From fb54114554940cdf9276d030d19e15600d672a68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Wed, 9 Sep 2026 15:01:32 +0200 Subject: [PATCH 1/2] Align national UC counts with paid source-window targets --- .../882-uc-paid-target-contract.fixed.md | 1 + .../uk-uc-882/source-target-diff.json | 645 ++++++++++++++++++ docs/uk-chronicle-feed-repin.md | 35 +- docs/uk-uc-paid-target-contract.md | 108 +++ .../src/microcosm/build/ledger_targets.py | 252 ++++++- .../build/target_reference_authoring.py | 19 +- .../microcosm/build/uk/country_package.json | 5 + .../build/uk/national_chronicle_feed.json | 16 + .../build/uk/target_reference_membership.json | 112 ++- .../microcosm/build/uk/target_references.json | 456 +++++++++++-- .../build/uk/uk_population_targets.json | 519 ++++++++++++-- .../build/uk_runtime/measure_simulation.py | 111 ++- .../uk_runtime/national_chronicle_feed.py | 103 +++ .../build/uk_runtime/uc_source_periods.py | 55 +- .../uk_runtime/uc_target_measurements.py | 120 ++++ .../uk_target_reference_feed_rows.jsonl | 120 ++++ .../tests/test_country_spec.py | 6 +- .../tests/test_ledger_targets.py | 302 ++++++++ .../tests/test_spec_engine_country_bundles.py | 2 +- .../tests/test_uk_calibration_seam_driver.py | 66 +- .../tests/test_uk_ledger_targets.py | 8 + .../tests/test_uk_national_chronicle_feed.py | 59 ++ .../tests/test_uk_population_targets.py | 137 +++- .../tests/test_uk_target_references.py | 318 +++++---- .../tests/test_uk_uc_family.py | 285 +++++++- .../tests/test_uk_uc_source_periods.py | 263 ++++++- tools/calibrate_uk_national_dataset.py | 31 +- tools/generate_uk_target_references.py | 23 +- 28 files changed, 3801 insertions(+), 376 deletions(-) create mode 100644 changelog.d/882-uc-paid-target-contract.fixed.md create mode 100644 docs/evidence/uk-uc-882/source-target-diff.json create mode 100644 docs/uk-uc-paid-target-contract.md create mode 100644 packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json create mode 100644 packages/microcosm-build/src/microcosm/build/uk_runtime/national_chronicle_feed.py create mode 100644 packages/microcosm-build/tests/test_uk_national_chronicle_feed.py diff --git a/changelog.d/882-uc-paid-target-contract.fixed.md b/changelog.d/882-uc-paid-target-contract.fixed.md new file mode 100644 index 000000000..5ed68e8c4 --- /dev/null +++ b/changelog.d/882-uc-paid-target-contract.fixed.md @@ -0,0 +1 @@ +Align ten national Universal Credit count targets with paid claims over all twelve months of 2025, using Chronicle's payment-status and child-entitlement tables. Add explicit, fully checked monthly source-window operations for calendar and fiscal comparisons, and classify model families using the standard allowance while retaining structural relationship measures. Record the remaining annual-payment, child-definition and open/nil-claim approximations. Pin the national Chronicle artifact independently from the existing local target surface, checking both its facts and manifest hashes before calibration. diff --git a/docs/evidence/uk-uc-882/source-target-diff.json b/docs/evidence/uk-uc-882/source-target-diff.json new file mode 100644 index 000000000..850b02cd8 --- /dev/null +++ b/docs/evidence/uk-uc-882/source-target-diff.json @@ -0,0 +1,645 @@ +{ + "purpose": "Public source-only compilation evidence; no population or fit results.", + "date": "2026-09-09", + "model_period": 2025, + "base_microcosm_commit": "a19582bd918829858a117f0d6aafa2b994e856ec", + "national_feed_pin": { + "version": 1, + "country": "uk", + "scope": "national_calibration", + "source_repo": "PolicyEngine/chronicle", + "source_commit": "ec7169b5db40b9f54117c80f70f14efc1dd0fedd", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "consumer_fact_schema_versions": [ + 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{ + "paid_publisher_entitlement_total": 1199.5833333333333, + "paid_and_child_entitlement_yes": 1199.5833333333333, + "nil_publisher_entitlement_total": 56.833333333333336 + }, + "Not available prior to April 2019": { + "paid_publisher_entitlement_total": 0.0, + "paid_and_child_entitlement_yes": 0.0, + "nil_publisher_entitlement_total": 0.0 + } + }, + "publisher_total_minus_entitlement_detail_sum": { + "cells": 192, + "nonzero_cells": 106, + "maximum_absolute_difference": 8, + "mean_difference": 0.020833333333333332, + "sum_difference": 4 + }, + "family_derived_total_minus_children_total_sum": { + "paid": { + "cells": 12, + "nonzero_cells": 12, + "maximum_absolute_difference": 8, + "mean_difference": 1.5833333333333333, + "sum_difference": 19 + }, + "nil": { + "cells": 12, + "nonzero_cells": 12, + "maximum_absolute_difference": 7, + "mean_difference": -1.25, + "sum_difference": -15 + } + }, + "paid_derived_grand_total_from_family": 6401649.416666666, + "paid_derived_grand_total_from_children": 6401647.833333333, + "paid_child_entitled_derived_total_from_family": 2823411.5833333335, + "paid_child_entitled_derived_total_from_children": 2823407.75 + } + }, + "limitations": [ + "All values are source facts or explicitly declared source arithmetic, not model outcomes.", + "CY and FY new targets average their exact twelve monthly observations. Monthly counts do not represent unique annual claimants.", + "FY 2025/26 includes provisional March 2026; the model remains year 2025.", + "The 101 other existing monthly UC references keep their original observation windows.", + "Family/headline paid counts sum all declared perturbed detail cells within month; child-count paid counts preserve publisher entitlement Totals.", + "Published unknown family cells remain in the headline; paid-and-child-entitled joints remain outside the active roster.", + "This source comparison does not establish model support, fit, claim history, own-child equivalence or population certification." + ] +} diff --git a/docs/uk-chronicle-feed-repin.md b/docs/uk-chronicle-feed-repin.md index 689d8072c..e02652661 100644 --- a/docs/uk-chronicle-feed-repin.md +++ b/docs/uk-chronicle-feed-repin.md @@ -1,26 +1,33 @@ # Re-pin the UK Chronicle consumer feed -The national and local target surfaces must always use one reviewed Chronicle -consumer artifact. Rebuild the UK bundle and consumer artifact in -`PolicyEngine/arch-data`, then copy the resulting `consumer_facts.jsonl` and -`manifest.json` into `.codex-work/consumer_facts_uk.jsonl` and -`.codex-work/consumer_facts_uk_manifest.json`. Do not commit either -file. +The national and local target surfaces have independently reviewed Chronicle +artifact pins. National calibration reads `uk/national_chronicle_feed.json`; +local calibration and validation retain their existing pins. A national +update does not authorize changes to local census membership or values. + +Rebuild the complete UK bundle and consumer artifact in +`PolicyEngine/chronicle` at the declared commit. Keep the resulting +`consumer_facts.jsonl` and `manifest.json` together; do not commit either file. Verify both SHA-256 digests and the manifest's `facts_sha256`, row count, and -schema version. Update `_LEDGER_FACT_FEED_PIN` in +schema version. For a national update, update +`uk/national_chronicle_feed.json` and regenerate the national references and +membership with `tools/generate_uk_target_references.py`. Verify the complete +compiled target diff, including targets outside the intended policy area. +The hermetic national regeneration test accepts `CHRONICLE_UK_FACTS`. + +For a separately reviewed local update, update `_LEDGER_FACT_FEED_PIN` in `uk_runtime/local_target_census.py`, the local validation-level pin, and their tests together. Regenerate the local census with `uv run --no-sync python tools/census_uk_local_targets.py`. -Regenerate the national and local reference surfaces from the same fact file -with `tools/generate_uk_target_references.py` and +Regenerate the local reference surface with `tools/generate_uk_local_target_references.py`, then rebuild the signed compile -parity receipts with +parity receipts affected by that update with `tools/build_uk_ledger_compile_parity_signed_differences.py`. The hermetic -regeneration test accepts either the default `.codex-work` files or a -`CHRONICLE_UK_FACTS` override and skips only when neither is present. +local regeneration test accepts either the default `.codex-work` files or a +`CHRONICLE_UK_LOCAL_FACTS` override and skips only when neither is present. -The calibration runner refuses a feed whose facts digest differs from the -committed pin. `--allow-unpinned-feed` is an explicit diagnostic override and +The national calibration runner refuses a feed whose facts or manifest digest +differs from its committed pin. `--allow-unpinned-feed` is an explicit diagnostic override and is recorded in the run manifest; it is not a re-pin procedure. diff --git a/docs/uk-uc-paid-target-contract.md b/docs/uk-uc-paid-target-contract.md new file mode 100644 index 000000000..d9209ec5a --- /dev/null +++ b/docs/uk-uc-paid-target-contract.md @@ -0,0 +1,108 @@ +# UK UC paid-claim target contract + +The ten national UC count targets now compare positive model UC with +administrative **paid claims**. Previously their source rows covered all claims, +including open claims with no payment. This repairs the comparison universe; +it does not establish an exact monthly claim history or guarantee a smaller +calibration residual. Related work remains in [#882](https://github.com/PolicyEngine/microcosm/issues/882). + +## Source and measurement bindings + +[Chronicle #252](https://github.com/PolicyEngine/chronicle/pull/252) supplies +payment indicator × child entitlement crossed separately with family type and +reported child count. The pinned complete UK artifact comes from +`ec7169b5db40b9f54117c80f70f14efc1dd0fedd`: all 100 source suites pass and its +131,450 facts validate. The packaged `uk/national_chronicle_feed.json` records +the facts, manifest and schema hashes. National calibration checks both artifact +digests before compiling targets; local surfaces retain their separate pins. + +| Target group | Source operation | Model counterpart | +|---|---|---| +| Paid headline | Within each month, sum five family categories × both child-entitlement states, with payment indicator Yes; average the 12 monthly sums | GB benefit units with annual `universal_credit > 0` | +| Four paid family counts | Within each month, sum entitlement No and Yes for the named family and payment Yes; average 12 months | Positive-UC benefit units classified by the model standard allowance and the reported-child proxy | +| Five paid child-count categories | Average each publisher `total_benefit_units` series with payment Yes and `child_entitlement: all` | Positive-UC benefit units by the existing declared-under-20 child-count proxy | + +Unknown family records contribute to the headline. A publisher zero must be +present; it cannot be replaced by an absent cell. The family cube has no +published grand Total, so its headline is explicitly derived. The child-count +cube has publisher Totals over entitlement; these are used directly rather +than reconstructed from independently perturbed detail cells. + +The administrative-family measurement follows DWP's standard-allowance rule: +an allowance above the maximum single rate implies a couple. Zero allowance +returns UNKNOWN. It preserves source claimant roles and the structural family +measurement. It does not recover ineligible-partner cases that the model cannot +identify. The four existing family payment-band bindings use the same new family +measurement. [DWP family definition](https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html) + +`uc_child_element > 0` is a separate child-entitlement diagnostic. It does not +turn reported children into own qualifying children. No family × child-count +joint is inferred from the two published marginal crosses. Thirty-nine +diagnostic masks expose paid, entitlement, family and child-definition support +without adding objective rows or changing target-family coefficients. + +## Years and source coverage + +The shipped count targets use January–December 2025. Model/calibration year 2025 +is distinct from the raw FRS FY2024/25 vintage: source assembly uses period 2024, +configured monetary inputs extend to 2025, and retained ages are not universally +advanced. UK government model parameters for 2025 generally represent FY2025/26. +An explicit April 2025–March 2026 sensitivity therefore tests a different source +window against the same annual model state. It is not a model-year change. + +`monthly_window_average` and `monthly_window_sum_average` require the paired +`source_window` period policy, explicit ordered unique months, and one fact for +every declared month × operand. They reject duplicate, missing, mixed-series or +mixed-publication cells. A sum divides by the **month count**, not the number of +cells. Receipts retain member identities, publication identity and the actual +denominator. Existing period policies retain their calendar/future-period guards. + +Only these ten count references acquire 12 months. The other 101 monthly UC +references retain their declared nine-month coverage. TCL remains an April +observation, and the Scotland infant target keeps its separate observation +contract. FY2025/26 includes provisional March 2026 in the pinned source; the +calendar-2025 observations are revised. + +## Reconciliation and complete target diff + +All 2,584 new joint facts match the archived publisher cubes cell by cell. +All 128,672 shared old/new feed records are byte-identical; the 45 replaced +April–December family records retain their old values under expanded-history +identities. Holding old references fixed, the source update changes **zero** +compiled target values. The repaired contract changes exactly ten values, +with 415 compiled references and the same 366 active rows after existing +exclusions. No non-UC value, target roster or coefficient changes. +The [source-only diff receipt](evidence/uk-uc-882/source-target-diff.json) +records all ten old/CY/FY values, complete compilation counts, source hashes +and monthly reconciliation. + +In calendar 2025 the family-derived paid headline is 6,197,311, including +1,168.92 unknown-family claims. Summed child-count publisher Totals differ from +that headline by 1.17 claims on average. The largest monthly within-category +publisher Total minus entitlement-detail sum is eight claims. These observed +disclosure residuals are reported without rewriting source values; they are +not optimizer tolerances. + +## Remaining comparison limits + +Positive annual UC is a proxy for a mean monthly paid population, not a count +of distinct annual claimants. A zero model award, a reported receipt flag or +take-up probability does not identify an administrative open nil-payment claim. +Those source totals remain separate diagnostics. [DWP payment indicator](https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Payment%20Indicator.html) + +Reported children, own qualifying children/young people, and a positive child +element remain distinct. The retained age inputs and source date precision do +not establish exact birthdays, April claim status or TCL exception histories. +The existing TCL receipt states its annual-positive-award and birth-year proxies. + +Annual recurring model UC divided by twelve does not reconstruct DWP monthly +cash payments, including advances in their issue month. Empty upper payment +bands therefore require an amount-concept and component audit before any +proposal to generate support. [DWP amount definition](https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20%28bands%29.html) + +Public synthetic tests cover grid completeness, literal zeros, mixed identities, +cross-year and direct-key guards, allowance precision, claimant/family roles, +own-unit child definitions and diagnostic partitions. Hash-pinned full-feed +regeneration checks cover national references and unchanged local membership. +Population fit and original-source concentration are separate development +diagnostics; this contract change does not close #882. diff --git a/packages/microcosm-build/src/microcosm/build/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/ledger_targets.py index 14e7c38cd..80edd5da6 100644 --- a/packages/microcosm-build/src/microcosm/build/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/ledger_targets.py @@ -12,6 +12,7 @@ import hashlib import json import math +import re from collections.abc import Iterable, Mapping from dataclasses import asdict, dataclass, field, replace from pathlib import Path @@ -21,7 +22,12 @@ SUPPORTED_LEDGER_AGGREGATIONS = frozenset(("sum",)) ALLOWED_ASSERTION_POLICIES = frozenset(("observed_only", "allow_source_projection")) -ALLOWED_PERIOD_MATCH_POLICIES = frozenset(("latest_not_after", "exact")) +ALLOWED_PERIOD_MATCH_POLICIES = frozenset( + ("latest_not_after", "exact", "source_window") +) +MONTHLY_WINDOW_OPERATIONS = frozenset( + ("monthly_window_average", "monthly_window_sum_average") +) ALLOWED_VALUE_OPERATIONS = frozenset( ( "identity", @@ -30,10 +36,18 @@ "calendar_year_average", "latest_plateau", "count_x_mean", + *MONTHLY_WINDOW_OPERATIONS, ) ) MULTI_FACT_VALUE_OPERATIONS = frozenset( - ("sum", "difference", "calendar_year_average", "latest_plateau", "count_x_mean") + ( + "sum", + "difference", + "calendar_year_average", + "latest_plateau", + "count_x_mean", + *MONTHLY_WINDOW_OPERATIONS, + ) ) EXACT_PERIOD_VALUE_OPERATIONS = frozenset( ("identity", "sum", "difference", "count_x_mean") @@ -149,6 +163,18 @@ def __post_init__(self) -> None: f"period_match_policy {self.period_match_policy!r}; expected " f"one of {sorted(ALLOWED_PERIOD_MATCH_POLICIES)!r}." ) + is_window = self.value_operation in MONTHLY_WINDOW_OPERATIONS + if is_window != (self.period_match_policy == "source_window"): + raise ValueError( + "A monthly window operation requires the paired source_window period policy." + ) + if is_window: + if self.period is None: + raise ValueError( + "A monthly window requires an explicit model target period." + ) + _declared_source_months(self) + _monthly_window_operands(self) if self.period_match_policy == "exact" and self.period is None: raise ValueError( f"LedgerTargetReference {self.name!r}: period_match_policy=" @@ -597,6 +623,8 @@ def target_spec_from_ledger_reference( facts = fact if isinstance(fact, tuple) else (fact,) if not facts: raise ValueError(f"Ledger target reference {reference.name!r} has no facts.") + if reference.value_operation in MONTHLY_WINDOW_OPERATIONS: + facts = _resolve_monthly_window_reference_facts(reference, list(facts)) if len(facts) > 1 and reference.value_operation not in MULTI_FACT_VALUE_OPERATIONS: raise ValueError( f"Ledger target reference {reference.name!r}: multiple Ledger facts " @@ -609,7 +637,9 @@ def target_spec_from_ledger_reference( numeric_values.append(_numeric_fact_value(member, reference)) _validate_fact_aggregation(member, reference) - if reference.value_operation == "calendar_year_average": + if reference.value_operation in MONTHLY_WINDOW_OPERATIONS: + numeric_value = sum(numeric_values) / len(_declared_source_months(reference)) + elif reference.value_operation == "calendar_year_average": numeric_value = sum(numeric_values) / len(numeric_values) elif reference.value_operation == "latest_plateau": numeric_value = numeric_values[-1] @@ -630,6 +660,21 @@ def target_spec_from_ledger_reference( facts, operation=reference.value_operation, ) + publication_metadata = {} + if reference.value_operation in MONTHLY_WINDOW_OPERATIONS: + # The window guard has proved these identities common to every member. + publication_metadata = { + key: value + for key, value in { + "ledger_source_release_key": _str_at( + representative_fact, "source_release_key" + ), + "ledger_source_sha256": _str_at( + representative_fact, "source", "source_sha256" + ), + }.items() + if value + } if not reference.measure: raise ValueError( @@ -664,6 +709,7 @@ def target_spec_from_ledger_reference( fact_key=_fact_key(representative_fact), ), **_multi_fact_reference_metadata(facts), + **publication_metadata, "ledger_resolved_assertion": _fact_assertion(representative_fact), **_reference_metadata(reference), }, @@ -980,6 +1026,8 @@ def _resolve_reference_fact( if _fact_matches_selector(fact, reference.ledger_selector) ] eligible_matches = _eligible_selector_matches(reference, matches) + if reference.value_operation in MONTHLY_WINDOW_OPERATIONS: + return _resolve_monthly_window_reference_facts(reference, eligible_matches) if reference.value_operation == "sum" and eligible_matches: return _resolve_sum_reference_facts(reference, eligible_matches) if reference.value_operation == "difference" and eligible_matches: @@ -1121,6 +1169,181 @@ def _resolve_difference_reference_facts( return tuple(resolved) +def _declared_source_months(reference: LedgerTargetReference) -> tuple[str, ...]: + months = reference.ledger_selector.get("period_value") + if ( + reference.ledger_selector.get("period_type") != "month" + or not isinstance(months, list) + or not months + or not all( + isinstance(month, str) + and re.fullmatch(r"[0-9]{4}-(?:0[1-9]|1[0-2])", month) + for month in months + ) + or months != sorted(set(months)) + ): + raise ValueError( + "A monthly window requires period_type=month and explicit unique ordered YYYY-MM period_value months." + ) + return tuple(months) + + +def _monthly_window_operands( + reference: LedgerTargetReference, +) -> tuple[Mapping[str, object], ...]: + if reference.value_operation == "monthly_window_average": + if reference.value_operands: + raise ValueError( + "A single-series monthly window does not accept value_operands." + ) + return () + dimensions = [] + for operand in reference.value_operands: + if not isinstance(operand, Mapping) or set(operand) != {"dimension_values"}: + raise ValueError( + "A monthly window sum operand requires only dimension_values." + ) + values = operand["dimension_values"] + if ( + not isinstance(values, Mapping) + or not values + or any( + not isinstance(key, str) + or not key + or value is None + or isinstance(value, (Mapping, list, tuple, set)) + for key, value in values.items() + ) + ): + raise ValueError( + "A monthly window sum operand requires complete scalar dimension_values." + ) + dimensions.append(values) + if not dimensions or any(set(value) != set(dimensions[0]) for value in dimensions): + raise ValueError( + "A monthly window sum requires operands with the same complete dimension keys." + ) + encodings = [ + json.dumps(value, sort_keys=True, separators=(",", ":")) for value in dimensions + ] + if len(set(encodings)) != len(encodings): + raise ValueError("A monthly window sum requires disjoint operand tuples.") + return tuple(dimensions) + + +def _monthly_window_source_key( + fact: object, dimensions: Mapping[str, object] +) -> tuple[str, ...]: + """All window members must share a publication and statistical measure.""" + return ( + _source_name(fact), + _str_at(fact, "source_release_key"), + _str_at(fact, "source", "source_sha256"), + _str_at(fact, "source", "vintage"), + _str_at(fact, "source", "source_file"), + _str_at(fact, "source", "source_table") + or _str_at(fact, "observed_measure", "source_table"), + _str_at(fact, "observed_measure", "source_measure_id") + or _str_at(fact, "layout", "measure_id"), + _source_measure_concept(fact), + _str_at(fact, "observed_measure", "unit"), + json.dumps(_at(fact, "entity"), sort_keys=True), + json.dumps(_at(fact, "geography"), sort_keys=True), + _normalized_record_set_id(_str_at(fact, "layout", "record_set_id")), + _normalized_record_set_id(_str_at(fact, "layout", "record_set_spec_id")), + _str_at(fact, "layout", "groupby_dimension"), + _str_at(fact, "aggregation", "method"), + _domain(fact), + json.dumps( + [ + constraint + for constraint in _constraint_rows(fact) + if _str_at(constraint, "variable") not in dimensions + ], + sort_keys=True, + ), + ) + + +def _resolve_monthly_window_reference_facts( + reference: LedgerTargetReference, + matches: list[object], +) -> tuple[object, ...]: + """Require a complete month-by-cell grid before averaging monthly totals. + + The same guard runs after direct-key resolution, so identifiers cannot + bypass the explicit window, selector, or operand coverage contract. + """ + months = _declared_source_months(reference) + operands = _monthly_window_operands(reference) + cell_count = len(operands) or 1 + expected_count = len(months) * cell_count + prefix = f"Ledger target reference {reference.name!r}: monthly window" + if reference.expected_member_count not in (None, expected_count): + raise ValueError( + f"{prefix} expected_member_count disagrees with its declared month-by-cell grid." + ) + if len(matches) != expected_count or any( + not _fact_matches_selector(fact, reference.ledger_selector) for fact in matches + ): + raise ValueError( + f"{prefix} requires exactly {expected_count} selected facts, one per declared month and cell; resolved {len(matches)}." + ) + grid: dict[tuple[str, int], object] = {} + partitions: dict[int, set[tuple[str, ...]]] = {} + for fact in matches: + publication = _at(fact, "source_release_key") + raw_hash = _at(fact, "source", "source_sha256") + if any( + not isinstance(value, str) or not value.strip() + for value in (publication, raw_hash) + ): + raise ValueError( + f"{prefix} requires nonempty source_release_key and " + "source.source_sha256 on every member." + ) + if operands: + cells = [ + index + for index, values in enumerate(operands) + if set(_dimensions(fact)) == set(values) + and _dimension_values_match(fact, values) + ] + else: + cells = [0] + if len(cells) != 1: + raise ValueError( + f"{prefix} requires every fact to match exactly one complete operand tuple." + ) + cell = cells[0] + key = (_str_at(fact, "period", "value"), cell) + if key in grid: + raise ValueError(f"{prefix} contains a duplicate month/cell observation.") + grid[key] = fact + partitions.setdefault(cell, set()).add(_selector_period_invariant_key(fact)) + if any(len(series) != 1 for series in partitions.values()): + raise ValueError( + f"{prefix} matched multiple semantic series for a declared cell." + ) + dimension_cells = operands[0] if operands else {} + if ( + len({_monthly_window_source_key(fact, dimension_cells) for fact in matches}) + != 1 + ): + raise ValueError( + f"{prefix} cells must share source publication, measure, entity and geography." + ) + expected_grid = [(month, cell) for month in months for cell in range(cell_count)] + if set(grid) != set(expected_grid): + raise ValueError(f"{prefix} is missing a declared month/cell observation.") + member_keys = [_fact_key(fact) or _source_record_id(fact) for fact in matches] + if not all(member_keys) or len(set(member_keys)) != len(member_keys): + raise ValueError( + f"{prefix} requires distinct non-empty member fact identities." + ) + return tuple(grid[key] for key in expected_grid) + + def _resolve_calendar_year_average_reference_facts( reference: LedgerTargetReference, eligible_matches: list[object], @@ -1332,6 +1555,13 @@ def _eligible_selector_matches( matches: list[object], ) -> list[object]: target_period_key = _period_key_from_value(reference.period) + if reference.period_match_policy == "source_window": + return [ + fact + for fact in matches + if _fact_matches_selector(fact, reference.ledger_selector) + and _assertion_allowed(reference, fact) + ] if reference.period_match_policy == "exact": return [ fact @@ -1404,6 +1634,12 @@ def _validate_reference_period(fact: object, reference: LedgerTargetReference) - binds the projected fact to the period it estimates. """ + if reference.period_match_policy == "source_window": + if _str_at(fact, "period", "type") != "month" or _str_at( + fact, "period", "value" + ) not in _declared_source_months(reference): + raise ValueError("Resolved fact lies outside the declared monthly window.") + return if reference.period_match_policy != "exact": if not _not_after_target_period( _period_key(fact), _period_key_from_value(reference.period) @@ -1769,6 +2005,16 @@ def _reference_metadata(reference: LedgerTargetReference) -> dict[str, str]: metadata["ledger_value_operation"] = reference.value_operation metadata["ledger_assertion_policy"] = reference.assertion_policy metadata["ledger_period_match_policy"] = reference.period_match_policy + if reference.value_operation in MONTHLY_WINDOW_OPERATIONS: + months = _declared_source_months(reference) + metadata["ledger_source_months"] = json.dumps(months, separators=(",", ":")) + metadata["ledger_source_month_count"] = str(len(months)) + metadata["ledger_source_cell_count_per_month"] = str( + len(reference.value_operands) or 1 + ) + metadata["ledger_value_formula"] = ( + "sum_of_declared_cells / declared_month_count" + ) if reference.value_operation == "difference": metadata["ledger_value_formula"] = "minuend - subtrahend" for key, value in sorted(reference.ledger_selector.items()): diff --git a/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py b/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py index a93803225..477af0e3f 100644 --- a/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py +++ b/packages/microcosm-build/src/microcosm/build/target_reference_authoring.py @@ -192,6 +192,10 @@ def author_target_references( "matched_fact_count_overall": len(matched), "matched_fact_count_at_or_before_period": len(eligible), } + if reference.period_match_policy == "source_window": + entry["matched_fact_count_in_source_window"] = sum( + _assertion_allowed(reference, fact) for fact in matched + ) try: registry = compile_ledger_target_references( matched, @@ -462,6 +466,14 @@ def target_references_resource( "calendar_year_average", "latest_plateau", "count_x_mean", + *sorted( + { + row["value_operation"] + for row in authored.references + if row.get("value_operation") + in {"monthly_window_average", "monthly_window_sum_average"} + } + ), ], "target_references": list(authored.references), } @@ -755,6 +767,9 @@ def _reference_row( assertion_policy = target.get("assertion_policy") if assertion_policy is not None: row["assertion_policy"] = assertion_policy + period_match_policy = target.get("period_match_policy") + if period_match_policy is not None: + row["period_match_policy"] = period_match_policy value_operation = config.value_operation_by_target_id.get(target_id) if value_operation is None and target_id in config.sum_target_ids: value_operation = "sum" @@ -763,6 +778,8 @@ def _reference_row( operands = target.get("value_operands") if operands is not None: row["value_operands"] = operands + if target.get("expected_member_count") is not None: + row["expected_member_count"] = target["expected_member_count"] dimension_values = selector.get("dimension_values") if value_operation == "sum" and isinstance(dimension_values, Mapping): member_count = 1 @@ -848,7 +865,7 @@ def _apply_uprating_hold( resolved_period: str, target_period: int | str, ) -> None: - if not resolved_period: + if not resolved_period or row.get("period_match_policy") == "source_window": return source_key = _period_key_from_value(resolved_period) target_key = _period_key_from_value(target_period) diff --git a/packages/microcosm-build/src/microcosm/build/uk/country_package.json b/packages/microcosm-build/src/microcosm/build/uk/country_package.json index df57587d6..6805e32df 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/country_package.json +++ b/packages/microcosm-build/src/microcosm/build/uk/country_package.json @@ -62,6 +62,11 @@ "kind": "legacy_json", "schema_id": "legacy_json" }, + { + "path": "national_chronicle_feed.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, { "path": "gates.json", "kind": "legacy_json", diff --git a/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json b/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json new file mode 100644 index 000000000..139a234f5 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/national_chronicle_feed.json @@ -0,0 +1,16 @@ +{ + "version": 1, + "country": "uk", + "scope": "national_calibration", + "source_repo": "PolicyEngine/chronicle", + "source_commit": "ec7169b5db40b9f54117c80f70f14efc1dd0fedd", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "consumer_fact_schema_versions": [ + "ledger.consumer_fact.v1" + ], + "consumer_fact_schema_sha256": "76ac268e626c86146cee51193e0cbecbb197ddbf3bf410156fe7da7c0edae3ad", + "fact_row_count": 131450, + "facts_sha256": "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", + "manifest_sha256": "a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f" +} diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 3f8ae6fa9..5ef0deeee 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,5 +1,5 @@ { - "source_fact_feed": ".codex-work/consumer_facts_uk.jsonl", + "source_fact_feed": "chronicle-uk-ec7169b5-consumer-facts", "target_period": 2025, "candidate_count": 430, "contract_target_count": 202, @@ -1684,26 +1684,6 @@ "from": "2025-12", "to": "2025" }, - { - "name": "dwp.uc.households_single_no_children", - "from": "2025-12", - "to": "2025" - }, - { - "name": "dwp.uc.households_single_with_children", - "from": "2025-12", - "to": "2025" - }, - { - "name": "dwp.uc.households_couple_no_children", - "from": "2025-12", - "to": "2025" - }, - { - "name": "dwp.uc.households_couple_with_children", - "from": "2025-12", - "to": "2025" - }, { "name": "dwp/uc_payment_dist/SINGLE_annual_payment_0_to_1_200", "from": "2025-12", @@ -2699,11 +2679,6 @@ "from": "2024", "to": "2025" }, - { - "name": "dwp.uc.households", - "from": "2025-12", - "to": "2025" - }, { "name": "dfe.funded_childcare.working_parent_children_2_to_4", "from": "2025-01", @@ -4906,12 +4881,12 @@ { "name": "dwp.uc.households_children_1", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, "resolved_period": 2025, - "resolved_value": 1278537.0, + "resolved_value": 1190123.8333333333, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_75d899ad408d26f0c2a0b4b2" + "resolved_fact_key": "ledger_aggregate_fact_v2_c1533879baf52aaa88758083" } ] }, @@ -4921,12 +4896,12 @@ { "name": "dwp.uc.households_children_2", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, "resolved_period": 2025, - "resolved_value": 1100617.5555555555, + "resolved_value": 1049895.5, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_970b3f74426e7a9ca57d4f3f" + "resolved_fact_key": "ledger_aggregate_fact_v2_46fca4f9d4e0519f0c190824" } ] }, @@ -4936,12 +4911,12 @@ { "name": "dwp.uc.households_children_3", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, "resolved_period": 2025, - "resolved_value": 494553.22222222225, + "resolved_value": 477887.6666666667, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_8d071172a1212492c8895356" + "resolved_fact_key": "ledger_aggregate_fact_v2_ba5bce203605d4e16037d311" } ] }, @@ -4951,12 +4926,12 @@ { "name": "dwp.uc.households_children_4", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, "resolved_period": 2025, - "resolved_value": 174668.88888888888, + "resolved_value": 170713.66666666666, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_d46a42194825aa8ac424c18d" + "resolved_fact_key": "ledger_aggregate_fact_v2_33541aafa05a63dcae4fc144" } ] }, @@ -4966,12 +4941,12 @@ { "name": "dwp.uc.households_children_5_or_more", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 12, + "matched_fact_count_at_or_before_period": 12, "resolved_period": 2025, - "resolved_value": 77376.77777777778, + "resolved_value": 76320.08333333333, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_c672eb5959db157315a3a3e6" + "resolved_fact_key": "ledger_aggregate_fact_v2_33d99593d166153ca2d0adec" } ] }, @@ -4981,12 +4956,13 @@ { "name": "dwp.uc.households_single_no_children", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 24, + "matched_fact_count_at_or_before_period": 24, + "matched_fact_count_in_source_window": 24, "resolved_period": 2025, - "resolved_value": 3446962.0, + "resolved_value": 2990070.1666666665, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_8f37a13228973c48fc995636" + "resolved_fact_key": "ledger_aggregate_fact_v2_c013630f1154eb7252466457" } ] }, @@ -4996,12 +4972,13 @@ { "name": "dwp.uc.households_single_with_children", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 24, + "matched_fact_count_at_or_before_period": 24, + "matched_fact_count_in_source_window": 24, "resolved_period": 2025, - "resolved_value": 2226220.0, + "resolved_value": 2138780.1666666665, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_0e4512db88ae6dfb508d365d" + "resolved_fact_key": "ledger_aggregate_fact_v2_4e21ff5e364647d7c75589d2" } ] }, @@ -5011,12 +4988,13 @@ { "name": "dwp.uc.households_couple_no_children", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 24, + "matched_fact_count_at_or_before_period": 24, + "matched_fact_count_in_source_window": 24, "resolved_period": 2025, - "resolved_value": 284345.55555555556, + "resolved_value": 241130.91666666666, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_5d1c98b108eaac7236138260" + "resolved_fact_key": "ledger_aggregate_fact_v2_ad46a5483afd6bad20dc50fe" } ] }, @@ -5026,12 +5004,13 @@ { "name": "dwp.uc.households_couple_with_children", "status": "active", - "matched_fact_count_overall": 9, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 24, + "matched_fact_count_at_or_before_period": 24, + "matched_fact_count_in_source_window": 24, "resolved_period": 2025, - "resolved_value": 899534.6666666666, + "resolved_value": 826160.8333333334, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_388f703e509e490c85497a81" + "resolved_fact_key": "ledger_aggregate_fact_v2_1a76243d3f616b19eaed3e0a" } ] }, @@ -7810,12 +7789,13 @@ { "name": "dwp.uc.households", "status": "active", - "matched_fact_count_overall": 11, - "matched_fact_count_at_or_before_period": 9, + "matched_fact_count_overall": 120, + "matched_fact_count_at_or_before_period": 120, + "matched_fact_count_in_source_window": 120, "resolved_period": 2025, - "resolved_value": 6758888.888888889, + "resolved_value": 6197311.0, "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_3270b0f656838ddd01b1ac5f" + "resolved_fact_key": "ledger_aggregate_fact_v2_71c060f3f2a732e2f836a5d8" } ] }, diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 912dfc18d..70e0d8ba0 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -1,13 +1,14 @@ { "country": "uk", - "description": "UK active-subset Ledger target references for the FRS 2024-25 line. Rows are generated from the national rows in uk_population_targets.json: name is the contract target_id or an incumbent-compatible fan-out row name; ledger_selector is the contract selector plus geography pins; entity is from_entity, then map_to, then household; measure is the prepared-column metric name or fan-out row name; family is the contract family; period is 2025 (opening-year convention for the FRS 2024-25 survey line). Observed values stay in Ledger facts and resolve by identity unless the reference declares value_operation=sum for a genuine multi-fact residue. Deferred classes and geography-pin decisions are recorded in uk/target_reference_membership.json. metadata.measure_kind records that measures are prepared columns produced from the contract binding payload referenced by metadata.contract_target_id.", + "description": "UK active-subset Ledger target references for the FRS 2024-25 line. Rows are generated from the national rows in uk_population_targets.json: name is the contract target_id or an incumbent-compatible fan-out row name; ledger_selector is the contract selector plus geography pins; entity is from_entity, then map_to, then household; measure is the prepared-column metric name or fan-out row name; family is the contract family; period is the model and calibration year 2025. Observation windows are declared separately from the FRS 2024-25 survey vintage. Observed values stay in Ledger facts and resolve through each reference's declared value operation. Deferred classes and geography-pin decisions are recorded in uk/target_reference_membership.json. metadata.measure_kind records that measures are prepared columns produced from the contract binding payload referenced by metadata.contract_target_id.", "allowed_value_operations": [ "identity", "sum", "difference", "calendar_year_average", "latest_plateau", - "count_x_mean" + "count_x_mean", + "monthly_window_sum_average" ], "target_references": [ { @@ -4146,11 +4147,31 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 1 - }, + "number_of_children": 1, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4161,7 +4182,7 @@ "metadata": { "contract_target_id": "dwp.uc.households_children_1", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4174,11 +4195,31 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 2 - }, + "number_of_children": 2, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4189,7 +4230,7 @@ "metadata": { "contract_target_id": "dwp.uc.households_children_2", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4202,11 +4243,31 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 3 - }, + "number_of_children": 3, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4217,7 +4278,7 @@ "metadata": { "contract_target_id": "dwp.uc.households_children_3", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4230,11 +4291,31 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 4 - }, + "number_of_children": 4, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4245,7 +4326,7 @@ "metadata": { "contract_target_id": "dwp.uc.households_children_4", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4258,11 +4339,31 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": "5 or more" - }, + "number_of_children": "5 or more", + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4273,7 +4374,7 @@ "metadata": { "contract_target_id": "dwp.uc.households_children_5_or_more", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4284,13 +4385,36 @@ "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Single, no children" }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4301,24 +4425,62 @@ "metadata": { "contract_target_id": "dwp.uc.households_single_no_children", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, - "value_operation": "calendar_year_average", - "uprating_from_period": "2025-12", - "uprating_to_period": 2025 + "period_match_policy": "source_window", + "value_operation": "monthly_window_sum_average", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "name": "dwp.uc.households_single_with_children", "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Single, with children" }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4329,24 +4491,62 @@ "metadata": { "contract_target_id": "dwp.uc.households_single_with_children", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, - "value_operation": "calendar_year_average", - "uprating_from_period": "2025-12", - "uprating_to_period": 2025 + "period_match_policy": "source_window", + "value_operation": "monthly_window_sum_average", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "name": "dwp.uc.households_couple_no_children", "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Couple, no children" }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4357,24 +4557,62 @@ "metadata": { "contract_target_id": "dwp.uc.households_couple_no_children", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, - "value_operation": "calendar_year_average", - "uprating_from_period": "2025-12", - "uprating_to_period": 2025 + "period_match_policy": "source_window", + "value_operation": "monthly_window_sum_average", + "value_operands": [ + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "name": "dwp.uc.households_couple_with_children", "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Couple, with children" }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -4385,11 +4623,26 @@ "metadata": { "contract_target_id": "dwp.uc.households_couple_with_children", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, - "value_operation": "calendar_year_average", - "uprating_from_period": "2025-12", - "uprating_to_period": 2025 + "period_match_policy": "source_window", + "value_operation": "monthly_window_sum_average", + "value_operands": [ + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "name": "dwp/uc_payment_dist/SINGLE_annual_payment_0_to_1_200", @@ -10576,9 +10829,43 @@ "name": "dwp.uc.households", "ledger_selector": { "source_name": "dwp", - "source_concept": "dwp.uc_benefit_units", - "source_measure_id": "total_units", - "groupby_dimension": "dwp.uc_deductions_month", + "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", + "groupby_dimension": "dwp.uc_family_type", + "dimensions": [ + "family_type", + "payment_indicator", + "child_entitlement" + ], + "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], + "family_type": [ + "Single, no children", + "Single, with children", + "Couple, no children", + "Couple, with children", + "Unknown or missing family type" + ] + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], "geography_level": "country", "geography_id": "K03000001" }, @@ -10589,11 +10876,82 @@ "metadata": { "contract_target_id": "dwp.uc.households", "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" }, - "value_operation": "calendar_year_average", - "uprating_from_period": "2025-12", - "uprating_to_period": 2025 + "period_match_policy": "source_window", + "value_operation": "monthly_window_sum_average", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Unknown or missing family type", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Unknown or missing family type", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "name": "hmrc.tfc.government_top_up", diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index e2eb93b84..ffa0f0e45 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -5,7 +5,9 @@ "allowed_value_operations": [ "identity", "sum", - "difference" + "difference", + "calendar_year_average", + "monthly_window_sum_average" ], "resolution_defaults": { "base_period_policy": "latest_not_after_build_base_period", @@ -3495,6 +3497,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3523,7 +3528,7 @@ "value": 1 } ], - "notes": "Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -3558,12 +3563,33 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 1 - } - } + "number_of_children": 1, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "calendar_year_average" }, { "target_id": "dwp.uc.households_children_2", @@ -3587,6 +3613,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3615,7 +3644,7 @@ "value": 2 } ], - "notes": "Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -3650,12 +3679,33 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 2 - } - } + "number_of_children": 2, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "calendar_year_average" }, { "target_id": "dwp.uc.households_children_3", @@ -3679,6 +3729,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3707,7 +3760,7 @@ "value": 3 } ], - "notes": "Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -3742,12 +3795,33 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 3 - } - } + "number_of_children": 3, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "calendar_year_average" }, { "target_id": "dwp.uc.households_children_4", @@ -3771,6 +3845,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3799,7 +3876,7 @@ "value": 4 } ], - "notes": "Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -3834,12 +3911,33 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": 4 - } - } + "number_of_children": 4, + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "calendar_year_average" }, { "target_id": "dwp.uc.households_children_5_or_more", @@ -3863,6 +3961,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3891,7 +3992,7 @@ "value": 5 } ], - "notes": "Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -3926,12 +4027,33 @@ "source_concept": "dwp.uc_households", "groupby_dimension": "dwp.uc_number_of_children", "dimensions": [ - "number_of_children" + "number_of_children", + "payment_indicator", + "child_entitlement" ], "dimension_values": { - "number_of_children": "5 or more" - } - } + "number_of_children": "5 or more", + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "calendar_year_average" }, { "target_id": "dwp.uc.households_single_no_children", @@ -3950,6 +4072,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -3973,12 +4098,12 @@ "value": 0 }, { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "SINGLE" } ], - "notes": "Benefit-unit count among positive UC awards. Family composition uses retained FRS claimant/parent roles, including cohabiting couples, and reported child members under 20 or UC qualifying children. This calibration proxy corrects generic age-only family categories; the native standard-allowance relationship calculation and eligibility exceptions remain separate model concerns. Benefit-unit grain retains microcosm#809. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. This is a derived sum of all declared family/entitlement cells within each month, followed by the mean of those twelve monthly sums; it is not a publisher grand Total. Include unknown/missing family type in the headline, retain both child-entitlement statuses, and require every declared month/category cell including literal zeros. Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -4011,14 +4136,55 @@ "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Single, no children" + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } } - } + ] }, { "target_id": "dwp.uc.households_single_with_children", @@ -4037,6 +4203,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -4060,12 +4229,12 @@ "value": 0 }, { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "LONE_PARENT" } ], - "notes": "Benefit-unit count among positive UC awards. Family composition uses retained FRS claimant/parent roles, including cohabiting couples, and reported child members under 20 or UC qualifying children. This calibration proxy corrects generic age-only family categories; the native standard-allowance relationship calculation and eligibility exceptions remain separate model concerns. Benefit-unit grain retains microcosm#809. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. This is a derived sum of all declared family/entitlement cells within each month, followed by the mean of those twelve monthly sums; it is not a publisher grand Total. Include unknown/missing family type in the headline, retain both child-entitlement statuses, and require every declared month/category cell including literal zeros. Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -4098,14 +4267,55 @@ "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Single, with children" + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } } - } + ] }, { "target_id": "dwp.uc.households_couple_no_children", @@ -4124,6 +4334,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -4147,12 +4360,12 @@ "value": 0 }, { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "COUPLE_NO_CHILDREN" } ], - "notes": "Benefit-unit count among positive UC awards. Family composition uses retained FRS claimant/parent roles, including cohabiting couples, and reported child members under 20 or UC qualifying children. This calibration proxy corrects generic age-only family categories; the native standard-allowance relationship calculation and eligibility exceptions remain separate model concerns. Benefit-unit grain retains microcosm#809. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. This is a derived sum of all declared family/entitlement cells within each month, followed by the mean of those twelve monthly sums; it is not a publisher grand Total. Include unknown/missing family type in the headline, retain both child-entitlement statuses, and require every declared month/category cell including literal zeros. Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -4185,14 +4398,55 @@ "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Couple, no children" + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": [ + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } } - } + ] }, { "target_id": "dwp.uc.households_couple_with_children", @@ -4211,6 +4465,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -4234,12 +4491,12 @@ "value": 0 }, { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "COUPLE_WITH_CHILDREN" } ], - "notes": "Benefit-unit count among positive UC awards. Family composition uses retained FRS claimant/parent roles, including cohabiting couples, and reported child members under 20 or UC qualifying children. This calibration proxy corrects generic age-only family categories; the native standard-allowance relationship calculation and eligibility exceptions remain separate model concerns. Benefit-unit grain retains microcosm#809. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. This is a derived sum of all declared family/entitlement cells within each month, followed by the mean of those twelve monthly sums; it is not a publisher grand Total. Include unknown/missing family type in the headline, retain both child-entitlement statuses, and require every declared month/category cell including literal zeros. Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -4272,14 +4529,55 @@ "ledger_selector": { "source_name": "dwp", "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", "groupby_dimension": "dwp.uc_family_type", "dimensions": [ - "family_type" + "family_type", + "payment_indicator", + "child_entitlement" ], "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], "family_type": "Couple, with children" + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": [ + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } } - } + ] }, { "target_id": "dwp.uc.payment_distribution_single", @@ -4317,7 +4615,7 @@ "groupby_variable": "universal_credit", "filters": [ { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "SINGLE" } @@ -4326,7 +4624,7 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Calibration composition uses retained FRS claimant/parent roles and UC child or qualifying-young-person status, including reported child members under 20; it does not change simulated entitlement. The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", "household_conditions": [ { "variable": "region", @@ -4407,7 +4705,7 @@ "groupby_variable": "universal_credit", "filters": [ { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "LONE_PARENT" } @@ -4416,7 +4714,7 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Calibration composition uses retained FRS claimant/parent roles and UC child or qualifying-young-person status, including reported child members under 20; it does not change simulated entitlement. The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", "household_conditions": [ { "variable": "region", @@ -4497,7 +4795,7 @@ "groupby_variable": "universal_credit", "filters": [ { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "COUPLE_NO_CHILDREN" } @@ -4506,7 +4804,7 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Calibration composition uses retained FRS claimant/parent roles and UC child or qualifying-young-person status, including reported child members under 20; it does not change simulated entitlement. The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", "household_conditions": [ { "variable": "region", @@ -4587,7 +4885,7 @@ "groupby_variable": "universal_credit", "filters": [ { - "variable": "uc_calibration_family_type", + "variable": "uc_calibration_administrative_family_type", "operator": "==", "value": "COUPLE_WITH_CHILDREN" } @@ -4596,7 +4894,7 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Calibration composition uses retained FRS claimant/parent roles and UC child or qualifying-young-person status, including reported child members under 20; it does not change simulated entitlement. The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at \u00a32,500 monthly, inclusive; the separately published \u00a32,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", "household_conditions": [ { "variable": "region", @@ -11503,6 +11801,9 @@ } ], "source_months": [ + "2025-01", + "2025-02", + "2025-03", "2025-04", "2025-05", "2025-06", @@ -11526,7 +11827,7 @@ "value": 0 } ], - "notes": "Prepared benefit-unit indicator for receipt of Universal Credit; DWP household means the UC unit of assessment, not an ONS household. This row binds DWP UC deductions statistics Table 1 total units. Its April-December 2025 calendar-year average is 6,758,889, 1.45% below the Stat-Xplore family-type sum of 6,858,287; chronicle#247 tracks a common-cube total. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset", + "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. This is a derived sum of all declared family/entitlement cells within each month, followed by the mean of those twelve monthly sums; it is not a publisher grand Total. Include unknown/missing family type in the headline, retain both child-entitlement statuses, and require every declared month/category cell including literal zeros. Positive annual model UC remains a proxy for monthly payment receipt, not an identified open/nil claim history. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", "household_conditions": [ { "variable": "region", @@ -11558,10 +11859,118 @@ }, "ledger_selector": { "source_name": "dwp", - "source_concept": "dwp.uc_benefit_units", - "source_measure_id": "total_units", - "groupby_dimension": "dwp.uc_deductions_month" - } + "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", + "groupby_dimension": "dwp.uc_family_type", + "dimensions": [ + "family_type", + "payment_indicator", + "child_entitlement" + ], + "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": [ + "No", + "Yes" + ], + "family_type": [ + "Single, no children", + "Single, with children", + "Couple, no children", + "Couple, with children", + "Unknown or missing family type" + ] + }, + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": [ + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Single, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, no children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Couple, with children", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + }, + { + "dimension_values": { + "family_type": "Unknown or missing family type", + "payment_indicator": "Yes", + "child_entitlement": "No" + } + }, + { + "dimension_values": { + "family_type": "Unknown or missing family type", + "payment_indicator": "Yes", + "child_entitlement": "Yes" + } + } + ] }, { "target_id": "hmrc.tfc.government_top_up", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py index 89708debd..dcbc451e3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py @@ -18,8 +18,12 @@ ) from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask from microcosm.build.uk_runtime.uc_target_measurements import ( + UC_PAID_TARGET_VARIABLES, UC_TARGET_VARIABLES, compute_uc_target_measure, + uc_administrative_family_type, + uc_child_entitlement, + uc_paid_comparison_contract, uc_tcl_comparison_contract, ) from microcosm.build.uk_runtime.weighted_integrity import ( @@ -93,10 +97,20 @@ def compute_uk_measure_input( return compute_uc_target_measure( frame, simulation, variable, year ), "uc_claim_statistical_proxy" - if variable in _UC_CALIBRATION_VARIABLES: + if variable == "uc_calibration_child_entitlement": + if entity != "benunit": + raise KeyError(f"UC child entitlement is benunit-only: {entity}") + return uc_child_entitlement( + frame, simulation, year + ), "uc_child_element_entitlement_proxy" + if variable in _UC_CALIBRATION_VARIABLES | UC_PAID_TARGET_VARIABLES: if entity != "benunit": raise KeyError(f"UC calibration composition is benunit-only: {entity}") family_type, child_count = _uc_calibration_composition(frame, simulation, year) + if variable == "uc_calibration_administrative_family_type": + return uc_administrative_family_type( + frame, simulation, year, child_count + ), "uc_allowance_and_reported_child_proxy" values = ( family_type if variable == "uc_calibration_family_type" else child_count ) @@ -152,6 +166,77 @@ def compute_uk_measure_input( return values, route +def compute_uc_paid_diagnostic_masks( + frame: Any, simulation: Any, year: int +) -> dict[str, np.ndarray]: + """Return out-of-fit Boolean masks in the frame's benefit-unit row order. + + The simulation must use that same row order, as for all UK measure inputs. + Callers retain their own weights and source-ancestry aggregation. Model + zero awards are labelled as such: they do not identify administrative open + nil-payment claims. Family and child-count partitions are separate, matching + the two available publisher cubes rather than inventing their joint. + """ + uc = _values(simulation.calculate("universal_credit", year)) + if ( + uc.ndim != 1 + or len(uc) != len(frame.table("benunit")) + or uc.dtype.kind not in "biuf" + or not np.isfinite(uc).all() + or (uc < 0).any() + ): + raise ValueError( + "UC diagnostic awards must be finite nonnegative benunit values." + ) + structural_family, reported_children = _uc_calibration_composition( + frame, simulation, year + ) + administrative_family = uc_administrative_family_type( + frame, simulation, year, reported_children + ) + own_qualifying_children = compute_uc_target_measure( + frame, simulation, "uc_tcl_qualifying_child_count", year + ) + entitled = uc_child_entitlement(frame, simulation, year) + paid = uc > 0 + masks = { + "paid.total": paid, + "model.zero_award": uc == 0, + "paid.child_entitled": paid & entitled, + "paid.no_child_entitlement": paid & ~entitled, + "paid.family_measure_disagreement": paid + & (administrative_family != structural_family), + "paid.child_count_measure_disagreement": paid + & (reported_children != own_qualifying_children), + } + partitions = { + "family": { + category: administrative_family == category + for category in ( + "SINGLE", + "LONE_PARENT", + "COUPLE_NO_CHILDREN", + "COUPLE_WITH_CHILDREN", + "UNKNOWN", + ) + }, + "children": { + **{str(count): reported_children == count for count in range(5)}, + "5_or_more": reported_children >= 5, + }, + } + for dimension, categories in partitions.items(): + for category, selected in categories.items(): + masks[f"paid.{dimension}.{category}"] = paid & selected + masks[f"paid_child_entitled.{dimension}.{category}"] = ( + paid & entitled & selected + ) + masks[f"paid_no_child_entitlement.{dimension}.{category}"] = ( + paid & ~entitled & selected + ) + return masks + + class UKMeasureResolver: """B2 measure provider backed by a policyengine-uk Microsimulation.""" @@ -193,6 +278,7 @@ def __init__( } self.contract_targets = _uk_contract_targets() self._uc_tcl_measures_used: set[str] = set() + self._uc_paid_measures_used: set[str] = set() def knows(self, entity: str, variable: str) -> bool: """Whether a route in :func:`compute_uk_measure_input` reaches here. @@ -202,7 +288,12 @@ def knows(self, entity: str, variable: str) -> bool: provider exception instead of the fence's message. """ - if variable in _UC_CALIBRATION_VARIABLES | UC_TARGET_VARIABLES: + if ( + variable + in _UC_CALIBRATION_VARIABLES + | UC_TARGET_VARIABLES + | UC_PAID_TARGET_VARIABLES + ): return entity == "benunit" definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None or entity not in _ENTITY_ID: @@ -220,7 +311,12 @@ def knows(self, entity: str, variable: str) -> bool: return getattr(definition, "value_type", None) in (int, float) def entity_for(self, variable: str) -> str | None: - if variable in _UC_CALIBRATION_VARIABLES | UC_TARGET_VARIABLES: + if ( + variable + in _UC_CALIBRATION_VARIABLES + | UC_TARGET_VARIABLES + | UC_PAID_TARGET_VARIABLES + ): return "benunit" definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None: @@ -233,6 +329,10 @@ def compute(self, entity: str, variable: str) -> tuple[np.ndarray, str]: ) if variable.startswith("uc_tcl_") and variable in UC_TARGET_VARIABLES: self._uc_tcl_measures_used.add(variable) + if variable in UC_PAID_TARGET_VARIABLES: + if not hasattr(self, "_uc_paid_measures_used"): + self._uc_paid_measures_used = set() + self._uc_paid_measures_used.add(variable) return result def receipt(self) -> dict[str, Any]: @@ -242,6 +342,11 @@ def receipt(self) -> dict[str, Any]: **uc_tcl_comparison_contract(self.year), "computed_measures": sorted(self._uc_tcl_measures_used), } + if getattr(self, "_uc_paid_measures_used", None): + receipt["uc_paid_comparison_contract"] = { + **uc_paid_comparison_contract(self.year), + "computed_measures": sorted(self._uc_paid_measures_used), + } return receipt diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_chronicle_feed.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_chronicle_feed.py new file mode 100644 index 000000000..2e2e3705f --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_chronicle_feed.py @@ -0,0 +1,103 @@ +"""Reviewed Chronicle artifact identity for UK national calibration. + +Local census and validation surfaces retain their independently reviewed feed +pins. Updating this national input does not promote those local surfaces. +""" + +from __future__ import annotations + +import hashlib +import json +import re +from dataclasses import asdict, dataclass +from importlib.resources import files + +from microcosm.build.chronicle_epoch import ( + is_accepted_consumer_artifact_schema_version, +) + + +@dataclass(frozen=True) +class UKNationalChronicleFeed: + version: int + country: str + scope: str + source_repo: str + source_commit: str + build: str + artifact_schema_version: str + consumer_fact_schema_versions: tuple[str, ...] + consumer_fact_schema_sha256: str + fact_row_count: int + facts_sha256: str + manifest_sha256: str + resource_sha256: str + resource_size_bytes: int + + def to_dict(self) -> dict[str, object]: + """Include the declaration's own digest in the run provenance.""" + result = asdict(self) + result["consumer_fact_schema_versions"] = list( + self.consumer_fact_schema_versions + ) + return result + + +def _feed_path(): + return files("microcosm.build.uk").joinpath("national_chronicle_feed.json") + + +def load_uk_national_chronicle_feed() -> UKNationalChronicleFeed: + """Load the packaged national pin without importing Chronicle or an engine.""" + content = _feed_path().read_bytes() + raw = json.loads(content) + if not isinstance(raw, dict): + raise ValueError("UK national Chronicle feed must contain a JSON object.") + for field, expected in ( + ("version", 1), + ("country", "uk"), + ("scope", "national_calibration"), + ("source_repo", "PolicyEngine/chronicle"), + ): + if type(raw.get(field)) is not type(expected) or raw[field] != expected: + raise ValueError( + f"UK national Chronicle feed {field} must be {expected!r}." + ) + for field, length in ( + ("source_commit", 40), + ("facts_sha256", 64), + ("manifest_sha256", 64), + ("consumer_fact_schema_sha256", 64), + ): + value = raw.get(field) + if not isinstance(value, str) or not re.fullmatch( + rf"[0-9a-f]{{{length}}}", value + ): + raise ValueError( + f"UK national Chronicle feed {field} must be {length} lowercase hex chars." + ) + if type(raw.get("fact_row_count")) is not int or raw["fact_row_count"] <= 0: + raise ValueError("UK national Chronicle feed fact_row_count must be positive.") + if not isinstance(raw.get("build"), str) or not raw["build"].strip(): + raise ValueError("UK national Chronicle feed build must be a nonempty string.") + if not is_accepted_consumer_artifact_schema_version( + raw.get("artifact_schema_version") + ): + raise ValueError( + "UK national Chronicle feed artifact_schema_version is unsupported." + ) + versions = raw.get("consumer_fact_schema_versions") + if ( + not isinstance(versions, list) + or not versions + or any(not isinstance(v, str) or not v for v in versions) + or len(set(versions)) != len(versions) + ): + raise ValueError( + "UK national Chronicle feed consumer_fact_schema_versions is invalid." + ) + return UKNationalChronicleFeed( + **{**raw, "consumer_fact_schema_versions": tuple(versions)}, + resource_sha256=hashlib.sha256(content).hexdigest(), + resource_size_bytes=len(content), + ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py index 5fa9eccdb..3e0d76cf7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py @@ -2,8 +2,9 @@ The shared ``calendar_year_average`` operation averages available months. UK UC contracts declare the intended source months separately so that a changed feed -cannot silently change that denominator. This does not turn the declared window -into a full calendar/fiscal year or align the model's annual observation date. +cannot silently change that denominator. Explicit monthly-window operations can +cross calendar years and sum declared cells within each month. Their coverage +receipt describes source observations, not the model's annual observation date. """ from __future__ import annotations @@ -14,13 +15,18 @@ from dataclasses import replace from typing import Any -from microcosm.build.ledger_targets import LedgerTargetReference +from microcosm.build.ledger_targets import ( + MONTHLY_WINDOW_OPERATIONS, + LedgerTargetReference, +) from microcosm.calibrate import TargetRegistry EXPECTED_SOURCE_MONTHS = "uk_uc_expected_source_months" -def uc_source_month_metadata(months: object) -> dict[str, str]: +def uc_source_month_metadata( + months: object, *, value_operation: str = "calendar_year_average" +) -> dict[str, str]: """Validate and encode a contract's explicit, ordered monthly coverage.""" valid = ( @@ -34,7 +40,10 @@ def uc_source_month_metadata(months: object) -> dict[str, str]: ) if not valid: raise ValueError("UK UC source_months must be a non-empty YYYY-MM list.") - if months != sorted(set(months)) or len({month[:4] for month in months}) != 1: + if months != sorted(set(months)) or ( + value_operation not in MONTHLY_WINDOW_OPERATIONS + and len({month[:4] for month in months}) != 1 + ): raise ValueError( "UK UC source_months must be unique, ordered months in one calendar year." ) @@ -51,12 +60,12 @@ def validate_uc_source_month_coverage( encoded = reference.metadata.get(EXPECTED_SOURCE_MONTHS) if encoded is None: return registry - if ( - reference.family != "dwp_universal_credit" - or reference.value_operation != "calendar_year_average" - ): + if reference.family != "dwp_universal_credit" or reference.value_operation not in { + "calendar_year_average", + *MONTHLY_WINDOW_OPERATIONS, + }: raise ValueError( - "UK UC source-month coverage requires a UC calendar_year_average reference." + "UK UC source-month coverage requires a UC calendar_year_average or explicit monthly window reference." ) try: expected = json.loads(encoded) @@ -64,7 +73,17 @@ def validate_uc_source_month_coverage( raise ValueError( "UK UC source-month coverage metadata is invalid JSON." ) from error - uc_source_month_metadata(expected) + uc_source_month_metadata(expected, value_operation=reference.value_operation) + is_window = reference.value_operation in MONTHLY_WINDOW_OPERATIONS + if is_window and expected != reference.ledger_selector.get("period_value"): + raise ValueError( + "UK UC source-month coverage metadata must equal the declared source window." + ) + cells_per_month = ( + len(reference.value_operands) + if reference.value_operation == "monthly_window_sum_average" + else 1 + ) by_key: dict[str, list[Mapping[str, Any]]] = {} for fact in facts: @@ -107,10 +126,10 @@ def validate_uc_source_month_coverage( ) resolved.append(str(period.get("value", ""))) resolved.sort() - if resolved != expected: + if resolved != sorted(expected * cells_per_month): raise ValueError( f"UK UC source-month coverage for {reference.name!r}: " - f"expected exactly one fact for each of {expected!r}; " + f"expected exactly one fact per cell ({cells_per_month}) for each of {expected!r}; " f"resolved {resolved!r}." ) validated.append( @@ -119,10 +138,14 @@ def validate_uc_source_month_coverage( metadata={ **dict(spec.metadata), "uk_uc_resolved_source_months": json.dumps( - resolved, separators=(",", ":") + expected, separators=(",", ":") + ), + "uk_uc_source_month_count": str(len(expected)), + "uk_uc_source_period_basis": ( + "mean_of_declared_monthly_cell_sums" + if reference.value_operation == "monthly_window_sum_average" + else "mean_of_declared_months" ), - "uk_uc_source_month_count": str(len(resolved)), - "uk_uc_source_period_basis": "mean_of_declared_months", }, ) ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py index 5b71c4afc..423fd2477 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py @@ -31,6 +31,126 @@ } ) +UC_PAID_TARGET_VARIABLES = frozenset( + { + "uc_calibration_administrative_family_type", + "uc_calibration_child_entitlement", + } +) + + +def uc_paid_comparison_contract(year: int) -> dict[str, Any]: + """Describe the paid-claim proxies without implying observed claim history.""" + return { + "source_unit": "UC claim (single claimant or couple), Great Britain", + "model_unit": "benunit", + "source_payment_state": "Payment Indicator = Yes in each observation month", + "model_payment_proxy": "universal_credit > 0", + "model_child_entitlement_proxy": "uc_child_element > 0", + "source_child_definition": "verified declared children or young people under 20", + "model_child_definition_proxy": ( + "own benefit-unit nonclaimants under 20 or qualifying for a UC child element" + ), + "own_qualifying_child_comparison": ( + "own benefit-unit nonclaimants satisfying the model UC child/QYP rule; " + "retained age and education inputs do not identify exact observation dates" + ), + "source_unknown_child_categories": ( + "retain in source reconciliation; no inferred model unknown-child cohort" + ), + "source_family_definition": ( + "standard allowance above the maximum single-person allowance means couple" + ), + "model_family_definition_proxy": ( + "model uc_standard_allowance compared with the model-year maximum single rate" + ), + "zero_standard_allowance_family": "UNKNOWN", + "family_limitations": ( + "The model allowance does not establish administrative ineligible partner " + "exceptions or missing declared-child records; structural claimant roles " + "remain unchanged." + ), + "open_nil_claims_identified": False, + "nil_claim_limitation": ( + "Zero model UC, reported receipt and would_claim_uc do not identify an " + "administrative open claim with a calculated nil award." + ), + "amount_limitation": ( + "Annual model UC is not an observed monthly cash payment or claim trajectory." + ), + "model_period": str(year), + "observation_period": "exact source months are declared on each target reference", + "status": "partial_alignment_with_explicit_proxies", + "source_metadata": { + "family_type": "https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html", + "child_entitlement": "https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Child%20Entitlement.html", + "payment_indicator": "https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Payment%20Indicator.html", + }, + } + + +def _uc_benunit_values( + frame: Any, simulation: Any, variable: str, year: int +) -> np.ndarray: + raw = simulation.calculate(variable, year) + values = np.asarray(raw.values if hasattr(raw, "values") else raw) + if values.ndim != 1 or len(values) != len(frame.table("benunit")): + raise ValueError(f"UC measurement {variable} must align with benunit rows.") + if values.dtype.kind not in "biuf" or not np.isfinite(values).all(): + raise ValueError(f"UC measurement {variable} must be finite numeric values.") + if (values < 0).any(): + raise ValueError(f"UC measurement {variable} must be nonnegative.") + return values + + +def uc_administrative_family_type( + frame: Any, simulation: Any, year: int, child_count: np.ndarray +) -> np.ndarray: + """Apply DWP's allowance-based family rule to the model's annual state. + + The child count remains an explicit reported-child proxy. This measurement + never changes which members are claimants or how the model awards UC: + https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html + """ + allowance = _uc_benunit_values(frame, simulation, "uc_standard_allowance", year) + rates = simulation.tax_benefit_system.parameters( + str(year) + ).gov.dwp.universal_credit.standard_allowance.amount + single_rates = np.asarray([rates.SINGLE_YOUNG, rates.SINGLE_OLD], dtype=float) + if not np.isfinite(single_rates).all() or (single_rates <= 0).any(): + raise ValueError( + "UC single standard allowance rates must be finite and positive." + ) + # The model multiplies the monthly parameter by twelve. Cast the comparison + # boundary to the amount's precision: a float32 single award must not become + # a couple merely because its float64 parameter representation is smaller. + amount_dtype = np.result_type(allowance.dtype, np.float32) + single_maximum = np.asarray(single_rates.max() * 12, dtype=amount_dtype) + if not np.isfinite(single_maximum): + raise ValueError("UC annual single standard allowance maximum must be finite.") + children = np.asarray(child_count) + if ( + children.shape != allowance.shape + or not np.isfinite(children).all() + or (children < 0).any() + or not np.equal(children, np.floor(children)).all() + ): + raise ValueError( + "UC reported child counts must be nonnegative benunit integers." + ) + couple = allowance > single_maximum + has_children = children > 0 + return np.select( + [allowance == 0, couple & has_children, couple, has_children], + ["UNKNOWN", "COUPLE_WITH_CHILDREN", "COUPLE_NO_CHILDREN", "LONE_PARENT"], + default="SINGLE", + ) + + +def uc_child_entitlement(frame: Any, simulation: Any, year: int) -> np.ndarray: + """Measure the recorded model child element, separately from reported children.""" + return _uc_benunit_values(frame, simulation, "uc_child_element", year) > 0 + def uc_tcl_comparison_contract(year: int) -> dict[str, Any]: """Expose the remaining source/model differences to run receipts.""" diff --git a/packages/microcosm-build/tests/fixtures/uk_target_reference_feed_rows.jsonl b/packages/microcosm-build/tests/fixtures/uk_target_reference_feed_rows.jsonl index 98b4b5108..ca82107f6 100644 --- a/packages/microcosm-build/tests/fixtures/uk_target_reference_feed_rows.jsonl +++ b/packages/microcosm-build/tests/fixtures/uk_target_reference_feed_rows.jsonl @@ -20,3 +20,123 @@ {"aggregate_fact_key":"ledger.aggregate_fact.v2:85f76449349c19bb159a6c91","aggregation":{"method":"sum"},"assertion":"observation","concept_alignment":{"authority":"dwp","canonical_concept":"dwp.uc_benefit_units","concept_alignment_key":"ledger.concept_alignment.v2:09dda486cf7db778ead24cee","evidence_notes":"Stat-Xplore 'Households on Universal Credit' dataset, April to December 2025, Great Britain, dimensioned by Family Type x Child Entitlement (Yes/No child element in the award); query echoed in the archived API response. 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"f4fb46fefd66a20b8d6d12c051a224743b6754fee53e1cee0be5ead4d34de550", "source_size_bytes": 22466, "source_table": "Households on Universal Credit by family type, payment indicator and child entitlement, April 2023 to May 2026", "url": "https://stat-xplore.dwp.gov.uk/webapi/rest/v1/table", "vintage": "stat_xplore_2026_09_08"}, "source_release_key": "ledger.source_release.v2:6872f79870d0a90db8362a75", "source_series_key": "ledger.source_series.v2:7fb31f6c00c414c098598aad", "universe_constraint_set_key": "ledger.universe_constraint_set.v2:ae6ec7d380eb3be11ea4e406", "universe_constraints": {"constraints": [{"operator": "==", "role": "filter", "value": "No", "variable": "child_entitlement"}, {"operator": "==", "role": "filter", "value": "Unknown or missing family type", "variable": "family_type"}, {"operator": "==", "role": "filter", "value": "Yes", "variable": "payment_indicator"}], "domain": "universal_credit"}, "value": 0, "value_type": "integer"} +{"aggregate_fact_key": "ledger.aggregate_fact.v2:71c060f3f2a732e2f836a5d8", "aggregation": {"method": "sum"}, "assertion": "observation", "concept_alignment": {"authority": "dwp", "canonical_concept": "dwp.uc_benefit_units", "concept_alignment_key": "ledger.concept_alignment.v2:08f8c4aaef975d6b11f3aab3", "evidence_notes": "DWP UC_Households cube cells for April 2023 to May 2026, by family type, payment indicator and child entitlement, preserve the publisher labels and value URIs. DWP defines a Universal Credit household as the UC unit of assessment; Chronicle normalizes that entity to benefit_unit.", "evidence_url": "https://stat-xplore.dwp.gov.uk/", "relation": "source_label", "source_concept": "dwp.uc_households"}, "dimension_set_key": "ledger.dimension_set.v2:0669a58d943af8c31456ac2d", "dimensions": {"child_entitlement": "Yes", "family_type": "Unknown or missing family type", "payment_indicator": "Yes"}, "entity": {"name": "benefit_unit", "role": "universal_credit_unit_of_assessment"}, "geography": {"id": "K03000001", "level": "country", "vintage": "current"}, "label": "Great Britain 2025-12 month count dwp uc benefit units for benefit unit (child entitlement=Yes, family type=Unknown or missing family type, payment indicator=Yes) [dwp Households on Universal Credit by family type, payment indicator and child entitlement, April 2023 to May 2026 uc_households_by_family_type_payment_indicator_and_child_entitlement_april_2023_may_2026.json stat_xplore_2026_09_08]", "layout": {"groupby_dimension": "dwp.uc_family_type", "groupby_ordinal": 19, "groupby_value_id": "unknown_or_missing_family_type__payment_indicator_yes__child_entitlement_yes", "groupby_value_label": "Unknown or missing family type, Yes, Yes", "measure_id": "benefit_units", "measure_label": "Universal Credit benefit units", "measure_ordinal": 0, "record_set_id": "dwp.statx.uc_benefit_units.family_type_payment_indicator_child_entitlement.month2025_12", "record_set_spec_hash": "3c229c1669874f43878e57cc", "record_set_spec_id": "dwp.statx.uc_benefit_units.family_type_payment_indicator_child_entitlement.month2025_12.v1", "source_column_id": "value", "source_row_id": "unknown_or_missing_family_type__payment_indicator_yes__child_entitlement_yes", "table_record_kind": "detail"}, "legacy_fact_key": "ledger.fact.v1:7af7bf378c0819a9735f92d3", "lineage": {"source_cell_keys": ["ledger.source_cell.v1:05e697819331ebac2b921b5d", "ledger.source_cell.v1:606a534ed95398c6fd197c77", "ledger.source_cell.v1:769247748b2f68de4fcfe22b", "ledger.source_cell.v1:66416d6193aa80c0ee972a4d", "ledger.source_cell.v1:884505764cf5e99f6613bb4e"], "source_record_id": "dwp.statx.uc_benefit_units.family_type_payment_indicator_child_entitlement.month2025_12.unknown_or_missing_family_type__payment_indicator_yes__child_entitlement_yes.benefit_units", "source_row_keys": ["ledger.source_row.v1:1de61b4677dcbd14f42eda59"]}, "observed_measure": {"source_concept": "dwp.uc_households", "source_measure_id": "benefit_units", "source_name": "dwp", "source_table": "Households on Universal Credit by family type, payment indicator and child entitlement, April 2023 to May 2026", "unit": "count"}, "observed_measure_key": "ledger.observed_measure.v2:a09cccfae59dd42ceb46e057", "period": {"type": "month", "value": "2025-12"}, "provenance_class": "administrative", "schema_version": "ledger.consumer_fact.v1", "semantic_fact_key": "ledger.semantic_fact.v2:6c90486fb3711b6d7971e7a9", "source": {"extracted_at": "2026-09-08", "extraction_method": "CellSelectorSpec and SourceRecordSpec resolved from cells", "method_notes": "Each fact carries source_record_id, source_cell_keys, and source_row_keys when available.", "raw_r2_bucket": "ledger-raw", "raw_r2_key": "raw/uk/dwp/dwp-uc-households-family-type-payment-indicator-child-entitlement-april-2023-may-2026/2025/f4fb46fefd66a20b8d6d12c051a224743b6754fee53e1cee0be5ead4d34de550/uc_households_by_family_type_payment_indicator_and_child_entitlement_april_2023_may_2026.json", "raw_r2_uri": "r2://ledger-raw/raw/uk/dwp/dwp-uc-households-family-type-payment-indicator-child-entitlement-april-2023-may-2026/2025/f4fb46fefd66a20b8d6d12c051a224743b6754fee53e1cee0be5ead4d34de550/uc_households_by_family_type_payment_indicator_and_child_entitlement_april_2023_may_2026.json", "source_file": "uc_households_by_family_type_payment_indicator_and_child_entitlement_april_2023_may_2026.json", "source_name": "dwp", "source_sha256": "f4fb46fefd66a20b8d6d12c051a224743b6754fee53e1cee0be5ead4d34de550", "source_size_bytes": 22466, "source_table": "Households on Universal Credit by family type, payment indicator and child entitlement, April 2023 to May 2026", "url": "https://stat-xplore.dwp.gov.uk/webapi/rest/v1/table", "vintage": "stat_xplore_2026_09_08"}, "source_release_key": "ledger.source_release.v2:6872f79870d0a90db8362a75", "source_series_key": "ledger.source_series.v2:7fb31f6c00c414c098598aad", "universe_constraint_set_key": "ledger.universe_constraint_set.v2:6b83817d0f0fde335b6c9654", "universe_constraints": {"constraints": [{"operator": "==", "role": "filter", "value": "Unknown or missing family type", "variable": "family_type"}, {"operator": "==", "role": "filter", "value": "Yes", "variable": "child_entitlement"}, {"operator": "==", "role": "filter", "value": "Yes", "variable": "payment_indicator"}], "domain": "universal_credit"}, "value": 1246, "value_type": "integer"} diff --git a/packages/microcosm-build/tests/test_country_spec.py b/packages/microcosm-build/tests/test_country_spec.py index 5cc95916e..84eb4e1b1 100644 --- a/packages/microcosm-build/tests/test_country_spec.py +++ b/packages/microcosm-build/tests/test_country_spec.py @@ -908,6 +908,7 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "efrs_parity_known_gaps.json", "efrs_parity_reference.json", "frs_release.json", + "national_chronicle_feed.json", "gates.json", "brma_rent_counts.json", "calibration_measure_exclusions.json", @@ -1001,6 +1002,7 @@ def test_uk_package_loads(self) -> None: "efrs_parity_known_gaps.json", "efrs_parity_reference.json", "frs_release.json", + "national_chronicle_feed.json", "gates.json", "brma_rent_counts.json", "calibration_measure_exclusions.json", @@ -1062,8 +1064,10 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: assert len(references) == 415 assert references["obr.esa"].value_operation == "sum" assert references["dwp.uc.households"].value_operation == ( - "calendar_year_average" + "monthly_window_sum_average" ) + assert references["dwp.uc.households"].period_match_policy == "source_window" + assert len(references["dwp.uc.households"].value_operands) == 10 assert ( references["obr.income_tax"].assertion_policy == "allow_source_projection" ) diff --git a/packages/microcosm-build/tests/test_ledger_targets.py b/packages/microcosm-build/tests/test_ledger_targets.py index e3753cbd2..8a693a164 100644 --- a/packages/microcosm-build/tests/test_ledger_targets.py +++ b/packages/microcosm-build/tests/test_ledger_targets.py @@ -20,6 +20,284 @@ ) from microcosm.calibrate import TargetRegistry, TargetSpec +_FISCAL_MONTHS = [f"2025-{month:02d}" for month in range(4, 13)] + [ + f"2026-{month:02d}" for month in range(1, 4) +] + + +def _window_fact_row(month, *, value): + row = _monthly_consumer_fact_row(month, value=value) + row["source"]["vintage"] = "release_2026_06" + row["source"]["source_file"] = "published-monthly-series.csv" + row["source_release_key"] = "ledger.source_release.v2:window-fixture" + row["source"]["source_sha256"] = "a" * 64 + return row + + +def _window_reference(**overrides): + fields = { + "name": "explicit fiscal observation window", + "ledger_selector": { + "source_name": "cms_medicaid", + "period_type": "month", + "period_value": _FISCAL_MONTHS, + }, + "value_operation": "monthly_window_average", + "period_match_policy": "source_window", + "entity": "person", + "measure": "enrollment", + "period": 2025, + } + fields.update(overrides) + return LedgerTargetReference(**fields) + + +def test_explicit_monthly_window_averages_cross_year_without_restamping_model(): + rows = [ + _window_fact_row(month, value=index) + for index, month in enumerate(_FISCAL_MONTHS) + ] + # These observations are deliberately outside the selected window. + rows += [ + _window_fact_row("2025-03", value=1000), + _window_fact_row("2026-04", value=1000), + ] + (target,) = compile_ledger_target_references( + rows[::-1], [_window_reference()], country="us" + ).specs + assert target.period == 2025 + assert target.value == 5.5 # Mean of the twelve source cells, including zero. + assert target.metadata["ledger_member_fact_count"] == "12" + assert target.metadata["ledger_fact_period"] == "2026-03" + assert target.metadata["ledger_period_match_policy"] == "source_window" + assert json.loads(target.metadata["ledger_source_months"]) == _FISCAL_MONTHS + assert target.metadata["ledger_source_month_count"] == "12" + assert ( + target.metadata["ledger_source_release_key"] + == "ledger.source_release.v2:window-fixture" + ) + assert target.metadata["ledger_source_sha256"] == "a" * 64 + + +@pytest.mark.parametrize("defect", ["missing", "duplicate", "other-series"]) +def test_monthly_window_requires_complete_unique_single_series(defect): + rows = [_window_fact_row(month, value=1) for month in _FISCAL_MONTHS] + if defect == "missing": + rows.pop(0) + elif defect == "duplicate": + rows.append(_window_fact_row(_FISCAL_MONTHS[0], value=2)) + rows[-1]["aggregate_fact_key"] += "-duplicate" + else: + rows[0]["layout"]["groupby_value_id"] = "other-enrollment" + with pytest.raises(ValueError, match="monthly window"): + compile_ledger_target_references(rows, [_window_reference()], country="us") + + +@pytest.mark.parametrize( + "fields", + [ + {"value_operation": "sum"}, + {"period_match_policy": "latest_not_after"}, + {"period_match_policy": "exact"}, + {"period": None}, + {"ledger_selector": {"period_type": "year", "period_value": _FISCAL_MONTHS}}, + {"ledger_selector": {"period_type": "month", "period_value": []}}, + {"ledger_selector": {"period_type": "month", "period_value": "2025-04"}}, + {"ledger_selector": {"period_type": "month", "period_value": ["2025-4"]}}, + {"ledger_selector": {"period_type": "month", "period_value": ["2025-13"]}}, + { + "ledger_selector": { + "period_type": "month", + "period_value": _FISCAL_MONTHS[::-1], + } + }, + { + "ledger_selector": { + "period_type": "month", + "period_value": ["2025-04", "2025-04"], + } + }, + ], +) +def test_monthly_window_requires_paired_policy_and_explicit_ordered_months(fields): + with pytest.raises(ValueError): + _window_reference(**fields) + + +def test_monthly_window_direct_fact_route_cannot_bypass_coverage_or_selector(): + reference = _window_reference() + with pytest.raises(ValueError, match="monthly window"): + target_spec_from_ledger_reference( + _window_fact_row("2026-03", value=99), reference + ) + rows = [_window_fact_row(month, value=1) for month in _FISCAL_MONTHS] + rows[-1]["period"]["value"] = "2026-04" + with pytest.raises(ValueError, match="monthly window"): + target_spec_from_ledger_reference(tuple(rows), reference) + + +def test_calendar_average_keeps_same_year_selection_and_future_fact_refusal(): + reference = _window_reference( + value_operation="calendar_year_average", period_match_policy="latest_not_after" + ) + rows = [ + _window_fact_row(month, value=index) + for index, month in enumerate(_FISCAL_MONTHS) + ] + (target,) = compile_ledger_target_references(rows, [reference], country="us").specs + assert target.value == 4 # Existing calendar semantics consume April–December. + assert target.metadata["ledger_member_fact_count"] == "9" + with pytest.raises(ValueError, match="at or before"): + target_spec_from_ledger_reference(rows[-1], reference) + + +def _window_cell_rows(): + rows = [] + for index, month in enumerate(_FISCAL_MONTHS): + for category, value in (("No", index), ("Yes", index + 20)): + row = _window_fact_row(month, value=value) + row["aggregate_fact_key"] += category + row["legacy_fact_key"] += category + row["semantic_fact_key"] += category + row["dimensions"] = {"family": "unknown", "entitlement": category} + row["source"]["vintage"] = "release_2026_06" + row["source"]["source_file"] = "published-joint.csv" + rows.append(row) + return rows + + +def _sum_window_reference(**overrides): + fields = { + "value_operation": "monthly_window_sum_average", + "value_operands": tuple( + {"dimension_values": {"family": "unknown", "entitlement": category}} + for category in ("No", "Yes") + ), + } + fields.update(overrides) + return _window_reference(**fields) + + +def test_monthly_window_sums_declared_cells_before_averaging_months(): + (target,) = compile_ledger_target_references( + _window_cell_rows()[::-1], [_sum_window_reference()], country="us" + ).specs + assert target.value == 31 # Mean(index + index + 20), not mean of 24 cells. + assert target.metadata["ledger_member_fact_count"] == "24" + assert target.metadata["ledger_source_month_count"] == "12" + assert target.metadata["ledger_source_cell_count_per_month"] == "2" + assert len(json.loads(target.metadata["ledger_member_fact_keys"])) == 24 + + +@pytest.mark.parametrize( + "defect", + [ + "missing-cell", + "duplicate-cell", + "extra-cell", + "source-release", + "measure", + "geography", + "entity", + "source-package", + "release-key", + "raw-hash", + "hidden-universe", + ], +) +def test_monthly_window_cell_grid_refuses_incomplete_or_incompatible_members(defect): + rows = _window_cell_rows() + if defect == "missing-cell": + rows.pop() + elif defect in {"duplicate-cell", "extra-cell"}: + row = _window_cell_rows()[0] + row["aggregate_fact_key"] += "extra" + row["legacy_fact_key"] += "extra" + row["semantic_fact_key"] += "extra" + if defect == "extra-cell": + row["dimensions"]["entitlement"] = "all" + rows.append(row) + elif defect == "source-release": + rows[0]["source"]["vintage"] = "different_release" + elif defect == "source-package": + rows[0]["layout"]["record_set_id"] = "different_package.2025-04" + elif defect == "release-key": + rows[0]["source_release_key"] = "other-release" + elif defect == "raw-hash": + rows[0]["source"]["source_sha256"] = "other-raw-source" + elif defect == "hidden-universe": + rows[0]["universe_constraints"]["constraints"] = [ + {"variable": "hidden_subset", "operator": "==", "value": "other"} + ] + elif defect == "measure": + rows[0]["observed_measure"]["source_measure_id"] = "different_measure" + elif defect == "geography": + rows[0]["geography"]["id"] = "different_geography" + else: + rows[0]["entity"]["name"] = "different_entity" + with pytest.raises(ValueError, match="monthly window"): + compile_ledger_target_references(rows, [_sum_window_reference()], country="us") + + +@pytest.mark.parametrize( + "operands", + [ + (), + ({"dimension_values": {"family": ["single", "couple"]}},), + ({"dimension_values": {"family": "single"}},) * 2, + ( + {"dimension_values": {"family": "single"}}, + {"dimension_values": {"entitlement": "Yes"}}, + ), + ], +) +def test_monthly_window_sum_requires_disjoint_complete_scalar_operands(operands): + with pytest.raises(ValueError, match="monthly window"): + _sum_window_reference(value_operands=operands) + + +def test_monthly_window_does_not_override_projection_policy_or_declared_grid_size(): + rows = _window_cell_rows() + rows[-1]["assertion"] = "source_projection" + with pytest.raises(ValueError, match="monthly window"): + compile_ledger_target_references(rows, [_sum_window_reference()], country="us") + with pytest.raises(ValueError, match="assertion_policy"): + target_spec_from_ledger_reference(tuple(rows), _sum_window_reference()) + (target,) = compile_ledger_target_references( + rows, + [_sum_window_reference(assertion_policy="allow_source_projection")], + country="us", + ).specs + assert target.value == 31 + with pytest.raises(ValueError, match="expected_member_count"): + compile_ledger_target_references( + rows, + [ + _sum_window_reference( + assertion_policy="allow_source_projection", expected_member_count=12 + ) + ], + country="us", + ) + + +def test_monthly_window_preserves_distinct_fact_identity_for_each_member(): + rows = _window_cell_rows() + rows[-1]["aggregate_fact_key"] = rows[0]["aggregate_fact_key"] + with pytest.raises(ValueError, match="member fact identities"): + target_spec_from_ledger_reference(tuple(rows), _sum_window_reference()) + + +@pytest.mark.parametrize("field", ["source_release_key", "source_sha256"]) +def test_single_series_monthly_window_refuses_mixed_publications(field): + rows = [_window_fact_row(month, value=1) for month in _FISCAL_MONTHS] + if field == "source_release_key": + rows[0][field] = "other-release" + else: + rows[0]["source"][field] = "other-raw-hash" + with pytest.raises(ValueError, match="source publication"): + compile_ledger_target_references(rows, [_window_reference()], country="us") + def _ledger_fact(**overrides): fact = { @@ -3262,3 +3540,27 @@ def test__given_chronicle_era_reference_pin__then_it_resolves_against_the_feed() registry.specs[0].metadata["ledger_fact_key"] == "chronicle.aggregate_fact.v3:def456" ) + + +# Equal missing identities must not count as a known common publication. +@pytest.mark.parametrize("summed", [False, True]) +@pytest.mark.parametrize("field", ["source_release_key", "source_sha256"]) +@pytest.mark.parametrize("bad_value", [None, "", " ", 1]) +def test_monthly_window_requires_actual_publication_identity(summed, field, bad_value): + rows = ( + _window_cell_rows() + if summed + else [_window_fact_row(month, value=1) for month in _FISCAL_MONTHS] + ) + reference = _sum_window_reference() if summed else _window_reference() + for row in rows: + owner = row if field == "source_release_key" else row["source"] + if bad_value is None: + owner.pop(field) + else: + owner[field] = bad_value + with pytest.raises(ValueError, match="requires nonempty source_release_key"): + compile_ledger_target_references(rows, [reference], country="us") + # The same authority must run if a caller bypasses selector resolution. + with pytest.raises(ValueError, match="requires nonempty source_release_key"): + target_spec_from_ledger_reference(tuple(rows), reference) diff --git a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py index 3f6f742c1..87df2dbe0 100644 --- a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py +++ b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py @@ -55,7 +55,7 @@ ), ( "uk", - "cfb7d43a35970b69177f4bc5097a7a108ab9c0442839cd6795bb42bebe37ba0e", + "6a8003d9246f8e4c7bd60b95f3f9ff599dfb5e4ffdc4323f6603f75a46f611b6", { "benunit.benunit_id", "household.household_id", diff --git a/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py b/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py index ac4d4dfa4..6b431828f 100644 --- a/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py +++ b/packages/microcosm-build/tests/test_uk_calibration_seam_driver.py @@ -126,30 +126,34 @@ def test_driver_refuses_bad_sha_and_path_alias(tmp_path: Path): def test_driver_refuses_feed_outside_the_committed_pin_without_override(): driver = _load_driver_module() - with pytest.raises(SystemExit, match="committed UK feed pin"): + with pytest.raises(SystemExit, match="committed UK national feed pin"): driver._check_committed_ledger_feed_pin( "b" * 64, + manifest_sha256=driver.load_uk_national_chronicle_feed().manifest_sha256, allow_unpinned_feed=False, ) driver._check_committed_ledger_feed_pin( "b" * 64, + manifest_sha256="c" * 64, allow_unpinned_feed=True, ) +@pytest.mark.parametrize("allow_unpinned_feed", [False, True]) def test_driver_threads_registry_exclusions_resolver_and_overrides( - monkeypatch, tmp_path, capsys + monkeypatch, tmp_path, capsys, allow_unpinned_feed ): driver = _load_driver_module() calls = [] registry = _registry() pruned_registry = TargetRegistry([], country="uk") + pin = driver.load_uk_national_chronicle_feed() artifact = SimpleNamespace( path=tmp_path / "ledger", facts=({"fact": 1},), - facts_sha256=driver._LEDGER_FACT_FEED_PIN["facts_sha256"], - manifest_sha256="c" * 64, + facts_sha256="b" * 64 if allow_unpinned_feed else pin.facts_sha256, + manifest_sha256="c" * 64 if allow_unpinned_feed else pin.manifest_sha256, ) artifact.path.mkdir() (artifact.path / "consumer_facts.jsonl").write_text("{}", encoding="utf-8") @@ -199,7 +203,8 @@ def fake_run(**kwargs): argv[argv.index("--input-sha256") + 1] = driver._sha256_file( Path(argv[argv.index("--input-h5") + 1]) ) - result = driver.main(argv + ["--epochs", "128"]) + extra = ["--allow-unpinned-feed"] if allow_unpinned_feed else [] + result = driver.main(argv + ["--epochs", "128", *extra]) assert result == 0 call = calls[0] @@ -214,12 +219,13 @@ def fake_run(**kwargs): call["measure_resolver"].kwargs["simulation_source"] == call["paths"].input_h5 ) assert call["source_pins"]["ledger_facts"] == { - "sha256": driver._LEDGER_FACT_FEED_PIN["facts_sha256"], + "sha256": artifact.facts_sha256, "size_bytes": 2, } assert call["run_config_extra"] == { "calibration_year": 2025, - "allow_unpinned_feed": False, + "allow_unpinned_feed": allow_unpinned_feed, + "national_chronicle_feed_pin": pin.to_dict(), } assert "uk_target_fit" in capsys.readouterr().out @@ -233,3 +239,49 @@ def test_driver_refuses_the_national_release_id(tmp_path: Path): args[base.index("--release-id") + 1] = "microcosm-uk-2024-25-national" with pytest.raises(SystemExit): driver._parse_args(args) + + +def test_driver_accepts_the_merged_national_feed_without_local_promotion(): + from microcosm.build.uk_runtime.local_target_census import _LEDGER_FACT_FEED_PIN + + driver = _load_driver_module() + driver._check_committed_ledger_feed_pin( + "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", + manifest_sha256="a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f", + allow_unpinned_feed=False, + ) + assert _LEDGER_FACT_FEED_PIN["facts_sha256"] == ( + "6ae49d7d7ab297df25a0b9bfe2d6776827c672d284fbb360957fe8337089549f" + ) + assert _LEDGER_FACT_FEED_PIN["manifest_sha256"] == ( + "dcda51d6496aea67f768a284e7955c7520e7c8b91e2bed3569f247567b7153f0" + ) + + +@pytest.mark.parametrize("manifest_sha256", ["c" * 64, None]) +def test_driver_refuses_unpinned_national_manifest(manifest_sha256): + driver = _load_driver_module() + with pytest.raises(SystemExit, match="manifest"): + driver._check_committed_ledger_feed_pin( + "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5", + manifest_sha256=manifest_sha256, + allow_unpinned_feed=False, + ) + + +def test_driver_checks_loaded_manifest_before_compiling_targets(monkeypatch, tmp_path): + driver = _load_driver_module() + pin = driver.load_uk_national_chronicle_feed() + artifact = SimpleNamespace(facts_sha256=pin.facts_sha256, manifest_sha256="c" * 64) + monkeypatch.setattr( + driver, "load_ledger_consumer_artifact", lambda *a, **k: artifact + ) + monkeypatch.setattr( + driver, + "compile_uk_target_registry", + lambda *a, **k: pytest.fail( + "Targets must not compile from an unpinned artifact" + ), + ) + with pytest.raises(SystemExit, match="manifest"): + driver.main(_args(tmp_path)) diff --git a/packages/microcosm-build/tests/test_uk_ledger_targets.py b/packages/microcosm-build/tests/test_uk_ledger_targets.py index 2977e0f91..1a011a760 100644 --- a/packages/microcosm-build/tests/test_uk_ledger_targets.py +++ b/packages/microcosm-build/tests/test_uk_ledger_targets.py @@ -801,6 +801,12 @@ def _uc_composition_frame(): "LONE_PARENT", "SINGLE", ], + "uc_calibration_administrative_family_type": [ + "LONE_PARENT", + "UNKNOWN", + "UNKNOWN", + "SINGLE", + ], "universal_credit": [100.0, 0.0, 0.0, 100.0], "uc_calibration_child_count": [1, 0, 1, 0], } @@ -885,6 +891,8 @@ def test_uc_national_counts_exclude_ni_and_unknown_regions_at_benunit_grain(): "benunit_id": np.arange(10), "universal_credit": np.full(10, 29_000.0), "uc_calibration_family_type": ["COUPLE_NO_CHILDREN"] * 10, + "uc_calibration_administrative_family_type": ["COUPLE_NO_CHILDREN"] + * 10, "uc_calibration_child_count": np.ones(10), } ), diff --git a/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py b/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py new file mode 100644 index 000000000..8b4935162 --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_national_chronicle_feed.py @@ -0,0 +1,59 @@ +from __future__ import annotations + +import hashlib +import json +from importlib.resources import files + +import pytest + + +def test_national_feed_records_the_complete_merged_source_artifact(): + from microcosm.build.uk_runtime.national_chronicle_feed import ( + load_uk_national_chronicle_feed, + ) + + pin = load_uk_national_chronicle_feed() + resource = files("microcosm.build.uk").joinpath("national_chronicle_feed.json") + raw = resource.read_bytes() + assert pin.source_commit == "ec7169b5db40b9f54117c80f70f14efc1dd0fedd" + assert pin.source_repo == "PolicyEngine/chronicle" + assert pin.fact_row_count == 131450 + assert pin.facts_sha256 == ( + "4a50ee9568a01bbb57f73d927084ed6b4b9e52249b51a2338455874ae6e382b5" + ) + assert pin.manifest_sha256 == ( + "a95d0ee9f87f36947eaecdb3de29cf81a91e47ccaa822fed42da677eedca877f" + ) + assert pin.artifact_schema_version == "policyengine_ledger.consumer_artifact.v2" + assert pin.consumer_fact_schema_sha256 == ( + "76ac268e626c86146cee51193e0cbecbb197ddbf3bf410156fe7da7c0edae3ad" + ) + assert pin.resource_sha256 == hashlib.sha256(raw).hexdigest() + assert pin.resource_size_bytes == len(raw) + assert pin.to_dict()["source_commit"] == pin.source_commit + + +@pytest.mark.parametrize( + ("field", "bad_value"), + [ + ("facts_sha256", "not-a-digest"), + ("manifest_sha256", "A" * 64), + ("consumer_fact_schema_sha256", "a" * 63), + ("source_commit", "ec7169b5"), + ("fact_row_count", True), + ("country", "us"), + ], +) +def test_national_feed_rejects_malformed_identity( + monkeypatch, tmp_path, field, bad_value +): + from microcosm.build.uk_runtime import national_chronicle_feed + + raw = json.loads(national_chronicle_feed._feed_path().read_text()) + raw[field] = bad_value + path = tmp_path / "pin.json" + path.write_text(json.dumps(raw)) + monkeypatch.setattr(national_chronicle_feed, "_feed_path", lambda: path) + + with pytest.raises(ValueError, match=field): + national_chronicle_feed.load_uk_national_chronicle_feed() diff --git a/packages/microcosm-build/tests/test_uk_population_targets.py b/packages/microcosm-build/tests/test_uk_population_targets.py index 94f6235c2..b57399a30 100644 --- a/packages/microcosm-build/tests/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/test_uk_population_targets.py @@ -50,6 +50,7 @@ "dimension_values", "entity_name", "period_type", + "period_value", "layout_groupby_value_id", } LOCAL_SELECTOR_KEYS = {"source_name", "source_measure_id", "record_set_spec_id"} @@ -163,7 +164,13 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: resource = _load() assert resource["country"] == "uk" - assert resource["allowed_value_operations"] == ["identity", "sum", "difference"] + assert resource["allowed_value_operations"] == [ + "identity", + "sum", + "difference", + "calendar_year_average", + "monthly_window_sum_average", + ] assert resource["resolution_defaults"] == { "base_period_policy": "latest_not_after_build_base_period", "operation": "sum", @@ -555,7 +562,7 @@ def test_uk_population_uc_households_target_counts_benunits() -> None: assert target["measurement"] == { "entity": "benunit", "concept": "uk.benefit_unit.count", - "source_months": [f"2025-{month:02}" for month in range(4, 13)], + "source_months": [f"2025-{month:02}" for month in range(1, 13)], "filters": [ { "concept": "uk.benefits.universal_credit.amount", @@ -575,12 +582,29 @@ def test_uk_population_uc_households_target_counts_benunits() -> None: ] assert target["ledger_selector"] == { "source_name": "dwp", - "source_concept": "dwp.uc_benefit_units", - "source_measure_id": "total_units", - "groupby_dimension": "dwp.uc_deductions_month", + "source_concept": "dwp.uc_households", + "source_measure_id": "benefit_units", + "groupby_dimension": "dwp.uc_family_type", + "dimensions": ["family_type", "payment_indicator", "child_entitlement"], + "dimension_values": { + "payment_indicator": "Yes", + "child_entitlement": ["No", "Yes"], + "family_type": [ + "Single, no children", + "Single, with children", + "Couple, no children", + "Couple, with children", + "Unknown or missing family type", + ], + }, + "period_type": "month", + "period_value": [f"2025-{month:02}" for month in range(1, 13)], } - assert "6,758,889" in target["bindings"]["policyengine"]["notes"] - assert "1.45%" in target["bindings"]["policyengine"]["notes"] + assert target["value_operation"] == "monthly_window_sum_average" + assert target["period_match_policy"] == "source_window" + assert len(target["value_operands"]) == 10 + assert "derived" in target["bindings"]["policyengine"]["notes"] + assert "publisher grand Total" in target["bindings"]["policyengine"]["notes"] def test_uk_uc_composition_and_disability_children_targets_are_rebound() -> None: @@ -599,7 +623,7 @@ def test_uk_uc_composition_and_disability_children_targets_are_rebound() -> None allowed_filter_variables = { "universal_credit", "uc_calibration_child_count", - "uc_calibration_family_type", + "uc_calibration_administrative_family_type", } for target_id in composition_target_ids: @@ -631,6 +655,103 @@ def test_uk_uc_composition_and_disability_children_targets_are_rebound() -> None assert binding["filters"] == [{"variable": flag, "operator": ">", "value": 0}] +def test_paid_uc_targets_preserve_entitlement_margins_and_exact_calendar_months() -> ( + None +): + """#252's paid margin is broader than its paid/child-entitled joint. + + No target may silently replace either entitlement status with Yes, or + reconstruct the independently published child-count Total from detail cells. + """ + targets = _load()["targets"] + months = [f"2025-{month:02}" for month in range(1, 13)] + paid_targets = [ + t + for t in targets + if t["target_id"] == "dwp.uc.households" + or t["target_id"].startswith("dwp.uc.households_children_") + or t["target_id"] + in { + "dwp.uc.households_single_no_children", + "dwp.uc.households_single_with_children", + "dwp.uc.households_couple_no_children", + "dwp.uc.households_couple_with_children", + } + ] + assert len(paid_targets) == 10 + for target in paid_targets: + selector = target["ledger_selector"] + assert target["measurement"]["source_months"] == months + assert selector["period_type"] == "month" + assert selector["period_value"] == months + assert selector["dimension_values"]["payment_indicator"] == "Yes" + assert not any( + f["variable"] == "uc_calibration_child_entitlement" + for f in target["bindings"]["policyengine"]["filters"] + ) + if target["target_id"].startswith("dwp.uc.households_children_"): + assert selector["source_measure_id"] == "total_benefit_units" + assert selector["dimension_values"]["child_entitlement"] == "all" + assert target["value_operation"] == "calendar_year_average" + assert "value_operands" not in target + else: + assert selector["source_measure_id"] == "benefit_units" + assert target["value_operation"] == "monthly_window_sum_average" + assert target["period_match_policy"] == "source_window" + terms = [o["dimension_values"] for o in target["value_operands"]] + assert all( + set(term) == {"family_type", "payment_indicator", "child_entitlement"} + for term in terms + ) + assert {term["child_entitlement"] for term in terms} == {"No", "Yes"} + assert len(terms) == ( + 10 if target["target_id"] == "dwp.uc.households" else 2 + ) + families = selector["dimension_values"]["family_type"] + if isinstance(families, str): + families = [families] + assert { + ( + term["family_type"], + term["payment_indicator"], + term["child_entitlement"], + ) + for term in terms + } == { + (family, "Yes", entitlement) + for family in families + for entitlement in ("No", "Yes") + } + + +def test_uc_payment_bands_share_administrative_family_but_keep_source_window() -> None: + targets = [ + t + for t in _load()["targets"] + if t["target_id"].startswith("dwp.uc.payment_distribution_") + ] + assert len(targets) == 4 + for target in targets: + assert target["measurement"]["source_months"] == [ + f"2025-{month:02}" for month in range(4, 13) + ] + assert ( + target["bindings"]["policyengine"]["filters"][0]["variable"] + == "uc_calibration_administrative_family_type" + ) + assert target["bindings"]["policyengine"]["band_upper_bound"] == 2500 + + +def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: + targets = _load()["targets"] + assert len(targets) == 235 + assert not any( + f.get("variable") == "uc_calibration_child_entitlement" + for target in targets + for f in target["bindings"]["policyengine"].get("filters", []) + ) + + def test_uc_gb_bindings_match_the_committed_source_geography_and_finite_bands(): """Source K03000001 is GB; DWP £2400.01–2500 is not its £2500.01+ row.""" resource = _load() diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 7ebee82dd..0d2a8df5c 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -2,7 +2,7 @@ ``uk/target_references.json`` is the typed activation surface derived from the value-free UK national contract. Observed values remain in Ledger facts; this -resource only declares which 2023 country-level facts Microcosm activates. +resource only declares which country-level facts Microcosm activates for 2025. """ from __future__ import annotations @@ -31,12 +31,16 @@ author_target_references, ) from microcosm.build.uk_runtime.local_target_census import _LEDGER_FACT_FEED_PIN +from microcosm.build.uk_runtime.national_chronicle_feed import ( + load_uk_national_chronicle_feed, +) from microcosm.calibrate.matrix import build_constraint_matrix from microcosm.frame import EntitySchema, Frame, WeightKind, Weights from tools.generate_uk_target_references import ( POLICYENGINE_BINDING_KEYS, _annual_uc_award_band_token, _geography_pins, + _reference_metadata, _sum_target_ids, _value_operation_by_target_id, ) @@ -74,12 +78,20 @@ def _sha256(path: Path) -> str: return hashlib.sha256(path.read_bytes()).hexdigest() -def test_committed_surfaces_regenerate_from_pinned_feed(tmp_path: Path) -> None: +@pytest.mark.parametrize("surface", ["national", "local"]) +def test_committed_surfaces_regenerate_from_pinned_feed( + tmp_path: Path, surface: str +) -> None: root = Path(__file__).resolve().parents[3] - configured = os.environ.get("CHRONICLE_UK_FACTS") + # Local references keep their independently reviewed historical input. + # Never fall back to the configured national feed for the local surface. + environment_key = ( + "CHRONICLE_UK_FACTS" if surface == "national" else "CHRONICLE_UK_LOCAL_FACTS" + ) + configured = os.environ.get(environment_key) feed = Path(configured) if configured else root / STABLE_UK_FACT_FEED_NAME if not feed.exists(): - pytest.skip("pinned UK Chronicle consumer feed is not present") + pytest.skip(f"pinned UK Chronicle {surface} consumer feed is not present") if feed.is_dir(): artifact_path = feed @@ -89,18 +101,28 @@ def test_committed_surfaces_regenerate_from_pinned_feed(tmp_path: Path) -> None: default_manifest if not configured else feed.with_name("manifest.json") ) if not manifest.is_file(): - pytest.skip("pinned UK Chronicle consumer manifest is not present") + pytest.skip( + f"pinned UK Chronicle {surface} consumer manifest is not present" + ) artifact_path = tmp_path / "consumer-artifact" artifact_path.mkdir() (artifact_path / "consumer_facts.jsonl").symlink_to(feed.resolve()) (artifact_path / "manifest.json").symlink_to(manifest.resolve()) + if surface == "national": + pin = load_uk_national_chronicle_feed() + facts_sha256, manifest_sha256 = pin.facts_sha256, pin.manifest_sha256 + expected_rows = pin.fact_row_count + else: + facts_sha256 = _LEDGER_FACT_FEED_PIN["facts_sha256"] + manifest_sha256 = _LEDGER_FACT_FEED_PIN["manifest_sha256"] + expected_rows = 128_717 artifact = load_ledger_consumer_artifact( artifact_path, - expected_facts_sha256=_LEDGER_FACT_FEED_PIN["facts_sha256"], - expected_manifest_sha256=_LEDGER_FACT_FEED_PIN["manifest_sha256"], + expected_facts_sha256=facts_sha256, + expected_manifest_sha256=manifest_sha256, ) - assert artifact.fact_row_count == 128_717 + assert artifact.fact_row_count == expected_rows facts_path = ( artifact.path / "consumer_facts.jsonl" if artifact.path.is_dir() @@ -110,58 +132,32 @@ def test_committed_surfaces_regenerate_from_pinned_feed(tmp_path: Path) -> None: package = root / "packages/microcosm-build/src/microcosm/build/uk" generated = tmp_path / "generated" generated.mkdir() - national = generated / "target_references.json" - national_membership = generated / "target_reference_membership.json" - local = generated / "local_target_references.json" - local_membership = generated / "local_target_reference_membership.json" - common = [sys.executable] - subprocess.run( - [ - *common, - str(root / "tools/generate_uk_target_references.py"), - "--contract", - str(package / "uk_population_targets.json"), - "--ledger-facts", - str(facts_path), - "--source-fact-feed", - STABLE_UK_FACT_FEED_NAME, - "--output", - str(national), - "--membership-report", - str(national_membership), - ], - cwd=root, - check=True, - ) - subprocess.run( - [ - *common, - str(root / "tools/generate_uk_local_target_references.py"), - "--contract", - str(package / "uk_population_targets.json"), - "--ledger-facts", - str(facts_path), - "--source-fact-feed", - STABLE_UK_FACT_FEED_NAME, - "--crosswalk", - str(package / "local_area_crosswalk.json"), - "--output", - str(local), - "--membership-report", - str(local_membership), - ], - cwd=root, - check=True, - ) - - for regenerated, committed_name in ( - (national, "target_references.json"), - (national_membership, "target_reference_membership.json"), - (local, "local_target_references.json"), - (local_membership, "local_target_reference_membership.json"), - ): - committed = package / committed_name - assert _sha256(regenerated) == _sha256(committed), committed_name + prefix = "" if surface == "national" else "local_" + references_name = f"{prefix}target_references.json" + membership_name = f"{prefix}target_reference_membership.json" + generator = f"generate_uk_{prefix}target_references.py" + # This stable provenance label is independent of the artifact location; + # the facts and manifest above must still match the surface-specific pin. + source_fact_feed = _load_uk_resource(membership_name)["source_fact_feed"] + arguments = [ + sys.executable, + str(root / "tools" / generator), + "--contract", + str(package / "uk_population_targets.json"), + "--ledger-facts", + str(facts_path), + "--source-fact-feed", + source_fact_feed, + "--output", + str(generated / references_name), + "--membership-report", + str(generated / membership_name), + ] + if surface == "local": + arguments.extend(["--crosswalk", str(package / "local_area_crosswalk.json")]) + subprocess.run(arguments, cwd=root, check=True) + for name in (references_name, membership_name): + assert _sha256(generated / name) == _sha256(package / name), name def _contract_targets_by_id() -> dict[str, dict]: @@ -174,27 +170,8 @@ def _expected_reference_entity(target: dict) -> str: return binding.get("from_entity") or binding.get("map_to") or "household" -def _uc_benefit_units_fact(period: str, *, value: float) -> dict: - normalized_period = period.replace("-", "_") - return { - "aggregate_fact_key": f"ledger.aggregate_fact.v2:uc-{normalized_period}", - "aggregation": {"method": "sum"}, - "assertion": "observation", - "geography": {"level": "country", "id": "K03000001"}, - "layout": { - "groupby_dimension": "dwp.uc_deductions_month", - "measure_id": "total_units", - "record_set_id": f"dwp.uc_deductions.{period}.total_units", - }, - "observed_measure": { - "source_name": "dwp", - "source_concept": "dwp.uc_benefit_units", - "source_measure_id": "total_units", - "unit": "count", - }, - "period": {"type": "month", "value": period}, - "value": value, - } +def _fixture_feed_rows() -> list[dict]: + return [json.loads(line) for line in FIXTURE_FEED_ROWS.read_text().splitlines()] def test_uk_target_references_load_as_typed_non_empty_resource() -> None: @@ -217,6 +194,7 @@ def test_uk_target_references_follow_contract_derivation_rules() -> None: "calendar_year_average", "latest_plateau", "count_x_mean", + "monthly_window_sum_average", ] assert len(names) == len(set(names)) @@ -377,13 +355,11 @@ def test_ons_age_total_targets_pin_exact_age_dimension_set() -> None: ] == ["age"] -def test_uc_composition_targets_pin_exact_dimension_sets_without_triggering_sum() -> ( - None -): +def test_uc_composition_targets_pin_paid_cells_and_explicit_month_windows() -> None: contract = _load_uk_resource("uk_population_targets.json") targets = {target["target_id"]: target for target in contract["targets"]} sum_target_ids = _sum_target_ids(contract) - + operations = _value_operation_by_target_id(contract) composition = { target_id: target for target_id, target in targets.items() @@ -393,20 +369,40 @@ def test_uc_composition_targets_pin_exact_dimension_sets_without_triggering_sum( assert composition for target_id, target in composition.items(): selector = target["ledger_selector"] - assert set(selector["dimension_values"]) <= set(selector["dimensions"]) - assert target_id not in sum_target_ids - - for target_id, target in composition.items(): - selector = target["ledger_selector"] + pins = selector["dimension_values"] + assert set(pins) <= set(selector["dimensions"]) if target_id.startswith("dwp.uc.households_children_"): - assert selector["dimensions"] == ["number_of_children"] - elif target_id.startswith( - ( - "dwp.uc.households_single_", - "dwp.uc.households_couple_", - ) + assert selector["dimensions"] == [ + "number_of_children", + "payment_indicator", + "child_entitlement", + ] + assert pins["payment_indicator"] == "Yes" + assert pins["child_entitlement"] == "all" + assert selector["source_measure_id"] == "total_benefit_units" + assert operations[target_id] == "calendar_year_average" + elif target_id == "dwp.uc.households" or target_id.startswith( + ("dwp.uc.households_single_", "dwp.uc.households_couple_") ): - assert selector["dimensions"] == ["family_type"] + assert selector["dimensions"] == [ + "family_type", + "payment_indicator", + "child_entitlement", + ] + assert pins["payment_indicator"] == "Yes" + assert pins["child_entitlement"] == ["No", "Yes"] + assert selector["source_measure_id"] == "benefit_units" + assert operations[target_id] == "monthly_window_sum_average" + else: + # Merely naming a dimension set still does not request a sum. + assert target_id not in sum_target_ids + continue + # The list of months makes these candidates for aggregation, but the + # declared operation averages months rather than summing a year's stock. + assert target_id in sum_target_ids + assert selector["period_value"] == [ + f"2025-{month:02d}" for month in range(1, 13) + ] def test_prefix_geography_pins_carry_scotgov_and_england_scoped_slc_families() -> None: @@ -661,67 +657,129 @@ def test_uk_target_references_compile_from_real_staged_feed_rows() -> None: assert targets["hmrc.cgt.gains_total"].metadata["ledger_fact_period"] == "2023" caseload = targets["dwp.uc.households"] - assert caseload.value == pytest.approx(6_758_888.888888889) - assert caseload.metadata["ledger_member_fact_count"] == "9" - assert caseload.metadata["ledger_value_operation"] == "calendar_year_average" + assert caseload.value == 6_197_311 + assert caseload.metadata["ledger_member_fact_count"] == "120" + assert caseload.metadata["ledger_value_operation"] == "monthly_window_sum_average" + assert caseload.metadata["ledger_source_month_count"] == "12" + assert caseload.metadata["ledger_source_cell_count_per_month"] == "10" family_type = targets["dwp.uc.households_single_no_children"] - assert family_type.value == 3_725_304 - assert "ledger_member_fact_count" not in family_type.metadata - assert family_type.metadata["ledger_value_operation"] == "calendar_year_average" + assert family_type.value == pytest.approx(2_990_070.1666666665) + assert family_type.metadata["ledger_member_fact_count"] == "24" + assert ( + family_type.metadata["ledger_value_operation"] == "monthly_window_sum_average" + ) + assert family_type.metadata["ledger_source_month_count"] == "12" + assert family_type.metadata["ledger_source_cell_count_per_month"] == "2" -def test_uk_generator_assigns_calendar_average_to_the_whole_uc_family() -> None: +def test_uk_generator_averages_paid_monthly_sums_and_preserves_other_uc_operations() -> ( + None +): contract = _load_uk_resource("uk_population_targets.json") operations = _value_operation_by_target_id(contract) + monthly_sum_ids = { + "dwp.uc.households", + "dwp.uc.households_single_no_children", + "dwp.uc.households_single_with_children", + "dwp.uc.households_couple_no_children", + "dwp.uc.households_couple_with_children", + } uc_target_ids = { target["target_id"] for target in contract["targets"] if target["family"] == "dwp_universal_credit" } - assert uc_target_ids + assert monthly_sum_ids < uc_target_ids + assert { + target_id + for target_id in uc_target_ids + if operations[target_id] == "monthly_window_sum_average" + } == monthly_sum_ids assert all( - operations[target_id] == "calendar_year_average" for target_id in uc_target_ids + operations[target_id] == "calendar_year_average" + for target_id in uc_target_ids - monthly_sum_ids ) - facts = [ - _uc_benefit_units_fact("2025-04", value=6_380_000.0), - *( - _uc_benefit_units_fact(f"2025-{month:02d}", value=6_600_000.0) - for month in range(5, 9) - ), - *( - _uc_benefit_units_fact(f"2025-{month:02d}", value=6_960_000.0) - for month in range(9, 12) - ), - _uc_benefit_units_fact("2025-12", value=7_170_000.0), - ] + facts = _fixture_feed_rows() authored = author_target_references( contract, facts, TargetReferenceAuthoringConfig( target_period=2025, geography_pins=_geography_pins(contract), - value_operation_by_target_id=_value_operation_by_target_id(contract), + value_operation_by_target_id=operations, + reference_metadata_by_target_id=_reference_metadata(contract), binding_vocabulary=POLICYENGINE_BINDING_KEYS, - source_fact_feed="synthetic-uc-benefit-units", + source_fact_feed="uk_target_reference_feed_rows.jsonl", ), ) references = {reference["name"]: reference for reference in authored.references} - uc_reference = references["dwp.uc.households"] - assert uc_reference["value_operation"] == "calendar_year_average" - assert authored.membership_report["targets"]["dwp.uc.households"]["status"] == ( - "active" + assert uc_reference["value_operation"] == "monthly_window_sum_average" + assert uc_reference["period_match_policy"] == "source_window" + assert len(uc_reference["value_operands"]) == 10 + assert ( + authored.membership_report["targets"]["dwp.uc.households"]["status"] == "active" ) - registry = compile_ledger_target_references( - facts, - [LedgerTargetReference(**uc_reference)], - country="uk", + facts, [LedgerTargetReference(**uc_reference)], country="uk" ) - assert registry.specs[0].value == pytest.approx( - (6_380_000.0 + 4 * 6_600_000.0 + 3 * 6_960_000.0 + 7_170_000.0) / 9 + # Public DWP detail cells sum to 74,367,732 across the twelve months; + # the reference is a mean monthly stock, including unknown family type. + assert registry.specs[0].value == 74_367_732 / 12 + + +def test_paid_uc_public_fixture_preserves_complete_source_cells_including_zeros() -> ( + None +): + # Exact Chronicle ec7169b5 producer rows for the public Stat-Xplore response. + source_sha256 = "f4fb46fefd66a20b8d6d12c051a224743b6754fee53e1cee0be5ead4d34de550" + rows = [ + row + for row in _fixture_feed_rows() + if row["source"]["source_sha256"] == source_sha256 + ] + assert len(rows) == 120 + assert {row["layout"]["table_record_kind"] for row in rows} == {"detail"} + assert {row["dimensions"]["payment_indicator"] for row in rows} == {"Yes"} + assert {row["period"]["value"] for row in rows} == { + f"2025-{month:02d}" for month in range(1, 13) + } + assert ( + len( + { + ( + row["period"]["value"], + row["dimensions"]["family_type"], + row["dimensions"]["child_entitlement"], + ) + for row in rows + } + ) + == 120 ) + assert sum(row["value"] for row in rows) == 74_367_732 + missing_zero = next( + row + for row in rows + if row["dimensions"]["family_type"] == "Single, no children" + and row["dimensions"]["child_entitlement"] == "Yes" + ) + assert missing_zero["value"] == 0 + reference = next( + reference + for reference in load_country_spec("uk").target_references + if reference.name == "dwp.uc.households_single_no_children" + ) + incomplete = [ + row + for row in _fixture_feed_rows() + if row["aggregate_fact_key"] != missing_zero["aggregate_fact_key"] + ] + with pytest.raises( + ValueError, match="requires exactly 24 selected facts.*resolved 23" + ): + compile_ledger_target_references(incomplete, [reference], country="uk") def test_uk_target_references_constrain_a_frame_with_prepared_columns() -> None: diff --git a/packages/microcosm-build/tests/test_uk_uc_family.py b/packages/microcosm-build/tests/test_uk_uc_family.py index 1017223c6..ff2d9d5ae 100644 --- a/packages/microcosm-build/tests/test_uk_uc_family.py +++ b/packages/microcosm-build/tests/test_uk_uc_family.py @@ -13,7 +13,10 @@ UKFrameTargetAdapter, materialize_uk_ledger_targets, ) -from microcosm.build.uk_runtime.measure_simulation import compute_uk_measure_input +from microcosm.build.uk_runtime.measure_simulation import ( + UKMeasureResolver, + compute_uk_measure_input, +) from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.calibrate.matrix import build_constraint_matrix from microcosm.frame import EntitySchema, Frame, WeightKind, Weights @@ -95,6 +98,266 @@ def test_uc_family_measure_refuses_ambiguous_claimant_count(): ) +def _administrative_fixture(allowances=None): + frame, original = _fixture() + # In benefit-unit order: lone parent; structural young couple receiving a + # single allowance; lone parent; unavailable allowance; cohabiting parents. + # The single-allowance couple is an explicit input scenario, not an assertion + # that the engine reconstructs administrative partner eligibility. + values = np.array([1200, 1200, 1200, 0, 1800], dtype=np.float32) + if allowances is not None: + values = np.asarray(allowances) + + def parameters(period): + assert period == "2025" + return SimpleNamespace( + gov=SimpleNamespace( + dwp=SimpleNamespace( + universal_credit=SimpleNamespace( + standard_allowance=SimpleNamespace( + amount=SimpleNamespace(SINGLE_YOUNG=80, SINGLE_OLD=100) + ) + ) + ) + ) + ) + + def calculate(variable, year): + if variable == "uc_standard_allowance": + return values + if variable == "uc_child_element": + # A reported 19-year-old need not attract a child element; a young + # claimant qualifying as a QYP does not make their own claim entitled. + return np.array([0, 0, 100, 0, 100]) + if variable == "universal_credit": + return np.array([100, 100, 0, 0, 100]) + return original.calculate(variable, year) + + return frame, SimpleNamespace( + calculate=calculate, + tax_benefit_system=SimpleNamespace(parameters=parameters, variables={}), + ) + + +def test_administrative_family_uses_allowance_without_rewriting_claimant_roles(): + """DWP Family Type uses the allowance, including single-rate couples. + + https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html + """ + frame, sim = _administrative_fixture() + before = frame.table("person").copy(deep=True) + values, route = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_administrative_family_type", 2025 + ) + assert values.tolist() == [ + "LONE_PARENT", + "SINGLE", + "LONE_PARENT", + "UNKNOWN", + "COUPLE_WITH_CHILDREN", + ] + assert route == "uc_allowance_and_reported_child_proxy" + structural, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_family_type", 2025 + ) + assert structural[1] == "COUPLE_NO_CHILDREN" + pd.testing.assert_frame_equal(before, frame.table("person")) + + +def test_administrative_family_uses_strict_single_maximum_in_amount_precision(): + # Compare at the precision of the model amount, not a tolerance that can + # relabel an actual amount above the maximum. Test both float widths. + for dtype in (np.float32, np.float64): + boundary = dtype(1200) + above = np.nextafter(boundary, dtype(np.inf)) + frame, sim = _administrative_fixture( + np.array([boundary, above, boundary, 0, 1800], dtype=dtype) + ) + values, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_administrative_family_type", 2025 + ) + assert values[0] == "LONE_PARENT" + assert values[1] == "COUPLE_NO_CHILDREN" + assert values[3] == "UNKNOWN" + + +@pytest.mark.parametrize("allowances", [[1200], [1200, 1200, np.nan, 0, 1800]]) +def test_administrative_family_refuses_unusable_allowances(allowances): + frame, sim = _administrative_fixture(allowances) + with pytest.raises(ValueError, match="UC.*allowance"): + compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_administrative_family_type", 2025 + ) + + +def test_child_entitlement_diagnostic_is_distinct_from_reported_children(): + frame, sim = _administrative_fixture() + children, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_child_count", 2025 + ) + entitled, route = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_child_entitlement", 2025 + ) + assert children.tolist() == [1, 0, 1, 0, 1] + assert entitled.tolist() == [False, False, True, False, True] + assert (entitled & (sim.calculate("universal_credit", 2025) > 0)).tolist() == [ + False, + False, + False, + False, + True, + ] + assert route == "uc_child_element_entitlement_proxy" + + +def test_paid_comparison_receipt_records_observation_and_family_limits(): + frame, sim = _administrative_fixture() + resolver = UKMeasureResolver.__new__(UKMeasureResolver) + resolver.frame, resolver.simulation, resolver.year = frame, sim, 2025 + resolver._receipt = {"mode": "synthetic"} + resolver._uc_tcl_measures_used = set() + assert "uc_paid_comparison_contract" not in resolver.receipt() + for variable in ( + "uc_calibration_administrative_family_type", + "uc_calibration_child_entitlement", + ): + assert resolver.knows("benunit", variable) + assert not resolver.knows("household", variable) + assert resolver.entity_for(variable) == "benunit" + resolver.compute("benunit", variable) + contract = resolver.receipt()["uc_paid_comparison_contract"] + assert contract["model_payment_proxy"] == "universal_credit > 0" + assert contract["model_child_entitlement_proxy"] == "uc_child_element > 0" + assert contract["open_nil_claims_identified"] is False + assert contract["model_period"] == "2025" + assert contract["zero_standard_allowance_family"] == "UNKNOWN" + assert "ineligible partner" in contract["family_limitations"] + assert len(contract["computed_measures"]) == 2 + + +def test_paid_diagnostic_masks_preserve_benunit_order_and_separate_joint_states(): + from microcosm.build.uk_runtime.measure_simulation import ( + compute_uc_paid_diagnostic_masks, + ) + + frame, sim = _administrative_fixture() + before = { + entity: frame.table(entity).copy(deep=True) for entity in ("person", "benunit") + } + masks = compute_uc_paid_diagnostic_masks(frame, sim, 2025) + assert frame.table("benunit").benunit_id.tolist() == [50, 30, 10, 40, 20] + assert all(mask.shape == (5,) and mask.dtype == bool for mask in masks.values()) + assert masks["paid.total"].tolist() == [True, True, False, False, True] + assert masks["model.zero_award"].tolist() == [False, False, True, True, False] + assert masks["paid.child_entitled"].tolist() == [False, False, False, False, True] + assert masks["paid.family_measure_disagreement"].tolist() == [ + False, + True, + False, + False, + False, + ] + assert masks["paid.child_count_measure_disagreement"].tolist() == [ + True, + False, + False, + False, + False, + ] + assert masks["paid.children.1"].tolist() == [True, False, False, False, True] + assert masks["paid_no_child_entitlement.family.LONE_PARENT"].tolist() == [ + True, + False, + False, + False, + False, + ] + assert masks["paid_child_entitled.family.COUPLE_WITH_CHILDREN"].tolist() == [ + False, + False, + False, + False, + True, + ] + assert not masks["paid.family.UNKNOWN"].any() + # Each partition independently conserves the paid mask; it does not create + # a family-by-child-count joint from the separate publisher tables. + for dimension, categories in { + "family": [ + "SINGLE", + "LONE_PARENT", + "COUPLE_NO_CHILDREN", + "COUPLE_WITH_CHILDREN", + "UNKNOWN", + ], + "children": ["0", "1", "2", "3", "4", "5_or_more"], + }.items(): + np.testing.assert_array_equal( + sum( + masks[f"paid.{dimension}.{category}"].astype(int) + for category in categories + ), + masks["paid.total"].astype(int), + ) + for category in categories: + np.testing.assert_array_equal( + masks[f"paid_child_entitled.{dimension}.{category}"] + | masks[f"paid_no_child_entitlement.{dimension}.{category}"], + masks[f"paid.{dimension}.{category}"], + ) + for entity in before: + pd.testing.assert_frame_equal(before[entity], frame.table(entity)) + assert not any("nil" in name or "open" in name for name in masks) + + +@pytest.mark.requires_uk +@pytest.mark.parametrize("year", [2024, 2025]) +def test_released_engine_single_allowance_is_not_misclassified_by_float_precision(year): + """Use the engine's processed year rates and real float32 allowance output.""" + from policyengine_uk import Simulation + + from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask + + frame, _ = _fixture() + person, benunit = frame.table("person"), frame.table("benunit") + claimants = frs_uc_claimant_mask(person, benunit) + people = { + str(int(row.person_id)): { + "age": {str(year): int(row.age)}, + "is_uc_claimant": {str(year): bool(claimants[i])}, + } + for i, row in enumerate(person.itertuples(index=False)) + } + units = { + str(int(bu)): { + "members": [ + str(int(pid)) + for pid in person.loc[person.person_benunit_id == bu, "person_id"] + ] + } + for bu in benunit.benunit_id + } + sim = Simulation( + situation={ + "people": people, + "benunits": units, + "households": { + key: {"members": unit["members"]} for key, unit in units.items() + }, + } + ) + values, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_administrative_family_type", year + ) + assert values.tolist() == [ + "LONE_PARENT", + "COUPLE_NO_CHILDREN", + "LONE_PARENT", + "SINGLE", + "COUPLE_WITH_CHILDREN", + ] + + def test_full_uc_payment_registry_matches_independent_relationship_and_band_cases(): """All active rows count benefit units, retaining edges of excluded bands.""" # Expected labels are stated by scenario, never calculated by the resolver. @@ -169,13 +432,31 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case EntitySchema(group_entities=("benunit", "household")), {"household": Weights(np.ones(len(hh)), WeightKind.DESIGN)}, ) - sim = SimpleNamespace(calculate=lambda variable, year: np.asarray(qualifying[::-1])) + allowance = np.array( + [1800 if family.startswith("COUPLE") else 1200 for family in expected_families] + ) + _, administrative_sim = _administrative_fixture() + sim = SimpleNamespace( + calculate=lambda variable, year: ( + allowance + if variable == "uc_standard_allowance" + else np.asarray(qualifying[::-1]) + ), + tax_benefit_system=administrative_sim.tax_benefit_system, + ) family, _ = compute_uk_measure_input( frame, sim, "benunit", "uc_calibration_family_type", 2025 ) np.testing.assert_array_equal(family, expected_families) adapter = UKFrameTargetAdapter(frame) adapter.set_column("benunit", "uc_calibration_family_type", family) + administrative_family, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_calibration_administrative_family_type", 2025 + ) + np.testing.assert_array_equal(administrative_family, expected_families) + adapter.set_column( + "benunit", "uc_calibration_administrative_family_type", administrative_family + ) refs = [ r diff --git a/packages/microcosm-build/tests/test_uk_uc_source_periods.py b/packages/microcosm-build/tests/test_uk_uc_source_periods.py index 0728e28e6..0ccee370f 100644 --- a/packages/microcosm-build/tests/test_uk_uc_source_periods.py +++ b/packages/microcosm-build/tests/test_uk_uc_source_periods.py @@ -10,14 +10,233 @@ from microcosm.build.country_spec import load_country_spec from microcosm.build.ledger_targets import LedgerTargetReference +from microcosm.build.target_reference_authoring import ( + TargetReferenceAuthoringConfig, + author_target_references, + target_references_resource, +) from microcosm.build.uk_runtime.ledger_targets import compile_uk_target_registry from microcosm.build.uk_runtime.uc_source_periods import ( EXPECTED_SOURCE_MONTHS, uc_source_month_metadata, ) -from tools.generate_uk_target_references import _reference_metadata +from tools.generate_uk_target_references import ( + _reference_metadata, + _value_operation_by_target_id, +) MONTHS = [f"2025-{month:02}" for month in range(4, 13)] +FISCAL_MONTHS = MONTHS + [f"2026-{month:02}" for month in range(1, 4)] + + +@pytest.mark.parametrize("family_count", [1, 5]) +def test_generator_and_uk_guard_count_months_separately_from_joint_cells( + monkeypatch, family_count +): + months = [f"2025-{month:02}" for month in range(1, 13)] + operands = [ + { + "dimension_values": { + "family_type": family, + "payment_indicator": "In payment", + "child_entitlement": entitlement, + } + } + for family in [ + "single", + "couple", + "single_children", + "couple_children", + "unknown", + ][:family_count] + for entitlement in ["No", "Yes"] + ] + facts = [] + for index, month in enumerate(months): + for cell, operand in enumerate(operands): + fact = _fact(month, suffix=f"-{cell}", value=index + cell) + fact["dimensions"] = operand["dimension_values"] + fact["layout"]["groupby_dimension"] = "dwp.uc_family_type" + fact["layout"]["record_set_id"] = f"dwp.uc_family_joint.{month}" + facts.append(fact) + target = { + "target_id": "uc.paid", + "family": "dwp_universal_credit", + "value_operation": "monthly_window_sum_average", + "period_match_policy": "source_window", + "value_operands": operands, + "ledger_selector": { + "source_concept": "dwp.uc_benefit_units", + "period_type": "month", + "period_value": months, + "groupby_dimension": "dwp.uc_family_type", + "dimensions": ["family_type", "payment_indicator", "child_entitlement"], + "dimension_values": { + "family_type": [ + item["dimension_values"]["family_type"] for item in operands + ], + "payment_indicator": "In payment", + "child_entitlement": ["No", "Yes"], + }, + }, + "measurement": {"source_months": months}, + "bindings": { + "policyengine": {"metric_name": "uc_households", "from_entity": "benunit"} + }, + } + contract = {"country": "uk", "targets": [target]} + config = TargetReferenceAuthoringConfig( + target_period=2025, + value_operation_by_target_id=_value_operation_by_target_id(contract), + reference_metadata_by_target_id=_reference_metadata(contract), + ) + (row,) = author_target_references(contract, facts, config).references + assert row["value_operands"] == operands + result = _compile(monkeypatch, facts, LedgerTargetReference(**row)) + assert not result.unsupported + (spec,) = result.registry.specs + assert spec.value == 5.5 * len(operands) + sum(range(len(operands))) + assert spec.metadata["uk_uc_source_month_count"] == "12" + assert spec.metadata["ledger_member_fact_count"] == str(12 * len(operands)) + assert json.loads(spec.metadata["uk_uc_resolved_source_months"]) == months + assert ( + spec.metadata["uk_uc_source_period_basis"] + == "mean_of_declared_monthly_cell_sums" + ) + mismatch = replace( + LedgerTargetReference(**row), metadata=uc_source_month_metadata(MONTHS) + ) + assert ( + "must equal" in _compile(monkeypatch, facts, mismatch).unsupported[0]["reason"] + ) + + +def test_legacy_calendar_authoring_keeps_original_resource_shape(): + target = { + "target_id": "uc.calendar", + "family": "dwp_universal_credit", + "ledger_selector": {"source_concept": "dwp.uc_benefit_units"}, + "bindings": { + "policyengine": {"metric_name": "uc_households", "from_entity": "benunit"} + }, + } + contract = {"country": "uk", "targets": [target]} + authored = author_target_references( + contract, + [_fact(month) for month in MONTHS], + TargetReferenceAuthoringConfig( + target_period=2025, + value_operation_by_target_id=_value_operation_by_target_id(contract), + ), + ) + resource = target_references_resource( + country="uk", description="Legacy", authored=authored + ) + assert resource == { + "country": "uk", + "description": "Legacy", + "allowed_value_operations": [ + "identity", + "sum", + "difference", + "calendar_year_average", + "latest_plateau", + "count_x_mean", + ], + "target_references": [ + { + "name": "uc.calendar", + "ledger_selector": target["ledger_selector"], + "entity": "benunit", + "measure": "uc_households", + "family": "dwp_universal_credit", + "period": 2025, + "metadata": { + "contract_target_id": "uc.calendar", + "measure_kind": "prepared_column", + }, + "value_operation": "calendar_year_average", + "uprating_from_period": "2025-12", + "uprating_to_period": 2025, + } + ], + } + target["value_operation"] = "sum" + assert _value_operation_by_target_id(contract) == { + "uc.calendar": "calendar_year_average" + } + + +def test_generator_authors_explicit_cross_year_uc_window_and_preserves_model_period( + monkeypatch, +): + contract = { + "country": "uk", + "targets": [ + { + "target_id": "uc.fiscal", + "family": "dwp_universal_credit", + "value_operation": "monthly_window_average", + "period_match_policy": "source_window", + "ledger_selector": { + "source_concept": "dwp.uc_benefit_units", + "period_type": "month", + "period_value": FISCAL_MONTHS, + }, + "measurement": {"source_months": FISCAL_MONTHS}, + "bindings": { + "policyengine": { + "metric_name": "uc_households", + "from_entity": "benunit", + } + }, + } + ], + } + facts = [_fact(month, value=index) for index, month in enumerate(FISCAL_MONTHS)] + config = TargetReferenceAuthoringConfig( + target_period=2025, + value_operation_by_target_id=_value_operation_by_target_id(contract), + reference_metadata_by_target_id=_reference_metadata(contract), + ) + authored = author_target_references(contract, facts, config) + (row,) = authored.references + assert row["period_match_policy"] == "source_window" + assert row["value_operation"] == "monthly_window_average" + assert row["period"] == 2025 + assert "uprating_from_period" not in row + assert ( + "monthly_window_average" + in target_references_resource( + country="uk", description="Synthetic source window", authored=authored + )["allowed_value_operations"] + ) + result = _compile(monkeypatch, facts, LedgerTargetReference(**row)) + assert not result.unsupported + (target,) = result.registry.specs + assert target.value == 5.5 + assert target.period == 2025 + assert target.metadata["uk_uc_source_month_count"] == "12" + assert json.loads(target.metadata["uk_uc_resolved_source_months"]) == FISCAL_MONTHS + entry = authored.membership_report["targets"]["uc.fiscal"]["candidates"][0] + assert entry["matched_fact_count_at_or_before_period"] == 9 + assert entry["matched_fact_count_in_source_window"] == 12 + + +def test_calendar_uc_selection_can_explicitly_choose_nine_from_twelve(monkeypatch): + reference = replace( + _reference(), + ledger_selector={ + "source_concept": "dwp.uc_benefit_units", + "period_type": "month", + "period_value": MONTHS, + }, + ) + facts = [_fact(f"2025-{month:02}", value=month) for month in range(1, 13)] + result = _compile(monkeypatch, facts, reference) + (target,) = result.registry.specs + assert target.value == 8 + assert target.metadata["uk_uc_source_month_count"] == "9" def _reference() -> LedgerTargetReference: @@ -36,6 +255,8 @@ def _reference() -> LedgerTargetReference: def _fact(month: str, *, suffix: str = "", value: float = 100.0) -> dict: return { "aggregate_fact_key": f"uc-{month}{suffix}", + "source_release_key": "ledger.source_release.v2:uc-window-fixture", + "source": {"source_sha256": "a" * 64}, "aggregation": {"method": "sum"}, "assertion": "observation", "geography": {"level": "country", "id": "K03000001"}, @@ -182,7 +403,7 @@ def test_generator_preserves_observation_basis_and_explicit_months(): _reference_metadata(contract) -def test_shipped_uc_monthly_references_declare_the_pinned_nine_month_window(): +def test_shipped_uc_monthly_references_preserve_each_declared_window(): references = load_country_spec("uk").target_references monthly = [ reference @@ -190,12 +411,40 @@ def test_shipped_uc_monthly_references_declare_the_pinned_nine_month_window(): if reference.family == "dwp_universal_credit" ] assert len(monthly) == 111 - assert {reference.metadata[EXPECTED_SOURCE_MONTHS] for reference in monthly} == { - uc_source_month_metadata(MONTHS)[EXPECTED_SOURCE_MONTHS] + new_paid = { + "dwp.uc.households", + *{ + f"dwp.uc.households_{family}" + for family in ( + "single_no_children", + "single_with_children", + "couple_no_children", + "couple_with_children", + ) + }, + *{ + f"dwp.uc.households_children_{children}" + for children in ("1", "2", "3", "4", "5_or_more") + }, } - assert all( - reference.value_operation == "calendar_year_average" for reference in monthly - ) + assert len(new_paid) == 10 + for reference in monthly: + if reference.name in new_paid: + expected_months = [f"2025-{month:02}" for month in range(1, 13)] + assert reference.ledger_selector["period_value"] == expected_months + if reference.value_operation == "monthly_window_sum_average": + assert reference.period_match_policy == "source_window" + assert len(reference.value_operands) == ( + 10 if reference.name == "dwp.uc.households" else 2 + ) + else: + assert reference.value_operation == "calendar_year_average" + assert reference.name.startswith("dwp.uc.households_children_") + else: + expected_months = MONTHS + assert reference.value_operation == "calendar_year_average" + assert json.loads(reference.metadata[EXPECTED_SOURCE_MONTHS]) == expected_months + assert new_paid <= {reference.name for reference in monthly} assert not any( EXPECTED_SOURCE_MONTHS in reference.metadata for reference in references diff --git a/tools/calibrate_uk_national_dataset.py b/tools/calibrate_uk_national_dataset.py index c05b2fb04..6f8e758ec 100644 --- a/tools/calibrate_uk_national_dataset.py +++ b/tools/calibrate_uk_national_dataset.py @@ -27,12 +27,15 @@ ) from microcosm.build.uk_runtime.frs_release import load_uk_frs_release from microcosm.build.uk_runtime.ledger_targets import compile_uk_target_registry -from microcosm.build.uk_runtime.local_target_census import _LEDGER_FACT_FEED_PIN from microcosm.build.uk_runtime.measure_simulation import ( UKMeasureResolver, apply_uk_calibration_measure_exclusions, load_uk_calibration_measure_exclusions, ) +from microcosm.build.uk_runtime.national_chronicle_feed import ( + UKNationalChronicleFeed, + load_uk_national_chronicle_feed, +) from microcosm.build.uk_runtime.national_doctrine import uk_doctrine_with_overrides from microcosm.build.uk_runtime.release_identity import UK_NATIONAL_RELEASE_ID from microcosm.calibrate import TargetRegistry @@ -46,6 +49,7 @@ def main(argv: list[str] | None = None) -> int: args = _parse_args(argv) + national_feed = load_uk_national_chronicle_feed() artifact = load_ledger_consumer_artifact( args.ledger_facts, expected_facts_sha256=args.ledger_facts_sha256, @@ -53,6 +57,8 @@ def main(argv: list[str] | None = None) -> int: ) _check_committed_ledger_feed_pin( artifact.facts_sha256, + manifest_sha256=artifact.manifest_sha256, + pin=national_feed, allow_unpinned_feed=args.allow_unpinned_feed, ) calibration_year = load_uk_frs_release().calibration_year @@ -121,6 +127,7 @@ def main(argv: list[str] | None = None) -> int: run_config_extra={ "calibration_year": calibration_year, "allow_unpinned_feed": args.allow_unpinned_feed, + "national_chronicle_feed_pin": national_feed.to_dict(), }, release_id=args.release_id, logbook_prev_row_digest=args.logbook_prev_row_digest, @@ -147,8 +154,8 @@ def _parse_args(argv: list[str] | None) -> argparse.Namespace: "--allow-unpinned-feed", action="store_true", help=( - "Allow a Chronicle fact feed whose content hash differs from the " - "committed UK feed pin; the override is recorded in the run manifest." + "Allow Chronicle facts or manifest hashes outside the committed UK " + "national feed pin; the override is recorded in the run manifest." ), ) parser.add_argument("--staging-h5", required=True, type=Path) @@ -226,13 +233,23 @@ def _sha256_file(path: Path) -> str: def _check_committed_ledger_feed_pin( facts_sha256: str, *, + manifest_sha256: str | None, allow_unpinned_feed: bool, + pin: UKNationalChronicleFeed | None = None, ) -> None: - committed = _LEDGER_FACT_FEED_PIN["facts_sha256"] - if facts_sha256 != committed and not allow_unpinned_feed: + pin = pin or load_uk_national_chronicle_feed() + mismatches = [] + for label, loaded, committed in ( + ("facts", facts_sha256, pin.facts_sha256), + ("manifest", manifest_sha256, pin.manifest_sha256), + ): + if loaded != committed: + mismatches.append(f"{label}: loaded {loaded}, committed {committed}") + if mismatches and not allow_unpinned_feed: raise SystemExit( - "error: Chronicle consumer facts differ from the committed UK feed pin: " - f"loaded {facts_sha256}, committed {committed}; pass " + "error: Chronicle artifact differs from the committed UK national feed pin: " + + "; ".join(mismatches) + + "; pass " "--allow-unpinned-feed only for an explicitly reviewed diagnostic run" ) diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index f89fed013..3d18e85e7 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -15,6 +15,7 @@ from pathlib import Path from typing import Any +from microcosm.build.ledger_targets import MONTHLY_WINDOW_OPERATIONS from microcosm.build.target_reference_authoring import ( TargetReferenceAuthoringConfig, author_target_references, @@ -73,10 +74,10 @@ "target_id or an incumbent-compatible fan-out row name; ledger_selector " "is the contract selector plus geography pins; entity is from_entity, " "then map_to, then household; measure is the prepared-column metric name " - "or fan-out row name; family is the contract family; period is 2025 " - "(opening-year convention for the FRS 2024-25 survey line). Observed " - "values stay in Ledger facts and resolve by identity unless the reference " - "declares value_operation=sum for a genuine multi-fact residue. Deferred " + "or fan-out row name; family is the contract family; period is the model " + "and calibration year 2025. Observation windows are declared separately " + "from the FRS 2024-25 survey vintage. Observed values stay in Ledger facts " + "and resolve through each reference's declared value operation. Deferred " "classes and geography-pin decisions are recorded in " "uk/target_reference_membership.json. metadata.measure_kind records that " "measures are prepared columns produced from the contract binding payload " @@ -287,7 +288,10 @@ def _value_operation_by_target_id(contract: Mapping[str, Any]) -> dict[str, str] declared = target.get("value_operation") if declared is not None: operations[target_id] = str(declared) - if target.get("family") == "dwp_universal_credit": + if ( + target.get("family") == "dwp_universal_credit" + and declared not in MONTHLY_WINDOW_OPERATIONS + ): operations[target_id] = "calendar_year_average" return operations @@ -341,7 +345,14 @@ def _reference_metadata(contract: Mapping[str, Any]) -> dict[str, dict[str, str] if "source_months" in measurement: if target.get("family") != "dwp_universal_credit": raise ValueError("source_months is currently a UK UC-only declaration.") - metadata.update(uc_source_month_metadata(measurement["source_months"])) + metadata.update( + uc_source_month_metadata( + measurement["source_months"], + value_operation=str( + target.get("value_operation", "calendar_year_average") + ), + ) + ) if metadata: result[str(target["target_id"])] = metadata return result From 2adb0cb85e14877f39023143b0e90a0054e8e41f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mar=C3=ADa=20Juaristi?= <127882282+juaristi22@users.noreply.github.com> Date: Wed, 9 Sep 2026 15:54:08 +0200 Subject: [PATCH 2/2] Fix UC national calibration fixtures for paid monthly targets --- .../tests/test_uk_national_calibration.py | 66 ++++++++++++++++++- 1 file changed, 64 insertions(+), 2 deletions(-) diff --git a/packages/microcosm-build/tests/test_uk_national_calibration.py b/packages/microcosm-build/tests/test_uk_national_calibration.py index bb29a8006..a144cca23 100644 --- a/packages/microcosm-build/tests/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/test_uk_national_calibration.py @@ -263,6 +263,25 @@ def _fact_for_reference( } +def _facts_for_reference(reference: LedgerTargetReference, value: float) -> list[dict]: + """Expand the paid headline into its declared synthetic month/cell grid.""" + if reference.name != "dwp.uc.households": + return [_fact_for_reference(reference, value)] + + facts = [] + for month in reference.ledger_selector["period_value"]: + for cell, operand in enumerate(reference.value_operands): + fact = _fact_for_reference(reference, value / len(reference.value_operands)) + fact["aggregate_fact_key"] += f":{month}:{cell}" + fact["dimensions"] = dict(operand["dimension_values"]) + fact["period"] = {"type": "month", "value": month} + fact["source_release_key"] = "ledger.source_release.v2:uc-paid-fixture" + fact["source"] = {"source_sha256": "a" * 64} + fact["observed_measure"]["unit"] = "count" + facts.append(fact) + return facts + + def _materialization_binding_frame( *, include_counterfactual_delta: bool = True, @@ -472,7 +491,29 @@ def test_chronicle_184_uc_and_obr_references_compile_fail_closed() -> None: for reference in spec.target_references if reference.name == "dwp.uc.households" ) - assert uc_reference.value_operation == "calendar_year_average" + assert uc_reference.value_operation == "monthly_window_sum_average" + assert uc_reference.period_match_policy == "source_window" + assert uc_reference.ledger_selector["period_value"] == [ + f"2025-{month:02d}" for month in range(1, 13) + ] + assert len(uc_reference.value_operands) == 10 + assert { + tuple( + operand["dimension_values"][key] + for key in ("family_type", "payment_indicator", "child_entitlement") + ) + for operand in uc_reference.value_operands + } == { + (family, "Yes", entitled) + for family in ( + "Single, no children", + "Single, with children", + "Couple, no children", + "Couple, with children", + "Unknown or missing family type", + ) + for entitled in ("No", "Yes") + } references = tuple( reference @@ -504,12 +545,13 @@ def test_packaged_binding_classes_materialize_through_national_stage() -> None: ) references = tuple(_reference_by_name(name) for name in selected_names) facts = [ - _fact_for_reference(reference, value) + fact for reference, value in zip( references, (20.0, 33.0, 2.0, 5.0, 3.0), strict=True, ) + for fact in _facts_for_reference(reference, value) ] from microcosm.build.ledger_targets import compile_ledger_target_references from microcosm.build.uk_runtime.ledger_targets import ( @@ -518,6 +560,9 @@ def test_packaged_binding_classes_materialize_through_national_stage() -> None: ) registry = compile_ledger_target_references(facts, references, country="uk") + headline = next(spec for spec in registry.specs if spec.name == "dwp.uc.households") + assert headline.value == 20.0 + assert headline.metadata["ledger_member_fact_count"] == "120" resolver = StubCrosstabResolver() resolver.contract_targets = _uk_contract_targets() stage = UKNationalCalibrationStage( @@ -983,3 +1028,20 @@ def test_unmapped_household_reduction_names_the_published_reducer(): "value": 1, } ) + + +@pytest.mark.parametrize("defect", ["missing", "duplicate"]) +def test_packaged_uc_headline_refuses_incomplete_month_cell_fixture(defect) -> None: + from microcosm.build.ledger_targets import compile_ledger_target_references + + reference = _reference_by_name("dwp.uc.households") + facts = _facts_for_reference(reference, 20.0) + if defect == "missing": + # Keep every month represented but omit one paid/entitlement cell. + facts.pop(0) + else: + # Keep the expected total size and unique IDs; repeat a cell instead. + facts[0]["dimensions"] = dict(facts[1]["dimensions"]) + + with pytest.raises(ValueError, match="monthly window"): + compile_ledger_target_references(facts, [reference], country="uk")