diff --git a/changelog.d/uc-calibration-best-iterate.fixed.md b/changelog.d/uc-calibration-best-iterate.fixed.md new file mode 100644 index 000000000..6a7201820 --- /dev/null +++ b/changelog.d/uc-calibration-best-iterate.fixed.md @@ -0,0 +1 @@ +Preserve the best feasible iterate within the requested budget for unregularized, free-mass Adam calibration, including a better warm start, and record the selected update in diagnostics. Free-mass national and rowwise-local calibration weights can change when an earlier iterate fits better; this does not establish national fit stability. Conserved-mass, L2-regularized and L0-gated paths retain their closing-state behavior, checked against exact pre-change output bytes. diff --git a/changelog.d/uk-uc-claim-target-contract.fixed.md b/changelog.d/uk-uc-claim-target-contract.fixed.md new file mode 100644 index 000000000..141eb9508 --- /dev/null +++ b/changelog.d/uk-uc-claim-target-contract.fixed.md @@ -0,0 +1 @@ +Measure UK two-child-limit targets over each benefit unit's own qualifying children and claimant roles, separating statistical affected-child proxies from element denial. Correct Scottish UC claims with a child under one to benefit-unit grain and record remaining annual-period, birth-year and positive-award approximations in measurement receipts. diff --git a/changelog.d/uk-uc-couple-donor-predictors.fixed.md b/changelog.d/uk-uc-couple-donor-predictors.fixed.md new file mode 100644 index 000000000..bbabaad4a --- /dev/null +++ b/changelog.d/uk-uc-couple-donor-predictors.fixed.md @@ -0,0 +1,3 @@ +Use retained FRS claimant and partner roles to classify couples in UC capital donor cells and reported-UC predictors, including cohabiting couples and excluding older dependent children. Preserve legal marital status, emit an explicit is_uc_claimant input from the raw FRS spine, and share the claimant-role derivation with UC calibration measurement. + +Preserve the Boolean claimant flag through graph storage, refresh the synthetic UK spine fixture, and bind the shared relationship helper into the three consuming stages' implementation hashes. diff --git a/changelog.d/uk-uc-development-replays.added.md b/changelog.d/uk-uc-development-replays.added.md new file mode 100644 index 000000000..f25cf18a2 --- /dev/null +++ b/changelog.d/uk-uc-development-replays.added.md @@ -0,0 +1 @@ +Add private, pinned UC relationship and target-matrix diagnostic replays with source and overlay hashes, fixed-weight comparisons, and baseline reproduction checks. diff --git a/changelog.d/uk-uc-released-claimant-input.fixed.md b/changelog.d/uk-uc-released-claimant-input.fixed.md new file mode 100644 index 000000000..a70b50638 --- /dev/null +++ b/changelog.d/uk-uc-released-claimant-input.fixed.md @@ -0,0 +1 @@ +Require PolicyEngine-UK 2.97.0 for the UK extras, allow recorded UC claimant roles through export, and refuse model loading that drops or changes those roles. Preserve historical candidate evidence at its recorded model version. diff --git a/changelog.d/uk-uc-source-month-coverage.fixed.md b/changelog.d/uk-uc-source-month-coverage.fixed.md new file mode 100644 index 000000000..3b9a3bee5 --- /dev/null +++ b/changelog.d/uk-uc-source-month-coverage.fixed.md @@ -0,0 +1 @@ +Declare the April–December 2025 source window for national UC monthly targets and reject missing, extra or repeated months during UK target compilation. Record the actual observation months and averaging denominator without changing target values or treating the window as a full calendar year. diff --git a/changelog.d/uk-uc-target-geography-and-finite-tail.fixed.md b/changelog.d/uk-uc-target-geography-and-finite-tail.fixed.md new file mode 100644 index 000000000..16723f63f --- /dev/null +++ b/changelog.d/uk-uc-target-geography-and-finite-tail.fixed.md @@ -0,0 +1,4 @@ +Align national UC calibration measures with their Great Britain source geography, +projecting household geography through benefit-unit links without changing family +composition. Keep the published £2,400.01–£2,500 monthly payment band finite instead +of counting the separately published, unbound higher-payment category in that row. diff --git a/docs/evidence/spec-engine/us-f0-coverage.json b/docs/evidence/spec-engine/us-f0-coverage.json index 3adf1102e..a8d2d977f 100644 --- a/docs/evidence/spec-engine/us-f0-coverage.json +++ b/docs/evidence/spec-engine/us-f0-coverage.json @@ -1656,13 +1656,13 @@ "compiler_ir.node_slices" ], "expected": { - "map_sha256": "6b4902d9a640dd459942e588ae2cc7fb937f1c42ad814365c7c1616c98ce1b68", - "protocol_sha256": "e63bbfa0f05302a672acd9914e3652c151cc7ee690182d8823dfb85329b1e911" + "map_sha256": "c94a5af8eb24866156b8cd4b77b93dc497b7df5b025204fe1022f80b1d8446ea", + "protocol_sha256": "1c53f1d9b3e185a41181fbe861f9b08d56c83880710fd1e18d7a4ceb5d6a354e" }, "failures": [], "observed": { - "map_sha256": "6b4902d9a640dd459942e588ae2cc7fb937f1c42ad814365c7c1616c98ce1b68", - "protocol_sha256": "e63bbfa0f05302a672acd9914e3652c151cc7ee690182d8823dfb85329b1e911" + "map_sha256": "c94a5af8eb24866156b8cd4b77b93dc497b7df5b025204fe1022f80b1d8446ea", + "protocol_sha256": "1c53f1d9b3e185a41181fbe861f9b08d56c83880710fd1e18d7a4ceb5d6a354e" }, "status": "covered" }, @@ -1677,7 +1677,7 @@ "compiler_ir.seed_stream_map" ], "expected": { - "implementation_sha256": "e63bbfa0f05302a672acd9914e3652c151cc7ee690182d8823dfb85329b1e911", + "implementation_sha256": "1c53f1d9b3e185a41181fbe861f9b08d56c83880710fd1e18d7a4ceb5d6a354e", "protocol": "legacy-v1", "streams": [ "build_model", @@ -1698,7 +1698,7 @@ }, "failures": [], "observed": { - "implementation_sha256": "e63bbfa0f05302a672acd9914e3652c151cc7ee690182d8823dfb85329b1e911", + "implementation_sha256": "1c53f1d9b3e185a41181fbe861f9b08d56c83880710fd1e18d7a4ceb5d6a354e", "protocol": "legacy-v1", "streams": [ "build_model", @@ -2599,7 +2599,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "9db29b4d33424fbb21a83c63927c7de55ba9a333d631f6323935f67a496eee46" + "spec_sha256": "8b1546bc97b540afd525fe88f36a315de18b4d57bd9fab73035dacd8962e302e" } }, "report_schema_version": 3, @@ -2609,7 +2609,7 @@ "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "9db29b4d33424fbb21a83c63927c7de55ba9a333d631f6323935f67a496eee46" + "spec_sha256": "8b1546bc97b540afd525fe88f36a315de18b4d57bd9fab73035dacd8962e302e" }, "status": "pass" } diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index 4747035cb..7521108f8 100644 --- a/packages/microcosm-build/pyproject.toml +++ b/packages/microcosm-build/pyproject.toml @@ -39,7 +39,7 @@ us = [ # The UK extra adds the rules engine for local metric generation from a # Microcosm UK H5. Target tables remain explicit inputs, and the base package # still does not import policyengine-uk at import time. -uk = ["policyengine-uk>=2.93", "h5py>=3", "tables>=3"] +uk = ["policyengine-uk>=2.97.0", "h5py>=3", "tables>=3"] [project.scripts] microcosm-export-us-l0-refit-h5 = "microcosm.build.us_runtime.l0_refit_export:main" diff --git a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py index a158ccfb5..d9413007e 100644 --- a/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py +++ b/packages/microcosm-build/src/microcosm/build/spec_engine/inventory_coverage.py @@ -359,8 +359,8 @@ "late_schedule": "e59c019d3d454eac99ac0ac209b6c5b6faaf9bdfcaeee18c36a25be19bf7da2f", "ownership": "5f64f0aac49e2313177564f71876bffc8c81b3ded4df701e70930e60e9c98356", "primary_tuples": "987b501c695e31f45521c4a178528f75ab3df22c09bc407b182213b2de99ee57", - "seed_map": "6b4902d9a640dd459942e588ae2cc7fb937f1c42ad814365c7c1616c98ce1b68", - "seed_protocol": "e63bbfa0f05302a672acd9914e3652c151cc7ee690182d8823dfb85329b1e911", + "seed_map": "c94a5af8eb24866156b8cd4b77b93dc497b7df5b025204fe1022f80b1d8446ea", + "seed_protocol": "1c53f1d9b3e185a41181fbe861f9b08d56c83880710fd1e18d7a4ceb5d6a354e", "source_manifest": "cd5ba8924d64da5425ee14cca82a774e3f4b2bb5aabe06df291cc3cc457287a9", "take_up": "fa186daea0f8dd641cc470e41d1a2953f887d45282ec990201298f47bedf8d4d", "tail": "ac92829c88a1a4fb6460d61190918d5d99c6c377fc8dd8f62f02b332d09bf59c", diff --git a/packages/microcosm-build/src/microcosm/build/target_materialization.py b/packages/microcosm-build/src/microcosm/build/target_materialization.py index a64f787af..592774add 100644 --- a/packages/microcosm-build/src/microcosm/build/target_materialization.py +++ b/packages/microcosm-build/src/microcosm/build/target_materialization.py @@ -25,11 +25,9 @@ # sibling's lower edge within the same compiled contract target. Edges must # never derive from an exclusion-pruned roster, or excluding a band silently # widens its lower neighbour; only the compiled register's top band runs to -# infinity. +# infinity unless the binding declares a source-unit ``band_upper_bound``. # Entity-count indicators: a value of one per record of the owning entity. -_COUNT_VALUE_VARIABLES = frozenset( - {"household_count", "person_count", "benunit_count"} -) +_COUNT_VALUE_VARIABLES = frozenset({"household_count", "person_count", "benunit_count"}) _BAND_LOWER_BOUND_SUFFIX = "_lower_bound" _LEDGER_FILTER_PREFIX = "ledger_filter_" @@ -182,9 +180,7 @@ def resolve_target_measures( "skipped": [skip.__dict__ for skip in skipped], } receipt["rounds"].append(round_receipt) - all_skips.extend( - {**skip.__dict__, "round": round_index} for skip in skipped - ) + all_skips.extend({**skip.__dict__, "round": round_index} for skip in skipped) if not skipped: return MeasureResolution( measure_inputs=MappingProxyType(dict(measure_inputs)), @@ -242,6 +238,10 @@ def resolve_target_measures( all_skips, ) measure_inputs[key] = np.asarray(values) + # Providers can learn the comparison contract while resolving + # inputs (for example, UC claim-state/date proxies). Preserve the + # post-computation receipt, not only the pre-resolution snapshot. + receipt["provider"] = _measure_provider_receipt(provider) provided_this_round.add(key) receipt["attached"][f"{entity}.{variable}"] = route progressed = True @@ -308,9 +308,7 @@ def _raise_measure_resolution( raise MeasureResolutionError(message, receipt=receipt) -def _band_lower_edge( - spec: Any, binding: Mapping[str, Any] -) -> float | None: +def _band_lower_edge(spec: Any, binding: Mapping[str, Any]) -> float | None: """This spec's band lower edge in model units, or None if unbanded. Reads the compiled spec's Ledger filter metadata: a ``*_lower_bound`` @@ -441,6 +439,30 @@ def _band_bounds( if edge > lower: upper = edge break + # A consumer may bind only the finite portion of a published histogram. + # Declare its source-unit ceiling instead of allowing the last included + # row to absorb a separately published, unbound top-coded category. + if "band_upper_bound" in binding: + inclusive = binding.get("band_upper_bound_inclusive") + factor = float(binding.get("band_period_factor", 1)) + declared_upper = float(binding["band_upper_bound"]) * factor + if ( + not isinstance(inclusive, bool) + or not math.isfinite(factor) + or factor <= 0 + or not math.isfinite(declared_upper) + or declared_upper <= lower + ): + raise ValueError( + "band_upper_bound requires a finite bound above the lower " + "edge, a positive period factor and an explicit boolean " + "band_upper_bound_inclusive" + ) + if inclusive: + declared_upper = np.nextafter(declared_upper, math.inf) + upper = min(upper, declared_upper) + elif "band_upper_bound_inclusive" in binding: + raise ValueError("band_upper_bound_inclusive requires band_upper_bound") return lower, upper diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 95a118f07..7a2c0fd7f 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -618,6 +618,7 @@ "person.is_before_universal_credit_qualifying_young_person_terminal_date", "person.is_in_non_advanced_education", "person.is_parent", + "person.is_uc_claimant", "person.legacy_jobseeker_proxy", "person.outpatient_visits", "person.pension_contributions_via_salary_sacrifice", diff --git a/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json b/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json index df64a4aea..3afdaedda 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json +++ b/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json @@ -473,7 +473,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2025, Table 2.1a", "survey": "HMRC Capital Gains Tax statistics Table 2.1a and Advani-Summers capital-gains incidence" @@ -496,7 +496,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, and Advani-Summers capital-gains incidence" @@ -525,7 +525,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab, DfT rail fare index, and public NHS activity/cost table.", "survey": "Effects of Taxes and Benefits 1977-2024 and NHS age-gender public table" @@ -545,7 +545,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab and cited VAT anchor resource.", "survey": "Effects of Taxes and Benefits 1977-2024" @@ -576,7 +576,7 @@ "superseded_by": { "reason": "The FRS spine build executes hmrc_cgt_gains_spine, which applies the same HMRC Table 3 amounts redraw directly in source_stages.json before calibration.", "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "stage": "hmrc_cgt_gains_spine" } }, @@ -596,7 +596,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024" @@ -610,7 +610,7 @@ "base_candidate_tier": "frs", "calibration_permitted": false, "canonical_source_manifest": "source_stages.json", - "canonical_source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "canonical_source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "effective_mass_requirements": { "charitable_investment_gifts": { "mass_share_denominator": "all_person_effective_mass", @@ -693,7 +693,7 @@ "superseded_by": { "reason": "The FRS spine build executes hmrc_spi_income_spine, which supersedes the June retained-leaves/hmrc_spi_income pair inside source_stages.json.", "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "stage": "hmrc_spi_income_spine" } }, @@ -727,7 +727,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "UK Data Service SN 9468 Living Costs and Food Survey 2023-24 household/person tabs, NEED 2023 headline energy tables, Ofgem Q2 2026 unit rates, and WAS round-8 bridge donor.", "survey": "Living Costs and Food Survey 2023-24" @@ -748,7 +748,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "MHCLG dwellings and ONS UK House Price Index December 2025 regional average prices.", "survey": "Public regional property reference" @@ -772,7 +772,7 @@ "employee_pension_contributions" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "HMRC, Salary sacrifice reform for pension contributions effective from 6 April 2029", "survey": "Family Resources Survey 2024-25 salary-sacrifice respondents and HMRC salary-sacrifice reform analysis" @@ -794,7 +794,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "Explore Education Statistics Table 6a, Higher education total", "survey": "Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England" @@ -829,7 +829,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "d83310aa9ebb309b4a9c6409a35916cc49390e330827cce8d7321c613679cd84", + "source_manifest_sha256": "f1bb134b5456be67c10f2e09b5dc9999c75b1bf3c47165092e260cbd79a54732", "source_vintages": { "source": "Office for National Statistics Wealth and Assets Survey, UK Data Service SN 7215, DOI 10.5255/UKDA-SN-7215-20; local licensed 2006-22 household tab.", "survey": "Wealth and Assets Survey round 8" diff --git a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json index 77bb87dad..52fe5be61 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -229,7 +229,7 @@ }, { "kind": "map_columns", - "scope": "direct raw-to-column E2 mappings only; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)" }, { "kind": "map_coded_amounts", @@ -251,6 +251,7 @@ "is_household_head", "is_benunit_head", "is_parent", + "is_uc_claimant", "employment_income", "self_employment_income", "private_pension_income", @@ -2538,6 +2539,7 @@ { "kind": "derive", "derived": { + "is_uc_couple": "count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2", "uc_reported_capital": "frs_benunit_capital", "positive_pre_takeup_uc_award": "max(0, uc_maximum_amount - uc_income_reduction) > 0", "has_non_child_member": "any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members", @@ -2571,7 +2573,7 @@ "rewrite_rows": "spi_channel", "screen": "claimable_screen", "predictors": [ - "is_married", + "is_uc_couple", "uc_child_band", "benunit_employment_income", "benunit_self_employment_income", @@ -2595,7 +2597,7 @@ "rewrites": [ "universal_credit_reported" ], - "notes": "Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses." + "notes": "Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. The consumed is_uc_couple predictor counts two retained FRS claimant/partner roles, independently of legal is_married; older dependent children do not become partners. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses." }, { "stage": "uc_capital_coherence", @@ -2623,7 +2625,7 @@ "2", "3+" ], - "couple_status": "is_married", + "couple_status": "is_uc_couple", "weight_mapping": "household_to_benunit", "draw": "weighted_empirical", "identity": "benunit_id", @@ -2645,7 +2647,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by couple-status cells, then applies the monotone reported-UC OR refresh." + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by is_uc_couple cells (two retained FRS claimant/partner roles, independently of legal is_married), then applies the monotone reported-UC OR refresh." }, { "stage": "uc_deduction_attributes", diff --git a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml index b8daecd4f..7a7308ff5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -204,7 +204,7 @@ stages: person_table: adult union child household_sort: set_index('household_id').sort_index() before positional household reads - kind: map_columns - scope: 'direct raw-to-column E2 mappings only; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)' + scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)' - kind: map_coded_amounts scope: region, tenure, accommodation, council tax band, benefit-code amount mappings - kind: annualize_periodic_amounts @@ -220,6 +220,7 @@ stages: - is_household_head - is_benunit_head - is_parent + - is_uc_claimant - employment_income - self_employment_income - private_pension_income @@ -2028,6 +2029,7 @@ stages: operations: - kind: derive derived: + is_uc_couple: count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2 uc_reported_capital: frs_benunit_capital positive_pre_takeup_uc_award: max(0, uc_maximum_amount - uc_income_reduction) > 0 has_non_child_member: any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members @@ -2052,7 +2054,7 @@ stages: rewrite_rows: spi_channel screen: claimable_screen predictors: - - is_married + - is_uc_couple - uc_child_band - benunit_employment_income - benunit_self_employment_income @@ -2071,7 +2073,7 @@ stages: outputs: [] rewrites: - universal_credit_reported - notes: Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses. + notes: Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. The consumed is_uc_couple predictor counts two retained FRS claimant/partner roles, independently of legal is_married; older dependent children do not become partners. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses. - stage: uc_capital_coherence survey: Family Resources Survey 2024-25 source: Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel. @@ -2092,7 +2094,7 @@ stages: - '1' - '2' - 3+ - couple_status: is_married + couple_status: is_uc_couple weight_mapping: household_to_benunit draw: weighted_empirical identity: benunit_id @@ -2107,7 +2109,7 @@ stages: rewrites: - frs_benunit_capital - would_claim_uc - notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by couple-status cells, then applies the monotone reported-UC OR refresh. + notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by is_uc_couple cells (two retained FRS claimant/partner roles, independently of legal is_married), then applies the monotone reported-UC OR refresh. - stage: uc_deduction_attributes survey: DWP Universal Credit deductions statistics, March 2025 to February 2026 source: Department for Work and Pensions supplementary Tables 2, 5 and 6, sha-pinned by Chronicle package dwp-uc-deductions-march-2025-february-2026. 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 c69ff3649..912dfc18d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -4160,7 +4160,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_children_1", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4187,7 +4188,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_children_2", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4214,7 +4216,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_children_3", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4241,7 +4244,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_children_4", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4268,7 +4272,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_children_5_or_more", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4295,7 +4300,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_single_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4322,7 +4328,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_single_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4349,7 +4356,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4376,7 +4384,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4405,7 +4414,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4434,7 +4444,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4463,7 +4474,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4492,7 +4504,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4521,7 +4534,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4550,7 +4564,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4579,7 +4594,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4608,7 +4624,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4637,7 +4654,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4666,7 +4684,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4695,7 +4714,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4724,7 +4744,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4753,7 +4774,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4782,7 +4804,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4811,7 +4834,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4840,7 +4864,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4869,7 +4894,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4898,7 +4924,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4927,7 +4954,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4956,7 +4984,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -4985,7 +5014,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5014,7 +5044,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5043,7 +5074,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5072,7 +5104,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5101,7 +5134,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_single", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5130,7 +5164,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5159,7 +5194,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5188,7 +5224,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5217,7 +5254,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5246,7 +5284,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5275,7 +5314,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5304,7 +5344,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5333,7 +5374,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5362,7 +5404,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5391,7 +5434,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5420,7 +5464,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5449,7 +5494,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5478,7 +5524,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5507,7 +5554,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5536,7 +5584,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5565,7 +5614,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5594,7 +5644,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5623,7 +5674,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5652,7 +5704,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5681,7 +5734,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5710,7 +5764,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5739,7 +5794,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5768,7 +5824,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5797,7 +5854,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5826,7 +5884,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_lone_parent", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5855,7 +5914,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5884,7 +5944,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5913,7 +5974,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5942,7 +6004,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -5971,7 +6034,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6000,7 +6064,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6029,7 +6094,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6058,7 +6124,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6087,7 +6154,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6116,7 +6184,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6145,7 +6214,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6174,7 +6244,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6203,7 +6274,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6232,7 +6304,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6261,7 +6334,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6290,7 +6364,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6319,7 +6394,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6348,7 +6424,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6377,7 +6454,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6406,7 +6484,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6435,7 +6514,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6464,7 +6544,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6493,7 +6574,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6522,7 +6604,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6551,7 +6634,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_no_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6580,7 +6664,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6609,7 +6694,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6638,7 +6724,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6667,7 +6754,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6696,7 +6784,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6725,7 +6814,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6754,7 +6844,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6783,7 +6874,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6812,7 +6904,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6841,7 +6934,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6870,7 +6964,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6899,7 +6994,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6928,7 +7024,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6957,7 +7054,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -6986,7 +7084,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7015,7 +7114,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7044,7 +7144,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7073,7 +7174,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7102,7 +7204,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7131,7 +7234,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7160,7 +7264,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7189,7 +7294,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7218,7 +7324,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7247,7 +7354,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7276,7 +7384,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.payment_distribution_couple_with_children", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7297,13 +7406,14 @@ "geography_level": "country", "geography_id": "S92000003" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/scotland_uc_households_child_under_1", "family": "dwp_universal_credit", "period": 2025, "metadata": { "contract_target_id": "dwp.uc.scotland_households_child_under_1", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", @@ -7320,7 +7430,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/households_affected", "family": "dwp_two_child_limit", "period": 2025, @@ -7342,7 +7452,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/children_affected", "family": "dwp_two_child_limit", "period": 2025, @@ -7364,7 +7474,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/children_in_affected_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7386,7 +7496,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/3_children_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7408,7 +7518,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/3_children_households_total_children", "family": "dwp_two_child_limit", "period": 2025, @@ -7430,7 +7540,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/4_children_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7452,7 +7562,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/4_children_households_total_children", "family": "dwp_two_child_limit", "period": 2025, @@ -7474,7 +7584,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/5_children_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7496,7 +7606,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/5_children_households_total_children", "family": "dwp_two_child_limit", "period": 2025, @@ -7518,7 +7628,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/6_children_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7540,7 +7650,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/6_children_households_total_children", "family": "dwp_two_child_limit", "period": 2025, @@ -7562,7 +7672,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/adult_pip_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7584,7 +7694,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/adult_pip_children", "family": "dwp_two_child_limit", "period": 2025, @@ -7606,7 +7716,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/disabled_child_element_households", "family": "dwp_two_child_limit", "period": 2025, @@ -7628,7 +7738,7 @@ "geography_level": "country", "geography_id": "K03000001" }, - "entity": "household", + "entity": "benunit", "measure": "dwp/uc/two_child_limit/disabled_child_element_children", "family": "dwp_two_child_limit", "period": 2025, @@ -10478,7 +10588,8 @@ "period": 2025, "metadata": { "contract_target_id": "dwp.uc.households", - "measure_kind": "prepared_column" + "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\"]" }, "value_operation": "calendar_year_average", "uprating_from_period": "2025-12", 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 ff68d8f60..e2eb93b84 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 @@ -3493,6 +3493,17 @@ "operator": "==", "value": 1 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3512,7 +3523,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_with_1_children", @@ -3552,6 +3585,17 @@ "operator": "==", "value": 2 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3571,7 +3615,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_with_2_children", @@ -3611,6 +3677,17 @@ "operator": "==", "value": 3 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3630,7 +3707,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_with_3_children", @@ -3670,6 +3769,17 @@ "operator": "==", "value": 4 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3689,7 +3799,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_with_4_children", @@ -3729,6 +3861,17 @@ "operator": ">=", "value": 5 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3748,7 +3891,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_with_5+_children", @@ -3783,6 +3948,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3802,7 +3978,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_single_no_children", @@ -3837,6 +4035,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3856,7 +4065,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_single_with_children", @@ -3891,6 +4122,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3910,7 +4152,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_couple_no_children", @@ -3945,6 +4209,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -3964,7 +4239,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/claimants_couple_with_children", @@ -4001,6 +4298,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -4018,7 +4326,31 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "band_upper_bound": 2500, + "band_upper_bound_inclusive": true }, "axiom": { "metric_name": "dwp/uc_payment_dist/SINGLE", @@ -4056,6 +4388,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -4073,7 +4416,31 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "band_upper_bound": 2500, + "band_upper_bound_inclusive": true }, "axiom": { "metric_name": "dwp/uc_payment_dist/LONE_PARENT", @@ -4111,6 +4478,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -4128,7 +4506,31 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "band_upper_bound": 2500, + "band_upper_bound_inclusive": true }, "axiom": { "metric_name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN", @@ -4166,6 +4568,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -4183,7 +4596,31 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "band_upper_bound": 2500, + "band_upper_bound_inclusive": true }, "axiom": { "metric_name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN", @@ -4211,57 +4648,55 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count", - "filters": [ - { - "concept": "uk.geography.region", - "equals": "scotland" - }, - { - "concept": "uk.benefits.universal_credit.amount", - "operator": ">", - "value": 0 - }, - { - "concept": "uk.demographics.youngest_child_age", - "operator": "<", - "value": 1 - } + "entity": "benunit", + "map_to": "household", + "concept": "uk.benefit_unit.count", + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { "policyengine": { "metric_name": "dwp/scotland_uc_households_child_under_1", - "reduce": "any", "household_conditions": [ { "variable": "region", + "entity": "household", + "reduce": "any", "operator": "==", - "value": "SCOTLAND" - }, + "value": "SCOTLAND", + "map_to": "benunit" + } + ], + "value_variable": "benunit_count", + "notes": "Count each Scottish UC benefit unit with its own reported child under one; a baby or UC claim in a different benefit unit in the same dwelling does not qualify it. Source April\u2013December 2025 mean covers nine observations. Annual positive model UC remains a proxy for the source claim/payment state.", + "from_entity": "benunit", + "map_to": "household", + "filters": [ { "variable": "universal_credit", - "entity": "benunit", - "reduce": "any", "operator": ">", "value": 0 }, { - "variable": "age", - "entity": "person", - "reduce": "any_child_under", - "operator": "<", - "value": 1 + "variable": "uc_calibration_has_child_under_one", + "operator": "==", + "value": true } - ], - "value_variable": "household_count", - "notes": "Scottish UC households with a child under 1 (compute/benefits.py:24-30). Household grain kept: the calibration provider has no categorical household-to-benefit-unit broadcast (a benefit-unit row cannot filter on region), so the #813-style re-bind is not available for a geography-filtered row; the Chronicle series is calendar-year averaged over April-December 2025 like the rest of the family." + ] }, "axiom": { "metric_name": "dwp/scotland_uc_households_child_under_1", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4283,22 +4718,46 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/households_affected", "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "from_entity": "benunit" }, "axiom": { "metric_name": "dwp/uc/two_child_limit/households_affected", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4316,22 +4775,41 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/children_affected", "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "uc_is_child_limit_affected", - "value_reduction": { - "variable": "uc_is_child_limit_affected", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_affected_child_count_proxy", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_children." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "from_entity": "benunit" }, "axiom": { "metric_name": "dwp/uc/two_child_limit/children_affected", @@ -4354,22 +4832,41 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/children_in_affected_households", "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: children_in_affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "from_entity": "benunit" }, "axiom": { "metric_name": "dwp/uc/two_child_limit/children_in_affected_households", @@ -4392,31 +4889,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/3_children_households", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 3 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 3 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/3_children_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4434,31 +4953,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/3_children_households_total_children", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 3 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Total children (not only affected children) in affected households of this size (compute/benefits.py:158-161). Row counts: children_in_affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 3 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/3_children_households_total_children", @@ -4481,31 +5017,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/4_children_households", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 4 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 4 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/4_children_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4523,31 +5081,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/4_children_households_total_children", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 4 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Total children (not only affected children) in affected households of this size (compute/benefits.py:158-161). Row counts: children_in_affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 4 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/4_children_households_total_children", @@ -4570,31 +5145,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/5_children_households", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 5 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 5 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/5_children_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4612,31 +5209,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/5_children_households_total_children", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": "==", - "value": 5 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Total children (not only affected children) in affected households of this size (compute/benefits.py:158-161). Row counts: children_in_affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": "==", + "value": 5 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/5_children_households_total_children", @@ -4659,31 +5273,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/6_children_households", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": ">=", - "value": 6 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Publisher's top band is 6-or-more children. Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": ">=", + "value": 6 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/6_children_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4701,31 +5337,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/6_children_households_total_children", "household_conditions": [ { - "variable": "is_child", - "entity": "person", - "reduce": "sum", - "operator": ">=", - "value": 6 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Total children (not only affected children) in affected households of this size (compute/benefits.py:158-161). Row counts: children_in_affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_qualifying_child_count", + "operator": ">=", + "value": 6 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/6_children_households_total_children", @@ -4748,31 +5401,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/adult_pip_households", "household_conditions": [ { - "variable": "pip", - "entity": "person", - "reduce": "sum", - "operator": ">", - "value": 0 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_claimant_receives_pip", + "operator": ">", + "value": 0 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/adult_pip_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4790,31 +5465,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/adult_pip_children", "household_conditions": [ { - "variable": "pip", - "entity": "person", - "reduce": "sum", - "operator": ">", - "value": 0 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "All children living in affected households, counted with the model is_child sum per household under the baseline affected flag and the disability condition (sheet 04B counts every child: the 3- and 4-children sibling rows divide to exactly 3.00 and 4.00 children per household; these cells run ~3.6-3.7). Re-bound from uc_is_child_limit_affected per microcosm#808 - the affected-children flag averages ~1.27 per household, a structural ~2.8x shortfall against the published all-children concept. Row counts: adult_pip_children." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_claimant_receives_pip", + "operator": ">", + "value": 0 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/adult_pip_children", @@ -4837,31 +5529,53 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.household.count" + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/disabled_child_element_households", "household_conditions": [ { - "variable": "uc_individual_disabled_child_element", - "entity": "person", - "reduce": "sum", - "operator": ">", - "value": 0 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "household_count", + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "benunit_count", "map_to": "household", - "notes": "Affected children are persons flagged by uc_is_child_limit_affected (household round-trip) who are children in UC-receiving households; an affected household contains at least one such child (compute/benefits.py:130-148). Row counts: affected_households." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_receives_disabled_child_element", + "operator": ">", + "value": 0 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/disabled_child_element_households", "status": "pending", - "value_rule": "uk.household.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { @@ -4879,31 +5593,48 @@ "country" ], "measurement": { - "entity": "household", - "concept": "uk.person.count" + "entity": "benunit", + "concept": "uk.person.count", + "map_to": "household" }, "bindings": { "policyengine": { "metric_name": "dwp/uc/two_child_limit/disabled_child_element_children", "household_conditions": [ { - "variable": "uc_individual_disabled_child_element", - "entity": "person", - "reduce": "sum", - "operator": ">", - "value": 0 + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ], "kind": "baseline_flag_crosstab", - "affected_flag_variable": "uc_is_child_limit_affected", - "value_variable": "is_child", - "value_reduction": { - "variable": "is_child", - "entity": "person", - "reduce": "sum" - }, + "affected_flag_variable": "uc_tcl_affected_benunit_proxy", + "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "All children living in affected households, counted with the model is_child sum per household under the baseline affected flag and the disability condition (sheet 04B counts every child: the 3- and 4-children sibling rows divide to exactly 3.00 and 4.00 children per household; these cells run ~3.6-3.7). Re-bound from uc_is_child_limit_affected per microcosm#808 - the affected-children flag averages ~1.27 per household, a structural ~2.8x shortfall against the published all-children concept. Row counts: disabled_child_element_children." + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "from_entity": "benunit", + "filters": [ + { + "variable": "uc_tcl_receives_disabled_child_element", + "operator": ">", + "value": 0 + } + ] }, "axiom": { "metric_name": "dwp/uc/two_child_limit/disabled_child_element_children", @@ -10770,6 +11501,17 @@ "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { @@ -10784,7 +11526,29 @@ "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." + "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", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] }, "axiom": { "metric_name": "dwp/uc/households", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py index 5a758c40b..61f139c63 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py @@ -15,6 +15,7 @@ uk_national_frame, validate_uk_national_frame, ) +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask from microcosm.frame import Frame, WeightKind __all__ = [ @@ -161,6 +162,7 @@ "is_household_head", "is_benunit_head", "is_parent", + "is_uc_claimant", "employment_income", "self_employment_income", "private_pension_income", @@ -429,6 +431,10 @@ def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame: pe_benunit["is_married"] = _number(benunit_raw, "famtypb2").isin([5, 7]) pe_benunit["dependent_children"] = dependent_children + # Preserve the FRS claimant/partner roles as a country-model input. Age + # does not promote a dependent child to a partner, and legal marriage + # alone does not establish that a partner lives in this benefit unit. + pe_person["is_uc_claimant"] = frs_uc_claimant_mask(pe_person, pe_benunit) _add_household_columns(pe_household, household, frs) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py index 6e68a56c6..d023c1f19 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -264,7 +264,12 @@ def owned(self, *, rewrite: bool = False) -> Owned: _ROOT_PERSON_STRING = {"gender", "marital_status"} -_ROOT_PERSON_BOOL = {"is_household_head", "is_benunit_head", "is_parent"} +_ROOT_PERSON_BOOL = { + "is_household_head", + "is_benunit_head", + "is_parent", + "is_uc_claimant", +} _ROOT_PERSON_INT = {"age"} _ROOT_PERSON_FLOAT: set[str] = set() _ROOT_BENUNIT_TYPES = { diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py index 540b46544..530e61470 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py @@ -27,6 +27,7 @@ import pandas as pd from microcosm.frame import Frame, MassChangeRecord, WeightKind +from microcosm.frame.adapters import policyengine_uk as uk_engine_adapter from microcosm.graph import ( Capabilities, Determinism, @@ -43,6 +44,7 @@ ) from microcosm.graph.population import dtype_for_token +from . import uc_relationships from .national_frame import UK_NATIONAL_SCHEMA from .rowwise_geography import id_multiplier_for_values @@ -91,6 +93,21 @@ "age_tail": "age_tail", } +# Imported modules are not traversed by ``source_hash``. Bind relationship +# helpers and the adapter's input-retention checks into every consuming stage. +_STAGE_HELPER_MODULES = { + "frs_spine": (uc_relationships,), + "frs_legacy_proxies": (uk_engine_adapter,), + "frs_education_grant_split": (uk_engine_adapter,), + "frs_brma": (uk_engine_adapter,), + "was_wealth": (uk_engine_adapter,), + "lcfs_consumption": (uk_engine_adapter,), + "etb_vat": (uk_engine_adapter,), + "etb_services": (uk_engine_adapter,), + "uc_reporter_redraw": (uc_relationships, uk_engine_adapter), + "uc_capital_coherence": (uc_relationships,), +} + _COMPUTE = Capabilities( determinism=Determinism.DETERMINISTIC, numeric=Numeric.BITWISE, @@ -132,7 +149,9 @@ def _implementation_hash(kernel: object, stage: str, transform: object | None) - # hermetic registries unhashable and, more importantly, would fail to bind # production edits made elsewhere in that stage's module. del transform - return source_hash(type(kernel), _stage_module(stage)) + return source_hash( + type(kernel), _stage_module(stage), *_STAGE_HELPER_MODULES.get(stage, ()) + ) def _mass_log_payload(before: Frame, after: Frame) -> list[dict[str, object]]: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 9f3b5f635..cd6067fb8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -41,6 +41,9 @@ load_uk_local_geography_contract, metric_names, ) +from microcosm.build.uk_runtime.uc_source_periods import ( + validate_uc_source_month_coverage, +) from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.frame import Frame @@ -260,6 +263,9 @@ def compile_uk_target_registry( [restamped], country="uk", ) + registry = validate_uc_source_month_coverage( + restamped, registry, candidate_facts + ) except ValueError as error: unsupported.append( { @@ -726,6 +732,12 @@ def entity_reduction(self, reduction: Mapping[str, Any]) -> np.ndarray: ) def household_condition(self, condition: Mapping[str, Any]) -> np.ndarray: + """Evaluate a household predicate, optionally project it to its members. + + A benefit-unit target can share its dwelling's geography while keeping + claimant/family filters at benefit-unit grain. ``map_to`` is explicit; + legacy household conditions keep their household-aligned result. + """ entity = str(condition.get("entity") or "household") source = self.tables[entity] household_ids = self._household_ids_for(entity) @@ -753,10 +765,21 @@ def household_condition(self, condition: Mapping[str, Any]) -> np.ndarray: households = self.tables["household"] ids = households["household_id"] - return np.asarray( - _compare_series(ids.map(aggregate).fillna(0.0), expected), - dtype=bool, - ) + matched = _compare_series(ids.map(aggregate).fillna(0.0), expected) + map_to = str(condition.get("map_to") or "household") + if map_to != "household": + if map_to not in {"person", "benunit"}: + raise ValueError( + f"Unsupported UK household condition map_to {map_to!r}." + ) + matched = self._household_ids_for(map_to).map( + pd.Series(matched.to_numpy(), index=ids) + ) + if matched.isna().any(): + raise ValueError( + "UK household condition has unmatched household links." + ) + return np.asarray(matched, dtype=bool) def to_frame(self) -> Frame: tables = {**self.tables, **self.link_tables} 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 959b5eab2..89708debd 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 @@ -16,10 +16,17 @@ load_uk_national_frame, write_uk_national_frame, ) +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask +from microcosm.build.uk_runtime.uc_target_measurements import ( + UC_TARGET_VARIABLES, + compute_uc_target_measure, + uc_tcl_comparison_contract, +) from microcosm.build.uk_runtime.weighted_integrity import ( exclusion_evaluation_date, ) from microcosm.calibrate import TargetRegistry +from microcosm.frame.adapters.policyengine_uk import validate_uc_claimant_input _ENTITY_LINK = {"benunit": "person_benunit_id", "household": "person_household_id"} _ENTITY_ID = { @@ -52,24 +59,10 @@ def _uc_calibration_composition( benunit = frame.table("benunit") member_ids = person["person_benunit_id"].to_numpy() ids = benunit["benunit_id"].to_numpy() - dependent = ( - pd.Series(benunit["dependent_children"].to_numpy(), index=ids) - .loc[member_ids] - .to_numpy() - ) - claimant = ( - person["is_benunit_head"].to_numpy(dtype=bool) - | person["is_parent"].to_numpy(dtype=bool) - | (dependent == 0) - ) + claimant = frs_uc_claimant_mask(person, benunit) claimant_count = ( pd.Series(claimant).groupby(member_ids).sum().reindex(ids).to_numpy() ) - if not np.isin(claimant_count, [1, 2]).all(): - raise ValueError( - "UC calibration families require one or two relationship-defined " - "claimants per benefit unit." - ) qualifying = _values( simulation.calculate( "is_child_or_qualifying_young_person_for_universal_credit", year @@ -94,6 +87,12 @@ def compute_uk_measure_input( ) -> tuple[np.ndarray, str]: """Compute one policyengine-uk variable at the requested entity grain.""" + if variable in UC_TARGET_VARIABLES: + if entity != "benunit": + raise KeyError(f"UC statistical measurements are benunit-only: {entity}") + return compute_uc_target_measure( + frame, simulation, variable, year + ), "uc_claim_statistical_proxy" if variable in _UC_CALIBRATION_VARIABLES: if entity != "benunit": raise KeyError(f"UC calibration composition is benunit-only: {entity}") @@ -186,12 +185,14 @@ def __init__( frame, _provenance = load_uk_national_frame(source_path) self.frame = frame self.simulation = factory(dataset=str(source_path)) + validate_uc_claimant_input(self.simulation, frame.table("person"), self.year) self._receipt = { "mode": mode, "source_path": str(source_path), "policyengine_uk_version": _policyengine_uk_version(policyengine_uk), } self.contract_targets = _uk_contract_targets() + self._uc_tcl_measures_used: set[str] = set() def knows(self, entity: str, variable: str) -> bool: """Whether a route in :func:`compute_uk_measure_input` reaches here. @@ -201,7 +202,7 @@ def knows(self, entity: str, variable: str) -> bool: provider exception instead of the fence's message. """ - if variable in _UC_CALIBRATION_VARIABLES: + if variable in _UC_CALIBRATION_VARIABLES | UC_TARGET_VARIABLES: return entity == "benunit" definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None or entity not in _ENTITY_ID: @@ -219,7 +220,7 @@ 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: + if variable in _UC_CALIBRATION_VARIABLES | UC_TARGET_VARIABLES: return "benunit" definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None: @@ -227,12 +228,21 @@ def entity_for(self, variable: str) -> str | None: return str(definition.entity.key) def compute(self, entity: str, variable: str) -> tuple[np.ndarray, str]: - return compute_uk_measure_input( + result = compute_uk_measure_input( self.frame, self.simulation, entity, variable, self.year ) - - def receipt(self) -> dict[str, str]: - return dict(self._receipt) + if variable.startswith("uc_tcl_") and variable in UC_TARGET_VARIABLES: + self._uc_tcl_measures_used.add(variable) + return result + + def receipt(self) -> dict[str, Any]: + receipt = dict(self._receipt) + if self._uc_tcl_measures_used: + receipt["uc_tcl_comparison_contract"] = { + **uc_tcl_comparison_contract(self.year), + "computed_measures": sorted(self._uc_tcl_measures_used), + } + return receipt #: Every field a reviewed measure exclusion must carry (the diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py index 3ce29b413..eb387d85e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py @@ -207,6 +207,7 @@ def __post_init__(self) -> None: "person.is_before_universal_credit_qualifying_young_person_terminal_date", "person.is_in_non_advanced_education", "person.is_parent", + "person.is_uc_claimant", "person.legacy_jobseeker_proxy", "person.outpatient_visits", "person.pension_contributions_via_salary_sacrifice", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py index 22a8d21a3..dfce66e6c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py @@ -33,6 +33,9 @@ SPI_SYNTHETIC_SUPPORT_CHANNEL, support_channel_column, ) +from microcosm.build.uk_runtime.uc_relationships import ( + frs_uc_couple_mask, +) from microcosm.frame import Frame UC_CAPITAL_REDRAW_OUTPUT = "frs_benunit_capital" @@ -107,7 +110,6 @@ def cohere_uc_capital(frame: Frame) -> UKUCCapitalCoherenceResult: support_channel_column("benunit"), "frs_benunit_capital", "dependent_children", - "is_married", "would_claim_uc", ), label="benunit", @@ -201,7 +203,7 @@ def _redraw_spi_reporter_capital( household_weights=household_weights, ) child_band = _dependent_children_band(benunit["dependent_children"]) - couple = _boolean_values(benunit["is_married"]) + couple = frs_uc_couple_mask(person, benunit) # Domain-validated upstream: every non-sentinel value is >= 0. available = capital >= 0.0 donor = base & reporter & available & (weights > 0.0) @@ -305,6 +307,7 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None: "output": UC_CAPITAL_REDRAW_OUTPUT, "seed": UC_CAPITAL_REDRAW_SEED, "salt": UC_CAPITAL_REDRAW_SALT, + "couple_status": "is_uc_couple", } actual = {key: parameters.get(key) for key in expected} if actual != expected: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py new file mode 100644 index 000000000..7e39c875a --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py @@ -0,0 +1,83 @@ +"""Relationship inputs shared by UC measurement and diagnostic model replays.""" + +from __future__ import annotations + +import numpy as np +import pandas as pd + +UC_COUPLE_DEFINITION = ( + "count(is_benunit_head OR is_parent OR (dependent_children == 0)) " + "over benefit-unit members == 2" +) + + +def frs_uc_claimant_mask(person: pd.DataFrame, benunit: pd.DataFrame) -> np.ndarray: + """Return FRS claimant/partner roles in person-row order. + + The FRS adult-file members of a unit with dependent children are retained + as ``is_parent``; the head is retained separately. A childless FRS benefit + unit consists of its claimant or couple. This source role does not assert + eligibility, receipt of a couple standard allowance, or legal marriage. + Dependent child-file members do not become partners on their 18th birthday. + Membership is never changed here. + + Persist this mask as the Boolean ``is_uc_claimant`` input. It is + authoritative over PolicyEngine-UK's calculator fallback: a 17-year-old + partner or an older nonqualifying dependent cannot reliably be inferred + from age and education alone. Reporter payment landing uses a separate + established convention; this mask identifies every claimant or partner. + """ + required_person = {"person_benunit_id", "is_benunit_head", "is_parent"} + required_benunit = {"benunit_id", "dependent_children"} + for table, required, label in ( + (person, required_person, "person"), + (benunit, required_benunit, "benunit"), + ): + missing = required - set(table.columns) + if missing: + raise KeyError(f"UC relationship {label} inputs missing: {sorted(missing)}") + ids = benunit["benunit_id"] + members = person["person_benunit_id"] + if ids.isna().any() or ids.duplicated().any() or members.isna().any(): + raise ValueError("UC relationship membership IDs must be present and unique.") + if set(ids) != set(members): + raise ValueError("UC relationship membership must cover every benefit unit.") + for name in ("is_benunit_head", "is_parent"): + values = person[name] + if values.isna().any() or not values.isin([0, 1, False, True]).all(): + raise ValueError(f"{name} must contain complete boolean source roles.") + dependent = pd.to_numeric(benunit["dependent_children"], errors="coerce") + if ( + dependent.isna().any() + or not np.isfinite(dependent).all() + or (dependent < 0).any() + or not np.equal(dependent, np.floor(dependent)).all() + ): + raise ValueError("dependent_children must contain nonnegative integer counts.") + child_count = pd.Series(dependent.to_numpy(), index=ids).reindex(members).to_numpy() + claimants = ( + person["is_benunit_head"].to_numpy(dtype=bool) + | person["is_parent"].to_numpy(dtype=bool) + | (child_count == 0) + ) + counts = pd.Series(claimants).groupby(members.to_numpy()).sum().reindex(ids) + if not counts.isin([1, 2]).all(): + raise ValueError("FRS UC roles require one or two claimants per benefit unit.") + return claimants + + +def frs_uc_couple_mask(person: pd.DataFrame, benunit: pd.DataFrame) -> np.ndarray: + """Return claimant/partner couple status in benefit-unit row order. + + UC treats cohabiting claimants as a couple regardless of legal marriage. + Source roles, rather than an adult-age count, also keep an older dependent + from becoming the lone claimant's partner. + """ + claimants = frs_uc_claimant_mask(person, benunit) + counts = ( + pd.Series(claimants) + .groupby(person["person_benunit_id"].to_numpy(), sort=False) + .sum() + .reindex(benunit["benunit_id"]) + ) + return counts.eq(2).to_numpy(dtype=bool) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py index 176603a4d..054b4c835 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py @@ -33,6 +33,10 @@ _boolean_values, _household_to_benunit_weights, ) +from microcosm.build.uk_runtime.uc_relationships import ( + UC_COUPLE_DEFINITION, + frs_uc_couple_mask, +) from microcosm.fit.qrf import RegimeGatedQRF from microcosm.frame import Frame from microcosm.frame.rules import assert_rules_engine_country @@ -51,7 +55,7 @@ "universal_credit_reported_amount": "universal_credit_reported", } UC_REPORTER_PREDICTORS = ( - "is_married", + "is_uc_couple", "uc_child_band", "benunit_employment_income", "benunit_self_employment_income", @@ -66,6 +70,7 @@ ) UC_REPORTER_TARGET = "universal_credit_reported_amount" UC_REPORTER_TEMPORARY_DERIVED = { + "is_uc_couple": UC_COUPLE_DEFINITION, "uc_reported_capital": "frs_benunit_capital", "positive_pre_takeup_uc_award": ( "max(0, uc_maximum_amount - uc_income_reduction) > 0" @@ -176,6 +181,8 @@ def redraw_spi_reported_uc( "person_id", "person_benunit_id", "person_household_id", + "is_benunit_head", + "is_parent", "age", UC_REPORTER_REDRAW_OUTPUT, *_PERSON_INCOME_COLUMNS, @@ -187,7 +194,7 @@ def redraw_spi_reported_uc( ( "benunit_id", "frs_benunit_capital", - "is_married", + "dependent_children", support_channel_column("benunit"), support_clone_index_column("benunit"), ), @@ -212,9 +219,7 @@ def redraw_spi_reported_uc( expected=len(benunit), ) uc_child = _strict_materialized_bool( - materialized[ - "is_child_or_qualifying_young_person_for_universal_credit" - ], + materialized["is_child_or_qualifying_young_person_for_universal_credit"], expected=len(person), label="UC child flag", ) @@ -261,7 +266,9 @@ def redraw_spi_reported_uc( training = base & ~is_cgt_clone & (support_clone == 0) & screen target = spi & screen if not training.any(): - raise ValueError("UC reporter redraw has no screened canonical FRS training rows.") + raise ValueError( + "UC reporter redraw has no screened canonical FRS training rows." + ) if float(weights[training].sum()) <= 0.0: raise ValueError("UC reporter redraw training weights must have positive mass.") if not target.any(): @@ -430,6 +437,12 @@ def _claimant_rows( uc_child: np.ndarray, sp_age: np.ndarray, ) -> np.ndarray: + """Choose the payment landing row, separately from claim membership. + + The shared FRS mask identifies one or two claimants. This established + landing rule selects one eldest non-child, preferring working age, and + does not redefine those claimants or their partner roles. + """ age = _finite_numeric(person["age"], label="person.age") person_id = person["person_id"].to_numpy() adult = ~uc_child @@ -474,14 +487,19 @@ def _benefit_unit_predictors( grouped = person_numeric.groupby("person_benunit_id", sort=False) employment = grouped["employment_income"].sum().reindex(benunit_index) self_employment = grouped["self_employment_income"].sum().reindex(benunit_index) - investment = grouped[ - [ - "savings_interest_income", - "dividend_income", - "property_income", - "other_investment_income", + investment = ( + grouped[ + [ + "savings_interest_income", + "dividend_income", + "property_income", + "other_investment_income", + ] ] - ].sum().sum(axis=1).reindex(benunit_index) + .sum() + .sum(axis=1) + .reindex(benunit_index) + ) child_count = ( pd.Series(uc_child.astype(np.int8), index=person.index) .groupby(person_ids, sort=False) @@ -513,7 +531,7 @@ def _benefit_unit_predictors( column="region", ) result = pd.DataFrame(index=benunit.index) - result["is_married"] = _boolean_values(benunit["is_married"]).astype(float) + result["is_uc_couple"] = frs_uc_couple_mask(person, benunit).astype(float) result["uc_child_band"] = np.minimum(child_count.to_numpy(dtype=float), 3.0) result["benunit_employment_income"] = employment.to_numpy(dtype=float) result["benunit_self_employment_income"] = self_employment.to_numpy(dtype=float) @@ -527,7 +545,9 @@ def _benefit_unit_predictors( result["claimant_age"] = claimant["claimant_age"].to_numpy(dtype=float) result["region"] = region.to_numpy() if result[list(UC_REPORTER_NUMERIC_PREDICTORS)].isna().any().any(): - raise ValueError("UC reporter redraw numeric predictors contain missing values.") + raise ValueError( + "UC reporter redraw numeric predictors contain missing values." + ) if result["region"].isna().any(): raise ValueError("UC reporter redraw region predictor contains missing values.") return result.loc[:, list(UC_REPORTER_PREDICTORS)] @@ -537,9 +557,9 @@ def _benefit_unit_reporter_amounts( person: pd.DataFrame, benunit: pd.DataFrame, ) -> np.ndarray: - amounts = pd.to_numeric( - person[UC_REPORTER_REDRAW_OUTPUT], errors="coerce" - ).fillna(0.0) + amounts = pd.to_numeric(person[UC_REPORTER_REDRAW_OUTPUT], errors="coerce").fillna( + 0.0 + ) if (amounts < 0.0).any(): raise ValueError("universal_credit_reported must be nonnegative.") by_benunit = amounts.groupby(person["person_benunit_id"], sort=False).sum() @@ -592,9 +612,7 @@ def _land_spi_draws( spi_people = person["person_benunit_id"].isin(spi_ids).to_numpy(dtype=bool) person.loc[spi_people, UC_REPORTER_REDRAW_OUTPUT] = 0.0 draw_by_benunit = pd.Series(draws, index=benunit["benunit_id"]) - claimant_draws = person.loc[claimant_rows, "person_benunit_id"].map( - draw_by_benunit - ) + claimant_draws = person.loc[claimant_rows, "person_benunit_id"].map(draw_by_benunit) claimant_spi = claimant_rows & spi_people landed = claimant_draws.loc[claimant_spi[claimant_rows]].to_numpy(dtype=float) child_claimants = uc_child[claimant_spi] @@ -652,8 +670,7 @@ def _assert_landing_invariants( ) if np.any(positive != (expected > 0.0).astype(np.int64)): raise RuntimeError( - "UC reporter redraw must land each positive draw on exactly one " - "person row." + "UC reporter redraw must land each positive draw on exactly one person row." ) @@ -672,7 +689,7 @@ def _reporter_transition_receipt( .sum() ) children = benunit["benunit_id"].map(child_count).fillna(0).to_numpy(dtype=int) - couple = _boolean_values(benunit["is_married"]) + couple = frs_uc_couple_mask(person, benunit) # A unit with no non-child member (a 16-19 qualifying young person # heading their own unit) is not a lone parent: file it under its own # label so the with-children cells the measurement reads stay clean. @@ -810,8 +827,7 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None: } if actual != expected: raise ValueError( - "uc_reporter_redraw parameters drifted: " - f"expected {expected}, got {actual}." + f"uc_reporter_redraw parameters drifted: expected {expected}, got {actual}." ) if stage.outputs != () or stage.rewrites != (UC_REPORTER_REDRAW_OUTPUT,): raise ValueError( 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 new file mode 100644 index 000000000..5fa9eccdb --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py @@ -0,0 +1,129 @@ +"""Declared observation-month coverage for UK UC calibration references. + +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. +""" + +from __future__ import annotations + +import json +import re +from collections.abc import Iterable, Mapping +from dataclasses import replace +from typing import Any + +from microcosm.build.ledger_targets import LedgerTargetReference +from microcosm.calibrate import TargetRegistry + +EXPECTED_SOURCE_MONTHS = "uk_uc_expected_source_months" + + +def uc_source_month_metadata(months: object) -> dict[str, str]: + """Validate and encode a contract's explicit, ordered monthly coverage.""" + + valid = ( + isinstance(months, list) + and bool(months) + and all( + isinstance(month, str) + and re.fullmatch(r"[0-9]{4}-(?:0[1-9]|1[0-2])", month) + for month in months + ) + ) + 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: + raise ValueError( + "UK UC source_months must be unique, ordered months in one calendar year." + ) + return {EXPECTED_SOURCE_MONTHS: json.dumps(months, separators=(",", ":"))} + + +def validate_uc_source_month_coverage( + reference: LedgerTargetReference, + registry: TargetRegistry, + facts: Iterable[Mapping[str, Any]], +) -> TargetRegistry: + """Refuse a different resolved month set; record coverage without revaluing.""" + + 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" + ): + raise ValueError( + "UK UC source-month coverage requires a UC calendar_year_average reference." + ) + try: + expected = json.loads(encoded) + except (TypeError, json.JSONDecodeError) as error: + raise ValueError( + "UK UC source-month coverage metadata is invalid JSON." + ) from error + uc_source_month_metadata(expected) + + by_key: dict[str, list[Mapping[str, Any]]] = {} + for fact in facts: + lineage = fact.get("lineage") + key = ( + fact.get("aggregate_fact_key") + or fact.get("fact_key") + or fact.get("legacy_fact_key") + or fact.get("source_record_id") + or ( + lineage.get("source_record_id") + if isinstance(lineage, Mapping) + else None + ) + ) + if key: + by_key.setdefault(str(key), []).append(fact) + + validated = [] + for spec in registry.specs: + member_keys = json.loads( + spec.metadata.get("ledger_member_fact_keys", "null") + ) or [ + spec.metadata.get("ledger_fact_key") + or spec.metadata.get("ledger_source_record_id") + ] + resolved = [] + for key in member_keys: + matches = by_key.get(key, ()) + if len(matches) != 1: + raise ValueError( + f"UK UC source-month coverage for {reference.name!r}: " + f"member {key!r} must identify exactly one fact." + ) + period = matches[0].get("period", {}) + if period.get("type") != "month": + raise ValueError( + f"UK UC source-month coverage for {reference.name!r}: " + "resolved member is not a monthly observation." + ) + resolved.append(str(period.get("value", ""))) + resolved.sort() + if resolved != expected: + raise ValueError( + f"UK UC source-month coverage for {reference.name!r}: " + f"expected exactly one fact for each of {expected!r}; " + f"resolved {resolved!r}." + ) + validated.append( + replace( + spec, + metadata={ + **dict(spec.metadata), + "uk_uc_resolved_source_months": json.dumps( + resolved, separators=(",", ":") + ), + "uk_uc_source_month_count": str(len(resolved)), + "uk_uc_source_period_basis": "mean_of_declared_months", + }, + ) + ) + return TargetRegistry(validated, country=registry.country) 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 new file mode 100644 index 000000000..5b71c4afc --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py @@ -0,0 +1,117 @@ +"""UC statistical measurements, separate from benefit entitlement formulas. + +DWP's two-child-limit statistics include exceptions and count the claim's +own dependent children/qualifying young people. They do not count everyone +under 18 in the dwelling, or only children losing an element: +https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology + +The retained frame does not identify April open claims or exact birth dates. +The affected measures therefore explicitly remain positive-award/birth-year +proxies. Their receipts record those gaps; improved fit is not evidence that +the administrative population has been reconstructed. +""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import pandas as pd + +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask + +UC_TARGET_VARIABLES = frozenset( + { + "uc_tcl_qualifying_child_count", + "uc_tcl_affected_child_count_proxy", + "uc_tcl_affected_benunit_proxy", + "uc_tcl_claimant_receives_pip", + "uc_tcl_receives_disabled_child_element", + "uc_calibration_has_child_under_one", + } +) + + +def uc_tcl_comparison_contract(year: int) -> dict[str, Any]: + """Expose the remaining source/model differences to run receipts.""" + return { + "source_unit": "UC claim (single claimant or couple)", + "model_unit": "benunit", + "source_child_definition": "own dependent children or qualifying young people", + "includes_exceptions": True, + "source_claim_state": "open claim within source observation month", + "model_claim_state_proxy": "universal_credit > 0", + "source_birth_cutoff": "2017-04-06", + "model_birth_cutoff_proxy": "model birth_year >= 2017", + "birth_precision": ( + "model year minus retained age input; source-age observation " + "date/advancement and exact birth date unavailable" + ), + "source_period": "April snapshot; retain matched fact's observation year", + "model_period": str(year), + "period_alignment": "annual model state; not an April claim-history replay", + "status": "partial_alignment_with_explicit_proxies", + } + + +def compute_uc_target_measure( + frame: Any, simulation: Any, variable: str, year: int +) -> np.ndarray: + """Return a statistical measurement in benefit-unit row order. + + All member reductions use the person's own benefit unit. Child elements and + PIP on another unit in the same dwelling must not leak into these counts. + """ + if variable not in UC_TARGET_VARIABLES: + raise KeyError(variable) + person, benunit = frame.table("person"), frame.table("benunit") + members = person["person_benunit_id"].to_numpy() + ids = benunit["benunit_id"].to_numpy() + claimant = frs_uc_claimant_mask(person, benunit) + + def calculate(name: str, *, entity: str = "person") -> np.ndarray: + raw = simulation.calculate(name, year) + values = np.asarray(raw.values if hasattr(raw, "values") else raw) + if values.ndim != 1 or len(values) != len(frame.table(entity)): + raise ValueError(f"UC measurement {name} must align with {entity} rows.") + if values.dtype.kind not in "biuf" or not np.isfinite(values).all(): + raise ValueError(f"UC measurement {name} must be finite numeric values.") + return values + + def count(mask: np.ndarray) -> np.ndarray: + return ( + pd.Series(mask.astype(float)) + .groupby(members, sort=False) + .sum() + .reindex(ids) + .to_numpy() + ) + + if variable == "uc_calibration_has_child_under_one": + age = calculate("age") + return count(~claimant & (age >= 0) & (age < 1)) > 0 + if variable == "uc_tcl_claimant_receives_pip": + return count(claimant & (calculate("pip") > 0)) > 0 + + children = ~claimant & calculate( + "is_child_or_qualifying_young_person_for_universal_credit" + ).astype(bool) + child_count = count(children) + if variable == "uc_tcl_qualifying_child_count": + return child_count + if variable == "uc_tcl_receives_disabled_child_element": + return ( + count(children & (calculate("uc_individual_disabled_child_element") > 0)) + > 0 + ) + + birth_year = calculate("birth_year") + if not np.equal(birth_year, np.floor(birth_year)).all(): + raise ValueError("UC birth-year proxy requires integer model birth years.") + # The first two children are the oldest. This count is invariant to person + # row order and ties, and deliberately includes excepted later children. + post_cutoff_count = count(children & (birth_year >= 2017)) + affected_count = np.minimum(post_cutoff_count, np.maximum(child_count - 2, 0)) + if variable == "uc_tcl_affected_child_count_proxy": + return affected_count + return (affected_count > 0) & (calculate("universal_credit", entity="benunit") > 0) diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py index 17104fd29..7c110669e 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py @@ -28,7 +28,7 @@ PRIMARY_QRF_WORKER_MODULE = "microcosm.build.us_runtime.puf_qrf_worker" PRIMARY_QRF_INTERPRETER_PLACEHOLDER = "{python_interpreter}" APPROVED_UV_LOCK_SHA256 = ( - "ca9305fcebb5854d8d8369eb10fd134c918da7a79ab90f21c932c946b35165e5" + "4ef1ef6eb39b65ebc47c2c00bafa44e7c872544b1b0146dd8493bcfe45b2da1b" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" 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 9f1fda9cf..3f6f742c1 100644 --- a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py +++ b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py @@ -25,7 +25,7 @@ "spine", "vintages", } -AM_SPEC_SHA256 = "b128d14f8e6351d745a16ef4537d5c1b8d71d11b9d96cb8a65f1a4fb953f13ed" +AM_SPEC_SHA256 = "b081464d575f63e54e13ce7a2a7c9009ed0bea62289b4f9ab5dd3bc214544b16" @pytest.mark.parametrize( @@ -45,7 +45,7 @@ ), ( "be", - "8c6018eaf518b239a5625f8df8bf783ce4723a03704d100f3dbf4c2bb7765772", + "70fc6b15de937f3d88c173d586bbe4856070644eb9e1c850bcdc93501a3534f9", { "household.household_id", "person.person_id", @@ -55,7 +55,7 @@ ), ( "uk", - "8cddc822e7d1207e8f36da4883cdeaa8bc5e93a27b1b134bb6b1f33cf976e45d", + "cfb7d43a35970b69177f4bc5097a7a108ab9c0442839cd6795bb42bebe37ba0e", { "benunit.benunit_id", "household.household_id", diff --git a/packages/microcosm-build/tests/test_spec_engine_loader.py b/packages/microcosm-build/tests/test_spec_engine_loader.py index a7effd506..a98f9cd15 100644 --- a/packages/microcosm-build/tests/test_spec_engine_loader.py +++ b/packages/microcosm-build/tests/test_spec_engine_loader.py @@ -236,7 +236,7 @@ def test_semantic_hash_has_golden_vector_and_surface_separation(tmp_path) -> Non # Pin the domain separator, normalization rules, schema-set receipt, and # exact normative projection as one reviewable golden vector. assert first.spec_sha256 == ( - "7a0fbb0a2a16aff28a9ac6e205e6679b013c580171db87b39597a0b14a97e423" + "8761376e420bf184dcd4623185a876e4453f7d0d567ed687009ac3533752a88c" ) second_root = _rich_minimal(tmp_path / "xy", note="second", store="local:b") diff --git a/packages/microcosm-build/tests/test_target_materialization.py b/packages/microcosm-build/tests/test_target_materialization.py index c9865cb9d..2a6d2c860 100644 --- a/packages/microcosm-build/tests/test_target_materialization.py +++ b/packages/microcosm-build/tests/test_target_materialization.py @@ -65,13 +65,9 @@ def set_column(self, entity, variable, values): class StubMeasureProvider: contract_targets = { - "needs_input_a": { - "bindings": {"policyengine": {"value_variable": "input_a"}} - }, + "needs_input_a": {"bindings": {"policyengine": {"value_variable": "input_a"}}}, "needs_input_a_and_b": { - "bindings": { - "policyengine": {"value_expression": "input_a + input_b"} - } + "bindings": {"policyengine": {"value_expression": "input_a + input_b"}} }, "needs_input_a_again": { "bindings": {"policyengine": {"value_variable": "input_a"}} @@ -328,9 +324,7 @@ def column(self, entity, variable): period=2025, ) - assert error.value.receipt["attached"] == { - "person.input_a": "stub:person.input_a" - } + assert error.value.receipt["attached"] == {"person.input_a": "stub:person.input_a"} assert error.value.receipt["skips"][-1]["name"] == "needs_input_a" @@ -507,8 +501,7 @@ def test_bands_slice_the_population_and_partition_it(): assert list(adapter.tables["person"]["income_band_40"]) == [0.0, 0.0, 0.0] # Every record lands in exactly one band: the bands partition the surface. total = sum( - adapter.tables["person"][f"income_band_{label}"] - for label in ("0", "20", "40") + adapter.tables["person"][f"income_band_{label}"] for label in ("0", "20", "40") ) assert list(total) == [1.0, 1.0, 1.0] @@ -701,6 +694,72 @@ def test_published_range_label_edges_survive_sibling_exclusion(): assert list(adapter.tables["person"]["award_low"]) == [0.0, 1.0, 0.0] +@pytest.mark.parametrize("inclusive", [True, False]) +def test_declared_finite_band_ceiling_does_not_absorb_unbound_source_tail(inclusive): + """DWP's finite £2400.01–2500 row excludes its separate £2500.01+ row.""" + adapter = StubAdapter() + adapter.tables["person"]["income"] = np.array( + [np.nextafter(30_000.0, -np.inf), 30_000.0, np.nextafter(30_000.0, np.inf)] + ) + spec = TargetSpec( + name="finite_top", + entity="person", + measure="finite_top", + value=1.0, + source="DWP Monthly Award Amount (payment bands)", + metadata={ + "contract_target_id": "uc.finite_bands", + "ledger_filter_monthly_award_bands": "£2400.01 to £2500.00", + }, + ) + registry = TargetRegistry([spec], country="uk") + contract = { + "uc.finite_bands": { + "bindings": { + "policyengine": { + "value_variable": "person_count", + "groupby_variable": "income", + "from_entity": "person", + "band_period_factor": 12, + "band_upper_bound": 2500, + "band_upper_bound_inclusive": inclusive, + } + } + } + } + result = materialize_target_bindings(adapter, registry, contract, period=2025) + assert not result.skipped + assert adapter.tables["person"]["finite_top"].tolist() == [1, int(inclusive), 0] + + +@pytest.mark.parametrize( + "bound_fields", + [ + {"band_upper_bound": 2500}, + {"band_upper_bound_inclusive": True}, + {"band_upper_bound": np.inf, "band_upper_bound_inclusive": True}, + {"band_upper_bound": 0, "band_upper_bound_inclusive": True}, + {"band_upper_bound": 2500, "band_upper_bound_inclusive": "yes"}, + { + "band_upper_bound": 2500, + "band_upper_bound_inclusive": True, + "band_period_factor": 0, + }, + ], +) +def test_invalid_declared_band_ceiling_refuses_materialization(bound_fields): + from copy import deepcopy + + contract = deepcopy(_BANDED_CONTRACT) + target_id = _banded_registry().specs[0].metadata["contract_target_id"] + contract[target_id]["bindings"]["policyengine"].update(bound_fields) + result = materialize_target_bindings( + StubAdapter(), _banded_registry(), contract, period=2025 + ) + assert result.skipped + assert all("band_upper_bound" in skip.reason for skip in result.skipped) + + def test_band_bounds_refuse_a_spec_absent_from_the_band_edge_register(): # A register that cannot bound a spec is a wrong-register problem for the # whole run: it must propagate as a refusal, never degrade into a skipped @@ -768,9 +827,7 @@ def adapter_factory(): band_edge_registry=registry, ) - assert resolution.receipt["attached"] == { - "person.input_a": "stub:person.input_a" - } + assert resolution.receipt["attached"] == {"person.input_a": "stub:person.input_a"} assert list(probes[-1].tables["person"]["income_band_0"]) == [1.0, 0.0, 0.0] assert list(probes[-1].tables["person"]["income_band_40"]) == [0.0, 0.0, 0.0] diff --git a/packages/microcosm-build/tests/test_uk_battery_bindings.py b/packages/microcosm-build/tests/test_uk_battery_bindings.py index ee991d88a..465b89ef8 100644 --- a/packages/microcosm-build/tests/test_uk_battery_bindings.py +++ b/packages/microcosm-build/tests/test_uk_battery_bindings.py @@ -75,6 +75,23 @@ def uk_gates(): return load_country_spec("uk").gates +def test_declared_export_surface_preserves_claimants_and_matches_runtime(uk_gates): + from microcosm.build.uk_runtime.terminal_gates import ( + UK_ALLOWED_EXTRA_EXPORT_COLUMNS, + ) + + entry = next(entry for entry in uk_gates.gates if entry.id == "uk_export_surface") + declared = set(entry.parameters["allowed_extra_columns"]) + assert "person.is_uc_claimant" in declared + assert declared == set(UK_ALLOWED_EXTRA_EXPORT_COLUMNS) + battery = _run_battery( + _tables(), + parity=_parity(candidate_columns={"person.age", "person.is_uc_claimant"}), + ) + outcome = next(o for o in battery.outcomes if o.entry.id == "uk_export_surface") + assert outcome.status is GateStatus.PASSED + + def _tables(*, n: int = 4, weights=None): if weights is None: weights = np.ones(n, dtype=float) diff --git a/packages/microcosm-build/tests/test_uk_calibration_run.py b/packages/microcosm-build/tests/test_uk_calibration_run.py index 068ee3cbf..2ca21390d 100644 --- a/packages/microcosm-build/tests/test_uk_calibration_run.py +++ b/packages/microcosm-build/tests/test_uk_calibration_run.py @@ -66,6 +66,7 @@ def _frame(): household=pd.DataFrame( { "household_id": ids, + "region": "LONDON", "household_weight": [10.0, 10.0, 10.0, 10.0], "household_is_spi_synthetic": [False, False, False, False], "household_is_capital_gains_clone": [False, False, False, False], diff --git a/packages/microcosm-build/tests/test_uk_frs_spine.py b/packages/microcosm-build/tests/test_uk_frs_spine.py index 5b5c7437e..18e35e77c 100644 --- a/packages/microcosm-build/tests/test_uk_frs_spine.py +++ b/packages/microcosm-build/tests/test_uk_frs_spine.py @@ -943,6 +943,41 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["salary_sacrifice_asked"] == 1 +@pytest.mark.parametrize("couple_has_children", [False, True]) +def test_uc_claimant_input_uses_frs_membership_not_age_or_marriage( + tmp_path: Path, couple_has_children: bool +) -> None: + tables = _fixture_tables() + # A lone parent with a 19-year-old child remains a single claimant. + tables["child"][0]["AGE"] = 19 + # A young cohabiting partner is a claimant even below the generic adult age. + tables["adult"].append( + { + **tables["adult"][0], + "PERSON": 2, + "UPERSON": 2, + "HRPID": 0, + "AGE": 17, + "MARITAL": 2, + } + ) + tables["benunit"][0]["FAMTYPB2"] = 6 + if couple_has_children: + tables["benunit"][0]["DEPCHLDB"] = 1 + tables["child"].append( + {**tables["child"][0], "SERNUM": 2, "PERSON": 3, "AGE": 18} + ) + stage = _write_fixture(tmp_path, tables) + frame = build_uk_frs_spine_frame(tmp_path, stage=stage) + person = frame.table("person").set_index("person_id") + expected = {1001: True, 1002: False, 2001: True, 2002: True} + if couple_has_children: + expected[2003] = False + assert person["is_uc_claimant"].to_dict() == expected + assert person["is_uc_claimant"].dtype == bool + assert not frame.table("benunit").set_index("benunit_id").loc[201, "is_married"] + + def test_benefit_code_splits_are_ported(tmp_path: Path) -> None: stage = _write_fixture(tmp_path) diff --git a/packages/microcosm-build/tests/test_uk_graph.py b/packages/microcosm-build/tests/test_uk_graph.py index ea15d9a0d..80a39f041 100644 --- a/packages/microcosm-build/tests/test_uk_graph.py +++ b/packages/microcosm-build/tests/test_uk_graph.py @@ -2,6 +2,8 @@ from __future__ import annotations +from pathlib import Path + import numpy as np import pandas as pd import pytest @@ -171,6 +173,11 @@ def test_uk_spine_graph_contains_manifest_stages_and_named_exclusions() -> None: } assert set(expected) <= ids assert not (UK_SPINE_EXCLUSIONS & ids) + root_dtypes = { + (owned.entity, owned.column): owned.dtype + for owned in graph.node("create_uk_frs").outputs + } + assert root_dtypes[("person", "is_uc_claimant")] == "bool" assert { node.id for node in graph.nodes if node.structural is StructuralDelta.EXPAND } == UK_SPINE_STRUCTURAL_STAGES @@ -257,6 +264,75 @@ def test_uk_registry_covers_every_kernel_ref_and_hashes_stage_modules() -> None: ) != registry.implementation_hash("uk.stage.frs_council_tax@1") +def test_uc_relationship_helper_changes_affected_kernel_hashes(monkeypatch) -> None: + from microcosm.build.uk_runtime import uc_relationships + + graph = uk_spine_graph() + registry = uk_registry(graph=graph) + affected_refs = ( + graph.node("create_uk_frs").kernel, + graph.node("uc_reporter_redraw").kernel, + graph.node("uc_capital_coherence").kernel, + ) + control_ref = graph.node("frs_council_tax").kernel + before = { + ref: registry.implementation_hash(ref) for ref in (*affected_refs, control_ref) + } + helper_path = Path(uc_relationships.__file__).resolve() + original_read_bytes = Path.read_bytes + + def changed_helper_bytes(path: Path) -> bytes: + content = original_read_bytes(path) + if path.resolve() == helper_path: + return content + b"\n# A helper-only source change.\n" + return content + + monkeypatch.setattr(Path, "read_bytes", changed_helper_bytes) + + for ref in affected_refs: + assert registry.implementation_hash(ref) != before[ref] + assert registry.implementation_hash(control_ref) == before[control_ref] + + +def test_uk_adapter_source_changes_invalidate_all_consuming_stages(monkeypatch): + from microcosm.frame.adapters import policyengine_uk + + graph = uk_spine_graph() + registry = uk_registry(graph=graph) + stages = ( + "frs_legacy_proxies", + "frs_education_grant_split", + "frs_brma", + "was_wealth", + "lcfs_consumption", + "etb_vat", + "etb_services", + "uc_reporter_redraw", + ) + affected = [graph.node(stage).kernel for stage in stages] + controls = [ + graph.node(stage).kernel + for stage in ("frs_council_tax", "uc_capital_coherence") + ] + before = {ref: registry.implementation_hash(ref) for ref in affected + controls} + adapter_path = Path(policyengine_uk.__file__).resolve() + original = Path.read_bytes + + def changed_adapter_bytes(path): + content = original(path) + return ( + content + b"\n# adapter-only change\n" + if path.resolve() == adapter_path + else content + ) + + monkeypatch.setattr(Path, "read_bytes", changed_adapter_bytes) + for ref in affected: + assert registry.implementation_hash(ref) != before[ref] + for ref in controls: + assert registry.implementation_hash(ref) == before[ref] + + def test_uk_graph_json_round_trip_is_canonical() -> None: graph = uk_spine_graph() serialized = graph_to_json(graph) diff --git a/packages/microcosm-build/tests/test_uk_ledger_targets.py b/packages/microcosm-build/tests/test_uk_ledger_targets.py index 0781c8711..2977e0f91 100644 --- a/packages/microcosm-build/tests/test_uk_ledger_targets.py +++ b/packages/microcosm-build/tests/test_uk_ledger_targets.py @@ -420,13 +420,11 @@ def test_local_parity_fixture_aligns_legacy_council_tax_band_names(): class StubUKAdapter: - """Seven people across three households, with distinct child concepts. + """Prepared benefit-unit measures distinguish flags and child counts. - The affected flag and the child counts are deliberately distinct here: - household 0 holds three affected children but four children in all, and - household 2 holds two of each. A household-grain read of the boolean - (1/0/1) can therefore be distinguished from both the affected-child counts - (3/0/2) and the all-child counts (4/0/2). + These independent prepared vectors test binding selection, not the + demographic calculation: the flag (1/0/1), affected counts (3/0/2), and + qualifying counts (4/0/2) must not substitute for one another. """ def __init__(self): @@ -446,6 +444,13 @@ def __init__(self): # household contains at least one flagged child". "uc_is_child_limit_affected": np.array([1.0, 0.0, 1.0]), }, + "benunit": { + "benunit_id": np.array([0, 1, 2]), + "uc_tcl_affected_benunit_proxy": np.array([True, False, True]), + "uc_tcl_affected_child_count_proxy": np.array([3.0, 0.0, 2.0]), + "uc_tcl_qualifying_child_count": np.array([4.0, 0.0, 2.0]), + "uc_tcl_claimant_receives_pip": np.array([True, True, False]), + }, } def column(self, entity, variable): @@ -469,6 +474,9 @@ def entity_reduction(self, reduction): ) def household_condition(self, condition): + if condition["variable"] == "region": + assert condition["map_to"] == "benunit" + return np.array([True, True, True]) assert condition["variable"] == "pip" assert condition["entity"] == "person" assert condition["reduce"] == "sum" @@ -685,7 +693,7 @@ def test_materialize_uk_ledger_targets_with_stub_adapter(): ), TargetSpec( name="dwp.uc.two_child_limit.children_affected", - entity="household", + entity="benunit", measure="dwp/uc/two_child_limit/children_affected", value=1.0, source="test", @@ -695,7 +703,7 @@ def test_materialize_uk_ledger_targets_with_stub_adapter(): ), TargetSpec( name="dwp.uc.two_child_limit.children_claimant_pip", - entity="household", + entity="benunit", measure="dwp/uc/two_child_limit/adult_pip_children", value=1.0, source="test", @@ -722,16 +730,15 @@ def test_materialize_uk_ledger_targets_with_stub_adapter(): 0.0, 1.0, ] - # Child counts, not household indicators: the declared value_reduction - # sums the flag over each household's people. A boolean any-collapse - # would have published [1.0, 0.0, 1.0] against a count target. - assert adapter.tables["household"][ + # The binding reads the prepared affected-child count, preserving its + # difference from the benefit-unit indicator [1.0, 0.0, 1.0]. + assert adapter.tables["benunit"][ "dwp/uc/two_child_limit/children_affected" ].tolist() == [3.0, 0.0, 2.0] - # Sheet 04B's claimant-PIP row counts every child in affected households - # satisfying the PIP condition, not only the children carrying the - # affected flag. Household 0 therefore contributes four, not three. - assert adapter.tables["household"][ + # Sheet 04B counts all qualifying children in affected claims whose own + # claimant receives PIP. The first prepared unit contributes four, not + # its three affected children or its one affected-unit indicator. + assert adapter.tables["benunit"][ "dwp/uc/two_child_limit/adult_pip_children" ].tolist() == [4.0, 0.0, 0.0] @@ -798,7 +805,9 @@ def _uc_composition_frame(): "uc_calibration_child_count": [1, 0, 1, 0], } ), - "household": pd.DataFrame({"household_id": np.arange(2)}), + "household": pd.DataFrame( + {"household_id": np.arange(2), "region": ["LONDON", "SCOTLAND"]} + ), }, EntitySchema(group_entities=("benunit", "household")), {"household": Weights(np.array([10.0, 20.0]), WeightKind.DESIGN)}, @@ -849,6 +858,110 @@ def test_uc_composition_materializes_at_benunit_grain(): ].tolist() == [0.0, 0.0, 0.0, 1.0] +def test_uc_national_counts_exclude_ni_and_unknown_regions_at_benunit_grain(): + """The GB source excludes Northern Ireland, not just on the fact selector. + + DWP methodology: Creating a Great Britain dataset. Multiple benefit units + share each dwelling; order differs between benefit-unit and household ids. + """ + import pandas as pd + + from microcosm.build.uk_runtime.ledger_targets import UKFrameTargetAdapter + from microcosm.frame import EntitySchema, Frame, WeightKind, Weights + + regions = ["NORTHERN_IRELAND", "LONDON", "WALES", "SCOTLAND", "UNKNOWN"] + household_ids = np.array([4, 3, 2, 1, 0, 4, 3, 2, 1, 0]) + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": np.arange(10), + "person_benunit_id": np.arange(10), + "person_household_id": household_ids, + } + ), + "benunit": pd.DataFrame( + { + "benunit_id": np.arange(10), + "universal_credit": np.full(10, 29_000.0), + "uc_calibration_family_type": ["COUPLE_NO_CHILDREN"] * 10, + "uc_calibration_child_count": np.ones(10), + } + ), + "household": pd.DataFrame( + {"household_id": np.arange(5), "region": regions} + ), + }, + EntitySchema(group_entities=("benunit", "household")), + {"household": Weights(np.ones(5), WeightKind.DESIGN)}, + ) + ids = [ + "dwp.uc.households", + "dwp.uc.households_children_1", + "dwp.uc.households_couple_no_children", + "dwp.uc.payment_distribution_couple_no_children", + ] + registry = TargetRegistry( + [ + TargetSpec( + name=target_id, + entity="benunit", + measure=target_id, + value=1.0, + source="synthetic; DWP UC_Households GB", + metadata={ + "contract_target_id": target_id, + "ledger_filter_monthly_award_amount_bands": "£2400.01 to £2500.00", + }, + ) + for target_id in ids + ], + country="uk", + ) + adapter = UKFrameTargetAdapter(frame) + result = materialize_uk_ledger_targets(adapter, registry, period=2025) + assert not result.skipped + expected = [0.0, 1.0, 1.0, 1.0, 0.0] * 2 + for target_id in ids: + assert adapter.tables["benunit"][target_id].tolist() == expected + + +def test_household_geography_condition_projects_through_benefit_unit_links(): + from microcosm.build.uk_runtime.ledger_targets import UKFrameTargetAdapter + + adapter = UKFrameTargetAdapter(_uc_composition_frame()) + adapter.tables["household"]["region"] = ["NORTHERN_IRELAND", "SCOTLAND"] + result = adapter.household_condition( + { + "entity": "household", + "variable": "region", + "reduce": "any", + "operator": "in", + "value": ["SCOTLAND"], + "map_to": "benunit", + } + ) + assert result.tolist() == [False, False, True, True] + + +def test_household_geography_projection_refuses_benunits_spanning_dwellings(): + from microcosm.build.uk_runtime.ledger_targets import UKFrameTargetAdapter + + adapter = UKFrameTargetAdapter(_uc_composition_frame()) + adapter.tables["person"].loc[1, "person_household_id"] = 1 + with pytest.raises(ValueError, match="groups span multiple households"): + adapter.household_condition( + { + "entity": "household", + "variable": "region", + "reduce": "any", + "operator": "==", + "value": "SCOTLAND", + "map_to": "benunit", + } + ) + + def test_household_condition_reduces_person_level_conditions(): # Person-level conditions collapse through person_household_id. Building # the group-membership column here would ask for "person_person_id", diff --git a/packages/microcosm-build/tests/test_uk_measure_simulation.py b/packages/microcosm-build/tests/test_uk_measure_simulation.py index 7f13fa4b5..da0eeddba 100644 --- a/packages/microcosm-build/tests/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/test_uk_measure_simulation.py @@ -64,6 +64,37 @@ def calculate(self, variable, year, map_to=None): return self.values[variable][1] +@pytest.mark.parametrize("missing_variable", [True, False]) +def test_direct_measure_resolver_refuses_dropped_uc_claimant_input( + monkeypatch, tmp_path, missing_variable +): + frame = FrameStub() + frame.table("person")["is_uc_claimant"] = [True, False, True] + simulation = SimulationStub( + {"is_uc_claimant": ("person", np.array([True, False, True]))} + ) + simulation.input_variables = [] + if missing_variable: + simulation.tax_benefit_system.variables.clear() + else: + simulation.tax_benefit_system.variables[ + "is_uc_claimant" + ].definition_period = "year" + monkeypatch.setattr( + measure_simulation, + "_policyengine_uk_module", + lambda: SimpleNamespace(__version__="2.94.0"), + ) + with pytest.raises(ValueError, match="is_uc_claimant"): + UKMeasureResolver( + simulation_source=tmp_path / "synthetic.h5", + scratch_dir=tmp_path, + year=2025, + frame=frame, + microsimulation_factory=lambda **kwargs: simulation, + ) + + def test_compute_uk_measure_input_native_route(): sim = SimulationStub({"income_tax": ("person", np.array([1.0, 2.0, 3.0]))}) diff --git a/packages/microcosm-build/tests/test_uk_national_calibration.py b/packages/microcosm-build/tests/test_uk_national_calibration.py index 0dadc852f..bb29a8006 100644 --- a/packages/microcosm-build/tests/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/test_uk_national_calibration.py @@ -116,7 +116,7 @@ def _frame() -> Frame: "benunit": pd.DataFrame( {"benunit_id": ids, "universal_credit": [1.0, 1.0, 0.0, 0.0]} ), - "household": pd.DataFrame({"household_id": ids}), + "household": pd.DataFrame({"household_id": ids, "region": "LONDON"}), }, EntitySchema(group_entities=("benunit", "household")), {"household": Weights(np.full(4, 10.0), WeightKind.DESIGN)}, @@ -167,23 +167,24 @@ def receipt(self): class StubCrosstabResolver: - """Supplies the household-grain affected flag, as production does. + """Supply separate prepared benefit-unit flags and affected-child counts. - ``uc_is_child_limit_affected`` is person-native in policyengine-uk, and - the Frame's global column-uniqueness rule forbids the same name on two - entity tables — so the household-grain flag can only ever arrive as a - table-scoped adapter injection. That is the production route, and it is - the route exercised here. + The national stage must inject both measurements temporarily, materialize + the target count, then remove the scratch inputs from the returned frame. """ contract_targets = None + measures = { + "uc_tcl_affected_benunit_proxy": np.array([True, False, True]), + "uc_tcl_affected_child_count_proxy": np.array([2.0, 0.0, 3.0]), + } def knows(self, entity, variable): - return (entity, variable) == ("household", "uc_is_child_limit_affected") + return entity == "benunit" and variable in self.measures def compute(self, entity, variable): - assert (entity, variable) == ("household", "uc_is_child_limit_affected") - return np.array([1.0, 0.0, 1.0]), "stub_any_collapse_person_to_household" + assert self.knows(entity, variable) + return self.measures[variable].copy(), "stub_benunit_tcl_measure" def receipt(self): return {"provider": "stub_crosstab_flag"} @@ -205,7 +206,9 @@ def _nested_frame() -> Frame: "universal_credit": [1.0, 0.0, 1.0, 1.0], } ), - "household": pd.DataFrame({"household_id": np.arange(3, dtype="int64")}), + "household": pd.DataFrame( + {"household_id": np.arange(3, dtype="int64"), "region": "LONDON"} + ), }, EntitySchema(group_entities=("benunit", "household")), {"household": Weights(np.array([10.0, 20.0, 30.0]), WeightKind.DESIGN)}, @@ -267,6 +270,7 @@ def _materialization_binding_frame( household = pd.DataFrame( { "household_id": np.arange(3, dtype="int64"), + "region": "LONDON", "esa_income": [10.0, 20.0, 0.0], "esa_contrib": [1.0, 2.0, 0.0], } @@ -544,17 +548,16 @@ def test_packaged_binding_classes_materialize_through_national_stage() -> None: # The binding classes produce the right prepared values on the adapter… adapter = UKFrameTargetAdapter(_materialization_binding_frame()) # The same table-scoped injection the resolution loop performs. - adapter.tables["household"]["uc_is_child_limit_affected"] = np.array( - [1.0, 0.0, 1.0] - ) + for variable, values in resolver.measures.items(): + adapter.tables["benunit"][variable] = values.copy() materialize_uk_ledger_targets(adapter, registry, period=2025) materialized = { ("benunit", "dwp/uc/households"): [1.0, 0.0, 1.0], ("household", "obr/esa"): [11.0, 22.0, 0.0], ("person", "hmrc/cgt_taxpayers"): [0.0, 1.0, 1.0, 0.0, 0.0, 0.0], - # Counts of flagged children, reduced from the person rows — not the - # household indicator [1.0, 0.0, 1.0] a same-grain boolean read gives. - ("household", "dwp/uc/two_child_limit/children_affected"): [2.0, 0.0, 3.0], + # The prepared affected-child counts stay distinct from the claim + # indicator [1.0, 0.0, 1.0] and legacy person-native entitlement flags. + ("benunit", "dwp/uc/two_child_limit/children_affected"): [2.0, 0.0, 3.0], ("person", "hmrc/salary_sacrifice_it_relief_basic_rate"): [ 1.0, 2.0, diff --git a/packages/microcosm-build/tests/test_uk_population_targets.py b/packages/microcosm-build/tests/test_uk_population_targets.py index c61d18de0..94f6235c2 100644 --- a/packages/microcosm-build/tests/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/test_uk_population_targets.py @@ -58,6 +58,8 @@ "band", "band_filter_dimension", "band_period_factor", + "band_upper_bound", + "band_upper_bound_inclusive", "count_of", "filters", "folded_into", @@ -469,6 +471,13 @@ def test_uk_population_targets_declare_chronicle_loader_guarantees() -> None: target["target_id"], predicate, ) + if "map_to" in predicate: + assert field == "household_conditions", target["target_id"] + assert predicate["map_to"] in PREDICATE_ENTITIES + assert predicate["map_to"] == binding.get( + "from_entity", "household" + ) + assert "reduce" in predicate assertion_policy = target.get("assertion_policy") if assertion_policy is not None: @@ -546,6 +555,7 @@ 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)], "filters": [ { "concept": "uk.benefits.universal_credit.amount", @@ -596,22 +606,87 @@ def test_uk_uc_composition_and_disability_children_targets_are_rebound() -> None binding = _target_by_id(resource, target_id)["bindings"]["policyengine"] assert binding["from_entity"] == "benunit" assert binding["value_variable"] == "benunit_count" - assert "household_conditions" not in binding + assert all( + condition["variable"] == "region" and condition["map_to"] == "benunit" + for condition in binding.get("household_conditions", ()) + ) assert "reduce" not in binding assert all( predicate["variable"] in allowed_filter_variables for predicate in binding["filters"] ) - for target_id in { - "dwp.uc.two_child_limit.children_claimant_pip", - "dwp.uc.two_child_limit.children_disabled_child_element", - }: + for target_id, flag in { + "dwp.uc.two_child_limit.children_claimant_pip": "uc_tcl_claimant_receives_pip", + "dwp.uc.two_child_limit.children_disabled_child_element": ( + "uc_tcl_receives_disabled_child_element" + ), + }.items(): binding = _target_by_id(resource, target_id)["bindings"]["policyengine"] - assert binding["value_variable"] == "is_child" - assert binding["value_reduction"]["variable"] == "is_child" + assert binding["from_entity"] == "benunit" + assert binding["value_variable"] == "uc_tcl_qualifying_child_count" + assert "value_reduction" not in binding assert binding["kind"] == "baseline_flag_crosstab" - assert binding["affected_flag_variable"] == "uc_is_child_limit_affected" + assert binding["affected_flag_variable"] == "uc_tcl_affected_benunit_proxy" + assert binding["filters"] == [{"variable": flag, "operator": ">", "value": 0}] + + +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() + references = json.loads( + importlib_resources.files("microcosm.build.uk") + .joinpath("target_references.json") + .read_text() + )["target_references"] + gb_ids = { + row["metadata"]["contract_target_id"] + for row in references + if row["metadata"]["contract_target_id"].startswith("dwp.uc.") + and row["ledger_selector"]["geography_id"] == "K03000001" + } + assert len(gb_ids) == 29 + for target_id in gb_ids: + binding = _target_by_id(resource, target_id)["bindings"]["policyengine"] + geographic = [ + condition + for condition in binding["household_conditions"] + if condition["variable"] == "region" + ] + assert len(geographic) == 1 + assert geographic[0]["entity"] == "household" + assert geographic[0]["operator"] == "in" + assert set(geographic[0]["value"]) == { + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND", + } + assert geographic[0].get("map_to", "household") == binding.get( + "from_entity", "household" + ) + if target_id.startswith("dwp.uc.payment_distribution_"): + assert binding["band_upper_bound"] == 2500 + assert binding["band_upper_bound_inclusive"] is True + assert binding["band_period_factor"] == 12 + # The omitted source top-coded row is not introduced or merged into a + # finite reference by these measurement-only changes. + payment = [ + row for row in references if row["name"].startswith("dwp/uc_payment_dist/") + ] + assert len(payment) == 100 + assert all( + "or over" + not in row["ledger_selector"]["dimension_values"]["monthly_award_amount_bands"] + for row in payment + ) def test_uk_population_cgt_contract_names_match_runtime_specs() -> None: diff --git a/packages/microcosm-build/tests/test_uk_release_input_coverage_manifest.py b/packages/microcosm-build/tests/test_uk_release_input_coverage_manifest.py index 2dd872ef8..504f17999 100644 --- a/packages/microcosm-build/tests/test_uk_release_input_coverage_manifest.py +++ b/packages/microcosm-build/tests/test_uk_release_input_coverage_manifest.py @@ -4,6 +4,7 @@ import importlib.util import json +from importlib.metadata import version from importlib.resources import files from pathlib import Path @@ -102,8 +103,9 @@ def test_committed_manifest_matches_regeneration() -> None: @requires_uk -def test_cached_candidate_regeneration_matches_committed_evidence() -> None: +def test_cached_candidate_current_reader_preserves_frozen_measurements() -> None: generator = _load_generator() + committed = _resource("efrs_parity_known_gaps.json")["candidate_evidence"] try: from huggingface_hub import try_to_load_from_cache except ImportError: @@ -118,8 +120,20 @@ def test_cached_candidate_regeneration_matches_committed_evidence() -> None: pytest.skip("pinned certified candidate revision is not cached") regenerated = generator.build_candidate_evidence(Path(cached)) - committed = _resource("efrs_parity_known_gaps.json")["candidate_evidence"] - assert regenerated == committed + # The reader's version is a new observation, not a replacement for the + # frozen receipt's provenance. Every actual measurement must still match. + assert regenerated["engine"]["version"] == version("policyengine-uk") + assert {k: v for k, v in regenerated["engine"].items() if k != "version"} == { + k: v for k, v in committed["engine"].items() if k != "version" + } + assert {k: v for k, v in regenerated.items() if k != "engine"} == { + k: v for k, v in committed.items() if k != "engine" + } + + +def test_frozen_candidate_retains_its_original_engine_provenance() -> None: + evidence = _resource("efrs_parity_known_gaps.json")["candidate_evidence"] + assert evidence["engine"]["version"] == "2.89.0" def test_hmrc_family_period_fields_come_from_the_bytes_their_hash_names() -> None: diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 2e46a2d5b..7ebee82dd 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -248,6 +248,11 @@ def test_uk_target_references_follow_contract_derivation_rules() -> None: observation_basis = target["measurement"].get("observation_basis") if observation_basis is not None: expected_metadata["observation_basis"] = observation_basis + source_months = target["measurement"].get("source_months") + if source_months is not None: + expected_metadata["uk_uc_expected_source_months"] = json.dumps( + source_months, separators=(",", ":") + ) assert reference["metadata"] == expected_metadata # The measure is a prepared column, so the pointed-to contract binding # must carry what the microcosm#622 materializer needs to prepare it. diff --git a/packages/microcosm-build/tests/test_uk_terminal_gates.py b/packages/microcosm-build/tests/test_uk_terminal_gates.py index d676fc0c4..96a1ed59b 100644 --- a/packages/microcosm-build/tests/test_uk_terminal_gates.py +++ b/packages/microcosm-build/tests/test_uk_terminal_gates.py @@ -270,6 +270,18 @@ def test_ported_june_parity_gates_retain_their_named_failures() -> None: assert fit.name == "target_fit" +def test_export_surface_allows_claimant_roles_but_not_unreviewed_columns() -> None: + reference = {"person.age"} + assert uk_export_surface_gate( + reference | {"person.is_uc_claimant"}, reference + ).passed + unrelated = uk_export_surface_gate( + reference | {"person.is_uc_claimant", "person.unreviewed_extra"}, reference + ) + assert not unrelated.passed + assert any("unreviewed_extra" in failure for failure in unrelated.failures) + + def _target_fit_exclusion( *, approved_on: str = "2026-08-30", diff --git a/packages/microcosm-build/tests/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/test_uk_uc_capital_coherence.py index 13fdcb83d..e16771f05 100644 --- a/packages/microcosm-build/tests/test_uk_uc_capital_coherence.py +++ b/packages/microcosm-build/tests/test_uk_uc_capital_coherence.py @@ -1,5 +1,7 @@ from __future__ import annotations +from dataclasses import replace + import numpy as np import pandas as pd import pytest @@ -49,8 +51,35 @@ def _frame(): "person_benunit_id": [row[1] for row in rows], "person_household_id": [row[0] for row in rows], "universal_credit_reported": [row[8] for row in rows], + "is_benunit_head": True, + "is_parent": [row[5] > 0 for row in rows], } ) + other_members = [] + for row in rows: + if row[6]: + other_members.append( + { + "person_id": row[2] + 1, + "person_benunit_id": row[1], + "person_household_id": row[0], + "universal_credit_reported": 0.0, + "is_benunit_head": False, + "is_parent": row[5] > 0, + } + ) + for child in range(row[5]): + other_members.append( + { + "person_id": row[2] + 10 + child, + "person_benunit_id": row[1], + "person_household_id": row[0], + "universal_credit_reported": 0.0, + "is_benunit_head": False, + "is_parent": False, + } + ) + person = pd.concat([person, pd.DataFrame(other_members)], ignore_index=True) benunit = pd.DataFrame( { "benunit_id": [row[1] for row in rows], @@ -83,10 +112,29 @@ def test_manifest_declares_exact_redraw_seed_and_output() -> None: assert redraw.parameters["output"] == UC_CAPITAL_REDRAW_OUTPUT assert redraw.parameters["seed"] == UC_CAPITAL_REDRAW_SEED assert redraw.parameters["salt"] == UC_CAPITAL_REDRAW_SALT + assert redraw.parameters["couple_status"] == "is_uc_couple" assert stage.outputs == ("uc_reported_capital",) assert stage.rewrites == ("frs_benunit_capital", "would_claim_uc") +def test_stage_refuses_a_stale_marriage_based_donor_contract(): + stage = _stage() + operations = tuple( + replace( + operation, + parameters={**operation.parameters, "couple_status": "is_married"}, + ) + if operation.kind == "redraw_spi_reporter_capital" + else operation + for operation in stage.operations + ) + transform = UKUCCapitalCoherenceStageTransform( + stage=replace(stage, operations=operations) + ) + with pytest.raises(ValueError, match="parameters drifted"): + transform(_frame()) + + def test_stage_orders_after_every_universal_credit_report_writer() -> None: spec = load_country_spec("uk") assert spec.sources is not None @@ -136,6 +184,23 @@ def test_redraw_is_reporter_conditioned_cell_exact_and_household_weighted() -> N assert result.redrawn_spi_reporter_count == 2 +def test_capital_donor_cells_use_claimant_partnership_and_preserve_marital_status(): + frame = _frame() + benunit = frame.table("benunit") + # Cohabiting couple and a married claimant whose spouse is not in this + # unit must use the couple and single donor cells respectively. + benunit.loc[benunit["benunit_id"] == 1007, "is_married"] = False + benunit.loc[benunit["benunit_id"] == 1005, "is_married"] = True + marital_before = benunit["is_married"].copy() + + result = cohere_uc_capital(frame).frame.table("benunit") + + by_id = result.set_index("benunit_id") + assert by_id.loc[1007, "frs_benunit_capital"] == 3_000.0 + assert by_id.loc[1005, "frs_benunit_capital"] == 200.0 + pd.testing.assert_series_equal(result["is_married"], marital_before) + + def test_transform_is_deterministic_and_idempotent() -> None: transform = UKUCCapitalCoherenceStageTransform(stage=_stage()) @@ -197,7 +262,7 @@ def redraw_tables( expected = redraw_tables(person, benunit, household, weights) order = np.asarray([7, 2, 5, 0, 6, 1, 4, 3]) actual = redraw_tables( - person.iloc[order].reset_index(drop=True), + person.sample(frac=1.0, random_state=91).reset_index(drop=True), benunit.iloc[order].reset_index(drop=True), household.iloc[order].reset_index(drop=True), weights[order], diff --git a/packages/microcosm-build/tests/test_uk_uc_family.py b/packages/microcosm-build/tests/test_uk_uc_family.py index f17b3ff52..1017223c6 100644 --- a/packages/microcosm-build/tests/test_uk_uc_family.py +++ b/packages/microcosm-build/tests/test_uk_uc_family.py @@ -117,12 +117,12 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case ), ] # Match the published decimal lower edges, independently of _band_bounds. - # KNOWN source mismatch: the highest INCLUDED band is bounded in DWP's - # labels (£2,400.01–£2,500/month); a separate £2,500.01+ category is absent - # from this registry. Current interpretation opens the included band to - # infinity. The £50,000 case pins that behavior, not source correctness. + # DWP's last included band is £2,400.01–£2,500/month, inclusive: + # https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html + # The separately published £2,500.01+ category remains unbound. It must + # never be silently absorbed into the last finite reference. edges = [float(f"{100 * i}.01") * 12 for i in range(25)] - awards = [-1.0, 0.0, 0.1, 50_000.0] + awards = [-1.0, 0.0, 0.1, 30_000.0, np.nextafter(30_000.0, np.inf), 50_000.0] for edge in edges: awards.extend([np.nextafter(edge, -np.inf), edge, np.nextafter(edge, np.inf)]) people, benunits, expected_families, qualifying = [], [], [], [] @@ -157,7 +157,9 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case # Reverse person-row order to expose positional aggregation assumptions; # Frame requires sorted group ids. bu = pd.DataFrame(benunits) - hh = pd.DataFrame({"household_id": np.arange(len(benunits) // 2)}) + hh = pd.DataFrame( + {"household_id": np.arange(len(benunits) // 2), "region": "LONDON"} + ) frame = Frame( { "person": pd.DataFrame(people).iloc[::-1].reset_index(drop=True), @@ -232,7 +234,9 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case family_name, band_name = spec.name.split("/")[-1].split("_annual_payment_") band_index = int(band_name.split("_to_")[0].replace("_", "")) // 1200 lower = edges[band_index] - upper = edges[band_index + 1] if band_index < 24 else np.inf + upper = ( + edges[band_index + 1] if band_index < 24 else np.nextafter(30_000.0, np.inf) + ) mask = ( (expected_family == family_name) & (uc > 0) & (uc >= lower) & (uc < upper) ) diff --git a/packages/microcosm-build/tests/test_uk_uc_relationships.py b/packages/microcosm-build/tests/test_uk_uc_relationships.py new file mode 100644 index 000000000..6f513817f --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_uc_relationships.py @@ -0,0 +1,212 @@ +"""FRS claimant roles must survive row reordering and older dependants.""" + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask + + +def test_frs_roles_distinguish_partner_from_older_dependent(): + person = pd.DataFrame( + { + "person_benunit_id": [20, 10, 20, 10, 20, 30], + "is_benunit_head": [False, True, True, False, False, True], + "is_parent": [True, True, True, False, False, False], + "age": [36, 42, 38, 19, 18, 17], + }, + index=[8, 6, 4, 2, 0, 9], + ) + benunit = pd.DataFrame( + {"benunit_id": [30, 20, 10], "dependent_children": [0, 1, 1]} + ) + np.testing.assert_array_equal( + frs_uc_claimant_mask(person, benunit), [True, True, True, False, False, True] + ) + + +def test_childless_cohabiting_partners_are_both_claimants(): + person = pd.DataFrame( + { + "person_benunit_id": [7, 7], + "is_benunit_head": [True, False], + "is_parent": [False, False], + } + ) + benunit = pd.DataFrame({"benunit_id": [7], "dependent_children": [0]}) + np.testing.assert_array_equal(frs_uc_claimant_mask(person, benunit), [True, True]) + + +def test_couple_mask_uses_source_roles_and_returns_benunit_row_order(): + from microcosm.build.uk_runtime.uc_relationships import frs_uc_couple_mask + + person = pd.DataFrame( + { + "person_benunit_id": [20, 10, 20, 10, 30, 30], + "is_benunit_head": [False, True, True, False, True, False], + "is_parent": [True, True, True, False, False, False], + "age": [36, 42, 38, 19, 26, 24], + }, + index=[8, 6, 4, 2, 0, 9], + ) + benunit = pd.DataFrame( + { + "benunit_id": [30, 20, 10], + "dependent_children": [0, 1, 1], + "is_married": [False, False, True], + } + ) + # Childless cohabitation; cohabiting parents; a lone married parent + # whose 19-year-old dependent must not become their partner. + np.testing.assert_array_equal( + frs_uc_couple_mask(person, benunit), [True, True, False] + ) + + +@pytest.mark.parametrize("bad_role", [None, 2, "False"]) +def test_invalid_roles_fail_instead_of_becoming_truthy(bad_role): + person = pd.DataFrame( + {"person_benunit_id": [7], "is_benunit_head": [bad_role], "is_parent": [False]} + ) + benunit = pd.DataFrame({"benunit_id": [7], "dependent_children": [1]}) + with pytest.raises(ValueError, match="is_benunit_head"): + frs_uc_claimant_mask(person, benunit) + + +def test_missing_membership_fails(): + person = pd.DataFrame( + {"person_benunit_id": [9], "is_benunit_head": [True], "is_parent": [False]} + ) + benunit = pd.DataFrame({"benunit_id": [7], "dependent_children": [0]}) + with pytest.raises(ValueError, match="membership"): + frs_uc_claimant_mask(person, benunit) + + +def test_more_than_two_claimants_requires_source_reconciliation(): + person = pd.DataFrame( + { + "person_benunit_id": [7, 7, 7], + "is_benunit_head": [True, False, False], + "is_parent": [False, False, False], + } + ) + benunit = pd.DataFrame({"benunit_id": [7], "dependent_children": [0]}) + with pytest.raises(ValueError, match="one or two"): + frs_uc_claimant_mask(person, benunit) + + +@pytest.mark.requires_uk +@pytest.mark.parametrize("route", ["adapter", "direct_h5"]) +def test_released_engine_retains_source_claimants_and_uc_awards(tmp_path, route): + """Explicit FRS roles survive both model-loading paths without fallback. + + This tests recorded claim membership, including a 17-year-old partner; + it does not add or establish eligibility for under-18 claimants. + 2025 monthly standard allowances are 400.14 single old, 497.55 couple + young and 628.10 couple old; an eldest child born before 2017 adds 339. + """ + from microcosm.build.uk_runtime.measure_simulation import UKMeasureResolver + from microcosm.build.uk_runtime.national_frame import ( + uk_national_frame, + write_uk_national_frame, + ) + from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine + + person = pd.DataFrame( + { + "person_id": range(1, 10), + "person_benunit_id": [10, 10, 20, 20, 30, 30, 40, 40, 40], + "person_household_id": [1, 1, 2, 2, 3, 3, 4, 4, 4], + "age": [40, 19, 24, 17, 40, 18, 40, 39, 19], + "is_benunit_head": [ + True, + False, + True, + False, + True, + False, + True, + False, + False, + ], + "is_parent": [True, False, False, False, True, False, True, True, False], + "is_in_non_advanced_education": [ + False, + False, + False, + True, + False, + True, + False, + False, + True, + ], + "age_started_or_accepted_current_education_or_training": [18] * 9, + "is_before_universal_credit_qualifying_young_person_terminal_date": [True] + * 9, + } + ) + benunit = pd.DataFrame( + { + "benunit_id": [10, 20, 30, 40], + "dependent_children": [1, 0, 1, 1], + "would_claim_uc": [True] * 4, + "uc_reported_capital": [0.0] * 4, + "uc_latent_deduction_rate": [0.0] * 4, + "uc_LCWRA_element": [0.0] * 4, + "benefit_cap_reduction": [0.0] * 4, + } + ) + claimant = frs_uc_claimant_mask(person, benunit) + person["is_uc_claimant"] = claimant + frame = uk_national_frame( + person=person, + benunit=benunit, + household=pd.DataFrame( + { + "household_id": [1, 2, 3, 4], + "region": ["LONDON"] * 4, + "council_tax": [0.0] * 4, + "rent": [0.0] * 4, + "tenure_type": ["OWNED_OUTRIGHT"] * 4, + } + ), + time_period=2025, + household_weights=np.ones(4), + ) + variables = [ + "is_uc_claimant", + "uc_standard_allowance", + "uc_child_element", + "universal_credit", + ] + if route == "adapter": + values = PolicyEngineUKEngine().materialize(frame, variables, 2025) + else: + path = tmp_path / "claimants.h5" + write_uk_national_frame(frame, path) + resolver = UKMeasureResolver( + simulation_source=path, scratch_dir=tmp_path, year=2025, frame=frame + ) + assert "is_uc_claimant" in resolver.simulation.input_variables + values = { + name: np.asarray(resolver.simulation.calculate(name, 2025)) + for name in variables + } + np.testing.assert_array_equal(values["is_uc_claimant"], claimant) + assert values["is_uc_claimant"].dtype.kind == "b" + np.testing.assert_allclose( + values["uc_standard_allowance"], + [4801.68, 5970.60, 4801.68, 7537.20], + atol=0.01, + rtol=0, + ) + np.testing.assert_allclose( + values["uc_child_element"], [0, 0, 4068, 4068], atol=0.01, rtol=0 + ) + np.testing.assert_allclose( + values["universal_credit"], + [4801.68, 5970.60, 8869.68, 11605.20], + atol=0.01, + rtol=0, + ) diff --git a/packages/microcosm-build/tests/test_uk_uc_reporter_redraw.py b/packages/microcosm-build/tests/test_uk_uc_reporter_redraw.py index db310db3d..09d6f969e 100644 --- a/packages/microcosm-build/tests/test_uk_uc_reporter_redraw.py +++ b/packages/microcosm-build/tests/test_uk_uc_reporter_redraw.py @@ -54,9 +54,9 @@ def materialize(self, frame, variables, period): reduction = np.zeros(len(benunit)) reduction[benunit["benunit_id"].eq(202).to_numpy()] = 100.0 if self.fail_all_spi: - reduction[benunit[support_channel_column("benunit")].eq("spi").to_numpy()] = ( - 100.0 - ) + reduction[ + benunit[support_channel_column("benunit")].eq("spi").to_numpy() + ] = 100.0 return { "uc_maximum_amount": maximum, "uc_income_reduction": reduction, @@ -88,6 +88,7 @@ def __init__(self, record: dict[str, object], *, seed: int) -> None: self.record = record def fit(self, table, predictors, targets, *, weights): + self.record["fit_table"] = table.copy() self.record["fit_index"] = table.index.tolist() self.record["fit_predictors"] = list(predictors) self.record["fit_targets"] = list(targets) @@ -155,6 +156,7 @@ def _frame(*, child_only: bool = False): "person_household_id": household_id, "age": age, "is_benunit_head": member == 0, + "is_parent": children > 0, "is_uc_child": benunit_id == 204, "employment_income": 10_000.0 + benunit_id + member, "self_employment_income": 100.0 * member, @@ -173,6 +175,7 @@ def _frame(*, child_only: bool = False): "person_household_id": household_id, "age": 10, "is_benunit_head": False, + "is_parent": False, "is_uc_child": True, "employment_income": 0.0, "self_employment_income": 0.0, @@ -219,6 +222,35 @@ def _stub_run(): return result, engine, factory.record +def test_reporter_predictor_and_receipt_use_claimant_couple_not_marriage(): + frame = _frame() + benunit = frame.table("benunit") + benunit.loc[benunit["benunit_id"].isin([101, 201]), "is_married"] = False + benunit.loc[benunit["benunit_id"] == 102, "is_married"] = True + marital_before = benunit["is_married"].copy() + factory = _StubQRFFactory() + + result = redraw_spi_reported_uc(frame, engine=_StubEngine(), qrf_factory=factory) + + assert "is_married" not in factory.record["fit_predictors"] + assert factory.record["fit_table"]["is_uc_couple"].tolist() == [1, 0, 0, 1] + assert ( + result.reporter_transitions["spi"]["couple_with_children"]["held_reporter"] == 1 + ) + pd.testing.assert_series_equal( + result.frame.table("benunit")["is_married"], marital_before + ) + + +def test_reporter_refuses_missing_relationship_inputs_before_engine_call(): + frame = _frame() + del frame.table("person")["is_parent"] + engine = _StubEngine() + with pytest.raises(ValueError, match="person columns missing.*is_parent"): + redraw_spi_reported_uc(frame, engine=engine, qrf_factory=_StubQRFFactory()) + assert engine.calls == [] + + def test_manifest_declares_benunit_rewrite_seed_and_exact_operations() -> None: stage = _stage() @@ -275,9 +307,11 @@ def test_training_mask_channel_containment_screen_and_landing_rule() -> None: positive = spi_201[spi_201[UC_REPORTER_REDRAW_OUTPUT] > 0.0] assert positive["person_id"].tolist() == [2011] assert positive[UC_REPORTER_REDRAW_OUTPUT].tolist() == [250.0] - assert person.loc[ - person["person_benunit_id"].eq(202), UC_REPORTER_REDRAW_OUTPUT - ].eq(0.0).all() + assert ( + person.loc[person["person_benunit_id"].eq(202), UC_REPORTER_REDRAW_OUTPUT] + .eq(0.0) + .all() + ) amounts = _benefit_unit_reporter_amounts(person, after.table("benunit")) assert amounts[after.table("benunit")["benunit_id"].eq(201).to_numpy()][0] == 250.0 assert result.training_benunits == 4 @@ -296,9 +330,7 @@ def test_twin_calls_are_byte_identical_and_emit_transition_receipt() -> None: check_exact=True, ) assert first.evidence() == twin.evidence() - assert first.evidence()["reporter_transitions"]["spi"][ - "couple_with_children" - ] == { + assert first.evidence()["reporter_transitions"]["spi"]["couple_with_children"] == { "promoted": 0, "demoted": 0, "held_reporter": 1, @@ -324,9 +356,10 @@ def test_downstream_capital_coherence_picks_up_redrawn_anchor() -> None: person = coherent.frame.table("person") benunit = coherent.frame.table("benunit").set_index("benunit_id") - assert person.loc[ - person["person_benunit_id"].eq(201), UC_REPORTER_REDRAW_OUTPUT - ].sum() == 250.0 + assert ( + person.loc[person["person_benunit_id"].eq(201), UC_REPORTER_REDRAW_OUTPUT].sum() + == 250.0 + ) assert benunit.loc[201, "would_claim_uc"] assert benunit.loc[201, "uc_reported_capital"] >= 0.0 gate = next( @@ -474,9 +507,7 @@ def test_claimant_prefers_working_age_by_the_engine_sp_age_flag() -> None: uc_child = np.array([False, False]) # The eldest adult is flagged state-pension-age by the engine (early SPA # cohort); the younger adult is the working-age claimant. - claimant = _claimant_rows( - person, uc_child=uc_child, sp_age=np.array([True, False]) - ) + claimant = _claimant_rows(person, uc_child=uc_child, sp_age=np.array([True, False])) assert claimant.tolist() == [False, True] diff --git a/packages/microcosm-build/tests/test_uk_uc_source_periods.py b/packages/microcosm-build/tests/test_uk_uc_source_periods.py new file mode 100644 index 000000000..0728e28e6 --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_uc_source_periods.py @@ -0,0 +1,203 @@ +"""UK UC source windows must not drift when monthly fact availability changes.""" + +from __future__ import annotations + +import json +from dataclasses import replace +from types import SimpleNamespace + +import pytest + +from microcosm.build.country_spec import load_country_spec +from microcosm.build.ledger_targets import LedgerTargetReference +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 + +MONTHS = [f"2025-{month:02}" for month in range(4, 13)] + + +def _reference() -> LedgerTargetReference: + return LedgerTargetReference( + name="dwp.uc.households", + ledger_selector={"source_concept": "dwp.uc_benefit_units"}, + value_operation="calendar_year_average", + entity="benunit", + measure="uc_households", + period=2025, + family="dwp_universal_credit", + metadata=uc_source_month_metadata(MONTHS), + ) + + +def _fact(month: str, *, suffix: str = "", value: float = 100.0) -> dict: + return { + "aggregate_fact_key": f"uc-{month}{suffix}", + "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.{month}.total_units", + }, + "observed_measure": { + "source_name": "dwp", + "source_concept": "dwp.uc_benefit_units", + "source_measure_id": "total_units", + "unit": "count", + }, + "period": {"type": "month", "value": month}, + "value": value, + } + + +def _compile(monkeypatch, facts: list[dict], reference=None, *, period=2025): + monkeypatch.setattr( + "microcosm.build.uk_runtime.ledger_targets.load_country_spec", + lambda country: SimpleNamespace(target_references=[reference or _reference()]), + ) + return compile_uk_target_registry(facts, target_period=period) + + +def test_declared_window_preserves_mean_and_receipts_each_actual_month(monkeypatch): + result = _compile( + monkeypatch, + [_fact(month, value=100.0 + index) for index, month in enumerate(MONTHS)], + ) + assert not result.unsupported + (target,) = result.registry.specs + assert (target.value, target.entity, target.measure) == ( + 104.0, + "benunit", + "uc_households", + ) + assert json.loads(target.metadata[EXPECTED_SOURCE_MONTHS]) == MONTHS + assert json.loads(target.metadata["uk_uc_resolved_source_months"]) == MONTHS + assert target.metadata["uk_uc_source_month_count"] == "9" + assert target.metadata["uk_uc_source_period_basis"] == "mean_of_declared_months" + + +@pytest.mark.parametrize( + "observed", + [ + MONTHS[1:], + ["2025-01", *MONTHS], + [f"2025-{month:02}" for month in range(1, 10)], + [*MONTHS, "2025-09"], + ], + ids=["missing", "extra", "same-count-wrong-months", "duplicate-month"], +) +def test_changed_month_coverage_is_unsupported_without_replacement( + monkeypatch, observed +): + result = _compile( + monkeypatch, + [_fact(month, suffix=f"-{index}") for index, month in enumerate(observed)], + ) + assert not result.registry.specs + assert len(result.unsupported) == 1 + assert "expected exactly one fact" in result.unsupported[0]["reason"] + assert "resolved" in result.unsupported[0]["reason"] + + +def test_restamping_model_year_does_not_silently_restamp_source_window(monkeypatch): + result = _compile( + monkeypatch, + [_fact(month.replace("2025", "2026")) for month in MONTHS], + period=2026, + ) + assert not result.registry.specs + assert "UK UC source-month coverage" in result.unsupported[0]["reason"] + + +def test_missing_member_identity_is_reported_instead_of_aborting_compilation( + monkeypatch, +): + facts = [_fact(month) for month in MONTHS] + facts[0].pop("aggregate_fact_key") + facts[0]["lineage"] = None + result = _compile(monkeypatch, facts) + assert not result.registry.specs + assert "must identify exactly one fact" in result.unsupported[0]["reason"] + + +def test_single_declared_month_uses_the_resolved_representative_fact(monkeypatch): + reference = replace(_reference(), metadata=uc_source_month_metadata(["2025-04"])) + result = _compile(monkeypatch, [_fact("2025-04")], reference) + (target,) = result.registry.specs + assert target.metadata["uk_uc_source_month_count"] == "1" + assert target.value == 100.0 + + +def test_undeclared_non_uc_reference_keeps_existing_available_month_behavior( + monkeypatch, +): + reference = replace(_reference(), family="other", metadata={}) + result = _compile(monkeypatch, [_fact("2025-06")], reference) + (target,) = result.registry.specs + assert target.value == 100.0 + assert EXPECTED_SOURCE_MONTHS not in target.metadata + + +@pytest.mark.parametrize( + "months", + [ + [], + "2025-04", + [4], + ["2025-13"], + ["2025-4"], + MONTHS[::-1], + [*MONTHS, "2025-12"], + ["2024-12", "2025-01"], + ], +) +def test_invalid_source_month_declarations_fail(months): + with pytest.raises(ValueError, match="source_months"): + uc_source_month_metadata(months) + + +def test_generator_preserves_observation_basis_and_explicit_months(): + contract = { + "targets": [ + { + "target_id": "uc", + "family": "dwp_universal_credit", + "measurement": { + "observation_basis": "monthly_stock", + "source_months": MONTHS, + }, + } + ] + } + assert _reference_metadata(contract) == { + "uc": {"observation_basis": "monthly_stock", **uc_source_month_metadata(MONTHS)} + } + contract["targets"][0]["family"] = "other" + with pytest.raises(ValueError, match="UK UC-only"): + _reference_metadata(contract) + + +def test_shipped_uc_monthly_references_declare_the_pinned_nine_month_window(): + references = load_country_spec("uk").target_references + monthly = [ + reference + for reference in references + 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] + } + assert all( + reference.value_operation == "calendar_year_average" for reference in monthly + ) + assert not any( + EXPECTED_SOURCE_MONTHS in reference.metadata + for reference in references + if reference.family != "dwp_universal_credit" + ) diff --git a/packages/microcosm-build/tests/test_uk_uc_target_measurements.py b/packages/microcosm-build/tests/test_uk_uc_target_measurements.py new file mode 100644 index 000000000..36213c3ad --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_uc_target_measurements.py @@ -0,0 +1,261 @@ +"""Statistical UC claims and own children cannot cross benefit-unit boundaries.""" + +import json +from importlib import resources +from types import SimpleNamespace + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.country_spec import load_country_spec +from microcosm.build.uk_runtime.ledger_targets import ( + UKFrameTargetAdapter, + materialize_uk_ledger_targets, +) +from microcosm.build.uk_runtime.measure_simulation import ( + UKMeasureResolver, + compute_uk_measure_input, +) +from microcosm.build.uk_runtime.uc_target_measurements import UC_TARGET_VARIABLES +from microcosm.calibrate import TargetRegistry, TargetSpec +from microcosm.calibrate.matrix import build_constraint_matrix +from microcosm.frame import EntitySchema, Frame, WeightKind, Weights + + +def _fixture(): + # Own QYP ages, award, dwelling. Units 1, 3, 4 share a dwelling: two + # affected claims and an unrelated claimant with a baby and PIP. + scenarios = [ + ([19, 17, 15, 13, 11], 100, 0), # five older qualifying children + ([19, 10, 3], 100, 1), # includes an older QYP; child gets PIP + ([12, 10, 3], 0, 2), # open-claim test input, no positive model award + ([0], 100, 1), # baby belongs only to this young claimant + ([10, 8, 4], 100, 1), + ([15, 14, 8], 100, 3), # ambiguous 2017 birth cohort + ([12, 10, 2], 100, 4), # excepted third child, no element denied + ] + people, units = [], [] + for bu, (ages, award, hh) in enumerate(scenarios): + units.append( + dict(benunit_id=bu, dependent_children=len(ages), universal_credit=award) + ) + for i, age in enumerate([19 if bu == 3 else 45, *ages]): + people.append( + dict( + person_id=len(people), + person_benunit_id=bu, + person_household_id=hh, + age=age, + birth_year=2025 - age, + is_benunit_head=i == 0, + is_parent=i == 0, + qyp=i > 0 or bu == 3, # young claimant also QYP: exclude + pip=100 if (bu == 1 and i == 1) or (bu == 3 and i == 0) else 0, + disabled_element=100 if bu == 1 and i == 1 else 0, + denied_element=bu == 0 or (bu == 1 and i == 3), + ) + ) + person = pd.DataFrame(people).iloc[::-1].reset_index(drop=True) + frame = Frame( + { + "person": person, + "benunit": pd.DataFrame(units), + "household": pd.DataFrame({"household_id": range(5), "region": "SCOTLAND"}), + }, + EntitySchema(group_entities=("benunit", "household")), + {"household": Weights(np.ones(5), WeightKind.DESIGN)}, + ) + columns = { + "is_child_or_qualifying_young_person_for_universal_credit": "qyp", + "uc_individual_disabled_child_element": "disabled_element", + "uc_is_child_limit_affected": "denied_element", + } + + def calculate(variable, year): + assert year == 2025 + if variable == "universal_credit": + return frame.table("benunit")[variable].to_numpy() + return person[columns.get(variable, variable)].to_numpy() + + return frame, SimpleNamespace(calculate=calculate) + + +@pytest.mark.parametrize( + "variable,expected", + [ + ("uc_tcl_qualifying_child_count", [5, 3, 3, 1, 3, 3, 3]), + ("uc_tcl_affected_child_count_proxy", [0, 1, 1, 0, 1, 1, 1]), + ("uc_tcl_affected_benunit_proxy", [0, 1, 0, 0, 1, 1, 1]), + ("uc_tcl_claimant_receives_pip", [0, 0, 0, 1, 0, 0, 0]), + ("uc_tcl_receives_disabled_child_element", [0, 1, 0, 0, 0, 0, 0]), + ("uc_calibration_has_child_under_one", [0, 0, 0, 1, 0, 0, 0]), + ], +) +def test_uc_statistical_measures_preserve_own_children_and_claimants( + variable, expected +): + frame, sim = _fixture() + result, route = compute_uk_measure_input(frame, sim, "benunit", variable, 2025) + np.testing.assert_array_equal(result, expected) + assert route == "uc_claim_statistical_proxy" + with pytest.raises(KeyError, match="benunit-only"): + compute_uk_measure_input(frame, sim, "household", variable, 2025) + + +def test_tcl_counts_include_exceptions_and_do_not_use_denied_element_flags(): + frame, sim = _fixture() + before, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_tcl_affected_benunit_proxy", 2025 + ) + frame.table("person")["denied_element"] = ~frame.table("person")["denied_element"] + after, _ = compute_uk_measure_input( + frame, sim, "benunit", "uc_tcl_affected_benunit_proxy", 2025 + ) + np.testing.assert_array_equal(before, after) + assert before[6] and not before[0] + + +def test_tcl_receipt_explicitly_records_remaining_claim_date_and_period_proxies(): + frame, sim = _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_tcl_comparison_contract" not in resolver.receipt() + for variable in UC_TARGET_VARIABLES: + 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_tcl_comparison_contract"] + assert contract["source_birth_cutoff"] == "2017-04-06" + assert contract["model_birth_cutoff_proxy"] == "model birth_year >= 2017" + assert contract["model_claim_state_proxy"] == "universal_credit > 0" + assert contract["status"] == "partial_alignment_with_explicit_proxies" + assert contract["model_period"] == "2025" + assert len(contract["computed_measures"]) == 5 + + +def test_tcl_and_scottish_registry_sum_claims_without_borrowing_other_units_children(): + frame, sim = _fixture() + adapter = UKFrameTargetAdapter(frame) + for variable in UC_TARGET_VARIABLES: + adapter.set_column( + "benunit", + variable, + compute_uk_measure_input(frame, sim, "benunit", variable, 2025)[0], + ) + refs = [ + r + for r in load_country_spec("uk").target_references + if r.name.startswith("dwp.uc.two_child_limit.") + or r.name == "dwp.uc.scotland_households_child_under_1" + ] + assert len(refs) == 16 + registry = TargetRegistry( + [ + TargetSpec( + name=r.name, + entity=r.entity, + measure=r.measure, + value=1, + period=2025, + source="synthetic", + metadata=dict(r.metadata), + ) + for r in refs + ], + country="uk", + ) + result = materialize_uk_ledger_targets(adapter, registry, period=2025) + assert not result.skipped + problem = build_constraint_matrix( + adapter.to_frame(), registry.to_target_set(), weight_entity="household" + ) + assert not problem.skipped + matrix = problem.matrix.toarray() + by_name = dict( + zip((name.rsplit("@", 1)[0] for name in problem.names), matrix, strict=True) + ) + np.testing.assert_array_equal( + by_name["dwp.uc.two_child_limit.households_affected"], [0, 2, 0, 1, 1] + ) + np.testing.assert_array_equal( + by_name["dwp.uc.two_child_limit.children_in_affected_households"], + [0, 6, 0, 3, 3], + ) + np.testing.assert_array_equal( + by_name["dwp.uc.two_child_limit.children_affected"], [0, 2, 0, 1, 1] + ) + np.testing.assert_array_equal( + by_name["dwp.uc.two_child_limit.households_claimant_pip"], [0] * 5 + ) + np.testing.assert_array_equal( + by_name["dwp.uc.two_child_limit.children_disabled_child_element"], + [0, 3, 0, 0, 0], + ) + np.testing.assert_array_equal( + by_name["dwp.uc.scotland_households_child_under_1"], [0, 1, 0, 0, 0] + ) + + +def test_tcl_contract_is_benefit_unit_native_and_geography_only_at_dwelling_grain(): + contract = json.loads( + resources.files("microcosm.build.uk") + .joinpath("uk_population_targets.json") + .read_text() + ) + targets = [ + t + for t in contract["targets"] + if t["target_id"].startswith("dwp.uc.two_child_limit.") + ] + assert len(targets) == 15 + for t in targets: + b = t["bindings"]["policyengine"] + assert t["measurement"]["entity"] == b["from_entity"] == "benunit" + assert b["affected_flag_variable"] == "uc_tcl_affected_benunit_proxy" + assert "value_reduction" not in b + assert all( + c["variable"] == "region" and c["map_to"] == "benunit" + for c in b["household_conditions"] + ) + + +def test_resolution_receipt_retains_proxy_contract_discovered_during_computation(): + from microcosm.build.target_materialization import resolve_target_measures + + frame, sim = _fixture() + resolver = UKMeasureResolver.__new__(UKMeasureResolver) + resolver.frame, resolver.simulation, resolver.year = frame, sim, 2025 + resolver._receipt = {"mode": "synthetic"} + resolver._uc_tcl_measures_used = set() + contract = json.loads( + resources.files("microcosm.build.uk") + .joinpath("uk_population_targets.json") + .read_text() + ) + resolver.contract_targets = {t["target_id"]: t for t in contract["targets"]} + name = "dwp.uc.two_child_limit.households_affected" + registry = TargetRegistry( + [ + TargetSpec( + name=name, + entity="benunit", + measure="dwp/uc/two_child_limit/households_affected", + value=1, + period=2025, + source="synthetic", + metadata={"contract_target_id": name}, + ) + ], + country="uk", + ) + resolved = resolve_target_measures( + lambda: UKFrameTargetAdapter(frame), registry, resolver, period=2025 + ) + receipt = resolved.receipt["provider"]["uc_tcl_comparison_contract"] + assert receipt["computed_measures"] == ["uc_tcl_affected_benunit_proxy"] + assert receipt["model_claim_state_proxy"] == "universal_credit > 0" + assert receipt["status"] == "partial_alignment_with_explicit_proxies" diff --git a/packages/microcosm-build/tests/test_us_multispine_pool_tool.py b/packages/microcosm-build/tests/test_us_multispine_pool_tool.py index 07e81d2fb..91811e9e8 100644 --- a/packages/microcosm-build/tests/test_us_multispine_pool_tool.py +++ b/packages/microcosm-build/tests/test_us_multispine_pool_tool.py @@ -2504,7 +2504,7 @@ def capture_equality(expected: object, actual: object) -> None: "country": "us", "schema_id": "country_spec", "schema_version": 1, - "spec_sha256": "9db29b4d33424fbb21a83c63927c7de55ba9a333d631f6323935f67a496eee46", + "spec_sha256": "8b1546bc97b540afd525fe88f36a315de18b4d57bd9fab73035dacd8962e302e", }, } diff --git a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py index 746ed9e6a..c37ed85c9 100644 --- a/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py +++ b/packages/microcosm-calibrate/src/microcosm/calibrate/solve.py @@ -173,6 +173,7 @@ class CalibrationResult: loss_trajectory: The optimizer-start loss at each epoch (length ``epochs``). With no warm start, the first value is the input-weight loss; with a warm start, the first value is the warm-start loss. + Best-iterate selection does not truncate this history. skipped: Targets that could not be compiled (carried through from the matrix build), each with its reason. problem: The compiled :class:`CalibrationProblem` (matrix, b, names). @@ -184,7 +185,8 @@ class CalibrationResult: closing_loss: The capped weighted-MAPE calibration loss evaluated once on the *returned* weights (after the closing mass/cap projections). Exposed as :attr:`final_loss`; recorded separately from the trajectory, whose - tail is a pre-step/pre-projection value. + tail is a pre-step/pre-projection value and may follow an earlier + selected best iterate. target_loss_weights: The effective non-negative target-importance vector, aligned to :attr:`diagnostics`. Uniform defaults are materialized as ones so diagnostics can publish the exact final loss basis. @@ -242,7 +244,8 @@ def final_loss(self) -> float: after the closing mass/cap projections — so it describes the calibrated vector. It is **not** ``loss_trajectory[-1]``: the trajectory records each epoch's loss before that epoch's step and before the closing projections, - so its tail can differ (e.g. under ``mass="conserve"`` with a cap). + so its tail can differ (e.g. under ``mass="conserve"`` with a cap or when + an earlier feasible iterate has a lower loss). """ return self.closing_loss @@ -769,6 +772,7 @@ def _optimize( progress_callback: Callable[[dict[str, object]], None] | None = None, progress_context: Mapping[str, object] | None = None, return_gate_open_probabilities: bool = False, + selection_receipt: dict[str, object] | None = None, ) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, np.ndarray | None]: """Run the torch optimization and return weights plus its trajectory. @@ -778,6 +782,13 @@ def _optimize( by projecting the realized weights after each step, so they hold on the returned vector exactly, not merely in expectation. + For deterministic, unregularized free-mass runs, return the best feasible + iterate seen within the requested budget, including the warm start and the + final update. Constant-step Adam can otherwise discard an earlier better + fit when it oscillates across the absolute-error kink. Gated, regularized, + and conserved-mass runs keep their closing-state selection: their stochastic + penalties or closing mass projection need a different selection criterion. + The private opt-in ``return_gate_open_probabilities`` adds a third return value without changing the two-item tuple used by existing workspace callers. The third value is aligned per-record ``pi_i`` for an L0 run and @@ -828,6 +839,10 @@ def _optimize( else: w0_t = None + retain_best = gates is None and not conserve_mass and l2_lambda == 0.0 + best_loss = float("inf") + best_epoch = 0 + best_log_w: torch.Tensor | None = None trajectory = np.empty(epochs, dtype=np.float64) for epoch in range(epochs): optimizer.zero_grad() @@ -856,6 +871,10 @@ def _optimize( ) total_loss = loss + penalty + l2_lambda * l2_penalty trajectory[epoch] = float(loss.item()) + if retain_best and trajectory[epoch] < best_loss: + best_loss = trajectory[epoch] + best_epoch = epoch + best_log_w = log_w.detach().clone() if progress_callback is not None: progress_callback( { @@ -894,6 +913,32 @@ def _optimize( gate_open_probabilities: np.ndarray | None = None with torch.no_grad(): weights = torch.exp(log_w) + if best_log_w is not None: + closing_loss = _relative_error_loss( + _apply_constraint(matrix, weights), + targets, + target_loss_weights, + target_loss_scales, + target_loss_cap, + ) + if float(closing_loss.item()) > best_loss: + weights = torch.exp(best_log_w) + selected_epoch = best_epoch + selected_loss = best_loss + else: + selected_epoch = epochs + selected_loss = float(closing_loss.item()) + if selection_receipt is not None: + selection_receipt.update( + { + "rule": "best_feasible_loss", + "selected_epoch": selected_epoch, + "epochs_executed": epochs, + "epoch_convention": "completed_optimizer_updates; zero is start", + "selected_loss_float32": selected_loss, + "closing_iterate_loss_float32": float(closing_loss.item()), + } + ) if gates is not None: gates.eval() weights = weights * gates() @@ -1368,8 +1413,9 @@ def calibrate( weight_entity: Entity whose weights to calibrate (default ``"household"``). method: Optimization method. ``"adam"`` (default) runs the torch Adam - optimizer on the log-weights described above (smooth objective; weights - stay strictly positive by construction). ``"prox"`` runs proximal + optimizer on the log-weights described above (weights stay strictly + positive by construction). Unregularized free-mass runs retain their + best feasible fit within the requested epoch budget. ``"prox"`` runs proximal gradient (ISTA) on raw non-negative weight ratios with a soft-threshold step, the optimizer required for the nonsmooth ``l1_lambda`` penalty: it drives unneeded records to exact zero, so @@ -1640,6 +1686,7 @@ def calibrate( ) target_loss_scales_t = torch.tensor(target_loss_scales_np, dtype=torch.float32) + iterate_selection_receipt: dict[str, object] = {} if method == "prox": # L1 path: proximal gradient (ISTA) on raw weights. The soft-threshold # drives unneeded records to exact zero, so L1 selects a sparse weighted @@ -1732,6 +1779,7 @@ def calibrate( temperature=temperature, progress_callback=progress_callback, return_gate_open_probabilities=True, + selection_receipt=iterate_selection_receipt, ) n_nonzero = int((final_weights > prune_atol).sum()) if effective_l0 > 0.0 and n_nonzero == 0: @@ -1795,6 +1843,16 @@ def calibrate( "method": method, "epochs": epochs, "learning_rate": learning_rate, + "iterate_selection": ( + "best_feasible_loss" + if method == "adam" + and mass == FREE_MASS + and l0_lambda == 0.0 + and l2_lambda == 0.0 + and target_records is None + else "closing_state" + ), + "iterate_selection_receipt": iterate_selection_receipt, "mass": mass, "mass_reason": mass_reason, "max_weight_ratio": max_weight_ratio, diff --git a/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/controls.json b/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/controls.json new file mode 100644 index 000000000..d3e83fdb2 --- /dev/null +++ b/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/controls.json @@ -0,0 +1,138 @@ +{ + "schema": "microcosm.solver_pre_best_iterate_controls.v1", + "source_commit": "1f7d676f2449894bfd794973eaa507aa79bfbfdb", + "source_path": "packages/microcosm-calibrate/src/microcosm/calibrate/solve.py", + "source_sha256": "f4b32c40a8675c37b0a4f3c1e48ca7b2078e803dac4413221aeb98c23b9e2066", + "provenance": "Generated by calling calibrate from the unmodified source blob at source_commit; no expected output was produced by the changed solver.", + "dependencies": { + "numpy": "2.4.6", + "pandas": "3.0.3", + "scipy": "1.17.1", + "torch": "2.12.0" + }, + "inputs": { + "initial_weights": [ + 2.0, + 3.0, + 5.0, + 7.0, + 11.0, + 13.0, + 17.0, + 19.0 + ], + "income": [ + 0.0, + 2.0, + 3.0, + 5.0, + 8.0, + 13.0, + 21.0, + 34.0 + ], + "eligible": [ + 1.0, + 1.0, + 0.0, + 1.0, + 0.0, + 0.0, + 1.0, + 0.0 + ] + }, + "targets": [ + { + "name": "income", + 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"2e50a07a704ad2ab9d6529a965b7554f7aaba3972ec046a14e5a519e4f0904ed", + "_project_to_total": "f220ae141fc9144a4a1eec2c894f49065e88bbc1fd18be837ddec2d96cdd52f0", + "_relative_error_loss": "e7bba6d694211827780e2d5a0c60436decbaa1dc9310a7caa62945ac4d50843d", + "_validate_target_loss_cap": "7d7a663cc1de3f3667ea713329387a7913063335a47bc693299fd5c2479f8553" + }, + "gates_source_path": "packages/microcosm-calibrate/src/microcosm/calibrate/gates.py", + "gates_module_sha256": "379e4adbb3ed7cdd13f7c7c548da45163f2a50330c874ee51095d38243d392e6", + "prune_rel_atol": 1e-06, + "note": "Compare this frozen function and the current solver in the same runtime with separate seed resets. Recorded Mac bytes remain an additional check only on the authoring runtime and dependency versions." + } +} diff --git a/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/optimizer_1f7d676f.py b/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/optimizer_1f7d676f.py new file mode 100644 index 000000000..382647af7 --- /dev/null +++ b/packages/microcosm-calibrate/tests/fixtures/pre_best_iterate/optimizer_1f7d676f.py @@ -0,0 +1,206 @@ +"""Immutable pre-best-iterate optimizer, extracted from commit 1f7d676f. + +The function body is copied verbatim from solve.py at the recorded revision. +Tests verify its SHA and the unchanged installed helper closure before use. +This module is only an oracle; it never replaces an installed solver function. +""" + +from __future__ import annotations + +from collections.abc import Callable, Mapping + +import numpy as np +import torch + +from microcosm.calibrate.gates import HardConcrete +from microcosm.calibrate.solve import ( + _PRUNE_REL_ATOL, + _apply_constraint, + _prepare_warm_start_weights, + _project_to_total, + _relative_error_loss, +) + + +def _optimize( + matrix: torch.Tensor, + targets: torch.Tensor, + target_loss_weights: torch.Tensor | None, + target_loss_scales: torch.Tensor, + target_loss_cap: float, + initial_weights: np.ndarray, + *, + warm_start_weights: np.ndarray | None = None, + epochs: int, + learning_rate: float, + conserve_mass: bool, + max_weight_ratio: float | None, + l0_lambda: float, + l2_lambda: float, + l2_anchor: str = "initial", + l2_anchor_weights: np.ndarray | None = None, + target_records: int | None, + init_mean: float, + temperature: float, + progress_callback: Callable[[dict[str, object]], None] | None = None, + progress_context: Mapping[str, object] | None = None, + return_gate_open_probabilities: bool = False, +) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, np.ndarray | None]: + """Run the torch optimization and return weights plus its trajectory. + + Optimizes the log-weights with Adam against capped weighted MAPE. + Positivity is by construction (``w = exp(log_w)`` times optional gates). The + hard constraints — mass conservation and ``max_weight_ratio`` — are applied + by projecting the realized weights after each step, so they hold on the + returned vector exactly, not merely in expectation. + + The private opt-in ``return_gate_open_probabilities`` adds a third return + value without changing the two-item tuple used by existing workspace + callers. The third value is aligned per-record ``pi_i`` for an L0 run and + ``None`` when no gates were active. + """ + w0 = np.asarray(initial_weights, dtype=np.float64) + start = _prepare_warm_start_weights( + w0, + warm_start_weights, + conserve_mass=conserve_mass, + max_weight_ratio=max_weight_ratio, + ) + total0 = float(w0.sum()) + # Same prune threshold the result's n_nonzero uses, so "pruned" here means + # exactly what the reported non-zero count means. + prune_atol = _PRUNE_REL_ATOL * float(np.mean(w0)) + log_w = torch.tensor(np.log(start), dtype=torch.float32, requires_grad=True) + + gates: HardConcrete | None = None + params: list[torch.Tensor] = [log_w] + if l0_lambda > 0.0 or target_records is not None: + gates = HardConcrete(len(w0), init_mean=init_mean, temperature=temperature) + params = [log_w, *gates.parameters()] + + optimizer = torch.optim.Adam(params, lr=learning_rate) + upper = ( + torch.tensor(max_weight_ratio * w0, dtype=torch.float32) + if max_weight_ratio is not None + else None + ) + if l2_lambda > 0.0: + # The L2 penalty's reference vector. "initial" divides by each record's + # own starting weight; "uniform" divides by the shared mean weight; an + # explicit vector (e.g. the pre-selection design weights during a + # refit) overrides both. Under mass conservation the penalty's + # constrained optimum is w_i ∝ anchor_i², so anchoring on + # heterogeneous starting weights (e.g. a refit whose start is a + # concentrated selection vector) pulls toward MORE concentration; the + # uniform anchor makes the penalty a direct 1/ESS control regardless + # of the starting distribution. + if l2_anchor_weights is not None: + anchor = np.asarray(l2_anchor_weights, dtype=np.float64) + elif l2_anchor == "initial": + anchor = w0 + else: + anchor = np.full_like(w0, w0.mean()) + w0_t = torch.tensor(anchor, dtype=torch.float32) + else: + w0_t = None + + trajectory = np.empty(epochs, dtype=np.float64) + for epoch in range(epochs): + optimizer.zero_grad() + weights = torch.exp(log_w) + if gates is not None: + weights = weights * gates() + estimate = _apply_constraint(matrix, weights) + loss = _relative_error_loss( + estimate, + targets, + target_loss_weights, + target_loss_scales, + target_loss_cap, + ) + penalty = ( + l0_lambda * gates.get_penalty() + if (gates is not None and l0_lambda > 0.0) + else torch.zeros((), dtype=torch.float32) + ) + # Penalize latent pre-gate weights. Under L0, a nearly closed gate + # should not be able to hide a very large exp(log_w). + l2_penalty = ( + ((torch.exp(log_w) / w0_t) ** 2).mean() + if w0_t is not None + else torch.zeros((), dtype=torch.float32) + ) + total_loss = loss + penalty + l2_lambda * l2_penalty + trajectory[epoch] = float(loss.item()) + if progress_callback is not None: + progress_callback( + { + **dict(progress_context or {}), + "kind": "calibration_epoch", + "epoch": epoch + 1, + "epochs": epochs, + "loss": trajectory[epoch], + } + ) + total_loss.backward() + optimizer.step() + + # Hard projections, applied to the realized weights every step so the + # guarantees hold on the returned vector, not just in expectation. + with torch.no_grad(): + if upper is not None: + # Clamp log-weights so exp(log_w) <= max_weight_ratio*w0. This is + # the landmine guard: a rare high-value near-zero-weight record + # can never be inflated past its bound. + log_w.clamp_(max=torch.log(upper)) + if conserve_mass: + realized = torch.exp(log_w) + if gates is not None: + realized = realized * gates() + current_total = float(realized.sum().item()) + if current_total > 0: + log_w.add_(float(np.log(total0 / current_total))) + if upper is not None: + # Re-clamp: the rescale may have pushed a record over its + # bound. The mass invariant is held within rtol by the + # final-vector rescale below; per-step we keep the bound + # hard so it can never be violated mid-run. + log_w.clamp_(max=torch.log(upper)) + + gate_open_probabilities: np.ndarray | None = None + with torch.no_grad(): + weights = torch.exp(log_w) + if gates is not None: + gates.eval() + weights = weights * gates() + if return_gate_open_probabilities: + gate_open_probabilities = ( + gates.get_active_prob() + .detach() + .cpu() + .numpy() + .astype(np.float64, copy=True) + ) + final = weights.detach().numpy().astype(np.float64) + + # Make the hard ratio bound exact on the returned vector. The per-step + # clamp is in float32, so exp() can overshoot the bound by a float epsilon + # (~1e-7 relative); a closing float64 cap guarantees no returned weight + # exceeds max_weight_ratio * w0, which downstream code may assert. + if max_weight_ratio is not None: + final = np.minimum(final, max_weight_ratio * np.asarray(w0, dtype=np.float64)) + + # Exact mass conservation on the returned vector: a single closing rescale + # to the input total. When a max_weight_ratio is also set, the rescale is + # capped at the bound and the residual is redistributed below the bound, so + # both invariants hold together. When L0 pruning is active, the deficit is + # redistributed only over surviving (gate-open) records so the cap fill never + # resurrects a pruned one. + if conserve_mass: + pruned = ( + final <= prune_atol if (gates is not None and l0_lambda > 0.0) else None + ) + final = _project_to_total(final, total0, max_weight_ratio, w0, pruned=pruned) + if return_gate_open_probabilities: + return final, trajectory, gate_open_probabilities + return final, trajectory diff --git a/packages/microcosm-calibrate/tests/test_solve.py b/packages/microcosm-calibrate/tests/test_solve.py index 37557a06e..6ffa0bad5 100644 --- a/packages/microcosm-calibrate/tests/test_solve.py +++ b/packages/microcosm-calibrate/tests/test_solve.py @@ -9,11 +9,19 @@ from __future__ import annotations +import hashlib +import importlib.util +import inspect +import json +import platform import warnings +from importlib.metadata import version +from pathlib import Path import numpy as np import pandas as pd import pytest +import torch from microcosm.calibrate import ( CalibrationResult, @@ -1879,6 +1887,84 @@ def test__given_warm_start_weights__then_optimizer_starts_from_warm_loss( assert warm.diagnostics[0].initial_estimate == cold.diagnostics[0].initial_estimate +def test_free_adam_preserves_a_better_warm_start_after_overshooting() -> None: + """A finite Adam budget must not discard an already better feasible fit. + + The warm start nearly hits a count target. A deliberately large step jumps + past it, so returning the last update makes the accepted warm start worse. + """ + frame = Frame( + { + "person": pd.DataFrame({"person_id": [0], "person_household_id": [0]}), + "household": pd.DataFrame({"household_id": [0], "household_count": [1]}), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(values=np.array([1.0]), kind=WeightKind.DESIGN)}, + ) + targets = TargetSet((_population_target(2.0, 1.005),)) + result = calibrate( + frame, + targets, + epochs=2, + learning_rate=0.2, + mass="free", + max_weight_ratio=3, + warm_start_weights=np.array([2.0]), + ) + assert result.final_loss <= result.initial_loss + 1e-7 + np.testing.assert_allclose(result.weights, [2.0], rtol=1e-7) + assert len(result.loss_trajectory) == 2 + assert result.options["iterate_selection_receipt"]["selected_epoch"] == 0 + + +def test_free_adam_considers_the_final_post_update_candidate() -> None: + """The final allowed step still counts when it improves the feasible fit.""" + frame = Frame( + { + "person": pd.DataFrame({"person_id": [0], "person_household_id": [0]}), + "household": pd.DataFrame({"household_id": [0], "household_count": [1]}), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(values=np.array([1.0]), kind=WeightKind.DESIGN)}, + ) + targets = TargetSet((_population_target(2.0, 1.0),)) + result = calibrate( + frame, + targets, + epochs=1, + learning_rate=0.1, + mass="free", + max_weight_ratio=1.05, + ) + assert result.final_loss < result.initial_loss + np.testing.assert_allclose(result.weights, [1.05], rtol=1e-7) + assert result.weights[0] <= 1.05 + assert result.options["iterate_selection_receipt"]["selected_epoch"] == 1 + + +def test_free_adam_retains_an_intermediate_fit_when_the_last_step_overshoots() -> None: + """Selection includes intermediate candidates, not only start and end.""" + frame = Frame( + { + "person": pd.DataFrame({"person_id": [0], "person_household_id": [0]}), + "household": pd.DataFrame({"household_id": [0], "household_count": [1]}), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(values=np.array([1.0]), kind=WeightKind.DESIGN)}, + ) + result = calibrate( + frame, + TargetSet((_population_target(1.5, 1.0),)), + epochs=3, + learning_rate=0.2, + mass="free", + max_weight_ratio=3, + ) + assert result.final_loss <= result.loss_trajectory.min() + 1e-7 + assert result.final_loss < 0.02 + assert result.options["iterate_selection_receipt"]["selected_epoch"] == 2 + + def test__given_bad_warm_start_shape__then_solver_rejects_it(feasible_frame) -> None: frame, truths = feasible_frame(n=20) targets = TargetSet((_population_target(truths["population"], 1.0),)) @@ -2027,3 +2113,124 @@ def test_mass_reason_rides_the_free_mass_record() -> None: calibrate(frame, targets, epochs=4, mass="conserve", mass_reason=reason) with pytest.raises(ValueError, match="non-empty string"): calibrate(frame, targets, epochs=4, mass_reason=" ") + + +def _pre_best_iterate_oracle(fixture: dict): + """Verify the immutable optimizer and installed dependency closure first.""" + from microcosm.calibrate import gates + + provenance = fixture["same_runtime_oracle"] + path = Path(__file__).parent / "fixtures/pre_best_iterate" / provenance["module"] + assert hashlib.sha256(path.read_bytes()).hexdigest() == provenance["module_sha256"] + for name, expected in provenance["helper_source_sha256"].items(): + source = inspect.getsource(getattr(solve_module, name)).rstrip("\n") + assert hashlib.sha256(source.encode()).hexdigest() == expected, name + # Pin the entire gate module, including the class's stretch constants. + assert ( + hashlib.sha256(Path(gates.__file__).read_bytes()).hexdigest() + == (provenance["gates_module_sha256"]) + ) + assert solve_module._PRUNE_REL_ATOL == provenance["prune_rel_atol"] + spec = importlib.util.spec_from_file_location("pre_best_iterate_oracle", path) + oracle = importlib.util.module_from_spec(spec) + spec.loader.exec_module(oracle) + source = inspect.getsource(oracle._optimize).rstrip("\n") + assert ( + hashlib.sha256(source.encode()).hexdigest() + == provenance["function_source_sha256"] + ) + return oracle + + +@pytest.mark.parametrize("case_id", ["conserved_mass", "l2_regularized", "l0_gated"]) +def test_best_iterate_preserves_pre_change_excluded_paths(case_id: str) -> None: + """Old and current solvers return exact bytes in every executing runtime. + + The immutable optimizer comes from the recorded pre-change source, not + the implementation under test. Mac snapshot bytes provide a separate + provenance check only on their authoring runtime. No tolerance or platform + skip replaces the exact old/new comparison. + """ + fixture = json.loads( + (Path(__file__).parent / "fixtures/pre_best_iterate/controls.json").read_text() + ) + oracle = _pre_best_iterate_oracle(fixture) + inputs = fixture["inputs"] + n = len(inputs["initial_weights"]) + frame = Frame( + { + "person": pd.DataFrame( + {"person_id": range(n), "person_household_id": range(n)} + ), + "household": pd.DataFrame( + { + "household_id": range(n), + "income": inputs["income"], + "eligible": inputs["eligible"], + } + ), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(np.array(inputs["initial_weights"]), WeightKind.DESIGN)}, + ) + targets = TargetSet(tuple(Target(**item) for item in fixture["targets"])) + case = next(item for item in fixture["cases"] if item["id"] == case_id) + options = case["options"] + # Construct the oracle's inputs from frozen data, independently of the + # current solver's compiled problem. These two targets preserve row order. + target_values = np.array([item["value"] for item in fixture["targets"]]) + torch.manual_seed(options["seed"]) + old_weights, old_trajectory, old_gates = oracle._optimize( + torch.tensor([inputs["income"], inputs["eligible"]], dtype=torch.float32), + torch.tensor(target_values, dtype=torch.float32), + None, + torch.tensor(np.maximum(np.abs(target_values), 1.0), dtype=torch.float32), + options["target_loss_cap"], + np.array(inputs["initial_weights"]), + epochs=options["epochs"], + learning_rate=options["learning_rate"], + conserve_mass=options["mass"] == "conserve", + max_weight_ratio=options["max_weight_ratio"], + l0_lambda=options.get("l0_lambda", 0.0), + l2_lambda=options.get("l2_lambda", 0.0), + target_records=None, + init_mean=options.get("init_mean", 0.999), + temperature=0.25, + return_gate_open_probabilities=True, + ) + torch.manual_seed(options["seed"]) + result = calibrate(frame, targets, **options) + expected_arrays = {"weights": old_weights, "loss_trajectory": old_trajectory} + if old_gates is not None: + expected_arrays["gate_open_probabilities"] = old_gates + else: + assert result.gate_open_probabilities is None + for name, expected in expected_arrays.items(): + actual = np.asarray(getattr(result, name)) + assert actual.dtype == expected.dtype == np.dtype("float64") + assert actual.tobytes() == expected.tobytes(), name + + runtime = { + "system": platform.system(), + "machine": platform.machine(), + "python_major_minor": ".".join(platform.python_version_tuple()[:2]), + } + if runtime == fixture["authoring_runtime"] and all( + version(name) == expected for name, expected in fixture["dependencies"].items() + ): + for name, expected in case["expected_float64_le_hex"].items(): + assert expected_arrays[name].astype(" 0 + else: + assert result.l0_lambda > 0 + assert np.all( + (result.gate_open_probabilities > 0) & (result.gate_open_probabilities < 1) + ) diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index 5f5e27ba9..4e1829781 100644 --- a/packages/microcosm-data/pyproject.toml +++ b/packages/microcosm-data/pyproject.toml @@ -28,7 +28,7 @@ us = ["policyengine-us>=1.745.0,<2"] # The UK artifact lives in a PRIVATE repo (UK Data Service licence): loads # require an authenticated HF token with access. The loader surfaces the # 401 with that explanation rather than retrying. -uk = ["policyengine-uk>=2.93"] +uk = ["policyengine-uk>=2.97.0"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index e928a141b..719c4cbca 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -383,13 +383,13 @@ # fingerprint derives from the manifest digest. Editing the spec moves all # three here in the same reviewed change. _UK_GATE_BATTERY_POLICY_SHA256 = ( - "76afdd6e14bb4fc3d7e03fcceac8c2498d235fbbc7b9e996968ddda607992c15" + "8deb610d25fced25e27be58989311efa217f49e6d644f37268cf0f7efd122193" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "f7460ed01621432d42c900013fe6edcf040cf825e90bc3640f2b507efdccda32" + "cd90537532e1800a76d4414a7d7454f5dedb4dfd574aca2dfca4ec1e1fe6cff3" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "5f17aaeffdbbca96a7db46878ed65ce6229ffb7fe74480a3f0284395866ef90e" + "8b28c7d23b9e8ebc9e7436abd7309e59f0ef14fb582506f874147a5c1c4f44ef" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -724,10 +724,10 @@ }, "release_cut": { "gates_manifest_sha256": ( - "969fd0f1bd4529006af00c9a7a0cdab76489cfc41b5b3f838670fcab52409188" + "79a1bd35f18ced387d988a1285aa26afb9404c635f69f32204aa020559d4a0e3" ), "policy_sha256": ( - "619dd84ee5748948358a03e2a209f41ab2a170b6dd45ad636a9a47c994d98de0" + "e9cdea8c7dad2ee582a6b71df0d5f25eed142f52bd352e29683a4977f6ac69d9" ), }, } diff --git a/packages/microcosm-data/tests/test_contract.py b/packages/microcosm-data/tests/test_contract.py index 39946bfaa..7ddb37f2f 100644 --- a/packages/microcosm-data/tests/test_contract.py +++ b/packages/microcosm-data/tests/test_contract.py @@ -137,13 +137,13 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: UK_GATE_BATTERY_PRODUCER = "microcosm.build.gate_battery" UK_GATE_BATTERY_SIGNING_KEY_ENV = "MICROCOSM_UK_TERMINAL_GATE_SIGNING_KEY" UK_GATE_BATTERY_POLICY_SHA256 = ( - "76afdd6e14bb4fc3d7e03fcceac8c2498d235fbbc7b9e996968ddda607992c15" + "8deb610d25fced25e27be58989311efa217f49e6d644f37268cf0f7efd122193" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "f7460ed01621432d42c900013fe6edcf040cf825e90bc3640f2b507efdccda32" + "cd90537532e1800a76d4414a7d7454f5dedb4dfd574aca2dfca4ec1e1fe6cff3" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "5f17aaeffdbbca96a7db46878ed65ce6229ffb7fe74480a3f0284395866ef90e" + "8b28c7d23b9e8ebc9e7436abd7309e59f0ef14fb582506f874147a5c1c4f44ef" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6f0243bcda09dad26945376230c44ec3cf55d4e417c3a25e29bae8c59bc1a69d" diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 70822aebf..ada61ca34 100644 --- a/packages/microcosm-frame/pyproject.toml +++ b/packages/microcosm-frame/pyproject.toml @@ -16,7 +16,7 @@ microcosm-graph = { workspace = true } [project.optional-dependencies] us = ["microunit>=0.1.0"] policyengine = ["policyengine-us>=1.745.0,<2", "microunit>=0.1.0"] -uk = ["policyengine-uk>=2.93"] +uk = ["policyengine-uk>=2.97.0"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py index f84d206a9..4dcb088f5 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py @@ -8,6 +8,7 @@ from __future__ import annotations from collections.abc import Sequence +from importlib import metadata from pathlib import Path from typing import Any @@ -18,7 +19,7 @@ from microcosm.frame.rules import ExportContract from microcosm.frame.schema import EntitySchema, VariableMetadata -__all__ = ["PolicyEngineUKEngine", "UK_SCHEMA"] +__all__ = ["PolicyEngineUKEngine", "UK_SCHEMA", "validate_uc_claimant_input"] UK_SCHEMA = EntitySchema(group_entities=("benunit", "household")) _PERSON_TABLE = "person" @@ -78,6 +79,7 @@ def materialize( microsimulation_class = self._import_policyengine_uk().Microsimulation dataset = self._build_dataset(bundle, period) simulation = microsimulation_class(dataset=dataset) + validate_uc_claimant_input(simulation, bundle.table("person"), period) results: dict[str, np.ndarray] = {} for name in variables: entity = self.variable_metadata(name).entity @@ -158,3 +160,45 @@ def _is_engine_computed(variable: Any) -> bool: if getattr(variable, "formula", None) is not None: return True return bool(getattr(variable, "formulas", None)) + + +def validate_uc_claimant_input(simulation: Any, person: Any, period: int | str) -> None: + """Require supplied UC roles to survive the model's dataset loader. + + The UK loader ignores unknown columns. FRS roles must therefore remain + explicit inputs; recalculating the model's relationship fallback can give + a different answer for young partners or older nonqualifying dependants. + Frames without recorded claimant roles retain ordinary calculator behavior. + """ + name = "is_uc_claimant" + if name not in person: + return + try: + installed = metadata.version("policyengine-uk") + except metadata.PackageNotFoundError: + installed = "unavailable" + guidance = ( + f"Supplied {name} requires policyengine-uk>=2.97.0 with explicit " + f"person/Boolean/year input support (installed {installed})." + ) + definition = simulation.tax_benefit_system.variables.get(name) + if ( + definition is None + or getattr(getattr(definition, "entity", None), "key", None) != "person" + or getattr(definition, "value_type", None) is not bool + or getattr(definition, "definition_period", None) != "year" + ): + raise ValueError(guidance) + expected = person[name].to_numpy() + if expected.ndim != 1 or expected.dtype.kind != "b": + raise ValueError( + f"{guidance} Source {name} must contain complete Boolean roles." + ) + if name not in getattr(simulation, "input_variables", ()): + raise ValueError(f"{guidance} The loader did not retain {name} as an input.") + raw = simulation.calculate(name, period) + actual = np.asarray(raw.values if hasattr(raw, "values") else raw) + if actual.dtype.kind != "b" or not np.array_equal(actual, expected): + raise ValueError( + f"{guidance} Loaded {name} differs from the supplied source roles." + ) diff --git a/packages/microcosm-frame/tests/test_policyengine_uk_adapter.py b/packages/microcosm-frame/tests/test_policyengine_uk_adapter.py index 5c6a1c009..7c56ce41e 100644 --- a/packages/microcosm-frame/tests/test_policyengine_uk_adapter.py +++ b/packages/microcosm-frame/tests/test_policyengine_uk_adapter.py @@ -2,6 +2,10 @@ from __future__ import annotations +from types import SimpleNamespace + +import numpy as np +import pandas as pd import pytest from microcosm.frame import RulesEngine @@ -63,3 +67,52 @@ def test_policyengine_uk_adapter_builds_a_real_engine_dataset() -> None: stamp = str(getattr(dataset, "time_period", getattr(dataset, "fiscal_year", ""))) assert stamp.startswith("2023") + + +@pytest.mark.parametrize( + "defect", ["missing", "not_loaded", "changed", "entity", "dtype", "period"] +) +def test_adapter_refuses_unconsumed_explicit_uc_roles(monkeypatch, defect): + definition = SimpleNamespace( + entity=SimpleNamespace(key="person"), value_type=bool, definition_period="year" + ) + variables = { + "is_uc_claimant": definition, + "universal_credit": SimpleNamespace( + entity=SimpleNamespace(key="benunit"), value_type=float + ), + } + if defect == "missing": + del variables["is_uc_claimant"] + elif defect == "entity": + definition.entity.key = "benunit" + elif defect == "dtype": + definition.value_type = float + elif defect == "period": + definition.definition_period = "month" + calls = [] + + def calculate(name, *args, **kwargs): + calls.append(name) + return np.array([False if defect == "changed" else True]) + + simulation = SimpleNamespace( + tax_benefit_system=SimpleNamespace(variables=variables), + input_variables=[] if defect == "not_loaded" else ["is_uc_claimant"], + calculate=calculate, + ) + engine = PolicyEngineUKEngine() + engine._system = simulation.tax_benefit_system + monkeypatch.setattr(engine, "_build_dataset", lambda *args: object()) + monkeypatch.setattr( + engine, + "_import_policyengine_uk", + lambda: SimpleNamespace(Microsimulation=lambda **kwargs: simulation), + ) + bundle = SimpleNamespace( + table=lambda entity: pd.DataFrame({"is_uc_claimant": [True]}), + n=lambda entity: 1, + ) + with pytest.raises(ValueError, match="is_uc_claimant"): + engine.materialize(bundle, ["universal_credit"], 2025) + assert "universal_credit" not in calls diff --git a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json index 8485a3916..4e83617d6 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json +++ b/packages/microcosm-graph/tests/fixtures/parity/kernels/calibrate/pins.json @@ -1 +1 @@ -{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"a7a0330fb7b7a4c80b62e2a44dcc9075aa6da9b27a20a4282ca6969f4570ef18","kernel":"calibrate.adam@1","node":"calibrate","node_key":"0168f69547e21ef71c67442a52622de83efd32fd23adc8faf93cc347f6c80b4c","numeric":"bitwise","platform":"arm64/darwin/py3.14","platforms":{"arm64/darwin/py3.14":{"direct":"direct.csv","node_key":"0168f69547e21ef71c67442a52622de83efd32fd23adc8faf93cc347f6c80b4c"}},"seed":0} +{"dependencies":{"numpy":"2.4.6","pandas":"3.0.3","scipy":"1.17.1","torch":"2.12.0"},"implementation_hash":"142dc3bf3eef39d37c36560c614e216d424ba61ae8d8f73915df66459373484b","kernel":"calibrate.adam@1","node":"calibrate","node_key":"31627146bf0703eb425f2a837f6747c3a687b9b9174b14ea4204c5aa473644e1","numeric":"bitwise","platform":"arm64/darwin/py3.13","platforms":{"arm64/darwin/py3.13":{"direct":"direct.csv","node_key":"31627146bf0703eb425f2a837f6747c3a687b9b9174b14ea4204c5aa473644e1"}},"seed":0} diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json index 043c0d296..38dd16be6 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json @@ -1170,7 +1170,7 @@ }, { "kind": "map_columns", - "scope": "direct raw-to-column E2 mappings only; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)" }, { "kind": "map_coded_amounts", @@ -1192,6 +1192,7 @@ "is_household_head", "is_benunit_head", "is_parent", + "is_uc_claimant", "employment_income", "self_employment_income", "private_pension_income", @@ -2694,7 +2695,7 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by couple-status cells, then applies the monotone reported-UC OR refresh.", + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws only SPI post-fill reporters from weighted base-FRS reporter capital within dependent-children band by is_uc_couple cells (two retained FRS claimant/partner roles, independently of legal is_married), then applies the monotone reported-UC OR refresh.", "operations": [ { "aggregates": { @@ -2705,7 +2706,7 @@ "method": "any_positive" }, { - "couple_status": "is_married", + "couple_status": "is_uc_couple", "dependent_children_bands": [ "0", "1", @@ -2811,12 +2812,13 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses.", + "notes": "Runs after the person-grain SPI income chain and before UC capital coherence. One temporary engine simulation screens both FRS and SPI benefit units to a positive pre-take-up award using FRS-carried capital AND at least one non-child member (uc_maximum_amount pays qualifying-young-person-only units mechanically), and materializes the UC child and state-pension-age flags; engine-known values are consumed only. The consumed is_uc_couple predictor counts two retained FRS claimant/partner roles, independently of legal is_married; older dependent children do not become partners. A benefit-unit RegimeGatedQRF fitted on the screened base domain draws the screened SPI domain (rows); the rewrite domain (rewrite_rows) is the whole SPI channel, so screen-failing SPI units are zeroed. Each positive amount lands on one deterministic adult claimant; base-FRS rows remain byte-unchanged; an empty screened SPI domain refuses.", "operations": [ { "derived": { "claimable_screen": "positive_pre_takeup_uc_award AND has_non_child_member", "has_non_child_member": "any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members", + "is_uc_couple": "count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2", "positive_pre_takeup_uc_award": "max(0, uc_maximum_amount - uc_income_reduction) > 0", "uc_reported_capital": "frs_benunit_capital" }, @@ -2850,7 +2852,7 @@ "landing": "eldest_working_age_adult_lowest_person_id", "output": "universal_credit_reported", "predictors": [ - "is_married", + "is_uc_couple", "uc_child_band", "benunit_employment_income", "benunit_self_employment_income", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv index e39dada6f..a7bb5f773 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ 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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json index 3486c157c..3129218d8 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json @@ -59,6 +59,7 @@ "is_benunit_head": "bool", "is_household_head": "bool", "is_parent": "bool", + "is_uc_claimant": "bool", "jsa_contrib_reported": "float64", "jsa_income_reported": "float64", "lump_sum_income": "float64", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json index c194f56a8..f238fb92b 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json @@ -1 +1 @@ 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UK spine stage student_loans.","id":"student_loans","inputs":[{"columns":["frs_benunit_capital"],"entity":"benunit","rows":"all"},{"columns":["region"],"entity":"household","rows":"all"},{"columns":["age","current_education","employee_pension_contributions","highest_education","student_loan_repayments","student_loans"],"entity":"person","rows":"all"}],"kernel":"uk.stage.student_loans@1","mass":"conserve","outputs":[{"column":"student_loan_plan","dtype":"string","entity":"person","ownership":"produced","rows":"all"}],"params":{"stage":"student_loans","stage_contract_sha256":"7c4c76398c2e80b41c7f941a5f06246b57d89b5eb0c36d42e3116ac35e82e203","time_period":"2024"},"population":"hmrc_cgt_gains_spine.boundary","sources":["frs"],"structural":"none","weights":null}],"sources":[{"codec":"csv-tables","description":"Content-bound UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/tools/diagnose_uk_uc_matrix.py b/tools/diagnose_uk_uc_matrix.py new file mode 100644 index 000000000..7946e0b91 --- /dev/null +++ b/tools/diagnose_uk_uc_matrix.py @@ -0,0 +1,230 @@ +"""Compile the full current national matrix for a pinned UC development replay. + +This writes private diagnostic matrices and aggregate row estimates. It does not +export calibrated microdata or issue a release certificate. Reviewed exclusions +and the complete band-edge register are applied through the production route. +""" + +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path + +import numpy as np +import pandas as pd +from diagnose_uk_uc_relationships import sha256, variable_overlay +from scipy import sparse + +from microcosm.build.ledger_artifact import load_ledger_consumer_artifact +from microcosm.build.target_materialization import resolve_target_measures +from microcosm.build.uk_runtime.ledger_targets import ( + compile_uk_target_registry, + materialize_uk_ledger_targets, +) +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_calibration import ( + CalibrationFrameAdapter, + drop_injected_measure_inputs, + inject_measure_inputs, +) +from microcosm.build.uk_runtime.national_doctrine import uk_national_target_loss_weights +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask +from microcosm.calibrate import calibrate +from microcosm.calibrate.matrix import build_constraint_matrix +from microcosm.frame import WeightKind + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--replay-dir", type=Path, required=True) + parser.add_argument("--ledger-dir", type=Path, required=True) + parser.add_argument("--facts-sha256", required=True) + parser.add_argument("--manifest-sha256", required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--epochs", type=int, default=0) + args = parser.parse_args() + os.umask(0o077) + args.output_dir.mkdir(parents=True, exist_ok=False) + receipt = json.loads((args.replay_dir / "aggregate.json").read_text()) + inputs = {Path(k): v for k, v in receipt["input_sha256"].items()} + for path, digest in inputs.items(): + assert sha256(path) == digest, path + paths = {p.name: p for p in inputs} + person, benunit, household = [ + pd.read_pickle(paths[f"{name}.pkl"]) + for name in ("person", "benunit", "household") + ] + inherited = np.load(paths["solve.npz"], allow_pickle=False) + if receipt["explicit_frs_claimant_input"]: + person["is_uc_claimant"] = frs_uc_claimant_mask(person, benunit) + frame = uk_national_frame( + person=person, + benunit=benunit, + household=household, + time_period=receipt["source_year"], + household_weights=inherited["initial_weights"], + weight_kind=WeightKind.IMPORTANCE, + ) + factory = None + if receipt["model_overlay"]: + from policyengine_uk import Microsimulation + + overlay_paths = [] + for item in receipt["model_overlay"]: + path = Path(item["path"]) + assert sha256(path) == item["sha256"], path + overlay_paths.append(path) + scenario, _ = variable_overlay(overlay_paths) + + def factory(**kwargs): + return Microsimulation(**kwargs, scenario=scenario) + + resolver = UKMeasureResolver( + simulation_source=None, + frame=frame, + year=receipt["model_year"], + scratch_dir=args.output_dir / "engine", + microsimulation_factory=factory, + ) + old = pd.read_pickle(args.replay_dir / "benefit_units_private.pkl") + np.testing.assert_array_equal( + resolver.compute("benunit", "universal_credit")[0], old.universal_credit + ) + artifact = load_ledger_consumer_artifact( + args.ledger_dir, + expected_facts_sha256=args.facts_sha256, + expected_manifest_sha256=args.manifest_sha256, + ) + compilation = compile_uk_target_registry( + artifact.facts, target_period=receipt["model_year"] + ) + assert not compilation.unsupported, compilation.unsupported + registry, exclusions = apply_uk_calibration_measure_exclusions( + compilation.registry, load_uk_calibration_measure_exclusions(None) + ) + print(f"Resolving {len(registry.specs)} active rows.", flush=True) + resolution = resolve_target_measures( + lambda: CalibrationFrameAdapter(frame), + registry, + resolver, + period=receipt["model_year"], + band_edge_registry=compilation.registry, + ) + adapter = CalibrationFrameAdapter(frame) + original_columns = { + entity: set(table.columns) for entity, table in adapter.tables.items() + } + inject_measure_inputs(adapter, resolution.measure_inputs) + materialized = materialize_uk_ledger_targets( + adapter, + registry, + period=receipt["model_year"], + band_edge_registry=compilation.registry, + ) + assert not materialized.skipped, materialized.skipped + drop_injected_measure_inputs(adapter, resolution.measure_inputs, original_columns) + prepared = adapter.prepared_frame() + targets = registry.to_target_set() + problem = build_constraint_matrix(prepared, targets, weight_entity="household") + assert not problem.skipped + np.testing.assert_array_equal(problem.names, inherited["names"]) + np.testing.assert_array_equal(problem.target_vector, inherited["target_values"]) + np.testing.assert_array_equal( + problem.initial_weights.values, inherited["initial_weights"] + ) + coefficients = uk_national_target_loss_weights( + [spec.family for spec in registry.specs], rule="family_equal" + ) + sparse.save_npz(args.output_dir / "matrix.npz", problem.matrix) + np.savez_compressed( + args.output_dir / "inputs_private.npz", + names=np.array(problem.names), + target_values=problem.target_vector, + initial_weights=problem.initial_weights.values, + inherited_weights=inherited["weights"], + target_loss_weights=coefficients, + household_ids=household["household_id"].to_numpy(), + target_families=np.array([spec.family for spec in registry.specs]), + ) + row_estimates = pd.DataFrame( + { + "name": problem.names, + "target": problem.target_vector, + "prior": problem.matrix @ inherited["initial_weights"], + "inherited_calibrated": problem.matrix @ inherited["weights"], + } + ) + run = None + if args.epochs: + print(f"Running diagnostic Adam for {args.epochs} epochs.", flush=True) + result = calibrate( + prepared, + targets, + weight_entity="household", + epochs=args.epochs, + learning_rate=0.02, + mass="free", + mass_reason="Development UC diagnosis on current measurement contract", + max_weight_ratio=10, + seed=0, + l0_lambda=0, + target_loss_cap=10.0, + target_loss_weights=coefficients, + ) + np.savez_compressed( + args.output_dir / "weights_private.npz", weights=result.weights + ) + row_estimates["new_calibrated"] = problem.matrix @ result.weights + run = { + "epochs": args.epochs, + "learning_rate": 0.02, + "seed": 0, + "mass": "free", + "max_weight_ratio": 10, + "l0_lambda": 0, + "target_loss_cap": 10.0, + "target_weight_rule": "family_equal", + "initial_loss": result.initial_loss, + "final_loss": result.final_loss, + } + row_estimates.to_json( + args.output_dir / "row_estimates.json", orient="records", indent=2 + ) + for path, digest in inputs.items(): + assert sha256(path) == digest, path + (args.output_dir / "receipt.json").write_text( + json.dumps( + { + "purpose": "Development matrix/solver diagnostic; no certified dataset export", + "replay_receipt_sha256": sha256(args.replay_dir / "aggregate.json"), + "tool_sha256": sha256(Path(__file__)), + "target_contract_sha256": sha256( + Path(__file__).resolve().parents[1] + / "packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json" + ), + "matrix_sha256": sha256(args.output_dir / "matrix.npz"), + "facts_sha256": args.facts_sha256, + "manifest_sha256": args.manifest_sha256, + "compiled_rows": len(compilation.registry.specs), + "active_rows": len(problem.names), + "exclusions": exclusions, + "target_values_and_roster_unchanged": True, + "measure_resolver": resolver.receipt(), + "solver": run, + }, + indent=2, + ) + + "\n" + ) + print(json.dumps({"rows": len(problem.names), "solver": run}), flush=True) + + +if __name__ == "__main__": + main() diff --git a/tools/diagnose_uk_uc_relationships.py b/tools/diagnose_uk_uc_relationships.py new file mode 100644 index 000000000..b79547ff3 --- /dev/null +++ b/tools/diagnose_uk_uc_relationships.py @@ -0,0 +1,409 @@ +"""Replay UC on captured source tables with fixed weights and explicit model pins. + +This development diagnostic never recalibrates, changes source artifacts, or +certifies a release. Person/benefit-unit output is private; the JSON is aggregate. +An optional list of variable source files isolates model fixes without upgrading +unrelated rules in the installed country model. +""" + +from __future__ import annotations + +import argparse +import hashlib +import importlib.metadata +import importlib.util +import json +import os +from pathlib import Path + +import numpy as np +import pandas as pd + +from microcosm.build.uk_runtime.measure_simulation import UKMeasureResolver +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.uc_relationships import frs_uc_claimant_mask +from microcosm.frame import WeightKind + +GB_REGIONS = { + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND", +} +BU_VARIABLES = ( + "universal_credit", + "universal_credit_pre_benefit_cap", + "uc_maximum_amount", + "uc_income_reduction", + "uc_earned_income", + "uc_unearned_income", + "uc_net_earned_income", + "uc_assessable_capital", + "uc_capital_income", + "uc_standard_allowance", + "uc_standard_allowance_claimant_type", + "uc_work_allowance", + "is_uc_work_allowance_eligible", + "uc_child_element", + "uc_disability_elements", + "uc_housing_costs_element", + "uc_childcare_element", + "uc_carer_element", + "uc_deductions", + "benefit_cap_reduction", + "is_benefit_cap_exempt", + "is_uc_eligible", + "family_type", + "relation_type", + "benunit_rent", + "would_claim_uc", + "uc_calibration_family_type", + "uc_calibration_child_count", +) + + +def sha256(path: Path) -> str: + with path.open("rb") as handle: + return hashlib.file_digest(handle, "sha256").hexdigest() + + +def variable_overlay(paths: list[Path]): + """Apply explicit variable bytes before loading their dataset inputs.""" + from policyengine_uk.utils.scenario import Scenario + + classes = [] + identities = [] + for index, path in enumerate(paths): + spec = importlib.util.spec_from_file_location(f"uc_diagnostic_{index}", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + classes.append(getattr(module, path.stem)) + identities.append( + {"variable": path.stem, "path": str(path), "sha256": sha256(path)} + ) + + def apply(simulation): + for variable in classes: + simulation.tax_benefit_system.update_variable(variable) + + return Scenario( + simulation_modifier=apply, applied_before_data_load=True + ), identities + + +def summarize_group(table: pd.DataFrame, mask: np.ndarray) -> dict: + rows = table.loc[mask] + claim = rows["universal_credit"].gt(0) + result = { + "benefit_unit_rows": len(rows), + "unique_source_benefit_units": int(rows["benunit_source_id"].nunique()), + "unique_source_households": int(rows["household_source_id"].nunique()), + "positive_uc_rows": int(claim.sum()), + } + for weight in ("prior_weight", "fixed_calibrated_weight"): + w = rows[weight] + source_mass = rows.loc[claim].groupby("household_source_id")[weight].sum() + result[weight] = { + "benefit_units": float(w.sum()), + "positive_uc": float(w[claim].sum()), + "uc_amount": float((w * rows["universal_credit"]).sum()), + "source_household_ess_positive_uc": ( + float(source_mass.sum() ** 2 / (source_mass**2).sum()) + if (source_mass**2).sum() + else 0.0 + ), + } + return result + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--capture-dir", type=Path, required=True) + parser.add_argument("--solve-dir", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--source-year", type=int, default=2024) + parser.add_argument("--year", type=int, default=2025) + parser.add_argument("--variable-overlay", type=Path, nargs="*", default=[]) + parser.add_argument("--supply-frs-claimants", action="store_true") + parser.add_argument("--baseline-output", type=Path) + parser.add_argument( + "--allow-changed-source-tables", + action="store_true", + help="Explicitly compare a diagnosed source-stage intervention.", + ) + args = parser.parse_args() + os.umask(0o077) + args.output_dir.mkdir(parents=True, exist_ok=False) + inputs = [args.capture_dir / f"{e}.pkl" for e in ("person", "benunit", "household")] + inputs.extend( + [args.solve_dir / "solve.npz", args.solve_dir / "uc_composition_private.pkl"] + ) + identities = {str(path): sha256(path) for path in inputs} + person, benunit, household = [pd.read_pickle(path) for path in inputs[:3]] + solution = np.load(args.solve_dir / "solve.npz", allow_pickle=False) + composition = pd.read_pickle(args.solve_dir / "uc_composition_private.pkl") + pd.testing.assert_series_equal( + benunit["benunit_id"], composition["benunit_id"], check_names=False + ) + membership = person.groupby("person_benunit_id")["person_household_id"].first() + hh_ids = benunit["benunit_id"].map(membership) + np.testing.assert_array_equal(hh_ids, composition["household_id"]) + prior = pd.Series(solution["initial_weights"], index=household["household_id"]) + weights = pd.Series(solution["weights"], index=household["household_id"]) + np.testing.assert_allclose( + hh_ids.map(weights), composition["final_weight"], rtol=0, atol=0 + ) + roles = frs_uc_claimant_mask(person, benunit) + if args.supply_frs_claimants: + person = person.copy() + person["is_uc_claimant"] = roles + frame = uk_national_frame( + person=person, + benunit=benunit, + household=household, + time_period=args.source_year, + household_weights=solution["initial_weights"], + weight_kind=WeightKind.IMPORTANCE, + ) + overlay = [] + factory = None + if args.variable_overlay: + from policyengine_uk import Microsimulation + + scenario, overlay = variable_overlay(args.variable_overlay) + + def factory(**kwargs): + return Microsimulation(**kwargs, scenario=scenario) + + elif args.supply_frs_claimants: + raise ValueError( + "Supplying new claimant input requires an explicit model overlay." + ) + resolver = UKMeasureResolver( + simulation_source=None, + scratch_dir=args.output_dir / "engine", + year=args.year, + frame=frame, + microsimulation_factory=factory, + ) + print("Simulation initialized; computing UC components.", flush=True) + if args.supply_frs_claimants: + np.testing.assert_array_equal( + resolver.compute("person", "is_uc_claimant")[0], roles + ) + result = benunit.copy() + result["household_id"] = hh_ids.to_numpy() + for name in ("region", "household_source_id", "household_support_channel", "rent"): + result[name] = hh_ids.map(household.set_index("household_id")[name]).to_numpy() + result["prior_weight"] = hh_ids.map(prior).to_numpy() + result["fixed_calibrated_weight"] = hh_ids.map(weights).to_numpy() + missing = [] + for variable in BU_VARIABLES: + if resolver.knows("benunit", variable): + values, _ = resolver.compute("benunit", variable) + result[variable] = values + else: + missing.append(variable) + np.testing.assert_array_equal(result["would_claim_uc"], benunit["would_claim_uc"]) + baseline_exact = None + if not overlay: + baseline_exact = np.array_equal(result["universal_credit"], composition["uc"]) + if not baseline_exact: + raise AssertionError( + "Baseline UC awards do not reproduce the retained full-model capture." + ) + np.testing.assert_array_equal( + result["uc_calibration_family_type"], composition["family"] + ) + np.testing.assert_array_equal( + result["uc_calibration_child_count"], composition["child_count"] + ) + person_metrics = person[["person_id", "person_benunit_id", "age"]].copy() + person_metrics["frs_claimant"] = roles + person_metrics["uc_individual_child_element"] = resolver.compute( + "person", "uc_individual_child_element" + )[0] + for variable in ( + "is_WA_adult", + "is_adult", + "is_child_or_qualifying_young_person_for_universal_credit", + ): + values, _ = resolver.compute("person", variable) + person_metrics[variable] = values.astype(bool) + grouping = person_metrics.groupby("person_benunit_id") + for column in ("frs_claimant", "is_adult", "is_WA_adult"): + result[f"count_{column}"] = ( + benunit["benunit_id"].map(grouping[column].sum()).to_numpy() + ) + qualifying = "is_child_or_qualifying_young_person_for_universal_credit" + person_metrics["dependent_qyp"] = ~roles & person_metrics[qualifying] + person_metrics["older_dependent"] = ~roles & person_metrics["age"].ge(18) + for column in ("dependent_qyp", "older_dependent"): + result[f"count_{column}"] = ( + benunit["benunit_id"] + .map(person_metrics.groupby("person_benunit_id")[column].sum()) + .to_numpy() + ) + result["raw_reported_uc"] = ( + benunit["benunit_id"] + .map(person.groupby("person_benunit_id")["universal_credit_reported"].sum()) + .to_numpy() + ) + capital_limit = resolver.simulation.tax_benefit_system.parameters( + args.year + ).gov.dwp.universal_credit.means_test.capital.limit + result["blocked_capital"] = result["uc_assessable_capital"].gt(capital_limit) + result["blocked_age"] = result["count_is_WA_adult"].eq(0) + result["pre_takeup_positive"] = ( + result["uc_maximum_amount"] - result["uc_income_reduction"] + ).gt(0) + result["blocked_takeup"] = ~result["would_claim_uc"].astype(bool) + result["income_exhausts_award"] = ( + result["is_uc_eligible"].astype(bool) + & result["uc_maximum_amount"].gt(0) + & ~result["pre_takeup_positive"] + ) + result["loss_reason"] = np.select( + [ + result["universal_credit"].gt(0), + result["blocked_age"], + result["blocked_capital"], + result["uc_maximum_amount"].le(0), + result["income_exhausts_award"], + result["blocked_takeup"], + ], + ["positive_uc", "age", "capital", "no_maximum_elements", "income", "takeup"], + default="cap_deductions_or_other", + ) + result.to_pickle(args.output_dir / "benefit_units_private.pkl") + person_metrics.to_pickle(args.output_dir / "people_private.pkl") + groups = {} + for scope, mask in ( + ("UK", np.ones(len(result), dtype=bool)), + ("GB", result["region"].isin(GB_REGIONS).to_numpy()), + ("NI", result["region"].eq("NORTHERN_IRELAND").to_numpy()), + ): + groups[scope] = summarize_group(result, mask) + for family in ( + "SINGLE", + "LONE_PARENT", + "COUPLE_NO_CHILDREN", + "COUPLE_WITH_CHILDREN", + ): + groups[f"{scope}/{family}"] = summarize_group( + result, + mask & result["uc_calibration_family_type"].eq(family).to_numpy(), + ) + for channel in result["benunit_support_channel"].unique(): + for reason in result["loss_reason"].unique(): + mask = ( + result["benunit_support_channel"].eq(channel) + & result["loss_reason"].eq(reason) + & result["uc_calibration_family_type"].eq("LONE_PARENT") + ) + groups[f"lone_parent/{channel}/{reason}"] = summarize_group( + result, mask.to_numpy() + ) + for name, mask in { + "adult_count_disagrees_with_claimants": result["count_is_adult"].ne( + result["count_frs_claimant"] + ), + "older_dependants": result["count_older_dependent"].gt(0), + "childless_couple_tail": result["uc_calibration_family_type"].eq( + "COUPLE_NO_CHILDREN" + ) + & result["universal_credit"].ge(26400.12), + }.items(): + groups[name] = summarize_group(result, mask.to_numpy()) + transitions = ( + result.groupby(["family_type", "uc_calibration_family_type"], observed=True) + .agg(rows=("benunit_id", "size"), mass=("fixed_calibrated_weight", "sum")) + .reset_index() + .to_dict("records") + ) + comparison = None + if args.baseline_output: + baseline_receipt = json.loads( + (args.baseline_output / "aggregate.json").read_text() + ) + assert baseline_receipt["source_year"] == args.source_year + assert baseline_receipt["model_year"] == args.year + if not args.allow_changed_source_tables: + assert baseline_receipt["input_sha256"] == identities + old = pd.read_pickle(args.baseline_output / "benefit_units_private.pkl") + np.testing.assert_array_equal(old["benunit_id"], result["benunit_id"]) + np.testing.assert_array_equal(old["household_id"], result["household_id"]) + for weight in ("prior_weight", "fixed_calibrated_weight"): + np.testing.assert_array_equal(old[weight], result[weight]) + comparison = {} + for variable in ( + "universal_credit", + "uc_standard_allowance", + "uc_work_allowance", + "uc_child_element", + "uc_housing_costs_element", + "uc_income_reduction", + ): + delta = result[variable] - old[variable] + comparison[variable] = { + "increased_rows": int(delta.gt(0.01).sum()), + "decreased_rows": int(delta.lt(-0.01).sum()), + "fixed_weight_delta": float( + (delta * result["fixed_calibrated_weight"]).sum() + ), + "prior_weight_delta": float((delta * result["prior_weight"]).sum()), + } + comparison["uc_zero_to_positive_rows"] = int( + (old["universal_credit"].le(0) & result["universal_credit"].gt(0)).sum() + ) + comparison["uc_positive_to_zero_rows"] = int( + (old["universal_credit"].gt(0) & result["universal_credit"].le(0)).sum() + ) + receipt = { + "purpose": "Development diagnostic; fixed inherited weights; no recalibration or certified release", + "source_year": args.source_year, + "model_year": args.year, + "versions": { + p: importlib.metadata.version(p) + for p in ("policyengine-uk", "policyengine-core", "numpy", "pandas") + }, + "input_sha256": identities, + "model_overlay": overlay, + "diagnostic_tool_sha256": sha256(Path(__file__)), + "loss_reason_semantics": "Ordered first-failing-condition partition, not independent causal attribution", + "changed_source_tables_allowed": args.allow_changed_source_tables, + "explicit_frs_claimant_input": args.supply_frs_claimants, + "baseline_awards_exact": baseline_exact, + "missing_variables": missing, + "groups": groups, + "family_transitions": transitions, + "comparison": comparison, + } + if identities != {str(path): sha256(path) for path in inputs}: + raise AssertionError("Diagnostic changed an input artifact.") + (args.output_dir / "aggregate.json").write_text( + json.dumps(receipt, indent=2) + "\n" + ) + print( + json.dumps( + { + "baseline_awards_exact": baseline_exact, + "UK": groups["UK"], + "GB": groups["GB"], + "comparison": comparison, + }, + indent=2, + ), + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index 8219e52ad..f89fed013 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -20,6 +20,7 @@ author_target_references, target_references_resource, ) +from microcosm.build.uk_runtime.uc_source_periods import uc_source_month_metadata UK_GEOGRAPHY_IDS = { "uk": "K02000001", @@ -36,6 +37,8 @@ "band", "band_filter_dimension", "band_period_factor", + "band_upper_bound", + "band_upper_bound_inclusive", "count_of", "filters", "folded_into", @@ -329,13 +332,19 @@ def _signed_exclusions(contract: Mapping[str, Any]) -> dict[str, str]: def _reference_metadata(contract: Mapping[str, Any]) -> dict[str, dict[str, str]]: - return { - str(target["target_id"]): { - "observation_basis": str(target["measurement"]["observation_basis"]) - } - for target in contract.get("targets", ()) - if target.get("measurement", {}).get("observation_basis") is not None - } + result = {} + for target in contract.get("targets", ()): + measurement = target.get("measurement", {}) + metadata = {} + if measurement.get("observation_basis") is not None: + metadata["observation_basis"] = str(measurement["observation_basis"]) + 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"])) + if metadata: + result[str(target["target_id"])] = metadata + return result def _fanout_name( diff --git a/uv.lock b/uv.lock index 177c79099..715727d69 100644 --- a/uv.lock +++ b/uv.lock @@ -780,7 +780,7 @@ requires-dist = [ { name = "microunit", marker = "extra == 'us'", specifier = ">=0.1.0" }, { name = "numpy", specifier = ">=1.26" }, { name = "pandas", specifier = ">=2" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.93" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.97.0" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=1.745.0,<2" }, { name = "pyarrow", specifier = ">=15" }, { name = "pyyaml", specifier = ">=6" }, @@ -853,7 +853,7 @@ requires-dist = [ { name = "h5py", specifier = ">=3" }, { name = "huggingface-hub", specifier = ">=0.20" }, { name = "packaging", specifier = ">=24" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.93" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.97.0" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=1.745.0,<2" }, ] provides-extras = ["us", "uk"] @@ -931,7 +931,7 @@ requires-dist = [ { name = "microunit", marker = "extra == 'us'", specifier = ">=0.1.0" }, { name = "numpy", specifier = ">=2" }, { name = "pandas", specifier = 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