diff --git a/changelog.d/1095-age-15-19-band-split.changed.md b/changelog.d/1095-age-15-19-band-split.changed.md new file mode 100644 index 0000000000..7e12842875 --- /dev/null +++ b/changelog.d/1095-age-15-19-band-split.changed.md @@ -0,0 +1 @@ +The two national UK age targets for 15 to 19 by sex are split into ages 15, 16-17 and 18-19 (uk-data#563, microcosm#1095; María's ruling of 2026-10-13). A fit to a five-year band leaves the ages inside it free to move: uk-data#563 found weights that fit the band while holding 35% too many 16-year-olds and about half the 18- and 19-year-olds, and nothing in this contract bound the mix inside the band either. The ages 16 and 18 are where the child and qualifying-young-person rules change. The six rows read the same ONS mid-year estimate by single year of age as the two they replace, so each sex's three rows sum to its old band. The contract has 400 targets (396 before) and 1,283 active references (1,279). In the registry accounting the incumbent's `ons/female_15_19` and `ons/male_15_19` move from mapped to excluded, since no single row has their definition, and the six rows are contract-only declarations; the compile-parity receipt signs the two incumbent rows as fixture-only and the six as ledger-only. Within the `ons_population` family every other row's loss weight falls to 149/153 of what it was. diff --git a/changelog.d/1095-approved-training-trainee.fixed.md b/changelog.d/1095-approved-training-trainee.fixed.md new file mode 100644 index 0000000000..1683f323dc --- /dev/null +++ b/changelog.d/1095-approved-training-trainee.fixed.md @@ -0,0 +1 @@ +The UK `frs_education` stage sets `is_in_approved_training` from the FRS TRAINEE flag on child-table records, porting uk-data#561 (microcosm#1095). The FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so a child-table record with TRAINEE = 1 is a dependant in approved training. The stage read the adult table's TRAIN2 scheme codes instead (microcosm#1063), which flagged adults on DWP employment schemes and no dependant, so those dependants failed the qualifying-young-person education-or-training condition for Universal Credit, Pension Credit, Child Benefit and child tax credit. No adult-table record is flagged now, a tab without TRAINEE refuses the build, and the entry age and the age-19 terminal-date flag follow the new flag. No engine change is needed. With the flag, fewer than 10 FRS source households separate DWP's count of Universal Credit households with five or more children from its two-child-limit counts; before it, the frame told them apart only through the dependants the missing flag miscounted. So `dwp.uc.households_children_5_or_more` is held out of the calibration until 26 November 2026 (`calibration_measure_exclusions.json`, María's ruling on microcosm#1271), and the two-child-limit rows stay bound. diff --git a/changelog.d/1095-birth-month-stored.fixed.md b/changelog.d/1095-birth-month-stored.fixed.md new file mode 100644 index 0000000000..73293d2f5f --- /dev/null +++ b/changelog.d/1095-birth-month-stored.fixed.md @@ -0,0 +1 @@ +The FRS spine's root stage now stores each person's `months_since_last_birthday` (`uk_runtime/birth_month.py`), so every engine read of a build, the stages', the calibration's and the release's, places the same date of birth (microcosm#1095). policyengine-uk places a date of birth from the whole age and this column, and the date decides the two-child limit, the State Pension scheme and the Pension Credit qualifying age. Left unset, the engine spreads each single year of age and sex evenly over the year by weight, in an order set by a hash of `person_id`, so a file's birth months depended on the weights and the identifiers it carried: the release, at the calibrated weights, read a different spread from the one the solve fitted. The stage spreads the months the engine's way once, at the FRS weights, in an order set by an identity-keyed draw on the person's stable identifier; a copy of a person keeps their months. The column is allow-listed on the export surface. An engine test holds the stage's spread equal to the engine's own `stratified_uniform` and shows that stored months do not move with the weights while unset ones do. diff --git a/changelog.d/1095-policyengine-uk-2-130.changed.md b/changelog.d/1095-policyengine-uk-2-130.changed.md new file mode 100644 index 0000000000..9e1442ee27 --- /dev/null +++ b/changelog.d/1095-policyengine-uk-2-130.changed.md @@ -0,0 +1 @@ +The lock's UK side moves from policyengine-uk 2.123.0 to 2.130.1, and the UK extras of microcosm-build, microcosm-data and microcosm-frame require >=2.130.1 (microcosm#1095). The branch first moved to 2.127.0 and then, after review, to 2.130.1, the first release published after it (2.128.0 to 2.130.0 never reached PyPI). policyengine-core moves to 3.33.3 on the UK side, which is what policyengine-uk's own floor resolves, and stays at the US side's own 3.32.5 pin (microcosm#1086); a test pins the engine and core that every UK install path resolves. Nothing the UK build writes is renamed, removed or re-typed. What the build's engine reads return does change. Up to 2.127.0: `uc_reported_capital` and `pension_credit_reported_capital` become formulas of the new input `benunit_reported_capital`, a stored value still wins, and a stored `uc_reported_capital` is projected by GDP per head as Pension Credit's already was (pe-uk#2201), which completes microcosm#1200's property rule for Universal Credit; a claimant whose partner cannot claim is assessed as a single person (pe-uk#2084); DWP's announced 2025-26 rates replace CPI projections (pe-uk#1925); the Housing Benefit Guarantee Credit passport needs receipt and each family's Council Tax Reduction uses its own scheme; a unit's own claimant or partner no longer counts as its child; Carer's Allowance stops above the earnings limit for reported recipients too; savings and dividends are banded above property income; the employer Class 1 secondary threshold is £5,000 a year; and the road-fuel litre proxies are re-based on DESNZ pump prices. From 2.128.0 to 2.130.1: families move onto Universal Credit when DWP closes a legacy benefit they receive, tax credits from 2025-26 (pe-uk#2119); statutory sick, maternity and paternity pay count as Universal Credit earned income, and paternity pay in household benefits and HBAI income (pe-uk#2365); Gift Aid extends the basic rate band instead of reducing allowances (pe-uk#2271); a child on the working parent childcare entitlement keeps the universal hours (pe-uk#2177); the benefit cap's ESA and disability exceptions follow the claimant and partner (2.129.0) and its earnings exception counts statutory pay (pe-uk#2399, 2.130.1); a reported income-related ESA or income-based JSA award ends when the claimant or partner is in remunerative work (2.129.1); and the Pension Age Winter Heating Payment counts in household income. Six parts of the build follow. The CGT taxable-income measure is net income less the allowances, as the engine now stacks gains on it, and no longer takes Gift Aid off the allowances. The salary-sacrifice relief by rate class extends a Gift Aid donor's bands by the grossed-up gift, as the engine's `earned_income_tax` now does (ITA 2007 s.414(2)(b)), so the classes still add up to the engine's tax; without it the national calibration refused, and the three relief rows declare the extension in the contract. The universal-only childcare row counts children on the universal hours who are not on the extended entitlement, as the published difference does. The UC take-up lockstep test names a third case the population does not mirror: a pension-age family moved onto Universal Credit at the legacy closure (SI 2014/1230 reg 60A). The concept mapping declares `would_claim_uc_at_legacy_closure` left at the engine's default, so every family moved off a legacy benefit claims Universal Credit; uk-data#534 draws it at DWP's Move to UC claim rates, which are not yet a Chronicle fact, so the calibration's 2025 Universal Credit rows rest on that default. And the liquid-asset reason names the three company-capital inputs of Universal Credit's regulation 77 (2.130.0), which default to no company and which the build sets none of. Re-derived with no change to data: the uprating pins (the version stamp; every pinned value, the ONS population growth included, is unchanged), the concept coverage (288 inputs to 299: eleven owned-company inputs and `would_claim_uc_at_legacy_closure` join, `uc_has_limited_capability_for_work` leaves; the build writes none of them), the stage texts that cite the locked engine, the H2 fixture's dependency, the coverage manifest's source digests and `APPROVED_UV_LOCK_SHA256`. One engine test changed its expectation at 2.127.0: the single-claim rule gives a 24-year-old with a 17-year-old partner the single young allowance. diff --git a/changelog.d/1095-release-identifiers-32-bit.fixed.md b/changelog.d/1095-release-identifiers-32-bit.fixed.md new file mode 100644 index 0000000000..6f867e38cb --- /dev/null +++ b/changelog.d/1095-release-identifiers-32-bit.fixed.md @@ -0,0 +1 @@ +A UK release's identifiers stay inside the range policyengine-uk stores (microcosm#1194, microcosm#1095; uk-data#558 is the incumbent's side). The clone stages number each copy by adding a power of ten to every identifier, so a built frame's identifiers run far past 2,147,483,647, and policyengine-uk keeps its five identifier variables as 32-bit integers: the stored values wrapped, so `calculate("household_id")` did not return the file's identifiers and every engine draw keyed on one read the wrapped number. The release boundary now renumbers (`national_frame.uk_release_identifier_tables`, called by `uk_release_export_frame` and by the local export): per entity the identifier is one plus the rank of the build identifier, the person table's two memberships map through the same arrays, and row order does not change. The build identifiers stay in the file as `person_build_id`, `benunit_build_id` and `household_build_id`, which the existing lineage columns describe, and are allow-listed on the export surface. The build and its checkpoints keep the build identifiers, because every draw in the build is keyed on them. The engine file the calibration measures on carries the release's identifiers (a per-clone block takes the ranks its rows have in the whole frame), so the solve fits the engine outputs the released file gives. Both readbacks refuse a written identifier that repeats, is below 1 or is above the 32-bit range, naming the column only. The release assembler pairs a release with its spine on the build identifiers, and the size evaluation and `tools/verify_uk_identity_stability.py` read them too. Engine tests hold both halves against the installed engine: the renumbered file's identifiers read back as written, and the build's own do not. Why the calibration follows: policyengine-uk spreads each person's birth month over the year by a hash of `person_id` (`months_since_last_birthday`), and the date of birth decides the two-child limit, the State Pension scheme and the Pension Credit qualifying age. Measured on a calibrated file, with the solve left on the build identifiers, the renumbered file moved two-child-limit and Universal Credit award rows by up to a tenth or more against the solve's own estimates; the receipts give the comparison. The Universal Credit deduction draws do not move: the build stores them. The spine now stores the birth months too (`1095-birth-month-stored`), so neither the identifiers nor the weights move them; the calibration keeps the release's identifiers so that any engine read keyed on one agrees with the release. The spine stages still read the engine on the build identifiers, as before. diff --git a/changelog.d/1095-salary-sacrifice-within-pay.fixed.md b/changelog.d/1095-salary-sacrifice-within-pay.fixed.md new file mode 100644 index 0000000000..1cebc84904 --- /dev/null +++ b/changelog.d/1095-salary-sacrifice-within-pay.fixed.md @@ -0,0 +1 @@ +The UK `salary_sacrifice` stage limits every salary-sacrifice amount to the pay it comes out of, porting the final rule of uk-data#518 (microcosm#1095). A sacrifice is pay given up for a pension contribution, so no amount, reported or predicted, now exceeds the pay the person still receives, and a person without pay has none; microcosm#1089 had done this only for people not asked the question. The limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The forest trains only on respondents with pay. The headcount conversion moves a contribution into salary sacrifice up to half the pay and leaves the rest as an employee contribution; it moved the whole contribution before and floored pay at zero, which could leave a converted record with a sacrifice and no pay. The stage refuses if any sacrifice ends above the pay that remains. policyengine-uk 2.126.2 ignores a sacrifice with no pay behind it (PolicyEngine/policyengine-uk#2063), so the stored column now agrees with what the engine relieves. The stage's manifest gains `clamp_maximum` and `move_pay_share_cap`, the evidence records how many amounts the limit and the half-pay cap touched, and the identity tool restores a converted record's contribution as what it kept plus what moved. diff --git a/changelog.d/1095-statutory-paternity-pay.added.md b/changelog.d/1095-statutory-paternity-pay.added.md new file mode 100644 index 0000000000..c1bcc04ac8 --- /dev/null +++ b/changelog.d/1095-statutory-paternity-pay.added.md @@ -0,0 +1 @@ +The UK root stage sets `statutory_paternity_pay` from the FRS adult table's SPPADJ ("SPP amount", a weekly amount for the adult's main job), annualised like `statutory_sick_pay` and `statutory_maternity_pay` beside it (uk-data#575, microcosm#1095). Nothing set it before, so every adult had none. policyengine-uk reads it in Class 1 National Insurance earnings and in the Universal Credit and Tax-Free Childcare paid-work tests, and from 2.128.0 also as Universal Credit earned income and in household benefits and HBAI income (pe-uk#2365). On an engine before 2.128.0 the column was only a National Insurance base, so it lowered the net income of the households that report it; this PR locks 2.130.1. Whether SPPADJ times the weeks in a year (365.25/7) is the right annual amount for a payment that lasts at most two weeks is still open (uk-data#575): it matches sick and maternity pay, and a grossed weekly amount from a reference week is the FRS's usual annual estimate in aggregate, but not per person. A tab without SPPADJ refuses the build. The column is new on the export, so it joins the reviewed extra columns of the export-surface gate; few adults report it, so its signed entry gives survey support as a band only. diff --git a/changelog.d/1095-weights-at-dataset-year.fixed.md b/changelog.d/1095-weights-at-dataset-year.fixed.md new file mode 100644 index 0000000000..ddb73c43c2 --- /dev/null +++ b/changelog.d/1095-weights-at-dataset-year.fixed.md @@ -0,0 +1 @@ +A UK release writes its household weights at the dataset's own year (microcosm#796 A19, microcosm#1095; uk-data#538 is the incumbent's side of the same mechanism). policyengine-uk projects a single-year dataset forward: from the dataset's year it multiplies `household_weight` by one plus the ONS population growth of each later year. Weights solved at 2025 and stored on the 2024-25 dataset therefore read 0.72% high at 2025, on every weighted count and total. The release write now divides the solved weights by the engine's own factor (`uk_runtime.weight_year`), so the engine returns the solved weights at the solve year. The division is the last record of the file's mass log, and the national and local readbacks expect exactly it. The solve, its gates, its diagnostics and the engine scratch file keep the solved weights. The growth is vendored in a new `dataset_projection` block of `uk/hmrc_uprating_engine_pins.json` and a test holds it against the installed engine, with a second test that `household_weight` is the only weight the engine projects and a third that loads a written file and reads the solved weights back at 2025. Readers of a written file follow the same projection: the rule-1 scorer reads each side with the weights the engine gives it at the scored year and its score block gains `weight_year`, so scores are not comparable with earlier ones; the release-cut certifier runs its gates on the solved weights and refuses a file divided for another year; `tools/evaluate_uk_incumbent_surface.py` reads the candidate the same way; and the assembler's build manifest says which year the weights NPZ stands at (it pairs with the H5 row by row, so it carries the file's own weights). diff --git a/changelog.d/uk-frs-empstati-other-inactive.fixed.md b/changelog.d/uk-frs-empstati-other-inactive.fixed.md new file mode 100644 index 0000000000..9518c1a86d --- /dev/null +++ b/changelog.d/uk-frs-empstati-other-inactive.fixed.md @@ -0,0 +1 @@ +The UK `frs_employment` stage maps FRS EMPSTATI code 11 ("Other Inactive" in the UKDS FRS 2024-25 data dictionary) to `employment_status` OTHER_INACTIVE instead of LONG_TERM_DISABLED, matching the incumbent's uk-data#526, so other-inactive adults no longer enter the ESA health-condition and support-group proxies. Codes 1-11 are mapped one data-dictionary label each; people outside `adult.tab` stay CHILD, and an adult with a blank or unknown code now refuses the build instead of defaulting to LONG_TERM_DISABLED. diff --git a/docs/concept-coverage/policyengine-uk.md b/docs/concept-coverage/policyengine-uk.md index b5ff0a14e9..5353ae7982 100644 --- a/docs/concept-coverage/policyengine-uk.md +++ b/docs/concept-coverage/policyengine-uk.md @@ -1,11 +1,11 @@ # Concept coverage: policyengine-uk -Engine version `2.123.0`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. +Engine version `2.130.1`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. -- Engine inputs: 288 +- Engine inputs: 299 - Fed by a concept: 40 - Structural: 11 -- Uncovered: 237 +- Uncovered: 248 - Mapped but missing from the engine: 0 ## Inputs each concept feeds @@ -44,20 +44,20 @@ Engine version `2.123.0`; concept schema `5a55669d0447`. Generated by `tools/ref | `fact:person.legal_marital_status` | policyengine-uk derives marital status from benefit-unit composition; marital_status and is_married are formula-owned. | | `fact:person.partner_person_id` | Partnerships reach policyengine-uk only through benefit-unit membership, a structure the bundle must already carry (the adapter builds no units); is_benunit_head and is_married are formula-owned. | | `fact:household.reference_person_id` | is_household_head is formula-owned (the oldest member). The UK build overrides it with the FRS household reference person (frs_spine.py), an override the loader accepts, but it is not an input, so the mapping does not target it. Every housing input is household-level, so nothing is allocated to the reference person. | -| `fact:person.liquid_financial_assets` | policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it would be a modelling choice for a UK build, not a mapping of this concept. | +| `fact:person.liquid_financial_assets` | policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital. It is formula-owned since policyengine-uk 2.124.0: unless set directly it is benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'). When it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it would be a modelling choice for a UK build, not a mapping of this concept. | | `fact:person.public_pension_income` | The State Pension is computed from state_pension_reported, which is formula-owned: the loader accepts it as an override, but it is not an input. | | `fact:household.source_household_id` | Original donor household lineage is build provenance and weight diagnostic support, not a policyengine-uk rules input. | ## Inputs no concept covers -### benunit (12) +### benunit (13) -`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `housing_benefit_board_and_attendance_determination`, `in_specified_or_temporary_accommodation`, `liable_for_share_of_household_rent`, `maximum_extended_childcare_hours_usage`, `meals_included_in_rent`, `pension_credit_reported_capital`, `tax_free_childcare_meets_uk_connection`, `uc_reported_capital`, `working_tax_credit_run_on` +`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `benunit_reported_capital`, `housing_benefit_board_and_attendance_determination`, `in_specified_or_temporary_accommodation`, `liable_for_share_of_household_rent`, `maximum_extended_childcare_hours_usage`, `meals_included_in_rent`, `tax_free_childcare_meets_uk_connection`, `uc_receives_new_claimant_health_element`, `working_tax_credit_run_on`, `would_claim_uc_at_legacy_closure` -### household (78) +### household (79) -`accommodation_type`, `alcohol_and_tobacco_consumption`, `beer_draught_share`, `beer_litres`, `brma`, `bus_fare_spending_reported`, `bus_subsidy_spending`, `cash_isa`, `cider_draught_share`, `cider_litres`, `cigarettes_per_week`, `cigars_grams_per_week`, `clothing_and_footwear_consumption`, `communication_consumption`, `consumer_debt`, `corporate_wealth`, `council_tax_band`, `cumulative_non_residential_rent`, `cumulative_residential_rent`, `dfe_education_spending`, `diesel_spending`, `domestic_energy_consumption`, `domestic_rates`, `education_consumption`, `electricity_consumption`, `external_child_payments`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gross_financial_wealth`, `hand_rolling_tobacco_grams_per_week`, `health_consumption`, `heated_tobacco_grams_per_week`, `household_furnishings_consumption`, `household_owns_tv`, `housing_service_charges`, `housing_water_and_electricity_consumption`, `in_original_frs`, `in_rural_fuel_duty_relief_area`, `is_shared_accommodation`, `local_authority`, `lpg_kg`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `miscellaneous_consumption`, `mortgage_debt`, `natural_gas_kg`, `net_financial_wealth`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_value`, `other_tobacco_grams_per_week`, `owned_land`, `petrol_spending`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `restaurants_and_hotels_consumption`, `savings`, `sparkling_cider_draught_share`, `sparkling_cider_litres`, `spi_imputed`, `spirits_draught_share`, `spirits_litres`, `stocks_and_shares_isa`, `structural_insurance_payments`, `transport_consumption`, `uc_migrated`, `water_and_sewerage_charges`, `wine_draught_share`, `wine_litres`, `would_evade_tv_licence_fee` +`accommodation_type`, `alcohol_and_tobacco_consumption`, `beer_draught_share`, `beer_litres`, `bus_fare_spending_reported`, `bus_subsidy_spending`, `cash_isa`, `cider_draught_share`, `cider_litres`, `cigarettes_per_week`, `cigars_grams_per_week`, `clothing_and_footwear_consumption`, `communication_consumption`, `consumer_debt`, `corporate_wealth`, `council_tax_band`, `cumulative_non_residential_rent`, `cumulative_residential_rent`, `dfe_education_spending`, `diesel_spending`, `domestic_energy_consumption`, `domestic_rates`, `ebr_energy_bills_credit_eligible`, `education_consumption`, `electricity_consumption`, `external_child_payments`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gross_financial_wealth`, `hand_rolling_tobacco_grams_per_week`, `health_consumption`, `heated_tobacco_grams_per_week`, `household_furnishings_consumption`, `household_owns_tv`, `housing_service_charges`, `housing_water_and_electricity_consumption`, `in_original_frs`, `in_rural_fuel_duty_relief_area`, `is_shared_accommodation`, `local_authority`, `lpg_kg`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `miscellaneous_consumption`, `mortgage_debt`, `natural_gas_kg`, `net_financial_wealth`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_value`, `other_tobacco_grams_per_week`, `owned_land`, `petrol_spending`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `restaurants_and_hotels_consumption`, `savings`, `sparkling_cider_draught_share`, `sparkling_cider_litres`, `spi_imputed`, `spirits_draught_share`, `spirits_litres`, `stocks_and_shares_isa`, `structural_insurance_payments`, `transport_consumption`, `uc_migrated`, `uses_energy_prepayment_meter`, `water_and_sewerage_charges`, `wine_draught_share`, `wine_litres`, `would_evade_tv_licence_fee` -### person (147) +### person (156) -`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `ssmg_reported`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `trading_loss`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_limited_capability_for_work`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_ineligible_partner`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` +`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_engaged_in_owned_company_trade`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `owned_company_capital`, `owned_company_carries_on_property_business`, `owned_company_carries_on_trade`, `owned_company_holding_value`, `owned_company_income_share`, `owned_company_intermediary_earnings_chapter`, `owned_company_intermediary_earnings_from_main_employment`, `owned_company_is_main_employment`, `owned_company_trade_assets`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `ssmg_reported`, `stands_as_sole_owner_or_partner_of_company`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `trading_loss`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` diff --git a/experiments/1095-uk-data-ports-4-receipts.md b/experiments/1095-uk-data-ports-4-receipts.md new file mode 100644 index 0000000000..ae12a619ea --- /dev/null +++ b/experiments/1095-uk-data-ports-4-receipts.md @@ -0,0 +1,475 @@ +# microcosm#1095 receipts: the fourth ports PR + +Branch `uk-data-ports-1095-4`, rebased on 2026-10-14 onto main `121fd4dd6` (#1256, #1279). The four +arms and the lanes below ran on the branch as it stood on main `65d7d153a` (#1260, the LCFS 2024-25 +tabs); the tree ids are the ones measured. #1256, merged since, draws the SPI channel's incomes +afresh, so a control and the head are rebuilt on `121fd4dd6` ("On the current main", below). +Chronicle feed 942a1fa throughout. The engine lock moves from policyengine-uk 2.123.0 to +2.130.1: the branch first locked 2.127.0, and after Vahid's review moved to 2.130.1, the first release +published after it (2.128.0 to 2.130.0 never reached PyPI). 2.131.0, published at 12:29 UTC on +14 October (trading losses), is left for the next bump. + +Every figure here is a weighted aggregate or a publisher's figure. Survey support is counted in +distinct source households, FRS and SPI channels together, and given as a band: `<10`, `10–29` or +`30+`. A cell under 10 has no weighted figure, and a target row's figure is given only where 30+ +source households stand behind it. Effective sample sizes are not given. A spine holds copies of +survey households, so its row counts overstate support and are not given. + +## Commits, in order + +Which items port merged incumbent code and which go first (item 5 of the review): + +- B1 `00040d929`, B2 `c2d305fd0`: the lock at policyengine-uk 2.127.0 and the stage texts that cite + it. +- R1 `3e62fa607`, `111391500`, `00519cf54`, `01c771ad2`: FRS EMPSTATI 11 is OTHER_INACTIVE, Max + Ghenis's commits from #1097. **Ports merged code**: uk-data#526. +- R2 `d309afb85`: approved training from the FRS TRAINEE flag on dependants. **Goes first**: + uk-data#561 is an open PR; the rule is its rule. +- R3 `6c3a0add8`: statutory paternity pay from the FRS adult table's SPPADJ. **Goes first**: + uk-data#575 is an open issue. +- R4 `f19416e42`: salary sacrifice limited to the pay it comes out of. **Ports merged code**: + uk-data#518, merged on 14 October after this commit was written; the review compared the two. +- T1 `2da556a5a`: the national 15–19 age bands split into 15, 16–17 and 18–19. **Goes first**: + uk-data#563 is an open issue. +- E1 `0f0bb869a`: release weights written at the dataset's year. **Goes first**: uk-data#538 is the + incumbent's open issue. +- E2 `e9aa8d333`, `1e88ac52c`: release identifiers inside the engine's 32-bit range. **Goes first**: + uk-data#558 is an open issue. +- E3 `ac6088f2c`, `ae08ba3bd`: the calibration measures the engine on the release's identifiers. + Microcosm only. +- B3 `3623ca57d`: the lock at policyengine-uk 2.130.1 and what the build follows (item 2). +- M `84ce97a2a`: each person's `months_since_last_birthday` stored in the spine. Microcosm only: the + incumbent leaves it to the engine's formula. +- X `e3342783a`: `dwp.uc.households_children_5_or_more` held out of the calibration until + 26 November 2026 (item 4). +- `0912388b6`: the derived surfaces regenerated on main after the rebase onto #1256 (the gate + digests, the coverage manifest and the H2 fixture; the rendered stages are unchanged). +- This commit: the receipts. + +## María's rulings + +Of 2026-10-13, at the plan: + +- The engine bump is in this PR. +- Statutory paternity pay is included. +- The 15–19 band splits into 15, 16–17 and 18–19. +- Additional-dwelling purchases and `obr.sdlt` wait for HMRC's stamp tax tables in Chronicle. This PR + only measures household SDLT by part (below). +- The measure exclusions that lapse from 15 October are not renewed here. The arms pass + `--review-date 2026-10-14`, for measurement only. + +Of 2026-10-14, during the work: + +- #1097's `experiments/` folder is not carried. Four of its eight commits are cherry-picked; the + other four touched only that folder. +- The incumbent's two 15–19 rows move to `excluded` in the registry accounting. +- The UK side takes the policyengine-core that uv resolves, 3.33.3. + +Of 2026-10-14, on Vahid's review: + +- The lock moves to 2.130.1 in this PR, with an arm of its own. +- R3 stays, and its changelog fragment and these receipts say what the engine reads and that the + annual amount is an open question. +- `dwp.uc.households_children_5_or_more` is held out with a reason and the sparse-band expiry, + 26 November 2026. +- The receipts carry no effective sample size, and no commit on the branch does: the earlier + receipts commit is replaced, not edited. +- The fix storing `months_since_last_birthday` is folded into this PR. + +## Arms + +Each arm builds the spine and runs the national calibration on one tree, on feed 942a1fa with the +review date above. + +- **control**: main `65d7d153a`, policyengine-uk 2.123.0. +- **bump**: main plus the three lock commits and nothing else, policyengine-uk 2.130.1. A + measurement-only local commit (`c1a8d91c8`), not in the PR: B1, B2 and B3 picked onto main with + the generated surfaces regenerated. Its spine was built before B3 took the rate-class change below, + which changes no stage. +- **head without M**: the PR at B3 plus X (`a3ae4faf1`, measurement only, not in the PR). +- **head**: `1da8d681f`, X before the rebases onto #1242 and #1256 (now `e3342783a`). + +What each calibration reached: + +- **control**: loss 0.00583; 98.8% of 1,214 rows within 10%; all 7 calibrated-seam gates pass. +- **bump**: loss 0.00593; 98.6% of 1,214; all 7 gates pass. +- **head without M**: loss 0.00588; 98.7% of 1,217; all 7 gates pass. +- **head**: loss 0.00590; 98.8% of 1,217; all 7 gates pass. It writes a release file, and E1, E2 and M + are checked on it. + +The head has three rows more than main: T1's six in place of the incumbent's two, less the row X +holds out. + +The input-mass gate, run as the certifier runs it on the head's calibrated weights, passes. + +**The rebase onto #1242.** #1242 came after the arms. It adds the UK local release path: target-weight +rules that the national role refuses, size-stage penalties that are off unless asked for, and the +local doctrine. It renews the `dwp.uc.households_housing_element` measure exclusion to 14 November, +which the arms already held at their review date, and its gate-register edits concern the release-cut +certifier. So the national calibration the arms ran computes the same on the rebased branch. The +rebase changed one file of this branch: the packaged-exclusions test, which now tells X's ruling +apart from the housing-element renewal of the same date by tracking issue (folded into X). The +derived surfaces regenerate unchanged on the rebased head. + +## On the current main + +#1256 draws the SPI channel's incomes at the FRS twin's conditional rank, which changes the spine +every arm above calibrates. So a control and the head were rebuilt on main `121fd4dd6`, with the same +feed and review date: + +- **control** (`121fd4dd6`, policyengine-uk 2.123.0): loss 0.00584; 98.4% of 1,214 rows within 10%; + all 7 calibrated-seam gates pass. +- **head** (`0912388b6`, this commit's parent): loss 0.00599; 98.6% of 1,217; all 7 gates pass. + +On the head's written file the checks below hold as before. The stored weights times 1.0072 equal the +solve's to 16 significant figures, and policyengine-uk's 2025 weights equal them to single precision. +The identifiers run 1 to n with the build identifiers equal to the spine's, and the engine returns all +five as written. Every other column equals the spine's, and the input-mass gate passes. With M, the +file read as the engine reads it differs from its own solve on 3 rows: private-school pupils by 3.4 +points, and the two land rows by 0.3 and 0.1. The engine returns the stored months for every person. + +The decomposition into the bump, the spine fixes and the stored months was measured on `65d7d153a` +and is not repeated on this base. + +## B. The engine bump, 2.123.0 to 2.130.1 + +Nothing the UK build writes is renamed, removed or re-typed. What the engine returns does change. +Up to 2.127.0: + +- `uc_reported_capital` and `pension_credit_reported_capital` become formulas of the new input + `benunit_reported_capital`. A stored value still wins, and a stored `uc_reported_capital` is now + projected by GDP per head, as Pension Credit's already was (pe-uk#2201). That completes #1200's + property rule for Universal Credit. +- A claimant whose partner cannot claim is assessed as a single person (pe-uk#2084, UC Regs 2013 + reg 3(3)). +- DWP's announced 2025-26 rates replace CPI projections (pe-uk#1925). +- The Housing Benefit Guarantee Credit passport needs receipt (pe-uk#1927), and each family's + Council Tax Reduction uses its own scheme (pe-uk#2015). +- A unit's own claimant or partner no longer counts as its child or young person (pe-uk#2285). +- Carer's Allowance stops above the earnings limit for reported recipients too (pe-uk#2312). +- Savings and dividends are banded above property income (pe-uk#2352). +- The employer Class 1 secondary threshold is £5,000 a year (pe-uk#2165). +- The road-fuel litre proxies are re-based on DESNZ pump prices (pe-uk#2186). + +From 2.128.0 to 2.130.1, the releases the review named: + +- **Legacy benefits close** (pe-uk#2119, 2.128.0): a family moves onto Universal Credit when DWP + closes a legacy benefit it receives, tax credits from 2025-26. Whether it claims is + `would_claim_uc_at_legacy_closure`, which the build leaves at the engine's default, true. + uk-data#534 draws it at DWP's Move to UC claim rates, which are not yet a Chronicle fact, so the + calibration's 2025 Universal Credit rows rest on that default. The concept mapping declares it. +- **Statutory pay in Universal Credit** (pe-uk#2365, 2.128.0): sick, maternity and paternity pay + count as UC earned income, and paternity pay in household benefits and HBAI income. +- **Gift Aid** (pe-uk#2271, 2.128.0) extends the basic rate band instead of reducing allowances. + Two parts of the build follow. The CGT taxable-income measure is net income less the allowances, + as the engine now stacks gains on it. And the salary-sacrifice relief by rate class extends a + donor's bands by the grossed-up gift, as `earned_income_tax` now does (ITA 2007 s.414(2)(b)). The + first head arm on 2.130.1 refused at that measure: its classes no longer added up to the engine's + tax for donors. B3 carries the extension, the three relief rows declare it, and hand-computed + donor cases hold it against the engine. +- **The benefit cap**: its ESA and disability exceptions follow the claimant and partner (2.129.0), + and its earnings exception counts statutory pay (pe-uk#2399, 2.130.1). +- A reported income-related ESA or income-based JSA award ends in remunerative work (2.129.1). +- A child on the working parent childcare entitlement keeps the universal hours (pe-uk#2177). The + universal-only childcare row now leaves the extended entitlement out itself, as the published + difference does. +- Universal Credit's regulation 77 for company owners (2.130.0): the `owned_company_*` inputs + default to no company, and the build sets none. +- Capital gains get their own projection path (2.130.0). With no scenario passed it copies GDP per + head exactly (`legacy_gdp_per_capita`), so the build's gains are projected as before. + +**The spine moves with the engine.** The stages read it: the take-up and reporter draws read +entitlement, the consumption and wealth stages read its indices, and the CGT stages read liability. +So the bump changes the spine although it changes no stage. At each spine's own weights: + +- Universal Credit held by reporters (`universal_credit_reported`) rises 2.3%, from £42.2bn to + £43.2bn. +- Pension contributions by salary sacrifice rise 2.3%, from £16.91bn to £17.30bn, and employee + contributions 0.5%. +- Gains that qualify for Business Asset Disposal Relief fall 0.75%, and all capital gains rise 0.25%. +- Every other total checked moves by less than 0.5%: pay, residential gains, main-home, other + residential and non-residential property, financial and corporate wealth, recorded capital for + Universal Credit and Pension Credit, the three take-up flags, petrol, diesel, domestic energy and + transport spending. + +**Before calibration**, at the spine's weights, 169 of the 1,214 rows move by more than 1%: 56 +Universal Credit rows, 35 CGT rows, 30 regional SPI rows, 14 Pension Credit rows and 34 others. The +rows the review named, and the largest moves: + +- Universal Credit is £52.1bn against the control's £51.9bn (+0.4%), on 0.1% fewer households. The + legacy closure and statutory pay as earned income both reach it; the arm measures them together. +- The benefit cap: 0.8% fewer capped households. +- ESA is £4.63bn against £4.77bn (−3.0%), with 5.1% fewer income-related claimants. +- OBR's Pension Credit line is £4.52bn against £4.24bn (+6.5%), with 1.05m benefit units against + 1.01m. Carer's Allowance is £3.06bn against £3.24bn (−5.5%). +- CGT liability rises 0.3%, and income tax 0.2%. + +**After calibration** the two arms fit alike: + +- loss 0.00593 against the control's 0.00583; +- 98.6% of the 1,214 rows within 10%, against 98.8%; +- all 7 calibrated-seam gates pass on both. + +Fifteen rows are outside 10% on both arms (three of them rest on fewer than 10 source households), +and the bump has two more: `dwp.uc.households_children_1` and +`dwp.uc.households_couple_with_children`. The Universal Credit family counts trade places with each +other: the number of households on Universal Credit is 3.9% under DWP's on the control and 4.0% +under on the bump. + +Row figures in this file are given only for rows with 30+ source households behind them. Of the +bump's 1,214 rows, 79 rest on 10–29 and 36 on fewer than 10; many of them are the sparse Universal +Credit award bands the support-floor PR is for. + +Re-derived with no change to data: the uprating pins (the version stamp; every pinned value, the ONS +population growth E1 divides by included, is unchanged), the concept coverage (288 inputs to 299: +eleven owned-company inputs and `would_claim_uc_at_legacy_closure` join, +`uc_has_limited_capability_for_work` leaves; the build writes none of them), the stage texts that +cite the locked engine, the H2 fixture, the coverage manifest's source digests and +`APPROVED_UV_LOCK_SHA256`. The target references and the compile-parity receipt regenerate +identically on feed 942a1fa. + +## R1 to R4. The spine fixes + +The head without M against the bump, each at its spine's own weights. + +- **R1. EMPSTATI 11.** 2.02m adults move from LONG_TERM_DISABLED to OTHER_INACTIVE: the first + group falls from 5.19m to 3.18m, and no other status changes (30+ source households). The ESA + rows read reported ESA and do not move. +- **R2. Approved training.** The flag leaves adults on employment schemes, who are mostly aged 20 + or over, and lands on dependants aged 16 to 19 in the child table. Each group rests on 10–29 FRS + source households, and the two share fewer than 10, so no weighted count is given. Those + dependants now pass the qualifying-young-person test, which moves their families up one size in + the engine's Universal Credit child counts. Its effect on the fit is below (X). +- **R3. Statutory paternity pay.** Fewer than 10 source households report it, so no weighted figure + is given. Statutory maternity pay is unchanged. +- **R4. Salary sacrifice within pay.** On the control's and the bump's spines some rows held a + sacrifice above their pay (30+ source households across the FRS and SPI channels, fewer than 10 + of them FRS). The stage now refuses a spine where any does, and the head's spines built. On + 2.130.1 the totals barely move: contributions by salary sacrifice are £17.30bn on both spines, + and employee contributions fall 0.25%. Before calibration, with R1 to R3 beside it, the + additional-rate relief row rises 7.5% and the basic- and higher-rate rows fall 1.7% and 1.6%; + users below the £2,000 cap rise 2.1% and those above it fall 2.3%. After calibration all seven + salary-sacrifice rows are within 0.1% of HMRC's on the head. + +Together, before calibration, 124 of the common rows move by more than 1%: 60 Universal Credit +rows, 26 CGT rows, 13 State Pension rows, 8 two-child-limit rows and 17 others. Universal Credit is +£52.5bn against the bump's £52.1bn (+0.7%), on 1.0% more households. No fix touches the State +Pension: those rows move because the fixes reweight the spine's households (the stages read the +engine), and without M the engine spreads birth months by those weights. At the spine's weights +Universal Credit held by reporters falls 0.7%, from £43.2bn to £42.9bn; every other total checked +moves by less than 0.5%. + +After calibration: loss 0.00588 against the bump's 0.00593; 98.7% of 1,217 rows within 10%, +against 98.6% of 1,214; all 7 gates pass on both. + +## R2 and the row held out (X) + +DWP counts 76,300 Universal Credit households with five or more children. It counts 55,300 +households under the two-child limit with five or more children (37,000 with five and 18,300 with +six or more). So about 21,000 large families on Universal Credit are outside the limit: their third +and later children were all born before 6 April 2017, or an exception applies. + +On main both rows pass the fit gate through an artefact of the missing training flag. A dependant +aged 16 to 19 in training is a child in the FRS and in the first row's count, but failed the +engine's qualifying-young-person test, so the two-child-limit rows counted one child fewer in that +family. Fewer than 10 source households were in the first row and in neither of the +two-child-limit size rows. They were the only way the frame could tell DWP's two counts apart. + +R2 gives those dependants the flag. The two counts then agree on every family, and the frame has +fewer than 10 source households with five or more children on Universal Credit and outside the +limit. The calibration cannot meet both DWP counts, and the two-child-limit rows carry six times the +loss weight. On the earlier arms, on 2.127.0, `dwp.uc.households_children_5_or_more` fell outside +the fit gate's 25% bound, and a blocked calibration writes no release file. + +The fix in R2 is right, and the earlier fit stood on fewer than 10 source households. María ruled +to hold the row out (X): `calibration_measure_exclusions.json` carries it with the reason, tracking +microcosm#1095 and the sparse-band expiry, 26 November 2026, and the packaged-exclusions test pins +the entry. The two-child-limit rows stay bound. With the row held out, the head and the head without +M pass all 7 gates and write a release file. + +## R3 on the locked engine + +policyengine-uk 2.130.1 reads `statutory_paternity_pay` in Class 1 National Insurance earnings, in +the Universal Credit and Tax-Free Childcare paid-work tests, as Universal Credit earned income, and +in household benefits and HBAI income (pe-uk#2365). So the column raises the net income of the +households that report it, as it should. On 2.127.0 it was only a National Insurance base and +lowered their net income; the changelog fragment says so. Fewer than 10 source households report +it, so no weighted figure is given. + +The annual amount is still open (uk-data#575): the stage takes SPPADJ, a weekly amount, times the +weeks in a year (365.25/7), as it does sick and maternity pay. For a payment that lasts at most two +weeks, a grossed weekly amount from a reference week is the FRS's usual annual estimate in +aggregate, but not per person. + +## T1. Ages 15 to 19 + +**Measured first, on the control's file.** The control fits each sex's five-year band within 0.1% +and binds nothing inside it. Against the ONS mid-year estimates the three new rows bind, it holds: + +- female: 3.7% too few at 15, 19.1% too many at 16–17, 18.0% too few at 18–19; +- male: 26.1% too many at 15, 14.0% too many at 16–17, 28.1% too few at 18–19. + +Every one of the six cells rests on 30+ source households. This is uk-data#563's finding on this +frame: a fit to the band leaves it holding too many dependants under 18 and too few adults of 18 +and 19. + +**On the head** the six rows fit within 0.1%. + +The contract has 400 targets (396) and 1,283 active references (1,279). In the registry accounting +the incumbent's two 15–19 rows move from mapped to excluded and the six rows are contract-only +declarations; the compile-parity receipt signs the two as fixture-only and the six as ledger-only. +Within the `ons_population` family every other row's loss weight falls to 149/153 of what it was. + +## E1. Weights at the dataset's year + +policyengine-uk projects a single-year dataset forward. From the dataset's own year it multiplies +`household_weight` by one plus the ONS population growth of each later year; no other weight is +stored, and the person and benefit-unit weights are formulas of it. The 2024-25 dataset's weights +are solved at 2025, where that growth is 0.72%. Written as solved, they read 0.72% high at 2025 on +every weighted count and total. + +The release write now divides the solved weights by the engine's own factor, 1.0072. The division +is the last record of the file's mass log, and the readback expects exactly it. The solve, its +gates, its diagnostics, the spine checkpoints and the engine scratch file keep the solved weights. +The growth is unchanged in 2.130.1, so the factor holds. + +**On the head's file:** + +- the stored weights times 1.0072 equal the solve's weights on every household, to 16 significant + figures; +- loaded in policyengine-uk 2.130.1, `household_weight` at 2025 equals the solve's weights to the + engine's single precision (within 6 parts in 100 million); +- every other column of the file equals the spine's column, row for row, the reviewed export drops + aside; +- the readback passes and records the solve year, the dataset year and the factor. + +**Engine tests** hold the mechanism against the installed engine: the vendored growth equals the +engine's parameter at each pinned instant; `household_weight` is the only weight in the engine's +uprating file, and the person and benefit-unit weights are formulas; a written release read at 2025 +returns the solved weights; and a file written as solved reads high by exactly the factor. + +**Readers of a written file** follow the same projection: + +- the rule-1 scorer reads each side with the weights the engine gives it at the scored year, and + its score block records each file's year and factor. The incumbent `enhanced_frs_2024_25` is a + 2024 dataset too, so every one of its estimates is now read 0.72% higher than before. **Scores + are not comparable with earlier ones;** +- the release-cut certifier runs its gates on the solved weights and refuses a file divided for + another year; +- `tools/evaluate_uk_incumbent_surface.py` reads the candidate the same way; +- the assembler's weights file pairs with the H5 row by row, so it carries the file's own weights, + and the build manifest says which year they stand at. + +## E2 and E3. Identifiers inside 32 bits + +**Before.** On main's file more than half of the rows of each entity carry an identifier above +2,147,483,647. policyengine-uk stores its five identifier variables as 32-bit integers, so those +wrapped: `calculate("household_id")` did not return the file's identifiers. + +**E2.** The release boundary renumbers. Per entity the identifier is one plus the rank of the build +identifier, the person table's two memberships map through the same arrays, and row order does not +change. The build identifiers stay in the file as `person_build_id`, `benunit_build_id` and +`household_build_id`. Both readbacks refuse an identifier that repeats, is below 1 or is out of +range, naming only the column. + +**E3.** The engine file the calibration measures on carries the release's identifiers, so any +engine read keyed on an identifier gives the solve what it gives the release. A per-clone engine +block takes the ranks its rows have in the whole frame, so the local solve numbers its rows as the +local release does. The resolver's own frame keeps the build identifiers, and nothing joins engine +outputs to it by identifier. Measured before M on 2.127.0, E2 alone made the release differ from the +solve on two-child-limit and Universal Credit award rows by up to a tenth or more, because the +engine dealt birth months by a hash of `person_id`; E3 removed that. With M the birth months no +longer depend on identifiers, and E3 remains for any other engine read keyed on one. + +**On the head's file:** each entity's identifiers are 1 to n, and the build identifiers beside them +equal the spine's, row for row; policyengine-uk 2.130.1 returns all five identifier variables +exactly as written, with no warning on load. + +## M. The birth months stored + +policyengine-uk places each person's date of birth from the whole age and +`months_since_last_birthday`. The date decides the two-child limit (born before or after 6 April +2017), the State Pension scheme (reaching pension age before or after 6 April 2016) and the Pension +Credit qualifying age. Left unset, the engine spreads each single year of age and sex evenly over +the year by weight, in an order set by a hash of `person_id`. So the months a file gets depend on +the weights it carries: the solve measures the engine at the spine's weights and the release +carries the calibrated ones, so a release read a different spread of birth dates from the one its +own solve fitted. That was so on main too. + +The root stage now stores the months, spread the engine's way once: at the FRS weights, in an order +set by an identity-keyed draw on the person's stable identifier. Every later engine read takes the +stored value, and a copy of a person keeps their months. An engine test holds the stage's spread +equal to the engine's own `stratified_uniform`, and shows that stored months do not move with the +weights while unset ones do. + +**On the spines.** Every person on the head's spine carries a value in [0, 12), and every copy of a +source person carries the same one. Storing the months moves the stages' engine reads: Universal +Credit held by reporters falls a further 0.75%, from £42.9bn to £42.6bn. Before calibration 98 of +the 1,217 rows move by more than 1%: 61 Universal Credit rows, 18 State Pension rows, 11 +two-child-limit rows, 4 Pension Credit rows, 3 benefit-cap rows and a childcare row. After it the +two arms fit alike: loss 0.00590 against 0.00588, 98.8% of rows within 10% against 98.7%, all 7 +gates on both. + +**The release against its own solve.** Each reading takes a written file as policyengine-uk 2.130.1 +reads it and compares every row's relative error with the solve's own, in percentage points. + +- **Without M:** 55 of the 1,217 rows differ, 8 by more than 1 point. Read from the file the loss is + 0.0080; the solve's is 0.0059. The State Pension rows move most: people on the new State Pension + at £300 a week or more read 18.8 points higher, and men aged 70 to 74 on the pre-2016 scheme 10.9 + points lower. Private-school pupils read 4.5 points lower, and two Pension Credit rows move by up + to 0.4 points. +- **With M:** 3 rows differ, 1 by more than 1 point; the loss is 0.0076 against the solve's 0.0059. + No State Pension, two-child-limit or Pension Credit row moves. The engine returns the stored + months for every person, and the written column equals the spine's. + +What is left depends on the weights themselves, and is main's too: the engine imputes private-school +attendance by each household's income rank at the file's own weights, so private-school pupils read +4.1 points lower, and two land rows 0.3 and 0.1 points lower. Every row named here rests on 30+ +source households. + +## Household SDLT by part + +`obr.sdlt` binds `stamp_duty_land_tax`. Split by part on calibrated files, with policyengine-uk +2.130.1 at the solved weights: + +- **The bump's file** (main's stages): £15.04bn in all, against OBR's £15.03bn. £9.80bn is on + main-home purchases. The rest, £5.24bn, is additional dwellings together with non-residential + purchases and rent. +- **The head's file**: £15.03bn in all. £9.70bn is on main-home purchases and £5.33bn on the rest. + +Non-residential purchases and rent each rest on fewer than 10 source households, so they are not +split out of the rest, and neither is any part that would give them back. `corporate_sdlt`, which +the bound measure leaves out, is £3.1bn on both. At the spine's weights household SDLT is £6.4bn, +so the calibration reaches OBR's total by raising it about 2.3 times through the weights. That is +the gap the purchases work is for: it waits for HMRC's Annual Stamp Tax Statistics in Chronicle. + +## Tests + +- CI's engine-uk job list on `1da8d681f`: 205 tests, all pass. +- CI's engine-free job list on `1da8d681f`: 17,475 tests. 17,318 pass and 155 skip; two fail on this + machine only: a US scorer test that needs Python 3.14.7, and a test that reads the live Hugging + Face pointer. +- The feed-gated tests on feed 942a1fa, on `1da8d681f`: 99 tests, 97 pass and 2 skip. +- `tools/ci_test_plan.py verify`, `tools/graph_acceptance_burndown.py --verify`, + `tools/pin_uk_uprating_engine_values.py --check`, `tools/refresh_concept_coverage.py --check` and + `ruff check .` pass on `1da8d681f`. +- On the head rebased onto #1242 (`cb922d69f`): CI's engine-uk job list, 205 tests, all pass; and + the engine-free test files that rebase touched, 837 tests, of which 836 pass and one, feed-gated, + skips. +- On this commit's parent (`0912388b6`, rebased onto #1256): CI's engine-uk job list, 207 tests, all + pass; and the engine-free test files the rebases or this PR's last commits touch, #1256's SPI tests + among them, 966 tests, of which 964 pass and two skip. CI runs every lane on the pushed branch. + +## Not in this PR + +- Additional-dwelling purchases and `obr.sdlt` (waiting on HMRC's Annual Stamp Tax Statistics in + Chronicle, and on pe-uk#2241 and pe-uk#2322). +- DWP's Move to UC claim rates as a Chronicle fact, for `would_claim_uc_at_legacy_closure` + (uk-data#534). +- policyengine-uk 2.131.0. +- The sparse-band support floor (its own PR, before the sparse-band exclusions expire on 26 November; + X's row has the same expiry). +- The renewals of the measure exclusions that lapse from 15 October. +- The signed-difference entries for the new and changed columns: R3's and M's net-new columns, E2's + three build-identifier columns, and the columns R1, R2 and R4 change. Their drafts stay outside the + repository until signed. diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index 9498457b89..09c2ab4ef7 100644 --- a/packages/microcosm-build/pyproject.toml +++ b/packages/microcosm-build/pyproject.toml @@ -46,7 +46,7 @@ us = [ # The UK extra adds the rules engine for local metric generation from a # Microcosm UK H5. Target tables remain explicit inputs, and the base package # still does not import policyengine-uk at import time. -uk = ["policyengine-uk>=2.123.0", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] +uk = ["policyengine-uk>=2.130.1", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] [project.scripts] microcosm-build-uk = "microcosm.build.uk_runtime.full_build_cli:main" diff --git a/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json b/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json index c1a7541749..88205b9ad4 100644 --- a/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json +++ b/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json @@ -7226,6 +7226,9 @@ "clamp_minimum": { "type": "number" }, + "clamp_maximum": { + "type": "string" + }, "preserve_asked_rows": { "type": "boolean" }, @@ -9374,6 +9377,9 @@ "rate_cap": { "type": "number" }, + "move_pay_share_cap": { + "type": "number" + }, "move": { "type": "string" }, diff --git a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json index cdc2ca5b6d..05120c2cd7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json @@ -585,6 +585,15 @@ "adjudication": "María's approval of 2026-10-08 of the VAT, road fuel and domestic energy plan (microcosm#1113, microcosm#1123 item 3), decision D2, with the binding confirmed on 2026-10-09: the stage sets the household road-fuel level and incidence, and the household-scope ONS 07.2.2 row is bound in place of the OBR cars receipts, which include business fuel. The reason's figures were restated on 2026-10-11 after her review of PolicyEngine/microcosm#1167, which found bases mixed. Receipts: experiments/1113-consumption-tax-levels-receipts.md.", "approved_on": "2026-10-08", "expires_on": "2027-01-08" + }, + { + "name": "dwp.uc.households_children_5_or_more", + "reason": "Two DWP counts the frame cannot both meet once dependants in approved training qualify as young people (microcosm#1271, uk-data#561). DWP's count of Universal Credit households with five or more children includes families outside the two-child limit, whose third and later children were all born before 6 April 2017 or who meet an exception; DWP's own two-child-limit counts of households with five and with six or more children leave them out, about 21,000 households fewer in DWP's published figures. Fewer than 10 FRS source households have five or more children on Universal Credit and sit outside the limit, so the frame has no real support for that difference and the calibration can reach this row only through the two-child-limit rows, which carry six times its loss weight and are kept. Before the fix the row passed the fit gate through an artefact: a dependant aged 16 to 19 in training counted as a child in this row but failed the engine's qualifying-young-person test, so the two-child-limit rows counted one child fewer in that family. Held out, the row is neither fitted nor gated, and the two-child-limit rows are. Rebind it when the frame can carry large families outside the limit.", + "tracking": "microcosm#1095", + "approved_by": "juaristi22", + "adjudication": "María's ruling of 2026-10-14 on microcosm#1271 (item 4 of Vahid's review, issue comment 6168355102): hold the row out with an expiry rather than keep a fit that rested on fewer than 10 source households", + "approved_on": "2026-10-14", + "expires_on": "2026-11-26" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index fe09bb0587..b566128d27 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -1021,6 +1021,7 @@ "person.salary_sacrifice_asked", "person.salary_sacrifice_reported", "person.sic_industry_division", + "person.statutory_paternity_pay", "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", @@ -1072,6 +1073,10 @@ "person.srp_regular_code5", "benunit.benunit_clone_index", "person.person_clone_index", + "benunit.benunit_build_id", + "household.household_build_id", + "person.person_build_id", + "person.months_since_last_birthday", "household.itl1_code", "household.itl2_code", "household.itl3_code", diff --git a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json index a635145ef9..444d41a8c2 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json +++ b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json @@ -2,7 +2,7 @@ "schema_version": 1, "engine": { "package": "policyengine-uk", - "version": "2.123.0" + "version": "2.130.1" }, "basis": "policyengine-uk parameter ratio between 1 January of the fact's opening year and 1 January of the calibration year (the SPI donor rebasing convention)", "written_by": "tools/pin_uk_uprating_engine_values.py", @@ -81,11 +81,26 @@ "2019-01-01": 0.138, "2020-01-01": 0.138, "2021-01-01": 0.138, - "2022-01-01": 0.138, + "2022-01-01": 0.1505, "2023-01-01": 0.138, "2024-01-01": 0.138, "2025-01-01": 0.15, "2026-01-01": 0.15 } + }, + "dataset_projection": { + "household_weight": { + "parameter": "gov.economic_assumptions.yoy_growth.ons.population", + "growth": { + "2019-01-01": 0.004, + "2020-01-01": 0.004, + "2021-01-01": 0.004, + "2022-01-01": 0.0093, + "2023-01-01": 0.0131, + "2024-01-01": 0.0107, + "2025-01-01": 0.0072, + "2026-01-01": 0.0038 + } + } } } diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 06eab16f39..4b0c8b8baa 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 1279, + "compiled_count": 1283, "counts_by_kind": { - "calibration_drift": 568, - "fixture_only": 43, - "ledger_only": 685 + "calibration_drift": 566, + "fixture_only": 45, + "ledger_only": 691 }, - "difference_count": 1296, + "difference_count": 1302, "differences": [ { "fixture_value": 1608000.0, @@ -168,6 +168,20 @@ "period": 2025, "reason": "microcosm#1069 (ruling R4) moves the OBR FY2025-26 State Pension forecast out of the fit: it counts Great Britain plus pensioners paid abroad and leaves out Northern Ireland, a different quantity from the resident level the DWP Stat-Xplore and DfC rows bind. The exact forecast is retained as diagnostic provenance on dwp.state_pension.amount, so the frozen fixture's fitted row is fixture-only on the current surface." }, + { + "fixture_value": 2036402.0, + "kind": "fixture_only", + "name": "ons.population.female_15_19", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): the five-year 15_19 sex band is split into ages 15, 16-17 and 18-19, because a fit to the band left the ages inside it free to drift. The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single current row has this definition, so the frozen fixture's row is fixture-only on the current surface." + }, + { + "fixture_value": 2149930.0, + "kind": "fixture_only", + "name": "ons.population.male_15_19", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): the five-year 15_19 sex band is split into ages 15, 16-17 and 18-19, because a fit to the band left the ages inside it free to drift. The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single current row has this definition, so the frozen fixture's row is fixture-only on the current surface." + }, { "fixture_value": 82000.0, "kind": "fixture_only", @@ -5047,6 +5061,27 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 408119.0, + "name": "ons.population.female_15", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, + { + "kind": "ledger_only", + "ledger_value": 812522.0, + "name": "ons.population.female_16_17", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, + { + "kind": "ledger_only", + "ledger_value": 781458.0, + "name": "ons.population.female_18_19", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, { "kind": "ledger_only", "ledger_value": 425428.0, @@ -5054,6 +5089,27 @@ "period": 2025, "reason": "María ruling 2026-08-21: new female 90_plus tail constrains ages 90+; the incumbent six-year terminal band left ages 91+ unconstrained." }, + { + "kind": "ledger_only", + "ledger_value": 427529.0, + "name": "ons.population.male_15", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, + { + "kind": "ledger_only", + "ledger_value": 856296.0, + "name": "ons.population.male_16_17", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, + { + "kind": "ledger_only", + "ledger_value": 823183.0, + "name": "ons.population.male_18_19", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 sex band, bound so the fit holds the age mix inside the band. The incumbent binds only the band, so this row has no fixture counterpart." + }, { "kind": "ledger_only", "ledger_value": 219915.0, @@ -9175,14 +9231,6 @@ "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, - { - "fixture_value": 2036402.0, - "kind": "calibration_drift", - "ledger_value": 2002099.0, - "name": "ons.population.female_15_19", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, { "fixture_value": 2033478.0, "kind": "calibration_drift", @@ -9319,14 +9367,6 @@ "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, - { - "fixture_value": 2149930.0, - "kind": "calibration_drift", - "ledger_value": 2107008.0, - "name": "ons.population.male_15_19", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, { "fixture_value": 2136737.0, "kind": "calibration_drift", 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 3b8b2ee243..1e4bafaf24 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 @@ -470,7 +470,7 @@ "required_mass_change_reason": "Capital-gains incidence anchor moves the mass of non-liable clone households back to their paired originals until the sub-exempt and loss-making clone mass match the Advani-Summers reporter composition at the redrawn liable mass; every pair's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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, the HMRC Table 3 redrawn gains on the spine and Advani-Summers capital-gains incidence" @@ -493,7 +493,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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" @@ -516,7 +516,7 @@ "required_mass_change_reason": "Capital-gains residential split divides every household carrying liable gainers into arms weighted by the solved residential probabilities, one arm per subset of residential gainers; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative) on the amounts the Table 3 redraw placed" @@ -538,7 +538,7 @@ "required_mass_change_reason": "Capital-gains support split divides the wealthiest households of each HMRC Table 3 income band into light copies at equal weight; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2026 release, Table 3 for 2024-25, via the vendored conditioning facts", "survey": "Family Resources Survey 2024-25, SPI synthetic support and HMRC Capital Gains Tax statistics Table 3 (2024-25 individuals by size of gain and taxable income)" @@ -567,7 +567,7 @@ "required_mass_change_reason": "ETB public-services imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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" @@ -587,7 +587,7 @@ "required_mass_change_reason": "ETB VAT expenditure-rate imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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" @@ -608,7 +608,7 @@ "required_mass_change_reason": "CGT asset-type assignment on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative), Table 4.1 (individuals claiming Business Asset Disposal Relief or Investors' Relief by band of qualifying gain, 2024-25, administrative) and Table 7 (disposals, proceeds and gains by asset type, 2023-24, sample-based), vendored from the pinned Chronicle feed" @@ -632,7 +632,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "417d662bb58a25db06dcd542a83553a6775bbc12a7c3a995f695b091483215ed", @@ -721,7 +721,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -763,7 +763,7 @@ "required_mass_change_reason": "LCFS consumption imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "UK Data Service SN 9603 Living Costs and Food Survey 2024-25 (2nd edition) household/person tabs and the vendored Chronicle facts for NEED 2024 mean kWh, DESNZ Energy Trends and QEP average prices paid for calendar 2024, road-fuel outturn (DESNZ, HMRC, ONS Consumer Trends) and road fuel by vehicle type (DESNZ), ONS Consumer Trends household spending by COICOP division, the DfT VEH1103 licensed-car stock, NTS bus use, and the DfT and devolved bus finance tables and fares indices.", "survey": "Living Costs and Food Survey 2024-25" @@ -789,7 +789,7 @@ "required_mass_change_reason": "NTS bus-travel imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "Department for Transport National Travel Survey, UK Data Service SN 5340 (End User Licence, 19th edition, November 2025), DOI 10.5255/UKDA-SN-5340-19; local licensed tab files (household, individual, trip); England residents only from 2013.", "survey": "National Travel Survey 2002-2024" @@ -812,7 +812,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "https://www.gov.uk/government/statistics/property-rental-income-statistics", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Property Rental Income Statistics 2026 (tax year 2024-25)" @@ -836,7 +836,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "MHCLG dwellings and ONS UK House Price Index December 2025 regional average prices.", "survey": "Public regional property reference" @@ -863,7 +863,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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" @@ -896,7 +896,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "source_vintages": { "source": "Reserved SPI support households for the HMRC Table 2.5 total-income bands from GBP 200,000, stacked at the published band taxpayer count over the donors per band (PolicyEngine/chronicle#280 lane).", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Income Tax liabilities statistics July 2026 Table 2.5" @@ -918,7 +918,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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" @@ -940,7 +940,7 @@ "required_mass_change_reason": "WAS Lifetime ISA imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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 round-8 person and household tabs (interviews April 2020 to March 2022, Great Britain).", "survey": "Wealth and Assets Survey round 8" @@ -975,7 +975,7 @@ "required_mass_change_reason": "WAS wealth imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "cf48c60759e39bc322dc646360caf46eeb797b157cfd0a02066a95a34380b851", + "source_manifest_sha256": "ec297426446c5840da4e2118a3db1229bc650bb4e50801a293fd5a99e9f80cac", "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 f38d43f2c8..b9997936fd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -255,6 +255,7 @@ "person_household_id", "age", "gender", + "months_since_last_birthday", "marital_status", "hours_worked", "care_hours", @@ -284,6 +285,7 @@ "student_loan_repayments", "statutory_sick_pay", "statutory_maternity_pay", + "statutory_paternity_pay", "student_loans", "access_fund", "education_grants", @@ -353,6 +355,7 @@ "rent_paid_as_boarder", "rent_paid_as_lodger", "age", + "months_since_last_birthday", "hours_worked", "employment_income", "self_employment_income", @@ -365,6 +368,7 @@ "student_loan_repayments", "statutory_sick_pay", "statutory_maternity_pay", + "statutory_paternity_pay", "childcare_expenses", "personal_pension_contributions", "employee_pension_contributions", @@ -377,7 +381,7 @@ "external_child_payments", "care_hours" ], - "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, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)." + "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, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)." }, { "stage": "age_tail", @@ -702,7 +706,7 @@ "nonnegative_outputs": [ "sic_industry_division" ], - "notes": "Ports FRS employment derivations. empstati code 11 preserves the incumbent truncated-map artifact as LONG_TERM_DISABLED, so OTHER_INACTIVE is not emitted; mjobsect and sic are direct-indexed and fail loudly if absent." + "notes": "Ports FRS employment derivations. empstati codes 1-11 each map to the status their UKDS SN 9563 adult-table data-dictionary label names, as uk-data#526 does: 11 (Other Inactive) is OTHER_INACTIVE and only 9 (Permanently sick/disabled) is LONG_TERM_DISABLED. People outside adult.tab are CHILD; an adult with a blank or unknown code refuses the build. mjobsect and sic are direct-indexed and fail loudly if absent." }, { "stage": "frs_council_tax", @@ -830,7 +834,7 @@ }, { "kind": "derive", - "scope": "current/highest education, QYP inputs, EMA cell-mean degenerate fills, and benefits-in-own-right flag" + "scope": "current/highest education, QYP inputs (approved training is TRAINEE = 1 on a child-table record), EMA cell-mean degenerate fills, and benefits-in-own-right flag" }, { "kind": "impute_cell_means", @@ -858,7 +862,7 @@ "child_ema", "age_started_or_accepted_current_education_or_training" ], - "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant." + "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build." }, { "stage": "frs_legacy_proxies", @@ -1079,7 +1083,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095)." + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095)." }, { "stage": "frs_person_draws", @@ -5066,7 +5070,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both." + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both." }, { "stage": "pension_credit_take_up", @@ -5730,7 +5734,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.123.0 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount" ], @@ -5811,7 +5815,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.123.0 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount", "gov.hmrc.cgt.badr.lifetime_limit", @@ -5967,7 +5971,7 @@ "operations": [ { "kind": "fit_weighted_qrf", - "training_population": "support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1", + "training_population": "support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1 and employment_income > 0", "target_population": "salary_sacrifice_asked != 1 and employment_income > 0, frame-wide; people not asked who have no employment income are set to zero", "predictors": [ "age", @@ -5981,6 +5985,7 @@ "seed": 42, "n_estimators": 100, "clamp_minimum": 0, + "clamp_maximum": "employment_income floored at zero; every row, reported or predicted, FRS and SPI channel", "preserve_asked_rows": true, "cache": false }, @@ -5990,7 +5995,8 @@ "target": 5400000, "donor_pool": "employee_pension_contributions > 0 and pension_contributions_via_salary_sacrifice == 0 and employment_income > 0", "rate_cap": 0.5, - "move": "full employee_pension_contributions to pension_contributions_via_salary_sacrifice; source zeroed; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere)", + "move_pay_share_cap": 0.5, + "move": "employee_pension_contributions, up to move_pay_share_cap of employment_income, to pension_contributions_via_salary_sacrifice; the rest stays in the source; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere)", "seed": 2024, "salt": "salary_sacrifice_conversion", "receipt": "weighted_headcount", @@ -6014,7 +6020,7 @@ "employee_pension_contributions", "employment_income" ], - "notes": "The QRF trains only on the 2024-25 FRS asked subset. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' full pension amounts into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief." + "notes": "The QRF trains only on the 2024-25 FRS asked subset with pay. Salary sacrifice is pay given up, so every amount, reported or predicted, is limited to the pay the person still receives, and a person without pay has none (uk-data#518, microcosm#1095); the limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' pension amounts, up to half their pay, into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; what does not move stays an employee contribution, so the sacrifice never exceeds the pay that remains. Before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief." }, { "stage": "student_loans", 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 3835c52700..6c14fdaeb5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -386,6 +386,7 @@ stages: - person_household_id - age - gender + - months_since_last_birthday - marital_status - hours_worked - care_hours @@ -415,6 +416,7 @@ stages: - student_loan_repayments - statutory_sick_pay - statutory_maternity_pay + - statutory_paternity_pay - student_loans - access_fund - education_grants @@ -483,6 +485,7 @@ stages: - rent_paid_as_boarder - rent_paid_as_lodger - age + - months_since_last_birthday - hours_worked - employment_income - self_employment_income @@ -495,6 +498,7 @@ stages: - student_loan_repayments - statutory_sick_pay - statutory_maternity_pay + - statutory_paternity_pay - childcare_expenses - personal_pension_contributions - employee_pension_contributions @@ -506,7 +510,7 @@ stages: - housing_service_charges - external_child_payments - care_hours - 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, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). + 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, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). - 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). @@ -708,7 +712,7 @@ stages: - sic_industry_division nonnegative_outputs: - sic_industry_division - notes: Ports FRS employment derivations. empstati code 11 preserves the incumbent truncated-map artifact as LONG_TERM_DISABLED, so OTHER_INACTIVE is not emitted; mjobsect and sic are direct-indexed and fail loudly if absent. + notes: "Ports FRS employment derivations. empstati codes 1-11 each map to the status their UKDS SN 9563 adult-table data-dictionary label names, as uk-data#526 does: 11 (Other Inactive) is OTHER_INACTIVE and only 9 (Permanently sick/disabled) is LONG_TERM_DISABLED. People outside adult.tab are CHILD; an adult with a blank or unknown code refuses the build. mjobsect and sic are direct-indexed and fail loudly if absent." - stage: frs_council_tax 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. @@ -805,7 +809,7 @@ stages: numeric_errors: coerce runtime_sha256_required: true - kind: derive - scope: current/highest education, QYP inputs, EMA cell-mean degenerate fills, and benefits-in-own-right flag + scope: current/highest education, QYP inputs (approved training is TRAINEE = 1 on a child-table record), EMA cell-mean degenerate fills, and benefits-in-own-right flag - kind: impute_cell_means cells: - 'single cell: EMA participants (code == 1; adema/ademaamt pair, eduma/edumaamt when adema is absent; chema/chemaamt for children)' @@ -826,7 +830,7 @@ stages: - adult_ema - child_ema - age_started_or_accepted_current_education_or_training - notes: 'Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant.' + notes: 'Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build.' - stage: frs_legacy_proxies 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. plus policyengine-uk DWP parameters. @@ -983,7 +987,7 @@ stages: - maximum_extended_childcare_hours_usage nonnegative_outputs: - maximum_extended_childcare_hours_usage - notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). + notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). - stage: frs_person_draws survey: Family Resources Survey 2024-25 source: Sourced UK take-up contract rates and identity-keyed deterministic draws. @@ -4105,7 +4109,7 @@ stages: rewrites: - frs_benunit_capital - would_claim_uc - notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. + notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. - stage: pension_credit_take_up survey: Family Resources Survey 2024-25 source: 'DWP Income-related benefits: estimates of take-up, financial year ending 2024 (Pension Credit caseload take-up by component), applied to PolicyEngine-UK pre-take-up Pension Credit entitlement after the SPI income chain.' @@ -4627,7 +4631,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.123.0 via microcosm-build[uk] + dependency: policyengine-uk>=2.130.1 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount instant_rule: raw dated parameter files evaluated at 1 June of the build period's tax year @@ -4692,7 +4696,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.123.0 via microcosm-build[uk] + dependency: policyengine-uk>=2.130.1 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount - gov.hmrc.cgt.badr.lifetime_limit @@ -4818,7 +4822,7 @@ stages: runtime_sha256_required: true operations: - kind: fit_weighted_qrf - training_population: support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1 + training_population: support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1 and employment_income > 0 target_population: salary_sacrifice_asked != 1 and employment_income > 0, frame-wide; people not asked who have no employment income are set to zero predictors: - age @@ -4830,6 +4834,7 @@ stages: seed: 42 n_estimators: 100 clamp_minimum: 0 + clamp_maximum: employment_income floored at zero; every row, reported or predicted, FRS and SPI channel preserve_asked_rows: true cache: false - kind: convert_donors_to_target_stock @@ -4837,7 +4842,8 @@ stages: target: 5400000 donor_pool: employee_pension_contributions > 0 and pension_contributions_via_salary_sacrifice == 0 and employment_income > 0 rate_cap: 0.5 - move: full employee_pension_contributions to pension_contributions_via_salary_sacrifice; source zeroed; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere) + move_pay_share_cap: 0.5 + move: employee_pension_contributions, up to move_pay_share_cap of employment_income, to pension_contributions_via_salary_sacrifice; the rest stays in the source; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere) seed: 2024 salt: salary_sacrifice_conversion receipt: weighted_headcount @@ -4856,7 +4862,7 @@ stages: - pension_contributions_via_salary_sacrifice - employee_pension_contributions - employment_income - notes: The QRF trains only on the 2024-25 FRS asked subset. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' full pension amounts into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief. + notes: The QRF trains only on the 2024-25 FRS asked subset with pay. Salary sacrifice is pay given up, so every amount, reported or predicted, is limited to the pay the person still receives, and a person without pay has none (uk-data#518, microcosm#1095); the limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' pension amounts, up to half their pay, into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; what does not move stays an employee contribution, so the sacrifice never exceeds the pay that remains. Before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief. - stage: student_loans survey: Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England source: Explore Education Statistics Table 6a, Higher education total diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 9f53ca6edd..7e4bb7e553 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,11 +1,11 @@ { "source_fact_feed": "chronicle-uk-942a1fa0-consumer-facts", "target_period": 2025, - "candidate_count": 1299, - "contract_target_count": 344, - "active_reference_count": 1279, + "candidate_count": 1303, + "contract_target_count": 348, + "active_reference_count": 1283, "status_counts": { - "active": 1279, + "active": 1283, "no_fact_at_or_before_period": 5, "signed_excluded": 15 }, @@ -818,7 +818,15 @@ "geography_level": "country", "geography_id": "K02000001" }, - "ons.population.female_15_19": { + "ons.population.female_15": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "ons.population.female_16_17": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "ons.population.female_18_19": { "geography_level": "country", "geography_id": "K02000001" }, @@ -894,7 +902,15 @@ "geography_level": "country", "geography_id": "K02000001" }, - "ons.population.male_15_19": { + "ons.population.male_15": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "ons.population.male_16_17": { + "geography_level": "country", + "geography_id": "K02000001" + }, + "ons.population.male_18_19": { "geography_level": "country", "geography_id": "K02000001" }, @@ -4341,7 +4357,9 @@ "ons.population.age_80_89_by_region@W92000004", "ons.population.female_0_4", "ons.population.female_10_14", - "ons.population.female_15_19", + "ons.population.female_15", + "ons.population.female_16_17", + "ons.population.female_18_19", "ons.population.female_20_24", "ons.population.female_25_29", "ons.population.female_30_34", @@ -4360,7 +4378,9 @@ "ons.population.female_90_plus", "ons.population.male_0_4", "ons.population.male_10_14", - "ons.population.male_15_19", + "ons.population.male_15", + "ons.population.male_16_17", + "ons.population.male_18_19", "ons.population.male_20_24", "ons.population.male_25_29", "ons.population.male_30_34", @@ -16303,21 +16323,51 @@ } ] }, - "ons.population.female_15_19": { + "ons.population.female_15": { "status": "active", "candidates": [ { - "name": "ons.population.female_15_19", + "name": "ons.population.female_15", "status": "active", - "matched_fact_count_overall": 15, - "matched_fact_count_at_or_before_period": 15, + "matched_fact_count_overall": 3, + "matched_fact_count_at_or_before_period": 3, "resolved_period": 2025, - "resolved_value": 2002099.0, + "resolved_value": 408119.0, "resolved_fact_period": "2025", "resolved_fact_key": "ledger_aggregate_fact_v2_5adba03177e435af73792b18" } ] }, + "ons.population.female_16_17": { + "status": "active", + "candidates": [ + { + "name": "ons.population.female_16_17", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, + "resolved_period": 2025, + "resolved_value": 812522.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_04ff61d39ac01fc622589dbb" + } + ] + }, + "ons.population.female_18_19": { + "status": "active", + "candidates": [ + { + "name": "ons.population.female_18_19", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, + "resolved_period": 2025, + "resolved_value": 781458.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_05db7085ea57cab32a14fcf6" + } + ] + }, "ons.population.female_20_24": { "status": "active", "candidates": [ @@ -16588,16 +16638,46 @@ } ] }, - "ons.population.male_15_19": { + "ons.population.male_15": { "status": "active", "candidates": [ { - "name": "ons.population.male_15_19", + "name": "ons.population.male_15", "status": "active", - "matched_fact_count_overall": 15, - "matched_fact_count_at_or_before_period": 15, + "matched_fact_count_overall": 3, + "matched_fact_count_at_or_before_period": 3, + "resolved_period": 2025, + "resolved_value": 427529.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_01bde34c40a1237a7f636f81" + } + ] + }, + "ons.population.male_16_17": { + "status": "active", + "candidates": [ + { + "name": "ons.population.male_16_17", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, + "resolved_period": 2025, + "resolved_value": 856296.0, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_888c3c10fc18dfe96e35faa9" + } + ] + }, + "ons.population.male_18_19": { + "status": "active", + "candidates": [ + { + "name": "ons.population.male_18_19", + "status": "active", + "matched_fact_count_overall": 6, + "matched_fact_count_at_or_before_period": 6, "resolved_period": 2025, - "resolved_value": 2107008.0, + "resolved_value": 823183.0, "resolved_fact_period": "2025", "resolved_fact_key": "ledger_aggregate_fact_v2_d4534116ee44f57db1120c83" } diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 678a75424b..5377976e45 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -487,7 +487,9 @@ "ons.population.age_80_89_by_region": "ons.population", "ons.population.female_0_4": "ons.population", "ons.population.female_10_14": "ons.population", - "ons.population.female_15_19": "ons.population", + "ons.population.female_15": "ons.population", + "ons.population.female_16_17": "ons.population", + "ons.population.female_18_19": "ons.population", "ons.population.female_20_24": "ons.population", "ons.population.female_25_29": "ons.population", "ons.population.female_30_34": "ons.population", @@ -506,7 +508,9 @@ "ons.population.female_90_plus": "ons.population", "ons.population.male_0_4": "ons.population", "ons.population.male_10_14": "ons.population", - "ons.population.male_15_19": "ons.population", + "ons.population.male_15": "ons.population", + "ons.population.male_16_17": "ons.population", + "ons.population.male_18_19": "ons.population", "ons.population.male_20_24": "ons.population", "ons.population.male_25_29": "ons.population", "ons.population.male_30_34": "ons.population", @@ -821,7 +825,9 @@ "ons.population.age_80_89_by_region": "Age 80 89 by region", "ons.population.female_0_4": "Female 0 4", "ons.population.female_10_14": "Female 10 14", - "ons.population.female_15_19": "Female 15 19", + "ons.population.female_15": "Female 15", + "ons.population.female_16_17": "Female 16 17", + "ons.population.female_18_19": "Female 18 19", "ons.population.female_20_24": "Female 20 24", "ons.population.female_25_29": "Female 25 29", "ons.population.female_30_34": "Female 30 34", @@ -840,7 +846,9 @@ "ons.population.female_90_plus": "Female 90 plus", "ons.population.male_0_4": "Male 0 4", "ons.population.male_10_14": "Male 10 14", - "ons.population.male_15_19": "Male 15 19", + "ons.population.male_15": "Male 15", + "ons.population.male_16_17": "Male 16 17", + "ons.population.male_18_19": "Male 18 19", "ons.population.male_20_24": "Male 20 24", "ons.population.male_25_29": "Male 25 29", "ons.population.male_30_34": "Male 30 34", @@ -13850,16 +13858,72 @@ "expected_member_count": 5 }, { - "name": "ons.population.female_15_19", + "name": "ons.population.female_15", + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "female", + "age": [ + 15 + ] + }, + "geography_level": "country", + "geography_id": "K02000001", + "dimensions": [ + "age", + "sex" + ] + }, + "entity": "person", + "measure": "ons/female_15", + "family": "ons_population", + "period": 2025, + "metadata": { + "contract_target_id": "ons.population.female_15", + "measure_kind": "prepared_column" + }, + "value_operation": "sum", + "expected_member_count": 1 + }, + { + "name": "ons.population.female_16_17", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "dimension_values": { "sex": "female", "age": [ - 15, 16, - 17, + 17 + ] + }, + "geography_level": "country", + "geography_id": "K02000001", + "dimensions": [ + "age", + "sex" + ] + }, + "entity": "person", + "measure": "ons/female_16_17", + "family": "ons_population", + "period": 2025, + "metadata": { + "contract_target_id": "ons.population.female_16_17", + "measure_kind": "prepared_column" + }, + "value_operation": "sum", + "expected_member_count": 2 + }, + { + "name": "ons.population.female_18_19", + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "female", + "age": [ 18, 19 ] @@ -13872,15 +13936,15 @@ ] }, "entity": "person", - "measure": "ons/female_15_19", + "measure": "ons/female_18_19", "family": "ons_population", "period": 2025, "metadata": { - "contract_target_id": "ons.population.female_15_19", + "contract_target_id": "ons.population.female_18_19", "measure_kind": "prepared_column" }, "value_operation": "sum", - "expected_member_count": 5 + "expected_member_count": 2 }, { "name": "ons.population.female_20_24", @@ -14473,16 +14537,72 @@ "expected_member_count": 5 }, { - "name": "ons.population.male_15_19", + "name": "ons.population.male_15", + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "male", + "age": [ + 15 + ] + }, + "geography_level": "country", + "geography_id": "K02000001", + "dimensions": [ + "age", + "sex" + ] + }, + "entity": "person", + "measure": "ons/male_15", + "family": "ons_population", + "period": 2025, + "metadata": { + "contract_target_id": "ons.population.male_15", + "measure_kind": "prepared_column" + }, + "value_operation": "sum", + "expected_member_count": 1 + }, + { + "name": "ons.population.male_16_17", "ledger_selector": { "source_name": "ons", "source_concept": "ons.mid_year_population_estimate", "dimension_values": { "sex": "male", "age": [ - 15, 16, - 17, + 17 + ] + }, + "geography_level": "country", + "geography_id": "K02000001", + "dimensions": [ + "age", + "sex" + ] + }, + "entity": "person", + "measure": "ons/male_16_17", + "family": "ons_population", + "period": 2025, + "metadata": { + "contract_target_id": "ons.population.male_16_17", + "measure_kind": "prepared_column" + }, + "value_operation": "sum", + "expected_member_count": 2 + }, + { + "name": "ons.population.male_18_19", + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "male", + "age": [ 18, 19 ] @@ -14495,15 +14615,15 @@ ] }, "entity": "person", - "measure": "ons/male_15_19", + "measure": "ons/male_18_19", "family": "ons_population", "period": 2025, "metadata": { - "contract_target_id": "ons.population.male_15_19", + "contract_target_id": "ons.population.male_18_19", "measure_kind": "prepared_column" }, "value_operation": "sum", - "expected_member_count": 5 + "expected_member_count": 2 }, { "name": "ons.population.male_20_24", diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index 81bc81ea30..47762cc114 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -25,8 +25,8 @@ "pinned_ref": "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3", "pinned_version": "1.56.16", "registry_rows": 652, - "mapped_rows": 622, - "excluded_rows": 30, + "mapped_rows": 620, + "excluded_rows": 32, "mapped": { "dwp/benefit_capped_households": "dwp.benefit_cap.capped_households", "dwp/esa_claimants": "dwp.esa_claimants", @@ -394,7 +394,6 @@ "ons/east_midlands_age_80_89": "ons.population.age_80_89_by_region", "ons/female_0_4": "ons.population.female_0_4", "ons/female_10_14": "ons.population.female_10_14", - "ons/female_15_19": "ons.population.female_15_19", "ons/female_20_24": "ons.population.female_20_24", "ons/female_25_29": "ons.population.female_25_29", "ons/female_30_34": "ons.population.female_30_34", @@ -427,7 +426,6 @@ "ons/lone_parent_non_dependent_children_households": "ons.household_composition.lone_parent_non_dependent_children_households", "ons/male_0_4": "ons.population.male_0_4", "ons/male_10_14": "ons.population.male_10_14", - "ons/male_15_19": "ons.population.male_15_19", "ons/male_20_24": "ons.population.male_20_24", "ons/male_25_29": "ons.population.male_25_29", "ons/male_30_34": "ons.population.male_30_34", @@ -681,7 +679,9 @@ "obr/salary_sacrifice_users_total": "R10 (microcosm#1069 c9): the salary-sacrifice users total contradicts the staged support and the two headcounts either side of £2,000 already bind it; it leaves the fit.", "obr/ni": "Diagnostic only (María's ruling of 2026-10-09, microcosm#1095; uk-data#537 drops the row): OBR's total NICs receipts (table 3.8 row 12) is a cash total, while the three bound class rows are accrued, and it carries statutory-payment recoveries, Class 1A, 1B and 3 and settlements, which no household carries; at the calendar-2025 window it sits about 1.8% above the class rows. The exact OBR FY2025-26 figure is retained only as diagnostic provenance on obr.ni_employee; no source value is adjusted.", "dwp/pip_dl_standard_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted.", - "dwp/pip_dl_enhanced_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted." + "dwp/pip_dl_enhanced_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted.", + "ons/female_15_19": "Split, not dropped (María's ruling of 2026-10-13 (microcosm#1095, uk-data#563)): a fit to the five-year band leaves the ages inside it free to drift, so the contract binds ages 15, 16-17 and 18-19 instead (ons.population.female_15, female_16_17 and female_18_19). The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single contract row has this row's definition.", + "ons/male_15_19": "Split, not dropped (María's ruling of 2026-10-13 (microcosm#1095, uk-data#563)): a fit to the five-year band leaves the ages inside it free to drift, so the contract binds ages 15, 16-17 and 18-19 instead (ons.population.male_15, male_16_17 and male_18_19). The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single contract row has this row's definition." }, "unmapped_declarations": { "hmrc.salary_sacrifice.it_relief_basic_rate": "New declaration backed by chronicle#139 salary-sacrifice facts; the incumbent source CSV is still 410-Gone at 12a1e028 and its rows are silently suppressed by the bare-except drop in build_loss_matrix.py:376-394.", @@ -838,7 +838,13 @@ "ons.households.english_regions": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", "nrs.households.scotland": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", "welshgov.households.wales": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", - "nisra.households.northern_ireland": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win)." + "nisra.households.northern_ireland": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", + "ons.population.female_15": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 female band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/female_15_19 is excluded.", + "ons.population.female_16_17": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 female band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/female_15_19 is excluded.", + "ons.population.female_18_19": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 female band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/female_15_19 is excluded.", + "ons.population.male_15": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 male band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/male_15_19 is excluded.", + "ons.population.male_16_17": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 male band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/male_15_19 is excluded.", + "ons.population.male_18_19": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the incumbent five-year 15_19 male band is split into ages 15, 16-17 and 18-19, the ages at which the child and qualifying-young-person rules change. The three rows partition the band, so no incumbent row has this row's definition and incumbent ons/male_15_19 is excluded." }, "suppressed_ancestors": { "hmrc/salary_sacrifice_it_relief_basic_rate": { @@ -869,7 +875,8 @@ "two_child_limit_year_surface": "Chronicle's 15 dwp/uc/two_child_limit/* facts are the April-2025 publication (period month 2025-04) and are the only TCL facts; there are no 2026 Ledger facts. At 12a1e028, the incumbent pins the same published numbers at {2026}, so at calibration year 2025 Ledger activates all 15 TCL targets while the incumbent drops them from the 652-row union to the 637-row effective surface; the 2026 effective surface remains 652. The same 12a1e028 refresh adds the new incumbent dwp/uc/households row. Fixture B treats the 15 TCL rows as enumerated ledger-side additions.", "geography_level_order_canonicalization": "The 9 voa.council_tax_stock.* rows use country, region order to match the rest of the contract; consumers use membership only.", "ons_terminal_sex_band_split": "María ruling 2026-08-21: split the incumbent terminal sex bands into uniform five-year 85_89 bands plus 90_plus tails. The incumbent six-year 85_90 terminal rows leave ages 91+ unconstrained; Microcosm keeps the uniform five-year bands and constrains the tail. The 85_89-vs-85_90 value gap is the single-age-90 share and is a signed semantic difference.", - "cgt_band_promotion": "The 18 banded CGT registry names (hmrc/cgt_taxpayers_band_ and hmrc/capital_gains_band_, nine bands from 12,300) move from excluded to mapped: Chronicle now carries HMRC Table 2.1a for 2024-25 (cgt_size_of_gain_2026), so the incumbent's unpackaged uk-data CSV is superseded by published individual rows bound at identity; the 3,000 and 6,000 bands the incumbent never carried are authored under the same naming form and the 0-2,999 band below the 2024 AEA is signed out (microcosm#467, #725). Chronicle's Table 2.1a package emits taxpayers and gains only, although the published sheet carries an amounts-of-tax column, so no liability-by-band target exists yet; it becomes possible once Chronicle emits that column." + "cgt_band_promotion": "The 18 banded CGT registry names (hmrc/cgt_taxpayers_band_ and hmrc/capital_gains_band_, nine bands from 12,300) move from excluded to mapped: Chronicle now carries HMRC Table 2.1a for 2024-25 (cgt_size_of_gain_2026), so the incumbent's unpackaged uk-data CSV is superseded by published individual rows bound at identity; the 3,000 and 6,000 bands the incumbent never carried are authored under the same naming form and the 0-2,999 band below the 2024 AEA is signed out (microcosm#467, #725). Chronicle's Table 2.1a package emits taxpayers and gains only, although the published sheet carries an amounts-of-tax column, so no liability-by-band target exists yet; it becomes possible once Chronicle emits that column.", + "ons_15_19_sex_band_split": "María's ruling of 2026-10-13 (microcosm#1095, uk-data#563): the two national 15_19 sex bands are split into ages 15, 16-17 and 18-19. uk-data#563 found weights that fit the five-year band while holding too many 16-year-olds and too few 18- and 19-year-olds, and nothing in this contract bound the mix inside the band either. The six rows partition the two bands and read the same ONS mid-year estimate by single year of age, so each sex's three rows sum to the band they replace. The incumbent ons/female_15_19 and ons/male_15_19 rows move from mapped to excluded: no single contract row has their definition." }, "scope_target_ids": [ "obr.income_tax", @@ -966,7 +973,9 @@ "ons.population.age_80_89_by_region", "ons.population.female_0_4", "ons.population.female_10_14", - "ons.population.female_15_19", + "ons.population.female_15", + "ons.population.female_16_17", + "ons.population.female_18_19", "ons.population.female_20_24", "ons.population.female_25_29", "ons.population.female_30_34", @@ -985,7 +994,9 @@ "ons.population.female_90_plus", "ons.population.male_0_4", "ons.population.male_10_14", - "ons.population.male_15_19", + "ons.population.male_15", + "ons.population.male_16_17", + "ons.population.male_18_19", "ons.population.male_20_24", "ons.population.male_25_29", "ons.population.male_30_34", @@ -3208,6 +3219,7 @@ "schedule_flag": "pays_scottish_income_tax", "basic_below_rate": 0.3, "additional": "the schedule's top rate", + "gift_aid_extends_bands": "gift_aid_grossed_up", "rate_class": "basic" }, "from_entity": "person", @@ -3256,6 +3268,7 @@ "schedule_flag": "pays_scottish_income_tax", "basic_below_rate": 0.3, "additional": "the schedule's top rate", + "gift_aid_extends_bands": "gift_aid_grossed_up", "rate_class": "higher" }, "from_entity": "person", @@ -3304,6 +3317,7 @@ "schedule_flag": "pays_scottish_income_tax", "basic_below_rate": 0.3, "additional": "the schedule's top rate", + "gift_aid_extends_bands": "gift_aid_grossed_up", "rate_class": "additional" }, "from_entity": "person", @@ -8136,7 +8150,7 @@ "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." }, { - "target_id": "ons.population.female_15_19", + "target_id": "ons.population.female_15", "family": "ons_population", "geography_levels": [ "country" @@ -8158,13 +8172,13 @@ { "concept": "uk.demographics.age", "operator": "<=", - "value": 19 + "value": 15 } ] }, "bindings": { "policyengine": { - "metric_name": "ons/female_15_19", + "metric_name": "ons/female_15", "filters": [ { "variable": "gender", @@ -8178,7 +8192,7 @@ { "variable": "age", "operator": "<=", - "value": 19 + "value": 15 } ], "value_variable": "person_count", @@ -8186,7 +8200,77 @@ "map_to": "household" }, "axiom": { - "metric_name": "ons/female_15_19", + "metric_name": "ons/female_15", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "female", + "age": [ + 15 + ] + } + }, + "category_id": "ons.population", + "label": "Female 15", + "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." + }, + { + "target_id": "ons.population.female_16_17", + "family": "ons_population", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "filters": [ + { + "concept": "uk.demographics.gender", + "equals": "female" + }, + { + "concept": "uk.demographics.age", + "operator": ">=", + "value": 16 + }, + { + "concept": "uk.demographics.age", + "operator": "<=", + "value": 17 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "ons/female_16_17", + "filters": [ + { + "variable": "gender", + "equals": "FEMALE" + }, + { + "variable": "age", + "operator": ">=", + "value": 16 + }, + { + "variable": "age", + "operator": "<=", + "value": 17 + } + ], + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household" + }, + "axiom": { + "metric_name": "ons/female_16_17", "status": "pending", "value_rule": "uk.person.count" } @@ -8197,16 +8281,84 @@ "dimension_values": { "sex": "female", "age": [ - 15, 16, - 17, + 17 + ] + } + }, + "category_id": "ons.population", + "label": "Female 16 17", + "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." + }, + { + "target_id": "ons.population.female_18_19", + "family": "ons_population", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "filters": [ + { + "concept": "uk.demographics.gender", + "equals": "female" + }, + { + "concept": "uk.demographics.age", + "operator": ">=", + "value": 18 + }, + { + "concept": "uk.demographics.age", + "operator": "<=", + "value": 19 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "ons/female_18_19", + "filters": [ + { + "variable": "gender", + "equals": "FEMALE" + }, + { + "variable": "age", + "operator": ">=", + "value": 18 + }, + { + "variable": "age", + "operator": "<=", + "value": 19 + } + ], + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household" + }, + "axiom": { + "metric_name": "ons/female_18_19", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "female", + "age": [ 18, 19 ] } }, "category_id": "ons.population", - "label": "Female 15 19", + "label": "Female 18 19", "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." }, { @@ -9528,7 +9680,7 @@ "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." }, { - "target_id": "ons.population.male_15_19", + "target_id": "ons.population.male_15", "family": "ons_population", "geography_levels": [ "country" @@ -9550,13 +9702,13 @@ { "concept": "uk.demographics.age", "operator": "<=", - "value": 19 + "value": 15 } ] }, "bindings": { "policyengine": { - "metric_name": "ons/male_15_19", + "metric_name": "ons/male_15", "filters": [ { "variable": "gender", @@ -9570,7 +9722,7 @@ { "variable": "age", "operator": "<=", - "value": 19 + "value": 15 } ], "value_variable": "person_count", @@ -9578,7 +9730,77 @@ "map_to": "household" }, "axiom": { - "metric_name": "ons/male_15_19", + "metric_name": "ons/male_15", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "male", + "age": [ + 15 + ] + } + }, + "category_id": "ons.population", + "label": "Male 15", + "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." + }, + { + "target_id": "ons.population.male_16_17", + "family": "ons_population", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "filters": [ + { + "concept": "uk.demographics.gender", + "equals": "male" + }, + { + "concept": "uk.demographics.age", + "operator": ">=", + "value": 16 + }, + { + "concept": "uk.demographics.age", + "operator": "<=", + "value": 17 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "ons/male_16_17", + "filters": [ + { + "variable": "gender", + "equals": "MALE" + }, + { + "variable": "age", + "operator": ">=", + "value": 16 + }, + { + "variable": "age", + "operator": "<=", + "value": 17 + } + ], + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household" + }, + "axiom": { + "metric_name": "ons/male_16_17", "status": "pending", "value_rule": "uk.person.count" } @@ -9589,16 +9811,84 @@ "dimension_values": { "sex": "male", "age": [ - 15, 16, - 17, + 17 + ] + } + }, + "category_id": "ons.population", + "label": "Male 16 17", + "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." + }, + { + "target_id": "ons.population.male_18_19", + "family": "ons_population", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "filters": [ + { + "concept": "uk.demographics.gender", + "equals": "male" + }, + { + "concept": "uk.demographics.age", + "operator": ">=", + "value": 18 + }, + { + "concept": "uk.demographics.age", + "operator": "<=", + "value": 19 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "ons/male_18_19", + "filters": [ + { + "variable": "gender", + "equals": "MALE" + }, + { + "variable": "age", + "operator": ">=", + "value": 18 + }, + { + "variable": "age", + "operator": "<=", + "value": 19 + } + ], + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household" + }, + "axiom": { + "metric_name": "ons/male_18_19", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "ons", + "source_concept": "ons.mid_year_population_estimate", + "dimension_values": { + "sex": "male", + "age": [ 18, 19 ] } }, "category_id": "ons.population", - "label": "Male 15 19", + "label": "Male 18 19", "selector_note": "The selector pins ages through dimension_values and no longer requires the table's layout to group by age: the ONS mid-2025 workbook lays its rows out by geography (microcosm#1123)." }, { @@ -14993,6 +15283,10 @@ "concept": "uk.childcare.is_child_receiving_universal_childcare", "equals": true }, + { + "concept": "uk.childcare.is_child_receiving_extended_childcare", + "equals": false + }, { "concept": "uk.geography.country", "equals": "ENGLAND" @@ -15010,12 +15304,16 @@ "variable": "is_child_receiving_universal_childcare", "equals": true }, + { + "variable": "is_child_receiving_extended_childcare", + "equals": false + }, { "variable": "country", "equals": "ENGLAND" } ], - "notes": "Universal-only caseload is the declared difference between the overlapping published universes; no entitlement spending row is bound." + "notes": "Universal-only caseload is the declared difference between the overlapping published universes; no entitlement spending row is bound. From policyengine-uk 2.128.1 a child on the working parent entitlement keeps the universal hours (Childcare Act 2016 s.1(6)(a); pe-uk#2177), so the measure counts children on the universal hours who are not receiving the extended entitlement, as the published difference does." }, "axiom": { "metric_name": "dfe/funded_childcare/universal_only_children", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/birth_month.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/birth_month.py new file mode 100644 index 0000000000..c222720fe1 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/birth_month.py @@ -0,0 +1,109 @@ +"""Each person's months since their last birthday, stored once in the spine. + +policyengine-uk places a date of birth from a whole age and +``months_since_last_birthday``: the months since the person's most recent +birthday on 6 October, the middle of the fiscal year. The date of birth +decides the two-child limit (born before or after 6 April 2017), the State +Pension scheme (reaching pension age before or after 6 April 2016) and the +Pension Credit qualifying age. + +Left unset, the engine spreads each single year of age and sex evenly over +the year by weight, in an order set by a hash of ``person_id``. So the months +a file gets depend on its weights and its identifiers. The calibration +measures the engine at the spine's weights and the released file carries the +calibrated ones, so a release read a different spread of birth dates from the +one its own solve fitted (microcosm#1095). + +The root stage therefore stores the months, drawn the engine's way once: at +the survey weights, in an order set by an identity-keyed draw on the person's +stable build identifier. Every later engine read (the stages, the solve and +the release) takes the stored value, and a copy of a person keeps their +months. +""" + +from __future__ import annotations + +import numpy as np + +from microcosm.build.stochastic_assignment import stable_identity_uniforms + +#: The engine variable the root stage stores. +UK_BIRTH_MONTH_COLUMN = "months_since_last_birthday" +UK_BIRTH_MONTH_SEED = 0 +UK_BIRTH_MONTH_SALT = "uk.frs_spine.months_since_last_birthday" +#: The months in a year: positions on [0, 1) scale to [0, 12). +MONTHS_IN_YEAR = 12.0 +#: The largest position below 1 that float32 keeps below 1 (the engine stores +#: the variable as float32, which would round a larger draw up to 12). +_LARGEST_POSITION = 1.0 - 2.0**-24 + + +def stratified_positions( + strata: np.ndarray, draws: np.ndarray, weights: np.ndarray +) -> np.ndarray: + """Spread records evenly over [0, 1) within each stratum, by weight. + + policyengine-uk's own ``utils.stochastic.stratified_uniform``, restated so + the stage needs no engine: records in a stratum are ordered by their draw + and each sits at the midpoint of its slice of the stratum's cumulative + weight. The result does not depend on the order of the records. A stratum + with no positive weight keeps the draws. + """ + + strata = np.asarray(strata) + draws = np.asarray(draws, dtype=np.float64) + weights = np.maximum(np.asarray(weights, dtype=np.float64), 0.0) + if not (len(strata) == len(draws) == len(weights)): + raise ValueError("Strata, draws and weights must have one value per record.") + if len(strata) == 0: + return np.zeros(0, dtype=np.float64) + order = np.lexsort((draws, strata)) + _, group = np.unique(strata[order], return_inverse=True) + sorted_weights = weights[order] + totals = np.bincount(group, weights=sorted_weights) + starts = np.concatenate([[0.0], np.cumsum(totals)[:-1]]) + within = np.cumsum(sorted_weights) - starts[group] - sorted_weights / 2.0 + group_totals = totals[group] + positions = np.where( + group_totals > 0, + within / np.where(group_totals > 0, group_totals, 1.0), + draws[order], + ) + result = np.empty_like(positions) + result[order] = positions + return np.clip(result, 0.0, _LARGEST_POSITION) + + +def uk_months_since_last_birthday( + *, + age: np.ndarray, + is_male: np.ndarray, + person_ids: np.ndarray, + weights: np.ndarray, +) -> np.ndarray: + """Months since the last birthday, spread evenly within each age and sex. + + ``age`` must be whole years, as the FRS records it; ``weights`` are the + people's survey weights. + """ + + age = np.asarray(age, dtype=np.float64) + if not np.array_equal(age, np.floor(age)): + raise ValueError("Birth months are spread over whole ages only.") + strata = age * 2.0 + np.asarray(is_male, dtype=bool).astype(np.float64) + draws = stable_identity_uniforms( + np.asarray(person_ids), + seed=UK_BIRTH_MONTH_SEED, + salt=UK_BIRTH_MONTH_SALT, + ) + return MONTHS_IN_YEAR * stratified_positions(strata, draws, weights) + + +__all__ = [ + "MONTHS_IN_YEAR", + "UK_BIRTH_MONTH_COLUMN", + "UK_BIRTH_MONTH_SALT", + "UK_BIRTH_MONTH_SEED", + "stratified_positions", + "uk_months_since_last_birthday", +] diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py index b69f8c0b24..7b67059d96 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py @@ -28,7 +28,14 @@ from microcosm.build.target_materialization import MeasureResolutionError from microcosm.build.uk_runtime.national_calibration import prepare_uk_target_frame -from microcosm.build.uk_runtime.national_frame import load_uk_national_frame +from microcosm.build.uk_runtime.national_frame import ( + load_uk_national_frame, + uk_time_period, +) +from microcosm.build.uk_runtime.weight_year import ( + uk_engine_year_frame, + uk_weight_projection_factor, +) from microcosm.build.uk_runtime.weighted_integrity import ( UKReviewedExclusion, exclusion_evaluation_date, @@ -149,6 +156,14 @@ def score_uk_national_candidate( "candidate": candidate_resolution, "incumbent": incumbent_resolution, }, + # Each file's own year and the factor the engine multiplies its + # household weights by to reach the scored year. Scores taken before + # this block existed used the stored weights and do not compare. + "weight_year": { + "scored_year": int(calibration_year), + "candidate": _weight_year(candidate_frame, calibration_year), + "incumbent": _weight_year(incumbent_frame, calibration_year), + }, "target_drift": _target_drift( target_registry, candidate_errors, incumbent_errors ), @@ -177,6 +192,16 @@ def score_uk_national_candidate( } +def _weight_year(frame: Any, calibration_year: int) -> dict[str, object]: + dataset_year = int(uk_time_period(frame)) + return { + "dataset_year": dataset_year, + "engine_projection_factor": uk_weight_projection_factor( + dataset_year, int(calibration_year) + ), + } + + def _relative_errors(result) -> dict[str, float]: return { diagnostic.name: float(diagnostic.relative_error) @@ -263,6 +288,10 @@ def _scored_frame( band_edge_registry: TargetRegistry | None = None, ) -> tuple[Any, Any]: frame, _provenance = load_uk_national_frame(h5_path) + # policyengine-uk projects a file's household weights from the file's own + # year to the scored year, so each side is scored with the weights its + # users get there (microcosm#796 A19). The engine reads the file itself. + frame = uk_engine_year_frame(frame, simulation_year=calibration_year) resolver = None if factory is None else factory(Path(h5_path), frame) return prepare_uk_target_frame( frame, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/donor_uprating.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/donor_uprating.py index 0aa2fa9f33..c0ff1ea514 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/donor_uprating.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/donor_uprating.py @@ -356,9 +356,12 @@ def _engine_index_audit( ) -> dict[str, Any]: """Record the engine's own index ratio beside the applied outturn factor. - The engine's ``*_spending_litre_proxy`` indices are not applied (they - reproduce its 2023 pump-price parameters, which disagree with DESNZ); - the ratio is recorded so the divergence stays visible in the evidence. + The engine's ``*_spending_litre_proxy`` indices are not applied. Up to + policyengine-uk 2.126.10 they reproduced its 2023 pump-price parameters, + which disagreed with DESNZ; 2.126.11 re-based them on DESNZ pump prices + (PolicyEngine/policyengine-uk#2186), and they still differ from the + outturn applied here. The ratio is recorded so the divergence stays + visible in the evidence. """ before = reader(path, from_period) 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 79ebf76b27..4630cc2032 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 @@ -30,13 +30,23 @@ "UPPER_SECONDARY", "POST_SECONDARY", ) -FRS_APPROVED_TRAINING_CODES = tuple(range(1, 10)) -#: FRS 2024-25 (SN 9563) renames ``TRAIN`` to ``TRAIN2`` and recodes it: the -#: scheme codes are 1-8 (Work Club or Enterprise Club, New Enterprise -#: Allowance, Work Experience, Work Trial, Work Programme, Fair Start -#: Scotland, other local employment support, any other government scheme) -#: and 10 (Kickstart Scheme); 9 is "None of these" (microcosm#1063 c9). -FRS_APPROVED_TRAINING_CODES_TRAIN2 = (1, 2, 3, 4, 5, 6, 7, 8, 10) +#: FRS TRAINEE, "Whether on a government scheme for employment training" +#: (1 yes, 2 no), on the adult and child tables of FRS 2024-25 (SN 9563). The +#: FRS counts a 16- to 19-year-old as a dependent child only in full-time +#: non-advanced education or unwaged government training, so a child-table +#: record with TRAINEE = 1 is a dependant in approved training (uk-data#560, +#: uk-data#561). An adult-table record is never flagged: the FRS has already +#: decided it is not a dependant, and its TRAINEE answer does not say the +#: training is unwaged, so it may be training under a contract of employment, +#: which is not approved training (UC Regs 2013 reg 5(3)). The adult table's +#: TRAIN2 names DWP employment schemes (Work Programme, Kickstart and others) +#: and is not read: microcosm#1063 read it, which flagged those adults and no +#: dependant. +FRS_TRAINEE_COLUMN = "trainee" +FRS_TRAINEE_YES = 1 +#: The tab a raw person row came from; set before the adult and child tabs are +#: concatenated, as ``frs_relationships`` does. +FRS_TABLE_COLUMN = "_frs_table" UNKNOWN_QUALIFYING_EDUCATION_OR_TRAINING_ENTRY_AGE = 1000 BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS = ( "universal_credit_reported", @@ -144,7 +154,20 @@ def add_frs_education( Path(raw_dir) / str(artifacts["child"]["locator"]), artifacts["child"] ) ) - raw_person = pd.concat([adult, child], ignore_index=True, sort=False) + for table, tab in (("adult", adult), ("child", child)): + if FRS_TRAINEE_COLUMN not in tab.columns: + raise KeyError( + f"The FRS {table} tab has no TRAINEE column, so approved " + "training cannot be derived." + ) + raw_person = pd.concat( + [ + adult.assign(**{FRS_TABLE_COLUMN: "adult"}), + child.assign(**{FRS_TABLE_COLUMN: "child"}), + ], + ignore_index=True, + sort=False, + ) person = frame.table("person").copy() derived = derive_frs_education(person, raw_person) for column in FRS_EDUCATION_OUTPUT_COLUMNS: @@ -179,13 +202,20 @@ def derive_frs_education( values["is_in_non_advanced_education"] = np.isin( current, NON_ADVANCED_EDUCATION_LEVELS ) - if "train" in raw.columns: - approved = _num(raw, "train").isin(FRS_APPROVED_TRAINING_CODES) - elif "train2" in raw.columns: - # The 2024-25 vintage's recoded variable; code 9 is "None of these". - approved = _num(raw, "train2").isin(FRS_APPROVED_TRAINING_CODES_TRAIN2) - else: - approved = pd.Series(False, index=raw.index) + missing = [ + column + for column in (FRS_TRAINEE_COLUMN, FRS_TABLE_COLUMN) + if column not in raw.columns + ] + if missing: + # Fail rather than mark nobody as in training: a silent default is + # what left every dependant unflagged before. + raise KeyError( + f"FRS person rows lack {missing}, so approved training cannot be derived." + ) + approved = _num(raw, FRS_TRAINEE_COLUMN).eq(FRS_TRAINEE_YES) & raw[ + FRS_TABLE_COLUMN + ].eq("child") values["is_in_approved_training"] = approved.to_numpy(dtype=bool) in_qualifying = values["is_in_non_advanced_education"].to_numpy( dtype=bool diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_employment.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_employment.py index 9f42a2d63f..9dddd51534 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_employment.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_employment.py @@ -4,8 +4,10 @@ from collections.abc import Mapping from pathlib import Path +from types import MappingProxyType from typing import Any +import numpy as np import pandas as pd from microcosm.build.source_manifest import SourceStageSpec @@ -21,20 +23,27 @@ ) from microcosm.frame import Frame -EMPLOYMENT_STATUS_MAP = { - 0: "CHILD", - 1: "FT_EMPLOYED", - 2: "PT_EMPLOYED", - 3: "FT_SELF_EMPLOYED", - 4: "PT_SELF_EMPLOYED", - 5: "UNEMPLOYED", - 6: "RETIRED", - 7: "STUDENT", - 8: "CARER", - 9: "LONG_TERM_DISABLED", - 10: "SHORT_TERM_DISABLED", - 11: "LONG_TERM_DISABLED", -} +# Adult-table EMPSTATI ("Adult - Employment Status - ILO definition") value +# labels from the UKDS FRS 2024-25 data dictionary (SN 9563), mapped to +# policyengine-uk EmploymentStatus names. Matches the incumbent's +# FRS_EMPSTATI_EMPLOYMENT_STATUS (uk-data#526). Child-table people have no +# EMPSTATI and are CHILD. +FRS_EMPSTATI_EMPLOYMENT_STATUS = MappingProxyType( + { + 1: "FT_EMPLOYED", # Full-time employee + 2: "PT_EMPLOYED", # Part-time employee + 3: "FT_SELF_EMPLOYED", # Full-time self-employed + 4: "PT_SELF_EMPLOYED", # Part-time self-employed + 5: "UNEMPLOYED", # Unemployed + 6: "RETIRED", # Retired + 7: "STUDENT", # Student + 8: "CARER", # Looking after family/home + 9: "LONG_TERM_DISABLED", # Permanently sick/disabled + 10: "SHORT_TERM_DISABLED", # Temporarily sick/injured + 11: "OTHER_INACTIVE", # Other inactive + } +) +CHILD_EMPLOYMENT_STATUS = "CHILD" EMPLOYMENT_SECTOR_MAP = { 0: "NOT_EMPLOYED", 1: "PRIVATE", @@ -69,7 +78,9 @@ def add_frs_employment( artifacts = _artifact_by_table(stage) adult = normalize_ids( - read_pinned_tab(Path(raw_dir) / str(artifacts["adult"]["locator"]), artifacts["adult"]) + read_pinned_tab( + Path(raw_dir) / str(artifacts["adult"]["locator"]), artifacts["adult"] + ) ) derived = derive_frs_employment(frame.table("person"), adult) person = frame.table("person").copy() @@ -93,31 +104,70 @@ def derive_frs_employment(person: pd.DataFrame, adult: pd.DataFrame) -> pd.DataF ``empstati``, ``mjobsect``, and ``sic`` are intentionally direct-indexed so missing source columns fail loudly, matching the incumbent FRS port. + A person is an adult record when their ``person_id`` is in ``adult.tab``; + everyone else is CHILD. """ raw = adult.set_index("person_id") aligned = raw.reindex(person["person_id"]) values = pd.DataFrame(index=person.index) - empstati = pd.to_numeric(aligned["empstati"], errors="coerce").fillna(0) - # Unmapped codes above the declared domain follow the incumbent's - # post-map fillna to LONG_TERM_DISABLED; 0/NaN rows land on CHILD via - # the explicit map entry. - values["employment_status"] = ( - empstati.astype(int) - .map(EMPLOYMENT_STATUS_MAP) - .fillna("LONG_TERM_DISABLED") - .to_numpy() + values["employment_status"] = derive_employment_status_from_frs( + aligned["empstati"], person["person_id"].isin(adult["person_id"]) ) sector = pd.to_numeric(aligned["mjobsect"], errors="coerce").fillna(0) values["employment_sector"] = ( sector.astype(int).map(EMPLOYMENT_SECTOR_MAP).fillna("NOT_EMPLOYED").to_numpy() ) values["sic_industry_division"] = ( - pd.to_numeric(aligned["sic"], errors="coerce").fillna(0).clip(lower=0).astype(int).to_numpy() + pd.to_numeric(aligned["sic"], errors="coerce") + .fillna(0) + .clip(lower=0) + .astype(int) + .to_numpy() ) return values +def derive_employment_status_from_frs(empstati, is_adult_record) -> np.ndarray: + """Map FRS EMPSTATI codes to ``employment_status``. + + People who are not adult records (child-table people) are CHILD whatever + their code. Every adult must carry a code in + ``FRS_EMPSTATI_EMPLOYMENT_STATUS``: a missing or unknown adult code refuses + the build rather than falling back to a guessed status, as the old fallback + silently made code 11 (Other inactive) LONG_TERM_DISABLED. The refusal + names the offending codes but never how many adults carry them. + """ + + codes = pd.Series( + np.asarray(pd.to_numeric(empstati, errors="coerce"), dtype="float64") + ) + adult = np.asarray(is_adult_record, dtype=bool) + if adult.shape != codes.shape: + raise ValueError( + "FRS EMPSTATI codes and adult-record flags must have the same length." + ) + adult_status = codes.map(FRS_EMPSTATI_EMPLOYMENT_STATUS).to_numpy(dtype=object) + unknown = adult & pd.isna(adult_status) + if unknown.any(): + bad = codes[unknown] + labels = [_code_label(code) for code in sorted(bad.dropna().unique())] + if bad.isna().any(): + labels.append("blank or non-numeric") + # Build logs are not licensed outputs, and adults are not survey + # households, so the refusal names the codes and never a count. + raise ValueError( + "FRS adult.tab carries adults with EMPSTATI code(s) " + f"{labels} outside FRS_EMPSTATI_EMPLOYMENT_STATUS; map them from the " + "release's data dictionary." + ) + return np.where(adult, adult_status, CHILD_EMPLOYMENT_STATUS).astype(object) + + +def _code_label(code: float) -> str: + return str(int(code)) if float(code).is_integer() else str(code) + + def _artifact_by_table(stage: SourceStageSpec) -> dict[str, Mapping[str, Any]]: by_table = {str(artifact.get("table")): artifact for artifact in stage.artifacts} if "adult" not in by_table: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py index 3fa8860fd6..1c0ba2e3b0 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py @@ -12,6 +12,10 @@ from microcosm.build.source_manifest import SourceStageSpec from microcosm.build.stochastic_assignment import stable_identity_uniforms from microcosm.build.trace import sha256_file +from microcosm.build.uk_runtime.birth_month import ( + UK_BIRTH_MONTH_COLUMN, + uk_months_since_last_birthday, +) from microcosm.build.uk_runtime.national_frame import ( uk_national_frame, validate_uk_national_frame, @@ -188,6 +192,7 @@ "person_household_id", "age", "gender", + UK_BIRTH_MONTH_COLUMN, "marital_status", "hours_worked", "care_hours", @@ -217,6 +222,7 @@ "student_loan_repayments", "statutory_sick_pay", "statutory_maternity_pay", + "statutory_paternity_pay", "student_loans", "access_fund", "education_grants", @@ -500,6 +506,19 @@ def _assemble_frame( raise ValueError("FRS age80/age columns must be integral to build person.age.") pe_person["age"] = age.astype("int64") pe_person["gender"] = np.where(_number(person, "sex") == 1, "MALE", "FEMALE") + # The months since each person's last birthday, stored once here so the + # engine reads the same date of birth at every later stage, in the solve + # and in the release: left unset, it spreads them by the weights and the + # identifiers each file carries (uk_runtime.birth_month, microcosm#1095). + pe_person[UK_BIRTH_MONTH_COLUMN] = uk_months_since_last_birthday( + age=pe_person["age"].to_numpy(), + is_male=pe_person["gender"].to_numpy() == "MALE", + person_ids=pe_person["person_id"].to_numpy(), + weights=person["household_id"] + .map(_raw_number(household, "gross4")) + .fillna(0.0) + .to_numpy(dtype=np.float64), + ) pe_person["marital_status"] = _map_codes(person, "marital", MARITAL_MAP, "SINGLE") pe_person["hours_worked"] = _positive(person, "tothours") * WEEKS_IN_YEAR pe_person["care_hours"] = frs_care_hours(person) @@ -688,6 +707,15 @@ def _add_person_income( pe_person["student_loan_repayments"] = _number(person, "slrepamt") * WEEKS_IN_YEAR pe_person["statutory_sick_pay"] = _number(person, "sspadj") * WEEKS_IN_YEAR pe_person["statutory_maternity_pay"] = _number(person, "smpadj") * WEEKS_IN_YEAR + # SPPADJ, "SPP amount": the weekly statutory paternity pay an adult gets + # for their main job (FRS 2024-25 adult table, beside SSPADJ and SMPADJ). + # A tab without it refuses, rather than set nobody's (uk-data#575). + if "sppadj" not in person.columns: + raise KeyError( + "The FRS adult tab has no SPPADJ column, so statutory paternity pay " + "cannot be set." + ) + pe_person["statutory_paternity_pay"] = _number(person, "sppadj") * WEEKS_IN_YEAR pe_person["student_loans"] = _positive(person, "tuborr") access_fund_evidence: dict[str, object] = {} pe_person["access_fund"] = access_fund_annual(person, evidence=access_fund_evidence) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py index 2925c400ac..16d95baaf3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py @@ -49,19 +49,23 @@ } UK_TAKE_UP_DECLARED_SEEDS = {output: 0 for output in FRS_TAKE_UP_OUTPUT_COLUMNS} # Universal Credit is claimable only by a benefit unit whose claimant or -# partner meets its age conditions: policyengine-uk 2.123.0's ``is_uc_eligible`` -# (capital aside) needs a member with ``is_uc_claimant`` who meets UC's minimum -# age and has not reached Pension Credit qualifying age. The draw's population -# is therefore the units with a UC claimant or partner aged 18 or over and -# under that age; any other unit is never drawn (#882, microcosm#867, -# microcosm#1095). The bounds come from the engine at the build instant through +# partner meets its age conditions: policyengine-uk 2.130.1's ``is_uc_eligible`` +# (capital aside) needs a single or joint claimant, taken from +# ``is_uc_claimant``, who meets UC's minimum age and has not reached Pension +# Credit qualifying age. The draw's population is therefore the units with a +# UC claimant or partner aged 18 or over and under that age; any other unit is +# never drawn (#882, microcosm#867, microcosm#1095). The bounds come from the +# engine at the build instant through # ``uk_take_up_population_policy``: the qualifying age is the State Pension age # the ``gov.dwp.state_pension.age`` timetable sets for the cohort reaching it # in the build year, which equals the engine's Pension Credit qualifying age at -# 2024 and 2025, and the minimum age is UC's 18. Two engine cases are not +# 2024 and 2025, and the minimum age is UC's 18. Three engine cases are not # mirrored, and the lockstep test names them: 16- and 17-year-old claimants -# under the UC Regs 2013 reg 8 exceptions, whom the engine includes, and -# mixed-age couples on the Pension Credit route, whom it excludes. The stage +# under the UC Regs 2013 reg 8 exceptions, whom the engine includes; mixed-age +# couples on the Pension Credit route, whom it excludes; and, from 2.128.0, a +# pension-age family moved onto Universal Credit when its legacy benefit +# closes, whom it includes (SI 2014/1230 reg 60A; the build leaves +# ``would_claim_uc_at_legacy_closure`` at the engine default). The stage # manifest declares the same population on the ``would_claim_uc`` operation; # ``assert_take_up_stage_population_declaration`` refuses a manifest that stops # saying so, so the declaration and the code cannot drift apart. diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py index 49e63ea1a7..9d19a9d9ae 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py @@ -216,6 +216,7 @@ ) from .size_checkpoint import uk_size_checkpoint_identity from .staging import UK_STAGED_DATASET_REPOSITORY, UK_STAGING_REPOSITORY +from .weight_year import uk_dataset_year_frame if TYPE_CHECKING: from microcosm.build.staging_v2 import StagingRunBundleWriterV2 @@ -1102,6 +1103,7 @@ def prepare_full_build( graph, population=full.population, bindings=bindings, + solve_year=int(config.calibration_year), artifact_inputs=( ArtifactInput( "full_gates", @@ -2755,8 +2757,15 @@ def _execute_national_build( # 7. The H5, then the graph's readback of it. stage(telemetry, "candidate_h5_creation", "started") # The release boundary (microcosm#1063 c9): the reviewed export exclusions - # leave the candidate here, and the graph readback expects the same. - write_uk_national_frame(uk_release_export_frame(frame), paths["dataset"]) + # leave the candidate here, and the graph readback expects the same. The + # weights are written at the dataset's year, so policyengine-uk's own + # projection returns the solved weights at the solve year (microcosm#796 + # A19); the solve, its gates and its evidence keep the solved weights. + solve_year = int(registry_document["calibration_year"]) + write_uk_national_frame( + uk_dataset_year_frame(uk_release_export_frame(frame), solve_year=solve_year), + paths["dataset"], + ) if state is not None: append_phase(state, "staging_h5_written") stage( @@ -2765,7 +2774,9 @@ def _execute_national_build( "completed", size_bytes=paths["dataset"].stat().st_size, ) - continued = add_uk_national_readback(graph, population=national.population) + continued = add_uk_national_readback( + graph, population=national.population, solve_year=solve_year + ) final = _run_graph_with_progress( compile_graph(continued), sources={**sources, "exported_dataset": paths["dataset"]}, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_measure.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_measure.py index 03bf9f0aba..764835d741 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_measure.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_measure.py @@ -321,6 +321,7 @@ def _run_engine_blocks( local_grains: tuple[str, ...], on_block, engine_weight_scales: Mapping[str, float] | None = None, + identifier_universe=None, ) -> tuple[ dict[str, list[pd.DataFrame]], list[Mapping[str, Any]], @@ -348,6 +349,10 @@ def _run_engine_blocks( resolver_kwargs["engine_weight_scale"] = float( engine_weight_scales[str(clone_index)] ) + if identifier_universe is not None and clone_index is not None: + # A block's rows take the identifiers they have in the release, + # which numbers the whole frame (microcosm#1194). + resolver_kwargs["engine_identifier_universe"] = identifier_universe resolver = resolver_factory(**resolver_kwargs) resolution = resolve_target_measures( lambda block_frame=block_frame: CalibrationFrameAdapter(block_frame), @@ -558,6 +563,7 @@ def collect(block_frame, block_inputs): scratch_dir=scratch_dir, resolver_factory=resolver_factory, engine_weight_scales=representation.get("factor_by_block"), + identifier_universe=frame, local_grains=local_grains, on_block=collect, ) @@ -785,6 +791,7 @@ def materialize(block_frame, block_inputs): scratch_dir=scratch_dir, resolver_factory=resolver_factory, engine_weight_scales=representation.get("factor_by_block"), + identifier_universe=frame, local_grains=local_grains, on_block=materialize, ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py index 7dd1d1ca8d..18fb979a38 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py @@ -121,12 +121,18 @@ national_calibration_mass_reason, ) from .national_doctrine import UKNationalSolveDoctrine, uk_national_target_loss_weights -from .national_frame import load_uk_national_frame, uk_release_export_frame +from .national_frame import ( + load_uk_national_frame, + uk_identifier_range_failures, + uk_release_export_frame, + uk_time_period, +) from .rowwise_posture import ( UK_ROWWISE_DENSE_POSTURE, UK_ROWWISE_NATIONAL_POSTURE, UKRowwisePosture, ) +from .weight_year import uk_dataset_year_frame, uk_weight_projection_factor NATIONAL_TARGET_TYPE = ArtifactType("microcosm.uk.national-target-registry", 1) NATIONAL_GATE_REPORT_TYPE = ArtifactType("microcosm.uk.national-gate-report", 1) @@ -416,9 +422,18 @@ def uk_national_graph( def add_uk_national_readback( - graph: Graph, *, population: str, dataset_source: str = "exported_dataset" + graph: Graph, + *, + population: str, + solve_year: int, + dataset_source: str = "exported_dataset", ) -> Graph: - """Continue the graph over the written H5: a readback gate on its content.""" + """Continue the graph over the written H5: a readback gate on its content. + + ``solve_year`` is the year the weights were solved at. The file carries + them at the dataset's year (``uk_runtime.weight_year``), and the readback + expects exactly that division of the calibrated population. + """ node = Node( NATIONAL_READBACK_NODE, @@ -426,7 +441,7 @@ def add_uk_national_readback( population=population, inputs=population_slices(population_columns(graph, population)), sources=(dataset_source,), - params={"dataset_source": dataset_source}, + params={"dataset_source": dataset_source, "solve_year": int(solve_year)}, artifact_outputs=(ArtifactOutput("readback", NATIONAL_READBACK_TYPE),), description="Read the written national H5 back and compare its content identity with the calibrated population.", ) @@ -989,9 +1004,9 @@ class UKNationalReadbackKernel(KernelBase): capabilities = Capabilities(Determinism.DETERMINISTIC, role=KernelRole.GATE) def implementation_hash(self) -> str: - from . import content_identity, national_frame + from . import content_identity, national_frame, weight_year - return source_hash(type(self), content_identity, national_frame) + return source_hash(type(self), content_identity, national_frame, weight_year) def run(self, context: KernelContext) -> KernelResult: from ..artifact_files import file_artifact @@ -1001,9 +1016,14 @@ def run(self, context: KernelContext) -> KernelResult: dataset = file_artifact(path) written, _provenance = load_uk_national_frame(path) failures = [] - # The writer drops the reviewed export exclusions (microcosm#1063 c9), - # so the expectation is the export frame, not the raw population. - expected = uk_frame_content_identity(uk_release_export_frame(frame)) + # The writer drops the reviewed export exclusions (microcosm#1063 c9) + # and writes the weights at the dataset's year (microcosm#796 A19), so + # the expectation is that export frame, not the raw population. + solve_year = int(context.params["solve_year"]) + release = uk_dataset_year_frame( + uk_release_export_frame(frame), solve_year=solve_year + ) + expected = uk_frame_content_identity(release) actual = uk_frame_content_identity(written) if actual != expected: failures.append( @@ -1011,10 +1031,17 @@ def run(self, context: KernelContext) -> KernelResult: ) if written.weights_for("household").kind is not WeightKind.CALIBRATED: failures.append("Written national H5 household weights are not calibrated.") - if written.mass_log != frame.mass_log: + if written.mass_log != release.mass_log: failures.append( "Written national H5 mass log differs from the calibrated population." ) + # The identifiers must survive the engine's 32-bit storage + # (microcosm#1194), whatever wrote the file. + failures.extend( + uk_identifier_range_failures( + {entity: written.table(entity) for entity in written.entities} + ) + ) if file_artifact(path) != dataset: raise ValueError("UK national H5 changed during graph readback validation.") report = { @@ -1025,6 +1052,15 @@ def run(self, context: KernelContext) -> KernelResult: "dataset": dataset, "content_identity": actual, "expected_content_identity": expected, + # The year the weights were solved at, the year the file carries + # them at, and the engine's own factor between the two. + "weight_year": { + "solve_year": solve_year, + "dataset_year": int(uk_time_period(release)), + "engine_projection_factor": uk_weight_projection_factor( + int(uk_time_period(release)), solve_year + ), + }, } return KernelResult( artifacts={"readback": canonical_json(report)}, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py index d5f769af0a..b313e60fbf 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py @@ -46,7 +46,7 @@ from microcosm.graph.codecs import SOURCE_CODECS from ..artifact_files import file_artifact -from . import geography_ladder, national_frame +from . import geography_ladder, national_frame, weight_year from .atomic_area_support import uk_area_code_frames, without_uk_native_alias_columns from .dataset_size import UK_SIZE_L2_PARAM_KEYS from .geography_ladder import ( @@ -60,6 +60,8 @@ _read_uk_national_tables, _write_uk_single_year_tables, uk_household_weight_kind, + uk_identifier_range_failures, + uk_release_identifier_tables, uk_time_period, validate_uk_national_frame, ) @@ -68,6 +70,7 @@ ladder_clone_index_column, load_uk_rowwise_dataset, ) +from .weight_year import uk_dataset_year_frame, uk_weight_projection_factor EXPORT_DESCRIPTOR_TYPE = ArtifactType("microcosm.uk.full-export-descriptor", 1) EXPORT_READBACK_TYPE = ArtifactType("microcosm.uk.full-export-readback", 1) @@ -101,7 +104,9 @@ def _ladder_tables(frame: Frame) -> dict[str, pd.DataFrame]: dropped = [c for c in columns if c in renamed[entity]] if dropped: renamed[entity] = renamed[entity].drop(columns=dropped) - return renamed + # The identifiers leave inside the range policyengine-uk stores, with the + # build identifiers beside them (microcosm#1194). + return uk_release_identifier_tables(renamed) def _tables(frame: Frame) -> dict[str, pd.DataFrame]: @@ -195,11 +200,17 @@ def _content_descriptor( def describe_uk_export( - frame: Frame, *, bindings: Mapping[str, object] + frame: Frame, *, bindings: Mapping[str, object], solve_year: int ) -> dict[str, object]: - """Validate and describe the maintained H5 layout without serializing it.""" + """Validate and describe the maintained H5 layout without serializing it. + + ``solve_year`` is the year the weights were solved at. The artifact + carries them at the dataset's year (``uk_runtime.weight_year``, + microcosm#796 A19); the geography gate reads the solved weights. + """ validate_uk_national_frame(frame) - ladder = _ladder_tables(frame) + release = uk_dataset_year_frame(frame, solve_year=solve_year) + ladder = _ladder_tables(release) gate = uk_geography_ladder_gate( ladder["household"], frame.weights_for("household").values ) @@ -208,15 +219,26 @@ def describe_uk_export( "UK export geography integrity failed: " + "; ".join(gate.failures) ) tables = {**ladder, "household": export_area_code_columns(ladder["household"])} + dataset_year = int(uk_time_period(release)) return { "schema_version": 1, "kind": "uk_full_build_export", **_content_descriptor( tables, - time_period=uk_time_period(frame), - weight_kind=uk_household_weight_kind(frame), - mass_log=frame.mass_log, + time_period=uk_time_period(release), + weight_kind=uk_household_weight_kind(release), + mass_log=release.mass_log, ), + # The year the weights were solved at, the year the file carries them + # at, and the engine's own factor between the two. The division itself + # is inside the content identity, as the last record of the mass log. + "weight_year": { + "solve_year": int(solve_year), + "dataset_year": dataset_year, + "engine_projection_factor": uk_weight_projection_factor( + dataset_year, int(solve_year) + ), + }, "bindings": dict(bindings), "geography_integrity": {"passed": gate.passed, "failures": list(gate.failures)}, "area_codes": _area_codes_block(tables["household"]), @@ -247,6 +269,10 @@ def materialize_uk_export( ) -> dict[str, object]: """Perform only the declared serialization, recreating files on cache hits.""" _check_descriptor(descriptor) + # The weights leave at the dataset's year, as the descriptor declared. + frame = uk_dataset_year_frame( + frame, solve_year=int(descriptor["weight_year"]["solve_year"]) + ) tables = _tables(frame) current = _content_descriptor( tables, @@ -291,6 +317,7 @@ def validate_uk_export( if actual[key] != descriptor[key]: failures.append(f"Exported {key} differs from its graph descriptor.") failures.extend(_area_code_failures(payload["household"], descriptor)) + failures.extend(uk_identifier_range_failures(payload)) if file_artifact(path) != dataset: raise ValueError("UK exported file changed during graph readback validation.") return { @@ -312,7 +339,9 @@ class UKExportPrepareKernel(KernelBase): ) def implementation_hash(self) -> str: - return source_hash(sys.modules[__name__], national_frame, geography_ladder) + return source_hash( + sys.modules[__name__], national_frame, geography_ladder, weight_year + ) def run(self, context: KernelContext) -> KernelResult: for value in context.artifacts.values(): @@ -326,7 +355,11 @@ def run(self, context: KernelContext) -> KernelResult: bindings["artifacts"] = { name: value.key for name, value in context.artifacts.items() } - descriptor = describe_uk_export(context_frame(context), bindings=bindings) + descriptor = describe_uk_export( + context_frame(context), + bindings=bindings, + solve_year=int(context.params["solve_year"]), + ) return KernelResult(artifacts={"export_descriptor": canonical_json(descriptor)}) @@ -414,6 +447,7 @@ def add_uk_export_preparation( *, population: str, bindings: Mapping[str, object], + solve_year: int, artifact_inputs: tuple[ArtifactInput, ...] = (), ) -> Graph: node = Node( @@ -421,7 +455,10 @@ def add_uk_export_preparation( kernel=UKExportPrepareKernel.ref, population=population, inputs=population_slices(population_columns(graph, population)), - params={"bindings": canonical_json(dict(bindings)).decode()}, + params={ + "bindings": canonical_json(dict(bindings)).decode(), + "solve_year": int(solve_year), + }, artifact_inputs=artifact_inputs, artifact_outputs=(ArtifactOutput("export_descriptor", EXPORT_DESCRIPTOR_TYPE),), description="Validate and describe exact H5 values, schema, weights and geographic lineage.", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py index ab498abd78..4aeb3b6033 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py @@ -18,6 +18,12 @@ uk-data#533 reads it. policyengine-uk puts the Scottish top-rate threshold at GBP 112,570 rather than the statutory GBP 125,140 (pe-uk#2130), so it files some advanced-rate relief as additional. + +A Gift Aid donor's bands are extended by the grossed-up gift (ITA 2007 +s.414(2)(b)), as policyengine-uk's earned_income_tax extends them from 2.128.0 +(pe-uk#2271): the rest-of-UK higher and additional limits move up by the gift, +and so does every Scottish limit above the starter band. Pay returned to a +donor is therefore relieved at the rates of the extended bands. """ from __future__ import annotations @@ -41,6 +47,7 @@ "schedule_flag": "pays_scottish_income_tax", "basic_below_rate": UK_BASIC_RATE_CLASS_CEILING, "additional": "the schedule's top rate", + "gift_aid_extends_bands": "gift_aid_grossed_up", } @@ -73,15 +80,37 @@ def rate_class_engine_tolerance(*amounts: np.ndarray) -> np.ndarray: ) +def gift_aid_extended_brackets(schedule: str, brackets: int) -> range: + """The in-force brackets whose lower limit a donor's gross gift moves up. + + policyengine-uk's ``calc_income_tax_with_gift_aid``, as earned_income_tax + calls it: the rest-of-UK higher and additional limits (not the model's + extra bracket above them), and every Scottish limit above the starter band + (above the basic band in the three-rate schedule before 2018). + """ + + if schedule == "rest_of_uk": + return range(1, min(3, brackets)) + if schedule == "scotland": + return range(1 if brackets == 3 else 2, brackets) + raise ValueError(f"unknown income tax schedule {schedule!r}") + + def tax_by_rate_class( income: np.ndarray, thresholds: Sequence[float], rates: Sequence[float | None], + *, + gross_gift: np.ndarray | None = None, + schedule: str | None = None, ) -> dict[str, np.ndarray]: """Tax on ``income`` within each of HMRC's rate categories. The categories sum to the schedule's tax on ``income``. A bracket whose - rate is ``None`` (not in force at the instant) is skipped. + rate is ``None`` (not in force at the instant) is skipped. With + ``gross_gift`` the ``schedule``'s extended limits move up by each person's + gift, as the engine moves them: the income between a limit and the limit + plus the gift is taxed at the rate below it instead. """ values = np.asarray(income, dtype=float) @@ -97,17 +126,31 @@ def tax_by_rate_class( above_basic = {rate for _, rate in brackets if rate >= UK_BASIC_RATE_CLASS_CEILING} top = brackets[-1][1] if len(above_basic) > 1 else None uppers = [threshold for threshold, _ in brackets[1:]] + [np.inf] + + def category(rate: float) -> str: + if rate < UK_BASIC_RATE_CLASS_CEILING: + return "basic" + return "additional" if rate == top else "higher" + classes = {name: np.zeros_like(values) for name in UK_RATE_CLASSES} for (lower, rate), upper in zip(brackets, uppers, strict=True): - if rate < UK_BASIC_RATE_CLASS_CEILING: - name = "basic" - elif rate == top: - name = "additional" - else: - name = "higher" + name = category(rate) classes[name] = classes[name] + rate * np.clip( values - lower, 0.0, upper - lower ) + if gross_gift is not None: + if schedule is None: + raise ValueError("a Gift Aid extension needs the schedule it extends.") + gift = np.asarray(gross_gift, dtype=float) + # Each moved limit taxes the slice above it, up to the gift, at the + # rate below it. Summed over the limits this is exact even when a gift + # spans several bands, as in the engine. + for index in gift_aid_extended_brackets(schedule, len(brackets)): + lower, rate = brackets[index] + below = brackets[index - 1][1] + overlap = np.clip(values - lower, 0.0, gift) + classes[category(rate)] = classes[category(rate)] - rate * overlap + classes[category(below)] = classes[category(below)] + below * overlap return classes @@ -117,19 +160,34 @@ def relief_by_rate_class( counterfactual_income: np.ndarray, scottish: np.ndarray, schedules: Mapping[str, tuple[Sequence[float], Sequence[float | None]]], + gross_gift: np.ndarray | None = None, ) -> dict[str, np.ndarray]: """Each person's rise in tax on earned income, by rate category. ``schedules`` maps ``rest_of_uk`` and ``scotland`` to their (thresholds, - rates); ``scottish`` picks each person's schedule. + rates); ``scottish`` picks each person's schedule. ``gross_gift`` is each + person's grossed-up Gift Aid, which extends their bands in both the + baseline and the counterfactual. """ scottish = np.asarray(scottish, dtype=bool) relief = {name: np.zeros(scottish.shape, dtype=float) for name in UK_RATE_CLASSES} for schedule, members in (("rest_of_uk", ~scottish), ("scotland", scottish)): thresholds, rates = schedules[schedule] - after = tax_by_rate_class(counterfactual_income, thresholds, rates) - before = tax_by_rate_class(baseline_income, thresholds, rates) + after = tax_by_rate_class( + counterfactual_income, + thresholds, + rates, + gross_gift=gross_gift, + schedule=schedule, + ) + before = tax_by_rate_class( + baseline_income, + thresholds, + rates, + gross_gift=gross_gift, + schedule=schedule, + ) for name in UK_RATE_CLASSES: relief[name] = relief[name] + np.where( members, after[name] - before[name], 0.0 diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py index fde52659d5..eb2c6c81d3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py @@ -27,8 +27,10 @@ relief_by_rate_class, ) from microcosm.build.uk_runtime.national_frame import ( + UK_BUILD_IDENTIFIER_COLUMNS, UK_RELEASE_EXPORT_DROPPED_COLUMNS, load_uk_national_frame, + uk_release_identifier_tables, write_uk_national_frame, ) from microcosm.build.uk_runtime.uc_relationships import ( @@ -83,22 +85,28 @@ #: (microcosm#1014). Derived from these engine variables at the measure year. _CGT_TAXABLE_INCOME = "cgt_taxable_income" _CGT_TAXABLE_INCOME_COMPONENTS = ( - "adjusted_net_income", + "net_income", "allowances", - "gift_aid", "personal_pension_contributions", "pension_contributions_relief", ) _CGT_TAXABLE_INCOME_DEFINITION = ( - "max(0, adjusted_net_income - max(0, allowances - gift_aid - " + "max(0, net_income - max(0, allowances - " "min(personal_pension_contributions, pension_contributions_relief))), as " "policyengine-uk's capital_gains_tax computes the taxable income it stacks " - "gains on" + "gains on; from 2.128.0 Gift Aid extends the basic rate band instead of " + "reducing allowances (pe-uk#2271)" ) def cgt_taxable_income_from_components(parts: Mapping[str, Any]) -> np.ndarray: - """The engine CGT formula's taxable income from its component arrays.""" + """The engine CGT formula's taxable income from its component arrays. + + policyengine-uk 2.128.0 and later stack gains on net income less the + allowances, with relief-at-source pension contributions taken off the + allowances; Gift Aid extends the basic rate band instead (pe-uk#2271), so + it moves where the band ends, not the income the band is measured on. + """ values = { name: np.asarray(parts[name], dtype=float) @@ -108,10 +116,8 @@ def cgt_taxable_income_from_components(parts: Mapping[str, Any]) -> np.ndarray: values["personal_pension_contributions"], values["pension_contributions_relief"], ) - allowances = np.maximum( - 0.0, values["allowances"] - values["gift_aid"] - band_extension - ) - return np.maximum(0.0, values["adjusted_net_income"] - allowances) + allowances = np.maximum(0.0, values["allowances"] - band_extension) + return np.maximum(0.0, values["net_income"] - allowances) #: Every binding key that names a measured variable: a dated CGT measure in @@ -364,8 +370,41 @@ def compute_uc_paid_diagnostic_masks( ) +def _release_identifier_tables( + tables: Mapping[str, pd.DataFrame], universe: Any | None +) -> dict[str, pd.DataFrame] | None: + """The engine tables on the release's identifiers, or None if they already are. + + The build identifiers the release keeps beside them are not engine inputs + and do not go to the scratch file. + """ + + whole = ( + None + if universe is None + else {entity: universe.table(entity) for entity in UK_BUILD_IDENTIFIER_COLUMNS} + ) + released = uk_release_identifier_tables( + {entity: tables[entity] for entity in UK_BUILD_IDENTIFIER_COLUMNS}, + universe=whole, + ) + result = {} + changed = False + for entity, carrier in UK_BUILD_IDENTIFIER_COLUMNS.items(): + if carrier in tables[entity].columns: + # Already on release identifiers (a loaded release): leave it. + return None + table = released[entity].drop(columns=[carrier]) + changed = changed or not table.equals(tables[entity]) + result[entity] = table + return result if changed else None + + def _engine_scratch_frame( - frame: Any, *, engine_weight_scale: float | None = None + frame: Any, + *, + engine_weight_scale: float | None = None, + identifier_universe: Any | None = None, ) -> tuple[Any, tuple[str, ...]]: """The frame a scratch-mode engine loads, and the columns it leaves behind. @@ -391,7 +430,16 @@ def _engine_scratch_frame( the pool's and the formula is exact for identical clone copies. The scaling is declared on the engine frame's mass log. - A frame carrying none of those columns and no scale is returned as is. + The identifiers leave as the release's too (microcosm#1194, + ``national_frame.uk_release_identifier_tables``): policyengine-uk stores + them as 32-bit integers and keys a person's birth month on ``person_id``, + so a solve measured on other identifiers fits a deal of birth dates the + released file does not have. ``identifier_universe`` is the whole frame + when this one is a per-clone block of it, so a block's rows take the + identifiers they have in the release. The resolver's own frame keeps the + build identifiers. + + A frame that needs none of this is returned as is. """ trimmed: dict[str, pd.DataFrame] = {} @@ -405,7 +453,22 @@ def _engine_scratch_frame( scale = None if engine_weight_scale is None else float(engine_weight_scale) if scale is not None and not (np.isfinite(scale) and scale > 0.0): raise ValueError("engine weight scale must be a positive finite factor.") - if not trimmed and kept is household and (scale is None or scale == 1.0): + renumbered = _release_identifier_tables( + { + **{ + entity: trimmed.get(entity, frame.table(entity)) + for entity in UK_BUILD_IDENTIFIER_COLUMNS + }, + "household": kept, + }, + identifier_universe, + ) + if ( + not trimmed + and kept is household + and renumbered is None + and (scale is None or scale == 1.0) + ): return frame, () dropped = tuple( column @@ -416,6 +479,8 @@ def _engine_scratch_frame( tables = {name: frame.table(name) for name in frame.entities} tables.update(trimmed) tables["household"] = kept + if renumbered is not None: + tables.update(renumbered) weights = {entity: frame.weights_for(entity) for entity in frame.weighted_entities} mass_log = frame.mass_log if scale is not None and scale != 1.0: @@ -460,6 +525,7 @@ def __init__( frame: Any, microsimulation_factory: Any | None = None, engine_weight_scale: float | None = None, + engine_identifier_universe: Any | None = None, ): self.year = int(year) self._engine_weight_scale = ( @@ -477,7 +543,9 @@ def __init__( scratch_dir.mkdir(parents=True, exist_ok=True) source_path = scratch_dir / "simulation-input.h5" engine_frame, dropped = _engine_scratch_frame( - frame, engine_weight_scale=self._engine_weight_scale + frame, + engine_weight_scale=self._engine_weight_scale, + identifier_universe=engine_identifier_universe, ) write_uk_national_frame(engine_frame, source_path) mode = "scratch_frame_export" @@ -487,6 +555,11 @@ def __init__( "engine weight scaling needs scratch-mode resolution; a direct " "H5 source carries its own weights." ) + if engine_identifier_universe is not None: + raise ValueError( + "an identifier universe needs scratch-mode resolution; a direct " + "H5 source carries its own identifiers." + ) dropped = () source_path = Path(simulation_source) mode = "direct_h5" @@ -745,6 +818,15 @@ def _rate_class_relief( scottish, _ = compute_uk_measure_input( self.frame, self.simulation, "person", flag, self.year ) + # A donor's grossed-up Gift Aid extends their bands; returning the + # sacrifice to pay does not change the gift. + gross_gift, _ = compute_uk_measure_input( + self.frame, + self.simulation, + "person", + str(UK_RATE_CLASS_ALLOCATION["gift_aid_extends_bands"]), + self.year, + ) # The simulation's own parameters at the measurement year are its # fiscal-year values, so the Scottish bands are the ones in force from # 6 April, as the engine taxes them. @@ -760,9 +842,11 @@ def _rate_class_relief( counterfactual_income=np.asarray(counterfactual_income, dtype=float), scottish=np.asarray(scottish, dtype=bool), schedules=schedules, + gross_gift=np.asarray(gross_gift, dtype=float), ) # The allocation re-implements the engine's earned_income_tax, the - # schedule applied to earned taxable income. Its classes must add up + # schedule applied to earned taxable income with a donor's bands + # extended by their gift. Its classes must add up # to that variable's rise, or the schedule has moved away from the # brackets read here. engine_variable = UK_RATE_CLASS_ENGINE_VARIABLE diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py index 798f5d2739..96b93474bd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py @@ -46,6 +46,8 @@ ) __all__ = [ + "UK_BUILD_IDENTIFIER_COLUMNS", + "UK_ENGINE_IDENTIFIER_MAX", "UK_EXPORTED_WEIGHT_COLUMNS", "UK_HOUSEHOLD_WEIGHT_KIND_ATTR", "UK_MASS_LOG_ATTR", @@ -55,8 +57,13 @@ "UKNationalStage", "UKStagingProvenance", "load_uk_national_frame", + "uk_build_identifier_frame", + "uk_build_identifier_tables", + "uk_build_identifiers", "uk_household_weight_kind", + "uk_identifier_range_failures", "uk_national_frame", + "uk_release_identifier_tables", "uk_household_mass_conservation_receipt", "uk_time_period", "validate_uk_national_frame", @@ -497,12 +504,244 @@ def _write_uk_single_year_tables( } +#: The largest identifier policyengine-uk stores. Its five identifier +#: variables (``person_id``, ``benunit_id``, ``household_id``, +#: ``person_benunit_id``, ``person_household_id``) are 32-bit integers, and a +#: larger stored value wraps (microcosm#1194, pe-uk#2273). +UK_ENGINE_IDENTIFIER_MAX = 2_147_483_647 + +#: The column that keeps each entity's build identifier beside its release +#: identifier. The clone stages number copies by adding a power of ten per +#: copy, so a built frame's identifiers run far past the engine's range; every +#: draw in the build is keyed on them, so the build keeps them and the release +#: boundary renumbers. The lineage columns (``*_source_id``, the clone +#: indices) describe the build identifier. +UK_BUILD_IDENTIFIER_COLUMNS: Mapping[str, str] = { + "person": "person_build_id", + "benunit": "benunit_build_id", + "household": "household_build_id", +} + +_UK_ENTITY_IDENTIFIER_COLUMNS: Mapping[str, str] = { + "person": "person_id", + "benunit": "benunit_id", + "household": "household_id", +} +_UK_PERSON_MEMBERSHIP_COLUMNS: Mapping[str, str] = { + "benunit": "person_benunit_id", + "household": "person_household_id", +} + + +def _uk_build_identifier_state(tables: Mapping[str, pd.DataFrame]) -> bool: + """Whether the tables carry the build-identifier columns: all or none.""" + + present = [ + entity + for entity, column in UK_BUILD_IDENTIFIER_COLUMNS.items() + if column in tables[entity].columns + ] + if present and len(present) != len(UK_BUILD_IDENTIFIER_COLUMNS): + missing = sorted(set(UK_BUILD_IDENTIFIER_COLUMNS) - set(present)) + raise ValueError( + "UK tables carry a build identifier on some entities only; " + f"{missing} lack theirs." + ) + return bool(present) + + +def _uk_mapped_identifiers( + values: np.ndarray, source: np.ndarray, target: np.ndarray, *, label: str +) -> np.ndarray: + """``values`` read through the one-to-one map ``source[i] -> target[i]``.""" + + order = np.argsort(source, kind="stable") + ordered = source[order] + if len(ordered) > 1 and (ordered[1:] == ordered[:-1]).any(): + raise ValueError(f"UK {label} identifiers are not unique.") + position = np.searchsorted(ordered, values) + inside = position < len(ordered) + if not inside.all() or not (ordered[position[inside]] == values[inside]).all(): + raise ValueError(f"UK {label} memberships name an identifier with no row.") + return target[order][position] + + +def uk_release_identifier_tables( + tables: Mapping[str, pd.DataFrame], + *, + universe: Mapping[str, pd.DataFrame] | None = None, +) -> dict[str, pd.DataFrame]: + """The three tables with identifiers inside the engine's range (microcosm#1194). + + Per entity the release identifier is one plus the rank of the build + identifier among the entity's rows, so row order is untouched and the + numbering depends on nothing but the identifiers themselves. The person + table's two memberships map through the same arrays. The build identifier + stays beside each release identifier (``UK_BUILD_IDENTIFIER_COLUMNS``). + + ``universe`` is the tables the release holds when ``tables`` are a subset + of their rows (a per-clone engine block): the rank is then taken in the + universe, so each row gets the identifier it has in the release. + + Idempotent: tables that already carry the build identifiers are returned + as they are. + """ + + if _uk_build_identifier_state(tables): + return dict(tables) + whole = tables if universe is None else universe + result: dict[str, pd.DataFrame] = {} + whole_build: dict[str, np.ndarray] = {} + whole_release: dict[str, np.ndarray] = {} + for entity, column in _UK_ENTITY_IDENTIFIER_COLUMNS.items(): + whole_build[entity] = whole[entity][column].to_numpy(dtype=np.int64) + order = np.argsort(whole_build[entity], kind="stable") + ordered = whole_build[entity][order] + if len(ordered) > 1 and (ordered[1:] == ordered[:-1]).any(): + raise ValueError(f"UK {entity} identifiers are not unique.") + whole_release[entity] = np.empty(len(order), dtype=np.int64) + whole_release[entity][order] = np.arange(1, len(order) + 1, dtype=np.int64) + table = tables[entity] + build = table[column].to_numpy(dtype=np.int64) + release = ( + whole_release[entity] + if universe is None + else _uk_mapped_identifiers( + build, whole_build[entity], whole_release[entity], label=entity + ) + ) + renumbered = table.copy() + renumbered[column] = release.astype(table[column].dtype) + renumbered.insert( + renumbered.columns.get_loc(column) + 1, + UK_BUILD_IDENTIFIER_COLUMNS[entity], + build, + ) + result[entity] = renumbered + person = result["person"] + for entity, column in _UK_PERSON_MEMBERSHIP_COLUMNS.items(): + person[column] = _uk_mapped_identifiers( + person[column].to_numpy(dtype=np.int64), + whole_build[entity], + whole_release[entity], + label=entity, + ).astype(tables["person"][column].dtype) + return result + + +def uk_build_identifier_tables( + tables: Mapping[str, pd.DataFrame], +) -> dict[str, pd.DataFrame]: + """The inverse of :func:`uk_release_identifier_tables`. + + A reader that replays the build's identifier-keyed draws, or joins a + release to its spine, needs the build identifiers back. Tables that + carry none are returned as they are. + """ + + if not _uk_build_identifier_state(tables): + return dict(tables) + result: dict[str, pd.DataFrame] = {} + build: dict[str, np.ndarray] = {} + release: dict[str, np.ndarray] = {} + for entity, column in _UK_ENTITY_IDENTIFIER_COLUMNS.items(): + table = tables[entity] + carrier = UK_BUILD_IDENTIFIER_COLUMNS[entity] + build[entity] = table[carrier].to_numpy(dtype=np.int64) + release[entity] = table[column].to_numpy(dtype=np.int64) + restored = table.drop(columns=[carrier]) + restored[column] = build[entity].astype(table[column].dtype) + result[entity] = restored + person = result["person"] + for entity, column in _UK_PERSON_MEMBERSHIP_COLUMNS.items(): + person[column] = _uk_mapped_identifiers( + person[column].to_numpy(dtype=np.int64), + release[entity], + build[entity], + label=entity, + ).astype(tables["person"][column].dtype) + return result + + +def uk_identifier_range_failures(tables: Mapping[str, pd.DataFrame]) -> list[str]: + """Why written identifiers would not survive policyengine-uk, if they would not. + + The engine stores them as 32-bit integers: a value above + ``UK_ENGINE_IDENTIFIER_MAX`` wraps, and two that wrap to the same value + merge. The messages name the column only: never an identifier, and never a + count of rows. + """ + + failures: list[str] = [] + columns = [ + *( + (entity, column, True) + for entity, column in _UK_ENTITY_IDENTIFIER_COLUMNS.items() + ), + *( + ("person", column, False) + for column in _UK_PERSON_MEMBERSHIP_COLUMNS.values() + ), + ] + for entity, column, unique in columns: + if column not in tables[entity].columns: + failures.append(f"Written {entity} table lacks {column!r}.") + continue + values = pd.to_numeric(tables[entity][column], errors="coerce") + if values.isna().any() or (values != np.floor(values)).any(): + failures.append(f"Written {column!r} is not a whole number on every row.") + continue + if (values < 1).any(): + failures.append(f"Written {column!r} has a value below 1.") + if (values > UK_ENGINE_IDENTIFIER_MAX).any(): + failures.append( + f"Written {column!r} has a value above the 32-bit range " + "policyengine-uk stores." + ) + if unique and values.duplicated().any(): + failures.append(f"Written {column!r} repeats a value.") + return failures + + +def uk_build_identifiers(table: pd.DataFrame, entity: str) -> np.ndarray: + """One entity table's build identifiers, whether or not it was renumbered.""" + + carrier = UK_BUILD_IDENTIFIER_COLUMNS[entity] + column = ( + carrier if carrier in table.columns else _UK_ENTITY_IDENTIFIER_COLUMNS[entity] + ) + return table[column].to_numpy(dtype=np.int64) + + +def uk_build_identifier_frame(frame: Frame) -> Frame: + """A loaded release frame back on its build identifiers; others unchanged.""" + + tables = { + entity: frame.table(entity) for entity in ("person", "benunit", "household") + } + if not _uk_build_identifier_state(tables): + return frame + restored = uk_build_identifier_tables(tables) + return uk_national_frame( + person=restored["person"], + benunit=restored["benunit"], + household=restored["household"], + time_period=uk_time_period(frame), + weight_kind=uk_household_weight_kind(frame), + household_weights=frame.weights_for("household").values, + mass_log=frame.mass_log, + ) + + def uk_release_export_frame(frame: Frame) -> Frame: """The frame the release boundary writes: reviewed export exclusions dropped. The three area codes leave under the consumers' names (microcosm#1114, ``geography_ladder.UK_EXPORT_AREA_CODE_COLUMNS``); a national frame - carries none and passes unchanged. + carries none. The identifiers leave inside the 32-bit range the engine + stores, with the build identifiers beside them (microcosm#1194, + :func:`uk_release_identifier_tables`). A frame that already left passes + unchanged. """ from .geography_ladder import export_area_code_columns @@ -525,6 +764,9 @@ def uk_release_export_frame(frame: Frame) -> Frame: table = renamed changed = True tables[entity] = table + if not _uk_build_identifier_state(tables): + tables = uk_release_identifier_tables(tables) + changed = True if not changed: return frame return uk_national_frame( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/salary_sacrifice.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/salary_sacrifice.py index e31364c5d1..016aa9922a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/salary_sacrifice.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/salary_sacrifice.py @@ -47,6 +47,10 @@ SALSAC_BELOW_2000_ANCHOR = 4_300_000.0 SALSAC_STAGING_RATIO = SALSAC_STAGE_TARGET / SALSAC_HMRC_ANCHOR SALSAC_RATE_CAP = 0.5 +#: The share of a converted record's pay that at most moves into salary +#: sacrifice. Half keeps the sacrifice within the pay that remains, and the +#: rest of the contribution stays an employee contribution (uk-data#518). +SALSAC_MOVE_PAY_SHARE_CAP = 0.5 SALSAC_QRF_SEED = 42 SALSAC_QRF_ESTIMATORS = 100 SALSAC_CONVERSION_SEED = 2024 @@ -59,6 +63,22 @@ "salary_sacrifice_asked != 1 and employment_income > 0, frame-wide; people " "not asked who have no employment income are set to zero" ) +#: Whom the QRF trains on. A respondent asked the question who has no pay +#: cannot show what a paid person sacrifices, and only predictions for paid +#: people are kept (uk-data#518). +SALSAC_TRAINING_POPULATION = ( + "support_channel == frs and not capital-gains clone and not CGT support " + "copy and salary_sacrifice_asked == 1 and employment_income > 0" +) +#: The limit on every amount. Salary sacrifice is pay given up, so no amount, +#: reported or predicted, exceeds the pay the person still receives, and a +#: person without pay has none. It is an imputation bound, not a legal one. +#: It bites on SPI-channel rows above all: they keep their FRS twin's answer +#: beside pay redrawn from the SPI (uk-data#518). +SALSAC_AMOUNT_LIMIT = ( + "employment_income floored at zero; every row, reported or predicted, " + "FRS and SPI channel" +) SALSAC_MASS_CHANGE_REASON = ( "Salary-sacrifice support stage rewrites pension columns and converted " "records' pay; household rows and typed household weights pass through and " @@ -85,6 +105,9 @@ class UKSalarySacrificeResult: prediction_rows: int not_asked_without_pay_rows: int not_asked_without_pay_mass: float + limited_to_pay_rows: int + limited_to_pay_mass: float + limited_to_pay_amount: float pre_headcount: float post_headcount: float shortfall: float @@ -94,6 +117,8 @@ class UKSalarySacrificeResult: converted_rows: int converted_mass: float moved_amount: float + pay_share_capped_rows: int + retained_employee_amount: float expected_converted_mass: float realization_deviation: float @@ -108,6 +133,13 @@ def evidence(self) -> dict[str, object]: # zero, never predicted for. "not_asked_without_pay_rows": self.not_asked_without_pay_rows, "not_asked_without_pay_mass": self.not_asked_without_pay_mass, + # Amounts above the person's pay, reported or predicted, + # limited to it: rows, weighted people and unweighted pounds + # removed. + "amount_limit": SALSAC_AMOUNT_LIMIT, + "limited_to_pay_rows": self.limited_to_pay_rows, + "limited_to_pay_mass": self.limited_to_pay_mass, + "limited_to_pay_amount": self.limited_to_pay_amount, "seed": SALSAC_QRF_SEED, }, "headcount_receipt": { @@ -122,6 +154,12 @@ def evidence(self) -> dict[str, object]: "converted_rows": self.converted_rows, "converted_mass": self.converted_mass, "moved_amount": self.moved_amount, + # Converted records whose contribution was above half their + # pay, and the unweighted pounds they keep as an employee + # contribution. + "move_pay_share_cap": SALSAC_MOVE_PAY_SHARE_CAP, + "pay_share_capped_rows": self.pay_share_capped_rows, + "retained_employee_amount": self.retained_employee_amount, "expected_converted_mass": self.expected_converted_mass, "realization_deviation": self.realization_deviation, "pre_conversion_pay_column": SALSAC_PRE_CONVERSION_PAY_COLUMN, @@ -160,7 +198,8 @@ def checkpoint_metadata(self) -> dict[str, object]: def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: - """Fit on the asked base-FRS subset, then create additional SS support.""" + """Fit on the asked, paid base-FRS subset, limit every amount to pay, then + create additional SS support.""" validate_uk_national_frame(frame) person = frame.table("person").copy() @@ -199,23 +238,28 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: asked = pd.to_numeric(person["salary_sacrifice_asked"], errors="coerce") if asked.isna().any(): raise ValueError("salary_sacrifice_asked contains non-numeric values.") + numeric = person.loc[:, [*SALSAC_PREDICTORS, SALSAC_OUTPUT]].apply( + pd.to_numeric, errors="coerce" + ) + if not np.isfinite(numeric.to_numpy(dtype=float)).all(): + raise ValueError("Salary-sacrifice QRF columns must be finite numeric values.") + paid = numeric["employment_income"].gt(0.0) + # The forest trains on respondents with pay: only predictions for paid + # people are kept, and a respondent without pay shows nothing about them. training_mask = ( channels.eq("frs") & ~clones.astype(bool) & ~support_copies.astype(bool) & asked.eq(1) + & paid ) if not training_mask.any(): - raise ValueError("Salary-sacrifice QRF has no eligible asked FRS rows.") - numeric = person.loc[:, [*SALSAC_PREDICTORS, SALSAC_OUTPUT]].apply( - pd.to_numeric, errors="coerce" - ) - if not np.isfinite(numeric.to_numpy(dtype=float)).all(): - raise ValueError("Salary-sacrifice QRF columns must be finite numeric values.") + raise ValueError( + "Salary-sacrifice QRF has no eligible asked FRS rows with pay." + ) # The prediction population is the people not asked who are paid; a # person not asked with no employment income sacrifices none of it. not_asked = ~asked.eq(1) - paid = numeric["employment_income"].gt(0.0) predict_mask = not_asked & paid without_pay = not_asked & ~paid person.loc[without_pay, SALSAC_OUTPUT] = 0.0 @@ -254,6 +298,13 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: raise ValueError("Salary-sacrifice amounts must be finite and non-negative.") if not np.isfinite(employee).all() or (employee < 0.0).any(): raise ValueError("Employee-pension amounts must be finite and non-negative.") + # A sacrifice comes out of pay, so every amount is limited to the pay the + # person still receives: reported and predicted alike, and SPI-channel + # rows too, whose kept answer is their FRS twin's beside redrawn pay. + pay_limit = np.maximum(employment_income, 0.0) + limited = final_ss > pay_limit + limited_to_pay_amount = float((final_ss[limited] - pay_limit[limited]).sum()) + final_ss = np.minimum(final_ss, pay_limit) has_ss = final_ss > 0.0 pre_headcount = float(person_weights[has_ss].sum()) shortfall = max(0.0, SALSAC_STAGE_TARGET - pre_headcount) @@ -267,8 +318,17 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: salt=SALSAC_CONVERSION_SALT, ) converted = donor_pool & (draws < rate) - moved_amount = float(employee[converted].sum()) - final_ss[converted] = employee[converted] + # The contribution moves into salary sacrifice, up to half the pay: the + # FRS pay it was deducted from is before the sacrifice, so pay falls by + # the amount moved and the sacrifice never exceeds the pay that remains. + # What does not move stays an employee contribution (uk-data#518). + moved = np.minimum(employee, SALSAC_MOVE_PAY_SHARE_CAP * employment_income) + pay_share_capped = converted & (moved < employee) + moved_amount = float(moved[converted].sum()) + retained_employee_amount = float( + (employee[pay_share_capped] - moved[pay_share_capped]).sum() + ) + final_ss[converted] = moved[converted] # Sacrificed pay leaves the contract: the engine reads employment_income # after sacrifice, so a converted record's pay drops by the amount that # becomes salary sacrifice. Without this the converted contribution left @@ -280,10 +340,12 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: pre_conversion_pay = np.zeros(len(person), dtype=float) pre_conversion_pay[converted] = employment_income[converted] employment_income = employment_income.copy() - employment_income[converted] = np.maximum( - employment_income[converted] - employee[converted], 0.0 - ) - employee[converted] = 0.0 + employment_income[converted] = employment_income[converted] - moved[converted] + employee[converted] = employee[converted] - moved[converted] + if (final_ss > np.maximum(employment_income, 0.0)).any(): + raise ValueError( + "Salary-sacrifice amounts must not exceed the pay that remains." + ) person[SALSAC_OUTPUT] = final_ss person["employee_pension_contributions"] = employee person["employment_income"] = employment_income @@ -314,6 +376,9 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: prediction_rows=int(predict_mask.sum()), not_asked_without_pay_rows=int(without_pay.sum()), not_asked_without_pay_mass=float(person_weights[without_pay.to_numpy()].sum()), + limited_to_pay_rows=int(limited.sum()), + limited_to_pay_mass=float(person_weights[limited].sum()), + limited_to_pay_amount=limited_to_pay_amount, pre_headcount=pre_headcount, post_headcount=post_headcount, shortfall=shortfall, @@ -323,6 +388,8 @@ def impute_salary_sacrifice(frame: Frame) -> UKSalarySacrificeResult: converted_rows=int(converted.sum()), converted_mass=converted_mass, moved_amount=moved_amount, + pay_share_capped_rows=int(pay_share_capped.sum()), + retained_employee_amount=retained_employee_amount, expected_converted_mass=rate * donor_pool_mass, realization_deviation=( (converted_mass - rate * donor_pool_mass) / (rate * donor_pool_mass) @@ -351,10 +418,7 @@ def _assert_salary_sacrifice_stage_parameters( ( "fit_weighted_qrf", { - "training_population": ( - "support_channel == frs and not capital-gains clone and " - "not CGT support copy and salary_sacrifice_asked == 1" - ), + "training_population": SALSAC_TRAINING_POPULATION, "target_population": SALSAC_TARGET_POPULATION, "predictors": list(SALSAC_PREDICTORS), "targets": [SALSAC_OUTPUT], @@ -363,6 +427,7 @@ def _assert_salary_sacrifice_stage_parameters( "seed": SALSAC_QRF_SEED, "n_estimators": SALSAC_QRF_ESTIMATORS, "clamp_minimum": 0, + "clamp_maximum": SALSAC_AMOUNT_LIMIT, "preserve_asked_rows": True, "cache": False, }, @@ -378,12 +443,15 @@ def _assert_salary_sacrifice_stage_parameters( "employment_income > 0" ), "rate_cap": SALSAC_RATE_CAP, + "move_pay_share_cap": SALSAC_MOVE_PAY_SHARE_CAP, "move": ( - "full employee_pension_contributions to " - "pension_contributions_via_salary_sacrifice; source zeroed; " - "employment_income lowered by the amount sacrificed; the " - "converted record's pay before the conversion kept on " - "salary_sacrifice_pre_conversion_pay (zero elsewhere)" + "employee_pension_contributions, up to move_pay_share_cap " + "of employment_income, to " + "pension_contributions_via_salary_sacrifice; the rest " + "stays in the source; employment_income lowered by the " + "amount sacrificed; the converted record's pay before the " + "conversion kept on salary_sacrifice_pre_conversion_pay " + "(zero elsewhere)" ), "seed": SALSAC_CONVERSION_SEED, "salt": SALSAC_CONVERSION_SALT, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/size_evaluation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/size_evaluation.py index 1e3f7984e2..4ca4f488f4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/size_evaluation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/size_evaluation.py @@ -18,6 +18,7 @@ uk_fit_by_family, uk_weight_summary, ) +from microcosm.build.uk_runtime.national_frame import UK_BUILD_IDENTIFIER_COLUMNS from microcosm.build.uk_runtime.rowwise_geography import id_multiplier_for_values __all__ = [ @@ -222,11 +223,17 @@ def load_run(path: str | Path, *, label: str, skip_h5: bool = False) -> RowwiseR h5_path = _find_h5(run_path, manifest) weights = None if not skip_h5 and h5_path is not None: + # The source-household arithmetic below is on the build's identifiers, + # which a release keeps beside its renumbered ones (microcosm#1194). + with pd.HDFStore(h5_path, mode="r") as store: + stored = store.select("household", stop=0).columns + carrier = UK_BUILD_IDENTIFIER_COLUMNS["household"] + identifier = carrier if carrier in stored else "household_id" weights = pd.read_hdf( h5_path, "household", - columns=["household_id", "household_weight", "clone_index"], - ) + columns=[identifier, "household_weight", "clone_index"], + ).rename(columns={identifier: "household_id"}) uk_diagnostics = _mapping(calibration.get("uk_diagnostics")) holdout_value = uk_diagnostics.get("rotated_holdout") holdout = dict(holdout_value) if isinstance(holdout_value, Mapping) else None diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py index 35a57a68d8..2a3cad9c8d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py @@ -292,6 +292,7 @@ def __post_init__(self) -> None: "person.salary_sacrifice_asked", "person.salary_sacrifice_reported", "person.sic_industry_division", + "person.statutory_paternity_pay", "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", @@ -356,6 +357,15 @@ def __post_init__(self) -> None: "person.srp_regular_code5", "benunit.benunit_clone_index", "person.person_clone_index", + # microcosm#1194: the build identifiers the release keeps beside the + # renumbered ones (national_frame.UK_BUILD_IDENTIFIER_COLUMNS). The + # lineage columns above describe them. + "benunit.benunit_build_id", + "household.household_build_id", + "person.person_build_id", + # The months since each person's last birthday, stored by the root stage + # so the engine reads one date of birth everywhere (microcosm#1095). + "person.months_since_last_birthday", "household.itl1_code", "household.itl2_code", "household.itl3_code", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py index 471411d8ad..3cfc869d8f 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py @@ -74,7 +74,7 @@ _UC_CAPITAL_LEVELS = ("exact_cell", *UC_CAPITAL_COARSENING) #: The UC capital limits the receipt measures the redraw against. _UC_CAPITAL_RECEIPT_LIMITS = (6_000.0, 16_000.0) -#: policyengine-uk 2.123.0 counts these household property values as Universal +#: policyengine-uk 2.130.1 counts these household property values as Universal #: Credit and Pension Credit capital (the ``sources`` lists under #: ``gov.dwp.universal_credit.means_test.capital`` and #: ``gov.dwp.pension_credit.income.capital``, beside ``savings`` and diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/weight_year.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/weight_year.py new file mode 100644 index 0000000000..5d7f3c97f3 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/weight_year.py @@ -0,0 +1,196 @@ +"""The year a UK release's household weights are written at (microcosm#796 A19). + +policyengine-uk projects a single-year dataset forward. From the dataset's own +year it multiplies ``household_weight`` by one plus the ONS population growth +of each later year (``policyengine_uk/data/uprating_indices.yaml`` lists the +column under ``gov.economic_assumptions.yoy_growth.ons.population``; no other +weight is stored, and the person and benefit-unit weights are formulas of it). + +The UK release is stamped with the survey year and its weights are solved at +the calibration year. Written as solved, they read high at the calibration +year by the growth between the two: 0.72% from 2024 to 2025. uk-data#538 is the +incumbent's version of the same mechanism, with the opposite sign. + +So the release writes the solved weights divided by the engine's own factor, +and the engine's projection then returns the solved weights at the solve year. +The division happens on a derived frame at the release write only. The solve, +its gates and the engine scratch file keep the solved weights. + +The growth values are vendored beside the uprating pins +(``uk/hmrc_uprating_engine_pins.json``, block ``dataset_projection``) by +``tools/pin_uk_uprating_engine_values.py``, so nothing here imports the engine; +a ``requires_uk`` test holds them in lockstep with the installed engine. +""" + +from __future__ import annotations + +import numpy as np + +from microcosm.build.uk_runtime.hmrc_uprating import ( + UK_ENGINE_PINS_RESOURCE, + UK_ENGINE_PINS_TOOL, + engine_parameter_pins, +) +from microcosm.build.uk_runtime.national_frame import uk_time_period +from microcosm.frame import Frame, MassChange, WeightKind, Weights + +#: The engine parameter policyengine-uk projects ``household_weight`` by: the +#: year-on-year growth it applies entering each year after the dataset's own. +UK_WEIGHT_PROJECTION_PARAMETER = "gov.economic_assumptions.yoy_growth.ons.population" +UK_WEIGHT_PROJECTION_VARIABLE = "household_weight" +#: The pins block that carries the growth, keyed by the projected variable. +UK_WEIGHT_PROJECTION_PINS_BLOCK = "dataset_projection" + +#: The mass-log reason of the division. It names the solve, not "calibration": +#: the solve's own evidence finds its record by that word, on the frame before +#: this one is derived. +UK_WEIGHT_YEAR_MASS_REASON = ( + "Release weights at the dataset year: the solved household weights divided " + "by policyengine-uk's own projection of household_weight from the dataset " + "year to the solve year, so the engine returns the solved weights at the " + "solve year (microcosm#796 A19)." +) +#: The mass-log reason of the lift a reader applies to see a stored file as +#: the engine does at a later year. +UK_ENGINE_YEAR_MASS_REASON = ( + "Household weights as policyengine-uk projects them from the dataset year " + "to the simulation year (microcosm#796 A19)." +) + + +def uk_weight_projection_growth(year: int) -> float: + """The pinned growth the engine applies to ``household_weight`` entering ``year``.""" + + block = engine_parameter_pins().get(UK_WEIGHT_PROJECTION_PINS_BLOCK) or {} + entry = block.get(UK_WEIGHT_PROJECTION_VARIABLE) or {} + growth = entry.get("growth") or {} + instant = f"{int(year)}-01-01" + if ( + entry.get("parameter") != UK_WEIGHT_PROJECTION_PARAMETER + or instant not in growth + ): + raise ValueError( + f"{UK_ENGINE_PINS_RESOURCE} does not pin {UK_WEIGHT_PROJECTION_PARAMETER!r} " + f"at {instant} for {UK_WEIGHT_PROJECTION_VARIABLE}; re-run " + f"{UK_ENGINE_PINS_TOOL} with the uk extra installed." + ) + value = float(growth[instant]) + if not np.isfinite(value) or value <= -1.0: + raise ValueError( + f"{UK_ENGINE_PINS_RESOURCE}: {UK_WEIGHT_PROJECTION_PARAMETER!r} at " + f"{instant} is {value!r}; a growth rate must be finite and above -1." + ) + return value + + +def uk_weight_projection_factor(dataset_year: int, simulation_year: int) -> float: + """What the engine multiplies ``household_weight`` by between the two years. + + One for the dataset's own year. The engine does not project backwards, so + an earlier simulation year refuses. + """ + + dataset_year, simulation_year = int(dataset_year), int(simulation_year) + if simulation_year < dataset_year: + raise ValueError( + f"policyengine-uk does not project a {dataset_year} dataset back to " + f"{simulation_year}." + ) + factor = 1.0 + for year in range(dataset_year + 1, simulation_year + 1): + factor *= 1.0 + uk_weight_projection_growth(year) + return factor + + +def uk_weights_are_at_dataset_year(frame: Frame) -> bool: + """Whether the frame's household weights were already divided for release.""" + + records = [record for record in frame.mass_log if record.entity == "household"] + return bool(records) and records[-1].reason == UK_WEIGHT_YEAR_MASS_REASON + + +def uk_dataset_year_frame(frame: Frame, *, solve_year: int) -> Frame: + """The release frame: solved household weights at the dataset's year. + + Idempotent: a frame that already carries the division is returned as it + is. Only calibrated weights are divided, since the division undoes the + engine's projection of a solve; anything else refuses. + """ + + if uk_weights_are_at_dataset_year(frame): + return frame + weights = frame.weights_for("household") + if weights.kind is not WeightKind.CALIBRATED: + raise ValueError( + "Only solved household weights are written at the dataset year; " + f"these are {weights.kind.value!r}." + ) + factor = uk_weight_projection_factor(int(uk_time_period(frame)), solve_year) + return frame.with_weights( + "household", + Weights(np.asarray(weights.values, dtype=np.float64) / factor, weights.kind), + mass=MassChange(factor=1.0 / factor, reason=UK_WEIGHT_YEAR_MASS_REASON), + ) + + +def uk_engine_year_frame(frame: Frame, *, simulation_year: int) -> Frame: + """The frame with the household weights the engine uses at ``simulation_year``. + + This is the engine's projection of the stored weights, whatever wrote + them: for a release written at the dataset year it gives back the solved + weights at the solve year, and for a file written as solved it shows the + overstatement its users get. The dataset's own year returns the frame. + """ + + factor = uk_weight_projection_factor(int(uk_time_period(frame)), simulation_year) + if factor == 1.0: + return frame + weights = frame.weights_for("household") + return frame.with_weights( + "household", + Weights(np.asarray(weights.values, dtype=np.float64) * factor, weights.kind), + mass=MassChange(factor=factor, reason=UK_ENGINE_YEAR_MASS_REASON), + ) + + +def uk_solve_year_frame(frame: Frame, *, solve_year: int) -> Frame: + """A written frame at the weights it was solved with. + + The gates that certify a release stand on the solved weights. A release + carries them divided, and the engine's projection to the solve year gives + them back; a frame that was never divided is returned as it is, since its + stored weights are the solved ones. A frame divided by anything but the + engine's factor to ``solve_year`` refuses: the engine would not return + its solve. + """ + + if not uk_weights_are_at_dataset_year(frame): + return frame + factor = uk_weight_projection_factor(int(uk_time_period(frame)), solve_year) + record = [r for r in frame.mass_log if r.entity == "household"][-1] + declared = record.declared_factor + if declared is None or not np.isclose( + float(declared) * factor, 1.0, rtol=1e-12, atol=0.0 + ): + raise ValueError( + "The frame's household weights were divided by a factor that is not " + f"policyengine-uk's projection of {UK_WEIGHT_PROJECTION_VARIABLE} to " + f"{int(solve_year)}; the engine would not return the solved weights " + "there." + ) + return uk_engine_year_frame(frame, simulation_year=solve_year) + + +__all__ = [ + "UK_ENGINE_YEAR_MASS_REASON", + "UK_WEIGHT_PROJECTION_PARAMETER", + "UK_WEIGHT_PROJECTION_PINS_BLOCK", + "UK_WEIGHT_PROJECTION_VARIABLE", + "UK_WEIGHT_YEAR_MASS_REASON", + "uk_dataset_year_frame", + "uk_engine_year_frame", + "uk_solve_year_frame", + "uk_weight_projection_factor", + "uk_weight_projection_growth", + "uk_weights_are_at_dataset_year", +] diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py index 4a071a1009..c0cfc29030 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py @@ -28,7 +28,7 @@ PRIMARY_QRF_WORKER_MODULE = "microcosm.build.us_runtime.puf_qrf_worker" PRIMARY_QRF_INTERPRETER_PLACEHOLDER = "{python_interpreter}" APPROVED_UV_LOCK_SHA256 = ( - "6b32bcb10b20c0ee3ce3174ab5da4794bb3ab2147b91d9729effde446576a156" + "c99b8d9ff73e1e4edb80340ac4c5d9b9992f2331e2fe450b19e7794ff84c4bbc" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_birth_month.py b/packages/microcosm-build/tests/engine/uk/test_uk_birth_month.py new file mode 100644 index 0000000000..44d1a08e4f --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_birth_month.py @@ -0,0 +1,130 @@ +"""policyengine-uk reads the birth months the spine stores (microcosm#1095). + +Left unset, the engine spreads each age and sex over the year by weight in an +order set by a hash of ``person_id``, so a file's birth dates depend on its +weights and identifiers. These tests hold the stored months against the +installed engine: the stage spreads them the engine's way, the engine returns +them as stored, and they no longer move with the weights. +""" + +from __future__ import annotations + +import warnings +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.birth_month import ( + UK_BIRTH_MONTH_COLUMN, + stratified_positions, + uk_months_since_last_birthday, +) +from microcosm.build.uk_runtime.national_frame import ( + uk_national_frame, + uk_release_export_frame, + write_uk_national_frame, +) +from microcosm.frame import WeightKind + +N = 40 + + +def _frame(weights: np.ndarray, *, store_months: bool): + ids = np.arange(1, N + 1, dtype=np.int64) + person = pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": 66, + "gender": "MALE", + } + ) + if store_months: + person[UK_BIRTH_MONTH_COLUMN] = uk_months_since_last_birthday( + age=person["age"].to_numpy(), + is_male=np.ones(N, dtype=bool), + person_ids=ids, + weights=np.full(N, 1_000.0), + ) + return uk_national_frame( + person=person, + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2024", + weight_kind=WeightKind.CALIBRATED, + household_weights=weights, + ) + + +def _engine_months(path: Path) -> np.ndarray: + import policyengine_uk + from policyengine_uk.data import UKSingleYearDataset + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + simulation = policyengine_uk.Microsimulation( + dataset=UKSingleYearDataset(file_path=path) + ) + return np.asarray(simulation.calculate(UK_BIRTH_MONTH_COLUMN, 2025)) + + +def test_the_stage_spreads_months_as_the_engine_does() -> None: + from policyengine_uk.utils.stochastic import stratified_uniform + + rng = np.random.default_rng(11) + strata = rng.integers(0, 6, 500) + draws = rng.uniform(0.0, 1.0, 500) + weights = rng.uniform(0.0, 5.0, 500) + weights[strata == 5] = 0.0 + + np.testing.assert_array_equal( + stratified_positions(strata, draws, weights), + stratified_uniform(strata, draws, weights), + ) + + +def test_stored_months_do_not_move_with_the_weights(tmp_path) -> None: + pytest.importorskip("tables") + even = np.full(N, 1_000.0) + uneven = np.linspace(100.0, 5_000.0, N) + + stored = [ + _engine_months( + write_uk_national_frame( + uk_release_export_frame(_frame(weights, store_months=True)), + tmp_path / f"stored-{name}.h5", + ) + ) + for name, weights in (("even", even), ("uneven", uneven)) + ] + np.testing.assert_allclose(stored[0], stored[1], rtol=0, atol=1e-5) + np.testing.assert_allclose( + stored[0], + _frame(even, store_months=True).table("person")[UK_BIRTH_MONTH_COLUMN], + rtol=0, + atol=1e-5, + ) + + # The mutation: left to the engine, the same people's months follow the + # weights the file carries. + unset = [ + _engine_months( + write_uk_national_frame( + uk_release_export_frame(_frame(weights, store_months=False)), + tmp_path / f"unset-{name}.h5", + ) + ) + for name, weights in (("even", even), ("uneven", uneven)) + ] + assert not np.allclose(unset[0], unset[1], rtol=0, atol=1e-3) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_childcare_targets.py b/packages/microcosm-build/tests/engine/uk/test_uk_childcare_targets.py index 3ec87320de..b48e99fe7e 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_childcare_targets.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_childcare_targets.py @@ -18,7 +18,23 @@ def test_childcare_target_formula_contracts_match_the_installed_engine() -> None ) tax_free_source = inspect.getsource(system.variables["tax_free_childcare"].formula) - assert "~has_extended_childcare" in universal_source + # From 2.128.1 a child on the working parent entitlement keeps the + # universal hours (pe-uk#2177), so the universal-only row must leave the + # extended entitlement out itself. + assert "has_extended_childcare" not in universal_source + universal_only = next( + target + for target in json.loads( + importlib_resources.files("microcosm.build.uk") + .joinpath("uk_population_targets.json") + .read_text(encoding="utf-8") + )["targets"] + if target["target_id"] == "dfe.funded_childcare.universal_only_children" + ) + assert { + "variable": "is_child_receiving_extended_childcare", + "equals": False, + } in universal_only["bindings"]["policyengine"]["filters"] assert "tax_free_childcare_spend_routed_share" in tax_free_source for name in ( diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_frs_employment.py b/packages/microcosm-build/tests/engine/uk/test_uk_frs_employment.py new file mode 100644 index 0000000000..8edd9f21b0 --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_frs_employment.py @@ -0,0 +1,24 @@ +"""FRS EMPSTATI statuses against the engine's EmploymentStatus enum.""" + +from policyengine_uk.variables.household.income.employment_status import ( + EmploymentStatus, +) + +from microcosm.build.uk_runtime.frs_employment import ( + CHILD_EMPLOYMENT_STATUS, + FRS_EMPSTATI_EMPLOYMENT_STATUS, +) +from microcosm.build.uk_runtime.frs_legacy_proxies import ( + ESA_HEALTH_EMPLOYMENT_STATUSES, +) + + +def test_adult_codes_and_child_rows_are_a_bijection_onto_the_engine_enum() -> None: + statuses = [*FRS_EMPSTATI_EMPLOYMENT_STATUS.values(), CHILD_EMPLOYMENT_STATUS] + assert len(set(statuses)) == len(statuses) + assert set(statuses) == set(EmploymentStatus.__members__) + + +def test_esa_health_statuses_are_engine_statuses() -> None: + assert set(ESA_HEALTH_EMPLOYMENT_STATUSES) <= set(EmploymentStatus.__members__) + assert "OTHER_INACTIVE" not in ESA_HEALTH_EMPLOYMENT_STATUSES diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py index 996735e9ba..1ea7dd218a 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py @@ -29,8 +29,8 @@ def test_uc_take_up_population_mirrors_the_engines_eligibility_rule() -> None: The take-up population holds a unit with a UC claimant or partner aged 18 to under Pension Credit qualifying age. One-person units aged 0 to 100 - agree with the engine; the two cases below are engine conditions the - population does not read, named so that an engine change to either fails + agree with the engine; the three cases below are engine conditions the + population does not read, named so that an engine change to any fails here. """ @@ -73,3 +73,14 @@ def test_uc_take_up_population_mirrors_the_engines_eligibility_rule() -> None: }, ) == [False] assert policy.working_age(np.array([70, 60])).tolist() == [False, True] + + # Named exception (iv): from 2.128.0 the closure of tax credits moves a + # family onto Universal Credit (pe-uk#2119), and a pension-age Working Tax + # Credit recipient claims it with the upper age condition waived (SI + # 2014/1230 reg 60A). The population leaves them out; the build leaves + # would_claim_uc_at_legacy_closure at the engine's default. + assert _engine_uc_eligible( + {"wtc": {"age": {2025: 70}, "working_tax_credit_reported": {2025: 2_000}}}, + {"b_wtc": {"members": ["wtc"]}}, + ) == [True] + assert not policy.working_age(np.array([70])).any() diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_full_graph_admission.py b/packages/microcosm-build/tests/engine/uk/test_uk_full_graph_admission.py index 923070595c..5466dd976d 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_full_graph_admission.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_full_graph_admission.py @@ -132,6 +132,7 @@ def test_real_full_graph_preflight_replays_and_blocks_dense_export_and_certifica graph, population=full.calibration.population, bindings={"target_scope": "all", "n_clones": full.config.n_clones}, + solve_year=int(full.config.calibration_year), artifact_inputs=(final_gate,), ) graph = add_uk_export_continuation( diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_release_identifiers.py b/packages/microcosm-build/tests/engine/uk/test_uk_release_identifiers.py new file mode 100644 index 0000000000..ac6b4e21cd --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_release_identifiers.py @@ -0,0 +1,185 @@ +"""policyengine-uk keeps a release's identifiers as written (microcosm#1194). + +The engine stores its five identifier variables as 32-bit integers. The clone +stages number copies past that range, so the release boundary renumbers +(``national_frame.uk_release_identifier_tables``). These tests hold it +against the installed engine: the renumbered file reads back exactly, the +build's own identifiers would not have, and the calibration's engine file deals +the same birth months as the release. +""" + +from __future__ import annotations + +import warnings +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.measure_simulation import UKMeasureResolver +from microcosm.build.uk_runtime.national_frame import ( + UK_ENGINE_IDENTIFIER_MAX, + uk_national_frame, + uk_release_export_frame, + write_uk_national_frame, +) +from microcosm.frame import WeightKind + +IDENTIFIERS = ( + "person_id", + "person_benunit_id", + "person_household_id", + "benunit_id", + "household_id", +) +# One survey household and its copy, numbered as the clone stages number them. +COPY = 10**13 +BUILD = np.asarray([1, 2, COPY + 1, COPY + 2], dtype=np.int64) + + +def _frame(): + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": BUILD, + "person_benunit_id": BUILD, + "person_household_id": BUILD, + "age": [40, 50, 40, 50], + } + ), + benunit=pd.DataFrame({"benunit_id": BUILD}), + household=pd.DataFrame( + { + "household_id": BUILD, + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2024", + weight_kind=WeightKind.CALIBRATED, + household_weights=np.ones(len(BUILD)), + ) + + +def _engine_identifiers(path: Path) -> dict[str, np.ndarray]: + import policyengine_uk + from policyengine_uk.data import UKSingleYearDataset + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + simulation = policyengine_uk.Microsimulation( + dataset=UKSingleYearDataset(file_path=path) + ) + return { + name: np.asarray(simulation.calculate(name, 2024)).astype(np.int64) + for name in IDENTIFIERS + } + + +def test_the_engine_returns_the_release_identifiers_as_written(tmp_path) -> None: + pytest.importorskip("tables") + path = write_uk_national_frame( + uk_release_export_frame(_frame()), tmp_path / "release.h5" + ) + + with pd.HDFStore(path, mode="r") as store: + stored = { + "person_id": store["person"]["person_id"], + "person_benunit_id": store["person"]["person_benunit_id"], + "person_household_id": store["person"]["person_household_id"], + "benunit_id": store["benunit"]["benunit_id"], + "household_id": store["household"]["household_id"], + } + engine = _engine_identifiers(path) + + for name in IDENTIFIERS: + written = stored[name].to_numpy(dtype=np.int64) + assert written.tolist() == [1, 2, 3, 4], name + np.testing.assert_array_equal(engine[name], written, err_msg=name) + + +def test_the_build_identifiers_do_not_survive_the_engine(tmp_path) -> None: + """The mutation check: what the renumbering prevents.""" + pytest.importorskip("tables") + assert BUILD.max() > UK_ENGINE_IDENTIFIER_MAX + path = write_uk_national_frame(_frame(), tmp_path / "build-ids.h5") + + engine = _engine_identifiers(path) + + for name in IDENTIFIERS: + assert not np.array_equal(engine[name], BUILD), name + + +def _same_age_frame(): + """Forty people of one age and sex, a copy of twenty: the engine spreads + their birth months over the year by a hash of ``person_id``.""" + build = np.concatenate([np.arange(1, 21), COPY + np.arange(1, 21)]).astype(np.int64) + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": build, + "person_benunit_id": build, + "person_household_id": build, + "age": 40, + "gender": "MALE", + } + ), + benunit=pd.DataFrame({"benunit_id": build}), + household=pd.DataFrame( + { + "household_id": build, + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2024", + weight_kind=WeightKind.CALIBRATED, + household_weights=np.ones(len(build)), + ) + + +def _birth_months(simulation) -> np.ndarray: + return np.asarray(simulation.calculate("months_since_last_birthday", 2025)) + + +def test_the_calibration_and_the_release_deal_the_same_birth_months(tmp_path) -> None: + """The date of birth decides the two-child limit, the State Pension scheme + and the Pension Credit qualifying age, so the solve must measure the deal + the released file has.""" + pytest.importorskip("tables") + import policyengine_uk + from policyengine_uk.data import UKSingleYearDataset + + frame = _same_age_frame() + + solve = UKMeasureResolver( + simulation_source=None, frame=frame, scratch_dir=tmp_path / "solve", year=2025 + ) + released = write_uk_national_frame( + uk_release_export_frame(frame), tmp_path / "release.h5" + ) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + release = policyengine_uk.Microsimulation( + dataset=UKSingleYearDataset(file_path=released) + ) + on_build_ids = policyengine_uk.Microsimulation( + dataset=UKSingleYearDataset( + file_path=write_uk_national_frame(frame, tmp_path / "build-ids.h5") + ) + ) + + months = _birth_months(release) + np.testing.assert_array_equal(_birth_months(solve.simulation), months) + assert len(np.unique(months)) > 20 + # The mutation: on the build's identifiers the engine deals them otherwise. + assert not np.array_equal(_birth_months(on_build_ids), months) + # The resolver's own frame keeps the build identifiers. + assert solve.frame.table("person")["person_id"].tolist() == ( + frame.table("person")["person_id"].tolist() + ) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py index 68b9fbaf3d..e2aa2ca7c7 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py @@ -193,6 +193,58 @@ def test_relief_bands_relieve_each_slice_at_its_rate(tmp_path): assert len(resolver._counterfactuals) == 1 +def test_a_donors_relief_bands_follow_the_engines_extended_bands(tmp_path): + """policyengine-uk extends a Gift Aid donor's bands by the grossed-up gift + (ITA 2007 s.414(2)(b), pe-uk#2271). uk-data#533's first two examples with a + GBP 800 gift (GBP 1,000 gross): rest of the UK 654 basic and 292 higher, + Scotland 559.02 basic and 561.96 higher, and the classes still add up to + the rise in the engine's earned_income_tax.""" + + ids = np.arange(2, dtype=np.int64) + frame = uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": [40, 40], + "employment_income": [48_000.0, 42_000.0], + "pension_contributions_via_salary_sacrifice": [4_000.0, 4_000.0], + "gift_aid": [800.0, 800.0], + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "household_weight": [1.0, 1.0], + "region": ["LONDON", "SCOTLAND"], + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2025", + weight_kind=WeightKind.DESIGN, + ) + resolver = UKMeasureResolver( + simulation_source=None, frame=frame, scratch_dir=tmp_path, year=2025 + ) + + relief = { + rate_class: resolver.counterfactual_delta(_band_binding(rate_class), 2025)[0] + for rate_class in UK_RATE_CLASSES + } + + np.testing.assert_allclose(relief["basic"], [654.0, 559.02], atol=0.01) + np.testing.assert_allclose(relief["higher"], [292.0, 561.96], atol=0.01) + np.testing.assert_allclose(relief["additional"], [0.0, 0.0], atol=0.01) + earned, _ = resolver.counterfactual_delta( + _relief_binding("earned_income_tax"), 2025 + ) + np.testing.assert_allclose(sum(relief.values()), earned, atol=0.01) + + def test_relief_bands_refuse_when_they_leave_the_engines_earned_income_tax( tmp_path, monkeypatch ): diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_uc_relationships.py b/packages/microcosm-build/tests/engine/uk/test_uk_uc_relationships.py index 5a65eac2ca..1ab17eef1f 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_uc_relationships.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_uc_relationships.py @@ -10,8 +10,13 @@ def test_released_engine_retains_source_claimants_and_uc_awards(tmp_path, route) This tests recorded claim membership, including a 17-year-old partner; it does not add or establish eligibility for under-18 claimants. - 2025 monthly standard allowances are 400.14 single old, 497.55 couple - young and 628.10 couple old; an eldest child born before 2017 adds 339. + 2025 monthly standard allowances are 316.98 single young, 400.14 single + old and 628.10 couple old; an eldest child born before 2017 adds 339. + The 24-year-old with the 17-year-old partner claims as a single person + (UC Regs 2013 reg 3(3)), which policyengine-uk models since 2.123.8 + (PolicyEngine/policyengine-uk#2084): the recorded role is kept and the + award takes the single young allowance, where it took the couple young + one (497.55) before. """ from microcosm.build.uk_runtime.measure_simulation import UKMeasureResolver from microcosm.build.uk_runtime.national_frame import ( @@ -105,7 +110,7 @@ def test_released_engine_retains_source_claimants_and_uc_awards(tmp_path, route) assert values["is_uc_claimant"].dtype.kind == "b" np.testing.assert_allclose( values["uc_standard_allowance"], - [4801.68, 5970.60, 4801.68, 7537.20], + [4801.68, 3803.76, 4801.68, 7537.20], atol=0.01, rtol=0, ) @@ -114,7 +119,7 @@ def test_released_engine_retains_source_claimants_and_uc_awards(tmp_path, route) ) np.testing.assert_allclose( values["universal_credit"], - [4801.68, 5970.60, 8869.68, 11605.20], + [4801.68, 3803.76, 8869.68, 11605.20], atol=0.01, rtol=0, ) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_weight_year.py b/packages/microcosm-build/tests/engine/uk/test_uk_weight_year.py new file mode 100644 index 0000000000..d159d944c0 --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_weight_year.py @@ -0,0 +1,138 @@ +"""policyengine-uk returns a release's solved weights at the solve year (microcosm#796 A19). + +The release write divides the solved household weights by the engine's own +projection of ``household_weight`` from the dataset's year to the solve year +(``uk_runtime.weight_year``). These tests hold the three things that rests on +against the installed engine: the pinned growth, that ``household_weight`` is +the only weight the engine projects, and the round trip itself. +""" + +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +import yaml + +from microcosm.build.uk_runtime import hmrc_uprating +from microcosm.build.uk_runtime.national_frame import ( + uk_national_frame, + uk_release_export_frame, + write_uk_national_frame, +) +from microcosm.build.uk_runtime.weight_year import ( + UK_WEIGHT_PROJECTION_PARAMETER, + UK_WEIGHT_PROJECTION_PINS_BLOCK, + UK_WEIGHT_PROJECTION_VARIABLE, + uk_dataset_year_frame, + uk_weight_projection_factor, +) +from microcosm.frame import WeightKind + +SOLVED = np.asarray([1_000.0, 2_500.0, 400.0]) + + +def _frame(): + ids = np.arange(3, dtype=np.int64) + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": [40, 50, 60], + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2024", + weight_kind=WeightKind.CALIBRATED, + household_weights=SOLVED, + ) + + +def _engine_weights(path: Path, year: int) -> np.ndarray: + import policyengine_uk + from policyengine_uk.data import UKSingleYearDataset + + simulation = policyengine_uk.Microsimulation( + dataset=UKSingleYearDataset(file_path=path) + ) + return np.asarray( + simulation.calculate(UK_WEIGHT_PROJECTION_VARIABLE, year), dtype=float + ) + + +def test_the_pinned_growth_is_the_installed_engines() -> None: + pins = hmrc_uprating.engine_parameter_pins() + entry = pins[UK_WEIGHT_PROJECTION_PINS_BLOCK][UK_WEIGHT_PROJECTION_VARIABLE] + assert entry["parameter"] == UK_WEIGHT_PROJECTION_PARAMETER + assert list(entry["growth"]) == pins["instants"] + for instant, value in entry["growth"].items(): + assert hmrc_uprating.live_engine_parameter_value( + UK_WEIGHT_PROJECTION_PARAMETER, instant + ) == float(value), instant + + +def test_household_weight_is_the_only_weight_the_engine_projects() -> None: + import policyengine_uk + from policyengine_uk import CountryTaxBenefitSystem + + path = Path(policyengine_uk.__file__).parent / "data" / "uprating_indices.yaml" + indices = yaml.safe_load(path.read_text(encoding="utf-8")) + projected = { + variable: parameter + for parameter, variables in indices.items() + for variable in variables + if "weight" in variable + } + assert projected == {UK_WEIGHT_PROJECTION_VARIABLE: UK_WEIGHT_PROJECTION_PARAMETER} + # The person and benefit-unit weights are formulas of it, never stored. + system = CountryTaxBenefitSystem() + for name in ("person_weight", "benunit_weight"): + assert not system.variables[name].is_input_variable(), name + + +def test_the_engine_returns_the_solved_weights_at_the_solve_year(tmp_path) -> None: + pytest.importorskip("tables") + frame = uk_release_export_frame(_frame()) + factor = uk_weight_projection_factor(2024, 2025) + + released = write_uk_national_frame( + uk_dataset_year_frame(frame, solve_year=2025), tmp_path / "release.h5" + ) + + np.testing.assert_allclose(_engine_weights(released, 2025), SOLVED, rtol=1e-6) + np.testing.assert_allclose( + _engine_weights(released, 2024), SOLVED / factor, rtol=1e-6 + ) + # Beyond the solve year the engine carries on with its own growth. + np.testing.assert_allclose( + _engine_weights(released, 2026), + SOLVED * uk_weight_projection_factor(2025, 2026), + rtol=1e-6, + ) + + +def test_weights_written_as_solved_read_high_at_the_solve_year(tmp_path) -> None: + """The mutation check: what the division prevents.""" + pytest.importorskip("tables") + factor = uk_weight_projection_factor(2024, 2025) + + as_solved = write_uk_national_frame( + uk_release_export_frame(_frame()), tmp_path / "as-solved.h5" + ) + + high = _engine_weights(as_solved, 2025) + np.testing.assert_allclose(high, SOLVED * factor, rtol=1e-6) + assert not np.allclose(high, SOLVED, rtol=1e-4, atol=0) diff --git a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py index 2e16cec137..41c7ccea09 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py @@ -1143,8 +1143,8 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: references = {reference.name: reference for reference in spec.target_references} assert ( - len(references) == 1279 - ) # microcosm#1106: + 13 SPI property amounts; microcosm#1113: + 1 ONS 07.2.2 household road fuel; PolicyEngine/chronicle#280 lane: 705 + 33 HMRC liabilities rows (Table 2.5, three + len(references) == 1283 + ) # microcosm#1095: + 4, the two national 15_19 sex bands split into ages 15, 16-17 and 18-19; microcosm#1106: + 13 SPI property amounts; microcosm#1113: + 1 ONS 07.2.2 household road fuel; PolicyEngine/chronicle#280 lane: 705 + 33 HMRC liabilities rows (Table 2.5, three # measures by eleven bands) + 26 SPI savings-interest rows (two measures by # thirteen bands) + 360 SPI Table 3.11 region-tier rows (three measures by # ten regional bands over twelve areas); microcosm#905: 424 - 18 country diff --git a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py index a1a1848bca..25c5696383 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py @@ -4,7 +4,9 @@ every extra that installs policyengine-us is declared in conflict with every extra that installs policyengine-uk, and uv locks the two sides in separate forks. The US side stays on the core the certified US default was built with -until its next certified build. The resolution checks read the committed +until its next certified build. The UK side resolves the locked policyengine-uk +and the core that release brings, and a lock change that moves either has to +move the pin below with it. The resolution checks read the committed ``uv.lock`` through ``uv export --frozen``, uv's own reading of what an install path gets, without the network or the environment. """ @@ -27,6 +29,12 @@ "policyengine-us": "2.2.1", "spm-calculator": "1.0.0", } +#: What every UK install path resolves: the locked policyengine-uk and the +#: policyengine-core its own floor brings. +UK_ENGINE = { + "policyengine-core": "3.33.3", + "policyengine-uk": "2.130.1", +} ENGINE_PACKAGES = ("microcosm-build", "microcosm-data", "microcosm-frame") @@ -117,6 +125,26 @@ def test_every_us_install_path_resolves_the_certified_us_engine( assert "policyengine-uk" not in resolved +def _uk_install_paths() -> list[tuple[str, ...]]: + extra_names = sorted({extra for _, extra in UK_EXTRAS}) + return [ + *(("--all-packages", "--extra", extra) for extra in extra_names), + *(("--package", package, "--extra", extra) for package, extra in UK_EXTRAS), + ] + + +@pytest.mark.parametrize("args", _uk_install_paths(), ids=" ".join) +def test_every_uk_install_path_resolves_the_locked_uk_engine( + args: tuple[str, ...], +) -> None: + result = _uv_export(*args) + + assert result.returncode == 0, result.stderr + resolved = _resolved(result.stdout) + assert {name: resolved.get(name) for name in UK_ENGINE} == UK_ENGINE + assert "policyengine-us" not in resolved + + def test_every_us_and_uk_pair_is_declared_and_locked_as_a_conflict() -> None: expected = { frozenset({us, uk}) for us, uk in itertools.product(US_EXTRAS, UK_EXTRAS) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_birth_month.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_birth_month.py new file mode 100644 index 0000000000..ba54553917 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_birth_month.py @@ -0,0 +1,117 @@ +"""The months since each person's last birthday, stored once in the spine (microcosm#1095).""" + +from __future__ import annotations + +import numpy as np +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st + +from microcosm.build.uk_runtime.birth_month import ( + MONTHS_IN_YEAR, + stratified_positions, + uk_months_since_last_birthday, +) + + +def test_positions_sit_at_the_midpoints_of_each_stratum_s_cumulative_weight() -> None: + strata = np.asarray([0, 0, 0, 1, 1]) + draws = np.asarray([0.9, 0.1, 0.5, 0.3, 0.2]) + weights = np.asarray([1.0, 1.0, 2.0, 3.0, 1.0]) + + positions = stratified_positions(strata, draws, weights) + + # Stratum 0 in draw order: 0.1 (w 1), 0.5 (w 2), 0.9 (w 1) of total 4. + np.testing.assert_allclose(positions[[1, 2, 0]], [0.125, 0.5, 0.875]) + # Stratum 1 in draw order: 0.2 (w 1), 0.3 (w 3) of total 4. + np.testing.assert_allclose(positions[[4, 3]], [0.125, 0.625]) + + +def test_a_stratum_without_weight_keeps_its_draws() -> None: + positions = stratified_positions( + np.asarray([0, 0]), np.asarray([0.25, 0.75]), np.asarray([0.0, 0.0]) + ) + + np.testing.assert_allclose(positions, [0.25, 0.75]) + + +@settings(max_examples=60, deadline=None) +@given( + st.lists( + st.tuples( + st.integers(0, 3), + st.floats(0.0, 0.999999, allow_nan=False), + st.floats(0.01, 100.0, allow_nan=False), + ), + min_size=1, + max_size=40, + # Identity-keyed draws do not tie; with a tie the order would decide. + unique_by=lambda row: row[1], + ), + st.randoms(use_true_random=False), +) +def test_positions_do_not_depend_on_record_order(rows, rng) -> None: + strata, draws, weights = (np.asarray(column) for column in zip(*rows, strict=True)) + order = list(range(len(rows))) + rng.shuffle(order) + + positions = stratified_positions(strata, draws, weights) + shuffled = stratified_positions(strata[order], draws[order], weights[order]) + + np.testing.assert_allclose(shuffled, positions[order]) + assert ((positions >= 0.0) & (positions < 1.0)).all() + + +def _people(n: int = 200): + rng = np.random.default_rng(7) + return { + "age": rng.integers(0, 90, n), + "is_male": rng.integers(0, 2, n).astype(bool), + "person_ids": np.arange(1, n + 1) * 100 + 1, + "weights": rng.uniform(500.0, 3_000.0, n), + } + + +def test_months_spread_each_age_and_sex_evenly_over_the_year() -> None: + people = _people(4_000) + people["age"] = np.full(4_000, 40) + people["is_male"] = np.zeros(4_000, dtype=bool) + + months = uk_months_since_last_birthday(**people) + + assert ((months >= 0.0) & (months < MONTHS_IN_YEAR)).all() + # Within one age and sex, the weight in each month is a twelfth of the + # whole, to within the largest single weight. + share = ( + np.bincount( + np.floor(months).astype(int), weights=people["weights"], minlength=12 + ) + / people["weights"].sum() + ) + assert ( + np.abs(share - 1.0 / 12.0).max() + < people["weights"].max() / (people["weights"].sum()) + 1e-12 + ) + + +def test_months_are_keyed_on_the_person_not_on_the_row() -> None: + people = _people() + months = uk_months_since_last_birthday(**people) + order = np.random.default_rng(3).permutation(len(months)) + + again = uk_months_since_last_birthday( + **{name: values[order] for name, values in people.items()} + ) + + np.testing.assert_array_equal(again, months[order]) + # Other identifiers deal the months otherwise. + renamed = dict(people, person_ids=people["person_ids"] + 7) + assert not np.array_equal(uk_months_since_last_birthday(**renamed), months) + + +def test_fractional_ages_refuse() -> None: + people = _people(3) + people["age"] = np.asarray([30.5, 40.0, 50.0]) + + with pytest.raises(ValueError, match="whole ages"): + uk_months_since_last_birthday(**people) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py index 8bece9c538..5dc79887d0 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py @@ -30,6 +30,10 @@ uk_national_frame, write_uk_national_frame, ) +from microcosm.build.uk_runtime.weight_year import ( + uk_dataset_year_frame, + uk_weight_projection_factor, +) from microcosm.build.uk_runtime.weighted_integrity import UKReviewedExclusion from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.frame import WeightKind @@ -63,7 +67,14 @@ def _registry() -> TargetRegistry: ) -def _write(path: Path, *, measure_a, measure_b=None) -> None: +def _write( + path: Path, + *, + measure_a, + measure_b=None, + time_period: str = "2025", + solve_year: int | None = None, +) -> None: n = 2 person = pd.DataFrame( { @@ -80,10 +91,12 @@ def _write(path: Path, *, measure_a, measure_b=None) -> None: person=person, benunit=benunit, household=pd.DataFrame(household), - time_period="2025", + time_period=time_period, weight_kind=WeightKind.CALIBRATED, household_weights=np.ones(n), ) + if solve_year is not None: + frame = uk_dataset_year_frame(frame, solve_year=solve_year) write_uk_national_frame(frame, path) @@ -351,6 +364,46 @@ def test_verdict_fails_when_the_incumbent_fits_better(tmp_path): assert evaluation_block(score) == score["evaluation"] +def test_each_file_is_scored_with_the_weights_the_engine_gives_it(tmp_path): + """microcosm#796 A19: a file is read at the scored year through the + engine's own projection of its household weights from the file's year.""" + pytest.importorskip("tables") + released = tmp_path / "released.h5" + as_solved = tmp_path / "as_solved.h5" + # Both hit the two targets exactly at their solved weights of one. One is + # written at its dataset year, as a release is; the other as solved. + _write( + released, + measure_a=[5.0, 5.0], + measure_b=[10.0, 10.0], + time_period="2024", + solve_year=2025, + ) + _write(as_solved, measure_a=[5.0, 5.0], measure_b=[10.0, 10.0], time_period="2024") + factor = uk_weight_projection_factor(2024, 2025) + + score = evaluate_uk_candidate_against_incumbent( + candidate_h5=released, + incumbent_h5=as_solved, + candidate_sha256=_sha256_file(released), + incumbent_sha256=_sha256_file(as_solved), + target_registry=_registry(), + calibration_year=2025, + ) + + rule_1 = score["evaluation"]["rule_1"] + # The release reads back its solved weights; the as-solved file reads + # high on every row by the population growth of the year after its own. + assert rule_1["candidate_full_loss"] == pytest.approx(0.0, abs=1e-12) + assert rule_1["incumbent_full_loss"] == pytest.approx(factor - 1.0, rel=1e-9) + side = {"dataset_year": 2024, "engine_projection_factor": factor} + assert score["weight_year"] == { + "scored_year": 2025, + "candidate": side, + "incumbent": side, + } + + def _evaluate(candidate, incumbent, **overrides): kwargs = { "candidate_h5": candidate, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py index 0ba3fe2db6..6c64b97d9b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py @@ -1,10 +1,15 @@ from __future__ import annotations +import hashlib +from types import SimpleNamespace + import numpy as np import pandas as pd +import pytest from microcosm.build.uk_runtime.frs_education import ( EDUCQUAL_MAP, + add_frs_education, derive_current_education, derive_frs_education, ) @@ -68,7 +73,11 @@ def test_training_qyp_ema_and_benefits_in_own_right() -> None: "fted": [1, 2, 2], "typeed2": [7, 0, 0], "educqual": [17, 86, 999], - "train": [9, 10, np.nan], + # A child-table 17-year-old outside education is a trainee; the + # adult-table record's TRAINEE answer is not read as approved + # training (uk-data#561). + "trainee": [2, 1, 1], + "_frs_table": ["child", "child", "adult"], # 2023-24-shaped vintage: no adema pair, eduma/edumaamt instead. "eduma": [1, 1, 0], "edumaamt": [2.0, -1.0, 5.0], @@ -79,19 +88,10 @@ def test_training_qyp_ema_and_benefits_in_own_right() -> None: result = derive_frs_education(person, raw) - assert result["is_in_approved_training"].tolist() == [True, False, False] - # FRS 2024-25 renames the variable to train2 and recodes it: 9 is "None - # of these" and 10 the Kickstart Scheme (microcosm#1063 c9). - recoded = derive_frs_education( - person, raw.drop(columns=["train"]).assign(train2=[9, 10, 1]) - ) - assert recoded["is_in_approved_training"].tolist() == [False, True, True] - assert derive_frs_education(person, raw.drop(columns=["train"]))[ - "is_in_approved_training" - ].tolist() == [False, False, False] + assert result["is_in_approved_training"].tolist() == [False, True, False] assert result["age_started_or_accepted_current_education_or_training"].tolist() == [ 18, - 1000, + 17, 1000, ] assert result[ @@ -124,6 +124,8 @@ def test_adult_ema_prefers_the_adema_pair_when_present() -> None: # adema pair present: it must win over a contradictory eduma pair # (the incumbent aliases eduma into adema only when adema is # absent). + "trainee": [2, 2], + "_frs_table": ["child", "adult"], "adema": [1, 1], "ademaamt": [4.0, -1.0], "eduma": [1, 1], @@ -139,3 +141,100 @@ def test_adult_ema_prefers_the_adema_pair_when_present() -> None: 4.0 * WEEKS_IN_YEAR, 4.0 * WEEKS_IN_YEAR, ] + + +def test_approved_training_is_trainee_on_child_table_records_only() -> None: + """TRAINEE = 1 flags a child-table record and never an adult-table one. + + The FRS counts a 16- to 19-year-old as a dependent child only in full-time + non-advanced education or unwaged government training, so a child-table + trainee is in approved training. An adult-table record is not a dependant, + and its answer does not say the training is unwaged (uk-data#561). + """ + + person = pd.DataFrame({"person_id": [1, 2, 3, 4, 5], "age": [16, 19, 18, 17, 40]}) + raw = pd.DataFrame( + { + "person_id": [1, 2, 3, 4, 5], + "fted": [2, 2, 2, 2, 2], + "typeed2": [0, 0, 0, 0, 0], + "educqual": [86, 86, 86, 86, 86], + # 1 yes, 2 no; a blank and an out-of-range code are not a yes. + "trainee": [1, 1, 1, np.nan, 1], + "_frs_table": ["child", "child", "adult", "child", "adult"], + "chema": [0, 0, 0, 0, 0], + "chemaamt": [0.0, 0.0, 0.0, 0.0, 0.0], + "eduma": [0, 0, 0, 0, 0], + "edumaamt": [0.0, 0.0, 0.0, 0.0, 0.0], + } + ) + + result = derive_frs_education(person, raw) + + assert result["is_in_approved_training"].tolist() == [ + True, + True, + False, + False, + False, + ] + # The entry age and the age-19 terminal-date flag follow the training flag. + assert result["age_started_or_accepted_current_education_or_training"].tolist() == [ + 16, + 18, + 1000, + 1000, + 1000, + ] + assert result[ + "is_before_universal_credit_qualifying_young_person_terminal_date" + ].tolist() == [False, True, False, False, False] + + +@pytest.mark.parametrize("column", ["trainee", "_frs_table"]) +def test_approved_training_refuses_rows_without_its_columns(column: str) -> None: + """A missing column refuses; it never reads as nobody in training.""" + + person = pd.DataFrame({"person_id": [1], "age": [17]}) + raw = pd.DataFrame( + { + "person_id": [1], + "fted": [2], + "typeed2": [0], + "educqual": [86], + "trainee": [1], + "_frs_table": ["child"], + } + ) + + with pytest.raises(KeyError, match="approved training cannot be derived"): + derive_frs_education(person, raw.drop(columns=[column])) + + +@pytest.mark.parametrize("table", ["adult", "child"]) +def test_the_stage_refuses_a_tab_without_trainee(tmp_path, table: str) -> None: + """Each tab is checked on its own, before the two are concatenated. + + After the concat a tab without TRAINEE would read as blanks beside the + other tab's answers, which is a silent "nobody on this table trains". + """ + + artifacts = [] + for name in ("adult", "child"): + columns = ["SERNUM", "BENUNIT", "PERSON", "TRAINEE"] + if name == table: + columns.remove("TRAINEE") + row = ["1", "1", "1" if name == "adult" else "2", "2"][: len(columns)] + path = tmp_path / f"{name}.tab" + path.write_text("\t".join(columns) + "\n" + "\t".join(row) + "\n") + artifacts.append( + { + "table": name, + "locator": path.name, + "sha256": hashlib.sha256(path.read_bytes()).hexdigest(), + "size_bytes": path.stat().st_size, + } + ) + + with pytest.raises(KeyError, match=f"The FRS {table} tab has no TRAINEE column"): + add_frs_education(None, tmp_path, stage=SimpleNamespace(artifacts=artifacts)) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_employment.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_employment.py index 23f44ca8f4..3b23d9f6dc 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_employment.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_employment.py @@ -1,33 +1,86 @@ from __future__ import annotations +import re + import numpy as np import pandas as pd import pytest +from hypothesis import given +from hypothesis import strategies as st + +from microcosm.build.uk_runtime.frs_employment import ( + CHILD_EMPLOYMENT_STATUS, + FRS_EMPSTATI_EMPLOYMENT_STATUS, + derive_employment_status_from_frs, + derive_frs_employment, +) + +# EMPSTATI ("Adult - Employment Status - ILO definition") value labels in the +# UKDS FRS 2024-25 data dictionary (SN 9563, adult table), with the status each +# label means. The incumbent's uk-data#526 pins the same table. +DATA_DICTIONARY = { + 1: ("Full-time Employee", "FT_EMPLOYED"), + 2: ("Part-time Employee", "PT_EMPLOYED"), + 3: ("Full-time Self-Employed", "FT_SELF_EMPLOYED"), + 4: ("Part-time Self-Employed", "PT_SELF_EMPLOYED"), + 5: ("Unemployed", "UNEMPLOYED"), + 6: ("Retired", "RETIRED"), + 7: ("Student", "STUDENT"), + 8: ("Looking after family/home", "CARER"), + 9: ("Permanently sick/disabled", "LONG_TERM_DISABLED"), + 10: ("Temporarily sick/injured", "SHORT_TERM_DISABLED"), + 11: ("Other Inactive", "OTHER_INACTIVE"), +} +ADULT_CODES = sorted(DATA_DICTIONARY) + +adult_rows = st.tuples(st.just(True), st.sampled_from(ADULT_CODES)) +# Child-table people have no EMPSTATI (NaN once aligned to adult.tab); the +# code must not matter for them. +child_rows = st.tuples( + st.just(False), + st.one_of(st.just(0), st.just(np.nan), st.integers(-9, 99)), +) +people = st.lists(st.one_of(adult_rows, child_rows), max_size=60) +unknown_adult_codes = st.one_of( + st.just(np.nan), + st.integers(-99, 0), + st.integers(12, 999), + st.floats(0.5, 11.5).filter(lambda x: not float(x).is_integer()), +) -from microcosm.build.uk_runtime.frs_employment import derive_frs_employment + +def _derive(rows): + is_adult = [adult for adult, _ in rows] + codes = [code for _, code in rows] + return derive_employment_status_from_frs(codes, is_adult) + + +def _expected(adult, code): + return DATA_DICTIONARY[code][1] if adult else "CHILD" def test_employment_maps_status_sector_and_sic() -> None: - person = pd.DataFrame({"person_id": [1, 2, 3, 4, 5]}) + # Person 6 is not in adult.tab (a child-table person), so every adult.tab + # column aligns to NaN for them. + person = pd.DataFrame({"person_id": [1, 2, 3, 4, 5, 6]}) adult = pd.DataFrame( { - "person_id": [1, 2, 3, 4, 5], - "empstati": [0, 10, 11, np.nan, 12], - "mjobsect": [0, 1, 2, np.nan, 1], - "sic": [-5, 84.9, np.nan, 7, 20], + "person_id": [5, 4, 3, 2, 1], + "empstati": [8, 1.0, 11, 10, 9], + "mjobsect": [1, np.nan, 2, 1, 0], + "sic": [20, 7, np.nan, 84.9, -5], } ) result = derive_frs_employment(person, adult) assert result["employment_status"].tolist() == [ - "CHILD", - "SHORT_TERM_DISABLED", "LONG_TERM_DISABLED", + "SHORT_TERM_DISABLED", + "OTHER_INACTIVE", + "FT_EMPLOYED", + "CARER", "CHILD", - # Beyond-domain codes take the incumbent's post-map fillna, not the - # CHILD default reserved for 0/NaN rows. - "LONG_TERM_DISABLED", ] assert result["employment_sector"].tolist() == [ "NOT_EMPLOYED", @@ -35,8 +88,153 @@ def test_employment_maps_status_sector_and_sic() -> None: "PUBLIC", "NOT_EMPLOYED", "PRIVATE", + "NOT_EMPLOYED", + ] + assert result["sic_industry_division"].tolist() == [0, 84, 0, 7, 20, 0] + + +def test_other_inactive_is_not_long_term_disabled() -> None: + result = derive_employment_status_from_frs([9, 11], [True, True]) + assert result.tolist() == ["LONG_TERM_DISABLED", "OTHER_INACTIVE"] + + +def test_only_code_11_moves_from_the_truncated_map() -> None: + # The map this replaces zipped codes 0-11 with 11 statuses and sent the + # unmatched code 11 to LONG_TERM_DISABLED; codes 1-10 keep their status. + truncated = { + code: status for code, (_, status) in DATA_DICTIONARY.items() if code != 11 + } | {11: "LONG_TERM_DISABLED"} + moved = { + code + for code in ADULT_CODES + if FRS_EMPSTATI_EMPLOYMENT_STATUS[code] != truncated[code] + } + assert moved == {11} + + +@pytest.mark.parametrize("code", range(12)) +def test_every_code_from_0_to_11(code) -> None: + assert derive_employment_status_from_frs([code], [False]).tolist() == ["CHILD"] + if code == 0: + with pytest.raises(ValueError, match="EMPSTATI"): + derive_employment_status_from_frs([code], [True]) + else: + label, status = DATA_DICTIONARY[code] + assert derive_employment_status_from_frs([code], [True]).tolist() == [status], ( + label + ) + + +def test_code_table_is_the_data_dictionary() -> None: + assert dict(FRS_EMPSTATI_EMPLOYMENT_STATUS) == { + code: status for code, (_, status) in DATA_DICTIONARY.items() + } + + +def test_code_table_is_immutable() -> None: + with pytest.raises(TypeError): + FRS_EMPSTATI_EMPLOYMENT_STATUS[12] = "OTHER_INACTIVE" # type: ignore[index] + + +def test_adult_codes_and_child_rows_cover_each_status_once() -> None: + statuses = [*FRS_EMPSTATI_EMPLOYMENT_STATUS.values(), CHILD_EMPLOYMENT_STATUS] + assert CHILD_EMPLOYMENT_STATUS == "CHILD" + assert len(set(statuses)) == len(statuses) == 12 + + +@pytest.mark.parametrize("code", [0, -1, 12, 11.5, np.nan, "x"]) +def test_unknown_adult_code_fails_the_build(code) -> None: + with pytest.raises(ValueError, match="EMPSTATI"): + derive_employment_status_from_frs([1, code], [True, True]) + + +@pytest.mark.parametrize("code", [0, 12, np.nan]) +def test_adult_record_with_unknown_or_blank_code_fails_the_stage(code) -> None: + person = pd.DataFrame({"person_id": [1, 2]}) + adult = pd.DataFrame( + {"person_id": [1, 2], "empstati": [1, code], "mjobsect": [1, 1], "sic": [1, 1]} + ) + + with pytest.raises(ValueError, match="EMPSTATI"): + derive_frs_employment(person, adult) + + +def _digits_outside_code_list(message: str) -> list[str]: + return re.findall(r"\d", re.sub(r"\[.*?\]", "", message)) + + +def test_unknown_code_message_names_codes_and_prints_no_count() -> None: + # Adults are not survey households, so even a count of 10 or more could + # describe fewer than 10 households: the refusal never prints one. + with pytest.raises(ValueError) as few: + derive_employment_status_from_frs([12, 12, np.nan, 11.5], [True] * 4) + message = str(few.value) + assert "['11.5', '12', 'blank or non-numeric']" in message + assert _digits_outside_code_list(message) == [] + + with pytest.raises(ValueError) as many: + derive_employment_status_from_frs([13] * 25, [True] * 25) + assert "['13']" in str(many.value) + assert _digits_outside_code_list(str(many.value)) == [] + + +def test_mismatched_lengths_fail() -> None: + with pytest.raises(ValueError, match="same length"): + derive_employment_status_from_frs([1, 2], [True]) + + +@given(people) +def test_each_row_maps_on_its_own(rows) -> None: + result = _derive(rows) + assert result.tolist() == [_expected(adult, code) for adult, code in rows] + + +@given(people, st.randoms(use_true_random=False)) +def test_mapping_commutes_with_row_order(rows, rng) -> None: + order = list(range(len(rows))) + rng.shuffle(order) + assert _derive([rows[i] for i in order]).tolist() == [ + _derive(rows)[i] for i in order + ] + + +@given(people, unknown_adult_codes, st.integers(0, 60)) +def test_any_unknown_adult_code_fails_the_build(rows, bad_code, position) -> None: + rows = list(rows) + rows.insert(min(position, len(rows)), (True, bad_code)) + with pytest.raises(ValueError, match="EMPSTATI"): + _derive(rows) + + +@given(st.lists(unknown_adult_codes, min_size=1, max_size=30), people) +def test_unknown_code_message_never_prints_a_count(bad_codes, rows) -> None: + rows = [*rows, *((True, code) for code in bad_codes)] + with pytest.raises(ValueError) as error: + _derive(rows) + assert _digits_outside_code_list(str(error.value)) == [] + + +@given(people) +def test_stage_marks_adults_by_adult_tab_membership(rows) -> None: + # The stage aligns adult.tab by person_id: people missing from it get NaN + # codes and are CHILD; everyone in it is mapped strictly. + person = pd.DataFrame({"person_id": np.arange(len(rows)) + 101}) + adult = pd.DataFrame( + { + "person_id": [ + pid + for pid, (is_adult, _) in zip(person["person_id"], rows, strict=True) + if is_adult + ], + "empstati": [code for is_adult, code in rows if is_adult], + } + ).assign(mjobsect=0, sic=0) + + result = derive_frs_employment(person, adult.iloc[::-1]) + + assert result["employment_status"].tolist() == [ + _expected(is_adult, code) for is_adult, code in rows ] - assert result["sic_industry_division"].tolist() == [0, 84, 0, 7, 20] @pytest.mark.parametrize("missing", ["empstati", "mjobsect", "sic"]) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_legacy_proxies.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_legacy_proxies.py index eba5fdd0d5..3a05e259b4 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_legacy_proxies.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_legacy_proxies.py @@ -1,7 +1,15 @@ from __future__ import annotations +import numpy as np import pandas as pd +import pytest +from hypothesis import given +from hypothesis import strategies as st +from microcosm.build.uk_runtime.frs_employment import ( + derive_employment_status_from_frs, + derive_frs_employment, +) from microcosm.build.uk_runtime.frs_legacy_proxies import derive_frs_legacy_proxies from microcosm.build.uk_runtime.frs_spine import WEEKS_IN_YEAR @@ -41,3 +49,77 @@ def test_legacy_proxy_truth_table_and_jsa_hours_boundary() -> None: assert result["legacy_jobseeker_proxy"].tolist() == [True, False, False, False] assert result["esa_health_condition_proxy"].tolist() == [False, False, True, True] assert result["esa_support_group_proxy"].tolist() == [False, False, False, True] + + +@pytest.mark.parametrize("empstati", range(1, 12)) +def test_esa_proxies_read_only_the_sick_or_disabled_codes(empstati) -> None: + # A working-age adult with no hours: only EMPSTATI 9 (permanently + # sick/disabled) and 10 (temporarily sick/injured) are ESA health states, + # and only 9 is in the support group. Code 11 (other inactive) is neither. + status = derive_frs_employment( + pd.DataFrame({"person_id": [1]}), + pd.DataFrame( + {"person_id": [1], "empstati": [empstati], "mjobsect": [0], "sic": [0]} + ), + )["employment_status"] + + result = derive_frs_legacy_proxies( + _working_age_person(status.to_numpy(), hours=[0]), + employment_status_reported=[True], + state_pension_age=[66], + max_annual_hours=16 * WEEKS_IN_YEAR, + ) + + assert result["esa_health_condition_proxy"].tolist() == [empstati in (9, 10)] + assert result["esa_support_group_proxy"].tolist() == [empstati == 9] + assert result["legacy_jobseeker_proxy"].tolist() == [empstati == 5] + + +@given( + st.lists( + st.tuples( + st.integers(0, 100), # age + st.sampled_from(range(1, 12)), # EMPSTATI + st.integers(60, 68), # State Pension age + st.integers(0, 3_000), # annual hours worked + st.booleans(), # EMPSTATI reported + ), + min_size=1, + max_size=60, + ) +) +def test_esa_proxies_follow_the_corrected_statuses(rows) -> None: + age, codes, spa, hours, reported = map(np.array, zip(*rows, strict=True)) + status = derive_employment_status_from_frs(codes, np.ones(len(rows), bool)) + person = _working_age_person(status, hours=hours).assign(age=age) + + result = derive_frs_legacy_proxies( + person, + employment_status_reported=reported, + state_pension_age=spa, + max_annual_hours=16 * WEEKS_IN_YEAR, + ) + health = result["esa_health_condition_proxy"].to_numpy() + support = result["esa_support_group_proxy"].to_numpy() + + working_age = (age >= 16) & (age < spa) + assert ( + health.tolist() == (reported & working_age & np.isin(codes, (9, 10))).tolist() + ) + assert ( + support.tolist() + == (reported & working_age & (codes == 9) & (hours <= 0)).tolist() + ) + assert not (support & ~health).any() + assert not health[codes == 11].any() + + +def _working_age_person(status, *, hours) -> pd.DataFrame: + return pd.DataFrame( + { + "age": np.full(len(status), 30), + "employment_status": status, + "hours_worked": hours, + "current_education": "NOT_IN_EDUCATION", + } + ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py index a06b467a3c..c03cae18ba 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py @@ -263,7 +263,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "FTED": 2, "TYPEED2": 0, "EDUCQUAL": 17, - "TRAIN": 10, + "TRAINEE": 2, "EMAAMT": 0.0, "CHEMAAMT": 0.0, "INEARNS": 10.0, @@ -291,6 +291,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "SLREPAMT": 2.0, "SSPADJ": 1.0, "SMPADJ": 0.5, + "SPPADJ": 0.25, "TUBORR": 500.0, "ACCSSAMT": 1.0, "ACCSSPD": 52.0, @@ -325,7 +326,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "FTED": 1, "TYPEED2": 2, "EDUCQUAL": 86, - "TRAIN": 9, + "TRAINEE": 1, "EMAAMT": 0.0, "CHEMAAMT": 1.0, "FSMVAL": 3.0, @@ -1155,6 +1156,7 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["student_loan_repayments"] == pytest.approx(2 * WEEKS_IN_YEAR) assert adult["statutory_sick_pay"] == pytest.approx(WEEKS_IN_YEAR) assert adult["statutory_maternity_pay"] == pytest.approx(0.5 * WEEKS_IN_YEAR) + assert adult["statutory_paternity_pay"] == pytest.approx(0.25 * WEEKS_IN_YEAR) assert adult["student_loans"] == pytest.approx(500) assert adult["access_fund"] == pytest.approx(WEEKS_IN_YEAR) assert adult["education_grants"] == pytest.approx(5) @@ -1254,6 +1256,20 @@ def test_household_subrent_column_floors_missing_value_codes( assert household.loc[2, "subrent"] == pytest.approx(6 * WEEKS_IN_YEAR) +def test_frs_spine_refuses_an_adult_tab_without_sppadj(tmp_path: Path) -> None: + """A vintage without SPPADJ refuses; it never sets nobody's paternity pay.""" + + tables = _fixture_tables() + tables["adult"] = [ + {key: value for key, value in row.items() if key != "SPPADJ"} + for row in tables["adult"] + ] + stage = _write_fixture(tmp_path, tables) + + with pytest.raises(KeyError, match="no SPPADJ column"): + build_uk_frs_spine_frame(tmp_path, stage=stage) + + def test_is_blind_reads_blind_registration_on_the_adult_and_child_tabs( tmp_path: Path, ) -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py index be1fb4c8fa..91a3aae194 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py @@ -178,7 +178,7 @@ def test_uc_take_up_population_excludes_units_without_a_working_age_adult() -> N def test_uc_take_up_population_needs_a_claimant_or_partner_of_working_age() -> None: """A working-age dependant does not bring a unit into the UC population. - policyengine-uk 2.123.0's is_uc_eligible reads the age of the unit's UC + policyengine-uk 2.130.1's is_uc_eligible reads the age of the unit's UC claimant or partner (is_uc_claimant), not of any adult: a pensioner unit with an 18-year-old dependant cannot claim Universal Credit. """ diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py index 5a4ec666c9..7f49430771 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py @@ -10,6 +10,7 @@ from microcosm.build.country_spec import load_country_spec from microcosm.build.uk_runtime import full_gates as runtime +from microcosm.build.uk_runtime.national_frame import uk_national_frame from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.calibrate.artifacts import OrderedProblem, OrderedSolution from microcosm.calibrate.matrix import CalibrationProblem @@ -114,12 +115,18 @@ def evidence(monkeypatch): problem, (1, 2), metadata, {"target_selection": _selection()}, "a" * 64 ) solution = OrderedSolution(np.ones(2), (1, 2), "a" * 64, {}, "b" * 64) - table = pd.DataFrame({"household_id": [1, 2], "income": [1.0, 1.0]}) - frame = SimpleNamespace( - entities=("household",), - table=lambda entity: table, - schema=SimpleNamespace(entity_id_column=lambda entity: "household_id"), - weights_for=lambda entity: weights, + # A real national frame: the context reads it through the release export + # boundary, which renumbers identifiers (microcosm#1194). + ids = np.asarray([1, 2], dtype="int64") + frame = uk_national_frame( + person=pd.DataFrame( + {"person_id": ids, "person_benunit_id": ids, "person_household_id": ids} + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame({"household_id": ids, "income": [1.0, 1.0]}), + time_period="2024", + weight_kind=weights.kind, + household_weights=weights.values, ) monkeypatch.setattr( runtime, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph_terminal.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph_terminal.py index c57a5ac5cb..f8e4b2fe45 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph_terminal.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph_terminal.py @@ -12,6 +12,10 @@ register_uk_terminal_kernels, validate_uk_export, ) +from microcosm.build.uk_runtime.weight_year import ( + UK_WEIGHT_YEAR_MASS_REASON, + uk_weight_projection_factor, +) from microcosm.frame import WeightKind from test_support.microcosm_build.uk_graph_terminal import _frame from test_support.microcosm_build.uk_hierarchy_fixtures import ( @@ -48,7 +52,9 @@ def test_export_drops_native_aliases_and_keeps_identity_keyed_assignment(tmp_pat "ward_code", "itl1_code", } <= set(exported) - descriptor = describe_uk_export(frame, bindings={"target_scope": "all"}) + descriptor = describe_uk_export( + frame, bindings={"target_scope": "all"}, solve_year=2025 + ) assert "output_area_code" not in descriptor["tables"]["household"]["columns"] path = tmp_path / "full.h5" materialize_uk_export(frame, descriptor, path) @@ -78,7 +84,9 @@ def test_export_writes_area_codes_under_consumer_names_and_refuses_ladder_names( assert exported[export_name].tolist() == ladder[ladder_name].tolist() # The in-memory frame is untouched: gates and diagnostics keep the ladder names. assert "constituency_code" in frame.table("household").columns - descriptor = describe_uk_export(frame, bindings={"target_scope": "all"}) + descriptor = describe_uk_export( + frame, bindings={"target_scope": "all"}, solve_year=2025 + ) assert descriptor["area_codes"]["columns"] == dict(UK_EXPORT_AREA_CODE_COLUMNS) frames = descriptor["area_codes"]["frames"] assert set(frames) == set(UK_EXPORT_AREA_CODE_COLUMNS.values()) @@ -125,6 +133,7 @@ def test_export_roundtrip_preserves_dtype_weights_lineage_period_and_gate(tmp_pa descriptor = describe_uk_export( frame, bindings={"pool_replicates": 1, "dataset_households": 2, "target_scope": "all"}, + solve_year=2025, ) path = tmp_path / "full.h5" record = materialize_uk_export(frame, descriptor, path) @@ -134,6 +143,22 @@ def test_export_roundtrip_preserves_dtype_weights_lineage_period_and_gate(tmp_pa assert descriptor["tables"]["person"]["dtypes"][-1] == "float32" assert descriptor["time_period"] == "2024" assert descriptor["weight_kind"] == "calibrated" + # The artifact carries the solved weights at the dataset's year + # (microcosm#796 A19); the frame the graph holds keeps them as solved. + factor = uk_weight_projection_factor(2024, 2025) + assert descriptor["weight_year"] == { + "solve_year": 2025, + "dataset_year": 2024, + "engine_projection_factor": factor, + } + assert descriptor["mass_log"][-1]["reason"] == UK_WEIGHT_YEAR_MASS_REASON + assert descriptor["mass_log"][-1]["declared_factor"] == 1.0 / factor + with pd.HDFStore(path) as store: + np.testing.assert_array_equal( + store["household"]["household_weight"].to_numpy(), + np.array([13.0, 87.0]) / factor, + ) + np.testing.assert_array_equal(frame.weights_for("household").values, [13.0, 87.0]) path.unlink() materialize_uk_export(frame, descriptor, path) assert validate_uk_export(path, descriptor)["passed"] is True @@ -141,7 +166,7 @@ def test_export_roundtrip_preserves_dtype_weights_lineage_period_and_gate(tmp_pa def test_export_refuses_population_changed_after_descriptor(tmp_path): frame = _frame() - descriptor = describe_uk_export(frame, bindings={}) + descriptor = describe_uk_export(frame, bindings={}, solve_year=2025) frame.table("person").loc[0, "income"] = 99.0 with pytest.raises(ValueError, match="descriptor"): materialize_uk_export(frame, descriptor, tmp_path / "changed.h5") @@ -150,7 +175,7 @@ def test_export_refuses_population_changed_after_descriptor(tmp_path): def test_export_readback_reports_changed_stored_values(tmp_path): pytest.importorskip("tables") frame = _frame() - descriptor = describe_uk_export(frame, bindings={}) + descriptor = describe_uk_export(frame, bindings={}, solve_year=2025) path = tmp_path / "full.h5" materialize_uk_export(frame, descriptor, path) with pd.HDFStore(path) as store: @@ -227,6 +252,7 @@ def run(self, context): graph, population="root", bindings={"pool_replicates": 1, "dataset_households": 2, "target_scope": "all"}, + solve_year=2025, ) registry = KernelRegistry() registry.register(Create()) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py index 6b4d9687b1..d1acbddb86 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py @@ -491,9 +491,10 @@ def _converted(frame): Household 6 is the band-37,700 family the split divides (wealth 20 over household 5's 10). Its carrier sacrifices enough pay to fall into band - 0, the way the stage rewrites a converted record: pay lowered, - employee contribution zeroed and moved whole into the salary-sacrifice - column, the pre-conversion pay kept on the carrier. + 0, the way the stage rewrites a converted record: pay lowered by the + amount moved into the salary-sacrifice column, and the pre-conversion + pay kept on the carrier. Here the whole contribution moves, so no + employee contribution remains. """ from microcosm.build.uk_runtime.salary_sacrifice import ( @@ -590,20 +591,48 @@ def test_the_restoration_reverses_the_conversion_exactly(self) -> None: ) assert tool._pre_salary_sacrifice_frame(frame) is frame + def test_a_contribution_the_record_kept_is_restored_with_the_amount_moved( + self, + ) -> None: + # The conversion moves at most half the pay and leaves the rest of the + # contribution in place (uk-data#518), so the contribution before it is + # what the record kept plus what moved. + tool = _load_tool() + _, frame = _split_then_cloned() + rewritten, carrier_id, pay, sacrificed = self._converted(frame) + person = rewritten.table("person").copy() + person.loc[ + person["person_id"] == carrier_id, "employee_pension_contributions" + ] = 125.0 + kept = uk_national_frame( + person=person, + benunit=rewritten.table("benunit"), + household=rewritten.table("household"), + time_period="2024", + weight_kind=WeightKind.IMPORTANCE, + household_weights=rewritten.weights_for("household").values, + mass_log=rewritten.mass_log, + ) + restored = tool._pre_salary_sacrifice_frame(kept).table("person") + restored = restored.set_index("person_id") + assert restored.loc[carrier_id, "employment_income"] == pay + assert restored.loc[carrier_id, "employee_pension_contributions"] == ( + 125.0 + sacrificed + ) + def test_a_carrier_disagreeing_with_the_columns_is_refused(self) -> None: from microcosm.build.uk_runtime.salary_sacrifice import ( + SALSAC_OUTPUT, SALSAC_PRE_CONVERSION_PAY_COLUMN, ) tool = _load_tool() _, frame = _split_then_cloned() - rewritten, carrier_id, _, _ = self._converted(frame) + rewritten, carrier_id, _, sacrificed = self._converted(frame) person = rewritten.table("person").copy() - # A positive employee contribution on a record the carrier calls - # converted is not the state the conversion leaves. - person.loc[ - person["person_id"] == carrier_id, "employee_pension_contributions" - ] = 1.0 + # Pay plus the sacrifice must give the pay before the conversion; a + # sacrifice that does not is not the state the conversion leaves. + person.loc[person["person_id"] == carrier_id, SALSAC_OUTPUT] = sacrificed + 1.0 tampered = uk_national_frame( person=person, benunit=rewritten.table("benunit"), diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py index 7c44edb9e8..09bf763a51 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py @@ -13,6 +13,7 @@ UK_RATE_CLASS_ENGINE_FLOAT32_STEPS, UK_RATE_CLASS_ENGINE_TOLERANCE_GBP, UK_RATE_CLASSES, + gift_aid_extended_brackets, rate_class_engine_tolerance, relief_by_rate_class, tax_by_rate_class, @@ -103,6 +104,78 @@ def test_relief_takes_each_persons_own_schedule() -> None: assert relief["additional"].tolist() == [0.0, 0.0] +def _engine_tax_with_gift_aid(income, thresholds, rates, gift, first, stop): + """policyengine-uk's calc_income_tax_with_gift_aid, restated: each extended + limit takes its rate step off the slice above it, up to the gift.""" + + tax = _schedule_tax(income, thresholds, rates) + for index in range(first, min(stop, len(thresholds))): + overlap = np.clip(income - thresholds[index], 0.0, gift) + tax = tax - (rates[index] - rates[index - 1]) * overlap + return tax + + +@pytest.mark.parametrize( + ("schedule", "name", "first", "stop"), + [(REST_OF_UK, "rest_of_uk", 1, 3), (SCOTLAND, "scotland", 2, 6)], + ids=["ruk", "scotland"], +) +def test_a_donors_classes_add_up_to_the_engines_extended_schedule( + schedule, name, first, stop +) -> None: + rng = np.random.default_rng(1) + income = rng.uniform(-1_000.0, 200_000.0, 5_000) + # Gifts from none to more than a whole band, so a gift spans several. + gift = rng.choice([0.0, 500.0, 20_000.0, 120_000.0], income.size) + + classes = tax_by_rate_class(income, *schedule, gross_gift=gift, schedule=name) + + assert list(gift_aid_extended_brackets(name, len(schedule[0]))) == list( + range(first, stop) + ) + np.testing.assert_allclose( + sum(classes.values()), + _engine_tax_with_gift_aid(income, *schedule, gift, first, stop), + atol=1e-6, + ) + for values in classes.values(): + assert np.all(values >= -1e-9) + # Without a gift nothing moves. + plain = tax_by_rate_class(income, *schedule) + none = gift == 0.0 + for rate_class in UK_RATE_CLASSES: + np.testing.assert_allclose(classes[rate_class][none], plain[rate_class][none]) + + +def test_a_donors_returned_pay_is_relieved_at_the_extended_bands() -> None: + """GBP 4,000 returned on GBP 35,430 (rest of the UK) and GBP 29,430 + (Scotland) of taxable income, by a donor of GBP 800 net, GBP 1,000 gross. + + Rest of the UK: the higher limit moves from 37,700 to 38,700, so 3,270 is + relieved at 20% (654) and 730 at 40% (292), against 454 and 692 without + the gift. Scotland: the higher limit moves from 31,092 to 32,092, so 2,662 + is relieved at 21% (559.02) and 1,338 at 42% (561.96).""" + + relief = relief_by_rate_class( + baseline_income=np.array([35_430.0, 29_430.0, 35_430.0]), + counterfactual_income=np.array([39_430.0, 33_430.0, 39_430.0]), + scottish=np.array([False, True, False]), + schedules={"rest_of_uk": REST_OF_UK, "scotland": SCOTLAND}, + gross_gift=np.array([1_000.0, 1_000.0, 0.0]), + ) + + assert relief["basic"].tolist() == pytest.approx([654.0, 559.02, 454.0]) + assert relief["higher"].tolist() == pytest.approx([292.0, 561.96, 692.0]) + assert relief["additional"].tolist() == [0.0, 0.0, 0.0] + + +def test_an_extension_needs_its_schedule() -> None: + with pytest.raises(ValueError, match="needs the schedule"): + tax_by_rate_class(np.array([1.0]), *REST_OF_UK, gross_gift=np.array([1.0])) + with pytest.raises(ValueError, match="unknown income tax schedule"): + gift_aid_extended_brackets("wales", 3) + + def test_a_schedule_without_an_additional_rate_files_its_top_rate_as_higher() -> None: # The top rate is the additional rate only where a higher rate sits below # it. With one rate above the basic class, that rate is the higher rate. diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py index 12e92b0b43..9e1350aa03 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py @@ -38,6 +38,24 @@ def table(self, entity): return self._tables[entity] +class ReleaseIdentifierFrameStub(FrameStub): + """The stub on the identifiers a release carries: one plus the rank per + entity (microcosm#1194), which a scratch engine file leaves as they are.""" + + def __init__(self): + self._tables = { + "person": pd.DataFrame( + { + "person_id": [1, 2, 3], + "person_benunit_id": [1, 1, 2], + "person_household_id": [1, 1, 2], + } + ), + "benunit": pd.DataFrame({"benunit_id": [1, 2]}), + "household": pd.DataFrame({"household_id": [1, 2]}), + } + + def _stub_value_type(values): """The policyengine ``value_type`` a real variable definition would carry.""" @@ -602,8 +620,9 @@ def test_exclusion_applier_warns_within_week_of_expiry(): # structural bias: the v20 measurement (-2.8 % unbound, at weights that # never solved toward the row) reproduces the 2.79 % other/unknown-tenure # gap the entry records, so it is a diagnostic, not a fit target - # (docs/evidence/uk-a16-v20/). - "dwp.uc.households_": 1, + # (docs/evidence/uk-a16-v20/). microcosm#1095 (2026-10-14): the five-or-more + # children row joins it, held out on María's ruling of microcosm#1271. + "dwp.uc.households_": 2, # microcosm#882 repairs (2026-09-16): the three Housing Benefit caseload # rows and the thirteen benefit-cap amount bands outside the 25 percent # bound are measured on every evaluation but held out of the objective; @@ -645,7 +664,7 @@ def test_exclusion_applier_warns_within_week_of_expiry(): def test_packaged_exclusions_load(): exclusions = load_uk_calibration_measure_exclusions() names = [entry["name"] for entry in exclusions] - assert len(names) == len(set(names)) == 65 + assert len(names) == len(set(names)) == 66 band_h_region_cells = [ entry for entry in exclusions @@ -773,6 +792,30 @@ def test_packaged_exclusions_load(): "ons.household_electricity_expenditure": "microcosm#1207", "ons.household_gas_expenditure": "microcosm#1207", } + # microcosm#1095 (2026-10-14): once dependants in approved training qualify + # as young people, fewer than 10 source households separate DWP's count of + # UC households with five or more children from its two-child-limit + # counts, so the row is held out with the sparse-band expiry and the + # two-child-limit rows stay bound (María's ruling on microcosm#1271). + # Two rulings share that date (the housing-element renewal below), so the + # tranche is told apart by its tracking issue. + assert sorted( + e["name"] for e in exclusions if e["approved_on"] == "2026-10-14" + ) == (sorted([*_UC_ELEMENT_REGISTER_ROWS, "dwp.uc.households_children_5_or_more"])) + large_families = [ + e + for e in exclusions + if e["approved_on"] == "2026-10-14" and e["tracking"] == "microcosm#1095" + ] + assert [e["name"] for e in large_families] == [ + "dwp.uc.households_children_5_or_more" + ] + (large_family,) = large_families + assert large_family["tracking"] == "microcosm#1095" + assert large_family["expires_on"] == "2026-11-26" + assert "microcosm#1271" in large_family["adjudication"] + assert "6168355102" in large_family["adjudication"] + assert "fewer than 10 frs source households" in large_family["reason"].lower() for entry in renewed: assert entry["expires_on"] == "2026-11-03", entry["name"] assert entry["tracking"] == a16_issues[entry["name"]], entry["name"] @@ -830,7 +873,11 @@ def test_packaged_exclusions_load(): # and renewed one month on 2026-10-14 (microcosm#1124 local release), so # nothing remains on the 2026-09-15 clock. assert [e for e in exclusions if e["approved_on"] == "2026-09-15"] == [] - elements = [e for e in exclusions if e["approved_on"] == "2026-10-14"] + elements = [ + e + for e in exclusions + if e["approved_on"] == "2026-10-14" and e["tracking"] == "microcosm#882" + ] assert sorted(e["name"] for e in elements) == sorted(_UC_ELEMENT_REGISTER_ROWS) for entry in elements: assert entry["expires_on"] == "2026-11-14", entry["name"] @@ -902,7 +949,7 @@ def __init__(self, *, dataset): lambda frame, path: writes.append((frame, path)) or path, ) - frame = FrameStub() + frame = ReleaseIdentifierFrameStub() direct = UKMeasureResolver( simulation_source=tmp_path / "input.h5", scratch_dir=tmp_path, @@ -919,7 +966,8 @@ def __init__(self, *, dataset): assert direct.receipt()["mode"] == "direct_h5" assert direct.receipt()["policyengine_uk_version"] == "9.9.9" assert scratch.receipt()["mode"] == "scratch_frame_export" - # A frame carrying no export-dropped column is written as it is. + # A frame carrying no export-dropped column, already on the release's + # identifiers, is written as it is. assert "engine_scratch_dropped_columns" not in scratch.receipt() assert writes == [(frame, tmp_path / "simulation-input.h5")] assert created == [ @@ -1089,19 +1137,20 @@ def test_packaged_exclusion_names_resolve_to_committed_references(): def test_cgt_taxable_income_nets_the_allowances_the_engine_formula_nets(): """Hand-computed rows: the band extension and the floors (microcosm#1014).""" parts = { - "adjusted_net_income": [0.0, 30_000.0, 60_000.0, 20_000.0, 80_000.0, 5_000.0], - "allowances": [12_570.0, 12_570.0, 20_570.0, 32_570.0, 27_570.0, 12_570.0], - "gift_aid": [0.0, 0.0, 8_000.0, 0.0, 0.0, 0.0], + "net_income": [0.0, 30_000.0, 60_000.0, 20_000.0, 80_000.0, 5_000.0], + "allowances": [12_570.0, 12_570.0, 12_570.0, 32_570.0, 27_570.0, 12_570.0], "personal_pension_contributions": [0.0, 0.0, 0.0, 30_000.0, 5_000.0, 0.0], "pension_contributions_relief": [0.0, 0.0, 0.0, 20_000.0, 15_000.0, 0.0], } np.testing.assert_allclose( measure_simulation.cgt_taxable_income_from_components(parts), - # 0 floor; 30,000 - 12,570; gift aid leaves the allowances (it extends - # the band instead); relief up to the personal contributions leaves - # them too; an allowance above income floors at 0. + # 0 floor; 30,000 - 12,570; a Gift Aid donor's income is net income + # less the allowances, the gift extending the band instead (2.128.0, + # pe-uk#2271); relief up to the personal contributions leaves the + # allowances; an allowance above income floors at 0. [0.0, 17_430.0, 47_430.0, 7_430.0, 57_430.0, 0.0], ) + assert "gift_aid" not in measure_simulation._CGT_TAXABLE_INCOME_COMPONENTS # microcosm#1069 c11: the live counterfactual route (salary sacrifice returned to pay). @@ -1200,6 +1249,8 @@ def test_counterfactual_delta_refuses_bands_and_other_periods(monkeypatch, tmp_p {**allocation, "basic_below_rate": 0.25}, {**allocation, "rate_class": "starter"}, {**allocation, "taxable_income": "adjusted_net_income"}, + # The bands a Gift Aid donor's gift extends are part of the allocation. + {k: v for k, v in allocation.items() if k != "gift_aid_extends_bands"}, ): with pytest.raises(ValueError, match="rate-class allocation must declare"): resolver.counterfactual_delta( @@ -1300,7 +1351,9 @@ def __init__(self, *, dataset): assert not set(aliases) & set(written.columns) assert not written.isna().any().any() assert written["oa_code"].tolist() == ["E00000001", "S00090001", "N00000101"] - assert written["household_id"].tolist() == [100, 200, 300] + # The engine file carries the release's identifiers (microcosm#1194); the + # resolver's own frame keeps the build's. + assert written["household_id"].tolist() == [1, 2, 3] # The resolver's own frame is untouched; only the engine's copy changed. pd.testing.assert_frame_equal(frame.table("household"), before) assert resolver.frame is frame @@ -1329,7 +1382,7 @@ def test_scratch_engine_input_without_aliases_is_the_frame_itself( "write_uk_national_frame", lambda frame, path: writes.append(frame) or path, ) - frame = FrameStub() + frame = ReleaseIdentifierFrameStub() resolver = UKMeasureResolver( simulation_source=None, scratch_dir=tmp_path, year=2025, frame=frame ) @@ -1380,6 +1433,92 @@ def _tiny_national_frame(weights): ) +def _tiny_release_identifier_frame(weights): + from microcosm.build.uk_runtime.national_frame import ( + uk_national_frame, + uk_release_export_frame, + ) + + released = uk_release_export_frame(_tiny_national_frame(weights)) + # The engine file carries no build-identifier column: a frame whose own + # identifiers are already the release's. + tables = { + entity: released.table(entity).drop(columns=[f"{entity}_build_id"]) + for entity in ("person", "benunit", "household") + } + return uk_national_frame( + **tables, + time_period="2024", + weight_kind=released.weights_for("household").kind, + household_weights=released.weights_for("household").values, + mass_log=released.mass_log, + ) + + +def test_engine_scratch_frame_carries_the_release_identifiers(): + """microcosm#1194: policyengine-uk keys a person's birth month on + ``person_id``, so the solve's engine file carries the identifiers the + release will: one plus the rank of the build identifier.""" + frame = _tiny_national_frame([250.0, 125.0]) + + engine_frame, dropped = measure_simulation._engine_scratch_frame(frame) + + assert dropped == () + assert engine_frame.table("person")["person_id"].tolist() == [1, 2, 3, 4] + assert engine_frame.table("person")["person_benunit_id"].tolist() == [1, 1, 2, 2] + assert engine_frame.table("person")["person_household_id"].tolist() == [1, 1, 2, 2] + assert engine_frame.table("benunit")["benunit_id"].tolist() == [1, 2] + assert engine_frame.table("household")["household_id"].tolist() == [1, 2] + # No build-identifier column goes to the engine, and nothing else moves. + for entity in ("person", "benunit", "household"): + assert f"{entity}_build_id" not in engine_frame.table(entity).columns + assert list(engine_frame.table(entity).columns) == list( + frame.table(entity).columns + ) + assert engine_frame.table("person")["age"].tolist() == [35, 8, 40, 38] + np.testing.assert_array_equal( + engine_frame.weights_for("household").values, + frame.weights_for("household").values, + ) + assert engine_frame.mass_log == frame.mass_log + # The resolver's own frame keeps the build identifiers. + assert frame.table("household")["household_id"].tolist() == [100, 200] + + +def test_engine_scratch_block_takes_its_identifiers_from_the_whole_frame(): + """A per-clone block's rows carry the identifiers they have in the release, + which numbers the whole frame, not the block.""" + whole = _tiny_national_frame([250.0, 125.0]) + block = whole.select(whole.table("person")["person_household_id"].eq(200)) + + alone, _ = measure_simulation._engine_scratch_frame(block) + in_whole, _ = measure_simulation._engine_scratch_frame( + block, identifier_universe=whole + ) + + assert alone.table("person")["person_id"].tolist() == [1, 2] + assert in_whole.table("person")["person_id"].tolist() == [3, 4] + assert in_whole.table("person")["person_household_id"].tolist() == [2, 2] + assert in_whole.table("benunit")["benunit_id"].tolist() == [2] + assert in_whole.table("household")["household_id"].tolist() == [2] + + +def test_an_identifier_universe_needs_scratch_mode(tmp_path, monkeypatch): + monkeypatch.setitem( + sys.modules, + "policyengine_uk", + SimpleNamespace(__version__="9.9.9", Microsimulation=lambda *, dataset: None), + ) + with pytest.raises(ValueError, match="identifier universe needs scratch-mode"): + UKMeasureResolver( + simulation_source=tmp_path / "input.h5", + scratch_dir=tmp_path, + year=2025, + frame=FrameStub(), + engine_identifier_universe=FrameStub(), + ) + + def test_engine_scratch_frame_scales_household_weights_for_the_engine_only(): frame = _tiny_national_frame([250.0, 125.0]) before = frame.weights_for("household").values.copy() @@ -1404,11 +1543,13 @@ def test_engine_scratch_frame_scales_household_weights_for_the_engine_only(): assert record.reason == measure_simulation.ENGINE_WEIGHT_SCALE_REASON # the resolver's own frame keeps the block's true mass np.testing.assert_array_equal(frame.weights_for("household").values, before) - # no scale (or a unit scale) on an alias-free frame: the frame itself - assert measure_simulation._engine_scratch_frame(frame)[0] is frame + # no scale (or a unit scale) on an alias-free frame already on the + # release's identifiers: the frame itself + released = _tiny_release_identifier_frame([250.0, 125.0]) + assert measure_simulation._engine_scratch_frame(released)[0] is released assert ( - measure_simulation._engine_scratch_frame(frame, engine_weight_scale=1.0)[0] - is frame + measure_simulation._engine_scratch_frame(released, engine_weight_scale=1.0)[0] + is released ) with pytest.raises(ValueError, match="positive finite"): measure_simulation._engine_scratch_frame(frame, engine_weight_scale=0.0) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py index 790b6d0274..83f61a17c7 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py @@ -53,7 +53,7 @@ from microcosm.calibrate.artifacts import decode_problem from microcosm.frame import EntitySchema, Frame, MassChange, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 1279 +ACTIVE_REFERENCE_COUNT = 1283 def _uc_reference(**overrides) -> LedgerTargetReference: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py index 50bf4714c8..af19b29452 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py @@ -222,8 +222,9 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: # rows they repeat, as do the two PIP daily-living caseload rows; # microcosm#1113 adds ONS 07.2.2 household road fuel; microcosm#1123 adds # the four nation household controls (LFS English regions, NRS, Welsh - # Government, NI). - assert len(resource["targets"]) == 396 + # Government, NI); microcosm#1095 splits the two national 15_19 sex + # bands into ages 15, 16-17 and 18-19 (uk-data#563), six rows for two. + assert len(resource["targets"]) == 400 providers = resource["hierarchy"]["providers"] categories = resource["hierarchy"]["categories"] @@ -237,10 +238,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: target_ids = [target["target_id"] for target in resource["targets"]] registry_scope = resource["registry_parity"]["scope_target_ids"] profile_scope = resource["profile_parity"]["scope_target_ids"] - assert len(registry_scope) == 344 + assert len(registry_scope) == 348 assert len(profile_scope) == 52 - assert target_ids[:344] == registry_scope - assert target_ids[344:] == profile_scope + assert target_ids[:348] == registry_scope + assert target_ids[348:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -254,9 +255,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope) assert all(reason for reason in unmapped_declarations.values()) # microcosm#1095: obr/ni and the two PIP daily-living caseload rows leave - # the mapped rows for diagnostics. - assert len(mapped_target_ids) == 189 - assert len(unmapped_declarations) == 155 + # the mapped rows for diagnostics, and the two incumbent 15_19 sex bands + # leave them for the six rows that partition them. + assert len(mapped_target_ids) == 187 + assert len(unmapped_declarations) == 161 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 assert set(suppressed_ancestors).isdisjoint(parity["mapped"]) @@ -371,11 +373,63 @@ def test_uk_population_targets_split_terminal_sex_age_bands() -> None: ] +def test_uk_population_targets_split_the_15_19_sex_bands() -> None: + """Ages 15, 16-17 and 18-19 partition each incumbent 15_19 sex band. + + A fit to the five-year band left the ages inside it free to drift + (uk-data#563), so the contract binds the three pieces instead and the + incumbent rows are excluded, not mapped onto one piece (microcosm#1095). + """ + + resource = _load() + parity = resource["registry_parity"] + pieces = {"15": [15], "16_17": [16, 17], "18_19": [18, 19]} + + for sex in ("female", "male"): + assert f"ons/{sex}_15_19" not in parity["mapped"] + assert "Split, not dropped" in parity["excluded"][f"ons/{sex}_15_19"] + ages: list[int] = [] + for suffix, expected in pieces.items(): + target = _target_by_id(resource, f"ons.population.{sex}_{suffix}") + assert target["target_id"] in parity["unmapped_declarations"] + assert target["ledger_selector"]["dimension_values"] == { + "sex": sex, + "age": expected, + } + bounds = { + item["operator"]: item["value"] + for item in target["bindings"]["policyengine"]["filters"] + if item["variable"] == "age" + } + assert bounds == {">=": expected[0], "<=": expected[-1]} + assert target["measurement"]["filters"][1:] == [ + { + "concept": "uk.demographics.age", + "operator": ">=", + "value": expected[0], + }, + { + "concept": "uk.demographics.age", + "operator": "<=", + "value": expected[-1], + }, + ] + ages += expected + # Disjoint, and together the band they replace. + assert ages == [15, 16, 17, 18, 19] + assert not any( + target["target_id"].endswith("_15_19") + and target["target_id"].startswith("ons.population.") + for target in resource["targets"] + ) + assert "partition" in parity["accounting_notes"]["ons_15_19_sex_band_split"] + + def test_uk_population_targets_have_unique_target_ids() -> None: resource = _load() target_ids = [target["target_id"] for target in resource["targets"]] - assert len(target_ids) == 396 + assert len(target_ids) == 400 assert len(target_ids) == len(set(target_ids)) @@ -856,7 +910,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 396 + assert len(targets) == 400 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_assembler.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_assembler.py index d7e568fb72..25b3fb38d7 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_assembler.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_assembler.py @@ -15,12 +15,17 @@ from microcosm.build.uk_runtime.local_targets import load_uk_population_contract from microcosm.build.uk_runtime.national_frame import ( uk_national_frame, + uk_release_export_frame, write_uk_national_frame, ) from microcosm.build.uk_runtime.release_certification import ( compose_uk_release_certification, ) from microcosm.build.uk_runtime.release_identity import UK_NATIONAL_RELEASE_ID +from microcosm.build.uk_runtime.weight_year import ( + uk_dataset_year_frame, + uk_weight_projection_factor, +) from microcosm.data.contract import validate_release_dir from microcosm.data.registry import REGISTRY from microcosm.frame import WeightKind @@ -215,15 +220,16 @@ def _build_assembler_inputs( tmp_path: Path, *, spine_frame=None, + candidate_frame=None, runtime=None, ): pytest.importorskip("tables") # pandas HDF backend pytest.importorskip("h5py") candidate = green_certification_inputs["candidate_path"] spine = tmp_path / "spine.h5" - write_uk_national_frame( - _frame([0.0, 4.0], weight_kind=WeightKind.CALIBRATED), candidate - ) + if candidate_frame is None: + candidate_frame = _frame([0.0, 4.0], weight_kind=WeightKind.CALIBRATED) + write_uk_national_frame(candidate_frame, candidate) if spine_frame is None: spine_frame = _frame([1.0, 2.0], weight_kind=WeightKind.DESIGN) write_uk_national_frame(spine_frame, spine) @@ -361,6 +367,11 @@ def test_assemble_green_release_dir(assembler_inputs, capsys) -> None: build_manifest = json.loads((release_dir / "build_manifest.json").read_text()) release_manifest = json.loads((release_dir / "release_manifest.json").read_text()) assert build_manifest["dataset"]["filename"] == driver._DATASET_FILENAME + # The fixture candidate was written as solved, never divided for release. + assert build_manifest["calibration"]["household_weight_year"] == { + "dataset_year": 2024, + "basis": "as_solved", + } assert build_manifest["staging"] == { "contract_version": 2, "enabled": False, @@ -588,6 +599,72 @@ def test_assemble_refuses_build_record_attempt_id_tamper(assembler_inputs) -> No _load_driver_module().main(assembler_inputs["argv"]) +def test_assemble_labels_weights_written_at_the_dataset_year( + green_certification_inputs, tmp_path: Path +) -> None: + """microcosm#796 A19: the NPZ pairs with the H5 it ships beside, so it + carries the dataset-year weights and the build manifest says so.""" + released = uk_dataset_year_frame( + _frame([0.0, 4.0], weight_kind=WeightKind.CALIBRATED), solve_year=2025 + ) + inputs = _build_assembler_inputs( + green_certification_inputs, tmp_path, candidate_frame=released + ) + driver = _load_driver_module() + + assert driver.main(inputs["argv"]) == 0 + + release_dir = inputs["out_dir"] / UK_NATIONAL_RELEASE_ID + build_manifest = json.loads((release_dir / "build_manifest.json").read_text()) + assert build_manifest["calibration"]["household_weight_year"] == { + "dataset_year": 2024, + "basis": "dataset_year", + } + calibration_path = inputs["candidate"].with_name( + "microcosm_uk_2024_25_calibration.npz" + ) + with np.load(calibration_path) as calibration: + np.testing.assert_array_equal( + calibration["household_weight"], + np.asarray([0.0, 4.0]) / uk_weight_projection_factor(2024, 2025), + ) + + +def test_assemble_pairs_a_renumbered_release_with_its_spine( + green_certification_inputs, tmp_path: Path +) -> None: + """microcosm#1194: the release renumbers its identifiers, so the row + pairing with the spine stands on the build identifiers it keeps.""" + ids = np.asarray([5, 6], dtype="int64") + + def frame(weights, weight_kind): + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame({"household_id": ids}), + time_period="2024", + weight_kind=weight_kind, + household_weights=np.asarray(weights, dtype="float64"), + ) + + released = uk_release_export_frame(frame([0.0, 4.0], WeightKind.CALIBRATED)) + assert released.table("household")["household_id"].tolist() == [1, 2] + inputs = _build_assembler_inputs( + green_certification_inputs, + tmp_path, + candidate_frame=released, + spine_frame=frame([1.0, 2.0], WeightKind.DESIGN), + ) + + assert _load_driver_module().main(inputs["argv"]) == 0 + + def test_assemble_refuses_spine_with_different_household_count( green_certification_inputs, tmp_path: Path ) -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_identifiers.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_identifiers.py new file mode 100644 index 0000000000..952e8ee718 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_identifiers.py @@ -0,0 +1,306 @@ +"""A UK release's identifiers stay inside the range policyengine-uk stores (microcosm#1194). + +The clone stages number copies by adding a power of ten per copy, so a built +frame's identifiers run far past the 32-bit integers the engine keeps its five +identifier variables in. Every draw in the build is keyed on those +identifiers, so the build keeps them; the release boundary renumbers and +keeps the build's beside the new ones. +""" + +from __future__ import annotations + +from types import SimpleNamespace + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.graph_national import UKNationalReadbackKernel +from microcosm.build.uk_runtime.graph_terminal import ( + describe_uk_export, + materialize_uk_export, + validate_uk_export, +) +from microcosm.build.uk_runtime.national_frame import ( + UK_BUILD_IDENTIFIER_COLUMNS, + UK_ENGINE_IDENTIFIER_MAX, + load_uk_national_frame, + uk_build_identifier_frame, + uk_build_identifier_tables, + uk_build_identifiers, + uk_identifier_range_failures, + uk_national_frame, + uk_release_export_frame, + uk_release_identifier_tables, + write_uk_national_frame, +) +from microcosm.build.uk_runtime.weight_year import uk_dataset_year_frame +from microcosm.frame import WeightKind +from test_support.microcosm_build.uk_graph_terminal import _frame as _ladder_frame + +# Two copies of a three-household survey, numbered the way the clone stages +# number them: the copy index times a power of ten past the largest identifier. +COPY = 10**13 +HOUSEHOLD = np.asarray([1, 7, COPY + 1, COPY + 7], dtype=np.int64) +BENUNIT = np.asarray([11, 12, 71, COPY + 11, COPY + 12, COPY + 71], dtype=np.int64) +PERSON = np.asarray( + [101, 102, 103, 701, COPY + 101, COPY + 102, COPY + 103, COPY + 701], + dtype=np.int64, +) +PERSON_BENUNIT = BENUNIT[[0, 0, 1, 2, 3, 3, 4, 5]] +PERSON_HOUSEHOLD = HOUSEHOLD[[0, 0, 0, 1, 2, 2, 2, 3]] + + +def _tables() -> dict[str, pd.DataFrame]: + return { + "person": pd.DataFrame( + { + "person_id": PERSON, + "person_benunit_id": PERSON_BENUNIT, + "person_household_id": PERSON_HOUSEHOLD, + "age": [40, 38, 9, 71, 40, 38, 9, 71], + } + ), + "benunit": pd.DataFrame({"benunit_id": BENUNIT}), + "household": pd.DataFrame({"household_id": HOUSEHOLD, "region": "LONDON"}), + } + + +def _frame(*, kind: WeightKind = WeightKind.CALIBRATED): + return uk_national_frame( + **_tables(), + time_period="2024", + weight_kind=kind, + household_weights=np.asarray([10.0, 20.0, 30.0, 40.0]), + ) + + +def test_release_identifiers_are_one_plus_the_rank_of_the_build_identifier() -> None: + built = _tables() + + released = uk_release_identifier_tables(built) + + assert released["household"]["household_id"].tolist() == [1, 2, 3, 4] + assert released["benunit"]["benunit_id"].tolist() == [1, 2, 3, 4, 5, 6] + assert released["person"]["person_id"].tolist() == [1, 2, 3, 4, 5, 6, 7, 8] + assert released["person"]["person_benunit_id"].tolist() == [1, 1, 2, 3, 4, 4, 5, 6] + assert released["person"]["person_household_id"].tolist() == [ + 1, + 1, + 1, + 2, + 3, + 3, + 3, + 4, + ] + # The build identifiers stay, right beside the ones they were. + for entity, carrier in UK_BUILD_IDENTIFIER_COLUMNS.items(): + table = released[entity] + assert table[carrier].tolist() == built[entity][f"{entity}_id"].tolist() + assert table.columns.get_loc(carrier) == 1 + assert table[f"{entity}_id"].dtype == built[entity][f"{entity}_id"].dtype + # Nothing else moves, and the inputs are not touched. + assert released["person"]["age"].tolist() == built["person"]["age"].tolist() + assert built["person"]["person_id"].tolist() == PERSON.tolist() + assert not uk_identifier_range_failures(released) + + +def test_the_rank_does_not_depend_on_row_order() -> None: + built = _tables() + order = [3, 0, 2, 1, 7, 4, 6, 5] + built["person"] = built["person"].iloc[order].reset_index(drop=True) + + released = uk_release_identifier_tables(built) + + # Each person keeps the number its build identifier ranks at. + assert released["person"]["person_id"].tolist() == [n + 1 for n in order] + assert released["person"]["person_build_id"].tolist() == PERSON[order].tolist() + + +def test_renumbering_is_applied_once_and_undone_exactly() -> None: + built = _tables() + released = uk_release_identifier_tables(built) + + again = uk_release_identifier_tables(released) + for entity in built: + assert again[entity] is released[entity] + + restored = uk_build_identifier_tables(released) + for entity in built: + pd.testing.assert_frame_equal(restored[entity], built[entity]) + # Tables that were never renumbered are their own build tables. + for entity, table in uk_build_identifier_tables(built).items(): + assert table is built[entity] + assert uk_build_identifiers(released["household"], "household").tolist() == ( + HOUSEHOLD.tolist() + ) + assert uk_build_identifiers(built["household"], "household").tolist() == ( + HOUSEHOLD.tolist() + ) + + +def test_tables_that_cannot_be_renumbered_refuse() -> None: + repeated = _tables() + repeated["household"].loc[1, "household_id"] = 1 + with pytest.raises(ValueError, match="household identifiers are not unique"): + uk_release_identifier_tables(repeated) + + orphan = _tables() + orphan["person"].loc[0, "person_benunit_id"] = 999 + with pytest.raises(ValueError, match="benunit memberships name an identifier"): + uk_release_identifier_tables(orphan) + + partial = uk_release_identifier_tables(_tables()) + partial["benunit"] = partial["benunit"].drop(columns=["benunit_build_id"]) + with pytest.raises(ValueError, match="on some entities only"): + uk_release_identifier_tables(partial) + + +def test_range_failures_name_the_column_only() -> None: + built = _tables() + + failures = uk_identifier_range_failures(built) + + above = "has a value above the 32-bit range policyengine-uk stores." + assert failures == [ + f"Written 'person_id' {above}", + f"Written 'benunit_id' {above}", + f"Written 'household_id' {above}", + f"Written 'person_benunit_id' {above}", + f"Written 'person_household_id' {above}", + ] + # Never an identifier, and never a count of rows. + assert not any( + character.isdigit() + for failure in failures + for character in failure.replace("32-bit", "") + ) + + edge = uk_release_identifier_tables(_tables()) + edge["household"]["household_id"] = [0, 2, 2, UK_ENGINE_IDENTIFIER_MAX] + assert uk_identifier_range_failures(edge) == [ + "Written 'household_id' has a value below 1.", + "Written 'household_id' repeats a value.", + ] + edge["household"]["household_id"] = [1, 2, 3, UK_ENGINE_IDENTIFIER_MAX + 1] + assert uk_identifier_range_failures(edge) == [f"Written 'household_id' {above}"] + + +def test_the_release_frame_leaves_renumbered_and_loads_back_to_the_build( + tmp_path, +) -> None: + pytest.importorskip("tables") + built = _frame() + + released = uk_release_export_frame(built) + + assert uk_release_export_frame(released) is released + assert released.weights_for("household").values.tolist() == [10, 20, 30, 40] + assert released.mass_log == built.mass_log + path = write_uk_national_frame(released, tmp_path / "release.h5") + with pd.HDFStore(path, mode="r") as store: + stored = { + entity: store[entity] for entity in ("person", "benunit", "household") + } + assert not uk_identifier_range_failures(stored) + assert stored["household"]["household_build_id"].tolist() == HOUSEHOLD.tolist() + + loaded, _provenance = load_uk_national_frame(path) + restored = uk_build_identifier_frame(loaded) + for entity in ("person", "benunit", "household"): + pd.testing.assert_frame_equal(restored.table(entity), built.table(entity)) + # A frame that was never renumbered is its own build frame. + assert uk_build_identifier_frame(built) is built + + +def _readback(frame, path, *, solve_year: int = 2025) -> dict: + import json + + context = SimpleNamespace( + tables={entity: frame.table(entity) for entity in frame.entities}, + frame_column_order={}, + weights={"household": frame.weights_for("household")}, + strata=frame.strata, + frame_mass_log=frame.mass_log, + frame_metadata=dict(frame.metadata), + params={"dataset_source": "exported_dataset", "solve_year": solve_year}, + sources={"exported_dataset": str(path)}, + ) + result = UKNationalReadbackKernel().run(context) + return { + "outcome": result.receipt["outcome"], + **json.loads(result.artifacts["readback"]), + } + + +def test_the_national_readback_passes_the_release_and_refuses_build_identifiers( + tmp_path, +) -> None: + pytest.importorskip("tables") + built = _frame() + release = uk_dataset_year_frame(uk_release_export_frame(built), solve_year=2025) + good = write_uk_national_frame(release, tmp_path / "release.h5") + + report = _readback(built, good) + assert report["passed"] is True and report["outcome"] == "pass" + + # The same population written on its build identifiers, as releases were. + unrenumbered = write_uk_national_frame( + uk_dataset_year_frame(built, solve_year=2025), tmp_path / "build-ids.h5" + ) + report = _readback(built, unrenumbered) + assert report["passed"] is False and report["outcome"] == "fail" + assert ( + "Written 'household_id' has a value above the 32-bit range " + "policyengine-uk stores." + ) in report["failures"] + assert not any(str(COPY) in failure for failure in report["failures"]) + + +def test_the_national_readback_refuses_a_repeated_identifier(tmp_path) -> None: + pytest.importorskip("tables") + built = _frame() + release = uk_dataset_year_frame(uk_release_export_frame(built), solve_year=2025) + path = write_uk_national_frame(release, tmp_path / "release.h5") + with pd.HDFStore(path) as store: + benunit = store["benunit"] + benunit.loc[1, "benunit_id"] = benunit.loc[0, "benunit_id"] + store.put("benunit", benunit, format="table") + + # The frame itself cannot hold two rows under one identifier. + with pytest.raises(ValueError): + _readback(built, path) + + +def test_the_local_readback_refuses_out_of_range_and_repeated_identifiers( + tmp_path, +) -> None: + pytest.importorskip("tables") + frame = _ladder_frame() + descriptor = describe_uk_export(frame, bindings={}, solve_year=2025) + path = tmp_path / "full.h5" + materialize_uk_export(frame, descriptor, path) + assert validate_uk_export(path, descriptor)["passed"] is True + with pd.HDFStore(path, mode="r") as store: + household = store["household"] + person = store["person"] + assert household["household_id"].tolist() == [1, 2] + assert household["household_build_id"].tolist() == [1, 2] + assert "household_build_id" in descriptor["tables"]["household"]["columns"] + + with pd.HDFStore(path) as store: + person["person_id"] = [1, UK_ENGINE_IDENTIFIER_MAX + 1] + store.put("person", person, format="table") + report = validate_uk_export(path, descriptor) + assert report["passed"] is False + assert ( + "Written 'person_id' has a value above the 32-bit range policyengine-uk stores." + ) in report["failures"] + + with pd.HDFStore(path) as store: + person["person_id"] = [1, 1] + store.put("person", person, format="table") + report = validate_uk_export(path, descriptor) + assert report["passed"] is False + assert "Written 'person_id' repeats a value." in report["failures"] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_rowwise_national_role.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_rowwise_national_role.py index 39612e4eb2..e684c94f39 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_rowwise_national_role.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_rowwise_national_role.py @@ -54,6 +54,11 @@ write_uk_national_frame, ) from microcosm.build.uk_runtime.release_identity import UK_NATIONAL_RELEASE_ID +from microcosm.build.uk_runtime.weight_year import ( + UK_WEIGHT_YEAR_MASS_REASON, + uk_engine_year_frame, + uk_weight_projection_factor, +) from microcosm.calibrate import TargetRegistry, calibrate from microcosm.frame import WeightKind from test_support.microcosm_build import uk_calibration_run as seam @@ -382,6 +387,13 @@ def test_uk_national_role_builds_through_the_graph_and_stages_locally( "uk.full.national.readback", } assert record["graph"]["readback"]["passed"] is True + # The fixture frame is a 2023 dataset solved at 2025 (microcosm#796 A19). + factor = uk_weight_projection_factor(2023, 2025) + assert record["graph"]["readback"]["weight_year"] == { + "solve_year": 2025, + "dataset_year": 2023, + "engine_projection_factor": factor, + } # The seam diagnostics with the build block, digest measured from the file. written = json.loads(diagnostics.read_text()) @@ -412,13 +424,24 @@ def test_uk_national_role_builds_through_the_graph_and_stages_locally( == signature ) - # The H5: calibrated weights, the seam's mass record. + # The H5: calibrated weights at the dataset's year. The seam's mass + # record, then the division by the engine's projection to the solve year. staged, _ = load_uk_national_frame(dataset) assert staged.weights_for("household").kind is WeightKind.CALIBRATED - assert len(staged.mass_log) == 1 + assert len(staged.mass_log) == 2 assert staged.mass_log[0].reason == national_calibration_mass_reason( [spec.family for spec in registry.specs] ) + assert staged.mass_log[1].reason == UK_WEIGHT_YEAR_MASS_REASON + assert staged.mass_log[1].declared_factor == 1.0 / factor + assert staged.mass_log[1].old_total == staged.mass_log[0].new_total + # microcosm#1194: identifiers inside the range policyengine-uk stores, + # the build's own beside them (the fixture numbers its rows from zero). + for entity in ("person", "benunit", "household"): + table = staged.table(entity) + assert table[f"{entity}_id"].tolist() == [1, 2, 3, 4] + assert table[f"{entity}_build_id"].tolist() == [0, 1, 2, 3] + assert staged.table("person")["person_household_id"].tolist() == [1, 2, 3, 4] frozen = TargetRegistry.from_json(out / "national_target_registry.json") assert frozen.version == registry.version @@ -518,7 +541,9 @@ def test_uk_national_role_builds_through_the_graph_and_stages_locally( def test_uk_national_role_solve_is_the_seam_solve(monkeypatch, tmp_path): """The graph's H5 carries the weights an in-process calibrate call under - the national doctrine produces on the same frame and register.""" + the national doctrine produces on the same frame and register, written at + the dataset's year: the engine's projection to the solve year returns + them (microcosm#796 A19).""" pytest.importorskip("tables") builder = candidate._load_builder_module() input_h5, registry, _artifact, _pin = _national_inputs(monkeypatch, tmp_path) @@ -528,7 +553,12 @@ def test_uk_national_role_solve_is_the_seam_solve(monkeypatch, tmp_path): staged, _ = load_uk_national_frame(out / "microcosm_uk_2024_25.h5") expected = _seam_weights(seam._frame(), registry, UKNationalSolveDoctrine(epochs=7)) - assert np.array_equal(staged.weights_for("household").values, expected) + factor = uk_weight_projection_factor(2023, 2025) + assert np.array_equal(staged.weights_for("household").values, expected / factor) + at_solve_year = uk_engine_year_frame(staged, simulation_year=2025) + np.testing.assert_allclose( + at_solve_year.weights_for("household").values, expected, rtol=1e-15, atol=0 + ) def test_uk_national_role_records_doctrine_overrides(monkeypatch, tmp_path): diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_salary_sacrifice.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_salary_sacrifice.py index 8ce6171fa7..0aa9556eff 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_salary_sacrifice.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_salary_sacrifice.py @@ -6,6 +6,8 @@ import numpy as np import pandas as pd import pytest +from hypothesis import given, settings +from hypothesis import strategies as st from microcosm.build.country_spec import load_country_spec from microcosm.build.source_manifest import SourceOperationSpec @@ -14,6 +16,7 @@ from microcosm.build.uk_runtime.cgt_support import HOUSEHOLD_IS_CGT_SUPPORT_COPY from microcosm.build.uk_runtime.national_frame import uk_national_frame from microcosm.build.uk_runtime.salary_sacrifice import ( + SALSAC_MOVE_PAY_SHARE_CAP, SALSAC_OUTPUT, SALSAC_PRE_CONVERSION_PAY_COLUMN, SALSAC_RATE_CAP, @@ -160,27 +163,105 @@ def test_qrf_predicts_only_for_people_not_asked_who_are_paid( result = impute_salary_sacrifice(frame) - # Then the paid person not asked is predicted for, the unpaid ones are - # zero, and an asked person keeps what they reported whatever their pay. + # Then the paid person not asked is predicted for and the unpaid ones are + # zero. The asked person without pay reported 20: a sacrifice comes out of + # pay, so it is limited to none (uk-data#518). assert result.frame.table("person")[SALSAC_OUTPUT].tolist() == [ 10.0, - 20.0, + 0.0, 900.0, 0.0, 0.0, ] assert result.prediction_rows == 1 + assert result.limited_to_pay_rows == 1 + assert result.limited_to_pay_mass == 1.0 + assert result.limited_to_pay_amount == 20.0 assert result.not_asked_without_pay_rows == 2 assert result.not_asked_without_pay_mass == 5.0 qrf = result.evidence()["qrf"] assert qrf["not_asked_without_pay_rows"] == 2 assert qrf["not_asked_without_pay_mass"] == 5.0 assert "employment_income > 0" in qrf["target_population"] - # The headcount the conversion tops up starts from true users only. - assert result.pre_headcount == 3.0 + assert qrf["limited_to_pay_rows"] == 1 + assert qrf["limited_to_pay_amount"] == 20.0 + assert "employment_income" in qrf["amount_limit"] + # The headcount the conversion tops up starts from true users only: the + # asked person without pay is no longer one of them. + assert result.pre_headcount == 2.0 -def test_conversion_moves_full_pension_zeros_source_and_records_cap( +def test_qrf_trains_only_on_asked_respondents_with_pay( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """A respondent without pay shows nothing about what paid people sacrifice.""" + + _FakeQRF.training_frames = [] + _Fitted.value = 0.0 + monkeypatch.setattr(salary_sacrifice, "QRF", _FakeQRF) + frame = _frame( + asked=[1, 1, 1, 0], + salary_sacrifice_values=[10.0, 0.0, 30.0, 0.0], + employee_pension=[0.0] * 4, + employment_income=[30_000.0, 0.0, 20_000.0, 25_000.0], + ) + + result = impute_salary_sacrifice(frame) + + assert _FakeQRF.training_frames[0].index.tolist() == [0, 2] + assert result.training_rows == 2 + + +def test_the_forest_needs_an_asked_respondent_with_pay( + monkeypatch: pytest.MonkeyPatch, +) -> None: + monkeypatch.setattr(salary_sacrifice, "QRF", _FakeQRF) + frame = _frame( + asked=[1, 0], + salary_sacrifice_values=[0.0, 0.0], + employee_pension=[0.0, 0.0], + employment_income=[0.0, 25_000.0], + ) + + with pytest.raises(ValueError, match="no eligible asked FRS rows with pay"): + impute_salary_sacrifice(frame) + + +def test_every_amount_is_limited_to_the_pay_that_remains( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """Reported and predicted amounts alike, on FRS and SPI-channel rows. + + An SPI-channel row keeps its FRS twin's answer beside pay redrawn from the + SPI, so its reported amount can sit above its pay. + """ + + _FakeQRF.training_frames = [] + _Fitted.value = 900.0 + monkeypatch.setattr(salary_sacrifice, "QRF", _FakeQRF) + frame = _frame( + asked=[1, 1, 0, 0, 1], + salary_sacrifice_values=[4_000.0, 4_000.0, 0.0, 0.0, 250.0], + employee_pension=[0.0] * 5, + employment_income=[30_000.0, 1_500.0, 500.0, 25_000.0, 250.0], + channels=["frs", "spi", "frs", "spi", "frs"], + weights=np.asarray([1.0, 2.0, 3.0, 4.0, 5.0]), + ) + + result = impute_salary_sacrifice(frame) + person = result.frame.table("person") + + # The SPI-channel respondent's 4,000 against pay of 1,500 and the + # prediction of 900 against pay of 500 are limited; an amount equal to pay + # stays. + assert person[SALSAC_OUTPUT].tolist() == [4_000.0, 1_500.0, 500.0, 900.0, 250.0] + assert (person[SALSAC_OUTPUT] <= person["employment_income"]).all() + assert result.limited_to_pay_rows == 2 + assert result.limited_to_pay_mass == 5.0 + assert result.limited_to_pay_amount == 2_500.0 + 400.0 + + +def test_conversion_moves_a_contribution_within_half_the_pay_whole( monkeypatch: pytest.MonkeyPatch, ) -> None: _Fitted.value = 0.0 @@ -222,6 +303,136 @@ def test_conversion_moves_full_pension_zeros_source_and_records_cap( assert evidence["target"] == SALSAC_STAGE_TARGET assert evidence["converted_rows"] == result.converted_rows assert evidence["pre_conversion_pay_column"] == SALSAC_PRE_CONVERSION_PAY_COLUMN + # No contribution here is above half the pay, so none is split. + assert evidence["move_pay_share_cap"] == SALSAC_MOVE_PAY_SHARE_CAP + assert evidence["pay_share_capped_rows"] == 0 + assert evidence["retained_employee_amount"] == 0.0 + + +def test_conversion_moves_at_most_half_the_pay_and_keeps_the_rest( + monkeypatch: pytest.MonkeyPatch, +) -> None: + """The sacrifice never exceeds the pay that remains (uk-data#518). + + The whole contribution used to move, and pay was floored at zero, which + left a converted record with a sacrifice and no pay. + """ + + _Fitted.value = 0.0 + monkeypatch.setattr(salary_sacrifice, "QRF", _FakeQRF) + n_donors = 100 + pay = np.asarray([30_000.0, *([1_000.0] * n_donors)]) + # Contributions from 100 to 1,500: above half the pay from 500 up, and + # above the whole pay from 1,000 up. + contributions = np.asarray([0.0, *np.linspace(100.0, 1_500.0, n_donors)]) + frame = _frame( + asked=[1, *([0] * n_donors)], + salary_sacrifice_values=[0.0] * (n_donors + 1), + employee_pension=contributions, + employment_income=pay, + weights=[1.0, *([100_000.0] * n_donors)], + ) + + result = impute_salary_sacrifice(frame) + person = result.frame.table("person") + converted = (person[SALSAC_PRE_CONVERSION_PAY_COLUMN] > 0.0).to_numpy() + + assert 0 < result.converted_rows < n_donors + moved = person.loc[converted, SALSAC_OUTPUT].to_numpy() + np.testing.assert_array_equal( + moved, np.minimum(contributions[converted], 0.5 * pay[converted]) + ) + # Pay falls by the amount moved, so pay plus sacrifice is the carrier and + # the sacrifice is within the pay that remains. + np.testing.assert_array_equal( + person.loc[converted, "employment_income"].to_numpy() + moved, + person.loc[converted, SALSAC_PRE_CONVERSION_PAY_COLUMN].to_numpy(), + ) + assert (person[SALSAC_OUTPUT] <= person["employment_income"]).all() + assert (person.loc[converted, "employment_income"] > 0.0).all() + # What does not move stays an employee contribution, so the two columns + # still add up to the contribution. + np.testing.assert_allclose( + person["employee_pension_contributions"].to_numpy() + + np.where(converted, person[SALSAC_OUTPUT].to_numpy(), 0.0), + contributions, + ) + capped = converted & (contributions > 0.5 * pay) + assert capped.any() and (converted & ~capped).any() + assert result.pay_share_capped_rows == int(capped.sum()) + assert result.moved_amount == pytest.approx(moved.sum()) + assert result.retained_employee_amount == pytest.approx( + (contributions[capped] - 0.5 * pay[capped]).sum() + ) + + +_AMOUNTS = st.sampled_from( + [0.0, 0.01, 250.0, 499.99, 500.0, 1_000.0, 2_500.0, 40_000.0] +) + + +@settings(max_examples=60, deadline=None) +@given( + rows=st.lists( + st.tuples( + st.booleans(), # asked + _AMOUNTS, # reported salary sacrifice + _AMOUNTS, # employee pension contribution + _AMOUNTS, # pay + st.sampled_from(["frs", "spi"]), + ), + min_size=1, + max_size=12, + ), + predicted=_AMOUNTS, +) +def test_no_amount_exceeds_pay_and_nothing_is_lost(rows, predicted) -> None: + """Whatever is reported, predicted or converted, the sacrifice is within pay. + + The invariants of uk-data#518's rule: a sacrifice is at most the pay that + remains and nil without pay; a converted record's pay and sacrifice add up + to its pay before; and a contribution is moved or kept, never dropped. + """ + + # One asked, paid FRS respondent always leads, so the forest has a row. + rows = [(True, 0.0, 0.0, 30_000.0, "frs"), *rows] + asked = [1 if row[0] else 0 for row in rows] + reported = np.asarray([row[1] if row[0] else 0.0 for row in rows]) + contributions = np.asarray([row[2] for row in rows]) + pay = np.asarray([row[3] for row in rows]) + frame = _frame( + asked=asked, + salary_sacrifice_values=reported, + employee_pension=contributions, + employment_income=pay, + channels=[row[4] for row in rows], + weights=np.full(len(rows), 1_000_000.0), + ) + original_qrf, original_value = salary_sacrifice.QRF, _Fitted.value + salary_sacrifice.QRF, _Fitted.value = _FakeQRF, predicted + try: + result = impute_salary_sacrifice(frame) + finally: + salary_sacrifice.QRF, _Fitted.value = original_qrf, original_value + person = result.frame.table("person") + sacrifice = person[SALSAC_OUTPUT].to_numpy() + pay_after = person["employment_income"].to_numpy() + carrier = person[SALSAC_PRE_CONVERSION_PAY_COLUMN].to_numpy() + converted = carrier > 0.0 + + assert (sacrifice >= 0.0).all() and (sacrifice <= pay_after).all() + assert (sacrifice[pay_after == 0.0] == 0.0).all() + np.testing.assert_array_equal(pay_after[~converted], pay[~converted]) + np.testing.assert_array_equal( + pay_after[converted] + sacrifice[converted], carrier[converted] + ) + np.testing.assert_array_equal(carrier[converted], pay[converted]) + kept = person["employee_pension_contributions"].to_numpy() + np.testing.assert_array_equal(kept[~converted], contributions[~converted]) + np.testing.assert_allclose( + kept[converted] + sacrifice[converted], contributions[converted] + ) + assert (sacrifice[converted] <= SALSAC_MOVE_PAY_SHARE_CAP * pay[converted]).all() def test_anchor_is_self_consistent() -> None: @@ -246,6 +457,7 @@ def test_anchor_is_self_consistent() -> None: "seed", "n_estimators", "clamp_minimum", + "clamp_maximum", "preserve_asked_rows", "cache", ) @@ -257,6 +469,7 @@ def test_anchor_is_self_consistent() -> None: "target", "donor_pool", "rate_cap", + "move_pay_share_cap", "move", "seed", "salt", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py index d13c106929..4b5e017120 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py @@ -65,7 +65,7 @@ _TEST_PATHS = paths_for("microcosm-build") -ACTIVE_REFERENCE_COUNT = 1279 +ACTIVE_REFERENCE_COUNT = 1283 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} @@ -805,7 +805,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 1279, + "active": 1283, "no_fact_at_or_before_period": 5, "signed_excluded": 15, } diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py index ed4563fedf..9fe2a1ee60 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py @@ -361,11 +361,13 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: import pandas as pd from microcosm.build.uk_runtime.national_frame import ( + UK_BUILD_IDENTIFIER_COLUMNS, UK_RELEASE_EXPORT_DROPPED_COLUMNS, uk_national_frame, uk_release_export_frame, ) from microcosm.build.uk_runtime.terminal_gates import ( + UK_ALLOWED_EXTRA_EXPORT_COLUMNS, UK_REVIEWED_EXPORT_EXCLUSIONS, uk_export_candidate_columns, ) @@ -402,12 +404,19 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: } exported = uk_release_export_frame(frame) # microcosm#1106: the WAS total leaves; its engine components stay. + # microcosm#1194: the build identifiers stay beside the renumbered ones, + # as reviewed extras of the export surface. + carriers = { + f"{entity}.{column}" for entity, column in UK_BUILD_IDENTIFIER_COLUMNS.items() + } assert uk_export_candidate_columns(exported) == { "person.age", "household.region", "household.main_residence_value", "household.household_weight", + *carriers, } + assert carriers <= set(UK_ALLOWED_EXTRA_EXPORT_COLUMNS) assert exported.weights_for("household").values.tolist() == [1.0, 2.0] assert exported.mass_log == frame.mass_log # The boundary drops the reviewed export exclusions, the internal diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_weight_year.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_weight_year.py new file mode 100644 index 0000000000..3a0b8d6b8a --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_weight_year.py @@ -0,0 +1,188 @@ +"""The year a UK release's household weights are written at (microcosm#796 A19).""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime import hmrc_uprating, weight_year +from microcosm.build.uk_runtime.national_frame import ( + load_uk_national_frame, + uk_national_frame, + write_uk_national_frame, +) +from microcosm.build.uk_runtime.weight_year import ( + UK_ENGINE_YEAR_MASS_REASON, + UK_WEIGHT_PROJECTION_PARAMETER, + UK_WEIGHT_PROJECTION_PINS_BLOCK, + UK_WEIGHT_PROJECTION_VARIABLE, + UK_WEIGHT_YEAR_MASS_REASON, + uk_dataset_year_frame, + uk_engine_year_frame, + uk_solve_year_frame, + uk_weight_projection_factor, + uk_weight_projection_growth, + uk_weights_are_at_dataset_year, +) +from microcosm.frame import WeightKind + +SOLVED = np.asarray([1_000.0, 2_500.0, 400.0]) + + +def _frame(*, kind: WeightKind = WeightKind.CALIBRATED, period: str = "2024"): + ids = np.asarray([1, 2, 3], dtype="int64") + return uk_national_frame( + person=pd.DataFrame( + {"person_id": ids, "person_benunit_id": ids, "person_household_id": ids} + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame({"household_id": ids}), + time_period=period, + weight_kind=kind, + household_weights=SOLVED, + ) + + +def _growth(year: int) -> float: + pins = hmrc_uprating.engine_parameter_pins() + block = pins[UK_WEIGHT_PROJECTION_PINS_BLOCK][UK_WEIGHT_PROJECTION_VARIABLE] + return float(block["growth"][f"{year}-01-01"]) + + +def test_the_pins_carry_the_engine_growth_of_household_weight() -> None: + pins = hmrc_uprating.engine_parameter_pins() + block = pins[UK_WEIGHT_PROJECTION_PINS_BLOCK] + assert list(block) == [UK_WEIGHT_PROJECTION_VARIABLE] + entry = block[UK_WEIGHT_PROJECTION_VARIABLE] + assert entry["parameter"] == UK_WEIGHT_PROJECTION_PARAMETER + # One growth rate per pinned instant, the same instants as the indices. + assert list(entry["growth"]) == pins["instants"] + assert uk_weight_projection_growth(2025) == _growth(2025) + + +def test_the_factor_compounds_the_growth_after_the_dataset_year() -> None: + assert uk_weight_projection_factor(2024, 2024) == 1.0 + assert uk_weight_projection_factor(2024, 2025) == 1.0 + _growth(2025) + assert uk_weight_projection_factor(2024, 2026) == pytest.approx( + (1.0 + _growth(2025)) * (1.0 + _growth(2026)), rel=0, abs=0 + ) + # The dataset's own year takes no growth: only the years after it do. + assert uk_weight_projection_factor(2025, 2026) == 1.0 + _growth(2026) + + +def test_the_engine_does_not_project_backwards() -> None: + with pytest.raises(ValueError, match="does not project a 2025 dataset back"): + uk_weight_projection_factor(2025, 2024) + + +def test_a_year_the_pins_do_not_cover_refuses() -> None: + with pytest.raises(ValueError, match="re-run"): + uk_weight_projection_growth(2099) + + +def test_pins_without_the_block_refuse(monkeypatch: pytest.MonkeyPatch) -> None: + pins = dict(hmrc_uprating.engine_parameter_pins()) + pins.pop(UK_WEIGHT_PROJECTION_PINS_BLOCK) + monkeypatch.setattr(weight_year, "engine_parameter_pins", lambda: pins) + + with pytest.raises(ValueError, match="does not pin"): + uk_weight_projection_growth(2025) + + +def test_release_weights_are_the_solved_weights_over_the_engine_factor() -> None: + frame = _frame() + factor = uk_weight_projection_factor(2024, 2025) + + released = uk_dataset_year_frame(frame, solve_year=2025) + + weights = released.weights_for("household") + assert weights.kind is WeightKind.CALIBRATED + np.testing.assert_array_equal(weights.values, SOLVED / factor) + record = released.mass_log[-1] + assert record.entity == "household" + assert record.reason == UK_WEIGHT_YEAR_MASS_REASON + assert record.declared_factor == 1.0 / factor + assert record.old_total == SOLVED.sum() + assert uk_weights_are_at_dataset_year(released) + # The solved frame is not touched, and it carries no such record. + np.testing.assert_array_equal(frame.weights_for("household").values, SOLVED) + assert not uk_weights_are_at_dataset_year(frame) + # The solve's evidence finds its own record by the word "calibration" on + # the frame before this one; the division must never be mistaken for it. + assert "calibration" not in UK_WEIGHT_YEAR_MASS_REASON + assert "calibration" not in UK_ENGINE_YEAR_MASS_REASON + + +def test_the_division_is_applied_once() -> None: + released = uk_dataset_year_frame(_frame(), solve_year=2025) + + again = uk_dataset_year_frame(released, solve_year=2025) + + assert again is released + + +def test_only_solved_weights_are_divided() -> None: + with pytest.raises(ValueError, match="Only solved household weights"): + uk_dataset_year_frame(_frame(kind=WeightKind.DESIGN), solve_year=2025) + + +def test_the_engine_projection_of_a_release_gives_back_the_solved_weights() -> None: + released = uk_dataset_year_frame(_frame(), solve_year=2025) + + at_solve_year = uk_engine_year_frame(released, simulation_year=2025) + + np.testing.assert_allclose( + at_solve_year.weights_for("household").values, SOLVED, rtol=1e-15, atol=0 + ) + assert at_solve_year.mass_log[-1].reason == UK_ENGINE_YEAR_MASS_REASON + # At the dataset's own year the engine applies no growth. + assert uk_engine_year_frame(released, simulation_year=2024) is released + + +def test_a_file_written_as_solved_reads_high_at_the_solve_year() -> None: + # The defect this fixes: weights solved at 2025 and stored on a 2024 + # dataset come back multiplied by the population growth of 2025. + as_solved = _frame() + + seen = uk_engine_year_frame(as_solved, simulation_year=2025) + + np.testing.assert_array_equal( + seen.weights_for("household").values, + SOLVED * uk_weight_projection_factor(2024, 2025), + ) + + +def test_a_release_goes_back_to_its_solved_weights_for_the_gates() -> None: + released = uk_dataset_year_frame(_frame(), solve_year=2025) + + solved = uk_solve_year_frame(released, solve_year=2025) + + np.testing.assert_allclose( + solved.weights_for("household").values, SOLVED, rtol=1e-15, atol=0 + ) + # A file written as solved already carries them. + as_solved = _frame() + assert uk_solve_year_frame(as_solved, solve_year=2025) is as_solved + + +def test_a_release_divided_for_another_year_refuses() -> None: + released = uk_dataset_year_frame(_frame(), solve_year=2026) + + with pytest.raises(ValueError, match="would not return the solved weights"): + uk_solve_year_frame(released, solve_year=2025) + + +def test_the_division_survives_the_release_file(tmp_path) -> None: + released = uk_dataset_year_frame(_frame(), solve_year=2025) + path = tmp_path / "release.h5" + + write_uk_national_frame(released, path) + loaded, _provenance = load_uk_national_frame(path) + + assert uk_weights_are_at_dataset_year(loaded) + np.testing.assert_array_equal( + loaded.weights_for("household").values, + released.weights_for("household").values, + ) + assert loaded.mass_log == released.mass_log diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index 8d7e880e6e..d37b166643 100644 --- a/packages/microcosm-data/pyproject.toml +++ b/packages/microcosm-data/pyproject.toml @@ -37,7 +37,7 @@ us = ["policyengine-us>=2.2.1,<3", "policyengine-core==3.32.5"] # The UK artifact lives in a PRIVATE repo (UK Data Service licence): loads # require an authenticated HF token with access. The loader surfaces the # 401 with that explanation rather than retrying. -uk = ["policyengine-uk>=2.123.0"] +uk = ["policyengine-uk>=2.130.1"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index f9d576f8cd..ba91d8c209 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -442,13 +442,13 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: # fingerprint derives from the manifest digest. Editing the spec moves all # three here in the same reviewed change. _UK_GATE_BATTERY_POLICY_SHA256 = ( - "382d3d5e16bda29b5cc3d6fa1109aa2ebf210513046723e4ff7eed38b4037fec" + "5243864991eac2990b92dbe9027322e8ff437f97a13fe1e7ba575bf701964aa2" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "59a94f17b2cd725ec76ab511bb433435a6ce5d7a22801f72f28a7085162bd8b7" + "f31c1320dcf30f488b27a96a19915eaa70639a6d6e69eb7fa709212b46ccc89b" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "7fc56fcdea81b8ddf08e18c4a3d483a76ff1533139d4f5fd1256f5e9c7fbcb13" + "d170880bc64d48add012ccac0c3d2bb2bede4b19e97bd526354e5e08bccc65db" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -902,10 +902,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "d6adeb2e203e3abd41a8037ea42c3cda14a1a2dd97d9aa973cbd23cb72b40990" + "437b1c298d730afb7f56d05bdd6d659df143fc0c7f376c606004aea6c79ab9e9" ), "policy_sha256": ( - "af0beb1143b1e6a4d778472d5c1f568a840536bb72ff57570b73eea443df208e" + "5e6260d9495ab5b29f1f08908bfef5c002004df35a031c49d1b98e16667c0b0f" ), }, } diff --git a/packages/microcosm-data/tests/engine_free/shared/test_contract.py b/packages/microcosm-data/tests/engine_free/shared/test_contract.py index 07a6b867c5..56a1a2bd58 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -214,13 +214,13 @@ 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 = ( - "382d3d5e16bda29b5cc3d6fa1109aa2ebf210513046723e4ff7eed38b4037fec" + "5243864991eac2990b92dbe9027322e8ff437f97a13fe1e7ba575bf701964aa2" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "59a94f17b2cd725ec76ab511bb433435a6ce5d7a22801f72f28a7085162bd8b7" + "f31c1320dcf30f488b27a96a19915eaa70639a6d6e69eb7fa709212b46ccc89b" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "7fc56fcdea81b8ddf08e18c4a3d483a76ff1533139d4f5fd1256f5e9c7fbcb13" + "d170880bc64d48add012ccac0c3d2bb2bede4b19e97bd526354e5e08bccc65db" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6a179222c6b6058ad9caacfd347f7bdb8cb98a62237b50d1fc1136df09337251" diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 13066a779c..10742b2594 100644 --- a/packages/microcosm-frame/pyproject.toml +++ b/packages/microcosm-frame/pyproject.toml @@ -22,7 +22,7 @@ policyengine = [ "policyengine-core==3.32.5", "microunit>=0.1.0", ] -uk = ["policyengine-uk>=2.123.0"] +uk = ["policyengine-uk>=2.130.1"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py index fa8f0b44ad..4f15afce00 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py @@ -25,14 +25,14 @@ ) from microcosm.frame.concepts import AlignmentRelation -__all__ = ["POLICYENGINE_UK_CONCEPT_MAPPING"] +__all__ = ["POLICYENGINE_UK_CONCEPT_MAPPING", "UK_ENGINE_DEFAULT_TAKE_UP_INPUTS"] # --------------------------------------------------------------------------- # Concept mapping # --------------------------------------------------------------------------- # # Which PolicyEngine-UK inputs each engine-neutral concept feeds -# (microcosm.frame.concepts), reviewed against policyengine-uk 2.123.0. Every +# (microcosm.frame.concepts), reviewed against policyengine-uk 2.130.1. Every # target is a pure input (in ``PolicyEngineUKEngine.variables``). The UK # loader also accepts some formula-owned variables as overrides # (``employment_income``, ``state_pension_reported``, ``is_household_head``); @@ -164,6 +164,21 @@ } +#: Take-up inputs of the engine the build leaves at the engine's default, +#: with why. ``would_claim_uc_at_legacy_closure`` (policyengine-uk 2.128.0, +#: pe-uk#2119) says whether a family DWP moves off a legacy benefit claims +#: Universal Credit; the default is that every such family does. uk-data#534 +#: draws it at DWP's Move to UC claim rates, which are not yet a Chronicle +#: fact (microcosm#1095). +UK_ENGINE_DEFAULT_TAKE_UP_INPUTS: dict[str, str] = { + "would_claim_uc_at_legacy_closure": ( + "Left at the engine's default (true): every family moved off a legacy " + "benefit claims Universal Credit. DWP's Move to UC claim rates, which " + "uk-data#534 draws it at, are not yet a Chronicle fact (microcosm#1095)." + ), +} + + def _uk_take_up(program: str, entity: str) -> InputBinding: person = entity == "person" documentation, build = _UK_TAKE_UP_EVIDENCE[program] @@ -185,7 +200,7 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: POLICYENGINE_UK_CONCEPT_MAPPING = ConceptMapping( engine="policyengine-uk", - engine_version="2.123.0", + engine_version="2.130.1", entity_correspondence={"person": "person", "household": "household"}, input_declaration=InputDeclaration.ENGINE_TYPED, bindings=( @@ -528,15 +543,20 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: "('Current value of the person's Lifetime ISAs, cash and stocks " "and shares together'), one savings wrapper, which the engine " "documents as already inside gross_financial_wealth and " - "net_financial_wealth. savings and corporate_wealth are two of " + "net_financial_wealth. Since policyengine-uk 2.130.0 three more " + "describe a company the person owns, for Universal Credit's " + "regulation 77 (owned_company_capital, owned_company_holding_value " + "and owned_company_trade_assets); every one defaults to no such " + "company, and the build sets none. savings and corporate_wealth are two of " "Universal Credit's capital sources " "(gov.dwp.universal_credit.means_test.capital.sources), and " "lifetime_isa_countable_capital, the Lifetime ISA at its surrender " "value, is its one person-level source (capital.person_sources). " - "uc_reported_capital (benefit unit; 'Claimant-level capital for " - "Universal Credit when household-level asset data cannot be " - "attributed across multiple benefit units', default -1) is " - "Universal Credit capital: when it is zero or more, " + "uc_reported_capital (benefit unit, default -1) is Universal " + "Credit capital. It is formula-owned since policyengine-uk 2.124.0: " + "unless set directly it is benunit_reported_capital, a benefit-unit " + "input ('Savings and investments of the benefit unit's adults (the " + "claimant and any partner) as recorded'). When it is zero or more, " "uc_assessable_capital takes it in place of both the " "household-capital proxy it otherwise sums from the household " "sources, net of capital other benefit units report, and " diff --git a/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py b/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py index ea65b4c665..be17424750 100644 --- a/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py +++ b/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py @@ -21,6 +21,9 @@ UK_SCHEMA, PolicyEngineUKEngine, ) +from microcosm.frame.adapters.policyengine_uk_concepts import ( + UK_ENGINE_DEFAULT_TAKE_UP_INPUTS, +) from microcosm.frame.concept_mapping import ( Recode, TakeUpThreshold, @@ -83,7 +86,10 @@ def test_take_up_bindings_are_the_engines_would_claim_inputs(engine) -> None: if isinstance(binding.transform, TakeUpThreshold) and binding.engine_input.startswith("would_claim_") } - assert bound == would_claim + # Every other take-up input is left at the engine default, with a reason. + left = set(UK_ENGINE_DEFAULT_TAKE_UP_INPUTS) + assert bound.isdisjoint(left) + assert bound | left == would_claim def test_structural_inputs_are_engine_inputs(engine) -> None: @@ -104,6 +110,7 @@ def test_the_mapping_notes_formula_ownership_claims_hold(engine) -> None: "is_household_head", "weekly_hours", "tax_free_savings_income", + "uc_reported_capital", } inputs = set(engine.variables()) for name in claimed: @@ -115,9 +122,11 @@ def test_the_liquid_asset_reason_holds(engine) -> None: # Financial wealth is a household input. The person-level GBP stocks are a # debt, a car's list price and the Lifetime ISA balance, which the engine # documents as already inside the household's financial wealth. - # uc_reported_capital is a benefit-unit input, unset at -1, that the - # uc_assessable_capital formula reads in place of both a household proxy - # apportioned by claimant and partner count and the person-level sources. + # uc_reported_capital is formula-owned since policyengine-uk 2.124.0: unless + # set directly it is benunit_reported_capital, a benefit-unit input unset at + # -1. The uc_assessable_capital formula reads it in place of both a + # household proxy apportioned by claimant and partner count and the + # person-level sources. inputs = set(engine.variables()) person_stocks = { name @@ -130,13 +139,23 @@ def test_the_liquid_asset_reason_holds(engine) -> None: "car_list_price", "lifetime_isa_balance", "student_loan_balance", + # Universal Credit's regulation 77 company inputs (2.130.0): no + # company by default, and the build sets none. + "owned_company_capital", + "owned_company_holding_value", + "owned_company_trade_assets", } for name in ("savings", "gross_financial_wealth", "net_financial_wealth"): assert name in inputs, name assert engine.variable_metadata(name).entity == "household", name - assert "uc_reported_capital" in inputs + assert "benunit_reported_capital" in inputs + assert engine.variable_metadata("benunit_reported_capital").entity == "benunit" + assert engine._variable("benunit_reported_capital").default_value == -1 + assert "uc_reported_capital" not in inputs assert engine.variable_metadata("uc_reported_capital").entity == "benunit" assert engine._variable("uc_reported_capital").default_value == -1 + reported = inspect.getsource(engine._variable("uc_reported_capital").get_formula()) + assert 'benunit("benunit_reported_capital", period)' in reported assert "uc_assessable_capital" not in inputs source = inspect.getsource(engine._variable("uc_assessable_capital").get_formula()) assert "uc_reported_capital" in source diff --git a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json index 192ad49f17..c33847a846 100644 --- a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json +++ b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json @@ -393,8 +393,8 @@ } ], "engine": "policyengine-uk", - "engine_version": "2.123.0", - "input_count": 288, + "engine_version": "2.130.1", + "input_count": 299, "schema_sha256": "5a55669d0447a686692911a6327ae298f19741dde29559f2654a75807878a141", "structural_inputs": [ { @@ -524,12 +524,12 @@ "name": "beer_litres" }, { - "entity": "person", - "name": "blind_persons_allowance" + "entity": "benunit", + "name": "benunit_reported_capital" }, { - "entity": "household", - "name": "brma" + "entity": "person", + "name": "blind_persons_allowance" }, { "entity": "person", @@ -739,6 +739,10 @@ "entity": "household", "name": "domestic_rates" }, + { + "entity": "household", + "name": "ebr_energy_bills_credit_eligible" + }, { "entity": "household", "name": "education_consumption" @@ -899,6 +903,10 @@ "entity": "person", "name": "is_blind" }, + { + "entity": "person", + "name": "is_engaged_in_owned_company_trade" + }, { "entity": "person", "name": "is_in_approved_training" @@ -1067,6 +1075,42 @@ "entity": "person", "name": "outpatient_visits" }, + { + "entity": "person", + "name": "owned_company_capital" + }, + { + "entity": "person", + "name": "owned_company_carries_on_property_business" + }, + { + "entity": "person", + "name": "owned_company_carries_on_trade" + }, + { + "entity": "person", + "name": "owned_company_holding_value" + }, + { + "entity": "person", + "name": "owned_company_income_share" + }, + { + "entity": "person", + "name": "owned_company_intermediary_earnings_chapter" + }, + { + "entity": "person", + "name": "owned_company_intermediary_earnings_from_main_employment" + }, + { + "entity": "person", + "name": "owned_company_is_main_employment" + }, + { + "entity": "person", + "name": "owned_company_trade_assets" + }, { "entity": "household", "name": "owned_land" @@ -1079,10 +1123,6 @@ "entity": "person", "name": "pension_credit_reported" }, - { - "entity": "benunit", - "name": "pension_credit_reported_capital" - }, { "entity": "person", "name": "pension_income" @@ -1215,6 +1255,10 @@ "entity": "person", "name": "ssmg_reported" }, + { + "entity": "person", + "name": "stands_as_sole_owner_or_partner_of_company" + }, { "entity": "person", "name": "statutory_maternity_pay" @@ -1311,10 +1355,6 @@ "entity": "person", "name": "travel_grant_receives_means_tested_nhs_bursary" }, - { - "entity": "person", - "name": "uc_has_limited_capability_for_work" - }, { "entity": "person", "name": "uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period" @@ -1335,10 +1375,6 @@ "entity": "person", "name": "uc_is_in_startup_period" }, - { - "entity": "person", - "name": "uc_is_ineligible_partner" - }, { "entity": "person", "name": "uc_is_responsible_foster_parent_of_child_under_one" @@ -1357,12 +1393,16 @@ }, { "entity": "benunit", - "name": "uc_reported_capital" + "name": "uc_receives_new_claimant_health_element" }, { "entity": "person", "name": "universal_credit_reported" }, + { + "entity": "household", + "name": "uses_energy_prepayment_meter" + }, { "entity": "household", "name": "water_and_sewerage_charges" @@ -1387,6 +1427,10 @@ "entity": "benunit", "name": "working_tax_credit_run_on" }, + { + "entity": "benunit", + "name": "would_claim_uc_at_legacy_closure" + }, { "entity": "household", "name": "would_evade_tv_licence_fee" @@ -1397,7 +1441,7 @@ "fact:household.reference_person_id": "is_household_head is formula-owned (the oldest member). The UK build overrides it with the FRS household reference person (frs_spine.py), an override the loader accepts, but it is not an input, so the mapping does not target it. Every housing input is household-level, so nothing is allocated to the reference person.", "fact:household.source_household_id": "Original donor household lineage is build provenance and weight diagnostic support, not a policyengine-uk rules input.", "fact:person.legal_marital_status": "policyengine-uk derives marital status from benefit-unit composition; marital_status and is_married are formula-owned.", - "fact:person.liquid_financial_assets": "policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it would be a modelling choice for a UK build, not a mapping of this concept.", + "fact:person.liquid_financial_assets": "policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital. It is formula-owned since policyengine-uk 2.124.0: unless set directly it is benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'). When it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it would be a modelling choice for a UK build, not a mapping of this concept.", "fact:person.partner_person_id": "Partnerships reach policyengine-uk only through benefit-unit membership, a structure the bundle must already carry (the adapter builds no units); is_benunit_head and is_married are formula-owned.", "fact:person.public_pension_income": "The State Pension is computed from state_pension_reported, which is formula-owned: the loader accepts it as an override, but it is not an input." } diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json index 6d492a73db..9062f08fcd 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json @@ -2,68 +2,68 @@ "resource": "synthetic-cgt-asset-type.json", "resource_sha256": "synthetic", "source_commit": "synthetic", - "table4_all_gains": 6514166.302732463, + "table4_all_gains": 6532579.106281655, "table4_all_tax": 0.0, "table4_all_taxpayers": 16.148473673276698, "table4_bands": [ { - "gains": 7783.990538546768, + "gains": 7798.971448970017, "lower_bound": 0, "tax": null, - "taxpayers": 2.0026411075377792, + "taxpayers": 2.00646205197416, "upper_bound": 10000 }, { - "gains": 26119.446564634374, + "gains": 26154.11642991817, "lower_bound": 10000, "tax": null, - "taxpayers": 1.487867248593902, + "taxpayers": 1.490050205249477, "upper_bound": 25000 }, { - "gains": 49781.01752911345, + "gains": 49827.4012523063, "lower_bound": 25000, "tax": null, - "taxpayers": 1.3455777298227016, + "taxpayers": 1.3468759603089053, "upper_bound": 50000 }, { - "gains": 106977.1033635592, + "gains": 108561.10221362385, "lower_bound": 50000, "tax": null, - "taxpayers": 1.4303755939511549, + "taxpayers": 1.451624803099124, "upper_bound": 100000 }, { - "gains": 388325.05202461896, + "gains": 378709.96801750694, "lower_bound": 100000, "tax": null, - "taxpayers": 2.4814642253794714, + "taxpayers": 2.4189199216423223, "upper_bound": 250000 }, { - "gains": 497308.1835164052, + "gains": 497130.99157994165, "lower_bound": 250000, "tax": null, - "taxpayers": 1.3559425316699736, + "taxpayers": 1.3554671278941939, "upper_bound": 500000 }, { - "gains": 1385347.683474949, + "gains": 1399717.9272849406, "lower_bound": 500000, "tax": null, - "taxpayers": 1.9920814106010807, + "taxpayers": 2.0143949750540693, "upper_bound": 1000000 }, { - "gains": 4052523.8257206357, + "gains": 4064678.6280544475, "lower_bound": 1000000, "tax": null, - "taxpayers": 4.052523825720636, + "taxpayers": 4.064678628054447, "upper_bound": null } ], - "table4_individuals_gains": 6514166.302732463, + "table4_individuals_gains": 6532579.106281655, "table4_individuals_tax": 0.0, "table4_individuals_taxpayers": 16.148473673276698, "table4_trusts_gains": 0.0, @@ -115,11 +115,11 @@ } ], "table8a_disposals_total": 30.451407498178924, - "table8a_gains_total": 46295466.727019295, - "table8a_tax_total": 9259093.34540386, + "table8a_gains_total": 46016748.440787, + "table8a_tax_total": 9203349.6881574, "table8a_taxpayers_total": 27.683097725617202, - "table8b_all_gains": 46295466.727019295, + "table8b_all_gains": 46016748.440787, "table8b_all_taxpayers": 27.683097725617202, - "table8b_individuals_gains": 46295466.727019295, + "table8b_individuals_gains": 46016748.440787, "table8b_individuals_taxpayers": 27.683097725617202 } 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 e342b596dc..49afe8f435 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 @@ -253,7 +253,7 @@ "cgt_residential_split": { "artifacts": [ { - "dependency": "policyengine-uk>=2.123.0 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -1153,8 +1153,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" }, @@ -1164,8 +1164,8 @@ "locator": "child.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2", - "size_bytes": 2185, + "sha256": "13e7c811f8dbdf1ee3ede64782ef14a29d5c045cd8b8d853b2fc78fafb3fc106", + "size_bytes": 2187, "table": "child", "vintage": "synthetic-h2" } @@ -1176,7 +1176,7 @@ "child_ema", "age_started_or_accepted_current_education_or_training" ], - "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant.", + "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build.", "operations": [ { "delimiter": "\t", @@ -1188,7 +1188,7 @@ }, { "kind": "derive", - "scope": "current/highest education, QYP inputs, EMA cell-mean degenerate fills, and benefits-in-own-right flag" + "scope": "current/highest education, QYP inputs (approved training is TRAINEE = 1 on a child-table record), EMA cell-mean degenerate fills, and benefits-in-own-right flag" }, { "cells": [ @@ -1269,8 +1269,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" } @@ -1279,7 +1279,7 @@ "nonnegative_outputs": [ "sic_industry_division" ], - "notes": "Ports FRS employment derivations. empstati code 11 preserves the incumbent truncated-map artifact as LONG_TERM_DISABLED, so OTHER_INACTIVE is not emitted; mjobsect and sic are direct-indexed and fail loudly if absent.", + "notes": "Ports FRS employment derivations. empstati codes 1-11 each map to the status their UKDS SN 9563 adult-table data-dictionary label names, as uk-data#526 does: 11 (Other Inactive) is OTHER_INACTIVE and only 9 (Permanently sick/disabled) is LONG_TERM_DISABLED. People outside adult.tab are CHILD; an adult with a blank or unknown code refuses the build. mjobsect and sic are direct-indexed and fail loudly if absent.", "operations": [ { "delimiter": "\t", @@ -1312,8 +1312,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" }, @@ -1530,8 +1530,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" } @@ -1641,8 +1641,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" }, @@ -1652,8 +1652,8 @@ "locator": "child.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2", - "size_bytes": 2185, + "sha256": "13e7c811f8dbdf1ee3ede64782ef14a29d5c045cd8b8d853b2fc78fafb3fc106", + "size_bytes": 2187, "table": "child", "vintage": "synthetic-h2" }, @@ -1872,8 +1872,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad", - "size_bytes": 30240, + "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", + "size_bytes": 30789, "table": "adult", "vintage": "synthetic-h2" }, @@ -1905,8 +1905,8 @@ "locator": "child.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2", - "size_bytes": 2185, + "sha256": "13e7c811f8dbdf1ee3ede64782ef14a29d5c045cd8b8d853b2fc78fafb3fc106", + "size_bytes": 2187, "table": "child", "vintage": "synthetic-h2" }, @@ -2015,6 +2015,7 @@ "rent_paid_as_boarder", "rent_paid_as_lodger", "age", + "months_since_last_birthday", "hours_worked", "employment_income", "self_employment_income", @@ -2027,6 +2028,7 @@ "student_loan_repayments", "statutory_sick_pay", "statutory_maternity_pay", + "statutory_paternity_pay", "childcare_expenses", "personal_pension_contributions", "employee_pension_contributions", @@ -2039,7 +2041,7 @@ "external_child_payments", "care_hours" ], - "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, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).", + "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, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).", "operations": [ { "delimiter": "\t", @@ -2089,6 +2091,7 @@ "person_household_id", "age", "gender", + "months_since_last_birthday", "marital_status", "hours_worked", "care_hours", @@ -2118,6 +2121,7 @@ "student_loan_repayments", "statutory_sick_pay", "statutory_maternity_pay", + "statutory_paternity_pay", "student_loans", "access_fund", "education_grants", @@ -2194,7 +2198,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095).", + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095).", "operations": [ { "aggregates": { @@ -2319,7 +2323,7 @@ "hmrc_cgt_asset_type_spine": { "artifacts": [ { - "dependency": "policyengine-uk>=2.123.0 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters and uk_cgt_badr_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -4918,10 +4922,11 @@ "employment_income", "salary_sacrifice_pre_conversion_pay" ], - "notes": "The QRF trains only on the 2024-25 FRS asked subset. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' full pension amounts into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief.", + "notes": "The QRF trains only on the 2024-25 FRS asked subset with pay. Salary sacrifice is pay given up, so every amount, reported or predicted, is limited to the pay the person still receives, and a person without pay has none (uk-data#518, microcosm#1095); the limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' pension amounts, up to half their pay, into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; what does not move stays an employee contribution, so the sacrifice never exceeds the pay that remains. Before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief.", "operations": [ { "cache": false, + "clamp_maximum": "employment_income floored at zero; every row, reported or predicted, FRS and SPI channel", "clamp_minimum": 0, "kind": "fit_weighted_qrf", "n_estimators": 100, @@ -4935,14 +4940,15 @@ "targets": [ "pension_contributions_via_salary_sacrifice" ], - "training_population": "support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1", + "training_population": "support_channel == frs and not capital-gains clone and not CGT support copy and salary_sacrifice_asked == 1 and employment_income > 0", "weight_mapping": "household_to_person", "weights": "household_weight" }, { "donor_pool": "employee_pension_contributions > 0 and pension_contributions_via_salary_sacrifice == 0 and employment_income > 0", "kind": "convert_donors_to_target_stock", - "move": "full employee_pension_contributions to pension_contributions_via_salary_sacrifice; source zeroed; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere)", + "move": "employee_pension_contributions, up to move_pay_share_cap of employment_income, to pension_contributions_via_salary_sacrifice; the rest stays in the source; employment_income lowered by the amount sacrificed; the converted record's pay before the conversion kept on salary_sacrifice_pre_conversion_pay (zero elsewhere)", + "move_pay_share_cap": 0.5, "rate_cap": 0.5, "reason": "Salary-sacrifice support stage rewrites pension columns and converted records' pay; household rows and typed household weights pass through and total household mass is conserved.", "receipt": "weighted_headcount", @@ -5416,7 +5422,7 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both.", + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both.", "operations": [ { "aggregates": { diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab index 3f0b193835..bc36ebd0bd 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab @@ -1,136 +1,136 @@ -SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL EMPSTATI SAMESIT SRENTAMT CONVBL SDEMP01 SDEMP02 SDEMP03 SDEMP04 SDEMP05 SDEMP06 SDEMP07 SDEMP08 SDEMP09 SDEMP10 SDEMP11 SDEMP12 MJOBSECT SIC FTED TYPEED2 EDUCQUAL TRAIN EMAAMT CHEMAAMT INEARNS SEINCAM2 MNTUS1 MNTUSAM1 MNTAMT1 MNTAMT2 CVPAY ROYYR1 ROYYR2 ROYYR3 ROYYR4 ALLPAY2 ALLPAY3 ALLPAY4 CHAMTERN CHAMTTST APAMT APDAMT PAREAMT REDAMT SLREPAMT SSPADJ SMPADJ TUBORR ACCSSAMT ACCSSPD SPCREG1 SPCREG2 GRTDIR1 GRTDIR2 HEARTVAL -1 1 1 0 80 2 21 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 11 2 0 2 10 0.0 0.0 101.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -2 1 1 0 22 1 22 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 12 2 0 3 10 0.0 0.0 102.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -3 1 1 0 23 2 23 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 13 2 0 4 10 0.0 0.0 103.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -4 1 1 0 24 1 24 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2 0 5 10 0.0 0.0 104.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -5 1 1 0 25 2 25 1 1 7 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 15 2 0 6 10 0.0 0.0 105.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -6 1 1 0 26 1 26 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 16 2 0 7 10 0.0 0.0 106.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -7 1 1 0 27 2 27 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 17 2 0 8 10 0.0 0.0 107.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -8 1 1 0 28 1 28 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 18 2 0 9 10 0.0 0.0 108.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -9 1 1 0 29 2 29 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 19 2 0 10 10 0.0 0.0 109.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -10 1 1 0 30 1 30 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 20 2 0 11 10 0.0 0.0 110.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -11 1 1 0 31 2 31 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 21 2 0 12 10 0.0 0.0 111.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -12 1 1 0 32 1 32 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 22 2 0 13 10 0.0 0.0 112.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -13 1 1 0 33 2 33 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 23 2 0 14 10 0.0 0.0 113.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -14 1 1 0 34 1 34 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 24 2 0 15 10 0.0 0.0 114.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -15 1 1 0 35 2 35 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 25 2 0 16 10 0.0 0.0 115.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -16 1 1 0 36 1 36 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 26 2 0 17 10 0.0 0.0 116.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -17 1 1 0 37 2 37 1 1 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 27 2 0 18 10 0.0 0.0 117.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -18 1 1 0 38 1 38 1 1 1 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 28 2 0 1 10 0.0 0.0 118.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -19 1 1 0 39 2 39 1 1 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 29 2 0 2 10 0.0 0.0 119.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -20 1 1 0 40 1 40 1 1 7 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 30 2 0 3 10 0.0 0.0 120.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -21 1 1 0 41 2 41 1 1 1 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 31 2 0 4 10 0.0 0.0 121.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -22 1 1 0 42 1 42 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 32 2 0 5 10 0.0 0.0 122.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -23 1 1 0 43 2 43 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 33 2 0 6 10 0.0 0.0 123.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -24 1 1 0 44 1 44 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 34 2 0 7 10 0.0 0.0 124.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -25 1 1 0 45 2 20 1 1 7 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 35 2 0 8 10 0.0 0.0 125.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -26 1 1 0 46 1 21 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 36 2 0 9 10 0.0 0.0 126.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -27 1 1 0 47 2 22 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 37 2 0 10 10 0.0 0.0 127.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -28 1 1 0 48 1 23 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 38 2 0 11 10 0.0 0.0 128.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -29 1 1 0 49 2 24 1 1 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 39 2 0 12 10 0.0 0.0 129.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -30 1 1 0 50 1 25 1 1 7 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 40 2 0 13 10 0.0 0.0 130.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -31 1 1 0 51 2 26 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 41 2 0 14 10 0.0 0.0 131.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -32 1 1 0 52 1 27 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 42 2 0 15 10 0.0 0.0 132.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -33 1 1 0 53 2 28 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 43 2 0 16 10 0.0 0.0 133.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -34 1 1 0 54 1 29 1 1 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 44 2 0 17 10 0.0 0.0 134.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -35 1 1 0 55 2 30 1 1 7 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 45 2 0 18 10 0.0 0.0 135.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 1 2.0 3.0 5.0 -36 1 1 0 56 1 31 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 46 2 0 1 10 0.0 0.0 136.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -37 1 1 0 57 2 32 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 47 2 0 2 10 0.0 0.0 137.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -38 1 1 0 58 1 33 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 48 2 0 3 10 0.0 0.0 138.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -39 1 1 0 59 2 34 1 1 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 49 2 0 4 10 0.0 0.0 139.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -40 1 1 0 60 1 35 1 1 7 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 50 2 0 5 10 0.0 0.0 140.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -41 1 1 0 61 2 36 1 1 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 51 2 0 6 10 0.0 0.0 141.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -42 1 1 0 62 1 37 1 1 1 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 52 2 0 7 10 0.0 0.0 142.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -43 1 1 0 63 2 38 1 1 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 53 2 0 8 10 0.0 0.0 143.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -44 1 1 0 64 1 39 1 1 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 54 2 0 9 10 0.0 0.0 144.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -45 1 1 0 65 2 40 1 1 7 1 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 55 2 0 10 10 0.0 0.0 145.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -46 1 1 0 66 1 41 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 56 2 0 11 10 0.0 0.0 146.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -47 1 1 0 67 2 42 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 57 2 0 12 10 0.0 0.0 147.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -48 1 1 0 68 1 43 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 58 2 0 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1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 61 2 0 6 10 0.0 0.0 231.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -132 1 1 0 42 1 27 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 62 2 0 7 10 0.0 0.0 232.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -133 1 1 0 43 2 28 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 63 2 0 8 10 0.0 0.0 233.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -134 1 1 0 44 1 29 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 64 2 0 9 10 0.0 0.0 234.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -135 1 1 0 45 2 30 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 65 2 0 10 10 0.0 0.0 235.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL EMPSTATI SAMESIT SRENTAMT CONVBL SDEMP01 SDEMP02 SDEMP03 SDEMP04 SDEMP05 SDEMP06 SDEMP07 SDEMP08 SDEMP09 SDEMP10 SDEMP11 SDEMP12 MJOBSECT SIC FTED TYPEED2 EDUCQUAL TRAINEE EMAAMT CHEMAAMT INEARNS SEINCAM2 MNTUS1 MNTUSAM1 MNTAMT1 MNTAMT2 CVPAY ROYYR1 ROYYR2 ROYYR3 ROYYR4 ALLPAY2 ALLPAY3 ALLPAY4 CHAMTERN CHAMTTST APAMT APDAMT PAREAMT REDAMT SLREPAMT SSPADJ SMPADJ SPPADJ TUBORR ACCSSAMT ACCSSPD SPCREG1 SPCREG2 GRTDIR1 GRTDIR2 HEARTVAL +1 1 1 0 80 2 21 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 11 2 0 2 2 0.0 0.0 101.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +2 1 1 0 22 1 22 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 12 2 0 3 2 0.0 0.0 102.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +3 1 1 0 23 2 23 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 13 2 0 4 2 0.0 0.0 103.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +4 1 1 0 24 1 24 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2 0 5 2 0.0 0.0 104.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +5 1 1 0 25 2 25 1 1 7 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 15 2 0 6 2 0.0 0.0 105.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +6 1 1 0 26 1 26 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 16 2 0 7 2 0.0 0.0 106.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +7 1 1 0 27 2 27 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 17 2 0 8 2 0.0 0.0 107.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +8 1 1 0 28 1 28 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 18 2 0 9 2 0.0 0.0 108.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +9 1 1 0 29 2 29 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 19 2 0 10 2 0.0 0.0 109.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +10 1 1 0 30 1 30 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 20 2 0 11 2 0.0 0.0 110.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +11 1 1 0 31 2 31 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 21 2 0 12 2 0.0 0.0 111.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +12 1 1 0 32 1 32 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 22 2 0 13 2 0.0 0.0 112.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 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1 1 1 1 1 1 1 1 1 62 2 0 7 2 0.0 0.0 232.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +133 1 1 0 43 2 28 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 63 2 0 8 2 0.0 0.0 233.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +134 1 1 0 44 1 29 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 64 2 0 9 2 0.0 0.0 234.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +135 1 1 0 45 2 30 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 65 2 0 10 2 0.0 0.0 235.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab index 7576f7b244..e8b063b54b 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab @@ -1,28 +1,28 @@ -SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL FTED TYPEED2 EDUCQUAL TRAIN EMAAMT CHEMAAMT FSMVAL FSFVVAL FSBVAL HEARTVAL SPCREG1 -5 1 2 0 13 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -10 1 2 0 10 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -15 1 2 0 15 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -20 1 2 0 12 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -25 1 2 0 9 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -30 1 2 0 14 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -35 1 2 0 11 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -40 1 2 0 8 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -45 1 2 0 13 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 1 -50 1 2 0 10 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -55 1 2 0 15 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -60 1 2 0 12 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -65 1 2 0 9 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -70 1 2 0 14 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -75 1 2 0 11 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -80 1 2 0 8 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -85 1 2 0 13 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -90 1 2 0 10 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 1 -95 1 2 0 15 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -100 1 2 0 12 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -105 1 2 0 9 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -110 1 2 0 14 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -115 1 2 0 11 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -120 1 2 0 8 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -125 1 2 0 13 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -130 1 2 0 10 1 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 2 -135 1 2 0 15 2 0 0 3 3 2 1 2 86 9 0.0 1.0 3.0 1.0 2.0 4.0 1 +SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL FTED TYPEED2 EDUCQUAL TRAINEE EMAAMT CHEMAAMT FSMVAL FSFVVAL FSBVAL HEARTVAL SPCREG1 +5 1 2 0 13 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +10 1 2 0 10 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +15 1 2 0 15 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +20 1 2 0 12 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +25 1 2 0 9 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +30 1 2 0 14 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +35 1 2 0 11 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +40 1 2 0 8 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +45 1 2 0 13 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 1 +50 1 2 0 10 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +55 1 2 0 15 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +60 1 2 0 12 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +65 1 2 0 9 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +70 1 2 0 14 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +75 1 2 0 11 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +80 1 2 0 8 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +85 1 2 0 13 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +90 1 2 0 10 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 1 +95 1 2 0 15 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +100 1 2 0 12 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +105 1 2 0 9 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +110 1 2 0 14 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +115 1 2 0 11 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +120 1 2 0 8 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +125 1 2 0 13 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +130 1 2 0 10 1 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 2 +135 1 2 0 15 2 0 0 3 3 2 1 2 86 1 0.0 1.0 3.0 1.0 2.0 4.0 1 diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv index 69bd87ae8a..c4160e56b4 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ -person_id,person_benunit_id,person_household_id,age,gender,marital_status,hours_worked,care_hours,is_household_head,is_benunit_head,is_parent,is_blind,employment_income,self_employment_income,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,property_income,reports_rent_from_other_property,maintenance_income,miscellaneous_income,private_transfer_income,lump_sum_income,student_loan_repayments,statutory_sick_pay,statutory_maternity_pay,student_loans,access_fund,education_grants,healthy_start_vouchers,free_school_breakfasts,free_school_fruit_veg,free_school_meals,child_benefit_reported,income_support_reported,housing_benefit_reported,attendance_allowance_reported,dla_sc_reported,dla_m_reported,iidb_reported,carers_allowance_reported,sda_reported,afcs_reported,ssmg_reported,pension_credit_reported,child_tax_credit_reported,working_tax_credit_reported,state_pension_reported,winter_fuel_allowance_reported,incapacity_benefit_reported,universal_credit_reported,pip_m_reported,pip_dl_reported,jsa_contrib_reported,jsa_income_reported,esa_contrib_reported,esa_income_reported,bsp_reported,would_claim_carers_allowance,council_tax_benefit_reported,maintenance_expenses,childcare_expenses,personal_pension_contributions,employee_pension_contributions,pension_contributions_via_salary_sacrifice,salary_sacrifice_reported,salary_sacrifice_asked,is_uc_claimant,rent_paid_as_boarder,rent_paid_as_lodger,is_claimant_or_partner,is_hbai_dependent_child,uc_is_in_startup_period,uc_is_in_gainful_self_employment,__stratum__ -1001,101,1,80,FEMALE,SINGLE,1.0_9575000000000000e+03,0.0_0000000000000000e+00,True,True,False,False,5.2_7003571428571468e+03,5.2_1785714285714306e+01,7.8_2678571428571445e+02,5.2_1785714285714306e+01,1.8_2625000000000000e+02,0.0_0000000000000000e+00,1.0_4357142857142861e+02,True,1.0_4357142857142861e+02,2.1_3932142857142844e+03,2.9_7417857142857156e+03,2.0_0000000000000000e+01,1.0_4357142857142861e+02,5.2_1785714285714306e+01,2.6_0892857142857153e+01,5.0_0000000000000000e+02,5.2_1785714285714306e+01,5.0_0000000000000000e+00,2.6_0892857142857167e+02,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,3.6_5250000000000000e+02,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,5.2_1785714285714306e+01,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,1.0_4357142857142861e+02,1.5_6535714285714278e+02,2.0_8714285714285722e+02,2.6_0892857142857167e+02,3.1_3071428571428555e+02,False,5.2_1785714285714306e+01,1.0_4357142857142861e+02,2.6_0892857142857167e+02,2.0_8714285714285722e+02,1.0_4357142857142861e+02,1.5_6535714285714278e+02,1.0_0000000000000000e+00,1.0_0000000000000000e+00,True,5.2_1785714285714306e+01,0.0_0000000000000000e+00,True,False,False,False,default -2001,201,2,22,MALE,SINGLE,1.1_4792857142857156e+03,0.0_0000000000000000e+00,True,True,False,False,5.3_2221428571428623e+03,1.0_4357142857142861e+02,7.8_2678571428571445e+02,5.2_1785714285714306e+01,1.8_2625000000000000e+02,0.0_0000000000000000e+00,1.0_4357142857142861e+02,True,1.0_4357142857142861e+02,2.1_3932142857142844e+03,2.9_7417857142857156e+03,2.0_0000000000000000e+01,1.0_4357142857142861e+02,5.2_1785714285714306e+01,2.6_0892857142857153e+01,5.0_0000000000000000e+02,5.2_1785714285714306e+01,5.0_0000000000000000e+00,2.6_0892857142857167e+02,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,3.6_5250000000000000e+02,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,5.2_1785714285714306e+01,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,1.0_4357142857142861e+02,1.5_6535714285714278e+02,2.0_8714285714285722e+02,2.6_0892857142857167e+02,3.1_3071428571428555e+02,False,1.0_4357142857142861e+02,1.0_4357142857142861e+02,2.6_0892857142857167e+02,2.0_8714285714285722e+02,1.0_4357142857142861e+02,1.5_6535714285714278e+02,1.0_0000000000000000e+00,1.0_0000000000000000e+00,True,0.0_0000000000000000e+00,5.2_1785714285714306e+01,True,False,False,True,default 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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json index 28e82f4053..2e20aa268d 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 @@ -72,6 +72,7 @@ "maintenance_income": "float64", "marital_status": "string", "miscellaneous_income": "float64", + "months_since_last_birthday": "float64", "pension_contributions_via_salary_sacrifice": "float64", "pension_credit_reported": "float64", "person_benunit_id": "int64", @@ -94,6 +95,7 @@ "ssmg_reported": "float64", "state_pension_reported": "float64", "statutory_maternity_pay": "float64", + "statutory_paternity_pay": "float64", "statutory_sick_pay": "float64", "student_loan_repayments": "float64", "student_loans": "float64", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json 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and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/uk_full_build_cli.py b/test_support/microcosm_build/uk_full_build_cli.py index 2d13aaf3dc..c47b11c253 100644 --- a/test_support/microcosm_build/uk_full_build_cli.py +++ b/test_support/microcosm_build/uk_full_build_cli.py @@ -928,6 +928,7 @@ def prepared(tmp_path, failed=None, *, release_candidate=False, size_households= graph, population=root.id, bindings={"target_scope": "all", "geography": synthetic_geography_binding()}, + solve_year=2025, artifact_inputs=( ArtifactInput( "gates", diff --git a/tools/assemble_uk_release_dir.py b/tools/assemble_uk_release_dir.py index 240afe8084..750b992c83 100644 --- a/tools/assemble_uk_release_dir.py +++ b/tools/assemble_uk_release_dir.py @@ -26,8 +26,13 @@ validate_staging_delivery, ) from microcosm.build.uk_runtime.local_targets import load_uk_population_contract -from microcosm.build.uk_runtime.national_frame import load_uk_national_frame +from microcosm.build.uk_runtime.national_frame import ( + load_uk_national_frame, + uk_build_identifiers, + uk_time_period, +) from microcosm.build.uk_runtime.release_identity import UK_NATIONAL_RELEASE_ID +from microcosm.build.uk_runtime.weight_year import uk_weights_are_at_dataset_year from microcosm.data.contract import validate_release_dir _ATTEMPT_PREFIX = "uk-frs-calibration-attempt-" @@ -287,8 +292,12 @@ def _assemble(args: argparse.Namespace) -> dict[str, object]: # must be identical — same ids, same order — before the pairing is # digested and shipped as authoritative. A digest over mis-paired bytes # authenticates the bytes, not the pairing. - candidate_ids = candidate_frame.table("household")["household_id"].to_numpy() - spine_ids = spine_frame.table("household")["household_id"].to_numpy() + # A release renumbers its identifiers and keeps the build's beside them + # (microcosm#1194); the spine carries the build's own. + candidate_ids = uk_build_identifiers( + candidate_frame.table("household"), "household" + ) + spine_ids = uk_build_identifiers(spine_frame.table("household"), "household") if candidate_ids.shape != spine_ids.shape or not np.array_equal( candidate_ids, spine_ids ): @@ -302,9 +311,20 @@ def _assemble(args: argparse.Namespace) -> dict[str, object]: f"({detail}); the calibration NPZ would pair weights across " "different households" ) + # The NPZ carries the dataset's own household weights, so it pairs with + # the H5 row by row. On a release they stand at the dataset's year + # (microcosm#796 A19); the build manifest says which. household_weight = np.asarray( candidate_frame.weights_for("household").values, dtype=np.float64 ) + household_weight_year = { + "dataset_year": int(uk_time_period(candidate_frame)), + "basis": ( + "dataset_year" + if uk_weights_are_at_dataset_year(candidate_frame) + else "as_solved" + ), + } initial_household_weight = np.asarray( spine_frame.weights_for("household").values, dtype=np.float64 ) @@ -320,6 +340,7 @@ def _assemble(args: argparse.Namespace) -> dict[str, object]: calibration_filename=calibration_filename, household_weight=household_weight, initial_household_weight=initial_household_weight, + household_weight_year=household_weight_year, measured=measured, cut_tag=cut_tag, attempt_id=attempt_id, @@ -348,6 +369,7 @@ def _stage_and_finalize( calibration_filename: str, household_weight: np.ndarray, initial_household_weight: np.ndarray, + household_weight_year: Mapping[str, object], measured: Mapping[str, str], cut_tag: str, attempt_id: str, @@ -387,6 +409,10 @@ def _stage_and_finalize( "calibration": { "filename": calibration_filename, "sha256": calibration_sha, + # The year ``household_weight`` stands at: the dataset's own on a + # release, where policyengine-uk's projection then returns the + # solved weights at the calibration year. + "household_weight_year": dict(household_weight_year), "target_surface": { "sha256": target_surface.get("sha256"), "n_targets": target_surface.get("n_targets"), diff --git a/tools/build_uk_ledger_compile_parity_signed_differences.py b/tools/build_uk_ledger_compile_parity_signed_differences.py index 55ae0cb5d5..d1185e7924 100644 --- a/tools/build_uk_ledger_compile_parity_signed_differences.py +++ b/tools/build_uk_ledger_compile_parity_signed_differences.py @@ -284,6 +284,29 @@ class ParityReceiptSpec: ), } +_ONS_15_19_BAND_SPLIT_FIXTURE_ONLY_RATIONALE = ( + "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): the five-year " + "15_19 sex band is split into ages 15, 16-17 and 18-19, because a fit to the " + "band left the ages inside it free to drift. The three rows partition this " + "row's population and read the same ONS mid-year estimate by single year of " + "age, so they sum to it. No single current row has this definition, so the " + "frozen fixture's row is fixture-only on the current surface." +) +_ONS_15_19_BAND_SPLIT_LEDGER_ONLY_RATIONALE = ( + "microcosm#1095 (María's ruling of 2026-10-13; uk-data#563): one of the three " + "rows (ages 15, 16-17 and 18-19) that partition the incumbent five-year 15_19 " + "sex band, bound so the fit holds the age mix inside the band. The incumbent " + "binds only the band, so this row has no fixture counterpart." +) +_ONS_15_19_BAND_ROWS = frozenset( + f"ons.population.{sex}_15_19" for sex in ("female", "male") +) +_ONS_15_19_BAND_SPLIT_ROWS = frozenset( + f"ons.population.{sex}_{ages}" + for sex in ("female", "male") + for ages in ("15", "16_17", "18_19") +) + _DEVOLVED_RENT_FIXTURE_ONLY_RATIONALE = ( "Ruled signed exclusion: the incumbent y-values for devolved private rent " "are hardcoded constants in devolved_housing.py with no source or year, " @@ -705,6 +728,10 @@ def _add_signed_rationale_notes( row["reason"] = _CGT_NEW_ROW_LEDGER_ONLY_RATIONALE elif name in _ONS_TERMINAL_BAND_RATIONALES: row["reason"] = _ONS_TERMINAL_BAND_RATIONALES[name] + elif name in _ONS_15_19_BAND_ROWS and row.get("kind") == "fixture_only": + row["reason"] = _ONS_15_19_BAND_SPLIT_FIXTURE_ONLY_RATIONALE + elif name in _ONS_15_19_BAND_SPLIT_ROWS and row.get("kind") == "ledger_only": + row["reason"] = _ONS_15_19_BAND_SPLIT_LEDGER_ONLY_RATIONALE def _add_local_signed_rationale_notes(report: dict[str, object]) -> None: diff --git a/tools/certify_uk_release_cut.py b/tools/certify_uk_release_cut.py index 0365fec7e3..62a035eee6 100644 --- a/tools/certify_uk_release_cut.py +++ b/tools/certify_uk_release_cut.py @@ -61,6 +61,7 @@ from microcosm.build.uk_runtime.release_input_coverage import ( PolicyEngineUKCoverageEngine, ) +from microcosm.build.uk_runtime.weight_year import uk_solve_year_frame from microcosm.build.uk_runtime.weighted_integrity import ( exclusion_evaluation_date, load_uk_input_mass_reference, @@ -282,6 +283,10 @@ def _run(args: argparse.Namespace, state: AttemptState) -> dict[str, object]: append_phase(state, "registries_compiled") frame, _provenance = load_uk_national_frame(args.candidate_h5) + # A release carries its weights at the dataset's year (microcosm#796 A19). + # The gates stand on the solved weights, which policyengine-uk's own + # projection to the calibration year gives back. + frame = uk_solve_year_frame(frame, solve_year=calibration_year) # The spine frame is the evidence for the stage families' build state # (importance weights, mass receipts); the candidate is calibrated. spine_frame, _spine_provenance = load_uk_national_frame(args.spine_h5) diff --git a/tools/evaluate_uk_incumbent_surface.py b/tools/evaluate_uk_incumbent_surface.py index e8bea8efb5..d93b753430 100644 --- a/tools/evaluate_uk_incumbent_surface.py +++ b/tools/evaluate_uk_incumbent_surface.py @@ -51,6 +51,7 @@ load_uk_calibration_measure_exclusions, ) from microcosm.build.uk_runtime.rowwise_dataset import load_uk_rowwise_dataset +from microcosm.build.uk_runtime.weight_year import uk_engine_year_frame from microcosm.calibrate import TargetRegistry from microcosm.calibrate.matrix import build_constraint_matrix from microcosm.data.contract import uk_incumbent_surface_assessment @@ -214,6 +215,10 @@ def digest(path): print("loading the candidate frame ...", file=sys.stderr, flush=True) frame, _ = load_uk_rowwise_dataset(args.candidate_h5) + # The candidate is read as policyengine-uk gives it at the calibration + # year: its stored household weights times the engine's own projection + # from the file's year (microcosm#796 A19). + frame = uk_engine_year_frame(frame, simulation_year=period) weights = np.asarray(frame.resolve_weights("household").values, dtype=np.float64) print("resolving the engine over the frame ...", file=sys.stderr, flush=True) diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index ec30b50219..7976d10538 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -421,7 +421,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "FTED": 2, "TYPEED2": 0, "EDUCQUAL": 1 + household_id % 18, - "TRAIN": 10, + "TRAINEE": 2, "EMAAMT": 0.0, "CHEMAAMT": 0.0, "INEARNS": 100.0 + household_id, @@ -447,6 +447,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "SLREPAMT": 2.0, "SSPADJ": 1.0, "SMPADJ": 0.5, + "SPPADJ": 0.25, "TUBORR": 500.0, "ACCSSAMT": 1.0, "ACCSSPD": 52.0, @@ -478,7 +479,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "FTED": 1, "TYPEED2": 2, "EDUCQUAL": 86, - "TRAIN": 9, + "TRAINEE": 1, "EMAAMT": 0.0, "CHEMAAMT": 1.0, "FSMVAL": 3.0, diff --git a/tools/pin_uk_uprating_engine_values.py b/tools/pin_uk_uprating_engine_values.py index ff7f178167..46ed71c067 100644 --- a/tools/pin_uk_uprating_engine_values.py +++ b/tools/pin_uk_uprating_engine_values.py @@ -10,6 +10,11 @@ engine version they came from. A ``requires_uk`` test holds the file in lockstep with the installed engine, so an engine bump re-runs this tool. +The file also carries the growth policyengine-uk applies to ``household_weight`` +when it projects a dataset to a later year (block ``dataset_projection``): the +release write divides the solved weights by it (``uk_runtime.weight_year``, +microcosm#796 A19). + Usage (with the uk extra installed): uv run --no-sync python tools/pin_uk_uprating_engine_values.py [--check] @@ -33,6 +38,11 @@ installed_engine_version, live_engine_parameter_value, ) +from microcosm.build.uk_runtime.weight_year import ( + UK_WEIGHT_PROJECTION_PARAMETER, + UK_WEIGHT_PROJECTION_PINS_BLOCK, + UK_WEIGHT_PROJECTION_VARIABLE, +) UK_PACKAGE_DIR = ( Path(__file__).resolve().parents[1] @@ -64,6 +74,15 @@ def build_pins() -> dict[str, object]: ) by_instant[instant] = value values[path] = by_instant + growth: dict[str, float] = {} + for instant in instants: + value = live_engine_parameter_value(UK_WEIGHT_PROJECTION_PARAMETER, instant) + if not value > -1: + raise SystemExit( + f"policyengine-uk {version}: {UK_WEIGHT_PROJECTION_PARAMETER!r} is " + f"{value!r} at {instant}; a growth rate must be above -1." + ) + growth[instant] = value return { "schema_version": 1, "engine": {"package": "policyengine-uk", "version": version}, @@ -71,6 +90,12 @@ def build_pins() -> dict[str, object]: "written_by": "tools/pin_uk_uprating_engine_values.py", "instants": instants, "values": values, + UK_WEIGHT_PROJECTION_PINS_BLOCK: { + UK_WEIGHT_PROJECTION_VARIABLE: { + "parameter": UK_WEIGHT_PROJECTION_PARAMETER, + "growth": growth, + } + }, } diff --git a/tools/verify_uk_identity_stability.py b/tools/verify_uk_identity_stability.py index d002afc049..182fca7047 100644 --- a/tools/verify_uk_identity_stability.py +++ b/tools/verify_uk_identity_stability.py @@ -36,6 +36,7 @@ ) from microcosm.build.uk_runtime.national_frame import ( load_uk_national_frame, + uk_build_identifier_frame, uk_time_period, ) from microcosm.build.uk_runtime.regional_uprating import ( @@ -1909,6 +1910,9 @@ def main() -> int: args = parser.parse_args() frame, _provenance = load_uk_national_frame(args.input_h5) + # Every draw these receipts replay is keyed on the build's identifiers; a + # release carries them beside its renumbered ones (microcosm#1194). + frame = uk_build_identifier_frame(frame) if args.check == "e4": from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine @@ -2474,19 +2478,23 @@ def _pre_salary_sacrifice_frame(frame): """The artifact with the salary-sacrifice conversion reversed. ``salary_sacrifice`` moves a converted record's employee pension - contribution whole into ``pension_contributions_via_salary_sacrifice``, - zeroes the source and lowers ``employment_income`` by the amount moved, + contribution, up to half its pay, into + ``pension_contributions_via_salary_sacrifice``, leaves the rest in the + source and lowers ``employment_income`` by the amount moved, after the capital-gains stages have banded every carrier on that pay (microcosm#1063: on the 2 October spine the E8 recomputes banded 2,797 clone carriers on post-sacrifice pay and derived different anchor targets and a different support family). The stage keeps a converted record's pre-conversion pay on ``SALSAC_PRE_CONVERSION_PAY_COLUMN`` (zero for - everyone else), so the rewrite reverses exactly: pay back to the carrier - and the employee contribution back from the salary-sacrifice column, - which the conversion set to zero on the record it converted. The stage's - QRF predictions for the records it did not convert stay as stored; no - receipted layer before the stage reads them. An artifact without the - carrier is returned as it is. + everyone else), so the rewrite reverses: pay back to the carrier, exactly, + and the amount moved back from the salary-sacrifice column onto the + employee contribution the record kept. That sum is the contribution + before the conversion, to the last bit unless the contribution was above + the pay it came from; no receipted layer before the stage reads it. The + stage's QRF predictions and its limit of every amount to pay stay as + stored for the records it did not convert; no receipted layer before the + stage reads them either. An artifact without the carrier is returned as + it is. """ from microcosm.build.uk_runtime.national_frame import ( @@ -2521,14 +2529,24 @@ def _pre_salary_sacrifice_frame(frame): sacrifice = pd.to_numeric(person[SALSAC_OUTPUT], errors="raise").to_numpy( dtype=float, copy=True ) - if (employee[converted] != 0.0).any() or (sacrifice[converted] <= 0.0).any(): + if ( + (employee[converted] < 0.0).any() + or (sacrifice[converted] <= 0.0).any() + or not np.allclose( + pay[converted] + sacrifice[converted], + carrier[converted], + rtol=1e-12, + atol=0.0, + ) + ): raise ValueError( - "A converted salary-sacrifice record must carry a zero employee " - "contribution and a positive salary sacrifice; the artifact's " - f"{SALSAC_PRE_CONVERSION_PAY_COLUMN} disagrees with its columns." + "A converted salary-sacrifice record must carry a positive salary " + "sacrifice that, added to its pay, gives its pay before the " + f"conversion; the artifact's {SALSAC_PRE_CONVERSION_PAY_COLUMN} " + "disagrees with its columns." ) pay[converted] = carrier[converted] - employee[converted] = sacrifice[converted] + employee[converted] = employee[converted] + sacrifice[converted] sacrifice[converted] = 0.0 person["employment_income"] = pay person["employee_pension_contributions"] = employee diff --git a/uv.lock b/uv.lock index 21ea6ba0aa..b3bc4b0c9c 100644 --- a/uv.lock +++ b/uv.lock @@ -857,7 +857,7 @@ requires-dist = [ { name = "openpyxl", marker = "extra == 'uk'", specifier = ">=3.1" }, { name = "pandas", specifier = ">=2" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.123.0" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.130.1" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, { name = "pyarrow", specifier = ">=15" }, { name = "pyyaml", specifier = ">=6" }, @@ -936,7 +936,7 @@ requires-dist = [ { name = "microcosm-diagnostics", editable = "packages/microcosm-diagnostics" }, { name = "packaging", specifier = ">=24" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - 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