diff --git a/changelog.d/uk-age-tail-reorder-785.changed.md b/changelog.d/uk-age-tail-reorder-785.changed.md new file mode 100644 index 000000000..35d9bb077 --- /dev/null +++ b/changelog.d/uk-age-tail-reorder-785.changed.md @@ -0,0 +1 @@ +Moved UK age-tail disaggregation immediately after the raw FRS spine and keyed its deterministic draw on base `person_id`, so every age-conditioned imputation now derives from the disaggregated age surface while later clones copy donor age; the age-tail gate now runs in the assembled phase and its `achieved_weighted` receipt measures raw FRS-grossed 80+ mass before support channels, the E6/E8 identity receipts pin the disaggregated basis, and NHS allocation uses the native 85–89, 90–94, and 95+ donor bands and `person.age` is declared int64 from the FRS root with age_tail rewriting it as int64, matching what the root produces (#785, #845). diff --git a/experiments/785-age-tail-reorder-receipts.md b/experiments/785-age-tail-reorder-receipts.md new file mode 100644 index 000000000..f3b9d4a32 --- /dev/null +++ b/experiments/785-age-tail-reorder-receipts.md @@ -0,0 +1,128 @@ +# 785 — age-tail reorder receipts + +Committed, disclosure-safe checklist for the licensed #785 acceptance lane. The +raw measurement scripts and receipts will live licensed-side under +`data/ukds/acceptance/spine-n-785/`, not in this repository. Every published +measurement below must name its artifact digest, PolicyEngine-UK version, period, +and disclosure-control threshold. + +The before artifact is the **graph-executed 27-stage build of main after #835 merged** +(its I5 re-mint candidate), not the legacy-executed spine-m: the graph executor's own +normalizations (text columns stored as StringDtype, root cells cast at CREATE) would +otherwise appear as payload differences that are not this change's. All before values +below are **to be measured at L2** from that digest-pinned artifact; none are inferred +from an earlier spine or from the planning estimates. The after artifact is the +twin-verified **spine-n** build of this branch. + +## Inputs and provenance + +- spine-m H5 path and sha256: **to be measured at L2** +- spine-m producing commit and stage-manifest digest: **to be measured at L2** +- spine-n primary H5 path and sha256: **to be measured at L2** +- spine-n twin H5 path and sha256: **to be measured at L2** +- PolicyEngine-UK version and build period: **to be measured at L2** +- minimum disclosure-control count: **to be measured at L2** + +## Receipt 1 — byte-identical final age + +- `person.age` value-surface absence from the payload diff: **to be measured at L2** +- `person.age` dtype: int64 on spine-n (#845 declares the root cell int64 and age_tail + rewrites it as int64) versus the before artifact's dtype — the only licensed + surface on this column: **to be measured at L2** +- sha256 over sorted shared-channel `(person_source_id, age)` pairs, spine-m: + **to be measured at L2** +- sha256 over sorted shared-channel `(person_source_id, age)` pairs, spine-n: + **to be measured at L2** +- equality verdict: **to be measured at L2** +- capital-gains donor-set and entity-count deltas, reported separately from the + shared-channel identity proof: **to be measured at L2** + +## Receipt 2 — payload classification + +The comparator compares column values only when a table's row counts agree, and +the reselected CGT band-donor households change the person and benunit counts. +So first derive the **donor-excluded, row-aligned slices** of spine-m and spine-n +(drop every household flagged `household_is_cgt_band_donor` together with its +benefit units and persons, on both sides; the pre-donor block is positionally +aligned because the donor rows are appended last), then run +`tools/compare_uk_h5_payload.py` on the two slices and classify its JSON report +with `tools/classify_uk_payload_diff.py` and +`tools/uk_age_tail_reorder_payload_expectation.json`. Any row-count inequality on +a slice is itself an unexpected difference. The committed classifier is the coarse +payload allowlist; each scoped claim below must also be checked on the named row +domain because the comparator deliberately reports counts, not unit-row +identities. The donor block is compared separately under "CGT donor selection". + +- slice derivation script and both slice digests: **to be measured at L2** + +- comparator report sha256: **to be measured at L2** +- classifier report sha256: **to be measured at L2** +- `unexpected` count and verdict: **to be measured at L2** + +### SPI stage-1 row-locality + +- changed-column incidence on recipients whose stage-time age moved out of the + top-coded pile: **to be measured at L2** +- byte equality outside that recipient domain: **to be measured at L2** + +### SPI stage-2 fill incidence + +- per-column SPI-channel changed-row incidence, including the reported-benefit + chain: **to be measured at L2** +- any incidence shift beyond the signed-difference band: **to be measured at L2** + +### UC reporter and capital follow-through + +- stage-19 SPI reporter-set and amount movement, with base-channel byte equality + (`uc-reporter-benefit-unit-redraw-incidence` re-derived; the refresh-lift entry was + retired by #835's third review pass): **to be measured at L2** +- stage-20 `frs_benunit_capital`, `uc_reported_capital`, and `would_claim_uc` + movement on the SPI reporter domain: **to be measured at L2** + +### Salary-sacrifice conditioning + +- `employee_pension_contributions` changed-row incidence: **to be measured at L2** +- `pension_contributions_via_salary_sacrifice` changed-row incidence: + **to be measured at L2** + +### CGT donor selection and entity counts + +- before and after donor-set digests and overlap: **to be measured at L2** +- household count and benunit/person count deltas: **to be measured at L2** +- household-count pin verdict: **to be measured at L2** + +## NHS reorder totals + +Use the native NHS donor table on spine-m and spine-n and report each service +separately. The before values come from spine-m, not an older planning receipt. + +- A&E realized total, before / after / anchor: **to be measured at L2** +- admitted-patient realized total, before / after / anchor: **to be measured at L2** +- outpatient realized total, before / after / anchor: **to be measured at L2** +- combined realized total, before / after / anchor: **to be measured at L2** +- `uk_aggregate_admin` verdict and margin: **to be measured at L2** + +## NHS fold-delta + +On the same spine-n frame, call `allocate_nhs_by_age_gender` once with the former +85+ folded table and once with the native table. + +- per-service folded and native realized totals: **to be measured at L2** +- exact totals-invariant verdict: **to be measured at L2** +- redistribution across 85–89 / 90–94 / 95+ recipients: **to be measured at L2** + +## Identity ladder + +- E4: **to be measured at L2** +- E5: **to be measured at L2** +- E6 (`stage_time_disaggregated` NHS basis): **to be measured at L2** +- E7: **to be measured at L2** +- E8 (`stage_time_disaggregated` donor basis): **to be measured at L2** +- permutation-stability verdict: **to be measured at L2** + +## Twin build + +- primary/twin payload comparison report sha256: **to be measured at L2** +- payload-identical verdict: **to be measured at L2** +- complete gate-battery verdict, including `uk_aggregate_admin`, + `uk_uc_capital_coherence`, and `uk_take_up_signal`: **to be measured at L2** diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index d290835b5..dccfc5855 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -376,7 +376,7 @@ { "id": "uk_stage_age_tail_targets", "gate": "stage_health", - "phase": "transferred", + "phase": "assembled", "criticality": "release_blocking", "evidence_absent_blocks": true, "parameters": { @@ -384,7 +384,7 @@ "check": "age_tail_targets", "maximum_relative_deviation": 1.0 }, - "notes": "Stage-time achieved-versus-target gate over the age-tail disaggregation receipt." + "notes": "Stage-time achieved-versus-target gate over the age-tail disaggregation receipt. At its post-frs_spine position, achieved_weighted measures raw FRS-grossed 80+ mass before clone or zero-weight support channels exist." }, { "id": "uk_release_input_coverage", 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 cc4d78e88..bbf0f3be0 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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "stage": "hmrc_cgt_gains_spine" } }, @@ -596,7 +596,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "canonical_source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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" @@ -827,7 +827,7 @@ "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": "1128013c99cd3592c06adab25cd0efa7f92667765445e2c6370c6c867a4a3517", + "source_manifest_sha256": "2cf4b9513a59b1c50c3a658b98bc2a61e6fd5e54ec5f0fadc3467ae0004680ad", "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 c403cf81b..e7e6d42a7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -355,6 +355,58 @@ ], "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity." }, + { + "stage": "age_tail", + "survey": "Family Resources Survey 2024-25 and ONS mid-year population estimates", + "source": "Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population).", + "grain": "person", + "artifacts": [ + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "ons_age_tail_band_populations.json", + "role": "age_tail_band_populations", + "runtime_sha256_required": true + } + ], + "operations": [ + { + "bands": [ + { + "low": 80, + "name": "80_84", + "width": 5 + }, + { + "low": 85, + "name": "85_89", + "width": 5 + }, + { + "low": 90, + "name": "90_plus", + "width": 8 + } + ], + "draw_key": "person_id", + "kind": "disaggregate_top_coded_ages", + "output": "age", + "reason": "Age disaggregation rewrites an existing person column in place; no rows move, no weights change, and total household mass is conserved.", + "resource": "ons_age_tail_band_populations.json", + "salt_streams": [ + "band", + "within" + ], + "seed": 0, + "top_code": 80 + } + ], + "outputs": [], + "notes": "The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled base person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_id. Runs immediately after frs_spine, before every stage that conditions on age, so all age-conditioned imputations derive from the disaggregated surface. The stage precedes person_source_id provenance; later support and capital-gains clones copy the donor's rewritten age, preserving the #623 final-age identity invariant. Ages are assigned toward the ONS distribution; calibration still owns the totals.", + "rewrites": [ + "age" + ] + }, { "stage": "frs_employment", "survey": "Family Resources Survey 2024-25", @@ -1492,7 +1544,7 @@ "resource": "nhs_consumption_by_age_gender.json", "budget_resource": "etb_services_anchors.json", "age_bands": "half_open", - "top_band_fold_in": "85+" + "top_band_fold_in": "none" } ], "outputs": [ @@ -1519,7 +1571,7 @@ "nhs_admitted_patient_spending", "nhs_outpatient_spending" ], - "notes": "Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age-band and 85+ fold-in fixes. The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage." + "notes": "Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age bands with the donor table's native 85-89, 90-94 and 95+ top bands (the former 85+ fold-in was retired once age_tail moved ahead of this stage and recipients could populate those bands). The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage." }, { "stage": "frs_hmrc_spine_leaves", @@ -2916,58 +2968,6 @@ ], "notes": "Reported PAYE repayers are classified without a country gate. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year; PLAN_4 is never imputed." }, - { - "stage": "age_tail", - "survey": "Family Resources Survey 2024-25 and ONS mid-year population estimates", - "source": "Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population).", - "grain": "person", - "artifacts": [ - { - "format": "json", - "kind": "public_aggregate_reference", - "resource": "ons_age_tail_band_populations.json", - "role": "age_tail_band_populations", - "runtime_sha256_required": true - } - ], - "operations": [ - { - "bands": [ - { - "low": 80, - "name": "80_84", - "width": 5 - }, - { - "low": 85, - "name": "85_89", - "width": 5 - }, - { - "low": 90, - "name": "90_plus", - "width": 8 - } - ], - "draw_key": "person_source_id", - "kind": "disaggregate_top_coded_ages", - "output": "age", - "reason": "Age disaggregation rewrites an existing person column in place; no rows move, no weights change, and total household mass is conserved.", - "resource": "ons_age_tail_band_populations.json", - "salt_streams": [ - "band", - "within" - ], - "seed": 0, - "top_code": 80 - } - ], - "outputs": [], - "notes": "The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_source_id so a household and its capital-gains clone twin receive the same age. Runs after student_loans — downstream of every stage that conditions on age, the position the", - "rewrites": [ - "age" - ] - }, { "stage": "frs_hmrc_retained_leaves", "survey": "Family Resources Survey 2024-25", 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 ca30dbece..fbe7f116b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -321,6 +321,42 @@ stages: - housing_service_charges - external_child_payments notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. +- stage: age_tail + survey: Family Resources Survey 2024-25 and ONS mid-year population estimates + source: Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population). + grain: person + artifacts: + - role: age_tail_band_populations + kind: public_aggregate_reference + resource: ons_age_tail_band_populations.json + format: json + runtime_sha256_required: true + operations: + - kind: disaggregate_top_coded_ages + output: age + resource: ons_age_tail_band_populations.json + top_code: 80 + bands: + - name: '80_84' + low: 80 + width: 5 + - name: '85_89' + low: 85 + width: 5 + - name: '90_plus' + low: 90 + width: 8 + seed: 0 + draw_key: person_id + salt_streams: + - band + - within + reason: Age disaggregation rewrites an existing person column in place; no rows + move, no weights change, and total household mass is conserved. + outputs: [] + rewrites: + - age + notes: "The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled base person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_id. Runs immediately after frs_spine, before every stage that conditions on age, so all age-conditioned imputations derive from the disaggregated surface. The stage precedes person_source_id provenance; later support and capital-gains clones copy the donor's rewritten age, preserving the #623 final-age identity invariant. Ages are assigned toward the ONS distribution; calibration still owns the totals." - stage: frs_employment survey: Family Resources Survey 2024-25 source: Department for Work and Pensions Family Resources Survey 2024-25, UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1; local licensed 2024_25 tabs. @@ -1180,7 +1216,7 @@ stages: resource: nhs_consumption_by_age_gender.json budget_resource: etb_services_anchors.json age_bands: half_open - top_band_fold_in: 85+ + top_band_fold_in: none outputs: - dfe_education_spending - rail_subsidy_spending @@ -1203,7 +1239,7 @@ stages: - nhs_a_and_e_spending - nhs_admitted_patient_spending - nhs_outpatient_spending - notes: Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age-band and 85+ fold-in fixes. The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage. + notes: Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age bands with the donor table's native 85-89, 90-94 and 95+ top bands (the former 85+ fold-in was retired once age_tail moved ahead of this stage and recipients could populate those bands). The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage. - stage: frs_hmrc_spine_leaves survey: Family Resources Survey 2024-25 source: Department for Work and Pensions Family Resources Survey 2024-25 raw adult.tab and benefits.tab, caller-supplied local input @@ -2318,42 +2354,6 @@ stages: rewrites: - student_loan_plan notes: Reported PAYE repayers are classified without a country gate. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year; PLAN_4 is never imputed. -- stage: age_tail - survey: Family Resources Survey 2024-25 and ONS mid-year population estimates - source: Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population). - grain: person - artifacts: - - role: age_tail_band_populations - kind: public_aggregate_reference - resource: ons_age_tail_band_populations.json - format: json - runtime_sha256_required: true - operations: - - kind: disaggregate_top_coded_ages - output: age - resource: ons_age_tail_band_populations.json - top_code: 80 - bands: - - name: '80_84' - low: 80 - width: 5 - - name: '85_89' - low: 85 - width: 5 - - name: '90_plus' - low: 90 - width: 8 - seed: 0 - draw_key: person_source_id - salt_streams: - - band - - within - reason: Age disaggregation rewrites an existing person column in place; no rows - move, no weights change, and total household mass is conserved. - outputs: [] - rewrites: - - age - notes: The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_source_id so a household and its capital-gains clone twin receive the same age. Runs after student_loans — downstream of every stage that conditions on age, the position the #623 calibration campaign proved — so imputation conditioning is unchanged. Ages are assigned toward the ONS distribution; calibration still owns the totals. - stage: frs_hmrc_retained_leaves survey: Family Resources Survey 2024-25 source: Department for Work and Pensions Family Resources Survey 2024-25 raw adult.tab and benefits.tab, caller-supplied local input diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/age_tail.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/age_tail.py index 7ca222d57..484acc320 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/age_tail.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/age_tail.py @@ -13,12 +13,19 @@ the imputation source and the target denominators cannot drift apart). The draw is: -- keyed on ``person_source_id``, so a household and its capital-gains clone - twin receive the same age and the payload-identity discipline holds; +- keyed on the base-row ``person_id`` before clone provenance exists; +- dtype-preserving: an integer ``age`` column stays integer (bands and + within-band offsets are integral), matching the graph's int64 declaration; - deterministic under a declared seed (sha256 counter stream, no global RNG); - sex-specific, using the MALE/FEMALE 80-84 / 85-89 / 90+ populations as an inverse CDF, with a uniform integer age within the drawn band. +The stage runs immediately after ``frs_spine``, before every imputation that +conditions on age. Later support and capital-gains stages clone whole rows, so +each clone copies its donor's already-disaggregated age. A runtime guard +refuses a frame that already carries ``person_source_id``: clone provenance is +proof that the position contract has been violated. + Ages are assigned, not weighted, toward the ONS distribution: the achieved weighted shares land near the ONS shares by construction and calibration still owns the totals, so the age-band targets remain honest constraints @@ -26,12 +33,9 @@ Written for the #623 assessment runner, proven over its nine-run calibration campaign, and ported here as the declarative WS-E source stage its docstring -always said it would become. In the spine it runs after ``student_loans`` — -the position the campaign exercised, downstream of everything that consumes -``age``, so imputation conditioning is unchanged and the stage's whole -effect is the pile's disaggregation. The band populations come from the -committed ``ons_age_tail_band_populations.json`` resource, each cell -carrying the register target id it must agree with. +always said it would become. The band populations come from the committed +``ons_age_tail_band_populations.json`` resource, each cell carrying the +register target id it must agree with. """ from __future__ import annotations @@ -149,6 +153,10 @@ def disaggregate_uk_age_top_code( """ person = frame.table("person") + if "person_source_id" in person.columns: + raise ValueError( + "age_tail must run before person_source_id clone provenance exists." + ) ages = pd.to_numeric(person["age"], errors="raise").to_numpy(dtype=float) if (ages > top_code).any(): raise ValueError( @@ -179,21 +187,28 @@ def disaggregate_uk_age_top_code( shares = np.asarray(populations) / sum(populations) cdf[gender] = np.cumsum(shares) - source_ids = person["person_source_id"].to_numpy() + person_ids = person["person_id"].to_numpy() new_ages = ages.copy() assigned_counts: dict[tuple[str, str], int] = {} for index in np.flatnonzero(piled): gender = genders[index] - band_draw = _unit_draw(source_ids[index], seed, "band") + band_draw = _unit_draw(person_ids[index], seed, "band") band_index = int(np.searchsorted(cdf[gender], band_draw, side="right")) band_index = min(band_index, len(band_names) - 1) name, low, width = UK_AGE_TAIL_BANDS[band_index] - within_draw = _unit_draw(source_ids[index], seed, "within") + within_draw = _unit_draw(person_ids[index], seed, "within") new_ages[index] = low + int(within_draw * width) key = (gender, name) assigned_counts[key] = assigned_counts.get(key, 0) + 1 - person["age"] = new_ages + # Bands and within-band offsets are integers, so an integer input keeps + # its dtype: the graph declares age int64 from the root (#845), and a + # float input (synthetic frames) stays float. + age_dtype = person["age"].dtype + if np.issubdtype(age_dtype, np.integer): + person["age"] = new_ages.astype(age_dtype) + else: + person["age"] = new_ages check = pd.to_numeric(frame.table("person")["age"], errors="raise") if int((check == float(top_code)).sum()) >= int(piled.sum()): raise RuntimeError("age disaggregation did not persist on the frame.") @@ -232,7 +247,7 @@ def disaggregate_uk_age_top_code( f"{gender}:{band}": float(value) for (gender, band), value in sorted(band_populations.items()) }, - "draw_key": "person_source_id (clone-twin consistent)", + "draw_key": "person_id (base rows only; clones copy the donor age)", } @@ -255,7 +270,7 @@ def _assert_age_tail_stage_parameters(stage: SourceStageSpec) -> None: for name, low, width in UK_AGE_TAIL_BANDS ], "seed": UK_AGE_TAIL_DECLARED_SEEDS["age"], - "draw_key": "person_source_id", + "draw_key": "person_id", "salt_streams": ["band", "within"], } declared = {"kind": operations[0].kind, **parameters} diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py index 82101f054..555d149bf 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py @@ -402,16 +402,6 @@ def build_nhs_cell_table( values="Total", aggfunc="sum", ).reset_index() - top = ( - pivot[pivot["Lower age"] >= 85] - .groupby(["Gender", "Service"], as_index=False)[ - ["Activity Count", "Total Cost"] - ] - .sum() - ) - top["Lower age"] = 85 - top["Upper age"] = 120 - pivot = pd.concat([pivot[pivot["Lower age"] < 85], top], ignore_index=True) counts = _weighted_person_counts(person, household) pivot["Total people"] = [ counts((row["Lower age"], row["Upper age"]), row["Gender"]) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py index 0b60f8d31..abb957c95 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py @@ -173,11 +173,13 @@ def derive_frs_education(person: pd.DataFrame, raw_person: pd.DataFrame) -> pd.D values["is_in_non_advanced_education"].to_numpy(dtype=bool) | values["is_in_approved_training"].to_numpy(dtype=bool) ) + # Integer by contract whatever dtype ``age`` arrives in: age_tail now + # rewrites age as float64 upstream of this stage. values["age_started_or_accepted_current_education_or_training"] = np.where( in_qualifying, np.minimum(age, 18), UNKNOWN_QUALIFYING_EDUCATION_OR_TRAINING_ENTRY_AGE, - ) + ).astype("int64") values["is_before_universal_credit_qualifying_young_person_terminal_date"] = ( (age == 19) & in_qualifying ) 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 ccc530ddb..38eb232bd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -85,12 +85,19 @@ _READER_ISOLATION_BOUNDARIES = frozenset( { "uc_capital_coherence", - # Earlier stages in this version genuinely condition on age. The - # terminal rewrite must live above them rather than becoming their - # same-version owner and reversing those dependencies. - "age_tail", + # ``frs_education_grant_split`` rewrites the root cell + # ``education_grants`` that the open-surface ``frs_legacy_proxies`` + # reader already bound to. In the root version that rewrite opened a + # boundary by the same-version owner rule; now that ``age_tail`` opens + # its own version right after the root, the root cells are inherited + # rather than owned there, so the isolation must be declared. + "frs_education_grant_split", } ) +# ``age_tail`` needs no isolation entry: it is declared immediately after the +# root stage, and the root version already owns ``age``, so the same-version +# owner rule opens its boundary. Every later age reader then binds to the +# disaggregated cell — the #785 contract — instead of the top-coded root. _SPLIT_STAGE_SOURCES: Mapping[str, tuple[str, ...]] = { "frs_spine": ("frs",), @@ -222,11 +229,12 @@ ("household", "region"), } ), + # Keyed on the structural person_id before any clone provenance exists; + # the transform refuses a frame that already carries person_source_id. "age_tail": frozenset( { ("person", "age"), ("person", "gender"), - ("person", "person_source_id"), } ), } @@ -256,8 +264,8 @@ 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_INT: set[str] = set() -_ROOT_PERSON_FLOAT = {"age"} +_ROOT_PERSON_INT = {"age"} +_ROOT_PERSON_FLOAT: set[str] = set() _ROOT_BENUNIT_TYPES = { "frs_benunit_capital": "float64", "is_married": "bool", @@ -295,12 +303,12 @@ def _root_cells(stage_outputs: Iterable[str]) -> tuple[_Cell, ...]: elif column in _ROOT_PERSON_BOOL: cells.append(_Cell("person", column, "bool")) elif column in _ROOT_PERSON_INT: + # The FRS root produces an integer age and age_tail rewrites it + # as int64 (integral bands by construction), so the declaration + # matches what the root produces and no CREATE-time cast is + # needed (#845). Rewrites cannot change their base's dtype. cells.append(_Cell("person", column, "int64")) elif column in _ROOT_PERSON_FLOAT: - # age_tail is an honest float64 rewrite. Rewrites cannot change - # their base's declared dtype, so the graph normalizes age at - # CREATE while the legacy spine reaches the same dtype at its - # final stage. cells.append(_Cell("person", column, "float64")) elif column.startswith("household_"): # No root data column currently takes this spelling; keep an @@ -549,7 +557,7 @@ def _cells( ), ), "student_loans": (_Cell("person", "student_loan_plan", "string"),), - "age_tail": (_Cell("person", "age", "float64"),), + "age_tail": (_Cell("person", "age", "int64"),), } _HMRC_SPI_FLOAT_COLUMNS = ( 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 225af1c0d..f222c473a 100644 --- a/packages/microcosm-build/tests/test_spec_engine_country_bundles.py +++ b/packages/microcosm-build/tests/test_spec_engine_country_bundles.py @@ -55,7 +55,7 @@ ), ( "uk", - "8e6f5861362e1e23b650c1d2937995bebee573bb9f1cab51fc8e1fef2cef8a9c", + "b29705c707b793d10b2c71ec62ff6e7e1ddb34029a0b7221f3969f9a5124e19e", { "benunit.benunit_id", "household.household_id", diff --git a/packages/microcosm-build/tests/test_uk_age_tail.py b/packages/microcosm-build/tests/test_uk_age_tail.py index 098e16980..890382480 100644 --- a/packages/microcosm-build/tests/test_uk_age_tail.py +++ b/packages/microcosm-build/tests/test_uk_age_tail.py @@ -3,9 +3,9 @@ The stage exists because the licensed FRS records no age above 80, so the 85+ population targets are structurally unbindable. These tests pin the properties that make its draw safe to run inside the spine: it is keyed on -the source identity (so clone twins agree), it is deterministic under a -declared seed, it moves nobody out of the 80+ population, and it refuses -rather than guesses when its preconditions do not hold. +the base person identity before clone provenance exists, it is deterministic +under a declared seed, it moves nobody out of the 80+ population, and it +refuses rather than guesses when its preconditions do not hold. """ from __future__ import annotations @@ -17,6 +17,7 @@ import pandas as pd import pytest +import microcosm.build.uk_runtime.age_tail as age_tail_runtime from microcosm.build.source_manifest import SourceManifest from microcosm.build.uk_runtime.age_tail import ( UK_AGE_TAIL_BANDS, @@ -40,7 +41,7 @@ } -def _frame(*, n_piled: int = 40, clone_twins: bool = False): +def _frame(*, n_piled: int = 40): """A minimal UK national frame with a top-code pile.""" ages = [30.0, 55.0] + [float(UK_AGE_TOP_CODE)] * n_piled @@ -49,19 +50,11 @@ def _frame(*, n_piled: int = 40, clone_twins: bool = False): ] count = len(ages) person_ids = list(range(1, count + 1)) - source_ids = [f"s{index}" for index in person_ids] - if clone_twins: - # A clone twin shares its original's source id *and* its sex — the - # band CDF is sex-specific, so a twin with a different gender would - # legitimately draw a different band. - source_ids[-1] = source_ids[-2] - genders[-1] = genders[-2] person = pd.DataFrame( { "person_id": person_ids, "person_benunit_id": person_ids, "person_household_id": person_ids, - "person_source_id": source_ids, "age": ages, "gender": genders, } @@ -79,6 +72,30 @@ def _frame(*, n_piled: int = 40, clone_twins: bool = False): ) +def _person_source_id_keyed_reference_ages(frame, *, seed: int = 0) -> np.ndarray: + """Reproduce the former key path for equal person/source digest inputs.""" + + person = frame.table("person") + ages = person["age"].to_numpy(dtype=float).copy() + genders = person["gender"].astype(str).to_numpy() + source_ids = person["person_source_id"].to_numpy() + cdf = {} + for gender in ("MALE", "FEMALE"): + populations = np.asarray( + [_BANDS[(gender, name)] for name, _, _ in UK_AGE_TAIL_BANDS], + dtype=float, + ) + cdf[gender] = np.cumsum(populations / populations.sum()) + for index in np.flatnonzero(ages == UK_AGE_TOP_CODE): + band_draw = age_tail_runtime._unit_draw(source_ids[index], seed, "band") + band_index = int(np.searchsorted(cdf[genders[index]], band_draw, side="right")) + band_index = min(band_index, len(UK_AGE_TAIL_BANDS) - 1) + _, low, width = UK_AGE_TAIL_BANDS[band_index] + within_draw = age_tail_runtime._unit_draw(source_ids[index], seed, "within") + ages[index] = low + int(within_draw * width) + return ages + + class TestDraw: def test_the_pile_disperses_across_every_band(self) -> None: frame = _frame(n_piled=400) @@ -115,14 +132,38 @@ def test_a_different_seed_moves_the_draw(self) -> None: second.table("person")["age"].to_numpy(), ) - def test_clone_twins_receive_the_same_age(self) -> None: - # The capital-gains clone shares its original's source id, so the two - # rows must agree or the payload-identity discipline breaks. - frame = _frame(n_piled=40, clone_twins=True) - disaggregate_uk_age_top_code(frame, band_populations=_BANDS) - person = frame.table("person") - by_source = person.groupby("person_source_id")["age"].nunique() - assert int(by_source.max()) == 1 + def test_person_id_key_matches_the_source_keyed_reference(self) -> None: + # Base rows copy person_id verbatim into person_source_id when the SPI + # channel is created. Equal digest inputs must therefore reproduce the + # former final-age draw exactly; later clones copy that rewritten age. + candidate = _frame(n_piled=120) + reference = _frame(n_piled=120) + reference.table("person")["person_source_id"] = reference.table("person")[ + "person_id" + ] + expected = _person_source_id_keyed_reference_ages(reference) + + disaggregate_uk_age_top_code(candidate, band_populations=_BANDS) + + assert np.array_equal(candidate.table("person")["age"], expected) + + def test_an_integer_age_column_stays_integer(self) -> None: + # The FRS root produces int64 ages and the graph declares the cell + # int64 end to end (#845); the draw's bands are integral, so nothing + # is lost by keeping the dtype. + as_float = _frame(n_piled=60) + as_int = _frame(n_piled=60) + as_int.table("person")["age"] = as_int.table("person")["age"].astype("int64") + + disaggregate_uk_age_top_code(as_float, band_populations=_BANDS) + disaggregate_uk_age_top_code(as_int, band_populations=_BANDS) + + assert as_int.table("person")["age"].dtype == np.dtype("int64") + assert as_float.table("person")["age"].dtype == np.dtype("float64") + assert np.array_equal( + as_int.table("person")["age"].to_numpy(), + as_float.table("person")["age"].to_numpy(), + ) def test_the_draw_follows_the_band_populations(self) -> None: # A degenerate CDF that puts all mass on 90+ must send the whole pile @@ -141,10 +182,18 @@ def test_the_receipt_records_what_it_did(self) -> None: assert receipt["top_code"] == UK_AGE_TOP_CODE assert sum(receipt["assigned_unweighted"].values()) == 60 assert set(receipt["achieved_weighted"]) == {"MALE", "FEMALE"} - assert receipt["draw_key"].startswith("person_source_id") + assert receipt["draw_key"] == ( + "person_id (base rows only; clones copy the donor age)" + ) class TestRefusals: + def test_clone_provenance_refuses_a_late_position(self) -> None: + frame = _frame(n_piled=10) + frame.table("person")["person_source_id"] = frame.table("person")["person_id"] + with pytest.raises(ValueError, match="before person_source_id"): + disaggregate_uk_age_top_code(frame, band_populations=_BANDS) + def test_an_already_disaggregated_surface_is_refused(self) -> None: frame = _frame(n_piled=10) disaggregate_uk_age_top_code(frame, band_populations=_BANDS) diff --git a/packages/microcosm-build/tests/test_uk_etb_services.py b/packages/microcosm-build/tests/test_uk_etb_services.py index 6abf3de5a..a5623cf36 100644 --- a/packages/microcosm-build/tests/test_uk_etb_services.py +++ b/packages/microcosm-build/tests/test_uk_etb_services.py @@ -203,33 +203,37 @@ def _nhs_raw() -> pd.DataFrame: return pd.DataFrame(rows) -def test_nhs_age_parsing_and_85_plus_fold_in_uses_full_table_denominator() -> None: +def test_nhs_age_parsing_preserves_native_top_bands() -> None: assert parse_nhs_age_bounds("0 years") == (0, 1) assert parse_nhs_age_bounds("95 years or older") == (95, 120) assert parse_nhs_age_bounds("85-89") == (85, 90) person = pd.DataFrame( { - "person_id": [1, 2, 3], - "person_household_id": [1, 1, 2], - "age": [0, 85, 95], - "gender": ["FEMALE", "FEMALE", "FEMALE"], + "person_id": [1, 2, 3, 4], + "person_household_id": [1, 1, 2, 3], + "age": [0, 85, 90, 95], + "gender": ["FEMALE", "FEMALE", "FEMALE", "FEMALE"], } ) household = pd.DataFrame( { - "household_id": [1, 2], - "household_weight": [2.0, 3.0], + "household_id": [1, 2, 3], + "household_weight": [2.0, 3.0, 4.0], } ) cells = build_nhs_cell_table(_nhs_raw(), person, household) - top = cells[cells["Lower age"] == 85].iloc[0] + top = cells[cells["Lower age"] >= 85].sort_values("Lower age") - assert top["Upper age"] == 120 - assert top["Activity Count"] == 90.0 - assert top["Total Cost"] == 1900.0 - assert top["Total people"] == 5.0 + assert list(zip(top["Lower age"], top["Upper age"], strict=True)) == [ + (85, 90), + (90, 95), + (95, 120), + ] + assert top["Activity Count"].tolist() == [20.0, 30.0, 40.0] + assert top["Total Cost"].tolist() == [300.0, 600.0, 1000.0] + assert top["Total people"].tolist() == [2.0, 3.0, 4.0] assert np.isclose( cells["Per-person average spending"].mul(cells["Total people"]).sum(), load_etb_services_anchors()["nhs_budget_2025_26"]["value"], diff --git a/packages/microcosm-build/tests/test_uk_identity_stability_receipts.py b/packages/microcosm-build/tests/test_uk_identity_stability_receipts.py index cc663745b..06099bb53 100644 --- a/packages/microcosm-build/tests/test_uk_identity_stability_receipts.py +++ b/packages/microcosm-build/tests/test_uk_identity_stability_receipts.py @@ -140,7 +140,7 @@ def test_a_corrupted_stored_channel_is_caught(self) -> None: class TestE6Receipt: - def test_nhs_receipt_uses_stage_time_top_coded_age( + def test_nhs_receipt_uses_stage_time_disaggregated_age( self, monkeypatch: pytest.MonkeyPatch ) -> None: class _FakeModel: @@ -206,7 +206,6 @@ def materialize(self, frame, variables, period): "person_id": [1], "person_benunit_id": [10], "person_household_id": [100], - "person_source_id": ["source-1"], "age": [float(UK_AGE_TOP_CODE)], "gender": ["FEMALE"], } @@ -218,6 +217,17 @@ def materialize(self, frame, variables, period): time_period="2024", weight_kind=WeightKind.DESIGN, ) + disaggregate_uk_age_top_code( + frame, + band_populations={ + ("MALE", "80_84"): 1.0, + ("MALE", "85_89"): 1.0, + ("MALE", "90_plus"): 1.0, + ("FEMALE", "80_84"): 1.0, + ("FEMALE", "85_89"): 1e9, + ("FEMALE", "90_plus"): 1.0, + }, + ) stage = SourceStageSpec( stage="etb_services", survey="etb", @@ -253,17 +263,6 @@ def materialize(self, frame, variables, period): frame = UKETBServicesStageTransform( stage=stage, engine=_FakeEngine(), donor=donor )(frame) - disaggregate_uk_age_top_code( - frame, - band_populations={ - ("MALE", "80_84"): 1.0, - ("MALE", "85_89"): 1.0, - ("MALE", "90_plus"): 1.0, - ("FEMALE", "80_84"): 1.0, - ("FEMALE", "85_89"): 1e9, - ("FEMALE", "90_plus"): 1.0, - }, - ) person = frame.table("person") household = frame.table("household") @@ -275,57 +274,73 @@ def materialize(self, frame, variables, period): ) stored = person.set_index("person_id")[list(UK_NHS_OUTPUT_COLUMNS)] final_age_nhs.index = stored.index + for column in UK_NHS_OUTPUT_COLUMNS: + assert np.allclose( + stored[column].to_numpy(dtype=float), + final_age_nhs[column].to_numpy(dtype=float), + ) + + clamped_person = person.copy() + clamped_person["age"] = np.minimum( + pd.to_numeric(clamped_person["age"], errors="raise").to_numpy( + dtype=float + ), + float(UK_AGE_TOP_CODE), + ) + clamped_nhs = allocate_nhs_by_age_gender( + clamped_person, + household_weights=frame.weights_for("household").values, + household=household, + nhs_table=None, + ) + clamped_nhs.index = stored.index assert any( not np.allclose( stored[column].to_numpy(dtype=float), - final_age_nhs[column].to_numpy(dtype=float), + clamped_nhs[column].to_numpy(dtype=float), ) for column in UK_NHS_OUTPUT_COLUMNS ) receipt = _load_tool().e6_identity_receipt(frame, permutation_seed=7) - assert receipt["nhs_age_basis"] == "stage_time_top_coded" - assert receipt["nhs_age_top_code"] == UK_AGE_TOP_CODE + assert receipt["nhs_age_basis"] == "stage_time_disaggregated" assert receipt["matches_stored_columns"] is True assert receipt["stored_column_mismatches"] == {} -def test_e8_carrier_recompute_is_invariant_to_the_age_tail_rewrite(): - """The donor stage picked carriers before age_tail ran; the recompute must too. +def test_e8_carrier_recompute_uses_disaggregated_age(): + """The donor stage and its receipt both use final disaggregated age. - Two adults tied at the top code at stage time: the stage's carrier is the - stable-order winner of that tie. After age_tail lifts one of them to 90, - an unclamped recompute flips the tie to the lifted person — the mechanism - behind the 255-donor mismatch on the first 25-stage ladder run — while the - stage-time clamp reproduces the stage's own choice exactly. + Two adults tie on the former top-coded surface, while disaggregation lifts + one to 90. The selected carrier must be the lifted person; clamping back to + 80 demonstrates that the basis choice is load-bearing. """ from microcosm.build.uk_runtime.age_tail import UK_AGE_TOP_CODE as TOP from microcosm.build.uk_runtime.cgt_structure import _oldest_adult_indices - stage_time = pd.DataFrame( + top_coded = pd.DataFrame( { "person_id": [0, 1], "person_household_id": [7, 7], "age": [float(TOP), float(TOP)], } ) - after_age_tail = stage_time.assign(age=[float(TOP), 90.0]) - - stage_choice = _oldest_adult_indices(stage_time, household_ids={7}) + disaggregated = top_coded.assign(age=[float(TOP), 90.0]) - unclamped = _oldest_adult_indices(after_age_tail, household_ids={7}) - assert unclamped.tolist() != stage_choice.tolist() + stage_choice = _oldest_adult_indices(disaggregated, household_ids={7}) + # The lifted person (row 1) wins outright; the top-coded tie would have + # gone to row 0 on the stable person_id order. + assert stage_choice.tolist() == [1] - clamped = after_age_tail.assign( + clamped = disaggregated.assign( age=np.minimum( - pd.to_numeric(after_age_tail["age"], errors="coerce").to_numpy( + pd.to_numeric(disaggregated["age"], errors="coerce").to_numpy( dtype=float ), float(TOP), ) ) - assert ( - _oldest_adult_indices(clamped, household_ids={7}).tolist() - == stage_choice.tolist() + assert _oldest_adult_indices(clamped, household_ids={7}).tolist() != ( + stage_choice.tolist() ) diff --git a/packages/microcosm-build/tests/test_uk_nhs_allocation.py b/packages/microcosm-build/tests/test_uk_nhs_allocation.py index d8f1e0e4f..fe49bf9d7 100644 --- a/packages/microcosm-build/tests/test_uk_nhs_allocation.py +++ b/packages/microcosm-build/tests/test_uk_nhs_allocation.py @@ -41,29 +41,33 @@ def test_nhs_age_bound_parsing_uses_half_open_top_code() -> None: assert parse_nhs_age_bounds("95 years or older") == (95, 120) -def test_nhs_85_plus_fold_in_and_budget_normalization_use_full_table() -> None: +def test_nhs_native_top_bands_and_budget_normalization_use_full_table() -> None: person = pd.DataFrame( { - "person_id": [1, 2], - "person_household_id": [1, 2], - "age": [84, 85], - "gender": ["female", "FEMALE"], + "person_id": [1, 2, 3, 4], + "person_household_id": [1, 2, 3, 4], + "age": [84, 85, 90, 95], + "gender": ["female", "FEMALE", "FEMALE", "FEMALE"], } ) household = pd.DataFrame( { - "household_id": [1, 2], - "household_weight": [2.0, 3.0], + "household_id": [1, 2, 3, 4], + "household_weight": [2.0, 3.0, 4.0, 5.0], } ) cells = build_nhs_cell_table(_raw_nhs_rows(), person, household) - top = cells[cells["Lower age"] == 85].iloc[0] + top = cells[cells["Lower age"] >= 85].sort_values("Lower age") - assert top["Upper age"] == 120 - assert top["Activity Count"] == 90.0 - assert top["Total Cost"] == 1900.0 - assert top["Total people"] == 3.0 + assert list(zip(top["Lower age"], top["Upper age"], strict=True)) == [ + (85, 90), + (90, 95), + (95, 120), + ] + assert top["Activity Count"].tolist() == [20.0, 30.0, 40.0] + assert top["Total Cost"].tolist() == [300.0, 600.0, 1000.0] + assert top["Total people"].tolist() == [3.0, 4.0, 5.0] assert np.isclose( cells["Per-person average spending"].mul(cells["Total people"]).sum(), load_etb_services_anchors()["nhs_budget_2025_26"]["value"], @@ -79,10 +83,11 @@ def test_nhs_85_plus_fold_in_and_budget_normalization_use_full_table() -> None: ) assert allocated.loc[0, "a_and_e_visits"] == 5.0 - assert allocated.loc[1, "a_and_e_visits"] == 30.0 - assert allocated.loc[0, "nhs_a_and_e_spending"] < allocated.loc[ - 1, "nhs_a_and_e_spending" - ] + assert allocated.loc[1, "a_and_e_visits"] == 20.0 / 3.0 + assert allocated.loc[2, "a_and_e_visits"] == 30.0 / 4.0 + assert allocated.loc[3, "a_and_e_visits"] == 40.0 / 5.0 + spending = allocated["nhs_a_and_e_spending"].to_numpy(dtype=float) + assert np.all(np.diff(spending) > 0) def test_nhs_age_bounds_parse_every_committed_resource_label() -> None: diff --git a/packages/microcosm-build/tests/test_uk_payload_diff_classifier.py b/packages/microcosm-build/tests/test_uk_payload_diff_classifier.py new file mode 100644 index 000000000..41577b37b --- /dev/null +++ b/packages/microcosm-build/tests/test_uk_payload_diff_classifier.py @@ -0,0 +1,287 @@ +"""Fail-closed classification of the #785 UK payload diff report.""" + +from __future__ import annotations + +import importlib.util +import json +import sys +from pathlib import Path + +import pytest + +ROOT = Path(__file__).resolve().parents[3] + + +def _classifier_module(): + path = ROOT / "tools" / "classify_uk_payload_diff.py" + spec = importlib.util.spec_from_file_location("classify_uk_payload_diff", path) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + sys.modules[spec.name] = module + spec.loader.exec_module(module) + return module + + +CLASSIFIER = _classifier_module() + + +def _table(**overrides: object) -> dict[str, object]: + table: dict[str, object] = { + "rows": {"left": 3, "right": 3}, + "row_count_equal": True, + "column_order_equal": True, + "columns_only_left": [], + "columns_only_right": [], + "index_type_equal": True, + "index_dtype_equal": True, + "index_name_equal": True, + "index_values_equal": True, + "dtype_mismatches": {}, + "value_mismatch_rows_by_column": {}, + "stored_kind_equal": True, + } + table.update(overrides) + return table + + +def _report(*, tables: dict[str, object]) -> dict[str, object]: + return { + "left": "spine-m.h5", + "left_sha256": "a" * 64, + "right": "spine-n.h5", + "right_sha256": "b" * 64, + "keys_equal": True, + "keys_only_left": [], + "keys_only_right": [], + "tables": tables, + "root_attrs": { + "names_in_order_equal": True, + "attrs_only_left": [], + "attrs_only_right": [], + "attrs_with_differing_values": [], + }, + } + + +def _expectation() -> dict[str, object]: + return { + "schema_version": 1, + "default_expectation": "expected_byte_equal", + "expected_byte_equal": [ + { + "entity": "person", + "columns": ["age"], + "scope": "all rows", + "reason": "identity invariant", + } + ], + "expected_changed": [ + { + "entity": "person", + "columns": ["employment_income"], + "scope": "SPI rows only", + "reason": "age-conditioned QRF", + }, + ], + } + + +def test_expected_change_is_classified_with_its_scope() -> None: + report = _report( + tables={ + "person": _table(value_mismatch_rows_by_column={"employment_income": 12}) + } + ) + + result = CLASSIFIER.classify_payload_diff(report, _expectation()) + + assert result["ok"] is True + assert result["unexpected"] == [] + assert result["classified_table"] == [ + { + "entity": "person", + "column": "employment_income", + "surface": "value", + "detail": 12, + "classification": "expected_changed", + "declared_expectation": "expected_changed", + "scope": "SPI rows only", + "reason": "age-conditioned QRF", + "declared_surfaces": None, + } + ] + + +def test_unlisted_and_expected_byte_equal_differences_are_unexpected() -> None: + report = _report( + tables={ + "person": _table( + value_mismatch_rows_by_column={"age": "< 10", "gender": 14} + ) + } + ) + + result = CLASSIFIER.classify_payload_diff(report, _expectation()) + + assert result["ok"] is False + assert [row["column"] for row in result["unexpected"]] == ["age", "gender"] + assert result["unexpected"][0]["declared_expectation"] == "expected_byte_equal" + assert result["unexpected"][1]["declared_expectation"] == ( + "expected_byte_equal (default)" + ) + + +def test_unequal_row_counts_fail_closed_because_columns_go_unobserved() -> None: + # The comparator compares column values only when row counts agree, so an + # unequal table means the column-level contract was never observed: the + # slices must be donor-excluded and row-aligned before classification. + report = _report( + tables={ + "person": _table( + rows={"left": 10, "right": 12}, + row_count_equal=False, + index_values_equal=False, + ) + } + ) + + result = CLASSIFIER.classify_payload_diff(report, _expectation()) + + assert result["ok"] is False + assert [row["column"] for row in result["unexpected"]] == [ + "__row_count__", + "__index_values__", + ] + assert result["expected_changed_not_observed"] == ["person.employment_income"] + + +def test_declared_surfaces_license_only_those_surfaces() -> None: + # A dtype-only expectation (person.age under #845) must not license a + # value change on the same column. + declared = _expectation() + declared["expected_byte_equal"] = [] + declared["expected_changed"].append( + { + "entity": "person", + "columns": ["age"], + "scope": "dtype only", + "reason": "int64 replaces float64", + "surfaces": ["dtype"], + } + ) + dtype_only = _report( + tables={ + "person": _table( + dtype_mismatches={"age": {"left": "float64", "right": "int64"}} + ) + } + ) + with_values = _report( + tables={ + "person": _table( + dtype_mismatches={"age": {"left": "float64", "right": "int64"}}, + value_mismatch_rows_by_column={"age": 12}, + ) + } + ) + + ok = CLASSIFIER.classify_payload_diff(dtype_only, declared) + bad = CLASSIFIER.classify_payload_diff(with_values, declared) + + assert ok["ok"] is True + assert ok["classified_table"][0]["declared_surfaces"] == ["dtype"] + assert bad["ok"] is False + assert [ + (row["surface"], row["classification"]) for row in bad["classified_table"] + ] == [ + ("dtype", "expected_changed"), + ("value", "unexpected"), + ] + + +def test_surfaces_are_validated() -> None: + declared = _expectation() + declared["expected_changed"][0]["surfaces"] = [] + with pytest.raises(ValueError, match="surfaces is invalid"): + CLASSIFIER.classify_payload_diff(_report(tables={"person": _table()}), declared) + declared = _expectation() + declared["expected_byte_equal"][0]["surfaces"] = ["value"] + with pytest.raises(ValueError, match="only applies to expected_changed"): + CLASSIFIER.classify_payload_diff(_report(tables={"person": _table()}), declared) + + +def test_structural_surfaces_cannot_be_declared_expected_changed() -> None: + declared = _expectation() + declared["expected_changed"].append( + { + "entity": "person", + "columns": ["__row_count__"], + "scope": "CGT donor rows", + "reason": "donor fan-out", + } + ) + + with pytest.raises(ValueError, match="structural surfaces cannot"): + CLASSIFIER.classify_payload_diff(_report(tables={"person": _table()}), declared) + + +def test_unexpected_structure_and_root_attribute_fail_closed() -> None: + report = _report(tables={"person": _table(column_order_equal=False)}) + report["root_attrs"]["attrs_with_differing_values"] = ["mystery"] + + result = CLASSIFIER.classify_payload_diff(report, _expectation()) + + assert result["ok"] is False + assert {(row["entity"], row["column"]) for row in result["unexpected"]} == { + ("person", "__column_order__"), + ("__root__", "mystery"), + } + + +def test_cli_returns_one_and_writes_classified_report( + tmp_path: Path, capsys: pytest.CaptureFixture[str] +) -> None: + report_path = tmp_path / "payload.json" + expectation_path = tmp_path / "expectation.json" + output_path = tmp_path / "classified.json" + report_path.write_text( + json.dumps( + _report( + tables={"person": _table(value_mismatch_rows_by_column={"gender": 11})} + ) + ), + encoding="utf-8", + ) + expectation_path.write_text(json.dumps(_expectation()), encoding="utf-8") + + exit_code = CLASSIFIER.main( + [ + str(report_path), + str(expectation_path), + "--json-out", + str(output_path), + ] + ) + captured = capsys.readouterr() + + assert exit_code == 1 + assert "UNEXPECTED: 1" in captured.err + assert json.loads(captured.out)["ok"] is False + assert json.loads(output_path.read_text(encoding="utf-8"))["ok"] is False + + +def test_malformed_or_duplicate_expectations_are_refused() -> None: + duplicate = _expectation() + duplicate["expected_changed"].append( + { + "entity": "person", + "columns": ["age"], + "scope": "some rows", + "reason": "contradiction", + } + ) + + with pytest.raises(ValueError, match="duplicate expectation for person.age"): + CLASSIFIER.classify_payload_diff( + _report(tables={"person": _table()}), duplicate + ) diff --git a/packages/microcosm-build/tests/test_uk_source_stages.py b/packages/microcosm-build/tests/test_uk_source_stages.py index 67c4bf59b..af1457d27 100644 --- a/packages/microcosm-build/tests/test_uk_source_stages.py +++ b/packages/microcosm-build/tests/test_uk_source_stages.py @@ -28,6 +28,9 @@ "frs_legacy_proxies", "frs_education_grant_split", ] +POST_FRS_SPINE_STAGE_NAMES = [ + "age_tail", +] E4_STAGE_NAMES = [ "frs_take_up", "frs_person_draws", @@ -61,15 +64,9 @@ "salary_sacrifice", "student_loans", ] -# Spine stages that land after the E8 block. `age_tail` rewrites `age` and so -# must run downstream of every stage that conditions on it — student_loans -# reads it for cohort start years, the CGT stages for the adult carrier — which -# is the position the #623 calibration campaign exercised. -POST_E8_STAGE_NAMES = [ - "age_tail", -] UK_SOURCE_STAGE_NAMES = [ "frs_spine", + *POST_FRS_SPINE_STAGE_NAMES, *E3_STAGE_NAMES, *E4_STAGE_NAMES, *E5_STAGE_NAMES, @@ -78,7 +75,6 @@ *UC_REPORTER_REDRAW_STAGE_NAMES, *UC_COHERENCE_STAGE_NAMES, *E8_STAGE_NAMES, - *POST_E8_STAGE_NAMES, "frs_hmrc_retained_leaves", "hmrc_spi_income", ] @@ -168,12 +164,28 @@ def test_e7_block_sits_between_e6_and_e8(self) -> None: ] ) - def test_e8_block_is_contiguous_and_the_certified_pair_stays_last(self) -> None: - # Two invariants, and only two: the E8 stages stay contiguous, and the + def test_age_tail_runs_immediately_after_frs_spine(self) -> None: + canonical = _load_json(CANONICAL_SOURCE_STAGES) + spine = [stage["stage"] for stage in canonical["stages"][:-2]] + + assert spine[1] == "age_tail" + + def test_age_tail_position_owns_the_only_later_age_rewrite_guard(self) -> None: + manifest = SourceManifest.from_mapping(_load_json(CANONICAL_SOURCE_STAGES)) + stages = list(manifest.stages) + names = [stage.stage for stage in stages] + age_tail_index = names.index("age_tail") + + assert age_tail_index == names.index("frs_spine") + 1 + for stage in stages[age_tail_index + 1 :]: + assert "age" not in stage.outputs, stage.stage + assert "age" not in stage.rewrites, stage.stage + + def test_e8_block_is_final_and_the_certified_pair_stays_last(self) -> None: + # The E8 stages stay contiguous at the end of the spine, while the # certified pair stays at [-2:] (the frozen-copy lockstep test reads - # them from there). E8 being the *final* spine block was an artifact - # of it having been the last increment — the spine may grow a tail - # after it, as `age_tail` does, without either invariant moving. + # them from there). age_tail is now the post-frs_spine block, before + # every stage that conditions on age. canonical = _load_json(CANONICAL_SOURCE_STAGES) names = [stage["stage"] for stage in canonical["stages"]] @@ -181,7 +193,7 @@ def test_e8_block_is_contiguous_and_the_certified_pair_stays_last(self) -> None: spine = names[:-2] start = spine.index(E8_STAGE_NAMES[0]) assert spine[start : start + len(E8_STAGE_NAMES)] == E8_STAGE_NAMES - assert spine[start + len(E8_STAGE_NAMES) :] == POST_E8_STAGE_NAMES + assert spine[start + len(E8_STAGE_NAMES) :] == [] def test_copy_is_lockstep_with_frozen_original_except_citation_rewrites( self, diff --git a/packages/microcosm-build/tests/test_uk_spine_acceptance_receipt.py b/packages/microcosm-build/tests/test_uk_spine_acceptance_receipt.py index 23be9019e..5c840cd49 100644 --- a/packages/microcosm-build/tests/test_uk_spine_acceptance_receipt.py +++ b/packages/microcosm-build/tests/test_uk_spine_acceptance_receipt.py @@ -2,10 +2,10 @@ microcosm#771: the previous acceptance evidence quietly described a 24-stage build after the plan had grown to 25. This binder makes that class of drift a -CI failure. The #828 and #832 stages are deliberately pending the licensed I5 -rebuild, so the historical receipt stays truthful while the test pins the two -reviewed roster differences from the current driver. I5 restores strict roster -equality when it re-mints the receipt. +CI failure. The #828/#832 insertions and #785 reorder are deliberately pending +their licensed re-mints, so the historical receipt stays truthful while the +test composes only those reviewed transformations. Each transformation pins +the receipt state it expects and must be deleted when that re-mint lands. """ from __future__ import annotations @@ -41,16 +41,33 @@ def _production_graph_stage_names() -> tuple[str, ...]: return tuple(node_id for node_id in compiled.order if node_id in declared) +def _apply_pending_roster_transformations( + accepted_roster: tuple[str, ...], +) -> tuple[str, ...]: + roster = list(accepted_roster) + + # #832 I5: both UC insertions are one pending receipt transformation. + assert "uc_reporter_redraw" not in roster + assert "uc_capital_coherence" not in roster + cgt_index = roster.index("cgt_incidence_clone") + roster[cgt_index:cgt_index] = [ + "uc_reporter_redraw", + "uc_capital_coherence", + ] + + # #785 L3: the historical receipt still has age_tail at the spine tail. + assert roster[-1] == "age_tail" + roster.remove("age_tail") + roster.insert(1, "age_tail") + return tuple(roster) + + def test_receipt_roster_is_the_production_plan(): receipt = _receipt() accepted_roster = tuple(receipt["candidate"]["stage_roster"]) production_roster = _production_graph_stage_names() - expected_extra_stages = {"uc_capital_coherence", "uc_reporter_redraw"} - assert set(production_roster) - set(accepted_roster) == expected_extra_stages - assert tuple( - stage for stage in production_roster if stage not in expected_extra_stages - ) == accepted_roster + assert _apply_pending_roster_transformations(accepted_roster) == production_roster assert receipt["candidate"]["stage_count"] == len(accepted_roster) @@ -64,6 +81,9 @@ def test_receipt_identity_and_verdicts_are_the_accepted_ones(): for check, row in ladder.items(): assert row["identical_under_permutation"] is True, check assert row["matches_stored_columns"] is True, check + # The historical spine-i receipt remains truthful. The #785 L3 re-mint + # flips both fields to stage_time_disaggregated and deletes the matching + # pending roster transformation above. assert ladder["e6"]["nhs_age_basis"] == "stage_time_top_coded" assert ladder["e8"]["donor_age_basis"] == "stage_time_top_coded" parity = receipt["strict_parity"] diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 91376f846..9aadac7ad 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -375,13 +375,13 @@ # fingerprint derives from the manifest digest. Editing the spec moves all # three here in the same reviewed change. _UK_GATE_BATTERY_POLICY_SHA256 = ( - "bcbcfd552424313b0843bc68ad64afea04c3b4146b01458cc9fdaf54b85aea82" + "6916c207d1b32c20d372f9555374e1968298712f28163e9109020e659a05bbb3" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "76c861d48d48d73fd8f18f3d5cddac2855294c2839105ca2b6ac9a8a9d4b5f2f" + "6f4566944b2e68b11c1dc6faa74cdcdcda998ca259b621440e1853b17501517f" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "dfbad2aa2930ef7e615742bd43bccd6cadaeb77ceb46f9161bb81b715dda20aa" + "e31fe1f929d9fc7c2f0da9c4ca7650fc2f3350e74da379a09d6b4c31f931ef47" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -446,7 +446,7 @@ "transferred", ), "uk_stage_student_loans_realization": ("stage_health", "transferred"), - "uk_stage_age_tail_targets": ("stage_health", "transferred"), + "uk_stage_age_tail_targets": ("stage_health", "assembled"), "uk_ledger_compile_parity_local_incumbent_2025": ( "ledger_compile_parity", "preflight", @@ -643,10 +643,10 @@ _UK_CERTIFICATION_PART_DIGESTS: Mapping[str, Mapping[str, str]] = { "spine": { "gates_manifest_sha256": ( - "1605cf3fe1be4983cfb4ed806a34d69375cdc3e4e0c8883cc49481ac5870399a" + "6947a30a35a875794c83eddc052b31c841a106807653084facd6fb5a7340c124" ), "policy_sha256": ( - "3d14ad24eff7f5afd343164560db24095d27fafb36c619ddf725c32e00b35a69" + "6ae3b9ba8d24184dfbf8c004209ab1810b2f1b9616d8e8512aef55bd27cdfacd" ), }, "calibration_seam": { diff --git a/packages/microcosm-data/tests/test_contract.py b/packages/microcosm-data/tests/test_contract.py index 9c536c332..0e29bfb3a 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 = ( - "bcbcfd552424313b0843bc68ad64afea04c3b4146b01458cc9fdaf54b85aea82" + "6916c207d1b32c20d372f9555374e1968298712f28163e9109020e659a05bbb3" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "76c861d48d48d73fd8f18f3d5cddac2855294c2839105ca2b6ac9a8a9d4b5f2f" + "6f4566944b2e68b11c1dc6faa74cdcdcda998ca259b621440e1853b17501517f" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "dfbad2aa2930ef7e615742bd43bccd6cadaeb77ceb46f9161bb81b715dda20aa" + "e31fe1f929d9fc7c2f0da9c4ca7650fc2f3350e74da379a09d6b4c31f931ef47" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "d0d024043132fa07c378c393dbe2b24fe99bf19e876bcc39997d2c80cc9bd4f6" @@ -213,7 +213,7 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: "transferred", None, ), - "uk_stage_age_tail_targets": ("stage_health", "transferred", None), + "uk_stage_age_tail_targets": ("stage_health", "assembled", None), "uk_ledger_compile_parity_local_incumbent_2025": ( "ledger_compile_parity", "preflight", 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 6688f27b8..318a9c731 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 @@ -38,7 +38,7 @@ ], "grain": "person", "nonnegative_outputs": [], - "notes": "The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_source_id so a household and its capital-gains clone twin receive the same age. Runs after student_loans \u2014 downstream of every stage that conditions on age, the position the", + "notes": "The licensed FRS records no age above 80, so every 80+ person arrives piled at exactly 80 and the 85-89 and 90+ population targets are structurally unbindable; the incumbent shares the defect. Each piled base person draws a band from the sex-specific ONS inverse CDF and a uniform integer age within it, keyed on person_id. Runs immediately after frs_spine, before every stage that conditions on age, so all age-conditioned imputations derive from the disaggregated surface. The stage precedes person_source_id provenance; later support and capital-gains clones copy the donor's rewritten age, preserving the #623 final-age identity invariant. Ages are assigned toward the ONS distribution; calibration still owns the totals.", "operations": [ { "bands": [ @@ -58,7 +58,7 @@ "width": 8 } ], - "draw_key": "person_source_id", + "draw_key": "person_id", "kind": "disaggregate_top_coded_ages", "output": "age", "reason": "Age disaggregation rewrites an existing person column in place; no rows move, no weights change, and total household mass is conserved.", @@ -241,7 +241,7 @@ "nhs_admitted_patient_spending", "nhs_outpatient_spending" ], - "notes": "Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age-band and 85+ fold-in fixes. The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage.", + "notes": "Ports ETB public-services QRF at household grain, computes rail_usage from the 2023 fare index, and allocates NHS visits/spending to persons using the signed half-open age bands with the donor table's native 85-89, 90-94 and 95+ top bands (the former 85+ fold-in was retired once age_tail moved ahead of this stage and recipients could populate those bands). The year-max donor filter resolves to 2023 on the pinned tab (the file labels financial year ending 2024 as year 2023), so the services training year coincides with the VAT training year and the fare-index year - the incumbent's apparent three-way year mismatch is vacuous on this vintage.", "operations": [ { "annualization_weeks": 52, @@ -303,7 +303,7 @@ "budget_resource": "etb_services_anchors.json", "kind": "allocate_per_capita_from_cell_table", "resource": "nhs_consumption_by_age_gender.json", - "top_band_fold_in": "85+" + "top_band_fold_in": "none" } ], "outputs": [ 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 bb34c7d3b..3486c157c 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 @@ -30,7 +30,7 @@ "__stratum__": "string", "access_fund": "float64", "afcs_reported": "float64", - "age": "float64", + "age": "int64", "attendance_allowance_reported": "float64", "bsp_reported": "float64", "carers_allowance_reported": "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 3b9bcb921..7bb66916b 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 @@ -{"country":"uk","nodes":[{"base":null,"citation":"","description":"Load the source-bound UK FRS root 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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/classify_uk_payload_diff.py b/tools/classify_uk_payload_diff.py new file mode 100644 index 000000000..693e167a2 --- /dev/null +++ b/tools/classify_uk_payload_diff.py @@ -0,0 +1,389 @@ +"""Classify a UK H5 payload report against the #785 blast-radius contract. + +The input report is the JSON emitted by ``compare_uk_h5_payload.py``. This +tool preserves that report's disclosure posture: it reads only structural +booleans, names, dtypes, and already-masked counts. It never opens either H5. + +Every unlisted difference defaults to ``expected_byte_equal`` and is emitted +as ``unexpected``. Expected scopes are carried into the classified table for +the licensed L2 slice checks; the comparator intentionally does not disclose +row identities, so this tool cannot prove a row-domain claim by itself. + +The contract is defined on the donor-excluded, row-aligned slice of the two +artifacts (every household flagged ``household_is_cgt_band_donor`` dropped +with its benefit units and persons on both sides). The comparator only +compares column values when a table's row counts agree, so a structural +surface (``__row_count__``, ``__index_values__``, ...) can never be declared +``expected_changed``: an unequal table is an unexpected difference, because it +means the column-level contract could not be observed at all. The reselected +donor set and its entity-count deltas are reported by Receipt 1, separately. + +An ``expected_changed`` group may name the ``surfaces`` it expects to move +(``value``, ``dtype``, ``column_only_left``, ...). A difference on any other +surface of those columns is still ``unexpected``: declaring a dtype change +does not license a value change. + +Exit code: 0 when no unexpected difference is present, 1 when at least one is +present, and 2 when either JSON input is unreadable or fails schema checks. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from collections.abc import Mapping +from pathlib import Path +from typing import Any + +_EXPECTATIONS = frozenset({"expected_byte_equal", "expected_changed"}) + + +def _require_mapping(value: object, *, label: str) -> Mapping[str, Any]: + if not isinstance(value, Mapping): + raise ValueError(f"{label} must be an object.") + return value + + +def _expectation_index( + payload: Mapping[str, Any], +) -> dict[tuple[str, str], dict[str, str]]: + if payload.get("schema_version") != 1: + raise ValueError("expectation schema_version must be 1.") + default = payload.get("default_expectation") + if default != "expected_byte_equal": + raise ValueError("default_expectation must be 'expected_byte_equal'.") + + index: dict[tuple[str, str], dict[str, str]] = {} + for expectation in _EXPECTATIONS: + groups = payload.get(expectation) + if not isinstance(groups, list): + raise ValueError(f"{expectation} must be an array.") + for ordinal, raw_group in enumerate(groups): + group = _require_mapping(raw_group, label=f"{expectation}[{ordinal}]") + entity = group.get("entity") + columns = group.get("columns") + scope = group.get("scope") + reason = group.get("reason") + surfaces = group.get("surfaces") + if surfaces is not None and ( + not isinstance(surfaces, list) + or not surfaces + or any( + not isinstance(surface, str) or not surface for surface in surfaces + ) + ): + raise ValueError(f"{expectation}[{ordinal}].surfaces is invalid.") + if surfaces is not None and expectation != "expected_changed": + raise ValueError( + f"{expectation}[{ordinal}].surfaces only applies to expected_changed." + ) + if not isinstance(entity, str) or not entity: + raise ValueError(f"{expectation}[{ordinal}].entity is invalid.") + if ( + not isinstance(columns, list) + or not columns + or any(not isinstance(column, str) or not column for column in columns) + ): + raise ValueError(f"{expectation}[{ordinal}].columns is invalid.") + if not isinstance(scope, str) or not scope: + raise ValueError(f"{expectation}[{ordinal}].scope is invalid.") + if not isinstance(reason, str) or not reason: + raise ValueError(f"{expectation}[{ordinal}].reason is invalid.") + for column in columns: + key = (entity, column) + if key in index: + raise ValueError(f"duplicate expectation for {entity}.{column}.") + if expectation == "expected_changed" and column.startswith("__"): + raise ValueError( + f"{entity}.{column}: structural surfaces cannot be " + "expected_changed; compare the donor-excluded, " + "row-aligned slices instead." + ) + index[key] = { + "expectation": expectation, + "scope": scope, + "reason": reason, + "surfaces": None if surfaces is None else tuple(surfaces), + } + return index + + +def _table_difference_rows(report: Mapping[str, Any]) -> list[dict[str, Any]]: + tables = report.get("tables") + if not isinstance(tables, Mapping): + raise ValueError("payload report tables must be an object.") + rows: list[dict[str, Any]] = [] + for entity, raw_table in tables.items(): + if not isinstance(entity, str): + raise ValueError("payload report table names must be strings.") + table = _require_mapping(raw_table, label=f"tables.{entity}") + structural_fields = { + "row_count_equal": "__row_count__", + "column_order_equal": "__column_order__", + "stored_kind_equal": "__stored_kind__", + "index_type_equal": "__index_type__", + "index_dtype_equal": "__index_dtype__", + "index_name_equal": "__index_name__", + "index_values_equal": "__index_values__", + } + for field, column in structural_fields.items(): + equal = table.get(field) + if not isinstance(equal, bool): + raise ValueError(f"tables.{entity}.{field} must be boolean.") + if not equal: + detail: object = ( + table.get("rows") if field == "row_count_equal" else None + ) + rows.append( + { + "entity": entity, + "column": column, + "surface": field.removesuffix("_equal"), + "detail": detail, + } + ) + + for side in ("left", "right"): + field = f"columns_only_{side}" + columns = table.get(field) + if not isinstance(columns, list) or any( + not isinstance(column, str) for column in columns + ): + raise ValueError(f"tables.{entity}.{field} must be a string array.") + rows.extend( + { + "entity": entity, + "column": column, + "surface": f"column_only_{side}", + "detail": None, + } + for column in columns + ) + + dtype_mismatches = table.get("dtype_mismatches") + if not isinstance(dtype_mismatches, Mapping): + raise ValueError(f"tables.{entity}.dtype_mismatches must be an object.") + rows.extend( + { + "entity": entity, + "column": str(column), + "surface": "dtype", + "detail": mismatch, + } + for column, mismatch in sorted(dtype_mismatches.items()) + ) + + value_mismatches = table.get("value_mismatch_rows_by_column") + if not isinstance(value_mismatches, Mapping): + raise ValueError( + f"tables.{entity}.value_mismatch_rows_by_column must be an object." + ) + rows.extend( + { + "entity": entity, + "column": str(column), + "surface": "value", + "detail": count, + } + for column, count in sorted(value_mismatches.items()) + ) + return rows + + +def _artifact_difference_rows(report: Mapping[str, Any]) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + keys_equal = report.get("keys_equal") + if not isinstance(keys_equal, bool): + raise ValueError("payload report keys_equal must be boolean.") + if not keys_equal: + rows.append( + { + "entity": "__artifact__", + "column": "__store_key_order__", + "surface": "store_key_order", + "detail": None, + } + ) + for side in ("left", "right"): + field = f"keys_only_{side}" + keys = report.get(field) + if not isinstance(keys, list) or any(not isinstance(key, str) for key in keys): + raise ValueError(f"payload report {field} must be a string array.") + rows.extend( + { + "entity": "__artifact__", + "column": key, + "surface": f"store_key_only_{side}", + "detail": None, + } + for key in keys + ) + + attrs = _require_mapping(report.get("root_attrs"), label="root_attrs") + names_equal = attrs.get("names_in_order_equal") + if not isinstance(names_equal, bool): + raise ValueError("root_attrs.names_in_order_equal must be boolean.") + if not names_equal: + rows.append( + { + "entity": "__root__", + "column": "__attribute_name_order__", + "surface": "root_attribute_name_order", + "detail": None, + } + ) + for side in ("left", "right"): + field = f"attrs_only_{side}" + names = attrs.get(field) + if not isinstance(names, list) or any( + not isinstance(name, str) for name in names + ): + raise ValueError(f"root_attrs.{field} must be a string array.") + rows.extend( + { + "entity": "__root__", + "column": name, + "surface": f"root_attribute_only_{side}", + "detail": None, + } + for name in names + ) + differing = attrs.get("attrs_with_differing_values") + if not isinstance(differing, list) or any( + not isinstance(name, str) for name in differing + ): + raise ValueError( + "root_attrs.attrs_with_differing_values must be a string array." + ) + rows.extend( + { + "entity": "__root__", + "column": name, + "surface": "root_attribute_value", + "detail": None, + } + for name in differing + ) + return rows + + +def classify_payload_diff( + report: Mapping[str, Any], expectation: Mapping[str, Any] +) -> dict[str, Any]: + """Return a disclosure-safe classified table and fail-closed summary.""" + + index = _expectation_index(expectation) + differences = [ + *_artifact_difference_rows(report), + *_table_difference_rows(report), + ] + classified: list[dict[str, Any]] = [] + observed_expected_changed: set[tuple[str, str]] = set() + for difference in differences: + key = (difference["entity"], difference["column"]) + declared = index.get(key) + surfaces = None if declared is None else declared["surfaces"] + if ( + declared is not None + and declared["expectation"] == "expected_changed" + and (surfaces is None or difference["surface"] in surfaces) + ): + classification = "expected_changed" + observed_expected_changed.add(key) + else: + classification = "unexpected" + classified.append( + { + **difference, + "classification": classification, + "declared_expectation": ( + declared["expectation"] + if declared is not None + else "expected_byte_equal (default)" + ), + "scope": declared["scope"] if declared is not None else "all rows", + "reason": ( + declared["reason"] + if declared is not None + else "No expected-changed entry names this surface." + ), + "declared_surfaces": None if surfaces is None else list(surfaces), + } + ) + + expected_not_observed = sorted( + f"{entity}.{column}" + for (entity, column), declared in index.items() + if declared["expectation"] == "expected_changed" + and (entity, column) not in observed_expected_changed + ) + unexpected = [row for row in classified if row["classification"] == "unexpected"] + return { + "schema_version": 1, + "left": report.get("left"), + "left_sha256": report.get("left_sha256"), + "right": report.get("right"), + "right_sha256": report.get("right_sha256"), + "classified_table": classified, + "expected_changed_not_observed": expected_not_observed, + "unexpected": unexpected, + "summary": { + "observed_differences": len(classified), + "expected_changed": len(classified) - len(unexpected), + "unexpected": len(unexpected), + "expected_changed_not_observed": len(expected_not_observed), + }, + "ok": not unexpected, + } + + +def _parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=( + "Classify compare_uk_h5_payload.py JSON against a committed " + "expected-byte-equal / expected-changed contract." + ) + ) + parser.add_argument("payload_report", type=Path) + parser.add_argument("expectation", type=Path) + parser.add_argument("--json-out", type=Path, default=None) + return parser + + +def _load_json(path: Path) -> Mapping[str, Any]: + return _require_mapping( + json.loads(path.read_text(encoding="utf-8")), label=str(path) + ) + + +def main(argv: list[str] | None = None) -> int: + args = _parser().parse_args(argv) + try: + classified = classify_payload_diff( + _load_json(args.payload_report), _load_json(args.expectation) + ) + rendered = json.dumps(classified, indent=2, sort_keys=True, allow_nan=False) + except Exception: + print( + "error: payload classification could not be completed; verify both " + "JSON inputs against their committed schemas.", + file=sys.stderr, + ) + return 2 + + print(rendered) + if args.json_out is not None: + args.json_out.write_text(rendered + "\n", encoding="utf-8") + if classified["ok"]: + return 0 + print( + f"UNEXPECTED: {classified['summary']['unexpected']} payload " + "difference(s) fall outside the committed expectation.", + file=sys.stderr, + ) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index 67634ffb6..682a8bc71 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -996,7 +996,19 @@ def _build_implementations( def capture_root(frame: Frame) -> Frame: result = root_transform(frame) - root_capture["frame"] = result + # Snapshot the root: the next legacy stage (age_tail) rewrites the + # person table in place, so an aliased capture would carry its ages. + root_capture["frame"] = Frame( + { + entity: result.table(entity).copy(deep=True) + for entity in result.entities + }, + result.schema, + {entity: result.weights_for(entity) for entity in result.weighted_entities}, + result.strata.copy(deep=True), + mass_log=result.mass_log, + metadata=dict(result.metadata), + ) return result implementations: dict[str, object] = { diff --git a/tools/uk_age_tail_reorder_payload_expectation.json b/tools/uk_age_tail_reorder_payload_expectation.json new file mode 100644 index 000000000..641d97fe5 --- /dev/null +++ b/tools/uk_age_tail_reorder_payload_expectation.json @@ -0,0 +1,147 @@ +{ + "schema_version": 1, + "scope_note": "#785 spine-m to spine-n payload contract, defined on the donor-excluded, row-aligned slice: drop every household flagged household_is_cgt_band_donor (with its benefit units and persons) from both artifacts before running compare_uk_h5_payload.py, so every table's row counts agree and column values are compared position-wise. Any unlisted value, dtype, structural, store-key, or root-attribute difference is expected_byte_equal and therefore unexpected. The reselected CGT donor set and its entity-count deltas are Receipt 1's separate report. The before-artifact must be graph-executed (the post-#835 build of main): the legacy-executed spine-m differs from any graph build in executor normalizations (text columns as StringDtype) that are not this change's. Scoped row-domain claims still require the companion L2 slice checks because compare_uk_h5_payload.py discloses counts, not unit-row identities.", + "default_expectation": "expected_byte_equal", + "expected_byte_equal": [ + { + "entity": "household", + "columns": [ + "__row_count__" + ], + "scope": "whole artifact", + "reason": "CGT donor selection changes person and benunit fan-out, but the household count remains pinned." + } + ], + "expected_changed": [ + { + "entity": "person", + "columns": [ + "age" + ], + "surfaces": [ + "dtype" + ], + "scope": "dtype only: int64 replaces float64 on every row (#845); the values are byte-identical, proven by Receipt 1", + "reason": "age is declared int64 from the root and age_tail rewrites it as int64; the reorder itself must not move a single age value." + }, + { + "entity": "person", + "columns": [ + "a_and_e_visits", + "admitted_patient_visits", + "outpatient_visits", + "nhs_a_and_e_spending", + "nhs_admitted_patient_spending", + "nhs_outpatient_spending" + ], + "scope": "persons originating in the FRS age-80 pile, including later whole-table clones", + "reason": "ETB services now prices NHS use on the disaggregated stage-time age surface." + }, + { + "entity": "person", + "columns": [ + "employment_income", + "self_employment_income", + "savings_interest_income", + "dividend_income", + "private_pension_income", + "property_income", + "other_investment_income", + "gift_aid", + "charitable_investment_gifts", + "hmrc_spi_pay", + "hmrc_spi_employment_benefits", + "hmrc_spi_employment_expenses", + "hmrc_spi_incapacity_benefit_income", + "hmrc_spi_other_social_security_income", + "hmrc_spi_taxable_termination_pay", + "hmrc_spi_unemployment_benefit_income", + "hmrc_spi_miscellaneous_employment_income", + "hmrc_spi_other_income", + "hmrc_spi_state_pension_income", + "hmrc_spi_employed_income", + "hmrc_spi_total_earned_income", + "hmrc_spi_total_investment_income", + "hmrc_spi_assessable_income" + ], + "scope": "stage-1 SPI recipient rows whose stage-time age moved out of the age-80 pile, plus base rows in the pile for the dividend_income base redraw", + "reason": "SPI stage 1 is recipient-row-local and age-conditioned." + }, + { + "entity": "person", + "columns": [ + "personal_pension_contributions", + "employer_pension_contributions", + "tax_free_savings_income", + "pension_credit_reported", + "child_benefit_reported", + "housing_benefit_reported", + "income_support_reported", + "working_tax_credit_reported", + "child_tax_credit_reported", + "attendance_allowance_reported", + "state_pension_reported", + "dla_sc_reported", + "dla_m_reported", + "pip_m_reported", + "pip_dl_reported", + "sda_reported", + "carers_allowance_reported", + "iidb_reported", + "afcs_reported", + "bsp_reported", + "winter_fuel_allowance_reported", + "council_tax_benefit_reported", + "jsa_contrib_reported", + "jsa_income_reported", + "esa_contrib_reported", + "esa_income_reported", + "aa_category", + "dla_sc_category", + "dla_m_category", + "pip_m_category", + "pip_dl_category", + "is_disabled_for_benefits", + "is_enhanced_disabled_for_benefits", + "is_severely_disabled_for_benefits" + ], + "scope": "SPI support-channel persons; the stage-2 model is retrained on the disaggregated base-FRS age surface and the SPI disability refresh follows the refilled reported amounts", + "reason": "The joint stage-2 QRF and its disability refresh may move across the full SPI recipient channel." + }, + { + "entity": "person", + "columns": [ + "universal_credit_reported" + ], + "scope": "SPI support-channel persons only; base-channel rows remain byte-equal", + "reason": "The stage-2 fill changes and the stage-19 benefit-unit reporter redraw consumes those changed incomes." + }, + { + "entity": "person", + "columns": [ + "employee_pension_contributions", + "pension_contributions_via_salary_sacrifice" + ], + "scope": "frame-wide target rows and the scalar-coupled donor conversion set", + "reason": "Salary sacrifice trains on age and couples its donor conversion rate globally." + }, + { + "entity": "benunit", + "columns": [ + "frs_benunit_capital", + "uc_reported_capital", + "would_claim_uc" + ], + "scope": "SPI post-fill reporter benefit units only", + "reason": "UC capital coherence follows the changed stage-19 SPI reporter set." + }, + { + "entity": "person", + "columns": [ + "capital_gains" + ], + "scope": "CGT incidence carriers whose oldest-adult tie moved, and every gainer downstream of the reshuffled gains-stage draw stream", + "reason": "Disaggregated oldest-adult ages change carrier ties, and the positional gains-stage stream reshuffles behind them." + } + ] +} diff --git a/tools/verify_uk_identity_stability.py b/tools/verify_uk_identity_stability.py index 2b9246aa9..cb22153ba 100644 --- a/tools/verify_uk_identity_stability.py +++ b/tools/verify_uk_identity_stability.py @@ -17,7 +17,6 @@ import numpy as np import pandas as pd -from microcosm.build.uk_runtime.age_tail import UK_AGE_TOP_CODE from microcosm.build.uk_runtime.frs_brma import ( UK_BRMA_DECLARED_SEEDS, _benunit_regions, @@ -341,10 +340,10 @@ def e6_identity_receipt( Covered: the domestic-energy fold (elec + gas), the rail_usage ratio, petrol/diesel zeroing idempotence for non-fuel households, and the NHS age-gender person allocation recomputed from the committed resource. - The production contract is stage-time-age-derived: ``etb_services`` runs - on the top-coded FRS age surface, then ``age_tail`` disaggregates the - top-code later as calibration support. Stored NHS columns are therefore - checked against ``min(final_age, UK_AGE_TOP_CODE)`` rather than final age. + The production contract is stage-time-age-derived: ``age_tail`` now runs + immediately after ``frs_spine``, so ``etb_services`` allocates NHS use on + the disaggregated age surface. Stored NHS columns are therefore checked + against final age without reconstructing the former top code. The QRF chain draws and the NEED raking outcome are covered by twin-build determinism and the aggregate_admin NEED-margin receipt respectively (raking inputs are consumed by the stage and are not @@ -384,11 +383,10 @@ def recompute(person_t, benunit_t, household_t) -> dict[str, pd.DataFrame]: ) if {"age", "gender"} <= set(person_t.columns): nhs_person = person_t.copy() - nhs_person["age"] = np.minimum( + nhs_person["age"] = ( pd.to_numeric(nhs_person["age"], errors="coerce") .fillna(0) - .to_numpy(dtype=float), - UK_AGE_TOP_CODE, + .to_numpy(dtype=float) ) nhs = allocate_nhs_by_age_gender( nhs_person, @@ -472,8 +470,7 @@ def recompute(person_t, benunit_t, household_t) -> dict[str, pd.DataFrame]: return { "check": "uk_e6_identity_stability", "permutation_seed": permutation_seed, - "nhs_age_basis": "stage_time_top_coded", - "nhs_age_top_code": UK_AGE_TOP_CODE, + "nhs_age_basis": "stage_time_disaggregated", "identical_under_permutation": not mismatches, "permutation_mismatches": mismatches, "matches_stored_columns": not stored_mismatches, @@ -726,21 +723,15 @@ def e8_identity_receipt( problems["clone_half_masses"] = [float(left.sum()), float(right.sum())] # (2) Band-donor selection recomputed from the committed resources. - # Same contract as the E6 NHS check: the donor stage ran on the top-coded - # FRS age surface, four stages before age_tail disaggregated it, so its - # oldest-adult carriers are stage-time-age-derived. Recompute on - # min(age, top_code) — exact, because age_tail refuses inputs above the - # top code and rewrites only persons at exactly it, upward. + # Same contract as the E6 NHS check: age_tail now runs immediately after + # frs_spine, so the donor stage selected its oldest-adult carriers from + # the disaggregated age surface stored in the artifact. distribution = load_advani_summers_distribution() bands = _retained_size_bands(load_hmrc_cgt_size_bands()) non_donor_ids = set(non_donor["household_id"].tolist()) nd_person = person.loc[ person["person_household_id"].isin(non_donor_ids) ].reset_index(drop=True) - nd_person["age"] = np.minimum( - pd.to_numeric(nd_person["age"], errors="coerce").to_numpy(dtype=float), - float(UK_AGE_TOP_CODE), - ) nd_benunit = benunit.loc[ benunit["benunit_id"].isin(set(nd_person["person_benunit_id"].tolist())) ].reset_index(drop=True) @@ -811,14 +802,8 @@ def select_donors(person_t: pd.DataFrame) -> np.ndarray: donor_person = person.loc[ person["person_household_id"].isin(set(stored_donors["household_id"])) ].copy() - # Stage-time age basis again: the stage picked each donor household's - # carrier before age_tail ran. - donor_person["age"] = np.minimum( - pd.to_numeric(donor_person["age"], errors="coerce").to_numpy( - dtype=float - ), - float(UK_AGE_TOP_CODE), - ) + # Stage-time age basis again: the stored disaggregated age selected + # each donor household's carrier before the donor rows were copied. carrier_rows = _oldest_adult_indices( donor_person, household_ids=set(stored_donors["household_id"]) ) @@ -864,7 +849,7 @@ def select_donors(person_t: pd.DataFrame) -> np.ndarray: structural_ok = not problems return { "check": "uk_e8_identity_stability", - "donor_age_basis": "stage_time_top_coded", + "donor_age_basis": "stage_time_disaggregated", "permutation_seed": permutation_seed, "identical_under_permutation": bool( "donor_selection_permutation" not in problems