Confidence Dataset - #93
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📝 WalkthroughWalkthroughAdded ChangesConfidence dataset
Estimated code review effort: 3 (Moderate) | ~20 minutes Mergeability Score: 🟡 Moderate · up to The dataset can emit candidate nodes without matching supervision, causing tensor-shape mismatches during training, and it currently permits unsafe candidate-file deserialization plus invalid parameter values. The PR is not merge-ready until the supervision contract and validation/loading safeguards are addressed. Sequence Diagram(s)sequenceDiagram
participant Caller
participant ConfidenceDataset
participant ProteinWaterDataset
participant CandidateFiles
Caller->>ConfidenceDataset: Request item by index
ConfidenceDataset->>ProteinWaterDataset: Load flow item
ConfidenceDataset->>CandidateFiles: Load candidate positions
ConfidenceDataset->>ConfidenceDataset: Compute targets and labels
ConfidenceDataset-->>Caller: Return HeteroData with candidates and metadata
🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
📝 Generate docstrings
🧪 Generate unit tests (beta)
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Pull request overview
Adds a training-time dataset wrapper for the confidence model that reuses the existing flow dataset/cache format and per-structure candidate .pt files, computing confidence targets on-the-fly rather than introducing a new cache schema.
Changes:
- Introduces
ConfidenceDatasetinsrc/confidence_dataset.pyto join cached candidate positions with flow dataset items and compute soft/hard targets + labels. - Adds unit tests covering candidate joining, missing-file handling, subsampling behavior, and oxygen feature encoding.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.
| File | Description |
|---|---|
src/confidence_dataset.py |
New ConfidenceDataset implementation that loads candidate clouds and computes confidence targets/labels dynamically. |
tests/test_confidence_dataset.py |
New unit tests validating candidate join behavior, target computation, and utility helpers like _oxygen_features. |
Suppressed comments (1)
tests/test_confidence_dataset.py:77
- Hard-coding oxygen’s one-hot index as
2makes this test brittle ifELEMENT_VOCABordering changes. UseELEM_IDX["O"](imported fromsrc.constants) instead so the test validates behavior rather than a specific vocab layout.
assert (sample["water"].x[:, 2] == 1.0).all() # oxygen one-hot
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Actionable comments posted: 1
🔇 Additional comments (2)
src/confidence_dataset.py (2)
177-179: 🔒 Security & Privacy | 🟠 Major | ⚡ Quick win
⚠️ Unverified finding
Sandbox verification was unavailable.Use tensor-only deserialization for candidate files.
weights_only=Falsepermits pickle execution when an attacker can modify a candidate file incandidate_dir. The documented candidate format contains onlycandidate_pos, and the tests save only tensors. Setweights_only=Trueafter confirming the cache producer does not serialize custom objects.Proposed fix
candidate_pos: Tensor = torch.load( - self._paths[idx], map_location="cpu", weights_only=False + self._paths[idx], map_location="cpu", weights_only=True )["candidate_pos"].float()
160-162: 🗄️ Data Integrity & Integration | 🟠 Major | 🏗️ Heavy lift
⚠️ Unverified finding
Sandbox verification was unavailable.Do not return candidate nodes without candidate-length supervision. When
candidate_posis non-empty andgt_posis empty, the dataset returnsnum_nodes == Ncbut zero-lengthtarget_confidence,label_1A, andgt_index. A model output forNcnodes cannot align with these tensors. Define and enforce the no-GT policy before the item reaches batching. The current test confirms that the dataset emits an invalid training item.
src/confidence_dataset.py#L160-L162: Filter entries with zero GT waters before they become selectable dataset items, or define candidate-length negative targets and a valid sentinel-index contract through every consumer.tests/test_confidence_dataset.py#L199-L209: Replace the assertion of mismatched tensor lengths with a test for the selected no-GT exclusion or valid-label policy.
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Inline comments:
In `@src/confidence_dataset.py`:
- Around line 100-104: Update the constructor that assigns r_in, r_out,
accept_radius, and max_candidates to validate parameters immediately: require
r_out to be greater than r_in, reject negative accept_radius, and reject
negative max_candidates before any dataset entries are indexed or sampled.
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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Configuration used: defaults
Review profile: CHILL
Plan: Pro Plus
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📒 Files selected for processing (2)
src/confidence_dataset.pytests/test_confidence_dataset.py
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Pull request overview
Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.
Suppressed comments (2)
src/confidence_dataset.py:136
- This FileNotFoundError also points to
scripts/cache_candidates.py, which does not exist in this repo, making the error message misleading. Consider making the guidance generic (or updating it to the actual candidate-generation entrypoint).
raise FileNotFoundError(
f"{len(missing)} structures lack a candidate file under "
f"{self.candidate_dir} (first: {missing[0]}). Generate them "
"with scripts/cache_candidates.py or pass strict=False."
)
src/confidence_dataset.py:99
- The FileNotFoundError message references
scripts/cache_candidates.py, but that script is not present in this repository (noscripts/cache_candidates.py). This will send users on a dead-end whencandidate_diris missing; also consider validating that the path is a directory (not just exists).
This issue also appears on line 132 of the same file.
self.candidate_dir = Path(candidate_dir)
if not self.candidate_dir.exists():
raise FileNotFoundError(
f"Candidate directory not found: {self.candidate_dir}. "
"Build it with scripts/cache_candidates.py first."
)
| candidate_pos: Tensor = torch.load( | ||
| self._paths[idx], map_location="cpu", weights_only=False | ||
| )["candidate_pos"].float() |
A wrapper dataset that builds a dataset for the confidence model using the sampled cache created from the trained flow generator + tests. Built on top of confidence model PR.
Summary by CodeRabbit
New Features
Tests