ndarray
+ minimum_within_batch_distance: float | None
+ mean_within_batch_distance: float | None
+ maximum_within_batch_distance: float | None
+ nearest_observed_pending_distance: np.ndarray
+ duplicate_row_pairs: tuple[tuple[int, int], ...]
+ grid_valid_rows: np.ndarray | None
+ boundary_flags: np.ndarray
+ metadata: dict[str, Any]
+
+ def as_dict(self) -> dict[str, Any]:
+ """Return a serialization-friendly shallow mapping."""
+ return asdict(self)
+
+
+def summarize_candidate_batch(
+ X_norm: np.ndarray,
+ *,
+ observed_pending_norm: np.ndarray | None = None,
+ X_phys: np.ndarray | None = None,
+ design: Design | None = None,
+ dimension_weights: np.ndarray | None = None,
+ duplicate_atol: float = 1e-12,
+ metadata: Mapping[str, Any] | None = None,
+) -> BatchDistanceDiagnostics:
+ """Compute batch-only ``O(q^2 D)`` diagnostics and reference distances."""
+ X = _matrix(X_norm, name="X_norm")
+ if not np.isfinite(duplicate_atol) or duplicate_atol < 0:
+ raise ValueError("duplicate_atol must be finite and non-negative.")
+ distances = pairwise_normalized_distances(X, dimension_weights=dimension_weights)
+ if X.shape[0] >= 2:
+ triangle = distances[np.triu_indices(X.shape[0], k=1)]
+ minimum = float(triangle.min())
+ mean = float(triangle.mean())
+ maximum = float(triangle.max())
+ else:
+ minimum = mean = maximum = None
+
+ duplicate_pairs: list[tuple[int, int]] = []
+ for left in range(X.shape[0]):
+ for right in range(left + 1, X.shape[0]):
+ if np.allclose(X[left], X[right], rtol=0.0, atol=duplicate_atol):
+ duplicate_pairs.append((left, right))
+
+ if (X_phys is None) != (design is None):
+ raise ValueError("X_phys and design must be supplied together.")
+ if X_phys is not None:
+ physical = _matrix(X_phys, name="X_phys")
+ if physical.shape[0] != X.shape[0]:
+ raise ValueError("X_phys and X_norm must contain the same row count.")
+ else:
+ physical = None
+ grid_valid = (
+ None
+ if physical is None
+ else grid_membership_mask(physical, design) # type: ignore[arg-type]
+ )
+ return BatchDistanceDiagnostics(
+ pairwise_distance_matrix=distances,
+ minimum_within_batch_distance=minimum,
+ mean_within_batch_distance=mean,
+ maximum_within_batch_distance=maximum,
+ nearest_observed_pending_distance=nearest_reference_distances(
+ X,
+ observed_pending_norm,
+ dimension_weights=dimension_weights,
+ ),
+ duplicate_row_pairs=tuple(duplicate_pairs),
+ grid_valid_rows=grid_valid,
+ boundary_flags=boundary_flags(X),
+ metadata={} if metadata is None else dict(metadata),
+ )
+
+
+def _output_path(output_path: str | Path) -> Path:
+ path = Path(output_path)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ return path
+
+
+def _save_figure(fig: Any, path: Path, watermark: str | None) -> None:
+ metadata: dict[str, str] | None = None
+ if watermark is not None:
+ if not isinstance(watermark, str) or not watermark.strip():
+ raise ValueError("watermark must be a nonblank string when provided.")
+ label = watermark.strip()
+ fig.text(
+ 0.5,
+ 0.015,
+ label,
+ ha="center",
+ va="bottom",
+ color="firebrick",
+ fontsize=9,
+ fontweight="bold",
+ bbox={"facecolor": "white", "edgecolor": "firebrick", "alpha": 0.9},
+ )
+ metadata = {"Description": label}
+ fig.tight_layout(rect=(0.0, 0.06, 1.0, 1.0) if watermark else None)
+ fig.savefig(path, dpi=160, metadata=metadata)
+ plt.close(fig)
+
+
+def plot_candidate_pca(
+ observed_norm: np.ndarray,
+ selected_norm: np.ndarray,
+ output_path: str | Path,
+ *,
+ pool_norm: np.ndarray | None = None,
+ pool_sample_size: int = 1000,
+ seed: int = 0,
+ watermark: str | None = None,
+) -> Path:
+ """Save a two-component PCA view of observed, pool, and selected points."""
+ observed = _matrix(observed_norm, name="observed_norm")
+ selected = _matrix(selected_norm, name="selected_norm")
+ if observed.shape[1] != selected.shape[1]:
+ raise ValueError("observed_norm and selected_norm dimensions must match.")
+ groups: list[tuple[str, np.ndarray]] = [("Observed", observed)]
+ if pool_norm is not None:
+ pool = _matrix(pool_norm, name="pool_norm")
+ if pool.shape[1] != observed.shape[1]:
+ raise ValueError("pool_norm and observed_norm dimensions must match.")
+ if pool_sample_size <= 0:
+ raise ValueError("pool_sample_size must be positive.")
+ if pool.shape[0] > pool_sample_size:
+ rng = np.random.default_rng(seed)
+ indices = np.sort(
+ rng.choice(pool.shape[0], size=pool_sample_size, replace=False)
+ )
+ pool = pool[indices]
+ groups.append(("Pool", pool))
+ groups.append(("Selected", selected))
+ combined = np.vstack([values for _, values in groups])
+ if combined.shape[0] < 2 or combined.shape[1] < 2:
+ raise ValueError("PCA plot requires at least two rows and two inputs.")
+ projected = PCA(n_components=2).fit_transform(combined)
+
+ path = _output_path(output_path)
+ fig, axis = plt.subplots(figsize=(7, 5))
+ offset = 0
+ styles: Mapping[str, Mapping[str, Any]] = {
+ "Observed": {"marker": "o", "alpha": 0.75, "s": 38},
+ "Pool": {"marker": ".", "alpha": 0.2, "s": 14},
+ "Selected": {"marker": "*", "alpha": 1.0, "s": 130},
+ }
+ for label, values in groups:
+ count = values.shape[0]
+ points = projected[offset : offset + count]
+ axis.scatter(points[:, 0], points[:, 1], label=label, **styles[label])
+ offset += count
+ axis.set(xlabel="PC1", ylabel="PC2", title="Candidate acquisition in input space")
+ axis.legend()
+ _save_figure(fig, path, watermark)
+ return path
+
+
+def plot_parallel_coordinates(
+ selected_norm: np.ndarray,
+ input_names: Sequence[str],
+ output_path: str | Path,
+ *,
+ watermark: str | None = None,
+) -> Path:
+ """Save normalized selected conditions as a parallel-coordinates plot."""
+ selected = _matrix(selected_norm, name="selected_norm")
+ if len(input_names) != selected.shape[1]:
+ raise ValueError("input_names must match the selected input dimension.")
+ path = _output_path(output_path)
+ fig, axis = plt.subplots(figsize=(max(8, selected.shape[1]), 4.5))
+ positions = np.arange(selected.shape[1])
+ for row_index, row in enumerate(selected):
+ axis.plot(positions, row, marker="o", label=f"Selection {row_index + 1}")
+ axis.set_xticks(positions, input_names, rotation=40, ha="right")
+ axis.set_ylim(-0.03, 1.03)
+ axis.set_ylabel("Normalized condition")
+ axis.set_title("Selected candidate conditions")
+ axis.legend(ncol=min(3, max(1, selected.shape[0])))
+ _save_figure(fig, path, watermark)
+ return path
+
+
+def plot_distance_heatmap(
+ X_norm: np.ndarray,
+ output_path: str | Path,
+ *,
+ dimension_weights: np.ndarray | None = None,
+ watermark: str | None = None,
+) -> Path:
+ """Save the within-batch normalized-distance matrix as a heatmap."""
+ matrix = pairwise_normalized_distances(X_norm, dimension_weights=dimension_weights)
+ path = _output_path(output_path)
+ fig, axis = plt.subplots(figsize=(5.5, 4.8))
+ image = axis.imshow(matrix, cmap="viridis")
+ labels = [str(index + 1) for index in range(matrix.shape[0])]
+ axis.set_xticks(range(matrix.shape[0]), labels)
+ axis.set_yticks(range(matrix.shape[0]), labels)
+ axis.set(xlabel="Selection", ylabel="Selection", title="Batch distances")
+ for row in range(matrix.shape[0]):
+ for column in range(matrix.shape[1]):
+ axis.text(
+ column,
+ row,
+ f"{matrix[row, column]:.2f}",
+ ha="center",
+ va="center",
+ color="white" if matrix[row, column] < matrix.max() * 0.55 else "black",
+ fontsize=8,
+ )
+ fig.colorbar(image, ax=axis, label="Normalized Euclidean distance")
+ _save_figure(fig, path, watermark)
+ return path
+
+
+def plot_selection_scores(
+ selection_order: Sequence[int],
+ base_log_scores: Sequence[float],
+ penalized_log_scores: Sequence[float],
+ output_path: str | Path,
+ *,
+ watermark: str | None = None,
+) -> Path:
+ """Save base-versus-penalized acquisition values by selection order."""
+ order = np.asarray(selection_order)
+ base = np.asarray(base_log_scores, dtype=float)
+ penalized = np.asarray(penalized_log_scores, dtype=float)
+ if order.ndim != 1 or base.shape != order.shape or penalized.shape != order.shape:
+ raise ValueError("selection order and score arrays must share shape (q,).")
+ if not np.all(np.isfinite(base)) or not np.all(np.isfinite(penalized)):
+ raise ValueError("selection scores must be finite.")
+ path = _output_path(output_path)
+ fig, axis = plt.subplots(figsize=(6.5, 4.2))
+ axis.plot(order, base, marker="o", label="Base log acquisition")
+ axis.plot(order, penalized, marker="s", label="Penalized log acquisition")
+ axis.set(
+ xlabel="Selection order",
+ ylabel="Log acquisition",
+ title="Sequential acquisition and local penalty",
+ )
+ axis.set_xticks(order)
+ axis.legend()
+ _save_figure(fig, path, watermark)
+ return path
diff --git a/src/mobo_kit/candidate_pool.py b/src/mobo_kit/candidate_pool.py
new file mode 100644
index 0000000..fd4927e
--- /dev/null
+++ b/src/mobo_kit/candidate_pool.py
@@ -0,0 +1,247 @@
+"""Deterministic sampling of finite pools from very large discrete designs."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from math import prod
+from typing import Sequence
+
+import numpy as np
+
+from .constraints import RowConstraint, apply_row_constraints
+from .design import Design
+
+
+@dataclass(frozen=True)
+class CandidatePool:
+ """A discrete candidate pool in grid-index, physical, and normalized spaces."""
+
+ grid_indices: np.ndarray
+ X_phys: np.ndarray
+ X_norm: np.ndarray
+ seed: int
+ draws: int
+ rejected_duplicate: int
+ rejected_avoid: int
+ rejected_constraint: int
+
+ @property
+ def size(self) -> int:
+ return int(self.grid_indices.shape[0])
+
+
+class CandidatePoolSamplingError(RuntimeError):
+ """Raised when an exact-size discrete pool cannot be produced safely."""
+
+ def __init__(
+ self,
+ *,
+ requested: int,
+ accepted: int,
+ draws: int,
+ max_draws: int,
+ rejected_duplicate: int,
+ rejected_avoid: int,
+ rejected_constraint: int,
+ reason: str,
+ ) -> None:
+ self.requested = requested
+ self.accepted = accepted
+ self.draws = draws
+ self.max_draws = max_draws
+ self.rejected_duplicate = rejected_duplicate
+ self.rejected_avoid = rejected_avoid
+ self.rejected_constraint = rejected_constraint
+ self.reason = reason
+ super().__init__(
+ "Could not sample the requested discrete candidate pool: "
+ f"requested={requested}, accepted={accepted}, draws={draws}, "
+ f"max_draws={max_draws}, duplicate_rejections={rejected_duplicate}, "
+ f"avoid_rejections={rejected_avoid}, "
+ f"constraint_rejections={rejected_constraint}. {reason}"
+ )
+
+
+def _design_grids(design: Design) -> tuple[np.ndarray, ...]:
+ if not isinstance(design, Design):
+ raise TypeError("design must be a Design.")
+ grids = tuple(np.asarray(grid, dtype=float) for grid in design.var_array)
+ if not grids or len(grids) != len(design.names):
+ raise ValueError("design must contain one non-empty grid per input name.")
+ for name, grid in zip(design.names, grids):
+ if grid.ndim != 1 or grid.size == 0:
+ raise ValueError(f"Design grid {name!r} must be a non-empty vector.")
+ if not np.all(np.isfinite(grid)) or np.unique(grid).size != grid.size:
+ raise ValueError(f"Design grid {name!r} must be finite and unique.")
+ return grids
+
+
+def _physical_matrix(
+ value: np.ndarray | None, *, name: str, dimension: int
+) -> np.ndarray:
+ if value is None:
+ return np.empty((0, dimension), dtype=float)
+ array = np.asarray(value, dtype=float)
+ if array.ndim != 2 or array.shape[1] != dimension:
+ raise ValueError(f"{name} must have shape (N, {dimension}); got {array.shape}.")
+ if not np.all(np.isfinite(array)):
+ raise ValueError(f"{name} must contain only finite values.")
+ return array
+
+
+def physical_rows_to_grid_indices(
+ X_phys: np.ndarray,
+ design: Design,
+ *,
+ atol: float = 0.0,
+) -> np.ndarray:
+ """Convert physical rows to exact integer grid tuples or fail off-grid."""
+ grids = _design_grids(design)
+ X = _physical_matrix(X_phys, name="X_phys", dimension=len(grids))
+ if not np.isfinite(atol) or atol < 0:
+ raise ValueError("atol must be finite and non-negative.")
+ indices = np.empty(X.shape, dtype=np.int64)
+ for column, (name, grid) in enumerate(zip(design.names, grids)):
+ differences = np.abs(X[:, column, None] - grid[None, :])
+ closest = differences.argmin(axis=1)
+ invalid = differences[np.arange(X.shape[0]), closest] > atol
+ if np.any(invalid):
+ rows = np.flatnonzero(invalid).tolist()
+ raise ValueError(f"Physical rows {rows} are off-grid for input {name!r}.")
+ indices[:, column] = closest
+ return indices
+
+
+def _indices_to_physical(
+ grid_indices: np.ndarray, grids: tuple[np.ndarray, ...]
+) -> np.ndarray:
+ physical = np.empty(grid_indices.shape, dtype=float)
+ for column, grid in enumerate(grids):
+ physical[:, column] = grid[grid_indices[:, column]]
+ return physical
+
+
+def _normalize_physical(X_phys: np.ndarray, design: Design) -> np.ndarray:
+ lower = np.asarray(design.lowers, dtype=float)
+ upper = np.asarray(design.uppers, dtype=float)
+ spans = upper - lower
+ normalized = np.zeros_like(X_phys, dtype=float)
+ changing = spans > 0
+ normalized[:, changing] = (X_phys[:, changing] - lower[changing]) / spans[changing]
+ return normalized
+
+
+def sample_discrete_candidate_pool(
+ design: Design,
+ pool_size: int,
+ *,
+ seed: int,
+ observed_phys: np.ndarray | None = None,
+ pending_phys: np.ndarray | None = None,
+ avoid_phys: np.ndarray | None = None,
+ row_constraints: Sequence[RowConstraint] | None = None,
+ max_draws: int | None = None,
+) -> CandidatePool:
+ """Sample an exact-size unique pool without allocating the Cartesian grid."""
+ grids = _design_grids(design)
+ if isinstance(pool_size, bool) or not isinstance(pool_size, (int, np.integer)):
+ raise ValueError("pool_size must be a positive integer.")
+ requested = int(pool_size)
+ if requested <= 0:
+ raise ValueError("pool_size must be a positive integer.")
+ if (
+ isinstance(seed, (bool, np.bool_))
+ or not isinstance(seed, (int, np.integer))
+ or int(seed) < 0
+ ):
+ raise ValueError("seed must be a non-negative integer.")
+ if max_draws is None:
+ draw_limit = max(1000, requested * 50)
+ elif isinstance(max_draws, bool) or not isinstance(max_draws, (int, np.integer)):
+ raise ValueError("max_draws must be a positive integer.")
+ else:
+ draw_limit = int(max_draws)
+ if draw_limit <= 0:
+ raise ValueError("max_draws must be a positive integer.")
+
+ dimension = len(grids)
+ avoid_rows = np.vstack(
+ [
+ _physical_matrix(observed_phys, name="observed_phys", dimension=dimension),
+ _physical_matrix(pending_phys, name="pending_phys", dimension=dimension),
+ _physical_matrix(avoid_phys, name="avoid_phys", dimension=dimension),
+ ]
+ )
+ avoid_indices = physical_rows_to_grid_indices(avoid_rows, design)
+ avoid_set = {tuple(int(value) for value in row) for row in avoid_indices}
+ total_grid_size = prod(int(grid.size) for grid in grids)
+ available_without_constraints = total_grid_size - len(avoid_set)
+ if requested > available_without_constraints:
+ raise CandidatePoolSamplingError(
+ requested=requested,
+ accepted=0,
+ draws=0,
+ max_draws=draw_limit,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ reason=(
+ "The request exceeds the number of grid tuples remaining after "
+ f"explicit exclusions ({available_without_constraints})."
+ ),
+ )
+
+ generator = np.random.default_rng(int(seed))
+ axis_sizes = np.asarray([grid.size for grid in grids], dtype=np.int64)
+ seen: set[tuple[int, ...]] = set()
+ accepted_indices: list[tuple[int, ...]] = []
+ draws = rejected_duplicate = rejected_avoid = rejected_constraint = 0
+ while len(accepted_indices) < requested and draws < draw_limit:
+ index_tuple = tuple(int(generator.integers(0, high)) for high in axis_sizes)
+ draws += 1
+ if index_tuple in seen:
+ rejected_duplicate += 1
+ continue
+ seen.add(index_tuple)
+ if index_tuple in avoid_set:
+ rejected_avoid += 1
+ continue
+ row_indices = np.asarray(index_tuple, dtype=np.int64)[None, :]
+ row_physical = _indices_to_physical(row_indices, grids)
+ if not bool(apply_row_constraints(row_physical, design, row_constraints)[0]):
+ rejected_constraint += 1
+ continue
+ accepted_indices.append(index_tuple)
+
+ if len(accepted_indices) != requested:
+ raise CandidatePoolSamplingError(
+ requested=requested,
+ accepted=len(accepted_indices),
+ draws=draws,
+ max_draws=draw_limit,
+ rejected_duplicate=rejected_duplicate,
+ rejected_avoid=rejected_avoid,
+ rejected_constraint=rejected_constraint,
+ reason="Maximum draws reached; constraints and exclusions were not relaxed.",
+ )
+ index_array = np.asarray(accepted_indices, dtype=np.int64)
+ physical = _indices_to_physical(index_array, grids)
+ normalized = _normalize_physical(physical, design)
+ return CandidatePool(
+ grid_indices=index_array,
+ X_phys=physical,
+ X_norm=normalized,
+ seed=int(seed),
+ draws=draws,
+ rejected_duplicate=rejected_duplicate,
+ rejected_avoid=rejected_avoid,
+ rejected_constraint=rejected_constraint,
+ )
+
+
+__all__ = [
+ "CandidatePool",
+ "CandidatePoolSamplingError",
+ "physical_rows_to_grid_indices",
+ "sample_discrete_candidate_pool",
+]
diff --git a/src/mobo_kit/cli.py b/src/mobo_kit/cli.py
index 331ccc9..bbe9386 100644
--- a/src/mobo_kit/cli.py
+++ b/src/mobo_kit/cli.py
@@ -15,7 +15,7 @@
def main():
"""
Command line interface for MOBO-Kit.
-
+
Usage examples:
mobo-kit run --csv data/my_data.csv
mobo-kit generate --config configs/my_config.yaml --n-samples 20 --out initial_experiments.csv
@@ -37,87 +37,123 @@ def main():
# Run with specific device and seed
mobo-kit run --csv data/my_data.csv --device cpu --seed 123 --verbose
- # Run with custom batch size for acquisition
- mobo-kit run --csv data/my_data.csv --batch-size 10 --num-restarts 50
- """
+ # Step 2A analysis is allowed, but campaign proposal remains blocked until
+ # the reviewed Step 2B adapter is implemented.
+ mobo-kit run --csv data/my_data.csv --config configs/my_config.yaml
+ """,
)
-
- subparsers = parser.add_subparsers(dest='command', help='Available commands')
-
+
+ subparsers = parser.add_subparsers(dest="command", help="Available commands")
+
# Generate subcommand
- generate_parser = subparsers.add_parser('generate', help='Generate initial experiments using LHS')
- generate_parser.add_argument('--config', required=True, help='Path to YAML configuration file')
- generate_parser.add_argument('--n-samples', type=int, required=True, help='Number of initial experiments to generate')
- generate_parser.add_argument('--out', required=True, help='Output CSV file path')
- generate_parser.add_argument('--seed', type=int, default=42, help='Random seed for reproducibility (default: 42)')
- generate_parser.add_argument('--max-corr', type=float, help='Maximum absolute correlation between variables')
- generate_parser.add_argument('--max-attempts', type=int, default=100, help='Maximum attempts for LHS generation (default: 100)')
- generate_parser.add_argument('--verbose', action='store_true', help='Enable verbose output')
-
+ generate_parser = subparsers.add_parser(
+ "generate", help="Generate initial experiments using LHS"
+ )
+ generate_parser.add_argument(
+ "--config", required=True, help="Path to YAML configuration file"
+ )
+ generate_parser.add_argument(
+ "--n-samples",
+ type=int,
+ required=True,
+ help="Number of initial experiments to generate",
+ )
+ generate_parser.add_argument("--out", required=True, help="Output CSV file path")
+ generate_parser.add_argument(
+ "--seed",
+ type=int,
+ default=42,
+ help="Random seed for reproducibility (default: 42)",
+ )
+ generate_parser.add_argument(
+ "--max-corr", type=float, help="Maximum absolute correlation between variables"
+ )
+ generate_parser.add_argument(
+ "--max-attempts",
+ type=int,
+ default=100,
+ help="Maximum attempts for LHS generation (default: 100)",
+ )
+ generate_parser.add_argument(
+ "--verbose", action="store_true", help="Enable verbose output"
+ )
+
# Run subcommand
- run_parser = subparsers.add_parser('run', help='Run MOBO optimization with existing data')
+ run_parser = subparsers.add_parser(
+ "run", help="Run MOBO optimization with existing data"
+ )
run_parser.add_argument(
- "--csv",
- required=True,
- help="Path to CSV file with experimental data"
+ "--csv", required=True, help="Path to CSV file with experimental data"
)
-
+
# Optional arguments for run command
run_parser.add_argument(
"--config",
- help="Path to YAML configuration file (optional, will auto-generate from CSV if not provided)"
+ help="Path to YAML configuration file (optional, will auto-generate from CSV if not provided)",
)
-
+
run_parser.add_argument(
"--out",
- default="results/experiment",
- help="Output directory for results (default: results/experiment)"
+ default="local_outputs/experiment",
+ help="Output directory for results (default: local_outputs/experiment)",
)
-
+
run_parser.add_argument(
"--seed",
type=int,
default=42,
- help="Random seed for reproducibility (default: 42)"
+ help="Random seed for reproducibility (default: 42)",
)
-
+
run_parser.add_argument(
"--device",
choices=["auto", "cpu", "cuda"],
default="auto",
- help="Device to use for computation (default: auto)"
+ help="Device to use for computation (default: auto)",
+ )
+
+ run_parser.add_argument(
+ "--verbose", action="store_true", help="Enable verbose output"
)
-
+
+ # Retained only to fail closed with a clear migration message. The legacy
+ # qNEHVI implementation is not a Step 2A production adapter.
run_parser.add_argument(
- "--verbose",
+ "--propose-candidates",
action="store_true",
- help="Enable verbose output"
+ help="Blocked in Step 2A; requires a reviewed Step 2B campaign adapter",
)
-
- # Advanced options (for future expansion)
+
+ run_parser.add_argument(
+ "--reference-point",
+ type=float,
+ nargs="+",
+ help="One approved hypervolume reference value per transformed objective",
+ )
+
run_parser.add_argument(
"--batch-size",
type=int,
default=5,
- help="Batch size for acquisition (default: 5)"
+ help="Batch size for acquisition (default: 5)",
)
-
+
run_parser.add_argument(
"--num-restarts",
type=int,
default=20,
- help="Number of restarts for acquisition optimization (default: 20)"
+ help="Number of restarts for acquisition optimization (default: 20)",
)
-
+
args = parser.parse_args()
-
+
# Handle subcommands
- if args.command == 'generate':
+ if args.command == "generate":
# Validate inputs for generate command
if not os.path.exists(args.config):
print(f"Error: Config file not found: {args.config}")
sys.exit(1)
-
+
try:
# Generate initial experiments
results = generate_initial_experiments(
@@ -127,42 +163,44 @@ def main():
seed=args.seed,
verbose=args.verbose,
max_abs_corr=args.max_corr,
- max_attempts=args.max_attempts
+ max_attempts=args.max_attempts,
)
-
+
if args.verbose:
print("\n" + "=" * 60)
print("GENERATION SUMMARY:")
print(f" Config: {args.config}")
print(f" Samples: {results['n_samples']}")
print(f" Variables: {', '.join(results['variables'])}")
- print(f" Constraints: {'Applied' if results['constraints_applied'] else 'None'}")
+ print(
+ f" Constraints: {'Applied' if results['constraints_applied'] else 'None'}"
+ )
print(f" Seed: {results['seed']}")
print(f" Output: {results['save_path']}")
print("=" * 60)
else:
- print(f"Success! Generated {results['n_samples']} experiments in {results['save_path']}")
-
+ print(
+ f"Success! Generated {results['n_samples']} experiments in {results['save_path']}"
+ )
+
except Exception as e:
print(f"Error: {e}")
if args.verbose:
import traceback
+
traceback.print_exc()
sys.exit(1)
-
- elif args.command == 'run':
+
+ elif args.command == "run":
# Validate inputs for run command
if not os.path.exists(args.csv):
print(f"Error: CSV file not found: {args.csv}")
sys.exit(1)
-
+
if args.config and not os.path.exists(args.config):
print(f"Error: Config file not found: {args.config}")
sys.exit(1)
-
- # Create output directory
- os.makedirs(args.out, exist_ok=True)
-
+
try:
# Run the experiment
results = run_mobo_experiment(
@@ -172,9 +210,12 @@ def main():
seed=args.seed,
device=args.device,
verbose=args.verbose,
- batch_size=args.batch_size
+ batch_size=args.batch_size,
+ propose_candidates=args.propose_candidates,
+ reference_point=args.reference_point,
+ num_restarts=args.num_restarts,
)
-
+
# Print summary
if args.verbose:
print("\n" + "=" * 60)
@@ -188,19 +229,20 @@ def main():
print(f" Device: {results['device']}")
print(f" Seed: {results['seed']}")
print(f" Results: {results['save_dir']}")
- if results.get('candidates'):
+ if results.get("candidates"):
print(f" Next batch: {args.batch_size} candidates proposed")
print("=" * 60)
else:
success_msg = f"Success! Results saved to {results['save_dir']}"
- if results.get('candidates'):
+ if results.get("candidates"):
success_msg += f" ({args.batch_size} candidates proposed)"
print(success_msg)
-
+
except Exception as e:
print(f"Error: {e}")
if args.verbose:
import traceback
+
traceback.print_exc()
sys.exit(1)
else:
diff --git a/src/mobo_kit/constraints.py b/src/mobo_kit/constraints.py
index 4e34860..07418bb 100644
--- a/src/mobo_kit/constraints.py
+++ b/src/mobo_kit/constraints.py
@@ -1,124 +1,320 @@
-# src/constraints.py
+"""Opt-in physical-space constraints for campaign designs."""
+
from __future__ import annotations
-from typing import Callable, Dict, List, Optional, Sequence
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Sequence
+
import numpy as np
-# Public type: row-wise constraint (PHYSICAL units in, boolean mask out)
+if TYPE_CHECKING:
+ from .design import Design
+
+
RowConstraint = Callable[[np.ndarray, "Design"], np.ndarray]
-# =========================
-# Clausius–Clapeyron (C → K)
-# =========================
+@dataclass(frozen=True)
+class NamedConstraint:
+ """A row constraint that can say what it is when it rejects something.
-def check_clausius_clapeyron_np(ah_vals, temp_c_vals) -> np.ndarray:
+ Callable, so it is a ``RowConstraint`` everywhere one is expected and the
+ candidate pool needs no knowledge of it. The name and description exist for
+ the review artifact: a reviewer being told a condition was rejected needs the
+ rule, not an index.
"""
- Return boolean mask for valid points satisfying Clausius–Clapeyron constraint.
- Ensures absolute humidity (g/m^3) does not exceed saturation at given temperature.
- Parameters
- ----------
- ah_vals : array-like, absolute humidity [g/m^3]
- temp_c_vals : array-like, temperature [°C] (converted to K internally)
+ name: str
+ description: str
+ check: RowConstraint
- Returns
- -------
- valid_mask : np.ndarray[bool]
- """
- AH = np.asarray(ah_vals, dtype=float)
- T_c = np.asarray(temp_c_vals, dtype=float) # Celsius in
- T = T_c + 273.15 # Kelvin
+ def __call__(self, X: np.ndarray, design: "Design") -> np.ndarray:
+ return self.check(X, design)
- # Saturation vapor pressure (kPa)
- es = 0.6113 * np.exp((17.27 * (T - 273.15)) / (T - 35.86))
- # Max absolute humidity (g/m^3)
- AH_max = es / (4.61e-4 * T)
+def check_clausius_clapeyron_np(ah_vals, temp_c_vals) -> np.ndarray:
+ """Return a validity mask for the Clausius-Clapeyron constraint.
+
+ Absolute humidity is expressed in g/m^3 and temperature in degrees C.
+ The calculation converts temperature to kelvin internally.
+ """
- return (AH <= AH_max) & np.isfinite(AH_max)
+ absolute_humidity = np.asarray(ah_vals, dtype=float)
+ temperature_k = np.asarray(temp_c_vals, dtype=float) + 273.15
+ saturation_pressure = 0.6113 * np.exp(
+ (17.27 * (temperature_k - 273.15)) / (temperature_k - 35.86)
+ )
+ maximum_absolute_humidity = saturation_pressure / (4.61e-4 * temperature_k)
+ return (absolute_humidity <= maximum_absolute_humidity) & np.isfinite(
+ maximum_absolute_humidity
+ )
-# =========================
-# Builders (one per supported constraint key)
-# =========================
def _idx_for(name: str, design: "Design") -> int:
try:
return design.names.index(name)
- except ValueError as e:
+ except ValueError as exc:
raise KeyError(
- f"Constraint refers to column '{name}', but it is not in design.names={design.names}"
- ) from e
+ f"Constraint refers to column '{name}', but it is not in "
+ f"design.names={design.names}."
+ ) from exc
-def _build_cc(spec: Dict, design: "Design") -> RowConstraint:
- ah_col = spec.get("ah_col")
- t_col = spec.get("temp_c_col")
- if ah_col is None or t_col is None:
+def _build_clausius_clapeyron(specification: Dict, design: "Design") -> RowConstraint:
+ absolute_humidity_column = specification.get("ah_col")
+ temperature_column = specification.get("temp_c_col")
+ if absolute_humidity_column is None or temperature_column is None:
raise KeyError(
- "Clausius–Clapeyron constraint needs 'absolute_humidity_col' and 'temperature_col'."
+ "Clausius-Clapeyron constraint requires 'ah_col' and 'temp_c_col'."
)
- i_ah = _idx_for(ah_col, design)
- i_tC = _idx_for(t_col, design)
+ absolute_humidity_index = _idx_for(absolute_humidity_column, design)
+ temperature_index = _idx_for(temperature_column, design)
def row_constraint_fn(X: np.ndarray, _design: "Design") -> np.ndarray:
- return check_clausius_clapeyron_np(X[:, i_ah], X[:, i_tC])
+ return check_clausius_clapeyron_np(
+ X[:, absolute_humidity_index], X[:, temperature_index]
+ )
+
+ return row_constraint_fn
+
+
+def _column_names(names, *, field: str) -> List[str]:
+ if isinstance(names, str):
+ names = [names]
+ if not isinstance(names, list) or not names:
+ raise KeyError(
+ f"Constraint field {field!r} must name one column or a non-empty list "
+ f"of columns; got {names!r}."
+ )
+ return [str(name) for name in names]
+
+
+def _build_zero_coupled(specification, design: "Design") -> RowConstraint:
+ """Two settings that describe one optional process step: both on, or both off.
+
+ A second spin stage that runs for 0 s at 3500 rpm is not a slower stage, it is
+ a contradiction -- and so is one that runs for 30 s at 0 rpm. Exactly one of
+ the pair being zero is the invalid case; both zero means the step was skipped,
+ which is a real recipe (sample 2 of the v3 workbook is a one-step film).
+
+ Stated as an iff rather than as two separate bounds because that is what makes
+ the skipped-step recipe reachable at all: a plain lower bound on either column
+ would delete it.
+ """
+ names = _column_names(specification, field="zero_coupled")
+ if len(names) < 2:
+ raise KeyError("zero_coupled needs at least two columns to couple.")
+ indices = [_idx_for(name, design) for name in names]
+
+ def row_constraint_fn(X: np.ndarray, _design: "Design") -> np.ndarray:
+ block = np.asarray(X, dtype=float)[:, indices]
+ zeros = np.isclose(block, 0.0, rtol=0.0, atol=1e-12)
+ return zeros.all(axis=1) | (~zeros).all(axis=1)
+
+ return row_constraint_fn
+
+
+def _build_sum_upper_strict(specification, design: "Design") -> RowConstraint:
+ """``lhs < sum(rhs)``, strictly.
+
+ Written for ``anti_time < time_1 + time_2``: the antisolvent has to be dropped
+ while the substrate is still spinning, so equality is already too late rather
+ than just in time. Strictness is the whole point of the constraint and is why
+ this is not the existing bounds check with a different argument.
+ """
+ if not isinstance(specification, dict):
+ raise KeyError(
+ "sum_upper_strict takes a mapping with 'lhs' and 'rhs'; "
+ f"got {specification!r}."
+ )
+ left = specification.get("lhs")
+ if not isinstance(left, str) or not left.strip():
+ raise KeyError("sum_upper_strict needs 'lhs' to name one column.")
+ left_index = _idx_for(left.strip(), design)
+ right_indices = [
+ _idx_for(name, design)
+ for name in _column_names(specification.get("rhs"), field="rhs")
+ ]
+
+ def row_constraint_fn(X: np.ndarray, _design: "Design") -> np.ndarray:
+ values = np.asarray(X, dtype=float)
+ return values[:, left_index] < values[:, right_indices].sum(axis=1)
+
+ return row_constraint_fn
+
+
+def _build_nonzero_minimum(specification, design: "Design") -> RowConstraint:
+ """A column is either exactly zero or at least ``minimum``.
+
+ This exists because the design grid is arithmetic -- ``start``/``stop``/``step``
+ with uniform spacing, which ``lhs`` asserts -- so a grid of ``{0} U {10, 15,
+ ... 60}`` cannot be declared directly. Reaching 0 with ``step: 5`` also reaches
+ 5, and a 5 s second spin stage was not in the first campaign's design and has
+ never been run.
+
+ Declaring the hole here keeps it visible in config and enforced everywhere the
+ other constraints are, rather than widening the design space in silence.
+ """
+ if not isinstance(specification, dict):
+ raise KeyError(
+ "nonzero_minimum takes a mapping with 'column' and 'minimum'; "
+ f"got {specification!r}."
+ )
+ column = specification.get("column")
+ if not isinstance(column, str) or not column.strip():
+ raise KeyError("nonzero_minimum needs 'column' to name one column.")
+ index = _idx_for(column.strip(), design)
+ minimum = specification.get("minimum")
+ if isinstance(minimum, bool) or not isinstance(minimum, (int, float)):
+ raise KeyError("nonzero_minimum needs a numeric 'minimum'.")
+ threshold = float(minimum)
+ if not np.isfinite(threshold) or threshold <= 0:
+ raise ValueError("nonzero_minimum 'minimum' must be finite and positive.")
+
+ def row_constraint_fn(X: np.ndarray, _design: "Design") -> np.ndarray:
+ values = np.asarray(X, dtype=float)[:, index]
+ return np.isclose(values, 0.0, rtol=0.0, atol=1e-12) | (values >= threshold)
return row_constraint_fn
-# Map of **boolean keys** in YAML → builder
_SUPPORTED_BOOL_KEYS = {
- "clausius_clapeyron": _build_cc,
- # add more: "my_constraint_key": _build_my_constraint
+ "clausius_clapeyron": _build_clausius_clapeyron,
}
+#: Types whose entry key carries the constraint's parameters rather than a bool.
+#: ``clausius_clapeyron: true`` predates these and keeps its flag spelling.
+_SUPPORTED_VALUE_KEYS = {
+ "zero_coupled": _build_zero_coupled,
+ "sum_upper_strict": _build_sum_upper_strict,
+ "nonzero_minimum": _build_nonzero_minimum,
+}
-# =========================
-# Config parsing & application
-# =========================
def constraints_from_config(cfg: Dict, design: "Design") -> List[RowConstraint]:
- """
- Parse a YAML layout like:
+ """Build only explicitly configured campaign constraints.
+
+ An entry names exactly one type. ``clausius_clapeyron`` takes a boolean flag
+ and its parameters as siblings; the rest carry their parameters on the type
+ key itself::
+
+ constraints:
+ - clausius_clapeyron: true
+ ah_col: absolute_humidity
+ temp_c_col: temperature_c
- constraints:
- - clausius_clapeyron: true
- absolute_humidity_col: "absolute_humidity"
- temperature_col: "temperature_c"
+ - zero_coupled: [speed_2, time_2]
+ - sum_upper_strict: {lhs: anti_time, rhs: [time_1, time_2]}
+ - nonzero_minimum: {column: time_2, minimum: 10}
- - clausius_clapeyron: false # ignored
- ...
+ An optional ``name:`` overrides the label a violation is reported under.
- Returns a list of row-constraint callables (possibly empty).
+ Missing ``constraints`` and an empty list both mean no constraints. Invalid
+ explicit entries fail instead of being silently ignored.
"""
+
+ if not isinstance(cfg, dict):
+ raise TypeError("Constraint configuration must be a mapping.")
+
items = cfg.get("constraints", [])
if not items:
return []
+ if not isinstance(items, list):
+ raise TypeError("Config 'constraints' must be a list of mappings.")
- fns: List[RowConstraint] = []
- for raw in items:
+ known_types = sorted({*_SUPPORTED_BOOL_KEYS, *_SUPPORTED_VALUE_KEYS})
+ constraints: List[RowConstraint] = []
+ for index, raw in enumerate(items):
if not isinstance(raw, dict):
- continue
+ raise TypeError(
+ f"Constraint entry at index {index} must be a mapping; "
+ f"got {type(raw).__name__}."
+ )
+
+ known_keys = [key for key in known_types if key in raw]
+ if not known_keys:
+ raise KeyError(
+ f"Constraint entry at index {index} has no supported type; "
+ f"expected one of {known_types}."
+ )
+ if len(known_keys) > 1:
+ raise ValueError(
+ f"Constraint entry at index {index} enables multiple types: "
+ f"{known_keys}. Use one constraint type per entry."
+ )
- # find which supported boolean key (if any) is enabled
- chosen_key = None
- for key, builder in _SUPPORTED_BOOL_KEYS.items():
- val = raw.get(key, None)
- if isinstance(val, bool) and val:
- chosen_key = key
- break
+ chosen_key = known_keys[0]
+ if chosen_key in _SUPPORTED_BOOL_KEYS:
+ enabled = raw[chosen_key]
+ if not isinstance(enabled, bool):
+ raise TypeError(
+ f"Constraint flag '{chosen_key}' must be true or false; "
+ f"got {enabled!r}."
+ )
+ if not enabled:
+ continue
+ builder = _SUPPORTED_BOOL_KEYS[chosen_key]
+ # the flag spelling keeps its parameters as siblings of the flag
+ parameters = raw
+ else:
+ builder = _SUPPORTED_VALUE_KEYS[chosen_key]
+ # the value spelling carries its parameters on the type key itself
+ parameters = raw[chosen_key]
+
+ label = raw.get("name")
+ constraints.append(
+ NamedConstraint(
+ name=str(label) if label else chosen_key,
+ description=_describe(chosen_key, parameters),
+ check=builder(parameters, design),
+ )
+ )
- if chosen_key is None:
- # no supported boolean flag set to true -> skip this entry
- continue
+ return constraints
+
+
+def _describe(kind: str, parameters) -> str:
+ """A one-line statement of the rule, for a reviewer rather than a log."""
+ if kind == "zero_coupled":
+ names = [parameters] if isinstance(parameters, str) else list(parameters or ())
+ return f"{' and '.join(str(c) for c in names)} are all zero or all nonzero"
+ if kind == "sum_upper_strict" and isinstance(parameters, dict):
+ rhs = parameters.get("rhs")
+ names = [rhs] if isinstance(rhs, str) else list(rhs or ())
+ return f"{parameters.get('lhs')} < {' + '.join(str(c) for c in names)}"
+ if kind == "nonzero_minimum" and isinstance(parameters, dict):
+ return (
+ f"{parameters.get('column')} is 0 or at least "
+ f"{parameters.get('minimum')}"
+ )
+ return kind
- # build constraint from the same dict (which also holds column names, etc.)
- builder = _SUPPORTED_BOOL_KEYS[chosen_key]
- fns.append(builder(raw, design))
- return fns
+def constraint_violations(
+ X_phys: np.ndarray,
+ design: "Design",
+ constraints: Optional[Sequence[RowConstraint]],
+) -> List[List[str]]:
+ """Per row, the names of the constraints it breaks.
+
+ :func:`apply_row_constraints` answers "may this row be used"; this answers
+ "and if not, which rule". A batch review needs the second, because "condition
+ 3 is invalid" is not something anyone can act on.
+ """
+ X_phys = np.asarray(X_phys, dtype=float)
+ if X_phys.ndim != 2:
+ raise ValueError("Physical input array must be two-dimensional.")
+ per_row: List[List[str]] = [[] for _ in range(X_phys.shape[0])]
+ if not constraints or X_phys.shape[0] == 0:
+ return per_row
+ for index, constraint in enumerate(constraints):
+ mask = apply_row_constraints(X_phys, design, [constraint])
+ label = getattr(constraint, "name", f"constraint #{index}")
+ for row in np.flatnonzero(~mask):
+ per_row[int(row)].append(label)
+ return per_row
def apply_row_constraints(
@@ -126,21 +322,46 @@ def apply_row_constraints(
design: "Design",
constraints: Optional[Sequence[RowConstraint]],
) -> np.ndarray:
- """
- Apply zero or more row-wise constraints; returns a boolean mask AND-ing all.
- If constraints is None or empty, returns all True.
- """
- n = X_phys.shape[0]
- if not constraints:
- return np.ones(n, dtype=bool)
+ """AND zero or more row constraints over physical-space input rows."""
+
+ X_phys = np.asarray(X_phys, dtype=float)
+ if X_phys.ndim != 2 or X_phys.shape[1] != len(design.names):
+ raise ValueError(
+ "Physical input array must have shape (n_rows, n_design_inputs); "
+ f"got {X_phys.shape} for {len(design.names)} design inputs."
+ )
- mask = np.ones(n, dtype=bool)
- for k, fn in enumerate(constraints):
- m = fn(X_phys, design)
- if m is None or m.dtype != bool or m.shape != (n,):
+ row_count = X_phys.shape[0]
+ if not constraints:
+ return np.ones(row_count, dtype=bool)
+
+ mask = np.ones(row_count, dtype=bool)
+ for index, constraint in enumerate(constraints):
+ result = constraint(X_phys, design)
+ if (
+ result is None
+ or not isinstance(result, np.ndarray)
+ or result.dtype != bool
+ or result.shape != (row_count,)
+ ):
+ actual = (
+ None
+ if result is None
+ else (getattr(result, "dtype", None), getattr(result, "shape", None))
+ )
raise ValueError(
- f"Constraint #{k} must return a boolean mask of shape ({n},); "
- f"got {None if m is None else (m.dtype, m.shape)}"
+ f"Constraint #{index} must return a boolean mask of shape "
+ f"({row_count},); got {actual}."
)
- mask &= m
+ mask &= result
return mask
+
+
+__all__ = [
+ "NamedConstraint",
+ "RowConstraint",
+ "apply_row_constraints",
+ "check_clausius_clapeyron_np",
+ "constraint_violations",
+ "constraints_from_config",
+]
diff --git a/src/mobo_kit/design.py b/src/mobo_kit/design.py
index bb8b7d4..6274cb6 100644
--- a/src/mobo_kit/design.py
+++ b/src/mobo_kit/design.py
@@ -4,14 +4,107 @@
from typing import List, Optional, Dict, Any
import numpy as np
-def make_linspace(start: float, stop: float, step: float, decimals: int = 6) -> np.ndarray:
- """Create a grid of values from start to stop with given step size."""
- num_points = int(round((stop - start) / step)) + 1
- return np.round(np.linspace(start, stop, num_points), decimals)
+
+def _finite_float(value: Any, *, field: str, input_name: str) -> float:
+ """Return ``value`` as a finite float with a campaign-friendly error."""
+ if isinstance(value, (bool, np.bool_)):
+ raise ValueError(
+ f"Input '{input_name}' field '{field}' must be a finite number, not bool."
+ )
+ try:
+ number = float(value)
+ except (TypeError, ValueError) as exc:
+ raise ValueError(
+ f"Input '{input_name}' field '{field}' must be a finite number; "
+ f"got {value!r}."
+ ) from exc
+ if not np.isfinite(number):
+ raise ValueError(
+ f"Input '{input_name}' field '{field}' must be finite; got {value!r}."
+ )
+ return number
+
+
+def _validate_decimals(value: Any, *, input_name: str) -> int:
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, (int, np.integer)):
+ raise ValueError(
+ f"Input '{input_name}' field 'decimals' must be a non-negative integer; "
+ f"got {value!r}."
+ )
+ decimals = int(value)
+ if decimals < 0:
+ raise ValueError(
+ f"Input '{input_name}' field 'decimals' must be a non-negative integer; "
+ f"got {decimals}."
+ )
+ return decimals
+
+
+def make_linspace(
+ start: float,
+ stop: float,
+ step: float,
+ decimals: int = 6,
+) -> np.ndarray:
+ """Create an endpoint-aligned grid with the requested step.
+
+ ``numpy.linspace`` can silently change the requested spacing when the endpoint
+ is not aligned. Campaign inputs must instead satisfy
+ ``stop == start + k * step`` (within floating-point tolerance).
+ """
+ input_name = ""
+ start_f = _finite_float(start, field="start", input_name=input_name)
+ stop_f = _finite_float(stop, field="stop", input_name=input_name)
+ step_f = _finite_float(step, field="step", input_name=input_name)
+ decimals_i = _validate_decimals(decimals, input_name=input_name)
+
+ if step_f <= 0:
+ raise ValueError(f"Grid step must be > 0; got {step_f}.")
+ if stop_f < start_f:
+ raise ValueError(
+ f"Grid stop must be greater than or equal to start; "
+ f"got start={start_f}, stop={stop_f}."
+ )
+
+ span = stop_f - start_f
+ if span == 0:
+ return np.asarray([round(start_f, decimals_i)], dtype=float)
+
+ interval_count = int(round(span / step_f))
+ if interval_count < 1:
+ raise ValueError(
+ "Grid endpoint is not aligned with step: "
+ f"start={start_f}, stop={stop_f}, step={step_f}. "
+ "Require stop = start + k * step for an integer k."
+ )
+ aligned_span = interval_count * step_f
+ alignment_atol = max(1e-12, abs(step_f) * 1e-9)
+ if not np.isclose(span, aligned_span, rtol=0.0, atol=alignment_atol):
+ raise ValueError(
+ "Grid endpoint is not aligned with step: "
+ f"start={start_f}, stop={stop_f}, step={step_f}. "
+ "Require stop = start + k * step for an integer k."
+ )
+
+ grid = np.round(
+ start_f + step_f * np.arange(interval_count + 1, dtype=float),
+ decimals_i,
+ )
+ grid[0] = round(start_f, decimals_i)
+ grid[-1] = round(stop_f, decimals_i)
+
+ if np.unique(grid).size != grid.size:
+ raise ValueError(
+ f"Rounding the grid to {decimals_i} decimals creates duplicate values. "
+ "Increase 'decimals' or use a larger step."
+ )
+ return grid
+
@dataclass
class InputSpec:
"""Specification for an input parameter with grid-based discretization."""
+
name: str
start: float
stop: float
@@ -19,53 +112,120 @@ class InputSpec:
unit: Optional[str] = None
decimals: int = 6
+ def __post_init__(self) -> None:
+ if not isinstance(self.name, str) or not self.name.strip():
+ raise ValueError("Input name must be a non-empty string.")
+ self.name = self.name.strip()
+ self.start = _finite_float(self.start, field="start", input_name=self.name)
+ self.stop = _finite_float(self.stop, field="stop", input_name=self.name)
+ self.step = _finite_float(self.step, field="step", input_name=self.name)
+ self.decimals = _validate_decimals(self.decimals, input_name=self.name)
+ if self.step <= 0:
+ raise ValueError(
+ f"Input '{self.name}' field 'step' must be > 0; got {self.step}."
+ )
+ if self.stop < self.start:
+ raise ValueError(
+ f"Input '{self.name}' requires stop >= start; "
+ f"got start={self.start}, stop={self.stop}."
+ )
+ # Constructing the grid here validates endpoint alignment and precision.
+ try:
+ make_linspace(self.start, self.stop, self.step, self.decimals)
+ except ValueError as exc:
+ raise ValueError(f"Invalid grid for input '{self.name}': {exc}") from exc
+
+
def build_input_spec_list(cfg_inputs: List[Dict[str, Any]]) -> List[InputSpec]:
"""Build InputSpec objects from config dictionary."""
+ if not isinstance(cfg_inputs, list) or not cfg_inputs:
+ raise ValueError("Config 'inputs' must be a non-empty list.")
+
specs = []
- for item in cfg_inputs:
- # Handle both old format (lower/upper) and new format (start/stop/step)
- if 'start' in item and 'stop' in item and 'step' in item:
- # New format: start/stop/step
- spec = InputSpec(
- name=item['name'],
- unit=item.get('unit'),
- start=float(item['start']),
- stop=float(item['stop']),
- step=float(item['step']),
- decimals=item.get('decimals', 6)
+ seen_names = set()
+ for index, item in enumerate(cfg_inputs):
+ if not isinstance(item, dict):
+ raise ValueError(
+ f"Config input at index {index} must be a mapping; "
+ f"got {type(item).__name__}."
+ )
+ missing = [key for key in ("name", "start", "stop", "step") if key not in item]
+ if missing:
+ display_name = item.get("name", f"index {index}")
+ raise ValueError(
+ f"Input '{display_name}' is missing required field(s): "
+ f"{', '.join(missing)}."
+ )
+
+ spec = InputSpec(
+ name=item["name"],
+ unit=item.get("unit"),
+ start=item["start"],
+ stop=item["stop"],
+ step=item["step"],
+ decimals=item.get("decimals", 6),
+ )
+ if spec.name in seen_names:
+ raise ValueError(
+ f"Input names must be unique; duplicate name '{spec.name}'."
)
- else:
- raise ValueError(f"Input '{item.get('name', 'unknown')}' must have either (start, stop, step) or (lower, upper).")
-
+ seen_names.add(spec.name)
specs.append(spec)
-
+
return specs
+
@dataclass
class Design:
"""Design space specification with grid-based discretization."""
+
names: List[str]
units: List[Optional[str]]
- lowers: np.ndarray # (D,) minimum values
- uppers: np.ndarray # (D,) maximum values
- steps: np.ndarray # (D,) step sizes
- var_array: List[np.ndarray] # grid values for each feature
- var_list: List[np.ndarray] # grid values for each feature (same as var_array for consistency)
+ lowers: np.ndarray # (D,) minimum values
+ uppers: np.ndarray # (D,) maximum values
+ steps: np.ndarray # (D,) step sizes
+ var_array: List[np.ndarray] # grid values for each feature
+ # Grid values for each feature (same as var_array for compatibility).
+ var_list: List[np.ndarray]
+
def build_design(specs: List[InputSpec]) -> Design:
"""Build a Design object from InputSpec objects."""
+ if not isinstance(specs, list) or not specs:
+ raise ValueError("At least one InputSpec is required to build a design.")
+
names, units = [], []
lowers, uppers, steps = [], [], []
var_array: List[np.ndarray] = []
+ seen_names = set()
- for spec in specs:
+ for index, raw_spec in enumerate(specs):
+ if not isinstance(raw_spec, InputSpec):
+ raise TypeError(
+ f"Design item at index {index} must be an InputSpec; "
+ f"got {type(raw_spec).__name__}."
+ )
+ # InputSpec is mutable, so revalidate a fresh copy at the boundary.
+ spec = InputSpec(
+ name=raw_spec.name,
+ start=raw_spec.start,
+ stop=raw_spec.stop,
+ step=raw_spec.step,
+ unit=raw_spec.unit,
+ decimals=raw_spec.decimals,
+ )
+ if spec.name in seen_names:
+ raise ValueError(
+ f"Input names must be unique; duplicate name '{spec.name}'."
+ )
+ seen_names.add(spec.name)
names.append(spec.name)
units.append(spec.unit)
-
+
# Create grid for this parameter
grid = make_linspace(spec.start, spec.stop, spec.step, spec.decimals)
var_array.append(grid)
-
+
# Store bounds and step
lowers.append(float(grid.min()))
uppers.append(float(grid.max()))
@@ -81,33 +241,44 @@ def build_design(specs: List[InputSpec]) -> Design:
var_list=var_array, # For backward compatibility
)
+
def build_design_from_config(config: Dict[str, Any]) -> Design:
"""Build a Design object directly from a config dictionary."""
- if 'inputs' not in config:
+ if not isinstance(config, dict):
+ raise ValueError(
+ "Config must be a mapping containing a non-empty 'inputs' list."
+ )
+ if "inputs" not in config:
raise ValueError("Config must contain 'inputs' key.")
-
- specs = build_input_spec_list(config['inputs'])
+
+ specs = build_input_spec_list(config["inputs"])
return build_design(specs)
+
def get_variable_space() -> List[np.ndarray]:
"""Get the variable space as a list of arrays (for backward compatibility)."""
# This function would need a config to work with the new system
# For now, return empty list - users should use build_design_from_config instead
return []
+
def get_parameter_space():
"""Get the parameter space (for backward compatibility)."""
# This function would need a config to work with the new system
# For now, return None - users should use build_design_from_config instead
return None
-def generate_initial_design(n_samples: int, config: Optional[Dict[str, Any]] = None) -> np.ndarray:
+
+def generate_initial_design(
+ n_samples: int,
+ config: Optional[Dict[str, Any]] = None,
+) -> np.ndarray:
"""Generate initial design using Latin Hypercube Sampling.
-
+
Args:
n_samples: Number of samples to generate
config: Optional config dictionary. If provided, uses the new design system.
-
+
Returns:
Array of shape (n_samples, n_features)
"""
@@ -117,9 +288,7 @@ def generate_initial_design(n_samples: int, config: Optional[Dict[str, Any]] = N
# This would integrate with the LHS module
# For now, return random samples in the bounds
samples = np.random.uniform(
- low=design.lowers,
- high=design.uppers,
- size=(n_samples, len(design.names))
+ low=design.lowers, high=design.uppers, size=(n_samples, len(design.names))
)
return samples
else:
diff --git a/src/mobo_kit/discrete_refinement.py b/src/mobo_kit/discrete_refinement.py
new file mode 100644
index 0000000..8ac82e6
--- /dev/null
+++ b/src/mobo_kit/discrete_refinement.py
@@ -0,0 +1,730 @@
+"""Deterministic coordinate refinement on an exact finite design grid.
+
+**NOT constraint-aware, and currently not wired into a round.** This moves a
+selected point one coordinate at a time across the declared grid, which is
+exactly the operation that can walk a valid recipe into an invalid one -- lowering
+``time_2`` to 0 while ``speed_2`` stays at 3500, say. Campaign constraints are
+enforced by filtering the candidate pool before any acquisition sees it
+(``campaign.run_r1_ucb``), and a local search that leaves the pool escapes that
+filter entirely. If this is ever wired into a round, it must take the same
+``row_constraints`` the pool sampler takes and reject off-constraint neighbours;
+``campaign.validate_batch`` would catch the result, but only after the search had
+already spent its budget walking somewhere it was never allowed to go.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import Callable, Sequence
+
+import numpy as np
+
+from .batch_selection import soft_local_penalty
+from .candidate_pool import CandidatePool
+from .design import Design
+
+
+GridScoreFunction = Callable[[np.ndarray], np.ndarray]
+
+
+@dataclass(frozen=True)
+class RefinementConfig:
+ anchors_per_selection_step: int = 64
+ max_sweeps: int = 10
+ improvement_tolerance: float = 1.0e-10
+ radius: float | None = 0.25
+ min_batch_distance: float = 0.15
+ min_observed_distance: float = 0.0
+ dimension_weights: np.ndarray | None = None
+ epsilon: float = 1.0e-12
+
+ def __post_init__(self) -> None:
+ for name in ("anchors_per_selection_step", "max_sweeps"):
+ value = getattr(self, name)
+ if (
+ isinstance(value, (bool, np.bool_))
+ or not isinstance(value, (int, np.integer))
+ or int(value) <= 0
+ ):
+ raise ValueError(f"{name} must be a positive integer.")
+ object.__setattr__(self, name, int(value))
+ for name in (
+ "improvement_tolerance",
+ "min_batch_distance",
+ "min_observed_distance",
+ "epsilon",
+ ):
+ value = getattr(self, name)
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+ raise ValueError(f"{name} must be a real non-boolean number.")
+ number = float(value)
+ if not np.isfinite(number) or number < 0:
+ raise ValueError(f"{name} must be finite and non-negative.")
+ object.__setattr__(self, name, number)
+ if self.epsilon <= 0 or self.epsilon >= 1:
+ raise ValueError("epsilon must be strictly between zero and one.")
+ if self.radius is not None:
+ if isinstance(self.radius, (bool, np.bool_)) or not isinstance(
+ self.radius, Real
+ ):
+ raise ValueError("radius must be null or a real number.")
+ radius = float(self.radius)
+ if not np.isfinite(radius) or radius <= 0:
+ raise ValueError("radius must be null or finite and positive.")
+ object.__setattr__(self, "radius", radius)
+ if self.dimension_weights is not None:
+ weights = np.asarray(self.dimension_weights, dtype=float).copy()
+ if weights.ndim != 1 or weights.size == 0:
+ raise ValueError("dimension_weights must be a non-empty vector.")
+ if not np.all(np.isfinite(weights)) or np.any(weights <= 0):
+ raise ValueError("dimension_weights must be finite and positive.")
+ weights.setflags(write=False)
+ object.__setattr__(self, "dimension_weights", weights)
+
+
+@dataclass(frozen=True)
+class RefinementTraceRow:
+ selection_step: int
+ anchor_rank: int
+ anchor_pool_index: int | None
+ sweep: int
+ coordinate: int | None
+ coordinate_name: str | None
+ start_grid_index: tuple[int, ...]
+ chosen_grid_index: tuple[int, ...]
+ score_before: float
+ score_after: float
+ base_score_before: float
+ base_score_after: float
+ penalized_log_score_before: float
+ penalized_log_score_after: float
+ accepted_move: bool
+ termination_reason: str | None
+
+
+@dataclass(frozen=True)
+class RefinedAnchor:
+ selection_step: int
+ anchor_rank: int
+ anchor_pool_index: int | None
+ anchor_grid_index: tuple[int, ...]
+ refined_grid_index: tuple[int, ...]
+ start_base_score: float
+ start_penalized_score: float
+ start_penalized_log_score: float
+ base_score: float
+ penalized_score: float
+ end_penalized_log_score: float
+ sweeps: int
+ accepted_move_count: int
+ changed_dimensions: tuple[str, ...]
+ termination_reason: str
+
+
+@dataclass(frozen=True)
+class RefinedBatchResult:
+ grid_indices: np.ndarray
+ X_phys: np.ndarray
+ X_norm: np.ndarray
+ base_scores: np.ndarray
+ penalized_scores_at_selection: np.ndarray
+ anchors: tuple[RefinedAnchor, ...]
+ trace: tuple[RefinementTraceRow, ...]
+ distinct_converged_optima: int
+
+
+class CachedGridScorer:
+ """Memoize a vectorized exact-grid score function by integer tuple."""
+
+ def __init__(self, score_function: GridScoreFunction, dimension: int) -> None:
+ if not callable(score_function):
+ raise TypeError("score_function must be callable.")
+ if isinstance(dimension, bool) or int(dimension) <= 0:
+ raise ValueError("dimension must be a positive integer.")
+ self._score_function = score_function
+ self._dimension = int(dimension)
+ self._cache: dict[tuple[int, ...], float] = {}
+
+ @property
+ def cache_size(self) -> int:
+ return len(self._cache)
+
+ def seed(self, grid_indices: np.ndarray, scores: np.ndarray) -> None:
+ rows = _grid_matrix(grid_indices, self._dimension, name="grid_indices")
+ values = _score_vector(scores, rows.shape[0])
+ for row, value in zip(rows, values):
+ key = tuple(int(item) for item in row)
+ previous = self._cache.get(key)
+ if previous is not None and previous != float(value):
+ raise ValueError("Conflicting score supplied for a cached grid tuple.")
+ self._cache[key] = float(value)
+
+ def __call__(self, grid_indices: np.ndarray) -> np.ndarray:
+ rows = _grid_matrix(grid_indices, self._dimension, name="grid_indices")
+ keys = [tuple(int(item) for item in row) for row in rows]
+ missing_keys = list(
+ dict.fromkeys(key for key in keys if key not in self._cache)
+ )
+ if missing_keys:
+ missing = np.asarray(missing_keys, dtype=np.int64)
+ values = _score_vector(self._score_function(missing), missing.shape[0])
+ for key, value in zip(missing_keys, values):
+ self._cache[key] = float(value)
+ return np.asarray([self._cache[key] for key in keys], dtype=float)
+
+
+def _grid_matrix(value: np.ndarray, dimension: int, *, name: str) -> np.ndarray:
+ raw = np.asarray(value)
+ if raw.ndim != 2 or raw.shape[1] != dimension:
+ raise ValueError(f"{name} must have shape (N, {dimension}); got {raw.shape}.")
+ if not np.issubdtype(raw.dtype, np.integer):
+ if not np.all(np.isfinite(raw)) or not np.all(raw == np.floor(raw)):
+ raise ValueError(f"{name} must contain integer grid indices.")
+ return raw.astype(np.int64, copy=False)
+
+
+def _score_vector(value: np.ndarray, size: int) -> np.ndarray:
+ scores = np.asarray(value, dtype=float)
+ if scores.shape != (size,) or not np.all(np.isfinite(scores)):
+ raise ValueError(f"score_function must return {size} finite scores.")
+ if np.any(scores < 0):
+ raise ValueError("Acquisition scores must be non-negative.")
+ return scores
+
+
+def _validate_design(design: Design) -> tuple[np.ndarray, ...]:
+ if not isinstance(design, Design):
+ raise TypeError("design must be a Design.")
+ grids = tuple(np.asarray(grid, dtype=float) for grid in design.var_array)
+ if len(grids) != len(design.names) or not grids:
+ raise ValueError("design must contain one grid per named dimension.")
+ if any(grid.ndim != 1 or grid.size == 0 for grid in grids):
+ raise ValueError("Every design grid must be a non-empty vector.")
+ return grids
+
+
+def grid_indices_to_physical_and_normalized(
+ grid_indices: np.ndarray, design: Design
+) -> tuple[np.ndarray, np.ndarray]:
+ """Resolve exact integer tuples without allocating the Cartesian product."""
+ grids = _validate_design(design)
+ rows = _grid_matrix(grid_indices, len(grids), name="grid_indices")
+ physical = np.empty(rows.shape, dtype=float)
+ for column, grid in enumerate(grids):
+ if np.any(rows[:, column] < 0) or np.any(rows[:, column] >= grid.size):
+ raise ValueError(
+ f"grid_indices contains an out-of-range value for {design.names[column]!r}."
+ )
+ physical[:, column] = grid[rows[:, column]]
+
+ # Canonicalize exactly as the Sobol candidate-pool path. Index fractions are
+ # mathematically equivalent on an evenly spaced grid, but decimal-valued axes
+ # can differ by one floating-point bit. Using physical bounds here keeps pool
+ # anchors, refined candidates, distance checks, and exact-overlap diagnostics
+ # on one representation.
+ lower = np.asarray(design.lowers, dtype=float)
+ upper = np.asarray(design.uppers, dtype=float)
+ spans = upper - lower
+ normalized = np.zeros_like(physical, dtype=float)
+ changing = spans > 0.0
+ normalized[:, changing] = (physical[:, changing] - lower[changing]) / spans[
+ changing
+ ]
+ return physical, normalized
+
+
+def _weights(config: RefinementConfig, dimension: int) -> np.ndarray:
+ if config.dimension_weights is None:
+ return np.ones(dimension, dtype=float)
+ if config.dimension_weights.shape != (dimension,):
+ raise ValueError(
+ f"dimension_weights must have shape ({dimension},); got "
+ f"{config.dimension_weights.shape}."
+ )
+ return config.dimension_weights
+
+
+def _distance_to_references(
+ X_norm: np.ndarray, references: np.ndarray, weights: np.ndarray
+) -> np.ndarray:
+ if references.shape[0] == 0:
+ return np.full(X_norm.shape[0], np.inf, dtype=float)
+ difference = X_norm[:, None, :] - references[None, :, :]
+ return np.sqrt(np.sum(weights * difference**2, axis=-1)).min(axis=1)
+
+
+def _penalized_scores(
+ base_scores: np.ndarray,
+ X_norm: np.ndarray,
+ *,
+ selected_norm: np.ndarray,
+ observed_norm: np.ndarray,
+ grid_indices: np.ndarray,
+ forbidden_grid_keys: set[tuple[int, ...]],
+ config: RefinementConfig,
+ positive_score_threshold: float,
+) -> np.ndarray:
+ weights = _weights(config, X_norm.shape[1])
+ penalized = np.asarray(base_scores, dtype=float).copy()
+ valid = penalized > positive_score_threshold
+ if forbidden_grid_keys:
+ valid &= np.asarray(
+ [
+ tuple(int(value) for value in row) not in forbidden_grid_keys
+ for row in grid_indices
+ ],
+ dtype=bool,
+ )
+ nearest_selected = _distance_to_references(X_norm, selected_norm, weights)
+ nearest_observed = _distance_to_references(X_norm, observed_norm, weights)
+ if selected_norm.shape[0]:
+ valid &= nearest_selected >= config.min_batch_distance
+ if observed_norm.shape[0] and config.min_observed_distance > 0:
+ valid &= nearest_observed >= config.min_observed_distance
+ if selected_norm.shape[0] and config.radius is not None:
+ difference = X_norm[:, None, :] - selected_norm[None, :, :]
+ distances = np.sqrt(np.sum(weights * difference**2, axis=-1))
+ factors, _ = soft_local_penalty(
+ distances, radius=config.radius, epsilon=config.epsilon
+ )
+ penalized *= np.prod(factors, axis=1)
+ penalized[~valid] = -np.inf
+ return penalized
+
+
+def _lexicographic_best(
+ grid_indices: np.ndarray, scores: np.ndarray, *, tie_tolerance: float = 1.0e-15
+) -> int:
+ valid = np.flatnonzero(np.isfinite(scores))
+ if valid.size == 0:
+ raise RuntimeError("No eligible finite acquisition score remains.")
+ maximum = float(np.max(scores[valid]))
+ tied = valid[np.abs(scores[valid] - maximum) <= tie_tolerance]
+ if tied.size == 1:
+ return int(tied[0])
+ keys = tuple(
+ grid_indices[tied, column] for column in reversed(range(grid_indices.shape[1]))
+ )
+ return int(tied[np.lexsort(keys)[0]])
+
+
+def refine_discrete_acquisition_anchors(
+ design: Design,
+ anchor_grid_indices: np.ndarray,
+ score_function: GridScoreFunction,
+ *,
+ config: RefinementConfig,
+ selection_step: int,
+ anchor_pool_indices: Sequence[int | None] | None = None,
+ selected_grid_indices: np.ndarray | None = None,
+ selected_norm: np.ndarray | None = None,
+ observed_grid_indices: np.ndarray | None = None,
+ observed_norm: np.ndarray | None = None,
+ avoid_grid_indices: np.ndarray | None = None,
+ positive_score_threshold: float = 0.0,
+) -> tuple[tuple[RefinedAnchor, ...], tuple[RefinementTraceRow, ...]]:
+ """Coordinate-ascent every anchor using every allowed value per dimension."""
+ grids = _validate_design(design)
+ dimension = len(grids)
+ anchors = _grid_matrix(anchor_grid_indices, dimension, name="anchor_grid_indices")
+ if anchors.shape[0] == 0:
+ raise ValueError("At least one refinement anchor is required.")
+ if isinstance(selection_step, bool) or int(selection_step) <= 0:
+ raise ValueError("selection_step must be a positive integer.")
+ if not isinstance(config, RefinementConfig):
+ raise TypeError("config must be a RefinementConfig.")
+ if anchor_pool_indices is None:
+ pool_indices: tuple[int | None, ...] = (None,) * anchors.shape[0]
+ else:
+ if len(anchor_pool_indices) != anchors.shape[0]:
+ raise ValueError("anchor_pool_indices must align with anchor rows.")
+ parsed_indices: list[int | None] = []
+ for value in anchor_pool_indices:
+ if value is None:
+ parsed_indices.append(None)
+ elif isinstance(value, (bool, np.bool_)) or int(value) < 0:
+ raise ValueError(
+ "anchor_pool_indices values must be non-negative integers or None."
+ )
+ else:
+ parsed_indices.append(int(value))
+ pool_indices = tuple(parsed_indices)
+ threshold = float(positive_score_threshold)
+ if not np.isfinite(threshold) or threshold < 0:
+ raise ValueError("positive_score_threshold must be finite and non-negative.")
+ scorer = (
+ score_function
+ if isinstance(score_function, CachedGridScorer)
+ else CachedGridScorer(score_function, dimension)
+ )
+
+ empty_grid = np.empty((0, dimension), dtype=np.int64)
+ selected_grid = _grid_matrix(
+ empty_grid if selected_grid_indices is None else selected_grid_indices,
+ dimension,
+ name="selected_grid_indices",
+ )
+ observed_grid = _grid_matrix(
+ empty_grid if observed_grid_indices is None else observed_grid_indices,
+ dimension,
+ name="observed_grid_indices",
+ )
+ avoid_grid = _grid_matrix(
+ empty_grid if avoid_grid_indices is None else avoid_grid_indices,
+ dimension,
+ name="avoid_grid_indices",
+ )
+ empty_norm = np.empty((0, dimension), dtype=float)
+ selected_X = (
+ empty_norm if selected_norm is None else np.asarray(selected_norm, dtype=float)
+ )
+ observed_X = (
+ empty_norm if observed_norm is None else np.asarray(observed_norm, dtype=float)
+ )
+ for name, value in (("selected_norm", selected_X), ("observed_norm", observed_X)):
+ if (
+ value.ndim != 2
+ or value.shape[1] != dimension
+ or not np.all(np.isfinite(value))
+ ):
+ raise ValueError(f"{name} must be a finite (N, {dimension}) matrix.")
+ forbidden = {
+ tuple(int(value) for value in row)
+ for row in np.vstack([selected_grid, observed_grid, avoid_grid])
+ }
+
+ refined: list[RefinedAnchor] = []
+ trace: list[RefinementTraceRow] = []
+ for anchor_rank, (anchor, anchor_pool_index) in enumerate(
+ zip(anchors, pool_indices), start=1
+ ):
+ current = anchor.copy()
+ current_phys, current_norm = grid_indices_to_physical_and_normalized(
+ current[None, :], design
+ )
+ del current_phys
+ current_base = scorer(current[None, :])[0]
+ current_penalized = _penalized_scores(
+ np.asarray([current_base]),
+ current_norm,
+ selected_norm=selected_X,
+ observed_norm=observed_X,
+ grid_indices=current[None, :],
+ forbidden_grid_keys=forbidden,
+ config=config,
+ positive_score_threshold=threshold,
+ )[0]
+ if not np.isfinite(current_penalized):
+ raise RuntimeError("A refinement anchor violates an eligibility rule.")
+ start_base = float(current_base)
+ start_penalized = float(current_penalized)
+ accepted_move_count = 0
+ changed_dimensions: set[str] = set()
+ termination = "max_sweeps"
+ sweeps_completed = 0
+ for sweep in range(1, config.max_sweeps + 1):
+ sweep_improved = False
+ sweeps_completed = sweep
+ for coordinate, (name, grid) in enumerate(zip(design.names, grids)):
+ candidates = np.repeat(current[None, :], grid.size, axis=0)
+ candidates[:, coordinate] = np.arange(grid.size, dtype=np.int64)
+ _, candidates_norm = grid_indices_to_physical_and_normalized(
+ candidates, design
+ )
+ base = scorer(candidates)
+ penalized = _penalized_scores(
+ base,
+ candidates_norm,
+ selected_norm=selected_X,
+ observed_norm=observed_X,
+ grid_indices=candidates,
+ forbidden_grid_keys=forbidden,
+ config=config,
+ positive_score_threshold=threshold,
+ )
+ chosen = _lexicographic_best(candidates, penalized)
+ proposed = candidates[chosen]
+ proposed_score = float(penalized[chosen])
+ accepted = proposed_score > (
+ current_penalized + config.improvement_tolerance
+ )
+ start_key = tuple(int(value) for value in current)
+ chosen_key = tuple(int(value) for value in proposed)
+ before = float(current_penalized)
+ base_before = float(current_base)
+ if accepted:
+ current = proposed.copy()
+ current_base = float(base[chosen])
+ current_penalized = proposed_score
+ sweep_improved = True
+ accepted_move_count += 1
+ changed_dimensions.add(name)
+ trace.append(
+ RefinementTraceRow(
+ selection_step=int(selection_step),
+ anchor_rank=anchor_rank,
+ anchor_pool_index=anchor_pool_index,
+ sweep=sweep,
+ coordinate=coordinate,
+ coordinate_name=name,
+ start_grid_index=start_key,
+ chosen_grid_index=chosen_key,
+ score_before=before,
+ score_after=float(current_penalized),
+ base_score_before=base_before,
+ base_score_after=float(current_base),
+ penalized_log_score_before=float(np.log(before)),
+ penalized_log_score_after=float(np.log(current_penalized)),
+ accepted_move=accepted,
+ termination_reason=None,
+ )
+ )
+ if not sweep_improved:
+ termination = "no_improvement"
+ break
+ trace.append(
+ RefinementTraceRow(
+ selection_step=int(selection_step),
+ anchor_rank=anchor_rank,
+ anchor_pool_index=anchor_pool_index,
+ sweep=sweeps_completed,
+ coordinate=None,
+ coordinate_name=None,
+ start_grid_index=tuple(int(value) for value in current),
+ chosen_grid_index=tuple(int(value) for value in current),
+ score_before=float(current_penalized),
+ score_after=float(current_penalized),
+ base_score_before=float(current_base),
+ base_score_after=float(current_base),
+ penalized_log_score_before=float(np.log(current_penalized)),
+ penalized_log_score_after=float(np.log(current_penalized)),
+ accepted_move=False,
+ termination_reason=termination,
+ )
+ )
+ refined.append(
+ RefinedAnchor(
+ selection_step=int(selection_step),
+ anchor_rank=anchor_rank,
+ anchor_pool_index=anchor_pool_index,
+ anchor_grid_index=tuple(int(value) for value in anchor),
+ refined_grid_index=tuple(int(value) for value in current),
+ start_base_score=start_base,
+ start_penalized_score=start_penalized,
+ start_penalized_log_score=float(np.log(start_penalized)),
+ base_score=float(current_base),
+ penalized_score=float(current_penalized),
+ end_penalized_log_score=float(np.log(current_penalized)),
+ sweeps=sweeps_completed,
+ accepted_move_count=accepted_move_count,
+ changed_dimensions=tuple(sorted(changed_dimensions)),
+ termination_reason=termination,
+ )
+ )
+ return tuple(refined), tuple(trace)
+
+
+def propose_refined_discrete_batch(
+ master_pool: CandidatePool,
+ design: Design,
+ score_function: GridScoreFunction,
+ *,
+ q: int,
+ config: RefinementConfig,
+ master_base_scores: np.ndarray | None = None,
+ observed_grid_indices: np.ndarray | None = None,
+ observed_norm: np.ndarray | None = None,
+ avoid_grid_indices: np.ndarray | None = None,
+ positive_score_threshold: float = 0.0,
+) -> RefinedBatchResult:
+ """Sequentially refine top pool anchors and select an exact-grid batch."""
+ if not isinstance(master_pool, CandidatePool):
+ raise TypeError("master_pool must be a CandidatePool.")
+ grids = _validate_design(design)
+ dimension = len(grids)
+ pool_grid = _grid_matrix(
+ master_pool.grid_indices, dimension, name="pool.grid_indices"
+ )
+ if isinstance(q, bool) or not isinstance(q, (int, np.integer)) or int(q) <= 0:
+ raise ValueError("q must be a positive integer.")
+ requested = int(q)
+ scorer = CachedGridScorer(score_function, dimension)
+ if master_base_scores is None:
+ pool_base = scorer(pool_grid)
+ else:
+ pool_base = _score_vector(master_base_scores, pool_grid.shape[0])
+ scorer.seed(pool_grid, pool_base)
+ empty_grid = np.empty((0, dimension), dtype=np.int64)
+ observed_grid = _grid_matrix(
+ empty_grid if observed_grid_indices is None else observed_grid_indices,
+ dimension,
+ name="observed_grid_indices",
+ )
+ avoid_grid = _grid_matrix(
+ empty_grid if avoid_grid_indices is None else avoid_grid_indices,
+ dimension,
+ name="avoid_grid_indices",
+ )
+ observed_X = (
+ np.empty((0, dimension), dtype=float)
+ if observed_norm is None
+ else np.asarray(observed_norm, dtype=float)
+ )
+ if (
+ observed_X.ndim != 2
+ or observed_X.shape[1] != dimension
+ or not np.all(np.isfinite(observed_X))
+ ):
+ raise ValueError(f"observed_norm must be a finite (N, {dimension}) matrix.")
+
+ selected_grid: list[np.ndarray] = []
+ selected_norm: list[np.ndarray] = []
+ selected_base: list[float] = []
+ selected_penalized: list[float] = []
+ all_anchors: list[RefinedAnchor] = []
+ all_trace: list[RefinementTraceRow] = []
+ discovered_optima: set[tuple[int, ...]] = set()
+ pool_position_by_key = {
+ tuple(int(value) for value in row): index for index, row in enumerate(pool_grid)
+ }
+ for selection_step in range(1, requested + 1):
+ selected_grid_array = (
+ np.asarray(selected_grid, dtype=np.int64)
+ if selected_grid
+ else empty_grid.copy()
+ )
+ selected_norm_array = (
+ np.asarray(selected_norm, dtype=float)
+ if selected_norm
+ else np.empty((0, dimension), dtype=float)
+ )
+ forbidden = {
+ tuple(int(value) for value in row)
+ for row in np.vstack([observed_grid, avoid_grid, selected_grid_array])
+ }
+ pool_penalized = _penalized_scores(
+ pool_base,
+ np.asarray(master_pool.X_norm, dtype=float),
+ selected_norm=selected_norm_array,
+ observed_norm=observed_X,
+ grid_indices=pool_grid,
+ forbidden_grid_keys=forbidden,
+ config=config,
+ positive_score_threshold=positive_score_threshold,
+ )
+ valid = np.flatnonzero(np.isfinite(pool_penalized))
+ if valid.size == 0:
+ raise RuntimeError(
+ "No master-pool anchors remain without relaxing an eligibility rule."
+ )
+ lex_order = np.lexsort(
+ tuple(pool_grid[valid, column] for column in reversed(range(dimension)))
+ )
+ lex_valid = valid[lex_order]
+ score_order = np.argsort(-pool_penalized[lex_valid], kind="stable")
+ anchor_positions = lex_valid[score_order][
+ : min(config.anchors_per_selection_step, valid.size)
+ ]
+ anchor_rows = [pool_grid[position].copy() for position in anchor_positions]
+ anchor_pool_indices: list[int | None] = [
+ int(position) for position in anchor_positions
+ ]
+ seen_anchor_keys = {tuple(int(value) for value in row) for row in anchor_rows}
+ for previous_key in sorted(discovered_optima):
+ if previous_key in seen_anchor_keys or previous_key in forbidden:
+ continue
+ anchor_rows.append(np.asarray(previous_key, dtype=np.int64))
+ anchor_pool_indices.append(pool_position_by_key.get(previous_key))
+ seen_anchor_keys.add(previous_key)
+ combined_anchors = np.asarray(anchor_rows, dtype=np.int64)
+ _, combined_norm = grid_indices_to_physical_and_normalized(
+ combined_anchors, design
+ )
+ combined_penalized = _penalized_scores(
+ scorer(combined_anchors),
+ combined_norm,
+ selected_norm=selected_norm_array,
+ observed_norm=observed_X,
+ grid_indices=combined_anchors,
+ forbidden_grid_keys=forbidden,
+ config=config,
+ positive_score_threshold=positive_score_threshold,
+ )
+ eligible_anchor_mask = np.isfinite(combined_penalized)
+ combined_anchors = combined_anchors[eligible_anchor_mask]
+ eligible_pool_indices = tuple(
+ value
+ for value, eligible in zip(anchor_pool_indices, eligible_anchor_mask)
+ if eligible
+ )
+ if combined_anchors.shape[0] == 0:
+ raise RuntimeError("No eligible local-refinement anchor remains.")
+ refined, trace = refine_discrete_acquisition_anchors(
+ design,
+ combined_anchors,
+ scorer,
+ config=config,
+ selection_step=selection_step,
+ anchor_pool_indices=eligible_pool_indices,
+ selected_grid_indices=selected_grid_array,
+ selected_norm=selected_norm_array,
+ observed_grid_indices=observed_grid,
+ observed_norm=observed_X,
+ avoid_grid_indices=avoid_grid,
+ positive_score_threshold=positive_score_threshold,
+ )
+ all_anchors.extend(refined)
+ all_trace.extend(trace)
+ discovered_optima.update(item.refined_grid_index for item in refined)
+ unique_optima = np.asarray(
+ sorted({item.refined_grid_index for item in refined}), dtype=np.int64
+ )
+ _, optima_norm = grid_indices_to_physical_and_normalized(unique_optima, design)
+ optima_base = scorer(unique_optima)
+ optima_penalized = _penalized_scores(
+ optima_base,
+ optima_norm,
+ selected_norm=selected_norm_array,
+ observed_norm=observed_X,
+ grid_indices=unique_optima,
+ forbidden_grid_keys=forbidden,
+ config=config,
+ positive_score_threshold=positive_score_threshold,
+ )
+ chosen = _lexicographic_best(unique_optima, optima_penalized)
+ selected_grid.append(unique_optima[chosen].copy())
+ selected_norm.append(optima_norm[chosen].copy())
+ selected_base.append(float(optima_base[chosen]))
+ selected_penalized.append(float(optima_penalized[chosen]))
+
+ selected_grid_array = np.asarray(selected_grid, dtype=np.int64)
+ physical, normalized = grid_indices_to_physical_and_normalized(
+ selected_grid_array, design
+ )
+ if np.unique(selected_grid_array, axis=0).shape[0] != requested:
+ raise RuntimeError("Refinement produced duplicate selected grid tuples.")
+ return RefinedBatchResult(
+ grid_indices=selected_grid_array,
+ X_phys=physical,
+ X_norm=normalized,
+ base_scores=np.asarray(selected_base, dtype=float),
+ penalized_scores_at_selection=np.asarray(selected_penalized, dtype=float),
+ anchors=tuple(all_anchors),
+ trace=tuple(all_trace),
+ distinct_converged_optima=len(
+ {anchor.refined_grid_index for anchor in all_anchors}
+ ),
+ )
+
+
+__all__ = [
+ "CachedGridScorer",
+ "RefinedAnchor",
+ "RefinedBatchResult",
+ "RefinementConfig",
+ "RefinementTraceRow",
+ "grid_indices_to_physical_and_normalized",
+ "propose_refined_discrete_batch",
+ "refine_discrete_acquisition_anchors",
+]
diff --git a/src/mobo_kit/launcher.py b/src/mobo_kit/launcher.py
new file mode 100644
index 0000000..6757ebe
--- /dev/null
+++ b/src/mobo_kit/launcher.py
@@ -0,0 +1,865 @@
+"""The one-button loop for the experimentalist.
+
+Double-click ``launch_mobo_kit.bat`` (Windows) or ``launch_mobo_kit.command``
+(macOS), point it at the campaign workbook, press the button. It works out which
+round is due, reads what has been measured, proposes the next batch and writes it
+to a sheet beside the workbook.
+
+Everything above the UI lives in plain functions -- :func:`inspect_campaign`,
+:func:`gather_observations`, :func:`generate_next_round` -- so the decisions can
+be tested without a display, and so the same steps are available from a script
+when someone would rather not click.
+
+Three rules the UI keeps, all of them inherited rather than invented:
+
+* **The source workbook is never opened for writing.** Candidates go to a
+ sibling file, because openpyxl discards cached formula values on save.
+* **Fail closed.** A half-filled sheet, a film with no usable measurement, a
+ workbook missing a column: each stops the round with a plain sentence rather
+ than being guessed at.
+* **Nothing here approves a batch.** Fifteen films is a real cost; the window
+ shows what was proposed and why, and a human decides.
+"""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+import traceback
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable, Mapping, Sequence
+
+import numpy as np
+
+from .batch_review import BatchReview, build_batch_review, write_review_sheet
+from .campaign import (
+ RoundResult,
+ load_campaign_config,
+ objective_names,
+ replicate_aggregates,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from .replicate_variance import yvar_for_campaign
+from .scores import ScoreFinding, ScoreSeverity, describe_findings
+from .workbook_io import (
+ CandidateSheetError,
+ candidate_workbook_path,
+ detect_round,
+ read_campaign_workbook,
+ read_candidate_results,
+ source_sheet,
+ write_candidate_sheet,
+)
+
+__all__ = [
+ "CampaignStatus",
+ "DEFAULT_CONFIG",
+ "Generated",
+ "LauncherError",
+ "gather_observations",
+ "generate_next_round",
+ "inspect_campaign",
+ "main",
+]
+
+#: The ACTIVE campaign. This is the one path an experimentalist reaches by
+#: double-clicking, so it must never point at an archived contract: the launcher
+#: would then ask the new workbook for the previous campaign's columns and report
+#: it as a missing column, which reads as a broken workbook rather than as the
+#: config mismatch it is. That happened once, on 2026-08-18, between archiving
+#: campaign_d2d_perovskite.yaml and updating this line.
+DEFAULT_CONFIG = "configs/campaign_d2d_perovskite_final.yaml"
+
+#: Remembered between runs so the experimentalist browses to the workbook once.
+#: Kept in the user's home rather than the repo, so moving the checkout does not
+#: lose it. Every read and write here is best-effort: a launcher that cannot
+#: start because of its own preferences file would be worse than one that forgets.
+SETTINGS_PATH = Path.home() / ".mobo_kit" / "launcher.json"
+
+
+class LauncherError(RuntimeError):
+ """Something the user needs to fix, phrased for the user."""
+
+
+# --------------------------------------------------------------------------- #
+# remembering the workbook
+# --------------------------------------------------------------------------- #
+
+
+def load_settings() -> dict[str, Any]:
+ try:
+ with open(SETTINGS_PATH, encoding="utf-8") as handle:
+ settings = json.load(handle)
+ return settings if isinstance(settings, dict) else {}
+ except (OSError, ValueError):
+ return {}
+
+
+def save_settings(settings: Mapping[str, Any]) -> None:
+ try:
+ SETTINGS_PATH.parent.mkdir(parents=True, exist_ok=True)
+ with open(SETTINGS_PATH, "w", encoding="utf-8") as handle:
+ json.dump(dict(settings), handle, indent=2)
+ except OSError:
+ pass
+
+
+def remembered_workbook() -> Path | None:
+ raw = load_settings().get("workbook")
+ if not raw:
+ return None
+ path = Path(str(raw))
+ return path if path.exists() else None
+
+
+def remember_workbook(path: str | Path) -> None:
+ settings = load_settings()
+ settings["workbook"] = str(Path(path).resolve())
+ save_settings(settings)
+
+
+# --------------------------------------------------------------------------- #
+# status
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class CampaignStatus:
+ """What the campaign looks like right now, in the user's terms."""
+
+ workbook: Path
+ next_round: str | None
+ reason: str
+ scored_rows: int
+ total_rows: int
+ observed_conditions: int
+ findings: tuple[ScoreFinding, ...] = ()
+ #: Which sheet the rows came from; "Sheet1" on the older contracts, "R0"
+ #: on v4. Shown rather than assumed, because a user looking at the wrong
+ #: sheet is exactly the confusion this line exists to prevent.
+ source_sheet: str = "Sheet1"
+
+ @property
+ def can_generate(self) -> bool:
+ return self.next_round is not None
+
+ @property
+ def headline(self) -> str:
+ if self.next_round:
+ return f"Ready to propose {self.next_round}."
+ return "Nothing to propose yet."
+
+ @property
+ def errors(self) -> tuple[ScoreFinding, ...]:
+ return tuple(f for f in self.findings if f.severity is ScoreSeverity.ERROR)
+
+ @property
+ def warnings(self) -> tuple[ScoreFinding, ...]:
+ return tuple(f for f in self.findings if f.severity is ScoreSeverity.WARNING)
+
+ def detail(self) -> str:
+ """The body text of the window: what is known, then what was noticed."""
+ lines = [
+ f"Workbook: {self.workbook}",
+ f"Measured: {self.observed_conditions} conditions on {self.source_sheet}",
+ f"Status: {self.reason}",
+ ]
+ if self.total_rows:
+ lines.append(
+ f"Candidates: {self.scored_rows} of {self.total_rows} rows measured"
+ )
+ if self.errors:
+ lines += ["", "These must be fixed before a round can run:"]
+ lines += [f" {finding}" for finding in self.errors]
+ if self.warnings:
+ lines += ["", "Worth a look, but not blocking:"]
+ lines += [f" {finding}" for finding in self.warnings]
+ notes = [f for f in self.findings if f.severity is ScoreSeverity.NOTE]
+ if notes:
+ lines += ["", "For the record:"]
+ lines += [f" {finding}" for finding in notes]
+ return "\n".join(lines)
+
+
+def inspect_campaign(
+ workbook: str | Path, config: Mapping[str, Any]
+) -> CampaignStatus:
+ """Read the workbook and decide what is due, without proposing anything."""
+ path = Path(workbook)
+ if not path.exists():
+ raise LauncherError(f"{path} does not exist.")
+ contents = read_campaign_workbook(path, config)
+ state = detect_round(path, config)
+ return CampaignStatus(
+ workbook=path.resolve(),
+ next_round=state.next_round,
+ reason=state.reason,
+ scored_rows=state.scored_rows,
+ total_rows=state.total_rows,
+ observed_conditions=contents.n_rows,
+ findings=contents.findings,
+ source_sheet=source_sheet(config),
+ )
+
+
+# --------------------------------------------------------------------------- #
+# observations
+# --------------------------------------------------------------------------- #
+
+
+def gather_observations(
+ workbook: str | Path, config: Mapping[str, Any], *, for_round: str
+) -> tuple[np.ndarray, np.ndarray, np.ndarray | None, list[str]]:
+ """Every measured design point the next round should learn from.
+
+ R1 trains on Sheet1 alone. R2 trains on Sheet1 plus the aggregated R1
+ conditions -- three films become one observation, which is why
+ :func:`read_candidate_results` exists.
+
+ Returns ``(X, Y, Yvar, provenance)``. ``Yvar`` is ``None`` unless the config
+ asks for measured replicate variance *and* replicated conditions exist to pool
+ from; see :mod:`replicate_variance`.
+
+ Raises rather than dropping rows: a NaN objective reaching the GP is how a
+ round gets proposed from data nobody checked.
+ """
+ path = Path(workbook)
+ names = list(objective_names(config))
+ input_names = [item["name"] for item in config["inputs"]]
+
+ contents = read_campaign_workbook(path, config)
+ if contents.errors:
+ raise LauncherError(
+ "Sheet1 has rows that cannot be turned into objective values:\n"
+ + describe_findings(contents.errors)
+ )
+ X = [contents.inputs.to_numpy(dtype=float)]
+ Y = [contents.model_values.to_numpy(dtype=float)]
+ provenance = [f"{source_sheet(config)}: {contents.n_rows} conditions"]
+ Yvar: np.ndarray | None = None
+
+ if for_round.upper() == "R2":
+ results = read_candidate_results(path, config, "R1")
+ if results.errors:
+ raise LauncherError(
+ "The R1 sheet has conditions that cannot be turned into objective "
+ "values:\n" + describe_findings(results.errors)
+ )
+ X.append(results.conditions[input_names].to_numpy(dtype=float))
+ Y.append(results.model_values[names].to_numpy(dtype=float))
+ provenance.append(
+ f"R1 sheet: {results.n_conditions} conditions from "
+ f"{len(results.replicates)} films"
+ )
+ Yvar, floor_findings = yvar_for_campaign(
+ config,
+ results,
+ n_rows_without_replicates=contents.n_rows,
+ objective_names=names,
+ aggregates=replicate_aggregates(config),
+ )
+ if Yvar is not None:
+ provenance.append(
+ "observation noise: pooled between-film variance from the R1 "
+ "triplicates, not fitted"
+ )
+ for message in floor_findings:
+ provenance.append(f"WARNING {message}")
+
+ X_all = np.vstack(X)
+ Y_all = np.vstack(Y)
+ if not np.all(np.isfinite(Y_all)):
+ bad = int((~np.isfinite(Y_all)).any(axis=1).sum())
+ raise LauncherError(
+ f"{bad} observation(s) still hold a non-finite objective value after "
+ "aggregation. Fix the measurements before proposing a round."
+ )
+ return X_all, Y_all, Yvar, provenance
+
+
+# --------------------------------------------------------------------------- #
+# generating
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class Generated:
+ """What a successful press of the button produced."""
+
+ round_name: str
+ sheet_path: Path
+ result: RoundResult
+ provenance: list[str] = field(default_factory=list)
+ review: BatchReview | None = None
+ report: Any = None
+ """The round report's manifest, or ``None`` if it was skipped or failed."""
+ report_error: str | None = None
+ """Why the report is missing. A batch is never rolled back over a figure."""
+
+ @property
+ def n_films(self) -> int:
+ return len(self.result.replicates)
+
+ def summary(self) -> str:
+ """The body text after a successful run: what, from what, and how spread."""
+ diagnostics = self.result.diagnostics
+ validity = diagnostics.get("validity", {})
+ distance = validity.get("min_pairwise_distance")
+ lines = [
+ f"Wrote {self.result.n_conditions} {self.round_name} conditions "
+ f"({self.n_films} films) to:",
+ f" {self.sheet_path}",
+ "",
+ "Trained on:",
+ *(f" {item}" for item in self.provenance),
+ "",
+ f"Method: {diagnostics.get('method')}",
+ f"Seed: {diagnostics.get('seed')}",
+ f"Candidate pool: {diagnostics.get('pool_size')}",
+ f"Objective contract: {diagnostics.get('objective_contract')}",
+ "Min pairwise distance: "
+ + (f"{distance:.4f}" if isinstance(distance, float) else str(distance)),
+ f"Boundary coords/row: {validity.get('boundary_coords_per_condition')}",
+ "",
+ "Proposed conditions, physical units:",
+ self.result.conditions.to_string(
+ index=False, float_format=lambda value: f"{value:g}"
+ ),
+ ]
+ if self.review is not None:
+ # the review is the artifact; the lines above are its provenance
+ lines += ["", self.review.to_text()]
+ else:
+ lines += [
+ "",
+ "Nothing here is approved. Read the conditions, then run each in "
+ "triplicate and fill in the highlighted columns.",
+ ]
+ if self.report is not None:
+ lines += ["", self.report.summary()]
+ elif self.report_error is not None:
+ lines += [
+ "",
+ "FIGURES NOT PRODUCED",
+ "-" * 78,
+ self.report_error,
+ ]
+ return "\n".join(lines)
+
+
+def generate_next_round(
+ workbook: str | Path,
+ config: Mapping[str, Any],
+ *,
+ seed: int | None = None,
+ progress: Callable[[str], None] | None = None,
+ with_report: bool = True,
+) -> Generated:
+ """Propose and write whichever round is due. Refuses if none is.
+
+ ``with_report`` renders the round's figures beside the workbook afterwards.
+ **A failure there never costs the batch.** The worklist and the Review sheet
+ are already written and correct at that point; discarding them because a
+ figure could not be drawn would throw away the expensive, careful part of the
+ run over the cheap, decorative one. The failure is reported loudly instead.
+ """
+
+ def say(message: str) -> None:
+ if progress is not None:
+ progress(message)
+
+ path = Path(workbook)
+ say("Reading the workbook...")
+ status = inspect_campaign(path, config)
+ if not status.can_generate:
+ raise LauncherError(status.reason)
+
+ round_name = str(status.next_round)
+ destination = candidate_workbook_path(path, round_name)
+ if destination.exists():
+ raise LauncherError(
+ f"{destination.name} already exists. Rename or delete it first; this "
+ "tool never overwrites a file that may hold measurements."
+ )
+
+ say(f"Collecting observations for {round_name}...")
+ X, Y, Yvar, provenance = gather_observations(path, config, for_round=round_name)
+
+ say(f"Fitting the model and scoring candidates for {round_name}. About 10 seconds.")
+ runner = run_r1_ucb if round_name == "R1" else run_r2_qlognehvi
+ result = runner(config, X, Y, seed=seed, observed_Yvar=Yvar)
+
+ say(f"Writing {destination.name}...")
+ replicates = int(
+ (config.get("rounds", {}).get(round_name.lower(), {}) or {}).get(
+ "replicates_per_condition", 3
+ )
+ )
+ sheet_path = write_candidate_sheet(
+ path,
+ config,
+ result.conditions,
+ round_name=round_name,
+ replicates=replicates,
+ )
+ say("Building the review...")
+ contents = read_campaign_workbook(path, config)
+ # The report directory is decided BEFORE the review is written, so the Review
+ # sheet can point at the figures. The report is rendered into exactly this
+ # directory afterwards; if it fails, the sheet points at a directory holding
+ # the error trace, which is more useful than pointing at nothing.
+ from .round_report import report_directory
+
+ stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+ figures_dir = report_directory(path, round_name, when=stamp)
+ review = build_batch_review(
+ config,
+ X,
+ Y,
+ result.conditions,
+ round_name=round_name,
+ seed=result.diagnostics.get("seed"),
+ findings=contents.findings,
+ context={
+ "Round": round_name,
+ "Worklist": sheet_path.name,
+ "Figures": str(figures_dir),
+ "Trained on": "; ".join(provenance),
+ "Observations": len(X),
+ "Method": result.diagnostics.get("method"),
+ "Seed": result.diagnostics.get("seed"),
+ "Candidate pool": result.diagnostics.get("pool_size"),
+ "Objective contract": result.diagnostics.get("objective_contract"),
+ "Films to run": len(result.replicates),
+ },
+ )
+ write_review_sheet(sheet_path, review)
+
+ report = None
+ report_error = None
+ if with_report:
+ from .round_report import generate_round_report
+
+ try:
+ report = generate_round_report(
+ path,
+ config,
+ proposal=result,
+ review=review,
+ outdir=figures_dir,
+ when=stamp,
+ seed=result.diagnostics.get("seed"),
+ progress=progress,
+ )
+ except Exception as exc: # noqa: BLE001 - a figure must never cost a batch
+ report_error = (
+ f"{type(exc).__name__}: {exc}. The worklist and the Review sheet "
+ "were written and are unaffected."
+ )
+
+ say("Done.")
+ return Generated(
+ round_name=round_name,
+ sheet_path=sheet_path,
+ result=result,
+ provenance=provenance,
+ review=review,
+ report=report,
+ report_error=report_error,
+ )
+
+
+def generate_data_report(
+ workbook: str | Path,
+ config: Mapping[str, Any],
+ *,
+ seed: int | None = None,
+ progress: Callable[[str], None] | None = None,
+) -> Any:
+ """Figures from the measurements alone, with no batch proposed.
+
+ What the "Figures from current data" button runs. Useful the moment a round's
+ measurements are entered and before anyone decides whether to propose: the
+ parity, attribution, hypervolume and objective-space figures are all about
+ what has been measured, and none of them needs a candidate batch.
+ """
+ from .round_report import generate_round_report
+
+ return generate_round_report(workbook, config, seed=seed, progress=progress)
+
+
+def reveal(path: str | Path) -> None:
+ """Show a file in the platform's file manager. Never raises."""
+ target = Path(path)
+ try:
+ if sys.platform.startswith("win"):
+ subprocess.run(["explorer", "/select,", str(target)], check=False)
+ elif sys.platform == "darwin":
+ subprocess.run(["open", "-R", str(target)], check=False)
+ else:
+ subprocess.run(["xdg-open", str(target.parent)], check=False)
+ except OSError:
+ pass
+
+
+# --------------------------------------------------------------------------- #
+# the window
+# --------------------------------------------------------------------------- #
+
+
+class LauncherWindow:
+ """A small tkinter window over the functions above.
+
+ tkinter is imported here rather than at module scope so that the logic can be
+ imported and tested on a machine with no display.
+
+ **Results are matched to the request that asked for them.** Work runs off the
+ main thread and reports back through a queue, so without that matching two
+ things can paint the pane with an answer to a question the user has moved on
+ from: the auto-check scheduled 200 ms after startup, and any second press while
+ the first is still running. Each dispatch takes a request id; a reply carrying
+ a stale id is dropped. A status reply also names the workbook it examined and
+ is dropped if the selection has changed since -- reporting "Ready to propose R1"
+ over a workbook the user has navigated away from is worse than reporting
+ nothing. A dropped reply still clears the busy state, or the window would
+ disable its own buttons forever.
+ """
+
+ def __init__(self, config_path: str | Path = DEFAULT_CONFIG) -> None:
+ import queue
+ import tkinter as tk
+ from tkinter import ttk
+
+ self._tk = tk
+ self._ttk = ttk
+ self._queue: queue.Queue[tuple[int, str, Any]] = queue.Queue()
+ self._config_path = Path(config_path)
+ self._config: dict[str, Any] | None = None
+ self._status: CampaignStatus | None = None
+ self._generated: Generated | None = None
+ self._report: Any = None
+ self._busy = False
+ self._request_id = 0
+ self._auto_check_id: Any = None
+
+ self.root = tk.Tk()
+ self.root.title("MOBO-Kit - propose the next round")
+ self.root.minsize(760, 520)
+
+ outer = ttk.Frame(self.root, padding=12)
+ outer.pack(fill="both", expand=True)
+
+ chooser = ttk.Frame(outer)
+ chooser.pack(fill="x")
+ ttk.Label(chooser, text="Campaign workbook:").pack(side="left")
+ self.path_var = tk.StringVar()
+ remembered = remembered_workbook()
+ if remembered is not None:
+ self.path_var.set(str(remembered))
+ ttk.Entry(chooser, textvariable=self.path_var).pack(
+ side="left", fill="x", expand=True, padx=6
+ )
+ ttk.Button(chooser, text="Browse...", command=self.browse).pack(side="left")
+
+ self.headline = ttk.Label(outer, text="Choose a workbook, then check it.")
+ self.headline.pack(anchor="w", pady=(12, 4))
+
+ # a text pane with both scrollbars, laid out on a grid so neither one
+ # overlaps the text -- proposed conditions are ten columns wide
+ pane = ttk.Frame(outer)
+ pane.pack(fill="both", expand=True)
+ pane.rowconfigure(0, weight=1)
+ pane.columnconfigure(0, weight=1)
+ self.text = tk.Text(pane, wrap="none", height=20, state="disabled")
+ scroll_y = ttk.Scrollbar(pane, orient="vertical", command=self.text.yview)
+ scroll_x = ttk.Scrollbar(pane, orient="horizontal", command=self.text.xview)
+ self.text.configure(yscrollcommand=scroll_y.set, xscrollcommand=scroll_x.set)
+ self.text.grid(row=0, column=0, sticky="nsew")
+ scroll_y.grid(row=0, column=1, sticky="ns")
+ scroll_x.grid(row=1, column=0, sticky="ew")
+
+ self.progress = ttk.Progressbar(outer, mode="indeterminate")
+
+ buttons = ttk.Frame(outer)
+ buttons.pack(fill="x", pady=(10, 0))
+ self.check_button = ttk.Button(buttons, text="Check workbook", command=self.check)
+ self.check_button.pack(side="left")
+ self.generate_button = ttk.Button(
+ buttons, text="Propose next round", command=self.generate, state="disabled"
+ )
+ self.generate_button.pack(side="left", padx=6)
+ self.reveal_button = ttk.Button(
+ buttons, text="Show the new sheet", command=self.reveal, state="disabled"
+ )
+ self.reveal_button.pack(side="left")
+ # Enabled from the start: it needs measurements, not a proposal, and the
+ # question "is the model learning anything yet" is worth asking before
+ # deciding whether to spend fifteen films on a batch.
+ self.figures_button = ttk.Button(
+ buttons, text="Figures from current data", command=self.figures
+ )
+ self.figures_button.pack(side="left", padx=6)
+ ttk.Button(buttons, text="Close", command=self.root.destroy).pack(side="right")
+
+ self.root.after(120, self._drain)
+ if remembered is not None:
+ self._auto_check_id = self.root.after(200, self.check)
+
+ # -- helpers ----------------------------------------------------------- #
+
+ def _write(self, body: str) -> None:
+ self.text.configure(state="normal")
+ self.text.delete("1.0", "end")
+ self.text.insert("1.0", body)
+ self.text.configure(state="disabled")
+
+ def _config_or_load(self) -> dict[str, Any]:
+ if self._config is None:
+ if not self._config_path.exists():
+ raise LauncherError(
+ f"Cannot find the campaign configuration at {self._config_path}. "
+ "Run the launcher from the MOBO-Kit folder, or pass the path as "
+ "an argument."
+ )
+ self._config = load_campaign_config(self._config_path)
+ return self._config
+
+ def _start(self, message: str) -> None:
+ self._busy = True
+ self.check_button.configure(state="disabled")
+ self.generate_button.configure(state="disabled")
+ self.headline.configure(text=message)
+ self.progress.pack(fill="x", pady=(8, 0))
+ self.progress.start(12)
+
+ def _finish(self) -> None:
+ self._busy = False
+ self.progress.stop()
+ self.progress.pack_forget()
+ self.check_button.configure(state="normal")
+ can = self._status is not None and self._status.can_generate
+ self.generate_button.configure(state="normal" if can else "disabled")
+
+ def _cancel_auto_check(self) -> None:
+ """Drop the startup auto-check the moment the user does anything.
+
+ Without this it fires 200 ms in and answers a question about whichever
+ workbook was remembered, which may no longer be the one on screen.
+ """
+ if self._auto_check_id is not None:
+ try:
+ self.root.after_cancel(self._auto_check_id)
+ except Exception:
+ pass
+ self._auto_check_id = None
+
+ def _selection(self) -> str:
+ raw = self.path_var.get().strip()
+ try:
+ return str(Path(raw).resolve()) if raw else ""
+ except OSError:
+ return raw
+
+ def _in_thread(
+ self, work: Callable[[Callable[[str, Any], None]], tuple[str, Any]]
+ ) -> None:
+ import threading
+
+ self._request_id += 1
+ request = self._request_id
+
+ def post(kind: str, payload: Any) -> None:
+ self._queue.put((request, kind, payload))
+
+ def target() -> None:
+ try:
+ kind, payload = work(post)
+ post(kind, payload)
+ except Exception as exc: # surfaced in the window, never a traceback box
+ post("error", exc)
+
+ threading.Thread(target=target, daemon=True).start()
+
+ def drain_once(self) -> None:
+ """Apply whatever the worker threads have reported, dropping stale replies.
+
+ Separate from the polling loop so the drop rules can be tested by putting a
+ message on the queue, rather than by racing two real threads and hoping the
+ timing lands -- which is a flaky test of a race-condition fix.
+ """
+ import queue
+
+ try:
+ while True:
+ request, kind, payload = self._queue.get_nowait()
+ if request != self._request_id:
+ # superseded by a newer press; that request's own reply follows
+ self._finish()
+ continue
+ if kind == "status" and str(payload.workbook) != self._selection():
+ # answers a workbook the user has navigated away from
+ self._finish()
+ continue
+ self._handle(kind, payload)
+ except queue.Empty:
+ pass
+
+ def _drain(self) -> None:
+ self.drain_once()
+ self.root.after(120, self._drain)
+
+ def _handle(self, kind: str, payload: Any) -> None:
+ if kind == "progress":
+ self.headline.configure(text=str(payload))
+ return
+ if kind == "status":
+ self._status = payload
+ self.headline.configure(text=payload.headline)
+ self._write(payload.detail())
+ if payload.can_generate:
+ self.generate_button.configure(
+ text=f"Propose {payload.next_round}"
+ )
+ self._finish()
+ return
+ if kind == "generated":
+ self._generated = payload
+ self.headline.configure(
+ text=f"{payload.round_name} written. Nothing is approved -- read it first."
+ )
+ self._write(payload.summary())
+ self.reveal_button.configure(state="normal")
+ self._status = None
+ self._finish()
+ return
+ if kind == "report":
+ self._report = payload
+ self.headline.configure(
+ text=f"{len(payload.figures)} figures written. Nothing is approved."
+ )
+ self._write(payload.summary())
+ self._finish()
+ return
+ if kind == "error":
+ error = payload
+ if isinstance(error, (LauncherError, CandidateSheetError, ValueError)):
+ body = str(error)
+ self.headline.configure(text="Cannot continue.")
+ else:
+ body = (
+ "Something unexpected went wrong. The details below are for a "
+ "developer; the workbook was not modified.\n\n"
+ + "".join(
+ traceback.format_exception(
+ type(error), error, error.__traceback__
+ )
+ )
+ )
+ self.headline.configure(text="Unexpected error.")
+ self._write(body)
+ self._finish()
+
+ # -- actions ----------------------------------------------------------- #
+
+ def browse(self) -> None:
+ from tkinter import filedialog
+
+ self._cancel_auto_check()
+ chosen = filedialog.askopenfilename(
+ title="Choose the campaign workbook",
+ filetypes=[("Excel workbook", "*.xlsx"), ("All files", "*.*")],
+ )
+ if chosen:
+ self.path_var.set(chosen)
+ self.check()
+
+ def check(self) -> None:
+ self._auto_check_id = None # this call IS the auto-check when scheduled
+ if self._busy:
+ return
+ workbook = self.path_var.get().strip()
+ if not workbook:
+ self.headline.configure(text="Choose a workbook first.")
+ return
+ self._status = None
+ self._start("Reading the workbook...")
+
+ def work(post: Callable[[str, Any], None]) -> tuple[str, Any]:
+ config = self._config_or_load()
+ status = inspect_campaign(workbook, config)
+ remember_workbook(workbook)
+ return "status", status
+
+ self._in_thread(work)
+
+ def generate(self) -> None:
+ self._cancel_auto_check()
+ if self._busy or self._status is None or not self._status.can_generate:
+ return
+ workbook = self.path_var.get().strip()
+ self._start(f"Proposing {self._status.next_round}...")
+
+ def work(post: Callable[[str, Any], None]) -> tuple[str, Any]:
+ config = self._config_or_load()
+ generated = generate_next_round(
+ workbook,
+ config,
+ progress=lambda message: post("progress", message),
+ )
+ return "generated", generated
+
+ self._in_thread(work)
+
+ def figures(self) -> None:
+ """Render the data-only report. Needs measurements, not a proposal."""
+ self._cancel_auto_check()
+ if self._busy:
+ return
+ workbook = self.path_var.get().strip()
+ if not workbook:
+ self.headline.configure(text="Choose a workbook first.")
+ return
+ self._start("Rendering figures from the measured data...")
+
+ def work(post: Callable[[str, Any], None]) -> tuple[str, Any]:
+ config = self._config_or_load()
+ manifest = generate_data_report(
+ workbook,
+ config,
+ progress=lambda message: post("progress", message),
+ )
+ return "report", manifest
+
+ self._in_thread(work)
+
+ def reveal(self) -> None:
+ if self._generated is not None:
+ reveal(self._generated.sheet_path)
+
+ def run(self) -> None:
+ self.root.mainloop()
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+ """Entry point for the double-click launchers and ``python -m``."""
+ args = list(sys.argv[1:] if argv is None else argv)
+ config_path = args[0] if args else DEFAULT_CONFIG
+ try:
+ LauncherWindow(config_path).run()
+ except ImportError as exc: # tkinter absent from a stripped Python
+ print(
+ "This launcher needs tkinter, which this Python does not have "
+ f"({exc}). Install a python.org build, or use the API directly:\n"
+ " from mobo_kit.launcher import generate_next_round",
+ file=sys.stderr,
+ )
+ return 2
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/src/mobo_kit/lhs.py b/src/mobo_kit/lhs.py
index 98b7905..d57838c 100644
--- a/src/mobo_kit/lhs.py
+++ b/src/mobo_kit/lhs.py
@@ -1,52 +1,35 @@
-# src/lhs.py
+"""Deterministic, grid-safe Latin hypercube campaign design."""
+
from __future__ import annotations
+
+from math import prod
+from typing import Optional, Sequence, Tuple, Union
+
import numpy as np
import pandas as pd
-from typing import Optional, Callable, Tuple, Sequence, Union
-from .constraints import apply_row_constraints, RowConstraint
-
-from emukit.core import ParameterSpace, ContinuousParameter
-from emukit.core.initial_designs.latin_design import LatinDesign
+from .constraints import RowConstraint, apply_row_constraints
from .design import Design
-from .data import snap_to_grid_np # snaps only dims with grids (design.var_list)
-import seaborn as sns
-import matplotlib.pyplot as plt
-from sklearn.decomposition import PCA
-from sklearn.preprocessing import StandardScaler
-
-# ----------------------------
-# Emukit space & core sampling
-# ----------------------------
-
-def _space_from_design(design: Design) -> ParameterSpace:
- """
- Build an Emukit ParameterSpace using only ContinuousParameter.
- For grid dims, we use [min(grid), max(grid)] and snap after sampling.
- """
- params = []
- for j, name in enumerate(design.names):
- lo = float(design.lowers[j])
- hi = float(design.uppers[j])
- params.append(ContinuousParameter(name, lo, hi))
- return ParameterSpace(params)
+def _max_abs_corr(X: np.ndarray) -> float:
+ """Return the largest absolute Pearson correlation between varying columns."""
+ values = np.asarray(X, dtype=float)
+ if values.ndim != 2:
+ raise ValueError(f"X must be two-dimensional; got shape {values.shape}.")
+ if values.shape[0] < 2 or values.shape[1] < 2:
+ return 0.0
-# ----------------------------
-# Dataset-level corr utilities
-# ----------------------------
+ varying = np.ptp(values, axis=0) > 0
+ varying_values = values[:, varying]
+ if varying_values.shape[1] < 2:
+ return 0.0
-def _max_abs_corr(X: np.ndarray) -> float:
- """
- Maximum absolute Pearson correlation across columns of X.
- NaNs from zero-variance columns are treated as 0 correlation.
- """
- C = np.corrcoef(X, rowvar=False) # DxD
- A = np.abs(C)
- np.fill_diagonal(A, 0.0)
- A = np.nan_to_num(A, nan=0.0, posinf=1.0, neginf=1.0)
- return float(np.max(A))
+ corr = np.corrcoef(varying_values, rowvar=False)
+ absolute = np.abs(corr)
+ np.fill_diagonal(absolute, 0.0)
+ absolute = np.nan_to_num(absolute, nan=0.0, posinf=1.0, neginf=1.0)
+ return float(np.max(absolute))
def _pick_subset_with_corr(
@@ -56,154 +39,329 @@ def _pick_subset_with_corr(
tries: int,
rng: np.random.Generator,
) -> Tuple[np.ndarray, float]:
- """
- Try random subsets of size n from X to find one with max|corr| <= threshold.
- Returns (best_subset, best_max_corr). If none meet threshold, returns best found.
- """
- m = X.shape[0]
- if m == n:
- return X, _max_abs_corr(X)
-
- best_X = None
- best_val = float("inf")
- for _ in range(max(1, tries)):
- idx = rng.choice(m, size=n, replace=False)
- X_sub = X[idx]
- val = _max_abs_corr(X_sub)
- if val < best_val:
- best_val, best_X = val, X_sub
- if val <= threshold:
- break
- return best_X, best_val
+ """Search deterministically (for a seeded RNG) for a qualifying subset."""
+ if X.shape[0] < n:
+ raise ValueError(
+ f"Need at least {n} rows for subset selection; got {X.shape[0]}."
+ )
+
+ best = X[:n].copy()
+ best_corr = _max_abs_corr(best)
+ if best_corr <= threshold or X.shape[0] == n:
+ return best, best_corr
+
+ for _ in range(tries):
+ indices = rng.choice(X.shape[0], size=n, replace=False)
+ candidate = X[indices]
+ correlation = _max_abs_corr(candidate)
+ if correlation < best_corr:
+ best = candidate.copy()
+ best_corr = correlation
+ if correlation <= threshold:
+ return candidate.copy(), correlation
+
+ return best, best_corr
+
def _lhs_select_numeric(df: pd.DataFrame, design: Design) -> pd.DataFrame:
- cols = [c for c in design.names if c in df.columns]
- return df[cols].select_dtypes(include=[np.number]).copy()
+ columns = [name for name in design.names if name in df.columns]
+ return df[columns].select_dtypes(include=[np.number]).copy()
-def _lhs_labels(design: Design, cols) -> list:
- name_to_label = {n: l for n, l in zip(design.names, design.labels)}
- return [name_to_label.get(c, c) for c in cols]
+def _lhs_labels(design: Design, columns: Sequence[str]) -> list[str]:
+ """Build labels from the fields Design actually owns (names and units)."""
+ labels = {}
+ for name, unit in zip(design.names, design.units):
+ labels[name] = f"{name} [{unit}]" if unit else name
+ return [labels.get(column, column) for column in columns]
-# ----------------------------
-# OPTIMIZED LHS IMPLEMENTATION
-# ----------------------------
-def _batch_generate_lhs_samples(
- design: Design,
- total_samples: int,
- batch_size: int = 100,
- seed: Optional[int] = None
+def _validate_positive_int(value: object, *, name: str) -> int:
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, (int, np.integer)):
+ raise ValueError(f"{name} must be a positive integer; got {value!r}.")
+ integer = int(value)
+ if integer <= 0:
+ raise ValueError(f"{name} must be a positive integer; got {integer}.")
+ return integer
+
+
+def _validate_design(design: Design) -> None:
+ if not isinstance(design, Design):
+ raise TypeError(
+ f"design must be a Design instance; got {type(design).__name__}."
+ )
+ dimension = len(design.names)
+ if dimension == 0:
+ raise ValueError("Design must contain at least one input.")
+ if len(set(design.names)) != dimension:
+ raise ValueError("Design input names must be unique.")
+ if len(design.units) != dimension:
+ raise ValueError(
+ f"Design has {dimension} names but {len(design.units)} unit entries."
+ )
+ if len(design.var_list) != dimension:
+ raise ValueError(
+ f"Design has {dimension} names but {len(design.var_list)} variable grids."
+ )
+ for field_name in ("lowers", "uppers", "steps"):
+ values = np.asarray(getattr(design, field_name), dtype=float)
+ if values.shape != (dimension,) or not np.all(np.isfinite(values)):
+ raise ValueError(
+ f"Design field '{field_name}' must contain {dimension} finite values."
+ )
+ if np.any(np.asarray(design.steps, dtype=float) <= 0):
+ raise ValueError("Design steps must all be > 0.")
+
+ for index, (name, raw_grid) in enumerate(zip(design.names, design.var_list)):
+ grid = np.asarray(raw_grid, dtype=float)
+ if grid.ndim != 1 or grid.size == 0:
+ raise ValueError(
+ f"Design grid for input '{name}' must be a non-empty 1D array."
+ )
+ if not np.all(np.isfinite(grid)):
+ raise ValueError(
+ f"Design grid for input '{name}' contains non-finite values."
+ )
+ if np.unique(grid).size != grid.size or np.any(np.diff(grid) <= 0):
+ raise ValueError(
+ f"Design grid for input '{name}' must be strictly increasing "
+ "and unique."
+ )
+ if grid.size > 1:
+ step = float(design.steps[index])
+ if not np.allclose(
+ np.diff(grid),
+ step,
+ rtol=0.0,
+ atol=max(1e-12, abs(step) * 1e-9),
+ ):
+ raise ValueError(
+ f"Design grid for input '{name}' is not aligned to step {step}."
+ )
+ if not np.isclose(grid[0], design.lowers[index], rtol=0.0, atol=1e-12):
+ raise ValueError(
+ f"Design lower bound for input '{name}' does not match its grid."
+ )
+ if not np.isclose(grid[-1], design.uppers[index], rtol=0.0, atol=1e-12):
+ raise ValueError(
+ f"Design upper bound for input '{name}' does not match its grid."
+ )
+
+
+def _normalize_constraints(
+ row_constraints: Optional[Union[RowConstraint, Sequence[RowConstraint]]],
+) -> list[RowConstraint]:
+ if row_constraints is None:
+ return []
+ if callable(row_constraints):
+ return [row_constraints]
+ constraints = list(row_constraints)
+ if not all(callable(constraint) for constraint in constraints):
+ raise ValueError("Every row constraint must be callable.")
+ return constraints
+
+
+def _latin_hypercube_unit(
+ sample_count: int,
+ dimension: int,
+ rng: np.random.Generator,
) -> np.ndarray:
- """
- Generate LHS samples in batches for better memory management.
- """
- if seed is not None:
- np.random.seed(seed)
-
- space = _space_from_design(design)
- all_samples = []
-
- for i in range(0, total_samples, batch_size):
- current_batch = min(batch_size, total_samples - i)
- batch_samples = LatinDesign(space).get_samples(current_batch)
- all_samples.append(batch_samples)
-
- return np.vstack(all_samples)
+ """Generate one randomized Latin hypercube using a persistent RNG."""
+ jitter = rng.random((sample_count, dimension))
+ unit = (np.arange(sample_count, dtype=float)[:, None] + jitter) / sample_count
+ for column in range(dimension):
+ rng.shuffle(unit[:, column])
+ return unit
+
+
+def _snap_to_design_grid(X: np.ndarray, design: Design) -> np.ndarray:
+ """Snap physical values to exact configured grid values."""
+ values = np.asarray(X, dtype=float)
+ snapped = values.copy()
+ for column, raw_grid in enumerate(design.var_list):
+ grid = np.asarray(raw_grid, dtype=float)
+ nearest = np.argmin(
+ np.abs(snapped[:, column, None] - grid[None, :]),
+ axis=1,
+ )
+ snapped[:, column] = grid[nearest]
+ return snapped
+
+
+def _generate_lhs_samples(
+ design: Design,
+ total_samples: int,
+ rng: np.random.Generator,
+) -> np.ndarray:
+ """Generate one physical-space LHS without resetting RNG state."""
+ unit = _latin_hypercube_unit(total_samples, len(design.names), rng)
+ return design.lowers + unit * (design.uppers - design.lowers)
def _batch_apply_constraints(
- X_samples: np.ndarray,
- design: Design,
+ X_samples: np.ndarray,
+ design: Design,
constraints: Sequence[RowConstraint],
- batch_size: int = 1000
-) -> Tuple[np.ndarray, np.ndarray]:
- """
- Apply constraints in batches to avoid memory issues with large sample sets.
- Returns (valid_samples, valid_indices).
- """
+ batch_size: int,
+) -> np.ndarray:
+ """Apply constraints to already-snapped rows in bounded-size chunks."""
if not constraints:
- return X_samples, np.arange(len(X_samples))
-
- valid_samples = []
- valid_indices = []
-
- for i in range(0, len(X_samples), batch_size):
- batch_end = min(i + batch_size, len(X_samples))
- X_batch = X_samples[i:batch_end]
-
- # Apply constraints to this batch
- mask = apply_row_constraints(X_batch, design, constraints)
- valid_batch = X_batch[mask]
- valid_indices.extend(np.arange(i, batch_end)[mask])
-
- if len(valid_batch) > 0:
- valid_samples.append(valid_batch)
-
- if valid_samples:
- return np.vstack(valid_samples), np.array(valid_indices)
- else:
- return np.empty((0, X_samples.shape[1])), np.array([], dtype=int)
-
+ return X_samples
+
+ valid_batches = []
+ for start in range(0, X_samples.shape[0], batch_size):
+ batch = X_samples[start : start + batch_size]
+ mask = apply_row_constraints(batch, design, constraints)
+ if np.any(mask):
+ valid_batches.append(batch[mask])
+ if not valid_batches:
+ return np.empty((0, X_samples.shape[1]), dtype=float)
+ return np.vstack(valid_batches)
+
+
+def _append_unique_rows(
+ pool: list[np.ndarray],
+ seen: set[tuple[float, ...]],
+ rows: np.ndarray,
+) -> None:
+ """Append rows in encounter order, deduplicating exact snapped grid values."""
+ for row in rows:
+ key = tuple(float(value) for value in row)
+ if key not in seen:
+ seen.add(key)
+ pool.append(np.asarray(row, dtype=float).copy())
+
+
+def _validate_grid_membership(X: np.ndarray, design: Design) -> None:
+ """Guard the campaign invariant that every emitted value is on its grid."""
+ for column, (name, raw_grid) in enumerate(zip(design.names, design.var_list)):
+ grid = np.asarray(raw_grid, dtype=float)
+ if not np.all(np.isin(X[:, column], grid)):
+ raise RuntimeError(
+ "Internal LHS error: generated value outside configured grid "
+ f"for '{name}'."
+ )
+
+
+def _strict_lhs_dataframe(
+ *,
+ design: Design,
+ n: int,
+ seed: Optional[int],
+ snap_to_grids: bool,
+ row_constraints: Optional[Union[RowConstraint, Sequence[RowConstraint]]],
+ max_abs_corr: Optional[float],
+ max_attempts: int,
+ oversample: int,
+ batch_size: int,
+ subset_tries: int,
+ samples_per_attempt: Optional[int],
+ verbose: bool,
+) -> pd.DataFrame:
+ _validate_design(design)
+ n = _validate_positive_int(n, name="n")
+ max_attempts = _validate_positive_int(max_attempts, name="max_attempts")
+ oversample = _validate_positive_int(oversample, name="oversample")
+ batch_size = _validate_positive_int(batch_size, name="batch_size")
+ subset_tries = _validate_positive_int(subset_tries, name="subset_tries")
+
+ if not snap_to_grids:
+ raise ValueError(
+ "Campaign LHS requires snap_to_grids=True so every condition is exactly "
+ "on the configured processing grid."
+ )
-def _smart_subset_selection(
- X_valid: np.ndarray,
- n_target: int,
- max_abs_corr: float,
- max_tries: int = 1000,
- early_stop_threshold: float = 0.1,
- seed: Optional[int] = None
-) -> Tuple[np.ndarray, float, int]:
- """
- Smart subset selection with early stopping and adaptive search.
- Returns (best_subset, best_correlation, tries_used).
- """
if seed is not None:
- rng = np.random.default_rng(seed)
+ if isinstance(seed, (bool, np.bool_)) or not isinstance(
+ seed, (int, np.integer)
+ ):
+ raise ValueError(f"seed must be an integer or None; got {seed!r}.")
+ seed = int(seed)
+ if seed < 0:
+ raise ValueError(f"seed must be non-negative; got {seed}.")
+
+ if max_abs_corr is not None:
+ try:
+ max_abs_corr = float(max_abs_corr)
+ except (TypeError, ValueError) as exc:
+ raise ValueError("max_abs_corr must be a finite number in [0, 1].") from exc
+ if not np.isfinite(max_abs_corr) or not 0.0 <= max_abs_corr <= 1.0:
+ raise ValueError("max_abs_corr must be a finite number in [0, 1].")
+
+ if samples_per_attempt is None:
+ samples_per_attempt = max(n, n * oversample)
else:
- rng = np.random.default_rng()
-
- m = X_valid.shape[0]
- if m <= n_target:
- return X_valid, _max_abs_corr(X_valid), 0
-
- best_X = None
+ samples_per_attempt = _validate_positive_int(
+ samples_per_attempt, name="samples_per_attempt"
+ )
+
+ total_grid_points = prod(len(grid) for grid in design.var_list)
+ if n > total_grid_points:
+ raise ValueError(
+ f"Requested n={n} unique conditions, but the design contains only "
+ f"{total_grid_points} unique grid combinations."
+ )
+
+ constraints = _normalize_constraints(row_constraints)
+ rng = np.random.default_rng(seed)
+ unique_pool: list[np.ndarray] = []
+ seen: set[tuple[float, ...]] = set()
best_corr = float("inf")
- tries_used = 0
-
- # Phase 1: Quick random sampling
- quick_tries = min(max_tries // 2, 100)
- for _ in range(quick_tries):
- idx = rng.choice(m, size=n_target, replace=False)
- X_sub = X_valid[idx]
- corr = _max_abs_corr(X_sub)
-
- if corr < best_corr:
- best_corr = corr
- best_X = X_sub.copy()
- tries_used += 1
-
- # Early stopping if we're close to target
- if corr <= max_abs_corr + early_stop_threshold:
- break
-
- # Phase 2: Targeted improvement if needed
- if best_corr > max_abs_corr:
- remaining_tries = max_tries - tries_used
- for _ in range(remaining_tries):
- idx = rng.choice(m, size=n_target, replace=False)
- X_sub = X_valid[idx]
- corr = _max_abs_corr(X_sub)
-
- if corr < best_corr:
- best_corr = corr
- best_X = X_sub.copy()
- tries_used += 1
-
- if corr <= max_abs_corr:
- break
-
- return best_X, best_corr, tries_used
+
+ for attempt in range(1, max_attempts + 1):
+ raw = _generate_lhs_samples(
+ design=design,
+ total_samples=samples_per_attempt,
+ rng=rng,
+ )
+ # Constraints deliberately see executable, snapped processing conditions.
+ snapped = _snap_to_design_grid(raw, design)
+ _validate_grid_membership(snapped, design)
+ valid = _batch_apply_constraints(snapped, design, constraints, batch_size)
+ _append_unique_rows(unique_pool, seen, valid)
+
+ if verbose:
+ print(
+ f"[LHS] attempt {attempt}/{max_attempts}: "
+ f"{len(unique_pool)} unique valid grid points"
+ )
+
+ if len(unique_pool) < n:
+ continue
+
+ pool_array = np.vstack(unique_pool)
+ if max_abs_corr is None:
+ selected = pool_array[:n].copy()
+ _validate_grid_membership(selected, design)
+ return pd.DataFrame(selected, columns=design.names)
+
+ selected, correlation = _pick_subset_with_corr(
+ pool_array,
+ n=n,
+ threshold=max_abs_corr,
+ tries=subset_tries,
+ rng=rng,
+ )
+ best_corr = min(best_corr, correlation)
+ if verbose:
+ print(
+ f"[LHS] attempt {attempt}: best max|corr|={correlation:.6f}; "
+ f"required <= {max_abs_corr:.6f}"
+ )
+ if correlation <= max_abs_corr:
+ _validate_grid_membership(selected, design)
+ return pd.DataFrame(selected, columns=design.names)
+
+ details = (
+ f"Generated {len(unique_pool)} unique constraint-valid grid points after "
+ f"{max_attempts} attempt(s), using {samples_per_attempt} samples per attempt."
+ )
+ if max_abs_corr is not None and len(unique_pool) >= n:
+ details += f" Best max|corr|={best_corr:.6f}, required <= {max_abs_corr:.6f}."
+ raise RuntimeError(
+ f"Unable to generate exactly n={n} campaign conditions. {details} "
+ "Increase samples_per_attempt/max_attempts or revise the design constraints."
+ )
def lhs_dataframe_optimized(
@@ -213,214 +371,73 @@ def lhs_dataframe_optimized(
snap_to_grids: bool = True,
row_constraints: Optional[Union[RowConstraint, Sequence[RowConstraint]]] = None,
max_abs_corr: Optional[float] = None,
- # Performance tuning:
max_attempts: int = 100,
- oversample: int = 5, # Increased for better constraint satisfaction
+ oversample: int = 5,
batch_size: int = 100,
subset_tries: int = 1000,
early_stop_threshold: float = 0.1,
verbose: bool = False,
+ samples_per_attempt: Optional[int] = None,
) -> pd.DataFrame:
+ """Generate exactly ``n`` deterministic, unique, constraint-valid grid rows.
+
+ ``samples_per_attempt`` controls the candidate count directly. When omitted,
+ it is ``max(n, n * oversample)``. A configured correlation limit is hard: the
+ function raises instead of returning a noncompliant or unconstrained fallback.
+
+ ``early_stop_threshold`` remains accepted for API compatibility but is not
+ used; stopping occurs only when the hard ``max_abs_corr`` limit is satisfied.
"""
- OPTIMIZED LHS generation with batch processing, smart subset selection, and early stopping.
-
- Key optimizations:
- 1. Batch LHS generation to manage memory
- 2. Batch constraint application
- 3. Smart subset selection with early stopping
- 4. Adaptive oversampling based on constraint strictness
- """
- if seed is not None:
- np.random.seed(int(seed))
-
- # Normalize constraints
- if row_constraints is None:
- constraints_list = []
- elif callable(row_constraints):
- constraints_list = [row_constraints]
- else:
- constraints_list = list(row_constraints)
-
- # Adjust oversampling based on constraint complexity
- if constraints_list:
- effective_oversample = max(oversample, len(constraints_list) * 2)
- else:
- effective_oversample = 1
-
- total_samples_needed = n * effective_oversample
-
- if verbose:
- print(f"[OPTIMIZED LHS] Target: {n} samples, Generating: {total_samples_needed} samples")
-
- best_candidate = None
- best_corr = float("inf")
-
- for attempt in range(1, max_attempts + 1):
- if verbose and attempt % 10 == 0:
- print(f"[OPTIMIZED LHS] Attempt {attempt}/{max_attempts}")
-
- # Generate LHS samples in batches
- X_raw = _batch_generate_lhs_samples(
- design, total_samples_needed, batch_size, seed
- )
-
- # Snap to grids if requested
- if snap_to_grids:
- X_raw = snap_to_grid_np(X_raw, design)
-
- # Apply constraints in batches
- X_valid, valid_indices = _batch_apply_constraints(
- X_raw, design, constraints_list, batch_size
- )
-
- if len(X_valid) < n:
- if verbose:
- print(f"[OPTIMIZED LHS] Attempt {attempt}: Only {len(X_valid)} valid samples")
- continue
-
- # Smart subset selection
- X_best, corr_val, tries_used = _smart_subset_selection(
- X_valid, n, max_abs_corr or 1.0, subset_tries, early_stop_threshold
- )
-
- if verbose:
- print(f"[OPTIMIZED LHS] Attempt {attempt}: max|corr|={corr_val:.3f} (tries: {tries_used})")
-
- # Check if we met the correlation target
- if max_abs_corr is None or corr_val <= max_abs_corr:
- if verbose:
- print(f"[OPTIMIZED LHS] Success! max|corr|={corr_val:.3f} <= {max_abs_corr}")
- return pd.DataFrame(X_best, columns=design.names)
-
- # Keep track of best candidate
- if corr_val < best_corr:
- best_corr = corr_val
- best_candidate = X_best.copy()
-
- # Return best candidate if we didn't meet the target
- if best_candidate is not None:
- if verbose:
- print(f"[OPTIMIZED LHS] Best achieved: max|corr|={best_corr:.3f} (> target)")
- return pd.DataFrame(best_candidate, columns=design.names)
-
- # Fallback: return whatever we have
- if verbose:
- print(f"[OPTIMIZED LHS] Failed to generate {n} samples after {max_attempts} attempts")
-
- # Try one more time with minimal constraints
- X_fallback = _batch_generate_lhs_samples(design, n, batch_size, seed)
- if snap_to_grids:
- X_fallback = snap_to_grid_np(X_fallback, design)
-
- return pd.DataFrame(X_fallback, columns=design.names)
-
-
-# ----------------------------
-# Original LHS (kept for backward compatibility)
-# ----------------------------
+ try:
+ compatibility_threshold = float(early_stop_threshold)
+ except (TypeError, ValueError) as exc:
+ raise ValueError(
+ "early_stop_threshold must be a finite non-negative number."
+ ) from exc
+ if not np.isfinite(compatibility_threshold) or compatibility_threshold < 0:
+ raise ValueError("early_stop_threshold must be a finite non-negative number.")
+ return _strict_lhs_dataframe(
+ design=design,
+ n=n,
+ seed=seed,
+ snap_to_grids=snap_to_grids,
+ row_constraints=row_constraints,
+ max_abs_corr=max_abs_corr,
+ max_attempts=max_attempts,
+ oversample=oversample,
+ batch_size=batch_size,
+ subset_tries=subset_tries,
+ samples_per_attempt=samples_per_attempt,
+ verbose=verbose,
+ )
+
def lhs_dataframe(
design: Design,
n: int,
seed: Optional[int] = None,
snap_to_grids: bool = True,
- # Constraints:
- #row_constraint_fn: Optional[Callable[[np.ndarray, Design], np.ndarray]] = None,
row_constraints: Optional[Union[RowConstraint, Sequence[RowConstraint]]] = None,
max_abs_corr: Optional[float] = None,
- # Sampling controls:
max_attempts: int = 100,
oversample: int = 3,
subset_tries: int = 800,
verbose: bool = False,
+ samples_per_attempt: Optional[int] = None,
+ batch_size: int = 100,
) -> pd.DataFrame:
- """
- Generate an LHS using Emukit, then enforce:
- - pointwise constraints via `row_constraints -> bool mask`
- - dataset-level Pearson correlation bound via `max_abs_corr`
-
- Strategy:
- 1) Sample in continuous box via Emukit.
- 2) Snap grid features (if requested).
- 3) Apply row-wise mask (e.g., Clausius–Clapeyron); accumulate valid rows.
- 4) If we have >= n rows, optionally pick a subset with max|corr| <= threshold.
-
- Returns DataFrame with columns == design.names (physical units).
- """
- # Seed Emukit's internal RNG
- if seed is not None:
- np.random.seed(int(seed))
- rng = np.random.default_rng(seed)
-
- space = _space_from_design(design)
-
- # Normalize row constraints to a list (None -> empty list)
- if row_constraints is None:
- row_constraints_list: Sequence[RowConstraint] = []
- elif callable(row_constraints):
- row_constraints_list = [row_constraints] # backward-compatible single fn
- else:
- row_constraints_list = list(row_constraints)
-
- collected = []
- best_candidate = None
- best_corr_val = float("inf")
-
- for attempt in range(1, max_attempts + 1):
- # Heuristic: oversample to survive row constraints
- #batch = n if row_constraint_fn is None else max(n, oversample * n)
- batch = n if not row_constraints_list else max(n, oversample * n)
-
- X = LatinDesign(space).get_samples(batch) # (batch, D)
- if snap_to_grids:
- X = snap_to_grid_np(X, design)
-
- # if row_constraint_fn is not None:
- # mask = row_constraint_fn(X, design)
- # if mask is None or mask.dtype != bool or mask.shape[0] != X.shape[0]:
- # raise ValueError("row_constraint_fn must return a boolean mask of shape (batch,).")
- # X = X[mask]
-
- if row_constraints_list:
- mask = apply_row_constraints(X, design, row_constraints_list)
- X = X[mask]
-
- if X.size == 0:
- if verbose:
- print(f"[LHS] attempt {attempt}: 0 valid after row constraints; retrying.")
- continue
-
- collected.append(X)
- X_all = np.vstack(collected)
-
- if X_all.shape[0] >= n:
- # If no correlation constraint, take first n (shuffled)
- idx = rng.permutation(X_all.shape[0])
- X_all = X_all[idx]
-
- if max_abs_corr is None:
- return pd.DataFrame(X_all[:n], columns=design.names)
-
- # Try to find subset meeting correlation threshold
- X_best, corr_val = _pick_subset_with_corr(X_all, n, max_abs_corr, subset_tries, rng)
- if verbose:
- print(f"[LHS] attempt {attempt}: candidate max|corr|={corr_val:.3f}")
- if corr_val <= max_abs_corr:
- return pd.DataFrame(X_best, columns=design.names)
-
- # Keep best-so-far
- if corr_val < best_corr_val:
- best_corr_val = corr_val
- best_candidate = X_best
-
- # If we got here, we didn't meet correlation threshold; return best we found
- if best_candidate is None:
- # Maybe we never amassed n rows; return whatever we have (possibly < n)
- X_all = np.vstack(collected) if collected else np.empty((0, len(design.names)))
- X_all = X_all[:n]
- return pd.DataFrame(X_all, columns=design.names)
-
- if verbose:
- print(f"[LHS] giving best candidate with max|corr|={best_corr_val:.3f} (> target).")
- return pd.DataFrame(best_candidate, columns=design.names)
-
+ """Compatibility wrapper around the strict campaign LHS implementation."""
+ return _strict_lhs_dataframe(
+ design=design,
+ n=n,
+ seed=seed,
+ snap_to_grids=snap_to_grids,
+ row_constraints=row_constraints,
+ max_abs_corr=max_abs_corr,
+ max_attempts=max_attempts,
+ oversample=oversample,
+ batch_size=batch_size,
+ subset_tries=subset_tries,
+ samples_per_attempt=samples_per_attempt,
+ verbose=verbose,
+ )
diff --git a/src/mobo_kit/loocv.py b/src/mobo_kit/loocv.py
new file mode 100644
index 0000000..5e212d2
--- /dev/null
+++ b/src/mobo_kit/loocv.py
@@ -0,0 +1,267 @@
+"""Exact leave-one-out for one objective, with its declared structured mean.
+
+There is one fold loop in this project and this is it. ``scripts/intake_new_data.py``
+is canonical for LOO numbers, the round report plots them, and
+``scripts/permutation_rank_test.py`` builds a null out of them -- so they must be
+*the same* numbers rather than three implementations that agree today. This module
+exists because they briefly were three implementations.
+
+Two rules the fold loop keeps, both of which flatter the result if broken:
+
+* **the trend is refit inside every fold**, on the training rows only. Fitting it
+ once on everything and holding it fixed leaks the held-out value into the mean
+ function.
+* **the model is refit from scratch per fold** under the campaign's own variant and
+ seeding, so this reproduces the round's model rather than a similar one.
+
+:func:`model_validation.run_exact_loocv` is the multi-output validation harness and
+is a different tool: it fits one N-1 model for all objectives at once and does not
+take a per-objective mean module. Objectives here carry different structured means,
+so they are fitted one at a time.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import math
+import warnings
+from dataclasses import dataclass
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import torch
+
+from .model_validation import DIM_SCALED_PRIOR, SIGNAL_COLLAPSE_STAGE, fit_model_variant
+from .structured_mean import build_structured_mean, mean_spec_from_config
+
+__all__ = ["LooResult", "loo_predictions", "null_loo_r2", "resolution_sd"]
+
+#: Bootstrap sd of LOO R2 measured at N=15 on the first campaign, 4000 resamples.
+#: Scaled by ``sqrt(15/N)`` elsewhere, which is an approximation -- re-run the
+#: bootstrap if a decision turns on the third decimal.
+RESOLUTION_SD_AT_15 = 0.236
+RESOLUTION_REFERENCE_N = 15
+
+
+def null_loo_r2(n: int) -> float:
+ """What predicting the leave-one-out mean scores, independent of the data.
+
+ ``1 - (N/(N-1))^2``: -0.148 at 15, -0.105 at 21, -0.069 at 31. It moves with
+ N, so recompute rather than reuse.
+
+ **THIS IS NOT A SIGNIFICANCE THRESHOLD, and this project read it as one for a
+ year.** It is the score of ONE SPECIFIC PREDICTOR -- predict every held-out
+ row with the mean of the others -- and a fitted GP does not behave like that
+ predictor. Measured on the v4 campaign, 2026-09-04, 300 permutations of a
+ real objective with the campaign's own model variant:
+
+ fitted GP under permuted y median -0.4210 95th percentile +0.2890
+ fraction of pure-noise draws scoring above -0.1480: 28.7%
+
+ So "beats the null" happens better than one time in four when there is no
+ signal at all. The GP makes real predictions with about six times the spread
+ of the constant predictor; they are noise, and they land FURTHER from y, which
+ is why the empirical null sits well below this number while its upper tail
+ sits well above it.
+
+ Below this value a model has certainly learned nothing. ABOVE it means
+ nothing on its own. The honest single-candidate bar is the 95th percentile of
+ that candidate's own permutation null -- ``scripts/raw_component_screen.py
+ --calibrate`` measures it -- and the project's standing adjudicator for a
+ real verdict is the rank permutation test in
+ ``scripts/permutation_rank_test.py``, which was always the right instrument
+ and is now the only one.
+
+ A declared mean function LOWERS this empirical null rather than raising it:
+ an OLS trend fitted on N-1 rows of shuffled y is a noise fit, and
+ extrapolating it to the held-out row adds error. Median goes -0.4075 with no
+ mean, -0.4368 with one feature, -0.5384 with two.
+ """
+ if n < 2:
+ raise ValueError("The leave-one-out null needs at least two rows.")
+ return 1.0 - (n / (n - 1)) ** 2
+
+
+def resolution_sd(n: int) -> float:
+ """Sampling sd of LOO R2 at this N, scaled from the N=15 bootstrap."""
+ if n < 1:
+ raise ValueError("resolution_sd needs at least one row.")
+ return RESOLUTION_SD_AT_15 * math.sqrt(RESOLUTION_REFERENCE_N / n)
+
+
+@contextlib.contextmanager
+def _single_threaded_torch():
+ """Run the fold loop on one thread, and put the setting back afterwards.
+
+ Every fold here fits a GP on an N-1 x D design -- 14 x 10 on this campaign.
+ At that size intra-op threading costs more than it buys. Measured on an IDLE
+ machine, 45 fits: **9.8 s at 1, 2 or 4 threads against 15.1 s at this box's
+ default of 12**, for bit-identical results (LOO R2 -0.6447 / -0.5842 / +0.6630
+ at every setting).
+
+ The idleness matters and is not a footnote. The first version of this comment
+ claimed 51 s against 117 s, which was measured while sixteen permutation
+ workers were saturating the CPU -- a real effect, but of the load and not of
+ the thread count. A timing taken under contention is a wrong number in the
+ same way any other unreproduced number is, so it was re-measured before being
+ written down.
+
+ Scoped rather than set globally, because the same setting would SLOW the
+ round itself down: scoring a 32768-point candidate pool is exactly the large
+ matrix work threads are for. Not safe to call while another thread in this
+ process is computing with torch.
+ """
+ previous = torch.get_num_threads()
+ torch.set_num_threads(1)
+ try:
+ yield
+ finally:
+ torch.set_num_threads(previous)
+
+
+@dataclass(frozen=True)
+class LooResult:
+ """One objective's exact leave-one-out predictions, in both spaces."""
+
+ objective: str
+ #: What the GP emits: ``log(y)`` when the objective declares a log-response
+ #: mean function, ``y`` otherwise. This is what a utility transform consumes.
+ mean_model_space: np.ndarray
+ variance_model_space: np.ndarray
+ #: The measurement's own units -- nanometres for thickness. This is what a
+ #: parity plot must show, because nobody reads log(nm).
+ predicted: np.ndarray
+ predictive_sd: np.ndarray
+ observed: np.ndarray
+ r2: float
+ spearman: float
+ has_mean_function: bool
+ model_link: str
+ collapse_warnings: tuple[str, ...]
+
+ @property
+ def n(self) -> int:
+ return len(self.observed)
+
+
+def loo_predictions(
+ config: Mapping[str, Any],
+ entry: Mapping[str, Any],
+ X_phys: np.ndarray,
+ y: np.ndarray,
+ *,
+ seed: int = 73,
+ use_mean_function: bool = True,
+) -> LooResult:
+ """Exact leave-one-out for one objective under the campaign's own contract.
+
+ ``y`` is in MEASUREMENT space, as :func:`workbook_io.read_campaign_workbook`
+ returns it. Set ``use_mean_function=False`` for the plain-GP comparison the
+ intake verdict rests on.
+ """
+ from .campaign import normalise_inputs
+ from scipy.stats import spearmanr
+
+ X_phys = np.asarray(X_phys, dtype=float)
+ y = np.asarray(y, dtype=float)
+ n = len(y)
+ if n < 3:
+ raise ValueError(f"Leave-one-out needs at least three rows; got {n}.")
+
+ mean_spec = mean_spec_from_config(entry) if use_mean_function else None
+ log_response = mean_spec is not None and mean_spec.response == "log"
+ design_names = [item["name"] for item in config["inputs"]]
+ lowers = np.array([float(item["start"]) for item in config["inputs"]])
+ uppers = np.array([float(item["stop"]) for item in config["inputs"]])
+ X_norm = normalise_inputs(config, X_phys)
+
+ mu = np.empty(n)
+ var = np.empty(n)
+ collapse: list[str] = []
+ with _single_threaded_torch():
+ for held in range(n):
+ keep = [i for i in range(n) if i != held]
+ target = y[keep]
+ module = None
+ if mean_spec is not None:
+ module, target = build_structured_mean(
+ X_phys[keep], y[keep], mean_spec, design_names, lowers, uppers
+ )
+ torch.manual_seed(seed)
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ record = fit_model_variant(
+ torch.tensor(X_norm[keep], dtype=torch.double),
+ torch.tensor(target, dtype=torch.double).unsqueeze(-1),
+ sample_ids=tuple(range(len(keep))),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ seed=seed,
+ mean_module=module,
+ )
+ collapse.extend(
+ w.message for w in record.warnings if w.stage == SIGNAL_COLLAPSE_STAGE
+ )
+ gp = record.model.models[0]
+ gp.eval()
+ with torch.no_grad():
+ posterior = gp.posterior(
+ torch.tensor(X_norm[held : held + 1], dtype=torch.double)
+ )
+ mu[held] = float(posterior.mean.reshape(-1)[0])
+ var[held] = float(max(0.0, float(posterior.variance.reshape(-1)[0])))
+
+ if log_response:
+ # The GP emits log(y), so the measurement-space POINT prediction is the
+ # median exp(mu), matching the convention the round simulation and the
+ # SHAP oracle both use. The sd is the lognormal one; it is asymmetric in
+ # nanometres, which is why the parity plot labels it as an interval rather
+ # than pretending to a symmetric error bar.
+ predicted = np.exp(mu)
+ predictive_sd = np.sqrt(np.clip(np.exp(var) - 1.0, 0.0, None)) * np.exp(
+ mu + var / 2.0
+ )
+ else:
+ predicted = mu
+ predictive_sd = np.sqrt(var)
+
+ ss_res = float(np.sum((y - predicted) ** 2))
+ ss_tot = float(np.sum((y - np.mean(y)) ** 2))
+ r2 = 1.0 - ss_res / ss_tot if ss_tot > 0 else float("nan")
+ rank = (
+ float(spearmanr(y, predicted).statistic)
+ if len(np.unique(predicted)) > 1
+ else float("nan")
+ )
+
+ return LooResult(
+ objective=str(entry.get("name", "")),
+ mean_model_space=mu,
+ variance_model_space=var,
+ predicted=predicted,
+ predictive_sd=predictive_sd,
+ observed=y,
+ r2=r2,
+ spearman=rank,
+ has_mean_function=mean_spec is not None,
+ model_link="log" if log_response else "identity",
+ collapse_warnings=tuple(dict.fromkeys(collapse)),
+ )
+
+
+def loo_for_objectives(
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ Y_measured: np.ndarray,
+ *,
+ names: Sequence[str],
+ seed: int = 73,
+) -> dict[str, LooResult]:
+ """One :class:`LooResult` per objective, each with its own declared mean."""
+ entries = config["objectives"]["specs"]
+ out: dict[str, LooResult] = {}
+ for index, name in enumerate(names):
+ out[name] = loo_predictions(
+ config, entries[index], X_phys, np.asarray(Y_measured)[:, index], seed=seed
+ )
+ return out
diff --git a/src/mobo_kit/main.py b/src/mobo_kit/main.py
index c222b44..18a9afb 100644
--- a/src/mobo_kit/main.py
+++ b/src/mobo_kit/main.py
@@ -7,17 +7,21 @@
import os
import sys
-from typing import Optional, Dict, Any
+from typing import Optional, Dict, Any, Sequence
+import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
import yaml
from .utils import (
- load_csv, split_XY, csv_to_config, set_seeds, select_device,
- get_objective_names
+ parse_campaign_csv,
+ split_XY,
+ set_seeds,
+ select_device,
+ get_objective_names,
)
-from .design import build_input_spec_list, build_design
+from .design import Design, build_design_from_config
from .data import x_normalizer_np
from .models import fit_gp_models, posterior_report
from .plotting import plot_parity_np
@@ -27,6 +31,22 @@
from .lhs import lhs_dataframe_optimized
+class CampaignProposalRedirect(RuntimeError):
+ """Raised when the legacy runner is asked to propose candidates."""
+
+
+_PROPOSE_REDIRECT = (
+ "This runner fits models and reports diagnostics; it does not propose "
+ "candidates.\n"
+ "Use mobo_kit.campaign instead:\n"
+ " from mobo_kit.campaign import load_campaign_config, run_r1_ucb\n"
+ " config = load_campaign_config('configs/.yaml')\n"
+ " batch = run_r1_ucb(config, X_phys, Y_model, n=5)\n"
+ "campaign.py carries the objective contract, the fixed scales, and the "
+ "batch validity checks that this path never had."
+)
+
+
def generate_initial_experiments(
config_path: str,
n_samples: int,
@@ -34,14 +54,14 @@ def generate_initial_experiments(
seed: int = 42,
verbose: bool = True,
max_abs_corr: Optional[float] = None,
- max_attempts: int = 100
+ max_attempts: int = 100,
) -> Dict[str, Any]:
"""
Generate initial experiments using Latin Hypercube Sampling.
-
+
This function is useful when you have a design space but no existing data.
It generates a CSV file with initial experiments to run.
-
+
Args:
config_path: Path to YAML configuration file
n_samples: Number of initial experiments to generate
@@ -50,7 +70,7 @@ def generate_initial_experiments(
verbose: Whether to print progress information
max_abs_corr: Maximum absolute correlation between variables (optional)
max_attempts: Maximum attempts for LHS generation
-
+
Returns:
Dictionary with generation results and metadata
"""
@@ -60,28 +80,28 @@ def generate_initial_experiments(
print(f"Generating {n_samples} initial experiments...")
print(f"Config: {config_path}")
print(f"Output: {save_path}")
-
+
# Set random seed
set_seeds(seed)
-
+
# Load configuration
- with open(config_path, 'r') as f:
+ with open(config_path, "r", encoding="utf-8") as f:
config = yaml.safe_load(f)
-
- # Build design space
- space = build_design(config)
-
+
+ # Build design space through the validated config-to-design path.
+ space = build_design_from_config(config)
+
if verbose:
print(f"Design space: {len(space.names)} variables")
print(f"Variables: {', '.join(space.names)}")
-
+
# Get constraints
row_constraints = constraints_from_config(config, space)
-
+
# Generate LHS samples
if verbose:
print("Generating Latin Hypercube samples...")
-
+
lhs_df = lhs_dataframe_optimized(
design=space,
n=n_samples,
@@ -90,17 +110,19 @@ def generate_initial_experiments(
row_constraints=row_constraints,
max_abs_corr=max_abs_corr,
max_attempts=max_attempts,
- verbose=verbose
+ verbose=verbose,
)
-
+
# Save to CSV
+ output_parent = os.path.dirname(os.path.abspath(save_path))
+ os.makedirs(output_parent, exist_ok=True)
lhs_df.to_csv(save_path, index=False)
-
+
if verbose:
print(f"Generated {len(lhs_df)} initial experiments")
print(f"Saved to: {save_path}")
print("=" * 50)
-
+
return {
"status": "success",
"n_samples": len(lhs_df),
@@ -108,23 +130,90 @@ def generate_initial_experiments(
"save_path": save_path,
"seed": seed,
"variables": space.names,
- "constraints_applied": len(row_constraints) > 0 if row_constraints else False
+ "constraints_applied": len(row_constraints) > 0 if row_constraints else False,
}
+def _validate_candidate_batch(
+ batch_result: Dict[str, Any],
+ design: Design,
+ observed_inputs: pd.DataFrame,
+ batch_size: int,
+) -> np.ndarray:
+ """Fail closed on incomplete, duplicate, observed, or off-grid batches."""
+
+ if not isinstance(batch_result, dict) or "X_phys" not in batch_result:
+ raise ValueError("Candidate proposal did not return an 'X_phys' array.")
+ try:
+ candidates = np.asarray(batch_result["X_phys"], dtype=float)
+ except (TypeError, ValueError) as exc:
+ raise ValueError("Candidate physical inputs must be numeric.") from exc
+
+ expected_shape = (batch_size, len(design.names))
+ if candidates.shape != expected_shape:
+ raise ValueError(
+ "Candidate proposal returned an incomplete or malformed batch: "
+ f"expected shape {expected_shape}, got {candidates.shape}."
+ )
+ if not np.isfinite(candidates).all():
+ raise ValueError("Candidate proposal contains non-finite physical inputs.")
+
+ tolerances = np.maximum(np.abs(design.steps) * 1e-9, 1e-10)
+ grid_indices = np.empty(expected_shape, dtype=np.int64)
+ for column_index, (name, grid) in enumerate(zip(design.names, design.var_list)):
+ distances = np.abs(candidates[:, column_index, None] - grid[None, :])
+ nearest_indices = np.argmin(distances, axis=1)
+ nearest_distances = distances[
+ np.arange(batch_size, dtype=np.int64), nearest_indices
+ ]
+ if np.any(nearest_distances > tolerances[column_index]):
+ bad_rows = np.flatnonzero(
+ nearest_distances > tolerances[column_index]
+ ).tolist()
+ raise ValueError(
+ f"Candidate input '{name}' is off-grid at batch rows {bad_rows}."
+ )
+ grid_indices[:, column_index] = nearest_indices
+
+ if np.unique(grid_indices, axis=0).shape[0] != batch_size:
+ raise ValueError("Candidate proposal contains duplicate snapped recipes.")
+
+ observed = observed_inputs.loc[:, design.names].to_numpy(dtype=float)
+ if observed.size:
+ matches_observed = np.all(
+ np.isclose(
+ candidates[:, None, :],
+ observed[None, :, :],
+ rtol=0.0,
+ atol=tolerances,
+ ),
+ axis=2,
+ )
+ repeated_rows = np.flatnonzero(matches_observed.any(axis=1)).tolist()
+ if repeated_rows:
+ raise ValueError(
+ "Candidate proposal repeats observed recipes at batch rows "
+ f"{repeated_rows}."
+ )
+
+ return candidates
+
+
def run_mobo_experiment(
csv_path: str,
- save_dir: str = "results/experiment",
+ save_dir: str = "local_outputs/experiment",
config_path: Optional[str] = None,
seed: int = 42,
device: str = "auto",
verbose: bool = True,
batch_size: int = 5,
- propose_candidates: bool = True
+ propose_candidates: bool = False,
+ reference_point: Optional[Sequence[float]] = None,
+ num_restarts: int = 20,
) -> Dict[str, Any]:
"""
Run a complete MOBO experiment from CSV data.
-
+
Args:
csv_path: Path to CSV file with experimental data
save_dir: Directory to save results
@@ -133,83 +222,108 @@ def run_mobo_experiment(
device: Device to use ("auto", "cpu", "cuda")
verbose: Whether to print progress information
batch_size: Number of candidates to propose for next batch
- propose_candidates: Whether to propose new candidates for next experiments
-
+ propose_candidates: Request campaign candidates. Step 2A always blocks
+ this legacy path; a reviewed Step 2B campaign adapter is required.
+ reference_point: Explicit hypervolume reference point in the exact same
+ transformed objective space as ``Y``. Required when proposing.
+ num_restarts: Number of acquisition-optimization restarts.
+
Returns:
Dictionary with experiment results and metadata
"""
if verbose:
print("MOBO-Kit: Multi-objective Bayesian Optimization Toolkit")
print("=" * 60)
-
+
# Set random seeds
set_seeds(seed)
-
+
# Select device
if device == "auto":
device_obj = select_device("cuda")
else:
device_obj = select_device(device)
-
+
if verbose:
print(f"Using device: {device_obj}")
print(f"Loading data from: {csv_path}")
-
- # Load and process data
- df = load_csv(csv_path)
-
- # Load or generate configuration
- if config_path and os.path.exists(config_path):
- import yaml
- with open(config_path, 'r') as f:
+
+ # Load the explicit configuration first when supplied, then parse the CSV
+ # exactly once against the expected objective names.
+ if config_path is not None:
+ if not os.path.isfile(config_path):
+ raise FileNotFoundError(f"Configuration file not found: {config_path}")
+ with open(config_path, "r", encoding="utf-8") as f:
config = yaml.safe_load(f)
+ if propose_candidates:
+ raise CampaignProposalRedirect(_PROPOSE_REDIRECT)
+ parsed_csv = parse_campaign_csv(
+ csv_path, expected_objectives=get_objective_names(config)
+ )
if verbose:
print(f"Loaded config from: {config_path}")
else:
- config = csv_to_config(csv_path)
+ if propose_candidates:
+ raise CampaignProposalRedirect(_PROPOSE_REDIRECT)
+ parsed_csv = parse_campaign_csv(csv_path)
+ config = parsed_csv.config
if verbose:
print("Auto-generated config from CSV metadata")
-
- # Build design space
- specs = build_input_spec_list(config["inputs"])
- space = build_design(specs)
-
+
+ df = parsed_csv.data
+
+ # Build the design through the same validated path used by LHS generation.
+ space = build_design_from_config(config)
+
# Split data into inputs and objectives
X, Y = split_XY(df, space, config)
-
+
+ if propose_candidates and reference_point is None:
+ raise ValueError(
+ "Candidate proposal requires an explicit reference_point in the "
+ "same transformed objective space as the model outputs. MOBO-Kit "
+ "will not invent a campaign reference point."
+ )
+
if verbose:
- print(f"Loaded {len(X)} samples with {X.shape[1]} inputs and {Y.shape[1]} objectives")
+ print(
+ f"Loaded {len(X)} samples with {X.shape[1]} inputs and {Y.shape[1]} objectives"
+ )
print(f"Objective names: {get_objective_names(config)}")
-
+
# Normalize inputs to [0,1] range
X_norm = x_normalizer_np(X, space)
-
+
# Convert to torch tensors (Y is used directly, not standardized)
X_t = torch.tensor(X_norm, dtype=torch.float64, device=device_obj)
Y_t = torch.tensor(Y.values, dtype=torch.float64, device=device_obj)
-
+
if verbose:
print("Fitting Gaussian Process models...")
-
+
# Fit GP models
model = fit_gp_models(X_t, Y_t)
-
+
if verbose:
print("Generating predictions...")
-
+
# Generate predictions (already in original units due to internal standardization)
pred_mean, pred_std = posterior_report(model, X_t)
-
+
# Create output directory
os.makedirs(save_dir, exist_ok=True)
-
+
# Predictions are generated but not saved to separate CSV
-
+
# Generate plots
try:
if verbose:
print("Generating plots...")
-
+
+ # The runner writes diagnostics to disk and must also work on lab PCs,
+ # CI workers, and managed Python installs without a Tcl/Tk GUI runtime.
+ plt.switch_backend("Agg")
+
# Create parity plots
parity_path = os.path.join(save_dir, "parity_plots.png")
fig, metrics_df = plot_parity_np(
@@ -218,35 +332,43 @@ def run_mobo_experiment(
pred_std=pred_std,
objective_names=get_objective_names(config),
save=parity_path,
- show_plot=False
+ show_plot=False,
)
plt.close(fig) # Close the figure to free memory
-
+
if verbose:
print(f"Parity plots saved to: {parity_path}")
-
+
except Exception as e:
if verbose:
print(f"Warning: Could not generate plots: {e}")
-
+
# Propose new candidates for next batch
candidates = None
if propose_candidates:
try:
if verbose:
print("Proposing new candidates for next batch...")
-
+
+ ref_point_np = np.asarray(reference_point, dtype=float)
+ if ref_point_np.shape != (Y.shape[1],):
+ raise ValueError(
+ "reference_point must contain exactly one value per objective "
+ f"({Y.shape[1]} expected, got shape {ref_point_np.shape})."
+ )
+
# Compute reference point and hypervolume
- _, pareto_Y_t, hv_val = compute_ref_pareto_hv(Y_t)
- ref_point_t = torch.tensor([-0.01] * Y.shape[1], dtype=X_t.dtype, device=device_obj)
-
+ ref_point_t, pareto_Y_t, hv_val = compute_ref_pareto_hv(
+ Y_t, ref_point_np=ref_point_np
+ )
+
if verbose:
print(f"Current hypervolume: {hv_val:.4f}")
print(f"Pareto points: {pareto_Y_t.shape[0]}")
-
+
# Get constraints
row_constraints = constraints_from_config(config, space)
-
+
# Propose batch
batch_result = propose_batch(
design=space,
@@ -255,41 +377,49 @@ def run_mobo_experiment(
ref_point_t=ref_point_t,
batch_size=batch_size,
row_constraints=row_constraints,
- verbose=verbose
+ num_restarts=num_restarts,
+ verbose=verbose,
)
-
+
+ candidate_array = _validate_candidate_batch(
+ batch_result=batch_result,
+ design=space,
+ observed_inputs=X,
+ batch_size=batch_size,
+ )
+ batch_result = dict(batch_result)
+ batch_result["X_phys"] = candidate_array
+
candidates = batch_result
candidates_path = os.path.join(save_dir, "next_batch.csv")
-
- # Create next_batch.csv in the same format as input CSV with metadata
- # Load the original CSV to get metadata structure
- original_df = load_csv(csv_path)
-
- # Create new candidates DataFrame with same structure
- new_candidates_df = pd.DataFrame(
- batch_result['X_phys'],
- columns=space.names
- )
-
+
+ # Retain the legacy flat-table export for the explicit qNEHVI path.
+ # It is not a metadata-style campaign CSV and therefore is not a
+ # supported round-trip input to parse_campaign_csv.
+ new_candidates_df = pd.DataFrame(candidate_array, columns=space.names)
+
# Add empty objective columns to match original format
objective_names = get_objective_names(config)
for obj_name in objective_names:
new_candidates_df[obj_name] = ""
-
+
# Combine original data with new candidates
- combined_df = pd.concat([original_df, new_candidates_df], ignore_index=True)
-
+ combined_df = pd.concat([df, new_candidates_df], ignore_index=True)
+
# Save the combined CSV
combined_df.to_csv(candidates_path, index=False)
-
+
if verbose:
print(f"Proposed {batch_size} candidates for next batch")
print(f"Candidates saved to: {candidates_path}")
-
+
except Exception as e:
if verbose:
- print(f"Warning: Could not propose candidates: {e}")
-
+ print(f"Candidate proposal failed: {e}")
+ raise RuntimeError(
+ "Candidate proposal failed; no batch was accepted."
+ ) from e
+
# Prepare results summary
results = {
"status": "success",
@@ -301,40 +431,38 @@ def run_mobo_experiment(
"seed": seed,
"save_dir": save_dir,
"config": config,
- "candidates": candidates
+ "candidates": candidates,
}
-
+
if verbose:
print(f"Experiment completed successfully!")
print(f"Results saved to: {save_dir}")
-
+
return results
def main():
"""
Main entry point for running MOBO-Kit with default settings.
-
+
This function runs a complete MOBO experiment using the default CSV file
and saves results to the default output directory.
"""
# Default paths
csv_path = "data/processed/configCSV_example.csv"
- save_dir = "results/demo"
-
+ save_dir = "local_outputs/demo"
+
# Check if default CSV exists
if not os.path.exists(csv_path):
print(f"Error: Default data file not found: {csv_path}")
print("Please provide a valid CSV file path or ensure the default file exists.")
sys.exit(1)
-
+
try:
results = run_mobo_experiment(
- csv_path=csv_path,
- save_dir=save_dir,
- verbose=True
+ csv_path=csv_path, save_dir=save_dir, verbose=True
)
-
+
print("\n" + "=" * 60)
print("EXPERIMENT SUMMARY:")
print(f" Samples: {results['n_samples']}")
@@ -343,11 +471,12 @@ def main():
print(f" Objectives: {', '.join(results['objective_names'])}")
print(f" Results: {results['save_dir']}")
print("=" * 60)
-
+
except Exception as e:
print(f"Error running MOBO experiment: {e}")
if "--verbose" in sys.argv:
import traceback
+
traceback.print_exc()
sys.exit(1)
diff --git a/src/mobo_kit/metrics.py b/src/mobo_kit/metrics.py
index 42fd266..e531eaf 100644
--- a/src/mobo_kit/metrics.py
+++ b/src/mobo_kit/metrics.py
@@ -14,16 +14,14 @@
def compute_ref_pareto_hv(
Y: torch.Tensor,
ref_point_np: Optional[np.ndarray] = None,
- eps: float = 1e-8
) -> Tuple[torch.Tensor, torch.Tensor, float]:
"""
Compute the Pareto front and hypervolume of the training data.
Args:
Y: (N, M) array of objectives.
- ref_point: (M,) array reference point for hypervolume calculation.
- eps: small margin to ensure auto reference point is strictly dominated.
-
+ ref_point_np: (M,) reference point. REQUIRED, and fixed for the campaign.
+
Returns:
ref_point: Reference point used for hypervolume.
pareto_Y: Pareto front points.
@@ -32,24 +30,68 @@ def compute_ref_pareto_hv(
Notes:
* All objectives are to be MAXIMIZED. If any are minimized, flip sign before calling.
* Returns tensors on the same device/dtype as `Y`.
+
+ The reference point is required rather than inferred, for two reasons that
+ both produced wrong numbers here.
+
+ A previous version defaulted to ``Y.min(dim=0) - 1e-8``, essentially the nadir
+ of whatever data it was handed. Every hypervolume slab is then 1e-8 thick: on
+ the campaign's R0 utilities that gave **6e-8 against 1.448** from BoTorch's
+ ``infer_reference_point`` on the same data. It also re-derived the reference
+ from the current data on every call, so two rounds' hypervolumes were measured
+ against two different reference points and were never comparable -- which is
+ the whole purpose of tracking hypervolume across rounds.
+
+ Pass the campaign's declared ``reference_point_utility`` from config, in
+ utility space, after the objective transforms.
"""
+ if ref_point_np is None:
+ raise ValueError(
+ "compute_ref_pareto_hv requires an explicit ref_point_np. Pass the "
+ "campaign's fixed reference: "
+ "np.asarray(config['reference_point_utility'], dtype=float), in utility "
+ "space after the objective transforms. A reference inferred from the "
+ "current data moves between rounds, which makes hypervolumes "
+ "incomparable across them."
+ )
+
device, dtype = Y.device, Y.dtype
N, M = Y.shape
+ del N
pareto_mask = is_non_dominated(Y)
pareto_Y = Y[pareto_mask]
- if ref_point_np is None:
- mins = torch.min(Y, dim=0).values
- ref_point_t = mins - eps
- else:
- if not isinstance(ref_point_np, np.ndarray):
- raise TypeError("ref_point_np must be a numpy.ndarray")
- if ref_point_np.ndim != 1:
- raise ValueError(f"ref_point must be 1D of length {M}, got shape {ref_point_np.shape}")
- if ref_point_np.size != M:
- raise ValueError(f"ref_point length {ref_point_np.size} does not match number of objectives M={M}")
- ref_point_t = torch.as_tensor(ref_point_np, device=device, dtype=dtype)
+ if not isinstance(ref_point_np, (np.ndarray, torch.Tensor)):
+ raise TypeError("ref_point_np must be a numpy.ndarray or torch.Tensor")
+ reference = np.asarray(
+ ref_point_np.detach().cpu().numpy()
+ if isinstance(ref_point_np, torch.Tensor)
+ else ref_point_np,
+ dtype=float,
+ )
+ if reference.ndim != 1:
+ raise ValueError(f"ref_point must be 1D of length {M}, got shape {reference.shape}")
+ if reference.size != M:
+ raise ValueError(f"ref_point length {reference.size} does not match number of objectives M={M}")
+ if not np.all(np.isfinite(reference)):
+ raise ValueError("ref_point must be finite.")
+ ref_point_t = torch.as_tensor(reference, device=device, dtype=dtype)
+
+ # BoTorch's Hypervolume assumes maximisation and SILENTLY DROPS points that do
+ # not dominate the reference -- no warning, no exception, just a smaller number
+ # or 0.0. If nothing dominates, the answer would be 0.0 and indistinguishable
+ # from a wrongly signed or badly placed reference, so say so instead.
+ dominating = bool((pareto_Y > ref_point_t).all(dim=-1).any())
+ if not dominating:
+ raise ValueError(
+ "No observation dominates the reference point, so the hypervolume "
+ "would be 0.0 for a reason the number cannot express. Check the sign "
+ "convention (every objective must be maximised here) and that the "
+ "reference sits below the achievable region. Reference: "
+ f"{reference.tolist()}; per-objective observed maxima: "
+ f"{Y.max(dim=0).values.detach().cpu().numpy().tolist()}."
+ )
hv = Hypervolume(ref_point=ref_point_t)
volume = float(hv.compute(pareto_Y))
diff --git a/src/mobo_kit/model_validation.py b/src/mobo_kit/model_validation.py
new file mode 100644
index 0000000..5026c86
--- /dev/null
+++ b/src/mobo_kit/model_validation.py
@@ -0,0 +1,1317 @@
+"""Strict GP fitting and exact leave-one-out validation for robustness studies.
+
+This module intentionally does not use the historical model-selection helper in
+``models.py``. That helper prints fitting failures and continues with fallback
+hyperparameters, which is appropriate for its exploratory notebook use but not
+for an auditable robustness study. Every fit here either returns a structured
+record or raises :class:`ModelFitError` without silently changing its contract.
+"""
+
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass, field
+import hashlib
+import json
+from math import log, pi
+from numbers import Real
+from time import perf_counter
+from typing import Any, Hashable, Iterable, Mapping, Sequence
+import warnings
+
+import gpytorch
+import numpy as np
+import pandas as pd
+from botorch.fit import fit_gpytorch_mll
+from botorch.models import SingleTaskGP
+from botorch.models.model_list_gp_regression import ModelListGP
+from botorch.models.transforms.outcome import Standardize
+from botorch.models.utils.gpytorch_modules import (
+ get_covar_module_with_dim_scaled_prior,
+ get_gaussian_likelihood_with_lognormal_prior,
+)
+from gpytorch.constraints import GreaterThan
+from gpytorch.kernels import MaternKernel, ScaleKernel
+from gpytorch.likelihoods import GaussianLikelihood
+from gpytorch.mlls import ExactMarginalLogLikelihood
+from scipy.stats import spearmanr
+import torch
+
+
+DIM_SCALED_PRIOR_NAME = "dim_scaled_prior"
+LEGACY_NO_PRIOR_NAME = "legacy_matern_no_prior"
+CONSERVATIVE_NAME = "conservative"
+#: BoTorch's MIN_INFERRED_NOISE_LEVEL, the floor that ships with its LogNormal
+#: noise prior. The prior, not the floor, is what stops the variance collapse.
+DIM_SCALED_PRIOR_MIN_NOISE = 1.0e-4
+LEGACY_NO_PRIOR_MIN_NOISE = 1.0e-3
+CONSERVATIVE_MIN_NOISE = 0.01
+CONSERVATIVE_MIN_LENGTHSCALE = 0.05
+GAUSSIAN_95_Z = 1.959963984540054
+VERY_SMALL_NORMALIZED_LENGTHSCALE = 0.05
+EXTREMELY_LARGE_NORMALIZED_LENGTHSCALE = 10.0
+
+
+def _finite_positive(value: Any, *, field_name: str) -> float:
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+ raise ValueError(f"{field_name} must be a real non-boolean number.")
+ result = float(value)
+ if not np.isfinite(result) or result <= 0.0:
+ raise ValueError(f"{field_name} must be finite and strictly positive.")
+ return result
+
+
+def _seed(value: Any) -> int:
+ if (
+ isinstance(value, (bool, np.bool_))
+ or not isinstance(value, (int, np.integer))
+ or int(value) < 0
+ ):
+ raise ValueError("seed must be a non-negative integer.")
+ return int(value)
+
+
+@dataclass(frozen=True)
+class ModelVariantSpec:
+ """One explicit GP model contract.
+
+ ``use_dim_scaled_prior`` selects BoTorch's dimension-scaled LogNormal
+ lengthscale prior. Without it, an unregularised ARD kernel fitted to 15
+ observations in 10 dimensions drives lengthscales to bimodal extremes
+ (measured: 0.13 to 3.8e4) and pins the likelihood noise at its floor, which
+ is interpolation rather than learning.
+
+ ``use_lognormal_noise_prior`` selects BoTorch's ``LogNormal(-4, 1)`` noise
+ prior. It is required alongside the lengthscale prior, not optional: with a
+ bare noise floor the fit has a second degenerate mode in which the
+ outputscale collapses to zero and the model declares the data pure noise.
+ That mode was observed on 10 of 15 leave-one-out folds of the thickness
+ score, producing a latent predictive sd of 1e-4 against a fitted noise of
+ 0.93, 68% interval coverage of 0.133, and a mean NLPD of 3.1e6.
+
+ See docs/GP_MODEL_DECISION.md.
+ """
+
+ name: str
+ min_noise: float
+ min_lengthscale: float | None
+ kernel_name: str = "matern_2.5_ard"
+ use_dim_scaled_prior: bool = False
+ use_lognormal_noise_prior: bool = False
+
+ def __post_init__(self) -> None:
+ if self.name not in {
+ DIM_SCALED_PRIOR_NAME,
+ LEGACY_NO_PRIOR_NAME,
+ CONSERVATIVE_NAME,
+ }:
+ raise ValueError(
+ "Model variant name must be 'dim_scaled_prior', "
+ "'legacy_matern_no_prior' or 'conservative'."
+ )
+ noise = _finite_positive(self.min_noise, field_name="min_noise")
+ lengthscale = (
+ None
+ if self.min_lengthscale is None
+ else _finite_positive(self.min_lengthscale, field_name="min_lengthscale")
+ )
+ if self.kernel_name != "matern_2.5_ard":
+ raise ValueError("Only the audited matern_2.5_ard kernel is supported.")
+ if self.name == DIM_SCALED_PRIOR_NAME and (
+ noise != DIM_SCALED_PRIOR_MIN_NOISE
+ or lengthscale is not None
+ or not self.use_dim_scaled_prior
+ or not self.use_lognormal_noise_prior
+ ):
+ raise ValueError(
+ "dim_scaled_prior must use min_noise=1e-4, no lengthscale floor, "
+ "and BOTH the dimension-scaled lengthscale prior and the "
+ "LogNormal noise prior."
+ )
+ if self.name == LEGACY_NO_PRIOR_NAME and (
+ noise != LEGACY_NO_PRIOR_MIN_NOISE
+ or lengthscale is not None
+ or self.use_dim_scaled_prior
+ or self.use_lognormal_noise_prior
+ ):
+ raise ValueError(
+ "legacy_matern_no_prior must preserve the retired Step 2B contract: "
+ "min_noise=1e-3, no lengthscale floor, and no priors."
+ )
+ if self.name == CONSERVATIVE_NAME and (
+ noise != CONSERVATIVE_MIN_NOISE
+ or lengthscale != CONSERVATIVE_MIN_LENGTHSCALE
+ or self.use_dim_scaled_prior
+ or self.use_lognormal_noise_prior
+ ):
+ raise ValueError(
+ "conservative must use min_noise=0.01, min_lengthscale=0.05 and "
+ "no priors."
+ )
+ object.__setattr__(self, "min_noise", noise)
+ object.__setattr__(self, "min_lengthscale", lengthscale)
+
+
+#: The campaign default. Matern 2.5 ARD with BoTorch's dimension-scaled
+#: LogNormal lengthscale prior and its LogNormal(-4, 1) noise prior. Both are
+#: required; see ModelVariantSpec for what happens with only the former.
+DIM_SCALED_PRIOR = ModelVariantSpec(
+ DIM_SCALED_PRIOR_NAME,
+ min_noise=DIM_SCALED_PRIOR_MIN_NOISE,
+ min_lengthscale=None,
+ use_dim_scaled_prior=True,
+ use_lognormal_noise_prior=True,
+)
+#: Retired. The prior-free contract used through Step 2C, kept only so archived
+#: runs remain reproducible and interpretable. Do not select for new work.
+LEGACY_NO_PRIOR = ModelVariantSpec(
+ LEGACY_NO_PRIOR_NAME,
+ min_noise=LEGACY_NO_PRIOR_MIN_NOISE,
+ min_lengthscale=None,
+)
+CONSERVATIVE = ModelVariantSpec(
+ CONSERVATIVE_NAME,
+ min_noise=CONSERVATIVE_MIN_NOISE,
+ min_lengthscale=CONSERVATIVE_MIN_LENGTHSCALE,
+)
+
+#: What new runs get unless a caller deliberately asks for something else.
+PRIMARY_VARIANT = DIM_SCALED_PRIOR
+
+
+def model_variant_spec(name: str) -> ModelVariantSpec:
+ """Return one of the fixed model contracts by name."""
+ if name == DIM_SCALED_PRIOR_NAME:
+ return DIM_SCALED_PRIOR
+ if name == LEGACY_NO_PRIOR_NAME:
+ return LEGACY_NO_PRIOR
+ if name == CONSERVATIVE_NAME:
+ return CONSERVATIVE
+ raise ValueError(f"Unsupported model variant {name!r}.")
+
+
+@dataclass(frozen=True)
+class ModelFitWarning:
+ variant_name: str
+ fit_key: str
+ omitted_sample_id: Hashable | None
+ objective_index: int
+ objective_name: str
+ stage: str
+ warning_category: str
+ message: str
+
+
+class ModelFitError(RuntimeError):
+ """A strict model-fit failure with all warnings observed before failure."""
+
+ def __init__(
+ self,
+ *,
+ variant_name: str,
+ fit_key: str,
+ omitted_sample_id: Hashable | None,
+ objective_index: int,
+ objective_name: str,
+ stage: str,
+ cause: BaseException,
+ fit_warnings: Sequence[ModelFitWarning],
+ ) -> None:
+ self.variant_name = variant_name
+ self.fit_key = fit_key
+ self.omitted_sample_id = omitted_sample_id
+ self.objective_index = objective_index
+ self.objective_name = objective_name
+ self.stage = stage
+ self.cause = cause
+ self.fit_warnings = tuple(fit_warnings)
+ super().__init__(
+ "Strict GP fit failed: "
+ f"variant={variant_name!r}, fit_key={fit_key!r}, "
+ f"objective={objective_name!r}, stage={stage!r}, "
+ f"cause={type(cause).__name__}: {cause}"
+ )
+
+
+@dataclass(frozen=True)
+class ModelFitCacheKey:
+ variant_name: str
+ cohort_fingerprint: str
+ omitted_sample_key: str
+ objective_names: tuple[str, ...]
+ seed: int
+
+
+@dataclass(frozen=True)
+class FittedModelRecord:
+ variant: ModelVariantSpec
+ model: ModelListGP
+ train_X: torch.Tensor
+ train_Y: torch.Tensor
+ sample_ids: tuple[Hashable, ...]
+ objective_names: tuple[str, ...]
+ fit_key: str
+ omitted_sample_id: Hashable | None
+ seed: int
+ cohort_fingerprint: str
+ training_fingerprint: str
+ fit_runtime_seconds: float
+ warnings: tuple[ModelFitWarning, ...]
+
+
+@dataclass
+class ModelFitCache:
+ """In-memory fit cache shared by LOOCV and observation influence."""
+
+ records: dict[ModelFitCacheKey, FittedModelRecord] = field(default_factory=dict)
+ hits: int = 0
+ misses: int = 0
+
+ def get(self, key: ModelFitCacheKey) -> FittedModelRecord | None:
+ record = self.records.get(key)
+ if record is None:
+ self.misses += 1
+ else:
+ self.hits += 1
+ return record
+
+ def store(self, key: ModelFitCacheKey, record: FittedModelRecord) -> None:
+ existing = self.records.get(key)
+ if existing is not None and existing is not record:
+ raise ValueError(f"A different fit already exists for cache key {key}.")
+ self.records[key] = record
+
+
+@dataclass(frozen=True)
+class LOOCVPrediction:
+ variant_name: str
+ omitted_sample_id: Hashable
+ row_role: str
+ is_control: bool
+ objective_index: int
+ objective_name: str
+ observed: float
+ predicted_mean: float
+ latent_std: float
+ predictive_std: float
+ prediction_error: float
+ residual: float
+ standardized_residual: float
+ within_68_percent_interval: bool
+ within_95_percent_interval: bool
+ gaussian_nlpd: float
+ fold_fit_key: str
+ fold_fit_warning_count: int
+
+
+@dataclass(frozen=True)
+class PredictionMetricRecord:
+ variant_name: str
+ objective_index: int
+ objective_name: str
+ prediction_count: int
+ mae: float
+ rmse: float
+ r_squared: float
+ r_squared_warning: str
+ spearman_rank_correlation: float
+ mean_signed_error: float
+ median_absolute_error: float
+ coverage_68_percent: float
+ coverage_95_percent: float
+ mean_standardized_residual: float
+ maximum_absolute_standardized_residual: float
+ mean_gaussian_nlpd: float
+
+
+@dataclass(frozen=True)
+class HyperparameterRecord:
+ variant_name: str
+ fit_key: str
+ omitted_sample_id: Hashable | None
+ objective_index: int
+ objective_name: str
+ kernel_type: str
+ likelihood_noise: float
+ outputscale: float
+ ard_lengthscales: tuple[float, ...]
+ input_names: tuple[str, ...]
+ configured_min_noise: float
+ configured_min_lengthscale: float | None
+ noise_constraint_lower_bound: float
+ lengthscale_constraint_lower_bound: float
+ noise_near_floor: bool
+ lengthscales_near_floor: tuple[bool, ...]
+ lengthscales_very_small_normalized_domain: tuple[bool, ...]
+ lengthscales_extremely_large_flat: tuple[bool, ...]
+ very_small_lengthscale_threshold: float = VERY_SMALL_NORMALIZED_LENGTHSCALE
+ extremely_large_lengthscale_threshold: float = (
+ EXTREMELY_LARGE_NORMALIZED_LENGTHSCALE
+ )
+ input_parameter_space: str = "normalized_0_1"
+ outcome_parameter_space: str = "standardized_internal"
+
+ def as_flat_dict(self) -> dict[str, Any]:
+ result = asdict(self)
+ result.pop("ard_lengthscales")
+ result.pop("input_names")
+ result.pop("lengthscales_near_floor")
+ result.pop("lengthscales_very_small_normalized_domain")
+ result.pop("lengthscales_extremely_large_flat")
+ result["any_lengthscale_very_small_normalized_domain"] = any(
+ self.lengthscales_very_small_normalized_domain
+ )
+ result["any_lengthscale_extremely_large_flat"] = any(
+ self.lengthscales_extremely_large_flat
+ )
+ for input_name, value, near_floor, very_small, extremely_large in zip(
+ self.input_names,
+ self.ard_lengthscales,
+ self.lengthscales_near_floor,
+ self.lengthscales_very_small_normalized_domain,
+ self.lengthscales_extremely_large_flat,
+ ):
+ result[f"ard_lengthscale_{input_name}"] = value
+ result[f"ard_lengthscale_{input_name}_near_floor"] = near_floor
+ result[f"ard_lengthscale_{input_name}_very_small_normalized_domain"] = (
+ very_small
+ )
+ result[f"ard_lengthscale_{input_name}_extremely_large_flat"] = (
+ extremely_large
+ )
+ return result
+
+
+@dataclass(frozen=True)
+class ExactLOOCVResult:
+ variant: ModelVariantSpec
+ predictions: tuple[LOOCVPrediction, ...]
+ metrics: tuple[PredictionMetricRecord, ...]
+ fold_records: Mapping[Hashable, FittedModelRecord]
+ cohort_fingerprint: str
+ cache: ModelFitCache
+
+ def predictions_frame(self) -> pd.DataFrame:
+ return pd.DataFrame(asdict(row) for row in self.predictions)
+
+ def metrics_frame(self) -> pd.DataFrame:
+ return pd.DataFrame(asdict(row) for row in self.metrics)
+
+
+@dataclass(frozen=True)
+class ModelValidationResult:
+ variant: ModelVariantSpec
+ full_fit: FittedModelRecord
+ loocv: ExactLOOCVResult
+ hyperparameters: tuple[HyperparameterRecord, ...]
+
+ def hyperparameters_frame(self) -> pd.DataFrame:
+ return pd.DataFrame(row.as_flat_dict() for row in self.hyperparameters)
+
+ def warnings_frame(self) -> pd.DataFrame:
+ records = [self.full_fit, *self.loocv.fold_records.values()]
+ return fit_warnings_frame(records)
+
+
+def _sample_key(value: Hashable | None) -> str:
+ if value is None:
+ return ""
+ return f"{type(value).__name__}:{value!r}"
+
+
+def _tensor_bytes(value: torch.Tensor) -> bytes:
+ tensor = value.detach().cpu().contiguous()
+ return tensor.numpy().tobytes()
+
+
+def dataset_fingerprint(
+ X: torch.Tensor,
+ Y: torch.Tensor,
+ sample_ids: Sequence[Hashable],
+) -> str:
+ """Return a deterministic fingerprint for cache and provenance checks."""
+ digest = hashlib.sha256()
+ for tensor in (X, Y):
+ digest.update(str(tensor.dtype).encode("utf-8"))
+ digest.update(json.dumps(tuple(tensor.shape)).encode("utf-8"))
+ digest.update(_tensor_bytes(tensor))
+ serialized_ids = [f"{type(value).__name__}:{value!r}" for value in sample_ids]
+ digest.update(json.dumps(serialized_ids, separators=(",", ":")).encode("utf-8"))
+ return digest.hexdigest()
+
+
+def _validate_dataset(
+ X: torch.Tensor,
+ Y: torch.Tensor,
+ sample_ids: Sequence[Hashable],
+ objective_names: Sequence[str],
+ *,
+ minimum_rows: int,
+) -> tuple[tuple[Hashable, ...], tuple[str, ...]]:
+ if not isinstance(X, torch.Tensor) or X.ndim != 2:
+ raise ValueError("X must be a torch tensor with shape (N, D).")
+ if not isinstance(Y, torch.Tensor) or Y.ndim != 2:
+ raise ValueError("Y must be a torch tensor with shape (N, M).")
+ if not X.is_floating_point() or not Y.is_floating_point():
+ raise TypeError("X and Y must use floating dtypes.")
+ if X.device != Y.device or X.dtype != Y.dtype:
+ raise ValueError("X and Y must share dtype and device.")
+ if X.shape[0] != Y.shape[0] or X.shape[0] < minimum_rows:
+ raise ValueError(
+ f"X and Y must share at least {minimum_rows} rows; "
+ f"got {X.shape[0]} and {Y.shape[0]}."
+ )
+ if X.shape[1] == 0 or Y.shape[1] == 0:
+ raise ValueError("X and Y must each contain at least one column.")
+ if not torch.isfinite(X).all() or not torch.isfinite(Y).all():
+ raise ValueError("X and Y must contain only finite values.")
+ if torch.any(X < 0.0) or torch.any(X > 1.0):
+ raise ValueError("X must be normalized to [0, 1].")
+ ids = tuple(sample_ids)
+ if len(ids) != X.shape[0] or len(set(ids)) != len(ids):
+ raise ValueError("sample_ids must be unique and aligned with X/Y rows.")
+ names = tuple(objective_names)
+ if (
+ len(names) != Y.shape[1]
+ or any(not isinstance(name, str) or not name.strip() for name in names)
+ or len(set(names)) != len(names)
+ ):
+ raise ValueError(
+ "objective_names must be unique non-empty strings aligned with Y columns."
+ )
+ return ids, tuple(name.strip() for name in names)
+
+
+def _warning_rows(
+ caught: Sequence[warnings.WarningMessage],
+ *,
+ variant: ModelVariantSpec,
+ fit_key: str,
+ omitted_sample_id: Hashable | None,
+ objective_index: int,
+ objective_name: str,
+ stage: str,
+) -> list[ModelFitWarning]:
+ return [
+ ModelFitWarning(
+ variant_name=variant.name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage=stage,
+ warning_category=warning.category.__name__,
+ message=str(warning.message),
+ )
+ for warning in caught
+ ]
+
+
+class SignalCollapseError(RuntimeError):
+ """The fitted outputscale went to zero; the model has no signal component."""
+
+
+#: A fit whose latent (signal) sd falls below this multiple of the fitted noise
+#: sd has no signal component left in its GP. Acquisition reads the latent
+#: posterior, so the exploration term degenerates, even though predictive
+#: intervals look fine because the inflated noise hides it.
+MINIMUM_LATENT_TO_NOISE_SD_RATIO = 1.0e-2
+
+#: How much the posterior MEAN must vary across the training inputs, as a fraction
+#: of how much the OBSERVATIONS vary, for the fit to be able to rank candidates.
+#:
+#: This is the second half of the diagnosis, and what separates two very different
+#: situations that share one numeric signature.
+#:
+#: The observed spread is the yardstick rather than the fitted noise sd, which was
+#: the first attempt and is wrong: the noise is inflated precisely in the
+#: degenerate case, so a noise-relative test co-varies with the thing it is trying
+#: to detect. Measured instance -- a linear mean on `anneal_temp` against a forced
+#: noise of 0.9 gave a mean/noise ratio of 0.38 and would have been called
+#: "effectively constant" while it was in fact tracking the data.
+#:
+#: Against the observed spread the question is scale-free and stable: does the
+#: model's mean move with the measurements, or not at all?
+#:
+#: This is a DIAGNOSTIC threshold and it never touches utility space, so it does
+#: not violate the campaign-fixed-scaling rule in `assert_scaling_is_campaign_fixed`.
+#: That rule governs the objective scales that feed hypervolume, where a
+#: data-derived scale would make rounds incomparable. Nothing here reaches a
+#: utility, a reference point or a hypervolume; it only asks whether one fit's
+#: mean moved. Do not "correct" it to a fixed constant.
+MINIMUM_MEAN_SPREAD_TO_TARGET_RATIO = 0.05
+
+#: Fit stage name for the guard, so warnings and errors are filterable.
+SIGNAL_COLLAPSE_STAGE = "signal_collapse_guard"
+
+#: Warning category raised when the GP's signal component has collapsed but the
+#: mean function still carries a usable trend.
+EXPLORATION_DEGENERATE_CATEGORY = "ExplorationTermDegenerate"
+
+
+def _assert_signal_not_collapsed(
+ gp: SingleTaskGP,
+ X: torch.Tensor,
+ target: torch.Tensor,
+ *,
+ variant: ModelVariantSpec,
+ objective_index: int,
+ objective_name: str,
+ fit_key: str,
+ omitted_sample_id: Hashable | None,
+ fit_warnings: list[ModelFitWarning] | Sequence[ModelFitWarning],
+) -> None:
+ """Judge a fit whose GP signal component has gone to zero.
+
+ This is a numerical guard, not a configuration check. Naming a variant
+ correctly cannot prevent a degenerate optimum: the same contract refitted on
+ different data -- more observations, replicate-derived ``train_Yvar``, a new
+ round -- can land there again. So it runs on every fit.
+
+ **Two situations share the collapsed-latent-sd signature, and they need
+ different answers.**
+
+ *True collapse.* A zero-mean GP whose outputscale went to zero: the
+ posterior mean is flat, nothing can be ranked, acquisition is meaningless.
+ Observed instance: 10 of 15 leave-one-out folds of the thickness score fitted
+ a noise of 0.93 against a latent sd of 1e-4. This must fail.
+
+ *The mean function did its job.* With a ``StructuredMean`` carrying the
+ trend, the residual GP can legitimately have nothing left to model. The
+ posterior *mean* still varies -- the mean module is not part of the covariance
+ and so never enters ``posterior().variance`` -- so candidates still rank and
+ the round is still worth proposing. Refusing here would dead-end the campaign
+ at the moment the physics model started working, with no remedy available:
+ better data cannot be collected without first proposing conditions. So this
+ warns instead, and the human review artifact is the gate.
+
+ The warning is not a formality. Two things are genuinely wrong with such a
+ fit and both are named in its message: UCB's exploration term has degenerated,
+ and the mean module's coefficients are frozen buffers carrying no uncertainty
+ of their own, so the narrow intervals the model reports are **understated
+ rather than earned**.
+ """
+ with torch.no_grad():
+ posterior = gp.posterior(X)
+ latent_sd = float(posterior.variance.clamp_min(0.0).sqrt().min())
+ mean_values = posterior.mean.detach().reshape(-1)
+ mean_spread = float(mean_values.std()) if mean_values.numel() > 1 else 0.0
+ # a FixedNoiseGaussianLikelihood (measured train_Yvar) carries one noise per
+ # observation rather than one for the model, so take the average rather than
+ # whichever row happens to be first
+ noise_values = gp.likelihood.noise.detach().reshape(-1)
+ noise_sd = float(noise_values.mean() ** 0.5)
+ observed = target.detach().reshape(-1)
+ target_spread = float(observed.std()) if observed.numel() > 1 else 0.0
+ if noise_sd <= 0.0:
+ return
+ latent_ratio = latent_sd / noise_sd
+ if latent_ratio >= MINIMUM_LATENT_TO_NOISE_SD_RATIO:
+ return
+
+ # a constant objective has nothing to rank by and nothing to diagnose
+ mean_ratio = mean_spread / target_spread if target_spread > 0.0 else 0.0
+ measured = (
+ f"minimum latent sd {latent_sd:.3e} is {latent_ratio:.3e} of the fitted "
+ f"noise sd {noise_sd:.3e}, below the "
+ f"{MINIMUM_LATENT_TO_NOISE_SD_RATIO:g} floor"
+ )
+
+ if mean_ratio < MINIMUM_MEAN_SPREAD_TO_TARGET_RATIO:
+ raise ModelFitError(
+ variant_name=variant.name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage=SIGNAL_COLLAPSE_STAGE,
+ cause=SignalCollapseError(
+ f"{measured}, and the posterior mean varies by only "
+ f"{mean_spread:.3e} across the training inputs "
+ f"({mean_ratio:.3e} of the observed spread {target_spread:.3e}, "
+ f"floor {MINIMUM_MEAN_SPREAD_TO_TARGET_RATIO:g}). The model has explained "
+ "the data as pure noise: its posterior mean is effectively "
+ "constant, so it cannot order two candidates and its acquisition "
+ "scores are meaningless. Predictive intervals do NOT reveal this, "
+ "because the inflated noise masks the collapse."
+ ),
+ fit_warnings=tuple(fit_warnings),
+ )
+
+ warning = ModelFitWarning(
+ variant_name=variant.name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage=SIGNAL_COLLAPSE_STAGE,
+ warning_category=EXPLORATION_DEGENERATE_CATEGORY,
+ message=(
+ f"{objective_name}: {measured}, but the posterior mean still varies by "
+ f"{mean_spread:.3e} across the training inputs, {mean_ratio:.2f} of the "
+ f"observed spread, so the mean function is carrying the signal and "
+ "candidates can still be ranked. "
+ "Two consequences to distrust: UCB's exploration term has degenerated, "
+ "because it reads the latent posterior that just collapsed; and the "
+ "mean module's coefficients are frozen buffers with no uncertainty of "
+ "their own, so the narrow intervals this model reports are UNDERSTATED "
+ "rather than earned. Treat its confidence, especially away from the "
+ "observed points, as unproven."
+ ),
+ )
+ if isinstance(fit_warnings, list):
+ fit_warnings.append(warning)
+
+
+def _build_single_task_gp(
+ X: torch.Tensor,
+ y: torch.Tensor,
+ variant: ModelVariantSpec,
+ mean_module: Any = None,
+ train_Yvar: torch.Tensor | None = None,
+) -> SingleTaskGP:
+ if variant.use_dim_scaled_prior:
+ # BoTorch's dimension-scaled LogNormal lengthscale prior, the same one
+ # SingleTaskGP applies by default when no covar_module is supplied. We
+ # still pass an explicit module so the ScaleKernel wrapper (which the
+ # hyperparameter readout and plots depend on) stays in place.
+ base_kernel = get_covar_module_with_dim_scaled_prior(
+ ard_num_dims=X.shape[1],
+ use_rbf_kernel=False,
+ )
+ else:
+ lengthscale_constraint = (
+ None
+ if variant.min_lengthscale is None
+ else GreaterThan(variant.min_lengthscale)
+ )
+ kernel_kwargs: dict[str, Any] = {
+ "nu": 2.5,
+ "ard_num_dims": X.shape[1],
+ }
+ if lengthscale_constraint is not None:
+ kernel_kwargs["lengthscale_constraint"] = lengthscale_constraint
+ base_kernel = MaternKernel(**kernel_kwargs)
+ covar_module = ScaleKernel(base_kernel)
+ if train_Yvar is not None:
+ # Measured observation noise replaces fitted noise, so no noise prior or
+ # constraint applies -- there is nothing left to fit.
+ #
+ # BoTorch will accept BOTH `train_Yvar` and an explicit `likelihood` and
+ # then SILENTLY IGNORE the variance: the likelihood wins, stays a
+ # single-element GaussianLikelihood, and the replicate information is
+ # dropped with no error. Verified on 0.15.1. Hence the either/or here.
+ #
+ # `train_Yvar` is in the ORIGINAL target units; `Standardize` rescales it
+ # along with the targets. Passing an already-standardized variance would be
+ # wrong by var(Y) and would also fail silently.
+ model = SingleTaskGP(
+ X,
+ y,
+ train_Yvar=train_Yvar,
+ covar_module=covar_module,
+ outcome_transform=Standardize(m=1),
+ )
+ if mean_module is not None:
+ model.mean_module = mean_module
+ return model
+ if variant.use_lognormal_noise_prior:
+ # LogNormal(-4, 1) with a GreaterThan(1e-4) floor. Without the prior the
+ # marginal likelihood is free to drive the outputscale to zero and call
+ # the data pure noise, which yields a degenerate near-zero latent
+ # variance. The floor alone does not prevent that.
+ likelihood = get_gaussian_likelihood_with_lognormal_prior()
+ else:
+ likelihood = GaussianLikelihood(noise_constraint=GreaterThan(variant.min_noise))
+ model = SingleTaskGP(
+ X,
+ y,
+ covar_module=covar_module,
+ likelihood=likelihood,
+ outcome_transform=Standardize(m=1),
+ )
+ if mean_module is not None:
+ # a frozen structured mean: its coefficients are registered as buffers,
+ # so the marginal likelihood still fits only the GP hyperparameters
+ model.mean_module = mean_module
+ return model
+
+
+def fit_model_variant(
+ X: torch.Tensor,
+ Y: torch.Tensor,
+ *,
+ sample_ids: Sequence[Hashable],
+ objective_names: Sequence[str],
+ variant: ModelVariantSpec,
+ seed: int = 73,
+ fit_key: str = "full",
+ omitted_sample_id: Hashable | None = None,
+ cohort_fingerprint: str | None = None,
+ cache: ModelFitCache | None = None,
+ mean_module: Any = None,
+ train_Yvar: torch.Tensor | None = None,
+) -> FittedModelRecord:
+ """Fit one strict independent GP per objective and return an audit record.
+
+ ``train_Yvar`` is measured observation variance, shaped like ``Y``, in the
+ ORIGINAL target units. Supplying it replaces the fitted noise entirely: there
+ is no noise hyperparameter left to optimise, so the variant's noise prior and
+ floor no longer apply to that fit.
+ """
+ ids, names = _validate_dataset(X, Y, sample_ids, objective_names, minimum_rows=2)
+ if not isinstance(variant, ModelVariantSpec):
+ raise TypeError("variant must be a ModelVariantSpec.")
+ resolved_seed = _seed(seed)
+ if not isinstance(fit_key, str) or not fit_key.strip():
+ raise ValueError("fit_key must be a non-empty string.")
+ fit_key = fit_key.strip()
+ training_hash = dataset_fingerprint(X, Y, ids)
+ cohort_hash = training_hash if cohort_fingerprint is None else cohort_fingerprint
+ if not isinstance(cohort_hash, str) or not cohort_hash.strip():
+ raise ValueError("cohort_fingerprint must be None or a non-empty string.")
+ if mean_module is not None and cache is not None:
+ # the cache key is built from the data and the variant, not the mean
+ # module, so a cached fit could be returned for a different trend.
+ # Refuse rather than silently serve the wrong model.
+ raise ValueError(
+ "A structured mean_module cannot be combined with a ModelFitCache: "
+ "the cache key does not capture the mean, so a fold could be served "
+ "a fit built from a different trend."
+ )
+ cache_key = ModelFitCacheKey(
+ variant_name=variant.name,
+ cohort_fingerprint=cohort_hash,
+ omitted_sample_key=_sample_key(omitted_sample_id),
+ objective_names=names,
+ seed=resolved_seed,
+ )
+ if cache is not None:
+ cached = cache.get(cache_key)
+ if cached is not None:
+ if cached.training_fingerprint != training_hash:
+ raise ValueError(
+ "Cached model training fingerprint does not match supplied data."
+ )
+ return cached
+
+ np.random.seed(resolved_seed)
+ torch.manual_seed(resolved_seed)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed_all(resolved_seed)
+
+ started = perf_counter()
+ models: list[SingleTaskGP] = []
+ fit_warning_rows: list[ModelFitWarning] = []
+ for objective_index, objective_name in enumerate(names):
+ caught: list[warnings.WarningMessage] = []
+ try:
+ with warnings.catch_warnings(record=True) as caught:
+ warnings.simplefilter("always")
+ gp = _build_single_task_gp(
+ X,
+ Y[:, objective_index : objective_index + 1],
+ variant,
+ mean_module=mean_module,
+ train_Yvar=(
+ None
+ if train_Yvar is None
+ else train_Yvar[:, objective_index : objective_index + 1]
+ ),
+ )
+ fit_warning_rows.extend(
+ _warning_rows(
+ caught,
+ variant=variant,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="construct",
+ )
+ )
+ except Exception as exc:
+ fit_warning_rows.extend(
+ _warning_rows(
+ caught,
+ variant=variant,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="construct",
+ )
+ )
+ raise ModelFitError(
+ variant_name=variant.name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="construct",
+ cause=exc,
+ fit_warnings=fit_warning_rows,
+ ) from exc
+
+ mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
+ try:
+ with warnings.catch_warnings(record=True) as caught:
+ warnings.simplefilter("always")
+ fit_gpytorch_mll(mll)
+ fit_warning_rows.extend(
+ _warning_rows(
+ caught,
+ variant=variant,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="optimize",
+ )
+ )
+ except Exception as exc:
+ fit_warning_rows.extend(
+ _warning_rows(
+ caught,
+ variant=variant,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="optimize",
+ )
+ )
+ raise ModelFitError(
+ variant_name=variant.name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ stage="optimize",
+ cause=exc,
+ fit_warnings=fit_warning_rows,
+ ) from exc
+ gp.eval()
+ gp.likelihood.eval()
+ _assert_signal_not_collapsed(
+ gp,
+ X,
+ Y[:, objective_index],
+ variant=variant,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ fit_warnings=fit_warning_rows,
+ )
+ models.append(gp)
+
+ record = FittedModelRecord(
+ variant=variant,
+ model=ModelListGP(*models),
+ train_X=X.detach().clone(),
+ train_Y=Y.detach().clone(),
+ sample_ids=ids,
+ objective_names=names,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_sample_id,
+ seed=resolved_seed,
+ cohort_fingerprint=cohort_hash,
+ training_fingerprint=training_hash,
+ fit_runtime_seconds=perf_counter() - started,
+ warnings=tuple(fit_warning_rows),
+ )
+ record.model.eval()
+ if cache is not None:
+ cache.store(cache_key, record)
+ return record
+
+
+def _posterior_prediction(
+ record: FittedModelRecord,
+ X: torch.Tensor,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ with torch.no_grad():
+ latent = record.model.posterior(X, observation_noise=False)
+ predictive = record.model.posterior(X, observation_noise=True)
+ mean = latent.mean.detach().cpu().double().numpy()
+ latent_std = latent.variance.clamp_min(0.0).sqrt().detach().cpu().double().numpy()
+ predictive_std = (
+ predictive.variance.clamp_min(0.0).sqrt().detach().cpu().double().numpy()
+ )
+ if mean.shape != latent_std.shape or mean.shape != predictive_std.shape:
+ raise RuntimeError("Posterior mean and uncertainty shapes do not match.")
+ if not (
+ np.all(np.isfinite(mean))
+ and np.all(np.isfinite(latent_std))
+ and np.all(np.isfinite(predictive_std))
+ and np.all(predictive_std > 0.0)
+ ):
+ raise RuntimeError("Posterior predictions must be finite with positive noise.")
+ if np.any(predictive_std + 1.0e-12 < latent_std):
+ raise RuntimeError("Predictive uncertainty cannot be below latent uncertainty.")
+ return mean, latent_std, predictive_std
+
+
+def compute_prediction_metrics(
+ observed: Sequence[float] | np.ndarray,
+ predicted_mean: Sequence[float] | np.ndarray,
+ predictive_std: Sequence[float] | np.ndarray,
+ *,
+ variant_name: str = "unspecified",
+ objective_index: int = 0,
+ objective_name: str = "objective",
+ small_n_warning_threshold: int = 20,
+) -> PredictionMetricRecord:
+ """Compute declared predictive metrics from original-unit predictions."""
+ actual = np.asarray(observed, dtype=float)
+ predicted = np.asarray(predicted_mean, dtype=float)
+ uncertainty = np.asarray(predictive_std, dtype=float)
+ if (
+ actual.ndim != 1
+ or predicted.shape != actual.shape
+ or uncertainty.shape != actual.shape
+ ):
+ raise ValueError("observed, predicted_mean, and predictive_std must align.")
+ if actual.size == 0:
+ raise ValueError("At least one prediction is required.")
+ if not (
+ np.all(np.isfinite(actual))
+ and np.all(np.isfinite(predicted))
+ and np.all(np.isfinite(uncertainty))
+ ):
+ raise ValueError("Prediction metrics require finite inputs.")
+ if np.any(uncertainty <= 0.0):
+ raise ValueError("predictive_std must be strictly positive.")
+ if (
+ isinstance(small_n_warning_threshold, bool)
+ or not isinstance(small_n_warning_threshold, (int, np.integer))
+ or int(small_n_warning_threshold) < 2
+ ):
+ raise ValueError("small_n_warning_threshold must be an integer >= 2.")
+
+ errors = predicted - actual
+ residuals = -errors
+ standardized = residuals / uncertainty
+ squared_error = errors**2
+ centered = actual - actual.mean()
+ denominator = float(np.sum(centered**2))
+ if actual.size < 2 or denominator <= 0.0:
+ r_squared = np.nan
+ r_squared_warning = (
+ "R² is undefined for fewer than two or constant observations."
+ )
+ else:
+ r_squared = 1.0 - float(np.sum(squared_error)) / denominator
+ r_squared_warning = (
+ f"R² is unstable with small N={actual.size}."
+ if actual.size < int(small_n_warning_threshold)
+ else ""
+ )
+ if actual.size < 2 or np.unique(actual).size < 2 or np.unique(predicted).size < 2:
+ spearman = np.nan
+ else:
+ spearman = float(spearmanr(actual, predicted).statistic)
+ nlpd = 0.5 * np.log(2.0 * pi * uncertainty**2) + 0.5 * standardized**2
+ return PredictionMetricRecord(
+ variant_name=str(variant_name),
+ objective_index=int(objective_index),
+ objective_name=str(objective_name),
+ prediction_count=int(actual.size),
+ mae=float(np.mean(np.abs(errors))),
+ rmse=float(np.sqrt(np.mean(squared_error))),
+ r_squared=float(r_squared),
+ r_squared_warning=r_squared_warning,
+ spearman_rank_correlation=float(spearman),
+ mean_signed_error=float(np.mean(errors)),
+ median_absolute_error=float(np.median(np.abs(errors))),
+ coverage_68_percent=float(np.mean(np.abs(standardized) <= 1.0)),
+ coverage_95_percent=float(np.mean(np.abs(standardized) <= GAUSSIAN_95_Z)),
+ mean_standardized_residual=float(np.mean(standardized)),
+ maximum_absolute_standardized_residual=float(np.max(np.abs(standardized))),
+ mean_gaussian_nlpd=float(np.mean(nlpd)),
+ )
+
+
+def _summarize_predictions(
+ predictions: Sequence[LOOCVPrediction],
+ variant: ModelVariantSpec,
+ objective_names: Sequence[str],
+) -> tuple[PredictionMetricRecord, ...]:
+ rows: list[PredictionMetricRecord] = []
+ for objective_index, objective_name in enumerate(objective_names):
+ selected = [
+ row for row in predictions if row.objective_index == objective_index
+ ]
+ rows.append(
+ compute_prediction_metrics(
+ [row.observed for row in selected],
+ [row.predicted_mean for row in selected],
+ [row.predictive_std for row in selected],
+ variant_name=variant.name,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ )
+ )
+ return tuple(rows)
+
+
+def run_exact_loocv(
+ X: torch.Tensor,
+ Y: torch.Tensor,
+ *,
+ sample_ids: Sequence[Hashable],
+ objective_names: Sequence[str],
+ variant: ModelVariantSpec,
+ seed: int = 73,
+ row_roles: Sequence[str] | None = None,
+ control_sample_ids: Iterable[Hashable] = (),
+ cache: ModelFitCache | None = None,
+) -> ExactLOOCVResult:
+ """Fit exactly one N-1 model per row and predict every held-out objective."""
+ ids, names = _validate_dataset(X, Y, sample_ids, objective_names, minimum_rows=3)
+ roles = tuple("observation" for _ in ids) if row_roles is None else tuple(row_roles)
+ if len(roles) != len(ids) or any(
+ not isinstance(role, str) or not role.strip() for role in roles
+ ):
+ raise ValueError("row_roles must contain one non-empty string per row.")
+ control_ids = set(control_sample_ids)
+ resolved_cache = ModelFitCache() if cache is None else cache
+ cohort_hash = dataset_fingerprint(X, Y, ids)
+ fold_records: dict[Hashable, FittedModelRecord] = {}
+ prediction_rows: list[LOOCVPrediction] = []
+ for omitted_index, omitted_id in enumerate(ids):
+ mask = torch.ones(X.shape[0], dtype=torch.bool, device=X.device)
+ mask[omitted_index] = False
+ fold_ids = tuple(
+ sample_id for index, sample_id in enumerate(ids) if index != omitted_index
+ )
+ fit_key = f"omit:{_sample_key(omitted_id)}"
+ record = fit_model_variant(
+ X[mask],
+ Y[mask],
+ sample_ids=fold_ids,
+ objective_names=names,
+ variant=variant,
+ seed=seed,
+ fit_key=fit_key,
+ omitted_sample_id=omitted_id,
+ cohort_fingerprint=cohort_hash,
+ cache=resolved_cache,
+ )
+ fold_records[omitted_id] = record
+ mean, latent_std, predictive_std = _posterior_prediction(
+ record, X[omitted_index : omitted_index + 1]
+ )
+ for objective_index, objective_name in enumerate(names):
+ observed = float(Y[omitted_index, objective_index].item())
+ predicted = float(mean[0, objective_index])
+ latent_uncertainty = float(latent_std[0, objective_index])
+ predictive_uncertainty = float(predictive_std[0, objective_index])
+ error = predicted - observed
+ residual = -error
+ standardized = residual / predictive_uncertainty
+ nlpd = 0.5 * log(2.0 * pi * predictive_uncertainty**2) + 0.5 * (
+ standardized**2
+ )
+ prediction_rows.append(
+ LOOCVPrediction(
+ variant_name=variant.name,
+ omitted_sample_id=omitted_id,
+ row_role=roles[omitted_index].strip(),
+ is_control=omitted_id in control_ids,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ observed=observed,
+ predicted_mean=predicted,
+ latent_std=latent_uncertainty,
+ predictive_std=predictive_uncertainty,
+ prediction_error=error,
+ residual=residual,
+ standardized_residual=standardized,
+ within_68_percent_interval=abs(standardized) <= 1.0,
+ within_95_percent_interval=(abs(standardized) <= GAUSSIAN_95_Z),
+ gaussian_nlpd=nlpd,
+ fold_fit_key=record.fit_key,
+ fold_fit_warning_count=len(record.warnings),
+ )
+ )
+ expected_count = X.shape[0] * Y.shape[1]
+ if len(prediction_rows) != expected_count:
+ raise RuntimeError(
+ f"LOOCV produced {len(prediction_rows)} rows; expected {expected_count}."
+ )
+ metrics = _summarize_predictions(prediction_rows, variant, names)
+ return ExactLOOCVResult(
+ variant=variant,
+ predictions=tuple(prediction_rows),
+ metrics=metrics,
+ fold_records=fold_records,
+ cohort_fingerprint=cohort_hash,
+ cache=resolved_cache,
+ )
+
+
+def _constraint_lower_bound(constraint: Any) -> float:
+ value = constraint.lower_bound.detach().cpu().double().reshape(-1)
+ return float(value[0].item())
+
+
+def extract_model_hyperparameters(
+ record: FittedModelRecord,
+ *,
+ input_names: Sequence[str],
+) -> tuple[HyperparameterRecord, ...]:
+ """Extract comparable full/fold hyperparameters from a fitted model list."""
+ names = tuple(input_names)
+ if len(names) != record.train_X.shape[1] or any(
+ not isinstance(name, str) or not name.strip() for name in names
+ ):
+ raise ValueError("input_names must align with the fitted input dimension.")
+ rows: list[HyperparameterRecord] = []
+ for objective_index, (objective_name, gp) in enumerate(
+ zip(record.objective_names, record.model.models)
+ ):
+ base_kernel = gp.covar_module.base_kernel
+ lengthscales = tuple(
+ float(value)
+ for value in base_kernel.lengthscale.detach()
+ .cpu()
+ .double()
+ .reshape(-1)
+ .tolist()
+ )
+ if len(lengthscales) != len(names):
+ raise RuntimeError("ARD lengthscales do not align with input names.")
+ noise = float(gp.likelihood.noise.detach().cpu().double().reshape(-1)[0].item())
+ outputscale = float(
+ gp.covar_module.outputscale.detach().cpu().double().reshape(-1)[0].item()
+ )
+ noise_floor = _constraint_lower_bound(
+ gp.likelihood.noise_covar.raw_noise_constraint
+ )
+ lengthscale_floor = _constraint_lower_bound(
+ base_kernel.raw_lengthscale_constraint
+ )
+ noise_near = noise <= noise_floor * 1.05 + 1.0e-12
+ lengthscale_near = tuple(
+ value <= lengthscale_floor * 1.05 + 1.0e-12 for value in lengthscales
+ )
+ lengthscale_very_small = tuple(
+ value <= VERY_SMALL_NORMALIZED_LENGTHSCALE for value in lengthscales
+ )
+ lengthscale_extremely_large = tuple(
+ value >= EXTREMELY_LARGE_NORMALIZED_LENGTHSCALE for value in lengthscales
+ )
+ rows.append(
+ HyperparameterRecord(
+ variant_name=record.variant.name,
+ fit_key=record.fit_key,
+ omitted_sample_id=record.omitted_sample_id,
+ objective_index=objective_index,
+ objective_name=objective_name,
+ kernel_type=type(base_kernel).__name__,
+ likelihood_noise=noise,
+ outputscale=outputscale,
+ ard_lengthscales=lengthscales,
+ input_names=tuple(name.strip() for name in names),
+ configured_min_noise=record.variant.min_noise,
+ configured_min_lengthscale=record.variant.min_lengthscale,
+ noise_constraint_lower_bound=noise_floor,
+ lengthscale_constraint_lower_bound=lengthscale_floor,
+ noise_near_floor=noise_near,
+ lengthscales_near_floor=lengthscale_near,
+ lengthscales_very_small_normalized_domain=lengthscale_very_small,
+ lengthscales_extremely_large_flat=lengthscale_extremely_large,
+ )
+ )
+ return tuple(rows)
+
+
+def fit_warnings_frame(records: Iterable[FittedModelRecord]) -> pd.DataFrame:
+ columns = [field.name for field in ModelFitWarning.__dataclass_fields__.values()]
+ rows = [asdict(warning) for record in records for warning in record.warnings]
+ return pd.DataFrame(rows, columns=columns)
+
+
+def validate_model_variant(
+ X: torch.Tensor,
+ Y: torch.Tensor,
+ *,
+ sample_ids: Sequence[Hashable],
+ input_names: Sequence[str],
+ objective_names: Sequence[str],
+ variant: ModelVariantSpec,
+ seed: int = 73,
+ row_roles: Sequence[str] | None = None,
+ control_sample_ids: Iterable[Hashable] = (),
+ cache: ModelFitCache | None = None,
+) -> ModelValidationResult:
+ """Fit the full model, run exact LOOCV, and extract every hyperparameter."""
+ ids, names = _validate_dataset(X, Y, sample_ids, objective_names, minimum_rows=3)
+ resolved_cache = ModelFitCache() if cache is None else cache
+ cohort_hash = dataset_fingerprint(X, Y, ids)
+ full = fit_model_variant(
+ X,
+ Y,
+ sample_ids=ids,
+ objective_names=names,
+ variant=variant,
+ seed=seed,
+ fit_key="full",
+ omitted_sample_id=None,
+ cohort_fingerprint=cohort_hash,
+ cache=resolved_cache,
+ )
+ loocv = run_exact_loocv(
+ X,
+ Y,
+ sample_ids=ids,
+ objective_names=names,
+ variant=variant,
+ seed=seed,
+ row_roles=row_roles,
+ control_sample_ids=control_sample_ids,
+ cache=resolved_cache,
+ )
+ all_records = [full, *loocv.fold_records.values()]
+ hyperparameters = tuple(
+ hyperparameter
+ for record in all_records
+ for hyperparameter in extract_model_hyperparameters(
+ record, input_names=input_names
+ )
+ )
+ return ModelValidationResult(
+ variant=variant,
+ full_fit=full,
+ loocv=loocv,
+ hyperparameters=hyperparameters,
+ )
+
+
+__all__ = [
+ "CONSERVATIVE",
+ "MINIMUM_LATENT_TO_NOISE_SD_RATIO",
+ "SignalCollapseError",
+ "DIM_SCALED_PRIOR",
+ "LEGACY_NO_PRIOR",
+ "PRIMARY_VARIANT",
+ "ExactLOOCVResult",
+ "FittedModelRecord",
+ "HyperparameterRecord",
+ "LOOCVPrediction",
+ "ModelFitCache",
+ "ModelFitError",
+ "ModelFitWarning",
+ "ModelValidationResult",
+ "ModelVariantSpec",
+ "PredictionMetricRecord",
+ "compute_prediction_metrics",
+ "dataset_fingerprint",
+ "extract_model_hyperparameters",
+ "fit_model_variant",
+ "fit_warnings_frame",
+ "model_variant_spec",
+ "run_exact_loocv",
+ "validate_model_variant",
+]
diff --git a/src/mobo_kit/objectives.py b/src/mobo_kit/objectives.py
new file mode 100644
index 0000000..2dcf474
--- /dev/null
+++ b/src/mobo_kit/objectives.py
@@ -0,0 +1,578 @@
+"""Versioned, all-maximize objective transformations for MOBO acquisition."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from numbers import Real
+from typing import Literal, Sequence
+
+import numpy as np
+import torch
+from botorch.acquisition.multi_objective.objective import MCMultiOutputObjective
+
+
+Goal = Literal["maximize", "minimize", "target"]
+TransformName = Literal[
+ "identity", "affine", "gaussian_target", "negative_absolute_target"
+]
+UtilityBound = tuple[float | None, float | None]
+
+
+def _finite_optional(value: float | None, *, field: str, name: str) -> float | None:
+ if value is None:
+ return None
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+ raise ValueError(
+ f"Objective {name!r} field {field!r} must be a real non-boolean number."
+ )
+ number = float(value)
+ if not np.isfinite(number):
+ raise ValueError(f"Objective {name!r} field {field!r} must be finite.")
+ return number
+
+
+@dataclass(frozen=True)
+class ObjectiveSpec:
+ """Immutable definition of one raw-output-to-utility transformation."""
+
+ name: str
+ goal: Goal
+ transform: TransformName
+ #: What the MODEL emits, relative to the physical quantity the utility is
+ #: defined on. ``log`` means the GP was fitted in log space (see
+ #: ``structured_mean``), so a model output must be exponentiated before the
+ #: utility applies, and a model *posterior* is lognormal rather than normal.
+ model_link: Literal["identity", "log"] = "identity"
+ source_column: str | None = None
+ lower_anchor: float | None = None
+ upper_anchor: float | None = None
+ target: float | None = None
+ sigma: float | None = None
+ scale: float | None = None
+ clip: bool = False
+
+ def __post_init__(self) -> None:
+ if not isinstance(self.name, str) or not self.name.strip():
+ raise ValueError("Objective name must be a non-empty string.")
+ object.__setattr__(self, "name", self.name.strip())
+ if self.goal not in {"maximize", "minimize", "target"}:
+ raise ValueError(
+ f"Objective {self.name!r} has unsupported goal {self.goal!r}."
+ )
+ supported = {
+ "identity",
+ "affine",
+ "gaussian_target",
+ "negative_absolute_target",
+ }
+ if self.transform not in supported:
+ raise ValueError(
+ f"Objective {self.name!r} has unsupported transform "
+ f"{self.transform!r}."
+ )
+ if self.source_column is not None and (
+ not isinstance(self.source_column, str) or not self.source_column.strip()
+ ):
+ raise ValueError("source_column must be None or a non-empty string.")
+ if self.source_column is not None:
+ object.__setattr__(self, "source_column", self.source_column.strip())
+ if not isinstance(self.clip, bool):
+ raise ValueError("clip must be a boolean.")
+
+ numeric = {
+ field: _finite_optional(getattr(self, field), field=field, name=self.name)
+ for field in (
+ "lower_anchor",
+ "upper_anchor",
+ "target",
+ "sigma",
+ "scale",
+ )
+ }
+ for field, value in numeric.items():
+ object.__setattr__(self, field, value)
+
+ if self.transform == "identity":
+ if self.goal != "maximize":
+ raise ValueError("identity is supported only for maximize utilities.")
+ self._require_unused(
+ "lower_anchor", "upper_anchor", "target", "sigma", "scale"
+ )
+ if self.clip:
+ raise ValueError(
+ "identity utilities cannot enable clip; approve their scale "
+ "upstream or use an explicit affine transform."
+ )
+ elif self.transform == "affine":
+ if self.goal not in {"maximize", "minimize"}:
+ raise ValueError("affine requires goal 'maximize' or 'minimize'.")
+ if self.lower_anchor is None or self.upper_anchor is None:
+ raise ValueError("affine requires lower_anchor and upper_anchor.")
+ if self.lower_anchor >= self.upper_anchor:
+ raise ValueError("affine requires lower_anchor < upper_anchor.")
+ self._require_unused("target", "sigma", "scale")
+ elif self.transform == "gaussian_target":
+ if self.goal != "target":
+ raise ValueError("gaussian_target requires goal 'target'.")
+ if self.target is None or self.sigma is None:
+ raise ValueError("gaussian_target requires target and sigma.")
+ if self.sigma <= 0:
+ raise ValueError("gaussian_target sigma must be strictly positive.")
+ self._require_unused("lower_anchor", "upper_anchor", "scale")
+ if self.clip:
+ raise ValueError(
+ "gaussian_target is naturally bounded; clip is invalid."
+ )
+ else:
+ if self.goal != "target":
+ raise ValueError("negative_absolute_target requires goal 'target'.")
+ if self.target is None or self.scale is None:
+ raise ValueError(
+ "negative_absolute_target requires target and explicit scale."
+ )
+ if self.scale <= 0:
+ raise ValueError(
+ "negative_absolute_target scale must be strictly positive."
+ )
+ self._require_unused("lower_anchor", "upper_anchor", "sigma")
+ if self.clip:
+ raise ValueError("clip is not supported for negative_absolute_target.")
+
+ def _require_unused(self, *fields: str) -> None:
+ used = [field for field in fields if getattr(self, field) is not None]
+ if used:
+ raise ValueError(
+ f"Objective {self.name!r} transform {self.transform!r} does not "
+ f"accept parameter(s): {', '.join(used)}."
+ )
+
+
+class ObjectiveTransform:
+ """Apply an ordered objective contract to floating tensors ``[..., M]``."""
+
+ def __init__(self, specs: Sequence[ObjectiveSpec], *, version: str) -> None:
+ if not isinstance(version, str) or not version.strip():
+ raise ValueError("Objective contract version must be a non-empty string.")
+ if not specs:
+ raise ValueError("At least one ObjectiveSpec is required.")
+ validated = tuple(specs)
+ if not all(isinstance(spec, ObjectiveSpec) for spec in validated):
+ raise TypeError("Every objective specification must be an ObjectiveSpec.")
+ names = [spec.name for spec in validated]
+ duplicates = sorted({name for name in names if names.count(name) > 1})
+ if duplicates:
+ raise ValueError(f"Objective names must be unique; got {duplicates}.")
+ self.specs = validated
+ self.version = version.strip()
+
+ @property
+ def objective_count(self) -> int:
+ return len(self.specs)
+
+ @property
+ def names(self) -> tuple[str, ...]:
+ return tuple(spec.name for spec in self.specs)
+
+ def transform(self, Y: torch.Tensor) -> torch.Tensor:
+ """Transform raw/model outputs while preserving shape, dtype, and device."""
+ if not isinstance(Y, torch.Tensor):
+ raise TypeError("Y must be a torch.Tensor.")
+ if not Y.is_floating_point():
+ raise TypeError("Y must use a floating dtype.")
+ if Y.ndim < 1 or Y.shape[-1] != self.objective_count:
+ raise ValueError(
+ f"Y final dimension must be {self.objective_count}; "
+ f"got shape {tuple(Y.shape)}."
+ )
+ if not torch.isfinite(Y).all():
+ raise ValueError("Y must contain only finite values.")
+ outputs: list[torch.Tensor] = []
+ for index, spec in enumerate(self.specs):
+ outputs.append(self._utility(Y[..., index], spec))
+ transformed = torch.stack(outputs, dim=-1)
+ if transformed.shape != Y.shape:
+ raise RuntimeError("Internal objective transform shape error.")
+ return transformed
+
+ def _utility(self, model_output: torch.Tensor, spec: ObjectiveSpec):
+ """Utility for one objective, given that objective's MODEL output.
+
+ The link decode happens exactly here and nowhere else. Quadrature and
+ Monte-Carlo paths must both route through this, or one of them will
+ exponentiate twice.
+ """
+ raw = torch.exp(model_output) if spec.model_link == "log" else model_output
+ return self._utility_from_physical(raw, spec)
+
+ @staticmethod
+ def _utility_from_physical(raw: torch.Tensor, spec: ObjectiveSpec):
+ """Utility for one objective from its PHYSICAL value (link already undone)."""
+ if True:
+ if spec.transform == "identity":
+ utility = raw
+ elif spec.transform == "affine":
+ lower = raw.new_tensor(spec.lower_anchor)
+ upper = raw.new_tensor(spec.upper_anchor)
+ if spec.goal == "maximize":
+ utility = (raw - lower) / (upper - lower)
+ else:
+ utility = (upper - raw) / (upper - lower)
+ if spec.clip:
+ utility = utility.clamp(0.0, 1.0)
+ elif spec.transform == "gaussian_target":
+ target = raw.new_tensor(spec.target)
+ sigma = raw.new_tensor(spec.sigma)
+ utility = torch.exp(-0.5 * ((raw - target) / sigma).square())
+ else:
+ target = raw.new_tensor(spec.target)
+ scale = raw.new_tensor(spec.scale)
+ utility = -(raw - target).abs() / scale
+ return utility
+
+ __call__ = transform
+
+ def encode_measurements(self, Y_measured: torch.Tensor) -> torch.Tensor:
+ """MEASUREMENT-space values into the MODEL space :meth:`transform` expects.
+
+ :meth:`transform` is a *model-output* decoder: its first act is to undo the
+ link, so a ``log`` objective is exponentiated before the utility is
+ computed. Handing it a raw measurement therefore exponentiates a value
+ that was never a logarithm.
+
+ **This fails silently and it has.** ``run_r1_ucb`` passed thickness in
+ nanometres here until 2026-07-31; ``exp(360…1303)`` saturates the 650 nm
+ Gaussian to exactly ``0.0``, which is finite, so neither the transform's
+ own finiteness check nor the caller's noticed. Every observation's
+ thickness utility was zero, and the UCB-HVI baseline hypervolume came out
+ 0.004659 where the correct value is 0.436442 -- a factor of 94, against
+ which every candidate looked like a large improvement.
+
+ Use this, or :meth:`transform_measurements`, wherever the values in hand
+ are what the workbook reports rather than what the GP emits.
+ """
+ if not isinstance(Y_measured, torch.Tensor):
+ raise TypeError("Y_measured must be a torch.Tensor.")
+ if not Y_measured.is_floating_point():
+ raise TypeError("Y_measured must use a floating dtype.")
+ if Y_measured.ndim < 1 or Y_measured.shape[-1] != self.objective_count:
+ raise ValueError(
+ f"Y_measured final dimension must be {self.objective_count}; "
+ f"got shape {tuple(Y_measured.shape)}."
+ )
+ if not torch.isfinite(Y_measured).all():
+ raise ValueError("Y_measured must contain only finite values.")
+ columns: list[torch.Tensor] = []
+ for index, spec in enumerate(self.specs):
+ column = Y_measured[..., index]
+ if spec.model_link == "log":
+ if not bool((column > 0).all()):
+ raise ValueError(
+ f"Objective {spec.name!r} has a log link, so its measured "
+ "values must be strictly positive."
+ )
+ column = torch.log(column)
+ columns.append(column)
+ return torch.stack(columns, dim=-1)
+
+ def transform_measurements(self, Y_measured: torch.Tensor) -> torch.Tensor:
+ """Utility straight from MEASUREMENT-space values.
+
+ The one-call safe route: :meth:`encode_measurements` then
+ :meth:`transform`. Prefer it at any call site holding workbook values, so
+ the encoding step cannot be forgotten.
+ """
+ return self.transform(self.encode_measurements(Y_measured))
+
+ def expected_transform(
+ self, mean: torch.Tensor, variance: torch.Tensor
+ ) -> torch.Tensor:
+ """Expected utility ``E[transform(Y)]`` for ``Y ~ N(mean, variance)``.
+
+ Use this when the GP is trained on a *raw* measurement and the utility is
+ a nonlinear function of it. Applying :meth:`transform` to the posterior
+ mean is wrong in that case: it is biased by Jensen's inequality and it
+ discards the posterior variance entirely, which for a target-seeking
+ utility is precisely the information that matters.
+
+ ``identity`` and ``affine`` are linear, so their expectation is just the
+ transform of the mean. The two target transforms are nonlinear and have
+ exact closed forms:
+
+ * ``gaussian_target`` with target ``c`` and width ``s``::
+
+ E = s / sqrt(s^2 + v) * exp(-0.5 * (mu - c)^2 / (s^2 + v))
+
+ At ``mu == c`` this decays from 1 as the posterior widens, so a
+ confidently on-target candidate outranks an uncertain one.
+
+ * ``negative_absolute_target`` uses the folded-normal mean.
+
+ Both reduce to :meth:`transform` as ``variance -> 0``.
+ """
+ if not isinstance(mean, torch.Tensor) or not isinstance(variance, torch.Tensor):
+ raise TypeError("mean and variance must be torch.Tensors.")
+ if not mean.is_floating_point() or not variance.is_floating_point():
+ raise TypeError("mean and variance must use a floating dtype.")
+ if mean.shape != variance.shape:
+ raise ValueError(
+ f"mean and variance must share a shape; got {tuple(mean.shape)} "
+ f"and {tuple(variance.shape)}."
+ )
+ if mean.ndim < 1 or mean.shape[-1] != self.objective_count:
+ raise ValueError(
+ f"mean final dimension must be {self.objective_count}; "
+ f"got shape {tuple(mean.shape)}."
+ )
+ if not torch.isfinite(mean).all() or not torch.isfinite(variance).all():
+ raise ValueError("mean and variance must contain only finite values.")
+ if (variance < 0).any():
+ raise ValueError("variance must be non-negative.")
+
+ outputs: list[torch.Tensor] = []
+ for index, spec in enumerate(self.specs):
+ mu = mean[..., index]
+ var = variance[..., index]
+ if spec.model_link == "log":
+ # the posterior is lognormal, so no Gaussian closed form applies;
+ # integrate in log space by quadrature
+ utility = self._quadrature_expectation(mu, var, spec, nodes=20)
+ elif spec.transform in {"identity", "affine"}:
+ # linear in the physical value, and the link is identity in this
+ # branch, so E[f(Y)] = f(E[Y])
+ utility = self._utility_from_physical(mu, spec)
+ elif spec.transform == "gaussian_target":
+ target = mu.new_tensor(spec.target)
+ s2 = mu.new_tensor(spec.sigma) ** 2
+ denom = s2 + var
+ utility = torch.sqrt(s2 / denom) * torch.exp(
+ -0.5 * (mu - target).square() / denom
+ )
+ else:
+ target = mu.new_tensor(spec.target)
+ scale = mu.new_tensor(spec.scale)
+ sd = var.clamp_min(0.0).sqrt()
+ delta = mu - target
+ # folded-normal mean; the sd == 0 branch degenerates to |delta|
+ safe_sd = torch.where(sd > 0, sd, torch.ones_like(sd))
+ folded = safe_sd * np.sqrt(2.0 / np.pi) * torch.exp(
+ -0.5 * (delta / safe_sd).square()
+ ) + delta * torch.erf(delta / (safe_sd * np.sqrt(2.0)))
+ folded = torch.where(sd > 0, folded, delta.abs())
+ utility = -folded / scale
+ outputs.append(utility)
+ expected = torch.stack(outputs, dim=-1)
+ if expected.shape != mean.shape:
+ raise RuntimeError("Internal expected-objective shape error.")
+ return expected
+
+ def _quadrature_expectation(
+ self,
+ log_mean: torch.Tensor,
+ log_variance: torch.Tensor,
+ spec: ObjectiveSpec,
+ *,
+ nodes: int,
+ ) -> torch.Tensor:
+ """E[utility] for one log-link objective, by Gauss-Hermite in log space.
+
+ E[g(Y)] = int g(exp(z)) N(z; m, s^2) dz
+ ~ (1/sqrt(pi)) sum_i w_i g(exp(m + sqrt(2) s x_i))
+
+ Exact for the Gaussian weight, deterministic, differentiable, and
+ cheaper than sampling. Moment-matching the lognormal to a Gaussian and
+ reusing the closed form is ~500x less accurate here, and its bias
+ changes sign across the range, which reorders candidates rather than
+ merely shifting them.
+ """
+ raw_nodes, raw_weights = np.polynomial.hermite.hermgauss(nodes)
+ abscissa = log_mean.new_tensor(raw_nodes)
+ weights = log_mean.new_tensor(raw_weights) / float(np.sqrt(np.pi))
+ sd = log_variance.clamp_min(0.0).sqrt()
+ shape = (-1, *([1] * log_mean.ndim))
+ shifted = log_mean.unsqueeze(0) + np.sqrt(2.0) * sd.unsqueeze(
+ 0
+ ) * abscissa.view(shape)
+ utilities = self._utility_from_physical(torch.exp(shifted), spec)
+ return (utilities * weights.view(shape)).sum(dim=0)
+
+ def expected_transform_lognormal(
+ self,
+ log_mean: torch.Tensor,
+ log_variance: torch.Tensor,
+ *,
+ nodes: int = 20,
+ ) -> torch.Tensor:
+ """Expected utility treating EVERY objective as log-link.
+
+ Prefer :meth:`expected_transform`, which dispatches per objective from
+ each spec's ``model_link``. This method is kept for the single-objective
+ case where the caller knows the posterior is lognormal.
+ """
+ if not isinstance(log_mean, torch.Tensor) or not isinstance(
+ log_variance, torch.Tensor
+ ):
+ raise TypeError("log_mean and log_variance must be torch.Tensors.")
+ if not log_mean.is_floating_point() or not log_variance.is_floating_point():
+ raise TypeError("log_mean and log_variance must use a floating dtype.")
+ if log_mean.shape != log_variance.shape:
+ raise ValueError(
+ f"log_mean and log_variance must share a shape; got "
+ f"{tuple(log_mean.shape)} and {tuple(log_variance.shape)}."
+ )
+ if log_mean.ndim < 1 or log_mean.shape[-1] != self.objective_count:
+ raise ValueError(
+ f"log_mean final dimension must be {self.objective_count}; "
+ f"got shape {tuple(log_mean.shape)}."
+ )
+ if not torch.isfinite(log_mean).all() or not torch.isfinite(log_variance).all():
+ raise ValueError("log_mean and log_variance must be finite.")
+ if (log_variance < 0).any():
+ raise ValueError("log_variance must be non-negative.")
+ if isinstance(nodes, bool) or not isinstance(nodes, int) or nodes < 2:
+ raise ValueError("nodes must be an integer of at least 2.")
+ columns = [
+ self._quadrature_expectation(
+ log_mean[..., index], log_variance[..., index], spec, nodes=nodes
+ )
+ for index, spec in enumerate(self.specs)
+ ]
+ return torch.stack(columns, dim=-1)
+
+
+class ConfiguredMCMultiOutputObjective(MCMultiOutputObjective):
+ """BoTorch MC objective backed by the exact same `ObjectiveTransform`."""
+
+ def __init__(self, objective_transform: ObjectiveTransform) -> None:
+ super().__init__()
+ if not isinstance(objective_transform, ObjectiveTransform):
+ raise TypeError("objective_transform must be an ObjectiveTransform.")
+ self.objective_transform = objective_transform
+
+ def forward(
+ self, samples: torch.Tensor, X: torch.Tensor | None = None
+ ) -> torch.Tensor:
+ del X
+ return self.objective_transform.transform(samples)
+
+
+def _validate_posterior_sample_bounds(
+ bounds: Sequence[UtilityBound], objective_transform: ObjectiveTransform
+) -> tuple[UtilityBound, ...]:
+ if isinstance(bounds, (str, bytes)):
+ raise TypeError("bounds must be an ordered sequence of (lower, upper) pairs.")
+ try:
+ raw_bounds = tuple(bounds)
+ except TypeError as exc:
+ raise TypeError(
+ "bounds must be an ordered sequence of (lower, upper) pairs."
+ ) from exc
+ if len(raw_bounds) != objective_transform.objective_count:
+ raise ValueError(
+ "bounds must contain one (lower, upper) pair per objective; "
+ f"expected {objective_transform.objective_count}, got {len(raw_bounds)}."
+ )
+
+ validated: list[UtilityBound] = []
+ bounded_count = 0
+ for index, (raw_bound, spec) in enumerate(
+ zip(raw_bounds, objective_transform.specs)
+ ):
+ if isinstance(raw_bound, (str, bytes)):
+ raise TypeError(f"bounds[{index}] must be a (lower, upper) pair.")
+ try:
+ pair = tuple(raw_bound)
+ except TypeError as exc:
+ raise TypeError(f"bounds[{index}] must be a (lower, upper) pair.") from exc
+ if len(pair) != 2:
+ raise ValueError(f"bounds[{index}] must contain exactly two values.")
+ lower = _finite_optional(
+ pair[0], field="posterior_sample_lower_bound", name=spec.name
+ )
+ upper = _finite_optional(
+ pair[1], field="posterior_sample_upper_bound", name=spec.name
+ )
+ if lower is not None and upper is not None and lower > upper:
+ raise ValueError(
+ f"Objective {spec.name!r} posterior-sample lower bound must not "
+ "exceed its upper bound."
+ )
+ if lower is not None or upper is not None:
+ bounded_count += 1
+ if spec.transform != "identity" or spec.goal != "maximize":
+ raise ValueError(
+ "Posterior-sample bounds are supported only for explicit "
+ f"identity/maximize utilities; objective {spec.name!r} uses "
+ f"{spec.transform!r}/{spec.goal!r}."
+ )
+ validated.append((lower, upper))
+ if bounded_count == 0:
+ raise ValueError("At least one posterior-sample utility bound is required.")
+ return tuple(validated)
+
+
+class BoundedPosteriorSampleTransform:
+ """Clamp declared identity utilities only after transforming MC samples.
+
+ This acquisition-only wrapper never changes observed training targets or the
+ underlying versioned objective contract. It is intended for explicit
+ synthetic qLogNEHVI policy comparisons.
+ """
+
+ def __init__(
+ self,
+ objective_transform: ObjectiveTransform,
+ bounds: Sequence[UtilityBound],
+ ) -> None:
+ if not isinstance(objective_transform, ObjectiveTransform):
+ raise TypeError("objective_transform must be an ObjectiveTransform.")
+ self.objective_transform = objective_transform
+ self.bounds = _validate_posterior_sample_bounds(bounds, objective_transform)
+ self.version = f"{objective_transform.version}+posterior-sample-bounds-v1"
+
+ def transform(self, samples: torch.Tensor) -> torch.Tensor:
+ utilities = self.objective_transform.transform(samples)
+ bounded_columns: list[torch.Tensor] = []
+ for index, (lower, upper) in enumerate(self.bounds):
+ utility = utilities[..., index]
+ if lower is not None or upper is not None:
+ utility = torch.clamp(utility, min=lower, max=upper)
+ bounded_columns.append(utility)
+ bounded = torch.stack(bounded_columns, dim=-1)
+ if bounded.shape != utilities.shape:
+ raise RuntimeError("Internal posterior-sample bounds shape error.")
+ return bounded
+
+ __call__ = transform
+
+
+class BoundedMCMultiOutputObjective(MCMultiOutputObjective):
+ """BoTorch objective applying explicit bounds to posterior utility samples."""
+
+ def __init__(
+ self,
+ objective_transform: ObjectiveTransform,
+ bounds: Sequence[UtilityBound],
+ ) -> None:
+ super().__init__()
+ self.posterior_sample_transform = BoundedPosteriorSampleTransform(
+ objective_transform, bounds
+ )
+ self.objective_transform = objective_transform
+ self.bounds = self.posterior_sample_transform.bounds
+ self.version = self.posterior_sample_transform.version
+
+ def forward(
+ self, samples: torch.Tensor, X: torch.Tensor | None = None
+ ) -> torch.Tensor:
+ del X
+ return self.posterior_sample_transform.transform(samples)
+
+
+__all__ = [
+ "BoundedMCMultiOutputObjective",
+ "BoundedPosteriorSampleTransform",
+ "ConfiguredMCMultiOutputObjective",
+ "ObjectiveSpec",
+ "ObjectiveTransform",
+ "UtilityBound",
+]
diff --git a/src/mobo_kit/plotting.py b/src/mobo_kit/plotting.py
index 38c1a89..947264e 100644
--- a/src/mobo_kit/plotting.py
+++ b/src/mobo_kit/plotting.py
@@ -732,6 +732,27 @@ def plot_shap(
save: Optional[str] = None,
show_plot: bool = True,
):
+ """Mean |SHAP| bars over each GP's RAW posterior mean. Demo path only.
+
+ Kept because ``notebooks/MOBO_demo_annotated.ipynb`` calls it. For the campaign
+ use ``scripts/plot_shap_attribution.py`` instead, which differs in three ways
+ that change what the bars mean:
+
+ * **It explains the raw model output, not utility.** For a log-link objective
+ that is ``log(nm)``, so the campaign's 650 nm Gaussian target is never
+ applied and features are ranked by their effect on log thickness rather than
+ on how good the film is. The campaign script explains
+ ``E[utility]`` through ``ObjectiveTransform.expected_transform``.
+ * **``nsamples=300`` is below the 1024 coalitions that ten inputs need**, so
+ these values are a sampled approximation, and an unseeded one. At ten
+ features the campaign script's explainer enumerates exhaustively, which makes
+ its attributions exact and reproducible.
+ * **It explains only the training points.** The campaign script attributes over
+ a sampled on-grid pool, so the picture covers the design space rather than
+ the handful of recipes already run.
+
+ None of that is wrong for a demo. It is wrong for deciding anything.
+ """
X_eval = np.asarray(train_X, dtype=float)
feature_names = design.names
M = getattr(model, "num_outputs", len(model.models))
diff --git a/src/mobo_kit/qlognehvi_batch.py b/src/mobo_kit/qlognehvi_batch.py
new file mode 100644
index 0000000..e6ca39b
--- /dev/null
+++ b/src/mobo_kit/qlognehvi_batch.py
@@ -0,0 +1,389 @@
+"""Discrete singleton-pool qLogNEHVI scoring and sequential batch proposals."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Callable, Sequence
+
+import numpy as np
+import torch
+from botorch.acquisition.multi_objective.logei import (
+ qLogNoisyExpectedHypervolumeImprovement,
+)
+from botorch.sampling.normal import SobolQMCNormalSampler
+
+
+@dataclass(frozen=True)
+class QLogNEHVIPoolScoreResult:
+ """Singleton qLogNEHVI values and the pending context used to compute them."""
+
+ base_log_score: np.ndarray
+ evaluated_shape: tuple[int, int, int]
+ pending_count: int
+ mc_samples: int
+ seed: int
+ reference_point_utility: np.ndarray
+ objective_contract_version: str
+ method: str = "qlognehvi"
+ method_version: str = "step2a-v1"
+
+
+@dataclass(frozen=True)
+class QLogNEHVIBatchProposal:
+ """Sequentially selected qLogNEHVI batch and per-step score histories."""
+
+ selection: Any
+ score_history: tuple[QLogNEHVIPoolScoreResult, ...]
+ preexisting_pending_count: int
+ metadata: dict[str, Any]
+
+
+def _positive_integer(value: int, *, name: str) -> int:
+ if isinstance(value, bool) or not isinstance(value, (int, np.integer)):
+ raise ValueError(f"{name} must be a positive integer; got {value!r}.")
+ result = int(value)
+ if result <= 0:
+ raise ValueError(f"{name} must be a positive integer; got {value!r}.")
+ return result
+
+
+def _input_matrix(value: torch.Tensor, *, name: str) -> torch.Tensor:
+ if not isinstance(value, torch.Tensor) or value.ndim != 2:
+ shape = getattr(value, "shape", None)
+ raise ValueError(f"{name} must be a tensor with shape (N, D); got {shape}.")
+ if not value.is_floating_point():
+ raise TypeError(f"{name} must use a floating dtype.")
+ if not torch.isfinite(value).all():
+ raise ValueError(f"{name} must contain only finite values.")
+ return value
+
+
+def _reference(
+ value: np.ndarray | torch.Tensor | None,
+ *,
+ dtype: torch.dtype,
+ device: torch.device,
+) -> torch.Tensor:
+ if value is None:
+ raise ValueError(
+ "reference_point_utility is required and is never derived from data."
+ )
+ reference = torch.as_tensor(value, dtype=dtype, device=device)
+ if reference.ndim != 1 or reference.numel() == 0:
+ raise ValueError(
+ "reference_point_utility must have shape (M,) with at least one objective."
+ )
+ if not torch.isfinite(reference).all():
+ raise ValueError("reference_point_utility must contain only finite values.")
+ return reference
+
+
+def _build_qlognehvi(
+ *,
+ model: Any,
+ train_X: torch.Tensor,
+ reference_point: torch.Tensor,
+ objective: Any,
+ mc_samples: int,
+ seed: int,
+ X_pending: torch.Tensor | None,
+ constraints: Sequence[Callable[[torch.Tensor], torch.Tensor]] | None,
+ eta: float | torch.Tensor,
+ prune_baseline: bool,
+) -> qLogNoisyExpectedHypervolumeImprovement:
+ sampler = SobolQMCNormalSampler(
+ sample_shape=torch.Size([mc_samples]), seed=int(seed)
+ )
+ return qLogNoisyExpectedHypervolumeImprovement(
+ model=model,
+ ref_point=reference_point,
+ X_baseline=train_X,
+ sampler=sampler,
+ objective=objective,
+ constraints=None if constraints is None else list(constraints),
+ eta=eta,
+ X_pending=X_pending,
+ prune_baseline=prune_baseline,
+ )
+
+
+def score_qlognehvi_singletons(
+ model: Any,
+ train_X_norm: torch.Tensor,
+ X_pool_norm: torch.Tensor,
+ objective: Any,
+ reference_point_utility: np.ndarray | torch.Tensor | None,
+ *,
+ mc_samples: int = 128,
+ seed: int = 0,
+ chunk_size: int = 512,
+ X_pending_norm: torch.Tensor | None = None,
+ constraints: Sequence[Callable[[torch.Tensor], torch.Tensor]] | None = None,
+ eta: float | torch.Tensor = 0.001,
+ prune_baseline: bool = False,
+) -> QLogNEHVIPoolScoreResult:
+ """Evaluate qLogNEHVI on a normalized pool with explicit ``N x 1 x D`` shape."""
+ from .objectives import (
+ BoundedMCMultiOutputObjective,
+ ConfiguredMCMultiOutputObjective,
+ )
+
+ train_X = _input_matrix(train_X_norm, name="train_X_norm")
+ pool = _input_matrix(X_pool_norm, name="X_pool_norm")
+ if pool.shape[0] == 0:
+ raise ValueError("X_pool_norm must contain at least one candidate.")
+ if pool.shape[1] != train_X.shape[1]:
+ raise ValueError("train_X_norm and X_pool_norm dimensions must match.")
+ sample_count = _positive_integer(mc_samples, name="mc_samples")
+ chunk = _positive_integer(chunk_size, name="chunk_size")
+ if (
+ isinstance(seed, (bool, np.bool_))
+ or not isinstance(seed, (int, np.integer))
+ or int(seed) < 0
+ ):
+ raise ValueError("seed must be a non-negative integer.")
+ approved_objective_types = (
+ ConfiguredMCMultiOutputObjective,
+ BoundedMCMultiOutputObjective,
+ )
+ if not isinstance(objective, approved_objective_types):
+ raise TypeError(
+ "objective must be a configured or bounded configured multi-output "
+ "objective so raw outcomes cannot silently bypass the approved utility "
+ "transform."
+ )
+ pending: torch.Tensor | None = None
+ if X_pending_norm is not None:
+ pending = _input_matrix(X_pending_norm, name="X_pending_norm")
+ if pending.shape[1] != train_X.shape[1]:
+ raise ValueError("X_pending_norm and train_X_norm dimensions must match.")
+ pending = pending.to(dtype=train_X.dtype, device=train_X.device)
+ reference = _reference(
+ reference_point_utility, dtype=train_X.dtype, device=train_X.device
+ )
+ objective_count = objective.objective_transform.objective_count
+ if reference.numel() != objective_count:
+ raise ValueError(
+ "reference_point_utility dimension must match the configured objective "
+ f"count ({objective_count}); got {reference.numel()}."
+ )
+ model_output_count = getattr(model, "num_outputs", None)
+ if model_output_count is not None and int(model_output_count) != objective_count:
+ raise ValueError(
+ f"Model has {int(model_output_count)} outputs but objective contract "
+ f"has {objective_count}."
+ )
+ objective_version = getattr(
+ objective, "version", objective.objective_transform.version
+ )
+ acquisition = _build_qlognehvi(
+ model=model,
+ train_X=train_X,
+ reference_point=reference,
+ objective=objective,
+ mc_samples=sample_count,
+ seed=int(seed),
+ X_pending=pending,
+ constraints=constraints,
+ eta=eta,
+ prune_baseline=bool(prune_baseline),
+ )
+
+ values: list[torch.Tensor] = []
+ with torch.no_grad():
+ for start in range(0, pool.shape[0], chunk):
+ singleton_batch = (
+ pool[start : start + chunk]
+ .to(dtype=train_X.dtype, device=train_X.device)
+ .unsqueeze(-2)
+ )
+ chunk_values = acquisition(singleton_batch)
+ if chunk_values.shape != (singleton_batch.shape[0],):
+ raise RuntimeError(
+ "qLogNEHVI singleton evaluation returned unexpected shape "
+ f"{tuple(chunk_values.shape)} for input "
+ f"{tuple(singleton_batch.shape)}."
+ )
+ values.append(chunk_values.detach().cpu().double())
+ scores = torch.cat(values).numpy()
+ if np.any(np.isnan(scores)) or np.any(np.isposinf(scores)):
+ raise RuntimeError("qLogNEHVI returned NaN or positive-infinite scores.")
+ return QLogNEHVIPoolScoreResult(
+ base_log_score=scores,
+ evaluated_shape=(int(pool.shape[0]), 1, int(pool.shape[1])),
+ pending_count=0 if pending is None else int(pending.shape[0]),
+ mc_samples=sample_count,
+ seed=int(seed),
+ reference_point_utility=reference.detach().cpu().double().numpy(),
+ objective_contract_version=objective_version,
+ )
+
+
+def _assert_no_reference_overlap(
+ pool_norm: np.ndarray,
+ reference_norm: np.ndarray | None,
+ *,
+ name: str,
+ atol: float = 1e-12,
+) -> None:
+ if reference_norm is None:
+ return
+ reference = np.asarray(reference_norm, dtype=float)
+ if reference.ndim != 2 or reference.shape[1] != pool_norm.shape[1]:
+ raise ValueError(
+ f"{name} must have shape (N, {pool_norm.shape[1]}); got {reference.shape}."
+ )
+ if not np.all(np.isfinite(reference)):
+ raise ValueError(f"{name} must contain only finite values.")
+ if reference.shape[0] == 0:
+ return
+ overlap = np.all(
+ np.isclose(
+ pool_norm[:, None, :],
+ reference[None, :, :],
+ rtol=0.0,
+ atol=atol,
+ ),
+ axis=-1,
+ )
+ if np.any(overlap):
+ pool_indices = np.flatnonzero(np.any(overlap, axis=1)).tolist()
+ raise ValueError(
+ f"Candidate pool overlaps {name} at pool indices {pool_indices}; "
+ "resample with those recipes in the avoid set."
+ )
+
+
+def propose_qlognehvi_penalized_batch(
+ candidate_pool: Any,
+ model: Any,
+ train_X_norm: torch.Tensor,
+ objective: Any,
+ reference_point_utility: np.ndarray | torch.Tensor | None,
+ *,
+ q: int,
+ local_penalization_config: Any,
+ X_pending_norm: torch.Tensor | None = None,
+ mc_samples: int = 128,
+ seed: int = 0,
+ chunk_size: int = 512,
+ constraints: Sequence[Callable[[torch.Tensor], torch.Tensor]] | None = None,
+ eta: float | torch.Tensor = 0.001,
+ prune_baseline: bool = False,
+) -> QLogNEHVIBatchProposal:
+ """Select a discrete batch, rebuilding qLogNEHVI pending state each step."""
+ from .batch_selection import BaseScoreResult, select_local_penalized_batch
+
+ train_X = _input_matrix(train_X_norm, name="train_X_norm")
+ pool_norm = np.asarray(candidate_pool.X_norm, dtype=float)
+ if pool_norm.ndim != 2 or pool_norm.shape[1] != train_X.shape[1]:
+ raise ValueError(
+ "candidate_pool.X_norm and train_X_norm must share input dimension."
+ )
+ train_numpy = train_X.detach().cpu().double().numpy()
+ _assert_no_reference_overlap(pool_norm, train_numpy, name="observed train_X_norm")
+
+ pending_tensor: torch.Tensor | None = None
+ pending_numpy: np.ndarray | None = None
+ if X_pending_norm is not None:
+ pending_tensor = _input_matrix(X_pending_norm, name="X_pending_norm").to(
+ dtype=train_X.dtype, device=train_X.device
+ )
+ pending_numpy = pending_tensor.detach().cpu().double().numpy()
+ _assert_no_reference_overlap(
+ pool_norm, pending_numpy, name="pre-existing X_pending_norm"
+ )
+
+ histories: list[QLogNEHVIPoolScoreResult] = []
+
+ def score_remaining(
+ remaining_indices: np.ndarray, selected_indices: np.ndarray
+ ) -> Any:
+ selected_tensor = torch.as_tensor(
+ pool_norm[selected_indices], dtype=train_X.dtype, device=train_X.device
+ )
+ if pending_tensor is None:
+ current_pending = selected_tensor if selected_indices.size else None
+ elif selected_indices.size:
+ current_pending = torch.cat([pending_tensor, selected_tensor], dim=0)
+ else:
+ current_pending = pending_tensor
+ result = score_qlognehvi_singletons(
+ model,
+ train_X,
+ torch.as_tensor(
+ pool_norm[remaining_indices],
+ dtype=train_X.dtype,
+ device=train_X.device,
+ ),
+ objective,
+ reference_point_utility,
+ mc_samples=mc_samples,
+ seed=seed,
+ chunk_size=chunk_size,
+ X_pending_norm=current_pending,
+ constraints=constraints,
+ eta=eta,
+ prune_baseline=prune_baseline,
+ )
+ histories.append(result)
+ raw_base_score = np.exp(np.clip(result.base_log_score, -745.0, 709.0))
+ raw_base_score[np.isneginf(result.base_log_score)] = 0.0
+ return BaseScoreResult(
+ base_log_score=result.base_log_score,
+ base_score=raw_base_score,
+ diagnostics={
+ "pending_count": result.pending_count,
+ "evaluated_shape": result.evaluated_shape,
+ "remaining_pool_indices": remaining_indices.copy(),
+ },
+ )
+
+ observed_pending = (
+ train_numpy
+ if pending_numpy is None
+ else np.vstack([train_numpy, pending_numpy])
+ )
+ selection = select_local_penalized_batch(
+ candidate_pool,
+ q,
+ score_remaining,
+ local_penalization_config,
+ observed_pending_norm=observed_pending,
+ )
+ return QLogNEHVIBatchProposal(
+ selection=selection,
+ score_history=tuple(histories),
+ preexisting_pending_count=(
+ 0 if pending_tensor is None else int(pending_tensor.shape[0])
+ ),
+ metadata={
+ "method": "qlognehvi",
+ "method_version": "step2a-v1",
+ "objective_contract_version": getattr(
+ objective, "version", objective.objective_transform.version
+ ),
+ "reference_point_utility": np.asarray(
+ reference_point_utility, dtype=float
+ ).copy(),
+ "pool_seed": candidate_pool.seed,
+ "pool_size": candidate_pool.size,
+ "pool_draws": candidate_pool.draws,
+ "pool_rejected_duplicate": candidate_pool.rejected_duplicate,
+ "pool_rejected_avoid": candidate_pool.rejected_avoid,
+ "pool_rejected_constraint": candidate_pool.rejected_constraint,
+ "mc_seed": int(seed),
+ "mc_samples": int(mc_samples),
+ "preexisting_pending_count": (
+ 0 if pending_tensor is None else int(pending_tensor.shape[0])
+ ),
+ "local_penalization": {
+ "radius": local_penalization_config.radius,
+ "min_batch_distance": local_penalization_config.min_batch_distance,
+ "min_observed_distance": local_penalization_config.min_observed_distance,
+ "dimension_weights": local_penalization_config.dimension_weights,
+ "epsilon": local_penalization_config.epsilon,
+ },
+ "selected_pool_indices": selection.selected_pool_indices.copy(),
+ },
+ )
diff --git a/src/mobo_kit/replicate_variance.py b/src/mobo_kit/replicate_variance.py
new file mode 100644
index 0000000..188b2c1
--- /dev/null
+++ b/src/mobo_kit/replicate_variance.py
@@ -0,0 +1,289 @@
+"""Turn replicate films into the observation variance the GP is told about.
+
+Each proposed condition is run in triplicate. Those three films are one design
+point, so they are aggregated to a single observation -- and the scatter that
+aggregation discards is the only direct measurement this campaign has of how
+reproducible its own process is. Handing it to the GP as ``train_Yvar`` stops the
+marginal likelihood from having to guess the noise from 15 points in 10 dimensions.
+
+**Two variances live in this campaign and they are not interchangeable.**
+
+*Between-film* variance is the quantity ``train_Yvar`` needs: two films made from
+the same recipe differ by everything that varies run to run -- ambient conditions,
+the operator, the substrate, the anneal. It only becomes measurable when the R1
+triplicates land.
+
+*Within-film* variance is the scatter of the 2-4 thickness points measured across
+one film. It is measurement plus spatial nonuniformity, and it is available today:
+pooled over the R0 rows it is 0.0593 on ``log T``, 24 dof. It is **not** a
+substitute. It excludes run-to-run variation entirely, so it is a *floor* -- if the
+pooled between-film variance ever comes out below it, something is wrong with the
+measurement or the pooling, because films cannot be more reproducible than points
+on a single film. :func:`sanity_floor_findings` says so rather than assuming it.
+
+**Space matters.** Variance must be in the space the GP trains in. Thickness
+trains on ``log T``, so its variance is of ``log T``; passing a variance in nm^2
+would be wrong by a factor of T^2, which over the observed range is 1.3e5 to 1.7e6
+-- not even a constant rescaling. :class:`workbook_io.CandidateResults` already
+reports ``replicate_spread`` in each objective's aggregation space for this reason,
+and that is the same space as the model's target by construction.
+
+**What the GP is told is the variance of the MEAN**, not of a single film. The
+observation handed to the model is an average of ``n`` films, so its variance is
+``pooled / n``. Passing the single-film variance instead would hand the model a
+number three times too large on a triplicate -- overstating its uncertainty, so it
+would trust a well-replicated condition less than it has earned -- and BoTorch will
+not complain.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+
+__all__ = [
+ "PooledVariance",
+ "REPLICATE_POOLED",
+ "WITHIN_FILM_LOG_THICKNESS_VARIANCE",
+ "pool_between_film_variance",
+ "sanity_floor_findings",
+ "train_yvar_for_rows",
+ "variance_config",
+ "yvar_for_campaign",
+]
+
+#: Pooled within-row variance of ``log T`` across the 15 R0 rows, 24 dof, from the
+#: 2-4 thickness points each row carries. A FLOOR for the between-film variance,
+#: never a replacement: it contains no run-to-run variation at all.
+WITHIN_FILM_LOG_THICKNESS_VARIANCE = 0.0593
+
+
+@dataclass(frozen=True)
+class PooledVariance:
+ """One objective's between-film variance, pooled across conditions."""
+
+ objective: str
+ variance: float
+ dof: int
+ n_conditions: int
+ space: str
+ """``value`` or ``log`` -- the space the aggregation and the GP both work in."""
+
+ @property
+ def sd(self) -> float:
+ return math.sqrt(self.variance)
+
+ def variance_of_mean(self, n_films: int) -> float:
+ """Variance of an observation that is the mean of ``n_films`` films."""
+ if n_films < 1:
+ raise ValueError("n_films must be at least 1.")
+ return self.variance / float(n_films)
+
+
+def pool_between_film_variance(
+ replicate_spread: pd.DataFrame,
+ films_used: pd.DataFrame,
+ *,
+ aggregates: Mapping[str, str] | Sequence[str] | None = None,
+) -> dict[str, PooledVariance]:
+ """Pool per-condition replicate scatter into one variance per objective.
+
+ ``replicate_spread`` holds the per-condition sample sd in each objective's
+ aggregation space, and ``films_used`` how many films each came from -- both
+ straight off :func:`workbook_io.read_candidate_results`.
+
+ Pooling is the usual dof-weighted estimate, ``sum((n_i - 1) * s_i^2) /
+ sum(n_i - 1)``. Conditions with one usable film contribute no dof and are
+ skipped rather than counted as zero variance: one film measures no
+ reproducibility, and treating that as perfect reproducibility is how a model
+ ends up certain about a process nobody has measured twice.
+ """
+ if not isinstance(replicate_spread, pd.DataFrame):
+ raise TypeError("replicate_spread must be a pandas DataFrame.")
+ if not isinstance(films_used, pd.DataFrame):
+ raise TypeError("films_used must be a pandas DataFrame.")
+ if list(replicate_spread.columns) != list(films_used.columns):
+ raise ValueError(
+ "replicate_spread and films_used must describe the same objectives; got "
+ f"{list(replicate_spread.columns)} and {list(films_used.columns)}."
+ )
+ if isinstance(aggregates, Mapping):
+ spaces = dict(aggregates)
+ elif aggregates is None:
+ spaces = {}
+ else:
+ spaces = dict(zip(replicate_spread.columns, aggregates))
+
+ pooled: dict[str, PooledVariance] = {}
+ for name in replicate_spread.columns:
+ weighted = 0.0
+ dof = 0
+ used = 0
+ for spread, films in zip(replicate_spread[name], films_used[name]):
+ count = int(films)
+ if count < 2 or not np.isfinite(spread):
+ continue
+ weighted += (count - 1) * float(spread) ** 2
+ dof += count - 1
+ used += 1
+ if dof == 0:
+ raise ValueError(
+ f"Objective {name!r} has no condition with two or more usable films, "
+ "so between-film variance cannot be estimated. Replicates are what "
+ "make it measurable; a single film per condition measures no "
+ "reproducibility at all."
+ )
+ rule = str(spaces.get(name, "mean"))
+ pooled[name] = PooledVariance(
+ objective=name,
+ variance=weighted / dof,
+ dof=dof,
+ n_conditions=used,
+ space="log" if rule == "mean_of_log" else "value",
+ )
+ return pooled
+
+
+def sanity_floor_findings(
+ pooled: Mapping[str, PooledVariance], floors: Mapping[str, float]
+) -> tuple[str, ...]:
+ """Report any objective whose between-film variance falls below its floor.
+
+ A floor comes from within-film scatter, which contains no run-to-run variation.
+ Between-film variance below it means films are apparently more reproducible than
+ points on one film, which is not a thing -- so it indicates a measurement or
+ pooling mistake, not a very good process.
+ """
+ messages: list[str] = []
+ for name, floor in floors.items():
+ estimate = pooled.get(name)
+ if estimate is None:
+ continue
+ if estimate.variance < float(floor):
+ messages.append(
+ f"{name}: pooled between-film variance {estimate.variance:.4g} "
+ f"({estimate.dof} dof) is BELOW the within-film floor {float(floor):.4g}. "
+ "Films cannot be more reproducible than points measured on a single "
+ "film, so this points at the measurements or the pooling, not at a "
+ "very reproducible process. Check before trusting train_Yvar."
+ )
+ return tuple(messages)
+
+
+def train_yvar_for_rows(
+ pooled: Mapping[str, PooledVariance],
+ films_per_row: pd.DataFrame | np.ndarray,
+ objective_names: Sequence[str],
+ *,
+ rows_without_replicates: int = 1,
+) -> np.ndarray:
+ """The ``(n_rows, n_objectives)`` variance array to hand the model.
+
+ Each entry is the variance of that row's observation, which is a mean of
+ ``n`` films, so ``pooled / n``.
+
+ ``rows_without_replicates`` is the film count assumed for rows that have none --
+ the R0 rows, which predate the triplicate policy. It defaults to 1: their
+ observation is a single film, so it carries the full between-film variance
+ rather than a third of it. That assumes the measurement process is unchanged
+ between rounds, which is an assumption and is recorded in the config as one.
+ """
+ names = list(objective_names)
+ counts = (
+ films_per_row[names].to_numpy(dtype=float)
+ if isinstance(films_per_row, pd.DataFrame)
+ else np.asarray(films_per_row, dtype=float)
+ )
+ if counts.ndim == 1:
+ counts = np.repeat(counts[:, None], len(names), axis=1)
+ if counts.shape[1] != len(names):
+ raise ValueError(
+ f"films_per_row must have one column per objective ({len(names)}); "
+ f"got {counts.shape[1]}."
+ )
+ missing = [name for name in names if name not in pooled]
+ if missing:
+ raise ValueError(f"No pooled variance for objective(s) {missing}.")
+
+ # A pooled variance of zero says every replicate of every condition agreed to
+ # the last digit. For a physical measurement that means the values were copied
+ # rather than measured -- and handing zero to the model asserts the observation
+ # is exact, which makes it interpolate through a number nobody verified.
+ degenerate = [name for name in names if pooled[name].variance <= 0.0]
+ if degenerate:
+ raise ValueError(
+ f"Pooled between-film variance is zero for {degenerate}. Replicate films "
+ "that agree exactly are a transcription, not a measurement, and a zero "
+ "train_Yvar tells the model the observation is exact. Check the entries "
+ "before enabling measured observation noise."
+ )
+
+ counts = np.where(counts >= 1, counts, float(rows_without_replicates))
+ variances = np.empty_like(counts, dtype=float)
+ for index, name in enumerate(names):
+ variances[:, index] = pooled[name].variance / counts[:, index]
+ return variances
+
+
+#: ``model.observation_noise`` value that switches measured replicate variance on.
+REPLICATE_POOLED = "replicate_pooled"
+
+
+def yvar_for_campaign(
+ config: Mapping[str, Any],
+ results: Any,
+ *,
+ n_rows_without_replicates: int,
+ objective_names: Sequence[str],
+ aggregates: Sequence[str] | Mapping[str, str] | None = None,
+) -> tuple[np.ndarray | None, tuple[str, ...]]:
+ """Build ``train_Yvar`` for the whole observed set, or return ``None``.
+
+ ``None`` unless ``model.observation_noise`` is ``replicate_pooled``: until the
+ triplicates land there is nothing to pool, and the marginal likelihood keeps
+ fitting the noise as it does today. Once they do land, turning this on is a
+ config edit, which is the point of wiring it before the data exists.
+
+ Rows without replicates -- the R0 block, which predates the triplicate policy --
+ come first and take the declared film count. The replicated conditions follow
+ in the order :func:`workbook_io.read_candidate_results` returned them, which is
+ the order they are appended to the observation matrix.
+ """
+ if str((config.get("model") or {}).get("observation_noise")) != REPLICATE_POOLED:
+ return None, ()
+
+ settings = variance_config(config)
+ names = list(objective_names)
+ pooled = pool_between_film_variance(
+ results.replicate_spread, results.films_used, aggregates=aggregates
+ )
+ findings = sanity_floor_findings(pooled, settings["sanity_floor"])
+
+ prior = np.full((int(n_rows_without_replicates), len(names)), 0.0)
+ replicated = results.films_used[names].to_numpy(dtype=float)
+ films = np.vstack([prior, replicated])
+ yvar = train_yvar_for_rows(
+ pooled,
+ films,
+ names,
+ rows_without_replicates=settings["rows_without_replicates"],
+ )
+ return yvar, findings
+
+
+def variance_config(config: Mapping[str, Any]) -> dict[str, Any]:
+ """The ``model.replicate_variance`` block, with its defaults filled in."""
+ block = (config.get("model") or {}).get("replicate_variance") or {}
+ if not isinstance(block, Mapping):
+ raise ValueError("model.replicate_variance must be a mapping.")
+ floors = block.get("sanity_floor") or {}
+ if not isinstance(floors, Mapping):
+ raise ValueError("model.replicate_variance.sanity_floor must be a mapping.")
+ return {
+ "rows_without_replicates": int(block.get("rows_without_replicates", 1)),
+ "sanity_floor": {str(k): float(v) for k, v in floors.items()},
+ }
diff --git a/src/mobo_kit/research_qnehvi.py b/src/mobo_kit/research_qnehvi.py
new file mode 100644
index 0000000..8051212
--- /dev/null
+++ b/src/mobo_kit/research_qnehvi.py
@@ -0,0 +1,286 @@
+"""qNEHVI as a research-only R2 variant, for comparison against qLogNEHVI.
+
+**This is not part of the campaign.** ``campaign.py`` proposes R2 with qLogNEHVI
+and nothing here changes that; the frozen acquisition modules
+(``qlognehvi_batch.py``, ``ucb_hvi.py``, ``batch_selection.py``) are untouched.
+This module exists so the two acquisitions can be compared on identical inputs,
+which is a question about the tooling rather than about the chemistry.
+
+**BoTorch itself recommends against qNEHVI.** Constructing one emits a
+``NumericsWarning`` saying it "has known numerical issues that lead to suboptimal
+optimization performance" and to use qLogNEHVI instead (arXiv:2310.20708). That
+warning is deliberately not silenced here -- if this module is used, the caller
+should see it.
+
+Measured on this campaign at the default cell (radius 0.25, beta 4.0, seed 73),
+the two acquisitions propose the **identical batch**. That is the useful finding,
+and it is what makes the comparison worth having run once rather than repeatedly.
+
+Derived from the structure of ``qlognehvi_batch.propose_qlognehvi_penalized_batch``
+and from the ``qnehvi_batch`` module on Annie Xu's ``ax_plots_simulation`` branch,
+which is where the idea of carrying a second acquisition came from. Two things are
+reviewed against current conventions rather than copied: the observed baseline
+reaches the objective transform in MODEL space, and the hypervolume reference is
+explicit rather than inferred.
+
+THE ONE REAL DIFFERENCE, and why it is handled the way it is. qLogNEHVI returns
+the *logarithm* of the expected improvement; qNEHVI returns the improvement
+itself. ``select_local_penalized_batch`` applies its soft penalty in log space, so
+a raw qNEHVI value must be logged before it enters the selector or the two
+variants would be penalised on different scales and would not be comparable. That
+conversion is exactly the numerically fragile step qLogNEHVI exists to avoid: for
+a small Monte-Carlo estimate, ``log(mean(...))`` loses precision where qLogNEHVI
+computes the log directly.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Callable, Sequence
+
+import numpy as np
+import pandas as pd
+import torch
+from botorch.acquisition.multi_objective import (
+ qNoisyExpectedHypervolumeImprovement,
+)
+from botorch.sampling.normal import SobolQMCNormalSampler
+
+from .batch_selection import LocalPenalizationConfig
+from .campaign import (
+ RoundResult,
+ build_objective_transform,
+ expand_replicates,
+ validate_batch,
+)
+from .candidate_pool import sample_discrete_candidate_pool
+from .constraints import constraints_from_config
+from .design import build_design_from_config
+
+__all__ = [
+ "propose_qnehvi_penalized_batch",
+ "run_r2_qnehvi_research",
+ "R2_ACQUISITIONS",
+]
+
+#: Selectable R2 acquisitions. ``qlognehvi`` is the campaign's own.
+R2_ACQUISITIONS = ("qlognehvi", "qnehvi")
+
+#: Below this the log of a Monte-Carlo improvement estimate is meaningless.
+_LOG_FLOOR = 1e-12
+
+
+def _score_qnehvi_singletons(
+ model: Any,
+ train_X: torch.Tensor,
+ pool_norm: np.ndarray,
+ objective: Any,
+ reference: torch.Tensor,
+ *,
+ mc_samples: int,
+ seed: int,
+ chunk_size: int,
+ pending: torch.Tensor | None,
+ constraints: Sequence[Callable[[torch.Tensor], torch.Tensor]] | None,
+ eta: float | torch.Tensor,
+ prune_baseline: bool,
+) -> np.ndarray:
+ """Evaluate qNEHVI on each pool row as its own ``1 x D`` batch."""
+ sampler = SobolQMCNormalSampler(
+ sample_shape=torch.Size([int(mc_samples)]), seed=int(seed)
+ )
+ acquisition = qNoisyExpectedHypervolumeImprovement(
+ model=model,
+ ref_point=reference,
+ X_baseline=train_X,
+ sampler=sampler,
+ objective=objective,
+ constraints=None if constraints is None else list(constraints),
+ eta=eta,
+ X_pending=pending,
+ prune_baseline=bool(prune_baseline),
+ )
+ values: list[torch.Tensor] = []
+ with torch.no_grad():
+ for start in range(0, pool_norm.shape[0], chunk_size):
+ batch = torch.as_tensor(
+ pool_norm[start : start + chunk_size],
+ dtype=train_X.dtype,
+ device=train_X.device,
+ ).unsqueeze(-2)
+ chunk_values = acquisition(batch)
+ if chunk_values.shape != (batch.shape[0],):
+ raise RuntimeError(
+ "qNEHVI singleton evaluation returned unexpected shape "
+ f"{tuple(chunk_values.shape)} for input {tuple(batch.shape)}."
+ )
+ values.append(chunk_values.detach().cpu().double())
+ scores = torch.cat(values).numpy()
+ if np.any(np.isnan(scores)) or np.any(np.isposinf(scores)):
+ raise RuntimeError("qNEHVI returned NaN or positive-infinite scores.")
+ return scores
+
+
+def propose_qnehvi_penalized_batch(
+ candidate_pool: Any,
+ model: Any,
+ train_X_norm: torch.Tensor,
+ objective: Any,
+ reference_point_utility: np.ndarray | torch.Tensor,
+ *,
+ q: int,
+ local_penalization_config: LocalPenalizationConfig,
+ mc_samples: int = 128,
+ seed: int = 0,
+ chunk_size: int = 512,
+ constraints: Sequence[Callable[[torch.Tensor], torch.Tensor]] | None = None,
+ eta: float | torch.Tensor = 0.001,
+ prune_baseline: bool = False,
+) -> Any:
+ """Select ``q`` locally penalised pool candidates by qNEHVI.
+
+ Mirrors ``propose_qlognehvi_penalized_batch`` step for step -- same pool, same
+ selector, same pending-state rebuild after each pick -- so any difference in
+ the batch is attributable to the acquisition and nothing else.
+ """
+ from .batch_selection import BaseScoreResult, select_local_penalized_batch
+
+ train_X = torch.as_tensor(train_X_norm, dtype=torch.double)
+ if train_X.ndim != 2:
+ raise ValueError("train_X_norm must be a 2-D (N, D) tensor.")
+ pool_norm = np.asarray(candidate_pool.X_norm, dtype=float)
+ if pool_norm.ndim != 2 or pool_norm.shape[1] != train_X.shape[1]:
+ raise ValueError(
+ "candidate_pool.X_norm and train_X_norm must share input dimension."
+ )
+ reference = torch.as_tensor(
+ np.asarray(reference_point_utility, dtype=float), dtype=torch.double
+ )
+
+ def score_remaining(
+ remaining_indices: np.ndarray, selected_indices: np.ndarray
+ ) -> Any:
+ pending = (
+ torch.as_tensor(pool_norm[selected_indices], dtype=torch.double)
+ if selected_indices.size
+ else None
+ )
+ raw = _score_qnehvi_singletons(
+ model,
+ train_X,
+ pool_norm[remaining_indices],
+ objective,
+ reference,
+ mc_samples=mc_samples,
+ seed=seed,
+ chunk_size=chunk_size,
+ pending=pending,
+ constraints=constraints,
+ eta=eta,
+ prune_baseline=prune_baseline,
+ )
+ # qNEHVI returns the improvement; the selector penalises in log space.
+ base_score = np.clip(raw, 0.0, None)
+ with np.errstate(divide="ignore"):
+ base_log_score = np.where(
+ base_score > _LOG_FLOOR, np.log(np.maximum(base_score, _LOG_FLOOR)),
+ -np.inf,
+ )
+ return BaseScoreResult(
+ base_log_score=base_log_score,
+ base_score=base_score,
+ diagnostics={"remaining_pool_indices": remaining_indices.copy()},
+ )
+
+ return select_local_penalized_batch(
+ candidate_pool,
+ q,
+ score_remaining,
+ local_penalization_config,
+ observed_pending_norm=train_X.detach().cpu().double().numpy(),
+ )
+
+
+def run_r2_qnehvi_research(
+ config: Any,
+ observed_X_phys: np.ndarray,
+ observed_Y_raw: np.ndarray,
+ *,
+ n: int | None = None,
+ seed: int | None = None,
+) -> RoundResult:
+ """An R2 round proposed by qNEHVI, for comparison only.
+
+ Same contract as ``campaign.run_r2_qlognehvi``: ``observed_Y_raw`` holds the
+ MODEL SOURCE values in objective order, so thickness arrives in nanometres.
+ """
+ from .campaign import _fit_models, _normalise, _penalization, _reference_point
+ from .campaign import _on_grid_mask, _round_settings
+ from .objectives import ConfiguredMCMultiOutputObjective
+
+ design = build_design_from_config(dict(config))
+ settings = _round_settings(config, "r2")
+ q = int(settings["batch_size"]) if n is None else int(n)
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ transform = build_objective_transform(config)
+ reference = _reference_point(config, transform.objective_count)
+ penalization = _penalization(config)
+
+ observed_norm = _normalise(design, observed_X_phys)
+ model, fit_warnings, raw_fit_warnings = _fit_models(
+ config,
+ np.asarray(observed_X_phys, dtype=float),
+ observed_norm,
+ observed_Y_raw,
+ resolved_seed,
+ )
+
+ on_grid = _on_grid_mask(design, observed_X_phys)
+ pool = sample_discrete_candidate_pool(
+ design,
+ int(settings.get("candidate_pool_size", 32768)),
+ seed=resolved_seed,
+ observed_phys=np.asarray(observed_X_phys, dtype=float)[on_grid],
+ row_constraints=constraints_from_config(dict(config), design) or None,
+ )
+
+ selection = propose_qnehvi_penalized_batch(
+ pool,
+ model,
+ torch.tensor(observed_norm, dtype=torch.double),
+ ConfiguredMCMultiOutputObjective(transform),
+ reference,
+ q=q,
+ local_penalization_config=penalization,
+ mc_samples=int(settings.get("mc_samples", 128)),
+ seed=resolved_seed,
+ )
+
+ conditions = pd.DataFrame(
+ np.asarray(selection.X_phys, dtype=float), columns=list(design.names)
+ )
+ report = validate_batch(
+ conditions,
+ design,
+ expected_count=q,
+ min_pairwise_distance=penalization.min_batch_distance,
+ )
+ replicates_per = int(settings.get("replicates_per_condition", 1))
+ return RoundResult(
+ round_name="R2",
+ conditions=conditions,
+ replicates=expand_replicates(
+ conditions, replicates=replicates_per, round_name="R2"
+ ),
+ diagnostics={
+ "method": "qnehvi",
+ "research_only": True,
+ "seed": resolved_seed,
+ "pool_size": pool.size,
+ "objective_contract": transform.version,
+ "model_fit_warnings": list(fit_warnings),
+ "fit_warnings_raw": list(raw_fit_warnings),
+ "validity": report,
+ },
+ )
diff --git a/src/mobo_kit/round_report.py b/src/mobo_kit/round_report.py
new file mode 100644
index 0000000..d20a93f
--- /dev/null
+++ b/src/mobo_kit/round_report.py
@@ -0,0 +1,1416 @@
+"""Figures an experimentalist sees when a round is proposed.
+
+One orchestrator, :func:`generate_round_report`, pure and headless. The launcher
+calls it after a successful propose; ``scripts/generate_round_report.py`` calls it
+from a terminal; neither owns any of the logic.
+
+**Every figure writes the numbers behind it.** A PNG whose data cannot be
+re-derived is the next plausible-finite-number bug waiting to happen -- this
+project has had three, and all three were quantities nothing recomputed. So each
+figure emits at least one CSV, ``manifest.json`` records what was produced, and
+determinism is checked against the CSVs rather than against PNG bytes.
+
+**Nothing here decides anything, and several figures exist to say so.** Two of
+the three objectives on the current campaign carry no learnable signal; their
+panels look exactly as convincing as thickness's and mean nothing. Each such panel
+is labelled on its face rather than in a caption somewhere else, because a figure
+travels without its documentation.
+
+**Output goes beside the workbook, never into it.** ``openpyxl`` discards cached
+formula values on save, so the source workbook is opened read-only for the life of
+this module.
+
+Three notebook conventions are deliberately NOT ported; see
+``docs/CAMPAIGN_STATUS.md``:
+
+* in-sample parity -- a model is being asked about points it was fitted on, which
+ measures memorisation. Parity here is leave-one-out.
+* ad-hoc sign flips at plot time -- objective polarity is a config contract
+ (``goal:``), and flipping it in a figure makes the figure disagree with the
+ optimiser.
+* auto-referenced hypervolume -- the reference point is required and campaign-fixed,
+ because a reference re-derived per call makes rounds incomparable.
+"""
+
+from __future__ import annotations
+
+import json
+import platform
+import subprocess
+import time
+import traceback
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable, Mapping, Sequence
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt # noqa: E402
+import numpy as np # noqa: E402
+import pandas as pd # noqa: E402
+import torch # noqa: E402
+
+from .campaign import ( # noqa: E402
+ build_design_from_config,
+ build_objective_transform,
+ fit_campaign_models,
+ normalise_inputs,
+ objective_names,
+)
+from .candidate_diagnostics import ( # noqa: E402
+ nearest_reference_distances,
+ pairwise_normalized_distances,
+)
+from .loocv import loo_predictions, null_loo_r2 # noqa: E402
+from .metrics import compute_ref_pareto_hv # noqa: E402
+from .scores import ScoreSeverity # noqa: E402
+from .workbook_io import ( # noqa: E402
+ candidate_workbook_path,
+ read_campaign_workbook,
+ read_candidate_results,
+)
+
+__all__ = [
+ "FigureRecord",
+ "ReportManifest",
+ "generate_round_report",
+ "report_directory",
+]
+
+#: The palette every figure in this project shares, so they read as one set.
+R0_COLOR, R1_COLOR, R2_COLOR = "#2a78d6", "#eb6834", "#1baf7a"
+PROPOSED_COLOR = "#7b3fbf"
+REFERENCE_COLOR = "#c0392b"
+GRID_COLOR = "#e6e5e1"
+SPINE = "#d8d7d2"
+OBSERVED_GREY = "#9a9894"
+
+ROUND_COLORS = {"R0": R0_COLOR, "R1": R1_COLOR, "R2": R2_COLOR}
+
+#: Posterior draws for the batch hypervolume diagnostic. Fixed, and recorded in
+#: the manifest: a distribution whose sample count moves between runs is not a
+#: distribution anyone can compare.
+HV_POSTERIOR_SAMPLES = 512
+
+
+# --------------------------------------------------------------------------- #
+# records
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class FigureRecord:
+ """One rendered figure and the data files that reproduce it."""
+
+ key: str
+ title: str
+ caption: str
+ png: str
+ data: tuple[str, ...] = ()
+ caveats: tuple[str, ...] = ()
+
+ def as_dict(self) -> dict[str, Any]:
+ return {
+ "key": self.key,
+ "title": self.title,
+ "caption": self.caption,
+ "png": self.png,
+ "data": list(self.data),
+ "caveats": list(self.caveats),
+ }
+
+
+@dataclass
+class ReportManifest:
+ """What one report run produced, and what a reader must know about it."""
+
+ directory: Path
+ round_name: str
+ mode: str
+ """``proposal`` when a batch was supplied, ``data_only`` otherwise."""
+ figures: tuple[FigureRecord, ...] = ()
+ skipped: tuple[tuple[str, str], ...] = ()
+ notices: tuple[str, ...] = ()
+ context: dict[str, Any] = field(default_factory=dict)
+ runtime_seconds: float = 0.0
+
+ def as_dict(self) -> dict[str, Any]:
+ return {
+ "round": self.round_name,
+ "mode": self.mode,
+ "generated_utc": self.context.get("generated_utc"),
+ "runtime_seconds": round(self.runtime_seconds, 2),
+ "context": self.context,
+ "figures": [figure.as_dict() for figure in self.figures],
+ "skipped": [{"key": key, "why": why} for key, why in self.skipped],
+ "notices": list(self.notices),
+ }
+
+ def summary(self) -> str:
+ """One line per figure, for the launcher pane."""
+ lines = [f"Round report ({self.mode}) -> {self.directory}"]
+ for figure in self.figures:
+ lines.append(f" {figure.png:<34} {figure.title}")
+ for key, why in self.skipped:
+ lines.append(f" {key:<34} SKIPPED: {why}")
+ if self.notices:
+ lines.append("")
+ lines.append(" Read with the figures:")
+ for notice in self.notices:
+ lines.append(f" - {notice}")
+ return "\n".join(lines)
+
+
+# --------------------------------------------------------------------------- #
+# plumbing
+# --------------------------------------------------------------------------- #
+
+
+def report_directory(workbook: str | Path, round_name: str, *, when: str) -> Path:
+ """``_reports/_/``, beside the workbook."""
+ source = Path(workbook)
+ return source.with_name(f"{source.stem}_reports") / f"{round_name}_{when}"
+
+
+def _git_describe() -> str:
+ try:
+ out = subprocess.run(
+ ["git", "describe", "--always", "--dirty"],
+ capture_output=True,
+ text=True,
+ timeout=5,
+ cwd=Path(__file__).resolve().parent,
+ )
+ return out.stdout.strip() or "unknown"
+ except Exception: # pragma: no cover - git absent or not a checkout
+ return "unknown"
+
+
+def _style(ax: plt.Axes) -> None:
+ ax.set_facecolor("white")
+ ax.grid(True, color=GRID_COLOR, linewidth=0.8, zorder=0)
+ ax.set_axisbelow(True)
+ for side in ("top", "right"):
+ ax.spines[side].set_visible(False)
+ for side in ("left", "bottom"):
+ ax.spines[side].set_color(SPINE)
+
+
+def _wrapped_caveats(fig: plt.Figure, caveats: Sequence[str]) -> list[str]:
+ """Hard-wrap to the figure width; matplotlib's own ``wrap=True`` is unreliable.
+
+ The footer is 7.2 pt monospace, so a character is about 0.06 in and an inch
+ holds ~16.5 of them. The estimate used to be 17 per inch with no right margin,
+ which overflowed the canvas on a three-panel figure: the last words of a long
+ caveat rendered past the edge and were cropped by ``savefig``. Nothing warns
+ when that happens -- the text is simply not in the PNG.
+ """
+ import textwrap
+
+ columns = max(60, int((fig.get_size_inches()[0] - 0.25) * 16))
+ lines: list[str] = []
+ for caveat in caveats:
+ wrapped = textwrap.wrap(caveat, width=columns) or [""]
+ lines.append(f"* {wrapped[0]}")
+ lines.extend(f" {piece}" for piece in wrapped[1:])
+ return lines
+
+
+def _save(
+ fig: plt.Figure, path: Path, caveats: Sequence[str], *, tight: bool = True
+) -> None:
+ """Reserve the footer's space BEFORE laying out, so nothing lands on an axis label.
+
+ The caveats are part of the figure rather than a caption in a document, because
+ a PNG gets pasted into a slide and the caption does not travel with it. That
+ only helps if they are legible, hence the explicit reservation rather than
+ trusting a default margin.
+
+ ``tight=False`` for any figure holding a 3-D axes: ``tight_layout`` does not
+ support them and warns that its result may be wrong, which on this project's
+ rules means not using it rather than ignoring the warning.
+ """
+ lines = _wrapped_caveats(fig, caveats)
+ reserved = float(min(0.42, (len(lines) * 0.155 + 0.30) / fig.get_size_inches()[1]))
+ if tight:
+ fig.tight_layout(rect=(0.0, reserved, 1.0, 0.99))
+ else:
+ fig.subplots_adjust(bottom=reserved + 0.09, top=0.9, left=0.055, right=0.985)
+ if lines:
+ fig.text(
+ 0.008,
+ 0.008,
+ "\n".join(lines),
+ ha="left",
+ va="bottom",
+ fontsize=7.2,
+ color="#5a5854",
+ family="monospace",
+ linespacing=1.35,
+ )
+ fig.savefig(path, dpi=150, facecolor="white")
+ plt.close(fig)
+
+
+def _write_csv(frame: pd.DataFrame, path: Path) -> str:
+ frame.to_csv(path, index=False)
+ return path.name
+
+
+# --------------------------------------------------------------------------- #
+# data gathering
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class _RoundBlock:
+ """One round's observations, in the order they entered the campaign."""
+
+ name: str
+ X_phys: np.ndarray
+ Y_measured: np.ndarray
+ labels: tuple[str, ...]
+
+
+def _observations_by_round(
+ workbook: Path, config: Mapping[str, Any]
+) -> list[_RoundBlock]:
+ """R0 from Sheet1, then whichever candidate sheets are filled in.
+
+ A round whose sheet exists but is not fully measured is skipped rather than
+ partially included: a hypervolume computed on half a round is not that round's
+ hypervolume, and it would silently make the trajectory wrong rather than short.
+ """
+ contents = read_campaign_workbook(workbook, config)
+ blocks = [
+ _RoundBlock(
+ "R0",
+ contents.inputs.to_numpy(float),
+ contents.model_values.to_numpy(float),
+ tuple(str(value) for value in contents.sample_ids),
+ )
+ ]
+ for round_name in ("R1", "R2"):
+ path = candidate_workbook_path(workbook, round_name)
+ if not path.exists():
+ break
+ try:
+ results = read_candidate_results(workbook, config, round_name)
+ except Exception:
+ break
+ values = results.model_values.to_numpy(float)
+ if values.size == 0 or not np.all(np.isfinite(values)):
+ break
+ blocks.append(
+ _RoundBlock(
+ round_name,
+ results.conditions.to_numpy(float),
+ values,
+ tuple(results.candidate_ids),
+ )
+ )
+ return blocks
+
+
+def _utility(transform: Any, Y_measured: np.ndarray) -> np.ndarray:
+ """Measurement space -> utility, by the one call that cannot forget the link."""
+ block = torch.tensor(np.asarray(Y_measured, dtype=float), dtype=torch.double)
+ return transform.transform_measurements(block).detach().cpu().numpy()
+
+
+def _pareto_mask(utility: np.ndarray) -> np.ndarray:
+ """Non-dominated rows, maximisation. Small N, so the O(n^2) form is fine."""
+ n = len(utility)
+ mask = np.ones(n, dtype=bool)
+ for i in range(n):
+ if not mask[i]:
+ continue
+ dominated = np.all(utility >= utility[i], axis=1) & np.any(
+ utility > utility[i], axis=1
+ )
+ if dominated.any():
+ mask[i] = False
+ return mask
+
+
+# --------------------------------------------------------------------------- #
+# figures
+# --------------------------------------------------------------------------- #
+
+
+def _figure_batch_placement(
+ directory: Path,
+ config: Mapping[str, Any],
+ observed_X: np.ndarray,
+ proposed_X: np.ndarray,
+ round_name: str,
+) -> FigureRecord:
+ """Where in recipe space the algorithm is asking to go.
+
+ The figure a coater operator actually reads. Parallel coordinates because ten
+ inputs will not fit on two axes and a projection would invent structure; the
+ distance panel because "is this batch spread out" is the question local
+ penalisation exists to answer and it is not visible in the lines.
+ """
+ design = build_design_from_config(dict(config))
+ names = list(design.names)
+ observed_norm = normalise_inputs(config, observed_X)
+ proposed_norm = normalise_inputs(config, proposed_X)
+
+ fig = plt.figure(figsize=(13.5, 5.4))
+ grid = fig.add_gridspec(1, 2, width_ratios=[1.85, 1.0], wspace=0.28)
+
+ ax = fig.add_subplot(grid[0, 0])
+ _style(ax)
+ xs = np.arange(len(names))
+ for row in observed_norm:
+ ax.plot(xs, row, color=OBSERVED_GREY, linewidth=1.0, alpha=0.55, zorder=2)
+ colour = ROUND_COLORS.get(round_name, PROPOSED_COLOR)
+ for index, row in enumerate(proposed_norm, start=1):
+ ax.plot(
+ xs,
+ row,
+ color=colour,
+ linewidth=2.2,
+ marker="o",
+ markersize=4.5,
+ zorder=3,
+ label=f"{round_name}_C{index:02d}",
+ )
+ ax.set_xticks(xs)
+ ax.set_xticklabels(names, rotation=35, ha="right", fontsize=8)
+ ax.set_ylim(-0.05, 1.05)
+ ax.set_ylabel("normalised to the declared grid")
+ ax.set_title(
+ f"{round_name} proposal against {len(observed_norm)} measured recipes",
+ fontsize=11,
+ )
+ ax.legend(fontsize=7.5, ncol=2, framealpha=0.9)
+
+ ax2 = fig.add_subplot(grid[0, 1])
+ within = pairwise_normalized_distances(proposed_norm)
+ image = ax2.imshow(within, cmap="magma_r", vmin=0.0)
+ ax2.set_xticks(range(len(proposed_norm)))
+ ax2.set_yticks(range(len(proposed_norm)))
+ labels = [f"C{i:02d}" for i in range(1, len(proposed_norm) + 1)]
+ ax2.set_xticklabels(labels, fontsize=8)
+ ax2.set_yticklabels(labels, fontsize=8)
+ for i in range(len(proposed_norm)):
+ for j in range(len(proposed_norm)):
+ ax2.text(
+ j,
+ i,
+ f"{within[i, j]:.2f}",
+ ha="center",
+ va="center",
+ fontsize=7.5,
+ color="white" if within[i, j] > within.max() * 0.55 else "#333333",
+ )
+ ax2.set_title("pairwise distance within the batch", fontsize=11)
+ fig.colorbar(image, ax=ax2, fraction=0.046, pad=0.04)
+
+ nearest = nearest_reference_distances(proposed_norm, observed_norm)
+ frame = pd.DataFrame(proposed_norm, columns=[f"{name}_norm" for name in names])
+ frame.insert(0, "candidate", labels)
+ frame["distance_to_nearest_observed"] = nearest
+ frame["min_distance_within_batch"] = [
+ np.min(np.delete(within[i], i)) if len(within) > 1 else np.nan
+ for i in range(len(within))
+ ]
+ data = _write_csv(frame, directory / "00_batch_placement.csv")
+
+ penalization = (config.get("local_penalization") or {})
+ caveats = [
+ "Normalised against the DECLARED GRID, not the observed range: 0 and 1 are "
+ "the range edges the config allows, so a line touching them is at a bound.",
+ f"Local penalisation radius {penalization.get('radius')}, minimum batch "
+ f"spacing {penalization.get('min_batch_distance')}; achieved minimum "
+ f"{np.min(within[within > 0]) if (within > 0).any() else float('nan'):.3f}.",
+ ]
+ _save(fig, directory / "00_batch_placement.png", caveats, tight=False)
+ return FigureRecord(
+ key="00_batch_placement",
+ title="Where the proposed batch sits in recipe space",
+ caption=(
+ "Each line is one recipe across the ten inputs, normalised to the "
+ "declared grid. Grey lines are what has been measured; coloured lines "
+ "are what is proposed. The heatmap is the batch's internal spacing."
+ ),
+ png="00_batch_placement.png",
+ data=(data,),
+ caveats=tuple(caveats),
+ )
+
+
+def _figure_loo_parity(
+ directory: Path,
+ config: Mapping[str, Any],
+ X_phys: np.ndarray,
+ Y_measured: np.ndarray,
+ names: Sequence[str],
+ labels: Sequence[str],
+ seed: int,
+) -> FigureRecord:
+ """Predicted against measured, leave-one-out, in the measurement's own units.
+
+ **Leave-one-out and not in-sample.** An in-sample parity plot asks the model
+ about points it was fitted on and therefore measures memorisation; at N=15 in
+ 10 dimensions it is close to a straight line no matter what the model knows.
+ That is one of the three notebook conventions deliberately not carried over.
+
+ The numbers come from :mod:`mobo_kit.loocv`, which is the same fold loop
+ ``scripts/intake_new_data.py`` uses -- not a reimplementation that agrees today.
+ """
+ entries = config["objectives"]["specs"]
+ results = {
+ name: loo_predictions(config, entries[index], X_phys, Y_measured[:, index], seed=seed)
+ for index, name in enumerate(names)
+ }
+ null = null_loo_r2(len(Y_measured))
+
+ fig, axes = plt.subplots(1, len(names), figsize=(5.4 * len(names), 5.6))
+ axes = np.atleast_1d(axes)
+ rows: list[dict[str, Any]] = []
+ for ax, name in zip(axes, names):
+ result = results[name]
+ _style(ax)
+ entry = entries[names.index(name)]
+ learnable = str(entry.get("signal_status", "")) == "learnable"
+ colour = R0_COLOR if learnable else OBSERVED_GREY
+ ax.errorbar(
+ result.observed,
+ result.predicted,
+ yerr=result.predictive_sd,
+ fmt="o",
+ markersize=6,
+ color=colour,
+ ecolor=colour,
+ elinewidth=1.0,
+ capsize=2.5,
+ alpha=0.9,
+ zorder=3,
+ )
+ lo = float(min(result.observed.min(), result.predicted.min()))
+ hi = float(max(result.observed.max(), result.predicted.max()))
+ pad = 0.07 * (hi - lo if hi > lo else 1.0)
+ line = np.array([lo - pad, hi + pad])
+ ax.plot(line, line, color="#666666", linewidth=1.0, linestyle="--", zorder=2)
+ ax.set_xlim(*line)
+ ax.set_ylim(*line)
+ ax.set_xlabel(f"measured {name}")
+ ax.set_ylabel("leave-one-out prediction")
+ ax.set_title(
+ f"{name}\nLOO R2 {result.r2:+.4f} null {null:+.4f}",
+ fontsize=11,
+ color="#222222" if learnable else "#8a3b2f",
+ )
+ if not learnable:
+ # inside the axes, not in the title: this is the single most important
+ # thing about the panel and it must not be croppable
+ ax.text(
+ 0.5,
+ 0.955,
+ "NO LEARNABLE SIGNAL",
+ transform=ax.transAxes,
+ ha="center",
+ va="top",
+ fontsize=10,
+ color="#8a3b2f",
+ bbox=dict(boxstyle="round,pad=0.35", fc="#fdeeea", ec="#e0b4a8"),
+ )
+ for position, label in enumerate(labels):
+ ax.annotate(
+ label,
+ (result.observed[position], result.predicted[position]),
+ fontsize=6.5,
+ color="#555555",
+ xytext=(3, 3),
+ textcoords="offset points",
+ )
+ for position, label in enumerate(labels):
+ rows.append(
+ {
+ "objective": name,
+ "sample": label,
+ "observed": result.observed[position],
+ "loo_predicted": result.predicted[position],
+ "loo_predictive_sd": result.predictive_sd[position],
+ "model_link": result.model_link,
+ "loo_r2": result.r2,
+ "loo_spearman": result.spearman,
+ "null_loo_r2": null,
+ "has_mean_function": result.has_mean_function,
+ }
+ )
+
+ data = _write_csv(pd.DataFrame(rows), directory / "01_loo_parity.csv")
+ caveats = [
+ "Leave-one-out, not in-sample: every point is predicted by a model that "
+ "never saw it. An in-sample version of this plot looks far better and "
+ "measures memorisation.",
+ f"The bar to clear is the NULL, {null:+.4f}, not zero. Predicting the "
+ "leave-one-out mean scores exactly that, whatever the data.",
+ "An axis marked NO LEARNABLE SIGNAL has a model that does not beat the "
+ "null. Its scatter is not a weak trend; it is nothing.",
+ ]
+ if any(results[name].model_link == "log" for name in names):
+ caveats.append(
+ "Where the model emits log(y), the point shown is the median exp(mu) "
+ "and the bar is the lognormal sd, which is asymmetric in the original "
+ "units."
+ )
+ _save(fig, directory / "01_loo_parity.png", caveats)
+ return FigureRecord(
+ key="01_loo_parity",
+ title="How well the model predicts a film it has not seen",
+ caption=(
+ "Leave-one-out prediction against measurement, one panel per objective, "
+ "in the measurement's own units. Points on the dashed line are perfect."
+ ),
+ png="01_loo_parity.png",
+ data=(data,),
+ caveats=tuple(caveats),
+ )
+
+
+def _figure_attribution(
+ directory: Path,
+ config: Mapping[str, Any],
+ model: Any,
+ transform: Any,
+ X_phys: np.ndarray,
+ names: Sequence[str],
+ seed: int,
+ max_instances: int,
+) -> FigureRecord:
+ """Mean |SHAP| per input per objective, from the campaign's own fitted model."""
+ from .attribution import mean_absolute_shap, shap_values_for
+
+ design = build_design_from_config(dict(config))
+ feature_names = list(design.names)
+ instances = X_phys[: max(1, min(max_instances, len(X_phys)))]
+
+ entries = config["objectives"]["specs"]
+ rows: list[dict[str, Any]] = []
+ magnitudes: dict[str, np.ndarray] = {}
+ for index, name in enumerate(names):
+ values = shap_values_for(
+ model, config, transform, index, X_phys, instances, seed=seed
+ )
+ magnitude = mean_absolute_shap(values)
+ magnitudes[name] = magnitude
+ declared = {
+ str(feature["column"])
+ for feature in (entries[index].get("mean_function") or {}).get(
+ "features", []
+ )
+ }
+ order = np.argsort(magnitude)[::-1]
+ for rank, position in enumerate(order, start=1):
+ rows.append(
+ {
+ "objective": name,
+ "feature": feature_names[position],
+ "mean_abs_shap": float(magnitude[position]),
+ "mean_shap": float(values[:, position].mean()),
+ "rank": rank,
+ "in_mean_function": feature_names[position] in declared,
+ "signal_status": str(entries[index].get("signal_status", "")),
+ }
+ )
+
+ fig, axes = plt.subplots(1, len(names), figsize=(5.4 * len(names), 5.6))
+ axes = np.atleast_1d(axes)
+ for ax, name in zip(axes, names):
+ _style(ax)
+ index = names.index(name)
+ magnitude = magnitudes[name]
+ order = np.argsort(magnitude)
+ declared = {
+ str(feature["column"])
+ for feature in (entries[index].get("mean_function") or {}).get(
+ "features", []
+ )
+ }
+ learnable = str(entries[index].get("signal_status", "")) == "learnable"
+ colours = [
+ R1_COLOR if feature_names[position] in declared else (
+ R0_COLOR if learnable else OBSERVED_GREY
+ )
+ for position in order
+ ]
+ ax.barh(
+ range(len(order)),
+ magnitude[order],
+ color=colours,
+ edgecolor="white",
+ zorder=3,
+ )
+ ax.set_yticks(range(len(order)))
+ ax.set_yticklabels([feature_names[position] for position in order], fontsize=8)
+ ax.set_xlabel("mean |SHAP| in utility units")
+ verdict = "" if learnable else " [fitted noise]"
+ ax.set_title(f"{name}{verdict}", fontsize=10.5,
+ color="#222222" if learnable else "#8a3b2f")
+
+ data = _write_csv(pd.DataFrame(rows), directory / "02_attribution.csv")
+ caveats = [
+ "Attributions explain the MODEL, not the world. Orange bars are features "
+ "the config TOLD the model about through a mean function, so recovering "
+ "them is a consistency check rather than a discovery.",
+ "On an axis with no learnable signal the bars are structure fitted to "
+ "noise. They have real magnitude and orderly ranking and mean nothing.",
+ "Explains E[utility] through the campaign transform, so thickness is "
+ "attributed on its 650 nm target and not on nanometres.",
+ f"Exact Shapley values: all 2^{len(feature_names)} coalitions are "
+ f"enumerated over {len(instances)} instances, so these do not depend on "
+ "the seed.",
+ ]
+ _save(fig, directory / "02_attribution.png", caveats)
+ return FigureRecord(
+ key="02_attribution",
+ title="Which process inputs move each objective, in the model",
+ caption=(
+ "Mean absolute SHAP value per input, per objective, computed on the "
+ "campaign's own fitted model in utility units."
+ ),
+ png="02_attribution.png",
+ data=(data,),
+ caveats=tuple(caveats),
+ )
+
+
+def _figure_batch_predictions(
+ directory: Path,
+ config: Mapping[str, Any],
+ review: Any,
+ names: Sequence[str],
+ hv_frame: pd.DataFrame,
+ round_name: str,
+) -> FigureRecord:
+ """What the model expects from each proposed condition, physical and utility.
+
+ **The numbers are read from the batch-review artifact, not recomputed.** The
+ Review sheet and this figure must not be able to disagree; one of them is the
+ source and it is the one already attached to the worklist.
+ """
+ candidates = review.candidates
+ labels = [f"C{i:02d}" for i in range(1, len(candidates) + 1)]
+ colour = ROUND_COLORS.get(round_name, PROPOSED_COLOR)
+
+ fig, axes = plt.subplots(2, len(names), figsize=(4.7 * len(names), 8.2))
+ axes = np.atleast_2d(axes)
+ rows: list[dict[str, Any]] = []
+ positions = np.arange(len(candidates))
+ for column, name in enumerate(names):
+ physical = candidates[f"{name}_predicted"].to_numpy(float)
+ lo = candidates[f"{name}_lo68"].to_numpy(float)
+ hi = candidates[f"{name}_hi68"].to_numpy(float)
+ utility = candidates[f"{name}_utility"].to_numpy(float)
+ utility_sd = candidates[f"{name}_sd"].to_numpy(float)
+
+ ax = axes[0, column]
+ _style(ax)
+ ax.bar(positions, physical, color=colour, edgecolor="white", zorder=3)
+ ax.errorbar(
+ positions,
+ physical,
+ yerr=[physical - lo, hi - physical],
+ fmt="none",
+ ecolor="#333333",
+ elinewidth=1.1,
+ capsize=3.5,
+ zorder=4,
+ )
+ ax.set_xticks(positions)
+ ax.set_xticklabels(labels, fontsize=8)
+ ax.set_title(f"{name} - predicted measurement", fontsize=10.5)
+ ax.set_ylabel("measurement units")
+
+ ax = axes[1, column]
+ _style(ax)
+ ax.bar(positions, utility, color=colour, edgecolor="white", zorder=3)
+ ax.errorbar(
+ positions,
+ utility,
+ yerr=utility_sd,
+ fmt="none",
+ ecolor="#333333",
+ elinewidth=1.1,
+ capsize=3.5,
+ zorder=4,
+ )
+ ax.set_xticks(positions)
+ ax.set_xticklabels(labels, fontsize=8)
+ ax.set_ylim(0.0, 1.05)
+ ax.set_title(f"{name} - utility (higher is better)", fontsize=10.5)
+ ax.set_ylabel("utility")
+
+ for position, label in enumerate(labels):
+ rows.append(
+ {
+ "candidate": label,
+ "objective": name,
+ "predicted_measurement": physical[position],
+ "lo68": lo[position],
+ "hi68": hi[position],
+ "utility_mean": utility[position],
+ "utility_sd": utility_sd[position],
+ }
+ )
+
+ data = _write_csv(pd.DataFrame(rows), directory / "03_batch_predictions.csv")
+ hv_data = _write_csv(hv_frame, directory / "03_batch_hypervolume.csv")
+ overall = hv_frame[hv_frame["candidate"] == "BATCH"]
+ caveats = [
+ "Predictions, not measurements. The bars are what the model expects before "
+ "anything is fabricated, and the whiskers are its own uncertainty.",
+ "The top row is in each measurement's units; the bottom row is utility, "
+ "which is what the optimiser maximises. Thickness utility peaks at the "
+ "650 nm target, so a thicker film is not a better one.",
+ "Numbers are read from the Review sheet's artifact, not recomputed here, "
+ "so the two cannot disagree.",
+ ]
+ if not overall.empty:
+ row = overall.iloc[0]
+ caveats.append(
+ f"Expected hypervolume gain {row['delta_hv_p50']:+.4f} "
+ f"(p05 {row['delta_hv_p05']:+.4f}, p95 {row['delta_hv_p95']:+.4f}), "
+ f"P(gain > 0) = {row['p_gain_positive']:.2f}, over "
+ f"{HV_POSTERIOR_SAMPLES} posterior draws."
+ )
+ _save(fig, directory / "03_batch_predictions.png", caveats)
+ return FigureRecord(
+ key="03_batch_predictions",
+ title="What the model expects from each proposed condition",
+ caption=(
+ "Per condition and objective: predicted measurement with a 68% interval "
+ "above, utility with its posterior sd below. The companion CSV carries "
+ "the batch's hypervolume-gain distribution."
+ ),
+ png="03_batch_predictions.png",
+ data=(data, hv_data),
+ caveats=tuple(caveats),
+ )
+
+
+def _batch_hypervolume_diagnostic(
+ config: Mapping[str, Any],
+ model: Any,
+ transform: Any,
+ observed_utility: np.ndarray,
+ proposed_X: np.ndarray,
+ reference: np.ndarray,
+ seed: int,
+) -> pd.DataFrame:
+ """How much this batch could add, as a distribution rather than a point.
+
+ A single expected utility per candidate cannot answer "is this batch worth
+ fabricating": hypervolume gain is a joint, nonlinear function of all of them.
+ So draw from the posterior at the proposed points, transform each draw to
+ utility, and recompute the hypervolume of ``observed + batch`` per draw.
+
+ ``P(non-dominated)`` per candidate is the share of draws in which that
+ condition is not dominated by anything already measured -- the question "is
+ this one pulling its weight" for a specific row.
+ """
+ X_norm = normalise_inputs(config, np.asarray(proposed_X, dtype=float))
+ torch.manual_seed(int(seed))
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(torch.tensor(X_norm, dtype=torch.double))
+ draws = posterior.rsample(
+ torch.Size([HV_POSTERIOR_SAMPLES])
+ ) # (S, q, m) in MODEL space
+ utility_draws = transform.transform(draws).detach().cpu().numpy()
+
+ base_ref = np.asarray(reference, dtype=float)
+ _, _, base_hv = compute_ref_pareto_hv(
+ torch.tensor(observed_utility, dtype=torch.double), base_ref
+ )
+ base_hv = float(base_hv)
+
+ gains = np.empty(HV_POSTERIOR_SAMPLES)
+ non_dominated = np.zeros(utility_draws.shape[1])
+ for s in range(HV_POSTERIOR_SAMPLES):
+ combined = np.vstack([observed_utility, utility_draws[s]])
+ _, _, volume = compute_ref_pareto_hv(
+ torch.tensor(combined, dtype=torch.double), base_ref
+ )
+ gains[s] = float(volume) - base_hv
+ for q in range(utility_draws.shape[1]):
+ point = utility_draws[s, q]
+ dominated = np.all(observed_utility >= point, axis=1) & np.any(
+ observed_utility > point, axis=1
+ )
+ if not dominated.any():
+ non_dominated[q] += 1.0
+ non_dominated /= HV_POSTERIOR_SAMPLES
+
+ rows = [
+ {
+ "candidate": f"C{q + 1:02d}",
+ "p_non_dominated": float(non_dominated[q]),
+ "delta_hv_p05": float("nan"),
+ "delta_hv_p50": float("nan"),
+ "delta_hv_p95": float("nan"),
+ "p_gain_positive": float("nan"),
+ "baseline_hv": base_hv,
+ "posterior_draws": HV_POSTERIOR_SAMPLES,
+ }
+ for q in range(utility_draws.shape[1])
+ ]
+ rows.append(
+ {
+ "candidate": "BATCH",
+ "p_non_dominated": float("nan"),
+ "delta_hv_p05": float(np.percentile(gains, 5)),
+ "delta_hv_p50": float(np.percentile(gains, 50)),
+ "delta_hv_p95": float(np.percentile(gains, 95)),
+ "p_gain_positive": float(np.mean(gains > 0.0)),
+ "baseline_hv": base_hv,
+ "posterior_draws": HV_POSTERIOR_SAMPLES,
+ }
+ )
+ return pd.DataFrame(rows)
+
+
+def _figure_hv_trajectory(
+ directory: Path,
+ blocks: Sequence[_RoundBlock],
+ transform: Any,
+ reference: np.ndarray,
+) -> FigureRecord:
+ """Cumulative observed hypervolume, one point per completed round."""
+ rows: list[dict[str, Any]] = []
+ cumulative_X: list[np.ndarray] = []
+ previous = 0.0
+ for block in blocks:
+ cumulative_X.append(block.Y_measured)
+ utility = _utility(transform, np.vstack(cumulative_X))
+ _, pareto, volume = compute_ref_pareto_hv(
+ torch.tensor(utility, dtype=torch.double), np.asarray(reference, float)
+ )
+ rows.append(
+ {
+ "round": block.name,
+ "cumulative_points": len(utility),
+ "points_added": len(block.Y_measured),
+ "hypervolume": float(volume),
+ "gain": float(volume) - previous,
+ "pareto_size": int(pareto.shape[0]),
+ }
+ )
+ previous = float(volume)
+
+ frame = pd.DataFrame(rows)
+ fig, ax = plt.subplots(figsize=(7.6, 5.0))
+ _style(ax)
+ xs = np.arange(len(frame))
+ ax.plot(xs, frame["hypervolume"], color="#444444", linewidth=1.4, zorder=2)
+ ax.scatter(
+ xs,
+ frame["hypervolume"],
+ s=110,
+ c=[ROUND_COLORS.get(name, PROPOSED_COLOR) for name in frame["round"]],
+ edgecolor="white",
+ linewidth=1.5,
+ zorder=4,
+ )
+ for position, row in frame.iterrows():
+ ax.annotate(
+ f"{row['hypervolume']:.4f}"
+ + ("" if position == 0 else f"\n(+{row['gain']:.4f})"),
+ (position, row["hypervolume"]),
+ fontsize=8.5,
+ ha="center",
+ va="bottom",
+ xytext=(0, 9),
+ textcoords="offset points",
+ )
+ ax.set_xticks(xs)
+ ax.set_xticklabels(
+ [f"{row['round']}\nn={row['cumulative_points']}" for _, row in frame.iterrows()]
+ )
+ ax.set_ylabel("cumulative hypervolume, utility space")
+ ax.set_title("Learning progress across measured rounds", fontsize=11.5)
+ if len(frame) == 1:
+ ax.set_xlim(-0.6, 0.6)
+ ax.text(
+ 0,
+ frame["hypervolume"].iloc[0],
+ " only R0 is measured, so there is\n no trajectory yet",
+ fontsize=9,
+ va="center",
+ ha="left",
+ color="#8a3b2f",
+ )
+
+ data = _write_csv(frame, directory / "04_hv_trajectory.csv")
+ caveats = [
+ "Cumulative hypervolume rises monotonically BY CONSTRUCTION -- adding "
+ "points can only grow a Pareto front. Random sampling produces a rising "
+ "line too, so this shows progress and is not evidence of optimisation.",
+ "OBSERVED outcomes only. No predicted point appears on this line.",
+ # `list(np.round(...))` yields np.float64 objects whose repr leaks the
+ # type into the caption. A figure that prints "np.float64(-0.01)" at an
+ # experimentalist is telling them about numpy, not about the campaign.
+ "Fixed campaign reference point "
+ + str([round(float(value), 4) for value in np.asarray(reference, float)])
+ + " in utility space. Re-deriving it per round would make these numbers "
+ "incomparable with each other.",
+ ]
+ _save(fig, directory / "04_hv_trajectory.png", caveats)
+ return FigureRecord(
+ key="04_hv_trajectory",
+ title="Hypervolume after each measured round",
+ caption=(
+ "Cumulative hypervolume of everything measured up to and including each "
+ "round, in utility space, against the campaign's fixed reference point."
+ ),
+ png="04_hv_trajectory.png",
+ data=(data,),
+ caveats=tuple(caveats),
+ )
+
+
+def _figure_objective_space(
+ directory: Path,
+ blocks: Sequence[_RoundBlock],
+ transform: Any,
+ reference: np.ndarray,
+ names: Sequence[str],
+ proposed_utility: np.ndarray | None,
+ proposed_sd: np.ndarray | None,
+) -> FigureRecord:
+ """The trade-off itself: pairwise panels, plus one 3D view for orientation.
+
+ Pairwise 2D is primary because a static 3D scatter cannot be read for
+ position -- depth is ambiguous without rotation, and "which point dominates
+ which" is exactly a position question. The 3D panel is kept for the shape of
+ the front, which the pairs do not convey.
+ """
+ all_utility = np.vstack([_utility(transform, block.Y_measured) for block in blocks])
+ round_of = [name for block in blocks for name in [block.name] * len(block.Y_measured)]
+ labels = [label for block in blocks for label in block.labels]
+ pareto = _pareto_mask(all_utility)
+
+ rows = [
+ {
+ "point": labels[i],
+ "round": round_of[i],
+ "kind": "observed",
+ "on_pareto": bool(pareto[i]),
+ **{f"utility_{name}": float(all_utility[i, j]) for j, name in enumerate(names)},
+ }
+ for i in range(len(all_utility))
+ ]
+ if proposed_utility is not None:
+ for i, point in enumerate(proposed_utility):
+ rows.append(
+ {
+ "point": f"C{i + 1:02d}",
+ "round": "proposed",
+ "kind": "proposed",
+ "on_pareto": False,
+ **{f"utility_{name}": float(point[j]) for j, name in enumerate(names)},
+ }
+ )
+
+ pairs = [(0, 1), (0, 2), (1, 2)][: max(1, len(names) * (len(names) - 1) // 2)]
+ fig = plt.figure(figsize=(5.2 * len(pairs) + 6.0, 5.8))
+ grid = fig.add_gridspec(1, len(pairs) + 1, wspace=0.34, width_ratios=[1] * len(pairs) + [1.25])
+
+ for position, (i, j) in enumerate(pairs):
+ ax = fig.add_subplot(grid[0, position])
+ _style(ax)
+ for block_name in dict.fromkeys(round_of):
+ mask = np.array([name == block_name for name in round_of])
+ ax.scatter(
+ all_utility[mask, i],
+ all_utility[mask, j],
+ s=52,
+ color=ROUND_COLORS.get(block_name, OBSERVED_GREY),
+ edgecolor="white",
+ linewidth=1.1,
+ label=block_name,
+ zorder=3,
+ )
+ # The 2-D front for THIS PAIR, computed on this pair alone. Sorting the
+ # 3-D Pareto set by one axis and joining it produces a zigzag that is not
+ # a front in any space -- a point can be non-dominated in 3-D while sitting
+ # well inside the 2-D trade-off, and the line then crosses itself and
+ # invites exactly the wrong reading.
+ pair_mask = _pareto_mask(all_utility[:, [i, j]])
+ pair_front = all_utility[pair_mask][np.argsort(all_utility[pair_mask][:, i])]
+ ax.step(
+ pair_front[:, i],
+ pair_front[:, j],
+ where="post",
+ color="#1baf7a",
+ linewidth=1.6,
+ alpha=0.85,
+ zorder=2,
+ label="front for this pair" if position == 0 else None,
+ )
+ # Points on the FULL 3-objective front, ringed rather than joined: they are
+ # what the optimiser is trading off, and several of them are interior here.
+ ax.scatter(
+ all_utility[pareto, i],
+ all_utility[pareto, j],
+ s=150,
+ facecolor="none",
+ edgecolor="#1baf7a",
+ linewidth=1.6,
+ zorder=3,
+ label="on the 3-objective front" if position == 0 else None,
+ )
+ if proposed_utility is not None:
+ ax.errorbar(
+ proposed_utility[:, i],
+ proposed_utility[:, j],
+ xerr=None if proposed_sd is None else proposed_sd[:, i],
+ yerr=None if proposed_sd is None else proposed_sd[:, j],
+ fmt="o",
+ markersize=9,
+ markerfacecolor="none",
+ markeredgecolor=PROPOSED_COLOR,
+ markeredgewidth=1.8,
+ ecolor=PROPOSED_COLOR,
+ elinewidth=1.0,
+ zorder=4,
+ label="proposed" if position == 0 else None,
+ )
+ # The reference point is at (-0.01, -0.01) while every observation lives
+ # above 0.35, so plotting it in scale spends about 40% of the panel on
+ # empty space and squeezes the region anyone needs to read. It is marked
+ # at the corner instead, labelled with its real coordinates, because
+ # hypervolume is measured from it and hiding it entirely would be worse.
+ drawn = [all_utility[:, [i, j]]]
+ if proposed_utility is not None:
+ drawn.append(proposed_utility[:, [i, j]])
+ visible = np.vstack(drawn)
+ lo = visible.min(axis=0)
+ hi = visible.max(axis=0)
+ pad = np.where(hi > lo, (hi - lo) * 0.09, 0.05)
+ ax.set_xlim(lo[0] - pad[0], hi[0] + pad[0])
+ ax.set_ylim(lo[1] - pad[1], hi[1] + pad[1])
+ # A star drawn at the corner sits at real data coordinates and reads as an
+ # observation. Text only, below the axes, where nothing can be mistaken
+ # for a measurement.
+ ax.annotate(
+ f"* reference ({reference[i]:g}, {reference[j]:g}) is off-scale, "
+ "down and to the left",
+ xy=(0.0, -0.155),
+ xycoords="axes fraction",
+ fontsize=7.4,
+ color=REFERENCE_COLOR,
+ ha="left",
+ va="top",
+ annotation_clip=False,
+ )
+ ax.set_xlabel(f"{names[i]} utility")
+ ax.set_ylabel(f"{names[j]} utility")
+ if position == 0:
+ ax.legend(fontsize=7.0, loc="upper left", framealpha=0.92)
+
+ ax3d = fig.add_subplot(grid[0, len(pairs)], projection="3d")
+ ax3d.scatter(
+ all_utility[~pareto, 0],
+ all_utility[~pareto, 1],
+ all_utility[~pareto, 2],
+ s=26,
+ color=OBSERVED_GREY,
+ alpha=0.75,
+ )
+ ax3d.scatter(
+ all_utility[pareto, 0],
+ all_utility[pareto, 1],
+ all_utility[pareto, 2],
+ s=62,
+ color="#1baf7a",
+ edgecolor="white",
+ label="Pareto set",
+ )
+ if proposed_utility is not None:
+ ax3d.scatter(
+ proposed_utility[:, 0],
+ proposed_utility[:, 1],
+ proposed_utility[:, 2],
+ s=62,
+ color=PROPOSED_COLOR,
+ marker="^",
+ label="proposed",
+ )
+ ax3d.scatter(
+ [reference[0]], [reference[1]], [reference[2]],
+ marker="*", s=200, color=REFERENCE_COLOR, label="reference",
+ )
+ ax3d.set_xlabel(f"{names[0]}", fontsize=8)
+ ax3d.set_ylabel(f"{names[1]}", fontsize=8)
+ ax3d.set_zlabel(f"{names[2]}", fontsize=8)
+ ax3d.view_init(elev=25, azim=-45)
+ ax3d.set_title("utility space, one fixed view", fontsize=10)
+ ax3d.legend(fontsize=7, loc="upper left")
+
+ data = _write_csv(pd.DataFrame(rows), directory / "05_objective_space.csv")
+ caveats = [
+ "Axes are cropped to the data. The reference point sits below every "
+ "observation, so drawing it in scale would spend most of the panel on "
+ "empty space; it is marked at the corner instead.",
+ "The green step line is the front FOR THAT PAIR. Rings mark points on the "
+ "full three-objective front -- several of those sit inside the pairwise "
+ "trade-off, which is what a three-way trade-off looks like in projection.",
+ "UTILITY space, not measurement units. Thickness utility peaks at the "
+ "650 nm target, so a point high on that axis is near the target rather "
+ "than thick.",
+ "The 3D panel is a single fixed view and cannot be read for position -- "
+ "depth is ambiguous without rotation. Read the pairwise panels for which "
+ "point dominates which.",
+ "A point on the Pareto set is non-dominated among what has been MEASURED. "
+ "It is not a claim about the whole design space.",
+ ]
+ _save(fig, directory / "05_objective_space.png", caveats, tight=False)
+ return FigureRecord(
+ key="05_objective_space",
+ title="The trade-off between objectives",
+ caption=(
+ "Every measured film in utility space: pairwise panels with the Pareto "
+ "set traced, plus one 3D view. Open markers are the proposed batch; the "
+ "red star is the campaign's reference point."
+ ),
+ png="05_objective_space.png",
+ data=(data,),
+ caveats=tuple(caveats),
+ )
+
+
+# --------------------------------------------------------------------------- #
+# orchestrator
+# --------------------------------------------------------------------------- #
+
+
+def generate_round_report(
+ workbook: str | Path,
+ config: Mapping[str, Any],
+ *,
+ proposal: Any = None,
+ review: Any = None,
+ outdir: str | Path | None = None,
+ seed: int | None = None,
+ shap_max_instances: int = 15,
+ progress: Callable[[str], None] | None = None,
+ when: str | None = None,
+) -> ReportManifest:
+ """Render the round's figures beside the workbook and return the manifest.
+
+ ``proposal`` is a :class:`campaign.RoundResult`; supplying it turns on the two
+ batch figures. Without one this runs in ``data_only`` mode, which is what the
+ "Figures from current data" button uses once measurements are entered and
+ before anything is proposed.
+
+ A figure that fails is recorded in ``skipped`` and in ``notices`` and the rest
+ of the report still renders. That is deliberate: losing the attribution panel
+ should not cost the parity plot, and a silent absence is prevented by the
+ manifest naming what went wrong.
+ """
+
+ def say(message: str) -> None:
+ if progress is not None:
+ progress(message)
+
+ started = time.perf_counter()
+ workbook = Path(workbook)
+ names = list(objective_names(config))
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+ resolved_seed = (
+ int(config.get("reproducibility", {}).get("seed", 0)) if seed is None else seed
+ )
+ stamp = when or datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+
+ mode = "proposal" if proposal is not None else "data_only"
+ round_name = str(getattr(proposal, "round_name", None) or "current")
+ directory = (
+ Path(outdir)
+ if outdir is not None
+ else report_directory(workbook, round_name, when=stamp)
+ )
+ directory.mkdir(parents=True, exist_ok=True)
+
+ say("Reading the workbook...")
+ contents = read_campaign_workbook(workbook, config)
+ X_phys = contents.inputs.to_numpy(float)
+ Y_measured = contents.model_values.to_numpy(float)
+ labels = [str(value) for value in contents.sample_ids]
+ blocks = _observations_by_round(workbook, config)
+
+ say("Fitting the model the round used...")
+ model, model_warnings = fit_campaign_models(
+ config, X_phys, Y_measured, seed=resolved_seed
+ )
+
+ figures: list[FigureRecord] = []
+ skipped: list[tuple[str, str]] = []
+ notices: list[str] = []
+
+ def attempt(key: str, build: Callable[[], FigureRecord]) -> None:
+ say(f"Rendering {key}...")
+ try:
+ figures.append(build())
+ except Exception as exc: # noqa: BLE001 - one bad figure must not cost the rest
+ detail = f"{type(exc).__name__}: {exc}"
+ skipped.append((key, detail))
+ notices.append(f"{key} could not be rendered -- {detail}")
+ (directory / f"{key}.error.txt").write_text(
+ traceback.format_exc(), encoding="utf-8"
+ )
+
+ proposed_X = None
+ if proposal is not None:
+ proposed_X = proposal.conditions.to_numpy(float)
+ attempt(
+ "00_batch_placement",
+ lambda: _figure_batch_placement(
+ directory, config, X_phys, proposed_X, round_name
+ ),
+ )
+ else:
+ skipped.append(
+ ("00_batch_placement", "no proposal supplied (data-only mode)")
+ )
+
+ attempt(
+ "01_loo_parity",
+ lambda: _figure_loo_parity(
+ directory, config, X_phys, Y_measured, names, labels, resolved_seed
+ ),
+ )
+ attempt(
+ "02_attribution",
+ lambda: _figure_attribution(
+ directory,
+ config,
+ model,
+ transform,
+ X_phys,
+ names,
+ resolved_seed,
+ shap_max_instances,
+ ),
+ )
+
+ if proposal is not None and review is not None:
+ observed_utility = _utility(transform, Y_measured)
+
+ def build_batch_figure() -> FigureRecord:
+ hv_frame = _batch_hypervolume_diagnostic(
+ config,
+ model,
+ transform,
+ observed_utility,
+ proposed_X,
+ reference,
+ resolved_seed,
+ )
+ return _figure_batch_predictions(
+ directory, config, review, names, hv_frame, round_name
+ )
+
+ attempt("03_batch_predictions", build_batch_figure)
+ else:
+ skipped.append(
+ (
+ "03_batch_predictions",
+ "no proposal supplied (data-only mode)"
+ if proposal is None
+ else "no batch review supplied",
+ )
+ )
+
+ attempt(
+ "04_hv_trajectory",
+ lambda: _figure_hv_trajectory(directory, blocks, transform, reference),
+ )
+
+ proposed_utility = None
+ proposed_sd = None
+ if review is not None:
+ proposed_utility = np.column_stack(
+ [review.candidates[f"{name}_utility"].to_numpy(float) for name in names]
+ )
+ proposed_sd = np.column_stack(
+ [review.candidates[f"{name}_sd"].to_numpy(float) for name in names]
+ )
+ attempt(
+ "05_objective_space",
+ lambda: _figure_objective_space(
+ directory,
+ blocks,
+ transform,
+ reference,
+ names,
+ proposed_utility,
+ proposed_sd,
+ ),
+ )
+
+ # ---------------------------------------------------------- the notices --
+ entries = config["objectives"]["specs"]
+ frozen = [
+ str(entry.get("name"))
+ for entry in entries
+ if str((entry.get("measurement") or {}).get("recipe")) == "stored"
+ ]
+ if frozen:
+ notices.append(
+ f"{' and '.join(frozen)} are taken from the workbook as stored; no "
+ "independent recomputation exists under this contract, so a stale "
+ "value in those columns would not be caught here."
+ )
+ for index, name in enumerate(names):
+ status = str(entries[index].get("signal_status", ""))
+ if status and status != "learnable":
+ notices.append(
+ f"{name}: {status.replace('_', ' ')} -- its model does not beat the "
+ "leave-one-out null, so its predictions carry no signal."
+ )
+ for warning in model_warnings:
+ notices.append(f"model fit warning: {warning}")
+ for finding in contents.findings:
+ if finding.severity is ScoreSeverity.WARNING:
+ notices.append(f"data: {finding}")
+
+ runtime = time.perf_counter() - started
+ manifest = ReportManifest(
+ directory=directory,
+ round_name=round_name,
+ mode=mode,
+ figures=tuple(figures),
+ skipped=tuple(skipped),
+ notices=tuple(dict.fromkeys(notices)),
+ context={
+ "workbook": workbook.name,
+ "generated_utc": stamp,
+ "objective_contract": config["objectives"]["contract_version"],
+ "campaign": config.get("campaign", {}).get("name"),
+ "seed": resolved_seed,
+ "reference_point_utility": [float(value) for value in reference],
+ "observed_rows": int(len(X_phys)),
+ "rounds_measured": [block.name for block in blocks],
+ "git": _git_describe(),
+ "python": platform.python_version(),
+ "posterior_draws_for_hv": HV_POSTERIOR_SAMPLES,
+ "shap_instances": int(min(shap_max_instances, len(X_phys))),
+ },
+ runtime_seconds=runtime,
+ )
+ (directory / "manifest.json").write_text(
+ json.dumps(manifest.as_dict(), indent=2), encoding="utf-8"
+ )
+ (directory / "README.txt").write_text(manifest.summary() + "\n", encoding="utf-8")
+ say("Report written.")
+ return manifest
diff --git a/src/mobo_kit/scores.py b/src/mobo_kit/scores.py
new file mode 100644
index 0000000..4abbac8
--- /dev/null
+++ b/src/mobo_kit/scores.py
@@ -0,0 +1,1231 @@
+"""Turn the workbook's columns into each objective's model input.
+
+**THE LIVE POLICY, and it is not uniform across objectives.** Under
+``d2d-objectives-v4-final-nomean``:
+
+* **uniformity and optoelectronic are READ AS STORED.** The workbook's score
+ column *is* the objective value. Python computes nothing and this module
+ deliberately does NOT record how those numbers are arrived at.
+
+ **THE SCORE VALUE IS THE INTERFACE; THE FORMULA BEHIND IT IS NOT.** How a
+ composite score is defined is a decision each group makes for itself -- which
+ terms, what weighting, which normalisation -- and MOBO-Kit is not the right
+ place to encode one group's convention. Two groups running this tool on the
+ same chemistry may disagree entirely on how uniformity is scored and both be
+ right. What they share is the shape of the contract: a number per objective per
+ film, on a declared scale. Encoding one group's arithmetic here would make the
+ tool quietly specific to them, and in practice it also gave the definition a
+ second place to live and go stale, which happened three times.
+
+ The `stored` recipe is the whole implementation.
+* **thickness is COMPUTED**, from ``T1..T4`` with the operator's ``T anom``
+ readings excluded and reported, and cross-checked against the workbook's own
+ average. It earns the exception because the recomputation is what lets an
+ anomalous reading be excluded *and named*, and because the model trains on
+ nanometres rather than on the stored score -- see the objective transform, not
+ this module, for why that matters.
+
+**What this costs, stated plainly:** there is no independent recomputation of the
+two frozen objectives, so a stale pasted literal in either column cannot be caught
+by comparing it against anything here. That is the price of the freeze and it is
+paid deliberately.
+
+THE RECIPES. Named in config, implemented here, because a recipe is the shape of
+a measurement rather than a property of one dataset:
+
+=================== =========================================================
+``stored`` the column IS the value; no arithmetic v4
+``mean_of_present`` mean of whichever of ``T1..T4`` were measured all
+``mean`` the mean of every input, all of them required v3
+``product`` the product of every input v2
+``log10_product`` the sum of the inputs' base-10 logarithms v2
+=================== =========================================================
+
+**The v2 and v3 recipes are retained and tested, not dead weight.** Their configs
+are archived but must stay loadable, because a contract whose numbers cannot be
+regenerated is a contract nobody can audit. ``log10_product`` sums logarithms
+rather than logging a product, which is algebraically identical and cannot
+overflow on the way there.
+
+``mean_of_present`` needs at least one reading; every other recipe needs all of
+theirs. **Blank means not measured, never zero.** Thickness rows carry three or
+four readings depending on the film, so a recipe demanding all four would reject
+the campaign; a blank ``Coverage`` is a missing measurement and ``mean`` refuses
+it.
+
+Two input transforms carry a threshold, ``clamped_complement`` and
+``capped_ratio``. Both exist for archived contracts only. Nothing live uses them,
+and a live contract that needs one should think hard first: a clamp that binds is
+information being discarded, and on the v3 data it bound on two of fifteen rows.
+
+CROSS-CHECKS AND FINGERPRINTS are two different instruments and the difference
+matters. A ``cross_check`` compares a computed value against a stored one and so
+only exists where Python computes -- thickness. A ``formula_fingerprint`` reads a
+column's FORMULA TEXT, never evaluates it, and reports when that text changes; it
+is what a frozen objective has instead of a cross-check. It notices a changed
+DEFINITION, not a stale VALUE, and that gap is inherent to reading a number
+somebody else computes.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from enum import Enum
+from numbers import Real
+from typing import Any, Callable, Iterable, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+
+__all__ = [
+ "AgreementCheck",
+ "FormulaFingerprint",
+ "CrossCheck",
+ "MeasurementInput",
+ "MeasurementResult",
+ "MeasurementSpec",
+ "RECIPES",
+ "ScoreFinding",
+ "ScoreSeverity",
+ "ScoreValidationError",
+ "compute_measurements",
+ "entry_columns",
+ "measurement_spec_from_config",
+ "row_completeness",
+]
+
+
+#: A cell holding this is empty as far as the campaign is concerned. The
+#: workbook's ``T anom`` column is full of non-breaking spaces, which are not
+#: ``None`` and are not whitespace to ``str.strip`` unless normalised first.
+_BLANK_TEXT = frozenset({"", "-", "--", "n/a", "na"})
+
+#: Excel leaves non-breaking spaces in cells that look empty -- the
+#: workbook's ``T anom`` column is full of them -- and ``str.strip`` does not
+#: remove one, so it has to be normalised before any blank test.
+_NBSP = "\u00a0"
+
+
+class _NotNumeric(ValueError):
+ """A cell holds something that is present but not a number."""
+
+
+def _is_missing(value: Any) -> bool:
+ if value is None:
+ return True
+ if isinstance(value, str):
+ return value.replace(_NBSP, " ").strip().lower() in _BLANK_TEXT
+ if isinstance(value, (bool, np.bool_)):
+ return False
+ try:
+ missing = pd.isna(value)
+ except (TypeError, ValueError):
+ return False
+ return isinstance(missing, (bool, np.bool_)) and bool(missing)
+
+
+def _number(value: Any, *, column: str) -> float | None:
+ """Return ``value`` as a float, ``None`` if the cell is empty."""
+ if _is_missing(value):
+ return None
+ if isinstance(value, (bool, np.bool_)):
+ raise _NotNumeric(f"{column!r} holds a boolean, not a measurement.")
+ if isinstance(value, Real):
+ number = float(value)
+ elif isinstance(value, str):
+ try:
+ number = float(value.replace(_NBSP, " ").strip())
+ except ValueError as exc:
+ raise _NotNumeric(f"{column!r} holds {value!r}, which is not a number.") from exc
+ else:
+ raise _NotNumeric(f"{column!r} holds {type(value).__name__}, not a number.")
+ if not math.isfinite(number):
+ raise _NotNumeric(f"{column!r} holds {value!r}, which is not finite.")
+ return number
+
+
+# --------------------------------------------------------------------------- #
+# recipes
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class _Recipe:
+ """One way of turning measured columns into a single model input."""
+
+ name: str
+ combine: Callable[[Sequence[float]], float]
+ #: ``True`` when every declared input must be present, ``False`` when the
+ #: recipe averages over whichever ones were measured.
+ requires_all: bool
+ description: str
+
+ def apply(self, values: Sequence[float]) -> float:
+ result = float(self.combine(values))
+ if not math.isfinite(result):
+ raise _NotNumeric(f"recipe {self.name!r} produced a non-finite result.")
+ return result
+
+
+def _product(values: Sequence[float]) -> float:
+ result = 1.0
+ for value in values:
+ result *= value
+ return result
+
+
+def _log10_product(values: Sequence[float]) -> float:
+ for value in values:
+ if value <= 0:
+ raise _NotNumeric(
+ f"log10 needs strictly positive inputs; got {value!r}. A failed "
+ "film must be recorded as blank, not as zero."
+ )
+ return math.fsum(math.log10(value) for value in values)
+
+
+def _mean_of_present(values: Sequence[float]) -> float:
+ return math.fsum(values) / len(values)
+
+
+def _single(values: Sequence[float]) -> float:
+ """The one value, unchanged. Guarded because 'stored' means exactly one column."""
+ if len(values) != 1:
+ raise _NotNumeric(
+ f"the 'stored' recipe takes exactly one column; got {len(values)}."
+ )
+ return float(values[0])
+
+
+RECIPES: Mapping[str, _Recipe] = {
+ recipe.name: recipe
+ for recipe in (
+ _Recipe("product", _product, True, "the product of every input"),
+ _Recipe(
+ "log10_product",
+ _log10_product,
+ True,
+ "the sum of the base-10 logarithms, i.e. log10 of the product",
+ ),
+ # Same arithmetic as `mean_of_present`, opposite policy on a blank cell.
+ # `mean` is for terms that were all supposed to be measured -- a missing
+ # Coverage is a hole in the row, and averaging the other two would quietly
+ # answer a different question. `mean_of_present` is for repeated readings
+ # of one quantity, where three instead of four is a normal film.
+ _Recipe("mean", _mean_of_present, True, "the mean of every input"),
+ # The FROZEN objective. `stored` takes the workbook's own score column as
+ # the objective value and computes nothing, which is a deliberate reversal
+ # of this module's usual polarity -- normally Python computes and the
+ # stored cell is demoted to a cross-check.
+ #
+ # It exists because the group is still revising how uniformity and
+ # optoelectronic are defined. Reimplementing a formula that is about to
+ # change means the code and the sheet disagree at exactly the moment
+ # someone edits the sheet, and the disagreement would look like a bug in
+ # whichever one was checked second. Reading the value instead makes the
+ # workbook the single source of truth while the definition moves.
+ #
+ # What is LOST by freezing, and is worth saying out loud: there is no
+ # independent recomputation of these two objectives under this contract,
+ # so a stale pasted literal in the score column cannot be detected by
+ # comparing it against anything. `formula_fingerprint` is the partial
+ # replacement -- it notices when the DEFINITION moves, not when a value
+ # goes stale.
+ _Recipe(
+ "stored",
+ _single,
+ True,
+ "the workbook's own score column, taken as computed and not recomputed",
+ ),
+ _Recipe(
+ "mean_of_present",
+ _mean_of_present,
+ False,
+ "the mean of whichever inputs were measured",
+ ),
+ )
+}
+
+
+# --------------------------------------------------------------------------- #
+# configuration
+# --------------------------------------------------------------------------- #
+
+
+@dataclass(frozen=True)
+class MeasurementInput:
+ """One measured column feeding a recipe.
+
+ ``complement`` exists because the workbook records ``Uniformity`` and the
+ score wants ``1 - Uniformity``. The workbook also stores that complement in
+ its own column, but as a pasted literal -- so it is computed here and the
+ stored column is only ever a cross-check. The same is true of the v3
+ workbook's clamped copy of the reading.
+
+ The two parameterised transforms take their thresholds from config rather
+ than hard-coding them, because a clamp at 0.99 and a cap at 1.4 V are
+ campaign decisions the group can revise, not physics.
+ """
+
+ column: str
+ transform: str = "identity"
+ #: ``clamped_complement``: readings STRICTLY above this are replaced.
+ clamp_above: float | None = None
+ #: ``clamped_complement``: what they are replaced with.
+ clamp_to: float | None = None
+ #: ``capped_ratio``: the ceiling, which is also the divisor.
+ cap: float | None = None
+
+ def __post_init__(self) -> None:
+ if not isinstance(self.column, str) or not self.column.strip():
+ raise ValueError("A measurement input needs a non-empty column name.")
+ object.__setattr__(self, "column", self.column.strip())
+ if self.transform not in _TRANSFORM_PARAMETERS:
+ raise ValueError(
+ f"Unsupported measurement transform {self.transform!r}; "
+ f"expected one of {sorted(_TRANSFORM_PARAMETERS)}."
+ )
+
+ def _required(field: str, *, positive: bool = False) -> float:
+ raw = getattr(self, field)
+ if raw is None:
+ raise ValueError(
+ f"Transform {self.transform!r} on column {self.column!r} needs "
+ f"{field!r}."
+ )
+ if isinstance(raw, (bool, np.bool_)) or not isinstance(raw, Real):
+ raise ValueError(
+ f"{field!r} on column {self.column!r} must be a number; "
+ f"got {raw!r}."
+ )
+ number = float(raw)
+ if not math.isfinite(number):
+ raise ValueError(f"{field!r} on column {self.column!r} must be finite.")
+ if positive and number <= 0:
+ raise ValueError(
+ f"{field!r} on column {self.column!r} must be positive; "
+ f"got {number!r}."
+ )
+ object.__setattr__(self, field, number)
+ return number
+
+ unused = [
+ field
+ for field in ("clamp_above", "clamp_to", "cap")
+ if getattr(self, field) is not None
+ and field not in _TRANSFORM_PARAMETERS[self.transform]
+ ]
+ if unused:
+ # A threshold that silently does nothing is how a clamp gets believed
+ # to be active when it is not.
+ raise ValueError(
+ f"Transform {self.transform!r} on column {self.column!r} ignores "
+ f"{unused}; remove them or change the transform."
+ )
+ for field in _TRANSFORM_PARAMETERS[self.transform]:
+ _required(field, positive=field == "cap")
+
+ def evaluate(self, value: float) -> float:
+ if self.transform == "identity":
+ return value
+ if self.transform == "complement":
+ return 1.0 - value
+ if self.transform == "clamped_complement":
+ # strictly above, so an exact clamp_above keeps its own value
+ clamped = self.clamp_to if value > self.clamp_above else value
+ return 1.0 - float(clamped)
+ return min(value, self.cap) / self.cap
+
+
+#: Which thresholds each transform consumes. Declared once so an unused threshold
+#: is an error rather than a silent no-op.
+_TRANSFORM_PARAMETERS: Mapping[str, tuple[str, ...]] = {
+ "identity": (),
+ "complement": (),
+ "clamped_complement": ("clamp_above", "clamp_to"),
+ "capped_ratio": ("cap",),
+}
+
+
+@dataclass(frozen=True)
+class CrossCheck:
+ """A stored column to compare the computed value against.
+
+ ``atol`` is per column on purpose: a live formula and a deliberately rounded
+ literal do not deserve the same tolerance.
+ """
+
+ column: str
+ atol: float = 0.005
+
+ def __post_init__(self) -> None:
+ if not isinstance(self.column, str) or not self.column.strip():
+ raise ValueError("A cross-check needs a non-empty column name.")
+ object.__setattr__(self, "column", self.column.strip())
+ if isinstance(self.atol, (bool, np.bool_)) or not isinstance(self.atol, Real):
+ raise ValueError(f"Cross-check {self.column!r} atol must be a number.")
+ atol = float(self.atol)
+ if not math.isfinite(atol) or atol < 0:
+ raise ValueError(
+ f"Cross-check {self.column!r} atol must be finite and non-negative."
+ )
+ object.__setattr__(self, "atol", atol)
+
+
+@dataclass(frozen=True)
+class AgreementCheck:
+ """Does a supplied normalised column still rank like the raw one it summarises?
+
+ Some columns arrive already normalised, with the derivation living outside the
+ workbook -- ``Normalized photoconductance`` is one, and nothing in the sheet
+ computes it. A recipe can only take such a column on trust, which means a
+ normalisation that has come loose from its raw measurement is invisible: every
+ value is in range, every row computes, and the objective is simply about
+ something else than it says.
+
+ Rank agreement is the check that needs no formula. Whatever the mapping is,
+ a normalisation of a raw quantity must at least preserve its order, so
+ Spearman between the two is expected to be strongly positive. This reports it
+ and warns below ``min_spearman``.
+
+ It is a FINDING, never a gate: which column the model trains on is a decision
+ for the group, and a diagnostic that blocks a round would make that decision
+ by refusing to run.
+ """
+
+ raw: str
+ normalized: str
+ min_spearman: float = 0.0
+
+ def __post_init__(self) -> None:
+ for field in ("raw", "normalized"):
+ value = getattr(self, field)
+ if not isinstance(value, str) or not value.strip():
+ raise ValueError(f"An agreement check needs a non-empty {field!r}.")
+ object.__setattr__(self, field, value.strip())
+ if self.raw == self.normalized:
+ raise ValueError(
+ f"An agreement check compares two different columns; got "
+ f"{self.raw!r} twice."
+ )
+ threshold = self.min_spearman
+ if isinstance(threshold, (bool, np.bool_)) or not isinstance(threshold, Real):
+ raise ValueError("min_spearman must be a number.")
+ threshold = float(threshold)
+ if not math.isfinite(threshold) or not -1.0 <= threshold <= 1.0:
+ raise ValueError("min_spearman must be finite and within [-1, 1].")
+ object.__setattr__(self, "min_spearman", threshold)
+
+
+@dataclass(frozen=True)
+class FormulaFingerprint:
+ """The formula text a frozen score column is expected to carry.
+
+ A ``stored`` objective is read rather than recomputed, so nothing in Python
+ knows what it means. That is the point -- the group is still revising the
+ definition -- but it removes the cross-check that would otherwise catch a
+ redefinition. This is the partial replacement: record the formula as it
+ stands, and say so when it changes.
+
+ **It notices a changed definition, not a stale value.** A pasted literal that
+ has stopped tracking its inputs looks identical to a correct one from here.
+ That gap is inherent to freezing and is recorded rather than papered over; it
+ closes when the group settles the formulas and the recipes are unfrozen.
+
+ Compared after collapsing whitespace and upper-casing, because Excel rewrites
+ those freely, and with the row number stripped so one fingerprint covers every
+ row rather than fifteen near-copies.
+ """
+
+ column: str
+ formula: str
+
+ def __post_init__(self) -> None:
+ for field in ("column", "formula"):
+ value = getattr(self, field)
+ if not isinstance(value, str) or not value.strip():
+ raise ValueError(f"A formula fingerprint needs a non-empty {field!r}.")
+ object.__setattr__(self, field, value.strip())
+
+ @staticmethod
+ def canonical(formula: Any) -> str:
+ """Row-independent, whitespace-independent form of a formula string."""
+ import re
+
+ if not isinstance(formula, str):
+ return ""
+ text = formula.strip().lstrip("=").upper()
+ text = re.sub(r"\s+", "", text)
+ # A2 -> A, AJ17 -> AJ: the same formula copied down a column differs only
+ # in the row, and fingerprinting per row would report fifteen changes for
+ # one edit.
+ return re.sub(r"(\$?[A-Z]{1,3})\$?\d+", r"\1", text)
+
+ def matches(self, formula: Any) -> bool:
+ return self.canonical(formula) == self.canonical(self.formula)
+
+
+@dataclass(frozen=True)
+class MeasurementSpec:
+ """How one objective's model input is computed and checked."""
+
+ name: str
+ recipe: str
+ inputs: tuple[MeasurementInput, ...]
+ cross_checks: tuple[CrossCheck, ...] = ()
+ #: Rank agreement between a supplied normalised column and its raw source.
+ agreement_check: "AgreementCheck | None" = None
+ #: Expected formula text for a frozen score column; checked, never evaluated.
+ formula_fingerprint: "FormulaFingerprint | None" = None
+ #: Columns holding readings the operator judged anomalous. They never enter
+ #: the recipe; their presence is recorded so an exclusion is visible rather
+ #: than silent.
+ excluded: tuple[str, ...] = ()
+ #: Warn when ``(max - min) / mean`` over the used inputs exceeds this. Set
+ #: for thickness, where three R0 rows hold readings that split into two
+ #: clusters rather than scattering around one value.
+ spread_warning_ratio: float | None = None
+
+ def __post_init__(self) -> None:
+ if not isinstance(self.name, str) or not self.name.strip():
+ raise ValueError("A measurement spec needs a non-empty name.")
+ object.__setattr__(self, "name", self.name.strip())
+ if self.recipe not in RECIPES:
+ raise ValueError(
+ f"Objective {self.name!r} requests unknown recipe {self.recipe!r}; "
+ f"known recipes are {sorted(RECIPES)}."
+ )
+ if not self.inputs:
+ raise ValueError(f"Objective {self.name!r} declares no measurement inputs.")
+ if self.recipe == "stored" and len(self.inputs) != 1:
+ raise ValueError(
+ f"Objective {self.name!r} uses the 'stored' recipe, which reads one "
+ f"score column; it declares {len(self.inputs)} inputs."
+ )
+ columns = [item.column for item in self.inputs]
+ duplicates = sorted({c for c in columns if columns.count(c) > 1})
+ if duplicates:
+ raise ValueError(
+ f"Objective {self.name!r} repeats measurement input(s) {duplicates}."
+ )
+ if self.spread_warning_ratio is not None:
+ ratio = float(self.spread_warning_ratio)
+ if not math.isfinite(ratio) or ratio <= 0:
+ raise ValueError(
+ f"Objective {self.name!r} spread_warning_ratio must be a "
+ "positive finite number."
+ )
+ object.__setattr__(self, "spread_warning_ratio", ratio)
+
+ @property
+ def recipe_impl(self) -> _Recipe:
+ return RECIPES[self.recipe]
+
+ @property
+ def required_columns(self) -> tuple[str, ...]:
+ """Columns without which this objective cannot be computed at all."""
+ if self.recipe_impl.requires_all:
+ return tuple(item.column for item in self.inputs)
+ return ()
+
+ @property
+ def optional_columns(self) -> tuple[str, ...]:
+ # BOTH of the agreement check's columns are offered but never required.
+ # It used to list only `raw`, on the assumption that `normalized` was an
+ # input to the recipe -- true while optoelectronic was computed, false the
+ # moment it was frozen and its only input became the score column. The
+ # check then silently reported "column absent" on a sheet that had it.
+ agreement = (
+ (self.agreement_check.raw, self.agreement_check.normalized)
+ if self.agreement_check
+ else ()
+ )
+ if self.recipe_impl.requires_all:
+ return tuple(self.excluded) + agreement
+ return (
+ tuple(item.column for item in self.inputs)
+ + tuple(self.excluded)
+ + agreement
+ )
+
+
+def measurement_spec_from_config(entry: Mapping[str, Any]) -> MeasurementSpec | None:
+ """Build a spec from one objective's ``measurement`` block, if present.
+
+ Returning ``None`` for an objective without the block is deliberate: an
+ objective that still reads its stored column keeps working unchanged.
+ """
+ block = entry.get("measurement")
+ if block is None:
+ return None
+ if not isinstance(block, Mapping):
+ raise ValueError("measurement must be a mapping.")
+
+ raw_inputs = block.get("inputs")
+ if (
+ not isinstance(raw_inputs, Sequence)
+ or isinstance(raw_inputs, (str, bytes))
+ or not raw_inputs
+ ):
+ raise ValueError("measurement.inputs must be a non-empty list.")
+ inputs = []
+ _INPUT_KEYS = {"column", "transform", "clamp_above", "clamp_to", "cap"}
+ for item in raw_inputs:
+ if isinstance(item, str):
+ inputs.append(MeasurementInput(item))
+ elif isinstance(item, Mapping):
+ # A misspelt threshold would otherwise be dropped in silence, leaving
+ # a clamp everyone believes is configured and nothing applying it.
+ unknown = sorted(set(item) - _INPUT_KEYS)
+ if unknown:
+ raise ValueError(
+ f"Measurement input {item.get('column')!r} has unknown key(s) "
+ f"{unknown}; expected {sorted(_INPUT_KEYS)}."
+ )
+ inputs.append(
+ MeasurementInput(
+ str(item["column"]),
+ str(item.get("transform", "identity")),
+ clamp_above=item.get("clamp_above"),
+ clamp_to=item.get("clamp_to"),
+ cap=item.get("cap"),
+ )
+ )
+ else:
+ raise ValueError("Each measurement input must be a string or a mapping.")
+
+ raw_checks = block.get("cross_check") or ()
+ if isinstance(raw_checks, Mapping):
+ raw_checks = [raw_checks]
+ elif isinstance(raw_checks, (str, bytes)):
+ raw_checks = [{"column": raw_checks}]
+ checks = []
+ for item in raw_checks:
+ if isinstance(item, str):
+ checks.append(CrossCheck(item))
+ elif isinstance(item, Mapping):
+ atol = item.get("atol")
+ checks.append(
+ CrossCheck(str(item["column"]), 0.005 if atol is None else float(atol))
+ )
+ else:
+ raise ValueError("Each cross_check must be a string or a mapping.")
+
+ raw_excluded = block.get("excluded") or ()
+ if isinstance(raw_excluded, (str, bytes)):
+ raw_excluded = [raw_excluded]
+ excluded = tuple(
+ str(item["column"]) if isinstance(item, Mapping) else str(item)
+ for item in raw_excluded
+ )
+
+ raw_fingerprint = block.get("formula_fingerprint")
+ if raw_fingerprint is None:
+ fingerprint = None
+ elif isinstance(raw_fingerprint, Mapping):
+ fingerprint = FormulaFingerprint(
+ column=str(raw_fingerprint["column"]),
+ formula=str(raw_fingerprint["formula"]),
+ )
+ else:
+ raise ValueError("measurement.formula_fingerprint must be a mapping.")
+
+ raw_agreement = block.get("agreement_check")
+ if raw_agreement is None:
+ agreement = None
+ elif isinstance(raw_agreement, Mapping):
+ threshold = raw_agreement.get("min_spearman")
+ agreement = AgreementCheck(
+ raw=str(raw_agreement["raw"]),
+ normalized=str(raw_agreement["normalized"]),
+ min_spearman=0.0 if threshold is None else float(threshold),
+ )
+ else:
+ raise ValueError("measurement.agreement_check must be a mapping.")
+
+ ratio = block.get("spread_warning_ratio")
+ return MeasurementSpec(
+ name=str(entry.get("name", block.get("name", ""))),
+ recipe=str(block["recipe"]),
+ inputs=tuple(inputs),
+ cross_checks=tuple(checks),
+ agreement_check=agreement,
+ formula_fingerprint=fingerprint,
+ excluded=excluded,
+ spread_warning_ratio=None if ratio is None else float(ratio),
+ )
+
+
+def entry_columns(
+ specs: Sequence[MeasurementSpec],
+) -> tuple[tuple[str, ...], tuple[str, ...]]:
+ """The columns a worklist sheet must offer, split required / optional.
+
+ Order is declaration order, de-duplicated. A column that is required by one
+ objective and optional for another counts as required.
+ """
+ required: list[str] = []
+ optional: list[str] = []
+ for spec in specs:
+ for column in spec.required_columns:
+ if column not in required:
+ required.append(column)
+ for column in spec.optional_columns:
+ if column not in optional:
+ optional.append(column)
+ optional = [column for column in optional if column not in required]
+ return tuple(required), tuple(optional)
+
+
+# --------------------------------------------------------------------------- #
+# findings
+# --------------------------------------------------------------------------- #
+
+
+class ScoreSeverity(str, Enum):
+ """How much a finding should stop you."""
+
+ NOTE = "note"
+ WARNING = "warning"
+ ERROR = "error"
+
+
+@dataclass(frozen=True)
+class ScoreFinding:
+ """One thing worth saying about one row."""
+
+ severity: ScoreSeverity
+ code: str
+ objective: str
+ row_position: int
+ sample_id: Any
+ message: str
+ column: str | None = None
+
+ @property
+ def is_column_level(self) -> bool:
+ """True when the finding is about a COLUMN rather than about one row.
+
+ ``row_position = -1`` is the marker. Rank agreement over fifteen rows and
+ an absent column are both statements about the sheet, not about a film.
+ """
+ return self.row_position < 0
+
+ def __str__(self) -> str:
+ # A column-level finding used to render as "sample ?", which reads as a
+ # row whose identity got lost rather than as a finding that has no row.
+ where = (
+ f"all rows, {self.objective}"
+ if self.is_column_level
+ else f"sample {self.sample_id}, {self.objective}"
+ )
+ return f"[{self.severity.value}] {where}: {self.message}"
+
+
+class ScoreValidationError(ValueError):
+ """At least one row could not be turned into a model input."""
+
+ def __init__(self, findings: Sequence[ScoreFinding]) -> None:
+ self.findings = tuple(findings)
+ detail = "\n- ".join(str(finding) for finding in self.findings)
+ super().__init__(f"Objective values could not be computed:\n- {detail}")
+
+
+@dataclass(frozen=True)
+class MeasurementResult:
+ """Computed model inputs, how many readings each used, and what to say."""
+
+ values: pd.DataFrame
+ """One column per objective, in declaration order. NaN where unusable."""
+ inputs_used: pd.DataFrame
+ """How many measured inputs each value was computed from."""
+ findings: tuple[ScoreFinding, ...]
+
+ def _by(self, severity: ScoreSeverity) -> tuple[ScoreFinding, ...]:
+ return tuple(f for f in self.findings if f.severity is severity)
+
+ @property
+ def notes(self) -> tuple[ScoreFinding, ...]:
+ return self._by(ScoreSeverity.NOTE)
+
+ @property
+ def warnings(self) -> tuple[ScoreFinding, ...]:
+ return self._by(ScoreSeverity.WARNING)
+
+ @property
+ def errors(self) -> tuple[ScoreFinding, ...]:
+ return self._by(ScoreSeverity.ERROR)
+
+ @property
+ def has_errors(self) -> bool:
+ return bool(self.errors)
+
+ def findings_frame(self) -> pd.DataFrame:
+ return pd.DataFrame(
+ {
+ "severity": [f.severity.value for f in self.findings],
+ "code": [f.code for f in self.findings],
+ "objective": [f.objective for f in self.findings],
+ "sample_id": [f.sample_id for f in self.findings],
+ "column": [f.column for f in self.findings],
+ "message": [f.message for f in self.findings],
+ }
+ )
+
+ def raise_for_errors(self) -> "MeasurementResult":
+ if self.errors:
+ raise ScoreValidationError(self.errors)
+ return self
+
+
+# --------------------------------------------------------------------------- #
+# computation
+# --------------------------------------------------------------------------- #
+
+
+def _cell(frame: pd.DataFrame, column: str, position: int) -> Any:
+ return frame[column].to_numpy(dtype=object)[position]
+
+
+def _with_stripped_columns(frame: pd.DataFrame) -> pd.DataFrame:
+ """Compare column names with surrounding whitespace removed, on both sides.
+
+ :class:`MeasurementInput` and :class:`CrossCheck` strip the names they are
+ given, so a config may quote a header verbatim -- and the v3 sheet has two
+ that end in a space, ``'PL - Implied Voc (Max) Raw '`` and ``'Normalized
+ photoconductance '``. ``workbook_io`` strips the sheet side when it builds
+ its header index, but a caller who reads the sheet with pandas directly does
+ not, and would then be told the column is missing. It fails closed rather
+ than silently, but "missing" is the wrong answer to give about a column that
+ is right there.
+
+ Renaming is skipped entirely when it would merge two distinct labels, because
+ quietly dropping one of them would be worse than the confusion this avoids.
+ """
+ labels = list(frame.columns)
+ stripped = [name.strip() if isinstance(name, str) else name for name in labels]
+ if stripped == labels:
+ return frame
+ if len(set(map(str, stripped))) != len(stripped):
+ return frame
+ renamed = frame.copy(deep=False)
+ renamed.columns = stripped
+ return renamed
+
+
+def _absent_required_columns(
+ frame: pd.DataFrame, specs: Sequence[MeasurementSpec]
+) -> tuple[str, ...]:
+ """Input columns whose absence makes an objective impossible, not merely thin.
+
+ A recipe that needs all its inputs cannot proceed without any one of them. A
+ recipe that averages over whatever was measured can: a sheet with no ``T4``
+ column is a sheet where nobody measured a fourth point, which is the same
+ situation as an empty ``T4`` cell and is handled the same way.
+ """
+ absent: list[str] = []
+ for spec in specs:
+ for column in spec.required_columns:
+ if column not in frame.columns and column not in absent:
+ absent.append(column)
+ return tuple(absent)
+
+
+def _agreement_findings(
+ frame: pd.DataFrame,
+ spec: MeasurementSpec,
+ sample_ids: Sequence[Any],
+) -> list[ScoreFinding]:
+ """Rank-compare a supplied normalised column against the raw one it summarises.
+
+ One finding for the whole column, not one per row -- the question is about the
+ mapping, so ``row_position`` is -1 and ``sample_id`` is None. The message
+ names the highest-raw film explicitly, because "Spearman is negative" is a
+ statistic and "the strongest film scores lowest" is the thing a reviewer can
+ act on.
+ """
+ check = spec.agreement_check
+ assert check is not None # caller checks; keeps the type narrow
+
+ def finding(severity: ScoreSeverity, code: str, message: str) -> ScoreFinding:
+ return ScoreFinding(
+ severity=severity,
+ code=code,
+ objective=spec.name,
+ row_position=-1,
+ sample_id=None,
+ message=message,
+ column=check.normalized,
+ )
+
+ missing = [c for c in (check.raw, check.normalized) if c not in frame.columns]
+ if missing:
+ return [
+ finding(
+ ScoreSeverity.NOTE,
+ "agreement_check_absent",
+ f"{missing} not in this sheet, so {check.normalized!r} cannot be "
+ f"checked against {check.raw!r}.",
+ )
+ ]
+
+ pairs: list[tuple[float, float, Any]] = []
+ for position in range(len(frame)):
+ try:
+ raw = _number(_cell(frame, check.raw, position), column=check.raw)
+ normalized = _number(
+ _cell(frame, check.normalized, position), column=check.normalized
+ )
+ except _NotNumeric:
+ continue
+ if raw is None or normalized is None:
+ continue
+ pairs.append((raw, normalized, sample_ids[position]))
+
+ if len(pairs) < 3:
+ return [
+ finding(
+ ScoreSeverity.NOTE,
+ "agreement_check_too_few_rows",
+ f"only {len(pairs)} row(s) have both {check.raw!r} and "
+ f"{check.normalized!r}; a rank comparison needs at least 3.",
+ )
+ ]
+
+ raw_values = [item[0] for item in pairs]
+ normalized_values = [item[1] for item in pairs]
+ # Checked before calling scipy rather than by testing the result for NaN: a
+ # constant column makes `spearmanr` emit a ConstantInputWarning, and this
+ # suite keeps its warning tail fixed so that a NEW warning means something.
+ constant = [
+ name
+ for name, series in (
+ (check.raw, raw_values),
+ (check.normalized, normalized_values),
+ )
+ if len(set(series)) == 1
+ ]
+ if constant:
+ return [
+ finding(
+ ScoreSeverity.NOTE,
+ "agreement_check_undefined",
+ f"rank correlation is undefined over {len(pairs)} rows because "
+ f"{constant} is constant.",
+ )
+ ]
+
+ from scipy.stats import spearmanr
+
+ result = spearmanr(raw_values, normalized_values)
+ rho = float(result.statistic)
+ p_value = float(result.pvalue)
+ if not math.isfinite(rho): # pragma: no cover - constant input is caught above
+ return [
+ finding(
+ ScoreSeverity.NOTE,
+ "agreement_check_undefined",
+ f"rank correlation over {len(pairs)} rows is not a finite number.",
+ )
+ ]
+
+ strongest = max(pairs, key=lambda item: item[0])
+ weakest = min(pairs, key=lambda item: item[0])
+ detail = (
+ f"Spearman({check.raw!r}, {check.normalized!r}) = {rho:+.4f} "
+ f"(p = {p_value:.4f}) over {len(pairs)} rows. The highest raw reading "
+ f"({strongest[0]:.4g}, sample {strongest[2]}) normalises to "
+ f"{strongest[1]:.4g}; the lowest ({weakest[0]:.4g}, sample {weakest[2]}) "
+ f"normalises to {weakest[1]:.4g}."
+ )
+ if rho < check.min_spearman:
+ return [
+ finding(
+ ScoreSeverity.WARNING,
+ "agreement_not_monotonic",
+ f"{check.normalized!r} does not rank like {check.raw!r}, so this "
+ f"objective is provisional until the group supplies the "
+ f"normalisation. {detail} Expected at least "
+ f"{check.min_spearman:+.4f}. The computed value still stands -- "
+ f"this is a finding, not a gate.",
+ )
+ ]
+ return [
+ finding(
+ ScoreSeverity.NOTE,
+ "agreement_monotonic",
+ f"{check.normalized!r} ranks like {check.raw!r}. {detail}",
+ )
+ ]
+
+
+def compute_measurements(
+ frame: pd.DataFrame,
+ specs: Sequence[MeasurementSpec],
+ *,
+ sample_ids: Sequence[Any] | None = None,
+) -> MeasurementResult:
+ """Compute one model input per objective per row, and check the workbook.
+
+ ``frame`` holds the raw cells as read -- blanks, non-breaking spaces and
+ strings are all handled here rather than by the caller, because the point of
+ this module is that nothing downstream has to know how the workbook spells
+ "not measured".
+
+ A row that cannot be computed gets ``NaN`` and an ``error`` finding rather
+ than an exception, so one bad row does not hide the state of the other
+ fourteen. Call :meth:`MeasurementResult.raise_for_errors` to fail closed.
+ """
+ if not isinstance(frame, pd.DataFrame):
+ raise TypeError("frame must be a pandas DataFrame.")
+ specs = tuple(specs)
+ if not specs:
+ raise ValueError("At least one MeasurementSpec is required.")
+ frame = _with_stripped_columns(frame)
+ absent = _absent_required_columns(frame, specs)
+ if absent:
+ raise ValueError(
+ f"Measurement column(s) missing from the sheet: {list(absent)}. The "
+ "objectives are computed from these, so they cannot be skipped."
+ )
+
+ n_rows = len(frame)
+ ids = list(sample_ids) if sample_ids is not None else [None] * n_rows
+ if len(ids) != n_rows:
+ raise ValueError("sample_ids must have one entry per row.")
+
+ findings: list[ScoreFinding] = []
+ for spec in specs:
+ for item in spec.inputs:
+ if item.column not in frame.columns:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.NOTE,
+ code="input_column_absent",
+ objective=spec.name,
+ row_position=-1,
+ sample_id=None,
+ message=(
+ f"there is no {item.column!r} column in this sheet, so "
+ "it counts as unmeasured for every row."
+ ),
+ column=item.column,
+ )
+ )
+ values: dict[str, list[float]] = {spec.name: [] for spec in specs}
+ counts: dict[str, list[int]] = {spec.name: [] for spec in specs}
+
+ for spec in specs:
+ recipe = spec.recipe_impl
+ for position in range(n_rows):
+ sample_id = ids[position]
+
+ def record(
+ severity: ScoreSeverity,
+ code: str,
+ message: str,
+ *,
+ column: str | None = None,
+ _position: int = position,
+ _sample_id: Any = sample_id,
+ _objective: str = spec.name,
+ ) -> None:
+ findings.append(
+ ScoreFinding(
+ severity=severity,
+ code=code,
+ objective=_objective,
+ row_position=_position,
+ sample_id=_sample_id,
+ message=message,
+ column=column,
+ )
+ )
+
+ used: list[float] = []
+ raw_used: list[float] = []
+ failed = False
+ for item in spec.inputs:
+ if item.column not in frame.columns:
+ continue # already reported once, above
+ try:
+ number = _number(_cell(frame, item.column, position), column=item.column)
+ except _NotNumeric as exc:
+ record(
+ ScoreSeverity.ERROR,
+ "input_not_numeric",
+ str(exc),
+ column=item.column,
+ )
+ failed = True
+ continue
+ if number is None:
+ if recipe.requires_all:
+ record(
+ ScoreSeverity.ERROR,
+ "input_missing",
+ f"{item.column!r} is empty, and {spec.recipe!r} needs "
+ "every input. Blank means not measured, not zero.",
+ column=item.column,
+ )
+ failed = True
+ continue
+ raw_used.append(number)
+ used.append(item.evaluate(number))
+
+ for column in spec.excluded:
+ if column not in frame.columns:
+ continue
+ try:
+ excluded_value = _number(_cell(frame, column, position), column=column)
+ except _NotNumeric:
+ excluded_value = None
+ if excluded_value is not None:
+ record(
+ ScoreSeverity.NOTE,
+ "reading_excluded",
+ f"{column!r} holds {excluded_value:g}, a reading judged "
+ "anomalous by the operator. It is recorded, not averaged.",
+ column=column,
+ )
+
+ if failed or not used:
+ if not failed:
+ record(
+ ScoreSeverity.ERROR,
+ "no_inputs_measured",
+ f"none of {[i.column for i in spec.inputs]} was measured, so "
+ f"{spec.name!r} cannot be computed for this row.",
+ )
+ values[spec.name].append(float("nan"))
+ counts[spec.name].append(len(used))
+ continue
+
+ try:
+ value = recipe.apply(used)
+ except _NotNumeric as exc:
+ record(ScoreSeverity.ERROR, "recipe_failed", str(exc))
+ values[spec.name].append(float("nan"))
+ counts[spec.name].append(len(used))
+ continue
+
+ if spec.spread_warning_ratio is not None and len(raw_used) > 1:
+ spread = max(raw_used) - min(raw_used)
+ centre = math.fsum(raw_used) / len(raw_used)
+ if centre != 0 and spread / abs(centre) > spec.spread_warning_ratio:
+ record(
+ ScoreSeverity.WARNING,
+ "readings_disagree",
+ f"{len(raw_used)} readings span {spread:g} around a mean of "
+ f"{centre:g} ({100 * spread / abs(centre):.0f}% of it): "
+ f"{[f'{v:g}' for v in raw_used]}. The mean may not describe "
+ "this film.",
+ )
+
+ for check in spec.cross_checks:
+ if check.column not in frame.columns:
+ record(
+ ScoreSeverity.NOTE,
+ "cross_check_absent",
+ f"cross-check column {check.column!r} is not in the sheet; "
+ "the computed value stands unchecked.",
+ column=check.column,
+ )
+ continue
+ try:
+ stored = _number(_cell(frame, check.column, position), column=check.column)
+ except _NotNumeric as exc:
+ record(
+ ScoreSeverity.WARNING,
+ "cross_check_not_numeric",
+ str(exc),
+ column=check.column,
+ )
+ continue
+ if stored is None:
+ record(
+ ScoreSeverity.WARNING,
+ "cross_check_empty",
+ f"{check.column!r} is empty. If it is a formula column, a "
+ "non-Excel tool has saved this file and dropped the cached "
+ "value.",
+ column=check.column,
+ )
+ continue
+ difference = abs(value - stored)
+ if difference > check.atol + 1e-12:
+ record(
+ ScoreSeverity.WARNING,
+ "cross_check_mismatch",
+ f"computed {value:.10g} against stored {stored:.10g} in "
+ f"{check.column!r}, a difference of {difference:.3g} above "
+ f"the {check.atol:g} tolerance. The computed value is what "
+ "the model uses.",
+ column=check.column,
+ )
+
+ values[spec.name].append(value)
+ counts[spec.name].append(len(used))
+
+ if spec.agreement_check is not None:
+ findings.extend(_agreement_findings(frame, spec, ids))
+
+ index = frame.index
+ return MeasurementResult(
+ values=pd.DataFrame(
+ {spec.name: values[spec.name] for spec in specs}, index=index, dtype=float
+ ),
+ inputs_used=pd.DataFrame(
+ {spec.name: counts[spec.name] for spec in specs}, index=index, dtype=int
+ ),
+ findings=tuple(findings),
+ )
+
+
+def row_completeness(
+ frame: pd.DataFrame, specs: Sequence[MeasurementSpec]
+) -> pd.Series:
+ """Which rows have enough measurements for every objective.
+
+ This is what "has this row been measured yet" means once objectives are
+ computed rather than read: ``product`` and ``log10_product`` need all their
+ inputs, ``mean_of_present`` needs one. Round detection uses it, so getting
+ it wrong either blocks a finished round or advances on a half-filled sheet.
+ """
+ specs = tuple(specs)
+ frame = _with_stripped_columns(frame)
+ complete = pd.Series(True, index=frame.index)
+ for spec in specs:
+ requires_all = spec.recipe_impl.requires_all
+ for position in range(len(frame)):
+ present = 0
+ for item in spec.inputs:
+ if item.column not in frame.columns:
+ continue
+ try:
+ number = _number(_cell(frame, item.column, position), column=item.column)
+ except _NotNumeric:
+ number = None
+ if number is not None:
+ present += 1
+ enough = (
+ present == len(spec.inputs) if requires_all else present >= 1
+ )
+ if not enough:
+ complete.iloc[position] = False
+ return complete
+
+
+def describe_findings(findings: Iterable[ScoreFinding]) -> str:
+ """A short human-readable block, worst first. Empty string when clean."""
+ ordered = sorted(
+ findings,
+ key=lambda f: (
+ {ScoreSeverity.ERROR: 0, ScoreSeverity.WARNING: 1, ScoreSeverity.NOTE: 2}[
+ f.severity
+ ],
+ f.row_position,
+ ),
+ )
+ return "\n".join(str(finding) for finding in ordered)
diff --git a/src/mobo_kit/sobol_pool.py b/src/mobo_kit/sobol_pool.py
new file mode 100644
index 0000000..172dd2f
--- /dev/null
+++ b/src/mobo_kit/sobol_pool.py
@@ -0,0 +1,399 @@
+"""Deterministic nested Sobol prefixes for finite discrete designs."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+from math import prod
+from typing import Sequence
+import warnings
+
+import numpy as np
+import scipy
+from scipy.stats import qmc
+
+from .candidate_pool import (
+ CandidatePool,
+ CandidatePoolSamplingError,
+ physical_rows_to_grid_indices,
+)
+from .constraints import RowConstraint, apply_row_constraints
+from .design import Design
+
+
+@dataclass(frozen=True)
+class NestedSobolPoolResult:
+ """Accepted-unique Sobol prefixes and their reproducibility metadata."""
+
+ pools: dict[int, CandidatePool]
+ prefix_hashes: dict[int, str]
+ accepted_sizes: tuple[int, ...]
+ scramble_seed: int
+ raw_sobol_draws: int
+ rejected_duplicate: int
+ rejected_avoid: int
+ rejected_constraint: int
+ ignored_off_grid_observed: int
+ scipy_version: str
+
+ @property
+ def largest_pool(self) -> CandidatePool:
+ """Return the largest accepted prefix."""
+
+ return self.pools[self.accepted_sizes[-1]]
+
+ @property
+ def accepted_count(self) -> int:
+ """Return the accepted count in the final master prefix."""
+
+ return self.largest_pool.size
+
+ @property
+ def pools_by_size(self) -> dict[int, CandidatePool]:
+ """Alias spelling useful to report-building callers."""
+
+ return self.pools
+
+
+@dataclass(frozen=True)
+class _PrefixSnapshot:
+ draws: int
+ rejected_duplicate: int
+ rejected_avoid: int
+ rejected_constraint: int
+
+
+def _validated_grids(design: Design) -> tuple[np.ndarray, ...]:
+ if not isinstance(design, Design):
+ raise TypeError("design must be a Design.")
+ grids = tuple(np.asarray(grid, dtype=float) for grid in design.var_array)
+ if not grids or len(grids) != len(design.names):
+ raise ValueError("design must contain one non-empty grid per input name.")
+ for name, grid in zip(design.names, grids):
+ if grid.ndim != 1 or grid.size == 0:
+ raise ValueError(f"Design grid {name!r} must be a non-empty vector.")
+ if not np.all(np.isfinite(grid)) or np.unique(grid).size != grid.size:
+ raise ValueError(f"Design grid {name!r} must be finite and unique.")
+ return grids
+
+
+def _positive_sizes(values: Sequence[int]) -> tuple[int, ...]:
+ if isinstance(values, (str, bytes)):
+ raise TypeError("accepted_sizes must be a non-empty sequence of integers.")
+ try:
+ raw = tuple(values)
+ except TypeError as exc:
+ raise TypeError(
+ "accepted_sizes must be a non-empty sequence of integers."
+ ) from exc
+ if not raw:
+ raise ValueError("accepted_sizes must not be empty.")
+ if any(
+ isinstance(value, (bool, np.bool_))
+ or not isinstance(value, (int, np.integer))
+ or int(value) <= 0
+ for value in raw
+ ):
+ raise ValueError("accepted_sizes must contain only positive integers.")
+ normalized = tuple(int(value) for value in raw)
+ if len(set(normalized)) != len(normalized):
+ raise ValueError("accepted_sizes must not contain duplicates.")
+ return tuple(sorted(normalized))
+
+
+def _non_negative_seed(value: int) -> int:
+ if (
+ isinstance(value, (bool, np.bool_))
+ or not isinstance(value, (int, np.integer))
+ or int(value) < 0
+ ):
+ raise ValueError("scramble_seed must be a non-negative integer.")
+ return int(value)
+
+
+def _physical_rows(
+ value: np.ndarray | None, *, name: str, dimension: int
+) -> np.ndarray:
+ if value is None:
+ return np.empty((0, dimension), dtype=float)
+ array = np.asarray(value, dtype=float)
+ if array.ndim != 2 or array.shape[1] != dimension:
+ raise ValueError(f"{name} must have shape (N, {dimension}); got {array.shape}.")
+ if not np.all(np.isfinite(array)):
+ raise ValueError(f"{name} must contain only finite values.")
+ return array
+
+
+def map_unit_points_to_grid_indices(
+ unit_points: np.ndarray,
+ design: Design,
+) -> np.ndarray:
+ """Map points in ``[0, 1)`` to exact discrete grid indices."""
+
+ grids = _validated_grids(design)
+ points = np.asarray(unit_points, dtype=float)
+ if points.ndim != 2 or points.shape[1] != len(grids):
+ raise ValueError(
+ "unit_points must have shape (N, n_design_inputs); "
+ f"got {points.shape} for {len(grids)} inputs."
+ )
+ if not np.all(np.isfinite(points)):
+ raise ValueError("unit_points must contain only finite values.")
+ if np.any(points < 0.0) or np.any(points >= 1.0):
+ raise ValueError("unit_points must lie in the half-open interval [0, 1).")
+ axis_sizes = np.asarray([grid.size for grid in grids], dtype=np.int64)
+ indices = np.floor(points * axis_sizes[None, :]).astype(np.int64)
+ return np.minimum(indices, axis_sizes[None, :] - 1)
+
+
+def _indices_to_physical(
+ grid_indices: np.ndarray,
+ grids: tuple[np.ndarray, ...],
+) -> np.ndarray:
+ physical = np.empty(grid_indices.shape, dtype=float)
+ for column, grid in enumerate(grids):
+ physical[:, column] = grid[grid_indices[:, column]]
+ return physical
+
+
+def _normalize_physical(X_phys: np.ndarray, design: Design) -> np.ndarray:
+ lower = np.asarray(design.lowers, dtype=float)
+ upper = np.asarray(design.uppers, dtype=float)
+ spans = upper - lower
+ normalized = np.zeros_like(X_phys, dtype=float)
+ changing = spans > 0.0
+ normalized[:, changing] = (X_phys[:, changing] - lower[changing]) / spans[changing]
+ return normalized
+
+
+def hash_grid_index_prefix(grid_indices: np.ndarray) -> str:
+ """Return a platform-stable SHA-256 for one integer-index prefix."""
+
+ indices = np.asarray(grid_indices)
+ if indices.ndim != 2:
+ raise ValueError("grid_indices must be a two-dimensional integer matrix.")
+ if not np.issubdtype(indices.dtype, np.integer):
+ raise TypeError("grid_indices must have an integer dtype.")
+ canonical = np.ascontiguousarray(indices, dtype=" np.ndarray:
+ if rows.shape[0] == 0:
+ return np.empty((0, len(design.names)), dtype=np.int64)
+ return physical_rows_to_grid_indices(rows, design)
+
+
+def _observed_exclusion_indices(
+ rows: np.ndarray,
+ design: Design,
+) -> tuple[np.ndarray, int]:
+ """Partition observed rows, deliberately skipping exact off-grid controls."""
+
+ accepted: list[np.ndarray] = []
+ ignored = 0
+ lower = np.asarray(design.lowers, dtype=float)
+ upper = np.asarray(design.uppers, dtype=float)
+ for row in rows:
+ if np.any(row < lower) or np.any(row > upper):
+ raise ValueError(
+ "observed_phys rows must remain within the design bounds even "
+ "when an observed recipe is off-grid."
+ )
+ try:
+ accepted.append(physical_rows_to_grid_indices(row[None, :], design)[0])
+ except ValueError as exc:
+ if "off-grid" not in str(exc):
+ raise
+ ignored += 1
+ if not accepted:
+ return np.empty((0, len(design.names)), dtype=np.int64), ignored
+ return np.asarray(accepted, dtype=np.int64), ignored
+
+
+def _sobol_prefix(dimension: int, draws: int, seed: int) -> np.ndarray:
+ engine = qmc.Sobol(d=dimension, scramble=True, seed=seed)
+ if draws > 0 and draws & (draws - 1) == 0:
+ return engine.random_base2(int(draws.bit_length() - 1))
+ with warnings.catch_warnings():
+ warnings.filterwarnings(
+ "ignore",
+ message="The balance properties of Sobol.*",
+ category=UserWarning,
+ )
+ return engine.random(draws)
+
+
+def build_nested_sobol_discrete_pool(
+ design: Design,
+ accepted_sizes: Sequence[int],
+ *,
+ scramble_seed: int,
+ observed_phys: np.ndarray | None = None,
+ pending_phys: np.ndarray | None = None,
+ avoid_phys: np.ndarray | None = None,
+ row_constraints: Sequence[RowConstraint] | None = None,
+ max_raw_draws: int | None = None,
+) -> NestedSobolPoolResult:
+ """Build exact accepted-unique nested prefixes from one Sobol scramble.
+
+ Observed rows that are not exact grid members are intentionally omitted from
+ index exclusion (the D2D control is such a row). Pending and explicit avoid
+ rows must be exact grid members and fail closed otherwise.
+ """
+
+ grids = _validated_grids(design)
+ sizes = _positive_sizes(accepted_sizes)
+ seed = _non_negative_seed(scramble_seed)
+ largest = sizes[-1]
+ if max_raw_draws is None:
+ draw_limit = max(1024, largest * 50)
+ elif (
+ isinstance(max_raw_draws, (bool, np.bool_))
+ or not isinstance(max_raw_draws, (int, np.integer))
+ or int(max_raw_draws) <= 0
+ ):
+ raise ValueError("max_raw_draws must be a positive integer.")
+ else:
+ draw_limit = int(max_raw_draws)
+ if draw_limit < largest:
+ raise CandidatePoolSamplingError(
+ requested=largest,
+ accepted=0,
+ draws=0,
+ max_draws=draw_limit,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ reason="max_raw_draws is smaller than the requested accepted prefix.",
+ )
+
+ dimension = len(grids)
+ observed = _physical_rows(observed_phys, name="observed_phys", dimension=dimension)
+ pending = _physical_rows(pending_phys, name="pending_phys", dimension=dimension)
+ explicit_avoid = _physical_rows(avoid_phys, name="avoid_phys", dimension=dimension)
+ observed_indices, ignored_off_grid = _observed_exclusion_indices(observed, design)
+ pending_indices = _strict_exclusion_indices(pending, design)
+ avoid_indices = _strict_exclusion_indices(explicit_avoid, design)
+ exclusions = np.vstack([observed_indices, pending_indices, avoid_indices])
+ avoid_set = {tuple(int(value) for value in row) for row in exclusions}
+
+ total_grid_size = prod(int(grid.size) for grid in grids)
+ available = total_grid_size - len(avoid_set)
+ if largest > available:
+ raise CandidatePoolSamplingError(
+ requested=largest,
+ accepted=0,
+ draws=0,
+ max_draws=draw_limit,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ reason=(
+ "The request exceeds the number of grid tuples remaining after "
+ f"exact exclusions ({available})."
+ ),
+ )
+
+ seen: set[tuple[int, ...]] = set()
+ accepted: list[tuple[int, ...]] = []
+ snapshots: dict[int, _PrefixSnapshot] = {}
+ draws = rejected_duplicate = rejected_avoid = rejected_constraint = 0
+ prefix_draws = 1 << max(0, (largest - 1).bit_length())
+
+ while len(accepted) < largest and draws < draw_limit:
+ target_draws = min(prefix_draws, draw_limit)
+ points = _sobol_prefix(dimension, target_draws, seed)
+ new_indices = map_unit_points_to_grid_indices(
+ points[draws:target_draws], design
+ )
+ for row in new_indices:
+ draws += 1
+ key = tuple(int(value) for value in row)
+ if key in seen:
+ rejected_duplicate += 1
+ continue
+ seen.add(key)
+ if key in avoid_set:
+ rejected_avoid += 1
+ continue
+ if row_constraints:
+ physical = _indices_to_physical(row[None, :], grids)
+ if not bool(
+ apply_row_constraints(physical, design, row_constraints)[0]
+ ):
+ rejected_constraint += 1
+ continue
+ accepted.append(key)
+ accepted_count = len(accepted)
+ if accepted_count in sizes:
+ snapshots[accepted_count] = _PrefixSnapshot(
+ draws=draws,
+ rejected_duplicate=rejected_duplicate,
+ rejected_avoid=rejected_avoid,
+ rejected_constraint=rejected_constraint,
+ )
+ if accepted_count == largest:
+ break
+ if target_draws == draw_limit:
+ break
+ prefix_draws *= 2
+
+ if len(accepted) != largest:
+ raise CandidatePoolSamplingError(
+ requested=largest,
+ accepted=len(accepted),
+ draws=draws,
+ max_draws=draw_limit,
+ rejected_duplicate=rejected_duplicate,
+ rejected_avoid=rejected_avoid,
+ rejected_constraint=rejected_constraint,
+ reason=(
+ "Maximum raw Sobol draws reached; exact exclusions and constraints "
+ "were not relaxed."
+ ),
+ )
+
+ all_indices = np.asarray(accepted, dtype=np.int64)
+ pools: dict[int, CandidatePool] = {}
+ hashes: dict[int, str] = {}
+ for size in sizes:
+ indices = all_indices[:size].copy()
+ physical = _indices_to_physical(indices, grids)
+ snapshot = snapshots[size]
+ pools[size] = CandidatePool(
+ grid_indices=indices,
+ X_phys=physical,
+ X_norm=_normalize_physical(physical, design),
+ seed=seed,
+ draws=snapshot.draws,
+ rejected_duplicate=snapshot.rejected_duplicate,
+ rejected_avoid=snapshot.rejected_avoid,
+ rejected_constraint=snapshot.rejected_constraint,
+ )
+ hashes[size] = hash_grid_index_prefix(indices)
+
+ return NestedSobolPoolResult(
+ pools=pools,
+ prefix_hashes=hashes,
+ accepted_sizes=sizes,
+ scramble_seed=seed,
+ raw_sobol_draws=draws,
+ rejected_duplicate=rejected_duplicate,
+ rejected_avoid=rejected_avoid,
+ rejected_constraint=rejected_constraint,
+ ignored_off_grid_observed=ignored_off_grid,
+ scipy_version=scipy.__version__,
+ )
+
+
+__all__ = [
+ "NestedSobolPoolResult",
+ "build_nested_sobol_discrete_pool",
+ "hash_grid_index_prefix",
+ "map_unit_points_to_grid_indices",
+]
diff --git a/src/mobo_kit/structured_mean.py b/src/mobo_kit/structured_mean.py
new file mode 100644
index 0000000..eca12e1
--- /dev/null
+++ b/src/mobo_kit/structured_mean.py
@@ -0,0 +1,303 @@
+"""Physics-informed mean functions for small-data GPs.
+
+With 15 observations in 10 dimensions a zero-mean GP spends most of its capacity
+rediscovering a trend that is already known from process physics. Giving it that
+trend as a mean function, and letting the GP model only the residual, roughly
+doubles the leave-one-out fit on this campaign's data.
+
+EVERY NUMBER BELOW IS FROM THE FIRST CAMPAIGN, contract
+``d2d-objectives-v2-nm-thickness``, on ``Summary Table.xlsx``. Objectives have
+been redefined twice since. Read them as the record of how these shapes were
+chosen, NOT as current fits -- and see the second bullet for one that has since
+been measured false.
+
+* **thickness** -- ``log T ~ log(speed_1) + log(precur_conc)``. Spin-coating
+ theory gives ``T ~ omega^-0.5``; the measured exponent was -0.38 on v2 and is
+ -0.255 on the current workbook. Neither term alone is worth much (LOO R2
+ +0.159 and +0.187); the *pair* carries the signal (+0.449 on v2, and the shape
+ still holds on v4). Modelled in log space, so the response is lognormal.
+ **WITHDRAWN FROM THE LIVE CONFIG 2026-09-06.** The shape transfers, but the
+ physics justification does not: the exponent's 95% interval on v4 is
+ [-0.385, -0.126], which EXCLUDES the textbook -0.5 by 4.1 standard errors, and
+ fixing the exponents at theory scores +0.5600 against +0.5823 for no trend at
+ all. What survives is that the VARIABLE choice beats matched-flexibility
+ controls (four unmotivated pairs scored +0.30 to +0.40, all below the plain GP)
+ -- the magnitudes were fitted, not predicted. This module stays wired and
+ tested for a prior that clears the bar; nothing currently does.
+* **optoelectronic** -- a single linear term on ``anneal_temp`` and nothing else,
+ LOO R2 +0.244 ON V2. **IT DOES NOT TRANSFER AND IS NO LONGER DECLARED
+ ANYWHERE.** v2's optoelectronic was ``log10(Voc x Photoconductance)``; the
+ current contract's is a different quantity, and on it the same mean function
+ scores **-1.0721** (and -0.7452 on raw Voc), far below even a constant. The
+ key was removed from the live config on the v3 intake verdict and nothing since
+ has argued for reinstating it. Measured 2026-09-04; reproduce with
+ ``scripts/raw_component_screen.py --spec
+ '[{"name":"x","expr":"score_opto","mean_features":["anneal_temp"]}]'``.
+
+Do not assume the two-term shape generalises: these are opposite patterns, and
+one of them turned out to be about an objective that no longer exists.
+
+The linear coefficients are refit on the training rows of every fold, so
+cross-validation stays honest. Feature *choice* is fixed in configuration from
+physics, before fitting -- it is not selected against the outcome.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Literal, Mapping, Sequence
+
+import gpytorch
+import numpy as np
+import torch
+
+__all__ = [
+ "MeanFeature",
+ "StructuredMeanSpec",
+ "StructuredPosterior",
+ "StructuredMean",
+ "build_structured_mean",
+ "fit_structured_mean",
+ "mean_spec_from_config",
+]
+
+Link = Literal["identity", "log"]
+
+
+@dataclass(frozen=True)
+class MeanFeature:
+ """One column of the linear mean's design matrix."""
+
+ column: str
+ transform: Link = "identity"
+
+ def evaluate(self, values: np.ndarray) -> np.ndarray:
+ if self.transform == "identity":
+ return values
+ if self.transform == "log":
+ if np.any(values <= 0):
+ raise ValueError(
+ f"Mean feature {self.column!r} uses a log transform but the "
+ "column contains non-positive values."
+ )
+ return np.log(values)
+ raise ValueError(f"Unsupported mean-feature transform {self.transform!r}.")
+
+
+@dataclass(frozen=True)
+class StructuredMeanSpec:
+ """A linear trend removed before GP fitting and added back after.
+
+ ``response`` is the space the GP works in. ``log`` means the GP models
+ ``log y``, which makes ``y`` lognormal -- the utility expectation must then
+ use Gauss-Hermite quadrature, not the Gaussian closed form.
+ """
+
+ response: Link
+ features: tuple[MeanFeature, ...]
+
+ def __post_init__(self) -> None:
+ if self.response not in ("identity", "log"):
+ raise ValueError(f"Unsupported response link {self.response!r}.")
+ if not self.features:
+ raise ValueError("A structured mean needs at least one feature.")
+ names = [feature.column for feature in self.features]
+ if len(set(names)) != len(names):
+ raise ValueError(f"Mean features must be unique; got {names}.")
+
+ def design_matrix(
+ self, X_phys: np.ndarray, input_names: Sequence[str]
+ ) -> np.ndarray:
+ columns = []
+ for feature in self.features:
+ try:
+ index = list(input_names).index(feature.column)
+ except ValueError as exc:
+ raise ValueError(
+ f"Mean feature {feature.column!r} is not a declared input."
+ ) from exc
+ columns.append(feature.evaluate(np.asarray(X_phys, float)[:, index]))
+ return np.column_stack(columns)
+
+
+@dataclass(frozen=True)
+class StructuredPosterior:
+ """Posterior in the response space, plus the link needed to interpret it.
+
+ When ``link == "log"`` these are the mean and variance of ``log y``, so the
+ utility expectation must integrate a lognormal.
+ """
+
+ mean: np.ndarray
+ variance: np.ndarray
+ link: Link
+
+
+def _ols(F: np.ndarray, target: np.ndarray) -> np.ndarray:
+ """Least squares with an intercept, returned as coefficients on [1, F]."""
+ design = np.column_stack([np.ones(len(F)), F])
+ coefficients, *_ = np.linalg.lstsq(design, target, rcond=None)
+ return coefficients
+
+
+def fit_structured_mean(
+ X_phys: np.ndarray,
+ y: np.ndarray,
+ spec: StructuredMeanSpec,
+ input_names: Sequence[str],
+) -> tuple[np.ndarray, np.ndarray]:
+ """Return the linear-mean coefficients and the residuals to hand the GP.
+
+ Call this with the TRAINING rows only. Refitting per fold is what keeps
+ cross-validation honest; fitting once on everything and holding it fixed
+ leaks the held-out value into the trend.
+ """
+ values = np.asarray(y, dtype=float)
+ if spec.response == "log":
+ if np.any(values <= 0):
+ raise ValueError("A log response requires strictly positive observations.")
+ target = np.log(values)
+ else:
+ target = values
+ F = spec.design_matrix(X_phys, input_names)
+ coefficients = _ols(F, target)
+ fitted = np.column_stack([np.ones(len(F)), F]) @ coefficients
+ return coefficients, target - fitted
+
+
+def apply_structured_mean(
+ coefficients: np.ndarray,
+ X_phys: np.ndarray,
+ spec: StructuredMeanSpec,
+ input_names: Sequence[str],
+ residual_mean: np.ndarray,
+ residual_variance: np.ndarray,
+) -> StructuredPosterior:
+ """Add the linear trend back to a GP residual posterior."""
+ F = spec.design_matrix(X_phys, input_names)
+ trend = np.column_stack([np.ones(len(F)), F]) @ coefficients
+ return StructuredPosterior(
+ mean=np.asarray(residual_mean, float) + trend,
+ variance=np.asarray(residual_variance, float),
+ link=spec.response,
+ )
+
+
+def mean_spec_from_config(entry: Mapping[str, Any]) -> StructuredMeanSpec | None:
+ """Build a spec from one objective's ``mean_function`` block, if present."""
+ block = entry.get("mean_function")
+ if block is None:
+ return None
+ if not isinstance(block, Mapping):
+ raise ValueError("mean_function must be a mapping.")
+ raw_features = block.get("features")
+ if not isinstance(raw_features, Sequence) or not raw_features:
+ raise ValueError("mean_function.features must be a non-empty list.")
+ features = []
+ for item in raw_features:
+ if isinstance(item, str):
+ features.append(MeanFeature(item))
+ elif isinstance(item, Mapping):
+ features.append(
+ MeanFeature(str(item["column"]), str(item.get("transform", "identity")))
+ )
+ else:
+ raise ValueError("Each mean feature must be a string or a mapping.")
+ return StructuredMeanSpec(
+ response=str(block.get("response", "identity")),
+ features=tuple(features),
+ )
+
+
+# --------------------------------------------------------------------------- #
+# as a GPyTorch mean module
+# --------------------------------------------------------------------------- #
+
+
+class StructuredMean(gpytorch.means.Mean):
+ """The linear trend as a GP mean module, with the coefficients frozen.
+
+ This is the two-stage pipeline -- OLS detrend, zero-mean GP on the residual --
+ expressed as a single model. ``posterior()`` is then correct by
+ construction: there is no trend to add back afterwards and therefore no code
+ path that can forget to.
+
+ Coefficients are registered as **buffers, not parameters**, so the marginal
+ likelihood fits only the GP hyperparameters and leaves the OLS fit alone.
+
+ The module operates in the model's *standardized* outcome space, because
+ ``SingleTaskGP`` is built with ``Standardize(m=1)``. Getting that wrong is
+ silent: the trend comes out shifted and scaled, and the model still looks
+ plausible. :func:`build_structured_mean` handles the conversion.
+ """
+
+ def __init__(
+ self,
+ lowers: torch.Tensor,
+ uppers: torch.Tensor,
+ feature_columns: Sequence[int],
+ feature_logs: Sequence[bool],
+ coefficients: torch.Tensor,
+ bias: torch.Tensor,
+ ) -> None:
+ super().__init__()
+ self.register_buffer("lowers", lowers)
+ self.register_buffer("uppers", uppers)
+ self.register_buffer("coefficients", coefficients)
+ self.register_buffer("bias", bias)
+ self.feature_columns = tuple(int(c) for c in feature_columns)
+ self.feature_logs = tuple(bool(f) for f in feature_logs)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ # the GP sees normalized inputs; the trend is defined on physical ones
+ physical = self.lowers + x * (self.uppers - self.lowers)
+ columns = []
+ for position, use_log in zip(self.feature_columns, self.feature_logs):
+ value = physical[..., position]
+ columns.append(torch.log(value) if use_log else value)
+ features = torch.stack(columns, dim=-1)
+ return (features * self.coefficients).sum(dim=-1) + self.bias
+
+
+def build_structured_mean(
+ X_phys: np.ndarray,
+ y: np.ndarray,
+ spec: StructuredMeanSpec,
+ input_names: Sequence[str],
+ lowers: np.ndarray,
+ uppers: np.ndarray,
+) -> tuple["StructuredMean", np.ndarray]:
+ """Fit the trend on these rows and return it as a mean module.
+
+ Returns the module plus the response-space target the GP should train on
+ (``log y`` for a log response, ``y`` otherwise). Pass only TRAINING rows;
+ refitting per fold is what keeps cross-validation honest.
+ """
+ values = np.asarray(y, dtype=float)
+ if spec.response == "log":
+ if np.any(values <= 0):
+ raise ValueError("A log response requires strictly positive observations.")
+ target = np.log(values)
+ else:
+ target = values
+
+ coefficients = _ols(spec.design_matrix(X_phys, input_names), target)
+ intercept, slopes = float(coefficients[0]), coefficients[1:]
+
+ # SingleTaskGP standardizes the outcome, so express the trend in that space:
+ # m_std(x) = (m_raw(x) - mu) / sigma
+ mu = float(np.mean(target))
+ sigma = float(np.std(target, ddof=1))
+ if not np.isfinite(sigma) or sigma <= 0:
+ sigma = 1.0
+
+ names = list(input_names)
+ module = StructuredMean(
+ lowers=torch.tensor(np.asarray(lowers, float), dtype=torch.double),
+ uppers=torch.tensor(np.asarray(uppers, float), dtype=torch.double),
+ feature_columns=[names.index(f.column) for f in spec.features],
+ feature_logs=[f.transform == "log" for f in spec.features],
+ coefficients=torch.tensor(slopes / sigma, dtype=torch.double),
+ bias=torch.tensor((intercept - mu) / sigma, dtype=torch.double),
+ )
+ return module, target
diff --git a/src/mobo_kit/ucb_hvi.py b/src/mobo_kit/ucb_hvi.py
new file mode 100644
index 0000000..be7ecf3
--- /dev/null
+++ b/src/mobo_kit/ucb_hvi.py
@@ -0,0 +1,860 @@
+"""Discrete multi-objective UCB-HVI scoring in transformed utility space.
+
+The module deliberately separates posterior sampling from deterministic
+hypervolume arithmetic. Nonlinear objective transforms are applied to every
+posterior Monte Carlo sample before utility moments are formed. All objective
+dimensions are maximized after transformation, and the reference point is
+always supplied explicitly by the caller.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from math import sqrt
+from numbers import Real
+from typing import Any, Callable, Literal, Sequence
+
+import numpy as np
+import torch
+from botorch.sampling.normal import SobolQMCNormalSampler
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+from botorch.utils.multi_objective.pareto import is_non_dominated
+
+
+TensorTransform = Callable[[torch.Tensor], torch.Tensor]
+MomentMethod = Literal["monte_carlo", "analytic_identity"]
+UCBBoundPolicy = Literal["none", "clip_ucb"]
+UtilityBound = tuple[float | None, float | None]
+
+
+@dataclass(frozen=True)
+class PosteriorIdentityMoments:
+ """Exact posterior moments for an all-identity maximize contract."""
+
+ utility_mean: torch.Tensor
+ utility_std: torch.Tensor
+ observation_noise: bool
+ objective_contract_version: str
+ moment_method: str = "analytic_identity"
+
+
+@dataclass(frozen=True)
+class BoundedUCBResult:
+ """Raw and policy-effective UCB vectors plus non-negative clip amounts."""
+
+ utility_ucb_raw: np.ndarray
+ utility_ucb_effective: np.ndarray
+ utility_ucb_clip_amount: np.ndarray
+ policy: str
+ bounds: tuple[UtilityBound, ...]
+
+
+@dataclass(frozen=True)
+class PosteriorUtilityMoments:
+ """Monte Carlo moments in all-maximize transformed utility space."""
+
+ utility_mean: np.ndarray
+ utility_std: np.ndarray
+ mc_samples: int
+ seed: int
+ observation_noise: bool
+ standard_deviation_correction: int = 0
+ moment_method: str = "monte_carlo"
+
+
+@dataclass(frozen=True)
+class UCBHVIScoreResult:
+ """Candidate-wise optimistic utilities and hypervolume improvements."""
+
+ base_score: np.ndarray
+ base_log_score: np.ndarray
+ utility_mean: np.ndarray
+ utility_std: np.ndarray
+ utility_ucb: np.ndarray
+ baseline_hypervolume: float
+ pareto_utility: np.ndarray
+ reference_point_utility: np.ndarray
+ beta: float
+ kappa: float
+ mc_samples: int | None
+ seed: int | None
+ observation_noise: bool
+ objective_contract_version: str
+ moment_method: str = "monte_carlo"
+ bound_policy: str = "none"
+ utility_bounds: tuple[UtilityBound, ...] | None = None
+ utility_ucb_raw: np.ndarray | None = None
+ utility_ucb_effective: np.ndarray | None = None
+ utility_ucb_clip_amount: np.ndarray | None = None
+ method: str = "ucb_hvi"
+ method_version: str = "step2a-v1"
+
+ def __post_init__(self) -> None:
+ effective = (
+ np.asarray(self.utility_ucb, dtype=float)
+ if self.utility_ucb_effective is None
+ else np.asarray(self.utility_ucb_effective, dtype=float)
+ )
+ raw = (
+ effective.copy()
+ if self.utility_ucb_raw is None
+ else np.asarray(self.utility_ucb_raw, dtype=float)
+ )
+ clip_amount = (
+ np.abs(raw - effective)
+ if self.utility_ucb_clip_amount is None
+ else np.asarray(self.utility_ucb_clip_amount, dtype=float)
+ )
+ expected_shape = np.asarray(self.utility_mean).shape
+ if (
+ effective.shape != expected_shape
+ or raw.shape != expected_shape
+ or clip_amount.shape != expected_shape
+ ):
+ raise ValueError(
+ "Raw/effective UCB and clip amounts must align with utility_mean."
+ )
+ object.__setattr__(self, "utility_ucb", effective)
+ object.__setattr__(self, "utility_ucb_raw", raw)
+ object.__setattr__(self, "utility_ucb_effective", effective)
+ object.__setattr__(self, "utility_ucb_clip_amount", clip_amount)
+
+
+@dataclass(frozen=True)
+class UCBHVIBatchProposal:
+ """A locally penalized batch plus its complete static UCB-HVI score table."""
+
+ selection: Any
+ scoring: UCBHVIScoreResult
+ positive_score_tolerance: float
+ metadata: dict[str, Any]
+
+
+def _finite_nonnegative(value: float, *, name: str) -> float:
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+ raise ValueError(f"{name} must be a real non-boolean number; got {value!r}.")
+ number = float(value)
+ if not np.isfinite(number) or number < 0:
+ raise ValueError(f"{name} must be finite and non-negative; got {value!r}.")
+ return number
+
+
+def _positive_integer(value: int, *, name: str) -> int:
+ if isinstance(value, bool) or not isinstance(value, (int, np.integer)):
+ raise ValueError(f"{name} must be a positive integer; got {value!r}.")
+ result = int(value)
+ if result <= 0:
+ raise ValueError(f"{name} must be a positive integer; got {value!r}.")
+ return result
+
+
+def _apply_objective_transform(transform: Any, samples: torch.Tensor) -> torch.Tensor:
+ if hasattr(transform, "transform"):
+ utility = transform.transform(samples)
+ elif callable(transform):
+ utility = transform(samples)
+ else:
+ raise TypeError("objective_transform must be callable or expose transform().")
+ if not isinstance(utility, torch.Tensor):
+ raise TypeError("objective_transform must return a torch.Tensor.")
+ if utility.shape != samples.shape:
+ raise ValueError(
+ "objective_transform must preserve shape; "
+ f"got {tuple(samples.shape)} -> {tuple(utility.shape)}."
+ )
+ if not torch.isfinite(utility).all():
+ raise ValueError("objective_transform produced non-finite utility values.")
+ return utility
+
+
+def _posterior(model: Any, X: torch.Tensor, observation_noise: bool) -> Any:
+ try:
+ return model.posterior(X, observation_noise=observation_noise)
+ except TypeError as exc:
+ raise TypeError(
+ "model.posterior must accept the explicit observation_noise keyword."
+ ) from exc
+
+
+def _identity_objective_contract(objective_transform: Any) -> Any:
+ if hasattr(objective_transform, "bounds"):
+ raise ValueError(
+ "analytic identity moments cannot be used with a posterior-sample "
+ "bounds wrapper; apply an explicit UCB bound policy instead."
+ )
+ contract = getattr(objective_transform, "objective_transform", objective_transform)
+ specs = getattr(contract, "specs", None)
+ if not specs:
+ raise ValueError(
+ "analytic identity moments require an explicit versioned objective "
+ "contract with objective specifications."
+ )
+ if any(
+ getattr(spec, "transform", None) != "identity"
+ or getattr(spec, "goal", None) != "maximize"
+ or bool(getattr(spec, "clip", False))
+ for spec in specs
+ ):
+ raise ValueError(
+ "analytic identity moments require every objective to use the "
+ "identity transform with maximize direction and no clipping."
+ )
+ version = getattr(contract, "version", None)
+ if not isinstance(version, str) or not version.strip():
+ raise ValueError(
+ "analytic identity moments require a non-empty objective contract version."
+ )
+ return contract
+
+
+def posterior_identity_moments(
+ model: Any,
+ X_pool_norm: torch.Tensor,
+ objective_transform: Any,
+ *,
+ chunk_size: int = 512,
+ observation_noise: bool = False,
+) -> PosteriorIdentityMoments:
+ """Return exact posterior mean and standard deviation for identity utilities.
+
+ Tensors remain on the posterior's device and retain its dtype and any model
+ batch dimensions. Candidate rows are concatenated along the posterior q
+ dimension, making the result invariant to the requested evaluation chunk.
+ """
+ if not isinstance(X_pool_norm, torch.Tensor) or X_pool_norm.ndim != 2:
+ shape = getattr(X_pool_norm, "shape", None)
+ raise ValueError(
+ f"X_pool_norm must be a tensor with shape (N, D); got {shape}."
+ )
+ if not X_pool_norm.is_floating_point():
+ raise TypeError("X_pool_norm must use a floating dtype.")
+ if X_pool_norm.shape[0] == 0:
+ raise ValueError("X_pool_norm must contain at least one candidate.")
+ if not torch.isfinite(X_pool_norm).all():
+ raise ValueError("X_pool_norm must contain only finite values.")
+ chunk = _positive_integer(chunk_size, name="chunk_size")
+ if not isinstance(observation_noise, bool):
+ raise ValueError("observation_noise must be a boolean.")
+ contract = _identity_objective_contract(objective_transform)
+ objective_count = len(contract.specs)
+
+ mean_parts: list[torch.Tensor] = []
+ std_parts: list[torch.Tensor] = []
+ with torch.no_grad():
+ for start in range(0, X_pool_norm.shape[0], chunk):
+ X_chunk = X_pool_norm[start : start + chunk]
+ posterior = _posterior(model, X_chunk, observation_noise)
+ mean = posterior.mean
+ variance = posterior.variance
+ if not isinstance(mean, torch.Tensor) or not isinstance(
+ variance, torch.Tensor
+ ):
+ raise TypeError("model.posterior mean and variance must be tensors.")
+ if mean.shape != variance.shape:
+ raise ValueError("Posterior mean and variance shapes must match.")
+ if mean.ndim < 2 or mean.shape[-2] != X_chunk.shape[0]:
+ raise ValueError(
+ "Posterior candidate dimension must align with the input chunk; "
+ f"got mean shape {tuple(mean.shape)} for {X_chunk.shape[0]} rows."
+ )
+ if mean.shape[-1] != objective_count:
+ raise ValueError(
+ "Posterior objective dimension does not match the identity "
+ f"contract; expected {objective_count}, got {mean.shape[-1]}."
+ )
+ if not mean.is_floating_point() or not variance.is_floating_point():
+ raise TypeError("Posterior mean and variance must use floating dtypes.")
+ if mean.dtype != variance.dtype or mean.device != variance.device:
+ raise ValueError(
+ "Posterior mean and variance must share dtype and device."
+ )
+ if not torch.isfinite(mean).all() or not torch.isfinite(variance).all():
+ raise ValueError("Posterior mean and variance must be finite.")
+ tolerance = 100.0 * torch.finfo(variance.dtype).eps
+ if torch.any(variance < -tolerance):
+ raise ValueError("Posterior variance cannot be materially negative.")
+ mean_parts.append(mean)
+ std_parts.append(variance.clamp_min(0.0).sqrt())
+
+ try:
+ means = torch.cat(mean_parts, dim=-2)
+ standard_deviations = torch.cat(std_parts, dim=-2)
+ except RuntimeError as exc:
+ raise ValueError(
+ "Posterior batch shape, dtype, and device must remain stable across chunks."
+ ) from exc
+ return PosteriorIdentityMoments(
+ utility_mean=means,
+ utility_std=standard_deviations,
+ observation_noise=observation_noise,
+ objective_contract_version=contract.version.strip(),
+ )
+
+
+def posterior_utility_moments(
+ model: Any,
+ X_pool_norm: torch.Tensor,
+ objective_transform: TensorTransform | Any,
+ *,
+ mc_samples: int = 128,
+ seed: int = 0,
+ chunk_size: int = 512,
+ observation_noise: bool = False,
+) -> PosteriorUtilityMoments:
+ """Estimate utility moments for a normalized ``(N, D)`` candidate pool.
+
+ Candidates are represented as independent singleton q-batches ``N x 1 x D``.
+ A seeded Sobol sampler is rebuilt for each chunk. BoTorch collapses the
+ singleton batch dimensions in its base-sample shape, so every candidate sees
+ the same reproducible QMC normal draws and results are invariant to chunking.
+ Population standard deviation (``correction=0``) is reported.
+ """
+ if not isinstance(X_pool_norm, torch.Tensor) or X_pool_norm.ndim != 2:
+ shape = getattr(X_pool_norm, "shape", None)
+ raise ValueError(
+ f"X_pool_norm must be a tensor with shape (N, D); got {shape}."
+ )
+ if not X_pool_norm.is_floating_point():
+ raise TypeError("X_pool_norm must use a floating dtype.")
+ if not torch.isfinite(X_pool_norm).all():
+ raise ValueError("X_pool_norm must contain only finite values.")
+ sample_count = _positive_integer(mc_samples, name="mc_samples")
+ chunk = _positive_integer(chunk_size, name="chunk_size")
+ if (
+ isinstance(seed, (bool, np.bool_))
+ or not isinstance(seed, (int, np.integer))
+ or int(seed) < 0
+ ):
+ raise ValueError("seed must be a non-negative integer.")
+ if not isinstance(observation_noise, bool):
+ raise ValueError("observation_noise must be a boolean.")
+
+ mean_parts: list[torch.Tensor] = []
+ std_parts: list[torch.Tensor] = []
+ with torch.no_grad():
+ for start in range(0, X_pool_norm.shape[0], chunk):
+ X_chunk = X_pool_norm[start : start + chunk].unsqueeze(-2)
+ posterior = _posterior(model, X_chunk, observation_noise)
+ sampler = SobolQMCNormalSampler(
+ sample_shape=torch.Size([sample_count]), seed=int(seed)
+ )
+ raw_samples = sampler(posterior)
+ utility_samples = _apply_objective_transform(
+ objective_transform, raw_samples
+ )
+ if utility_samples.shape[-2] != 1:
+ raise ValueError(
+ "Singleton posterior evaluation must retain q=1 in the "
+ f"penultimate dimension; got {tuple(utility_samples.shape)}."
+ )
+ utility_samples = utility_samples.squeeze(-2)
+ mean_parts.append(utility_samples.mean(dim=0))
+ std_parts.append(utility_samples.std(dim=0, correction=0))
+
+ if mean_parts:
+ means = torch.cat(mean_parts, dim=0)
+ standard_deviations = torch.cat(std_parts, dim=0)
+ else:
+ # The output dimension cannot be discovered safely without a posterior.
+ raise ValueError("X_pool_norm must contain at least one candidate.")
+ return PosteriorUtilityMoments(
+ utility_mean=means.detach().cpu().double().numpy(),
+ utility_std=standard_deviations.detach().cpu().double().numpy(),
+ mc_samples=sample_count,
+ seed=int(seed),
+ observation_noise=bool(observation_noise),
+ )
+
+
+def _utility_matrix(value: np.ndarray | torch.Tensor, *, name: str) -> np.ndarray:
+ if isinstance(value, torch.Tensor):
+ array = value.detach().cpu().double().numpy()
+ else:
+ array = np.asarray(value, dtype=float)
+ if array.ndim != 2:
+ raise ValueError(f"{name} must have shape (N, M); got {array.shape}.")
+ if array.shape[1] == 0:
+ raise ValueError(f"{name} must include at least one objective.")
+ if not np.all(np.isfinite(array)):
+ raise ValueError(f"{name} must contain only finite values.")
+ return array
+
+
+def _validated_utility_bounds(
+ bounds: Sequence[UtilityBound] | None,
+ objective_count: int,
+ *,
+ require_bounded: bool,
+) -> tuple[UtilityBound, ...]:
+ if bounds is None:
+ if require_bounded:
+ raise ValueError("clip_ucb requires explicit per-objective utility bounds.")
+ return tuple((None, None) for _ in range(objective_count))
+ if isinstance(bounds, (str, bytes)):
+ raise TypeError("bounds must be an ordered sequence of (lower, upper) pairs.")
+ try:
+ raw_bounds = tuple(bounds)
+ except TypeError as exc:
+ raise TypeError(
+ "bounds must be an ordered sequence of (lower, upper) pairs."
+ ) from exc
+ if len(raw_bounds) != objective_count:
+ raise ValueError(
+ "bounds must contain one (lower, upper) pair per objective; "
+ f"expected {objective_count}, got {len(raw_bounds)}."
+ )
+ validated: list[UtilityBound] = []
+ bounded_count = 0
+ for index, raw_bound in enumerate(raw_bounds):
+ if isinstance(raw_bound, (str, bytes)):
+ raise TypeError(f"bounds[{index}] must be a (lower, upper) pair.")
+ try:
+ pair = tuple(raw_bound)
+ except TypeError as exc:
+ raise TypeError(f"bounds[{index}] must be a (lower, upper) pair.") from exc
+ if len(pair) != 2:
+ raise ValueError(f"bounds[{index}] must contain exactly two values.")
+ converted: list[float | None] = []
+ for label, value in zip(("lower", "upper"), pair):
+ if value is None:
+ converted.append(None)
+ continue
+ if isinstance(value, (bool, np.bool_)) or not isinstance(value, Real):
+ raise ValueError(
+ f"bounds[{index}] {label} must be finite, real, and non-boolean."
+ )
+ number = float(value)
+ if not np.isfinite(number):
+ raise ValueError(f"bounds[{index}] {label} must be finite.")
+ converted.append(number)
+ lower, upper = converted
+ if lower is not None and upper is not None and lower > upper:
+ raise ValueError(
+ f"bounds[{index}] lower value must not exceed its upper value."
+ )
+ if lower is not None or upper is not None:
+ bounded_count += 1
+ validated.append((lower, upper))
+ if require_bounded and bounded_count == 0:
+ raise ValueError("clip_ucb requires at least one finite utility bound.")
+ return tuple(validated)
+
+
+def apply_ucb_bound_policy(
+ raw_ucb: np.ndarray | torch.Tensor,
+ bounds: Sequence[UtilityBound] | None,
+ policy: UCBBoundPolicy | str,
+) -> BoundedUCBResult:
+ """Apply an explicit optimistic-utility policy without changing targets."""
+ raw = _utility_matrix(raw_ucb, name="raw_ucb").copy()
+ if policy not in {"none", "clip_ucb"}:
+ raise ValueError("policy must be exactly 'none' or 'clip_ucb'.")
+ validated = _validated_utility_bounds(
+ bounds, raw.shape[1], require_bounded=policy == "clip_ucb"
+ )
+ effective = raw.copy()
+ if policy == "clip_ucb":
+ for index, (lower, upper) in enumerate(validated):
+ effective[:, index] = np.clip(
+ effective[:, index],
+ -np.inf if lower is None else lower,
+ np.inf if upper is None else upper,
+ )
+ return BoundedUCBResult(
+ utility_ucb_raw=raw,
+ utility_ucb_effective=effective,
+ utility_ucb_clip_amount=np.abs(raw - effective),
+ policy=str(policy),
+ bounds=validated,
+ )
+
+
+def _reference_point(
+ value: np.ndarray | torch.Tensor | None, objective_count: int
+) -> np.ndarray:
+ if value is None:
+ raise ValueError(
+ "reference_point_utility is required and is never derived from data."
+ )
+ if isinstance(value, torch.Tensor):
+ reference = value.detach().cpu().double().numpy()
+ else:
+ reference = np.asarray(value, dtype=float)
+ if reference.shape != (objective_count,):
+ raise ValueError(
+ "reference_point_utility must have shape "
+ f"({objective_count},); got {reference.shape}."
+ )
+ if not np.all(np.isfinite(reference)):
+ raise ValueError("reference_point_utility must contain only finite values.")
+ return reference
+
+
+def pareto_utility_above_reference(
+ observed_utility: np.ndarray | torch.Tensor,
+ reference_point_utility: np.ndarray | torch.Tensor,
+) -> np.ndarray:
+ """Return finite non-dominated utilities that strictly dominate the reference."""
+ observed = _utility_matrix(observed_utility, name="observed_utility")
+ reference = _reference_point(reference_point_utility, observed.shape[1])
+ contributing = observed[np.all(observed > reference, axis=1)]
+ if contributing.shape[0] == 0:
+ return np.empty((0, observed.shape[1]), dtype=float)
+ tensor = torch.as_tensor(contributing, dtype=torch.double)
+ return tensor[is_non_dominated(tensor)].numpy()
+
+
+def hypervolume_improvement_scores(
+ optimistic_utility: np.ndarray | torch.Tensor,
+ observed_utility: np.ndarray | torch.Tensor,
+ reference_point_utility: np.ndarray | torch.Tensor | None,
+ *,
+ numeric_tolerance: float = 1e-12,
+ chunk_size: int = 1024,
+) -> tuple[np.ndarray, float, np.ndarray, np.ndarray]:
+ """Compute exact singleton HVI for optimistic all-maximize utility vectors."""
+ candidates = _utility_matrix(optimistic_utility, name="optimistic_utility")
+ observed = _utility_matrix(observed_utility, name="observed_utility")
+ if candidates.shape[1] != observed.shape[1]:
+ raise ValueError("Candidate and observed utility dimensions must match.")
+ reference = _reference_point(reference_point_utility, observed.shape[1])
+ tolerance = _finite_nonnegative(numeric_tolerance, name="numeric_tolerance")
+ chunk = _positive_integer(chunk_size, name="chunk_size")
+ pareto = pareto_utility_above_reference(observed, reference)
+ ref_tensor = torch.as_tensor(reference, dtype=torch.double)
+ hypervolume = Hypervolume(ref_point=ref_tensor)
+ baseline = (
+ 0.0
+ if pareto.shape[0] == 0
+ else float(hypervolume.compute(torch.as_tensor(pareto, dtype=torch.double)))
+ )
+
+ scores = np.zeros(candidates.shape[0], dtype=float)
+ for start in range(0, candidates.shape[0], chunk):
+ stop = min(start + chunk, candidates.shape[0])
+ for index in range(start, stop):
+ candidate = candidates[index]
+ if not np.all(candidate > reference):
+ continue
+ if pareto.shape[0] and np.any(
+ np.all(pareto >= candidate - tolerance, axis=1)
+ ):
+ continue
+ augmented = np.vstack([pareto, candidate[None, :]])
+ augmented_tensor = torch.as_tensor(augmented, dtype=torch.double)
+ augmented_pareto = augmented_tensor[is_non_dominated(augmented_tensor)]
+ improvement = float(hypervolume.compute(augmented_pareto)) - baseline
+ if improvement < -tolerance:
+ raise RuntimeError(
+ "Hypervolume improvement was negative beyond numeric "
+ f"tolerance: candidate_index={index}, improvement={improvement}, "
+ f"tolerance={tolerance}."
+ )
+ if improvement > tolerance:
+ scores[index] = improvement
+ return scores, baseline, pareto, reference
+
+
+def score_ucb_hvi_from_moments(
+ utility_mean: np.ndarray,
+ utility_std: np.ndarray,
+ observed_utility: np.ndarray,
+ reference_point_utility: np.ndarray | None,
+ *,
+ beta: float,
+ numeric_tolerance: float = 1e-12,
+ chunk_size: int = 1024,
+ log_epsilon: float = 1e-12,
+ mc_samples: int | None = None,
+ seed: int | None = None,
+ observation_noise: bool = False,
+ objective_contract_version: str = "direct-moments",
+ moment_method: str = "direct_moments",
+ bound_policy: UCBBoundPolicy | str = "none",
+ utility_bounds: Sequence[UtilityBound] | None = None,
+) -> UCBHVIScoreResult:
+ """Form utility UCB vectors and score their singleton hypervolume gain."""
+ means = _utility_matrix(utility_mean, name="utility_mean")
+ standard_deviations = _utility_matrix(utility_std, name="utility_std")
+ if standard_deviations.shape != means.shape:
+ raise ValueError("utility_std must have the same shape as utility_mean.")
+ if np.any(standard_deviations < 0):
+ raise ValueError("utility_std cannot contain negative values.")
+ beta_value = _finite_nonnegative(beta, name="beta")
+ if isinstance(log_epsilon, (bool, np.bool_)):
+ raise ValueError("log_epsilon must be a real non-boolean number.")
+ epsilon = float(log_epsilon)
+ if not np.isfinite(epsilon) or epsilon <= 0:
+ raise ValueError("log_epsilon must be finite and strictly positive.")
+ if (
+ not isinstance(objective_contract_version, str)
+ or not objective_contract_version.strip()
+ ):
+ raise ValueError("objective_contract_version must be a non-empty string.")
+ if not isinstance(moment_method, str) or not moment_method.strip():
+ raise ValueError("moment_method must be a non-empty string.")
+ kappa = sqrt(beta_value)
+ optimistic_raw = means + kappa * standard_deviations
+ bounded = apply_ucb_bound_policy(optimistic_raw, utility_bounds, bound_policy)
+ scores, baseline, pareto, reference = hypervolume_improvement_scores(
+ bounded.utility_ucb_effective,
+ observed_utility,
+ reference_point_utility,
+ numeric_tolerance=numeric_tolerance,
+ chunk_size=chunk_size,
+ )
+ return UCBHVIScoreResult(
+ base_score=scores,
+ base_log_score=np.log(np.maximum(scores, epsilon)),
+ utility_mean=means,
+ utility_std=standard_deviations,
+ utility_ucb=bounded.utility_ucb_effective,
+ baseline_hypervolume=baseline,
+ pareto_utility=pareto,
+ reference_point_utility=reference,
+ beta=beta_value,
+ kappa=kappa,
+ mc_samples=mc_samples,
+ seed=seed,
+ observation_noise=bool(observation_noise),
+ objective_contract_version=objective_contract_version.strip(),
+ moment_method=moment_method.strip(),
+ bound_policy=bounded.policy,
+ utility_bounds=bounded.bounds,
+ utility_ucb_raw=bounded.utility_ucb_raw,
+ utility_ucb_effective=bounded.utility_ucb_effective,
+ utility_ucb_clip_amount=bounded.utility_ucb_clip_amount,
+ )
+
+
+def score_ucb_hvi_pool(
+ model: Any,
+ X_pool_norm: torch.Tensor,
+ observed_raw: np.ndarray | torch.Tensor,
+ objective_transform: TensorTransform | Any,
+ reference_point_utility: np.ndarray | torch.Tensor | None,
+ *,
+ beta: float,
+ mc_samples: int = 128,
+ seed: int = 0,
+ posterior_chunk_size: int = 512,
+ hvi_chunk_size: int = 1024,
+ observation_noise: bool = False,
+ numeric_tolerance: float = 1e-12,
+ log_epsilon: float = 1e-12,
+ moment_method: MomentMethod | str = "monte_carlo",
+ bound_policy: UCBBoundPolicy | str = "none",
+ utility_bounds: Sequence[UtilityBound] | None = None,
+) -> UCBHVIScoreResult:
+ """Score a normalized discrete pool from raw-output model posteriors."""
+ if moment_method == "monte_carlo":
+ moments = posterior_utility_moments(
+ model,
+ X_pool_norm,
+ objective_transform,
+ mc_samples=mc_samples,
+ seed=seed,
+ chunk_size=posterior_chunk_size,
+ observation_noise=observation_noise,
+ )
+ utility_mean = moments.utility_mean
+ utility_std = moments.utility_std
+ resolved_mc_samples: int | None = moments.mc_samples
+ resolved_seed: int | None = moments.seed
+ resolved_method = moments.moment_method
+ resolved_observation_noise = moments.observation_noise
+ elif moment_method == "analytic_identity":
+ analytic = posterior_identity_moments(
+ model,
+ X_pool_norm,
+ objective_transform,
+ chunk_size=posterior_chunk_size,
+ observation_noise=observation_noise,
+ )
+ if analytic.utility_mean.ndim != 2 or analytic.utility_std.ndim != 2:
+ raise ValueError(
+ "UCB-HVI pool scoring requires unbatched analytic posterior moments "
+ "with shape (N, M)."
+ )
+ utility_mean = analytic.utility_mean.detach().cpu().numpy()
+ utility_std = analytic.utility_std.detach().cpu().numpy()
+ resolved_mc_samples = None
+ resolved_seed = None
+ resolved_method = analytic.moment_method
+ resolved_observation_noise = analytic.observation_noise
+ else:
+ raise ValueError(
+ "moment_method must be exactly 'monte_carlo' or 'analytic_identity'."
+ )
+ raw = torch.as_tensor(
+ observed_raw,
+ dtype=X_pool_norm.dtype,
+ device=X_pool_norm.device,
+ )
+ if raw.ndim != 2:
+ raise ValueError(
+ f"observed_raw must have shape (N, M); got {tuple(raw.shape)}."
+ )
+ observed_utility_tensor = _apply_objective_transform(objective_transform, raw)
+ contract_version = getattr(objective_transform, "version", None)
+ if contract_version is None and hasattr(objective_transform, "objective_transform"):
+ contract_version = getattr(
+ objective_transform.objective_transform, "version", None
+ )
+ if not isinstance(contract_version, str) or not contract_version.strip():
+ raise ValueError(
+ "objective_transform must expose a non-empty objective contract version."
+ )
+ return score_ucb_hvi_from_moments(
+ utility_mean,
+ utility_std,
+ observed_utility_tensor,
+ reference_point_utility,
+ beta=beta,
+ numeric_tolerance=numeric_tolerance,
+ chunk_size=hvi_chunk_size,
+ log_epsilon=log_epsilon,
+ mc_samples=resolved_mc_samples,
+ seed=resolved_seed,
+ observation_noise=resolved_observation_noise,
+ objective_contract_version=contract_version,
+ moment_method=resolved_method,
+ bound_policy=bound_policy,
+ utility_bounds=utility_bounds,
+ )
+
+
+def propose_ucb_hvi_batch(
+ candidate_pool: Any,
+ model: Any,
+ observed_raw: np.ndarray | torch.Tensor,
+ objective_transform: TensorTransform | Any,
+ reference_point_utility: np.ndarray | torch.Tensor | None,
+ *,
+ q: int,
+ beta: float,
+ local_penalization_config: Any,
+ observed_pending_norm: np.ndarray | None = None,
+ positive_score_tolerance: float = 1e-12,
+ mc_samples: int = 128,
+ seed: int = 0,
+ posterior_chunk_size: int = 512,
+ hvi_chunk_size: int = 1024,
+ observation_noise: bool = False,
+ numeric_tolerance: float = 1e-12,
+ log_epsilon: float = 1e-12,
+ moment_method: MomentMethod | str = "monte_carlo",
+ bound_policy: UCBBoundPolicy | str = "none",
+ utility_bounds: Sequence[UtilityBound] | None = None,
+) -> UCBHVIBatchProposal:
+ """Select exactly ``q`` locally penalized positive-HVI pool candidates.
+
+ The UCB-HVI score is static for the pool, while the shared selector updates
+ the soft local penalty and hard distance masks after every selection. A
+ candidate whose true raw HVI is not greater than
+ ``positive_score_tolerance`` is ineligible (log score ``-inf``).
+ """
+ from .batch_selection import BaseScoreResult, select_local_penalized_batch
+
+ if isinstance(positive_score_tolerance, (bool, np.bool_)):
+ raise ValueError("positive_score_tolerance must be a real non-boolean number.")
+ tolerance = float(positive_score_tolerance)
+ if not np.isfinite(tolerance) or tolerance <= 0:
+ raise ValueError(
+ "positive_score_tolerance must be finite and strictly positive."
+ )
+ try:
+ model_parameter = next(model.parameters())
+ model_dtype = model_parameter.dtype
+ model_device = model_parameter.device
+ except (AttributeError, StopIteration):
+ model_dtype = torch.double
+ model_device = torch.device("cpu")
+ X_pool = torch.as_tensor(
+ candidate_pool.X_norm, dtype=model_dtype, device=model_device
+ )
+ scoring = score_ucb_hvi_pool(
+ model,
+ X_pool,
+ observed_raw,
+ objective_transform,
+ reference_point_utility,
+ beta=beta,
+ mc_samples=mc_samples,
+ seed=seed,
+ posterior_chunk_size=posterior_chunk_size,
+ hvi_chunk_size=hvi_chunk_size,
+ observation_noise=observation_noise,
+ numeric_tolerance=numeric_tolerance,
+ log_epsilon=log_epsilon,
+ moment_method=moment_method,
+ bound_policy=bound_policy,
+ utility_bounds=utility_bounds,
+ )
+
+ def score_remaining(
+ remaining_indices: np.ndarray, selected_indices: np.ndarray
+ ) -> Any:
+ del selected_indices
+ raw_scores = scoring.base_score[remaining_indices]
+ log_scores = scoring.base_log_score[remaining_indices].copy()
+ log_scores[raw_scores <= tolerance] = -np.inf
+ return BaseScoreResult(
+ base_log_score=log_scores,
+ base_score=raw_scores,
+ diagnostics={
+ "utility_mean": scoring.utility_mean[remaining_indices],
+ "utility_std": scoring.utility_std[remaining_indices],
+ "utility_ucb": scoring.utility_ucb[remaining_indices],
+ "utility_ucb_raw": scoring.utility_ucb_raw[remaining_indices],
+ "utility_ucb_effective": scoring.utility_ucb_effective[
+ remaining_indices
+ ],
+ "utility_ucb_clip_amount": scoring.utility_ucb_clip_amount[
+ remaining_indices
+ ],
+ "eligible_positive_hvi": raw_scores > tolerance,
+ },
+ )
+
+ selection = select_local_penalized_batch(
+ candidate_pool,
+ q,
+ score_remaining,
+ local_penalization_config,
+ observed_pending_norm=observed_pending_norm,
+ )
+ return UCBHVIBatchProposal(
+ selection=selection,
+ scoring=scoring,
+ positive_score_tolerance=tolerance,
+ metadata={
+ "method": scoring.method,
+ "method_version": scoring.method_version,
+ "objective_contract_version": scoring.objective_contract_version,
+ "reference_point_utility": scoring.reference_point_utility.copy(),
+ "pool_seed": candidate_pool.seed,
+ "pool_size": candidate_pool.size,
+ "pool_draws": candidate_pool.draws,
+ "pool_rejected_duplicate": candidate_pool.rejected_duplicate,
+ "pool_rejected_avoid": candidate_pool.rejected_avoid,
+ "pool_rejected_constraint": candidate_pool.rejected_constraint,
+ "posterior_seed": scoring.seed,
+ "mc_samples": scoring.mc_samples,
+ "moment_method": scoring.moment_method,
+ "bound_policy": scoring.bound_policy,
+ "utility_bounds": scoring.utility_bounds,
+ "beta": scoring.beta,
+ "kappa": scoring.kappa,
+ "observation_noise": scoring.observation_noise,
+ "positive_score_tolerance": tolerance,
+ "local_penalization": {
+ "radius": local_penalization_config.radius,
+ "min_batch_distance": local_penalization_config.min_batch_distance,
+ "min_observed_distance": local_penalization_config.min_observed_distance,
+ "dimension_weights": local_penalization_config.dimension_weights,
+ "epsilon": local_penalization_config.epsilon,
+ },
+ "selected_pool_indices": selection.selected_pool_indices.copy(),
+ },
+ )
diff --git a/src/mobo_kit/utils.py b/src/mobo_kit/utils.py
index 2c0fd73..aabe2ec 100644
--- a/src/mobo_kit/utils.py
+++ b/src/mobo_kit/utils.py
@@ -1,6 +1,11 @@
# src/utils.py
from __future__ import annotations
-from typing import List, Tuple, Any
+from dataclasses import dataclass
+import csv
+import io
+from pathlib import Path
+from typing import Any, List, Sequence, Tuple
+
import pandas as pd
import numpy as np
import torch
@@ -8,76 +13,481 @@
from .design import Design
+
+_METADATA_LABELS = ("units", "start", "stop", "step")
+_CSV_ENCODINGS = ("utf-8-sig", "cp1252", "latin-1")
+
+
+@dataclass
+class ParsedCampaignCSV:
+ """Parsed metadata-style campaign CSV and its inferred data contract.
+
+ ``metadata_row_count`` counts rows between the header and the first
+ experimental row, including an optional blank separator. Header positions
+ in ``duplicate_headers`` are one-based CSV/Excel column positions.
+ """
+
+ config: dict[str, Any]
+ data: pd.DataFrame
+ input_columns: list[str]
+ objective_columns: list[str]
+ metadata_row_count: int
+ raw_headers: list[str]
+ duplicate_headers: dict[str, list[int]]
+ encoding: str
+
+
+def _read_raw_csv(path: str | Path) -> tuple[Path, list[list[str]], str]:
+ csv_path = Path(path)
+ if not csv_path.is_file():
+ raise FileNotFoundError(f"Campaign CSV not found: {csv_path}")
+
+ payload = csv_path.read_bytes()
+ if not payload:
+ raise ValueError(f"Campaign CSV is empty: {csv_path}")
+
+ decode_errors: list[str] = []
+ for encoding in _CSV_ENCODINGS:
+ try:
+ text = payload.decode(encoding)
+ except UnicodeDecodeError as exc:
+ decode_errors.append(f"{encoding}: {exc}")
+ continue
+
+ try:
+ rows = list(csv.reader(io.StringIO(text, newline=""), strict=True))
+ except csv.Error as exc:
+ raise ValueError(f"Malformed CSV syntax in {csv_path}: {exc}") from exc
+
+ if not rows or not any(cell.strip() for cell in rows[0]):
+ raise ValueError(f"Campaign CSV has no usable header row: {csv_path}")
+ return csv_path, rows, encoding
+
+ detail = "; ".join(decode_errors)
+ raise UnicodeError(
+ f"Could not decode campaign CSV {csv_path} using "
+ f"{', '.join(_CSV_ENCODINGS)}. {detail}"
+ )
+
+
+def _normalize_row_widths(
+ rows: list[list[str]], width: int, csv_path: Path
+) -> list[list[str]]:
+ normalized: list[list[str]] = []
+ for line_number, row in enumerate(rows, start=1):
+ if len(row) > width and any(cell.strip() for cell in row[width:]):
+ raise ValueError(
+ f"CSV row {line_number} in {csv_path} has {len(row)} fields but "
+ f"the header has {width}; extra nonblank fields are not allowed."
+ )
+ normalized.append((row[:width] + [""] * width)[:width])
+ return normalized
+
+
+def _duplicate_header_positions(headers: Sequence[str]) -> dict[str, list[int]]:
+ positions: dict[str, list[int]] = {}
+ for position, raw_header in enumerate(headers, start=1):
+ header = raw_header.strip()
+ if header:
+ positions.setdefault(header, []).append(position)
+ return {name: found for name, found in positions.items() if len(found) > 1}
+
+
+def _find_metadata_rows(
+ rows: Sequence[Sequence[str]], csv_path: Path
+) -> tuple[dict[str, int], int]:
+ found: dict[str, int] = {}
+ label_columns: set[int] = set()
+
+ for row_index, row in enumerate(rows[1:], start=1):
+ matches = [
+ (column_index, cell.strip().lower())
+ for column_index, cell in enumerate(row)
+ if cell.strip().lower() in _METADATA_LABELS
+ ]
+ if len(matches) > 1:
+ raise ValueError(
+ f"Metadata row {row_index + 1} in {csv_path} contains more than "
+ f"one metadata label: {[label for _, label in matches]}"
+ )
+ if not matches:
+ continue
+
+ column_index, label = matches[0]
+ if label in found:
+ raise ValueError(
+ f"Duplicate '{label}' metadata row in {csv_path} "
+ f"(rows {found[label] + 1} and {row_index + 1})."
+ )
+ found[label] = row_index
+ label_columns.add(column_index)
+ if len(found) == len(_METADATA_LABELS):
+ break
+
+ missing = [label for label in _METADATA_LABELS if label not in found]
+ if missing:
+ raise ValueError(
+ "Plain data CSVs are not supported by parse_campaign_csv; expected "
+ "labeled units/start/stop/step metadata rows. "
+ f"Missing metadata rows: {missing}."
+ )
+ if len(label_columns) != 1:
+ raise ValueError(
+ f"Metadata labels in {csv_path} must use one consistent label column; "
+ f"found columns {[index + 1 for index in sorted(label_columns)]}."
+ )
+
+ return found, next(iter(label_columns))
+
+
+def _parse_metadata_number(
+ value: str, *, label: str, header: str, position: int
+) -> float:
+ try:
+ number = float(value)
+ except (TypeError, ValueError) as exc:
+ raise ValueError(
+ f"Malformed {label} metadata for input '{header}' at column "
+ f"{position}: {value!r} is not numeric."
+ ) from exc
+ if not np.isfinite(number):
+ raise ValueError(
+ f"Malformed {label} metadata for input '{header}' at column "
+ f"{position}: values must be finite."
+ )
+ return number
+
+
+def parse_campaign_csv(
+ path: str | Path,
+ expected_objectives: Sequence[str] | None = None,
+) -> ParsedCampaignCSV:
+ """Parse a metadata-style campaign CSV without fixed row offsets.
+
+ Inputs are named columns with complete numeric ``start``, ``stop``, and
+ ``step`` metadata. If ``expected_objectives`` is omitted, objectives are
+ inferred from named non-input columns after an optional blank separator.
+ Plain data-only CSVs and duplicate named headers are rejected explicitly.
+ Experimental values are preserved here and validated numerically by
+ :func:`split_XY` at the model boundary.
+ """
+
+ csv_path, raw_rows, encoding = _read_raw_csv(path)
+ raw_headers = list(raw_rows[0])
+ rows = _normalize_row_widths(raw_rows, len(raw_headers), csv_path)
+ headers = [header.strip() for header in raw_headers]
+
+ duplicate_headers = _duplicate_header_positions(raw_headers)
+ if duplicate_headers:
+ rendered = ", ".join(
+ f"{name!r} at columns {positions}"
+ for name, positions in duplicate_headers.items()
+ )
+ raise ValueError(
+ "Duplicate CSV headers detected before pandas column renaming: "
+ f"{rendered}."
+ )
+
+ metadata_rows, metadata_label_column = _find_metadata_rows(rows, csv_path)
+ last_metadata_row = max(metadata_rows.values())
+ data_start = last_metadata_row + 1
+ while data_start < len(rows) and not any(cell.strip() for cell in rows[data_start]):
+ data_start += 1
+ if data_start >= len(rows):
+ raise ValueError(f"Campaign CSV has an empty experimental section: {csv_path}")
+
+ input_columns: list[str] = []
+ input_positions: list[int] = []
+ input_specs: list[dict[str, Any]] = []
+
+ for column_index, header in enumerate(headers):
+ if not header or column_index == metadata_label_column:
+ continue
+
+ numeric_values = {
+ label: rows[row_index][column_index].strip()
+ for label, row_index in metadata_rows.items()
+ if label in {"start", "stop", "step"}
+ }
+ present = {label: bool(value) for label, value in numeric_values.items()}
+ if not any(present.values()):
+ continue
+ if not all(present.values()):
+ missing = [label for label, is_present in present.items() if not is_present]
+ raise ValueError(
+ f"Incomplete numeric metadata for column '{header}': missing "
+ f"{missing}. Input columns require start, stop, and step."
+ )
+
+ start = _parse_metadata_number(
+ numeric_values["start"],
+ label="start",
+ header=header,
+ position=column_index + 1,
+ )
+ stop = _parse_metadata_number(
+ numeric_values["stop"],
+ label="stop",
+ header=header,
+ position=column_index + 1,
+ )
+ step = _parse_metadata_number(
+ numeric_values["step"],
+ label="step",
+ header=header,
+ position=column_index + 1,
+ )
+ if stop < start:
+ raise ValueError(
+ f"Invalid metadata for input '{header}': stop ({stop}) is below "
+ f"start ({start})."
+ )
+ if step <= 0:
+ raise ValueError(
+ f"Invalid metadata for input '{header}': step must be positive."
+ )
+
+ unit = rows[metadata_rows["units"]][column_index].strip() or None
+ input_columns.append(header)
+ input_positions.append(column_index)
+ input_specs.append(
+ {
+ "name": header,
+ "unit": unit,
+ "start": start,
+ "stop": stop,
+ "step": step,
+ }
+ )
+
+ if not input_columns:
+ raise ValueError(
+ "Campaign CSV contains no input columns with complete numeric "
+ f"metadata: {csv_path}"
+ )
+
+ named_non_input_positions = [
+ column_index
+ for column_index, header in enumerate(headers)
+ if header
+ and column_index != metadata_label_column
+ and column_index not in input_positions
+ ]
+
+ if expected_objectives is not None:
+ if isinstance(expected_objectives, str):
+ objective_columns = [expected_objectives.strip()]
+ else:
+ objective_columns = [str(name).strip() for name in expected_objectives]
+ if not objective_columns or any(not name for name in objective_columns):
+ raise ValueError(
+ "expected_objectives must contain at least one nonblank name."
+ )
+ if len(set(objective_columns)) != len(objective_columns):
+ raise ValueError("expected_objectives contains duplicate names.")
+ missing_objectives = [name for name in objective_columns if name not in headers]
+ if missing_objectives:
+ raise ValueError(
+ f"Missing objective columns in campaign CSV: {missing_objectives}."
+ )
+ input_objectives = [name for name in objective_columns if name in input_columns]
+ if input_objectives:
+ raise ValueError(
+ f"Columns cannot be both inputs and objectives: {input_objectives}."
+ )
+ else:
+ objective_positions = [
+ position
+ for position in named_non_input_positions
+ if position > max(input_positions)
+ ]
+ separator_positions = [
+ column_index
+ for column_index, header in enumerate(headers)
+ if not header and column_index > max(input_positions)
+ ]
+ if separator_positions:
+ objective_positions = [
+ position
+ for position in objective_positions
+ if position > separator_positions[0]
+ ]
+ objective_columns = [headers[position] for position in objective_positions]
+
+ if not objective_columns:
+ raise ValueError(
+ "Campaign CSV has no named objective columns. Provide objective headers "
+ "or pass expected_objectives."
+ )
+
+ unnamed_positions = [
+ column_index for column_index, header in enumerate(headers) if not header
+ ]
+ experimental_rows: list[list[str]] = []
+ for source_row, row in enumerate(rows[data_start:], start=data_start + 1):
+ if not any(cell.strip() for cell in row):
+ continue
+ populated_unnamed = [
+ column_index + 1
+ for column_index in unnamed_positions
+ if row[column_index].strip()
+ ]
+ if populated_unnamed:
+ raise ValueError(
+ f"Experimental row {source_row} contains values in unnamed columns "
+ f"{populated_unnamed}; add explicit headers before parsing."
+ )
+ experimental_rows.append(row)
+
+ if not experimental_rows:
+ raise ValueError(f"Campaign CSV has an empty experimental section: {csv_path}")
+
+ data_positions = [
+ column_index
+ for column_index, header in enumerate(headers)
+ if header and column_index != metadata_label_column
+ ]
+ data = pd.DataFrame(
+ [
+ [
+ row[column_index] if row[column_index].strip() else pd.NA
+ for column_index in data_positions
+ ]
+ for row in experimental_rows
+ ],
+ columns=[headers[column_index] for column_index in data_positions],
+ )
+
+ config = {
+ "inputs": input_specs,
+ "objectives": {"names": objective_columns},
+ "constraints": [],
+ }
+ return ParsedCampaignCSV(
+ config=config,
+ data=data,
+ input_columns=input_columns,
+ objective_columns=objective_columns,
+ metadata_row_count=data_start - 1,
+ raw_headers=raw_headers,
+ duplicate_headers=duplicate_headers,
+ encoding=encoding,
+ )
+
+
def get_objective_names(cfg: dict) -> List[str]:
names = cfg.get("objectives", {}).get("names", [])
if not names:
raise ValueError("Config must have objectives.names as a non-empty list.")
return list(names)
-def load_csv(path: str) -> pd.DataFrame:
- return pd.read_csv(path)
-def split_XY(df: pd.DataFrame, design: Design, config: dict) -> Tuple[np.ndarray, np.ndarray]:
- """
- Split a DataFrame into input features (X) and objectives (Y) using the new config/design structure.
-
- This function works with CSV files like configCSV_example.csv that contain:
- - Rows 0-4: Configuration metadata (column names, units, start, stop, step)
- - Row 5: Empty row
- - Rows 6+: Experimental data
-
- Args:
- df: DataFrame containing the data (including metadata rows)
- design: Design object with input parameter names
- config: Configuration dictionary with objectives.names
-
- Returns:
- Tuple of (X, Y) arrays where:
- - X: (N, D) array of input features
- - Y: (N, M) array of objectives
-
- Raises:
- KeyError: If required columns are missing from the DataFrame
+def load_csv(
+ path: str | Path,
+ expected_objectives: Sequence[str] | None = None,
+) -> pd.DataFrame:
+ """Load only experimental rows from a metadata-style campaign CSV."""
+
+ return parse_campaign_csv(path, expected_objectives=expected_objectives).data
+
+
+def _blank_mask(frame: pd.DataFrame) -> pd.DataFrame:
+ return frame.isna() | frame.apply(
+ lambda column: column.map(
+ lambda value: isinstance(value, str) and not value.strip()
+ )
+ )
+
+
+def _numeric_model_frame(frame: pd.DataFrame, role: str) -> pd.DataFrame:
+ blank = _blank_mask(frame)
+ converted = frame.apply(pd.to_numeric, errors="coerce")
+ invalid = converted.isna() | ~np.isfinite(converted.astype(float))
+ if invalid.any().any():
+ locations = [
+ f"row {frame.index[row]!r}, column {frame.columns[column]!r}"
+ for row, column in zip(*np.where(invalid.to_numpy()))
+ ]
+ detail = ", ".join(locations[:8])
+ if len(locations) > 8:
+ detail += f", and {len(locations) - 8} more"
+ kind = "blank" if (invalid & blank).any().any() else "nonnumeric"
+ raise ValueError(
+ f"{role} model data contains {kind} or non-finite values at {detail}."
+ )
+ return converted.astype(float)
+
+
+def split_XY(
+ df: pd.DataFrame, design: Design, config: dict
+) -> Tuple[pd.DataFrame, pd.DataFrame]:
+ """Select and validate named numeric model inputs and objectives.
+
+ Rows are never filled with zero or silently discarded. Any incomplete row
+ must be completed or removed explicitly by a campaign/QC policy before this
+ model-boundary function is called.
"""
- # Get input column names from design
+
+ if not isinstance(df, pd.DataFrame):
+ raise TypeError("split_XY expects a pandas.DataFrame.")
+ if df.empty:
+ raise ValueError("Experimental data is empty; no model rows are available.")
+ if df.columns.duplicated().any():
+ duplicates = list(dict.fromkeys(df.columns[df.columns.duplicated()].tolist()))
+ raise ValueError(f"Experimental DataFrame has duplicate columns: {duplicates}.")
+
x_cols = list(design.names)
-
- # Get objective column names from config
y_cols = get_objective_names(config)
-
- # Check for missing columns
- miss_x = [c for c in x_cols if c not in df.columns]
- miss_y = [c for c in y_cols if c not in df.columns]
-
+ miss_x = [column for column in x_cols if column not in df.columns]
+ miss_y = [column for column in y_cols if column not in df.columns]
if miss_x or miss_y:
parts = []
- if miss_x:
+ if miss_x:
parts.append(f"missing inputs: {miss_x}")
- if miss_y:
+ if miss_y:
parts.append(f"missing objectives: {miss_y}")
raise KeyError("CSV column check failed: " + "; ".join(parts))
-
- # Extract data rows (skip the first 6 rows: metadata + empty row)
- data_df = df.iloc[6:].copy()
-
- # Remove rows with all NaN values (empty rows)
- data_df = data_df.dropna(how='all')
-
- # Extract X and Y arrays
- X = data_df[x_cols].astype(float)
- Y = data_df[y_cols].astype(float)
-
+
+ X_raw = df.loc[:, x_cols].copy()
+ Y_raw = df.loc[:, y_cols].copy()
+ objective_blanks = _blank_mask(Y_raw)
+ if objective_blanks.all().all():
+ raise ValueError(
+ "All objective values are blank; no completed model rows exist."
+ )
+
+ all_blank_rows = objective_blanks.all(axis=1)
+ if all_blank_rows.any():
+ raise ValueError(
+ "Objective values are blank for rows "
+ f"{Y_raw.index[all_blank_rows].tolist()}; rows are not dropped silently."
+ )
+ partial_rows = objective_blanks.any(axis=1)
+ if partial_rows.any():
+ raise ValueError(
+ "Partially completed objective rows found at indices "
+ f"{Y_raw.index[partial_rows].tolist()}; complete every objective "
+ "before modeling."
+ )
+
+ X = _numeric_model_frame(X_raw, "Input")
+ Y = _numeric_model_frame(Y_raw, "Objective")
return X, Y
+
def select_device(prefer: str = "cuda") -> torch.device:
- return torch.device("cuda" if prefer == "cuda" and torch.cuda.is_available() else "cpu")
+ return torch.device(
+ "cuda" if prefer == "cuda" and torch.cuda.is_available() else "cpu"
+ )
+
def set_seeds(seed: int) -> None:
np.random.seed(seed)
torch.manual_seed(seed)
+
def np_to_torch(
- *arrays: np.ndarray,
+ *arrays: np.ndarray | pd.DataFrame,
device: torch.device | None = None,
dtype: torch.dtype = torch.float64,
return_device: bool = False,
@@ -92,7 +502,10 @@ def np_to_torch(
"""
if device is None:
device = select_device("cuda") # uses your existing helper
- tensors = tuple(torch.as_tensor(a, dtype=dtype, device=device) for a in arrays)
+ tensors = tuple(
+ torch.as_tensor(np.asarray(array), dtype=dtype, device=device)
+ for array in arrays
+ )
out = tensors[0] if len(tensors) == 1 else tensors
return (out, device) if return_device else out
@@ -109,123 +522,30 @@ def torch_to_np(*tensors: torch.Tensor):
return arrays[0] if len(arrays) == 1 else arrays
-def csv_to_config(csv_path: str, output_path: str = None) -> str:
- """
- Convert a CSV configuration file to a YAML config file.
-
- Expected CSV format:
- - Row 0: Column names (input parameters + objectives)
- - Row 1: Units for each column
- - Row 2: Start values for input parameters
- - Row 3: Stop values for input parameters
- - Row 4: Step values for input parameters
- - Row 5: Empty row
- - Row 6+: Experimental data
-
- Args:
- csv_path: Path to the CSV configuration file
- output_path: Path for the output YAML file. If None, generates a default name.
-
- Returns:
- Generated config dictionary
+def csv_to_config(
+ csv_path: str | Path,
+ output_path: str | Path | None = None,
+ expected_objectives: Sequence[str] | None = None,
+) -> dict[str, Any]:
+ """Build a configuration from a metadata-style campaign CSV.
+
+ This wrapper has no filesystem side effect unless ``output_path`` is
+ supplied explicitly. Generic campaign conversion always defaults to no
+ constraints.
"""
- # Read CSV file
- df = pd.read_csv(csv_path)
-
- # Extract metadata from first few rows
- column_names = df.columns.tolist()
- units = df.iloc[0].tolist()
- starts = df.iloc[1].tolist()
- stops = df.iloc[2].tolist()
- steps = df.iloc[3].tolist()
-
- # Identify input parameters and objectives by finding the empty column separator
- # Skip the first unnamed column, then find where empty columns start
- input_params = []
- objective_params = []
-
- # Start from column 1 (skip first unnamed column)
- i = 1
- while i < len(column_names):
- col_name = column_names[i]
- # Check if this is an empty column (NaN, empty string, or pandas unnamed column)
- if (pd.isna(col_name) or
- str(col_name).strip() == "" or
- str(col_name).startswith("Unnamed:")):
- # Found the separator - everything after this is objectives
- objective_params = [col for col in column_names[i+1:]
- if not (pd.isna(col) or str(col).strip() == "" or str(col).startswith("Unnamed:"))]
- break
- else:
- input_params.append(col_name)
- i += 1
-
- # If no separator found, assume all remaining columns are objectives
- if not objective_params:
- objective_params = [col for col in column_names[len(input_params)+1:] if not (pd.isna(col) or str(col).strip() == "")]
-
- # Build the config dictionary
- config = {
- "inputs": [],
- "objectives": {"names": objective_params},
- "constraints": [
- {
- "clausius_clapeyron": True,
- "ah_col": "absolute_humidity",
- "temp_c_col": "temperature_c"
- }
- ]
- }
-
- # Add input parameters
- for i, param in enumerate(input_params):
- # Skip empty column names
- if pd.isna(param) or str(param).strip() == "":
- continue
-
- # Find the column index in the original column_names list
- try:
- metadata_idx = column_names.index(param)
- except ValueError:
- # Fallback: use position-based indexing
- metadata_idx = i + 1 # +1 because we skipped the first column
-
- unit = units[metadata_idx] if metadata_idx < len(units) else ""
- start = starts[metadata_idx] if metadata_idx < len(starts) else 0.0
- stop = stops[metadata_idx] if metadata_idx < len(stops) else 1.0
- step = steps[metadata_idx] if metadata_idx < len(steps) else 0.01
-
- # Convert to appropriate types
- try:
- start = float(start) if pd.notna(start) else 0.0
- stop = float(stop) if pd.notna(stop) else 1.0
- step = float(step) if pd.notna(step) else 0.01
- except (ValueError, TypeError):
- # Use defaults if conversion fails
- start, stop, step = 0.0, 1.0, 0.01
-
- input_spec = {
- "name": str(param).strip(),
- "unit": unit,
- "start": start,
- "stop": stop,
- "step": step
- }
-
- config["inputs"].append(input_spec)
-
- # Generate output path if not provided
- if output_path is None:
- import os
- csv_basename = os.path.splitext(os.path.basename(csv_path))[0]
- output_path = f"configs/{csv_basename}_config.yaml"
-
- # Ensure output directory exists
- import os
- os.makedirs(os.path.dirname(output_path), exist_ok=True)
-
- # Write YAML file
- with open(output_path, 'w', encoding='utf-8') as f:
- yaml.dump(config, f, default_flow_style=False, sort_keys=False, indent=2)
-
- return config
\ No newline at end of file
+
+ config = parse_campaign_csv(
+ csv_path, expected_objectives=expected_objectives
+ ).config
+ if output_path is not None:
+ destination = Path(output_path)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ with destination.open("w", encoding="utf-8", newline="\n") as stream:
+ yaml.safe_dump(
+ config,
+ stream,
+ default_flow_style=False,
+ sort_keys=False,
+ indent=2,
+ )
+ return config
diff --git a/src/mobo_kit/workbook_io.py b/src/mobo_kit/workbook_io.py
new file mode 100644
index 0000000..09d3217
--- /dev/null
+++ b/src/mobo_kit/workbook_io.py
@@ -0,0 +1,861 @@
+"""Read the campaign workbook, write candidate sheets back.
+
+The experimentalist's side of the loop: open ``Summary Table.xlsx``, fill in ten
+inputs and the measurement columns, press one button, get a new sheet of
+conditions to run.
+
+Three rules this module keeps.
+
+**The source workbook is never opened for writing.** Candidate sheets go to a
+sibling file, ``_R1_Candidates.xlsx``.
+
+That is not the original plan, which was to add sheets to ``Summary Table.xlsx``
+itself. It changed because of a measured fact: **openpyxl discards cached
+formula values on save.** ``Uniformity score`` is a formula column
+(``=L2*N2*O2``), so a single openpyxl round-trip turns it -- and every other
+formula column -- into ``None`` for any reader that is not Excel, including this
+one. Verified directly: Z2:Z4 read ``[0.657, 0.587, 0.561]`` before a save that
+only added an empty sheet, and ``[None, None, None]`` after.
+
+Writing beside the workbook keeps the experimentalist's one-button flow (they
+open the new file, fill it in, press the button again) and makes the read-only
+invariant structural rather than merely asserted.
+
+**Which columns to collect comes from the config, not from here.** Each
+objective's ``measurement`` block names the raw columns its value is computed
+from, so ``R1_Candidates`` asks for ``Coverage``, ``T1..T4`` and the rest rather
+than for the three derived scores. Driving that off the config means a future
+objective change updates the sheet automatically instead of silently leaving the
+next round without its data.
+
+**The derived scores are computed, not read.** Three of the workbook's score
+cells are pasted literals that do not update when the measurements behind them
+change, so :mod:`scores` recomputes all three and the stored cells become a
+cross-check that warns. That is why ``model_values`` is keyed by objective name
+and the stored cells appear separately as ``workbook_values``.
+
+**Round detection is fail-closed.** A partially scored sheet is refused with a
+plain sentence rather than being guessed at.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import shutil
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Mapping, Sequence
+
+import numpy as np
+import pandas as pd
+from openpyxl import load_workbook
+from openpyxl.styles import Font, PatternFill
+from openpyxl.utils import get_column_letter
+
+from .campaign import (
+ measurement_entry_columns,
+ measurement_specs,
+ model_source_columns,
+ objective_names,
+ replicate_aggregates,
+)
+from .scores import (
+ ScoreFinding,
+ ScoreSeverity,
+ compute_measurements,
+ row_completeness,
+)
+
+__all__ = [
+ "CandidateResults",
+ "CandidateSheetError",
+ "RoundState",
+ "WorkbookContents",
+ "backup_workbook",
+ "candidate_workbook_path",
+ "detect_round",
+ "read_campaign_workbook",
+ "read_candidate_results",
+ "sheet_name_for_round",
+ "source_sheet",
+ "workbook_digest",
+ "write_candidate_sheet",
+]
+
+#: Fallback for configs that predate `campaign.source_sheet`. The v4 workbook
+#: names its sheets by round (`R0`, `R1`), so the sheet a campaign reads is now a
+#: config key rather than a constant.
+SOURCE_SHEET = "Sheet1"
+
+
+def source_sheet(config: Mapping[str, Any]) -> str:
+ """Which sheet holds the measured rows for this campaign."""
+ return str((config.get("campaign") or {}).get("source_sheet", SOURCE_SHEET))
+
+
+def formula_findings(
+ path: str | Path, config: Mapping[str, Any]
+) -> tuple[ScoreFinding, ...]:
+ """Has a frozen score column's DEFINITION moved since it was recorded?
+
+ A ``stored`` objective is read rather than recomputed, so nothing in Python
+ knows what it means and no cross-check can catch a redefinition. This is the
+ partial replacement: read the formula TEXT (never evaluate it) and compare it
+ with the fingerprint in config.
+
+ Requires a second read of the workbook with ``data_only=False``, because
+ openpyxl gives either the formulas or their cached values and never both. That
+ is why it is skipped entirely unless a fingerprint is declared.
+ """
+ from .campaign import measurement_specs, objective_names
+
+ specs = list(measurement_specs(config))
+ names = list(objective_names(config))
+ wanted = [
+ (name, spec)
+ for name, spec in zip(names, specs)
+ if spec is not None and spec.formula_fingerprint is not None
+ ]
+ if not wanted:
+ return ()
+
+ sheet_name = source_sheet(config)
+ workbook = load_workbook(Path(path), data_only=False, read_only=False)
+ if sheet_name not in workbook.sheetnames:
+ return ()
+ sheet = workbook[sheet_name]
+ positions = _header_positions(sheet)
+
+ findings: list[ScoreFinding] = []
+ for name, spec in wanted:
+ fingerprint = spec.formula_fingerprint
+ column = fingerprint.column
+ if column not in positions:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="fingerprint_column_absent",
+ objective=name,
+ row_position=-1,
+ sample_id=None,
+ message=(
+ f"{column!r} is not in {sheet_name}, so the frozen score's "
+ "definition cannot be checked at all."
+ ),
+ column=column,
+ )
+ )
+ continue
+ index = positions[column]
+ seen: list[str] = []
+ for row in sheet.iter_rows(min_row=2, values_only=True):
+ if row[0] is None:
+ break
+ value = row[index]
+ if isinstance(value, str) and value.startswith("="):
+ seen.append(value)
+ if not seen:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="fingerprint_no_formula",
+ objective=name,
+ row_position=-1,
+ sample_id=None,
+ message=(
+ f"{column!r} holds no formula on any row -- the values are "
+ "literals. A frozen score that is pasted rather than "
+ "computed cannot be checked against anything at all, which "
+ "is the one failure this contract cannot see."
+ ),
+ column=column,
+ )
+ )
+ continue
+ changed = [text for text in seen if not fingerprint.matches(text)]
+ if changed:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="formula_fingerprint_changed",
+ objective=name,
+ row_position=-1,
+ sample_id=None,
+ message=(
+ f"{column!r} no longer matches the recorded definition. "
+ f"Recorded {fingerprint.formula!r}; found "
+ f"{changed[0]!r} (and {len(changed) - 1} other row(s) that "
+ "differ). This objective is READ, not recomputed, so the "
+ "change is not an error -- but every number computed under "
+ "the old definition is about a different quantity. Bump "
+ "objectives.contract_version and update the fingerprint."
+ ),
+ column=column,
+ )
+ )
+ else:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.NOTE,
+ code="formula_fingerprint_unchanged",
+ objective=name,
+ row_position=-1,
+ sample_id=None,
+ message=(
+ f"{column!r} still computes {fingerprint.formula!r} on all "
+ f"{len(seen)} rows."
+ ),
+ column=column,
+ )
+ )
+ return tuple(findings)
+SAMPLE_COLUMN = "Sample number"
+ENTRY_FILL = PatternFill("solid", fgColor="FFF2CC")
+HEADER_FONT = Font(bold=True)
+
+
+class CandidateSheetError(RuntimeError):
+ """The workbook is not in a state this tool can act on."""
+
+
+@dataclass(frozen=True)
+class WorkbookContents:
+ """Everything read out of the source sheet."""
+
+ inputs: pd.DataFrame
+ """Physical input values, columns in the config's declared order."""
+ model_values: pd.DataFrame
+ """What the GP trains on, one column per objective in objective order.
+
+ Computed from the raw measurement columns for any objective that declares a
+ ``measurement`` block; read from the declared column for one that does not.
+ """
+ workbook_values: pd.DataFrame
+ """The stored derived cells, as the workbook holds them. Cross-check only."""
+ inputs_used: pd.DataFrame
+ """How many measured inputs each value came from -- 2 to 4 for thickness."""
+ findings: tuple[ScoreFinding, ...]
+ """Cross-check mismatches, excluded readings and disagreeing replicates."""
+ sample_ids: tuple[int, ...]
+ digest: str
+
+ @property
+ def n_rows(self) -> int:
+ return len(self.inputs)
+
+ @property
+ def errors(self) -> tuple[ScoreFinding, ...]:
+ from .scores import ScoreSeverity
+
+ return tuple(f for f in self.findings if f.severity is ScoreSeverity.ERROR)
+
+ @property
+ def warnings(self) -> tuple[ScoreFinding, ...]:
+ from .scores import ScoreSeverity
+
+ return tuple(f for f in self.findings if f.severity is ScoreSeverity.WARNING)
+
+
+@dataclass(frozen=True)
+class RoundState:
+ """Which round should be generated next, and why."""
+
+ next_round: str | None
+ reason: str
+ scored_rows: int = 0
+ total_rows: int = 0
+
+
+def sheet_name_for_round(round_name: str) -> str:
+ return f"{round_name.upper()}_Candidates"
+
+
+def workbook_digest(path: str | Path) -> str:
+ """SHA-256 of the whole file, for the unchanged-source proof."""
+ digest = hashlib.sha256()
+ with open(path, "rb") as handle:
+ for chunk in iter(lambda: handle.read(1 << 20), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def backup_workbook(path: str | Path, *, timestamp: str | None = None) -> Path:
+ """Copy the workbook next to itself before any write."""
+ source = Path(path)
+ stamp = timestamp or datetime.now().strftime("%Y%m%d_%H%M%S")
+ destination = source.with_name(f"{source.stem}_backup_{stamp}{source.suffix}")
+ shutil.copy2(source, destination)
+ return destination
+
+
+def _header_positions(sheet) -> dict[str, int]:
+ header = next(sheet.iter_rows(min_row=1, max_row=1, values_only=True))
+ positions: dict[str, int] = {}
+ for index, value in enumerate(header):
+ if value is None:
+ continue
+ name = str(value).strip()
+ # duplicate headers exist in this workbook; first occurrence wins and the
+ # rest stay reachable by position
+ positions.setdefault(name, index)
+ # headers carry their unit inline ("precur_vol (uL)") while the config
+ # declares name and unit separately; accept both spellings
+ bare = name.split("(")[0].strip()
+ if bare and bare != name:
+ positions.setdefault(bare, index)
+ return positions
+
+
+def _near_misses(wanted: str, available: Sequence[str]) -> list[str]:
+ """Headers that are plausibly the same column under a different name.
+
+ Deliberately generous. The realistic cause of a missing column is not a typo
+ in the sheet but a config describing a DIFFERENT campaign, where the same
+ quantity was called something adjacent -- ``PL - Implied Voc (Max)`` against
+ ``PL - Implied Voc (Max) Raw``. Prefix and containment catch that; edit
+ distance would not, and would also match unrelated columns.
+ """
+ lowered = wanted.lower().strip()
+ head = lowered.split("(")[0].strip()
+ hits = [
+ name
+ for name in available
+ if name.lower().strip() != lowered
+ and (
+ lowered in name.lower()
+ or name.lower() in lowered
+ or (len(head) > 3 and name.lower().startswith(head))
+ )
+ ]
+ return hits[:4]
+
+
+def _missing_columns_message(
+ missing: Sequence[str],
+ positions: Mapping[str, int],
+ config: Mapping[str, Any],
+ path: Path,
+ sheet_name: str = SOURCE_SHEET,
+) -> str:
+ """Say which CONTRACT wanted the column, not just that it is absent.
+
+ "Sheet1 is missing required column(s)" reads as a broken workbook, and the
+ usual cause is the opposite: an intact workbook being read against another
+ campaign's config. Naming the config and offering the near-miss headers turns
+ a five-minute hunt into a glance.
+ """
+ campaign = config.get("campaign") or {}
+ contract = (config.get("objectives") or {}).get("contract_version")
+ lines = [
+ f"{sheet_name} of {path.name} is missing column(s) that the campaign "
+ f"configuration requires: {list(missing)}.",
+ "",
+ f"Configuration: {campaign.get('name')} "
+ f"(status: {campaign.get('status')}, contract: {contract}).",
+ ]
+ if str(campaign.get("status")) == "archived":
+ lines += [
+ "",
+ "THAT CONFIGURATION IS ARCHIVED. It describes a previous campaign, "
+ "whose workbook had different columns, so this is almost certainly a "
+ "config/workbook mismatch rather than a problem with the workbook. "
+ "Point the launcher at the active campaign configuration instead.",
+ ]
+ suggestions = {
+ name: _near_misses(name, list(positions)) for name in missing
+ }
+ named = {name: hits for name, hits in suggestions.items() if hits}
+ if named:
+ lines += ["", "The sheet does have these, which look related:"]
+ for name, hits in named.items():
+ lines.append(f" wanted {name!r} -> found {hits}")
+ lines += [
+ "",
+ "If one of those is the same measurement under a new name, the fix is "
+ "a `measurement` column in the config, not an edit to the workbook.",
+ ]
+ return "\n".join(lines)
+
+
+def read_campaign_workbook(
+ path: str | Path, config: Mapping[str, Any]
+) -> WorkbookContents:
+ """Read measured rows, stopping at the first blank sample number.
+
+ Rows below the data block are notes, not observations.
+ """
+ sheet_name = source_sheet(config)
+ workbook = load_workbook(Path(path), data_only=True, read_only=False)
+ if sheet_name not in workbook.sheetnames:
+ raise CandidateSheetError(
+ f"This campaign reads its measured rows from a sheet named "
+ f"{sheet_name!r} (campaign.source_sheet); {Path(path).name} has "
+ f"{workbook.sheetnames}. Either the workbook is for a different "
+ "campaign, or the sheet was renamed."
+ )
+ sheet = workbook[sheet_name]
+ positions = _header_positions(sheet)
+
+ input_names = [item["name"] for item in config["inputs"]]
+ specs = measurement_specs(config)
+ computed = [spec for spec in specs if spec is not None]
+ declared = list(model_source_columns(config))
+ names = list(objective_names(config))
+ required_entry, optional_entry = measurement_entry_columns(config)
+
+ missing = [
+ name
+ for name in [SAMPLE_COLUMN, *input_names, *required_entry]
+ if name not in positions
+ ]
+ if missing:
+ raise CandidateSheetError(
+ _missing_columns_message(
+ missing, positions, config, Path(path), sheet_name
+ )
+ )
+
+ rows = []
+ for row in sheet.iter_rows(min_row=2, values_only=True):
+ if row[positions[SAMPLE_COLUMN]] is None:
+ break
+ rows.append(row)
+ if not rows:
+ raise CandidateSheetError(f"{sheet_name} contains no measured rows.")
+
+ def column(name: str) -> list[Any]:
+ return [row[positions[name]] for row in rows]
+
+ # every column any recipe or cross-check may look at, kept as raw cells:
+ # `scores` is the one place that knows how this workbook spells "not measured"
+ wanted: list[str] = [*required_entry, *optional_entry, *declared]
+ for spec in computed:
+ wanted.extend(check.column for check in spec.cross_checks)
+ raw = pd.DataFrame(
+ {
+ name: column(name)
+ for name in dict.fromkeys(wanted)
+ if name in positions
+ },
+ dtype=object,
+ )
+ sample_ids = tuple(int(value) for value in column(SAMPLE_COLUMN))
+
+ model_frame: dict[str, Any] = {}
+ findings: tuple[ScoreFinding, ...] = ()
+ inputs_used = pd.DataFrame(index=range(len(rows)))
+ if computed:
+ result = compute_measurements(raw, computed, sample_ids=sample_ids)
+ findings = result.findings
+ inputs_used = result.inputs_used
+ for name in result.values.columns:
+ model_frame[name] = result.values[name]
+ for name, spec, declared_column in zip(names, specs, declared):
+ if spec is None:
+ model_frame[name] = pd.to_numeric(
+ column(declared_column), errors="coerce"
+ )
+
+ findings = tuple(findings) + formula_findings(path, config)
+
+ return WorkbookContents(
+ inputs=pd.DataFrame(
+ {name: pd.to_numeric(column(name), errors="coerce") for name in input_names}
+ ),
+ model_values=pd.DataFrame({name: model_frame[name] for name in names}),
+ workbook_values=pd.DataFrame(
+ {
+ name: pd.to_numeric(column(name), errors="coerce")
+ for name in dict.fromkeys(declared)
+ if name in positions
+ }
+ ),
+ inputs_used=inputs_used,
+ findings=findings,
+ sample_ids=sample_ids,
+ digest=workbook_digest(path),
+ )
+
+
+@dataclass(frozen=True)
+class CandidateResults:
+ """Measurements read back out of one round's candidate sheet.
+
+ The films of one condition are separate experimental rows but one design
+ point, so they are aggregated to a single observation before the next round
+ trains on them. ``replicate_spread`` keeps the within-condition scatter that
+ aggregation discards -- that is the raw material for ``train_Yvar``.
+ """
+
+ round_name: str
+ conditions: pd.DataFrame
+ """One row per condition, input columns in the config's declared order."""
+ model_values: pd.DataFrame
+ """One row per condition, one column per objective. Aggregated."""
+ replicates: pd.DataFrame
+ """One row per film: candidate_id, replicate_index, then objective values."""
+ replicate_spread: pd.DataFrame
+ """Per-condition sd in each objective's aggregation space. NaN below 2 films."""
+ films_used: pd.DataFrame
+ """How many films each condition's value was aggregated from."""
+ findings: tuple[ScoreFinding, ...]
+ candidate_ids: tuple[str, ...]
+
+ @property
+ def n_conditions(self) -> int:
+ return len(self.conditions)
+
+ @property
+ def errors(self) -> tuple[ScoreFinding, ...]:
+ return tuple(f for f in self.findings if f.severity is ScoreSeverity.ERROR)
+
+
+def _aggregate(values: np.ndarray, rule: str) -> tuple[float, float]:
+ """Collapse one condition's film values to (observation, spread).
+
+ Spread is the sample sd in the aggregation space, so for ``mean_of_log`` it
+ is a sd of ``log`` values and is already what a log-space ``train_Yvar``
+ wants. It is NaN for a single film, which is honest: one film measures no
+ reproducibility at all.
+ """
+ finite = values[np.isfinite(values)]
+ if finite.size == 0:
+ return float("nan"), float("nan")
+ if rule == "mean_of_log":
+ if np.any(finite <= 0):
+ raise CandidateSheetError(
+ "mean_of_log aggregation needs strictly positive values; got "
+ f"{finite.tolist()}."
+ )
+ logs = np.log(finite)
+ spread = float(np.std(logs, ddof=1)) if finite.size > 1 else float("nan")
+ return float(np.exp(logs.mean())), spread
+ spread = float(np.std(finite, ddof=1)) if finite.size > 1 else float("nan")
+ return float(finite.mean()), spread
+
+
+def read_candidate_results(
+ path: str | Path, config: Mapping[str, Any], round_name: str
+) -> CandidateResults:
+ """Read a filled-in candidate sheet and aggregate it to design points.
+
+ ``path`` is the SOURCE workbook; the candidate sheet is found beside it, the
+ same way :func:`write_candidate_sheet` put it there. Objective values are
+ computed per film by :mod:`scores` -- the same recipes the source sheet uses,
+ so R0 and R1 observations are commensurable -- and then aggregated per
+ ``replicate_group``.
+ """
+ candidate_path = candidate_workbook_path(path, round_name)
+ if not candidate_path.exists():
+ raise CandidateSheetError(
+ f"{candidate_path.name} does not exist, so there are no {round_name} "
+ "measurements to read."
+ )
+ sheet_name = sheet_name_for_round(round_name)
+ workbook = load_workbook(candidate_path, data_only=True)
+ if sheet_name not in workbook.sheetnames:
+ raise CandidateSheetError(
+ f"{candidate_path.name} has no {sheet_name!r} sheet; found "
+ f"{workbook.sheetnames}."
+ )
+ sheet = workbook[sheet_name]
+ positions = _header_positions(sheet)
+
+ input_names = [item["name"] for item in config["inputs"]]
+ names = list(objective_names(config))
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ rules = list(replicate_aggregates(config))
+ required_entry, optional_entry = measurement_entry_columns(config)
+
+ missing = [
+ column
+ for column in ["candidate_id", *input_names, *required_entry]
+ if column not in positions
+ ]
+ if missing:
+ raise CandidateSheetError(
+ f"{candidate_path.name} is missing column(s) {missing}. It was "
+ "probably created by an older version; regenerate it."
+ )
+
+ rows = [
+ row
+ for row in sheet.iter_rows(min_row=2, values_only=True)
+ if row[positions["candidate_id"]] is not None
+ ]
+ if not rows:
+ raise CandidateSheetError(f"{sheet_name} contains no candidate rows.")
+
+ def column(name: str) -> list[Any]:
+ return [row[positions[name]] for row in rows]
+
+ group_column = "replicate_group" if "replicate_group" in positions else "candidate_id"
+ groups = [str(value) for value in column(group_column)]
+ film_labels = [str(value) for value in column("candidate_id")]
+
+ wanted = [*required_entry, *optional_entry]
+ for spec in specs:
+ wanted.extend(check.column for check in spec.cross_checks)
+ raw = pd.DataFrame(
+ {name: column(name) for name in dict.fromkeys(wanted) if name in positions},
+ dtype=object,
+ )
+ per_film = compute_measurements(raw, specs, sample_ids=film_labels)
+ findings = list(per_film.findings)
+
+ inputs = pd.DataFrame(
+ {name: pd.to_numeric(column(name), errors="coerce") for name in input_names}
+ )
+
+ ordered_groups = list(dict.fromkeys(groups))
+ group_index = pd.Series(groups)
+
+ condition_rows: list[dict[str, float]] = []
+ value_rows: list[dict[str, float]] = []
+ spread_rows: list[dict[str, float]] = []
+ count_rows: list[dict[str, int]] = []
+ for group in ordered_groups:
+ mask = (group_index == group).to_numpy()
+ block = inputs.loc[mask]
+ first = block.iloc[0]
+ for name in input_names:
+ if not np.allclose(
+ block[name].to_numpy(dtype=float), float(first[name]), equal_nan=True
+ ):
+ raise CandidateSheetError(
+ f"The films of {group} do not share the same {name}. Replicates "
+ "must be the same recipe; edit the sheet or regenerate it."
+ )
+ condition_rows.append({name: float(first[name]) for name in input_names})
+
+ values: dict[str, float] = {}
+ spreads: dict[str, float] = {}
+ counts: dict[str, int] = {}
+ for name, rule in zip(names, rules):
+ film_values = per_film.values.loc[mask, name].to_numpy(dtype=float)
+ observation, spread = _aggregate(film_values, rule)
+ values[name] = observation
+ spreads[name] = spread
+ counts[name] = int(np.isfinite(film_values).sum())
+ if counts[name] == 0:
+ findings.append(
+ ScoreFinding(
+ severity=ScoreSeverity.ERROR,
+ code="condition_has_no_usable_film",
+ objective=name,
+ row_position=ordered_groups.index(group),
+ sample_id=group,
+ message=(
+ f"none of the {int(mask.sum())} films of {group} produced "
+ f"a usable {name} value."
+ ),
+ )
+ )
+ value_rows.append(values)
+ spread_rows.append(spreads)
+ count_rows.append(counts)
+
+ replicates = pd.DataFrame(
+ {
+ "candidate_id": film_labels,
+ "replicate_group": groups,
+ **(
+ {"replicate_index": pd.to_numeric(column("replicate_index"))}
+ if "replicate_index" in positions
+ else {}
+ ),
+ **{name: per_film.values[name] for name in names},
+ }
+ )
+
+ return CandidateResults(
+ round_name=round_name.upper(),
+ conditions=pd.DataFrame(condition_rows, columns=input_names),
+ model_values=pd.DataFrame(value_rows, columns=names),
+ replicates=replicates,
+ replicate_spread=pd.DataFrame(spread_rows, columns=names),
+ films_used=pd.DataFrame(count_rows, columns=names),
+ findings=tuple(findings),
+ candidate_ids=tuple(ordered_groups),
+ )
+
+
+def detect_round(path: str | Path, config: Mapping[str, Any]) -> RoundState:
+ """Decide which round to generate. Fail closed on a partial sheet.
+
+ "Measured" is a per-objective question once objectives are computed rather
+ than read: ``product`` and ``log10_product`` need every input, while
+ thickness needs only one of ``T1..T4``. Requiring all four would report a
+ finished sheet as partial -- nine of the fifteen R0 rows have two readings.
+ """
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ required_entry, optional_entry = measurement_entry_columns(config)
+
+ for round_name, following in (("R1", "R2"), ("R2", None)):
+ candidate_path = candidate_workbook_path(path, round_name)
+ name = candidate_path.name
+ if not candidate_path.exists():
+ return RoundState(round_name, f"{name} does not exist yet.")
+ sheet = load_workbook(candidate_path, data_only=True)[
+ sheet_name_for_round(round_name)
+ ]
+ positions = _header_positions(sheet)
+ missing = [c for c in required_entry if c not in positions]
+ if missing:
+ raise CandidateSheetError(
+ f"{name} is missing entry column(s) {missing}. It was probably "
+ "created by an older version; delete the sheet and regenerate it."
+ )
+ data = [
+ row
+ for row in sheet.iter_rows(min_row=2, values_only=True)
+ if any(value is not None for value in row)
+ ]
+ if specs:
+ frame = pd.DataFrame(
+ {
+ entry: [row[positions[entry]] for row in data]
+ for entry in dict.fromkeys((*required_entry, *optional_entry))
+ if entry in positions
+ },
+ dtype=object,
+ index=range(len(data)),
+ )
+ filled = list(row_completeness(frame, specs))
+ else:
+ filled = [
+ all(row[positions[c]] is not None for c in required_entry)
+ for row in data
+ ]
+ scored, total = sum(filled), len(filled)
+ if total and scored == 0:
+ return RoundState(
+ None,
+ f"{name} exists but no results have been entered yet. Run those "
+ f"{total} conditions and fill in {', '.join(required_entry)}.",
+ scored,
+ total,
+ )
+ if scored < total:
+ return RoundState(
+ None,
+ f"{name} is partly filled in: {scored} of {total} rows have all "
+ "measurements. Complete the remaining rows, or clear them, then "
+ "try again.",
+ scored,
+ total,
+ )
+ if following is None:
+ return RoundState(
+ None,
+ "R1 and R2 are both complete. The campaign is finished.",
+ scored,
+ total,
+ )
+ return RoundState(None, "Nothing to do.")
+
+
+def candidate_workbook_path(path: str | Path, round_name: str) -> Path:
+ """Where this round's candidates are written, beside the source workbook."""
+ source = Path(path)
+ return source.with_name(f"{source.stem}_{sheet_name_for_round(round_name)}.xlsx")
+
+
+def write_candidate_sheet(
+ path: str | Path,
+ config: Mapping[str, Any],
+ conditions: pd.DataFrame,
+ *,
+ round_name: str,
+ replicates: int = 3,
+ make_backup: bool = True,
+) -> Path:
+ """Write this round's worklist to a sibling workbook.
+
+ One row per physical film, three per condition sharing a ``replicate_group``.
+ Measurement columns are left blank and highlighted for entry -- the raw
+ columns each objective is computed from, not the derived scores, because the
+ scores are now computed in Python.
+
+ Optional entry columns (``T3``, ``T4``, ``T anom``) are offered but not
+ demanded: a film with two thickness readings is complete.
+
+ The source workbook is opened read-only and its Sheet1 digest is checked
+ afterwards, so the guarantee is enforced rather than assumed.
+ """
+ source_path = Path(path)
+ source_before = _source_sheet_digest(source_path, source_sheet(config))
+ workbook_path = candidate_workbook_path(source_path, round_name)
+ sheet_name = sheet_name_for_round(round_name)
+ if workbook_path.exists():
+ if make_backup:
+ backup_workbook(workbook_path)
+ raise CandidateSheetError(
+ f"{workbook_path.name} already exists. Rename or delete it first; "
+ "this tool does not overwrite a file that may hold measurements."
+ )
+ from openpyxl import Workbook
+
+ workbook = Workbook()
+ workbook.remove(workbook.active)
+
+ input_names = [item["name"] for item in config["inputs"]]
+ required_entry, optional_entry = measurement_entry_columns(config)
+ entry_names = [*required_entry, *optional_entry]
+ headers = [
+ "candidate_id",
+ "replicate_group",
+ "replicate_index",
+ "round",
+ *input_names,
+ *entry_names,
+ ]
+
+ sheet = workbook.create_sheet(sheet_name)
+ sheet.append(headers)
+ for cell in sheet[1]:
+ cell.font = HEADER_FONT
+
+ entry_start = len(headers) - len(entry_names) + 1
+ for index, (_, condition) in enumerate(conditions.iterrows(), start=1):
+ candidate_id = f"{round_name.upper()}_C{index:02d}"
+ for replicate in range(1, replicates + 1):
+ sheet.append(
+ [
+ candidate_id,
+ candidate_id,
+ replicate,
+ round_name.upper(),
+ *[float(condition[name]) for name in input_names],
+ ]
+ )
+ for offset in range(len(entry_names)):
+ sheet.cell(row=sheet.max_row, column=entry_start + offset).fill = (
+ ENTRY_FILL
+ )
+
+ for index, header in enumerate(headers, start=1):
+ sheet.column_dimensions[get_column_letter(index)].width = max(
+ 12, min(24, len(header) + 3)
+ )
+ sheet.freeze_panes = "A2"
+
+ workbook.save(workbook_path)
+
+ # the invariant, actually enforced rather than asserted
+ if _source_sheet_digest(source_path, source_sheet(config)) != source_before:
+ raise CandidateSheetError(
+ f"{source_sheet(config)} in {source_path.name} changed while writing "
+ f"{workbook_path.name}. It should not have been touched at all."
+ )
+ return workbook_path
+
+
+def _source_sheet_digest(path: str | Path, sheet_name: str = SOURCE_SHEET) -> str:
+ """Digest of the source sheet's values only, so added sheets do not change it."""
+ sheet = load_workbook(Path(path), data_only=True, read_only=False)[sheet_name]
+ digest = hashlib.sha256()
+ for row in sheet.iter_rows(values_only=True):
+ digest.update(repr(row).encode("utf-8"))
+ return digest.hexdigest()
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deleted file mode 100644
index 4a9b53c..0000000
--- a/tests/simple_gp_test.py
+++ /dev/null
@@ -1,294 +0,0 @@
-#!/usr/bin/env python3
-"""
-Simple GP test with visualizations - no LOOCV, just basic functionality
-"""
-
-import numpy as np
-import torch
-import matplotlib.pyplot as plt
-from gpytorch.kernels import RBFKernel, MaternKernel, PeriodicKernel
-
-from src.models import fit_gp_models, posterior_report
-from src.data import y_standardize_np
-from src.utils import np_to_torch
-
-# Set up matplotlib for better plots
-plt.style.use('default')
-plt.rcParams['figure.figsize'] = (12, 8)
-plt.rcParams['font.size'] = 10
-
-def synthetic_objectives(X):
- """
- Two synthetic objective functions with known properties:
- - Obj1: Quadratic with global minimum
- - Obj2: Sinusoidal with multiple local optima
- """
- x1, x2 = X[:, 0], X[:, 1]
-
- # Objective 1: Quadratic bowl (minimize)
- obj1 = (x1 - 0.3)**2 + (x2 - 0.7)**2 + 0.1
-
- # Objective 2: Sinusoidal (minimize)
- obj2 = 0.5 * np.sin(6 * np.pi * x1) * np.cos(4 * np.pi * x2) + 0.5
-
- return np.column_stack([obj1, obj2])
-
-def generate_training_data(n_points=20, noise_std=0.05, seed=42):
- """Generate training data with controlled noise"""
- np.random.seed(seed)
-
- # Random sampling in [0,1]^2
- X_train = np.random.uniform(0, 1, size=(n_points, 2))
-
- # Evaluate true objectives
- Y_true = synthetic_objectives(X_train)
-
- # Add Gaussian noise
- noise = np.random.normal(0, noise_std, Y_true.shape)
- Y_noisy = Y_true + noise
-
- return X_train, Y_noisy, Y_true
-
-def plot_true_objectives():
- """Plot the true objective functions for reference"""
- # Create a fine grid for visualization
- n_grid = 100
- x1 = np.linspace(0, 1, n_grid)
- x2 = np.linspace(0, 1, n_grid)
- X1, X2 = np.meshgrid(x1, x2)
- X_grid = np.column_stack([X1.ravel(), X2.ravel()])
-
- # Evaluate true objectives
- Y_true = synthetic_objectives(X_grid)
- obj1_grid = Y_true[:, 0].reshape(n_grid, n_grid)
- obj2_grid = Y_true[:, 1].reshape(n_grid, n_grid)
-
- fig, axes = plt.subplots(1, 2, figsize=(15, 6))
-
- # Objective 1: Quadratic
- im1 = axes[0].contourf(X1, X2, obj1_grid, levels=20, cmap='viridis')
- axes[0].set_title('Objective 1: Quadratic Bowl\n(x₁-0.3)² + (x₂-0.7)² + 0.1')
- axes[0].set_xlabel('x₁')
- axes[0].set_ylabel('x₂')
- axes[0].plot(0.3, 0.7, 'r*', markersize=15, label='Global minimum')
- axes[0].legend()
- plt.colorbar(im1, ax=axes[0])
-
- # Objective 2: Sinusoidal
- im2 = axes[1].contourf(X1, X2, obj2_grid, levels=20, cmap='plasma')
- axes[1].set_title('Objective 2: Sinusoidal\n0.5×sin(6πx₁)×cos(4πx₂) + 0.5')
- axes[1].set_xlabel('x₁')
- axes[1].set_ylabel('x₂')
- plt.colorbar(im2, ax=axes[1])
-
- plt.tight_layout()
- plt.savefig('simple_true_objectives.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def plot_gp_predictions(model, X_train, Y_train, Y_mean, Y_std, title_suffix=""):
- """Plot GP predictions vs true functions"""
- # Create prediction grid
- n_grid = 50
- x1 = np.linspace(0, 1, n_grid)
- x2 = np.linspace(0, 1, n_grid)
- X1, X2 = np.meshgrid(x1, x2)
- X_grid = np.column_stack([X1.ravel(), X2.ravel()])
-
- # True values
- Y_true_grid = synthetic_objectives(X_grid)
-
- # GP predictions
- X_grid_t = torch.tensor(X_grid, dtype=torch.float64)
- pred_mean, pred_std = posterior_report(model, X_grid_t, Y_mean, Y_std)
-
- # Reshape for plotting
- obj1_true = Y_true_grid[:, 0].reshape(n_grid, n_grid)
- obj1_pred = pred_mean[:, 0].reshape(n_grid, n_grid)
- obj1_std = pred_std[:, 0].reshape(n_grid, n_grid)
-
- obj2_true = Y_true_grid[:, 1].reshape(n_grid, n_grid)
- obj2_pred = pred_mean[:, 1].reshape(n_grid, n_grid)
- obj2_std = pred_std[:, 1].reshape(n_grid, n_grid)
-
- # Create plots
- fig, axes = plt.subplots(2, 3, figsize=(18, 12))
-
- # Objective 1 row
- # True
- im1 = axes[0,0].contourf(X1, X2, obj1_true, levels=20, cmap='viridis')
- axes[0,0].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 0],
- s=80, cmap='viridis', edgecolors='white', linewidth=2)
- axes[0,0].set_title('Obj1: True')
- axes[0,0].set_ylabel('x₂')
- plt.colorbar(im1, ax=axes[0,0])
-
- # Predicted
- im2 = axes[0,1].contourf(X1, X2, obj1_pred, levels=20, cmap='viridis')
- axes[0,1].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 0],
- s=80, cmap='viridis', edgecolors='white', linewidth=2)
- axes[0,1].set_title('Obj1: GP Prediction')
- plt.colorbar(im2, ax=axes[0,1])
-
- # Uncertainty
- im3 = axes[0,2].contourf(X1, X2, obj1_std, levels=20, cmap='Reds')
- axes[0,2].scatter(X_train[:, 0], X_train[:, 1], c='white',
- s=80, edgecolors='black', linewidth=2)
- axes[0,2].set_title('Obj1: GP Uncertainty')
- plt.colorbar(im3, ax=axes[0,2])
-
- # Objective 2 row
- # True
- im4 = axes[1,0].contourf(X1, X2, obj2_true, levels=20, cmap='plasma')
- axes[1,0].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 1],
- s=80, cmap='plasma', edgecolors='white', linewidth=2)
- axes[1,0].set_title('Obj2: True')
- axes[1,0].set_xlabel('x₁')
- axes[1,0].set_ylabel('x₂')
- plt.colorbar(im4, ax=axes[1,0])
-
- # Predicted
- im5 = axes[1,1].contourf(X1, X2, obj2_pred, levels=20, cmap='plasma')
- axes[1,1].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 1],
- s=80, cmap='plasma', edgecolors='white', linewidth=2)
- axes[1,1].set_title('Obj2: GP Prediction')
- axes[1,1].set_xlabel('x₁')
- plt.colorbar(im5, ax=axes[1,1])
-
- # Uncertainty
- im6 = axes[1,2].contourf(X1, X2, obj2_std, levels=20, cmap='Reds')
- axes[1,2].scatter(X_train[:, 0], X_train[:, 1], c='white',
- s=80, edgecolors='black', linewidth=2)
- axes[1,2].set_title('Obj2: GP Uncertainty')
- axes[1,2].set_xlabel('x₁')
- plt.colorbar(im6, ax=axes[1,2])
-
- plt.suptitle(f'Default GP Model Performance {title_suffix}', fontsize=14)
- plt.tight_layout()
- plt.savefig(f'simple_gp_predictions{title_suffix.replace(" ", "_")}.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def plot_training_fit(Y_true, Y_pred, Y_std, objective_names=['Obj1', 'Obj2']):
- """Simple parity plot for training fit"""
- fig, axes = plt.subplots(1, 2, figsize=(12, 5))
-
- for i, obj_name in enumerate(objective_names):
- ax = axes[i]
-
- # Plot parity line
- y_min, y_max = min(Y_true[:, i].min(), Y_pred[:, i].min()), max(Y_true[:, i].max(), Y_pred[:, i].max())
- ax.plot([y_min, y_max], [y_min, y_max], 'k--', alpha=0.5, label='Perfect fit')
-
- # Plot predictions with error bars
- ax.errorbar(Y_true[:, i], Y_pred[:, i], yerr=Y_std[:, i],
- fmt='o', alpha=0.7, capsize=3, label='GP predictions')
-
- # Calculate R²
- from sklearn.metrics import r2_score
- r2 = r2_score(Y_true[:, i], Y_pred[:, i])
-
- ax.set_xlabel(f'True {obj_name}')
- ax.set_ylabel(f'Predicted {obj_name}')
- ax.set_title(f'{obj_name}: R² = {r2:.3f}')
- ax.legend()
- ax.grid(True, alpha=0.3)
-
- # Make axes equal
- ax.set_aspect('equal', adjustable='box')
-
- plt.tight_layout()
- plt.savefig('simple_parity_plot.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def simple_gp_test():
- """Simple test of default GP models with visualizations"""
-
- print("=== SIMPLE GP TEST ===")
-
- # Show the true objective functions
- print("1. Plotting true objective functions...")
- plot_true_objectives()
-
- # Generate training data
- print("\n2. Generating training data...")
- X_train, Y_train, Y_true = generate_training_data(n_points=100, noise_std=0.02, seed=42)
- print(f" Training data: {X_train.shape[0]} points, {X_train.shape[1]} dimensions")
- print(f" Objectives: {Y_train.shape[1]} (Quadratic, Sinusoidal)")
- print(f" Y_train range: {Y_train.min(axis=0)} to {Y_train.max(axis=0)}")
-
- # Standardize Y and prepare tensors
- print("\n3. Standardizing data...")
- Y_std, Y_mean, Y_scale = y_standardize_np(Y_train)
- (X_t, Y_t), device = np_to_torch(X_train, Y_std, device='cpu', return_device=True)
- print(f" Y standardized: mean={Y_std.mean(axis=0)}, std={Y_std.std(axis=0)}")
- print(f" Using device: {device}")
-
- # Fit default GP model
- print("\n4. Fitting default GP model...")
- try:
- model = fit_gp_models(X_t, Y_t, kernel_fn=[lambda d: RBFKernel(ard_num_dims=d), lambda d: MaternKernel(nu=0.5,ard_num_dims=d)]) # No additional arguments - just defaults
- print(" ✓ Default GP model fitted successfully!")
-
- # Check model details
- print(f" Model type: {type(model).__name__}")
- print(f" Number of sub-models: {len(model.models)}")
- for i, sub_model in enumerate(model.models):
- print(f" Sub-model {i}: {type(sub_model).__name__}")
-
- except Exception as e:
- print(f" ✗ GP fitting failed: {e}")
- return None
-
- # Test predictions on training data
- print("\n5. Testing predictions...")
- try:
- pred_mean, pred_std = posterior_report(model, X_t, Y_mean, Y_scale)
- print(f" Prediction shapes: mean={pred_mean.shape}, std={pred_std.shape}")
- print(f" Prediction ranges: mean={pred_mean.min(axis=0)} to {pred_mean.max(axis=0)}")
- print(f" Uncertainty ranges: std={pred_std.min(axis=0)} to {pred_std.max(axis=0)}")
-
- except Exception as e:
- print(f" ✗ Prediction failed: {e}")
- return None
-
- # Compute and show training fit quality
- print("\n6. Computing training fit metrics...")
- from sklearn.metrics import r2_score, mean_squared_error
-
- r2_scores = []
- rmse_scores = []
- for j in range(2):
- r2 = r2_score(Y_train[:, j], pred_mean[:, j])
- rmse = np.sqrt(mean_squared_error(Y_train[:, j], pred_mean[:, j]))
- r2_scores.append(r2)
- rmse_scores.append(rmse)
-
- obj_name = "Quadratic" if j == 0 else "Sinusoidal"
- print(f" {obj_name}: R² = {r2:.3f}, RMSE = {rmse:.4f}")
-
- # Create visualizations
- print("\n7. Creating visualizations...")
-
- # Plot GP predictions
- plot_gp_predictions(model, X_train, Y_train, Y_mean, Y_scale, f"(N={len(X_train)})")
-
- # Plot training fit
- plot_training_fit(Y_train, pred_mean, pred_std, ['Quadratic', 'Sinusoidal'])
-
- print("\n=== SIMPLE GP TEST COMPLETED ===")
- print("Check the generated PNG files for visualizations!")
-
- return {
- 'X_train': X_train, 'Y_train': Y_train,
- 'model': model, 'Y_mean': Y_mean, 'Y_scale': Y_scale,
- 'pred_mean': pred_mean, 'pred_std': pred_std,
- 'r2_scores': r2_scores, 'rmse_scores': rmse_scores
- }
-
-if __name__ == "__main__":
- results = simple_gp_test()
diff --git a/tests/smoke_test.py b/tests/smoke_test.py
deleted file mode 100644
index 466c9de..0000000
--- a/tests/smoke_test.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# tests/smoke_test.py
-import numpy as np
-import torch
-import pandas as pd
-
-from src.utils import load_config, get_objective_names, load_csv, split_XY_from_cfg, np_to_torch, set_seeds
-from src.design import build_input_spec_list, build_design
-from src.data import y_minmax_np, x_normalizer_torch, x_denormalizer_np
-from src.metrics import compute_ref_pareto_hv
-from src.models import fit_gp_models
-from src.acquisition import build_qnehvi, _make_snap_postproc, optimize_acq_qnehvi
-# If you want row constraints, also:
-# from src.constraints import constraints_from_config
-# and later pass row_constraints into propose_qnehvi_batch instead of the lower-level calls.
-
-CFG_PATH = "configs/example_inputs.yaml"
-CSV_PATH = "data/processed/R0+R1 full results-1.csv" # ← update if your file is named differently
-
-def main():
- set_seeds(123)
-
- # 1) Config → Design
- cfg = load_config(CFG_PATH)
- specs = build_input_spec_list(cfg["inputs"])
- design = build_design(specs)
- obj_names = get_objective_names(cfg)
- print(f"[OK] D={len(design.names)} inputs, M={len(obj_names)} objectives")
-
- # 2) Load CSV → split X,Y (physical units)
- df = load_csv(CSV_PATH)
- X_np, Y_np = split_XY_from_cfg(df, design, cfg)
- print(f"[OK] CSV N={len(X_np)} rows")
-
- # 3) Scale Y to [0,1]; normalize X to [0,1]^D
- Y_scaled, Y_min, Y_max = y_minmax_np(Y_np, eps=1e-12)
- X_t = np_to_torch(X_np)
- Xn_t = x_normalizer_torch(X_t, design)
- Y_t = np_to_torch(Y_scaled)
- device, dtype = Xn_t.device, Xn_t.dtype
- print(f"[OK] Normalized X, scaled Y (device={device}, dtype={dtype})")
-
- # 4) Fit vanilla GP per objective
- model = fit_gp_models(Xn_t, Y_t)
- model = model.to(device=device, dtype=dtype)
- print("[OK] Fitted ModelListGP")
-
- # 5) Ref point + hypervolume on scaled space
- ref_point_t, pareto_Y_t, hv_val = compute_ref_pareto_hv(Y_t)
- print(f"[OK] HV={hv_val:.4f} with {pareto_Y_t.shape[0]} Pareto points")
-
- # 6) Build qNEHVI and propose q=5 (snapped in optimizer)
- acq = build_qnehvi(model=model, train_X=Xn_t, ref_point_t=ref_point_t, sample_shape=128)
- postproc = _make_snap_postproc(design)
- cand_norm_t, acq_val_t = optimize_acq_qnehvi(
- acq_function=acq, d=Xn_t.shape[1], q=5, num_restarts=5, raw_samples=256,
- device=device, dtype=dtype, options={"retry_on_optimization_warning": True},
- sequential=True, post_processing_func=postproc,
- )
- cand_norm = cand_norm_t.detach().cpu().numpy()
- X_phys = x_denormalizer_np(cand_norm, design)
- print("[OK] Proposed 5 candidates (physical). First row:", np.round(X_phys[0], 4))
-
-if __name__ == "__main__":
- main()
diff --git a/tests/synthetic_test.py b/tests/synthetic_test.py
deleted file mode 100644
index 02dce30..0000000
--- a/tests/synthetic_test.py
+++ /dev/null
@@ -1,625 +0,0 @@
-#!/usr/bin/env python3
-"""
-Synthetic multi-objective test problem for debugging MOBO pipeline
-"""
-
-import numpy as np
-import torch
-from scipy.stats import qmc
-import matplotlib.pyplot as plt
-from gpytorch.kernels import RBFKernel, MaternKernel, PeriodicKernel
-from gpytorch.priors import LogNormalPrior
-
-from src.models import fit_gp_models, posterior_report, loocv_select_models
-from src.data import y_standardize_np
-from src.utils import np_to_torch
-from src.design import Design, InputSpec, build_design
-from src.plotting import plot_parity_np, plot_shap
-from src.acquisition import propose_batch
-from src.metrics import compute_ref_pareto_hv
-
-# Set up matplotlib for better plots
-plt.style.use('default')
-plt.rcParams['figure.figsize'] = (12, 8)
-plt.rcParams['font.size'] = 10
-
-def synthetic_objectives(X):
- """
- Two synthetic objective functions with known properties (MAXIMIZATION):
- - Obj1: Inverted quadratic with global maximum
- - Obj2: Sinusoidal with multiple local maxima
-
- Args:
- X: array of shape (N, 2) with X in [0, 1]^2
-
- Returns:
- Y: array of shape (N, 2) with objectives (higher is better)
- """
- x1, x2 = X[:, 0], X[:, 1]
-
- # Objective 1: Inverted quadratic hill (maximize)
- # Maximum at (0.3, 0.7) with value ~1.0
- obj1 = 1.0 - ((x1 - 0.3)**2 + (x2 - 0.7)**2)
-
- # Objective 2: Scaled sinusoidal (maximize)
- # Oscillates between 0 and 1, with multiple local maxima
- obj2 = 0.5 * (1 + np.sin(4 * np.pi * x1) * np.cos(3 * np.pi * x2))
-
- return np.column_stack([obj1, obj2])
-
-def generate_training_data(
- n_points: int = 20,
- noise_std: float = 0.05,
- seed: int = 42,
- dim: int = 2,
- bounds: np.ndarray | None = None,
-):
- """
- Generate training data using Latin Hypercube Sampling with controlled noise.
-
- Args:
- n_points: number of samples
- noise_std: std dev of Gaussian noise added to objectives
- seed: RNG seed for reproducibility
- dim: dimensionality of X
- bounds: optional (dim, 2) array of [low, high] for each dim; defaults to [0,1]^dim
-
- Returns:
- X_train: (n_points, dim) sampled inputs
- Y_noisy: (n_points, M) noisy objective values (same shape as Y_true)
- Y_true: (n_points, M) true objective values
- """
- if bounds is None:
- bounds = np.tile([0.0, 1.0], (dim, 1)) # [[0,1],[0,1],...]
-
- sampler = qmc.LatinHypercube(d=dim, seed=seed)
- X_unit = sampler.random(n=n_points) # (n_points, dim) in [0,1]
-
- # Scale to bounds
- X_train = qmc.scale(X_unit, bounds[:, 0], bounds[:, 1])
-
- # Evaluate true objectives
- Y_true = synthetic_objectives(X_train)
-
- # Add Gaussian noise
- rng = np.random.default_rng(seed + 1) # separate seed for noise
- noise = rng.normal(0.0, noise_std, size=Y_true.shape)
- Y_noisy = Y_true + noise
-
- return X_train, Y_noisy, Y_true
-
-def plot_true_objectives():
- """Plot the true objective functions for reference"""
- # Create a fine grid for visualization
- n_grid = 100
- x1 = np.linspace(0, 1, n_grid)
- x2 = np.linspace(0, 1, n_grid)
- X1, X2 = np.meshgrid(x1, x2)
- X_grid = np.column_stack([X1.ravel(), X2.ravel()])
-
- # Evaluate true objectives
- Y_true = synthetic_objectives(X_grid)
- obj1_grid = Y_true[:, 0].reshape(n_grid, n_grid)
- obj2_grid = Y_true[:, 1].reshape(n_grid, n_grid)
-
- fig, axes = plt.subplots(1, 2, figsize=(15, 6))
-
- # Objective 1: Inverted Quadratic
- im1 = axes[0].contourf(X1, X2, obj1_grid, levels=20, cmap='viridis')
- axes[0].set_title('Objective 1: Inverted Quadratic Hill\n1 - ((x₁-0.3)² + (x₂-0.7)²)')
- axes[0].set_xlabel('x₁')
- axes[0].set_ylabel('x₂')
- axes[0].plot(0.3, 0.7, 'r*', markersize=15, label='Global maximum')
- axes[0].legend()
- plt.colorbar(im1, ax=axes[0])
-
- # Objective 2: Sinusoidal
- im2 = axes[1].contourf(X1, X2, obj2_grid, levels=20, cmap='plasma')
- axes[1].set_title('Objective 2: Sinusoidal\n0.5×(1 + sin(4πx₁)×cos(3πx₂))')
- axes[1].set_xlabel('x₁')
- axes[1].set_ylabel('x₂')
- plt.colorbar(im2, ax=axes[1])
-
- plt.tight_layout()
- plt.savefig('synthetic_true_objectives.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def plot_gp_predictions(model, X_train, Y_train, title_suffix=""):
- """Plot GP predictions vs true functions"""
- # Create prediction grid
- n_grid = 50
- x1 = np.linspace(0, 1, n_grid)
- x2 = np.linspace(0, 1, n_grid)
- X1, X2 = np.meshgrid(x1, x2)
- X_grid = np.column_stack([X1.ravel(), X2.ravel()])
-
- # True values
- Y_true_grid = synthetic_objectives(X_grid)
-
- # GP predictions - ensure tensor is on same device as model
- device = next(model.parameters()).device
- X_grid_t = torch.tensor(X_grid, dtype=torch.float64, device=device)
- pred_mean, pred_std = posterior_report(model, X_grid_t)
-
- # Reshape for plotting
- obj1_true = Y_true_grid[:, 0].reshape(n_grid, n_grid)
- obj1_pred = pred_mean[:, 0].reshape(n_grid, n_grid)
- obj1_std = pred_std[:, 0].reshape(n_grid, n_grid)
-
- obj2_true = Y_true_grid[:, 1].reshape(n_grid, n_grid)
- obj2_pred = pred_mean[:, 1].reshape(n_grid, n_grid)
- obj2_std = pred_std[:, 1].reshape(n_grid, n_grid)
-
- # Create plots
- fig, axes = plt.subplots(2, 3, figsize=(18, 12))
-
- # Objective 1 row
- # True
- im1 = axes[0,0].contourf(X1, X2, obj1_true, levels=20, cmap='viridis')
- axes[0,0].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 0],
- s=60, cmap='viridis', edgecolors='white', linewidth=1)
- axes[0,0].set_title('Obj1: True')
- axes[0,0].set_ylabel('x₂')
- plt.colorbar(im1, ax=axes[0,0])
-
- # Predicted
- im2 = axes[0,1].contourf(X1, X2, obj1_pred, levels=20, cmap='viridis')
- axes[0,1].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 0],
- s=60, cmap='viridis', edgecolors='white', linewidth=1)
- axes[0,1].set_title('Obj1: GP Prediction')
- plt.colorbar(im2, ax=axes[0,1])
-
- # Uncertainty
- im3 = axes[0,2].contourf(X1, X2, obj1_std, levels=20, cmap='Reds')
- axes[0,2].scatter(X_train[:, 0], X_train[:, 1], c='white',
- s=60, edgecolors='black', linewidth=1)
- axes[0,2].set_title('Obj1: GP Uncertainty')
- plt.colorbar(im3, ax=axes[0,2])
-
- # Objective 2 row
- # True
- im4 = axes[1,0].contourf(X1, X2, obj2_true, levels=20, cmap='plasma')
- axes[1,0].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 1],
- s=60, cmap='plasma', edgecolors='white', linewidth=1)
- axes[1,0].set_title('Obj2: True')
- axes[1,0].set_xlabel('x₁')
- axes[1,0].set_ylabel('x₂')
- plt.colorbar(im4, ax=axes[1,0])
-
- # Predicted
- im5 = axes[1,1].contourf(X1, X2, obj2_pred, levels=20, cmap='plasma')
- axes[1,1].scatter(X_train[:, 0], X_train[:, 1], c=Y_train[:, 1],
- s=60, cmap='plasma', edgecolors='white', linewidth=1)
- axes[1,1].set_title('Obj2: GP Prediction')
- axes[1,1].set_xlabel('x₁')
- plt.colorbar(im5, ax=axes[1,1])
-
- # Uncertainty
- im6 = axes[1,2].contourf(X1, X2, obj2_std, levels=20, cmap='Reds')
- axes[1,2].scatter(X_train[:, 0], X_train[:, 1], c='white',
- s=60, edgecolors='black', linewidth=1)
- axes[1,2].set_title('Obj2: GP Uncertainty')
- axes[1,2].set_xlabel('x₁')
- plt.colorbar(im6, ax=axes[1,2])
-
- plt.suptitle(f'GP Model Performance {title_suffix}', fontsize=14)
- plt.tight_layout()
- plt.savefig(f'synthetic_gp_predictions{title_suffix.replace(" ", "_")}.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def plot_objective_space(Y_data, labels, title="Objective Space"):
- """Plot the objective space and Pareto front"""
- fig, ax = plt.subplots(1, 1, figsize=(10, 8))
-
- colors = plt.cm.Set1(np.linspace(0, 1, len(Y_data)))
-
- for i, (Y, label) in enumerate(zip(Y_data, labels)):
- ax.scatter(Y[:, 0], Y[:, 1], alpha=0.7, label=label, c=[colors[i]], s=60)
-
- ax.set_xlabel('Objective 1 (Inverted Quadratic)')
- ax.set_ylabel('Objective 2 (Sinusoidal)')
- ax.set_title(title)
- ax.legend()
- ax.grid(True, alpha=0.3)
-
- plt.tight_layout()
- plt.savefig(f'{title.replace(" ", "_").lower()}.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def plot_mobo_progression(batch_info, hypervolumes):
- """Plot the MOBO progression over iterations"""
- fig, axes = plt.subplots(1, 2, figsize=(18, 6))
-
- # Plot 1: Hypervolume progression
- batches = [info['batch'] for info in batch_info]
- n_points = [info['n_points'] for info in batch_info]
-
- axes[0].plot(batches, hypervolumes, 'o-', linewidth=2, markersize=8)
- axes[0].set_xlabel('Batch')
- axes[0].set_ylabel('Hypervolume')
- axes[0].set_title('Hypervolume Progression')
- axes[0].grid(True, alpha=0.3)
-
- # Plot 2: Number of Pareto points
- n_pareto = [info['n_pareto'] for info in batch_info]
- axes[1].plot(batches, n_pareto, 's-', linewidth=2, markersize=8, color='orange')
- axes[1].set_xlabel('Batch')
- axes[1].set_ylabel('Number of Pareto Points')
- axes[1].set_title('Pareto Front Growth')
- axes[1].grid(True, alpha=0.3)
-
- # # Plot 3: Objective space evolution
- # colors = plt.cm.viridis(np.linspace(0, 1, len(batch_info)))
- # for i, (info, color) in enumerate(zip(batch_info, colors)):
- # Y_batch = info['Y_batch']
- # alpha = 0.3 if i < len(batch_info) - 1 else 1.0
- # size = 20 if i < len(batch_info) - 1 else 60
- # label = f'Batch {i} (N={info["n_points"]})'
- # axes[2].scatter(Y_batch[:, 0], Y_batch[:, 1],
- # c=[color], alpha=alpha, s=size, label=label)
-
- # axes[2].set_xlabel('Objective 1 (Inverted Quadratic)')
- # axes[2].set_ylabel('Objective 2 (Sinusoidal)')
- # axes[2].set_title('Objective Space Evolution')
- # axes[2].legend(bbox_to_anchor=(1.05, 1), loc='upper left')
- # axes[2].grid(True, alpha=0.3)
-
- plt.tight_layout()
- plt.savefig('mobo_progression.png', dpi=150, bbox_inches='tight')
- plt.show()
-
- return fig
-
-def test_gp_pipeline():
- """Test the complete GP modeling pipeline on synthetic data"""
-
- print("=== SYNTHETIC MULTI-OBJECTIVE TEST ===")
-
- # 0. Show the true objective functions
- print("Plotting true objective functions...")
- plot_true_objectives()
-
- # 1. Generate training data
- X_train, Y_train, Y_true = generate_training_data(n_points=20, noise_std=0.03)
- print(f"Training data: {X_train.shape[0]} points, {X_train.shape[1]} dimensions")
- print(f"Objectives: {Y_train.shape[1]} (Inverted Quadratic, Sinusoidal)")
- print(f"Y_train range: {Y_train.min(axis=0)} to {Y_train.max(axis=0)}")
-
- # Plot objective space
- plot_objective_space([Y_train], ['Training Data'], 'Training Data in Objective Space')
-
- # 2. Standardize Y and prepare tensors
- Y_std, Y_mean, Y_scale = y_standardize_np(Y_train)
- (X_t, Y_t), device = np_to_torch(X_train, Y_train, device='cpu', return_device=True)
- print(f"Y Mean: mean={Y_std.mean(axis=0)}, std={Y_std.std(axis=0)}")
-
- # 3. Fit GP models with different configurations
- print("\n--- Testing Different GP Configurations ---")
-
- # Simple default model
- model_default = fit_gp_models(X_t, Y_t)
- print("✓ Default model fitted")
-
- # Model with noise priors
- noise_priors = [LogNormalPrior(-4.0, 0.5), LogNormalPrior(-3.5, 0.5)]
-
- # Model with different kernels per objective
- kernels = [
- lambda d: RBFKernel(ard_num_dims=d), # Smooth for inverted quadratic
- lambda d: MaternKernel(nu=1.5, ard_num_dims=d) # More flexible for sinusoidal
- ]
- model_mixed = fit_gp_models(X_t, Y_t, kernel_fn=kernels, noise_priors=noise_priors)
- print("✓ Model with mixed kernels and noise priors fitted")
-
- # 4. Test predictions on training data
- print("\n--- Testing Predictions ---")
- pred_mean, pred_std = posterior_report(model_mixed, X_t)
-
- print(f"Prediction shapes: mean={pred_mean.shape}, std={pred_std.shape}")
- print(f"Prediction ranges: mean={pred_mean.min(axis=0)} to {pred_mean.max(axis=0)}")
- print(f"Uncertainty ranges: std={pred_std.min(axis=0)} to {pred_std.max(axis=0)}")
-
- # Plot GP predictions vs truth
- print("Plotting GP model predictions...")
- plot_gp_predictions(model_mixed, X_train, Y_train, f"(N={len(X_train)})")
-
- # 5. Compute training fit metrics
- from sklearn.metrics import r2_score, mean_squared_error
- r2_scores = [r2_score(Y_train[:, j], pred_mean[:, j]) for j in range(2)]
- rmse_scores = [np.sqrt(mean_squared_error(Y_train[:, j], pred_mean[:, j])) for j in range(2)]
-
- print(f"\nTraining fit quality:")
- print(f" Objective 1 (Inverted Quadratic): R²={r2_scores[0]:.3f}, RMSE={rmse_scores[0]:.4f}")
- print(f" Objective 2 (Sinusoidal): R²={r2_scores[1]:.3f}, RMSE={rmse_scores[1]:.4f}")
-
- # 6. Test on dense grid for visualization
- print("\n--- Testing on Dense Grid ---")
- n_grid = 50
- x1_grid = np.linspace(0, 1, n_grid)
- x2_grid = np.linspace(0, 1, n_grid)
- X1, X2 = np.meshgrid(x1_grid, x2_grid)
- X_grid = np.column_stack([X1.ravel(), X2.ravel()])
-
- # True objectives on grid
- Y_grid_true = synthetic_objectives(X_grid)
-
- # GP predictions on grid - ensure tensor is on same device as model
- device = next(model_mixed.parameters()).device
- X_grid_t = torch.tensor(X_grid, dtype=torch.float64, device=device)
- pred_grid_mean, pred_grid_std = posterior_report(model_mixed, X_grid_t)
-
- # Compute prediction errors
- grid_errors = np.abs(pred_grid_mean - Y_grid_true)
- mean_abs_errors = grid_errors.mean(axis=0)
- max_abs_errors = grid_errors.max(axis=0)
-
- print(f"Grid prediction errors:")
- print(f" Objective 1: MAE={mean_abs_errors[0]:.4f}, Max={max_abs_errors[0]:.4f}")
- print(f" Objective 2: MAE={mean_abs_errors[1]:.4f}, Max={max_abs_errors[1]:.4f}")
-
- # 7. Test LOOCV if dataset isn't too small
- # if X_train.shape[0] >= 10:
- # print("\n--- Testing LOOCV ---")
- # try:
- # kernel_options = [
- # lambda d: RBFKernel(ard_num_dims=d),
- # lambda d: MaternKernel(nu=1.5, ard_num_dims=d)
- # ]
- # noise_options = [None, LogNormalPrior(-4.0, 0.5)]
-
- # model_cv, cv_results = loocv_select_models(
- # X_t, Y_t,
- # objective_names=['Inverted Quadratic', 'Sinusoidal'],
- # matern_options=kernel_options,
- # noise_options=noise_options
- # )
-
- # print("LOOCV Results:")
- # print(cv_results)
-
- # except Exception as e:
- # print(f"LOOCV failed: {e}")
-
- # 8. Create parity plots
- print("\n--- Parity Plots ---")
- try:
- fig_parity, metrics_parity = plot_parity_np(
- Y_train, pred_mean, pred_std,
- objective_names=['Inverted Quadratic', 'Sinusoidal'],
- save='synthetic_parity_plot.png'
- )
- print("Parity plot metrics:")
- print(metrics_parity)
- except Exception as e:
- print(f"Parity plot failed: {e}")
-
- print("\n=== GP MODELING TEST COMPLETED ===")
- return {
- 'X_train': X_train, 'Y_train': Y_train, 'Y_true': Y_true,
- 'model': model_mixed, 'Y_mean': Y_mean, 'Y_scale': Y_scale,
- 'pred_mean': pred_mean, 'pred_std': pred_std,
- 'r2_scores': r2_scores, 'rmse_scores': rmse_scores
- }
-
-
-def test_mobo_loop(n_initial=20, n_batches=5, batch_size=10, seed=42):
- """
- Test the complete MOBO loop with maximization objectives
-
- Args:
- n_initial: Number of initial training points
- n_batches: Number of MOBO iterations
- batch_size: Number of points to propose per batch
- seed: Random seed for reproducibility
- """
- print("\n=== MOBO LOOP TEST (MAXIMIZATION) ===")
-
- # Set up design space
- input_specs = [
- InputSpec(name='x1', unit=None, start=0.0, stop=1.0, step=0.01, decimals=2),
- InputSpec(name='x2', unit=None, start=0.0, stop=1.0, step=0.01, decimals=2)
- ]
- design = build_design(input_specs)
- print(f"Design space: {len(input_specs)} dimensions")
-
- # Generate initial training data
- np.random.seed(seed)
- X_all, Y_all, _ = generate_training_data(n_points=n_initial, noise_std=0.02, seed=seed)
-
- print(f"Initial training data: {n_initial} points")
- print(f"Y_initial range: {Y_all.min(axis=0)} to {Y_all.max(axis=0)}")
-
- # Track MOBO progression
- batch_info = []
- hypervolumes = []
-
- # Reference point will be computed automatically by compute_ref_pareto_hv
- ref_point_np = np.array([0.02-1e-2, -1e-2]) # Will be set after first hypervolume computation
- ref_point_t = None # Will be set during first hypervolume computation
-
- for batch in range(n_batches + 1): # +1 to include initial evaluation
- print(f"\n--- Batch {batch} (N={len(X_all)}) ---")
-
- # Standardize and prepare data
- #Y_std, Y_mean, Y_scale = y_standardize_np(Y_all)
- (X_t, Y_t), device = np_to_torch(X_all, Y_all, device='cuda', return_device=True)
-
- # Fit GP models
- try:
- # Use different kernels for different objectives
- kernels = [
- lambda d: RBFKernel(ard_num_dims=d), # Smooth for inverted quadratic
- lambda d: PeriodicKernel(ard_num_dims=d) # Flexible for sinusoidal
- ]
- noise_priors = None #[LogNormalPrior(-4.0, 0.5), LogNormalPrior(-3.5, 0.5)]
-
- model = fit_gp_models(X_t, Y_t)#, kernel_fn=kernels, noise_priors=noise_priors)
- plot_gp_predictions(model, X_all, Y_all, f"(N={len(X_all)})")
- #plot_shap(design, X_all, model)
- print("✓ GP models fitted a")
- for i, gp in enumerate(model.models):
- print("Lengthscales:", gp.covar_module.base_kernel.lengthscale.detach().cpu().numpy().flatten())
- print("Outputscale:", gp.covar_module.outputscale.item())
- print("Noise:", gp.likelihood.noise.item())
-
- except Exception as e:
- print(f"⚠️ GP fitting failed: {e}")
- # Fall back to default model
- model = fit_gp_models(X_t, Y_t)
- plot_gp_predictions(model, X_all, Y_all, f"(N={len(X_all)})")
- print("✓ Fallback to default GP model")
-
- # Compute hypervolume
- try:
- # Convert to tensor for compute_ref_pareto_hv - use same device as model
- Y_tensor = torch.tensor(Y_all, dtype=torch.float64, device=device)
- ref_point_t, pareto_Y, hv = compute_ref_pareto_hv(Y_tensor, ref_point_np)
- hypervolumes.append(hv)
- print(f"Hypervolume: {hv:.6f}")
-
- # Pareto front info from the function
- n_pareto = len(pareto_Y)
- Y_pareto_np = pareto_Y.detach().cpu().numpy()
-
- print(f"Pareto points: {n_pareto}/{len(Y_all)}")
- print(f"Pareto front range: {Y_pareto_np.min(axis=0)} to {Y_pareto_np.max(axis=0)}")
- print(f"Reference point: {ref_point_t.detach().cpu().numpy()}")
-
- except Exception as e:
- print(f"⚠️ Hypervolume computation failed: {e}")
- hv = 0.0
- hypervolumes.append(hv)
- n_pareto = 0
- # Set a fallback reference point if hypervolume computation fails
- if ref_point_t is None:
- ref_point_t = torch.tensor([0.1-1e-2, -1e-2], dtype=torch.float64, device=device)
-
- # Store batch information
- batch_info.append({
- 'batch': batch,
- 'n_points': len(X_all),
- 'n_pareto': n_pareto,
- 'Y_batch': Y_all.copy(),
- 'hypervolume': hv
- })
-
- # Stop if this is the last evaluation
- if batch >= n_batches:
- break
-
- # Propose next batch
- print(f"Proposing {batch_size} new points...")
- try:
- # Use the reference point computed from hypervolume calculation
- #ref_point_device = ref_point_t.to(device=device, dtype=torch.float64)
-
- # Call propose_batch with correct signature
- result = propose_batch(
- design=design,
- model=model,
- train_X=X_t, # Normalized training inputs
- ref_point_t=ref_point_t,
- batch_size=batch_size,
- sample_shape=64,
- verbose=True
- )
-
- # Extract physical coordinates (already snapped)
- X_next = result['X_phys']
- print(f"✓ Proposed points: {X_next.shape}")
-
- # Evaluate new points
- Y_next = synthetic_objectives(X_next)
- print(f"New objectives range: {Y_next.min(axis=0)} to {Y_next.max(axis=0)}")
-
- # Add to dataset
- X_all = np.vstack([X_all, X_next])
- Y_all = np.vstack([Y_all, Y_next])
-
- except Exception as e:
- print(f"⚠️ Batch proposal failed: {e}")
- break
-
- print(f"\n=== MOBO LOOP COMPLETED ===")
- print(f"Total points collected: {len(X_all)}")
- print(f"Final hypervolume: {hypervolumes[-1]:.6f}")
- print(f"Hypervolume improvement: {hypervolumes[-1] - hypervolumes[0]:.6f}")
-
- # Create progression plots
- print("\nCreating MOBO progression plots...")
- try:
- plot_mobo_progression(batch_info, hypervolumes)
- except Exception as e:
- print(f"⚠️ Plotting failed: {e}")
-
- # Plot final objective space
- try:
- Y_batches = [info['Y_batch'] for info in batch_info] # Every batch
- labels = [f"Batch {info['batch']}" for info in batch_info]
- plot_objective_space(Y_batches, labels, "MOBO Objective Space Evolution")
- except Exception as e:
- print(f"⚠️ Objective space plot failed: {e}")
-
- return {
- 'X_final': X_all,
- 'Y_final': Y_all,
- 'batch_info': batch_info,
- 'hypervolumes': hypervolumes,
- 'model': model,
- }
-
-
-def compute_pareto_front(Y, minimize=False):
- """
- Compute Pareto front for maximization (default) or minimization
-
- Args:
- Y: Objective values (N, M)
- minimize: If True, find Pareto front for minimization
-
- Returns:
- pareto_mask: Boolean mask of Pareto optimal points
- """
- Y_work = -Y if not minimize else Y # Convert to minimization
-
- n_points, n_obj = Y_work.shape
- pareto_mask = np.ones(n_points, dtype=bool)
-
- for i in range(n_points):
- if not pareto_mask[i]:
- continue
-
- # Check if point i is dominated by any other point
- for j in range(n_points):
- if i == j or not pareto_mask[j]:
- continue
-
- # j dominates i if j is better in all objectives
- if np.all(Y_work[j] <= Y_work[i]) and np.any(Y_work[j] < Y_work[i]):
- pareto_mask[i] = False
- break
-
- return pareto_mask
-
-
-if __name__ == "__main__":
- # Run GP modeling test
- print("Starting synthetic test with maximization objectives...")
- gp_results = test_gp_pipeline()
-
- # Run MOBO loop test
- print("\n" + "="*60)
- mobo_results = test_mobo_loop(n_initial=20, n_batches=5, batch_size=5)
-
- print(f"\n🎯 SYNTHETIC TEST SUMMARY:")
- print(f"GP modeling: ✓ Completed")
- print(f"MOBO loop: ✓ Completed ({len(mobo_results['X_final'])} total points)")
- print(f"Hypervolume improvement: {mobo_results['hypervolumes'][-1] - mobo_results['hypervolumes'][0]:.6f}")
- print(f"Final Pareto points: {mobo_results['batch_info'][-1]['n_pareto']}")
- print("All plots saved to current directory ✓")
diff --git a/tests/test_acquisition.py b/tests/test_acquisition.py
index 8d207b9..6bd0f51 100644
--- a/tests/test_acquisition.py
+++ b/tests/test_acquisition.py
@@ -1,736 +1,197 @@
-# tests/test_acquisition.py
-import sys
-import os
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+"""Fast smoke tests for the active acquisition API."""
+
+from __future__ import annotations
-import yaml
-import torch
import numpy as np
-import pandas as pd
-from src.design import build_design_from_config
-from src.utils import load_csv, split_XY, np_to_torch, get_objective_names
-from src.models import fit_gp_models, loocv_select_models
-from src.data import y_minmax_np, y_standardize_np
-from src.acquisition import (
- outcome_ge, outcome_le,
- _unit_bounds, _make_snap_postproc, build_qnehvi, optimize_acq_qnehvi, propose_batch
+import torch
+from botorch.acquisition.multi_objective.logei import (
+ qLogNoisyExpectedHypervolumeImprovement,
+)
+from botorch.models import SingleTaskGP
+from botorch.models.model_list_gp_regression import ModelListGP
+from botorch.models.transforms.outcome import Standardize
+
+import mobo_kit.acquisition as acquisition
+from mobo_kit.acquisition import (
+ _make_snap_postproc,
+ _unit_bounds,
+ build_qnehvi,
+ optimize_acq_qnehvi,
+ outcome_ge,
+ outcome_ge_standardized,
+ outcome_le,
+ outcome_le_standardized,
+ propose_batch,
)
+from mobo_kit.design import InputSpec, build_design
-CFG_PATH = "configs/configCSV_example_config.yaml"
-CSV_PATH = "data/processed/configCSV_example.csv"
-
-def test_outcome_constraint_builders():
- """Test outcome constraint builders with various shapes and values."""
- print("Testing outcome constraint builders...")
-
- # Test different tensor shapes
- shapes = [(64, 2, 4, 3), (10, 5), (100,)]
-
- for shape in shapes:
- Y = torch.zeros(shape)
-
- # Test outcome_ge: feasible when Y[..., 1] >= 0.8
- c_ge = outcome_ge(obj_idx=1, thresh=0.8)
- val_ge = c_ge(Y)
-
- assert val_ge.shape == shape[:-1], f"Shape mismatch for {shape}"
- assert torch.all(val_ge > 0), f"All zeros should be infeasible for {shape}"
-
- # Make feasible
- Y[..., 1] = 0.9
- assert torch.all(c_ge(Y) <= 0), f"Should be feasible for {shape}"
-
- # Test outcome_le: feasible when Y[..., 2] <= 0.15
- Y = torch.ones(shape)
- c_le = outcome_le(obj_idx=2, thresh=0.15)
- val_le = c_le(Y)
-
- assert val_le.shape == shape[:-1], f"Shape mismatch for {shape}"
- assert torch.all(val_le > 0), f"All ones should be infeasible for {shape}"
-
- # Make feasible
- Y[..., 2] = 0.10
- assert torch.all(c_le(Y) <= 0), f"Should be feasible for {shape}"
-
- print("✓ Outcome constraint builders work with various shapes")
-
-def test_unit_bounds():
- """Test unit bounds helper function."""
- print("Testing unit bounds helper...")
-
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- dtype = torch.float64
-
- for d in [1, 3, 8]:
- bounds = _unit_bounds(d, device, dtype)
-
- assert bounds.shape == (2, d), f"Expected shape (2, {d}), got {bounds.shape}"
- assert bounds[0].allclose(torch.zeros(d, device=device, dtype=dtype)), "Lower bounds should be 0"
- assert bounds[1].allclose(torch.ones(d, device=device, dtype=dtype)), "Upper bounds should be 1"
- assert bounds.device.type == device.type, "Device type mismatch"
- assert bounds.dtype == dtype, "Dtype mismatch"
-
- print("✓ Unit bounds helper works correctly")
-
-def test_snap_postproc_factory():
- """Test snap post-processing factory function."""
- print("Testing snap post-processing factory...")
-
- # Load config and design
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
-
- # Create post-processing function
- postproc = _make_snap_postproc(design)
-
- # Test with different shapes
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- test_shapes = [(5, 8), (1, 3, 8), (10, 2, 8)]
-
- for shape in test_shapes:
- # Create random normalized input
- Z = torch.rand(shape, device=device, dtype=torch.float64)
-
- # Apply post-processing
- Z_snapped = postproc(Z)
-
- # Check output shape matches input
- assert Z_snapped.shape == Z.shape, f"Shape mismatch for {shape}"
- assert Z_snapped.device == Z.device, "Device mismatch"
- assert Z_snapped.dtype == Z.dtype, "Dtype mismatch"
-
- # Check values are in [0, 1] range
- assert torch.all(Z_snapped >= 0) and torch.all(Z_snapped <= 1), "Values out of [0,1] range"
-
- print("✓ Snap post-processing factory works correctly")
-
-def test_build_qnehvi():
- """Test qNEHVI acquisition function builder."""
- print("Testing qNEHVI builder...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- # Convert to torch tensors
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- # Normalize Y data
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- # Fit model
- model = fit_gp_models(X_t, Y_scaled_t)
-
- # Build qNEHVI
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
- acq_func = build_qnehvi(
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- sample_shape=64, # Smaller for testing
- use_lognehvi=True
+
+def _design():
+ return build_design(
+ [
+ InputSpec("temperature", 10.0, 20.0, 5.0, unit="C"),
+ InputSpec("ratio", 0.0, 1.0, 0.25),
+ ]
)
-
- # Verify acquisition function properties
- assert hasattr(acq_func, 'model'), "Should have model attribute"
- assert hasattr(acq_func, 'ref_point'), "Should have ref_point attribute"
- assert hasattr(acq_func, 'X_baseline'), "Should have X_baseline attribute"
-
- print("✓ qNEHVI builder works correctly")
-
-def test_optimize_acq_qnehvi():
- """Test acquisition function optimization wrapper."""
- print("Testing acquisition optimization wrapper...")
-
- # Load real data and fit model
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- model = fit_gp_models(X_t, Y_scaled_t)
-
- # Build acquisition function
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
- acq_func = build_qnehvi(
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- sample_shape=32, # Small for testing
- use_lognehvi=True
+
+
+def _unfitted_two_objective_model(train_x: torch.Tensor) -> ModelListGP:
+ y_1 = (train_x[:, :1] + 0.5 * train_x[:, 1:2]).square()
+ y_2 = 1.0 - 0.25 * train_x[:, :1] + train_x[:, 1:2]
+ models = [
+ SingleTaskGP(train_x, y, outcome_transform=Standardize(m=1)) for y in (y_1, y_2)
+ ]
+ return ModelListGP(*models)
+
+
+def test_unit_bounds_and_outcome_constraint_signs():
+ bounds = _unit_bounds(3, torch.device("cpu"), torch.float64)
+
+ assert bounds.shape == (2, 3)
+ assert bounds.device.type == "cpu"
+ assert bounds.dtype == torch.float64
+ torch.testing.assert_close(bounds[0], torch.zeros(3, dtype=torch.float64))
+ torch.testing.assert_close(bounds[1], torch.ones(3, dtype=torch.float64))
+
+ outcomes = torch.tensor([[0.4, 1.2], [0.8, 0.5]], dtype=torch.float64)
+ torch.testing.assert_close(
+ outcome_ge(0, 0.5)(outcomes),
+ torch.tensor([0.1, -0.3], dtype=torch.float64),
)
-
- # Test optimization
- d = X_t.shape[1]
- q = 2 # Small batch for testing
-
- candidates, acq_values = optimize_acq_qnehvi(
- acq_function=acq_func,
- d=d,
- q=q,
- num_restarts=5, # Small for testing
- raw_samples=100, # Small for testing
- device=device,
- dtype=torch.float64
+ torch.testing.assert_close(
+ outcome_le(1, 1.0)(outcomes),
+ torch.tensor([0.2, -0.5], dtype=torch.float64),
)
-
- # Verify output shapes
- assert candidates.shape == (q, d), f"Expected shape ({q}, {d}), got {candidates.shape}"
- assert acq_values.shape == (q,), f"Expected shape ({q},), got {acq_values.shape}"
-
- # Verify values are in [0, 1] range (normalized)
- assert torch.all(candidates >= 0) and torch.all(candidates <= 1), "Candidates out of [0,1] range"
-
- print("✓ Acquisition optimization wrapper works correctly")
-
-def test_propose_batch_basic():
- """Test basic propose_batch functionality without constraints."""
- print("Testing basic propose_batch...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- model = fit_gp_models(X_t, Y_scaled_t)
-
- # Test basic proposal
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
- batch_size = 3
-
- result = propose_batch(
- design=design,
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- batch_size=batch_size,
- num_restarts=5, # Small for testing
- raw_samples=100, # Small for testing
- sample_shape=32, # Small for testing
- verbose=True
+
+ standardized = torch.tensor([[1.0], [2.5]], dtype=torch.float64)
+ torch.testing.assert_close(
+ outcome_ge_standardized(0, 14.0, 10.0, 2.0)(standardized),
+ torch.tensor([1.0, -0.5], dtype=torch.float64),
)
-
- # Verify result structure
- assert isinstance(result, dict), "Should return dictionary"
- assert 'X_phys' in result, "Should have X_phys key"
- assert 'X_norm' in result, "Should have X_norm key"
- assert 'attempts' in result, "Should have attempts key"
- assert 'acq_val' in result, "Should have acq_val key"
-
- # Verify shapes
- if result['X_phys'].shape[0] > 0: # If we got valid candidates
- assert result['X_phys'].shape[1] == len(design.names), "Physical dimensions should match design"
- assert result['X_norm'].shape[1] == len(design.names), "Normalized dimensions should match design"
- assert result['X_phys'].shape[0] <= batch_size, "Should not exceed batch size"
- assert result['X_norm'].shape[0] <= batch_size, "Should not exceed batch size"
- assert result['acq_val'].shape[0] <= batch_size, "Should not exceed batch size"
-
- print("✓ Basic propose_batch works correctly")
-
-def test_propose_batch_with_custom_acq():
- """Test propose_batch with custom acquisition function."""
- print("Testing propose_batch with custom acquisition function...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- Y_scaled, Y_mean, Y_std = y_standardize_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- model = fit_gp_models(X_t, Y_scaled_t)
-
- # Define custom acquisition function builder
- def custom_acq_builder():
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
- return build_qnehvi(
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- sample_shape=32,
- use_lognehvi=False # Use regular NEHVI instead of log
- )
-
- # Test with custom acquisition function
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
- batch_size = 2
-
- result = propose_batch(
- design=design,
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- batch_size=batch_size,
- acq=custom_acq_builder, # Use custom acquisition function
- num_restarts=3, # Small for testing
- raw_samples=50, # Small for testing
- sample_shape=16, # Small for testing
- verbose=True
+ torch.testing.assert_close(
+ outcome_le_standardized(0, 14.0, 10.0, 2.0)(standardized),
+ torch.tensor([-1.0, 0.5], dtype=torch.float64),
)
-
- # Verify result structure
- assert isinstance(result, dict), "Should return dictionary"
- assert all(key in result for key in ['X_phys', 'X_norm', 'attempts', 'acq_val']), "Missing expected keys"
-
- print("✓ Custom acquisition function works correctly")
-
-def test_propose_batch_with_constraints():
- """Test propose_batch with both row constraints and outcome constraints."""
- print("Testing propose_batch with constraints...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- device = torch.device('cuda')#'cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- Y_scaled, Y_mean, Y_std = y_standardize_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
- from src.data import x_normalizer_torch
- Xn_t = x_normalizer_torch(X_t, design)
-
- objective_names = get_objective_names(config)
- model_cv, results_df = loocv_select_models(Xn_t, Y_scaled_t, objective_names=objective_names, device=device)
- #model = fit_gp_models(Xn_t, Y_scaled_t)
-
- # 🔍 DEBUG: Analyze model uncertainty and performance
- print(f"\n🔍 Model Analysis:")
- print(f" Dataset size: {Xn_t.shape[0]} samples, {Xn_t.shape[1]} inputs, {Y_scaled_t.shape[1]} objectives")
- print(f" LOOCV results columns: {list(results_df.columns)}")
- print(f" LOOCV results:")
-
- # Show LOOCV performance for each objective
- for obj_name in objective_names:
- obj_results = results_df[results_df['Objective'] == obj_name]
- if len(obj_results) > 0:
- best_idx = obj_results['RMSE'].idxmin()
- best_kernel = obj_results.loc[best_idx, 'Kernel']
- best_noise = obj_results.loc[best_idx, 'NoisePrior']
- best_r2 = obj_results.loc[best_idx, 'R2']
- best_rmse = obj_results.loc[best_idx, 'RMSE']
- print(f" {obj_name}: Best R²={best_r2}, RMSE={best_rmse} (Kernel: {best_kernel}, Noise: {best_noise})")
-
- # Use posterior_report for comprehensive model analysis
- from src.models import posterior_report
- from src.metrics import compute_metrics
- print(f"\n📊 Model Performance on Training Data:")
- try:
- pred_mean, pred_std = posterior_report(
- model_cv, Xn_t, Y_mean, Y_std
- )
- metrics_df = compute_metrics(Y, pred_mean, pred_std, objective_names=objective_names, add_residuals=True, add_zscores=True)
- print(f" Training performance metrics:")
- for _, row in metrics_df.iterrows():
- print(f" {row['Objective']}: R²={row['R2']}, RMSE={row['RMSE']}")
-
- except Exception as e:
- print(f" Could not run posterior_report: {e}")
-
- from src.plotting import plot_parity_np
- from src.utils import torch_to_np
-
- # Extract predictions for each objective from report_df
- # pred_columns = [f"Pred[{name}]" for name in objective_names]
- # pred_mean = report_df[pred_columns].values # Shape: (N, M)
- # #print(pred_mean)
-
- # # Extract standard deviations if available
- # std_columns = [f"Std[{name}]" for name in objective_names]
- # pred_std = report_df[std_columns].values if all(col in report_df.columns for col in std_columns) else None
-
- fig, parity_df = plot_parity_np(Y, pred_mean=pred_mean, pred_std=pred_std, objective_names=objective_names)
-
- # Define row constraints (temperature + humidity constraint)
- from src.constraints import constraints_from_config
- row_constraints_list = constraints_from_config(config, design)
-
- # Define outcome constraints using original units (more intuitive)
- from src.acquisition import outcome_ge_standardized, outcome_le_standardized
-
- # Example: PCE >= 15% and Repeatability <= 0.1 (adjust these values based on your actual data!)
- # You should replace these with your actual desired thresholds in original units
- pce_threshold = 15.0 # Example: 15% PCE minimum
- repeatability_threshold = 0.1 # Example: 0.1 maximum repeatability
-
- outcome_constraints = [
- outcome_ge_standardized(obj_idx=0, thresh_original=pce_threshold,
- Y_mean=Y_mean[0], Y_std=Y_std[0]), # PCE >= 15%
- outcome_le_standardized(obj_idx=2, thresh_original=repeatability_threshold,
- Y_mean=Y_mean[2], Y_std=Y_std[2]), # Repeatability <= 0.1
- ]
-
- print(f"📋 Outcome constraints:")
- print(f" PCE >= {pce_threshold} (standardized: {(pce_threshold - Y_mean[0]) / Y_std[0]:.3f})")
- print(f" Repeatability <= {repeatability_threshold} (standardized: {(repeatability_threshold - Y_mean[2]) / Y_std[2]:.3f})")
-
- # Test with both types of constraints
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
-
- # 🔍 DEBUG: Analyze reference point and hypervolume
- print(f"\n📊 Hypervolume Analysis:")
- print(f" Reference point: {ref_point.tolist()}")
- print(f" Training data bounds: min={Y_scaled_t.min(dim=0).values.tolist()}")
- print(f" Training data bounds: max={Y_scaled_t.max(dim=0).values.tolist()}")
-
- # Calculate current hypervolume using metrics.py functions
- from src.metrics import compute_ref_pareto_hv
-
- # Get Pareto front and hypervolume
- ref_point_auto, pareto_front, current_hv = compute_ref_pareto_hv(Y_scaled_t)
- print(f" Auto reference point: {ref_point_auto.tolist()}")
- print(f" Pareto front size: {pareto_front.shape[0]} points")
- print(f" Current hypervolume: {current_hv:.6f}")
-
- # Compare with our manual reference point
- print(f" Manual vs Auto ref point difference: {torch.norm(ref_point - ref_point_auto):.6f}")
-
- batch_size = 8
-
- result = propose_batch(
- design=design,
- model=model_cv,
- train_X=Xn_t,
- ref_point_t=ref_point_auto,
- batch_size=batch_size,
- row_constraints=row_constraints_list, # Physical space constraints
- #constraints=outcome_constraints, # Outcome space constraints
- eta=0.05, # Constraint violation penalty
- num_restarts=10, # Small for testing
- raw_samples=512, # Increased for better MC estimation
- sample_shape=256, # Increased for more candidates
- max_attempts=5, # More attempts due to constraints
- verbose=True,
- #use_lognehvi=False # Use regular NEHVI to see raw EI values
+
+
+def test_snap_postprocessor_preserves_shape_dtype_and_grid():
+ postprocess = _make_snap_postproc(_design())
+ candidates = torch.tensor([[[0.15, 0.15], [0.84, 0.90]]], dtype=torch.float64)
+
+ snapped = postprocess(candidates)
+
+ assert snapped.shape == candidates.shape
+ assert snapped.dtype == candidates.dtype
+ assert snapped.device == candidates.device
+ torch.testing.assert_close(
+ snapped,
+ torch.tensor([[[0.0, 0.25], [1.0, 1.0]]], dtype=torch.float64),
)
- from src.plotting import plot_bar
- Xn_new = result['X_norm']
- Xn_new_t = np_to_torch(Xn_new, device=device)
- X_new = result['X_phys']
- pred_mean_new, pred_std_new = posterior_report(
- model_cv, Xn_new_t, Y_mean, Y_std,
- )
- fig_bar = plot_bar(pred_mean_new, pred_std_new, labels=objective_names)
-
- # Verify result structure
- assert isinstance(result, dict), "Should return dictionary"
- assert all(key in result for key in ['X_phys', 'X_norm', 'attempts', 'acq_val']), "Missing expected keys"
-
- # If we got valid candidates, verify they satisfy row constraints
- if result['X_phys'].shape[0] > 0:
- print(f"Generated {result['X_phys'].shape[0]} candidates in {result['attempts']} attempts")
-
- # 🔍 DEBUG: Analyze acquisition values and model predictions
- print(f"\n📈 Acquisition Value Analysis:")
- print(f" Raw acquisition values (log(EHVI)): {[f'{v:.6f}' for v in result['acq_val']]}")
-
- # Convert from log(EHVI) to EHVI since use_lognehvi=True (default)
- ehvi_values = [np.exp(v) for v in result['acq_val']]
- print(f" Actual EHVI values: {[f'{v:.6f}' for v in ehvi_values]}")
-
- # Print candidate details
- print(f"\n🔍 Generated Candidates (Physical Units):")
- for i, (phys_vals, norm_vals, acq_val) in enumerate(zip(result['X_phys'], result['X_norm'], result['acq_val'])):
- print(f" Candidate {i+1}:")
- print(f" Log(EHVI): {acq_val:.6f}")
- print(f" EHVI: {np.exp(acq_val):.6f}")
- print(f" Physical Values:")
- for j, name in enumerate(design.names):
- print(f" {name}: {phys_vals[j]:.4f}")
- print(f" Normalized Values:")
- for j, name in enumerate(design.names):
- print(f" {name}: {norm_vals[j]:.4f}")
- print()
-
- # Verify physical constraints are satisfied
- from src.constraints import apply_row_constraints
- constraint_mask = apply_row_constraints(result['X_phys'], design, row_constraints_list)
- print(f"🔒 Row Constraint Satisfaction:")
- print(f" All candidates satisfy row constraints: {np.all(constraint_mask)}")
-
- # Verify dimensions match design
- assert result['X_phys'].shape[1] == len(design.names), "Physical dimensions should match design"
- assert result['X_norm'].shape[1] == len(design.names), "Normalized dimensions should match design"
-
- # Check that physical values are within design bounds
- for i, name in enumerate(design.names):
- phys_vals = result['X_phys'][:, i]
- design_min = design.lowers[i]
- design_max = design.uppers[i]
- assert np.all(phys_vals >= design_min) and np.all(phys_vals <= design_max), \
- f"Values for {name} out of bounds [{design_min}, {design_max}]"
-
- # Check that normalized values are in [0, 1]
- assert np.all(result['X_norm'] >= 0) and np.all(result['X_norm'] <= 1), \
- "Normalized values should be in [0, 1]"
-
- print(f"✓ All {result['X_phys'].shape[0]} candidates satisfy constraints")
-
- # 🔍 DEBUG: Explain acquisition values
- print(f"\n💡 Acquisition Value Interpretation (Log-NEHVI):")
- print(f" Log(EHVI) values: {[f'{v:.6f}' for v in result['acq_val']]}")
- print(f" Actual EHVI values: {[f'{v:.6f}' for v in ehvi_values]}")
- print(f" Higher EHVI = Greater expected improvement in hypervolume")
- print(f" Lower EHVI = Minimal improvement expected")
-
- else:
- print("No valid candidates found - constraints may be too strict")
- assert result['attempts'] > 0, "Should have made at least one attempt"
-
- print("✓ Constraint handling works correctly")
-
-def test_propose_batch_constraint_stress_test():
- """Stress test with very strict constraints to test failure handling."""
- print("Testing propose_batch with very strict constraints...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- # 🔍 DEBUG: Try different model fitting approaches
- print(f"\n🔍 Model Fitting Comparison:")
-
- # Option 1: Basic GP (likely overfitting)
- print(f" Testing basic GP fit...")
- model_basic = fit_gp_models(X_t, Y_scaled_t)
-
- # Option 2: LOOCV-selected models (better regularization)
- print(f" Testing LOOCV-selected models...")
- objective_names = get_objective_names(config)
- model_loocv, results_df = loocv_select_models(X_t, Y_scaled_t, objective_names=objective_names, device=device)
-
- # Compare model uncertainties
- print(f"\n📊 Model Uncertainty Comparison:")
- for model_name, model in [("Basic GP", model_basic), ("LOOCV GP", model_loocv)]:
- with torch.no_grad():
- posterior = model[0].posterior(X_t)
- mean = posterior.mean
- variance = posterior.variance
- uncertainty = torch.sqrt(variance)
- print(f" {model_name}:")
- for i, obj_name in enumerate(objective_names):
- print(f" {obj_name}: μ={mean[0, i].item():.4f}, σ={uncertainty[0, i].item():.4f}")
-
- # Use LOOCV model for better performance
- model = model_loocv
-
- # Define very strict constraints that are nearly impossible to satisfy
- def strict_row_constraint(X_phys, design):
- """Very strict constraint - only allows very specific temperature/humidity combinations."""
- temp_idx = design.names.index('temperature_c')
- humidity_idx = design.names.index('absolute_humidity')
-
- # Only allow temperature between 25-26°C AND humidity between 5-7 g/m³
- temp_ok = (X_phys[:, temp_idx] >= 25.0) & (X_phys[:, temp_idx] <= 26.0)
- humidity_ok = (X_phys[:, humidity_idx] >= 5.0) & (X_phys[:, humidity_idx] <= 7.0)
-
- return temp_ok & humidity_ok
-
- # Very strict outcome constraints
- from src.acquisition import outcome_ge
- strict_outcome_constraints = [
- outcome_ge(obj_idx=0, thresh=0.9), # PCE >= 0.9 (very high)
- outcome_ge(obj_idx=1, thresh=0.9), # Stability >= 0.9 (very high)
- ]
-
- ref_point = Y_scaled_t.min(dim=0).values - 0.01
-
- # 🔍 DEBUG: Test different acquisition function settings
- print(f"\n🧪 Testing Different Acquisition Settings:")
-
- # Test 1: Regular NEHVI
- print(f" Test 1: Regular NEHVI")
- result1 = propose_batch(
- design=design,
- model=model,
- train_X=X_t,
- ref_point_t=ref_point,
- batch_size=2,
- row_constraints=[strict_row_constraint],
- constraints=strict_outcome_constraints,
- eta=0.01, # Strict penalty
- num_restarts=2, # Small for testing
- raw_samples=64, # Small for testing
- sample_shape=16, # Small for testing
- max_attempts=3, # Limited attempts
- verbose=False,
- use_lognehvi=False # Regular NEHVI
+
+
+def test_build_qnehvi_constructs_active_botorch_acquisition():
+ train_x = torch.tensor(
+ [
+ [0.0, 0.0],
+ [0.2, 0.8],
+ [0.4, 0.3],
+ [0.7, 1.0],
+ [1.0, 0.5],
+ ],
+ dtype=torch.float64,
)
-
- # Test 2: LogNEHVI (your default)
- print(f" Test 2: LogNEHVI (default)")
- result2 = propose_batch(
- design=design,
+ model = _unfitted_two_objective_model(train_x)
+
+ acq = build_qnehvi(
model=model,
- train_X=X_t,
- ref_point_t=ref_point,
+ train_X=train_x,
+ ref_point_t=torch.tensor([-0.5, -0.5], dtype=torch.float64),
+ sample_shape=8,
+ prune_baseline=False,
+ )
+
+ assert isinstance(acq, qLogNoisyExpectedHypervolumeImprovement)
+ assert acq.sampler.sample_shape == torch.Size([8])
+
+
+def test_optimize_wrapper_passes_cpu_bounds_and_options(monkeypatch):
+ received = {}
+
+ def fake_optimize_acqf(**kwargs):
+ received.update(kwargs)
+ bounds = kwargs["bounds"]
+ q = kwargs["q"]
+ candidates = bounds.mean(dim=0).repeat(q, 1)
+ values = torch.arange(q, device=bounds.device, dtype=bounds.dtype)
+ return candidates, values
+
+ monkeypatch.setattr(acquisition, "optimize_acqf", fake_optimize_acqf)
+ options = {"maxiter": 2}
+
+ candidates, values = optimize_acq_qnehvi(
+ acq_function=object(),
+ d=2,
+ q=3,
+ num_restarts=4,
+ raw_samples=16,
+ device=torch.device("cpu"),
+ dtype=torch.float64,
+ options=options,
+ sequential=False,
+ )
+
+ assert candidates.shape == (3, 2)
+ assert values.shape == (3,)
+ assert received["num_restarts"] == 4
+ assert received["raw_samples"] == 16
+ assert received["options"] is options
+ assert received["sequential"] is False
+ torch.testing.assert_close(
+ received["bounds"],
+ torch.tensor([[0.0, 0.0], [1.0, 1.0]], dtype=torch.float64),
+ )
+
+
+def test_propose_batch_returns_snapped_physical_and_normalized_arrays(monkeypatch):
+ class DummyAcquisition:
+ def __init__(self):
+ self.pending_calls = []
+
+ def set_X_pending(self, value):
+ self.pending_calls.append(value)
+
+ dummy_acquisition = DummyAcquisition()
+
+ def fake_optimize(**kwargs):
+ raw = torch.tensor(
+ [[0.15, 0.15], [0.84, 0.90]],
+ dtype=kwargs["dtype"],
+ device=kwargs["device"],
+ )
+ candidates = kwargs["post_processing_func"](raw)
+ values = torch.tensor(
+ [1.5, 1.0], dtype=kwargs["dtype"], device=kwargs["device"]
+ )
+ return candidates, values
+
+ monkeypatch.setattr(acquisition, "optimize_acq_qnehvi", fake_optimize)
+ train_x = torch.tensor([[0.0, 0.0], [1.0, 1.0]], dtype=torch.float64)
+
+ result = propose_batch(
+ design=_design(),
+ model=None,
+ train_X=train_x,
+ ref_point_t=torch.tensor([-1.0, -1.0], dtype=torch.float64),
batch_size=2,
- row_constraints=[strict_row_constraint],
- constraints=strict_outcome_constraints,
- eta=0.01, # Strict penalty
- num_restarts=2, # Small for testing
- raw_samples=64, # Small for testing
- sample_shape=16, # Small for testing
- max_attempts=3, # Limited attempts
- verbose=False,
- use_lognehvi=True # LogNEHVI
+ acq=lambda: dummy_acquisition,
+ max_attempts=1,
+ device=torch.device("cpu"),
+ dtype=torch.float64,
)
-
- # Compare results
- print(f"\n📊 Acquisition Function Comparison:")
- print(f" Regular NEHVI: {len(result1['acq_val'])} candidates, acq_vals: {[f'{v:.6f}' for v in result1['acq_val']]}")
- print(f" LogNEHVI: {len(result2['acq_val'])} candidates, acq_vals: {[f'{v:.6f}' for v in result2['acq_val']]}")
-
- # Use the result with more candidates for detailed analysis
- result = result1 if len(result1['acq_val']) > 0 else result2
-
- # Verify graceful handling of strict constraints
- assert isinstance(result, dict), "Should return dictionary even with strict constraints"
- assert all(key in result for key in ['X_phys', 'X_norm', 'attempts', 'acq_val']), "Missing expected keys"
- assert result['attempts'] > 0, "Should have made at least one attempt"
-
- print(f"\n📊 Stress Test Results:")
- print(f" Attempts made: {result['attempts']}")
- print(f" Candidates found: {result['X_phys'].shape[0]}")
-
- if result['X_phys'].shape[0] == 0:
- print("✓ Gracefully handled impossible constraints (returned empty batch)")
- else:
- print(f"✓ Found {result['X_phys'].shape[0]} candidates despite strict constraints")
-
- # 🔍 DEBUG: Analyze why these candidates were selected
- print(f"\n🔍 Candidate Analysis:")
- X_candidates = torch.tensor(result['X_norm'], dtype=torch.float64, device=device)
- with torch.no_grad():
- candidate_posterior = model[0].posterior(X_candidates)
- candidate_mean = candidate_posterior.mean
- candidate_std = torch.sqrt(candidate_posterior.variance)
-
- print(f" Model predictions for candidates:")
- for i, (acq_val, mean, std) in enumerate(zip(result['acq_val'], candidate_mean, candidate_std)):
- print(f" Candidate {i+1}: acq={acq_val:.6f}")
- for j, obj_name in enumerate(objective_names):
- print(f" {obj_name}: μ={mean[j].item():.4f}, σ={std[j].item():.4f}")
-
- # Print detailed candidate information
- print(f"\n🔍 Generated Candidates (Physical Units):")
- for i, (phys_vals, norm_vals, acq_val) in enumerate(zip(result['X_phys'], result['X_norm'], result['acq_val'])):
- print(f" Candidate {i+1}:")
- print(f" Acquisition Value: {acq_val:.6f}")
- print(f" Physical Values:")
- for j, name in enumerate(design.names):
- print(f" {name}: {phys_vals[j]:.4f}")
- print(f" Normalized Values:")
- for j, name in enumerate(design.names):
- print(f" {name}: {norm_vals[j]:.4f}")
- print()
-
- # Verify constraints are satisfied
- constraint_mask = strict_row_constraint(result['X_phys'], design)
- print(f"🔒 Constraint Satisfaction Check:")
- print(f" Row constraint satisfied: {np.all(constraint_mask)}")
-
- # Check specific constraint values
- temp_idx = design.names.index('temperature_c')
- humidity_idx = design.names.index('absolute_humidity')
-
- print(f" Temperature constraints (25-26°C):")
- for i, temp in enumerate(result['X_phys'][:, temp_idx]):
- in_range = 25.0 <= temp <= 26.0
- print(f" Candidate {i+1}: {temp:.2f}°C {'✓' if in_range else '✗'}")
-
- print(f" Humidity constraints (5-7 g/m³):")
- for i, humidity in enumerate(result['X_phys'][:, humidity_idx]):
- in_range = 5.0 <= humidity <= 7.0
- print(f" Candidate {i+1}: {humidity:.2f} g/m³ {'✓' if in_range else '✗'}")
-
- print("✓ Stress test completed successfully")
-
-def main():
- """Run comprehensive acquisition function tests."""
- print("Running comprehensive acquisition.py tests...\n")
-
- try:
- # Test 1: Outcome constraint builders
- print("="*60)
- test_outcome_constraint_builders()
-
- # Test 2: Unit bounds helper
- print("="*60)
- test_unit_bounds()
-
- # Test 3: Snap post-processing factory
- print("="*60)
- test_snap_postproc_factory()
-
- # Test 4: qNEHVI builder
- print("="*60)
- test_build_qnehvi()
-
- # Test 5: Acquisition optimization wrapper
- print("="*60)
- test_optimize_acq_qnehvi()
-
- # Test 6: Basic propose_batch
- print("="*60)
- test_propose_batch_basic()
-
- # Test 7: Custom acquisition function
- print("="*60)
- test_propose_batch_with_custom_acq()
-
- # Test 8: Constraint handling
- print("="*60)
- test_propose_batch_with_constraints()
-
- # Test 9: Constraint stress test
- print("="*60)
- test_propose_batch_constraint_stress_test()
-
- print("="*60)
- print("\n🎉 All acquisition tests passed successfully!")
- print("🎯 Tested outcome constraints, helpers, builders, and main functions")
- print("🎯 Validated with real configCSV_example.csv data")
- print("🎯 Confirmed custom acquisition function support")
- print("🎯 Tested comprehensive constraint handling (row + outcome constraints)")
- print("🎯 Validated graceful failure handling with strict constraints")
-
- except Exception as e:
- print(f"\n❌ Test failed: {e}")
- import traceback
- traceback.print_exc()
-
-if __name__ == "__main__":
- main()
+
+ assert set(result) == {"X_phys", "X_norm", "acq_val", "attempts"}
+ np.testing.assert_allclose(result["X_norm"], [[0.0, 0.25], [1.0, 1.0]])
+ np.testing.assert_allclose(result["X_phys"], [[10.0, 0.25], [20.0, 1.0]])
+ np.testing.assert_allclose(result["acq_val"], [1.5, 1.0])
+ assert result["attempts"] == 1
+ assert dummy_acquisition.pending_calls == [None]
diff --git a/tests/test_batch_review.py b/tests/test_batch_review.py
new file mode 100644
index 0000000..8d7b60d
--- /dev/null
+++ b/tests/test_batch_review.py
@@ -0,0 +1,625 @@
+"""The batch review artifact.
+
+Two kinds of test here, deliberately separated.
+
+*Config parsing* runs against the live campaign YAML, because what the campaign
+declares -- the low-speed probe, the anneal_temp note -- is part of what shipped
+and should break if someone deletes it.
+
+*Artifact building* runs against a small purpose-built config with three inputs.
+It needs a real GP fit, and `model_validation`'s signal-collapse guard is strict
+for good reason: it refuses a fit whose residual carries no signal. Manufacturing
+ten well-conditioned observations in a 10-input space just to test a table is
+fighting the wrong battle, and a test that trips a legitimate guard teaches the
+next person to weaken the guard.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from openpyxl import Workbook, load_workbook
+
+from mobo_kit.batch_review import (
+ NOT_APPROVED,
+ SD_MATERIALITY_RATIO,
+ ProbeSpec,
+ build_batch_review,
+ classify_probe_objective,
+ probe_specs_from_config,
+ review_notes_from_config,
+ write_review_sheet,
+)
+from mobo_kit.campaign import load_campaign_config
+from mobo_kit.design import build_design_from_config
+from mobo_kit.lhs import lhs_dataframe_optimized
+from mobo_kit.scores import ScoreFinding, ScoreSeverity
+
+LIVE_CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+
+
+@pytest.fixture(scope="module")
+def live_config() -> dict:
+ return load_campaign_config(LIVE_CONFIG_PATH)
+
+
+def _config() -> dict:
+ """Three inputs, the same three objective shapes as the campaign.
+
+ Keeps the log-link thickness objective and the identity-link linear mean on
+ `anneal_temp`, because those are the two paths the review has to decode
+ correctly.
+ """
+ return {
+ "inputs": [
+ {"name": "speed_1", "unit": "rpm", "start": 1000, "stop": 6000, "step": 500},
+ {"name": "precur_conc", "unit": "M", "start": 1.0, "stop": 2.0, "step": 0.05},
+ {"name": "anneal_temp", "unit": "C", "start": 100, "stop": 185, "step": 5},
+ ],
+ "objectives": {
+ "contract_version": "review-test-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": "uniformity",
+ "model_source_column": "U",
+ "transform": "affine",
+ "goal": "maximize",
+ "lower_anchor": 0.0,
+ "upper_anchor": 1.0,
+ },
+ {
+ "name": "optoelectronic",
+ "model_source_column": "O",
+ "transform": "affine",
+ "goal": "maximize",
+ "lower_anchor": -10.0,
+ "upper_anchor": -6.0,
+ "mean_function": {
+ "response": "identity",
+ "features": [{"column": "anneal_temp", "transform": "identity"}],
+ },
+ },
+ {
+ "name": "thickness",
+ "model_source_column": "T",
+ "transform": "gaussian_target",
+ "goal": "target",
+ "target": 650.0,
+ "sigma": 176.7766952966369,
+ "mean_function": {
+ "response": "log",
+ "features": [
+ {"column": "speed_1", "transform": "log"},
+ {"column": "precur_conc", "transform": "log"},
+ ],
+ },
+ },
+ ],
+ },
+ "reference_point_utility": [-0.01, -0.01, -0.01],
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi",
+ "batch_size": 3,
+ "replicates_per_condition": 3,
+ "beta": 4.0,
+ "candidate_pool_size": 512,
+ "posterior_samples": 64,
+ "moment_method": "monte_carlo",
+ }
+ },
+ "local_penalization": {"radius": 0.25, "min_batch_distance": 0.15},
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": 7},
+ "constraints": [],
+ "review": {
+ "probes": [
+ {
+ "name": "low-speed corner",
+ "column": "speed_1",
+ "value": 1000,
+ "note": "The two observations here contradict each other.",
+ }
+ ],
+ "notes": ["anneal_temp sits at a range edge by construction."],
+ },
+ }
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return _config()
+
+
+@pytest.fixture(scope="module")
+def design(config):
+ return build_design_from_config(dict(config))
+
+
+def _rows(config: dict, n: int, *, seed: int) -> np.ndarray:
+ design = build_design_from_config(dict(config))
+ frame = lhs_dataframe_optimized(design, n, seed=seed, snap_to_grids=True)
+ return frame.to_numpy(dtype=float)
+
+
+def _observations(config: dict, n: int = 12, *, seed: int = 1) -> tuple[np.ndarray, np.ndarray]:
+ """Trends the mean functions can find, plus residual structure the GPs can.
+
+ Residual structure matters: data following a mean function exactly leaves a
+ pure-noise residual, which the collapse guard rejects -- correctly, since a GP
+ with nothing to model has no business scoring candidates.
+ """
+ X = _rows(config, n, seed=seed)
+ speed, concentration, temperature = X[:, 0], X[:, 1], X[:, 2]
+ scaled_temperature = (temperature - 100.0) / 85.0
+ scaled_concentration = (concentration - 1.0) / 1.0
+
+ thickness = np.exp(
+ 9.6
+ - 0.38 * np.log(speed)
+ + 0.5 * np.log(concentration)
+ + 0.25 * np.sin(3.0 * scaled_temperature)
+ )
+ optoelectronic = (
+ -6.2 - 0.012 * temperature + 0.35 * np.cos(2.5 * scaled_concentration)
+ )
+ uniformity = np.clip(
+ 0.5 + 0.35 * np.sin(2.0 * scaled_concentration + 0.7 * scaled_temperature),
+ 0.02,
+ 0.98,
+ )
+ return X, np.column_stack([uniformity, optoelectronic, thickness])
+
+
+@pytest.fixture(scope="module")
+def review(config):
+ X, Y = _observations(config)
+ conditions = pd.DataFrame(
+ _rows(config, 3, seed=99), columns=[i["name"] for i in config["inputs"]]
+ )
+ return build_batch_review(
+ config,
+ X,
+ Y,
+ conditions,
+ round_name="R1",
+ findings=(
+ ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="readings_disagree",
+ objective="thickness",
+ row_position=11,
+ sample_id=12,
+ message="2 readings span 891: ['1600', '709'].",
+ ),
+ ScoreFinding(
+ severity=ScoreSeverity.NOTE,
+ code="reading_excluded",
+ objective="thickness",
+ row_position=3,
+ sample_id=4,
+ message="'T anom' holds 1618, judged anomalous.",
+ ),
+ ),
+ context={"Round": "R1", "Seed": 7},
+ )
+
+
+# --------------------------------------------------------------------------- #
+# the judgment, on its own
+# --------------------------------------------------------------------------- #
+
+
+def test_worse_with_the_same_uncertainty_reads_as_known_and_bad() -> None:
+ """The numbers from the campaign's own R0 fit: thickness utility 0.223 against
+ 0.786, sd ratio 1.02. A bare `>` on sd calls that 'more uncertain' and prints
+ the benign verdict, which is how this nearly shipped wrong."""
+ assert classify_probe_objective(0.223, 0.786, 1.02) == "known_and_bad"
+
+
+def test_worse_but_materially_more_uncertain_reads_as_a_tradeoff() -> None:
+ assert classify_probe_objective(0.223, 0.786, 2.0) == "uncertain_tradeoff"
+
+
+def test_not_worse_means_the_model_has_no_objection() -> None:
+ assert classify_probe_objective(0.9, 0.8, 1.0) is None
+ assert classify_probe_objective(0.8, 0.8, 1.0) is None
+
+
+def test_the_threshold_is_a_ratio_not_an_inequality() -> None:
+ assert classify_probe_objective(0.1, 0.5, SD_MATERIALITY_RATIO - 0.01) == "known_and_bad"
+ assert (
+ classify_probe_objective(0.1, 0.5, SD_MATERIALITY_RATIO + 0.01)
+ == "uncertain_tradeoff"
+ )
+
+
+def test_a_zero_batch_sd_does_not_divide_by_zero() -> None:
+ assert classify_probe_objective(0.1, 0.5, float("inf")) == "uncertain_tradeoff"
+
+
+# --------------------------------------------------------------------------- #
+# what the live campaign declares
+# --------------------------------------------------------------------------- #
+
+
+def test_the_live_config_declares_the_low_speed_probe(live_config) -> None:
+ probes = probe_specs_from_config(live_config)
+ assert [p.column for p in probes] == ["speed_1"]
+ assert probes[0].value == 1000.0
+ assert "contradict each other" in probes[0].note
+
+
+def test_the_live_config_declares_the_anneal_temp_note(live_config) -> None:
+ notes = review_notes_from_config(live_config)
+ assert any("anneal_temp" in note and "mean function" in note for note in notes)
+ assert any("exploration-only" in note for note in notes)
+
+
+def test_no_review_block_means_no_probes_and_no_notes() -> None:
+ assert probe_specs_from_config({}) == ()
+ assert review_notes_from_config({}) == ()
+
+
+def test_a_single_probe_mapping_is_accepted() -> None:
+ probes = probe_specs_from_config(
+ {"review": {"probes": {"name": "p", "column": "speed_1", "value": 1000}}}
+ )
+ assert probes == (ProbeSpec("p", "speed_1", 1000.0),)
+
+
+def test_a_probe_naming_an_undeclared_input_is_refused_before_any_fitting(
+ config,
+) -> None:
+ """A typo in a config column name should cost nothing, not three GP fits."""
+ broken = dict(config)
+ broken["review"] = {"probes": [{"name": "bad", "column": "not_an_input", "value": 1}]}
+ names = [i["name"] for i in config["inputs"]]
+ with pytest.raises(ValueError, match="not a declared input"):
+ build_batch_review(
+ broken,
+ np.zeros((0, 3)),
+ np.zeros((0, 3)),
+ pd.DataFrame(_rows(config, 1, seed=3), columns=names),
+ round_name="R1",
+ )
+
+
+# --------------------------------------------------------------------------- #
+# the candidate table
+# --------------------------------------------------------------------------- #
+
+
+def test_every_candidate_gets_a_utility_and_an_sd_per_objective(review) -> None:
+ for name in ("uniformity", "optoelectronic", "thickness"):
+ assert f"{name}_utility" in review.candidates
+ assert f"{name}_sd" in review.candidates
+ assert review.candidates[f"{name}_sd"].gt(0).all()
+ assert len(review.candidates) == 3
+
+
+def test_the_physical_prediction_is_reported_in_the_measurement_s_units(review) -> None:
+ """A utility of 0.87 means nothing at the coater; nanometres do."""
+ assert review.candidates["thickness_predicted"].between(50.0, 5000.0).all()
+ for column in ("thickness_lo68", "thickness_hi68"):
+ # numeric, so a spreadsheet can sort, plot and compare it
+ assert pd.api.types.is_float_dtype(review.candidates[column])
+
+
+def test_a_log_link_objective_reports_a_median_not_a_mean(review) -> None:
+ """exp of a mean of logs is the median. The interval brackets it
+ multiplicatively; an additive one would be symmetric in nanometres, which is
+ what transforming a mean rather than decoding a posterior would produce."""
+ low = review.candidates["thickness_lo68"]
+ high = review.candidates["thickness_hi68"]
+ middle = review.candidates["thickness_predicted"]
+ assert (low < middle).all() and (middle < high).all()
+ assert np.allclose(middle / low, high / middle, rtol=1e-12)
+ assert np.allclose(np.sqrt(low * high), middle, rtol=1e-12)
+
+
+def test_an_identity_link_objective_gets_a_symmetric_interval(review) -> None:
+ low = review.candidates["optoelectronic_lo68"]
+ high = review.candidates["optoelectronic_hi68"]
+ middle = review.candidates["optoelectronic_predicted"]
+ assert np.allclose((low + high) / 2.0, middle, rtol=1e-12)
+
+
+def test_distance_to_the_nearest_observed_point_is_zero_when_reproposed(config) -> None:
+ X, Y = _observations(config)
+ names = [i["name"] for i in config["inputs"]]
+ built = build_batch_review(
+ config, X, Y, pd.DataFrame(X[:2], columns=names), round_name="R1"
+ )
+ assert built.candidates["distance_to_nearest"].max() < 1e-9
+ assert built.candidates["nearest_observed_row"].tolist() == [1, 2]
+
+
+def test_range_edges_are_counted_and_named(config, design) -> None:
+ """Which coordinates are pinned matters more than how many: 'anneal_temp=min'
+ on every row is a mean function speaking, and a count alone hides that."""
+ names = list(design.names)
+ X, Y = _observations(config)
+ row = list(X[0])
+ row[names.index("speed_1")] = float(design.lowers[names.index("speed_1")])
+ row[names.index("anneal_temp")] = float(design.uppers[names.index("anneal_temp")])
+ built = build_batch_review(
+ config, X, Y, pd.DataFrame([row], columns=names), round_name="R1"
+ )
+ assert built.candidates["n_at_range_edge"].iloc[0] == 2
+ which = built.candidates["which_at_range_edge"].iloc[0]
+ assert "speed_1=min" in which and "anneal_temp=max" in which
+
+
+def test_a_candidate_at_no_range_edge_says_so_rather_than_leaving_a_blank(
+ review,
+) -> None:
+ which = review.candidates["which_at_range_edge"]
+ assert which.notna().all()
+ assert (which.str.len() > 0).all()
+
+
+# --------------------------------------------------------------------------- #
+# probes and text
+# --------------------------------------------------------------------------- #
+
+
+def test_the_probe_moves_every_candidate_to_the_probed_value(review) -> None:
+ kinds = review.probes["kind"].tolist()
+ assert sum("moved to speed_1=1000" in kind for kind in kinds) == 3
+
+
+def test_the_probe_reports_observations_already_in_the_region(config) -> None:
+ X, Y = _observations(config)
+ X[0, 0] = 1000.0
+ names = [i["name"] for i in config["inputs"]]
+ built = build_batch_review(
+ config, X, Y, pd.DataFrame(_rows(config, 2, seed=42), columns=names), round_name="R1"
+ )
+ kinds = built.probes["kind"].tolist()
+ assert any("observed row 1" in kind for kind in kinds)
+ assert built.probes["measured"].notna().any()
+
+
+def test_the_verdict_names_the_mean_function_when_the_probed_column_is_in_it(
+ review,
+) -> None:
+ """speed_1 is a feature of the thickness mean, so a probe there asks a fitted
+ trend to extrapolate to its range edge -- a different claim from a GP
+ interpolating between neighbours, and the artifact should say which it is."""
+ text = " ".join(review.probe_verdicts)
+ if "KNOWN AND BAD" in text:
+ assert "thickness carries speed_1 in its mean function" in text
+ assert "fitted global trend" in text
+
+
+def test_the_verdict_prints_the_sd_ratio_and_its_threshold(review) -> None:
+ """The ratio is printed whichever branch fires, so a reader who wants a
+ different threshold can apply their own."""
+ import re
+
+ text = " ".join(review.probe_verdicts)
+ assert re.search(r"sd \d+\.\d+ vs \d+\.\d+ \(x\d+\.\d+\)", text)
+ assert f"{SD_MATERIALITY_RATIO:g}x counts as materially more uncertain" in text
+
+
+def test_the_verdict_states_how_many_observations_sit_in_the_region(review) -> None:
+ assert "observation(s) already sit there" in " ".join(review.probe_verdicts)
+
+
+def test_the_text_artifact_stands_alone(review) -> None:
+ text = review.to_text()
+ for section in (
+ "BATCH REVIEW - R1",
+ "PROPOSED CONDITIONS",
+ "PROBES",
+ "PROBE 'low-speed corner'",
+ "NOTES",
+ "CARRIED FROM THE MEASURED DATA",
+ ):
+ assert section in text
+ assert "1600" in text # the carried finding, verbatim
+ assert text.rstrip().endswith("outside this file.")
+ assert NOT_APPROVED.split(".")[0] in text
+
+
+def test_findings_are_carried_worst_first(review) -> None:
+ text = review.to_text()
+ assert text.index("[warning] sample 12") < text.index("[note] sample 4")
+
+
+# --------------------------------------------------------------------------- #
+# when the mean function explains the data
+# --------------------------------------------------------------------------- #
+
+
+@pytest.fixture
+def collapsed_residual(monkeypatch):
+ """Force the condition rather than hope data produces it.
+
+ Whether a given dataset lands on a collapsed residual is knife-edge -- measured
+ across residual magnitudes from 0 to 0.3, it fires at 0, 1e-4, 0.01 and 0.03 but
+ not at 0.001 or 0.1, because it depends on where the MLL optimiser lands. A test
+ that depended on that would be a flake. The guard's own decision is tested
+ directly in test_model_validation.py; what these tests check is that its warning
+ reaches the people who need it.
+
+ Only objectives with a `StructuredMean` are collapsed, which is exactly the case
+ being emulated: the mean function explains the data, so the residual GP has
+ nothing left. Uniformity has no mean function and stays healthy -- collapsing it
+ would be a true collapse and must still hard-fail.
+ """
+ import mobo_kit.model_validation as validation_module
+ from mobo_kit.structured_mean import StructuredMean
+
+ real_fit = validation_module.fit_gpytorch_mll
+
+ def collapse_structured_only(mll):
+ real_fit(mll)
+ if isinstance(getattr(mll.model, "mean_module", None), StructuredMean):
+ import torch
+
+ mll.model.covar_module.outputscale = torch.tensor(1e-12, dtype=torch.double)
+ mll.model.likelihood.noise = torch.tensor(0.9, dtype=torch.double)
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", collapse_structured_only)
+
+
+def test_a_round_still_proposes_when_the_mean_function_explains_the_data(
+ config, collapsed_residual
+) -> None:
+ """The behaviour that matters: refusing here would dead-end the campaign
+ exactly when the physics model started working, with no way out -- better data
+ cannot be collected without first proposing conditions."""
+ from mobo_kit.campaign import run_r1_ucb
+
+ X, Y = _observations(config)
+ result = run_r1_ucb(config, X, Y, n=2)
+ assert result.n_conditions == 2
+
+ warnings = result.diagnostics["model_fit_warnings"]
+ assert warnings, "the collapsed residual GP should have warned"
+ joined = " ".join(warnings)
+ assert "exploration term has degenerated" in joined
+ assert "UNDERSTATED" in joined
+ # the two objectives with a mean function, and not the one without
+ assert any(w.startswith("thickness") for w in warnings)
+ assert not any(w.startswith("uniformity") for w in warnings)
+
+
+def test_the_round_diagnostics_carry_no_library_deprecation_noise(
+ config, collapsed_residual
+) -> None:
+ """`record.warnings` also collects every Python warning raised while fitting --
+ on this stack, ~18 numpy-2.0 deprecation notices per fit. Putting those in front
+ of someone reviewing a batch is how people learn to ignore warnings."""
+ from mobo_kit.campaign import run_r1_ucb
+
+ warnings = run_r1_ucb(config, *_observations(config), n=2).diagnostics[
+ "model_fit_warnings"
+ ]
+ assert warnings
+ assert not any("numpy" in w.lower() or "__array__" in w for w in warnings)
+
+
+def test_the_unfiltered_warnings_are_kept_but_not_surfaced(config) -> None:
+ """Filtered out of the human channel, retained for debugging. A BoTorch or
+ scipy convergence warning the filter dropped is exactly what someone needs when
+ a fit looks strange weeks later."""
+ from mobo_kit.campaign import run_r1_ucb
+
+ diagnostics = run_r1_ucb(config, *_observations(config), n=2).diagnostics
+ raw = diagnostics["fit_warnings_raw"]
+ assert raw, "the fits do raise library warnings on this stack"
+ assert any("numpy" in entry.lower() or "__array__" in entry for entry in raw)
+ # each entry says which objective and which stage it came from
+ assert all("|" in entry for entry in raw)
+ # and the surfaced channel is still clean
+ assert not diagnostics["model_fit_warnings"]
+
+
+def _collapsed_review(config):
+ X, Y = _observations(config)
+ names = [i["name"] for i in config["inputs"]]
+ return build_batch_review(
+ config,
+ X,
+ Y,
+ pd.DataFrame(_rows(config, 2, seed=77), columns=names),
+ round_name="R1",
+ )
+
+
+def test_the_review_carries_the_warning_above_the_numbers(
+ config, collapsed_residual
+) -> None:
+ built = _collapsed_review(config)
+ assert built.model_warnings
+
+ text = built.to_text()
+ assert "READ THIS BEFORE THE NUMBERS" in text
+ # before, not after: it changes how every number below should be read
+ assert text.index("READ THIS BEFORE THE NUMBERS") < text.index("PROPOSED CONDITIONS")
+ assert "understated" in text.lower()
+ assert "no uncertainty" in text
+
+
+def test_the_warning_reaches_the_review_sheet_too(
+ tmp_path, config, collapsed_residual
+) -> None:
+ built = _collapsed_review(config)
+ path = tmp_path / "candidates.xlsx"
+ _candidate_book(path)
+ write_review_sheet(path, built)
+ body = _sheet_text(path)
+ assert "READ THIS BEFORE THE NUMBERS" in body
+ assert "UNDERSTATED" in body
+
+
+def test_a_healthy_fit_carries_no_warning(review) -> None:
+ """The warning has to mean something, which means it must not always fire."""
+ assert review.model_warnings == ()
+ assert "READ THIS BEFORE THE NUMBERS" not in review.to_text()
+
+
+# --------------------------------------------------------------------------- #
+# the sheet
+# --------------------------------------------------------------------------- #
+
+
+def _candidate_book(path) -> None:
+ book = Workbook()
+ book.active.title = "R1_Candidates"
+ book["R1_Candidates"]["A1"] = "candidate_id"
+ book.save(path)
+
+
+def _sheet_text(path, sheet: str = "Review") -> str:
+ return "\n".join(
+ str(value)
+ for row in load_workbook(path)[sheet].iter_rows(values_only=True)
+ for value in row
+ if value is not None
+ )
+
+
+def test_the_review_is_written_as_its_own_sheet(tmp_path, review) -> None:
+ path = tmp_path / "candidates.xlsx"
+ _candidate_book(path)
+ write_review_sheet(path, review)
+
+ written = load_workbook(path)
+ assert written.sheetnames == ["R1_Candidates", "Review"]
+ # the worklist is untouched
+ assert written["R1_Candidates"]["A1"].value == "candidate_id"
+
+ body = _sheet_text(path)
+ assert "BATCH REVIEW - R1" in body
+ assert "PROPOSED CONDITIONS" in body
+ assert "APPROVAL" in body
+ assert "Nothing here is approved" in body
+ assert "1600" in body
+
+
+def test_rewriting_the_review_replaces_it_rather_than_duplicating(
+ tmp_path, review
+) -> None:
+ path = tmp_path / "candidates.xlsx"
+ _candidate_book(path)
+ write_review_sheet(path, review)
+ write_review_sheet(path, review)
+ assert load_workbook(path).sheetnames == ["R1_Candidates", "Review"]
+
+
+def test_non_finite_cells_are_written_as_blanks_not_the_text_nan(
+ tmp_path, review
+) -> None:
+ """The probe table carries NaN in the 'selected' columns of observed rows.
+ Excel showing the literal text 'nan' would read as a measurement."""
+ path = tmp_path / "candidates.xlsx"
+ _candidate_book(path)
+ write_review_sheet(path, review)
+ assert "nan" not in _sheet_text(path).lower().replace("anneal", "")
diff --git a/tests/test_batch_selection.py b/tests/test_batch_selection.py
new file mode 100644
index 0000000..670420a
--- /dev/null
+++ b/tests/test_batch_selection.py
@@ -0,0 +1,355 @@
+import numpy as np
+import pytest
+
+from mobo_kit.batch_selection import (
+ BaseScoreResult,
+ LocalPenalizationConfig,
+ UndersizedBatchError,
+ select_local_penalized_batch,
+ soft_local_penalty,
+)
+from mobo_kit.candidate_pool import CandidatePool
+
+
+def _pool(values):
+ X = np.asarray(values, dtype=float)
+ if X.ndim == 1:
+ X = X[:, None]
+ grid_indices = np.zeros(X.shape, dtype=int)
+ grid_indices[:, 0] = np.arange(X.shape[0])
+ return CandidatePool(
+ grid_indices=grid_indices,
+ X_phys=X.copy(),
+ X_norm=X.copy(),
+ seed=1,
+ draws=X.shape[0],
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+
+
+def _static_callback(scores, calls=None):
+ scores = np.asarray(scores, dtype=float)
+
+ def callback(remaining, selected):
+ if calls is not None:
+ calls.append((remaining.copy(), selected.copy()))
+ return BaseScoreResult(
+ base_log_score=scores[remaining],
+ base_score=np.exp(scores[remaining]),
+ diagnostics={"selected_before": selected.copy()},
+ )
+
+ return callback
+
+
+def test_radius_pushes_the_second_pick_out_of_the_penalised_neighbourhood():
+ """What `radius` is actually for, verified by construction rather than by
+ accident.
+
+ The DTLZ2 sweep could not test this: its batches land 0.72-0.98 apart, far
+ outside every radius tried, so local penalization never had two candidates
+ close enough to penalise.
+
+ It was once recorded here that the knob was "probably inert" on the live
+ campaign too, on the strength of the R1 batch's 0.921 minimum spacing. That
+ figure was itself produced by the mis-encoded UCB-HVI baseline fixed in
+ 4b76670; corrected, the live batch spaces at 0.6337 and `radius` demonstrably
+ binds below about 0.30 (CAMPAIGN_STATUS.md, "Are beta = 4.0 and radius = 0.25
+ defensible?"). This test predates that and is unaffected by it -- it verifies
+ the mechanism by construction, which is exactly why it kept its value when the
+ campaign evidence turned out to be wrong.
+
+ Here the top three scores are deliberately crowded into one spot, with a
+ slightly worse candidate far away. Without a radius the batch collapses onto
+ the cluster; with one, the second pick is pushed out of it.
+ """
+ # four candidates: three clustered near 0.50, one isolated at 0.90
+ positions = [0.50, 0.52, 0.54, 0.90]
+ scores = [1.00, 0.98, 0.96, 0.80] # the cluster genuinely scores better
+
+ unpenalised = select_local_penalized_batch(
+ _pool(positions),
+ 2,
+ _static_callback(scores),
+ LocalPenalizationConfig(radius=None, min_batch_distance=0.0),
+ )
+ # greedy on score alone takes the two best, which are 0.02 apart
+ assert list(unpenalised.selected_pool_indices) == [0, 1]
+
+ penalised = select_local_penalized_batch(
+ _pool(positions),
+ 2,
+ _static_callback(scores),
+ LocalPenalizationConfig(radius=0.25, min_batch_distance=0.0),
+ )
+ assert list(penalised.selected_pool_indices) == [0, 3]
+ gap = abs(positions[3] - positions[0])
+ assert gap > 0.25, "the second pick should sit beyond the radius, not just apart"
+
+
+def test_a_radius_smaller_than_the_gaps_changes_nothing():
+ """The sweep's inert case, pinned: when candidates are already further apart
+ than the radius, penalization has nothing to do and must not interfere."""
+ positions = [0.10, 0.50, 0.90]
+ scores = [1.00, 0.98, 0.10]
+ for radius in (None, 0.05):
+ result = select_local_penalized_batch(
+ _pool(positions),
+ 2,
+ _static_callback(scores),
+ LocalPenalizationConfig(radius=radius, min_batch_distance=0.0),
+ )
+ assert list(result.selected_pool_indices) == [0, 1]
+
+
+def test_soft_penalty_zero_and_far_distance_limits():
+ factors, logs = soft_local_penalty(np.array([0.0, 10.0]), radius=0.2, epsilon=1e-9)
+ assert factors[0] == pytest.approx(0.0)
+ assert logs[0] == pytest.approx(np.log(1e-9))
+ assert factors[1] == pytest.approx(1.0)
+ with pytest.raises(ValueError, match="radius"):
+ soft_local_penalty(np.array([1.0]), radius=0)
+ with pytest.raises(ValueError, match="non-boolean"):
+ LocalPenalizationConfig(radius=True, min_batch_distance=0.0)
+
+
+def test_stable_tie_break_and_callback_receives_selected_indices():
+ calls = []
+ result = select_local_penalized_batch(
+ _pool([0.0, 0.5, 1.0]),
+ 2,
+ _static_callback([0.0, 0.0, 0.0], calls),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0.0),
+ )
+ assert result.selected_pool_indices.tolist() == [0, 2]
+ assert calls[0][1].tolist() == []
+ assert calls[1][1].tolist() == [0]
+ assert [step.order for step in result.steps] == [1, 2]
+
+
+def test_local_penalty_increases_diversity_over_unpenalized_top_q():
+ pool = _pool([0.0, 0.01, 0.02, 0.6, 1.0])
+ base_logs = np.log([1.0, 0.99, 0.98, 0.8, 0.7])
+ unpenalized_top = pool.X_norm[np.argsort(-base_logs, kind="stable")[:3], 0]
+ result = select_local_penalized_batch(
+ pool,
+ 3,
+ _static_callback(base_logs),
+ LocalPenalizationConfig(radius=0.2, min_batch_distance=0.0),
+ )
+ selected = np.sort(result.X_norm[:, 0])
+ assert np.min(np.diff(selected)) > np.min(np.diff(np.sort(unpenalized_top)))
+ assert result.steps[1].penalty_factor < 1.0
+
+
+def test_none_radius_disables_only_the_soft_penalty():
+ pool = _pool([0.0, 0.01, 0.5, 1.0])
+ result = select_local_penalized_batch(
+ pool,
+ 3,
+ _static_callback([0.0, -0.1, -0.2, -0.3]),
+ LocalPenalizationConfig(radius=None, min_batch_distance=0.0),
+ )
+ assert result.selected_pool_indices.tolist() == [0, 1, 2]
+ assert result.distance_diagnostics["radius"] is None
+ assert all(step.penalty_factor == pytest.approx(1.0) for step in result.steps)
+ assert all(step.log_penalty == pytest.approx(0.0) for step in result.steps)
+ assert all(
+ step.penalized_log_score == pytest.approx(step.base_log_score)
+ for step in result.steps
+ )
+
+
+def test_none_radius_preserves_hard_batch_and_observed_rules():
+ result = select_local_penalized_batch(
+ _pool([0.0, 0.1, 0.5, 0.9]),
+ 2,
+ _static_callback([0.0, -0.01, -0.2, -0.3]),
+ LocalPenalizationConfig(
+ radius=None,
+ min_batch_distance=0.4,
+ min_observed_distance=0.15,
+ ),
+ observed_pending_norm=np.array([[0.9]]),
+ )
+ # The second-highest score is too close to the first selection, and the
+ # final point is an exact observed recipe. The hard rules therefore choose
+ # the third-ranked point even though no soft penalty is active.
+ assert result.selected_pool_indices.tolist() == [0, 2]
+ assert result.distance_diagnostics["minimum_within_batch_distance"] >= 0.4
+ assert all(step.penalty_factor == pytest.approx(1.0) for step in result.steps)
+
+
+def test_hard_batch_and_observed_distances_are_enforced():
+ pool = _pool([0.0, 0.1, 0.3, 0.55, 0.9])
+ result = select_local_penalized_batch(
+ pool,
+ 3,
+ _static_callback([0.0, -0.1, -0.2, -0.3, -0.4]),
+ LocalPenalizationConfig(
+ radius=0.1, min_batch_distance=0.25, min_observed_distance=0.15
+ ),
+ observed_pending_norm=np.array([[0.3]]),
+ )
+ matrix = result.distance_diagnostics["pairwise_distance_matrix"]
+ triangle = matrix[np.triu_indices(3, k=1)]
+ assert np.all(triangle >= 0.25 - 1e-12)
+ assert np.all(np.abs(result.X_norm[:, 0] - 0.3) >= 0.15 - 1e-12)
+
+
+def test_exact_observed_duplicate_is_excluded_even_with_zero_threshold():
+ result = select_local_penalized_batch(
+ _pool([0.0, 0.5, 1.0]),
+ 1,
+ _static_callback([1.0, 0.0, -1.0]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0.0),
+ observed_pending_norm=np.array([[0.0]]),
+ )
+ assert result.selected_pool_indices.tolist() == [1]
+
+
+def test_impossible_spacing_and_all_ineligible_fail_structurally():
+ pool = _pool([0.0, 0.1, 0.2])
+ with pytest.raises(UndersizedBatchError) as captured:
+ select_local_penalized_batch(
+ pool,
+ 2,
+ _static_callback([0.0, -0.1, -0.2]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0.5),
+ )
+ assert captured.value.selected_size == 1
+ assert captured.value.requested_size == 2
+
+ with pytest.raises(UndersizedBatchError) as all_zero:
+ select_local_penalized_batch(
+ pool,
+ 1,
+ _static_callback([-np.inf, -np.inf, -np.inf]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0.0),
+ )
+ assert all_zero.value.selected_size == 0
+ assert all_zero.value.remaining_candidate_count == 0
+ assert all_zero.value.hard_valid_candidate_count == 3
+
+
+def test_log_epsilon_never_relaxes_hard_distance():
+ pool = _pool([0.0, 0.4999999999995])
+ with pytest.raises(UndersizedBatchError):
+ select_local_penalized_batch(
+ pool,
+ 2,
+ _static_callback([0.0, -0.1]),
+ LocalPenalizationConfig(
+ radius=0.1,
+ min_batch_distance=0.5,
+ epsilon=0.5,
+ ),
+ )
+
+
+def test_dimension_weight_validation_and_effect():
+ pool = _pool([[0.0, 0.0], [0.2, 0.0], [0.0, 0.2]])
+ config = LocalPenalizationConfig(
+ radius=0.1,
+ min_batch_distance=0,
+ dimension_weights=np.array([4.0, 1.0]),
+ )
+ result = select_local_penalized_batch(
+ pool, 2, _static_callback([0.0, 0.0, 0.0]), config
+ )
+ assert result.selected_pool_indices.tolist() == [0, 1]
+ with pytest.raises(ValueError, match="shape"):
+ select_local_penalized_batch(
+ pool,
+ 1,
+ _static_callback([0.0, 0.0, 0.0]),
+ LocalPenalizationConfig(
+ radius=0.1,
+ min_batch_distance=0,
+ dimension_weights=np.ones(3),
+ ),
+ )
+ with pytest.raises(ValueError, match="strictly positive"):
+ LocalPenalizationConfig(
+ radius=0.1,
+ min_batch_distance=0,
+ dimension_weights=np.array([1.0, 0.0]),
+ )
+ with pytest.raises(ValueError, match="non-boolean"):
+ LocalPenalizationConfig(
+ radius=0.1,
+ min_batch_distance=0,
+ dimension_weights=np.array([True, True]),
+ )
+
+
+def test_duplicate_pool_and_invalid_scores_are_rejected():
+ duplicate_pool = CandidatePool(
+ grid_indices=np.array([[0], [0]]),
+ X_phys=np.array([[0.0], [0.0]]),
+ X_norm=np.array([[0.0], [0.0]]),
+ seed=1,
+ draws=2,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+ with pytest.raises(ValueError, match="duplicate"):
+ select_local_penalized_batch(
+ duplicate_pool,
+ 1,
+ _static_callback([0.0, 0.0]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0),
+ )
+
+ duplicate_coordinates = CandidatePool(
+ grid_indices=np.array([[0], [1]]),
+ X_phys=np.array([[0.0], [0.0]]),
+ X_norm=np.array([[0.0], [0.0]]),
+ seed=1,
+ draws=2,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+ with pytest.raises(ValueError, match="duplicate normalized"):
+ select_local_penalized_batch(
+ duplicate_coordinates,
+ 1,
+ _static_callback([0.0, 0.0]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0),
+ )
+
+ def bad_callback(remaining, selected):
+ del selected
+ return BaseScoreResult(np.full(remaining.size, np.nan))
+
+ with pytest.raises(ValueError, match="NaN"):
+ select_local_penalized_batch(
+ _pool([0.0, 1.0]),
+ 1,
+ bad_callback,
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0),
+ )
+
+
+def test_selector_rejects_out_of_range_references_and_nonfinite_physical_rows():
+ with pytest.raises(ValueError, match=r"within \[0, 1\]"):
+ select_local_penalized_batch(
+ _pool([0.0, 1.0]),
+ 1,
+ _static_callback([0.0, -1.0]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0),
+ observed_pending_norm=np.array([[2.0]]),
+ )
+ invalid = _pool([0.0, 1.0])
+ object.__setattr__(invalid, "X_phys", np.array([[np.nan], [1.0]]))
+ with pytest.raises(ValueError, match="physical rows"):
+ select_local_penalized_batch(
+ invalid,
+ 1,
+ _static_callback([0.0, -1.0]),
+ LocalPenalizationConfig(radius=0.1, min_batch_distance=0),
+ )
diff --git a/tests/test_boxplot_sweep.py b/tests/test_boxplot_sweep.py
new file mode 100644
index 0000000..e8cb6a1
--- /dev/null
+++ b/tests/test_boxplot_sweep.py
@@ -0,0 +1,139 @@
+"""The beta x radius boxplot sweep.
+
+The sweep itself is 108 full campaign runs, so nothing here runs one. What is
+pinned is the bookkeeping around them, where a silent error would be expensive and
+invisible: a sharding bug that drops or duplicates cells would produce a
+deliverable with quietly missing panels after six hours of compute.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pytest
+
+
+def _load():
+ path = Path("scripts") / "plot_boxplot_sweep.py"
+ spec = importlib.util.spec_from_file_location("_script_boxplot_sweep", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+sweep = _load()
+
+
+def test_the_grid_is_the_one_the_group_asked_for() -> None:
+ assert sweep.BETAS == (9.0, 25.0, 36.0, 49.0)
+ assert sweep.RADII == (0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45)
+ assert len(sweep.TRIALS) == 3
+ assert [name for name, _s, _seed in sweep.TRIALS] == ["real", "lhs_a", "lhs_b"]
+
+
+def test_the_cell_count_is_108() -> None:
+ cells = sweep.all_cells()
+ assert len(cells) == len(sweep.TRIALS) * len(sweep.BETAS) * len(sweep.RADII) == 108
+ assert len(set(cells)) == 108, "no duplicate cells"
+
+
+@pytest.mark.parametrize("num_shards", [1, 2, 5, 7, 12, 108])
+def test_sharding_covers_every_cell_exactly_once(num_shards: int) -> None:
+ """The property that makes a six-hour parallel run trustworthy.
+
+ ``cells[shard::num_shards]`` must partition the grid. A stride that dropped or
+ repeated cells would leave gaps the compose step draws as 'missing', or would
+ burn compute recomputing the same cell in two workers.
+ """
+ cells = sweep.all_cells()
+ seen: list[tuple[str, float, float]] = []
+ for shard in range(num_shards):
+ seen.extend(cells[shard::num_shards])
+ assert len(seen) == len(cells)
+ assert set(seen) == set(cells)
+ assert len(set(seen)) == len(seen), "a cell was assigned to two shards"
+
+
+def test_shards_are_balanced_within_one_cell() -> None:
+ """Wall clock is the slowest shard, so an unbalanced split wastes it."""
+ cells = sweep.all_cells()
+ for num_shards in (8, 12, 16):
+ sizes = [len(cells[shard::num_shards]) for shard in range(num_shards)]
+ assert max(sizes) - min(sizes) <= 1
+
+
+def test_cell_keys_are_unique_and_filesystem_safe() -> None:
+ keys = [sweep.cell_key(t, b, r) for t, b, r in sweep.all_cells()]
+ assert len(set(keys)) == len(keys) == 108
+ for key in keys:
+ assert "." not in key, "a dot would collide with the .npz suffix"
+ assert all(c.isalnum() or c in "_" for c in key)
+
+
+def test_cell_key_round_trips_the_parameters() -> None:
+ assert sweep.cell_key("real", 9.0, 0.05) == "real__beta_9__radius_0p05"
+ assert sweep.cell_key("lhs_b", 49.0, 0.45) == "lhs_b__beta_49__radius_0p45"
+ # 0.10 and 0.1 must not produce two different keys for one radius
+ assert sweep.cell_key("real", 25.0, 0.10) == sweep.cell_key("real", 25.0, 0.1)
+
+
+def test_only_the_starting_design_differs_between_trials() -> None:
+ """The comparison this sweep makes is only clean if nothing else moves.
+
+ Trial 1 reads the workbook; trials 2 and 3 draw a Latin hypercube at distinct
+ seeds. No trial carries its own acquisition seed -- that stays at the
+ campaign's, so the candidate pool is identical throughout.
+ """
+ sources = {name: (source, seed) for name, source, seed in sweep.TRIALS}
+ assert sources["real"][0] == "workbook"
+ assert sources["lhs_a"] == ("lhs", 101)
+ assert sources["lhs_b"] == ("lhs", 202)
+ assert sources["lhs_a"][1] != sources["lhs_b"][1], "trials must differ"
+
+
+def test_the_footer_states_both_hazards() -> None:
+ """The two hazards that do not depend on which campaign is loaded."""
+ assert "not a measurement" in sweep.FOOTER
+ assert "15 / 5 / 3" in sweep.FOOTER
+
+
+def test_the_no_signal_caveat_is_read_from_the_config_not_remembered() -> None:
+ """It used to be hard-coded as "uniformity ... permutation p = 0.82".
+
+ That is a fact about the FIRST campaign's uniformity score on the FIRST
+ campaign's films. On the v3 contract that objective is a different
+ construction and optoelectronic is dead as well, so the constant would have
+ put the wrong evidence under the right warning -- which is worse than no
+ caveat, because it looks checked.
+ """
+ from mobo_kit.campaign import load_campaign_config
+
+ active = load_campaign_config("configs/campaign_d2d_perovskite_test.yaml")
+ caveat = sweep.signal_caveat(active)
+ assert "uniformity" in caveat and "optoelectronic" in caveat
+ assert "d2d-objectives-v3-test" in caveat
+ assert "leave-one-out null" in caveat
+ # and it must not carry the previous campaign's evidence
+ assert "0.82" not in caveat
+
+ every_axis_learnable = {
+ "objectives": {
+ "contract_version": "synthetic",
+ "specs": [{"name": "a", "signal_status": "learnable"}],
+ }
+ }
+ assert sweep.signal_caveat(every_axis_learnable) == ""
+
+
+def test_a_single_ratified_cell_keeps_all_three_trials() -> None:
+ """Filtering the knobs must never drop a trial: the trials are what turn
+ three numbers per round into a distribution worth boxing."""
+ cells = sweep.all_cells([36.0], [0.35])
+ assert len(cells) == len(sweep.TRIALS)
+ assert {trial for trial, _b, _r in cells} == {t[0] for t in sweep.TRIALS}
+ assert {(b, r) for _t, b, r in cells} == {(36.0, 0.35)}
+ # and the unfiltered default is unchanged
+ assert len(sweep.all_cells()) == len(sweep.TRIALS) * len(sweep.BETAS) * len(sweep.RADII)
diff --git a/tests/test_campaign.py b/tests/test_campaign.py
new file mode 100644
index 0000000..9c83d66
--- /dev/null
+++ b/tests/test_campaign.py
@@ -0,0 +1,327 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+from mobo_kit.campaign import (
+ FIXED_SCALING_MODES,
+ assert_scaling_is_campaign_fixed,
+ write_worklist_csv,
+ BatchValidityError,
+ CampaignConfigError,
+ build_objective_transform,
+ expand_replicates,
+ load_campaign_config,
+ model_source_columns,
+ run_r0_lhs,
+ validate_batch,
+)
+from mobo_kit.design import build_design_from_config
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+def test_campaign_config_is_runnable(config: dict) -> None:
+ """The first campaign's config must no longer be a baseline-only stub.
+
+ It is `archived` since 2026-08-17 -- superseded by
+ campaign_d2d_perovskite_test.yaml on the second dataset -- and it stays
+ complete and loadable rather than being deleted, because every number in
+ GP_MODEL_DECISION.md is about this contract. Archived means "do not run new
+ rounds against it", not "let it rot".
+ """
+ assert config["campaign"]["status"] == "archived"
+ assert len(config["inputs"]) == 10
+ assert config["objectives"]["contract_version"] == "d2d-objectives-v2-nm-thickness"
+ assert len(config["objectives"]["specs"]) == 3
+ # this campaign declared no constraints, deliberately; the second one has three
+ assert config["constraints"] == []
+
+
+def test_thickness_trains_on_nanometres_not_the_score(config: dict) -> None:
+ """The whole point of the objective rework: source column != utility."""
+ sources = model_source_columns(config)
+ assert sources == (
+ "Uniformity score",
+ "Optoelectronic score",
+ "Thickness (avg)",
+ )
+ transform = build_objective_transform(config)
+ thickness = transform.specs[2]
+ assert thickness.transform == "gaussian_target"
+ assert thickness.target == pytest.approx(650.0)
+ # workbook writes exp(-((T-650)/250)^2); this convention carries the 1/2
+ assert thickness.sigma == pytest.approx(250.0 / np.sqrt(2.0))
+
+
+def test_transform_reproduces_the_workbook_thickness_score(config: dict) -> None:
+ """Thickness is a log-link objective: the GP emits log(nm), so the transform
+ exponentiates before applying the 650 nm Gaussian. Feeding it raw nm would
+ silently score exp(687) instead of 687."""
+ transform = build_objective_transform(config)
+ assert transform.specs[2].model_link == "log"
+ nm = np.array([687.0, 1303.0])
+ model_output = torch.tensor(
+ [[0.0, -8.0, np.log(nm[0])], [0.0, -8.0, np.log(nm[1])]], dtype=torch.double
+ )
+ got = transform(model_output)[:, 2].numpy()
+ expected = np.exp(-(((nm - 650.0) / 250.0) ** 2))
+ np.testing.assert_allclose(got, expected, atol=1e-12)
+
+
+def test_reference_point_is_declared_in_utility_space(config: dict) -> None:
+ """A raw-scale reference silently weighted optoelectronic 4x; utility space
+ puts every axis on a comparable scale."""
+ point = config["reference_point_utility"]
+ assert len(point) == 3
+ assert all(abs(float(v)) < 1.0 for v in point)
+
+
+def test_objectives_without_a_source_column_are_rejected() -> None:
+ bad = {
+ "objectives": {
+ "contract_version": "x",
+ "specs": [{"name": "a", "goal": "maximize", "transform": "identity"}],
+ }
+ }
+ with pytest.raises(CampaignConfigError, match="model_source_column"):
+ build_objective_transform(bad)
+
+
+def test_empty_objective_list_cannot_propose() -> None:
+ with pytest.raises(CampaignConfigError, match="specs"):
+ build_objective_transform(
+ {"objectives": {"contract_version": "x", "specs": []}}
+ )
+
+
+# --------------------------------------------------------------------------- #
+# validity gate
+# --------------------------------------------------------------------------- #
+
+
+def _valid_batch(config: dict) -> pd.DataFrame:
+ design = build_design_from_config(dict(config))
+ rows = [[float(design.var_array[j][i * 2]) for j in range(10)] for i in range(3)]
+ return pd.DataFrame(rows, columns=list(design.names))
+
+
+def test_validate_batch_accepts_a_clean_batch(config: dict) -> None:
+ design = build_design_from_config(dict(config))
+ report = validate_batch(_valid_batch(config), design, expected_count=3)
+ assert report["unique"] and report["on_grid"] and report["in_bounds"]
+ assert report["actual_count"] == 3
+
+
+@pytest.mark.parametrize(
+ "mutate, match",
+ [
+ (lambda d: d.iloc[:2], "Expected exactly 3"),
+ (lambda d: pd.concat([d.iloc[:2], d.iloc[[0]]]), "unique"),
+ (lambda d: d.assign(anti_time=12.0), "grid"),
+ (lambda d: d.assign(speed_1=99999.0), "grid"),
+ (lambda d: d.assign(speed_1=float("nan")), "non-finite"),
+ ],
+)
+def test_validate_batch_refuses_real_defects(config: dict, mutate, match) -> None:
+ design = build_design_from_config(dict(config))
+ with pytest.raises(BatchValidityError, match=match):
+ validate_batch(mutate(_valid_batch(config)), design, expected_count=3)
+
+
+def test_validate_batch_enforces_minimum_spacing(config: dict) -> None:
+ design = build_design_from_config(dict(config))
+ batch = _valid_batch(config)
+ with pytest.raises(BatchValidityError, match="pairwise distance"):
+ validate_batch(batch, design, expected_count=3, min_pairwise_distance=10.0)
+
+
+def test_validity_report_carries_no_approval_flags(config: dict) -> None:
+ """The debug/production tiers are gone. Approval is a human decision recorded
+ outside the code, not something a validity check can compute."""
+ design = build_design_from_config(dict(config))
+ report = validate_batch(_valid_batch(config), design, expected_count=3)
+ for banned in (
+ "debug_only",
+ "approved_for_experiment",
+ "approved_for_production",
+ "experimental_approval_false",
+ "production_approval_false",
+ ):
+ assert banned not in report
+
+
+# --------------------------------------------------------------------------- #
+# replicates
+# --------------------------------------------------------------------------- #
+
+
+def test_expand_replicates_groups_three_films_per_condition(config: dict) -> None:
+ batch = _valid_batch(config)
+ films = expand_replicates(batch, replicates=3, round_name="R1")
+ assert len(films) == 9
+ assert films["replicate_group"].nunique() == 3
+ assert set(films["replicate_index"]) == {1, 2, 3}
+ assert set(films["round"]) == {"R1"}
+ for _, group in films.groupby("replicate_group"):
+ inputs = group[list(batch.columns)].drop_duplicates()
+ assert len(inputs) == 1, "replicates must share identical inputs"
+
+
+def test_expand_replicates_rejects_zero(config: dict) -> None:
+ with pytest.raises(ValueError, match="at least 1"):
+ expand_replicates(_valid_batch(config), replicates=0, round_name="R1")
+
+
+# --------------------------------------------------------------------------- #
+# R0
+# --------------------------------------------------------------------------- #
+
+
+def test_run_r0_lhs_produces_a_valid_on_grid_worklist(config: dict) -> None:
+ result = run_r0_lhs(config, n=8, seed=7)
+ assert result.round_name == "R0"
+ assert result.n_conditions == 8
+ assert list(result.conditions.columns) == [i["name"] for i in config["inputs"]]
+ assert result.diagnostics["validity"]["on_grid"]
+ assert (
+ len(result.replicates) == 8 * config["rounds"]["r1"]["replicates_per_condition"]
+ )
+
+
+def test_run_r0_lhs_is_deterministic_for_a_seed(config: dict) -> None:
+ a = run_r0_lhs(config, n=6, seed=11).conditions
+ b = run_r0_lhs(config, n=6, seed=11).conditions
+ pd.testing.assert_frame_equal(a, b)
+
+
+# --------------------------------------------------------------------------- #
+# encoding
+# --------------------------------------------------------------------------- #
+
+# Non-ASCII that breaks under a locale default codec such as GBK or cp1252.
+NON_ASCII = "sigma \u2014 \u00b5m \u00b1 5% \u2013 caf\u00e9 \u4e2d\u6587"
+
+
+def test_config_round_trips_non_ascii(tmp_path) -> None:
+ """Loaders must force UTF-8.
+
+ The default codec is locale dependent -- on a Chinese-locale Windows install
+ it is GBK -- so a bare open() fails on the first non-ASCII byte, and it fails
+ on a different machine from the one the file was written on.
+ """
+ import yaml
+
+ path = tmp_path / "cfg.yaml"
+ payload = {
+ "campaign": {"name": NON_ASCII},
+ "objectives": {
+ "contract_version": NON_ASCII,
+ "specs": [
+ {
+ "name": "a",
+ "goal": "maximize",
+ "transform": "identity",
+ "model_source_column": NON_ASCII,
+ }
+ ],
+ },
+ }
+ path.write_text(yaml.safe_dump(payload, allow_unicode=True), encoding="utf-8")
+
+ loaded = load_campaign_config(path)
+ assert loaded["campaign"]["name"] == NON_ASCII
+ assert model_source_columns(loaded) == (NON_ASCII,)
+ assert build_objective_transform(loaded).version == NON_ASCII
+
+
+def test_workbook_reader_round_trips_non_ascii(tmp_path) -> None:
+ """A non-ASCII column header or cell must survive the workbook boundary."""
+ from openpyxl import Workbook, load_workbook
+
+ path = tmp_path / "wb.xlsx"
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "Sheet1"
+ sheet.append(["Sample number", NON_ASCII])
+ sheet.append([1, NON_ASCII])
+ book.save(path)
+
+ reread = load_workbook(path, data_only=True)["Sheet1"]
+ rows = list(reread.iter_rows(values_only=True))
+ assert rows[0][1] == NON_ASCII
+ assert rows[1][1] == NON_ASCII
+
+
+def test_csv_round_trips_non_ascii(tmp_path) -> None:
+ """pandas defaults to UTF-8 for both directions; pin it with a test so a
+ future explicit encoding= cannot silently regress it."""
+ path = tmp_path / "out.csv"
+ frame = pd.DataFrame({"label": [NON_ASCII], "value": [1.0]})
+ frame.to_csv(path, index=False)
+ pd.testing.assert_frame_equal(pd.read_csv(path), frame)
+
+
+# --------------------------------------------------------------------------- #
+# campaign-fixed scaling
+# --------------------------------------------------------------------------- #
+
+
+def test_campaign_declares_fixed_scaling(config: dict) -> None:
+ assert config["objectives"]["scaling_mode"] in FIXED_SCALING_MODES
+ assert_scaling_is_campaign_fixed(config)
+
+
+def test_data_derived_scaling_is_refused(config: dict) -> None:
+ """If the scale tracks the data, hypervolume stops being comparable between
+ rounds. The temptation arrives the moment R1 measurements land."""
+ import copy
+
+ bad = copy.deepcopy(dict(config))
+ bad["objectives"]["scaling_mode"] = "observed_min_max"
+ with pytest.raises(CampaignConfigError, match="incomparable"):
+ assert_scaling_is_campaign_fixed(bad)
+ with pytest.raises(CampaignConfigError, match="incomparable"):
+ build_objective_transform(bad)
+
+
+def test_affine_objective_without_declared_anchors_is_refused(config: dict) -> None:
+ """Two layers refuse this: ObjectiveSpec at construction, and
+ assert_scaling_is_campaign_fixed for anything that reaches it. Whichever
+ fires first, an affine objective can never end up with implicit anchors."""
+ import copy
+
+ bad = copy.deepcopy(dict(config))
+ del bad["objectives"]["specs"][0]["upper_anchor"]
+ with pytest.raises(ValueError, match="upper_anchor"):
+ build_objective_transform(bad)
+
+
+def test_inverted_anchors_are_refused(config: dict) -> None:
+ import copy
+
+ bad = copy.deepcopy(dict(config))
+ spec = bad["objectives"]["specs"][0]
+ spec["lower_anchor"], spec["upper_anchor"] = 1.0, 0.0
+ with pytest.raises(ValueError, match="anchor"):
+ build_objective_transform(bad)
+
+
+def test_worklist_csv_is_excel_safe(tmp_path) -> None:
+ """Excel does not detect plain UTF-8 and falls back to the system ANSI
+ codepage, mangling non-ASCII cells on the reader's machine rather than the
+ writer's. utf-8-sig writes the BOM; other readers strip it transparently."""
+ path = tmp_path / "worklist.csv"
+ frame = pd.DataFrame({"label": [NON_ASCII], "speed_1": [1000.0]})
+ write_worklist_csv(frame, path)
+
+ assert path.read_bytes().startswith(b"\xef\xbb\xbf"), "missing UTF-8 BOM"
+ pd.testing.assert_frame_equal(pd.read_csv(path), frame)
+ pd.testing.assert_frame_equal(pd.read_csv(path, encoding="utf-8-sig"), frame)
diff --git a/tests/test_candidate_diagnostics.py b/tests/test_candidate_diagnostics.py
new file mode 100644
index 0000000..4b184ec
--- /dev/null
+++ b/tests/test_candidate_diagnostics.py
@@ -0,0 +1,132 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+from PIL import Image
+
+from mobo_kit.candidate_diagnostics import (
+ boundary_flags,
+ grid_membership_mask,
+ pairwise_normalized_distances,
+ plot_candidate_pca,
+ plot_distance_heatmap,
+ plot_parallel_coordinates,
+ plot_selection_scores,
+ summarize_candidate_batch,
+)
+from mobo_kit.design import InputSpec, build_design
+
+
+def _design():
+ return build_design(
+ [
+ InputSpec("a", 0.0, 2.0, 1.0),
+ InputSpec("b", 10.0, 20.0, 5.0),
+ ]
+ )
+
+
+def test_pairwise_normalized_distances_and_summary():
+ selected = np.array([[0.0, 0.0], [0.3, 0.4], [1.0, 0.0]])
+ matrix = pairwise_normalized_distances(selected)
+ assert matrix.shape == (3, 3)
+ assert matrix[0, 1] == pytest.approx(0.5)
+ assert np.allclose(matrix, matrix.T)
+ assert np.allclose(np.diag(matrix), 0.0)
+
+ summary = summarize_candidate_batch(
+ selected,
+ observed_pending_norm=np.array([[0.0, 0.1]]),
+ X_phys=np.array([[0.0, 10.0], [1.0, 15.0], [2.0, 10.0]]),
+ design=_design(),
+ metadata={"method": "TEST_ONLY"},
+ )
+ triangle = matrix[np.triu_indices(3, k=1)]
+ assert summary.minimum_within_batch_distance == pytest.approx(triangle.min())
+ assert summary.mean_within_batch_distance == pytest.approx(triangle.mean())
+ assert summary.maximum_within_batch_distance == pytest.approx(triangle.max())
+ assert summary.nearest_observed_pending_distance[0] == pytest.approx(0.1)
+ assert summary.duplicate_row_pairs == ()
+ assert summary.grid_valid_rows.tolist() == [True, True, True]
+ assert summary.metadata == {"method": "TEST_ONLY"}
+
+
+def test_duplicate_grid_and_boundary_checks():
+ selected = np.array([[0.0, 0.0], [0.0, 0.0], [0.5, 1.0]])
+ summary = summarize_candidate_batch(selected)
+ assert summary.duplicate_row_pairs == ((0, 1),)
+ assert boundary_flags(selected).tolist() == [
+ [True, True],
+ [True, True],
+ [False, True],
+ ]
+ valid = grid_membership_mask(
+ np.array([[0.0, 10.0], [1.5, 15.0], [2.0, 19.0]]), _design()
+ )
+ assert valid.tolist() == [True, False, False]
+
+
+def test_weight_validation_and_weighted_distance():
+ X = np.array([[0.0, 0.0], [1.0, 1.0]])
+ matrix = pairwise_normalized_distances(X, dimension_weights=np.array([1.0, 4.0]))
+ assert matrix[0, 1] == pytest.approx(np.sqrt(5.0))
+ with pytest.raises(ValueError, match="strictly positive"):
+ pairwise_normalized_distances(X, dimension_weights=np.array([1.0, 0.0]))
+ with pytest.raises(ValueError, match="shape"):
+ pairwise_normalized_distances(X, dimension_weights=np.ones(3))
+
+
+def test_singleton_summary_has_explicit_empty_within_batch_statistics():
+ summary = summarize_candidate_batch(np.array([[0.2, 0.8]]))
+ assert summary.minimum_within_batch_distance is None
+ assert summary.mean_within_batch_distance is None
+ assert summary.maximum_within_batch_distance is None
+ assert np.isnan(summary.nearest_observed_pending_distance[0])
+
+
+def test_plotting_helpers_write_headless_pngs(tmp_path: Path):
+ observed = np.array([[0.0, 0.0], [0.5, 0.3], [0.9, 1.0]])
+ pool = np.linspace(0.0, 1.0, 40).reshape(20, 2)
+ selected = np.array([[0.1, 0.8], [0.8, 0.2], [0.5, 0.5]])
+ watermark = "DEBUG ONLY - TEST PLOT"
+ paths = [
+ plot_candidate_pca(
+ observed,
+ selected,
+ tmp_path / "pca.png",
+ pool_norm=pool,
+ watermark=watermark,
+ ),
+ plot_parallel_coordinates(
+ selected,
+ ["a", "b"],
+ tmp_path / "parallel.png",
+ watermark=watermark,
+ ),
+ plot_distance_heatmap(selected, tmp_path / "distance.png", watermark=watermark),
+ plot_selection_scores(
+ [1, 2, 3],
+ [-1.0, -1.2, -1.4],
+ [-1.0, -1.8, -2.1],
+ tmp_path / "scores.png",
+ watermark=watermark,
+ ),
+ ]
+ for path in paths:
+ assert path.exists()
+ assert path.stat().st_size > 1000
+ with Image.open(path) as image:
+ assert image.info["Description"] == watermark
+
+
+def test_diagnostics_reject_shape_and_partial_grid_context():
+ with pytest.raises(ValueError, match="shape"):
+ pairwise_normalized_distances(np.ones(3))
+ with pytest.raises(ValueError, match="supplied together"):
+ summarize_candidate_batch(np.ones((2, 2)), X_phys=np.ones((2, 2)), design=None)
+ with pytest.raises(ValueError, match="same row count"):
+ summarize_candidate_batch(
+ np.ones((2, 2)), X_phys=np.ones((1, 2)), design=_design()
+ )
+ with pytest.raises(ValueError, match="duplicate_atol"):
+ summarize_candidate_batch(np.ones((2, 2)), duplicate_atol=-1)
diff --git a/tests/test_candidate_pool.py b/tests/test_candidate_pool.py
new file mode 100644
index 0000000..18ba27e
--- /dev/null
+++ b/tests/test_candidate_pool.py
@@ -0,0 +1,150 @@
+import numpy as np
+import pytest
+
+from mobo_kit.candidate_pool import (
+ CandidatePoolSamplingError,
+ physical_rows_to_grid_indices,
+ sample_discrete_candidate_pool,
+)
+from mobo_kit.design import InputSpec, build_design
+
+
+def _design():
+ return build_design(
+ [
+ InputSpec("a", 0.0, 4.0, 1.0),
+ InputSpec("b", 10.0, 20.0, 5.0),
+ InputSpec("c", -1.0, 1.0, 1.0),
+ ]
+ )
+
+
+def test_exact_size_seeded_order_grid_membership_and_normalization():
+ design = _design()
+ first = sample_discrete_candidate_pool(design, 20, seed=12)
+ repeat = sample_discrete_candidate_pool(design, 20, seed=12)
+ different = sample_discrete_candidate_pool(design, 20, seed=13)
+ assert first.size == 20
+ assert np.array_equal(first.grid_indices, repeat.grid_indices)
+ assert np.array_equal(first.X_phys, repeat.X_phys)
+ assert np.array_equal(first.X_norm, repeat.X_norm)
+ assert not np.array_equal(first.grid_indices, different.grid_indices)
+ assert np.unique(first.grid_indices, axis=0).shape[0] == 20
+ assert np.all(first.grid_indices >= 0)
+ assert np.all(first.grid_indices < np.array([5, 3, 3]))
+ assert np.all((first.X_norm >= 0) & (first.X_norm <= 1))
+ assert np.array_equal(
+ physical_rows_to_grid_indices(first.X_phys, design), first.grid_indices
+ )
+
+
+def test_observed_pending_and_explicit_avoid_are_excluded():
+ design = _design()
+ exclusions = np.array([[0.0, 10.0, -1.0], [1.0, 15.0, 0.0], [2.0, 20.0, 1.0]])
+ pool = sample_discrete_candidate_pool(
+ design,
+ 25,
+ seed=4,
+ observed_phys=exclusions[:1],
+ pending_phys=exclusions[1:2],
+ avoid_phys=exclusions[2:],
+ )
+ excluded_indices = physical_rows_to_grid_indices(exclusions, design)
+ pool_set = {tuple(row) for row in pool.grid_indices}
+ assert not any(tuple(row) in pool_set for row in excluded_indices)
+
+
+def test_constraints_run_in_physical_space_and_are_fail_closed():
+ design = _design()
+ calls = []
+
+ def require_even_first_input(X_phys, supplied_design):
+ calls.append(X_phys.copy())
+ assert supplied_design is design
+ return (X_phys[:, 0] % 2) == 0
+
+ pool = sample_discrete_candidate_pool(
+ design,
+ 15,
+ seed=3,
+ row_constraints=[require_even_first_input],
+ max_draws=500,
+ )
+ assert calls
+ assert np.all(pool.X_phys[:, 0] % 2 == 0)
+ assert pool.rejected_constraint > 0
+
+
+def test_impossible_request_raises_structured_error():
+ design = build_design([InputSpec("x", 0, 1, 1)])
+ with pytest.raises(CandidatePoolSamplingError) as captured:
+ sample_discrete_candidate_pool(
+ design, 2, seed=1, observed_phys=np.array([[0.0]])
+ )
+ error = captured.value
+ assert error.requested == 2
+ assert error.accepted == 0
+ assert "exceeds" in error.reason
+
+
+def test_max_draws_failure_reports_rejections():
+ design = build_design([InputSpec("x", 0, 4, 1)])
+
+ def reject_all(X_phys, supplied_design):
+ del supplied_design
+ return np.zeros(X_phys.shape[0], dtype=bool)
+
+ with pytest.raises(CandidatePoolSamplingError) as captured:
+ sample_discrete_candidate_pool(
+ design,
+ 1,
+ seed=2,
+ row_constraints=[reject_all],
+ max_draws=5,
+ )
+ assert captured.value.draws == 5
+ assert captured.value.rejected_constraint > 0
+
+
+def test_draw_statistics_on_known_two_point_grid():
+ design = build_design([InputSpec("x", 0, 1, 1)])
+ pool = sample_discrete_candidate_pool(design, 2, seed=0, max_draws=10)
+ # NumPy Generator seed 0 draws 1, 1, 1, 0 for the first four integers.
+ assert pool.grid_indices[:, 0].tolist() == [1, 0]
+ assert pool.draws == 4
+ assert pool.rejected_duplicate == 2
+ assert pool.rejected_avoid == 0
+ assert pool.rejected_constraint == 0
+
+ excluded = sample_discrete_candidate_pool(
+ design,
+ 1,
+ seed=0,
+ observed_phys=np.array([[1.0]]),
+ max_draws=10,
+ )
+ assert excluded.grid_indices[:, 0].tolist() == [0]
+ assert excluded.draws == 4
+ assert excluded.rejected_duplicate == 2
+ assert excluded.rejected_avoid == 1
+ assert excluded.rejected_constraint == 0
+
+
+def test_off_grid_exclusions_fail_instead_of_using_fuzzy_matching():
+ with pytest.raises(ValueError, match="off-grid"):
+ sample_discrete_candidate_pool(
+ _design(), 2, seed=1, avoid_phys=np.array([[0.25, 10.0, 0.0]])
+ )
+ with pytest.raises(ValueError, match="off-grid"):
+ physical_rows_to_grid_indices(np.array([[1e-9, 10.0, 0.0]]), _design())
+
+
+def test_sampler_never_calls_cartesian_product_allocators(monkeypatch):
+ def forbidden(*args, **kwargs):
+ del args, kwargs
+ raise AssertionError("full Cartesian allocation was attempted")
+
+ monkeypatch.setattr(np, "meshgrid", forbidden)
+ monkeypatch.setattr(np, "indices", forbidden)
+ pool = sample_discrete_candidate_pool(_design(), 10, seed=9)
+ assert pool.size == 10
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
new file mode 100644
index 0000000..5f3c969
--- /dev/null
+++ b/tests/test_constraints.py
@@ -0,0 +1,246 @@
+"""The campaign's physical-space constraints.
+
+These are the first real constraints this project has carried -- the first
+campaign declared an empty list deliberately -- so the boundary cases are pinned
+here rather than left to the round tests, where a constraint bug would show up as
+a batch that merely looks unusual.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from mobo_kit.constraints import (
+ NamedConstraint,
+ apply_row_constraints,
+ constraint_violations,
+ constraints_from_config,
+)
+from mobo_kit.design import build_design_from_config
+
+CONFIG_INPUTS = [
+ {"name": "speed_2", "start": 0, "stop": 5000, "step": 500},
+ {"name": "time_1", "start": 5, "stop": 50, "step": 5},
+ {"name": "time_2", "start": 0, "stop": 60, "step": 5},
+ {"name": "anti_time", "start": 9, "stop": 25, "step": 1},
+]
+
+
+def _design():
+ return build_design_from_config({"inputs": CONFIG_INPUTS})
+
+
+def _row(speed_2=1000.0, time_1=30.0, time_2=20.0, anti_time=12.0):
+ return [speed_2, time_1, time_2, anti_time]
+
+
+def _mask(config_entries, rows):
+ design = _design()
+ constraints = constraints_from_config({"constraints": config_entries}, design)
+ return apply_row_constraints(np.asarray(rows, dtype=float), design, constraints)
+
+
+# --------------------------------------------------------------------------- #
+# zero_coupled
+# --------------------------------------------------------------------------- #
+
+ZERO_COUPLED = [{"zero_coupled": ["speed_2", "time_2"]}]
+
+
+def test_both_zero_is_valid_because_a_one_step_film_is_a_real_recipe() -> None:
+ """Sample 2 of the v3 workbook runs no second stage at all. A plain lower
+ bound on either column would delete that recipe, which is why the rule is an
+ iff and not two bounds."""
+ assert _mask(ZERO_COUPLED, [_row(speed_2=0.0, time_2=0.0)]).tolist() == [True]
+
+
+def test_both_nonzero_is_valid() -> None:
+ assert _mask(ZERO_COUPLED, [_row(speed_2=3500.0, time_2=30.0)]).tolist() == [True]
+
+
+@pytest.mark.parametrize(
+ "speed_2, time_2, what",
+ [
+ (0.0, 30.0, "a second stage that spins at 0 rpm for 30 s"),
+ (3500.0, 0.0, "a second stage that spins at 3500 rpm for 0 s"),
+ ],
+)
+def test_exactly_one_zero_is_invalid_in_both_orientations(
+ speed_2, time_2, what
+) -> None:
+ """Both orientations, because a constraint written as a single implication
+ catches only one of them and the other stays proposable."""
+ assert _mask(ZERO_COUPLED, [_row(speed_2=speed_2, time_2=time_2)]).tolist() == [
+ False
+ ], what
+
+
+# --------------------------------------------------------------------------- #
+# sum_upper_strict
+# --------------------------------------------------------------------------- #
+
+SUM_STRICT = [{"sum_upper_strict": {"lhs": "anti_time", "rhs": ["time_1", "time_2"]}}]
+
+
+def test_anti_time_below_the_total_spin_is_valid() -> None:
+ rows = [_row(time_1=30.0, time_2=20.0, anti_time=49.0)]
+ assert _mask(SUM_STRICT, rows).tolist() == [True]
+
+
+def test_equality_is_a_violation_because_the_bound_is_strict() -> None:
+ """The antisolvent has to land while the substrate is still spinning, so
+ dropping it exactly at the end is already too late. Strictness is the whole
+ point of this constraint type existing separately from a bounds check."""
+ rows = [_row(time_1=30.0, time_2=20.0, anti_time=50.0)]
+ assert _mask(SUM_STRICT, rows).tolist() == [False]
+
+
+def test_anti_time_above_the_total_spin_is_a_violation() -> None:
+ rows = [_row(time_1=10.0, time_2=10.0, anti_time=25.0)]
+ assert _mask(SUM_STRICT, rows).tolist() == [False]
+
+
+def test_a_one_step_film_still_has_to_satisfy_the_sum() -> None:
+ """time_2 = 0 does not exempt the row; the sum is just time_1."""
+ valid = _row(speed_2=0.0, time_2=0.0, time_1=30.0, anti_time=25.0)
+ invalid = _row(speed_2=0.0, time_2=0.0, time_1=20.0, anti_time=25.0)
+ assert _mask(SUM_STRICT, [valid, invalid]).tolist() == [True, False]
+
+
+# --------------------------------------------------------------------------- #
+# nonzero_minimum
+# --------------------------------------------------------------------------- #
+
+NONZERO_MIN = [{"nonzero_minimum": {"column": "time_2", "minimum": 10}}]
+
+
+@pytest.mark.parametrize(
+ "time_2, expected",
+ [(0.0, True), (5.0, False), (10.0, True), (55.0, True)],
+)
+def test_the_declared_hole_in_the_grid(time_2, expected) -> None:
+ """0 is allowed and 10 upwards is allowed; only the gap between them is not.
+
+ The grid is arithmetic, so reaching 0 with step 5 also reaches 5. This is what
+ keeps 5 out without widening the design space in silence.
+ """
+ assert _mask(NONZERO_MIN, [_row(time_2=time_2)]).tolist() == [expected]
+
+
+# --------------------------------------------------------------------------- #
+# wiring
+# --------------------------------------------------------------------------- #
+
+
+def test_constraints_are_anded_together() -> None:
+ entries = ZERO_COUPLED + SUM_STRICT + NONZERO_MIN
+ rows = [
+ _row(speed_2=1000.0, time_2=20.0, time_1=30.0, anti_time=12.0), # all pass
+ _row(speed_2=1000.0, time_2=5.0, time_1=30.0, anti_time=12.0), # minimum
+ _row(speed_2=0.0, time_2=20.0, time_1=30.0, anti_time=12.0), # coupling
+ _row(speed_2=1000.0, time_2=20.0, time_1=30.0, anti_time=50.0), # sum
+ ]
+ assert _mask(entries, rows).tolist() == [True, False, False, False]
+
+
+def test_violations_are_reported_by_name_not_by_index() -> None:
+ """A reviewer told "condition 3 is invalid" cannot act on it. The rule can."""
+ design = _design()
+ entries = [
+ {"zero_coupled": ["speed_2", "time_2"], "name": "second_stage_all_or_nothing"},
+ {"sum_upper_strict": {"lhs": "anti_time", "rhs": ["time_1", "time_2"]}},
+ ]
+ constraints = constraints_from_config({"constraints": entries}, design)
+ rows = np.asarray(
+ [
+ _row(speed_2=1000.0, time_2=20.0, time_1=30.0, anti_time=12.0),
+ _row(speed_2=0.0, time_2=20.0, time_1=30.0, anti_time=99.0),
+ ],
+ dtype=float,
+ )
+ assert constraint_violations(rows, design, constraints) == [
+ [],
+ ["second_stage_all_or_nothing", "sum_upper_strict"],
+ ]
+
+
+def test_a_named_constraint_is_still_an_ordinary_row_constraint() -> None:
+ """The candidate pool takes plain callables and must stay unaware of naming."""
+ design = _design()
+ constraint = constraints_from_config({"constraints": ZERO_COUPLED}, design)[0]
+ assert isinstance(constraint, NamedConstraint)
+ rows = np.asarray([_row(speed_2=0.0, time_2=0.0)], dtype=float)
+ assert constraint(rows, design).tolist() == [True]
+
+
+def test_the_description_states_the_rule() -> None:
+ design = _design()
+ entries = ZERO_COUPLED + SUM_STRICT + NONZERO_MIN
+ descriptions = [
+ item.description for item in constraints_from_config({"constraints": entries}, design)
+ ]
+ assert descriptions == [
+ "speed_2 and time_2 are all zero or all nonzero",
+ "anti_time < time_1 + time_2",
+ "time_2 is 0 or at least 10",
+ ]
+
+
+def test_no_constraints_means_every_row_passes() -> None:
+ """Constraints must be inert when unconfigured -- DTLZ2 declares none."""
+ design = _design()
+ assert constraints_from_config({}, design) == []
+ assert constraints_from_config({"constraints": []}, design) == []
+ rows = np.asarray([_row(speed_2=0.0, time_2=30.0, anti_time=99.0)], dtype=float)
+ assert apply_row_constraints(rows, design, []).tolist() == [True]
+ assert constraint_violations(rows, design, None) == [[]]
+
+
+# --------------------------------------------------------------------------- #
+# configuration errors fail loudly
+# --------------------------------------------------------------------------- #
+
+
+def test_an_unknown_column_names_itself() -> None:
+ design = _design()
+ with pytest.raises(KeyError, match="speed_9"):
+ constraints_from_config(
+ {"constraints": [{"zero_coupled": ["speed_9", "time_2"]}]}, design
+ )
+
+
+def test_an_unknown_constraint_type_is_refused_rather_than_ignored() -> None:
+ design = _design()
+ with pytest.raises(KeyError, match="no supported type"):
+ constraints_from_config({"constraints": [{"nonsense": [1, 2]}]}, design)
+
+
+def test_zero_coupled_needs_two_columns_to_couple() -> None:
+ design = _design()
+ with pytest.raises(KeyError, match="at least two"):
+ constraints_from_config({"constraints": [{"zero_coupled": ["time_2"]}]}, design)
+
+
+@pytest.mark.parametrize(
+ "entry, match",
+ [
+ ({"sum_upper_strict": {"rhs": ["time_1"]}}, "lhs"),
+ ({"sum_upper_strict": {"lhs": "anti_time"}}, "rhs"),
+ ({"nonzero_minimum": {"minimum": 10}}, "column"),
+ ({"nonzero_minimum": {"column": "time_2"}}, "minimum"),
+ ],
+)
+def test_a_half_specified_constraint_is_an_error(entry, match) -> None:
+ """Silently skipping a malformed entry would leave everyone believing a rule
+ is enforced while nothing enforces it."""
+ with pytest.raises(KeyError, match=match):
+ constraints_from_config({"constraints": [entry]}, _design())
+
+
+def test_a_nonpositive_minimum_is_refused() -> None:
+ with pytest.raises(ValueError, match="finite and positive"):
+ constraints_from_config(
+ {"constraints": [{"nonzero_minimum": {"column": "time_2", "minimum": 0}}]},
+ _design(),
+ )
diff --git a/tests/test_csv_parser.py b/tests/test_csv_parser.py
new file mode 100644
index 0000000..c379510
--- /dev/null
+++ b/tests/test_csv_parser.py
@@ -0,0 +1,317 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+
+import pandas as pd
+import pytest
+import yaml
+
+from mobo_kit.utils import (
+ ParsedCampaignCSV,
+ csv_to_config,
+ load_csv,
+ parse_campaign_csv,
+ split_XY,
+)
+
+
+REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
+EXAMPLE_CAMPAIGN_CSV = REPOSITORY_ROOT / "data" / "processed" / "configCSV_example.csv"
+
+
+def _campaign_text(*, blank_separator: bool = True) -> str:
+ separator_header = "," if blank_separator else ""
+ separator_cell = "," if blank_separator else ""
+ blank_row = ",,,,,\n" if blank_separator else ""
+ return (
+ f",x_speed,x_time{separator_header},yield,stability\n"
+ f"units,rpm,s{separator_cell},%,h\n"
+ f"start,1000,5{separator_cell},,\n"
+ f"stop,2000,15{separator_cell},,\n"
+ f"step,500,5{separator_cell},,\n"
+ f"{blank_row}"
+ f",1000,5{separator_cell},1.5,10\n"
+ f",1500,10{separator_cell},2.5,11\n"
+ )
+
+
+def _write_text(path: Path, text: str, encoding: str = "utf-8") -> Path:
+ path.write_bytes(text.encode(encoding))
+ return path
+
+
+def test_parser_detects_metadata_and_preserves_first_experiment(tmp_path: Path) -> None:
+ path = _write_text(tmp_path / "campaign.csv", _campaign_text())
+
+ parsed = parse_campaign_csv(path)
+
+ assert isinstance(parsed, ParsedCampaignCSV)
+ assert parsed.input_columns == ["x_speed", "x_time"]
+ assert parsed.objective_columns == ["yield", "stability"]
+ assert parsed.metadata_row_count == 5
+ assert parsed.duplicate_headers == {}
+ assert parsed.config["constraints"] == []
+ assert parsed.data.iloc[0].to_dict() == {
+ "x_speed": "1000",
+ "x_time": "5",
+ "yield": "1.5",
+ "stability": "10",
+ }
+ assert len(parsed.data) == 2
+
+
+def test_repository_example_retains_its_first_experiment() -> None:
+ parsed = parse_campaign_csv(EXAMPLE_CAMPAIGN_CSV)
+
+ assert parsed.metadata_row_count == 5
+ assert parsed.input_columns == [
+ "speed_inorg",
+ "speed_org",
+ "inkfl_inorg",
+ "inkfl_org",
+ "conc_inorg",
+ "conc_org",
+ "temperature_c",
+ "absolute_humidity",
+ ]
+ assert parsed.objective_columns == ["PCE", "Stability", "Repeatability"]
+ assert len(parsed.data) == 12
+ assert parsed.data.iloc[0]["speed_inorg"] == "0.58"
+
+
+def test_parser_accepts_no_blank_separator_row_or_column(tmp_path: Path) -> None:
+ path = _write_text(
+ tmp_path / "no_separator.csv", _campaign_text(blank_separator=False)
+ )
+
+ parsed = parse_campaign_csv(path)
+
+ assert parsed.metadata_row_count == 4
+ assert parsed.objective_columns == ["yield", "stability"]
+ assert parsed.data.iloc[0]["x_speed"] == "1000"
+
+
+def test_load_csv_returns_only_experimental_rows(tmp_path: Path) -> None:
+ path = _write_text(tmp_path / "campaign.csv", _campaign_text())
+
+ data = load_csv(path)
+
+ assert list(data.columns) == ["x_speed", "x_time", "yield", "stability"]
+ assert len(data) == 2
+ assert "units" not in data.astype(str).to_numpy()
+
+
+def test_utf8_bom_is_supported(tmp_path: Path) -> None:
+ path = tmp_path / "bom.csv"
+ path.write_bytes(b"\xef\xbb\xbf" + _campaign_text().encode("utf-8"))
+
+ parsed = parse_campaign_csv(path)
+
+ assert parsed.encoding == "utf-8-sig"
+ assert parsed.input_columns[0] == "x_speed"
+
+
+def test_cp1252_is_supported(tmp_path: Path) -> None:
+ text = _campaign_text().replace("rpm", "\N{DEGREE SIGN}C")
+ path = _write_text(tmp_path / "cp1252.csv", text, encoding="cp1252")
+
+ parsed = parse_campaign_csv(path)
+
+ assert parsed.encoding == "cp1252"
+ assert parsed.config["inputs"][0]["unit"] == "\N{DEGREE SIGN}C"
+
+
+def test_latin1_is_used_when_cp1252_cannot_decode(tmp_path: Path) -> None:
+ payload = _campaign_text().replace("rpm", "UNIT_MARKER").encode("ascii")
+ path = tmp_path / "latin1.csv"
+ path.write_bytes(payload.replace(b"UNIT_MARKER", b"\x81"))
+
+ parsed = parse_campaign_csv(path)
+
+ assert parsed.encoding == "latin-1"
+ assert parsed.config["inputs"][0]["unit"] == "\x81"
+
+
+def test_duplicate_headers_are_rejected_before_pandas_mangling(tmp_path: Path) -> None:
+ text = _campaign_text().replace(",yield,stability", ",yield,yield", 1)
+ path = _write_text(tmp_path / "duplicates.csv", text)
+
+ with pytest.raises(ValueError, match=r"Duplicate CSV headers.*columns \[5, 6\]"):
+ parse_campaign_csv(path)
+
+
+def test_plain_data_csv_is_rejected_explicitly(tmp_path: Path) -> None:
+ path = _write_text(tmp_path / "plain.csv", "x,y\n1,2\n")
+
+ with pytest.raises(ValueError, match="Plain data CSVs are not supported"):
+ parse_campaign_csv(path)
+
+
+@pytest.mark.parametrize(
+ ("old", "new", "message"),
+ [
+ ("start,1000,5", "start,not-a-number,5", "Malformed start metadata"),
+ ("step,500,5", "step,,5", "Incomplete numeric metadata"),
+ ],
+)
+def test_malformed_or_missing_numeric_metadata_is_rejected(
+ tmp_path: Path, old: str, new: str, message: str
+) -> None:
+ path = _write_text(
+ tmp_path / "bad_metadata.csv", _campaign_text().replace(old, new)
+ )
+
+ with pytest.raises(ValueError, match=message):
+ parse_campaign_csv(path)
+
+
+def test_expected_objectives_are_validated_and_ordered(tmp_path: Path) -> None:
+ path = _write_text(tmp_path / "campaign.csv", _campaign_text())
+
+ parsed = parse_campaign_csv(path, expected_objectives=["stability", "yield"])
+ assert parsed.objective_columns == ["stability", "yield"]
+
+ with pytest.raises(ValueError, match="Missing objective columns.*missing_score"):
+ parse_campaign_csv(path, expected_objectives=["yield", "missing_score"])
+
+
+def test_missing_objectives_and_empty_experiment_section_are_clear(
+ tmp_path: Path,
+) -> None:
+ no_objective = ",x\nunits,rpm\nstart,0\nstop,1\nstep,0.5\n\n,0\n"
+ path = _write_text(tmp_path / "no_objective.csv", no_objective)
+ with pytest.raises(ValueError, match="no named objective columns"):
+ parse_campaign_csv(path)
+
+ empty = (
+ ",x_speed,x_time,,yield,stability\n"
+ "units,rpm,s,,%,h\n"
+ "start,1000,5,,,\n"
+ "stop,2000,15,,,\n"
+ "step,500,5,,,\n"
+ ",,,,,\n"
+ )
+ empty_path = _write_text(tmp_path / "empty.csv", empty)
+ with pytest.raises(ValueError, match="empty experimental section"):
+ parse_campaign_csv(empty_path)
+
+
+def test_csv_to_config_is_opt_in_for_output_and_constraints(
+ tmp_path: Path, monkeypatch: pytest.MonkeyPatch
+) -> None:
+ path = _write_text(tmp_path / "campaign.csv", _campaign_text())
+ monkeypatch.chdir(tmp_path)
+
+ config = csv_to_config(path)
+
+ assert config["constraints"] == []
+ assert not (tmp_path / "configs").exists()
+
+ output_path = tmp_path / "generated" / "campaign.yaml"
+ written = csv_to_config(path, output_path)
+ assert yaml.safe_load(output_path.read_text(encoding="utf-8")) == written
+
+
+def _design() -> SimpleNamespace:
+ return SimpleNamespace(names=["x_speed", "x_time"])
+
+
+def _model_config() -> dict:
+ return {"objectives": {"names": ["yield", "stability"]}}
+
+
+def test_split_xy_returns_named_numeric_dataframes() -> None:
+ data = pd.DataFrame(
+ {
+ "x_speed": ["1000", "1500"],
+ "x_time": ["5", "10"],
+ "yield": ["1.5", "2.5"],
+ "stability": ["10", "11"],
+ },
+ index=["sample-a", "sample-b"],
+ )
+
+ X, Y = split_XY(data, _design(), _model_config())
+
+ assert isinstance(X, pd.DataFrame)
+ assert isinstance(Y, pd.DataFrame)
+ assert X.columns.tolist() == ["x_speed", "x_time"]
+ assert Y.columns.tolist() == ["yield", "stability"]
+ assert X.index.tolist() == ["sample-a", "sample-b"]
+ assert X.dtypes.tolist() == ["float64", "float64"]
+ assert Y.iloc[0].tolist() == [1.5, 10.0]
+
+
+def test_split_xy_rejects_missing_columns() -> None:
+ data = pd.DataFrame({"x_speed": [1], "yield": [2], "stability": [3]})
+
+ with pytest.raises(KeyError, match="missing inputs.*x_time"):
+ split_XY(data, _design(), _model_config())
+
+
+@pytest.mark.parametrize(
+ ("data", "message"),
+ [
+ (
+ pd.DataFrame(
+ {
+ "x_speed": [1000],
+ "x_time": [5],
+ "yield": [pd.NA],
+ "stability": [pd.NA],
+ }
+ ),
+ "All objective values are blank",
+ ),
+ (
+ pd.DataFrame(
+ {
+ "x_speed": [1000, 1500],
+ "x_time": [5, 10],
+ "yield": [1.5, pd.NA],
+ "stability": [10, pd.NA],
+ }
+ ),
+ "Objective values are blank for rows",
+ ),
+ (
+ pd.DataFrame(
+ {
+ "x_speed": [1000],
+ "x_time": [5],
+ "yield": [1.5],
+ "stability": [pd.NA],
+ }
+ ),
+ "Partially completed objective rows",
+ ),
+ (
+ pd.DataFrame(
+ {
+ "x_speed": ["invalid"],
+ "x_time": [5],
+ "yield": [1.5],
+ "stability": [10],
+ }
+ ),
+ "Input model data contains",
+ ),
+ (
+ pd.DataFrame(
+ {
+ "x_speed": [1000],
+ "x_time": [5],
+ "yield": ["invalid"],
+ "stability": [10],
+ }
+ ),
+ "Objective model data contains",
+ ),
+ ],
+)
+def test_split_xy_rejects_incomplete_or_nonnumeric_model_rows(
+ data: pd.DataFrame, message: str
+) -> None:
+ with pytest.raises(ValueError, match=message):
+ split_XY(data, _design(), _model_config())
diff --git a/tests/test_design.py b/tests/test_design.py
index 3898803..33d7f2a 100644
--- a/tests/test_design.py
+++ b/tests/test_design.py
@@ -1,38 +1,114 @@
-import sys
-import os
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
import numpy as np
-from src.design import (
+import pytest
+
+from mobo_kit.design import (
+ InputSpec,
+ build_design,
+ build_design_from_config,
+ build_input_spec_list,
make_linspace,
- get_variable_space,
- get_parameter_space,
- generate_initial_design,
)
-from src.utils import get_closest_array
-def test_make_linspace():
- arr = make_linspace(0, 1, 0.2)
- assert np.allclose(arr, [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]), "Incorrect linspace result"
-def test_variable_space_shape():
- var_array = get_variable_space()
- assert isinstance(var_array, list) and len(var_array) == 8
- assert all(isinstance(v, np.ndarray) or isinstance(v, list) for v in var_array)
+def _valid_inputs():
+ return [
+ {
+ "name": "speed",
+ "unit": "rpm",
+ "start": 1000,
+ "stop": 2000,
+ "step": 250,
+ },
+ {
+ "name": "time",
+ "unit": "s",
+ "start": 5,
+ "stop": 20,
+ "step": 5,
+ },
+ ]
+
+
+def test_make_linspace_preserves_requested_step_and_endpoints():
+ grid = make_linspace(0.0, 1.0, 0.2)
+
+ assert np.array_equal(grid, np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0]))
+ assert np.allclose(np.diff(grid), 0.2)
+
+
+def test_make_linspace_allows_a_single_fixed_value():
+ assert np.array_equal(make_linspace(3.5, 3.5, 0.25), np.array([3.5]))
+
+
+@pytest.mark.parametrize(
+ ("start", "stop", "step", "message"),
+ [
+ (0.0, 1.0, 0.3, "not aligned"),
+ (1.0, 0.0, 0.1, "stop must be"),
+ (0.0, 1.0, 0.0, "step must be > 0"),
+ (0.0, 1.0, -0.1, "step must be > 0"),
+ (0.0, np.inf, 0.1, "must be finite"),
+ ],
+)
+def test_make_linspace_rejects_ambiguous_grids(start, stop, step, message):
+ with pytest.raises(ValueError, match=message):
+ make_linspace(start, stop, step)
+
+
+def test_build_design_from_config_constructs_exact_grids():
+ design = build_design_from_config({"inputs": _valid_inputs()})
+
+ assert design.names == ["speed", "time"]
+ assert design.units == ["rpm", "s"]
+ assert np.array_equal(
+ design.var_list[0], np.array([1000.0, 1250.0, 1500.0, 1750.0, 2000.0])
+ )
+ assert np.array_equal(design.var_list[1], np.array([5.0, 10.0, 15.0, 20.0]))
+ assert np.array_equal(design.lowers, np.array([1000.0, 5.0]))
+ assert np.array_equal(design.uppers, np.array([2000.0, 20.0]))
+
+
+@pytest.mark.parametrize(
+ ("inputs", "message"),
+ [
+ ([], "non-empty list"),
+ ([{"name": "", "start": 0, "stop": 1, "step": 1}], "non-empty string"),
+ ([{"name": "x", "start": 0, "stop": 1}], "missing required"),
+ ([{"name": "x", "start": "bad", "stop": 1, "step": 1}], "finite number"),
+ ([{"name": "x", "start": 0, "stop": np.nan, "step": 1}], "must be finite"),
+ ([{"name": "x", "start": 1, "stop": 0, "step": 1}], "stop >= start"),
+ ([{"name": "x", "start": 0, "stop": 1, "step": -1}], "step.*> 0"),
+ ([{"name": "x", "start": 0, "stop": 1, "step": 0.3}], "not aligned"),
+ (
+ [
+ {"name": " x ", "start": 0, "stop": 1, "step": 1},
+ {"name": "x", "start": 0, "stop": 1, "step": 1},
+ ],
+ "duplicate name",
+ ),
+ ],
+)
+def test_input_schema_validation_is_explicit(inputs, message):
+ with pytest.raises(ValueError, match=message):
+ build_input_spec_list(inputs)
+
+
+def test_rounding_that_collapses_grid_points_is_rejected():
+ with pytest.raises(ValueError, match="duplicate values"):
+ InputSpec(name="x", start=0.0, stop=0.02, step=0.005, decimals=2)
+
-def test_parameter_space_format():
- space = get_parameter_space()
- assert len(space.parameters) == 8
- names = [p.name for p in space.parameters]
- assert "temp" in names and "humidity" in names
+def test_build_design_revalidates_mutated_specs_and_unique_names():
+ first = InputSpec("x", 0, 1, 1)
+ second = InputSpec("y", 0, 1, 1)
+ second.name = "x"
-def test_design_sampling_and_snapping():
- raw_samples = generate_initial_design(n_samples=5)
- assert raw_samples.shape == (5, 8), "Design sample shape incorrect"
+ with pytest.raises(ValueError, match="duplicate name"):
+ build_design([first, second])
- var_array = get_variable_space()
- snapped = get_closest_array(raw_samples, var_array)
- assert snapped.shape == (5, 8)
- for i in range(8):
- assert np.all(np.isin(snapped[:, i], var_array[i])), f"Column {i} has invalid snapped values"
\ No newline at end of file
+def test_build_design_requires_input_specs():
+ with pytest.raises(ValueError, match="At least one"):
+ build_design([])
+ with pytest.raises(TypeError, match="InputSpec"):
+ build_design([{"name": "x"}])
diff --git a/tests/test_discrete_refinement.py b/tests/test_discrete_refinement.py
new file mode 100644
index 0000000..81a1aa7
--- /dev/null
+++ b/tests/test_discrete_refinement.py
@@ -0,0 +1,258 @@
+import numpy as np
+
+from mobo_kit.candidate_pool import CandidatePool
+from mobo_kit.design import InputSpec, build_design
+from mobo_kit.discrete_refinement import (
+ CachedGridScorer,
+ RefinementConfig,
+ grid_indices_to_physical_and_normalized,
+ propose_refined_discrete_batch,
+ refine_discrete_acquisition_anchors,
+)
+
+
+def _design():
+ return build_design(
+ [
+ InputSpec("x", 0, 4, 1),
+ InputSpec("y", 0, 3, 1),
+ ]
+ )
+
+
+def _pool(design) -> CandidatePool:
+ indices = np.asarray([[x, y] for x in range(5) for y in range(4)], dtype=np.int64)
+ physical, normalized = grid_indices_to_physical_and_normalized(indices, design)
+ return CandidatePool(
+ grid_indices=indices,
+ X_phys=physical,
+ X_norm=normalized,
+ seed=73,
+ draws=len(indices),
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+
+
+def test_refinement_reaches_coordinate_local_optimum_and_is_monotone() -> None:
+ design = _design()
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ return 100.0 - (rows[:, 0] - 3) ** 2 - 2.0 * (rows[:, 1] - 2) ** 2
+
+ refined, trace = refine_discrete_acquisition_anchors(
+ design,
+ np.asarray([[0, 0], [4, 3]], dtype=np.int64),
+ score,
+ config=RefinementConfig(
+ anchors_per_selection_step=2,
+ max_sweeps=10,
+ improvement_tolerance=1e-12,
+ radius=None,
+ min_batch_distance=0.0,
+ ),
+ selection_step=1,
+ )
+
+ assert {item.refined_grid_index for item in refined} == {(3, 2)}
+ assert all(item.termination_reason == "no_improvement" for item in refined)
+ assert all(row.score_after >= row.score_before for row in trace)
+ assert any(row.accepted_move for row in trace)
+ assert all(np.isfinite(row.base_score_before) for row in trace)
+ assert all(np.isfinite(row.base_score_after) for row in trace)
+ assert all(np.isfinite(row.penalized_log_score_before) for row in trace)
+ assert all(np.isfinite(row.penalized_log_score_after) for row in trace)
+ assert all(row.termination_reason in {None, "no_improvement"} for row in trace)
+
+
+def test_refinement_tie_break_and_trace_are_deterministic() -> None:
+ design = _design()
+ config = RefinementConfig(
+ anchors_per_selection_step=1,
+ max_sweeps=3,
+ radius=None,
+ min_batch_distance=0.0,
+ )
+
+ def flat(rows: np.ndarray) -> np.ndarray:
+ return np.ones(rows.shape[0])
+
+ first = refine_discrete_acquisition_anchors(
+ design,
+ np.asarray([[2, 2]], dtype=np.int64),
+ flat,
+ config=config,
+ selection_step=1,
+ )
+ second = refine_discrete_acquisition_anchors(
+ design,
+ np.asarray([[2, 2]], dtype=np.int64),
+ flat,
+ config=config,
+ selection_step=1,
+ )
+
+ assert first == second
+ assert first[0][0].refined_grid_index == (2, 2)
+ assert first[0][0].termination_reason == "no_improvement"
+
+
+def test_grid_normalization_matches_physical_bound_canonicalization_bitwise() -> None:
+ design = build_design([InputSpec("decimal_axis", 1.0, 2.0, 0.05, decimals=2)])
+ indices = np.arange(design.var_array[0].size, dtype=np.int64)[:, None]
+
+ physical, normalized = grid_indices_to_physical_and_normalized(indices, design)
+ expected = (physical - design.lowers) / (design.uppers - design.lowers)
+ index_fraction = indices / float(design.var_array[0].size - 1)
+
+ np.testing.assert_array_equal(normalized, expected)
+ assert np.any(normalized != index_fraction)
+
+
+def test_refined_batch_enforces_observed_exclusion_and_hard_distance() -> None:
+ design = _design()
+ pool = _pool(design)
+ observed_grid = np.asarray([[4, 3]], dtype=np.int64)
+ _, observed_norm = grid_indices_to_physical_and_normalized(observed_grid, design)
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ # The forbidden observed point is the nominal maximum.
+ return 100.0 + 10.0 * rows[:, 0] + rows[:, 1]
+
+ result = propose_refined_discrete_batch(
+ pool,
+ design,
+ score,
+ q=2,
+ config=RefinementConfig(
+ anchors_per_selection_step=6,
+ max_sweeps=5,
+ radius=None,
+ min_batch_distance=0.75,
+ ),
+ observed_grid_indices=observed_grid,
+ observed_norm=observed_norm,
+ )
+
+ assert result.grid_indices.shape == (2, 2)
+ assert not np.any(np.all(result.grid_indices == observed_grid[0], axis=1))
+ assert np.unique(result.grid_indices, axis=0).shape[0] == 2
+ assert np.linalg.norm(result.X_norm[0] - result.X_norm[1]) >= 0.75
+ assert all(value > 0 for value in result.base_scores)
+ assert result.distinct_converged_optima >= 2
+
+
+def test_refined_batch_forwards_pending_rows_through_avoid_grid_indices() -> None:
+ design = _design()
+ pool = _pool(design)
+ pending_grid = np.asarray([[4, 3]], dtype=np.int64)
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ return 100.0 + 10.0 * rows[:, 0] + rows[:, 1]
+
+ result = propose_refined_discrete_batch(
+ pool,
+ design,
+ score,
+ q=1,
+ config=RefinementConfig(
+ anchors_per_selection_step=6,
+ max_sweeps=5,
+ radius=None,
+ min_batch_distance=0.0,
+ ),
+ avoid_grid_indices=pending_grid,
+ )
+
+ assert result.grid_indices.shape == (1, 2)
+ assert not np.array_equal(result.grid_indices[0], pending_grid[0])
+
+
+def test_refinement_reports_max_sweeps_without_off_grid_moves() -> None:
+ design = _design()
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ return 10.0 + rows[:, 0] + rows[:, 0] * rows[:, 1]
+
+ refined, trace = refine_discrete_acquisition_anchors(
+ design,
+ np.asarray([[0, 0]], dtype=np.int64),
+ score,
+ config=RefinementConfig(
+ anchors_per_selection_step=1,
+ max_sweeps=1,
+ radius=None,
+ min_batch_distance=0.0,
+ ),
+ selection_step=1,
+ )
+
+ assert refined[0].termination_reason == "max_sweeps"
+ assert trace[-1].termination_reason == "max_sweeps"
+ assert np.all(np.asarray(refined[0].refined_grid_index) >= 0)
+ assert refined[0].refined_grid_index[0] < 5
+ assert refined[0].refined_grid_index[1] < 4
+
+
+def test_cached_grid_scorer_deduplicates_vectorized_requests() -> None:
+ calls: list[np.ndarray] = []
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ calls.append(rows.copy())
+ return rows.sum(axis=1).astype(float)
+
+ cached = CachedGridScorer(score, dimension=2)
+ requested = np.asarray([[1, 2], [1, 2], [2, 3]], dtype=np.int64)
+ np.testing.assert_array_equal(cached(requested), [3.0, 3.0, 5.0])
+ np.testing.assert_array_equal(cached(requested[::-1]), [5.0, 3.0, 3.0])
+
+ assert cached.cache_size == 2
+ assert len(calls) == 1
+ assert calls[0].shape == (2, 2)
+
+
+def test_later_steps_readd_eligible_previously_refined_optima() -> None:
+ design = build_design([InputSpec("x", 0, 2, 1), InputSpec("y", 0, 2, 1)])
+ pool_indices = np.asarray([[1, 0], [1, 2]], dtype=np.int64)
+ physical, normalized = grid_indices_to_physical_and_normalized(pool_indices, design)
+ pool = CandidatePool(
+ grid_indices=pool_indices,
+ X_phys=physical,
+ X_norm=normalized,
+ seed=73,
+ draws=2,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+ table = np.ones((3, 3), dtype=float)
+ table[0, 0] = 9.0
+ table[1, 0] = 5.0
+ table[1, 2] = 8.0
+ table[2, 2] = 10.0
+
+ def score(rows: np.ndarray) -> np.ndarray:
+ return table[rows[:, 0], rows[:, 1]]
+
+ result = propose_refined_discrete_batch(
+ pool,
+ design,
+ score,
+ q=2,
+ config=RefinementConfig(
+ anchors_per_selection_step=2,
+ max_sweeps=5,
+ radius=None,
+ min_batch_distance=0.3,
+ ),
+ )
+
+ assert any(
+ anchor.selection_step == 2 and anchor.anchor_pool_index is None
+ for anchor in result.anchors
+ )
+ assert all(anchor.accepted_move_count >= 0 for anchor in result.anchors)
+ assert all(
+ isinstance(anchor.changed_dimensions, tuple) for anchor in result.anchors
+ )
diff --git a/tests/test_dtlz2_acceptance.py b/tests/test_dtlz2_acceptance.py
new file mode 100644
index 0000000..6bf90e0
--- /dev/null
+++ b/tests/test_dtlz2_acceptance.py
@@ -0,0 +1,457 @@
+"""End-to-end acceptance test on a synthetic problem with a known Pareto front.
+
+DTLZ2 with 3 objectives and 10 inputs, run through the real campaign path:
+``run_r0_lhs`` -> ``run_r1_ucb(5)`` -> ``run_r2_qlognehvi(3)``. Nothing here
+touches the experimental data, so it answers "does the algorithm work" separately
+from "are the measurements right".
+
+Two conventions had to be got right, and both fail silently if you don't.
+
+**DTLZ2 minimises by default.** ``negate=True`` is mandatory. Without it the
+objectives are positive, no point dominates the reference, and the test would
+measure the opposite of optimisation.
+
+**BoTorch's Hypervolume assumes maximisation and silently drops points that do
+not dominate the reference** -- there is no warning and no exception, you simply
+get a smaller number, or 0.0. So :func:`_hypervolume` asserts that at least one
+point dominates before trusting the result.
+
+The optimisation claim is deliberately weak, because the honest one is:
+cumulative hypervolume rises monotonically *by construction* (adding points can
+only grow the dominated region), so "HV increased" would pass for random
+sampling too. The meaningful comparison is against a random baseline at the same
+budget, and BO wins **on average, not on every seed** -- measured 5/8 seeds with
+a mean gain ratio of 1.35x. Asserting a per-seed win would be a flaky test
+asserting something untrue.
+"""
+
+from __future__ import annotations
+
+import warnings
+
+import numpy as np
+import pytest
+import torch
+from botorch.test_functions.multi_objective import DTLZ2
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+from botorch.utils.multi_objective.pareto import is_non_dominated
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ run_r0_lhs,
+ run_r1_ucb,
+ run_r2_qlognehvi,
+)
+from mobo_kit.design import build_design_from_config
+from mobo_kit.ucb_hvi import pareto_utility_above_reference
+
+INPUT_DIM = 10
+OBJECTIVES = 3
+R0_SIZE = 15
+R1_SIZE = 5
+R2_SIZE = 3
+
+
+def _problem() -> DTLZ2:
+ # negate=True -> maximisation, which is what the campaign and BoTorch's
+ # hypervolume both assume
+ return DTLZ2(dim=INPUT_DIM, num_objectives=OBJECTIVES, negate=True).to(
+ dtype=torch.double
+ )
+
+
+def _config(pool: int = 1024, mc_samples: int = 32) -> dict:
+ """A campaign config for DTLZ2. Pool sizes are shrunk for test runtime.
+
+ The production config uses 32768, which costs ~124 s for one R0->R1->R2 pass.
+ R2 is the bottleneck and ``mc_samples`` is the cheapest lever, so that is what
+ is reduced most.
+ """
+ return {
+ "inputs": [
+ {"name": f"x{i}", "start": 0.0, "stop": 1.0, "step": 0.05}
+ for i in range(INPUT_DIM)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-dtlz2-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": f"f{i}",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": f"f{i}",
+ # negated DTLZ2 lands in roughly [-1.9, 0]
+ "lower_anchor": -2.0,
+ "upper_anchor": 0.0,
+ }
+ for i in range(OBJECTIVES)
+ ],
+ },
+ "reference_point_utility": [-0.01] * OBJECTIVES,
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi",
+ "batch_size": R1_SIZE,
+ "replicates_per_condition": 3,
+ "beta": 4.0,
+ "candidate_pool_size": pool,
+ "posterior_samples": 256,
+ "moment_method": "monte_carlo",
+ },
+ "r2": {
+ "method": "qlognehvi",
+ "batch_size": R2_SIZE,
+ "replicates_per_condition": 3,
+ "candidate_pool_size": pool,
+ "mc_samples": mc_samples,
+ },
+ },
+ "local_penalization": {
+ "radius": 0.25,
+ "min_batch_distance": 0.15,
+ "min_observed_distance": 0.0,
+ "dimension_weights": None,
+ },
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": 73},
+ "constraints": [],
+ }
+
+
+def _evaluate(problem: DTLZ2, X_phys: np.ndarray) -> np.ndarray:
+ """DTLZ2 lives on [0,1]^10, which is exactly the declared design domain."""
+ return problem(torch.tensor(np.asarray(X_phys, float), dtype=torch.double)).numpy()
+
+
+def _hypervolume(config: dict, Y_raw: np.ndarray) -> float:
+ """Hypervolume in UTILITY space against the fixed campaign reference.
+
+ Computing it in utility space rather than raw space is what makes values
+ comparable across rounds: the campaign's scales are fixed, so the reference
+ does not drift as data arrives.
+ """
+ transform = build_objective_transform(config)
+ reference = torch.tensor(config["reference_point_utility"], dtype=torch.double)
+ # transform_measurements, not transform: Y_raw holds MEASUREMENT-space values,
+ # and transform decodes the link itself. The two are the same call while every
+ # objective is affine, which is exactly why this file could not see the R1
+ # baseline bug -- see test_measurement_space_encoding.py.
+ utility = transform.transform_measurements(
+ torch.tensor(np.asarray(Y_raw, float), dtype=torch.double)
+ )
+ if not bool((utility >= reference).all(dim=-1).any()):
+ raise AssertionError(
+ "No point dominates the reference point. BoTorch would silently drop "
+ "every point and return 0.0 rather than raising, so this is checked "
+ "explicitly."
+ )
+ return Hypervolume(ref_point=reference).compute(utility[is_non_dominated(utility)])
+
+
+def _run_campaign(config: dict, seed: int, evaluate=None) -> dict:
+ """One full R0 -> R1 -> R2 pass, evaluating DTLZ2 at each proposed batch."""
+ problem = _problem()
+ evaluate = _evaluate if evaluate is None else evaluate
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore")
+ r0 = run_r0_lhs(config, n=R0_SIZE, seed=seed)
+ X0 = r0.conditions.to_numpy(float)
+ Y0 = evaluate(problem, X0)
+
+ r1 = run_r1_ucb(config, X0, Y0, seed=seed)
+ X1 = r1.conditions.to_numpy(float)
+ Y1 = evaluate(problem, X1)
+
+ X01, Y01 = np.vstack([X0, X1]), np.vstack([Y0, Y1])
+ r2 = run_r2_qlognehvi(config, X01, Y01, seed=seed)
+ X2 = r2.conditions.to_numpy(float)
+ Y2 = evaluate(problem, X2)
+
+ return {
+ "r0": r0,
+ "r1": r1,
+ "r2": r2,
+ "hv": [
+ _hypervolume(config, Y0),
+ _hypervolume(config, Y01),
+ _hypervolume(config, np.vstack([Y01, Y2])),
+ ],
+ "X": [X0, X1, X2],
+ }
+
+
+@pytest.fixture(scope="module")
+def campaign() -> dict:
+ return _run_campaign(_config(), seed=73)
+
+
+# --------------------------------------------------------------------------- #
+# the algorithm produces well-formed batches
+# --------------------------------------------------------------------------- #
+
+
+def test_each_round_proposes_exactly_the_requested_count(campaign: dict) -> None:
+ assert len(campaign["r0"].conditions) == R0_SIZE
+ assert len(campaign["r1"].conditions) == R1_SIZE
+ assert len(campaign["r2"].conditions) == R2_SIZE
+ # 23 distinct conditions, 3 films each
+ total = R0_SIZE + R1_SIZE + R2_SIZE
+ assert total == 23
+ assert len(campaign["r2"].replicates) == R2_SIZE * 3
+
+
+@pytest.mark.parametrize("round_key", ["r0", "r1", "r2"])
+def test_every_batch_is_unique_on_grid_and_in_bounds(
+ campaign: dict, round_key: str
+) -> None:
+ report = campaign[round_key].diagnostics["validity"]
+ assert report["unique"]
+ assert report["on_grid"]
+ assert report["in_bounds"]
+ assert report["finite"]
+
+
+@pytest.mark.parametrize("round_key", ["r1", "r2"])
+def test_local_penalization_spreads_the_batch(campaign: dict, round_key: str) -> None:
+ """Without penalization a batch collapses onto the single best pool point.
+
+ The floor is the configured 0.15; the measured values are far above it, which
+ is the signal that penalization is doing work rather than merely not failing.
+ """
+ minimum = campaign[round_key].diagnostics["validity"]["min_pairwise_distance"]
+ assert minimum >= 0.15
+ assert minimum > 0.5, f"batch is unexpectedly clustered: {minimum:.4f}"
+
+
+def test_proposed_points_are_distinct_from_the_observed_set(campaign: dict) -> None:
+ """Re-proposing an already-measured recipe would waste a film."""
+ X0, X1, X2 = campaign["X"]
+ seen = {tuple(np.round(row, 9)) for row in X0}
+ for row in np.vstack([X1, X2]):
+ assert tuple(np.round(row, 9)) not in seen
+
+
+# --------------------------------------------------------------------------- #
+# the algorithm optimises
+# --------------------------------------------------------------------------- #
+
+
+def test_cumulative_hypervolume_never_decreases(campaign: dict) -> None:
+ """True by construction -- adding points cannot shrink the dominated region.
+
+ It is asserted anyway because a violation would mean something is broken in
+ the transform, the reference point, or the sign convention. It is NOT
+ evidence of optimisation; see the random-baseline test for that.
+ """
+ hv0, hv1, hv2 = campaign["hv"]
+ assert hv0 <= hv1 <= hv2
+ assert hv0 > 0.0
+
+
+def test_hypervolume_actually_improves_over_the_initial_design(
+ campaign: dict,
+) -> None:
+ hv0, _, hv2 = campaign["hv"]
+ assert hv2 > hv0
+ # DTLZ2's optimum against its own reference is 0.807; we are in the right
+ # order of magnitude rather than chasing a specific value
+ assert 0.0 < hv2 < 1.0
+
+
+def test_the_batches_are_deterministic_for_a_fixed_seed(campaign: dict) -> None:
+ """A reproducible campaign is a precondition for auditing one."""
+ repeat = _run_campaign(_config(), seed=73)
+ np.testing.assert_allclose(
+ repeat["r1"].conditions.to_numpy(float), campaign["X"][1]
+ )
+ np.testing.assert_allclose(
+ repeat["r2"].conditions.to_numpy(float), campaign["X"][2]
+ )
+
+
+# --------------------------------------------------------------------------- #
+# every link type the campaign uses, exercised end to end
+# --------------------------------------------------------------------------- #
+#
+# Added 2026-07-31, after `run_r1_ucb` was found to have been handing the objective
+# transform measurement-space values for the life of the campaign. This file could
+# not have caught it: every objective above is affine, and for an affine objective
+# measurement space and model space are the same numbers, so a link-encoding
+# mistake is invisible BY CONSTRUCTION.
+#
+# The live campaign has a log-link objective (thickness trains on log(nm)), so the
+# synthetic acceptance test must have one too, or "the loop passes end to end"
+# keeps meaning "the loop passes end to end for half of the link types in use".
+
+
+def _config_with_log_link(pool: int = 1024, mc_samples: int = 32) -> dict:
+ """The same DTLZ2 problem with its third objective reached through a log link.
+
+ ``f2`` is reported as ``exp(f2)`` -- a strictly positive measurement -- and the
+ objective declares ``response: log``. The GP therefore trains on
+ ``log(exp(f2)) = f2``: the SAME latent quantity the affine config models,
+ reached by a different route. Any mis-encoding shows up as a difference in
+ something that ought to be identical.
+ """
+ config = _config(pool=pool, mc_samples=mc_samples)
+ config["objectives"]["contract_version"] = "TEST_ONLY-dtlz2-loglink-v1"
+ config["objectives"]["specs"][2] = {
+ "name": "f2",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "f2",
+ # negated DTLZ2 lands in roughly [-1.9, 0], so exp() lands in [0.15, 1]
+ "lower_anchor": float(np.exp(-2.0)),
+ "upper_anchor": 1.0,
+ "mean_function": {
+ "response": "log",
+ "features": [{"column": "x0", "transform": "identity"}],
+ },
+ }
+ return config
+
+
+def _evaluate_log_linked(problem: DTLZ2, X_phys: np.ndarray) -> np.ndarray:
+ Y = _evaluate(problem, X_phys)
+ return np.column_stack([Y[:, 0], Y[:, 1], np.exp(Y[:, 2])])
+
+
+@pytest.fixture(scope="module")
+def log_linked_campaign() -> dict:
+ return _run_campaign(
+ _config_with_log_link(), seed=73, evaluate=_evaluate_log_linked
+ )
+
+
+def test_the_log_link_config_really_is_log_linked() -> None:
+ """Guards the guard: if this reverts to identity the tests below go quiet."""
+ specs = build_objective_transform(_config_with_log_link()).specs
+ assert [spec.model_link for spec in specs] == ["identity", "identity", "log"]
+ # and the plain config remains the affine-only case, so both are covered
+ assert [s.model_link for s in build_objective_transform(_config()).specs] == [
+ "identity"
+ ] * OBJECTIVES
+
+
+def test_a_log_linked_campaign_runs_end_to_end(log_linked_campaign: dict) -> None:
+ assert len(log_linked_campaign["r0"].conditions) == R0_SIZE
+ assert len(log_linked_campaign["r1"].conditions) == R1_SIZE
+ assert len(log_linked_campaign["r2"].conditions) == R2_SIZE
+ for key in ("r0", "r1", "r2"):
+ report = log_linked_campaign[key].diagnostics["validity"]
+ assert report["unique"] and report["on_grid"] and report["in_bounds"]
+ hv0, hv1, hv2 = log_linked_campaign["hv"]
+ assert 0.0 < hv0 <= hv1 <= hv2
+
+
+def test_no_observed_utility_collapses_to_zero_under_a_log_link(
+ log_linked_campaign: dict,
+) -> None:
+ """The invariant the R1 baseline bug violated.
+
+ Under the mis-encoding every observation scored exactly 0.0 on the log-linked
+ axis -- a finite, unremarkable number that no check rejected. A measured point
+ with a finite value inside its anchors has non-zero utility; a hard zero means
+ an encoding step was skipped.
+
+ Only points whose raw value lies strictly INSIDE the objective's anchors are
+ checked. An affine objective legitimately clips to 0.0 when a measurement falls
+ at or below its lower anchor, and DTLZ2 does produce such points; asserting on
+ those would be asserting that clipping is a bug.
+ """
+ config = _config_with_log_link()
+ transform = build_objective_transform(config)
+ spec = transform.specs[2]
+ problem = _problem()
+ checked = 0
+ for X in log_linked_campaign["X"]:
+ Y = _evaluate_log_linked(problem, X)
+ utility = transform.transform_measurements(
+ torch.tensor(Y, dtype=torch.double)
+ ).numpy()
+ assert np.isfinite(utility).all()
+ raw = Y[:, 2]
+ inside = (raw > spec.lower_anchor) & (raw < spec.upper_anchor)
+ assert not np.any(utility[inside, 2] == 0.0), (
+ "a finite measurement strictly inside its anchors scored exactly zero"
+ )
+ checked += int(inside.sum())
+ # the assertion above is vacuous if nothing was inside the anchors
+ assert checked >= 15, f"only {checked} points were in range; test has no teeth"
+
+
+def test_the_r1_baseline_is_right_when_a_link_has_to_be_decoded(
+ log_linked_campaign: dict,
+) -> None:
+ """End-to-end version of the comparator that did not exist.
+
+ ``run_r1_ucb`` reports the baseline hypervolume it actually used; this
+ recomputes it by an independent route. On the pre-fix code the reported value
+ is the collapsed one and this fails.
+ """
+ config = _config_with_log_link()
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+
+ X0 = log_linked_campaign["r0"].conditions.to_numpy(float)
+ Y0 = _evaluate_log_linked(_problem(), X0)
+ utility = transform.transform_measurements(
+ torch.tensor(Y0, dtype=torch.double)
+ ).numpy()
+ pareto = pareto_utility_above_reference(utility, reference)
+ expected = float(
+ Hypervolume(ref_point=torch.tensor(reference, dtype=torch.double)).compute(
+ torch.tensor(pareto, dtype=torch.double)
+ )
+ )
+ reported = log_linked_campaign["r1"].diagnostics["observed_baseline_hypervolume"]
+ assert reported == pytest.approx(expected, rel=1e-9)
+ assert log_linked_campaign["r1"].diagnostics[
+ "observed_baseline_pareto_size"
+ ] == len(pareto)
+
+
+@pytest.mark.slow
+def test_bayesian_optimisation_beats_random_search_on_average() -> None:
+ """The test that makes the hypervolume numbers mean something.
+
+ Cumulative HV rises for random sampling too, so the only informative
+ comparison is against a random baseline at the same budget (8 extra points
+ from the same 15-point start).
+
+ BO wins on the MEAN, not on every seed: measured 5 of 8 seeds with a mean
+ gain ratio of 1.35x. With 8 added points in 10 dimensions that is the
+ honest expectation, and asserting a per-seed win would be a flaky test
+ asserting something false.
+ """
+ config = _config()
+ design = build_design_from_config(config)
+ problem = _problem()
+ grids = [np.asarray(g, float) for g in design.var_array]
+
+ bo_gains, random_gains, wins = [], [], 0
+ for seed in (1, 2, 3, 4, 5):
+ result = _run_campaign(config, seed=seed)
+ hv0, _, hv_bo = result["hv"]
+
+ rng = np.random.default_rng(seed)
+ X_random = np.column_stack(
+ [rng.choice(g, size=R1_SIZE + R2_SIZE) for g in grids]
+ )
+ Y_random = np.vstack(
+ [_evaluate(problem, result["X"][0]), _evaluate(problem, X_random)]
+ )
+ hv_random = _hypervolume(config, Y_random)
+
+ bo_gains.append(hv_bo - hv0)
+ random_gains.append(hv_random - hv0)
+ wins += (hv_bo - hv0) > (hv_random - hv0)
+
+ mean_bo, mean_random = float(np.mean(bo_gains)), float(np.mean(random_gains))
+ assert mean_bo > 0, "BO made no hypervolume progress at all"
+ assert mean_random > 0, "the random baseline is broken, not a fair comparison"
+ assert mean_bo > mean_random, (
+ f"BO mean gain {mean_bo:.4f} did not beat random {mean_random:.4f}; "
+ f"won {wins}/5 seeds"
+ )
diff --git a/tests/test_final_campaign.py b/tests/test_final_campaign.py
new file mode 100644
index 0000000..0265029
--- /dev/null
+++ b/tests/test_final_campaign.py
@@ -0,0 +1,426 @@
+"""The v4 contract: frozen scores, a per-round source sheet, and what freezing costs.
+
+Three objective contracts have existed and this is the live one. What is new here
+is a deliberate reversal of this project's usual polarity: uniformity and
+optoelectronic are READ from the workbook rather than recomputed, because the
+group is still revising how they are defined.
+
+That reversal removes a cross-check, and the tests below are mostly about the
+consequences of removing it:
+
+* a frozen score must still be *validated* -- numeric, present, inside its
+ declared anchors -- because nothing else looks at it;
+* a frozen score's DEFINITION must be watched, since its value cannot be;
+* and the one thing freezing cannot see -- a stale literal that has stopped
+ tracking its inputs -- is asserted to be exactly what the fingerprint does
+ **not** catch, so nobody later mistakes the fingerprint for a value check.
+
+The synthetic workbook here uses the v4 layout, so none of it needs the ignored
+private one.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from openpyxl import Workbook
+
+from mobo_kit.campaign import load_campaign_config, objective_names
+from mobo_kit.scores import (
+ FormulaFingerprint,
+ MeasurementInput,
+ MeasurementSpec,
+ ScoreSeverity,
+ compute_measurements,
+ entry_columns,
+ measurement_spec_from_config,
+)
+from mobo_kit.workbook_io import (
+ CandidateSheetError,
+ formula_findings,
+ read_campaign_workbook,
+ source_sheet,
+)
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite_final.yaml"
+V3_CONFIG_PATH = "configs/campaign_d2d_perovskite_test.yaml"
+SOURCE = "local_inputs/Final Summary Table.xlsx"
+
+UNIFORMITY_COLUMN = "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))"
+OPTO_COLUMN = (
+ "Optoelectronic score (Normalized (Voc + (0.75*Photoconductance + "
+ "0.25*Photosensitivity))/2"
+)
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+# --------------------------------------------------------------------------- #
+# the contract
+# --------------------------------------------------------------------------- #
+
+
+def test_three_contracts_exist_and_only_one_is_active(config) -> None:
+ """Naming them is not bookkeeping. An objective that keeps its name while
+ changing its construction makes every cross-contract number incomparable
+ while every plot still renders.
+
+ The live contract became ``-nomean`` on 2026-09-06, when the thickness mean
+ function was withdrawn. That is a MODEL change rather than an objective
+ redefinition -- the three quantities are unchanged -- but it moves every
+ fitted number on the learnable axis (+0.7423 to +0.5814) and therefore every
+ hypervolume, so it earns a version. This assertion failing is this test doing
+ its job; update it deliberately, never to make a run go green.
+ """
+ v3 = load_campaign_config(V3_CONFIG_PATH)
+ archived = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+
+ assert config["objectives"]["contract_version"] == "d2d-objectives-v4-final-nomean"
+ assert v3["objectives"]["contract_version"] == "d2d-objectives-v3-test"
+ assert archived["objectives"]["contract_version"] == "d2d-objectives-v2-nm-thickness"
+
+ assert config["campaign"]["status"] == "active"
+ assert v3["campaign"]["status"] == "archived"
+ assert archived["campaign"]["status"] == "archived"
+
+
+def test_no_objective_declares_a_mean_function(config) -> None:
+ """The live campaign carries NO physics prior, by decision on 2026-09-06.
+
+ The thickness prior ``log T ~ log(speed_1) + log(precur_conc)`` was withdrawn
+ after its justification failed: the fitted speed exponent's 95% interval is
+ [-0.385, -0.126], which excludes spin-coating theory's -0.5 by 4.1 standard
+ errors, and fixing the exponents at their theoretical values scores +0.5600
+ against +0.5823 for no trend at all.
+
+ ``structured_mean`` stays wired and tested for a prior that clears the bar --
+ established physics, declared before fitting, beating matched-flexibility
+ controls, and surviving a permutation test. Nothing currently does. If this
+ test fails, someone has added one; make them show the four pieces of evidence
+ before updating it.
+ """
+ from mobo_kit.structured_mean import mean_spec_from_config
+
+ declared = {
+ entry["name"]: mean_spec_from_config(entry)
+ for entry in config["objectives"]["specs"]
+ }
+ assert declared == {name: None for name in declared}, (
+ f"a mean function reappeared: "
+ f"{ {k: v for k, v in declared.items() if v is not None} }"
+ )
+
+
+def test_the_launcher_defaults_to_the_live_contract() -> None:
+ """The one path an experimentalist reaches by double-clicking. Archiving a
+ config without moving this is how a user once got a missing-column error on
+ an intact workbook."""
+ from mobo_kit.launcher import DEFAULT_CONFIG
+
+ assert DEFAULT_CONFIG == CONFIG_PATH
+ assert load_campaign_config(DEFAULT_CONFIG)["campaign"]["status"] == "active"
+
+
+def test_the_source_sheet_is_configuration_not_a_constant(config) -> None:
+ """The v4 workbook names its sheets by round, so `Sheet1` stopped being true.
+ Older contracts must keep working without declaring the key."""
+ assert source_sheet(config) == "R0"
+ assert source_sheet(load_campaign_config(V3_CONFIG_PATH)) == "Sheet1"
+ assert source_sheet({}) == "Sheet1"
+
+
+def test_both_score_objectives_are_frozen_and_thickness_is_not(config) -> None:
+ """Thickness stays computed: its definition has been stable across all three
+ contracts, and the recomputation is what lets `T anom` be excluded and
+ reported rather than silently dropped."""
+ recipes = {
+ spec["name"]: spec["measurement"]["recipe"]
+ for spec in config["objectives"]["specs"]
+ }
+ assert recipes == {
+ "uniformity": "stored",
+ "optoelectronic": "stored",
+ "thickness": "mean_of_present",
+ }
+
+
+# --------------------------------------------------------------------------- #
+# the stored recipe
+# --------------------------------------------------------------------------- #
+
+
+def _stored_spec(**kwargs) -> MeasurementSpec:
+ return MeasurementSpec(
+ name="uniformity",
+ recipe="stored",
+ inputs=(MeasurementInput("Uniformity score"),),
+ **kwargs,
+ )
+
+
+def test_a_stored_score_is_taken_exactly_as_the_workbook_computed_it() -> None:
+ frame = pd.DataFrame({"Uniformity score": [0.877272, 0.599033]})
+ result = compute_measurements(frame, [_stored_spec()], sample_ids=[1, 4])
+ assert result.values["uniformity"].tolist() == [0.877272, 0.599033]
+ assert not result.has_errors
+
+
+def test_a_blank_frozen_score_is_an_error_not_a_gap() -> None:
+ """`mean_of_present` tolerates a missing reading because a film can carry
+ three instead of four. A missing SCORE is different: nothing can recompute it
+ under this contract, so the row simply has no objective value."""
+ frame = pd.DataFrame({"Uniformity score": [0.87, None]})
+ result = compute_measurements(frame, [_stored_spec()], sample_ids=[1, 2])
+ codes = [f.code for f in result.findings if f.severity is ScoreSeverity.ERROR]
+ assert "input_missing" in codes
+ assert result.values["uniformity"].tolist()[0] == 0.87
+ assert np.isnan(result.values["uniformity"].tolist()[1])
+
+
+def test_a_formula_cell_with_no_cached_value_reads_as_blank_and_errors() -> None:
+ """openpyxl discards cached formula values on save, so a workbook written by
+ a non-Excel tool hands back None for every formula column. Under a freeze that
+ is every objective at once, and it must stop the round rather than train on
+ nothing."""
+ frame = pd.DataFrame({"Uniformity score": [None, None, None]})
+ result = compute_measurements(frame, [_stored_spec()], sample_ids=[1, 2, 3])
+ assert result.has_errors
+ assert all(np.isnan(v) for v in result.values["uniformity"])
+
+
+def test_a_non_numeric_frozen_score_is_an_error() -> None:
+ frame = pd.DataFrame({"Uniformity score": ["n/a", "not a number"]})
+ result = compute_measurements(frame, [_stored_spec()], sample_ids=[1, 2])
+ codes = [f.code for f in result.findings if f.severity is ScoreSeverity.ERROR]
+ assert codes, "a score column full of text must not pass silently"
+
+
+def test_stored_takes_exactly_one_column() -> None:
+ """Two columns would mean something is being combined, which is precisely what
+ a freeze exists to avoid."""
+ with pytest.raises(ValueError, match="one score column"):
+ MeasurementSpec(
+ name="uniformity",
+ recipe="stored",
+ inputs=(MeasurementInput("a"), MeasurementInput("b")),
+ )
+
+
+def test_the_v3_recipes_survive_unwired_for_when_the_group_unfreezes() -> None:
+ """`mean`, `clamped_complement` and `capped_ratio` are not deleted. The freeze
+ is temporary by the group's own description, and deleting the code would mean
+ rebuilding it from a doc rather than un-commenting it."""
+ from mobo_kit.scores import RECIPES
+
+ assert {"stored", "mean", "mean_of_present", "product", "log10_product"} <= set(RECIPES)
+ spec = measurement_spec_from_config(
+ {
+ "name": "uniformity",
+ "measurement": {
+ "recipe": "mean",
+ "inputs": [
+ {"column": "Coverage"},
+ {
+ "column": "Uniformity",
+ "transform": "clamped_complement",
+ "clamp_above": 1.0,
+ "clamp_to": 0.99,
+ },
+ {"column": "Phase purity"},
+ ],
+ },
+ }
+ )
+ frame = pd.DataFrame(
+ {"Coverage": [0.989], "Uniformity": [0.324584], "Phase purity": [0.9674]}
+ )
+ result = compute_measurements(frame, [spec], sample_ids=[1])
+ # the v4 workbook's own AJ for sample 1
+ assert result.values["uniformity"][0] == pytest.approx(0.877272, abs=1e-6)
+
+
+# --------------------------------------------------------------------------- #
+# the fingerprint: what replaces the cross-check, and what it cannot replace
+# --------------------------------------------------------------------------- #
+
+
+def _workbook_with(tmp_path, formula, *, column=UNIFORMITY_COLUMN, rows=3):
+ path = tmp_path / "Fingerprint.xlsx"
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "R0"
+ sheet.append(["Sample number", "Coverage", column])
+ for i in range(rows):
+ value = formula.replace("2", str(i + 2)) if formula else 0.5
+ sheet.append([i + 1, 0.9, value])
+ book.save(path)
+ return path
+
+
+def _fingerprint_config(formula="=(L2+O2+P2)/3", column=UNIFORMITY_COLUMN) -> dict:
+ return {
+ "campaign": {"source_sheet": "R0"},
+ "inputs": [{"name": "x", "start": 0, "stop": 1, "step": 1}],
+ "objectives": {
+ "contract_version": "synthetic",
+ "specs": [
+ {
+ "name": "uniformity",
+ "model_source_column": column,
+ "transform": "affine",
+ "goal": "maximize",
+ "lower_anchor": 0.0,
+ "upper_anchor": 1.0,
+ "measurement": {
+ "recipe": "stored",
+ "inputs": [{"column": column}],
+ "formula_fingerprint": {"column": column, "formula": formula},
+ },
+ }
+ ],
+ },
+ }
+
+
+def test_an_unchanged_definition_is_a_note(tmp_path) -> None:
+ path = _workbook_with(tmp_path, "=(L2+O2+P2)/3")
+ (finding,) = formula_findings(path, _fingerprint_config())
+ assert finding.code == "formula_fingerprint_unchanged"
+ assert finding.severity is ScoreSeverity.NOTE
+
+
+def test_a_changed_definition_is_a_warning_that_names_both_formulas(tmp_path) -> None:
+ """The value is still read and still used -- the change is not an error. But
+ every number computed under the old definition is about a different quantity,
+ so it has to be audible."""
+ path = _workbook_with(tmp_path, "=(L2+O2+P2+Q2)/4")
+ (finding,) = formula_findings(path, _fingerprint_config())
+ assert finding.code == "formula_fingerprint_changed"
+ assert finding.severity is ScoreSeverity.WARNING
+ assert "(L2+O2+P2)/3" in finding.message
+ assert "contract_version" in finding.message
+
+
+def test_the_same_formula_copied_down_a_column_is_not_a_change(tmp_path) -> None:
+ """Fingerprinting per row would report fifteen changes for one edit."""
+ path = _workbook_with(tmp_path, "=(L2+O2+P2)/3", rows=5)
+ (finding,) = formula_findings(path, _fingerprint_config())
+ assert finding.code == "formula_fingerprint_unchanged"
+ assert "5 rows" in finding.message
+
+
+def test_a_pasted_literal_score_is_flagged_as_uncheckable(tmp_path) -> None:
+ """The one failure this contract cannot see, called out rather than left
+ silent: a literal cannot be checked against anything at all."""
+ path = _workbook_with(tmp_path, None)
+ (finding,) = formula_findings(path, _fingerprint_config())
+ assert finding.code == "fingerprint_no_formula"
+ assert finding.severity is ScoreSeverity.WARNING
+
+
+def test_the_fingerprint_cannot_catch_a_stale_value(tmp_path) -> None:
+ """Asserted deliberately, so nobody later mistakes the fingerprint for a value
+ check. A formula whose inputs have changed still matches its own text; only a
+ recomputation would notice, and a freeze is the decision not to have one."""
+ path = _workbook_with(tmp_path, "=(L2+O2+P2)/3")
+ (finding,) = formula_findings(path, _fingerprint_config())
+ assert finding.severity is ScoreSeverity.NOTE, (
+ "the definition is unchanged, so the fingerprint is silent -- whatever the "
+ "values behind it have done"
+ )
+
+
+def test_no_fingerprint_declared_means_no_second_workbook_read(tmp_path) -> None:
+ """The check needs data_only=False, a second full read. Configs that do not
+ freeze anything must not pay for it."""
+ config = _fingerprint_config()
+ del config["objectives"]["specs"][0]["measurement"]["formula_fingerprint"]
+ assert formula_findings(tmp_path / "does-not-exist.xlsx", config) == ()
+
+
+def test_the_agreement_check_columns_are_offered_even_when_neither_is_an_input() -> None:
+ """This broke when optoelectronic was frozen: the check listed only its `raw`
+ column, on the assumption that `normalized` was a recipe input. Under a freeze
+ the only input is the score column, so the check reported "column absent" on a
+ sheet that had it."""
+ from mobo_kit.scores import AgreementCheck
+
+ spec = MeasurementSpec(
+ name="optoelectronic",
+ recipe="stored",
+ inputs=(MeasurementInput("Optoelectronic score"),),
+ agreement_check=AgreementCheck(raw="Photoconductance", normalized="Normalized"),
+ )
+ required, optional = entry_columns([spec])
+ assert "Photoconductance" in optional
+ assert "Normalized" in optional
+
+
+# --------------------------------------------------------------------------- #
+# the wrong workbook
+# --------------------------------------------------------------------------- #
+
+
+def test_a_workbook_without_the_configured_sheet_says_which_sheet(tmp_path, config) -> None:
+ path = tmp_path / "Wrong.xlsx"
+ book = Workbook()
+ book.active.title = "Sheet1"
+ book.active.append(["Sample number"])
+ book.save(path)
+ with pytest.raises(CandidateSheetError) as caught:
+ read_campaign_workbook(path, config)
+ message = str(caught.value)
+ assert "'R0'" in message and "campaign.source_sheet" in message
+ assert "Sheet1" in message
+
+
+# --------------------------------------------------------------------------- #
+# the real workbook
+# --------------------------------------------------------------------------- #
+
+requires_workbook = pytest.mark.skipif(
+ not __import__("pathlib").Path(SOURCE).is_file(),
+ reason=f"{SOURCE} is not present in this checkout",
+)
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_the_final_workbook_reads_clean(config) -> None:
+ contents = read_campaign_workbook(SOURCE, config)
+ assert contents.n_rows == 15
+ assert contents.errors == ()
+ assert contents.warnings == ()
+ values = contents.model_values
+ assert list(values.columns) == list(objective_names(config))
+ # the frozen scores are the sheet's own numbers, not a recomputation
+ stored = contents.workbook_values
+ for name in ("uniformity", "optoelectronic"):
+ column = [c for c in stored.columns if c.lower().startswith(name[:6])][0]
+ np.testing.assert_allclose(
+ values[name].to_numpy(float), stored[column].to_numpy(float), atol=0.0
+ )
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_the_recorded_fingerprints_match_the_final_workbook(config) -> None:
+ findings = formula_findings(SOURCE, config)
+ assert len(findings) == 2
+ assert {f.code for f in findings} == {"formula_fingerprint_unchanged"}
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_the_photoconductance_inversion_is_fixed(config) -> None:
+ """The v3 contract's optoelectronic axis was provisional because its
+ normalised column ranked BACKWARDS against its own raw measurement (Spearman
+ -0.5484). The group's fix landed; this pins that it did."""
+ contents = read_campaign_workbook(SOURCE, config)
+ finding = next(f for f in contents.findings if f.code.startswith("agreement_"))
+ assert finding.code == "agreement_monotonic"
+ assert "+1.0000" in finding.message
diff --git a/tests/test_launcher.py b/tests/test_launcher.py
new file mode 100644
index 0000000..14b2fd6
--- /dev/null
+++ b/tests/test_launcher.py
@@ -0,0 +1,646 @@
+"""The launcher's decisions, tested without a display.
+
+The tkinter window is a thin shell over `inspect_campaign`, `gather_observations`
+and `generate_next_round`; those are what can go wrong, so those are what is
+tested here. Importing `mobo_kit.launcher` must not require tkinter, and one test
+asserts that.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import shutil
+
+import numpy as np
+import pandas as pd
+import pytest
+from openpyxl import load_workbook
+
+from mobo_kit.campaign import (
+ load_campaign_config,
+ measurement_entry_columns,
+ objective_names,
+)
+from mobo_kit.launcher import (
+ CampaignStatus,
+ LauncherError,
+ gather_observations,
+ generate_next_round,
+ inspect_campaign,
+)
+from mobo_kit.workbook_io import (
+ CandidateSheetError,
+ candidate_workbook_path,
+ read_candidate_results,
+ sheet_name_for_round,
+ write_candidate_sheet,
+)
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+SOURCE = "local_inputs/Summary Table.xlsx"
+
+pytestmark = pytest.mark.skipif(
+ not __import__("pathlib").Path(SOURCE).exists(),
+ reason="requires the ignored private campaign workbook",
+)
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+@pytest.fixture
+def workbook(tmp_path):
+ destination = tmp_path / "Summary Table.xlsx"
+ shutil.copy2(SOURCE, destination)
+ return destination
+
+
+def _conditions(config: dict, n: int = 5) -> pd.DataFrame:
+ names = [item["name"] for item in config["inputs"]]
+ rows = [
+ [float(item["start"]) + i * float(item["step"]) for item in config["inputs"]]
+ for i in range(n)
+ ]
+ return pd.DataFrame(rows, columns=names)
+
+
+def _fill_candidate_sheet(
+ path, config, *, thickness=(700.0, 720.0), rows=None, coverage=1.0
+) -> None:
+ """Enter plausible measurements into every film of an R1 sheet."""
+ book = load_workbook(path)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ values = {
+ "Coverage": coverage,
+ "Uniformity": 0.3,
+ "Phase purity": 0.95,
+ "PL - Implied Voc (Max)": 0.05,
+ "Photoconductance (Max)": 5e-07,
+ "T1": thickness[0],
+ "T2": thickness[1],
+ }
+ target_rows = rows or range(2, sheet.max_row + 1)
+ for row in target_rows:
+ for column, value in values.items():
+ sheet.cell(row=row, column=headers.index(column) + 1).value = value
+ book.save(path)
+
+
+# --------------------------------------------------------------------------- #
+# status
+# --------------------------------------------------------------------------- #
+
+
+def test_a_fresh_workbook_is_ready_for_r1(workbook, config) -> None:
+ status = inspect_campaign(workbook, config)
+ assert status.next_round == "R1"
+ assert status.can_generate
+ assert status.observed_conditions == 15
+ assert "Ready to propose R1" in status.headline
+
+
+def test_the_detail_text_surfaces_the_read_findings(workbook, config) -> None:
+ """The experimentalist should see that samples 8, 12 and 15 hold thickness
+ readings that disagree, without going looking for it."""
+ detail = inspect_campaign(workbook, config).detail()
+ assert "Worth a look" in detail
+ assert "1600" in detail and "709" in detail
+ assert "For the record" in detail # the excluded T anom readings
+
+
+def test_a_missing_workbook_is_a_plain_sentence(tmp_path, config) -> None:
+ with pytest.raises(LauncherError, match="does not exist"):
+ inspect_campaign(tmp_path / "nope.xlsx", config)
+
+
+def test_an_unmeasured_r1_sheet_blocks_the_next_round(workbook, config) -> None:
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ status = inspect_campaign(workbook, config)
+ assert not status.can_generate
+ assert "no results have been entered" in status.reason
+ assert "Coverage" in status.reason # says what to fill in
+
+
+# --------------------------------------------------------------------------- #
+# observations
+# --------------------------------------------------------------------------- #
+
+
+def test_r1_trains_on_sheet1_alone(workbook, config) -> None:
+ X, Y, Yvar, provenance = gather_observations(workbook, config, for_round="R1")
+ assert X.shape == (15, 10)
+ assert Y.shape == (15, 3)
+ assert provenance == ["Sheet1: 15 conditions"]
+ # no replicates exist yet, so the noise is still fitted rather than measured
+ assert Yvar is None
+
+
+def test_r2_trains_on_sheet1_plus_the_aggregated_r1_conditions(
+ workbook, config
+) -> None:
+ """Three films are one design point, so R2 sees 15 + 5, not 15 + 15."""
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ _fill_candidate_sheet(out, config)
+ X, Y, Yvar, provenance = gather_observations(workbook, config, for_round="R2")
+ assert X.shape == (20, 10)
+ assert Y.shape == (20, 3)
+ assert "5 conditions from 15 films" in provenance[1]
+ # the live config still fits the noise; measured variance is one key away
+ assert Yvar is None
+
+
+def test_measured_replicate_variance_switches_on_from_config(workbook, config) -> None:
+ """The promise of wiring this before the data exists: when the triplicates
+ land, enabling it is a config edit, not a code change."""
+ import copy
+
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ _fill_candidate_sheet(out, config, thickness=(700.0, 760.0))
+ # the films of a condition must actually differ, or there is no variance to pool
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ for row in range(2, sheet.max_row + 1):
+ offset = row % 3
+ sheet.cell(row=row, column=headers.index("T1") + 1).value = 700.0 + 40.0 * offset
+ sheet.cell(row=row, column=headers.index("Coverage") + 1).value = 0.9 + 0.02 * offset
+ # every objective needs film-to-film variation, or its pooled variance is
+ # zero -- which the pooling refuses, because identical replicates are a
+ # transcription rather than a measurement
+ sheet.cell(row=row, column=headers.index("Photoconductance (Max)") + 1).value = (
+ 5e-07 * (1.0 + 0.1 * offset)
+ )
+ book.save(out)
+
+ enabled = copy.deepcopy(dict(config))
+ enabled["model"] = dict(enabled["model"])
+ enabled["model"]["observation_noise"] = "replicate_pooled"
+
+ X, Y, Yvar, provenance = gather_observations(workbook, enabled, for_round="R2")
+ assert Yvar is not None
+ assert Yvar.shape == Y.shape
+ assert np.all(Yvar > 0)
+ # the 15 R0 rows carry the full between-film variance; the R1 conditions,
+ # being means of three films, carry a third of it
+ assert Yvar[0, 2] == pytest.approx(3.0 * Yvar[15, 2])
+ assert any("pooled between-film variance" in item for item in provenance)
+
+
+def test_gathering_refuses_a_half_measured_film(workbook, config) -> None:
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ _fill_candidate_sheet(out, config)
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ # blank every thickness reading of one whole condition
+ for row in (2, 3, 4):
+ for column in ("T1", "T2"):
+ sheet.cell(row=row, column=headers.index(column) + 1).value = None
+ book.save(out)
+
+ with pytest.raises(LauncherError, match="cannot be turned into objective values"):
+ gather_observations(workbook, config, for_round="R2")
+
+
+# --------------------------------------------------------------------------- #
+# replicate aggregation
+# --------------------------------------------------------------------------- #
+
+
+def test_replicates_aggregate_to_one_observation_per_condition(
+ workbook, config
+) -> None:
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ _fill_candidate_sheet(out, config)
+ results = read_candidate_results(workbook, config, "R1")
+ assert results.n_conditions == 5
+ assert len(results.replicates) == 15
+ assert list(results.model_values.columns) == list(objective_names(config))
+ assert (results.films_used["thickness"] == 3).all()
+
+
+def test_thickness_aggregates_as_a_geometric_mean(workbook, config) -> None:
+ """`response: log` means the GP trains on log(T), so three films are averaged
+ in that space. With identical films the two means agree, which is why the
+ check uses films that differ."""
+ out = write_candidate_sheet(workbook, config, _conditions(config, 1), round_name="R1")
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ values = {
+ "Coverage": 1.0,
+ "Uniformity": 0.3,
+ "Phase purity": 0.95,
+ "PL - Implied Voc (Max)": 0.05,
+ "Photoconductance (Max)": 5e-07,
+ }
+ per_film = (400.0, 700.0, 1000.0)
+ for offset, thickness in enumerate(per_film):
+ row = 2 + offset
+ for column, value in values.items():
+ sheet.cell(row=row, column=headers.index(column) + 1).value = value
+ sheet.cell(row=row, column=headers.index("T1") + 1).value = thickness
+ book.save(out)
+
+ results = read_candidate_results(workbook, config, "R1")
+ observed = float(results.model_values["thickness"].iloc[0])
+ assert observed == pytest.approx(float(np.exp(np.mean(np.log(per_film)))))
+ assert observed == pytest.approx(654.2, abs=0.1) # (400*700*1000) ** (1/3)
+ # and not the arithmetic mean, which is 700
+ assert abs(observed - 700.0) > 40.0
+
+
+def test_the_spread_is_kept_in_the_aggregation_space(workbook, config) -> None:
+ """What Phase 4 needs: thickness spread already in log space, matching the
+ config's decision to pool train_Yvar there."""
+ out = write_candidate_sheet(workbook, config, _conditions(config, 1), round_name="R1")
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ for offset, thickness in enumerate((400.0, 700.0, 1000.0)):
+ row = 2 + offset
+ for column, value in {
+ "Coverage": 1.0,
+ "Uniformity": 0.3,
+ "Phase purity": 0.95,
+ "PL - Implied Voc (Max)": 0.05,
+ "Photoconductance (Max)": 5e-07,
+ }.items():
+ sheet.cell(row=row, column=headers.index(column) + 1).value = value
+ sheet.cell(row=row, column=headers.index("T1") + 1).value = thickness
+ book.save(out)
+
+ results = read_candidate_results(workbook, config, "R1")
+ expected = float(np.std(np.log([400.0, 700.0, 1000.0]), ddof=1))
+ assert float(results.replicate_spread["thickness"].iloc[0]) == pytest.approx(expected)
+ # uniformity is identical across the three films, so its spread is zero
+ assert float(results.replicate_spread["uniformity"].iloc[0]) == pytest.approx(0.0)
+
+
+def test_films_of_one_condition_must_share_a_recipe(workbook, config) -> None:
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ _fill_candidate_sheet(out, config)
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [cell.value for cell in sheet[1]]
+ sheet.cell(row=3, column=headers.index("speed_1") + 1).value = 4242.0
+ book.save(out)
+
+ with pytest.raises(CandidateSheetError, match="do not share the same speed_1"):
+ read_candidate_results(workbook, config, "R1")
+
+
+def test_reading_a_sheet_that_was_never_written_says_so(workbook, config) -> None:
+ with pytest.raises(CandidateSheetError, match="does not exist"):
+ read_candidate_results(workbook, config, "R1")
+
+
+# --------------------------------------------------------------------------- #
+# generating
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.slow
+def test_generating_r1_writes_a_sheet_and_leaves_the_source_alone(
+ workbook, config
+) -> None:
+ import hashlib
+
+ before = hashlib.sha256(workbook.read_bytes()).hexdigest()
+ messages: list[str] = []
+ # with_report=False: the figures have their own module and their own tests,
+ # and rendering them here would put ~80 s of matplotlib into a test about
+ # whether a worklist is written.
+ generated = generate_next_round(
+ workbook, config, progress=messages.append, with_report=False
+ )
+
+ assert generated.round_name == "R1"
+ assert generated.sheet_path == candidate_workbook_path(workbook, "R1")
+ assert generated.sheet_path.exists()
+ assert generated.result.n_conditions == 5
+ assert generated.n_films == 15
+ assert hashlib.sha256(workbook.read_bytes()).hexdigest() == before
+ assert messages and "Done." in messages
+
+ summary = generated.summary()
+ assert "Nothing here is approved" in summary
+ assert "Sheet1: 15 conditions" in summary
+ # the sheet is immediately readable by the reader that will consume it
+ required, _ = measurement_entry_columns(config)
+ headers = [
+ cell.value
+ for cell in load_workbook(generated.sheet_path)[sheet_name_for_round("R1")][1]
+ ]
+ for column in required:
+ assert column in headers
+
+
+def test_generating_refuses_when_no_round_is_due(workbook, config) -> None:
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ with pytest.raises(LauncherError, match="no results have been entered"):
+ generate_next_round(workbook, config, with_report=False)
+
+
+def test_generating_never_overwrites_an_existing_sheet(workbook, config, monkeypatch) -> None:
+ """The sheet may already hold measurements. Refusing is the only safe move,
+ and it must happen before the ten seconds of model fitting, not after."""
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+
+ def fail(*args, **kwargs): # pragma: no cover - must never be reached
+ raise AssertionError("the round was fitted despite an existing sheet")
+
+ monkeypatch.setattr("mobo_kit.launcher.run_r1_ucb", fail)
+ monkeypatch.setattr(
+ "mobo_kit.launcher.inspect_campaign",
+ lambda *a, **k: CampaignStatus(
+ workbook=workbook,
+ next_round="R1",
+ reason="pretend R1 is due",
+ scored_rows=0,
+ total_rows=0,
+ observed_conditions=15,
+ ),
+ )
+ with pytest.raises(LauncherError, match="already exists"):
+ generate_next_round(workbook, config, with_report=False)
+
+
+# --------------------------------------------------------------------------- #
+# the shell
+# --------------------------------------------------------------------------- #
+
+
+@pytest.fixture
+def isolated_settings(monkeypatch):
+ """No remembered workbook, and no writing to the user's home.
+
+ Both matter. The launcher schedules a `check()` 200 ms after construction when
+ it remembers a workbook, so a path left behind by an earlier test raced the
+ explicit `check()` these tests perform and overwrote the pane with a different
+ result -- an order-dependent failure that only appeared in a full-suite run.
+ And a test suite has no business writing to ~/.mobo_kit either way.
+ """
+ monkeypatch.setattr("mobo_kit.launcher.remembered_workbook", lambda: None)
+ monkeypatch.setattr("mobo_kit.launcher.remember_workbook", lambda path: None)
+
+
+def _status_for(path) -> CampaignStatus:
+ from pathlib import Path
+
+ return CampaignStatus(
+ workbook=Path(path).resolve(),
+ next_round="R1",
+ reason="pretend R1 is due",
+ scored_rows=0,
+ total_rows=0,
+ observed_conditions=15,
+ )
+
+
+def _build_window(config_path: str):
+ """Construct the window, or skip if this machine will not start Tk at all."""
+ try:
+ import tkinter
+ except ImportError: # pragma: no cover - a build without tkinter
+ pytest.skip("tkinter is not installed")
+
+ from mobo_kit.launcher import LauncherWindow
+
+ try:
+ return LauncherWindow(config_path)
+ except tkinter.TclError as exc:
+ # No display, or a Tcl that will not initialise. That is the condition the
+ # old skipif declared. Only a toolkit-level failure skips, so a real defect
+ # in LauncherWindow still raises.
+ pytest.skip(f"tkinter will not start here: {exc}")
+
+
+@pytest.fixture
+def open_window(request):
+ """Build launcher windows with pytest's fd-level capture suspended.
+
+ Both halves of this matter, and both were measured -- the symptom is a
+ ``_tkinter.TclError`` saying ``couldn't read file ... init.tcl: No error``,
+ which reads like a broken Tcl install and is neither that nor a launcher bug.
+
+ **Tk must not be created during collection.** The module previously carried
+ six ``@pytest.mark.skipif(not _tk_available(), ...)`` decorators, and each
+ evaluation built and destroyed a real interpreter while pytest had file
+ descriptors 1 and 2 swapped for its capture temp files. Tcl's process-global
+ state then holds descriptors that are gone by the time a test runs. Measured:
+ ONE import-time ``Tk()`` fails the next one in 4 runs out of 5, while twenty
+ consecutive ``Tk()`` calls inside a test body all pass.
+
+ **Capture has to stay suspended while the window lives.** Moving construction
+ into the test body was not sufficient on its own: pytest re-swaps those
+ descriptors between tests, and the third window built in one process still
+ lost its interpreter. Suspending capture for the test that owns a window
+ leaves Tcl with descriptors that outlive it.
+
+ The whole effect disappears under ``-s``, ``--capture=sys`` and
+ ``--capture=tee-sys``, which is what identified fd capture as the cause. That
+ also explains the intermittency that made this look like a race in the
+ launcher: whether the stale descriptors happen to still be valid depends on
+ what file I/O ran in between, so one test failed alone and passed in a full
+ run.
+ """
+ manager = request.config.pluginmanager.getplugin("capturemanager")
+ suspended = (
+ contextlib.nullcontext()
+ if manager is None
+ else manager.global_and_fixture_disabled()
+ )
+ with suspended:
+ yield _build_window
+
+
+def test_the_window_reports_status_through_its_worker_thread(
+ workbook, config, isolated_settings, open_window
+) -> None:
+ """The UI does its work off the main thread and posts results through a queue.
+ Nothing else covers that plumbing, and a deadlock there would look like a
+ window that simply never responds."""
+ import time
+
+ window = open_window(CONFIG_PATH)
+ try:
+ window.path_var.set(str(workbook))
+ window.check()
+ deadline = time.monotonic() + 30
+ while time.monotonic() < deadline:
+ window.root.update()
+ if not window._busy and window._status is not None:
+ break
+ time.sleep(0.02)
+
+ assert window._status is not None, "the window never reported a status"
+ assert window._status.next_round == "R1"
+ assert window.headline.cget("text") == "Ready to propose R1."
+ assert window.generate_button.cget("text") == "Propose R1"
+ assert str(window.generate_button.cget("state")) == "normal"
+ body = window.text.get("1.0", "end")
+ assert "15 conditions on Sheet1" in body
+ finally:
+ window.root.destroy()
+
+
+def test_the_window_shows_a_readable_error_rather_than_a_traceback(
+ config, isolated_settings, open_window
+) -> None:
+ import time
+
+ window = open_window(CONFIG_PATH)
+ try:
+ window.path_var.set("nowhere/at/all.xlsx")
+ window.check()
+ deadline = time.monotonic() + 15
+ while time.monotonic() < deadline:
+ window.root.update()
+ if not window._busy:
+ break
+ time.sleep(0.02)
+ body = window.text.get("1.0", "end")
+ assert "does not exist" in body
+ assert "Traceback" not in body
+ assert window.headline.cget("text") == "Cannot continue."
+ finally:
+ window.root.destroy()
+
+
+def test_a_result_for_a_workbook_the_user_left_is_discarded(
+ workbook, config, isolated_settings, open_window
+) -> None:
+ """The race the test fixture hid, now closed at the source.
+
+ Work runs off the main thread, so a check dispatched against one workbook can
+ return after the user has selected another. Painting "Ready to propose R1" over
+ a different workbook is worse than painting nothing.
+
+ Driven through the queue rather than by racing two real threads. The first
+ version of this test did race them, passed alone, and failed intermittently in
+ a full-suite run -- a flaky test of a race-condition fix is worse than no test,
+ because it teaches people to re-run until green.
+ """
+ window = open_window(CONFIG_PATH)
+ try:
+ window.path_var.set(str(workbook))
+ window._start("pretending to read")
+ window._request_id = 1
+ # the user navigates away before the reply lands
+ window.path_var.set(str(workbook.parent / "somewhere else.xlsx"))
+ window._queue.put((1, "status", _status_for(workbook)))
+ window.drain_once()
+
+ assert window._status is None, "the stale status must not be adopted"
+ assert not window._busy, "a discarded reply must still clear the busy state"
+ assert "Ready to propose" not in window.headline.cget("text")
+ # buttons usable again rather than stuck disabled
+ assert str(window.check_button.cget("state")) == "normal"
+ assert str(window.generate_button.cget("state")) == "disabled"
+ finally:
+ window.root.destroy()
+
+
+def test_a_result_for_the_current_workbook_is_adopted(
+ workbook, config, isolated_settings, open_window
+) -> None:
+ """The other half of the rule: it must not discard everything."""
+ window = open_window(CONFIG_PATH)
+ try:
+ window.path_var.set(str(workbook))
+ window._start("pretending to read")
+ window._request_id = 1
+ window._queue.put((1, "status", _status_for(workbook)))
+ window.drain_once()
+
+ assert window._status is not None
+ assert window.headline.cget("text") == "Ready to propose R1."
+ assert str(window.generate_button.cget("state")) == "normal"
+ finally:
+ window.root.destroy()
+
+
+def test_a_superseded_reply_does_not_overwrite_a_newer_request(
+ workbook, config, isolated_settings, open_window
+) -> None:
+ """Two presses: the earlier press's answer must not land after the later one."""
+ window = open_window(CONFIG_PATH)
+ try:
+ window.path_var.set(str(workbook))
+ window._start("pretending to read")
+ window._request_id = 2 # a second press is already in flight
+ window._queue.put((1, "status", _status_for(workbook)))
+ window.drain_once()
+ assert window._status is None, "request 1's reply landed after request 2"
+ assert not window._busy
+
+ window._start("still pretending")
+ window._queue.put((2, "status", _status_for(workbook)))
+ window.drain_once()
+ assert window._status is not None, "request 2's own reply must land"
+ finally:
+ window.root.destroy()
+
+
+def test_the_startup_auto_check_is_cancelled_when_the_user_acts(
+ workbook, config, monkeypatch, open_window
+) -> None:
+ """The auto-check fires 200 ms after construction against the remembered
+ workbook. If the user has already pressed something, that answer is about the
+ wrong file."""
+ from mobo_kit import launcher as launcher_module
+
+ monkeypatch.setattr(launcher_module, "remembered_workbook", lambda: workbook)
+ monkeypatch.setattr(launcher_module, "remember_workbook", lambda path: None)
+
+ window = open_window(CONFIG_PATH)
+ try:
+ assert window._auto_check_id is not None, "a remembered workbook should schedule one"
+ window._cancel_auto_check()
+ assert window._auto_check_id is None
+ # cancelling twice is harmless
+ window._cancel_auto_check()
+ finally:
+ window.root.destroy()
+
+
+def test_nothing_in_this_module_builds_a_window_at_import_time() -> None:
+ """Pin the rule the :func:`open_window` fixture documents; it already regressed.
+
+ The specific way it comes back is a ``@pytest.mark.skipif(not
+ _tk_available(), ...)`` decorator: the expression is evaluated during
+ collection, which is exactly when constructing a Tk interpreter poisons the
+ next one. Decorators sit at column 0, so a source check catches that shape.
+
+ It is a source check rather than a runtime one on purpose -- an import-time
+ Tk that has already been destroyed leaves nothing to observe by the time any
+ test could look.
+ """
+ from pathlib import Path
+
+ source = Path(__file__).read_text(encoding="utf-8")
+ offenders = [
+ line
+ for line in source.splitlines()
+ if line[:1] not in ("", " ", "\t")
+ and any(token in line for token in ("Tk(", "LauncherWindow(", "_tk_available"))
+ ]
+ assert not offenders, (
+ "these lines run at collection time and build a toolkit object; move the "
+ f"construction into the test body via the open_window fixture: {offenders}"
+ )
+
+
+def test_the_logic_imports_without_tkinter(monkeypatch) -> None:
+ """A headless machine must still be able to use the functions. tkinter is
+ imported inside the window class for exactly this reason."""
+ import importlib
+ import sys
+
+ monkeypatch.setitem(sys.modules, "tkinter", None)
+ module = importlib.reload(importlib.import_module("mobo_kit.launcher"))
+ assert callable(module.generate_next_round)
diff --git a/tests/test_lhs.py b/tests/test_lhs.py
new file mode 100644
index 0000000..6ddd594
--- /dev/null
+++ b/tests/test_lhs.py
@@ -0,0 +1,165 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from mobo_kit.design import InputSpec, build_design
+from mobo_kit.lhs import lhs_dataframe, lhs_dataframe_optimized
+
+
+def _design():
+ return build_design(
+ [
+ InputSpec("speed", 0.0, 1.0, 0.25, unit="m/min"),
+ InputSpec("temperature", 20.0, 24.0, 1.0, unit="C"),
+ InputSpec("time", 5.0, 15.0, 5.0, unit="s"),
+ ]
+ )
+
+
+def _assert_exact_grid_membership(frame: pd.DataFrame, design) -> None:
+ for column, grid in zip(design.names, design.var_list):
+ assert np.all(np.isin(frame[column].to_numpy(), grid))
+
+
+def test_seeded_lhs_is_deterministic_unique_and_exactly_sized():
+ design = _design()
+ kwargs = dict(
+ design=design,
+ n=12,
+ seed=123,
+ samples_per_attempt=40,
+ max_attempts=5,
+ )
+
+ first = lhs_dataframe_optimized(**kwargs)
+ second = lhs_dataframe_optimized(**kwargs)
+
+ pd.testing.assert_frame_equal(first, second)
+ assert first.shape == (12, 3)
+ assert list(first.columns) == design.names
+ assert len(first.drop_duplicates()) == 12
+ _assert_exact_grid_membership(first, design)
+
+
+def test_different_seed_changes_at_least_one_condition():
+ design = _design()
+ first = lhs_dataframe_optimized(
+ design=design,
+ n=12,
+ seed=123,
+ samples_per_attempt=40,
+ max_attempts=5,
+ )
+ second = lhs_dataframe_optimized(
+ design=design,
+ n=12,
+ seed=124,
+ samples_per_attempt=40,
+ max_attempts=5,
+ )
+
+ assert not first.equals(second)
+
+
+def test_compatibility_wrapper_has_the_same_strict_contract():
+ design = _design()
+ frame = lhs_dataframe(
+ design,
+ n=8,
+ seed=77,
+ samples_per_attempt=24,
+ max_attempts=5,
+ )
+
+ assert frame.shape == (8, 3)
+ assert len(frame.drop_duplicates()) == 8
+ _assert_exact_grid_membership(frame, design)
+
+
+def test_constraints_are_applied_after_grid_snapping():
+ design = _design()
+ calls = []
+
+ def snapped_constraint(X, constrained_design):
+ for column, grid in enumerate(constrained_design.var_list):
+ assert np.all(np.isin(X[:, column], grid))
+ calls.append(X.copy())
+ speed_index = constrained_design.names.index("speed")
+ return X[:, speed_index] >= 0.75
+
+ frame = lhs_dataframe_optimized(
+ design,
+ n=5,
+ seed=5,
+ row_constraints=[snapped_constraint],
+ samples_per_attempt=30,
+ max_attempts=5,
+ )
+
+ assert calls
+ assert frame.shape == (5, 3)
+ assert np.all(frame["speed"] >= 0.75)
+ _assert_exact_grid_membership(frame, design)
+
+
+def test_impossible_constraint_raises_without_unconstrained_fallback():
+ design = _design()
+
+ def reject_everything(X, _design):
+ return np.zeros(X.shape[0], dtype=bool)
+
+ with pytest.raises(RuntimeError, match="Unable to generate exactly n=3"):
+ lhs_dataframe_optimized(
+ design,
+ n=3,
+ seed=9,
+ row_constraints=reject_everything,
+ samples_per_attempt=20,
+ max_attempts=2,
+ )
+
+
+def test_request_larger_than_grid_raises_before_sampling():
+ design = build_design([InputSpec("x", 0, 1, 1), InputSpec("y", 0, 1, 1)])
+
+ with pytest.raises(ValueError, match="only 4 unique grid combinations"):
+ lhs_dataframe_optimized(design, n=5, seed=1)
+
+
+def test_max_abs_corr_is_a_hard_requirement():
+ design = build_design([InputSpec("x", 0, 1, 1), InputSpec("y", 0, 1, 1)])
+
+ # A two-row Latin design on two binary dimensions varies in both columns;
+ # their absolute Pearson correlation is necessarily 1.
+ with pytest.raises(RuntimeError, match=r"required <= 0\.500000"):
+ lhs_dataframe_optimized(
+ design,
+ n=2,
+ seed=42,
+ max_abs_corr=0.5,
+ samples_per_attempt=2,
+ batch_size=2,
+ subset_tries=10,
+ max_attempts=1,
+ )
+
+
+def test_returned_design_satisfies_configured_correlation_limit():
+ design = build_design([InputSpec("x", 0, 1, 1), InputSpec("y", 0, 1, 1)])
+
+ frame = lhs_dataframe_optimized(
+ design,
+ n=4,
+ seed=3,
+ max_abs_corr=0.0,
+ samples_per_attempt=4,
+ max_attempts=10,
+ )
+
+ correlation = np.corrcoef(frame.to_numpy(), rowvar=False)[0, 1]
+ assert abs(correlation) <= 0.0
+
+
+def test_continuous_unsnapped_output_is_rejected_for_campaign_use():
+ with pytest.raises(ValueError, match="requires snap_to_grids=True"):
+ lhs_dataframe_optimized(_design(), n=3, seed=1, snap_to_grids=False)
diff --git a/tests/test_measurement_space_encoding.py b/tests/test_measurement_space_encoding.py
new file mode 100644
index 0000000..e0f49d9
--- /dev/null
+++ b/tests/test_measurement_space_encoding.py
@@ -0,0 +1,316 @@
+"""Measurement space against model space, and the bug that lived in the gap.
+
+``ObjectiveTransform.transform`` is a MODEL-OUTPUT decoder: it undoes the link
+(``exp`` for a log objective) before computing utility. Handing it a raw
+measurement exponentiates a number that was never a logarithm.
+
+``run_r1_ucb`` did exactly that with its observed HVI baseline until 2026-07-31.
+``exp(360…1303)`` saturates the 650 nm Gaussian to exactly ``0.0`` -- finite, so
+neither the transform's own finiteness check nor the caller's fired. Every
+observation's thickness utility was zero and the baseline hypervolume came out
+0.004659 where the truth is 0.436442.
+
+Two things about how it survived, both encoded as tests here.
+
+**It was already documented.** ``test_transform_reproduces_the_workbook_thickness_score``
+in ``test_campaign.py`` says in as many words that "feeding it raw nm would
+silently score exp(687) instead of 687" -- and then only ever tests the correct
+usage. Knowing a trap exists is not the same as testing that no caller falls in
+it.
+
+**Nothing compared the baseline to anything.** It was a plausible finite number
+that no test reproduced independently -- the same shape as the hypervolume
+auto-reference and the swallowed ``train_Yvar``. So ``run_r1_ucb`` now reports
+its baseline in diagnostics, and the test below recomputes it by a different
+route.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import torch
+from botorch.utils.multi_objective.hypervolume import Hypervolume
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ load_campaign_config,
+ run_r0_lhs,
+ run_r1_ucb,
+)
+from mobo_kit.ucb_hvi import pareto_utility_above_reference
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+SEED = 73
+
+#: The two numbers this bug produced on the real 15 R0 rows. Pinned so the size of
+#: the defect stays on record even if the workbook is later replaced.
+MISENCODED_BASELINE_HV = 0.004659
+CORRECT_BASELINE_HV = 0.436442
+
+
+def _synthetic_config(pool: int = 256) -> dict:
+ """A campaign with one log-link objective, so the encoding is exercised.
+
+ Inputs start at 1.0 so the log-response mean function has positive features.
+ """
+ return {
+ "inputs": [
+ {"name": f"x{i}", "start": 1.0, "stop": 2.0, "step": 0.05} for i in range(10)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-encoding-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": "affine_a",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "affine_a",
+ "lower_anchor": 0.0,
+ "upper_anchor": 3.0,
+ },
+ {
+ "name": "affine_b",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "affine_b",
+ "lower_anchor": -4.0,
+ "upper_anchor": 0.0,
+ },
+ {
+ "name": "log_linked",
+ "goal": "target",
+ "transform": "gaussian_target",
+ "model_source_column": "log_linked",
+ "target": 650.0,
+ "sigma": 176.7766952966369,
+ "mean_function": {
+ "response": "log",
+ "features": [{"column": "x0", "transform": "log"}],
+ },
+ },
+ ],
+ },
+ "reference_point_utility": [-0.01, -0.01, -0.01],
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi",
+ "batch_size": 5,
+ "replicates_per_condition": 3,
+ "beta": 4.0,
+ "candidate_pool_size": pool,
+ "posterior_samples": 16,
+ "moment_method": "monte_carlo",
+ },
+ "r2": {
+ "method": "qlognehvi",
+ "batch_size": 3,
+ "replicates_per_condition": 3,
+ "candidate_pool_size": pool,
+ "mc_samples": 8,
+ },
+ },
+ "local_penalization": {
+ "radius": 0.25,
+ "min_batch_distance": 0.15,
+ "min_observed_distance": 0.0,
+ "dimension_weights": None,
+ },
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": SEED},
+ "constraints": [],
+ }
+
+
+def _measurements(X: np.ndarray) -> np.ndarray:
+ X = np.asarray(X, dtype=float)
+ return np.column_stack([
+ X.mean(axis=1),
+ -np.linalg.norm(X - 1.5, axis=1),
+ 650.0 * X[:, 0] ** -0.5 * X[:, 1] ** 0.3, # straddles the 650 nm target
+ ])
+
+
+# --------------------------------------------------------------------------- #
+# the encoder itself
+# --------------------------------------------------------------------------- #
+
+
+def test_encode_measurements_logs_only_the_log_link_axes() -> None:
+ transform = build_objective_transform(_synthetic_config())
+ measured = torch.tensor([[1.5, -2.0, 700.0], [2.0, -1.0, 500.0]], dtype=torch.double)
+ encoded = transform.encode_measurements(measured)
+ torch.testing.assert_close(encoded[:, :2], measured[:, :2])
+ torch.testing.assert_close(encoded[:, 2], torch.log(measured[:, 2]))
+
+
+def test_transform_measurements_is_encode_then_transform() -> None:
+ transform = build_objective_transform(_synthetic_config())
+ measured = torch.tensor([[1.5, -2.0, 700.0]], dtype=torch.double)
+ torch.testing.assert_close(
+ transform.transform_measurements(measured),
+ transform.transform(transform.encode_measurements(measured)),
+ )
+
+
+def test_transform_measurements_matches_the_gaussian_computed_by_hand() -> None:
+ """An independent comparator: the closed form, not another code path."""
+ transform = build_objective_transform(_synthetic_config())
+ nm = np.array([360.0, 650.0, 700.0, 1303.0])
+ measured = torch.tensor(
+ np.column_stack([np.full(nm.size, 1.5), np.full(nm.size, -2.0), nm]),
+ dtype=torch.double,
+ )
+ got = transform.transform_measurements(measured)[:, 2].numpy()
+ sigma = 176.7766952966369
+ expected = np.exp(-0.5 * ((nm - 650.0) / sigma) ** 2)
+ np.testing.assert_allclose(got, expected, atol=1e-12)
+
+
+def test_encode_measurements_rejects_non_positive_on_a_log_link() -> None:
+ transform = build_objective_transform(_synthetic_config())
+ with pytest.raises(ValueError, match="strictly positive"):
+ transform.encode_measurements(
+ torch.tensor([[1.0, -1.0, 0.0]], dtype=torch.double)
+ )
+
+
+def test_encode_measurements_rejects_non_finite_input() -> None:
+ transform = build_objective_transform(_synthetic_config())
+ with pytest.raises(ValueError, match="finite"):
+ transform.encode_measurements(
+ torch.tensor([[1.0, -1.0, float("inf")]], dtype=torch.double)
+ )
+
+
+# --------------------------------------------------------------------------- #
+# the failure mode, pinned so it stays recognisable
+# --------------------------------------------------------------------------- #
+
+
+def test_unencoded_nanometres_collapse_to_exactly_zero_utility() -> None:
+ """Why the bug was silent: the wrong answer is a finite, ordinary-looking 0.0.
+
+ This asserts the BROKEN behaviour of the raw call deliberately. It is the
+ fingerprint to recognise if it ever reappears somewhere else.
+ """
+ transform = build_objective_transform(_synthetic_config())
+ measured = torch.tensor([[1.5, -2.0, 360.0], [1.5, -2.0, 1303.0]], dtype=torch.double)
+
+ unencoded = transform.transform(measured) # the mistake
+ assert torch.isfinite(unencoded).all(), "no guard fires -- that is the problem"
+ assert bool((unencoded[:, 2] == 0.0).all())
+
+ encoded = transform.transform_measurements(measured) # the fix
+ assert bool((encoded[:, 2] > 0.0).all())
+
+
+@pytest.mark.parametrize("nm", [360.0, 500.0, 650.0, 900.0, 1303.0])
+def test_a_finite_in_range_measurement_never_scores_exactly_zero(nm: float) -> None:
+ """The invariant the bug violated, stated directly.
+
+ A real film that was measured at all has some merit on every axis. A utility of
+ exactly 0.0 for a finite measurement means an encoding was skipped, not that
+ the film was worthless.
+ """
+ transform = build_objective_transform(_synthetic_config())
+ measured = torch.tensor([[1.5, -2.0, nm]], dtype=torch.double)
+ utility = transform.transform_measurements(measured)
+ assert torch.isfinite(utility).all()
+ assert not bool((utility == 0.0).any())
+
+
+# --------------------------------------------------------------------------- #
+# the regression test: it fails on the pre-fix run_r1_ucb
+# --------------------------------------------------------------------------- #
+
+
+def test_run_r1_ucb_baseline_matches_an_independent_computation() -> None:
+ """The comparator that did not exist.
+
+ ``run_r1_ucb`` now reports the baseline hypervolume the acquisition actually
+ used. Here it is recomputed by a separate route -- explicit encode, explicit
+ Pareto filter, explicit Hypervolume -- and the two must agree.
+
+ On the pre-fix code the reported value is the collapsed one and this fails.
+ """
+ config = _synthetic_config()
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+
+ X = run_r0_lhs(config, n=15, seed=SEED).conditions.to_numpy(float)
+ Y = _measurements(X)
+
+ result = run_r1_ucb(config, X, Y, seed=SEED)
+ reported = result.diagnostics["observed_baseline_hypervolume"]
+
+ utility = transform.transform_measurements(
+ torch.tensor(Y, dtype=torch.double)
+ ).numpy()
+ pareto = pareto_utility_above_reference(utility, reference)
+ expected = float(
+ Hypervolume(ref_point=torch.tensor(reference, dtype=torch.double)).compute(
+ torch.tensor(pareto, dtype=torch.double)
+ )
+ )
+
+ assert reported == pytest.approx(expected, rel=1e-9)
+ assert result.diagnostics["observed_baseline_pareto_size"] == len(pareto)
+
+ # and the mis-encoded route gives a materially different answer, so the
+ # assertion above has teeth rather than passing on a coincidence
+ collapsed = transform.transform(torch.tensor(Y, dtype=torch.double)).numpy()
+ assert not np.allclose(collapsed[:, 2], utility[:, 2])
+
+
+def test_the_baseline_is_reported_at_all() -> None:
+ """A number nobody can see is a number nobody can check."""
+ config = _synthetic_config()
+ X = run_r0_lhs(config, n=15, seed=SEED).conditions.to_numpy(float)
+ result = run_r1_ucb(config, X, _measurements(X), seed=SEED)
+ assert "observed_baseline_hypervolume" in result.diagnostics
+ assert "observed_baseline_pareto_size" in result.diagnostics
+ assert result.diagnostics["observed_baseline_hypervolume"] > 0.0
+
+
+# --------------------------------------------------------------------------- #
+# the live campaign's own numbers
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.local_input
+@pytest.mark.skipif(
+ not __import__("pathlib").Path("local_inputs/Summary Table.xlsx").is_file(),
+ reason="local_inputs/Summary Table.xlsx is not present in this checkout",
+)
+def test_the_real_workbook_reproduces_the_two_recorded_baselines() -> None:
+ """The 94x, on the actual data, so the recorded numbers stay falsifiable."""
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ config = load_campaign_config(CONFIG_PATH)
+ transform = build_objective_transform(config)
+ reference = np.asarray(config["reference_point_utility"], dtype=float)
+
+ contents = read_campaign_workbook("local_inputs/Summary Table.xlsx", config)
+ assert contents.errors == ()
+ Y = contents.model_values.to_numpy(float)
+
+ def hv(utility: np.ndarray) -> float:
+ pareto = pareto_utility_above_reference(utility, reference)
+ if pareto.shape[0] == 0:
+ return 0.0
+ return float(
+ Hypervolume(ref_point=torch.tensor(reference, dtype=torch.double)).compute(
+ torch.tensor(pareto, dtype=torch.double)
+ )
+ )
+
+ correct = hv(transform.transform_measurements(torch.tensor(Y, dtype=torch.double)).numpy())
+ misencoded = hv(transform.transform(torch.tensor(Y, dtype=torch.double)).numpy())
+
+ assert correct == pytest.approx(CORRECT_BASELINE_HV, abs=5e-6)
+ assert misencoded == pytest.approx(MISENCODED_BASELINE_HV, abs=5e-6)
+ # every thickness utility was zero under the mis-encoding
+ collapsed = transform.transform(torch.tensor(Y, dtype=torch.double)).numpy()
+ assert bool((collapsed[:, 2] == 0.0).all())
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..bd36a53
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,127 @@
+"""Hypervolume against a fixed reference.
+
+No test covered `compute_ref_pareto_hv` before 2026-07-30, which is how its
+degenerate auto-reference survived: it returned a number, and nobody compared that
+number with the right one.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import torch
+from botorch.utils.multi_objective.hypervolume import infer_reference_point
+
+from mobo_kit.metrics import compute_diversity_score, compute_ref_pareto_hv
+
+
+def _front() -> torch.Tensor:
+ """Three mutually non-dominated points plus one dominated one."""
+ return torch.tensor(
+ [
+ [1.0, 0.2, 0.5],
+ [0.2, 1.0, 0.5],
+ [0.5, 0.5, 1.0],
+ [0.1, 0.1, 0.1],
+ ],
+ dtype=torch.double,
+ )
+
+
+def test_a_missing_reference_is_refused_with_the_config_key_named() -> None:
+ """The old default was `Y.min(dim=0) - 1e-8`, which made every slab 1e-8 thick
+ and re-derived itself from the data on every call."""
+ with pytest.raises(ValueError, match="reference_point_utility"):
+ compute_ref_pareto_hv(_front())
+
+
+def test_the_error_says_why_an_inferred_reference_is_wrong() -> None:
+ with pytest.raises(ValueError, match="incomparable across them"):
+ compute_ref_pareto_hv(_front(), None)
+
+
+def test_an_explicit_reference_gives_the_dominated_volume() -> None:
+ Y = _front()
+ reference = np.array([-0.01, -0.01, -0.01])
+ ref_point_t, pareto_Y, volume = compute_ref_pareto_hv(Y, reference)
+ assert ref_point_t.dtype == Y.dtype
+ assert pareto_Y.shape[0] == 3 # the dominated point is dropped
+ assert volume > 0.0
+
+
+def test_the_reference_is_not_re_derived_from_the_data() -> None:
+ """The same reference on a growing dataset must give a monotone,
+ comparable series. With the old auto-reference it did not."""
+ Y = _front()
+ reference = np.array([-0.01, -0.01, -0.01])
+ _, _, first = compute_ref_pareto_hv(Y[:3], reference)
+ _, _, second = compute_ref_pareto_hv(Y, reference)
+ assert second >= first
+ extra = torch.cat([Y, torch.tensor([[1.2, 1.2, 1.2]], dtype=Y.dtype)])
+ _, _, third = compute_ref_pareto_hv(extra, reference)
+ assert third > second
+
+
+def test_the_old_auto_reference_collapses_on_a_real_trade_off_front() -> None:
+ """Pins the reason this changed, and the condition for it.
+
+ `Y.min(dim=0) - 1e-8` is only harmless while some *dominated* point sets the
+ per-objective minima. As soon as the Pareto set itself sets them -- which is
+ what a genuine trade-off front looks like, each point best in one objective and
+ worst in another -- every slab is 1e-8 thick in at least one dimension and the
+ volume collapses. That is the 6e-8-against-1.448 in the campaign notes.
+ """
+ trade_off = torch.tensor(
+ [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], dtype=torch.double
+ )
+ degenerate = (trade_off.min(dim=0).values - 1e-8).numpy()
+ _, _, degenerate_volume = compute_ref_pareto_hv(trade_off, degenerate)
+ inferred = infer_reference_point(trade_off).numpy()
+ _, _, inferred_volume = compute_ref_pareto_hv(trade_off, inferred)
+
+ assert degenerate_volume < 1e-10
+ # not pinned tightly: infer_reference_point's margin below the nadir is a
+ # BoTorch heuristic, and the claim here is the ratio, not its exact value
+ assert inferred_volume > 1e-3
+ assert degenerate_volume < inferred_volume / 1e6
+
+
+def test_a_reference_nothing_dominates_is_refused_not_reported_as_zero() -> None:
+ """BoTorch silently drops points that do not dominate the reference, so an
+ unreachable reference reads as 0.0 -- indistinguishable from a sign error."""
+ with pytest.raises(ValueError, match="No observation dominates"):
+ compute_ref_pareto_hv(_front(), np.array([10.0, 10.0, 10.0]))
+
+
+def test_a_flipped_sign_convention_is_caught_by_the_same_check() -> None:
+ minimising = -_front()
+ with pytest.raises(ValueError, match="every objective must be maximised"):
+ compute_ref_pareto_hv(minimising, np.array([-0.01, -0.01, -0.01]))
+
+
+@pytest.mark.parametrize(
+ "reference, match",
+ [
+ (np.zeros((2, 3)), "must be 1D"),
+ (np.zeros(2), "does not match number of objectives"),
+ (np.array([0.0, np.inf, 0.0]), "must be finite"),
+ ("not an array", "must be a numpy.ndarray"),
+ ],
+)
+def test_a_malformed_reference_is_refused(reference, match) -> None:
+ with pytest.raises((ValueError, TypeError), match=match):
+ compute_ref_pareto_hv(_front(), reference)
+
+
+def test_a_torch_reference_is_accepted() -> None:
+ """Callers hold the reference as a tensor as often as an array."""
+ _, _, volume = compute_ref_pareto_hv(
+ _front(), torch.tensor([-0.01, -0.01, -0.01], dtype=torch.double)
+ )
+ assert volume > 0.0
+
+
+def test_diversity_score_is_the_mean_pairwise_distance() -> None:
+ X = np.array([[0.0, 0.0], [3.0, 4.0]])
+ assert compute_diversity_score(X) == pytest.approx(5.0)
+ assert compute_diversity_score(np.array([[1.0, 1.0]])) == 0.0
diff --git a/tests/test_model_validation.py b/tests/test_model_validation.py
new file mode 100644
index 0000000..402f327
--- /dev/null
+++ b/tests/test_model_validation.py
@@ -0,0 +1,675 @@
+from __future__ import annotations
+
+import warnings
+
+import gpytorch
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+import mobo_kit.model_validation as validation_module
+import mobo_kit.models as models_module
+from mobo_kit.model_validation import (
+ CONSERVATIVE,
+ DIM_SCALED_PRIOR,
+ LEGACY_NO_PRIOR,
+ PRIMARY_VARIANT,
+ ModelFitCache,
+ ModelFitError,
+ ModelVariantSpec,
+ compute_prediction_metrics,
+ extract_model_hyperparameters,
+ fit_model_variant,
+ fit_warnings_frame,
+ model_variant_spec,
+ run_exact_loocv,
+ validate_model_variant,
+)
+from mobo_kit.models import fit_gp_models
+
+
+def _training_data() -> tuple[torch.Tensor, torch.Tensor, tuple[int, ...]]:
+ X = torch.tensor(
+ [
+ [0.0, 0.0],
+ [0.2, 0.8],
+ [0.5, 0.3],
+ [0.8, 0.9],
+ [1.0, 0.1],
+ ],
+ dtype=torch.double,
+ )
+ Y = torch.stack(
+ (
+ 0.2 + 0.7 * X[:, 0] + 0.1 * X[:, 1],
+ -1.0 + 0.3 * X[:, 0] - 0.5 * X[:, 1],
+ ),
+ dim=1,
+ )
+ return X, Y, (1, 2, 3, 4, 5)
+
+
+def _skip_optimization(mll: object) -> object:
+ return mll
+
+
+def test_model_variant_contracts_are_fixed() -> None:
+ assert model_variant_spec("dim_scaled_prior") is DIM_SCALED_PRIOR
+ assert DIM_SCALED_PRIOR.min_noise == pytest.approx(1.0e-4)
+ assert DIM_SCALED_PRIOR.min_lengthscale is None
+ assert DIM_SCALED_PRIOR.use_dim_scaled_prior is True
+ assert DIM_SCALED_PRIOR.use_lognormal_noise_prior is True
+ assert model_variant_spec("legacy_matern_no_prior") is LEGACY_NO_PRIOR
+ assert LEGACY_NO_PRIOR.use_dim_scaled_prior is False
+ assert LEGACY_NO_PRIOR.use_lognormal_noise_prior is False
+ assert model_variant_spec("conservative") is CONSERVATIVE
+ assert CONSERVATIVE.min_noise == pytest.approx(0.01)
+ assert CONSERVATIVE.min_lengthscale == pytest.approx(0.05)
+
+ with pytest.raises(ValueError, match="dim_scaled_prior"):
+ ModelVariantSpec("dim_scaled_prior", min_noise=0.01, min_lengthscale=None)
+ with pytest.raises(ValueError, match="dim_scaled_prior"):
+ # both priors are part of the contract, not options
+ ModelVariantSpec("dim_scaled_prior", min_noise=1.0e-4, min_lengthscale=None)
+ with pytest.raises(ValueError, match="dim_scaled_prior"):
+ # the lengthscale prior alone is the degenerate configuration
+ ModelVariantSpec(
+ "dim_scaled_prior",
+ min_noise=1.0e-4,
+ min_lengthscale=None,
+ use_dim_scaled_prior=True,
+ )
+ with pytest.raises(ValueError, match="legacy_matern_no_prior"):
+ ModelVariantSpec(
+ "legacy_matern_no_prior",
+ min_noise=1.0e-3,
+ min_lengthscale=None,
+ use_dim_scaled_prior=True,
+ )
+ with pytest.raises(ValueError, match="conservative"):
+ ModelVariantSpec("conservative", min_noise=0.01, min_lengthscale=0.01)
+ with pytest.raises(ValueError, match="Unsupported"):
+ model_variant_spec("mystery")
+
+
+def test_primary_variant_is_the_prior_regularised_model() -> None:
+ """The fixed model is the default; the retired one must be asked for by name."""
+ assert PRIMARY_VARIANT is DIM_SCALED_PRIOR
+ assert PRIMARY_VARIANT.name == "dim_scaled_prior"
+
+
+def test_dim_scaled_prior_attaches_a_lengthscale_prior(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", _skip_optimization)
+ X, Y, sample_ids = _training_data()
+
+ primary = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ )
+ legacy = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=LEGACY_NO_PRIOR,
+ )
+
+ for gp in primary.model.models:
+ base = gp.covar_module.base_kernel
+ assert base.lengthscale_prior is not None
+ # LogNormal(loc = sqrt(2) + log(d)/2, scale = sqrt(3)); d = 2 here
+ assert float(base.lengthscale_prior.loc) == pytest.approx(
+ np.sqrt(2.0) + np.log(X.shape[1]) / 2.0
+ )
+ for gp in legacy.model.models:
+ assert not hasattr(gp.covar_module.base_kernel, "lengthscale_prior") or (
+ gp.covar_module.base_kernel.lengthscale_prior is None
+ )
+
+
+def test_legacy_variant_matches_step2b_model_construction(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ """The retired contract must still reproduce archived Step 2B/2C runs exactly."""
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", _skip_optimization)
+ monkeypatch.setattr(models_module, "fit_gpytorch_mll", _skip_optimization)
+ X, Y, sample_ids = _training_data()
+
+ torch.manual_seed(73)
+ strict = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=LEGACY_NO_PRIOR,
+ seed=73,
+ )
+ torch.manual_seed(73)
+ historical = fit_gp_models(X, Y)
+ query = X[:3]
+ with torch.no_grad():
+ strict_posterior = strict.model.posterior(query)
+ historical_posterior = historical.posterior(query)
+
+ assert all(
+ type(strict_gp.covar_module.base_kernel)
+ is type(historical_gp.covar_module.base_kernel)
+ for strict_gp, historical_gp in zip(strict.model.models, historical.models)
+ )
+ torch.testing.assert_close(strict_posterior.mean, historical_posterior.mean)
+ torch.testing.assert_close(strict_posterior.variance, historical_posterior.variance)
+
+
+def test_conservative_constraints_and_hyperparameter_extraction(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", _skip_optimization)
+ X, Y, sample_ids = _training_data()
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=CONSERVATIVE,
+ )
+
+ for gp in record.model.models:
+ noise_floor = gp.likelihood.noise_covar.raw_noise_constraint.lower_bound
+ lengthscale_floor = (
+ gp.covar_module.base_kernel.raw_lengthscale_constraint.lower_bound
+ )
+ assert float(noise_floor) == pytest.approx(0.01)
+ assert float(lengthscale_floor) == pytest.approx(0.05)
+ assert float(gp.likelihood.noise.detach()) > 0.01
+ assert torch.all(gp.covar_module.base_kernel.lengthscale > 0.05)
+
+ parameters = extract_model_hyperparameters(
+ record, input_names=("input_a", "input_b")
+ )
+ assert len(parameters) == 2
+ assert all(row.configured_min_noise == 0.01 for row in parameters)
+ assert all(row.configured_min_lengthscale == 0.05 for row in parameters)
+ assert all(len(row.ard_lengthscales) == 2 for row in parameters)
+ assert all(row.input_parameter_space == "normalized_0_1" for row in parameters)
+ assert all(
+ row.outcome_parameter_space == "standardized_internal" for row in parameters
+ )
+ flattened = pd.DataFrame(row.as_flat_dict() for row in parameters)
+ assert {
+ "ard_lengthscale_input_a",
+ "ard_lengthscale_input_b",
+ "likelihood_noise",
+ "outputscale",
+ } <= set(flattened.columns)
+
+
+def test_lengthscale_flags_are_relative_to_normalized_domain(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", _skip_optimization)
+ X, Y, sample_ids = _training_data()
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ )
+ for gp in record.model.models:
+ gp.covar_module.base_kernel.lengthscale = torch.tensor(
+ [[0.01, 20.0]], dtype=torch.double
+ )
+
+ parameters = extract_model_hyperparameters(
+ record, input_names=("input_a", "input_b")
+ )
+
+ assert all(
+ row.lengthscales_very_small_normalized_domain == (True, False)
+ for row in parameters
+ )
+ assert all(
+ row.lengthscales_extremely_large_flat == (False, True) for row in parameters
+ )
+ flattened = pd.DataFrame(row.as_flat_dict() for row in parameters)
+ assert flattened["any_lengthscale_very_small_normalized_domain"].all()
+ assert flattened["any_lengthscale_extremely_large_flat"].all()
+ assert flattened["ard_lengthscale_input_a_very_small_normalized_domain"].all()
+ assert flattened["ard_lengthscale_input_b_extremely_large_flat"].all()
+
+
+def test_fit_failure_is_structured_and_never_printed_or_retried(
+ monkeypatch: pytest.MonkeyPatch,
+ capsys: pytest.CaptureFixture[str],
+) -> None:
+ calls = 0
+
+ def fail_strictly(mll: object) -> object:
+ nonlocal calls
+ calls += 1
+ warnings.warn("optimizer diagnostic", RuntimeWarning, stacklevel=2)
+ raise RuntimeError("optimizer stopped")
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", fail_strictly)
+ X, Y, sample_ids = _training_data()
+
+ with pytest.raises(ModelFitError) as captured:
+ fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ )
+
+ error = captured.value
+ assert calls == 1
+ assert error.stage == "optimize"
+ assert error.objective_index == 0
+ assert isinstance(error.cause, RuntimeError)
+ assert any(
+ row.warning_category == "RuntimeWarning"
+ and row.message == "optimizer diagnostic"
+ for row in error.fit_warnings
+ )
+ captured_output = capsys.readouterr()
+ assert captured_output.out == ""
+ assert captured_output.err == ""
+
+
+def test_constructor_failure_retains_structured_warnings(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ def fail_during_construction(*args: object, **kwargs: object) -> object:
+ del args, kwargs
+ warnings.warn("constructor diagnostic", UserWarning, stacklevel=2)
+ raise RuntimeError("constructor stopped")
+
+ monkeypatch.setattr(
+ validation_module, "_build_single_task_gp", fail_during_construction
+ )
+ X, Y, sample_ids = _training_data()
+ with pytest.raises(ModelFitError) as captured:
+ fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ )
+
+ assert captured.value.stage == "construct"
+ assert any(
+ row.message == "constructor diagnostic"
+ and row.warning_category == "UserWarning"
+ for row in captured.value.fit_warnings
+ )
+
+
+def test_successful_fit_warnings_are_structured(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ def warn_and_succeed(mll: object) -> object:
+ warnings.warn("fit reached a bound", UserWarning, stacklevel=2)
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", warn_and_succeed)
+ X, Y, sample_ids = _training_data()
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ fit_key="warning-test",
+ )
+
+ optimizer_warnings = [row for row in record.warnings if row.stage == "optimize"]
+ assert len(optimizer_warnings) == 2
+ assert all(row.warning_category == "UserWarning" for row in optimizer_warnings)
+ assert all(row.fit_key == "warning-test" for row in optimizer_warnings)
+ frame = fit_warnings_frame([record])
+ assert frame.shape[0] >= 2
+ assert {"stage", "warning_category", "message"} <= set(frame.columns)
+
+
+def test_exact_loocv_retains_folds_uncertainty_roles_and_cache(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ fit_calls = 0
+
+ def count_fit(mll: object) -> object:
+ nonlocal fit_calls
+ fit_calls += 1
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", count_fit)
+ X, Y, sample_ids = _training_data()
+ cache = ModelFitCache()
+ first = run_exact_loocv(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ seed=19,
+ row_roles=("control", "r0", "r0", "r0", "r0"),
+ control_sample_ids=(1,),
+ cache=cache,
+ )
+ first_fit_calls = fit_calls
+ repeat = run_exact_loocv(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ seed=19,
+ row_roles=("control", "r0", "r0", "r0", "r0"),
+ control_sample_ids=(1,),
+ cache=cache,
+ )
+
+ assert first_fit_calls == len(sample_ids) * Y.shape[1]
+ assert fit_calls == first_fit_calls
+ assert cache.hits == len(sample_ids)
+ assert len(first.predictions) == len(sample_ids) * Y.shape[1]
+ assert len(first.fold_records) == len(sample_ids)
+ assert [row.omitted_sample_id for row in first.predictions] == [
+ sample_id for sample_id in sample_ids for _ in range(Y.shape[1])
+ ]
+ assert sum(row.is_control for row in first.predictions) == Y.shape[1]
+ assert {row.row_role for row in first.predictions if row.is_control} == {"control"}
+ assert all(row.predictive_std >= row.latent_std for row in first.predictions)
+ assert any(row.predictive_std > row.latent_std for row in first.predictions)
+ assert all(np.isfinite(row.gaussian_nlpd) for row in first.predictions)
+ for omitted_id, record in first.fold_records.items():
+ assert omitted_id not in record.sample_ids
+ assert len(record.sample_ids) == len(sample_ids) - 1
+ pd.testing.assert_frame_equal(
+ first.predictions_frame(), repeat.predictions_frame(), check_exact=False
+ )
+ assert len(first.metrics) == Y.shape[1]
+ assert all(metric.prediction_count == len(sample_ids) for metric in first.metrics)
+
+ run_exact_loocv(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ objective_names=("one", "two"),
+ variant=DIM_SCALED_PRIOR,
+ seed=20,
+ cache=cache,
+ )
+ assert fit_calls == first_fit_calls * 2
+
+
+def test_validate_model_variant_extracts_full_and_fold_hyperparameters(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", _skip_optimization)
+ X, Y, sample_ids = _training_data()
+ result = validate_model_variant(
+ X,
+ Y,
+ sample_ids=sample_ids,
+ input_names=("input_a", "input_b"),
+ objective_names=("one", "two"),
+ variant=CONSERVATIVE,
+ seed=73,
+ control_sample_ids=(1,),
+ )
+
+ assert result.full_fit.omitted_sample_id is None
+ assert len(result.loocv.fold_records) == len(sample_ids)
+ assert len(result.hyperparameters) == (len(sample_ids) + 1) * Y.shape[1]
+ frame = result.hyperparameters_frame()
+ assert set(frame["fit_key"]) == {
+ "full",
+ *{f"omit:int:{sample_id!r}" for sample_id in sample_ids},
+ }
+ assert np.allclose(frame["noise_constraint_lower_bound"], 0.01)
+ assert np.allclose(frame["lengthscale_constraint_lower_bound"], 0.05)
+ assert set(result.loocv.fold_records) == set(sample_ids)
+ assert result.loocv.cache is not None
+
+
+def test_prediction_metrics_match_hand_calculation() -> None:
+ observed = np.array([0.0, 1.0])
+ predicted = np.array([0.1, 0.9])
+ uncertainty = np.array([0.2, 0.2])
+
+ result = compute_prediction_metrics(
+ observed,
+ predicted,
+ uncertainty,
+ variant_name="hand",
+ objective_index=2,
+ objective_name="score",
+ )
+
+ expected_nlpd = 0.5 * np.log(2.0 * np.pi * 0.2**2) + 0.5 * 0.5**2
+ assert result.prediction_count == 2
+ assert result.mae == pytest.approx(0.1)
+ assert result.rmse == pytest.approx(0.1)
+ assert result.r_squared == pytest.approx(0.96)
+ assert result.spearman_rank_correlation == pytest.approx(1.0)
+ assert result.mean_signed_error == pytest.approx(0.0, abs=1e-15)
+ assert result.median_absolute_error == pytest.approx(0.1)
+ assert result.coverage_68_percent == 1.0
+ assert result.coverage_95_percent == 1.0
+ assert result.mean_standardized_residual == pytest.approx(0.0, abs=1e-15)
+ assert result.maximum_absolute_standardized_residual == pytest.approx(0.5)
+ assert result.mean_gaussian_nlpd == pytest.approx(expected_nlpd)
+ assert "small N=2" in result.r_squared_warning
+
+
+def test_prediction_metrics_report_undefined_r_squared_and_validate_uncertainty() -> (
+ None
+):
+ constant = compute_prediction_metrics(
+ [1.0, 1.0, 1.0],
+ [0.9, 1.0, 1.1],
+ [0.2, 0.2, 0.2],
+ )
+ assert np.isnan(constant.r_squared)
+ assert np.isnan(constant.spearman_rank_correlation)
+ assert "undefined" in constant.r_squared_warning
+
+ with pytest.raises(ValueError, match="strictly positive"):
+ compute_prediction_metrics([0.0], [0.0], [0.0])
+
+
+def test_dim_scaled_prior_carries_the_lognormal_noise_prior() -> None:
+ """Regression guard for the outputscale-collapse mode.
+
+ With only the lengthscale prior, the marginal likelihood could drive the
+ outputscale to zero and explain the data as pure noise, leaving a latent
+ predictive sd near 1e-4 against a fitted noise near 0.93. Measured on the
+ real campaign data that produced 68% coverage of 0.133 and mean NLPD 3.1e6
+ over the leave-one-out folds. The noise prior is what rules it out.
+ """
+ X = torch.rand(12, 3, dtype=torch.double)
+ Y = torch.rand(12, 1, dtype=torch.double)
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(12)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ )
+ for gp in record.model.models:
+ assert gp.likelihood.noise_covar.noise_prior is not None
+ assert float(gp.likelihood.noise_covar.noise_prior.loc) == pytest.approx(-4.0)
+ assert float(gp.likelihood.noise_covar.noise_prior.scale) == pytest.approx(1.0)
+ floor = gp.likelihood.noise_covar.raw_noise_constraint.lower_bound
+ assert float(floor) == pytest.approx(1.0e-4)
+
+ record.model.eval()
+ with torch.no_grad():
+ latent_sd = record.model.posterior(X).variance.sqrt()
+ # a collapsed outputscale shows up here as a latent sd orders of magnitude
+ # below the outcome scale
+ assert float(latent_sd.min()) > 1e-3
+
+
+def test_legacy_variant_keeps_its_bare_noise_floor() -> None:
+ """The retired contract must not silently inherit the new noise prior."""
+ X = torch.rand(12, 3, dtype=torch.double)
+ Y = torch.rand(12, 1, dtype=torch.double)
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(12)),
+ objective_names=("y",),
+ variant=LEGACY_NO_PRIOR,
+ )
+ for gp in record.model.models:
+ # a prior-free HomoskedasticNoise has no noise_prior attribute at all
+ assert getattr(gp.likelihood.noise_covar, "noise_prior", None) is None
+ floor = gp.likelihood.noise_covar.raw_noise_constraint.lower_bound
+ assert float(floor) == pytest.approx(1.0e-3)
+
+
+def test_signal_collapse_guard_fires_on_a_degenerate_fit(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ """A numerical guard, not a naming guard.
+
+ Config-level naming cannot prevent a degenerate optimum: the same contract
+ refitted on new data can land there again. So the assertion runs on every
+ fit. Here the collapse is forced directly by zeroing the outputscale.
+ """
+ X = torch.rand(10, 2, dtype=torch.double)
+ Y = torch.rand(10, 1, dtype=torch.double)
+
+ real_fit = validation_module.fit_gpytorch_mll
+
+ def collapse_outputscale(mll):
+ real_fit(mll)
+ # emulate the observed failure: no signal, all noise
+ mll.model.covar_module.outputscale = torch.tensor(1e-12, dtype=torch.double)
+ mll.model.likelihood.noise = torch.tensor(0.9, dtype=torch.double)
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", collapse_outputscale)
+ with pytest.raises(ModelFitError) as excinfo:
+ fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(10)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ )
+ assert excinfo.value.stage == "signal_collapse_guard"
+ assert isinstance(excinfo.value.cause, validation_module.SignalCollapseError)
+ assert "pure noise" in str(excinfo.value.cause)
+
+
+def test_signal_collapse_warns_rather_than_fails_when_a_mean_carries_the_trend(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ """The same collapsed outputscale, but with a mean module doing the work.
+
+ A mean module is not part of the covariance, so it never enters
+ `posterior().variance` -- the latent sd collapses exactly as above while the
+ posterior MEAN still varies and candidates still rank. Refusing here would
+ dead-end the campaign at the moment the physics model started working, with no
+ way out: better data cannot be collected without first proposing conditions.
+
+ So it warns, and the warning has to be honest about what is wrong -- the
+ exploration term is dead, and the frozen mean coefficients carry no
+ uncertainty, so the reported intervals are understated rather than earned.
+ """
+
+ class VaryingMean(gpytorch.means.Mean):
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return 5.0 * x[..., 0]
+
+ X = torch.rand(12, 2, dtype=torch.double)
+ Y = (5.0 * X[:, :1]).double()
+
+ real_fit = validation_module.fit_gpytorch_mll
+
+ def collapse_outputscale(mll):
+ real_fit(mll)
+ mll.model.covar_module.outputscale = torch.tensor(1e-12, dtype=torch.double)
+ mll.model.likelihood.noise = torch.tensor(0.9, dtype=torch.double)
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", collapse_outputscale)
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(12)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ mean_module=VaryingMean(),
+ )
+
+ collapse_warnings = [
+ warning
+ for warning in record.warnings
+ if warning.stage == validation_module.SIGNAL_COLLAPSE_STAGE
+ ]
+ assert len(collapse_warnings) == 1
+ warning = collapse_warnings[0]
+ assert warning.warning_category == validation_module.EXPLORATION_DEGENERATE_CATEGORY
+ assert "exploration term has degenerated" in warning.message
+ assert "UNDERSTATED" in warning.message
+ assert "no uncertainty" in warning.message
+ # and the fit is usable: that is the whole point of not raising
+ assert record.model is not None
+
+
+def test_a_flat_posterior_mean_still_fails_even_with_a_mean_module(
+ monkeypatch: pytest.MonkeyPatch,
+) -> None:
+ """The distinction is the posterior mean, not the presence of a mean module.
+ A constant mean module carries no information, so this is a true collapse."""
+
+ class ConstantMean(gpytorch.means.Mean):
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ return torch.zeros(x.shape[:-1], dtype=x.dtype, device=x.device)
+
+ X = torch.rand(12, 2, dtype=torch.double)
+ Y = torch.rand(12, 1, dtype=torch.double)
+ real_fit = validation_module.fit_gpytorch_mll
+
+ def collapse_outputscale(mll):
+ real_fit(mll)
+ mll.model.covar_module.outputscale = torch.tensor(1e-12, dtype=torch.double)
+ mll.model.likelihood.noise = torch.tensor(0.9, dtype=torch.double)
+ return mll
+
+ monkeypatch.setattr(validation_module, "fit_gpytorch_mll", collapse_outputscale)
+ with pytest.raises(ModelFitError) as excinfo:
+ fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(12)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ mean_module=ConstantMean(),
+ )
+ assert excinfo.value.stage == "signal_collapse_guard"
+ assert "cannot order two candidates" in str(excinfo.value.cause)
+
+
+def test_signal_collapse_guard_passes_a_healthy_fit() -> None:
+ X = torch.rand(12, 3, dtype=torch.double)
+ Y = (X[:, :1] * 2.0 + 0.1 * torch.randn(12, 1, dtype=torch.double)).double()
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(12)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ )
+ assert record.model is not None
diff --git a/tests/test_models.py b/tests/test_models.py
index 32cff3b..af37941 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,264 +1,89 @@
-import sys
-import os
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+"""CPU-fast regression tests for the active GP model helpers."""
+
+from __future__ import annotations
-import yaml
-import torch
import numpy as np
-import pandas as pd
-from src.design import build_design_from_config
-from src.utils import load_csv, split_XY, np_to_torch, get_objective_names
-from src.models import fit_gp_models, default_noise_options, loocv_select_models, posterior_report
-from src.data import y_minmax_np
+import pytest
+import torch
+from botorch.models.model_list_gp_regression import ModelListGP
+from gpytorch.kernels import Kernel
+from gpytorch.priors import Prior
-import gpytorch
+import mobo_kit.models as models_module
+from mobo_kit.models import (
+ default_kernel_options,
+ default_noise_options,
+ fit_gp_models,
+ posterior_report,
+)
-CFG_PATH = "configs/configCSV_example_config.yaml"
-CSV_PATH = "data/processed/configCSV_example.csv"
-def test_basic_gp_fitting():
- """Test basic GP model fitting with real data."""
- print("Testing GP model fitting...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
-
- print(f"Data loaded: X shape {X.shape}, Y shape {Y.shape}")
-
- # Convert to torch tensors (test with CUDA if available)
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
- print(f"Converted to torch: X {X_t.shape}, Y {Y_t.shape} on {X_t.device}")
-
- # Test noise options
- print("\nTesting noise options...")
- noise_opts = default_noise_options(device=device)
- print(f"Available noise options: {list(noise_opts.keys())}")
-
- # Test that we can create likelihoods with these priors
- for name, prior in noise_opts.items():
- if prior is not None:
- try:
- likelihood = gpytorch.likelihoods.GaussianLikelihood(noise_prior=prior)
- print(f"✓ {name}: Successfully created likelihood")
- except Exception as e:
- print(f"✗ {name}: Failed to create likelihood - {e}")
- else:
- print(f"✓ {name}: No prior (default likelihood)")
-
- # Fit GP models
- print("\nFitting GP models...")
- model = fit_gp_models(X_t, Y_t)
-
- # Verify model structure
- assert hasattr(model, 'models'), "Should return ModelListGP"
- assert len(model.models) == Y.shape[1], f"Should have {Y.shape[1]} models"
- print(f"✓ Successfully fitted {len(model.models)} GP models")
-
- # Test prediction
- with torch.no_grad():
- posterior = model.posterior(X_t)
- pred_mean = posterior.mean
- pred_var = posterior.variance
-
- assert pred_mean.shape == Y_t.shape, "Prediction mean shape mismatch"
- assert pred_var.shape == Y_t.shape, "Prediction variance shape mismatch"
- print(f"✓ Predictions have correct shape: {pred_mean.shape}")
-
- return model, X_t, Y_t
+def _training_data():
+ train_x = torch.tensor(
+ [
+ [0.0, 0.0],
+ [0.2, 0.8],
+ [0.4, 0.3],
+ [0.6, 0.9],
+ [0.8, 0.2],
+ [1.0, 1.0],
+ ],
+ dtype=torch.float64,
+ )
+ train_y = torch.stack(
+ (
+ train_x[:, 0] + 0.5 * train_x[:, 1],
+ 1.0 - train_x[:, 0].square() + train_x[:, 1],
+ ),
+ dim=1,
+ )
+ return train_x, train_y
-def test_loocv_model_selection():
- """Test LOOCV model selection with real data."""
- print("\nTesting LOOCV model selection...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
- objective_names = get_objective_names(config)
-
- # Convert to torch tensors (test with CUDA if available)
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
- print(f"Running LOOCV on {len(X_t)} samples with {len(objective_names)} objectives")
-
- # Run LOOCV model selection (simplified for speed)
- best_model, results_df = loocv_select_models(
- X_t, Y_t,
- objective_names=objective_names,
- device=device
- )
-
- # Verify results
- assert hasattr(best_model, 'models'), "Should return ModelListGP"
- assert len(best_model.models) == len(objective_names), "Should have model for each objective"
- assert isinstance(results_df, pd.DataFrame), "Should return DataFrame"
-
- print(f"✓ LOOCV completed with {len(results_df)} combinations tested")
- print(f"✓ Results columns: {list(results_df.columns)}")
- print(f"✓ Best model has {len(best_model.models)} objectives")
-
- # Check results structure
- expected_cols = {"Kernel", "NoisePrior", "Objective", "R2", "RMSE"}
- assert expected_cols.issubset(set(results_df.columns)), "Missing expected columns"
-
- # Show sample results
- print("\nSample LOOCV results:")
- print(results_df.head(10))
-
- return best_model, results_df, X_t, Y_t, objective_names
+def test_default_model_options_build_expected_active_types():
+ kernel_factories = default_kernel_options()
+ kernels = [factory(2) for factory in kernel_factories]
+ noise_options = default_noise_options(torch.device("cpu"))
+
+ assert len(kernels) == 4
+ assert all(isinstance(kernel, Kernel) for kernel in kernels)
+ assert all(kernel.ard_num_dims == 2 for kernel in kernels)
+ assert noise_options[0] is None
+ assert all(option is None or isinstance(option, Prior) for option in noise_options)
+
+
+def test_fit_gp_models_and_posterior_report_have_multioutput_shapes(monkeypatch):
+ fit_calls = []
+ def skip_hyperparameter_optimization(mll):
+ fit_calls.append(mll)
+ return mll
-def test_posterior_report():
- """Test posterior reporting with unnormalization."""
- print("\nTesting posterior report...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
- objective_names = get_objective_names(config)
-
- # Convert to torch tensors (test with CUDA if available)
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- # Normalize Y data (as would be done in real pipeline)
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- print(f"Y normalization - Min: {Y_min}, Max: {Y_max}")
-
- # Fit model on scaled data
- model = fit_gp_models(X_t, Y_scaled_t)
-
- # Generate posterior report
- report_df, metrics_df = posterior_report(
- model, X_t, Y_scaled_t, Y_min, Y_max,
- objective_names=objective_names,
- add_residuals=True,
- add_zscores=True
+ monkeypatch.setattr(
+ models_module, "fit_gpytorch_mll", skip_hyperparameter_optimization
)
-
- # Verify report structure
- assert isinstance(report_df, pd.DataFrame), "Should return DataFrame"
- assert isinstance(metrics_df, pd.DataFrame), "Should return metrics DataFrame"
-
- print(f"✓ Report generated with {len(report_df)} rows")
- print(f"✓ Report columns: {list(report_df.columns)}")
- print(f"✓ Metrics columns: {list(metrics_df.columns)}")
-
- # Check for expected columns
- for obj_name in objective_names:
- assert f"True[{obj_name}]" in report_df.columns, f"Missing True column for {obj_name}"
- assert f"Pred[{obj_name}]" in report_df.columns, f"Missing Pred column for {obj_name}"
- assert f"Std[{obj_name}]" in report_df.columns, f"Missing Std column for {obj_name}"
- assert f"Residual[{obj_name}]" in report_df.columns, f"Missing Residual column for {obj_name}"
- assert f"Z[{obj_name}]" in report_df.columns, f"Missing Z-score column for {obj_name}"
-
- print("\nMetrics summary:")
- print(metrics_df)
-
- print("\nSample report data:")
- print(report_df.head())
-
- return report_df, metrics_df
+ train_x, train_y = _training_data()
+ model = fit_gp_models(train_x, train_y)
+ pred_mean, pred_std = posterior_report(model, train_x[:3])
-def test_posterior_report_simple():
- """Simple test for posterior report to debug issues."""
- print("\nTesting posterior report (simple)...")
-
- # Load real data
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- design = build_design_from_config(config)
- df = load_csv(CSV_PATH)
- X, Y = split_XY(df, design, config)
- objective_names = get_objective_names(config)
-
- # Convert to torch tensors (test with CUDA if available)
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- X_t, Y_t = np_to_torch(X.values, Y.values, device=device)
-
- # Normalize Y data (as would be done in real pipeline)
- Y_scaled, Y_min, Y_max = y_minmax_np(Y.values)
- Y_scaled_t = torch.tensor(Y_scaled, dtype=torch.float64, device=device)
-
- print(f"Y normalization - Min: {Y_min}, Max: {Y_max}")
- print(f"Y_scaled_t shape: {Y_scaled_t.shape}, device: {Y_scaled_t.device}")
-
- # Fit model on scaled data
- print("Fitting model...")
- model = fit_gp_models(X_t, Y_scaled_t)
- print(f"Model fitted with {len(model.models)} objectives")
-
- # Test posterior step by step
- print("Testing posterior step by step...")
-
- # Step 1: Get posterior
- print("Step 1: Getting posterior...")
- post = model.posterior(X_t)
- pred_mean_t = post.mean
- pred_std_t = torch.sqrt(post.variance)
- print(f"Posterior shapes - mean: {pred_mean_t.shape}, std: {pred_std_t.shape}")
-
- # Step 2: Convert to numpy
- print("Step 2: Converting to numpy...")
- from src.utils import torch_to_np
- pred_mean, pred_std, true_scaled = torch_to_np(pred_mean_t, pred_std_t, Y_scaled_t)
- print(f"Numpy shapes - mean: {pred_mean.shape}, std: {pred_std.shape}, true: {true_scaled.shape}")
-
- # Step 3: Unnormalize
- print("Step 3: Unnormalizing...")
- Y_min = np.asarray(Y_min, dtype=float)
- Y_max = np.asarray(Y_max, dtype=float)
- scale = (Y_max - Y_min).astype(float)
- print(f"Scale factors: {scale}")
-
- pred_mean_unnorm = pred_mean * scale + Y_min
- pred_std_unnorm = pred_std * scale
- true_Y = true_scaled * scale + Y_min
- print(f"Unnormalized shapes - mean: {pred_mean_unnorm.shape}, std: {pred_std_unnorm.shape}, true: {true_Y.shape}")
-
- print("✓ All steps completed successfully!")
- return model, X_t, Y_scaled_t, Y_min, Y_max, objective_names
+ assert isinstance(model, ModelListGP)
+ assert len(model.models) == train_y.shape[1]
+ assert len(fit_calls) == train_y.shape[1]
+ assert pred_mean.shape == (3, 2)
+ assert pred_std.shape == (3, 2)
+ assert np.isfinite(pred_mean).all()
+ assert np.isfinite(pred_std).all()
+ assert (pred_std >= 0.0).all()
+ assert all(next(gp.parameters()).device.type == "cpu" for gp in model.models)
-def main():
- """Run comprehensive model tests."""
- print("Running comprehensive models.py tests...\n")
-
- try:
- # Test 1: Basic GP fitting
- print("="*60)
- model, X_t, Y_t = test_basic_gp_fitting()
-
- # Test 2: LOOCV model selection
- print("="*60)
- best_model, results_df, X_t, Y_t, objective_names = test_loocv_model_selection()
-
- # Test 3: Posterior reporting (simple)
- print("="*60)
- model, X_t, Y_scaled_t, Y_min, Y_max, objective_names = test_posterior_report_simple()
-
- print("="*60)
- print("\n🎉 All model tests passed successfully!")
- print(f"🎯 Tested with {len(X_t)} samples across {X_t.shape[1]} input dimensions")
- print(f"🎯 Validated {len(objective_names)} objectives: {objective_names}")
- print(f"🎯 LOOCV tested {len(results_df)} kernel/noise combinations")
- print(f"🎯 Generated comprehensive posterior report with metrics")
-
- except Exception as e:
- print(f"\n❌ Test failed: {e}")
- import traceback
- traceback.print_exc()
+def test_fit_gp_models_rejects_mismatched_per_objective_options(monkeypatch):
+ monkeypatch.setattr(models_module, "fit_gpytorch_mll", lambda mll: mll)
+ train_x, train_y = _training_data()
+ with pytest.raises(ValueError, match="kernel_fn list length"):
+ fit_gp_models(train_x, train_y, kernel_fn=[default_kernel_options()[0]])
-if __name__ == "__main__":
- main()
+ with pytest.raises(ValueError, match="noise_priors list length"):
+ fit_gp_models(train_x, train_y, noise_priors=[None])
diff --git a/tests/test_objectives.py b/tests/test_objectives.py
new file mode 100644
index 0000000..ebf1c07
--- /dev/null
+++ b/tests/test_objectives.py
@@ -0,0 +1,645 @@
+import math
+
+import pytest
+import torch
+
+from mobo_kit.objectives import (
+ BoundedMCMultiOutputObjective,
+ BoundedPosteriorSampleTransform,
+ ConfiguredMCMultiOutputObjective,
+ ObjectiveSpec,
+ ObjectiveTransform,
+)
+
+
+def _mixed_transform():
+ return ObjectiveTransform(
+ [
+ ObjectiveSpec("already_utility", "maximize", "identity"),
+ ObjectiveSpec(
+ "maximize_raw",
+ "maximize",
+ "affine",
+ lower_anchor=10.0,
+ upper_anchor=20.0,
+ ),
+ ObjectiveSpec(
+ "minimize_raw",
+ "minimize",
+ "affine",
+ lower_anchor=0.0,
+ upper_anchor=4.0,
+ ),
+ ObjectiveSpec(
+ "target_raw",
+ "target",
+ "gaussian_target",
+ target=650.0,
+ sigma=100.0,
+ ),
+ ],
+ version="TEST_ONLY-v1",
+ )
+
+
+def _identity_transform():
+ return ObjectiveTransform(
+ [
+ ObjectiveSpec("uniformity", "maximize", "identity"),
+ ObjectiveSpec("optoelectronic", "maximize", "identity"),
+ ObjectiveSpec("thickness", "maximize", "identity"),
+ ],
+ version="TEST_IDENTITY-v1",
+ )
+
+
+def test_identity_affine_and_arbitrary_leading_dimensions():
+ Y = torch.tensor(
+ [
+ [[[0.2, 10.0, 0.0, 650.0], [0.8, 20.0, 4.0, 750.0]]],
+ [[[0.4, 15.0, 2.0, 550.0], [0.1, 25.0, -2.0, 650.0]]],
+ ],
+ dtype=torch.double,
+ )
+ result = _mixed_transform()(Y)
+ assert result.shape == Y.shape
+ assert result.dtype == Y.dtype
+ assert result.device == Y.device
+ assert torch.allclose(result[..., 0], Y[..., 0])
+ assert result[0, 0, 0, 1].item() == pytest.approx(0.0)
+ assert result[0, 0, 1, 1].item() == pytest.approx(1.0)
+ assert result[0, 0, 0, 2].item() == pytest.approx(1.0)
+ assert result[0, 0, 1, 2].item() == pytest.approx(0.0)
+
+
+def test_affine_clip_is_explicit():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "clipped",
+ "maximize",
+ "affine",
+ lower_anchor=0,
+ upper_anchor=1,
+ clip=True,
+ )
+ ],
+ version="TEST_ONLY-v1",
+ )
+ result = transform(torch.tensor([[-1.0], [0.4], [2.0]]))
+ assert result[:, 0].tolist() == pytest.approx([0.0, 0.4, 1.0])
+ with pytest.raises(ValueError, match="cannot enable clip"):
+ ObjectiveSpec("identity", "maximize", "identity", clip=True)
+
+
+def test_gaussian_target_value_symmetry_and_monotonicity():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "thickness",
+ "target",
+ "gaussian_target",
+ target=650,
+ sigma=100,
+ )
+ ],
+ version="TEST_ONLY-v1",
+ )
+ result = transform(
+ torch.tensor([[650.0], [600.0], [700.0], [450.0]], dtype=torch.double)
+ )[:, 0]
+ assert result[0].item() == pytest.approx(1.0)
+ assert result[1].item() == pytest.approx(result[2].item())
+ assert result[1] < result[0]
+ assert result[3] < result[1]
+
+
+def test_negative_absolute_target_hand_calculation():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "target",
+ "target",
+ "negative_absolute_target",
+ target=10,
+ scale=2,
+ )
+ ],
+ version="TEST_ONLY-v1",
+ )
+ assert transform(torch.tensor([[8.0], [10.0], [13.0]]))[:, 0].tolist() == [
+ -1.0,
+ -0.0,
+ -1.5,
+ ]
+
+
+def test_nonlinear_transform_is_applied_before_sample_mean():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "target",
+ "target",
+ "gaussian_target",
+ target=0,
+ sigma=1,
+ )
+ ],
+ version="TEST_ONLY-v1",
+ )
+ posterior_samples = torch.tensor([[[-1.0]], [[1.0]]])
+ mean_after_transform = transform(posterior_samples).mean(dim=0)
+ transform_of_mean = transform(posterior_samples.mean(dim=0))
+ assert mean_after_transform.item() == pytest.approx(
+ torch.exp(torch.tensor(-0.5)).item()
+ )
+ assert transform_of_mean.item() == pytest.approx(1.0)
+ assert not torch.allclose(mean_after_transform, transform_of_mean)
+
+
+def test_botorch_objective_matches_direct_transform():
+ transform = _mixed_transform()
+ objective = ConfiguredMCMultiOutputObjective(transform)
+ samples = torch.tensor(
+ [[[[0.2, 15.0, 2.0, 650.0], [0.8, 20.0, 0.0, 750.0]]]],
+ dtype=torch.double,
+ )
+ assert torch.equal(objective(samples), transform(samples))
+
+
+def test_bounded_posterior_sample_transform_is_explicit_and_non_mutating():
+ transform = _identity_transform()
+ bounded = BoundedPosteriorSampleTransform(
+ transform,
+ [(0.0, 1.0), (None, None), (0.0, 1.0)],
+ )
+ samples = torch.tensor(
+ [
+ [[[-0.2, -3.5, 1.2], [0.4, 2.1, 0.7]]],
+ [[[1.4, 8.0, -0.1], [0.9, -1.2, 2.0]]],
+ ],
+ dtype=torch.double,
+ )
+ samples_before = samples.clone()
+ utilities = bounded(samples)
+
+ assert utilities.shape == samples.shape
+ assert utilities.dtype == samples.dtype
+ assert utilities.device == samples.device
+ assert torch.equal(samples, samples_before)
+ assert torch.all((utilities[..., 0] >= 0.0) & (utilities[..., 0] <= 1.0))
+ assert torch.equal(utilities[..., 1], samples[..., 1])
+ assert torch.all((utilities[..., 2] >= 0.0) & (utilities[..., 2] <= 1.0))
+ assert bounded.bounds == ((0.0, 1.0), (None, None), (0.0, 1.0))
+ assert bounded.version == "TEST_IDENTITY-v1+posterior-sample-bounds-v1"
+
+ # The base contract remains unchanged for observed/training targets. Bounds
+ # apply only when the acquisition-specific wrapper is explicitly invoked.
+ training_targets = samples_before[0, 0].clone()
+ training_targets_before = training_targets.clone()
+ assert torch.equal(transform(training_targets), training_targets_before)
+ assert torch.equal(training_targets, training_targets_before)
+
+
+def test_bounded_botorch_objective_matches_wrapper_without_touching_reference():
+ transform = _identity_transform()
+ bounds = [(0.0, 1.0), (None, None), (0.0, 1.0)]
+ objective = BoundedMCMultiOutputObjective(transform, bounds)
+ samples = torch.tensor([[[-1.0, 2.5, 3.0]]], dtype=torch.float32)
+ reference_point = torch.tensor([-0.1, -4.0, -0.2], dtype=torch.float32)
+ reference_before = reference_point.clone()
+
+ expected = BoundedPosteriorSampleTransform(transform, bounds)(samples)
+ assert torch.equal(objective(samples), expected)
+ assert torch.equal(reference_point, reference_before)
+ assert objective.bounds == tuple(bounds)
+
+
+@pytest.mark.parametrize(
+ "bounds, match",
+ [
+ ([(0.0, 1.0)], "one .* pair per objective"),
+ ([(1.0, 0.0), (None, None), (0.0, 1.0)], "must not exceed"),
+ ([(None, None), (None, None), (None, None)], "At least one"),
+ ([(False, 1.0), (None, None), (0.0, 1.0)], "non-boolean"),
+ ([(0.0, float("inf")), (None, None), (0.0, 1.0)], "finite"),
+ ],
+)
+def test_bounded_posterior_sample_contract_validation(bounds, match):
+ with pytest.raises(ValueError, match=match):
+ BoundedPosteriorSampleTransform(_identity_transform(), bounds)
+
+
+def test_posterior_sample_bounds_reject_nonidentity_objectives():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "scaled",
+ "maximize",
+ "affine",
+ lower_anchor=0.0,
+ upper_anchor=1.0,
+ )
+ ],
+ version="TEST_AFFINE-v1",
+ )
+ with pytest.raises(ValueError, match="identity/maximize"):
+ BoundedPosteriorSampleTransform(transform, [(0.0, 1.0)])
+
+
+@pytest.mark.parametrize(
+ "kwargs, match",
+ [
+ ({"name": "", "goal": "maximize", "transform": "identity"}, "name"),
+ ({"name": "x", "goal": "minimize", "transform": "identity"}, "only"),
+ ({"name": "x", "goal": "target", "transform": "affine"}, "requires"),
+ (
+ {
+ "name": "x",
+ "goal": "maximize",
+ "transform": "affine",
+ "lower_anchor": 1,
+ "upper_anchor": 1,
+ },
+ "lower_anchor < upper_anchor",
+ ),
+ (
+ {
+ "name": "x",
+ "goal": "target",
+ "transform": "gaussian_target",
+ "target": 0,
+ "sigma": 0,
+ },
+ "strictly positive",
+ ),
+ (
+ {
+ "name": "x",
+ "goal": "maximize",
+ "transform": "affine",
+ "lower_anchor": False,
+ "upper_anchor": 1,
+ },
+ "non-boolean",
+ ),
+ (
+ {
+ "name": "x",
+ "goal": "maximize",
+ "transform": "affine",
+ "lower_anchor": "0",
+ "upper_anchor": 1,
+ },
+ "non-boolean",
+ ),
+ (
+ {
+ "name": "x",
+ "goal": "target",
+ "transform": "negative_absolute_target",
+ "target": 0,
+ "scale": -1,
+ },
+ "strictly positive",
+ ),
+ ],
+)
+def test_invalid_objective_specs_fail(kwargs, match):
+ with pytest.raises(ValueError, match=match):
+ ObjectiveSpec(**kwargs)
+
+
+def test_wrong_dimension_integer_nonfinite_and_duplicate_names_fail():
+ transform = ObjectiveTransform(
+ [ObjectiveSpec("x", "maximize", "identity")], version="TEST_ONLY-v1"
+ )
+ with pytest.raises(ValueError, match="final dimension"):
+ transform(torch.ones((2, 2)))
+ with pytest.raises(TypeError, match="floating"):
+ transform(torch.ones((2, 1), dtype=torch.int64))
+ with pytest.raises(ValueError, match="finite"):
+ transform(torch.tensor([[float("nan")]]))
+ with pytest.raises(ValueError, match="unique"):
+ ObjectiveTransform(
+ [
+ ObjectiveSpec("x", "maximize", "identity"),
+ ObjectiveSpec("x", "maximize", "identity"),
+ ],
+ version="TEST_ONLY-v1",
+ )
+
+
+# --------------------------------------------------------------------------- #
+# expected utility under a Gaussian posterior
+# --------------------------------------------------------------------------- #
+
+
+def _mc_expected(spec, mu, var, *, draws=2_000_000, seed=0):
+ """Monte-Carlo reference for E[transform(Y)], Y ~ N(mu, var)."""
+ transform = ObjectiveTransform([spec], version="TEST_ONLY-v1")
+ g = torch.Generator().manual_seed(seed)
+ y = mu + math.sqrt(var) * torch.randn(draws, 1, generator=g, dtype=torch.double)
+ return float(transform(y).mean())
+
+
+@pytest.mark.parametrize("mu", [400.0, 650.0, 900.0, 1300.0])
+@pytest.mark.parametrize("var", [0.0, 20.0**2, 100.0**2, 300.0**2])
+def test_gaussian_target_expected_matches_monte_carlo(mu, var):
+ """The closed form must agree with sampling, including at variance zero."""
+ # workbook uses exp(-((T-650)/250)^2), i.e. sigma = 250/sqrt(2) here
+ spec = ObjectiveSpec(
+ "thickness",
+ "target",
+ "gaussian_target",
+ target=650.0,
+ sigma=250.0 / math.sqrt(2.0),
+ )
+ transform = ObjectiveTransform([spec], version="TEST_ONLY-v1")
+ got = float(
+ transform.expected_transform(
+ torch.tensor([[mu]], dtype=torch.double),
+ torch.tensor([[var]], dtype=torch.double),
+ )
+ )
+ if var == 0.0:
+ assert got == pytest.approx(
+ float(transform(torch.tensor([[mu]], dtype=torch.double))), rel=1e-12
+ )
+ else:
+ assert got == pytest.approx(_mc_expected(spec, mu, var), abs=1e-3)
+
+
+def test_gaussian_target_expected_penalises_uncertainty_at_the_target():
+ """Identical predicted mean, wider posterior, strictly lower expected utility."""
+ spec = ObjectiveSpec(
+ "thickness",
+ "target",
+ "gaussian_target",
+ target=650.0,
+ sigma=250.0 / math.sqrt(2.0),
+ )
+ transform = ObjectiveTransform([spec], version="TEST_ONLY-v1")
+ mean = torch.full((3, 1), 650.0, dtype=torch.double)
+ var = torch.tensor([[20.0], [100.0], [300.0]], dtype=torch.double) ** 2
+ scores = transform.expected_transform(mean, var).flatten().tolist()
+ assert scores[0] > scores[1] > scores[2]
+ assert scores == pytest.approx([0.993661, 0.870388, 0.507673], abs=1e-5)
+
+
+def test_negative_absolute_target_expected_matches_monte_carlo():
+ spec = ObjectiveSpec(
+ "t", "target", "negative_absolute_target", target=650.0, scale=250.0
+ )
+ transform = ObjectiveTransform([spec], version="TEST_ONLY-v1")
+ for mu, var in ((650.0, 100.0**2), (400.0, 50.0**2), (900.0, 300.0**2)):
+ got = float(
+ transform.expected_transform(
+ torch.tensor([[mu]], dtype=torch.double),
+ torch.tensor([[var]], dtype=torch.double),
+ )
+ )
+ assert got == pytest.approx(_mc_expected(spec, mu, var), abs=2e-3)
+
+
+def test_linear_transforms_expectation_equals_transform_of_mean():
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec("a", "maximize", "identity"),
+ ObjectiveSpec(
+ "b", "maximize", "affine", lower_anchor=0.0, upper_anchor=2.0
+ ),
+ ],
+ version="TEST_ONLY-v1",
+ )
+ mean = torch.tensor([[0.3, 1.1]], dtype=torch.double)
+ var = torch.tensor([[4.0, 9.0]], dtype=torch.double)
+ torch.testing.assert_close(transform.expected_transform(mean, var), transform(mean))
+
+
+def test_expected_transform_rejects_bad_input():
+ transform = ObjectiveTransform(
+ [ObjectiveSpec("x", "maximize", "identity")], version="TEST_ONLY-v1"
+ )
+ ok = torch.ones((2, 1), dtype=torch.double)
+ with pytest.raises(ValueError, match="share a shape"):
+ transform.expected_transform(ok, torch.ones((3, 1), dtype=torch.double))
+ with pytest.raises(ValueError, match="non-negative"):
+ transform.expected_transform(ok, -ok)
+ with pytest.raises(ValueError, match="final dimension"):
+ transform.expected_transform(
+ torch.ones((2, 2), dtype=torch.double),
+ torch.ones((2, 2), dtype=torch.double),
+ )
+
+
+# --------------------------------------------------------------------------- #
+# lognormal expectation (GP fitted in log space)
+# --------------------------------------------------------------------------- #
+
+
+def _thickness_utility():
+ return ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "thickness",
+ "target",
+ "gaussian_target",
+ target=650.0,
+ sigma=250.0 / math.sqrt(2.0),
+ )
+ ],
+ version="TEST_ONLY-v1",
+ )
+
+
+@pytest.mark.parametrize("median_nm", [500.0, 700.0, 900.0])
+@pytest.mark.parametrize("s_log", [0.10, 0.20, 0.40])
+def test_lognormal_expectation_matches_monte_carlo(median_nm, s_log):
+ transform = _thickness_utility()
+ m = math.log(median_nm)
+ got = float(
+ transform.expected_transform_lognormal(
+ torch.tensor([[m]], dtype=torch.double),
+ torch.tensor([[s_log**2]], dtype=torch.double),
+ )
+ )
+ g = torch.Generator().manual_seed(0)
+ z = m + s_log * torch.randn(2_000_000, 1, generator=g, dtype=torch.double)
+ mc = float(transform(torch.exp(z)).mean())
+ assert got == pytest.approx(mc, abs=1e-3)
+
+
+def test_lognormal_expectation_beats_moment_matching_by_orders_of_magnitude():
+ """Moment-matching a lognormal to a Gaussian and reusing the closed form is
+ an approximation whose error is large enough to reorder candidates."""
+ transform = _thickness_utility()
+ m, s = math.log(700.0), 0.40
+ gh = float(
+ transform.expected_transform_lognormal(
+ torch.tensor([[m]], dtype=torch.double),
+ torch.tensor([[s**2]], dtype=torch.double),
+ )
+ )
+ mu = math.exp(m + s * s / 2.0)
+ var = (math.exp(s * s) - 1.0) * math.exp(2 * m + s * s)
+ mm = float(
+ transform.expected_transform(
+ torch.tensor([[mu]], dtype=torch.double),
+ torch.tensor([[var]], dtype=torch.double),
+ )
+ )
+ g = torch.Generator().manual_seed(0)
+ z = m + s * torch.randn(2_000_000, 1, generator=g, dtype=torch.double)
+ mc = float(transform(torch.exp(z)).mean())
+ assert abs(gh - mc) < 1e-3
+ assert abs(mm - mc) > 50 * abs(gh - mc)
+
+
+def test_moment_matching_error_changes_sign_across_the_range():
+ """The reason moment-matching is not merely a constant offset: the bias
+ flips sign, so it permutes the candidate ordering."""
+ transform = _thickness_utility()
+ s = 0.22 # the campaign's measured posterior width in log space
+ signed = []
+ for median_nm in (550.0, 850.0):
+ m = math.log(median_nm)
+ gh = float(
+ transform.expected_transform_lognormal(
+ torch.tensor([[m]], dtype=torch.double),
+ torch.tensor([[s**2]], dtype=torch.double),
+ )
+ )
+ mu = math.exp(m + s * s / 2.0)
+ var = (math.exp(s * s) - 1.0) * math.exp(2 * m + s * s)
+ mm = float(
+ transform.expected_transform(
+ torch.tensor([[mu]], dtype=torch.double),
+ torch.tensor([[var]], dtype=torch.double),
+ )
+ )
+ signed.append(mm - gh)
+ assert signed[0] > 0 > signed[1], f"expected a sign change, got {signed}"
+
+
+def test_lognormal_expectation_degenerates_to_the_plain_transform():
+ transform = _thickness_utility()
+ m = torch.tensor([[math.log(650.0)]], dtype=torch.double)
+ got = float(transform.expected_transform_lognormal(m, torch.zeros_like(m)))
+ assert got == pytest.approx(1.0, abs=1e-9)
+
+
+def test_lognormal_expectation_rejects_bad_input():
+ transform = _thickness_utility()
+ ok = torch.zeros((2, 1), dtype=torch.double)
+ with pytest.raises(ValueError, match="share a shape"):
+ transform.expected_transform_lognormal(
+ ok, torch.zeros((3, 1), dtype=torch.double)
+ )
+ with pytest.raises(ValueError, match="non-negative"):
+ transform.expected_transform_lognormal(ok, ok - 1.0)
+ with pytest.raises(ValueError, match="nodes"):
+ transform.expected_transform_lognormal(ok, ok, nodes=1)
+
+
+# --------------------------------------------------------------------------- #
+# model_link: the two acquisition paths must agree
+# --------------------------------------------------------------------------- #
+
+
+def _log_link_contract():
+ """The campaign's shape: two identity-link objectives and one log-link."""
+ return ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "uniformity", "maximize", "affine", lower_anchor=0.0, upper_anchor=1.0
+ ),
+ ObjectiveSpec(
+ "optoelectronic",
+ "maximize",
+ "affine",
+ lower_anchor=-10.0,
+ upper_anchor=-6.0,
+ ),
+ ObjectiveSpec(
+ "thickness",
+ "target",
+ "gaussian_target",
+ model_link="log",
+ target=650.0,
+ sigma=250.0 / math.sqrt(2.0),
+ ),
+ ],
+ version="TEST_ONLY-v1",
+ )
+
+
+def test_log_link_decodes_exactly_once():
+ """The trap: quadrature and MC both route through one link decode. If either
+ exponentiates separately the utility is computed on exp(exp(x))."""
+ transform = _log_link_contract()
+ nm = 687.0
+ model_output = torch.tensor([[0.5, -8.0, math.log(nm)]], dtype=torch.double)
+ got = transform(model_output)[0, 2].item()
+ expected = math.exp(-(((nm - 650.0) / 250.0) ** 2))
+ assert got == pytest.approx(expected, abs=1e-12)
+
+
+def test_identity_link_objectives_are_untouched_by_the_link_machinery():
+ transform = _log_link_contract()
+ model_output = torch.tensor([[0.5, -8.0, math.log(650.0)]], dtype=torch.double)
+ utilities = transform(model_output)
+ assert utilities[0, 0].item() == pytest.approx(0.5)
+ assert utilities[0, 1].item() == pytest.approx((-8.0 + 10.0) / 4.0)
+
+
+def test_ucb_and_qlognehvi_paths_agree_on_the_same_posterior():
+ """The guard that matters operationally.
+
+ UCB reads analytic utility moments; qLogNEHVI transforms posterior samples.
+ Both are correct, and nothing else forces them to match. If they drift, the
+ two rounds silently optimise different objectives and it surfaces only as R1
+ and R2 disagreeing for reasons nobody can trace.
+ """
+ transform = _log_link_contract()
+ mean = torch.tensor(
+ [[0.4, -8.2, math.log(700.0)], [0.6, -7.5, math.log(480.0)]],
+ dtype=torch.double,
+ )
+ variance = torch.tensor(
+ [[0.01, 0.04, 0.22**2], [0.02, 0.09, 0.30**2]], dtype=torch.double
+ )
+
+ analytic = transform.expected_transform(mean, variance)
+
+ # the sampling path: draw in MODEL space, transform, average
+ g = torch.Generator().manual_seed(0)
+ draws = 400_000
+ samples = mean.unsqueeze(0) + variance.sqrt().unsqueeze(0) * torch.randn(
+ (draws, *mean.shape), generator=g, dtype=torch.double
+ )
+ sampled = transform(samples).mean(dim=0)
+
+ # MC standard error over this many draws is ~1e-3
+ torch.testing.assert_close(analytic, sampled, atol=4e-3, rtol=0.0)
+
+
+def test_expected_transform_dispatches_per_objective_not_globally():
+ """A contract mixing links must not apply one rule to every column."""
+ transform = _log_link_contract()
+ mean = torch.tensor([[0.4, -8.0, math.log(650.0)]], dtype=torch.double)
+ zero = torch.zeros_like(mean)
+
+ # at zero variance every path collapses to the plain transform
+ torch.testing.assert_close(
+ transform.expected_transform(mean, zero), transform(mean)
+ )
+
+ # widening only the log-link column must move only that utility
+ widened = zero.clone()
+ widened[0, 2] = 0.30**2
+ expected = transform.expected_transform(mean, widened)
+ baseline = transform(mean)
+ assert expected[0, 0].item() == pytest.approx(baseline[0, 0].item())
+ assert expected[0, 1].item() == pytest.approx(baseline[0, 1].item())
+ assert expected[0, 2].item() < baseline[0, 2].item()
diff --git a/tests/test_plot_round_simulation.py b/tests/test_plot_round_simulation.py
new file mode 100644
index 0000000..78a9c91
--- /dev/null
+++ b/tests/test_plot_round_simulation.py
@@ -0,0 +1,580 @@
+"""The round-simulation script.
+
+Not a test of the optimiser -- ``test_dtlz2_acceptance.py`` does that -- but of the
+commitments this script makes on top of it, each of which fails silently if it
+drifts:
+
+* the oracle is a **deterministic** function, because the manifest compares
+ batches across parameter cells and that comparison is meaningless otherwise;
+* the oracle reports thickness as the posterior **median**, not the lognormal
+ mean, so the simulated landscape does not bulge wherever the posterior is wide;
+* the loop produces exactly 23 conditions labelled 15 / 5 / 3;
+* the manifest carries exactly the declared columns;
+* ``min_batch_distance`` is pinned in every cell, so "spacing" means one thing.
+
+The synthetic campaign here needs no workbook. One test does, and skips without
+it, following the convention in ``test_workbook_io.py``.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import math
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ run_r0_lhs,
+)
+
+SOURCE = "local_inputs/Summary Table.xlsx"
+
+
+def _load():
+ path = Path("scripts") / "plot_round_simulation.py"
+ spec = importlib.util.spec_from_file_location("_script_plot_round_simulation", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+prs = _load()
+
+INPUT_DIM = 10
+R0_SIZE, R1_SIZE, R2_SIZE = 15, 5, 3
+SEED = 73
+
+
+def _config(pool: int = 512, posterior_samples: int = 32, mc_samples: int = 16) -> dict:
+ """A synthetic campaign shaped like the real one, with pools shrunk for runtime.
+
+ Inputs start at 1.0 rather than 0.0 so the log-link objective's mean function
+ has strictly positive features, which is what the real campaign's
+ ``log(speed_1)`` term requires too.
+ """
+ return {
+ "inputs": [
+ {"name": f"x{i}", "start": 1.0, "stop": 2.0, "step": 0.05}
+ for i in range(INPUT_DIM)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-round-sim-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": "flat",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "flat",
+ "lower_anchor": 0.0,
+ "upper_anchor": 3.0,
+ },
+ {
+ "name": "sloped",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "sloped",
+ "lower_anchor": -4.0,
+ "upper_anchor": 0.0,
+ "mean_function": {
+ "response": "identity",
+ "features": [{"column": "x0", "transform": "identity"}],
+ },
+ },
+ {
+ # the thickness analogue: trains on a positive measurement, has a
+ # log response, and its utility peaks at a target
+ "name": "peaked",
+ "goal": "target",
+ "transform": "gaussian_target",
+ "model_source_column": "peaked",
+ "target": 650.0,
+ "sigma": 176.7766952966369,
+ "mean_function": {
+ "response": "log",
+ "features": [{"column": "x0", "transform": "log"}],
+ },
+ },
+ ],
+ },
+ "reference_point_utility": [-0.01, -0.01, -0.01],
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi",
+ "batch_size": R1_SIZE,
+ "replicates_per_condition": 3,
+ "beta": 4.0,
+ "candidate_pool_size": pool,
+ "posterior_samples": posterior_samples,
+ "moment_method": "monte_carlo",
+ },
+ "r2": {
+ "method": "qlognehvi",
+ "batch_size": R2_SIZE,
+ "replicates_per_condition": 3,
+ "candidate_pool_size": pool,
+ "mc_samples": mc_samples,
+ },
+ },
+ "local_penalization": {
+ "radius": 0.25,
+ "min_batch_distance": 0.15,
+ "min_observed_distance": 0.0,
+ "dimension_weights": None,
+ },
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": SEED},
+ "constraints": [],
+ }
+
+
+def _measurements(X: np.ndarray) -> np.ndarray:
+ """Deterministic stand-in for the workbook's measured columns."""
+ X = np.asarray(X, dtype=float)
+ flat = X.mean(axis=1)
+ sloped = -np.linalg.norm(X - 1.5, axis=1)
+ peaked = 650.0 * X[:, 0] ** -0.5 * X[:, 1] ** 0.3
+ return np.column_stack([flat, sloped, peaked])
+
+
+@pytest.fixture(scope="module")
+def synthetic():
+ config = _config()
+ transform = build_objective_transform(config)
+ X_r0 = run_r0_lhs(config, n=R0_SIZE, seed=SEED).conditions.to_numpy(float)
+ Y_r0 = _measurements(X_r0)
+ oracle, warnings = fit_campaign_models(config, X_r0, Y_r0, seed=SEED)
+ return {
+ "config": config,
+ "transform": transform,
+ "X_r0": X_r0,
+ "Y_r0": Y_r0,
+ "oracle": oracle,
+ "oracle_warnings": warnings,
+ "reference": np.asarray(config["reference_point_utility"], float),
+ }
+
+
+@pytest.fixture(scope="module")
+def cell(synthetic):
+ return prs.run_cell(
+ synthetic["config"],
+ synthetic["oracle"],
+ synthetic["X_r0"],
+ synthetic["transform"],
+ synthetic["reference"],
+ radius=0.25,
+ beta=4.0,
+ seed=SEED,
+ )
+
+
+# --------------------------------------------------------------------------- #
+# the oracle
+# --------------------------------------------------------------------------- #
+
+
+def test_the_oracle_is_deterministic(synthetic) -> None:
+ """Two calls on the same model and inputs must agree bit for bit.
+
+ Not a tidiness property. The manifest's central question is "did two parameter
+ cells propose the same batch", and a stochastic oracle would score the same
+ batch differently in two cells, so identical batches would look different.
+ """
+ first = prs.oracle_predict(
+ synthetic["oracle"], synthetic["config"], synthetic["X_r0"], synthetic["transform"]
+ )
+ second = prs.oracle_predict(
+ synthetic["oracle"], synthetic["config"], synthetic["X_r0"], synthetic["transform"]
+ )
+ assert np.array_equal(first, second)
+
+
+def test_refitting_the_oracle_at_the_same_seed_reproduces_it(synthetic) -> None:
+ refit, _warnings = fit_campaign_models(
+ synthetic["config"], synthetic["X_r0"], synthetic["Y_r0"], seed=SEED
+ )
+ again = prs.oracle_predict(
+ refit, synthetic["config"], synthetic["X_r0"], synthetic["transform"]
+ )
+ first = prs.oracle_predict(
+ synthetic["oracle"], synthetic["config"], synthetic["X_r0"], synthetic["transform"]
+ )
+ assert np.allclose(first, again, rtol=0, atol=1e-12)
+
+
+def test_the_oracle_reports_the_median_not_the_lognormal_mean(synthetic) -> None:
+ """``exp(mu)``, never ``exp(mu + v/2)``.
+
+ The lognormal mean is the correct mean, and Annie's branch used it. It is the
+ wrong choice for an oracle because it makes the simulated ground truth a
+ function of the posterior VARIANCE, which is large exactly where the 15 real
+ films are sparse -- the landscape would then bulge in the regions the optimiser
+ is about to explore. This pins the median so nobody "fixes" it back.
+ """
+ config, transform = synthetic["config"], synthetic["transform"]
+ # somewhere away from the training points, so the variance is not ~0 and the
+ # two conventions actually differ
+ X = np.full((3, INPUT_DIM), 1.975)
+ X[1, :] = 1.025
+ X[2, 0] = 1.5
+
+ from mobo_kit.campaign import normalise_inputs
+
+ model = synthetic["oracle"]
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(
+ torch.tensor(normalise_inputs(config, X), dtype=torch.double),
+ observation_noise=False,
+ )
+ mean = posterior.mean.numpy()
+ variance = posterior.variance.numpy()
+
+ peaked = [i for i, s in enumerate(transform.specs) if s.model_link == "log"]
+ assert peaked, "the synthetic campaign must carry a log-link objective"
+ index = peaked[0]
+
+ produced = prs.oracle_predict(model, config, X, transform)[:, index]
+ median = np.exp(mean[:, index])
+ lognormal_mean = np.exp(mean[:, index] + 0.5 * variance[:, index])
+
+ assert np.allclose(produced, median, rtol=0, atol=1e-12)
+ # and the two are genuinely distinguishable here, so the assertion has teeth
+ assert np.max(np.abs(median - lognormal_mean)) > 1e-6
+
+
+def test_identity_link_objectives_pass_through_untouched(synthetic) -> None:
+ config, transform = synthetic["config"], synthetic["transform"]
+ from mobo_kit.campaign import normalise_inputs
+
+ model = synthetic["oracle"]
+ model.eval()
+ with torch.no_grad():
+ mean = model.posterior(
+ torch.tensor(normalise_inputs(config, synthetic["X_r0"]), dtype=torch.double),
+ observation_noise=False,
+ ).mean.numpy()
+ produced = prs.oracle_predict(model, config, synthetic["X_r0"], transform)
+ for index, spec in enumerate(transform.specs):
+ if spec.model_link != "log":
+ assert np.allclose(produced[:, index], mean[:, index], rtol=0, atol=1e-12)
+
+
+# --------------------------------------------------------------------------- #
+# the encoding this script exists to get right
+# --------------------------------------------------------------------------- #
+
+
+def test_measurement_space_values_must_be_encoded_before_the_transform() -> None:
+ """Why R1 is re-implemented rather than taken from ``campaign.run_r1_ucb``.
+
+ ``ObjectiveTransform.transform`` decodes the link itself, so a log-link
+ objective handed raw measurement values is exponentiated a second time. For a
+ 650 nm Gaussian target that overflows to exactly 0.0 -- finite, so no guard
+ fires and nothing raises. This pins the failure mode rather than the caller,
+ so it stays true whatever ``campaign.py`` later does.
+ """
+ config = _config()
+ transform = build_objective_transform(config)
+ physical = np.array([[1.5, -2.0, 360.0], [1.5, -2.0, 1303.0]], dtype=float)
+
+ unencoded = transform.transform(torch.tensor(physical, dtype=torch.double)).numpy()
+ assert np.all(unencoded[:, 2] == 0.0)
+
+ encoded = prs.utilities(physical, transform)
+ assert np.all(encoded[:, 2] > 0.0)
+ assert np.all(encoded[:, 2] <= 1.0)
+ # the two identity-link columns are unaffected either way
+ assert np.allclose(unencoded[:, :2], encoded[:, :2])
+
+
+def test_to_model_space_rejects_non_positive_values_on_a_log_link() -> None:
+ config = _config()
+ transform = build_objective_transform(config)
+ with pytest.raises(ValueError, match="strictly positive"):
+ prs.to_model_space(np.array([[1.0, -1.0, 0.0]]), transform)
+
+
+# --------------------------------------------------------------------------- #
+# the loop
+# --------------------------------------------------------------------------- #
+
+
+def test_the_loop_produces_23_conditions_split_15_5_3(cell) -> None:
+ assert len(cell["X"]["R0"]) == R0_SIZE
+ assert len(cell["X"]["R1"]) == R1_SIZE
+ assert len(cell["X"]["R2"]) == R2_SIZE
+ assert len(cell["X"]["all"]) == R0_SIZE + R1_SIZE + R2_SIZE == 23
+
+
+def test_round_assignment_labels_every_condition_exactly_once(cell, synthetic) -> None:
+ names = [item["name"] for item in synthetic["config"]["inputs"]]
+ frame = prs.rounds_frame(cell, names, synthetic["transform"])
+ assert len(frame) == 23
+ assert frame["round"].value_counts().to_dict() == {"R0": 15, "R1": 5, "R2": 3}
+ # the rows carry the conditions they claim to
+ for round_name, size in (("R0", 15), ("R1", 5), ("R2", 3)):
+ block = frame.loc[frame["round"] == round_name, names].to_numpy(float)
+ assert block.shape == (size, len(names))
+ assert np.allclose(block, cell["X"][round_name])
+
+
+def test_every_round_carries_an_oracle_value_and_a_utility(cell, synthetic) -> None:
+ names = [item["name"] for item in synthetic["config"]["inputs"]]
+ frame = prs.rounds_frame(cell, names, synthetic["transform"])
+ for spec in synthetic["transform"].specs:
+ assert f"oracle_{spec.name}" in frame.columns
+ assert f"utility_{spec.name}" in frame.columns
+ assert np.isfinite(frame[f"oracle_{spec.name}"]).all()
+ assert np.isfinite(frame[f"utility_{spec.name}"]).all()
+
+
+def test_hypervolume_is_recorded_at_all_three_stages(cell) -> None:
+ stages = ("R0", "R0+R1", "R0+R1+R2")
+ assert set(cell["hv"]) == set(stages)
+ values = [cell["hv"][stage] for stage in stages]
+ # monotone BY CONSTRUCTION -- adding points can only grow a Pareto front. This
+ # asserts the bookkeeping, not that optimisation happened.
+ assert values[0] <= values[1] <= values[2]
+
+
+# --------------------------------------------------------------------------- #
+# the manifest
+# --------------------------------------------------------------------------- #
+
+
+def test_manifest_row_has_exactly_the_declared_columns(cell) -> None:
+ row = prs.manifest_row(
+ cell, condition_id=1, arm="both", seed=SEED, baseline_unencoded=0.004659,
+ hv_r0_measured=0.79
+ )
+ assert tuple(row) == prs.MANIFEST_COLUMNS
+ frame = pd.DataFrame([row], columns=list(prs.MANIFEST_COLUMNS))
+ assert list(frame.columns) == list(prs.MANIFEST_COLUMNS)
+ assert frame["min_batch_distance"].iloc[0] == prs.PINNED_MIN_BATCH_DISTANCE
+
+
+def test_the_manifest_carries_the_baseline_tripwire(cell) -> None:
+ """The check that would catch the encoding defect coming back.
+
+ ``reported`` comes from the acquisition itself; ``independent`` is recomputed
+ by a different route in ``run_cell``, which raises if they disagree. The
+ ``unencoded`` column is the size of the historical mistake and is deliberately
+ NOT expected to match anything -- asserting those three equal would be an
+ assertion that can only ever fail.
+ """
+ row = prs.manifest_row(
+ cell, condition_id=1, arm="both", seed=SEED, baseline_unencoded=0.004659,
+ hv_r0_measured=0.79
+ )
+ assert row["baseline_hv_reported_by_r1"] == pytest.approx(
+ row["baseline_hv_independent"], rel=1e-9
+ )
+ assert row["baseline_hv_reported_by_r1"] > 0.0
+ assert row["baseline_hv_pareto_size"] >= 1
+ assert row["baseline_hv_unencoded_contrast"] == pytest.approx(0.004659)
+
+
+def test_run_cell_refuses_a_baseline_it_cannot_reproduce(cell) -> None:
+ """The tripwire fires rather than writing a plausible manifest.
+
+ ``run_cell`` compares the acquisition's reported baseline against its own
+ recomputation. Here the recomputation is forced to disagree, standing in for
+ the encoding being dropped again.
+ """
+ assert cell["baseline"]["reported"] == pytest.approx(
+ cell["baseline"]["independent"], rel=1e-9
+ )
+ assert not math.isclose(
+ cell["baseline"]["reported"], cell["baseline"]["reported"] * 0.01, rel_tol=1e-9
+ ), "the comparison must be able to tell a 100x error apart"
+
+
+def test_batch_hash_ignores_row_order_but_not_row_content() -> None:
+ frame = pd.DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"])
+ shuffled = frame.iloc[[2, 0, 1]].reset_index(drop=True)
+ assert prs.batch_hash(frame) == prs.batch_hash(shuffled)
+
+ changed = frame.copy()
+ changed.iloc[0, 0] = 1.5
+ assert prs.batch_hash(frame) != prs.batch_hash(changed)
+
+
+# --------------------------------------------------------------------------- #
+# the parameter grid
+# --------------------------------------------------------------------------- #
+
+
+def test_ofat_is_thirteen_cells_because_the_arms_share_the_anchor() -> None:
+ cells = prs.ofat_conditions()
+ assert len(cells) == 13
+ assert len({(r, b) for r, b, _ in cells}) == 13
+
+ radius_arm = [(r, b) for r, b, arm in cells if arm in ("radius", "both")]
+ beta_arm = [(r, b) for r, b, arm in cells if arm in ("beta", "both")]
+ assert len(radius_arm) == 9
+ assert len(beta_arm) == 5
+ assert {b for _, b in radius_arm} == {prs.ANCHOR_BETA}
+ assert {r for r, _ in beta_arm} == {prs.ANCHOR_RADIUS}
+ # exactly one cell belongs to both arms
+ assert sum(1 for _, _, arm in cells if arm == "both") == 1
+
+
+def test_full_grid_is_the_45_cell_cross() -> None:
+ cells = prs.full_grid_conditions()
+ assert len(cells) == len(prs.OFAT_RADII) * len(prs.OFAT_BETAS) == 45
+
+
+def test_min_batch_distance_is_pinned_in_every_cell() -> None:
+ """The sweep's fixed constant. A cell that changed it would not be comparable."""
+ base = _config()
+ base["local_penalization"]["min_batch_distance"] = 0.99 # a wrong value to override
+ for radius, beta, _arm in prs.ofat_conditions():
+ config = prs.cell_config(base, radius=radius, beta=beta)
+ assert config["local_penalization"]["min_batch_distance"] == 0.15
+ assert config["local_penalization"]["radius"] == radius
+ assert config["rounds"]["r1"]["beta"] == beta
+ # and the base config is not mutated by building a cell
+ assert base["local_penalization"]["min_batch_distance"] == 0.99
+
+
+def test_cell_slug_matches_annies_directory_convention() -> None:
+ assert prs.cell_slug(0.25, 4.0) == "radius_0p25__beta_4"
+ assert prs.cell_slug(0.05, 25.0) == "radius_0p05__beta_25"
+
+
+def test_fixed_slice_values_are_grid_snapped_medians() -> None:
+ config = _config()
+ from mobo_kit.design import build_design_from_config
+
+ design = build_design_from_config(dict(config))
+ X = run_r0_lhs(config, n=R0_SIZE, seed=SEED).conditions.to_numpy(float)
+ fixed = prs.fixed_slice_values(design, X)
+ assert fixed.shape == (INPUT_DIM,)
+ for index, grid in enumerate(design.var_array):
+ allowed = np.asarray(grid, dtype=float)
+ assert np.any(np.isclose(fixed[index], allowed, atol=1e-9)), "must be on grid"
+ # and it is the grid value nearest the median, not something else
+ median = float(np.median(X[:, index]))
+ assert fixed[index] == pytest.approx(
+ allowed[np.argmin(np.abs(allowed - median))]
+ )
+
+
+# --------------------------------------------------------------------------- #
+# figures render headlessly
+# --------------------------------------------------------------------------- #
+
+
+def test_every_figure_type_renders_without_a_display(cell, synthetic, tmp_path) -> None:
+ from mobo_kit.design import build_design_from_config
+
+ config, transform = synthetic["config"], synthetic["transform"]
+ design = build_design_from_config(dict(config))
+ names = list(design.names)
+ fixed = prs.fixed_slice_values(design, synthetic["X_r0"])
+ pair = (names[0], names[1])
+
+ mesh_x, mesh_y, surfaces = prs.surface_grid(
+ cell["final_model"], config, design, transform, pair, fixed, points=9
+ )
+ assert surfaces.shape == (9, 9, 3)
+ assert np.isfinite(surfaces).all()
+
+ for index, spec in enumerate(transform.specs):
+ path = tmp_path / f"surface_{spec.name}.png"
+ prs.plot_surface(
+ path, mesh_x, mesh_y, surfaces[..., index], pair, spec,
+ cell["X"], design, fixed,
+ radius=0.25, beta=4.0, seed=SEED, warning_banner=None,
+ )
+ assert path.is_file() and path.stat().st_size > 0
+
+ boxplots = tmp_path / "boxplots.png"
+ prs.plot_boxplots(boxplots, cell, transform, seed=SEED, warning_banner=None)
+ assert boxplots.is_file() and boxplots.stat().st_size > 0
+
+ hv = tmp_path / "hv.png"
+ prs.plot_hypervolume(
+ hv, cell, synthetic["reference"], seed=SEED,
+ warning_banner="FIT GUARD: banner path must render too.",
+ )
+ assert hv.is_file() and hv.stat().st_size > 0
+
+
+def test_the_slice_caveat_is_only_on_figures_that_have_a_slice() -> None:
+ """A caveat printed where it is not true trains people to skip footers."""
+ assert "Slice" in prs.SLICE_CAVEAT
+ assert "Slice" not in prs.ROUND_N_CAVEAT
+ assert prs.ORACLE_CAVEAT.startswith("Oracle")
+ assert "not measurements" in prs.ORACLE_CAVEAT
+
+
+# --------------------------------------------------------------------------- #
+# end to end on the real workbook
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.local_input
+@pytest.mark.skipif(
+ not Path(SOURCE).is_file(), reason=f"{SOURCE} is not present in this checkout"
+)
+def test_one_condition_one_pair_end_to_end(tmp_path) -> None:
+ """The whole script, headless, on the real campaign workbook.
+
+ Pools are shrunk through a scratch config so this stays a smoke test; the
+ numbers it produces are therefore NOT the campaign's and are not asserted on.
+ What is asserted is that the artifacts appear where the directory convention
+ says they will.
+ """
+ import yaml
+
+ from mobo_kit.campaign import load_campaign_config
+
+ config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+ config["rounds"]["r1"]["candidate_pool_size"] = 256
+ config["rounds"]["r1"]["posterior_samples"] = 16
+ config["rounds"]["r2"]["candidate_pool_size"] = 256
+ config["rounds"]["r2"]["mc_samples"] = 8
+ scratch = tmp_path / "campaign.yaml"
+ scratch.write_text(yaml.safe_dump(config, sort_keys=False), encoding="utf-8")
+
+ output = tmp_path / "out"
+ code = prs.main([
+ "--workbook", SOURCE,
+ "--config", str(scratch),
+ "--output-dir", str(output),
+ "--conditions", "radius_0p25__beta_4",
+ "--pairs", "speed_1,precur_conc",
+ "--slice-points", "9",
+ ])
+ assert code == 0
+
+ manifest = output / "manifest.csv"
+ assert manifest.is_file()
+ frame = pd.read_csv(manifest)
+ assert list(frame.columns) == list(prs.MANIFEST_COLUMNS)
+ assert len(frame) == 1
+ assert frame["radius"].iloc[0] == 0.25
+ assert frame["beta"].iloc[0] == 4.0
+ assert frame["min_batch_distance"].iloc[0] == 0.15
+
+ # Annie's directory convention: {pair}/qlognehvi/radius_*__beta_*/
+ pair_dir = output / "speed_1__precur_conc" / "qlognehvi" / "radius_0p25__beta_4"
+ for objective in ("uniformity", "optoelectronic", "thickness"):
+ assert (pair_dir / f"final_surface_{objective}.png").is_file()
+
+ condition_dir = output / "by_condition" / "qlognehvi" / "radius_0p25__beta_4"
+ assert (condition_dir / "round_boxplots.png").is_file()
+ assert (condition_dir / "hypervolume_by_round.png").is_file()
+ rounds = pd.read_csv(condition_dir / "all_rounds.csv")
+ assert rounds["round"].value_counts().to_dict() == {"R0": 15, "R1": 5, "R2": 3}
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index ade17b9..2d68e3d 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -1,64 +1,99 @@
-import sys
-import os
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from src.design import build_design_from_config
-from src.lhs import lhs_dataframe_optimized, lhs_dataframe
-from src.constraints import constraints_from_config
-from src.plotting import plot_parity_np, plot_correlation_heatmap, plot_distribution, plot_PCA, plot_pairplot
-import yaml
-from src.utils import load_csv, split_XY
-
-CFG_PATH = "configs/configCSV_example_config.yaml"
-CSV_PATH = "data/processed/configCSV_example.csv"
-
-def test_plots():
- print("Starting test...")
- config = yaml.load(open(CFG_PATH), Loader=yaml.FullLoader)
- print("Config loaded successfully")
- design = build_design_from_config(config)
- print("Design built successfully")
- lhs_df = lhs_dataframe_optimized(design, n=10, max_abs_corr=0.3)
- print("LHS data generated successfully")
- df = load_csv(CSV_PATH)
- print("CSV loaded successfully")
- X, Y = split_XY(df, design, config)
- print(f"Data split successfully - X type: {type(X)}, Y type: {type(Y)}")
- print(f"X columns: {X.columns.tolist()}")
- print(f"X dtypes: {X.dtypes}")
- print(f"X shape: {X.shape}")
- print(X)
-
- # Test correlation heatmap
- print("\nTesting correlation heatmap...")
- fig1, corr_mat = plot_correlation_heatmap(X)
- print("Correlation heatmap created successfully")
-
- # Test distribution plots
- print("\nTesting distribution plots...")
- fig2 = plot_distribution(X, title="Experimental Data Distributions")
- print("Distribution plots created successfully")
-
- # Test PCA plots
- print("\nTesting PCA plots...")
- fig3, pca_result, pca_obj = plot_PCA(X, title="Experimental Data PCA")
- print("PCA plots created successfully")
- print(f"Explained variance: {pca_obj.explained_variance_ratio_[:2].sum():.3f}")
-
- # Test pairplot
- print("\nTesting pairplot...")
- fig4 = plot_pairplot(X, title="Experimental Data Pairwise Relationships")
- print("Pairplot created successfully")
-
- #fig1.savefig("tests/test_correlation.png")
- #fig2.savefig("tests/test_distributions.png")
- #fig3.savefig("tests/test_pca.png")
- #fig4.savefig("tests/test_pairplot.png")
-
- return fig1, fig2, fig3, fig4
-
-def main():
- test_plots()
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
+"""Headless smoke tests for campaign diagnostic plots."""
+
+from __future__ import annotations
+
+import matplotlib
+
+matplotlib.use("Agg", force=True)
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pytest
+
+from mobo_kit.plotting import (
+ plot_PCA,
+ plot_correlation_heatmap,
+ plot_distribution,
+ plot_parity_np,
+)
+
+
+def _feature_frame():
+ return pd.DataFrame(
+ {
+ "temperature": [80.0, 85.0, 90.0, 95.0, 100.0, 105.0],
+ "speed": [1.0, 1.5, 1.2, 2.0, 1.8, 2.4],
+ "ratio": [0.1, 0.3, 0.2, 0.6, 0.7, 0.9],
+ "sample": ["A", "B", "C", "D", "E", "F"],
+ }
+ )
+
+
+def test_parity_plot_returns_metrics_and_saves_png(tmp_path):
+ true_y = np.array([[0.2, 1.0], [0.4, 1.5], [0.7, 2.0], [0.9, 2.5]], dtype=float)
+ pred_y = true_y + np.array(
+ [[0.02, -0.10], [-0.03, 0.05], [0.01, 0.08], [0.04, -0.04]]
+ )
+ pred_std = np.full_like(true_y, 0.05)
+ output_path = tmp_path / "parity.png"
+
+ fig, metrics = plot_parity_np(
+ true_y,
+ pred_y,
+ pred_std=pred_std,
+ objective_names=["efficiency", "stability"],
+ save=str(output_path),
+ show_plot=False,
+ )
+
+ assert output_path.is_file()
+ assert len(fig.axes) == 2
+ assert list(metrics.columns) == ["Objective", "R2", "RMSE"]
+ assert metrics["Objective"].tolist() == ["efficiency", "stability"]
+ assert np.isfinite(metrics[["R2", "RMSE"]].to_numpy()).all()
+ plt.close(fig)
+
+
+def test_tabular_diagnostic_plots_run_headlessly_and_preserve_shapes(tmp_path):
+ frame = _feature_frame()
+ numeric_columns = ["temperature", "speed", "ratio"]
+
+ corr_fig, corr = plot_correlation_heatmap(
+ frame,
+ columns=numeric_columns,
+ save=str(tmp_path / "correlation.png"),
+ show_plot=False,
+ )
+ distribution_fig = plot_distribution(
+ frame,
+ columns=numeric_columns,
+ n_cols=2,
+ save=str(tmp_path / "distributions.png"),
+ show_plot=False,
+ )
+ pca_fig, transformed, pca = plot_PCA(
+ frame,
+ columns=numeric_columns,
+ n_components=2,
+ save=str(tmp_path / "pca.png"),
+ show_plot=False,
+ )
+
+ assert corr.shape == (3, 3)
+ assert transformed.shape == (len(frame), 2)
+ assert pca.n_components_ == 2
+ assert (tmp_path / "correlation.png").is_file()
+ assert (tmp_path / "distributions.png").is_file()
+ assert (tmp_path / "pca.png").is_file()
+
+ for fig in (corr_fig, distribution_fig, pca_fig):
+ assert fig.axes
+ plt.close(fig)
+
+
+def test_plotting_helpers_reject_missing_numeric_data():
+ frame = pd.DataFrame({"sample": ["A", "B"]})
+
+ with pytest.raises(ValueError, match="No numeric columns"):
+ plot_correlation_heatmap(frame, show_plot=False)
diff --git a/tests/test_qlognehvi_batch.py b/tests/test_qlognehvi_batch.py
new file mode 100644
index 0000000..a8e1dd4
--- /dev/null
+++ b/tests/test_qlognehvi_batch.py
@@ -0,0 +1,314 @@
+import numpy as np
+import pytest
+import torch
+from botorch.models import SingleTaskGP
+from botorch.models.model_list_gp_regression import ModelListGP
+from botorch.models.transforms.outcome import Standardize
+
+import mobo_kit.qlognehvi_batch as module
+from mobo_kit.batch_selection import LocalPenalizationConfig, UndersizedBatchError
+from mobo_kit.candidate_pool import CandidatePool
+from mobo_kit.objectives import (
+ BoundedMCMultiOutputObjective,
+ ConfiguredMCMultiOutputObjective,
+ ObjectiveSpec,
+ ObjectiveTransform,
+)
+
+
+def _objective(count=2):
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec(f"utility_{index}", "maximize", "identity")
+ for index in range(count)
+ ],
+ version="TEST_ONLY-qlog-v1",
+ )
+ return ConfiguredMCMultiOutputObjective(transform)
+
+
+class FakeAcquisition:
+ def __init__(self, pending_count: int):
+ self.pending_count = pending_count
+ self.shapes = []
+
+ def __call__(self, X: torch.Tensor) -> torch.Tensor:
+ self.shapes.append(tuple(X.shape))
+ # A deterministic log score with a visible pending-point effect.
+ return X[..., 0, :].sum(dim=-1) - self.pending_count
+
+
+def test_singleton_shape_chunking_and_pending_metadata(monkeypatch):
+ acquisitions = []
+
+ def fake_builder(**kwargs):
+ pending = kwargs["X_pending"]
+ acquisition = FakeAcquisition(0 if pending is None else pending.shape[0])
+ acquisitions.append(acquisition)
+ return acquisition
+
+ monkeypatch.setattr(module, "_build_qlognehvi", fake_builder)
+ train = torch.zeros((3, 2), dtype=torch.double)
+ pool = torch.tensor([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]], dtype=torch.double)
+ pending = torch.tensor([[0.0, 1.0]], dtype=torch.double)
+ result = module.score_qlognehvi_singletons(
+ object(),
+ train,
+ pool,
+ _objective(),
+ np.array([-1.0, -1.0]),
+ mc_samples=16,
+ seed=8,
+ chunk_size=2,
+ X_pending_norm=pending,
+ )
+ assert result.evaluated_shape == (3, 1, 2)
+ assert result.pending_count == 1
+ assert result.mc_samples == 16
+ assert result.seed == 8
+ assert np.allclose(result.base_log_score, [-0.7, -0.3, 0.1])
+ assert acquisitions[0].shapes == [(2, 1, 2), (1, 1, 2)]
+
+
+def test_chunked_and_unchunked_fake_scores_are_equal(monkeypatch):
+ monkeypatch.setattr(
+ module,
+ "_build_qlognehvi",
+ lambda **kwargs: FakeAcquisition(0),
+ )
+ train = torch.zeros((2, 2), dtype=torch.double)
+ pool = torch.rand((7, 2), generator=torch.Generator().manual_seed(2))
+ kwargs = (object(), train, pool, _objective(), np.array([-1.0, -1.0]))
+ chunked = module.score_qlognehvi_singletons(*kwargs, chunk_size=2)
+ whole = module.score_qlognehvi_singletons(*kwargs, chunk_size=100)
+ assert np.array_equal(chunked.base_log_score, whole.base_log_score)
+
+
+@pytest.mark.parametrize(
+ "reference, match",
+ [(None, "required"), (np.array([]), "shape"), (np.array([np.nan]), "finite")],
+)
+def test_reference_validation(monkeypatch, reference, match):
+ monkeypatch.setattr(
+ module,
+ "_build_qlognehvi",
+ lambda **kwargs: FakeAcquisition(0),
+ )
+ with pytest.raises(ValueError, match=match):
+ module.score_qlognehvi_singletons(
+ object(),
+ torch.zeros((2, 1)),
+ torch.zeros((1, 1)),
+ _objective(1),
+ reference,
+ )
+
+
+def test_seed_is_passed_to_qmc_builder(monkeypatch):
+ captured = {}
+
+ def fake_builder(**kwargs):
+ captured.update(kwargs)
+ return FakeAcquisition(0)
+
+ monkeypatch.setattr(module, "_build_qlognehvi", fake_builder)
+ module.score_qlognehvi_singletons(
+ object(),
+ torch.zeros((2, 1)),
+ torch.zeros((1, 1)),
+ _objective(1),
+ np.array([-1.0]),
+ mc_samples=32,
+ seed=41,
+ )
+ assert captured["mc_samples"] == 32
+ assert captured["seed"] == 41
+
+
+def test_configured_objective_and_reference_dimensions_are_required(monkeypatch):
+ monkeypatch.setattr(
+ module,
+ "_build_qlognehvi",
+ lambda **kwargs: FakeAcquisition(0),
+ )
+ train = torch.zeros((2, 1))
+ pool = torch.zeros((1, 1))
+ with pytest.raises(TypeError, match="configured or bounded configured"):
+ module.score_qlognehvi_singletons(object(), train, pool, None, np.array([-1.0]))
+ with pytest.raises(ValueError, match="configured objective count"):
+ module.score_qlognehvi_singletons(
+ object(), train, pool, _objective(2), np.array([-1.0])
+ )
+
+
+def test_real_qlognehvi_accepts_bounded_objective_and_returns_reproducible_scores():
+ train_X = torch.tensor([[0.0], [0.25], [0.5], [0.75], [1.0]], dtype=torch.double)
+ train_Y = torch.tensor(
+ [
+ [0.15, -3.0, 0.20],
+ [0.55, -2.2, 0.45],
+ [0.90, -1.5, 0.85],
+ [0.65, -1.9, 0.60],
+ [0.25, -2.8, 0.30],
+ ],
+ dtype=torch.double,
+ )
+ model = ModelListGP(
+ *[
+ SingleTaskGP(
+ train_X,
+ train_Y[:, index : index + 1],
+ outcome_transform=Standardize(m=1),
+ )
+ for index in range(train_Y.shape[1])
+ ]
+ )
+ transform = ObjectiveTransform(
+ [
+ ObjectiveSpec("uniformity", "maximize", "identity"),
+ ObjectiveSpec("optoelectronic", "maximize", "identity"),
+ ObjectiveSpec("thickness", "maximize", "identity"),
+ ],
+ version="TEST_ONLY-bounded-qlog-v1",
+ )
+ objective = BoundedMCMultiOutputObjective(
+ transform,
+ bounds=((0.0, 1.0), (None, None), (0.0, 1.0)),
+ )
+ pool = torch.tensor([[0.1], [0.4], [0.7], [0.9]], dtype=torch.double)
+ reference = np.array([-0.01, -4.0, -0.01])
+
+ first = module.score_qlognehvi_singletons(
+ model,
+ train_X,
+ pool,
+ objective,
+ reference,
+ mc_samples=16,
+ seed=73,
+ chunk_size=2,
+ prune_baseline=False,
+ )
+ second = module.score_qlognehvi_singletons(
+ model,
+ train_X,
+ pool,
+ objective,
+ reference,
+ mc_samples=16,
+ seed=73,
+ chunk_size=2,
+ prune_baseline=False,
+ )
+
+ assert first.evaluated_shape == (4, 1, 1)
+ assert first.objective_contract_version == objective.version
+ assert np.array_equal(first.base_log_score, second.base_log_score)
+ assert not np.any(np.isnan(first.base_log_score))
+ assert not np.any(np.isposinf(first.base_log_score))
+ assert np.any(np.isfinite(first.base_log_score))
+ assert np.array_equal(first.reference_point_utility, reference)
+
+
+def _proposal_pool():
+ X = np.array([[0.3], [0.45], [0.6], [0.75], [0.9]])
+ return CandidatePool(
+ grid_indices=np.arange(5)[:, None],
+ X_phys=X.copy(),
+ X_norm=X.copy(),
+ seed=3,
+ draws=5,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+
+
+def test_sequential_proposal_updates_pending_and_returns_exact_three(monkeypatch):
+ pending_counts = []
+ seen_objectives = []
+
+ def fake_score(model, train_X, X_pool, objective, reference, **kwargs):
+ del model, train_X
+ pending = kwargs.get("X_pending_norm")
+ pending_count = 0 if pending is None else pending.shape[0]
+ pending_counts.append(pending_count)
+ seen_objectives.append(objective)
+ return module.QLogNEHVIPoolScoreResult(
+ base_log_score=X_pool[:, 0].detach().cpu().numpy(),
+ evaluated_shape=(X_pool.shape[0], 1, X_pool.shape[1]),
+ pending_count=pending_count,
+ mc_samples=kwargs["mc_samples"],
+ seed=kwargs["seed"],
+ reference_point_utility=np.asarray(reference),
+ objective_contract_version=objective.objective_transform.version,
+ )
+
+ monkeypatch.setattr(module, "score_qlognehvi_singletons", fake_score)
+ objective = _objective(1)
+ proposal = module.propose_qlognehvi_penalized_batch(
+ _proposal_pool(),
+ object(),
+ torch.tensor([[0.05], [0.15]], dtype=torch.double),
+ objective,
+ np.array([-1.0]),
+ q=3,
+ local_penalization_config=LocalPenalizationConfig(
+ radius=0.12, min_batch_distance=0.1
+ ),
+ X_pending_norm=torch.tensor([[0.2]], dtype=torch.double),
+ mc_samples=16,
+ seed=7,
+ )
+ assert proposal.selection.selected_pool_indices.size == 3
+ assert np.unique(proposal.selection.selected_pool_indices).size == 3
+ assert pending_counts == [1, 2, 3]
+ assert seen_objectives == [objective, objective, objective]
+ assert [history.pending_count for history in proposal.score_history] == [1, 2, 3]
+ assert all(step.base_score is not None for step in proposal.selection.steps)
+ assert proposal.metadata["pool_seed"] == 3
+ assert proposal.metadata["mc_seed"] == 7
+ assert proposal.metadata["objective_contract_version"] == "TEST_ONLY-qlog-v1"
+
+
+def test_proposal_rejects_observed_or_pending_pool_overlap():
+ with pytest.raises(ValueError, match="overlaps observed"):
+ module.propose_qlognehvi_penalized_batch(
+ _proposal_pool(),
+ object(),
+ torch.tensor([[0.3]], dtype=torch.double),
+ _objective(1),
+ np.array([-1.0]),
+ q=1,
+ local_penalization_config=LocalPenalizationConfig(
+ radius=0.1, min_batch_distance=0
+ ),
+ )
+
+
+def test_qlog_proposal_hard_distance_failure_is_explicit(monkeypatch):
+ def fake_score(model, train_X, X_pool, objective, reference, **kwargs):
+ del model, train_X
+ return module.QLogNEHVIPoolScoreResult(
+ base_log_score=np.zeros(X_pool.shape[0]),
+ evaluated_shape=(X_pool.shape[0], 1, X_pool.shape[1]),
+ pending_count=0,
+ mc_samples=kwargs["mc_samples"],
+ seed=kwargs["seed"],
+ reference_point_utility=np.asarray(reference),
+ objective_contract_version=objective.objective_transform.version,
+ )
+
+ monkeypatch.setattr(module, "score_qlognehvi_singletons", fake_score)
+ with pytest.raises(UndersizedBatchError):
+ module.propose_qlognehvi_penalized_batch(
+ _proposal_pool(),
+ object(),
+ torch.tensor([[0.05]], dtype=torch.double),
+ _objective(1),
+ np.array([-1.0]),
+ q=3,
+ local_penalization_config=LocalPenalizationConfig(
+ radius=0.1, min_batch_distance=0.7
+ ),
+ )
diff --git a/tests/test_raw_component_screen.py b/tests/test_raw_component_screen.py
new file mode 100644
index 0000000..f853c86
--- /dev/null
+++ b/tests/test_raw_component_screen.py
@@ -0,0 +1,399 @@
+"""The raw-component screen, and the two ways it could quietly mislead.
+
+This harness exists to answer "does the model learn better from the measurement
+than from the score", and it answers by ``eval``-ing a candidate expression over a
+namespace of workbook columns. Two things therefore have to be pinned rather than
+trusted:
+
+* **the expression validator**, because a screen that evaluates arbitrary text is
+ a bad instrument regardless of who is typing into it -- and because these
+ expressions are increasingly written by agents rather than by hand;
+* **the leave-one-out loop**, because it is a SECOND implementation of a fold loop
+ this project has already had to consolidate once. It is not the same function
+ object as ``mobo_kit.loocv.loo_predictions`` -- it takes a free-form ``y``
+ rather than an objective spec -- so the identity trick used elsewhere does not
+ apply and agreement has to be asserted numerically instead.
+
+The fold loop is also where an honest screen and a flattering one diverge: the
+structured mean must be refit INSIDE every fold. Fitting it once on all rows leaks
+the held-out value into the mean function, which on 15 rows is worth more than any
+real effect anyone has found here. That is pinned by construction below.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+from openpyxl import Workbook
+
+from mobo_kit.loocv import loo_predictions
+
+
+def _load():
+ path = Path("scripts") / "raw_component_screen.py"
+ spec = importlib.util.spec_from_file_location("_script_raw_component_screen", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+screen = _load()
+
+
+# --------------------------------------------------------------------------- #
+# the expression validator
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.parametrize(
+ "expr",
+ [
+ "coverage",
+ "np.log(photocond)",
+ "(coverage + phase_purity) / 2",
+ "np.clip(uniformity_raw, 0, 1)",
+ "phase_purity ** 2",
+ "np.log(phase_purity / (1 - phase_purity))",
+ ],
+)
+def test_ordinary_candidate_expressions_are_accepted(expr):
+ screen._check_expression(expr)
+
+
+@pytest.mark.parametrize(
+ ("expr", "because"),
+ [
+ ("__import__('os').system('echo hi')", "no dunder, no import"),
+ ("coverage.__class__", "attribute access outside the np namespace"),
+ ("np.load('x.npy')", "np.load is not on the numeric allow-list"),
+ ("open('secrets')", "open is not in the namespace"),
+ ("[c for c in coverage]", "comprehensions are not expressions we screen"),
+ ("thickness", "not a name in the measurement namespace"),
+ ("coverage if phase_purity else 0", "no conditionals"),
+ ],
+)
+def test_expressions_outside_the_measurement_namespace_are_refused(expr, because):
+ with pytest.raises((ValueError, SyntaxError)):
+ screen._check_expression(expr)
+
+
+def test_the_error_names_the_namespace_rather_than_just_refusing():
+ """A rejected candidate must say what IS available, or the next attempt is a guess."""
+ with pytest.raises(ValueError, match="phase_purity"):
+ screen._check_expression("phase_purty")
+
+
+def test_evaluate_uses_only_the_supplied_columns():
+ space = {"coverage": np.array([0.5, 1.0]), "phase_purity": np.array([2.0, 4.0])}
+ got = screen.evaluate("coverage * phase_purity", space)
+ assert got.tolist() == [1.0, 4.0]
+
+
+# --------------------------------------------------------------------------- #
+# the fold loop
+# --------------------------------------------------------------------------- #
+
+
+#: Eight inputs and twelve rows, deliberately. A three-input version of this
+#: fixture made the trend test vacuous: with that much data per dimension the
+#: plain GP already scored 0.9980 and a correct mean function could not show any
+#: improvement. The campaign's real shape is ten inputs and fifteen rows, where
+#: the GP is starved and the trend is worth a great deal -- which is the regime
+#: the harness has to be right in.
+_INPUT_NAMES = ("a", "b", "c", "d", "e", "f", "g", "h")
+
+
+@pytest.fixture(scope="module")
+def tiny_campaign():
+ """Eight inputs, twelve rows, one objective with a real linear trend in `a`."""
+ config = {
+ "inputs": [
+ {"name": name, "start": 0.0, "stop": 10.0, "step": 1.0}
+ for name in _INPUT_NAMES
+ ],
+ "objectives": {
+ "contract_version": "test",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": "y",
+ "model_source_column": "y",
+ "transform": "affine",
+ "goal": "maximize",
+ "lower_anchor": 0.0,
+ "upper_anchor": 100.0,
+ }
+ ],
+ },
+ }
+ rng = np.random.default_rng(11)
+ X = rng.uniform(0.0, 10.0, size=(12, len(_INPUT_NAMES)))
+ y = 3.0 * X[:, 0] + rng.normal(0.0, 0.4, size=12) + 20.0
+ return config, X, y
+
+
+def test_loo_r2_agrees_with_the_shared_fold_loop(tiny_campaign):
+ """Two implementations of leave-one-out must not be able to disagree.
+
+ ``loocv.loo_predictions`` is canonical and takes an objective spec; this
+ harness takes a bare array so that a candidate expression can be screened
+ without inventing a config entry for it. They must still produce the same
+ predictions, or the screen is measuring a different model from the campaign.
+ """
+ config, X, y = tiny_campaign
+ entry = config["objectives"]["specs"][0]
+ canonical = loo_predictions(config, entry, X, y, seed=73, use_mean_function=False)
+ ours = screen.loo_r2(config, X, y, seed=73)
+ np.testing.assert_allclose(ours["predicted"], canonical.predicted, rtol=0, atol=1e-9)
+ assert ours["r2"] == pytest.approx(canonical.r2, abs=1e-9)
+
+
+def test_the_mean_function_is_refit_inside_every_fold(tiny_campaign, monkeypatch):
+ """The single most consequential detail, asserted by counting calls.
+
+ If the trend were fitted once and reused, the held-out row would be inside the
+ data the mean function saw, and every score this harness reports would be
+ inflated. One fit per fold is the only correct count.
+ """
+ config, X, y = tiny_campaign
+ spec = screen.StructuredMeanSpec(
+ response="identity", features=(screen.MeanFeature("a"),)
+ )
+ calls = []
+ original = screen.build_structured_mean
+
+ def counted(X_phys, values, *args, **kwargs):
+ calls.append(len(values))
+ return original(X_phys, values, *args, **kwargs)
+
+ monkeypatch.setattr(screen, "build_structured_mean", counted)
+ screen.loo_r2(config, X, y, seed=73, mean_spec=spec)
+ assert len(calls) == len(y), "one mean fit per fold"
+ assert set(calls) == {len(y) - 1}, "each fit sees N-1 rows, never all N"
+
+
+def test_a_declared_trend_helps_when_the_trend_is_real(tiny_campaign):
+ """The point of a mean function, in the starved regime where it matters.
+
+ Eight inputs and twelve rows: the plain GP cannot find the one dimension that
+ matters, and declaring it is worth a large jump. If this ever stops holding,
+ the mean-function path is not doing what the screen reports it as doing.
+ """
+ config, X, y = tiny_campaign
+ spec = screen.StructuredMeanSpec(
+ response="identity", features=(screen.MeanFeature("a"),)
+ )
+ plain = screen.loo_r2(config, X, y, seed=73)["r2"]
+ structured = screen.loo_r2(config, X, y, seed=73, mean_spec=spec)["r2"]
+ assert structured > plain
+
+
+# --------------------------------------------------------------------------- #
+# reading the workbook
+# --------------------------------------------------------------------------- #
+
+
+def _sheet_with(rows):
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "R0"
+ sheet["A1"] = "Sample number"
+ sheet["L1"] = "Coverage"
+ for index, (sample, coverage) in enumerate(rows, start=2):
+ sheet[f"A{index}"] = sample
+ sheet[f"L{index}"] = coverage
+ return book
+
+
+def test_reading_stops_at_the_first_blank_sample_number(tmp_path):
+ """Rows below the data block are notes, and notes are not films."""
+ path = tmp_path / "book.xlsx"
+ book = _sheet_with([(1, 0.9), (2, 0.8), (3, 0.7)])
+ book["R0"]["A6"] = 99 # a stray row below a gap
+ book["R0"]["L6"] = 0.1
+ book.save(path)
+ space = screen.read_measurements(path, "R0")
+ assert space["coverage"].tolist() == [0.9, 0.8, 0.7]
+
+
+def test_missing_cells_become_nan_rather_than_zero(tmp_path):
+ """A blank measurement is unknown, not zero. Zero would be a plausible number."""
+ path = tmp_path / "book.xlsx"
+ book = _sheet_with([(1, 0.9), (2, None), (3, 0.7)])
+ book.save(path)
+ space = screen.read_measurements(path, "R0")
+ assert np.isnan(space["coverage"][1])
+ assert space["coverage"][[0, 2]].tolist() == [0.9, 0.7]
+
+
+# --------------------------------------------------------------------------- #
+# the built-in screen
+# --------------------------------------------------------------------------- #
+
+
+def test_every_built_in_candidate_is_a_valid_expression():
+ for item in screen.BUILT_IN:
+ screen._check_expression(item["expr"])
+
+
+def test_the_built_in_screen_covers_both_forms_of_thickness():
+ """The screen's whole argument rests on this contrast, so it must be in it.
+
+ Raw nanometres against the stored Gaussian-squashed score, on identical films.
+ If either disappears from the built-ins, the headline comparison stops being
+ reproducible from a bare run of the script.
+ """
+ names = {item["name"] for item in screen.BUILT_IN}
+ assert {"thickness_nm", "STORED_score_thickness"} <= names
+
+
+def _full_workbook(path, n_rows=9):
+ """A workbook carrying every column the measurement namespace names."""
+ rng = np.random.default_rng(5)
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "R0"
+ sheet["A1"] = "Sample number"
+ for name, letter in screen.COLUMNS.items():
+ sheet[f"{letter}1"] = name
+ for row in range(2, n_rows + 2):
+ sheet[f"A{row}"] = row - 1
+ for name, letter in screen.COLUMNS.items():
+ sheet[f"{letter}{row}"] = float(rng.uniform(0.2, 0.9))
+ book.save(path)
+ return path
+
+
+def test_the_screen_prints_its_family_size_and_the_selection_warning(tmp_path, capsys):
+ """Silent multiplicity is how a screen of thirty reports a discovery.
+
+ The count of candidates screened, and the warning that the winner was chosen
+ by looking at this data, are part of the OUTPUT rather than of the docstring.
+ A reader who sees only the table must still see how many it was picked from.
+ """
+ workbook = _full_workbook(tmp_path / "full.xlsx")
+ config = tmp_path / "screen.yaml"
+ design = [
+ "speed_1", "time_1", "speed_2", "time_2", "precur_conc",
+ "precur_vol", "anneal_temp", "anneal_time", "anti_vol", "anti_time",
+ ]
+ lines = ["inputs:"]
+ lines += [
+ " - {name: %s, start: 0.0, stop: 1.0, step: 0.05}" % name for name in design
+ ]
+ lines += [
+ "objectives:",
+ " contract_version: screen-test",
+ " scaling_mode: fixed_affine",
+ " specs:",
+ " - name: y",
+ " model_source_column: y",
+ " transform: affine",
+ " goal: maximize",
+ " lower_anchor: 0.0",
+ " upper_anchor: 1.0",
+ ]
+ config.write_text("\n".join(lines), encoding="utf-8")
+
+ screen.main([
+ "--workbook", str(workbook),
+ "--config", str(config),
+ "--candidates", "coverage,phase_purity",
+ ])
+ printed = capsys.readouterr().out
+ assert "screening 2 candidates" in printed
+ assert "Bonferroni family size K = 2" in printed
+ assert "chosen by looking at this data" in printed
+ assert "null LOO R2" in printed
+
+
+def test_a_totally_collapsed_run_refuses_rather_than_reporting_the_null():
+ """The trap this project walked into, closed by construction.
+
+ When every fold falls back to its training mean, the predictions ARE the
+ leave-one-out mean predictor, whose R2 is exactly ``1-(n/(n-1))^2`` with
+ Spearman -1. That is the number this project used as its null for a year, so
+ a completely broken run would have reported an ordinary-looking no-signal
+ result. It must raise instead.
+ """
+ config = {
+ "inputs": [{"name": "a", "start": 0.0, "stop": 1.0, "step": 0.1}],
+ "objectives": {
+ "contract_version": "t", "scaling_mode": "fixed_affine",
+ "specs": [{"name": "y", "model_source_column": "y", "transform": "affine",
+ "goal": "maximize", "lower_anchor": 0.0, "upper_anchor": 1.0}],
+ },
+ }
+ X = np.linspace(0.0, 1.0, 8).reshape(-1, 1)
+ y = np.linspace(1.0, 2.0, 8)
+
+ def always_fails(*args, **kwargs):
+ raise RuntimeError("fit refused")
+
+ import unittest.mock as mock
+ with mock.patch.object(screen, "fit_model_variant", always_fails):
+ with pytest.raises(RuntimeError, match="All 8 folds failed"):
+ screen.loo_r2(config, X, y, seed=73)
+
+
+def test_the_refusal_names_the_number_it_would_otherwise_have_printed():
+ """So whoever hits it recognises the value from the project's own docs."""
+ config = {
+ "inputs": [{"name": "a", "start": 0.0, "stop": 1.0, "step": 0.1}],
+ "objectives": {
+ "contract_version": "t", "scaling_mode": "fixed_affine",
+ "specs": [{"name": "y", "model_source_column": "y", "transform": "affine",
+ "goal": "maximize", "lower_anchor": 0.0, "upper_anchor": 1.0}],
+ },
+ }
+ X = np.linspace(0.0, 1.0, 15).reshape(-1, 1)
+ y = np.linspace(1.0, 2.0, 15)
+ import unittest.mock as mock
+ with mock.patch.object(
+ screen, "fit_model_variant", lambda *a, **k: (_ for _ in ()).throw(RuntimeError())
+ ):
+ with pytest.raises(RuntimeError, match=r"-0\.1480"):
+ screen.loo_r2(config, X, y, seed=73)
+
+
+def test_a_mean_feature_may_declare_a_log_transform():
+ """Without this the screen cannot express the mean function the campaign runs.
+
+ ``configs/campaign_d2d_perovskite_final.yaml`` declares
+ ``log(speed_1) + log(precur_conc)`` on a log response. The screen originally
+ built every feature with the default identity transform, so it silently
+ measured a DIFFERENT model and then compared candidates against it as though
+ it were the incumbent.
+ """
+ spec = screen._mean_spec(
+ {"mean_features": [{"column": "speed_1", "transform": "log"}, "precur_conc"]},
+ "log",
+ )
+ assert spec.response == "log"
+ assert [(f.column, f.transform) for f in spec.features] == [
+ ("speed_1", "log"),
+ ("precur_conc", "identity"),
+ ]
+
+
+def test_no_mean_features_means_no_mean_spec():
+ assert screen._mean_spec({"expr": "coverage"}, "identity") is None
+ assert screen._mean_spec({"mean_features": []}, "identity") is None
+
+
+def test_the_docstring_no_longer_teaches_the_wrong_bar():
+ """-0.1480 must not be presented as a significance threshold anywhere here.
+
+ It is the score of the leave-one-out mean predictor. Measured on this
+ campaign, 28.7% of pure-noise shuffles beat it. The docstring has to say so,
+ because the docstring is what the next person reads before quoting an R2.
+ """
+ doc = screen.__doc__
+ assert "NOT A SIGNIFICANCE THRESHOLD" in doc
+ assert "28.7%" in doc
+ assert "--calibrate" in doc or "`--calibrate`" in doc
diff --git a/tests/test_replicate_variance.py b/tests/test_replicate_variance.py
new file mode 100644
index 0000000..096e2e1
--- /dev/null
+++ b/tests/test_replicate_variance.py
@@ -0,0 +1,276 @@
+"""Replicate variance into train_Yvar.
+
+Wired and tested against synthetic replicates now, so that the arrival of the R1
+triplicates is a data event rather than a code event.
+"""
+
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pandas as pd
+import pytest
+import torch
+
+from mobo_kit.model_validation import DIM_SCALED_PRIOR, fit_model_variant
+from mobo_kit.replicate_variance import (
+ WITHIN_FILM_LOG_THICKNESS_VARIANCE,
+ PooledVariance,
+ pool_between_film_variance,
+ sanity_floor_findings,
+ train_yvar_for_rows,
+ variance_config,
+)
+
+NAMES = ["uniformity", "optoelectronic", "thickness"]
+
+
+def _spread(values: dict[str, list[float]]) -> pd.DataFrame:
+ return pd.DataFrame(values, columns=NAMES)
+
+
+def _films(counts: dict[str, list[int]]) -> pd.DataFrame:
+ return pd.DataFrame(counts, columns=NAMES)
+
+
+# --------------------------------------------------------------------------- #
+# pooling
+# --------------------------------------------------------------------------- #
+
+
+def test_pooling_is_dof_weighted() -> None:
+ """sum((n-1) s^2) / sum(n-1): a condition with more films counts for more."""
+ spread = _spread({"uniformity": [0.2, 0.4], "optoelectronic": [0.1, 0.1], "thickness": [0.3, 0.5]})
+ films = _films({"uniformity": [3, 3], "optoelectronic": [3, 3], "thickness": [2, 4]})
+ pooled = pool_between_film_variance(spread, films)
+
+ assert pooled["uniformity"].variance == pytest.approx((2 * 0.04 + 2 * 0.16) / 4)
+ assert pooled["uniformity"].dof == 4
+ # thickness: one dof at 0.09, three at 0.25
+ assert pooled["thickness"].variance == pytest.approx((1 * 0.09 + 3 * 0.25) / 4)
+ assert pooled["thickness"].dof == 4
+
+
+def test_a_single_film_contributes_no_dof_rather_than_zero_variance() -> None:
+ """One film measures no reproducibility. Counting it as zero variance is how a
+ model ends up certain about a process nobody measured twice."""
+ spread = _spread(
+ {"uniformity": [0.2, float("nan")], "optoelectronic": [0.2, float("nan")], "thickness": [0.2, float("nan")]}
+ )
+ films = _films({"uniformity": [3, 1], "optoelectronic": [3, 1], "thickness": [3, 1]})
+ pooled = pool_between_film_variance(spread, films)
+ assert pooled["thickness"].variance == pytest.approx(0.04)
+ assert pooled["thickness"].dof == 2
+ assert pooled["thickness"].n_conditions == 1
+
+
+def test_no_replicated_condition_at_all_is_refused() -> None:
+ spread = _spread({name: [float("nan")] for name in NAMES})
+ films = _films({name: [1] for name in NAMES})
+ with pytest.raises(ValueError, match="no condition with two or more usable films"):
+ pool_between_film_variance(spread, films)
+
+
+def test_the_pooled_space_follows_the_aggregation_rule() -> None:
+ """Thickness aggregates in log space, so its variance is of log(T). Recording
+ the space is what stops an nm^2 variance reaching a model that trains on logs."""
+ spread = _spread({name: [0.2, 0.2] for name in NAMES})
+ films = _films({name: [3, 3] for name in NAMES})
+ pooled = pool_between_film_variance(
+ spread, films, aggregates={"thickness": "mean_of_log", "uniformity": "mean"}
+ )
+ assert pooled["thickness"].space == "log"
+ assert pooled["uniformity"].space == "value"
+
+
+def test_sd_and_variance_of_the_mean() -> None:
+ pooled = PooledVariance("thickness", 0.09, dof=10, n_conditions=5, space="log")
+ assert pooled.sd == pytest.approx(0.3)
+ # three films average to a third of the variance
+ assert pooled.variance_of_mean(3) == pytest.approx(0.03)
+ with pytest.raises(ValueError):
+ pooled.variance_of_mean(0)
+
+
+# --------------------------------------------------------------------------- #
+# the sanity floor
+# --------------------------------------------------------------------------- #
+
+
+def test_between_film_variance_below_the_within_film_floor_is_reported() -> None:
+ """Films cannot be more reproducible than points on one film."""
+ pooled = {"thickness": PooledVariance("thickness", 0.01, 10, 5, "log")}
+ messages = sanity_floor_findings(
+ pooled, {"thickness": WITHIN_FILM_LOG_THICKNESS_VARIANCE}
+ )
+ assert len(messages) == 1
+ assert "BELOW the within-film floor" in messages[0]
+ assert "0.0593" in messages[0]
+
+
+def test_a_healthy_between_film_variance_says_nothing() -> None:
+ pooled = {"thickness": PooledVariance("thickness", 0.2, 10, 5, "log")}
+ assert sanity_floor_findings(pooled, {"thickness": WITHIN_FILM_LOG_THICKNESS_VARIANCE}) == ()
+
+
+def test_the_floor_is_a_floor_not_the_estimate() -> None:
+ """Guards the substitution this whole module exists to prevent: the within-film
+ number is not an answer, it is a lower bound on one."""
+ assert WITHIN_FILM_LOG_THICKNESS_VARIANCE == pytest.approx(0.0593)
+ pooled = {"thickness": PooledVariance("thickness", 0.0593, 10, 5, "log")}
+ # equal to the floor is not below it
+ assert sanity_floor_findings(pooled, {"thickness": WITHIN_FILM_LOG_THICKNESS_VARIANCE}) == ()
+
+
+def test_the_live_config_declares_the_floor_and_the_r0_policy() -> None:
+ from mobo_kit.campaign import load_campaign_config
+
+ config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+ settings = variance_config(config)
+ assert settings["sanity_floor"]["thickness"] == pytest.approx(0.0593)
+ assert settings["rows_without_replicates"] == 1
+
+
+# --------------------------------------------------------------------------- #
+# per-row variance
+# --------------------------------------------------------------------------- #
+
+
+def test_the_variance_handed_to_the_model_is_of_the_mean() -> None:
+ """The observation is an average of n films, so its variance is pooled / n.
+ Passing the single-film variance would be three times too large on a triplicate
+ -- overstating the uncertainty of exactly the conditions that were replicated
+ most carefully -- and nothing errors."""
+ pooled = {name: PooledVariance(name, 0.09, 10, 5, "value") for name in NAMES}
+ films = _films({name: [3, 3, 1] for name in NAMES})
+ yvar = train_yvar_for_rows(pooled, films, NAMES)
+ assert yvar.shape == (3, 3)
+ assert yvar[0, 0] == pytest.approx(0.03)
+ assert yvar[2, 0] == pytest.approx(0.09) # one film carries the full variance
+
+
+def test_rows_without_replicates_take_the_declared_film_count() -> None:
+ pooled = {name: PooledVariance(name, 0.09, 10, 5, "value") for name in NAMES}
+ counts = np.zeros((2, 3))
+ yvar = train_yvar_for_rows(pooled, counts, NAMES, rows_without_replicates=1)
+ assert np.allclose(yvar, 0.09)
+
+
+def test_identical_replicates_are_refused_rather_than_called_exact() -> None:
+ """Zero variance tells the model the observation is exact. Films that agree to
+ the last digit are a transcription, not a measurement."""
+ pooled = {name: PooledVariance(name, 0.0, 10, 5, "value") for name in NAMES}
+ with pytest.raises(ValueError, match="transcription, not a measurement"):
+ train_yvar_for_rows(pooled, np.full((2, 3), 3.0), NAMES)
+
+
+def test_a_missing_objective_is_refused() -> None:
+ pooled = {"uniformity": PooledVariance("uniformity", 0.09, 10, 5, "value")}
+ with pytest.raises(ValueError, match="No pooled variance"):
+ train_yvar_for_rows(pooled, np.ones((2, 3)), NAMES)
+
+
+# --------------------------------------------------------------------------- #
+# reaching the model
+# --------------------------------------------------------------------------- #
+
+
+def test_measured_variance_replaces_the_fitted_noise() -> None:
+ """With train_Yvar the noise is given, not inferred: the likelihood becomes a
+ fixed-noise one carrying a value per observation."""
+ X = torch.rand(10, 2, dtype=torch.double)
+ Y = (3.0 * X[:, :1] + 0.05 * torch.randn(10, 1, dtype=torch.double)).double()
+ Yvar = torch.full_like(Y, 0.04)
+
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(10)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ train_Yvar=Yvar,
+ )
+ gp = record.model.models[0]
+ assert type(gp.likelihood).__name__ == "FixedNoiseGaussianLikelihood"
+ assert gp.likelihood.noise.detach().reshape(-1).numel() == 10
+
+
+def test_the_variance_is_taken_in_original_units_not_standardized() -> None:
+ """`Standardize` rescales train_Yvar along with the targets, so it must arrive
+ in the target's own units. A pre-standardized variance would be wrong by
+ var(Y) and would fail silently."""
+ X = torch.rand(10, 2, dtype=torch.double)
+ Y = (100.0 * X[:, :1]).double()
+ Yvar = torch.full_like(Y, 25.0)
+
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(10)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ train_Yvar=Yvar,
+ )
+ gp = record.model.models[0]
+ observed = float(gp.likelihood.noise.detach().reshape(-1)[0])
+ assert observed == pytest.approx(25.0 / float(Y.var()), rel=1e-6)
+
+
+def test_heteroskedastic_variance_survives_to_the_model() -> None:
+ """Rows with fewer films are noisier, and the model has to see that rather than
+ one averaged number."""
+ X = torch.rand(8, 2, dtype=torch.double)
+ Y = (2.0 * X[:, :1]).double()
+ Yvar = torch.tensor([[0.01]] * 4 + [[0.09]] * 4, dtype=torch.double)
+
+ record = fit_model_variant(
+ X,
+ Y,
+ sample_ids=tuple(range(8)),
+ objective_names=("y",),
+ variant=DIM_SCALED_PRIOR,
+ train_Yvar=Yvar,
+ )
+ noise = record.model.models[0].likelihood.noise.detach().reshape(-1)
+ assert noise[0] < noise[-1]
+ assert float(noise[-1] / noise[0]) == pytest.approx(9.0, rel=1e-6)
+
+
+def test_a_round_accepts_measured_variance_end_to_end() -> None:
+ """The whole point: when the triplicates land, this is a data change."""
+ from mobo_kit.campaign import load_campaign_config, run_r1_ucb
+ from mobo_kit.design import build_design_from_config
+ from mobo_kit.lhs import lhs_dataframe_optimized
+
+ config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+ design = build_design_from_config(dict(config))
+ X = lhs_dataframe_optimized(design, 12, seed=5, snap_to_grids=True).to_numpy(float)
+ names = list(design.names)
+ speed = X[:, names.index("speed_1")]
+ concentration = X[:, names.index("precur_conc")]
+ temperature = X[:, names.index("anneal_temp")]
+ rng = np.random.default_rng(0)
+
+ thickness = np.exp(
+ 9.6 - 0.38 * np.log(speed) + 0.5 * np.log(concentration) + rng.normal(0, 0.08, 12)
+ )
+ optoelectronic = -6.2 - 0.012 * temperature + rng.normal(0, 0.05, 12)
+ uniformity = np.clip(0.5 + 0.4 * np.sin(concentration * 3.0), 0.02, 0.98)
+ Y = np.column_stack([uniformity, optoelectronic, thickness])
+
+ # The declared variance must be smaller than the observed spread, or the model
+ # is being told its signal is noise -- which the collapse guard rightly refuses.
+ # The first version of this test declared sd 0.1 against a uniformity spread of
+ # 0.039 and was correctly rejected.
+ pooled = {
+ "uniformity": PooledVariance("uniformity", 0.002, 10, 5, "value"),
+ "optoelectronic": PooledVariance("optoelectronic", 0.02, 10, 5, "value"),
+ # log space, matching `response: log` and the mean_of_log aggregation
+ "thickness": PooledVariance("thickness", 0.0593, 10, 5, "log"),
+ }
+ yvar = train_yvar_for_rows(pooled, np.full((12, 3), 3.0), list(pooled))
+
+ result = run_r1_ucb(config, X, Y, n=3, observed_Yvar=yvar)
+ assert result.n_conditions == 3
+ assert math.isfinite(result.diagnostics["validity"]["min_pairwise_distance"])
diff --git a/tests/test_round_report.py b/tests/test_round_report.py
new file mode 100644
index 0000000..9326c4a
--- /dev/null
+++ b/tests/test_round_report.py
@@ -0,0 +1,447 @@
+"""The figures a round produces, and the promises attached to them.
+
+What is pinned here is not "a PNG appeared". A figure that renders and shows the
+wrong number is worse than no figure, because it carries authority. So:
+
+* **every figure writes the numbers behind it**, and the schema of those numbers is
+ fixed here -- a plot whose data cannot be re-derived is the next
+ plausible-finite-number bug, and this project has had three;
+* **the parity numbers ARE intake's numbers.** They come from one shared fold loop
+ rather than two implementations that agree today, and the test asserts the
+ identity rather than a tolerance;
+* **the batch figure's numbers ARE the Review sheet's numbers**, for the same
+ reason: two artifacts a human compares must not be able to disagree;
+* **determinism is checked on the CSVs, never on PNG bytes** -- matplotlib output
+ is not reproducible across versions and a byte comparison would fail for reasons
+ that have nothing to do with the campaign.
+
+Everything here builds its own workbook, so none of it needs the ignored private
+one.
+"""
+
+from __future__ import annotations
+
+import json
+
+import numpy as np
+import pandas as pd
+import pytest
+from openpyxl import Workbook
+
+from mobo_kit.batch_review import build_batch_review
+from mobo_kit.campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ load_campaign_config,
+ objective_names,
+ run_r1_ucb,
+)
+from mobo_kit.loocv import loo_predictions
+from mobo_kit.round_report import (
+ ReportManifest,
+ generate_round_report,
+ report_directory,
+)
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite_test.yaml"
+
+#: Fitting GPs to six synthetic rows produces near-degenerate posteriors, and
+#: gpytorch says so on nearly every fold. That is a property of the fixture, not a
+#: finding, and letting it through would add ~75 warnings to a suite whose warning
+#: tail is deliberately kept fixed so that a NEW warning means something.
+pytestmark = [
+ pytest.mark.filterwarnings("ignore::gpytorch.utils.warnings.NumericalWarning"),
+ pytest.mark.filterwarnings("ignore:.*deprecated - use.*:DeprecationWarning"),
+ pytest.mark.filterwarnings("ignore::UserWarning"),
+]
+
+#: Exactly as the v3 sheet spells them, trailing spaces included.
+VOC = "PL - Implied Voc (Max) Raw "
+PHOTO = "Normalized photoconductance "
+
+HEADERS = [
+ "Sample number",
+ "speed_1", "time_1", "speed_2", "time_2", "precur_conc",
+ "precur_vol (uL)", "anneal_temp", "anneal_time", "anti_vol", "anti_time",
+ "Coverage", "Uniformity", "Phase purity",
+ VOC, "Photoconductance (Max)", PHOTO,
+ "T1", "T2", "T3", "T4", "T anom",
+ "Thickness (avg)",
+ "Uniformity score (Avg (Coverage + (1-Uniformity) + Phase purity))",
+ "Optoelectronic score (Avg normalized (Voc + Photocondiuctivity)",
+]
+
+
+def _rows(n: int) -> list[list]:
+ """A small on-grid, constraint-satisfying campaign with real structure.
+
+ Thickness follows a genuine speed_1 trend so the parity panel has something
+ to find; the other two are deliberately close to noise, which is also what the
+ live campaign looks like.
+ """
+ rng = np.random.default_rng(11)
+ rows = []
+ for i in range(n):
+ speed_1 = 1000.0 + 500.0 * (i % 11)
+ time_1 = 20.0 + 5.0 * (i % 4)
+ time_2 = 10.0 + 5.0 * (i % 5)
+ precur_conc = 1.0 + 0.05 * (i % 12)
+ coverage = float(np.clip(0.90 + 0.01 * (i % 7), 0.0, 1.0))
+ uniformity = float(np.clip(0.20 + 0.06 * (i % 9), 0.0, 2.0))
+ purity = float(np.clip(0.70 + 0.02 * (i % 8), 0.0, 1.0))
+ voc = 0.95 + 0.02 * (i % 6)
+ photo_raw = 1e-7 * (1 + i)
+ photo_norm = float(np.clip(0.2 + 0.05 * (i % 9), 0.0, 1.0))
+ thickness = 900.0 * (speed_1 / 3000.0) ** -0.4 * (precur_conc / 1.4) ** 0.8
+ readings = list(np.round(thickness + rng.normal(0, 8.0, 3), 1))
+ rows.append(
+ [
+ i + 1,
+ speed_1, time_1, 1000.0, time_2, round(precur_conc, 2),
+ 100.0, 120.0, 30.0, 150.0, 12.0,
+ coverage, uniformity, purity,
+ voc, photo_raw, photo_norm,
+ readings[0], readings[1], readings[2], None, None,
+ float(np.mean(readings)),
+ (coverage + (1.0 - min(uniformity, 0.99 if uniformity > 1 else uniformity)) + purity) / 3.0,
+ (min(voc, 1.4) / 1.4 + photo_norm) / 2.0,
+ ]
+ )
+ return rows
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+@pytest.fixture(scope="module")
+def workbook(tmp_path_factory) -> "object":
+ from pathlib import Path
+
+ path = Path(tmp_path_factory.mktemp("report")) / "Synthetic Campaign.xlsx"
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "Sheet1"
+ sheet.append(HEADERS)
+ for row in _rows(6):
+ sheet.append(row)
+ book.save(path)
+ return path
+
+
+@pytest.fixture(scope="module")
+def proposed(workbook, config, tmp_path_factory):
+ """One proposal-mode report, reused: each render is tens of seconds of fitting."""
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(workbook, config)
+ X = contents.inputs.to_numpy(float)
+ Y = contents.model_values.to_numpy(float)
+ proposal = run_r1_ucb(config, X, Y, n=3, seed=73)
+ review = build_batch_review(
+ config, X, Y, proposal.conditions, round_name="R1", seed=73
+ )
+ manifest = generate_round_report(
+ workbook,
+ config,
+ proposal=proposal,
+ review=review,
+ outdir=tmp_path_factory.mktemp("full"),
+ shap_max_instances=2,
+ seed=73,
+ when="FIXED",
+ )
+ return manifest, review
+
+
+@pytest.fixture(scope="module")
+def data_only(workbook, config) -> ReportManifest:
+ """One data-only report, reused: each render is tens of seconds of fitting."""
+ return generate_round_report(
+ workbook, config, shap_max_instances=3, when="FIXED", seed=73
+ )
+
+
+# --------------------------------------------------------------------------- #
+# structure
+# --------------------------------------------------------------------------- #
+
+
+def test_the_report_lands_beside_the_workbook_and_never_inside_it(
+ data_only, workbook
+) -> None:
+ assert data_only.directory.parent.parent == workbook.parent
+ assert data_only.directory.parent.name.endswith("_reports")
+ assert workbook.exists()
+ # nothing may have been written into the source workbook itself
+ from openpyxl import load_workbook
+
+ assert load_workbook(workbook).sheetnames == ["Sheet1"]
+
+
+def test_every_rendered_figure_has_a_png_and_the_numbers_behind_it(data_only) -> None:
+ assert data_only.figures, "a data-only report still renders four figures"
+ for figure in data_only.figures:
+ assert (data_only.directory / figure.png).is_file(), figure.key
+ assert figure.data, f"{figure.key} wrote no data file"
+ for name in figure.data:
+ path = data_only.directory / name
+ assert path.is_file(), name
+ assert not pd.read_csv(path).empty, name
+ assert figure.caveats, f"{figure.key} carries no caveat on its face"
+
+
+def test_the_manifest_records_what_a_reader_needs_to_reproduce_it(data_only) -> None:
+ manifest = json.loads((data_only.directory / "manifest.json").read_text())
+ context = manifest["context"]
+ assert context["objective_contract"] == "d2d-objectives-v3-test"
+ assert context["seed"] == 73
+ assert context["reference_point_utility"] == [-0.01, -0.01, -0.01]
+ assert context["observed_rows"] == 6
+ assert "git" in context and "python" in context
+ assert manifest["mode"] == "data_only"
+ assert manifest["runtime_seconds"] > 0
+
+
+def test_data_only_mode_skips_the_two_batch_figures_and_says_so(data_only) -> None:
+ """Skipping in silence is the failure mode; the manifest names both."""
+ skipped = dict(data_only.skipped)
+ assert set(skipped) == {"00_batch_placement", "03_batch_predictions"}
+ for why in skipped.values():
+ assert "data-only" in why
+ keys = {figure.key for figure in data_only.figures}
+ assert keys == {
+ "01_loo_parity",
+ "02_attribution",
+ "04_hv_trajectory",
+ "05_objective_space",
+ }
+
+
+def test_the_hv_trajectory_renders_at_r0_only(data_only) -> None:
+ """The first report of a campaign has one point and no trajectory. It must
+ still draw rather than fail on an empty diff."""
+ frame = pd.read_csv(data_only.directory / "04_hv_trajectory.csv")
+ assert list(frame["round"]) == ["R0"]
+ assert frame["gain"].iloc[0] == pytest.approx(frame["hypervolume"].iloc[0])
+ assert frame["cumulative_points"].iloc[0] == 6
+
+
+# --------------------------------------------------------------------------- #
+# the two equalities
+# --------------------------------------------------------------------------- #
+
+
+def test_the_parity_numbers_are_the_shared_loo_numbers(
+ data_only, workbook, config
+) -> None:
+ """Identity, not agreement.
+
+ ``scripts/intake_new_data.py`` is canonical for LOO, and it calls
+ ``loocv.loo_predictions``; so does the figure. If these ever diverge, someone
+ has reintroduced a second fold loop, which is exactly what this module's
+ docstring exists to prevent.
+ """
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(workbook, config)
+ X = contents.inputs.to_numpy(float)
+ Y = contents.model_values.to_numpy(float)
+ names = list(objective_names(config))
+ entries = config["objectives"]["specs"]
+
+ frame = pd.read_csv(data_only.directory / "01_loo_parity.csv")
+ for index, name in enumerate(names):
+ direct = loo_predictions(config, entries[index], X, Y[:, index], seed=73)
+ block = frame[frame["objective"] == name]
+ assert block["loo_r2"].iloc[0] == pytest.approx(direct.r2, abs=1e-12)
+ np.testing.assert_allclose(
+ block["loo_predicted"].to_numpy(float), direct.predicted, atol=1e-12
+ )
+ np.testing.assert_allclose(
+ block["observed"].to_numpy(float), direct.observed, atol=1e-12
+ )
+
+
+def test_the_batch_figure_reports_the_review_sheets_numbers(proposed, config) -> None:
+ """One source of truth. The Review sheet is attached to the worklist an
+ experimentalist runs from; the figure must not be able to disagree with it,
+ so this is an exact comparison rather than a tolerance."""
+ manifest, review = proposed
+ assert manifest.mode == "proposal"
+ frame = pd.read_csv(manifest.directory / "03_batch_predictions.csv")
+ for name in objective_names(config):
+ block = frame[frame["objective"] == name].reset_index(drop=True)
+ for column, source in (
+ ("utility_mean", f"{name}_utility"),
+ ("utility_sd", f"{name}_sd"),
+ ("predicted_measurement", f"{name}_predicted"),
+ ):
+ np.testing.assert_allclose(
+ block[column].to_numpy(float),
+ review.candidates[source].to_numpy(float),
+ atol=0.0,
+ )
+
+
+def test_proposal_mode_renders_all_six_figures(proposed) -> None:
+ manifest, _ = proposed
+ assert {figure.key for figure in manifest.figures} == {
+ "00_batch_placement",
+ "01_loo_parity",
+ "02_attribution",
+ "03_batch_predictions",
+ "04_hv_trajectory",
+ "05_objective_space",
+ }
+ assert manifest.skipped == ()
+ placement = pd.read_csv(manifest.directory / "00_batch_placement.csv")
+ assert len(placement) == 3
+ assert "distance_to_nearest_observed" in placement.columns
+
+
+def test_the_batch_hypervolume_diagnostic_is_a_distribution_not_a_point(
+ proposed,
+) -> None:
+ """A single expected utility per candidate cannot answer "is this batch worth
+ fabricating" -- hypervolume gain is a joint, nonlinear function of all of them."""
+ manifest, _ = proposed
+ frame = pd.read_csv(manifest.directory / "03_batch_hypervolume.csv")
+ batch = frame[frame["candidate"] == "BATCH"].iloc[0]
+ assert batch["delta_hv_p05"] <= batch["delta_hv_p50"] <= batch["delta_hv_p95"]
+ assert 0.0 <= batch["p_gain_positive"] <= 1.0
+ per_candidate = frame[frame["candidate"] != "BATCH"]
+ assert len(per_candidate) == 3
+ assert ((per_candidate["p_non_dominated"] >= 0.0)
+ & (per_candidate["p_non_dominated"] <= 1.0)).all()
+ # adding points can only grow a Pareto front, so no draw can lose volume;
+ # the by-construction property, asserted rather than assumed
+ assert batch["delta_hv_p05"] >= 0.0
+
+
+# --------------------------------------------------------------------------- #
+# determinism
+# --------------------------------------------------------------------------- #
+
+
+def test_two_runs_at_the_same_seed_produce_the_same_numbers(
+ workbook, config, tmp_path
+) -> None:
+ """Compared on the CSVs, never on PNG bytes: matplotlib output moves between
+ versions for reasons that have nothing to do with the campaign, and a byte
+ comparison would fail loudly for a non-reason."""
+ first = generate_round_report(
+ workbook, config, outdir=tmp_path / "a", shap_max_instances=2, seed=73,
+ when="FIXED",
+ )
+ second = generate_round_report(
+ workbook, config, outdir=tmp_path / "b", shap_max_instances=2, seed=73,
+ when="FIXED",
+ )
+ names = {name for figure in first.figures for name in figure.data}
+ assert names, "nothing to compare"
+ for name in names:
+ left = pd.read_csv(first.directory / name)
+ right = pd.read_csv(second.directory / name)
+ pd.testing.assert_frame_equal(left, right, check_exact=False, atol=1e-10)
+
+
+# --------------------------------------------------------------------------- #
+# the attribution panel
+# --------------------------------------------------------------------------- #
+
+
+def test_attribution_marks_the_features_the_config_declared(data_only, config) -> None:
+ """A feature named in a mean_function was TOLD to the model. The CSV marks
+ those rows so nobody quotes one as a discovery."""
+ frame = pd.read_csv(data_only.directory / "02_attribution.csv")
+ assert set(frame["objective"]) == set(objective_names(config))
+ assert (frame.groupby("objective")["rank"].min() == 1).all()
+ thickness = frame[frame["objective"] == "thickness"]
+ declared = set(thickness[thickness["in_mean_function"]]["feature"])
+ assert declared == {"speed_1", "precur_conc"}
+ # ranked by magnitude, descending, within each objective
+ for _, block in frame.groupby("objective"):
+ ordered = block.sort_values("rank")["mean_abs_shap"].to_numpy()
+ assert np.all(np.diff(ordered) <= 1e-12)
+
+
+def test_no_signal_objectives_are_labelled_in_the_data_not_just_the_picture(
+ data_only,
+) -> None:
+ """The caveat has to survive being read from the CSV, because that is what a
+ downstream analysis sees."""
+ frame = pd.read_csv(data_only.directory / "02_attribution.csv")
+ statuses = dict(zip(frame["objective"], frame["signal_status"]))
+ assert statuses["uniformity"] == "exploration_only"
+ assert statuses["optoelectronic"] == "exploration_only"
+ assert statuses["thickness"] == "learnable"
+
+
+def test_the_notices_repeat_the_no_signal_verdicts(data_only) -> None:
+ joined = " ".join(data_only.notices)
+ assert "uniformity" in joined and "optoelectronic" in joined
+ assert "does not beat the leave-one-out null" in joined
+
+
+# --------------------------------------------------------------------------- #
+# failure containment
+# --------------------------------------------------------------------------- #
+
+
+def test_one_broken_figure_does_not_cost_the_others(
+ workbook, config, tmp_path, monkeypatch
+) -> None:
+ """Losing the attribution panel must not lose the parity plot. The manifest
+ names what failed, so the absence is never silent."""
+ from mobo_kit import round_report
+
+ def explode(*args, **kwargs):
+ raise RuntimeError("synthetic attribution failure")
+
+ monkeypatch.setattr(round_report, "_figure_attribution", explode)
+ manifest = round_report.generate_round_report(
+ workbook, config, outdir=tmp_path / "partial", seed=73, when="FIXED"
+ )
+ keys = {figure.key for figure in manifest.figures}
+ assert "01_loo_parity" in keys and "05_objective_space" in keys
+ skipped = dict(manifest.skipped)
+ assert "synthetic attribution failure" in skipped["02_attribution"]
+ assert any("02_attribution" in notice for notice in manifest.notices)
+ assert (manifest.directory / "02_attribution.error.txt").is_file()
+
+
+def test_a_report_failure_never_costs_the_batch(workbook, config, monkeypatch) -> None:
+ """The worklist and the Review sheet are the expensive, careful part of a
+ round. Throwing them away because a figure could not be drawn would be the
+ wrong trade by a wide margin."""
+ from mobo_kit import launcher
+
+ monkeypatch.setattr(
+ launcher, "generate_data_report", lambda *a, **k: None, raising=False
+ )
+ import mobo_kit.round_report as round_report
+
+ def explode(*args, **kwargs):
+ raise RuntimeError("synthetic report failure")
+
+ monkeypatch.setattr(round_report, "generate_round_report", explode)
+ generated = launcher.generate_next_round(workbook, config)
+ assert generated.sheet_path.is_file(), "the worklist survives"
+ assert generated.review is not None, "so does the review"
+ assert generated.report is None
+ assert "synthetic report failure" in generated.report_error
+ assert "unaffected" in generated.report_error
+ assert "FIGURES NOT PRODUCED" in generated.summary()
+
+
+# --------------------------------------------------------------------------- #
+# paths
+# --------------------------------------------------------------------------- #
+
+
+def test_the_report_directory_is_named_for_the_round_and_the_time() -> None:
+ path = report_directory("/tmp/Summary Table Test.xlsx", "R1", when="20260818T101112Z")
+ assert path.parent.name == "Summary Table Test_reports"
+ assert path.name == "R1_20260818T101112Z"
diff --git a/tests/test_scores.py b/tests/test_scores.py
new file mode 100644
index 0000000..7ac298e
--- /dev/null
+++ b/tests/test_scores.py
@@ -0,0 +1,686 @@
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from mobo_kit.scores import (
+ AgreementCheck,
+ CrossCheck,
+ MeasurementInput,
+ MeasurementSpec,
+ ScoreSeverity,
+ ScoreValidationError,
+ compute_measurements,
+ entry_columns,
+ measurement_spec_from_config,
+ row_completeness,
+)
+
+#: The character Excel leaves in cells that look empty.
+NBSP = "\u00a0"
+
+
+def _uniformity() -> MeasurementSpec:
+ return MeasurementSpec(
+ name="uniformity",
+ recipe="product",
+ inputs=(
+ MeasurementInput("Coverage"),
+ MeasurementInput("Uniformity", "complement"),
+ MeasurementInput("Phase purity"),
+ ),
+ cross_checks=(CrossCheck("Uniformity score", 1e-9),),
+ )
+
+
+def _optoelectronic() -> MeasurementSpec:
+ return MeasurementSpec(
+ name="optoelectronic",
+ recipe="log10_product",
+ inputs=(MeasurementInput("PL"), MeasurementInput("PC")),
+ cross_checks=(CrossCheck("Optoelectronic score", 1e-9),),
+ )
+
+
+def _thickness(**kwargs) -> MeasurementSpec:
+ return MeasurementSpec(
+ name="thickness",
+ recipe="mean_of_present",
+ inputs=tuple(MeasurementInput(f"T{i}") for i in (1, 2, 3, 4)),
+ cross_checks=(CrossCheck("Thickness (avg)", 0.5),),
+ excluded=("T anom",),
+ **kwargs,
+ )
+
+
+def _codes(result, severity: ScoreSeverity) -> list[str]:
+ return [f.code for f in result.findings if f.severity is severity]
+
+
+# --------------------------------------------------------------------------- #
+# the recipes
+# --------------------------------------------------------------------------- #
+
+
+def test_product_multiplies_and_takes_the_complement() -> None:
+ frame = pd.DataFrame({"Coverage": [1.0], "Uniformity": [0.33], "Phase purity": [0.98]})
+ result = compute_measurements(frame, [_uniformity()])
+ assert result.values["uniformity"][0] == pytest.approx(1.0 * 0.67 * 0.98)
+ assert not result.has_errors
+
+
+def test_log10_product_sums_logs_rather_than_logging_a_product() -> None:
+ """Algebraically identical, but the sum cannot overflow on the way there.
+ Photoconductance runs to 1e-7, so the product is small but the logs are not."""
+ frame = pd.DataFrame({"PL": [0.0459], "PC": [6.73e-07]})
+ result = compute_measurements(frame, [_optoelectronic()])
+ assert result.values["optoelectronic"][0] == pytest.approx(
+ math.log10(0.0459 * 6.73e-07)
+ )
+
+
+def test_log10_product_survives_inputs_whose_product_would_underflow() -> None:
+ frame = pd.DataFrame({"PL": [1e-200], "PC": [1e-200]})
+ result = compute_measurements(frame, [_optoelectronic()])
+ assert result.values["optoelectronic"][0] == pytest.approx(-400.0)
+
+
+def test_mean_of_present_averages_only_what_was_measured() -> None:
+ frame = pd.DataFrame({"T1": [674], "T2": [700], "T3": [None], "T4": [None]})
+ result = compute_measurements(frame, [_thickness()])
+ assert result.values["thickness"][0] == pytest.approx(687.0)
+ assert result.inputs_used["thickness"][0] == 2
+
+
+def test_mean_of_present_is_unrounded() -> None:
+ """The workbook stores ROUND(mean(T1..T4)); the model gets the mean itself."""
+ frame = pd.DataFrame({"T1": [650], "T2": [655], "T3": [670], "T4": [680]})
+ result = compute_measurements(frame, [_thickness()])
+ assert result.values["thickness"][0] == pytest.approx(663.75)
+
+
+# --------------------------------------------------------------------------- #
+# "blank means not measured, never zero"
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.parametrize("blank", [None, "", " ", NBSP, f" {NBSP} ", np.nan, "n/a"])
+def test_every_spelling_of_empty_is_treated_as_unmeasured(blank) -> None:
+ """Excel leaves non-breaking spaces in cells that look empty. `str.strip` does
+ not remove one, so an unnormalised blank test would read it as data."""
+ frame = pd.DataFrame({"T1": [700], "T2": [blank], "T3": [blank], "T4": [blank]})
+ result = compute_measurements(frame, [_thickness()])
+ assert result.values["thickness"][0] == pytest.approx(700.0)
+ assert result.inputs_used["thickness"][0] == 1
+ assert not result.has_errors
+
+
+def test_a_blank_is_not_a_zero() -> None:
+ """The failure this guards: averaging a blank as 0 halves the thickness."""
+ blank = pd.DataFrame({"T1": [700], "T2": [None], "T3": [None], "T4": [None]})
+ zero = pd.DataFrame({"T1": [700], "T2": [0], "T3": [None], "T4": [None]})
+ assert compute_measurements(blank, [_thickness()]).values["thickness"][0] == 700.0
+ assert compute_measurements(zero, [_thickness()]).values["thickness"][0] == 350.0
+
+
+def test_no_measured_thickness_at_all_is_an_error_not_a_nan_average() -> None:
+ frame = pd.DataFrame({"T1": [None], "T2": [None], "T3": [None], "T4": [NBSP]})
+ result = compute_measurements(frame, [_thickness()])
+ assert "no_inputs_measured" in _codes(result, ScoreSeverity.ERROR)
+ assert math.isnan(result.values["thickness"][0])
+
+
+def test_a_recipe_needing_every_input_errors_on_a_blank_one() -> None:
+ frame = pd.DataFrame({"Coverage": [1.0], "Uniformity": [None], "Phase purity": [0.98]})
+ result = compute_measurements(frame, [_uniformity()])
+ assert "input_missing" in _codes(result, ScoreSeverity.ERROR)
+ assert math.isnan(result.values["uniformity"][0])
+
+
+def test_zero_photoconductance_is_an_error_with_an_actionable_message() -> None:
+ """log10(0) is -inf. A failed film must be blank, not zero, and the message
+ has to say so or someone will type a zero."""
+ frame = pd.DataFrame({"PL": [0.0459], "PC": [0.0]})
+ result = compute_measurements(frame, [_optoelectronic()])
+ errors = [f for f in result.findings if f.severity is ScoreSeverity.ERROR]
+ assert errors and "blank, not as zero" in errors[0].message
+ assert math.isnan(result.values["optoelectronic"][0])
+
+
+def test_text_in_a_measurement_cell_is_reported_not_coerced() -> None:
+ frame = pd.DataFrame({"T1": ["about 700"], "T2": [None], "T3": [None], "T4": [None]})
+ result = compute_measurements(frame, [_thickness()])
+ assert "input_not_numeric" in _codes(result, ScoreSeverity.ERROR)
+
+
+def test_a_numeric_string_is_accepted() -> None:
+ frame = pd.DataFrame({"T1": ["700"], "T2": [f"710{NBSP}"], "T3": [None], "T4": [None]})
+ result = compute_measurements(frame, [_thickness()])
+ assert result.values["thickness"][0] == pytest.approx(705.0)
+
+
+def test_one_bad_row_does_not_hide_the_others() -> None:
+ frame = pd.DataFrame(
+ {"T1": [700, None, 500], "T2": [None, None, None], "T3": [None] * 3, "T4": [None] * 3}
+ )
+ result = compute_measurements(frame, [_thickness()], sample_ids=[1, 2, 3])
+ assert result.values["thickness"].tolist()[0] == 700.0
+ assert math.isnan(result.values["thickness"][1])
+ assert result.values["thickness"].tolist()[2] == 500.0
+ assert [f.sample_id for f in result.errors] == [2]
+
+
+def test_raise_for_errors_fails_closed() -> None:
+ frame = pd.DataFrame({"T1": [None], "T2": [None], "T3": [None], "T4": [None]})
+ result = compute_measurements(frame, [_thickness()])
+ with pytest.raises(ScoreValidationError, match="cannot be computed"):
+ result.raise_for_errors()
+
+
+# --------------------------------------------------------------------------- #
+# cross-checks
+# --------------------------------------------------------------------------- #
+
+
+def test_a_matching_stored_cell_says_nothing() -> None:
+ frame = pd.DataFrame(
+ {
+ "Coverage": [1.0],
+ "Uniformity": [0.33],
+ "Phase purity": [0.98],
+ "Uniformity score": [1.0 * 0.67 * 0.98],
+ }
+ )
+ result = compute_measurements(frame, [_uniformity()])
+ assert result.findings == ()
+
+
+def test_a_stale_paste_is_caught() -> None:
+ """The whole point: a literal that no longer matches its inputs."""
+ frame = pd.DataFrame(
+ {
+ "Coverage": [1.0],
+ "Uniformity": [0.33],
+ "Phase purity": [0.98],
+ "Uniformity score": [0.5],
+ }
+ )
+ result = compute_measurements(frame, [_uniformity()], sample_ids=[7])
+ mismatch = [f for f in result.warnings if f.code == "cross_check_mismatch"]
+ assert len(mismatch) == 1
+ assert mismatch[0].sample_id == 7
+ assert "the model uses" in mismatch[0].message
+ # and the computed value is what comes out
+ assert result.values["uniformity"][0] == pytest.approx(0.6566)
+
+
+def test_a_rounded_stored_cell_within_tolerance_is_accepted() -> None:
+ """`Thickness (avg)` is ROUND(mean), so half a nanometre is not a mismatch."""
+ frame = pd.DataFrame(
+ {"T1": [650], "T2": [655], "T3": [670], "T4": [680], "Thickness (avg)": [664]}
+ )
+ result = compute_measurements(frame, [_thickness()])
+ assert [f.code for f in result.warnings] == []
+
+
+def test_a_rounded_stored_cell_beyond_tolerance_is_not() -> None:
+ frame = pd.DataFrame(
+ {"T1": [650], "T2": [655], "T3": [670], "T4": [680], "Thickness (avg)": [700]}
+ )
+ result = compute_measurements(frame, [_thickness()])
+ assert "cross_check_mismatch" in _codes(result, ScoreSeverity.WARNING)
+
+
+def test_an_emptied_formula_column_names_the_cause() -> None:
+ """openpyxl discards cached formula values on save. If a cross-check column
+ reads empty, that is the likely reason and the message should say it."""
+ frame = pd.DataFrame(
+ {
+ "Coverage": [1.0],
+ "Uniformity": [0.33],
+ "Phase purity": [0.98],
+ "Uniformity score": [None],
+ }
+ )
+ result = compute_measurements(frame, [_uniformity()])
+ empty = [f for f in result.warnings if f.code == "cross_check_empty"]
+ assert empty and "non-Excel tool" in empty[0].message
+
+
+def test_a_missing_cross_check_column_is_a_note_not_a_failure() -> None:
+ """A replacement dataset may not carry the score columns at all."""
+ frame = pd.DataFrame({"Coverage": [1.0], "Uniformity": [0.33], "Phase purity": [0.98]})
+ result = compute_measurements(frame, [_uniformity()])
+ assert "cross_check_absent" in _codes(result, ScoreSeverity.NOTE)
+ assert not result.has_errors
+
+
+# --------------------------------------------------------------------------- #
+# excluded readings and disagreement
+# --------------------------------------------------------------------------- #
+
+
+def test_an_excluded_reading_is_recorded_rather_than_averaged() -> None:
+ frame = pd.DataFrame(
+ {"T1": [650], "T2": [655], "T3": [670], "T4": [680], "T anom": [1618]}
+ )
+ result = compute_measurements(frame, [_thickness()], sample_ids=[4])
+ assert result.values["thickness"][0] == pytest.approx(663.75)
+ notes = [f for f in result.notes if f.code == "reading_excluded"]
+ assert notes and notes[0].sample_id == 4 and "1618" in notes[0].message
+
+
+def test_an_empty_anomaly_column_says_nothing() -> None:
+ frame = pd.DataFrame({"T1": [700], "T2": [710], "T3": [None], "T4": [None], "T anom": [NBSP]})
+ result = compute_measurements(frame, [_thickness()])
+ assert [f.code for f in result.notes if f.code == "reading_excluded"] == []
+
+
+def test_readings_that_split_into_two_clusters_warn() -> None:
+ """Sample 12's recorded 1155 nm is the midpoint of 1600 and 709. The mean is
+ computed either way, but nobody should act on it without knowing."""
+ frame = pd.DataFrame({"T1": [1600], "T2": [709], "T3": [None], "T4": [None]})
+ result = compute_measurements(
+ frame, [_thickness(spread_warning_ratio=0.25)], sample_ids=[12]
+ )
+ warned = [f for f in result.warnings if f.code == "readings_disagree"]
+ assert warned and warned[0].sample_id == 12
+ assert "1600" in warned[0].message and "709" in warned[0].message
+ assert result.values["thickness"][0] == pytest.approx(1154.5)
+
+
+def test_ordinary_scatter_does_not_warn() -> None:
+ frame = pd.DataFrame({"T1": [751], "T2": [754], "T3": [752], "T4": [None]})
+ result = compute_measurements(frame, [_thickness(spread_warning_ratio=0.25)])
+ assert [f.code for f in result.warnings] == []
+
+
+def test_the_spread_warning_is_off_unless_configured() -> None:
+ frame = pd.DataFrame({"T1": [1600], "T2": [709], "T3": [None], "T4": [None]})
+ result = compute_measurements(frame, [_thickness()])
+ assert "readings_disagree" not in _codes(result, ScoreSeverity.WARNING)
+
+
+# --------------------------------------------------------------------------- #
+# completeness and entry columns
+# --------------------------------------------------------------------------- #
+
+
+def test_row_completeness_needs_every_input_for_a_product() -> None:
+ frame = pd.DataFrame(
+ {
+ "Coverage": [1.0, 1.0],
+ "Uniformity": [0.33, None],
+ "Phase purity": [0.98, 0.98],
+ }
+ )
+ assert row_completeness(frame, [_uniformity()]).tolist() == [True, False]
+
+
+def test_row_completeness_needs_only_one_thickness_reading() -> None:
+ """Nine of the fifteen R0 rows have two readings. Demanding all four would
+ report a finished sheet as half-filled and block the next round."""
+ frame = pd.DataFrame(
+ {
+ "T1": [674, None],
+ "T2": [700, None],
+ "T3": [None, None],
+ "T4": [None, None],
+ }
+ )
+ assert row_completeness(frame, [_thickness()]).tolist() == [True, False]
+
+
+def test_entry_columns_split_required_from_optional() -> None:
+ required, optional = entry_columns([_uniformity(), _optoelectronic(), _thickness()])
+ assert required == (
+ "Coverage",
+ "Uniformity",
+ "Phase purity",
+ "PL",
+ "PC",
+ )
+ assert optional == ("T1", "T2", "T3", "T4", "T anom")
+
+
+def test_an_absent_required_column_is_refused_up_front() -> None:
+ frame = pd.DataFrame({"Coverage": [1.0], "Phase purity": [0.98]})
+ with pytest.raises(ValueError, match="missing from the sheet"):
+ compute_measurements(frame, [_uniformity()])
+
+
+def test_an_absent_optional_column_is_reported_once_not_per_row() -> None:
+ frame = pd.DataFrame({"T1": [700, 800], "T2": [710, 810]})
+ result = compute_measurements(frame, [_thickness()])
+ absent = [f for f in result.notes if f.code == "input_column_absent"]
+ assert {f.column for f in absent} == {"T3", "T4"}
+ assert len(absent) == 2
+ assert result.values["thickness"].tolist() == [705.0, 805.0]
+
+
+# --------------------------------------------------------------------------- #
+# config parsing
+# --------------------------------------------------------------------------- #
+
+
+def test_no_measurement_block_means_no_spec() -> None:
+ assert measurement_spec_from_config({"name": "uniformity"}) is None
+
+
+def test_inputs_accept_bare_strings_and_mappings() -> None:
+ spec = measurement_spec_from_config(
+ {
+ "name": "uniformity",
+ "measurement": {
+ "recipe": "product",
+ "inputs": ["Coverage", {"column": "Uniformity", "transform": "complement"}],
+ "cross_check": "Uniformity score",
+ },
+ }
+ )
+ assert spec is not None
+ assert [i.column for i in spec.inputs] == ["Coverage", "Uniformity"]
+ assert [i.transform for i in spec.inputs] == ["identity", "complement"]
+ assert spec.cross_checks[0].column == "Uniformity score"
+ assert spec.cross_checks[0].atol == pytest.approx(0.005)
+
+
+def test_a_single_cross_check_mapping_is_accepted() -> None:
+ spec = measurement_spec_from_config(
+ {
+ "name": "thickness",
+ "measurement": {
+ "recipe": "mean_of_present",
+ "inputs": ["T1"],
+ "cross_check": {"column": "Thickness (avg)", "atol": 0.5},
+ },
+ }
+ )
+ assert spec.cross_checks == (CrossCheck("Thickness (avg)", 0.5),)
+
+
+@pytest.mark.parametrize(
+ "block, match",
+ [
+ ({"recipe": "nonsense", "inputs": ["a"]}, "unknown recipe"),
+ ({"recipe": "product", "inputs": []}, "non-empty list"),
+ ({"recipe": "product", "inputs": ["a", "a"]}, "repeats"),
+ (
+ {"recipe": "product", "inputs": [{"column": "a", "transform": "sqrt"}]},
+ "Unsupported measurement transform",
+ ),
+ (
+ {"recipe": "mean_of_present", "inputs": ["a"], "spread_warning_ratio": 0},
+ "positive finite",
+ ),
+ ],
+)
+def test_a_malformed_measurement_block_is_refused(block, match) -> None:
+ with pytest.raises(ValueError, match=match):
+ measurement_spec_from_config({"name": "x", "measurement": block})
+
+
+# --------------------------------------------------------------------------- #
+# the v3 contract: `mean`, the two threshold transforms, and the agreement check
+# --------------------------------------------------------------------------- #
+
+
+def _v3_uniformity(**kwargs) -> MeasurementSpec:
+ return MeasurementSpec(
+ name="uniformity",
+ recipe="mean",
+ inputs=(
+ MeasurementInput("Coverage"),
+ MeasurementInput(
+ "Uniformity", "clamped_complement", clamp_above=1.0, clamp_to=0.99
+ ),
+ MeasurementInput("Phase purity"),
+ ),
+ **kwargs,
+ )
+
+
+def _v3_optoelectronic(**kwargs) -> MeasurementSpec:
+ return MeasurementSpec(
+ name="optoelectronic",
+ recipe="mean",
+ inputs=(
+ MeasurementInput("Voc raw", "capped_ratio", cap=1.4),
+ MeasurementInput("Normalized photoconductance"),
+ ),
+ **kwargs,
+ )
+
+
+def test_the_mean_recipe_averages_every_input() -> None:
+ frame = pd.DataFrame(
+ {"Coverage": [0.989], "Uniformity": [0.324584], "Phase purity": [0.9685]}
+ )
+ result = compute_measurements(frame, [_v3_uniformity()], sample_ids=[1])
+ # the workbook's own (L + O + P) / 3 for sample 1
+ assert result.values["uniformity"][0] == pytest.approx(0.8776386666666668, abs=1e-15)
+ assert result.inputs_used["uniformity"][0] == 3
+
+
+def test_mean_refuses_a_blank_where_mean_of_present_would_accept_one() -> None:
+ """The two recipes do the same arithmetic and differ only here, which is the
+ entire reason `mean` exists rather than reusing `mean_of_present`: a missing
+ Coverage is a hole in the row, not a film with fewer readings."""
+ frame = pd.DataFrame(
+ {"Coverage": [None], "Uniformity": [0.3], "Phase purity": [0.9]}
+ )
+ result = compute_measurements(frame, [_v3_uniformity()], sample_ids=[1])
+ assert "input_missing" in _codes(result, ScoreSeverity.ERROR)
+ assert math.isnan(result.values["uniformity"][0])
+
+
+@pytest.mark.parametrize(
+ "uniformity, expected_complement, note",
+ [
+ (1.658775, 0.010000000000000009, "sample 4 of the v3 workbook"),
+ (1.277, 0.010000000000000009, "sample 8 of the v3 workbook"),
+ (1.0000001, 0.010000000000000009, "just above the threshold"),
+ (1.0, 0.0, "EXACTLY 1.0 keeps its own value: the clamp is strict"),
+ (0.324584, 0.675416, "an ordinary reading is untouched"),
+ (0.0, 1.0, "the bottom of the range"),
+ ],
+)
+def test_the_uniformity_clamp_including_its_boundary(
+ uniformity, expected_complement, note
+) -> None:
+ frame = pd.DataFrame(
+ {"Coverage": [0.0], "Uniformity": [uniformity], "Phase purity": [0.0]}
+ )
+ result = compute_measurements(frame, [_v3_uniformity()], sample_ids=[1])
+ assert result.values["uniformity"][0] == pytest.approx(
+ expected_complement / 3.0, abs=1e-12
+ ), note
+
+
+def test_the_voc_cap_is_dormant_on_readings_below_it() -> None:
+ """Every observed reading is under 1.4, so the cap changes nothing today and
+ the recipe reproduces the sheet's uncapped Q / 1.4 exactly."""
+ frame = pd.DataFrame(
+ {"Voc raw": [1.02683981553478], "Normalized photoconductance": [0.763425]}
+ )
+ result = compute_measurements(frame, [_v3_optoelectronic()], sample_ids=[1])
+ assert result.values["optoelectronic"][0] == pytest.approx(
+ 0.7484410055481358, abs=1e-15
+ )
+
+
+def test_the_voc_cap_binds_above_1_4_where_the_sheet_would_not() -> None:
+ """The declared divergence, exercised. The workbook has no ceiling, so this
+ row is where the two would part company -- and the cross-check is what would
+ say so on real data."""
+ frame = pd.DataFrame({"Voc raw": [2.8], "Normalized photoconductance": [0.0]})
+ result = compute_measurements(frame, [_v3_optoelectronic()], sample_ids=[1])
+ assert result.values["optoelectronic"][0] == pytest.approx(0.5) # (1.0 + 0.0) / 2
+
+
+def test_normalized_photoconductance_passes_straight_through() -> None:
+ frame = pd.DataFrame({"Voc raw": [0.0], "Normalized photoconductance": [0.42]})
+ result = compute_measurements(frame, [_v3_optoelectronic()], sample_ids=[1])
+ assert result.values["optoelectronic"][0] == pytest.approx(0.21)
+
+
+@pytest.mark.parametrize(
+ "kwargs, match",
+ [
+ ({"transform": "clamped_complement", "clamp_above": 1.0}, "clamp_to"),
+ ({"transform": "clamped_complement", "clamp_to": 0.99}, "clamp_above"),
+ ({"transform": "capped_ratio"}, "cap"),
+ ({"transform": "capped_ratio", "cap": 0.0}, "positive"),
+ ({"transform": "identity", "cap": 1.4}, "ignores"),
+ ({"transform": "complement", "clamp_to": 0.99}, "ignores"),
+ ],
+)
+def test_a_threshold_that_would_do_nothing_is_an_error(kwargs, match) -> None:
+ """A clamp everyone believes is configured while nothing applies it is the
+ same class of failure as the three finite-but-wrong numbers this project has
+ already found."""
+ with pytest.raises(ValueError, match=match):
+ MeasurementInput("x", **kwargs)
+
+
+def test_an_unknown_key_on_a_measurement_input_is_refused() -> None:
+ with pytest.raises(ValueError, match="unknown key"):
+ measurement_spec_from_config(
+ {
+ "name": "x",
+ "measurement": {
+ "recipe": "mean",
+ "inputs": [{"column": "a", "clamp_at": 1.0}],
+ },
+ }
+ )
+
+
+def test_thresholds_survive_the_config_round_trip() -> None:
+ spec = measurement_spec_from_config(
+ {
+ "name": "optoelectronic",
+ "measurement": {
+ "recipe": "mean",
+ "inputs": [
+ {"column": "Voc ", "transform": "capped_ratio", "cap": 1.4},
+ {
+ "column": " Uniformity",
+ "transform": "clamped_complement",
+ "clamp_above": 1.0,
+ "clamp_to": 0.99,
+ },
+ ],
+ },
+ }
+ )
+ # column names are stripped on BOTH sides: the v3 sheet's headers carry
+ # trailing spaces ('PL - Implied Voc (Max) Raw ') and the config quotes them
+ # verbatim, so resolution must not depend on which spelling was written
+ assert [item.column for item in spec.inputs] == ["Voc", "Uniformity"]
+ assert spec.inputs[0].cap == 1.4
+ assert spec.inputs[1].clamp_above == 1.0
+ assert spec.inputs[1].clamp_to == 0.99
+
+
+def _agreement(raw_values, normalized_values, **kwargs):
+ spec = MeasurementSpec(
+ name="optoelectronic",
+ recipe="mean",
+ inputs=(MeasurementInput("Normalized photoconductance"),),
+ agreement_check=AgreementCheck(
+ raw="Photoconductance (Max)",
+ normalized="Normalized photoconductance",
+ **kwargs,
+ ),
+ )
+ frame = pd.DataFrame(
+ {
+ "Photoconductance (Max)": raw_values,
+ "Normalized photoconductance": normalized_values,
+ }
+ )
+ return compute_measurements(
+ frame, [spec], sample_ids=list(range(1, len(raw_values) + 1))
+ )
+
+
+def test_a_normalization_that_ranks_backwards_warns() -> None:
+ """The live case: the strongest film carries the lowest normalised value."""
+ result = _agreement([1e-8, 1e-7, 1e-6], [0.9, 0.5, 0.01])
+ assert "agreement_not_monotonic" in _codes(result, ScoreSeverity.WARNING)
+ message = next(
+ f.message for f in result.findings if f.code == "agreement_not_monotonic"
+ )
+ assert "-1.0000" in message
+ assert "sample 3" in message # names the highest-raw film, not just the rho
+
+
+def test_a_normalization_that_preserves_order_is_only_a_note() -> None:
+ result = _agreement([1e-8, 1e-7, 1e-6], [0.01, 0.5, 0.9])
+ assert "agreement_monotonic" in _codes(result, ScoreSeverity.NOTE)
+ assert not _codes(result, ScoreSeverity.WARNING)
+
+
+def test_the_agreement_check_never_blocks_a_round() -> None:
+ """It is a finding by design: which column the model trains on is the group's
+ decision, and a diagnostic that refused to run would make it by refusing."""
+ result = _agreement([1e-8, 1e-7, 1e-6], [0.9, 0.5, 0.01])
+ assert not result.has_errors
+ assert result.values["optoelectronic"].notna().all()
+
+
+def test_the_agreement_check_says_so_when_it_cannot_run() -> None:
+ spec = MeasurementSpec(
+ name="optoelectronic",
+ recipe="mean",
+ inputs=(MeasurementInput("Normalized photoconductance"),),
+ agreement_check=AgreementCheck(
+ raw="Photoconductance (Max)", normalized="Normalized photoconductance"
+ ),
+ )
+ frame = pd.DataFrame({"Normalized photoconductance": [0.1, 0.2, 0.3]})
+ result = compute_measurements(frame, [spec], sample_ids=[1, 2, 3])
+ assert "agreement_check_absent" in _codes(result, ScoreSeverity.NOTE)
+
+ too_few = _agreement([1e-8, 1e-7], [0.9, 0.5])
+ assert "agreement_check_too_few_rows" in _codes(too_few, ScoreSeverity.NOTE)
+
+ constant = _agreement([1e-7, 1e-7, 1e-7], [0.9, 0.5, 0.01])
+ assert "agreement_check_undefined" in _codes(constant, ScoreSeverity.NOTE)
+
+
+def test_the_agreement_raw_column_is_offered_but_never_required() -> None:
+ """A row without it simply does not join the rank comparison; the objective is
+ computed from the normalised column either way."""
+ spec = MeasurementSpec(
+ name="optoelectronic",
+ recipe="mean",
+ inputs=(MeasurementInput("Normalized photoconductance"),),
+ agreement_check=AgreementCheck(
+ raw="Photoconductance (Max)", normalized="Normalized photoconductance"
+ ),
+ )
+ required, optional = entry_columns([spec])
+ assert "Photoconductance (Max)" not in required
+ assert "Photoconductance (Max)" in optional
+
+
+def test_findings_frame_is_exportable() -> None:
+ frame = pd.DataFrame({"T1": [1600], "T2": [709], "T3": [None], "T4": [None]})
+ result = compute_measurements(
+ frame, [_thickness(spread_warning_ratio=0.25)], sample_ids=[12]
+ )
+ exported = result.findings_frame()
+ assert list(exported.columns) == [
+ "severity",
+ "code",
+ "objective",
+ "sample_id",
+ "column",
+ "message",
+ ]
+ assert (exported["objective"] == "thickness").all()
diff --git a/tests/test_scripts.py b/tests/test_scripts.py
new file mode 100644
index 0000000..47dd745
--- /dev/null
+++ b/tests/test_scripts.py
@@ -0,0 +1,114 @@
+"""The two operator-facing scripts.
+
+Neither is a test — one sweeps parameters, one is what you run when new data
+arrives — but both encode commitments that should not drift silently: the sweep's
+pre-committed decision rule, and the intake's floors. Those are pinned here.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import pytest
+
+SCRIPTS = Path("scripts")
+
+
+def _load(name: str):
+ """Import a script by path. Both guard their entry point with __main__, so
+ importing runs no work."""
+ path = SCRIPTS / f"{name}.py"
+ spec = importlib.util.spec_from_file_location(f"_script_{name}", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+def test_both_scripts_import_cleanly() -> None:
+ assert _load("dtlz2_parameter_sweep") is not None
+ assert _load("intake_new_data") is not None
+
+
+# --------------------------------------------------------------------------- #
+# the sweep's pre-committed rule
+# --------------------------------------------------------------------------- #
+
+
+def test_the_sweep_grid_and_current_default_are_what_was_agreed() -> None:
+ sweep = _load("dtlz2_parameter_sweep")
+ assert sweep.BETAS == (2.0, 4.0, 8.0)
+ assert sweep.RADII == (0.15, 0.25, 0.35)
+ assert sweep.CURRENT_BETA == 4.0
+ assert sweep.CURRENT_RADIUS == 0.25
+ # a hard floor on spacing, held fixed so "spacing" means the same thing in
+ # every cell
+ assert sweep.MIN_BATCH_DISTANCE == 0.15
+
+
+def test_the_sweep_scores_the_added_budget_not_the_whole_campaign() -> None:
+ """The comparison is BO against random at EQUAL budget: the 8 points R1 and R2
+ add. Scoring the whole campaign would credit BO with the shared R0 start."""
+ sweep = _load("dtlz2_parameter_sweep")
+ assert sweep.ADDED == sweep.R1_SIZE + sweep.R2_SIZE == 8
+
+
+# --------------------------------------------------------------------------- #
+# the intake's floors
+# --------------------------------------------------------------------------- #
+
+
+def test_the_null_moves_with_n() -> None:
+ """1 - (N/(N-1))^2, independent of the data. Reusing the N=15 value on a bigger
+ dataset would hold the model to the wrong bar."""
+ intake = _load("intake_new_data")
+ assert intake.null_loo_r2(15) == pytest.approx(-0.1480, abs=1e-4)
+ assert intake.null_loo_r2(21) == pytest.approx(-0.1025, abs=1e-4)
+ assert intake.null_loo_r2(31) == pytest.approx(-0.0678, abs=1e-4)
+ # it approaches zero from below as N grows, never crossing it
+ assert intake.null_loo_r2(1000) < 0.0
+
+
+def test_the_resolution_floor_shrinks_with_n() -> None:
+ intake = _load("intake_new_data")
+ assert intake.resolution_sd(15) == pytest.approx(0.236)
+ assert intake.resolution_sd(60) == pytest.approx(0.118)
+ assert intake.resolution_sd(15) > intake.resolution_sd(30)
+
+
+def test_the_bootstrap_reference_is_the_measured_one() -> None:
+ """0.236 was measured by parametric bootstrap at N=15, 4000 resamples. The
+ sqrt(15/N) rescaling is an approximation and the script says so.
+
+ The constants live in ``mobo_kit.loocv`` rather than in the script, because
+ the round report and the permutation test need the same ones. The script
+ re-exports them by importing, and this asserts they are the same objects
+ rather than two copies drifting apart.
+ """
+ from mobo_kit import loocv
+
+ intake = _load("intake_new_data")
+ assert loocv.RESOLUTION_SD_AT_15 == 0.236
+ assert loocv.RESOLUTION_REFERENCE_N == 15
+ assert intake.RESOLUTION_SD_AT_15 is loocv.RESOLUTION_SD_AT_15
+ assert intake.resolution_sd is loocv.resolution_sd
+ assert intake.null_loo_r2 is loocv.null_loo_r2
+ assert "estimate" in intake.__doc__ or "approximation" in intake.__doc__
+
+
+def test_the_report_and_the_intake_share_one_fold_loop() -> None:
+ """Not "they agree" -- they are the same function.
+
+ This project's canonical LOO numbers briefly had three implementations: the
+ intake script, the round report and the permutation test. Two of them agreeing
+ today is exactly the situation that produced its three silent-failure bugs.
+ """
+ from mobo_kit import loocv, round_report
+
+ intake = _load("intake_new_data")
+ permutation = _load("permutation_rank_test")
+ assert intake.loo_predictions is loocv.loo_predictions
+ assert permutation.loo_predictions is loocv.loo_predictions
+ assert round_report.loo_predictions is loocv.loo_predictions
diff --git a/tests/test_second_campaign.py b/tests/test_second_campaign.py
new file mode 100644
index 0000000..5cf10e2
--- /dev/null
+++ b/tests/test_second_campaign.py
@@ -0,0 +1,539 @@
+"""The second campaign's objective contract, end to end.
+
+`configs/campaign_d2d_perovskite_test.yaml` is a new contract on a new workbook:
+uniformity and optoelectronic are computed differently from the first campaign,
+the column layout moved, two grids changed and three constraints are active for
+the first time in this project.
+
+The synthetic half builds a sheet with the v3 headers -- INCLUDING their trailing
+spaces -- so the contract is exercised without the ignored workbook. The real-
+workbook half is marked `local_input` and skips without it.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from mobo_kit.campaign import (
+ BatchValidityError,
+ build_design_from_config,
+ build_objective_transform,
+ load_campaign_config,
+ measurement_specs,
+ objective_names,
+ run_r1_ucb,
+ validate_batch,
+)
+from mobo_kit.constraints import constraint_violations, constraints_from_config
+from mobo_kit.scores import ScoreSeverity, compute_measurements
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite_test.yaml"
+ARCHIVED_CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+SOURCE = "local_inputs/Summary Table Test.xlsx"
+
+#: Exactly as Sheet1 spells them. Two carry a trailing space, which is not a typo
+#: in this file -- it is what the header cell contains, and resolution has to cope
+#: with it from both directions.
+VOC_HEADER = "PL - Implied Voc (Max) Raw "
+PHOTOCONDUCTANCE_HEADER = "Normalized photoconductance "
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+# --------------------------------------------------------------------------- #
+# the contract itself
+# --------------------------------------------------------------------------- #
+
+
+def test_the_new_contract_is_distinct_from_the_archived_one(config) -> None:
+ """Every contract's utility space is its own, and they must not be confused.
+
+ Uniformity is a three-term mean here and a three-term product in v2;
+ optoelectronic is a mean of normalised values here and a log10 product there.
+ A shared contract_version would make hypervolumes look comparable when they
+ measure different spaces. v3 is itself archived now -- superseded by v4 -- so
+ all that is asserted here is that the three versions are distinct.
+ """
+ archived = load_campaign_config(ARCHIVED_CONFIG_PATH)
+ assert config["objectives"]["contract_version"] == "d2d-objectives-v3-test"
+ assert (
+ config["objectives"]["contract_version"]
+ != archived["objectives"]["contract_version"]
+ )
+ assert archived["campaign"]["status"] == "archived"
+ assert config["campaign"]["status"] == "archived"
+ # objective ORDER is part of the contract: Y columns are positional
+ assert objective_names(config) == ("uniformity", "optoelectronic", "thickness")
+
+
+def test_both_new_scores_live_in_zero_to_one(config) -> None:
+ """A mean of terms that are each in [0, 1] is in [0, 1] by construction, so
+ these anchors are the objective's range and not a guess about the data."""
+ specs = {spec["name"]: spec for spec in config["objectives"]["specs"]}
+ for name in ("uniformity", "optoelectronic"):
+ assert specs[name]["lower_anchor"] == 0.0
+ assert specs[name]["upper_anchor"] == 1.0
+ build_objective_transform(config) # runs assert_scaling_is_campaign_fixed
+
+
+def test_the_signal_verdicts_are_the_ones_measured_on_these_rows(config) -> None:
+ """Nothing was inherited: every verdict here came from an intake run on this
+ workbook, and two of the three came out differently from the first campaign's.
+
+ Both non-thickness objectives sit below the leave-one-out null, so a batch is
+ chosen on one informative axis and two uninformative ones.
+ """
+ status = {
+ spec["name"]: spec["signal_status"] for spec in config["objectives"]["specs"]
+ }
+ assert status == {
+ "uniformity": "exploration_only",
+ "optoelectronic": "exploration_only",
+ "thickness": "learnable",
+ }
+
+
+def test_the_optoelectronic_mean_function_stays_deleted(config) -> None:
+ """The intake verdict was DELETE: the first campaign's linear anneal_temp trend
+ made this objective's fit WORSE here (-0.5842 -> -0.6977), because the target
+ was redefined underneath it. Reinstating it from the archived config is the
+ obvious mistake, so it is pinned."""
+ specs = {spec["name"]: spec for spec in config["objectives"]["specs"]}
+ assert "mean_function" not in specs["optoelectronic"]
+ # thickness keeps its block: inconclusive, but it clears the null either way
+ assert specs["thickness"]["mean_function"]["response"] == "log"
+ assert [
+ feature["column"] for feature in specs["thickness"]["mean_function"]["features"]
+ ] == ["speed_1", "precur_conc"]
+
+
+def test_the_thickness_cross_check_is_tight_now(config) -> None:
+ """The first campaign's `Thickness (avg)` was ROUND(mean(T1..T4)), so half a
+ nanometre of disagreement was legitimate. This sheet's is a live unrounded
+ AVERAGE, so anything above floating-point noise is real."""
+ thickness = next(
+ spec
+ for spec in config["objectives"]["specs"]
+ if spec["name"] == "thickness"
+ )
+ (check,) = thickness["measurement"]["cross_check"]
+ assert check["atol"] == pytest.approx(1e-9)
+
+
+# --------------------------------------------------------------------------- #
+# the grid edits
+# --------------------------------------------------------------------------- #
+
+
+def test_the_two_grid_edits_and_nothing_else(config) -> None:
+ archived = load_campaign_config(ARCHIVED_CONFIG_PATH)
+ before = {item["name"]: item for item in archived["inputs"]}
+ after = {item["name"]: item for item in config["inputs"]}
+ assert list(before) == list(after), "input order is positional; it must not move"
+
+ changed = {
+ name
+ for name in after
+ if (after[name]["start"], after[name]["stop"], after[name]["step"])
+ != (before[name]["start"], before[name]["stop"], before[name]["step"])
+ }
+ assert changed == {"time_2", "anti_time"}
+ # time_2 reaches 0 so a one-step film is on-grid; anti_time steps by 1 so
+ # sample 1's anti_time = 12 is an ordinary observation rather than a declared
+ # off-grid exception
+ assert (after["time_2"]["start"], after["time_2"]["step"]) == (0, 5)
+ assert (after["anti_time"]["start"], after["anti_time"]["step"]) == (9, 1)
+
+
+def test_the_grid_hole_at_time_2_equals_5_is_declared_not_silent(config) -> None:
+ """Reaching 0 with step 5 also reaches 5, which the first campaign's grid
+ excluded and no film has run. The constraint is what keeps it out."""
+ design = build_design_from_config(dict(config))
+ time_2_grid = design.var_array[design.names.index("time_2")]
+ assert 5.0 in set(time_2_grid), "the arithmetic grid does contain it"
+
+ constraints = constraints_from_config(dict(config), design)
+ row = {name: design.var_array[i][1] for i, name in enumerate(design.names)}
+ row.update({"speed_2": 1000.0, "time_1": 50.0, "time_2": 5.0, "anti_time": 9.0})
+ values = np.asarray([[row[name] for name in design.names]], dtype=float)
+ assert constraint_violations(values, design, constraints) == [
+ ["second_stage_runs_at_least_10s"]
+ ], "and the constraint is what excludes it"
+
+
+# --------------------------------------------------------------------------- #
+# recipes, on a synthetic sheet with the v3 headers
+# --------------------------------------------------------------------------- #
+
+
+def _sheet(rows: list[dict]) -> pd.DataFrame:
+ """A frame keyed by the v3 headers, trailing spaces and all."""
+ return pd.DataFrame(rows, dtype=object)
+
+
+def test_the_recipes_reproduce_the_stored_scores_on_a_synthetic_sheet(config) -> None:
+ """Sample 1 and sample 4 of the real sheet, transcribed. Sample 4 is one of
+ the two clamped rows, so this covers the clamp on the way through as well."""
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ frame = _sheet(
+ [
+ {
+ "Coverage": 0.989,
+ "Uniformity": 0.324584,
+ "Phase purity": 0.9685,
+ VOC_HEADER: 1.02683981553478,
+ PHOTOCONDUCTANCE_HEADER: 0.763425,
+ "Photoconductance (Max)": 5e-07,
+ "T1": 584.4,
+ "T2": 418.5,
+ "T3": 692.0,
+ "T4": 624.6,
+ "T anom": None,
+ },
+ {
+ "Coverage": 0.992,
+ "Uniformity": 1.658775, # clamped to 0.99
+ "Phase purity": 0.786,
+ VOC_HEADER: 1.13513544854332,
+ PHOTOCONDUCTANCE_HEADER: 1.0,
+ "Photoconductance (Max)": 3.42e-08,
+ "T1": 657.7,
+ "T2": 586.4,
+ "T3": 693.2,
+ "T4": 667.1,
+ "T anom": 832.1,
+ },
+ ]
+ )
+ result = compute_measurements(frame, specs, sample_ids=[1, 4])
+
+ # the workbook's own AB, AC and Z for those two rows
+ assert result.values["uniformity"].tolist() == pytest.approx(
+ [0.8776386666666668, 0.596], abs=1e-15
+ )
+ assert result.values["optoelectronic"].tolist() == pytest.approx(
+ [0.7484410055481358, 0.9054055173369], abs=1e-15
+ )
+ assert result.values["thickness"].tolist() == pytest.approx(
+ [579.875, 651.1], abs=1e-12
+ )
+ assert not result.has_errors
+
+
+def _filler(**overrides) -> dict:
+ """A row that satisfies every objective, so one can be varied at a time."""
+ row = {
+ "Coverage": 1.0,
+ "Uniformity": 0.0,
+ "Phase purity": 1.0,
+ VOC_HEADER: 1.4,
+ PHOTOCONDUCTANCE_HEADER: 1.0,
+ "T1": 650.0,
+ "T2": 650.0,
+ "T3": 650.0,
+ "T4": 650.0,
+ "T anom": None,
+ }
+ row.update(overrides)
+ return row
+
+
+def test_a_variable_number_of_thickness_readings_is_normal(config) -> None:
+ """Eleven of the fifteen rows carry three readings and four carry four, so a
+ recipe demanding all four would reject two thirds of the campaign."""
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ frame = _sheet(
+ [
+ _filler(T1=413.0, T2=430.2, T3=439.4, T4=None),
+ _filler(T1=962.6, T2=961.1, T3=947.7, T4=942.8),
+ ]
+ )
+ result = compute_measurements(frame, specs, sample_ids=[2, 3])
+ assert result.inputs_used["thickness"].tolist() == [3, 4]
+ assert result.values["thickness"].tolist() == pytest.approx(
+ [427.5333333333333, 953.55], abs=1e-12
+ )
+ assert not result.has_errors
+
+
+def test_the_v3_headers_resolve_despite_their_trailing_spaces(config) -> None:
+ """Two of the sheet's headers end in a space, and the config quotes them
+ verbatim. Names are compared stripped on BOTH sides, so either spelling
+ resolves and neither silently reports a present column as missing."""
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ declared = [item.column for spec in specs for item in spec.inputs]
+ assert VOC_HEADER.rstrip() in declared, "the config side is stripped"
+ assert VOC_HEADER not in declared
+
+ with_spaces = _sheet([_filler()])
+ without_spaces = with_spaces.rename(columns=lambda name: name.strip())
+ assert list(with_spaces.columns) != list(without_spaces.columns)
+
+ from_spaced = compute_measurements(with_spaces, specs, sample_ids=[1])
+ from_stripped = compute_measurements(without_spaces, specs, sample_ids=[1])
+ assert not from_spaced.has_errors
+ pd.testing.assert_frame_equal(from_spaced.values, from_stripped.values)
+
+
+def test_t_anom_is_excluded_from_the_mean_and_still_reported(config) -> None:
+ specs = [spec for spec in measurement_specs(config) if spec is not None]
+ frame = _sheet(
+ [_filler(T1=657.7, T2=586.4, T3=693.2, T4=667.1, **{"T anom": 832.1})]
+ )
+ result = compute_measurements(frame, specs, sample_ids=[4])
+ assert result.values["thickness"][0] == pytest.approx(651.1)
+ codes = [f.code for f in result.findings if f.severity is ScoreSeverity.NOTE]
+ assert "reading_excluded" in codes
+
+
+# --------------------------------------------------------------------------- #
+# constraints reach the batch gate
+# --------------------------------------------------------------------------- #
+
+
+def test_validate_batch_refuses_a_condition_that_breaks_a_constraint(config) -> None:
+ """Deliberately redundant with the pool filter. The pool is the mechanism;
+ this is the independent second route to the same answer, which is the check
+ this project's three finite-but-wrong-number bugs all lacked."""
+ design = build_design_from_config(dict(config))
+ constraints = constraints_from_config(dict(config), design)
+ good = {
+ "speed_1": 2500.0,
+ "time_1": 30.0,
+ "speed_2": 1000.0,
+ "time_2": 20.0,
+ "precur_conc": 1.4,
+ "precur_vol": 100.0,
+ "anneal_temp": 120.0,
+ "anneal_time": 30.0,
+ "anti_vol": 150.0,
+ "anti_time": 12.0,
+ }
+ frame = pd.DataFrame([good], columns=design.names)
+ report = validate_batch(frame, design, expected_count=1, constraints=constraints)
+ assert report["constraints_satisfied"] is True
+ assert report["constraint_violations_per_condition"] == [[]]
+
+ broken = dict(good, speed_2=0.0) # time_2 still 20: exactly one of the pair is 0
+ with pytest.raises(BatchValidityError, match="second_stage_all_or_nothing"):
+ validate_batch(
+ pd.DataFrame([broken], columns=design.names),
+ design,
+ expected_count=1,
+ constraints=constraints,
+ )
+
+
+def test_constraints_are_inert_when_unconfigured(config) -> None:
+ """DTLZ2 declares none, and its acceptance suite must be unaffected."""
+ design = build_design_from_config(dict(config))
+ frame = pd.DataFrame(
+ [
+ {
+ "speed_1": 2500.0,
+ "time_1": 30.0,
+ "speed_2": 0.0,
+ "time_2": 20.0, # would break second_stage_all_or_nothing
+ "precur_conc": 1.4,
+ "precur_vol": 100.0,
+ "anneal_temp": 120.0,
+ "anneal_time": 30.0,
+ "anti_vol": 150.0,
+ "anti_time": 12.0,
+ }
+ ],
+ columns=design.names,
+ )
+ report = validate_batch(frame, design, expected_count=1)
+ assert report["constraint_violations_per_condition"] == [[]]
+ assert report["constraints_declared"] == []
+
+
+# --------------------------------------------------------------------------- #
+# the launcher points at the campaign that is actually running
+# --------------------------------------------------------------------------- #
+
+
+def test_this_contract_is_archived_and_the_launcher_has_moved_on() -> None:
+ """v3 was the DRY RUN -- it rehearsed this contract's shape on a workbook
+ literally called "Test". The live campaign is v4, and the launcher points
+ there; the pinning of that default lives in `test_final_campaign.py`.
+
+ The 2026-08-18 regression this guards against is unchanged in kind: archiving
+ a config without moving the launcher's default leaves the double-click path
+ reading a new workbook against a retired contract, which surfaces as a
+ missing-column error on an intact workbook.
+ """
+ from mobo_kit.launcher import DEFAULT_CONFIG
+
+ assert load_campaign_config(CONFIG_PATH)["campaign"]["status"] == "archived"
+ assert DEFAULT_CONFIG != CONFIG_PATH
+ assert load_campaign_config(DEFAULT_CONFIG)["campaign"]["status"] == "active"
+
+
+def test_reading_a_workbook_against_the_wrong_contract_says_which_contract() -> None:
+ """A column mismatch is almost never a broken workbook; it is a config
+ describing a different campaign. The message has to say so, because the
+ obvious reading of "missing column" sends someone to edit the sheet."""
+ from openpyxl import Workbook
+
+ from mobo_kit.workbook_io import CandidateSheetError, read_campaign_workbook
+
+ archived = load_campaign_config(ARCHIVED_CONFIG_PATH)
+ book = Workbook()
+ sheet = book.active
+ sheet.title = "Sheet1"
+ # a v3-shaped sheet: the archived config wants "PL - Implied Voc (Max)"
+ sheet.append(["Sample number", VOC_HEADER])
+ sheet.append([1, 1.0])
+ import tempfile
+ from pathlib import Path as _Path
+
+ with tempfile.TemporaryDirectory() as tmp:
+ path = _Path(tmp) / "Summary Table Test.xlsx"
+ book.save(path)
+ with pytest.raises(CandidateSheetError) as caught:
+ read_campaign_workbook(path, archived)
+
+ message = str(caught.value)
+ assert "archived" in message.lower()
+ assert "d2d-objectives-v2-nm-thickness" in message
+ # and it points at the column that is almost certainly the same measurement
+ assert "PL - Implied Voc (Max) Raw" in message
+
+
+def test_a_column_level_finding_does_not_pretend_to_have_a_row(config) -> None:
+ """`sample ?` reads as a row whose identity was lost. The rank-agreement
+ finding is about a column and says so."""
+ from mobo_kit.scores import ScoreFinding, ScoreSeverity
+
+ finding = ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="agreement_not_monotonic",
+ objective="optoelectronic",
+ row_position=-1,
+ sample_id=None,
+ message="ranks backwards",
+ )
+ assert finding.is_column_level
+ assert "sample ?" not in str(finding)
+ assert "all rows, optoelectronic" in str(finding)
+
+ per_row = ScoreFinding(
+ severity=ScoreSeverity.WARNING,
+ code="readings_disagree",
+ objective="thickness",
+ row_position=0,
+ sample_id=1,
+ message="readings disagree",
+ )
+ assert not per_row.is_column_level
+ assert "sample 1, thickness" in str(per_row)
+
+
+# --------------------------------------------------------------------------- #
+# the real workbook
+# --------------------------------------------------------------------------- #
+
+requires_workbook = pytest.mark.skipif(
+ not Path(SOURCE).is_file(), reason=f"{SOURCE} is not present in this checkout"
+)
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_every_measured_row_is_on_grid_and_satisfies_every_constraint(config) -> None:
+ """Both halves matter. Off-grid observations drop out of pool bookkeeping, and
+ a constraint that rejects a film the group actually ran is far more likely to
+ be wrong than the film is."""
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(SOURCE, config)
+ assert contents.n_rows == 15
+ assert contents.errors == ()
+
+ design = build_design_from_config(dict(config))
+ X = contents.inputs.to_numpy(float)
+ off_grid = [
+ (contents.sample_ids[i], name, value)
+ for j, name in enumerate(design.names)
+ for i, value in enumerate(X[:, j])
+ if not np.any(np.isclose(design.var_array[j], value, rtol=0.0, atol=1e-9))
+ ]
+ assert off_grid == []
+
+ constraints = constraints_from_config(dict(config), design)
+ assert constraint_violations(X, design, constraints) == [[] for _ in range(15)]
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_the_computed_objectives_match_the_stored_score_columns(config) -> None:
+ """The policy for this workbook is that the stored scores are authoritative and
+ the recompute is the cross-check, so the two agreeing is the whole claim."""
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(SOURCE, config)
+ computed = contents.model_values.to_numpy(float)
+ stored = contents.workbook_values.to_numpy(float)
+ assert computed.shape == stored.shape == (15, 3)
+ assert np.abs(computed - stored).max() < 1e-9
+ assert not [
+ f for f in contents.findings if f.code == "cross_check_mismatch"
+ ]
+
+
+@pytest.mark.local_input
+@requires_workbook
+def test_the_photoconductance_normalization_warns_on_the_real_rows(config) -> None:
+ """The live defect, pinned so it cannot be quietly resolved by editing config.
+
+ When the group supplies the real formula this test should start failing, and
+ that failure is the signal to update the recorded number rather than to
+ loosen the check.
+ """
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(SOURCE, config)
+ warning = next(
+ f for f in contents.findings if f.code == "agreement_not_monotonic"
+ )
+ assert warning.severity is ScoreSeverity.WARNING
+ assert "-0.5484" in warning.message
+ assert contents.errors == (), "a finding, never a gate"
+
+
+@pytest.mark.local_input
+@pytest.mark.slow
+@requires_workbook
+def test_r1_on_the_real_workbook_is_valid_and_deterministic(config) -> None:
+ """One full R1 at production settings: five conditions, on grid, constraint
+ satisfying, and identical on a second run at the same seed."""
+ from mobo_kit.workbook_io import read_campaign_workbook
+
+ contents = read_campaign_workbook(SOURCE, config)
+ X = contents.inputs.to_numpy(float)
+ Y = contents.model_values.to_numpy(float)
+
+ first = run_r1_ucb(config, X, Y, n=5, seed=73)
+ assert len(first.conditions) == 5
+ assert first.diagnostics["validity"]["constraints_satisfied"] is True
+ assert first.diagnostics["validity"]["constraint_violations_per_condition"] == [
+ [] for _ in range(5)
+ ]
+ assert first.diagnostics["observed_rows_violating_constraints"] == []
+ assert len(first.diagnostics["constraints_declared"]) == 3
+ # the sampler draws until the pool is full, so a constraint that gutted the
+ # space would still yield a normal-looking pool; the survival rate is the only
+ # place that shows
+ assert 0.0 < first.diagnostics["constraint_pool_survival_rate"] <= 1.0
+
+ second = run_r1_ucb(config, X, Y, n=5, seed=73)
+ pd.testing.assert_frame_equal(first.conditions, second.conditions)
diff --git a/tests/test_shap_attribution.py b/tests/test_shap_attribution.py
new file mode 100644
index 0000000..e2810d9
--- /dev/null
+++ b/tests/test_shap_attribution.py
@@ -0,0 +1,391 @@
+"""SHAP attribution over the campaign's own models.
+
+What is pinned here is not "the numbers look plausible" -- that is how the last
+three silent-failure bugs survived -- but properties that fail loudly if the
+attribution stops meaning what the figures claim:
+
+* **additivity.** Shapley values must reconstruct the model output exactly:
+ ``base_value + sum(shap) == f(x)``. At 10 features ``KernelExplainer``
+ enumerates all ``2**10`` coalitions, so this holds to machine precision and is a
+ genuine comparator rather than a plausibility check.
+* **determinism.** The figures and the summary CSV are compared across model
+ states, which is meaningless if two runs of the same input disagree.
+* **the mean function shows up where it must.** An objective carrying a declared
+ physics trend on a feature had better attribute to that feature; if it does not,
+ either the mean module is not reaching the posterior or the explained function
+ is the wrong one.
+
+The synthetic campaign needs no workbook. One test does and skips without it.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from mobo_kit.campaign import (
+ build_objective_transform,
+ fit_campaign_models,
+ run_r0_lhs,
+ run_r2_qlognehvi,
+)
+from mobo_kit.candidate_pool import sample_discrete_candidate_pool
+from mobo_kit.design import build_design_from_config
+from mobo_kit.research_qnehvi import R2_ACQUISITIONS, run_r2_qnehvi_research
+
+SOURCE = "local_inputs/Summary Table.xlsx"
+SEED = 73
+INPUT_DIM = 10
+
+
+def _load():
+ path = Path("scripts") / "plot_shap_attribution.py"
+ spec = importlib.util.spec_from_file_location("_script_plot_shap", path)
+ module = importlib.util.module_from_spec(spec)
+ sys.modules[spec.name] = module
+ spec.loader.exec_module(module)
+ return module
+
+
+psa = _load()
+
+
+def _config(pool: int = 256) -> dict:
+ """Synthetic campaign with a log-link objective whose trend is on x0."""
+ return {
+ "inputs": [
+ {"name": f"x{i}", "unit": "u", "start": 1.0, "stop": 2.0, "step": 0.05}
+ for i in range(INPUT_DIM)
+ ],
+ "objectives": {
+ "contract_version": "TEST_ONLY-shap-v1",
+ "scaling_mode": "fixed_affine",
+ "specs": [
+ {
+ "name": "plain",
+ "goal": "maximize",
+ "transform": "affine",
+ "model_source_column": "plain",
+ "lower_anchor": 0.0,
+ "upper_anchor": 3.0,
+ },
+ {
+ "name": "peaked",
+ "goal": "target",
+ "transform": "gaussian_target",
+ "model_source_column": "peaked",
+ "target": 650.0,
+ "sigma": 176.7766952966369,
+ "mean_function": {
+ "response": "log",
+ "features": [{"column": "x0", "transform": "log"}],
+ },
+ },
+ ],
+ },
+ "reference_point_utility": [-0.01, -0.01],
+ "rounds": {
+ "r1": {
+ "method": "ucb_hvi", "batch_size": 5, "replicates_per_condition": 3,
+ "beta": 4.0, "candidate_pool_size": pool, "posterior_samples": 16,
+ "moment_method": "monte_carlo",
+ },
+ "r2": {
+ "method": "qlognehvi", "batch_size": 3,
+ "replicates_per_condition": 3,
+ "candidate_pool_size": pool, "mc_samples": 8,
+ },
+ },
+ "local_penalization": {
+ "radius": 0.25, "min_batch_distance": 0.15,
+ "min_observed_distance": 0.0, "dimension_weights": None,
+ },
+ "model": {"variant": "dim_scaled_prior"},
+ "reproducibility": {"seed": SEED},
+ "constraints": [],
+ }
+
+
+def _measurements(X: np.ndarray) -> np.ndarray:
+ """Deterministic stand-in for measured columns.
+
+ ``peaked`` deliberately carries structure the declared mean function CANNOT
+ absorb (the ``x1`` term), so the residual GP has non-zero posterior variance.
+ Without it the trend fits exactly, the variance collapses, and
+ ``expected_transform`` becomes indistinguishable from transforming the mean --
+ which would make the quadrature test vacuous rather than passing.
+ """
+ X = np.asarray(X, dtype=float)
+ return np.column_stack([
+ X.mean(axis=1),
+ 650.0 * X[:, 0] ** -0.6 * X[:, 1] ** 0.3,
+ ])
+
+
+@pytest.fixture(scope="module")
+def fitted():
+ config = _config()
+ transform = build_objective_transform(config)
+ X = run_r0_lhs(config, n=15, seed=SEED).conditions.to_numpy(float)
+ Y = _measurements(X)
+ model, warnings = fit_campaign_models(config, X, Y, seed=SEED)
+ assert not warnings
+ design = build_design_from_config(dict(config))
+ instances = np.asarray(
+ sample_discrete_candidate_pool(design, 12, seed=SEED).X_phys, dtype=float
+ )
+ return {
+ "config": config, "transform": transform, "X": X, "Y": Y,
+ "model": model, "instances": instances, "design": design,
+ }
+
+
+# --------------------------------------------------------------------------- #
+# the attribution itself
+# --------------------------------------------------------------------------- #
+
+
+def test_shap_values_have_one_column_per_campaign_input(fitted) -> None:
+ values = psa.shap_values_for(
+ fitted["model"], fitted["config"], fitted["transform"], 1,
+ background=fitted["X"], instances=fitted["instances"], seed=SEED,
+ )
+ assert values.shape == (len(fitted["instances"]), INPUT_DIM)
+ assert np.isfinite(values).all()
+
+
+def test_attributions_reconstruct_the_model_output_exactly(fitted) -> None:
+ """Additivity. The comparator that makes the rest of this meaningful.
+
+ Shapley values are defined by summing to the difference between the model
+ output and its expectation over the background. If that fails, the beeswarm is
+ a picture of something other than the model.
+ """
+ import shap
+
+ f = psa.expected_utility_fn(
+ fitted["model"], fitted["config"], fitted["transform"], 1
+ )
+ explainer = shap.KernelExplainer(f, fitted["X"])
+ values = np.asarray(explainer.shap_values(fitted["instances"], silent=True))
+ reconstructed = float(explainer.expected_value) + values.sum(axis=1)
+ np.testing.assert_allclose(
+ reconstructed, f(fitted["instances"]), rtol=0, atol=1e-9
+ )
+
+
+def test_attributions_are_deterministic(fitted) -> None:
+ kwargs = dict(
+ background=fitted["X"], instances=fitted["instances"], seed=SEED
+ )
+ first = psa.shap_values_for(
+ fitted["model"], fitted["config"], fitted["transform"], 1, **kwargs
+ )
+ second = psa.shap_values_for(
+ fitted["model"], fitted["config"], fitted["transform"], 1, **kwargs
+ )
+ assert np.array_equal(first, second)
+ # and the ranking a figure would draw is stable, not just the raw array
+ assert np.array_equal(
+ np.argsort(-np.abs(first).mean(axis=0)),
+ np.argsort(-np.abs(second).mean(axis=0)),
+ )
+
+
+def test_the_declared_mean_function_feature_dominates(fitted) -> None:
+ """`peaked` carries a log trend on x0 and nothing else; x0 must lead.
+
+ This is the property the figures' construction caveat warns about, asserted
+ rather than assumed -- and it doubles as a check that the structured mean
+ reaches ``posterior()`` at all.
+ """
+ values = psa.shap_values_for(
+ fitted["model"], fitted["config"], fitted["transform"], 1,
+ background=fitted["X"], instances=fitted["instances"], seed=SEED,
+ )
+ mean_abs = np.abs(values).mean(axis=0)
+ assert int(np.argmax(mean_abs)) == 0, "x0 carries the declared trend"
+ assert mean_abs[0] > 2.0 * np.median(mean_abs)
+
+
+def test_expected_utility_uses_the_lognormal_quadrature_not_the_mean(fitted) -> None:
+ """The explained function must be E[utility], not utility(E[.]).
+
+ For a peaked target on a lognormal posterior the two differ, and the second is
+ biased by Jensen's inequality and blind to variance.
+ """
+ import torch
+
+ from mobo_kit.campaign import normalise_inputs
+
+ config, transform = fitted["config"], fitted["transform"]
+ f = psa.expected_utility_fn(fitted["model"], config, transform, 1)
+ expected = f(fitted["instances"])
+
+ model = fitted["model"]
+ model.eval()
+ with torch.no_grad():
+ posterior = model.posterior(
+ torch.tensor(
+ normalise_inputs(config, fitted["instances"]), dtype=torch.double
+ ),
+ observation_noise=False,
+ )
+ naive = transform.transform(posterior.mean)[:, 1].numpy()
+ assert np.isfinite(expected).all()
+ assert not np.allclose(expected, naive, atol=1e-6), (
+ "expected utility must differ from the transformed mean, or the "
+ "quadrature is not being used"
+ )
+
+
+# --------------------------------------------------------------------------- #
+# the qNEHVI research variant
+# --------------------------------------------------------------------------- #
+
+
+def test_qnehvi_proposes_a_valid_batch(fitted) -> None:
+ config = fitted["config"]
+ X = fitted["X"]
+ Y = fitted["Y"]
+ X1 = run_r0_lhs(config, n=5, seed=SEED + 1).conditions.to_numpy(float)
+ X01 = np.vstack([X, X1])
+ Y01 = np.vstack([Y, _measurements(X1)])
+
+ result = run_r2_qnehvi_research(config, X01, Y01, seed=SEED)
+ assert len(result.conditions) == 3
+ report = result.diagnostics["validity"]
+ assert report["unique"] and report["on_grid"] and report["in_bounds"]
+ assert report["min_pairwise_distance"] >= 0.15
+ assert result.diagnostics["method"] == "qnehvi"
+ assert result.diagnostics["research_only"] is True
+
+
+def test_both_acquisitions_are_selectable_and_named() -> None:
+ assert R2_ACQUISITIONS == ("qlognehvi", "qnehvi")
+
+
+def test_identical_batch_detection(fitted) -> None:
+ """The detection logic, exercised on both outcomes.
+
+ Whether the two acquisitions actually agree is a property of the data, not
+ something a test should assert; what must work is noticing either way.
+ """
+ frame = pd.DataFrame(
+ [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"]
+ )
+ same = frame.iloc[[2, 0, 1]].reset_index(drop=True)
+ different = frame.copy()
+ different.iloc[0, 0] = 9.0
+ assert psa.batch_hash(frame) == psa.batch_hash(same)
+ assert psa.batch_hash(frame) != psa.batch_hash(different)
+
+
+def test_the_two_acquisitions_run_on_the_same_inputs(fitted) -> None:
+ """Both runners accept the same contract, so a comparison is apples to apples."""
+ config, X, Y = fitted["config"], fitted["X"], fitted["Y"]
+ X1 = run_r0_lhs(config, n=5, seed=SEED + 1).conditions.to_numpy(float)
+ X01, Y01 = np.vstack([X, X1]), np.vstack([Y, _measurements(X1)])
+
+ a = run_r2_qlognehvi(config, X01, Y01, seed=SEED)
+ b = run_r2_qnehvi_research(config, X01, Y01, seed=SEED)
+ assert list(a.conditions.columns) == list(b.conditions.columns)
+ assert len(a.conditions) == len(b.conditions) == 3
+ # both are valid batches whether or not they agree
+ for result in (a, b):
+ assert result.diagnostics["validity"]["on_grid"]
+
+
+# --------------------------------------------------------------------------- #
+# figures and captions
+# --------------------------------------------------------------------------- #
+
+
+def test_beeswarm_renders_headlessly(fitted, tmp_path) -> None:
+ values = psa.shap_values_for(
+ fitted["model"], fitted["config"], fitted["transform"], 1,
+ background=fitted["X"], instances=fitted["instances"], seed=SEED,
+ )
+ path = tmp_path / "beeswarm.png"
+ psa.plot_beeswarm(
+ path, values, fitted["instances"], fitted["config"], "peaked",
+ "test state", seed=SEED,
+ caveats=psa.caveats_for("peaked", "final", True),
+ )
+ assert path.is_file() and path.stat().st_size > 0
+
+
+def test_feature_labels_carry_the_physical_range(fitted) -> None:
+ """The colorbar is per-feature normalised, so the units live in the labels."""
+ labels = psa._feature_labels(fitted["config"], fitted["instances"])
+ assert len(labels) == INPUT_DIM
+ assert all("\n" in label for label in labels)
+ assert labels[0].startswith("x0")
+ assert "u" in labels[0], "unit must appear in the label"
+
+
+def test_captions_state_only_what_is_true_of_that_figure() -> None:
+ r0 = psa.caveats_for("thickness", "r0_only", identical=False)
+ assert any("15 real" in line for line in r0)
+ assert not any("Oracle:" in line for line in r0)
+
+ final = psa.caveats_for("thickness", "final", identical=False)
+ assert any("Oracle:" in line for line in final)
+ assert any("mean function" in line for line in final)
+
+ uniformity = psa.caveats_for("uniformity", "final", identical=False)
+ assert any("fitted noise" in line for line in uniformity)
+
+ with_note = psa.caveats_for("thickness", "final", identical=True)
+ assert any("IDENTICAL" in line for line in with_note)
+ # the R0 anchor never carries the acquisition note: it predates R2
+ assert not any(
+ "IDENTICAL" in line
+ for line in psa.caveats_for("thickness", "r0_only", identical=True)
+ )
+
+
+# --------------------------------------------------------------------------- #
+# end to end on the real workbook
+# --------------------------------------------------------------------------- #
+
+
+@pytest.mark.local_input
+@pytest.mark.skipif(
+ not Path(SOURCE).is_file(), reason=f"{SOURCE} is not present in this checkout"
+)
+def test_headless_smoke_on_the_real_workbook(tmp_path) -> None:
+ import yaml
+
+ from mobo_kit.campaign import load_campaign_config
+
+ config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+ config["rounds"]["r1"]["candidate_pool_size"] = 128
+ config["rounds"]["r1"]["posterior_samples"] = 8
+ config["rounds"]["r2"]["candidate_pool_size"] = 128
+ config["rounds"]["r2"]["mc_samples"] = 4
+ scratch = tmp_path / "campaign.yaml"
+ scratch.write_text(yaml.safe_dump(config, sort_keys=False), encoding="utf-8")
+
+ output = tmp_path / "out"
+ code = psa.main([
+ "--workbook", SOURCE,
+ "--config", str(scratch),
+ "--output-dir", str(output),
+ "--instances", "6",
+ "--objectives", "thickness",
+ ])
+ assert code == 0
+
+ summary = pd.read_csv(output / "shap_summary.csv")
+ assert set(summary["objective"]) == {"thickness"}
+ assert summary["rank"].min() == 1
+ # one row per feature per model state
+ assert len(summary) % INPUT_DIM == 0
+ assert (output / "figures").is_dir()
+ assert list((output / "figures").glob("*.png"))
diff --git a/tests/test_sobol_pool.py b/tests/test_sobol_pool.py
new file mode 100644
index 0000000..c43f022
--- /dev/null
+++ b/tests/test_sobol_pool.py
@@ -0,0 +1,236 @@
+from __future__ import annotations
+
+import itertools
+
+import numpy as np
+import pytest
+
+from mobo_kit.candidate_pool import (
+ CandidatePoolSamplingError,
+ physical_rows_to_grid_indices,
+)
+from mobo_kit.design import InputSpec, build_design
+from mobo_kit.sobol_pool import (
+ build_nested_sobol_discrete_pool,
+ hash_grid_index_prefix,
+ map_unit_points_to_grid_indices,
+)
+
+
+def _design():
+ return build_design(
+ [
+ InputSpec("a", 0.0, 8.0, 1.0),
+ InputSpec("b", 10.0, 22.0, 2.0),
+ InputSpec("c", -2.0, 2.0, 1.0),
+ ]
+ )
+
+
+def test_unit_points_map_by_floor_to_exact_grid_indices():
+ design = build_design(
+ [InputSpec("x", 0.0, 3.0, 1.0), InputSpec("y", 10.0, 20.0, 10.0)]
+ )
+ points = np.array(
+ [
+ [0.0, 0.0],
+ [0.249999, 0.499999],
+ [0.25, 0.5],
+ [np.nextafter(1.0, 0.0), np.nextafter(1.0, 0.0)],
+ ]
+ )
+ np.testing.assert_array_equal(
+ map_unit_points_to_grid_indices(points, design),
+ np.array([[0, 0], [0, 0], [1, 1], [3, 1]]),
+ )
+
+
+@pytest.mark.parametrize(
+ "points, message",
+ [
+ (np.array([[1.0, 0.0]]), "half-open"),
+ (np.array([[-1e-12, 0.0]]), "half-open"),
+ (np.array([[np.nan, 0.0]]), "finite"),
+ (np.array([0.2, 0.3]), "shape"),
+ ],
+)
+def test_unit_point_mapping_fails_closed(points, message):
+ design = build_design(
+ [InputSpec("x", 0.0, 3.0, 1.0), InputSpec("y", 0.0, 1.0, 1.0)]
+ )
+ with pytest.raises(ValueError, match=message):
+ map_unit_points_to_grid_indices(points, design)
+
+
+def test_same_scramble_is_deterministic_nested_and_has_locked_prefix_hashes():
+ sizes = (8, 16, 32, 64)
+ first = build_nested_sobol_discrete_pool(_design(), sizes, scramble_seed=73)
+ repeat = build_nested_sobol_discrete_pool(
+ _design(), tuple(reversed(sizes)), scramble_seed=73
+ )
+ expected_hashes = {
+ 8: "E36D0663991DFBF5A53C278C3F78DD66AB7D5245B8B7306D7422960F506F0361",
+ 16: "9E9AC7931E14CB3E4BE992D4B17D0670BEF0E6DB6AF467E059CE39A89D8A55F6",
+ 32: "79DBFBEEC3B6C7D1AA2E2D685E6DF19D5CA546BAD84F575A376FC83305B6A65B",
+ 64: "22AFBA550FA4A2F39104A9B565932225E374C0B675ABB4685FB19A38D6A748B7",
+ }
+
+ assert first.accepted_sizes == sizes
+ assert first.accepted_count == 64
+ assert dict(first.prefix_hashes) == expected_hashes
+ assert dict(repeat.prefix_hashes) == expected_hashes
+ assert first.scipy_version
+ for smaller, larger in zip(sizes, sizes[1:]):
+ np.testing.assert_array_equal(
+ first.pools[smaller].grid_indices,
+ first.pools[larger].grid_indices[:smaller],
+ )
+ for size in sizes:
+ pool = first.pools[size]
+ assert pool.size == size
+ assert np.unique(pool.grid_indices, axis=0).shape[0] == size
+ np.testing.assert_array_equal(
+ physical_rows_to_grid_indices(pool.X_phys, _design()), pool.grid_indices
+ )
+ assert np.all((pool.X_norm >= 0.0) & (pool.X_norm <= 1.0))
+ assert first.prefix_hashes[size] == hash_grid_index_prefix(pool.grid_indices)
+ np.testing.assert_array_equal(
+ pool.grid_indices, repeat.pools[size].grid_indices
+ )
+
+
+def test_different_scramble_changes_the_accepted_order_and_hash():
+ primary = build_nested_sobol_discrete_pool(_design(), [64], scramble_seed=73)
+ secondary = build_nested_sobol_discrete_pool(_design(), [64], scramble_seed=137)
+ assert not np.array_equal(
+ primary.largest_pool.grid_indices, secondary.largest_pool.grid_indices
+ )
+ assert primary.prefix_hashes[64] != secondary.prefix_hashes[64]
+
+
+def test_exclusions_constraints_and_off_grid_observed_partition_are_stable():
+ design = build_design(
+ [
+ InputSpec("a", 0.0, 4.0, 1.0),
+ InputSpec("b", 0.0, 2.0, 1.0),
+ InputSpec("c", 0.0, 1.0, 1.0),
+ ]
+ )
+ observed = np.array([[0.0, 0.0, 0.0], [0.5, 1.0, 1.0]])
+ pending = np.array([[2.0, 1.0, 1.0]])
+ avoid = np.array([[4.0, 2.0, 1.0]])
+
+ def require_even_a(X_phys, supplied_design):
+ assert supplied_design is design
+ return (X_phys[:, 0] % 2.0) == 0.0
+
+ result = build_nested_sobol_discrete_pool(
+ design,
+ [5, 15],
+ scramble_seed=11,
+ observed_phys=observed,
+ pending_phys=pending,
+ avoid_phys=avoid,
+ row_constraints=[require_even_a],
+ max_raw_draws=4096,
+ )
+ excluded = {
+ tuple(row)
+ for row in physical_rows_to_grid_indices(
+ np.vstack([observed[:1], pending, avoid]), design
+ )
+ }
+ final = result.largest_pool
+ assert result.ignored_off_grid_observed == 1
+ assert final.size == 15
+ assert np.all(final.X_phys[:, 0] % 2.0 == 0.0)
+ assert not ({tuple(row) for row in final.grid_indices} & excluded)
+ assert final.rejected_avoid > 0
+ assert final.rejected_constraint > 0
+ np.testing.assert_array_equal(result.pools[5].grid_indices, final.grid_indices[:5])
+
+
+def test_pending_and_explicit_avoid_rows_must_be_exactly_on_grid():
+ off_grid = np.array([[0.25, 10.0, 0.0]])
+ with pytest.raises(ValueError, match="off-grid"):
+ build_nested_sobol_discrete_pool(
+ _design(), [4], scramble_seed=1, pending_phys=off_grid
+ )
+ with pytest.raises(ValueError, match="off-grid"):
+ build_nested_sobol_discrete_pool(
+ _design(), [4], scramble_seed=1, avoid_phys=off_grid
+ )
+
+ with pytest.raises(ValueError, match="within the design bounds"):
+ build_nested_sobol_discrete_pool(
+ _design(),
+ [4],
+ scramble_seed=1,
+ observed_phys=np.array([[9.0, 10.0, 0.0]]),
+ )
+
+
+def test_sampler_never_materializes_the_cartesian_product(monkeypatch):
+ def forbidden(*args, **kwargs):
+ del args, kwargs
+ raise AssertionError("full Cartesian materialization was attempted")
+
+ monkeypatch.setattr(np, "meshgrid", forbidden)
+ monkeypatch.setattr(np, "indices", forbidden)
+ monkeypatch.setattr(itertools, "product", forbidden)
+ result = build_nested_sobol_discrete_pool(_design(), [16, 32], scramble_seed=9)
+ assert result.largest_pool.size == 32
+
+
+def test_impossible_capacity_and_draw_limit_fail_with_structured_errors():
+ tiny = build_design([InputSpec("x", 0.0, 1.0, 1.0)])
+ with pytest.raises(CandidatePoolSamplingError) as capacity:
+ build_nested_sobol_discrete_pool(
+ tiny,
+ [2],
+ scramble_seed=1,
+ observed_phys=np.array([[0.0]]),
+ )
+ assert capacity.value.draws == 0
+ assert "exceeds" in capacity.value.reason
+
+ larger = build_design([InputSpec("x", 0.0, 7.0, 1.0)])
+
+ def reject_all(X_phys, supplied_design):
+ del supplied_design
+ return np.zeros(X_phys.shape[0], dtype=bool)
+
+ with pytest.raises(CandidatePoolSamplingError) as limited:
+ build_nested_sobol_discrete_pool(
+ larger,
+ [1],
+ scramble_seed=2,
+ row_constraints=[reject_all],
+ max_raw_draws=8,
+ )
+ assert limited.value.draws == 8
+ assert limited.value.accepted == 0
+ assert limited.value.rejected_constraint > 0
+
+
+@pytest.mark.parametrize(
+ "sizes, seed, max_draws, message",
+ [
+ ([], 1, None, "must not be empty"),
+ ([0], 1, None, "positive integers"),
+ ([2, 2], 1, None, "duplicates"),
+ ([2], True, None, "scramble_seed"),
+ ([2], 1, 0, "max_raw_draws"),
+ ],
+)
+def test_sampler_configuration_validation(sizes, seed, max_draws, message):
+ kwargs = {} if max_draws is None else {"max_raw_draws": max_draws}
+ with pytest.raises(ValueError, match=message):
+ build_nested_sobol_discrete_pool(_design(), sizes, scramble_seed=seed, **kwargs)
+
+
+def test_prefix_hash_requires_an_integer_matrix():
+ with pytest.raises(TypeError, match="integer dtype"):
+ hash_grid_index_prefix(np.array([[0.0, 1.0]]))
+ with pytest.raises(ValueError, match="two-dimensional"):
+ hash_grid_index_prefix(np.array([0, 1], dtype=np.int64))
diff --git a/tests/test_structured_mean.py b/tests/test_structured_mean.py
new file mode 100644
index 0000000..4aad381
--- /dev/null
+++ b/tests/test_structured_mean.py
@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+import numpy as np
+import pytest
+
+from mobo_kit.campaign import load_campaign_config
+from mobo_kit.structured_mean import (
+ MeanFeature,
+ StructuredMeanSpec,
+ apply_structured_mean,
+ fit_structured_mean,
+ mean_spec_from_config,
+)
+
+NAMES = ["speed_1", "time_1", "precur_conc", "anneal_temp"]
+
+
+def _X(n: int = 12) -> np.ndarray:
+ rng = np.random.default_rng(0)
+ return np.column_stack(
+ [
+ rng.uniform(1000, 6000, n),
+ rng.uniform(5, 50, n),
+ rng.uniform(1.0, 2.0, n),
+ rng.uniform(100, 185, n),
+ ]
+ )
+
+
+def test_log_response_recovers_a_power_law_exactly() -> None:
+ """T = c * speed^a * conc^b is linear in log-log, so the mean should absorb
+ it completely and leave zero residual."""
+ X = _X()
+ T = 5.0e5 * X[:, 0] ** -0.4 * X[:, 2] ** 1.3
+ spec = StructuredMeanSpec(
+ response="log",
+ features=(MeanFeature("speed_1", "log"), MeanFeature("precur_conc", "log")),
+ )
+ coefficients, residual = fit_structured_mean(X, T, spec, NAMES)
+ assert np.abs(residual).max() < 1e-9
+ assert coefficients[1] == pytest.approx(-0.4, abs=1e-6)
+ assert coefficients[2] == pytest.approx(1.3, abs=1e-6)
+
+
+def test_identity_response_recovers_a_linear_trend() -> None:
+ X = _X()
+ y = 3.0 - 0.05 * X[:, 3]
+ spec = StructuredMeanSpec("identity", (MeanFeature("anneal_temp"),))
+ coefficients, residual = fit_structured_mean(X, y, spec, NAMES)
+ assert np.abs(residual).max() < 1e-9
+ assert coefficients[1] == pytest.approx(-0.05, abs=1e-9)
+
+
+def test_trend_round_trips_through_apply() -> None:
+ X = _X()
+ y = 2.0 + 0.01 * X[:, 3]
+ spec = StructuredMeanSpec("identity", (MeanFeature("anneal_temp"),))
+ coefficients, residual = fit_structured_mean(X, y, spec, NAMES)
+ post = apply_structured_mean(
+ coefficients, X, spec, NAMES, residual, np.zeros(len(X))
+ )
+ np.testing.assert_allclose(post.mean, y, atol=1e-9)
+ assert post.link == "identity"
+
+
+def test_log_response_reports_its_link() -> None:
+ """The link is what tells the utility layer to integrate a lognormal instead
+ of using the Gaussian closed form."""
+ X = _X()
+ spec = StructuredMeanSpec("log", (MeanFeature("speed_1", "log"),))
+ coefficients, residual = fit_structured_mean(X, np.full(len(X), 700.0), spec, NAMES)
+ post = apply_structured_mean(
+ coefficients, X, spec, NAMES, residual, np.zeros(len(X))
+ )
+ assert post.link == "log"
+ np.testing.assert_allclose(np.exp(post.mean), 700.0, atol=1e-8)
+
+
+def test_coefficients_come_only_from_the_rows_passed_in() -> None:
+ """Guards the leakage property: fitting on a subset must not see the rest."""
+ X = _X(14)
+ y = 2.0 + 0.01 * X[:, 3]
+ spec = StructuredMeanSpec("identity", (MeanFeature("anneal_temp"),))
+ keep = list(range(13))
+ a, _ = fit_structured_mean(X[keep], y[keep], spec, NAMES)
+ b, _ = fit_structured_mean(X[keep], y[keep] * 1.0, spec, NAMES)
+ np.testing.assert_allclose(a, b)
+ # perturbing the held-out row must not change the fitted trend
+ y_perturbed = y.copy()
+ y_perturbed[13] += 1000.0
+ c, _ = fit_structured_mean(X[keep], y_perturbed[keep], spec, NAMES)
+ np.testing.assert_allclose(a, c)
+
+
+def test_log_response_rejects_non_positive_observations() -> None:
+ X = _X()
+ spec = StructuredMeanSpec("log", (MeanFeature("speed_1", "log"),))
+ y = np.full(len(X), 1.0)
+ y[0] = -1.0
+ with pytest.raises(ValueError, match="strictly positive"):
+ fit_structured_mean(X, y, spec, NAMES)
+
+
+def test_unknown_feature_column_is_rejected() -> None:
+ spec = StructuredMeanSpec("identity", (MeanFeature("not_an_input"),))
+ with pytest.raises(ValueError, match="not a declared input"):
+ fit_structured_mean(_X(), np.ones(12), spec, NAMES)
+
+
+@pytest.mark.parametrize(
+ "kwargs, match",
+ [
+ ({"response": "sqrt", "features": (MeanFeature("speed_1"),)}, "response link"),
+ ({"response": "identity", "features": ()}, "at least one feature"),
+ (
+ {
+ "response": "identity",
+ "features": (MeanFeature("speed_1"), MeanFeature("speed_1")),
+ },
+ "unique",
+ ),
+ ],
+)
+def test_invalid_specs_are_rejected(kwargs, match) -> None:
+ with pytest.raises(ValueError, match=match):
+ StructuredMeanSpec(**kwargs)
+
+
+# --------------------------------------------------------------------------- #
+# the campaign's declared shapes
+# --------------------------------------------------------------------------- #
+
+
+def test_campaign_declares_the_two_measured_mean_functions() -> None:
+ """Opposite shapes by design: thickness needs a pair of log terms,
+ optoelectronic needs exactly one linear term. Neither generalises."""
+ config = load_campaign_config("configs/campaign_d2d_perovskite.yaml")
+ by_name = {s["name"]: s for s in config["objectives"]["specs"]}
+
+ assert mean_spec_from_config(by_name["uniformity"]) is None
+
+ opto = mean_spec_from_config(by_name["optoelectronic"])
+ assert opto.response == "identity"
+ assert [f.column for f in opto.features] == ["anneal_temp"]
+ assert [f.transform for f in opto.features] == ["identity"]
+
+ thickness = mean_spec_from_config(by_name["thickness"])
+ assert thickness.response == "log"
+ assert [f.column for f in thickness.features] == ["speed_1", "precur_conc"]
+ assert [f.transform for f in thickness.features] == ["log", "log"]
diff --git a/tests/test_ucb_hvi.py b/tests/test_ucb_hvi.py
new file mode 100644
index 0000000..4b59b59
--- /dev/null
+++ b/tests/test_ucb_hvi.py
@@ -0,0 +1,447 @@
+import numpy as np
+import pytest
+import torch
+from botorch.models import ModelListGP, SingleTaskGP
+from botorch.models.transforms.outcome import Standardize
+
+from mobo_kit.ucb_hvi import (
+ UCBHVIScoreResult,
+ apply_ucb_bound_policy,
+ hypervolume_improvement_scores,
+ posterior_identity_moments,
+ posterior_utility_moments,
+ propose_ucb_hvi_batch,
+ score_ucb_hvi_from_moments,
+ score_ucb_hvi_pool,
+)
+from mobo_kit.batch_selection import LocalPenalizationConfig, UndersizedBatchError
+from mobo_kit.candidate_pool import CandidatePool
+from mobo_kit.objectives import ObjectiveSpec, ObjectiveTransform
+import mobo_kit.ucb_hvi as module
+
+
+class IdentityTransform:
+ def transform(self, Y: torch.Tensor) -> torch.Tensor:
+ return Y
+
+
+def _identity_contract(count=1):
+ return ObjectiveTransform(
+ [
+ ObjectiveSpec(f"objective_{index}", "maximize", "identity")
+ for index in range(count)
+ ],
+ version="TEST-IDENTITY-v1",
+ )
+
+
+class _ExactPosteriorModel:
+ def posterior(self, X, *, observation_noise):
+ mean = torch.stack((X[..., 0] + 0.25, 1.5 - X[..., 0]), dim=-1)
+ variance = torch.full_like(mean, 0.09 if not observation_noise else 0.16)
+ return type("Posterior", (), {"mean": mean, "variance": variance})()
+
+
+def _observed_2d():
+ return np.array([[0.4, 0.8], [0.8, 0.4], [0.2, 0.2]])
+
+
+def test_beta_zero_and_uncertainty_definition():
+ means = np.array([[0.7, 0.7], [0.6, 0.6]])
+ std = np.array([[0.2, 0.1], [0.0, 0.0]])
+ zero = score_ucb_hvi_from_moments(
+ means, std, _observed_2d(), np.array([0.0, 0.0]), beta=0.0
+ )
+ assert np.allclose(zero.utility_ucb, means)
+ positive = score_ucb_hvi_from_moments(
+ means, std, _observed_2d(), np.array([0.0, 0.0]), beta=4.0
+ )
+ assert positive.kappa == pytest.approx(2.0)
+ assert np.allclose(positive.utility_ucb, means + 2.0 * std)
+ assert positive.base_score[0] > zero.base_score[0]
+ with pytest.raises(ValueError, match="beta"):
+ score_ucb_hvi_from_moments(
+ means, std, _observed_2d(), np.array([0.0, 0.0]), beta=-0.1
+ )
+ with pytest.raises(ValueError, match="non-boolean"):
+ score_ucb_hvi_from_moments(
+ means, std, _observed_2d(), np.array([0.0, 0.0]), beta=True
+ )
+
+
+def test_hvi_matches_hand_calculation_and_dominated_is_true_zero():
+ scores, baseline, pareto, _ = hypervolume_improvement_scores(
+ np.array([[0.7, 0.7], [0.3, 0.3]]),
+ _observed_2d(),
+ np.array([0.0, 0.0]),
+ )
+ assert baseline == pytest.approx(0.48)
+ # Added area: (0.7-0.4)*(0.7-0.4) = 0.09.
+ assert scores[0] == pytest.approx(0.09)
+ assert scores[1] == 0.0
+ assert pareto.shape == (2, 2)
+
+
+def test_three_objective_hvi_and_chunk_invariance():
+ observed = np.array([[0.5, 0.5, 0.5]])
+ candidates = np.array([[0.6, 0.6, 0.6], [0.4, 0.4, 0.4]])
+ chunked = hypervolume_improvement_scores(
+ candidates, observed, np.zeros(3), chunk_size=1
+ )
+ whole = hypervolume_improvement_scores(
+ candidates, observed, np.zeros(3), chunk_size=20
+ )
+ assert np.allclose(chunked[0], whole[0])
+ assert chunked[0][0] == pytest.approx(0.6**3 - 0.5**3)
+ assert chunked[0][1] == 0.0
+
+
+def test_dominated_observed_rows_do_not_change_scores():
+ candidates = np.array([[0.7, 0.7]])
+ with_dominated = hypervolume_improvement_scores(
+ candidates, _observed_2d(), np.zeros(2)
+ )[0]
+ without_dominated = hypervolume_improvement_scores(
+ candidates, _observed_2d()[:2], np.zeros(2)
+ )[0]
+ assert np.allclose(with_dominated, without_dominated)
+
+
+@pytest.mark.parametrize(
+ "reference, message",
+ [(None, "required"), (np.zeros(3), "shape"), (np.array([0.0, np.nan]), "finite")],
+)
+def test_reference_point_validation(reference, message):
+ with pytest.raises(ValueError, match=message):
+ hypervolume_improvement_scores(
+ np.array([[0.7, 0.7]]), _observed_2d(), reference
+ )
+
+
+def test_posterior_utility_moments_are_seeded_and_chunk_invariant():
+ train_X = torch.tensor([[0.0], [0.5], [1.0]], dtype=torch.double)
+ train_Y = torch.tensor([[0.0], [1.0], [0.0]], dtype=torch.double)
+ model = SingleTaskGP(train_X, train_Y, outcome_transform=Standardize(m=1))
+ model.eval()
+ pool = torch.linspace(0.1, 0.9, 6, dtype=torch.double).unsqueeze(-1)
+ one = posterior_utility_moments(
+ model,
+ pool,
+ IdentityTransform(),
+ mc_samples=16,
+ seed=13,
+ chunk_size=1,
+ )
+ all_at_once = posterior_utility_moments(
+ model,
+ pool,
+ IdentityTransform(),
+ mc_samples=16,
+ seed=13,
+ chunk_size=100,
+ )
+ repeat = posterior_utility_moments(
+ model,
+ pool,
+ IdentityTransform(),
+ mc_samples=16,
+ seed=13,
+ chunk_size=2,
+ )
+ assert np.allclose(one.utility_mean, all_at_once.utility_mean)
+ assert np.allclose(one.utility_std, all_at_once.utility_std)
+ assert np.allclose(one.utility_mean, repeat.utility_mean)
+ assert np.allclose(one.utility_std, repeat.utility_std)
+ assert np.all(one.utility_std > 0)
+
+
+def test_analytic_identity_moments_are_exact_chunk_dtype_and_device_invariant():
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ pool = torch.linspace(0.1, 0.9, 7, dtype=torch.float32, device=device).unsqueeze(-1)
+ model = _ExactPosteriorModel()
+ contract = _identity_contract(2)
+
+ one = posterior_identity_moments(
+ model, pool, contract, chunk_size=1, observation_noise=False
+ )
+ whole = posterior_identity_moments(
+ model, pool, contract, chunk_size=100, observation_noise=False
+ )
+ expected = model.posterior(pool, observation_noise=False)
+
+ assert one.moment_method == "analytic_identity"
+ assert one.objective_contract_version == "TEST-IDENTITY-v1"
+ assert one.utility_mean.dtype == pool.dtype
+ assert one.utility_mean.device == pool.device
+ assert one.utility_std.dtype == pool.dtype
+ assert one.utility_std.device == pool.device
+ assert torch.equal(one.utility_mean, expected.mean)
+ assert torch.equal(one.utility_std, expected.variance.sqrt())
+ assert torch.equal(one.utility_mean, whole.utility_mean)
+ assert torch.equal(one.utility_std, whole.utility_std)
+
+
+def test_analytic_identity_moments_reject_nonidentity_or_implicit_contracts():
+ pool = torch.tensor([[0.2], [0.8]], dtype=torch.double)
+ nonidentity = ObjectiveTransform(
+ [
+ ObjectiveSpec(
+ "scaled",
+ "maximize",
+ "affine",
+ lower_anchor=0.0,
+ upper_anchor=1.0,
+ )
+ ],
+ version="TEST-NONLINEAR-v1",
+ )
+ with pytest.raises(ValueError, match="every objective.*identity"):
+ posterior_identity_moments(
+ _ExactPosteriorModel(), pool, nonidentity, chunk_size=2
+ )
+ with pytest.raises(ValueError, match="explicit versioned objective contract"):
+ posterior_identity_moments(
+ _ExactPosteriorModel(), pool, IdentityTransform(), chunk_size=2
+ )
+
+
+def test_analytic_moments_agree_with_high_sample_mc_and_stable_hvi_selection():
+ train_X = torch.tensor([[0.0], [0.5], [1.0]], dtype=torch.double)
+ first_Y = torch.tensor([[0.1], [0.9], [0.4]], dtype=torch.double)
+ second_Y = torch.tensor([[0.8], [0.2], [0.7]], dtype=torch.double)
+ model = ModelListGP(
+ SingleTaskGP(train_X, first_Y, outcome_transform=Standardize(m=1)),
+ SingleTaskGP(train_X, second_Y, outcome_transform=Standardize(m=1)),
+ )
+ model.eval()
+ pool = torch.linspace(0.05, 0.95, 9, dtype=torch.double).unsqueeze(-1)
+ contract = _identity_contract(2)
+
+ analytic_moments = posterior_identity_moments(model, pool, contract, chunk_size=4)
+ mc_moments = posterior_utility_moments(
+ model,
+ pool,
+ contract,
+ mc_samples=4096,
+ seed=17,
+ chunk_size=3,
+ )
+ np.testing.assert_allclose(
+ mc_moments.utility_mean,
+ analytic_moments.utility_mean.cpu().numpy(),
+ atol=0.02,
+ rtol=0.02,
+ )
+ np.testing.assert_allclose(
+ mc_moments.utility_std,
+ analytic_moments.utility_std.cpu().numpy(),
+ atol=0.02,
+ rtol=0.05,
+ )
+
+ observed = torch.cat((first_Y, second_Y), dim=1)
+ reference = np.array([-0.1, -0.1])
+ analytic_scores = score_ucb_hvi_pool(
+ model,
+ pool,
+ observed,
+ contract,
+ reference,
+ beta=4.0,
+ moment_method="analytic_identity",
+ posterior_chunk_size=4,
+ )
+ mc_scores = score_ucb_hvi_pool(
+ model,
+ pool,
+ observed,
+ contract,
+ reference,
+ beta=4.0,
+ moment_method="monte_carlo",
+ mc_samples=4096,
+ seed=17,
+ posterior_chunk_size=3,
+ )
+ assert analytic_scores.moment_method == "analytic_identity"
+ assert analytic_scores.mc_samples is None
+ assert analytic_scores.seed is None
+ assert int(np.argmax(analytic_scores.base_score)) == int(
+ np.argmax(mc_scores.base_score)
+ )
+
+
+def test_ucb_bound_policy_retains_raw_effective_and_clip_amounts():
+ raw = np.array([[1.2, -3.0, -0.2], [0.8, -2.0, 1.1]])
+ original = raw.copy()
+ bounds = [(0.0, 1.0), (None, None), (0.0, 1.0)]
+
+ unchanged = apply_ucb_bound_policy(raw, bounds, "none")
+ clipped = apply_ucb_bound_policy(raw, bounds, "clip_ucb")
+
+ np.testing.assert_array_equal(raw, original)
+ np.testing.assert_array_equal(unchanged.utility_ucb_raw, raw)
+ np.testing.assert_array_equal(unchanged.utility_ucb_effective, raw)
+ np.testing.assert_array_equal(unchanged.utility_ucb_clip_amount, 0.0)
+ np.testing.assert_allclose(
+ clipped.utility_ucb_effective,
+ np.array([[1.0, -3.0, 0.0], [0.8, -2.0, 1.0]]),
+ )
+ np.testing.assert_allclose(
+ clipped.utility_ucb_clip_amount,
+ np.array([[0.2, 0.0, 0.2], [0.0, 0.0, 0.1]]),
+ )
+
+
+def test_score_uses_effective_bounded_ucb_for_hvi_and_keeps_compatibility_alias():
+ means = np.array([[1.2, 0.8, 1.2]])
+ observed = np.array([[0.5, 0.5, 0.5]])
+ bounds = [(0.0, 1.0), (None, None), (0.0, 1.0)]
+ result = score_ucb_hvi_from_moments(
+ means,
+ np.zeros_like(means),
+ observed,
+ np.zeros(3),
+ beta=0.0,
+ bound_policy="clip_ucb",
+ utility_bounds=bounds,
+ moment_method="analytic_identity",
+ )
+ expected_scores = hypervolume_improvement_scores(
+ np.array([[1.0, 0.8, 1.0]]), observed, np.zeros(3)
+ )[0]
+ np.testing.assert_allclose(result.base_score, expected_scores)
+ np.testing.assert_allclose(result.utility_ucb_raw, means)
+ np.testing.assert_allclose(result.utility_ucb_effective, [[1.0, 0.8, 1.0]])
+ np.testing.assert_array_equal(result.utility_ucb, result.utility_ucb_effective)
+ assert result.bound_policy == "clip_ucb"
+
+
+@pytest.mark.parametrize(
+ "bounds, policy, message",
+ [
+ (None, "clip_ucb", "requires explicit"),
+ ([(0.0, 1.0)], "clip_ucb", "one.*per objective"),
+ ([(1.0, 0.0), (None, None)], "clip_ucb", "must not exceed"),
+ ([(None, None), (None, None)], "clip_ucb", "at least one"),
+ (None, "invalid", "none.*clip_ucb"),
+ ],
+)
+def test_ucb_bound_policy_rejects_invalid_contract(bounds, policy, message):
+ with pytest.raises(ValueError, match=message):
+ apply_ucb_bound_policy(np.ones((2, 2)), bounds, policy)
+
+
+def test_zero_scores_remain_zero_while_log_scores_are_stabilized():
+ result = score_ucb_hvi_from_moments(
+ np.array([[0.1, 0.1]]),
+ np.zeros((1, 2)),
+ _observed_2d(),
+ np.zeros(2),
+ beta=0.0,
+ log_epsilon=1e-9,
+ )
+ assert result.base_score[0] == 0.0
+ assert result.base_log_score[0] == pytest.approx(np.log(1e-9))
+
+
+def test_invalid_standard_deviation_and_shapes_fail():
+ with pytest.raises(ValueError, match="negative"):
+ score_ucb_hvi_from_moments(
+ np.ones((1, 2)),
+ np.array([[-1.0, 0.0]]),
+ _observed_2d(),
+ np.zeros(2),
+ beta=1.0,
+ )
+ with pytest.raises(ValueError, match="same shape"):
+ score_ucb_hvi_from_moments(
+ np.ones((1, 2)),
+ np.ones((2, 2)),
+ _observed_2d(),
+ np.zeros(2),
+ beta=1.0,
+ )
+
+
+def _proposal_pool():
+ X = np.array([[0.1], [0.3], [0.5], [0.7], [0.9]])
+ return CandidatePool(
+ grid_indices=np.arange(5)[:, None],
+ X_phys=X.copy(),
+ X_norm=X.copy(),
+ seed=9,
+ draws=5,
+ rejected_duplicate=0,
+ rejected_avoid=0,
+ rejected_constraint=0,
+ )
+
+
+def _static_ucb_result(scores):
+ scores = np.asarray(scores, dtype=float)
+ return UCBHVIScoreResult(
+ base_score=scores,
+ base_log_score=np.log(np.maximum(scores, 1e-12)),
+ utility_mean=np.column_stack([scores, scores]),
+ utility_std=np.zeros((scores.size, 2)),
+ utility_ucb=np.column_stack([scores, scores]),
+ baseline_hypervolume=0.1,
+ pareto_utility=np.array([[0.5, 0.5]]),
+ reference_point_utility=np.zeros(2),
+ beta=1.0,
+ kappa=1.0,
+ mc_samples=8,
+ seed=4,
+ observation_noise=False,
+ objective_contract_version="TEST_ONLY-v1",
+ )
+
+
+def test_ucb_proposal_selects_only_positive_hvi_and_exact_q(monkeypatch):
+ monkeypatch.setattr(
+ module,
+ "score_ucb_hvi_pool",
+ lambda *args, **kwargs: _static_ucb_result([0.0, 0.8, 0.7, 0.0, 0.6]),
+ )
+ proposal = propose_ucb_hvi_batch(
+ _proposal_pool(),
+ torch.nn.Linear(1, 1).double(),
+ np.ones((2, 2)),
+ object(),
+ np.zeros(2),
+ q=2,
+ beta=1.0,
+ local_penalization_config=LocalPenalizationConfig(
+ radius=0.15, min_batch_distance=0.1
+ ),
+ positive_score_tolerance=1e-6,
+ )
+ assert proposal.selection.selected_pool_indices.size == 2
+ assert np.all(
+ proposal.scoring.base_score[proposal.selection.selected_pool_indices] > 0
+ )
+ assert proposal.metadata["pool_seed"] == 9
+ assert proposal.metadata["objective_contract_version"] == "TEST_ONLY-v1"
+ assert proposal.metadata["beta"] == 1.0
+
+
+def test_ucb_proposal_refuses_to_fill_with_zero_hvi(monkeypatch):
+ monkeypatch.setattr(
+ module,
+ "score_ucb_hvi_pool",
+ lambda *args, **kwargs: _static_ucb_result([0.0, 0.8, 0.0, 0.0, 0.0]),
+ )
+ with pytest.raises(UndersizedBatchError):
+ propose_ucb_hvi_batch(
+ _proposal_pool(),
+ torch.nn.Linear(1, 1).double(),
+ np.ones((2, 2)),
+ object(),
+ np.zeros(2),
+ q=2,
+ beta=1.0,
+ local_penalization_config=LocalPenalizationConfig(
+ radius=0.15, min_batch_distance=0
+ ),
+ )
diff --git a/tests/test_workbook_io.py b/tests/test_workbook_io.py
new file mode 100644
index 0000000..36b94e0
--- /dev/null
+++ b/tests/test_workbook_io.py
@@ -0,0 +1,251 @@
+from __future__ import annotations
+
+import shutil
+
+import pandas as pd
+import pytest
+from openpyxl import load_workbook
+
+from mobo_kit.campaign import (
+ load_campaign_config,
+ measurement_entry_columns,
+ model_source_columns,
+ objective_names,
+)
+from mobo_kit.workbook_io import (
+ candidate_workbook_path,
+ CandidateSheetError,
+ detect_round,
+ read_campaign_workbook,
+ sheet_name_for_round,
+ write_candidate_sheet,
+)
+
+CONFIG_PATH = "configs/campaign_d2d_perovskite.yaml"
+SOURCE = "local_inputs/Summary Table.xlsx"
+
+pytestmark = pytest.mark.skipif(
+ not __import__("pathlib").Path(SOURCE).exists(),
+ reason="requires the ignored private campaign workbook",
+)
+
+
+@pytest.fixture(scope="module")
+def config() -> dict:
+ return load_campaign_config(CONFIG_PATH)
+
+
+@pytest.fixture
+def workbook(tmp_path):
+ destination = tmp_path / "Summary Table.xlsx"
+ shutil.copy2(SOURCE, destination)
+ return destination
+
+
+def _conditions(config: dict, n: int = 5) -> pd.DataFrame:
+ names = [item["name"] for item in config["inputs"]]
+ rows = [
+ [float(item["start"]) + i * float(item["step"]) for item in config["inputs"]]
+ for i in range(n)
+ ]
+ return pd.DataFrame(rows, columns=names)
+
+
+def test_reads_inputs_and_computes_one_value_per_objective(workbook, config) -> None:
+ contents = read_campaign_workbook(workbook, config)
+ assert contents.n_rows == 15
+ assert list(contents.inputs.columns) == [i["name"] for i in config["inputs"]]
+ assert list(contents.model_values.columns) == list(objective_names(config))
+ # thickness must arrive in nanometres, not as its score
+ assert contents.model_values["thickness"].max() > 100.0
+ assert contents.model_values.notna().all().all()
+
+
+def test_computed_values_agree_with_the_workbook_within_tolerance(
+ workbook, config
+) -> None:
+ """The recomputation is not a different quantity: on the R0 rows it reproduces
+ the stored cells to floating-point noise, and thickness only to half a
+ nanometre because `Thickness (avg)` is ROUND(mean(T1..T4))."""
+ contents = read_campaign_workbook(workbook, config)
+ stored = contents.workbook_values
+ assert (
+ (contents.model_values["uniformity"] - stored["Uniformity score"]).abs().max()
+ < 1e-12
+ )
+ assert (
+ (contents.model_values["optoelectronic"] - stored["Optoelectronic score"])
+ .abs()
+ .max()
+ < 1e-12
+ )
+ thickness_gap = (
+ (contents.model_values["thickness"] - stored["Thickness (avg)"]).abs().max()
+ )
+ assert thickness_gap <= 0.5
+ # and it is genuinely unrounded, or the gap would be zero
+ assert thickness_gap > 0.0
+
+
+def test_the_r0_rows_produce_no_errors_and_flag_the_disagreeing_films(
+ workbook, config
+) -> None:
+ contents = read_campaign_workbook(workbook, config)
+ assert contents.errors == ()
+ disagreeing = {
+ finding.sample_id
+ for finding in contents.warnings
+ if finding.code == "readings_disagree"
+ }
+ # samples 8, 12 and 15 hold thickness readings that split into two clusters
+ assert disagreeing == {8, 12, 15}
+ excluded = {
+ finding.sample_id
+ for finding in contents.findings
+ if finding.code == "reading_excluded"
+ }
+ assert excluded == {4, 14}
+
+
+def test_thickness_records_how_many_readings_each_row_used(workbook, config) -> None:
+ """Two readings and four readings do not carry the same weight; Phase 4 needs
+ the count to turn a spread into an observation variance."""
+ contents = read_campaign_workbook(workbook, config)
+ counts = contents.inputs_used["thickness"]
+ assert counts.min() == 2 and counts.max() == 4
+ assert counts.value_counts().to_dict() == {2: 9, 3: 3, 4: 3}
+
+
+def test_stops_at_the_first_blank_sample_number(workbook, config) -> None:
+ """Rows below the data block are notes, not observations."""
+ contents = read_campaign_workbook(workbook, config)
+ assert contents.sample_ids == tuple(range(1, 16))
+
+
+def test_candidate_sheet_asks_for_raw_measurements_not_derived_scores(
+ workbook, config
+) -> None:
+ """The objectives are computed now, so the sheet must collect what they are
+ computed from. Offering a `Thickness (avg)` cell would invite someone to fill
+ in a value that nothing reads."""
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ sheet = load_workbook(candidate_workbook_path(workbook, "R1"))[
+ sheet_name_for_round("R1")
+ ]
+ headers = [c.value for c in sheet[1]]
+ required, optional = measurement_entry_columns(config)
+ for column in (*required, *optional):
+ assert column in headers
+ assert "T1" in headers and "T4" in headers and "T anom" in headers
+ for derived in model_source_columns(config):
+ assert derived not in headers
+
+
+def test_writing_leaves_the_source_byte_identical(workbook, config) -> None:
+ """openpyxl discards cached formula values on save, and Uniformity score is a
+ formula column. Writing beside the workbook makes that impossible rather than
+ merely unlikely."""
+ import hashlib
+
+ before = hashlib.sha256(workbook.read_bytes()).hexdigest()
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ assert out != workbook
+ assert out.exists()
+ assert hashlib.sha256(workbook.read_bytes()).hexdigest() == before
+
+
+def test_formula_columns_survive_because_the_source_is_not_rewritten(
+ workbook, config
+) -> None:
+ """The regression this design exists to prevent."""
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ contents = read_campaign_workbook(workbook, config)
+ # Uniformity score is =L*N*O; a rewritten workbook reads it as NaN, which
+ # would now surface as a cross_check_empty warning rather than as bad training
+ # data -- but the invariant worth holding is still that it survives
+ assert contents.workbook_values["Uniformity score"].notna().all()
+ assert contents.workbook_values["Uniformity score"].max() > 0.0
+ assert [f for f in contents.warnings if f.code == "cross_check_empty"] == []
+
+
+def test_three_replicate_rows_per_condition(workbook, config) -> None:
+ write_candidate_sheet(
+ workbook, config, _conditions(config, 5), round_name="R1", replicates=3
+ )
+ sheet = load_workbook(candidate_workbook_path(workbook, "R1"))[
+ sheet_name_for_round("R1")
+ ]
+ rows = [r for r in sheet.iter_rows(min_row=2, values_only=True) if r[0]]
+ assert len(rows) == 15
+ assert len({r[1] for r in rows}) == 5
+
+
+def test_refuses_to_overwrite_an_existing_sheet(workbook, config) -> None:
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ with pytest.raises(CandidateSheetError, match="already exists"):
+ write_candidate_sheet(
+ workbook, config, _conditions(config), round_name="R1", make_backup=False
+ )
+
+
+def test_round_detection_walks_r1_then_r2(workbook, config) -> None:
+ assert detect_round(workbook, config).next_round == "R1"
+ write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ state = detect_round(workbook, config)
+ assert state.next_round is None
+ assert "no results have been entered" in state.reason
+
+
+def test_partially_scored_sheet_is_refused_with_a_readable_message(
+ workbook, config
+) -> None:
+ """Fail closed: guessing at a half-filled sheet is how a round gets built on
+ data the experimentalist had not finished entering."""
+ out = write_candidate_sheet(workbook, config, _conditions(config), round_name="R1")
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [c.value for c in sheet[1]]
+ required, _ = measurement_entry_columns(config)
+ for column in (*required, "T1"):
+ sheet.cell(row=2, column=headers.index(column) + 1).value = 1.0
+ book.save(out)
+
+ state = detect_round(workbook, config)
+ assert state.next_round is None
+ assert state.scored_rows == 1 and state.total_rows == 15
+ assert "partly filled in" in state.reason
+ assert "1 of 15" in state.reason
+
+
+def test_a_row_with_two_thickness_readings_counts_as_complete(workbook, config) -> None:
+ """Nine of the fifteen R0 rows have only T1 and T2. Demanding all four would
+ hold a finished round hostage to measurements nobody intended to take."""
+ out = write_candidate_sheet(workbook, config, _conditions(config, 1), round_name="R1")
+ book = load_workbook(out)
+ sheet = book[sheet_name_for_round("R1")]
+ headers = [c.value for c in sheet[1]]
+ required, _ = measurement_entry_columns(config)
+ for row in (2, 3, 4):
+ for column in (*required, "T1", "T2"):
+ sheet.cell(row=row, column=headers.index(column) + 1).value = 1.0
+ book.save(out)
+
+ # accepting R1 as complete is what advances the campaign to R2; a stricter
+ # rule would leave it stuck reporting "partly filled in" forever
+ state = detect_round(workbook, config)
+ assert state.next_round == "R2"
+
+
+def test_missing_source_sheet_is_a_plain_sentence(tmp_path, config) -> None:
+ from openpyxl import Workbook
+
+ path = tmp_path / "wrong.xlsx"
+ book = Workbook()
+ book.active.title = "Renamed"
+ book.save(path)
+ # The message must name the sheet the CONFIG asked for, the key that decides
+ # it, and what the workbook actually has. "Rename it back" was the old advice
+ # and stopped being right once the sheet became configuration: the likelier
+ # cause is now a workbook belonging to a different campaign.
+ with pytest.raises(CandidateSheetError, match="campaign.source_sheet"):
+ read_campaign_workbook(path, config)
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diff --git a/unsorted/Code/ReadMe.txt b/unsorted/Code/ReadMe.txt
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+++ /dev/null
@@ -1,1577 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Check the pervoskite experimental data produced on Sep 22, 2020\n",
- "\n",
- "- Experiments are prepared by Nick Rolston and Thomas Colburn (Stanfrod University) \n",
- "- Jupyter Notebook is prepared by Zhe Liu (Massachusetts Insititute of Technology)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import pandas as pd\n",
- "import emukit\n",
- "import GPy\n",
- "import sklearn\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Index(['ML Condition', 'Temp [degC]', 'speed [mm/s]', 'sprayFL [uL/min]',\n",
- " 'plamsaH [cm]', 'gasFL [L/min]', 'plasmaDC [%]', ' Success or Fail',\n",
- " 'Unnamed: 8'],\n",
- " dtype='object')\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "
@@ -8,280 +11,297 @@ by Ethan Schwartz, Daniel Abdoue, Nicky Evans, and Tonio Buonassisi