diff --git a/changelog.d/1095-uk-bind-thin-target-rows.changed.md b/changelog.d/1095-uk-bind-thin-target-rows.changed.md new file mode 100644 index 000000000..60e528434 --- /dev/null +++ b/changelog.d/1095-uk-bind-thin-target-rows.changed.md @@ -0,0 +1 @@ +A UK target row is no longer left out of the calibration because few source households carry it (María's rule of 2026-10-21): the support floor now drops only rows that no household carries, the benefit-cap amount bands join its scope, and the per-row exclusions that rested on thin support alone retire (the benefit-cap amount bands and the HMRC £500k to £1m dividend and property-income-count rows). A bound national, regional or BRMA row that fewer than ten source households carry keeps its model figures in the build, as BRMA rows' do (constituency and local-authority rows keep the area-support rule): the diagnostics, the national gate evidence, the certifier, the national role and the Orrery summary withhold them, the national fit gate decides on the published rows and counts the withheld ones, and no run forwards an epoch's loss. A published row that, with the other published rows, would give back a combination of withheld rows resting on fewer than ten source households is withheld beside them (the next-smallest band, not the total), so no difference of published figures isolates fewer than ten cases. The A/B that retired the thirteen entries is in `experiments/1095-uk-thin-row-binding-receipts.md`. diff --git a/changelog.d/1331-uk-bus-fare-receipts-engine-window.changed.md b/changelog.d/1331-uk-bus-fare-receipts-engine-window.changed.md new file mode 100644 index 000000000..fe5226375 --- /dev/null +++ b/changelog.d/1331-uk-bus-fare-receipts-engine-window.changed.md @@ -0,0 +1 @@ +The 2025 England and London bus fare receipt rows now move from 2024-25 on policyengine-uk's own window (María's ruling of 2026-10-21, replacing the calendar-year basis of 2026-09-10): each area's BUS0415a quarter-end index is day-weighted over 6 April to 5 April, as the engine reads `gov.dft.bus.local_bus_fares_index` (PolicyEngine/policyengine-uk#2546), and England's receipts move as London's plus England outside London's. The bus parity receipt ties each area's factor to the engine's index ratio, and the engine test holds the tie within 0.1% at the locked engine (microcosm#1331). diff --git a/changelog.d/1331-uk-bus-single-fare-share-cap-years.changed.md b/changelog.d/1331-uk-bus-single-fare-share-cap-years.changed.md new file mode 100644 index 000000000..b5a19947f --- /dev/null +++ b/changelog.d/1331-uk-bus-single-fare-share-cap-years.changed.md @@ -0,0 +1 @@ +The NTS bus stage reads the single-ticket share from the years under England's £2 fare cap (2023 and 2024), as nine months of the FRS 2024-25 base year are (its last three sit under the £3 cap, which the extract does not observe), and its ticket receipt adds fare-mix lines for England outside London: the share of point-of-use boardings above £2 before the cap and their mean fare, and the share paid at £2 under it, each withheld below 10 boardings or 10 households. The receipt now weights boardings by the full trip weight, as the stage's share does (microcosm#1331). diff --git a/changelog.d/uk-policyengine-uk-2-145.changed.md b/changelog.d/uk-policyengine-uk-2-145.changed.md new file mode 100644 index 000000000..ecf562369 --- /dev/null +++ b/changelog.d/uk-policyengine-uk-2-145.changed.md @@ -0,0 +1 @@ +The UK lock moves from policyengine-uk 2.134.4 to 2.145.0, the release that carries pe-uk#2536 and pe-uk#2546 (policyengine-core stays at 3.33.3): the uprating pins' stamp, the concept coverage (302 to 311 inputs), the coverage manifest, the H2 fixture, the cited versions and `APPROVED_UV_LOCK_SHA256` re-derive, and the concept mapping's capital reason follows 2.136.1's complete-override rule. The build leaves `defined_benefit_pension_share`, the pre-2015 salary-sacrifice input, the PIP points inputs and `local_bus_uncapped_single_fare` at their defaults, and the stage texts or the PR say why. diff --git a/changelog.d/uk-uc-capital-sentinel-receipt.changed.md b/changelog.d/uk-uc-capital-sentinel-receipt.changed.md new file mode 100644 index 000000000..47aa4b626 --- /dev/null +++ b/changelog.d/uk-uc-capital-sentinel-receipt.changed.md @@ -0,0 +1 @@ +The UC capital stage follows policyengine-uk 2.136.1, which values property apart and shares it across a household's claimants and partners: its texts and engine tests say so. A benefit unit left on the unavailable sentinel beside a unit that records the household's property, for UC or Pension Credit, is the one case the engine counts twice: the stage's receipt counts such units, and the UC capital gate now refuses any. The stage's texts also note that both recorded figures project to later years by GDP per head, while from 2.139.1 the engine projects other residential property by house prices. diff --git a/changelog.d/uk-war-widows-pension-split.fixed.md b/changelog.d/uk-war-widows-pension-split.fixed.md new file mode 100644 index 000000000..eac661904 --- /dev/null +++ b/changelog.d/uk-war-widows-pension-split.fixed.md @@ -0,0 +1 @@ +The FRS spine writes War Widow's or Widower's Pension (FRS benefit code 9) to policyengine-uk's own `war_widows_pension_reported` input, added in 2.135.7 (pe-uk#2382), instead of adding it to `bsp_reported` (#1222): the pension rises with CPI, while Bereavement Support Payment's rates are frozen. An SPI-channel row keeps its FRS twin's value of the new column, as it does for `bsp_reported`, and the column follows `bsp_reported` through the graph, the SPI stages, the benefit coherence gate and the H2 fixture. diff --git a/docs/concept-coverage/policyengine-uk.md b/docs/concept-coverage/policyengine-uk.md index fb1020abe..df7bda628 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 `2.134.4`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. +Engine version `2.145.0`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. -- Engine inputs: 302 +- Engine inputs: 311 - Fed by a concept: 42 - Structural: 11 -- Uncovered: 249 +- Uncovered: 258 - Mapped but missing from the engine: 0 ## Inputs each concept feeds @@ -44,7 +44,7 @@ Engine version `2.134.4`; 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. 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.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; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either 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. | @@ -54,10 +54,10 @@ Engine version `2.134.4`; concept schema `5a55669d0447`. Generated by `tools/ref `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` -### household (81) +### household (83) -`accommodation_type`, `additional_residential_property_purchased`, `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_purchased`, `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` +`accommodation_type`, `additional_residential_property_purchased`, `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_purchased`, `non_residential_property_secured_debt`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_secured_debt`, `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 (156) +### person (163) -`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`, `loss_relief`, `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`, `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` +`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`, `defined_benefit_pension_share`, `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_full_year_category_a_director`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `local_bus_uncapped_single_fare`, `loss_relief`, `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_annual_allowance_taxable_lump_sum_death_benefits`, `pension_contributions_via_salary_sacrifice`, `pension_contributions_via_salary_sacrifice_pre_2015`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_points`, `pip_m_points`, `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`, `salary_sacrifice_returned_to_income`, `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`, `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`, `war_widows_pension_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` diff --git a/docs/evidence/uk-thin-rows-1095/scripts/ab_binding.py b/docs/evidence/uk-thin-rows-1095/scripts/ab_binding.py new file mode 100644 index 000000000..505e90fa2 --- /dev/null +++ b/docs/evidence/uk-thin-rows-1095/scripts/ab_binding.py @@ -0,0 +1,168 @@ +"""A/B of the thin-row binding on one spine (microcosm#1095 U3, #1333). + +Usage: ab_binding.py + +Both runs calibrate the same spine; the treatment binds the rows that María's rule of +2026-10-21 brought back (thin support alone leaves no row out). Every figure is recomputed +from each run's own target-support sidecars (the compiled matrix, the targets, the design and +final weights), so a row whose published figures are withheld is still measured here. + +Prints, for each run: the share of rows within 10%, the mean absolute relative error and the +weight anatomy (Kish ESS, the share of design mass whose weight was stretched 9.5 times or +more, and the max over median of the positive weights), all over the rows bound in BOTH runs; +the shared rows that cross the 10% line either way; and, per family of newly bound rows, the +fit of each row in both runs (the control's weights read through the treatment's matrix row), +its carriers' support band and how far the binding stretched their weights. + +Its output describes single rows and small groups of licensed records, so it stays with the +licensed spine, outside the repository; experiments/1095-uk-thin-row-binding-receipts.md +gives what may be published. +""" + +from __future__ import annotations + +import sys +from collections import defaultdict +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy import sparse + +STRETCH = 9.5 + + +def band(n: int) -> str: + return "<10" if n < 10 else ("10-29" if n < 30 else "30+") + + +def family(name: str) -> str: + if name.startswith("dwp.benefit_cap."): + return "benefit-cap amount bands" + if "uc_payment_dist" in name or "payment_distribution" in name: + return "UC payment bands" + if name.startswith("hmrc/"): + return "HMRC 500k-1m cells" + return "other" + + +def load(run: Path) -> dict: + matrix = sparse.csr_matrix(sparse.load_npz(run / "target_support_matrix.npz")) + vectors = np.load(run / "target_support_vectors.npz", allow_pickle=True) + names = [str(n) for n in vectors["names"]] + return { + "matrix": matrix, + "names": names, + "index": {n: i for i, n in enumerate(names)}, + "target": np.asarray(vectors["target_vector"], dtype=float), + "design": np.asarray(vectors["design_weights"], dtype=float), + "final": np.asarray(vectors["final_weights"], dtype=float), + "ids": np.asarray(vectors["household_ids"]) + if "household_ids" in vectors + else None, + } + + +def errors(run: dict, weights: np.ndarray) -> np.ndarray: + estimate = run["matrix"] @ weights + with np.errstate(divide="ignore", invalid="ignore"): + return (estimate - run["target"]) / run["target"] + + +def anatomy(run: dict) -> dict: + final, design = run["final"], run["design"] + positive = final > 0 + ratio = np.divide(final, design, out=np.zeros_like(final), where=design > 0) + return { + "ess": float(final.sum() ** 2 / np.square(final).sum()), + "stretched_mass": float(design[ratio >= STRETCH].sum() / design.sum()), + "max_over_median": float(final[positive].max() / np.median(final[positive])), + } + + +def main() -> None: + control, treatment = load(Path(sys.argv[1])), load(Path(sys.argv[2])) + if control["ids"] is not None and treatment["ids"] is not None: + assert np.array_equal(control["ids"], treatment["ids"]), ( + "the runs order households differently" + ) + with pd.HDFStore(sys.argv[3], mode="r") as store: + household = store.select( + "household", columns=["household_id", "source_household_id"] + ) + source = ( + household.set_index("household_id")["source_household_id"] + .reindex(treatment["ids"]) + .to_numpy() + if treatment["ids"] is not None + else None + ) + shared = sorted(set(control["names"]) & set(treatment["names"])) + added = sorted(set(treatment["names"]) - set(control["names"])) + dropped = sorted(set(control["names"]) - set(treatment["names"])) + for label, run in (("control", control), ("treatment", treatment)): + rel = errors(run, run["final"]) + picked = np.asarray([rel[run["index"][n]] for n in shared]) + a = anatomy(run) + print( + f"== {label}: {len(run['names'])} rows; over the {len(shared)} shared rows " + f"{np.mean(np.abs(picked) <= 0.10):.4f} within 10%, mean |error| {np.nanmean(np.abs(picked)):.4f}; " + f"ESS {a['ess']:.0f}; design mass stretched {STRETCH}x+ {a['stretched_mass']:.4f}; " + f"LOCAL max/median {a['max_over_median']:.1f}" + ) + print( + f"rows bound in the treatment only: {len(added)}; in the control only: {dropped}" + ) + rel_c = errors(control, control["final"]) + rel_t = errors(treatment, treatment["final"]) + out_of, into = [], [] + for name in shared: + c, t = rel_c[control["index"][name]], rel_t[treatment["index"][name]] + if abs(c) <= 0.10 < abs(t): + out_of.append(name) + if abs(t) <= 0.10 < abs(c): + into.append(name) + print(f"shared rows leaving 10% under the binding: {len(out_of)} {out_of}") + print(f"shared rows entering 10% under the binding: {len(into)} {into}") + # Newly bound rows: the control's weights read through the treatment's matrix row. + by_family: dict[str, list[str]] = defaultdict(list) + for name in added: + by_family[family(name)].append(name) + ratio_c = np.divide( + control["final"], + control["design"], + out=np.zeros_like(control["final"]), + where=control["design"] > 0, + ) + ratio_t = np.divide( + treatment["final"], + treatment["design"], + out=np.zeros_like(treatment["final"]), + where=treatment["design"] > 0, + ) + for fam, names in sorted(by_family.items()): + print(f"-- {fam}: {len(names)} rows (LOCAL ONLY below)") + carriers_all = np.zeros(len(treatment["final"]), dtype=bool) + for name in names: + row = treatment["matrix"][treatment["index"][name]] + cols = row.indices[row.data != 0] + carriers_all[cols] = True + target = treatment["target"][treatment["index"][name]] + est_c = float(np.ravel(row @ control["final"])[0]) + est_t = float(np.ravel(row @ treatment["final"])[0]) + support = band(len(pd.unique(source[cols]))) if source is not None else "?" + print( + f" {name}: support {support}; error unbound {est_c / target - 1:+.3f} -> bound {est_t / target - 1:+.3f}; " + f"carriers stretched {STRETCH}x+ {np.mean(ratio_c[cols] >= STRETCH) if len(cols) else 0:.2f} -> {np.mean(ratio_t[cols] >= STRETCH) if len(cols) else 0:.2f}" + ) + design = treatment["design"] + mass = design[carriers_all].sum() + print( + f" family carriers' design mass stretched {STRETCH}x+: control " + f"{design[carriers_all & (ratio_c >= STRETCH)].sum() / mass if mass else 0:.3f}, treatment " + f"{design[carriers_all & (ratio_t >= STRETCH)].sum() / mass if mass else 0:.3f}" + ) + + +if __name__ == "__main__": + main() diff --git a/experiments/1095-uk-thin-row-binding-receipts.md b/experiments/1095-uk-thin-row-binding-receipts.md new file mode 100644 index 000000000..55c70535a --- /dev/null +++ b/experiments/1095-uk-thin-row-binding-receipts.md @@ -0,0 +1,46 @@ +# microcosm#1095 U3: binding thin target rows, receipts for #1333 + +This receipt gives no row's model figure, and no count or weighted figure that rests on fewer than ten source households. Figures from the SPI channel stay out of it. Publisher figures appear as published. + +## The question + +María's rule of 2026-10-21: a target row is not left out because few source households carry it. Only a measured distortion of the calibration keeps it out. Thirteen per-row entries in `uk/calibration_measure_exclusions.json` rested on thin support, and #1333 retires them: + +- the eleven benefit-cap amount bands, held out after #882: on its arms the bands sat far outside their bound, and the solve moved weight onto cloned households; +- the HMRC £500k to £1m dividend and property-income-count cells, held out after #807: fitting them distorted neighbouring cells. + +The support floor now drops only the rows that no source household carries, which no weights can fit. For each retired entry the question is whether binding it distorts the calibration now. + +## Method + +- One spine, built on #1333's head before its second review (policyengine-uk 2.145.0's release commit, whose package files the PyPI wheel matches), was calibrated twice with `UK_REVIEW_DATE=2026-10-21`: + - as pushed (the treatment); + - with the thin-row commit reverted (the control), so the two runs differ only in the binding. +- Both runs pass all seven terminal gates. +- `docs/evidence/uk-thin-rows-1095/scripts/ab_binding.py` recomputes fit and weight anatomy from each run's own target-support sidecars (`target_support_matrix.npz`, `target_support_vectors.npz`). So it measures a withheld row too. Its output stays with the licensed spine, outside the repository. +- A newly bound row is read through the treatment's compiled row, at each run's weights. + +## Results + +- **Rows.** The control binds 1,216 rows and the treatment 1,244. The 28 new rows are 16 UC payment-distribution bands, 10 benefit-cap amount bands and the two HMRC cells. Of these, 22 rest on fewer than ten source households, so their figures are withheld. The floor drops the bands that no household carries. +- **Shared rows.** Over the 1,216 rows bound in both runs, 99.0% are within 10% of their target in the control and 98.9% in the treatment. The mean absolute relative error is 0.6% in both. One shared row moves just past 10%. It is SPI-derived, so this receipt does not name it. No shared row moves the other way. +- **Weights.** The effective sample size is about 6,900 in both runs. The share of design weight stretched 9.5 times or more is 0.8% in both. The weight-ratio gate passes in both. +- **New rows.** Each newly bound row whose figures are published reaches its target. In the two DWP families, binding does not raise the share of the carriers' design weight that is stretched 9.5 times or more. The HMRC cells' carriers are SPI-derived, so their figures stay out of this receipt. + +No retired entry goes back. + +## Rows given back by difference (Vahid's second review, item 10) + +A withheld row's figure can come back as a family total less its published bands, or through any other linear difference of published figures. The build now withholds a published row beside the thin ones while such a difference isolates fewer than ten source households (`uk_withheld_row_sets` in `uk_runtime/diagnostics.py`). + +On the treatment run's compiled rows and weights: + +- before the fix, the published rows gave back seven of the 38 withheld rows exactly, each on its own; +- with the fix, the build withholds eight more published rows. Each is a row that one of those differences needs, chosen with the fewest carriers. Most are the next band beside a thin one; +- an independent check then finds no combination of withheld rows that the published rows give back and that rests on fewer than ten source households. The combinations that are still given back, such as the withheld bands of a family together, each rest on ten or more. + +This receipt names none of these rows. Naming a row withheld beside another would show that its neighbour is thin. + +## To repeat on a later spine + +Calibrate the spine twice, with and without the thin rows bound, then run the script on the two runs and the spine. Compare the shared rows' share within 10%, the effective sample size and the stretched share with the figures above. diff --git a/experiments/930-nts-bus-journeys-receipts.md b/experiments/930-nts-bus-journeys-receipts.md index 0f28a807c..04c85c31a 100644 --- a/experiments/930-nts-bus-journeys-receipts.md +++ b/experiments/930-nts-bus-journeys-receipts.md @@ -570,3 +570,74 @@ and #979's own CGT files): 1,118 tests, 0 failures. The incidence anchor runs after `hmrc_cgt_asset_type_spine`, downstream of the three bus stages, so every stage receipt in Parts E–K stands as written; only the terminal weights and a calibration on the rebased tree would move, and neither is re-run here. + +## Part M — the single-ticket share from the cap years, and fare-mix lines (microcosm#1331, 2026-10-21) + +microcosm#1331 items 3 and 4, folded into #1333 ahead of policyengine-uk's index-based fares +(PolicyEngine/policyengine-uk#2546). + +**The share from the cap years (item 3).** The `clean_nts_travel_tables` operation declares +`fare_cap_regime`: England's £2 single-fare cap outside London (`cap_fare` 2.0) covers the whole of +the 2023 and 2024 survey years (`cap_years`; an NTS survey year is a calendar year, and the cap ran +from 1 January 2023 to 31 December 2024) and none of 2022 (`pre_cap_years`), with a penny's +tolerance. The stage reads the single-ticket share on the cap years only, because nine months of +the FRS 2024-25 base year (April to December 2024) sit under the same cap; January to March 2025, +and the whole of the model year 2025-26, sit under the £3 cap. The band and trip fit keep all three +years. An engine-free run +on the pinned tabs reads, for the other-local-bus series outside London, a share of fare-paying +boardings paid at the point of use of 0.70 pooled over 2022–24, 0.57 in 2022 alone and 0.73 on +2023–24. The bus-in-London share in London stays at 0.90. One band cell has no fare-paying +boardings in the cap years and takes its group's share (receipted). Once the engine prices fares +from the index, the share scales the reform's cap term only. The share under the £3 cap (from +January 2025) or with no cap is not observed: the extract ends in 2024. + +**The receipt's weight.** The ticket receipt weighted each boarding by its trip factor with the +household weight W2 divided out, the form the person-level sums need before the W2-weighted shares +multiply it back. So every diary household counted alike in the receipt's shares and fares. The +receipt now multiplies W2 back, which gives the full trip weight (W5 x JJXSC) that the stage's +share uses; no stage output read the receipt. Rest of England, single share of fare-paying +boardings: 0.58 in 2022, 0.73 in 2023 and 0.74 in 2024 (Part J, on the old weight: 0.60, 0.74, +0.74); fare paid per boarding £1.91, £1.78 and £1.83 (Part J: £1.97, £1.76, £1.82). London: 0.92, +0.89 and 0.90; £1.46, £1.64 and £1.63. Parts I and J read the old weight. + +**Fare-mix lines (item 4, report only).** For residents of England outside London, over +point-of-use boardings (singles, returns and day tickets bought for the journey), on the full trip +weight: + +- 2022, before the cap: 0.35 of boardings paid more than £2, at a mean fare of £2.83 (2022 prices). +- 2023 and 2024, under the cap: 0.46 and 0.53 paid £2, within a penny. + +A line reads `<10` unless it and its complement each rest on at least 10 unweighted boardings and +10 distinct households (one respondent makes many boardings); none is withheld here. Shares are +rounded to two decimals and fares to the penny, and no minimum, maximum or percentile is reported. +For comparison, DfT's evaluation of the cap (Frontier Economics and SYSTRA, Table 6) reports +single tickets above £2 averaging £2.73 before the cap and under half of pre-cap trips. The fitted +table of PolicyEngine/policyengine-uk#2546 puts 0.45 of single tickets above £2 at a mean of £2.76, +at FY2024-25 prices. The NTS lines agree in shape. They cover returns and day tickets as well as +singles and are at 2022 prices, so uprate them by the BUS0415a index before a closer comparison. +The frame's share moves on #1333's next arm. + +## Part N — the fare receipts on the engine's window (microcosm#1331 items 1 and 2, 2026-10-21) + +policyengine-uk 2.145.0 (PolicyEngine/policyengine-uk#2546) moves each area's benchmark yield by +its BUS0415a index, blended over the engine's model year: 6 April to 5 April, each quarter-end +value in force from the start of the quarter it closes. María's ruling of 2026-10-21 (option (b) +of #1331) aligns the 2025 England and London fare-receipt rows the same way, London and England +outside London apart, in place of the calendar-year basis of 2026-09-10. On DfT's June 2026 +release: + +- England outside London: 2024-25 blend 190.2, 2025-26 blend 214.2, factor 1.1259 (1.112 on the + calendar basis). +- London: factor 1.0000086. Fares were held; DfT's unrounded March 2026 value lifts the blend. +- England: London's £1,347.4m and England outside London's £2,070.0m of 2024-25 receipts move + by their own factors, £3,678.0m in all, a factor of 1.0763 (1.0570 on the calendar basis). + +At policyengine-uk 2.145.0 the engine's index ratio for each area equals these factors to +machine precision. The bus parity receipt records the tie, and the engine test holds it within +0.1%. The membership and the national compile-parity receipts re-derive from the pinned feed, +and only the two fare rows move. + +Both sides pin DfT's June 2026 release. DfT's address now serves the September release, which +revises the December 2025 and March 2026 values, so the 2025 factors will move a little when the +engine's index and the vendored anchors are re-pinned together (Vahid's second review of #1333, +item 14). diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index 9a19d4901..349abaae0 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>=2.134.4", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] +uk = ["policyengine-uk>=2.145.0", "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 5271d6972..c072cfb3f 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 @@ -10386,6 +10386,7 @@ "kind", "lowercase_columns", "survey_years", + "fare_cap_regime", "cars_clip", "age_band_edges", "trip_weight_basis", @@ -10406,6 +10407,36 @@ "type": "integer" } }, + "fare_cap_regime": { + "type": "object", + "additionalProperties": false, + "required": [ + "cap_fare", + "cap_years", + "pre_cap_years", + "cap_tolerance" + ], + "properties": { + "cap_fare": { + "type": "number" + }, + "cap_years": { + "type": "array", + "items": { + "type": "integer" + } + }, + "pre_cap_years": { + "type": "array", + "items": { + "type": "integer" + } + }, + "cap_tolerance": { + "type": "number" + } + } + }, "cars_clip": { "type": "array", "items": { 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 95e3f876d..eaa149a34 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 @@ -5,17 +5,31 @@ "dwp.uc.payment_distribution_single", "dwp.uc.payment_distribution_lone_parent", "dwp.uc.payment_distribution_couple_no_children", - "dwp.uc.payment_distribution_couple_with_children" + "dwp.uc.payment_distribution_couple_with_children", + "dwp.benefit_cap.capped_households_up_to_100", + "dwp.benefit_cap.capped_households_100_01_to_200", + "dwp.benefit_cap.capped_households_200_01_to_300", + "dwp.benefit_cap.capped_households_300_01_to_400", + "dwp.benefit_cap.capped_households_400_01_to_500", + "dwp.benefit_cap.capped_households_500_01_to_600", + "dwp.benefit_cap.capped_households_600_01_to_700", + "dwp.benefit_cap.capped_households_700_01_to_800", + "dwp.benefit_cap.capped_households_800_01_to_900", + "dwp.benefit_cap.capped_households_900_01_to_1000", + "dwp.benefit_cap.capped_households_1000_01_to_1100", + "dwp.benefit_cap.capped_households_1100_01_to_1200", + "dwp.benefit_cap.capped_households_1200_01_to_1300", + "dwp.benefit_cap.capped_households_1300_01_and_above" ], "metric": "distinct_source_households_with_positive_prior_weight", - "floor": 10, + "floor": 1, "max_dropped_rows": 24, "max_dropped_published_share": 0.05, - "reason": "Sparse-band support floor: a UC payment-distribution band whose compiled matrix row reaches fewer than 10 distinct source households (household_source_id) with a positive prior weight is fitted only by stretching those few weights, and a fit that rests on fewer than 10 source households is a cell the release must not report (the UK Data Service working floor of 10, CD171 section 5.2.1). Such rows are dropped from the solve, not pooled; the floor's receipt reports each family's dropped published total, and the run refuses when the drops pass either ceiling or when a per-row exclusion names a row the floor drops.", + "reason": "Zero-support floor: a UC payment-distribution band or a benefit-cap amount band whose compiled matrix row reaches no source household (household_source_id) with a positive prior weight cannot be fitted by any weights (the solver's initialiser refuses a nonzero target with no support), so it is dropped from the solve, not pooled. A row that some households carry stays bound however few they are; when fewer than 10 distinct source households carry it, its model figures are withheld from every written surface (the UK Data Service working floor, CD171 section 5.2.1). The floor's receipt reports each target's dropped published total, and the run refuses when the drops pass either ceiling or when a per-row exclusion names a row the floor drops.", "tracking": "microcosm#1095", "approved_by": "juaristi22", - "adjudication": "microcosm#1095 U3, ruled 2026-10-16 with the fifth ports PR: floor 10 distinct source households with a positive prior weight, rows dropped not pooled, dropped published totals reported; the per-row payment-band entries the floor covers retire", - "approved_on": "2026-10-16", + "adjudication": "microcosm#1095 U3, ruled 2026-10-16 with the fifth ports PR (floor 10) and revised 2026-10-21 with #1333: a cell that rests on fewer than 10 records is no reason to leave a row out, only a measured distortion of the calibration is. The floor drops only rows no household carries, the benefit-cap amount bands join its scope and their per-row entries retire, and the figures of rows under 10 source households are withheld, as the BRMA rows' are. The thirteen entries it retired (the eleven benefit-cap amount bands and the two HMRC £500k to £1m cells) and the A/B that retired them: experiments/1095-uk-thin-row-binding-receipts.md.", + "approved_on": "2026-10-21", "expires_on": "2027-01-08" }, "exclusions": [ @@ -262,24 +276,6 @@ "approved_on": "2026-08-26", "expires_on": "2026-11-26" }, - { - "name": "hmrc/dividend_income_income_band_500_000_to_1_000_000", - "reason": "Sparse cell support: fewer than ten in-band carrier records under the SPI assessable-income concept and a similarly thin count under the component-sum proxy, measured identically on spine-i and spine-j (microcosm#807) - too few for the solver to hit the published value without distorting neighbouring cells. One of the three thin 500k-1m cells retained when the microcosm#807 revision lifted the three mid-band cells whose measured support was in the hundreds; re-measure on the next spine build before renewal.", - "tracking": "microcosm#757", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411); reason revised 2026-08-28 per microcosm#807 (the spine-i/j excluded-cell support re-measurement)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, - { - "name": "hmrc/property_income_count_income_band_500_000_to_1_000_000", - "reason": "Sparse cell support: fewer than ten in-band carrier records under the SPI assessable-income concept and under the component-sum proxy, measured identically on spine-i and spine-j (microcosm#807) - too few for the solver to hit the published value without distorting neighbouring cells. One of the three thin 500k-1m cells retained when the microcosm#807 revision lifted the three mid-band cells whose measured support was in the hundreds; re-measure on the next spine build before renewal.", - "tracking": "microcosm#757", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411); reason revised 2026-08-28 per microcosm#807 (the spine-i/j excluded-cell support re-measurement)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, { "name": "ons.savings_interest_income", "reason": "Concept, not a weight question: the ONS UKEA HAXV row is households' (S.14) D.41 interest resources in the national accounts (FISIM-adjusted, including the unincorporated-business part of the sector; GBP 95.5bn at 2025) while the model's savings_interest_income is cash interest, so the row is unreachable by design (-82 percent on the v20 national candidate). From 2026-09-22 the fitting anchor for interest is the SPI 2023-24 Table 3.7 interest rows by total-income band, uprated to the calibration year (hmrc.spi.savings_interest_income.*, PolicyEngine/chronicle#280 lane); this row is measured on every evaluation as the D.41 diagnostic and never fitted. Retire only with a declared D.41-to-cash bridge.", @@ -388,105 +384,6 @@ "approved_on": "2026-09-16", "expires_on": "2026-12-08" }, - { - "name": "dwp.benefit_cap.capped_households_100_01_to_200", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 100 .01 to 200 measures far below its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_300_01_to_400", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 300 .01 to 400 measures far below its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_500_01_to_600", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 500 .01 to 600 measures far above its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_600_01_to_700", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 600 .01 to 700 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_700_01_to_800", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 700 .01 to 800 measures far above its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_800_01_to_900", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 800 .01 to 900 measures far above its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_900_01_to_1000", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 900 .01 to 1000 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_1000_01_to_1100", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 1000 .01 to 1100 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_1100_01_to_1200", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 1100 .01 to 1200 measures far above its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_1200_01_to_1300", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 1200 .01 to 1300 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, - { - "name": "dwp.benefit_cap.capped_households_1300_01_and_above", - "reason": "Support, not a weight question (#882): the benefit-cap amount band 1300 .01 and above measures far above its published count, outside the 25 percent bound on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty, and the share of capped units above GBP 1,300 a month is many times the published 0.6 percent, a tail of large families outside London on full child elements and large housing elements. The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", - "tracking": "microcosm#882", - "approved_by": "juaristi22", - "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", - "approved_on": "2026-09-16", - "expires_on": "2026-12-08" - }, { "name": "obr.vat", "reason": "Level set by an engine factor, not the weights: policyengine-uk's vat divides household VAT by gov.simulation.microdata_vat_coverage (0.38, a constant since January 2023 with no recorded derivation; policyengine-uk#1996), which folds the household share of VAT receipts (about 70 percent per OBR and the HMRC VAT gap estimates) together with the survey's capture of household spending. The frame's raw household VAT is about 46 percent of the OBR receipts, so the row reads +24.7 percent at design weights and +24.1 percent calibrated on microcosm#1121's head calibration (policyengine-uk 2.122.2), against the 25 percent bound. On the 2026-09-30 national problem (1,090 rows, before this stage set any level) the calibration with the four consumption-tax rows bound left total consumption GBP 55bn (5 percent) below a re-solve with the four consumption-tax rows masked together (GBP 982bn against GBP 1,037bn). In that re-solve the other 1,086 rows fitted no worse (98.5 against 98.4 percent within 10 percent, none past 25 percent against one). Measured on every evaluation with tools/diagnose_uk_consumption_taxes.py; rebind when a locked policyengine-uk splits the factor into the published household share and a declared survey capture and the row fits.", diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 084646d7b..62a882fd6 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -541,7 +541,8 @@ "restored_columns": [ "iidb_reported", "afcs_reported", - "bsp_reported" + "bsp_reported", + "war_widows_pension_reported" ] }, "notes": "Stage-time structural gate over the SPI benefit coherence receipt (microcosm#1095, uk-data#514): no SPI-channel row keeps a zeroed legacy report, every restored report equals the FRS twin's, receives_benefits_in_own_right agrees with each person's own reports, FRS rows are unchanged, every SPI benefit unit with a Universal Credit reporter claims, and no SPI unit outside the Universal Credit age population claims without reporting." @@ -884,7 +885,7 @@ }, "sum_tolerance": 0.01 }, - "notes": "Benunit-aggregated reported Universal Credit must imply would_claim_uc. Both recorded capitals, uc_reported_capital and pension_credit_reported_capital, are bounded below by the named -1 unavailable sentinel and may use it only where the FRS carrier frs_benunit_capital is unavailable. Otherwise each equals the carrier plus the household property the unit holds, re-derived here with the stage's own rule (microcosm#1095): the household reference person's benefit unit holds the household's property at 90% of its value, market value less 10% for the costs of sale, and no other unit holds any. UC counts other residential and non-residential property, and a unit that reports Universal Credit keeps its receipt and carries none; Pension Credit counts those and owned land. The sum may differ by GBP 0.01 plus one float32 step of the expected value, so a float32 round-trip of the export does not fail it." + "notes": "Benunit-aggregated reported Universal Credit must imply would_claim_uc. Both recorded capitals, uc_reported_capital and pension_credit_reported_capital, are bounded below by the named -1 unavailable sentinel and may use it only where the FRS carrier frs_benunit_capital is unavailable. Otherwise each equals the carrier plus the household property the unit holds, re-derived here with the stage's own rule (microcosm#1095): the household reference person's benefit unit holds the household's property at 90% of its value, market value less 10% for the costs of sale, and no other unit holds any. UC counts other residential and non-residential property, and a unit that reports Universal Credit keeps its receipt and carries none; Pension Credit counts those and owned land. The sum may differ by GBP 0.01 plus one float32 step of the expected value, so a float32 round-trip of the export does not fail it. No unit may sit on the -1 sentinel beside a unit that records a property share for either scheme: since policyengine-uk 2.136.1 the sentinel unit would take its own share of property the recording unit already holds, so the engine would count it twice (Vahid's review of microcosm#1333)." }, { "id": "uk_support", 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 25fd68b85..9fdb6aafb 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": "2.134.4" + "version": "2.145.0" }, "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", 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 239f3140a..097726560 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 @@ -380,14 +380,14 @@ }, { "kind": "ledger_only", - "ledger_value": 3612036036.2194014, + "ledger_value": 3678026476.522709, "name": "dft.bus_fare_receipts.england", "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, { "kind": "ledger_only", - "ledger_value": 1347434943.01459, + "ledger_value": 1347446557.7536979, "name": "dft.bus_fare_receipts.london", "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." 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 2c031116c..7232877dd 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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -609,7 +609,7 @@ "required_mass_change_reason": "High-value home support divides every owner household in the top value stratum of its region 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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "source": "Microcosm structural operator (microcosm#1240 fix 1); no publisher input", "survey": "Family Resources Survey 2024-25 and SPI synthetic support, after the WAS wealth draw and the regional property uprating" @@ -630,7 +630,7 @@ "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -654,7 +654,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "054b1ee1a66cae6ed3793053eb0d3b9eea6b3f8976953479948ae11f7eedcd35", @@ -743,7 +743,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -785,7 +785,7 @@ "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -811,7 +811,7 @@ "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -831,7 +831,7 @@ "required_mass_change_reason": "Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "source": "policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine", "survey": "policyengine-uk private-school attendance imputation, stored at the spine's weights" @@ -854,7 +854,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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)" @@ -875,7 +875,7 @@ "required_mass_change_reason": "Annual property purchases imputed on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "source": "https://www.gov.uk/government/statistics/uk-stamp-tax-statistics", "survey": "HMRC Annual Stamp Tax Statistics 2024 to 2025 (corrected 11 March 2026), Tables 6b, 8a and 8b" @@ -898,7 +898,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "source": "uk/high_value_home_value_knots.json, built by tools/build_uk_high_value_home_stock.py from PolicyEngine/chronicle#343's registrations", "survey": "HM Land Registry Price Paid latest sales uprated with the UK House Price Index, owner-weighted by region (the dense value node's knots, region level)" @@ -922,7 +922,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "source_vintages": { "source": "MHCLG dwellings and the UK House Price Index regional average price averaged over the twelve months of calendar 2024 from the July 2026 full file, the frame's valuation basis, so the engine's annual uprating from 2024 projects the value once (microcosm#1240 fix 5).", "survey": "Public regional property reference" @@ -949,7 +949,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -982,7 +982,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -1004,7 +1004,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -1026,7 +1026,7 @@ "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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" @@ -1061,7 +1061,7 @@ "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": "8599f34df31a02c99296a7daa03edbee543898a18cb672187c3b0d441ab88491", + "source_manifest_sha256": "8ac2ff840181bc1789c88155f3f4ffe64d0f98b393e8d3b1b9326f80def1aa7e", "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 572590e5f..b42e70a0e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -343,6 +343,7 @@ "esa_income_reported", "esa_includes_support_component", "bsp_reported", + "war_widows_pension_reported", "benunit_id", "frs_benunit_capital", "is_married", @@ -1106,7 +1107,7 @@ "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 2.134.4'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)." + "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 2.145.0'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", @@ -2062,6 +2063,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", @@ -2445,6 +2447,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", @@ -3476,6 +3479,17 @@ 2023, 2024 ], + "fare_cap_regime": { + "cap_fare": 2.0, + "cap_years": [ + 2023, + 2024 + ], + "pre_cap_years": [ + 2022 + ], + "cap_tolerance": 0.01 + }, "cars_clip": [ 0, 5 @@ -3797,7 +3811,7 @@ "local_bus_trips", "household_local_bus_trips" ], - "notes": "Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, María's ask of 2026-09-23). The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed." + "notes": "Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, María's ask of 2026-09-23). The share is read on the cap years declared in fare_cap_regime only (England's £2 single-fare cap outside London ran from 1 January 2023 to 31 December 2024, and an NTS survey year is a calendar year), because nine months of the FRS 2024-25 base year (April to December 2024) sit under that cap and the last three under the £3 cap that followed it; 2022, before it, stays in the band and trip fit. The share under the £3 cap or with no cap is not observed, because the extract ends in 2024 (microcosm#1331). For England outside London the receipt also carries fare-mix lines, aggregates only. Before the cap they give the share of point-of-use boardings paid above the cap and their mean fare, at that year's prices; under it, the share paid at the cap, within the declared tolerance. A line that rests on fewer than 10 unweighted boardings or 10 distinct households, or whose complement does, reads <10. The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed." }, { "stage": "lcfs_consumption", @@ -5186,7 +5200,8 @@ "columns": [ "iidb_reported", "afcs_reported", - "bsp_reported" + "bsp_reported", + "war_widows_pension_reported" ], "linkage": "person_source_id -> FRS-channel person_id", "reason": "follows an injury, service or a death, not income" @@ -5241,6 +5256,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "is_disabled_for_benefits", "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits", @@ -5317,7 +5333,7 @@ "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 2.134.4 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 2.145.0 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 shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). 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. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base." }, { "stage": "uc_legacy_closure_take_up", @@ -6072,7 +6088,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.134.4 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.145.0 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount" ], @@ -6153,7 +6169,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.134.4 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.145.0 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount", "gov.hmrc.cgt.badr.lifetime_limit", 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 ee41fe54b..2be3b5659 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -472,6 +472,7 @@ stages: - esa_income_reported - esa_includes_support_component - bsp_reported + - war_widows_pension_reported - benunit_id - frs_benunit_capital - is_married @@ -1008,7 +1009,7 @@ 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 2.134.4'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). + 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 2.145.0'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. @@ -1777,6 +1778,7 @@ stages: - iidb_reported - afcs_reported - bsp_reported + - war_widows_pension_reported - winter_fuel_allowance_reported - council_tax_benefit_reported - jsa_contrib_reported @@ -2094,6 +2096,7 @@ stages: - iidb_reported - afcs_reported - bsp_reported + - war_widows_pension_reported - winter_fuel_allowance_reported - council_tax_benefit_reported - jsa_contrib_reported @@ -2893,6 +2896,14 @@ stages: - 2022 - 2023 - 2024 + fare_cap_regime: + cap_fare: 2.0 + cap_years: + - 2023 + - 2024 + pre_cap_years: + - 2022 + cap_tolerance: 0.01 cars_clip: - 0 - 5 @@ -3147,7 +3158,7 @@ stages: - other_local_bus_trips - local_bus_trips - household_local_bus_trips - notes: Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, María's ask of 2026-09-23). The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed. + notes: Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, María's ask of 2026-09-23). The share is read on the cap years declared in fare_cap_regime only (England's £2 single-fare cap outside London ran from 1 January 2023 to 31 December 2024, and an NTS survey year is a calendar year), because nine months of the FRS 2024-25 base year (April to December 2024) sit under that cap and the last three under the £3 cap that followed it; 2022, before it, stays in the band and trip fit. The share under the £3 cap or with no cap is not observed, because the extract ends in 2024 (microcosm#1331). For England outside London the receipt also carries fare-mix lines, aggregates only. Before the cap they give the share of point-of-use boardings paid above the cap and their mean fare, at that year's prices; under it, the share paid at the cap, within the declared tolerance. A line that rests on fewer than 10 unweighted boardings or 10 distinct households, or whose complement does, reads <10. The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed. - stage: lcfs_consumption survey: Living Costs and Food Survey 2024-25 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. @@ -4210,6 +4221,7 @@ stages: - iidb_reported - afcs_reported - bsp_reported + - war_widows_pension_reported linkage: person_source_id -> FRS-channel person_id reason: follows an injury, service or a death, not income - kind: derive @@ -4249,6 +4261,7 @@ stages: - iidb_reported - afcs_reported - bsp_reported + - war_widows_pension_reported - is_disabled_for_benefits - is_enhanced_disabled_for_benefits - is_severely_disabled_for_benefits @@ -4308,7 +4321,7 @@ 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 2.134.4 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 2.145.0 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 shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). 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. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base. - stage: uc_legacy_closure_take_up survey: DWP Stat-Xplore, Households invited to Move to Universal Credit by legacy benefit and move status, notices July 2022 to June 2026 source: https://stat-xplore.dwp.gov.uk @@ -4898,7 +4911,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.134.4 via microcosm-build[uk] + dependency: policyengine-uk>=2.145.0 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 @@ -4963,7 +4976,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.134.4 via microcosm-build[uk] + dependency: policyengine-uk>=2.145.0 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount - gov.hmrc.cgt.badr.lifetime_limit 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 1478206d8..f7b817e1e 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 @@ -7003,7 +7003,7 @@ "from": "2024", "to": "2025", "index": "dft.local_bus_fares_index", - "factor": "1.05695792880259", + "factor": "1.07626812349731", "value_before_uprating": "3417388656.43538" }, { @@ -7016,7 +7016,7 @@ "from": "2024", "to": "2025", "index": "dft.local_bus_fares_index", - "factor": "1", + "factor": "1.0000086198886", "value_before_uprating": "1347434943.01459" }, { @@ -19310,12 +19310,12 @@ "matched_fact_count_overall": 3, "matched_fact_count_at_or_before_period": 3, "resolved_period": 2025, - "resolved_value": 3612036036.2194014, + "resolved_value": 3678026476.522709, "resolved_fact_period": "2024", "resolved_fact_key": "ledger_aggregate_fact_v2_2ece7c3d283b6de43b30389f", "uprating": { "index": "dft.local_bus_fares_index", - "factor": "1.05695792880259", + "factor": "1.07626812349731", "value_before_uprating": "3417388656.43538" } } @@ -19345,12 +19345,12 @@ "matched_fact_count_overall": 3, "matched_fact_count_at_or_before_period": 3, "resolved_period": 2025, - "resolved_value": 1347434943.01459, + "resolved_value": 1347446557.7536979, "resolved_fact_period": "2024", "resolved_fact_key": "ledger_aggregate_fact_v2_33611c64d2048fe660501802", "uprating": { "index": "dft.local_bus_fares_index", - "factor": "1", + "factor": "1.0000086198886", "value_before_uprating": "1347434943.01459" } } 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 1edb833f2..3b31ade36 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 @@ -18040,6 +18040,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 1200 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18116,6 +18125,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 2400 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18192,6 +18210,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 3600 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18268,6 +18295,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 4800 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18344,6 +18380,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 6000 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18420,6 +18465,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 7200 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18496,6 +18550,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 8400 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18572,6 +18635,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 9600 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18648,6 +18720,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 10800 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18724,6 +18805,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 12000 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18800,6 +18890,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 13200 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18876,6 +18975,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 14400 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -18952,6 +19060,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": "<=", "value": 15600 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, @@ -19023,6 +19140,15 @@ "concept": "uk.benefits.benefit_cap.reduction", "operator": ">", "value": 15600 + }, + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] } ] }, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py index f89d19741..bf30171de 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py @@ -575,6 +575,31 @@ def _evaluate_column_implication( "same_source_mismatch_count": int(sum_mismatch.sum()), "nonfinite_capital_count": int(nonfinite.sum()), } + # From policyengine-uk 2.136.1 a unit on the sentinel takes its own share + # of its household's property for each scheme, which a recording unit + # beside it already holds, so the property would count twice there + # (Vahid's review of microcosm#1333). No unit may sit on the sentinel + # beside a unit that records a property share for either scheme. + person = frame.table("person") + unit_household = ( + person.drop_duplicates("person_benunit_id") + .set_index("person_benunit_id")["person_household_id"] + .reindex(target[target_id_column]) + .to_numpy() + ) + on_sentinel = carrier == sentinel + sentinel_beside: dict[str, int] = {} + for column in property_shares: + share = np.asarray(shares.get(column, 0.0), dtype=float) + recording = (carrier >= 0.0) & (share > 0.0) + beside = on_sentinel & np.isin(unit_household, unit_household[recording]) + sentinel_beside[column] = int(beside.sum()) + if beside.any(): + failures.append( + f"{target_entity}: {int(beside.sum())} unit(s) on the " + f"{sentinel:g} sentinel beside a unit that records {column}'s " + "property share; the engine would count that property twice." + ) return GateResult( name="column_implication", passed=not failures, @@ -595,6 +620,7 @@ def _evaluate_column_implication( f"{target_entity}.{column}": counts[column] for column in capital_columns }, + "sentinel_units_beside_recorded_property": sentinel_beside, }, ) @@ -1750,12 +1776,34 @@ def _evaluate_target_fit( exclusions, _source = _resolve_target_fit_exclusions(context) exclusions, out_of_line = _in_line(exclusions, context) parity = context.artifacts["parity_evidence"] + # María's rule of 2026-10-21 binds a national row however few source + # households carry it, and keeps the figures of one under ten in the + # build; the gate decides on the published rows and counts the withheld + # ones, as it does for the local rows (her ruling of 2026-10-19). Every + # producer states the set, empty or not, so a missing one fails closed. + withheld = frozenset(parity.withheld_targets) + errors = { + name: error + for name, error in parity.target_relative_errors.items() + if name not in withheld + } + exclusions = { + name: record for name, record in exclusions.items() if name not in withheld + } + out_of_line = [name for name in out_of_line if name not in withheld] result = uk_target_fit_gate( - parity.target_relative_errors, + errors, reviewed_exclusions=exclusions, now=_exclusion_clock(context), **kwargs, ) + if withheld: + result = GateResult( + name=result.name, + passed=result.passed, + failures=result.failures, + details={**result.details, "withheld_targets": len(withheld)}, + ) return _with_out_of_line(result, context, out_of_line) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/bus_fare_pricing.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/bus_fare_pricing.py index d963a62b9..9f37ac656 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/bus_fare_pricing.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/bus_fare_pricing.py @@ -369,8 +369,9 @@ def own_fiscal_yield_price_factor( def bus_engine_parity( prices: BusFarePrices, - engine: Mapping[str, Mapping[str, float]], + engine: Mapping[str, Any], *, + alignment_factors: Mapping[str, float] | None = None, tolerance: float = UK_BUS_ENGINE_PARITY_TOLERANCE, ) -> dict[str, Any]: """The engine's bus pricing parameters against the stage's, area by area. @@ -380,8 +381,16 @@ def bus_engine_parity( and ``yield_per_boarding``, while the national fare rows calibrate the stage's priced column; this is the check microcosm#1207 asks for. Both sides are fiscal-year values, before the stage's calendar re-pricing. - ``engine`` maps each parameter name to its value by engine area. Report - only: a gap beyond ``tolerance`` marks the area, it refuses nothing. + ``engine`` maps each parameter name to its value by engine area. + + With ``alignment_factors`` (the fare-receipt rows' factor by engine area, + :func:`~microcosm.build.uk_runtime.ledger_targets.uk_bus_fare_alignment_factors`) + and the engine's ``local_bus_fares_index_ratio`` (its index in the + measurement year over the benchmark year), each area's factor is checked + against the engine's ratio too (microcosm#1331): the receipts move on the + engine's window, so the two must tie. Report only: a gap beyond + ``tolerance`` marks the area, it refuses nothing; the engine test holds + the tie at the locked engine. """ stage_areas = { @@ -409,16 +418,34 @@ def bus_engine_parity( } areas[label] = entry priced = [entry for entry in areas.values() if entry["engine_area"] is not None] - return { + within = all( + entry[name]["within_tolerance"] + for entry in priced + for name in ("boardings_per_trip", "yield_per_boarding") + ) + result: dict[str, Any] = { "report_only": True, "tolerance": float(tolerance), "areas": areas, - "within_tolerance": all( - entry[name]["within_tolerance"] - for entry in priced - for name in ("boardings_per_trip", "yield_per_boarding") - ), } + ratios = engine.get("local_bus_fares_index_ratio") + if alignment_factors is not None and isinstance(ratios, Mapping): + index_ratio: dict[str, Any] = {} + for area, factor in sorted(alignment_factors.items()): + engine_ratio = float(ratios[area]) + relative = float(factor) / engine_ratio - 1.0 if engine_ratio else None + index_ratio[area] = { + "targets": float(factor), + "engine": engine_ratio, + "relative_gap": relative, + "within_tolerance": relative is not None and abs(relative) <= tolerance, + } + result["fares_index_ratio"] = index_ratio + within = within and all( + entry["within_tolerance"] for entry in index_ratio.values() + ) + result["within_tolerance"] = within + return result def bus_fare_prices( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py index 7bcf1ea29..a8af91ae9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py @@ -26,6 +26,7 @@ import numpy as np import pandas as pd from pydantic import ValidationError +from scipy import sparse from microcosm.calibrate import ( TargetRegistry, @@ -71,10 +72,15 @@ _TARGET_PASS_RELATIVE_ERROR = 0.10 _AREA_FIT_LIMIT = 15 -#: A BRMA row's model figures leave the build only when at least this many -#: distinct source households carry them (María's ruling of 2026-10-17 on the -#: #1305 review, UKDS CD137 clause 8); the support gate covers constituencies -#: and authorities, not the partial BRMA grain. +#: A target row's model figures leave the build only when at least this many +#: distinct source households carry them (UKDS CD137 clause 8): María's ruling +#: of 2026-10-17 on the #1305 review for BRMA rows, extended to national and +#: regional rows by her rule of 2026-10-21, which binds a thin row rather +#: than leave it out (local rows keep the area-support rule). +UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS = 10 +#: The BRMA rows' floor, the same ten under its own ruling of 2026-10-17 (the +#: support gate covers constituencies and authorities, not the partial BRMA +#: grain). UK_BRMA_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS = 10 #: The fields of a target row that the model's weights produce (the shared #: tuple the release contract checks too). @@ -82,6 +88,10 @@ _WITHHELD_NOTE = ( "model figures withheld: fewer than 10 distinct source households carry this row" ) +_COMPLEMENT_NOTE = ( + "model figures withheld: with the published rows they would give back a row " + "that fewer than 10 distinct source households carry" +) _COUNTRY_BY_AREA_PREFIX = { "E": "England", @@ -959,52 +969,320 @@ def _declared_target_partition( return declared, compiled, skipped_names -def uk_withheld_brma_rows( +#: The local area levels whose rows the area-support rule governs +#: (``uk_local_area_support``) rather than the per-row floor below: María's +#: rule of 2026-10-21 has not been applied to the local line, and the report +#: guard matches a withheld local row by its bare area code, which suits a +#: BRMA's one family but not an area that binds many. +UK_AREA_SUPPORT_LEVELS = frozenset({"constituency", "local_authority"}) + + +#: How near a combination of withheld rows may come to the published rows' +#: span and still count as given back: the squared distance of its unit-normed +#: row, so within 0.1% in norm, which the published figures reproduce to the +#: same precision. Exact recoveries sit near 1e-9 and the rest above 1e-3. +_RECOVERY_TOLERANCE = 1e-6 +#: Below this share of the largest entry, an entry of a row-reduced +#: combination, of its values or of its published coefficients is rounding +#: (the eigenvectors carry errors near 1e-8). Treating a small entry as zero +#: can only shrink a combination's support, so it errs toward withholding. +_RECOVERY_ROUNDING = 1e-6 + + +def _row_level(geography_levels: Mapping[str, object] | None): + def level(name: str) -> str: + raw = (geography_levels or {}).get(name, "") + text = str(getattr(raw, "value", raw)).strip().lower() + return _GEOGRAPHY_LEVEL_ALIASES.get(text, text) + + return level + + +def _scoped_rows( problem: Any, weights: Sequence[float] | np.ndarray, frame: Frame, target_rows: Sequence[Mapping[str, object]], - geography_levels: Mapping[str, str], -) -> set[str]: - """The BRMA rows whose model figures rest on fewer than ten source households. + geography_levels: Mapping[str, object] | None, +) -> tuple[list[str], sparse.csr_matrix, np.ndarray, np.ndarray, np.ndarray]: + """The rows the per-row floor governs, sorted, over the positive-weight households. - ``problem`` is the numerical problem the rows were scored on (its - ``matrix`` and ``names``) and ``weights`` the weights they were scored at. - A row's carriers are the distinct ``source_household_id`` values of the - positive-weight households with a nonzero entry in its problem row, as the - area support counts them. + Returns their names, their problem rows (stored zeros dropped: a household + with a zero entry does not carry the row), those households' source ids, + each row's distinct source households and each row's floor. """ - names = [ - str(row["name"]) - for row in target_rows - if geography_levels.get(str(row["name"])) == "brma" - ] + level = _row_level(geography_levels) + names = sorted( + { + str(row["name"]) + for row in target_rows + if level(str(row["name"])) not in UK_AREA_SUPPORT_LEVELS + } + ) if not names: - return set() + empty = np.zeros(0) + return [], sparse.csr_matrix((0, 0)), empty, empty, empty household = frame.table("household") if "source_household_id" not in household.columns: raise ValueError( - "UK BRMA diagnostics need household source_household_id to count " - "each row's carriers." + "UK diagnostics need household source_household_id to count each " + "row's carriers." ) sources = household["source_household_id"].to_numpy() positive = np.asarray(weights, dtype=float) > 0 if positive.shape != sources.shape: - raise ValueError("UK BRMA carriers need one weight per household.") - matrix = problem.matrix.tocsr() + raise ValueError("UK row carriers need one weight per household.") index = {str(name): position for position, name in enumerate(problem.names)} - withheld = set() for name in names: - position = index.get(name) - if position is None: - raise ValueError(f"UK BRMA diagnostic {name!r} has no problem row.") - span = slice(matrix.indptr[position], matrix.indptr[position + 1]) - columns = matrix.indices[span][matrix.data[span] != 0] - columns = columns[positive[columns]] - if len(pd.unique(sources[columns])) < UK_BRMA_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS: - withheld.add(name) - return withheld + if name not in index: + raise ValueError(f"UK target diagnostic {name!r} has no problem row.") + matrix = sparse.csr_matrix(problem.matrix)[[index[name] for name in names]] + matrix = sparse.csr_matrix(matrix[:, np.flatnonzero(positive)]) + matrix.eliminate_zeros() + sources = sources[positive] + carriers = np.array( + [ + len(pd.unique(sources[matrix.indices[start:stop]])) + for start, stop in zip(matrix.indptr[:-1], matrix.indptr[1:], strict=True) + ] + ) + floors = np.array( + [ + UK_BRMA_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS + if level(name) == "brma" + else UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS + for name in names + ] + ) + return names, matrix, sources, carriers, floors + + +def uk_thin_rows( + problem: Any, + weights: Sequence[float] | np.ndarray, + frame: Frame, + target_rows: Sequence[Mapping[str, object]], + geography_levels: Mapping[str, object] | None = None, +) -> set[str]: + """The rows whose model figures rest on fewer than ten source households. + + National, regional and BRMA rows: María's rule of 2026-10-21 binds a row + however few households carry it, and keeps its figures in the build, as + BRMA rows' have been kept since her ruling of 2026-10-17. A row whose + level in ``geography_levels`` is a constituency or a local authority is + left to the area-support rule (``UK_AREA_SUPPORT_LEVELS``). ``problem`` is + the numerical problem the rows were scored on (its ``matrix`` and + ``names``) and ``weights`` the weights they were scored at. A row's + carriers are the distinct ``source_household_id`` values of the + positive-weight households with a nonzero entry in its problem row, as the + area support counts them. + """ + + names, _, _, carriers, floors = _scoped_rows( + problem, weights, frame, target_rows, geography_levels + ) + return { + name + for name, count, floor in zip(names, carriers, floors, strict=True) + if count < floor + } + + +def uk_withheld_row_sets( + problem: Any, + weights: Sequence[float] | np.ndarray, + frame: Frame, + target_rows: Sequence[Mapping[str, object]], + geography_levels: Mapping[str, object] | None = None, +) -> tuple[set[str], set[str]]: + """The thin rows (:func:`uk_thin_rows`) and the published rows withheld beside them. + + A published row's model figure is its problem row times the weights, so a + combination of withheld rows is given back exactly when its row lies in + the span of the published rows over the positive-weight households (a + family total less its published bands gives back the bands withheld). + María's hard rule (UKDS CD171 section 5.2.1): no difference of published + figures may isolate fewer than ten cases, and where a total would give a + withheld cell back, one more cell is withheld (Vahid's second review of + #1333, item 10). While the published rows give back a combination of + withheld rows that rests on fewer source households than its rows' floor, + the published row with the fewest carriers among those that give it back + is withheld too (the next-smallest band beside a thin one, not its total), + and the span is measured again. Constituency and authority rows stay + outside, as they stay outside the per-row floor: whether a local row can + give back a national one is part of the local-line ruling still owed. + """ + + names, matrix, sources, carriers, floors = _scoped_rows( + problem, weights, frame, target_rows, geography_levels + ) + thin = { + name + for name, count, floor in zip(names, carriers, floors, strict=True) + if count < floor + } + if not thin: + return thin, set() + norms = np.sqrt(np.asarray(matrix.multiply(matrix).sum(axis=1)).ravel()) + scale = np.divide(1.0, norms, out=np.zeros_like(norms), where=norms > 0) + unit = sparse.csr_matrix(sparse.diags(scale) @ matrix) + gram = (unit @ unit.T).toarray() + withheld = set(thin) + for _ in range(len(names)): + held = [i for i, name in enumerate(names) if name in withheld and norms[i]] + published = [ + i for i, name in enumerate(names) if name not in withheld and norms[i] + ] + if not held or not published: + break + recovery = _small_recovery(unit, gram, sources, held, published, floors) + if recovery is None: + break + coefficients, leverage = recovery + size = np.abs(coefficients) + givers = np.flatnonzero(size > _RECOVERY_ROUNDING * size.max()) + if not len(givers): + raise ValueError( + "UK diagnostics found a thin combination the published rows give " + "back but no published row that gives it." + ) + # A giver that no other published row can stand in for (it sits in no + # dependency among them) carries the same coefficient in every way of + # giving the combination back, so withholding it breaks them all. When + # every giver has a stand-in, two relations give the combination back, + # and withholding a giver breaks one of them. + essential = [j for j in givers if leverage[j] > 1 - _RECOVERY_TOLERANCE] + pick = min( + (published[j] for j in (essential or givers)), + key=lambda i: (carriers[i], names[i]), + ) + withheld.add(names[pick]) + return thin, withheld - thin + + +def _small_recovery( + unit: sparse.csr_matrix, + gram: np.ndarray, + sources: np.ndarray, + held: list[int], + published: list[int], + floors: np.ndarray, +) -> tuple[np.ndarray, np.ndarray] | None: + """How the published rows give back a too-small combination of held rows. + + ``unit`` holds the unit-normed rows and ``gram`` their inner products. A + combination of the held rows is given back when what is left of it off + the published rows' span is a negligible share of it (its squared norm + under ``_RECOVERY_TOLERANCE`` of the combination's own). Each held row is + tried alone first (a total less its published bands gives one back); then + the combinations, from the generalized eigenvectors of that remainder + against the combination's own size, so that two proportional held rows, + whose difference is nothing, give nothing back. The first combination + that rests on fewer distinct source households than the highest floor + among its rows returns the minimum-norm published coefficients that give + it back, and each published row's leverage (1 when no other published row + can stand in for it); ``None`` when there is none. + """ + + epsilon = np.finfo(float).eps + values, vectors = np.linalg.eigh(gram[np.ix_(published, published)]) + keep = values > values.max() * len(values) * epsilon + values, vectors = values[keep], vectors[:, keep] + cross = gram[np.ix_(published, held)] + reach = (vectors.T @ cross) / np.sqrt(values)[:, None] + own = gram[np.ix_(held, held)] + remainder = own - reach.T @ reach + + def rests_below_floor(alpha: np.ndarray) -> bool: + rows = np.flatnonzero(alpha) + combination = np.asarray(unit[[held[i] for i in rows]].T @ alpha[rows]).ravel() + size = np.abs(combination) + if not size.any(): + return False + support = size > _RECOVERY_ROUNDING * size.max() + floor = max(int(floors[held[i]]) for i in rows) + return len(pd.unique(sources[support])) < floor + + def found(alpha: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + coefficients = vectors @ ((vectors.T @ (cross @ alpha)) / values) + return coefficients, np.sum(vectors**2, axis=1) + + for row in np.flatnonzero(np.diag(remainder) < _RECOVERY_TOLERANCE): + alpha = np.zeros(len(held)) + alpha[row] = 1.0 + if rests_below_floor(alpha): + return found(alpha) + own_values, own_vectors = np.linalg.eigh(own) + nonzero = own_values > own_values.max() * len(own_values) * epsilon + lift = own_vectors[:, nonzero] / np.sqrt(own_values[nonzero]) + share = lift.T @ remainder @ lift + share_values, share_vectors = np.linalg.eigh((share + share.T) / 2) + given_back = lift @ share_vectors[:, share_values < _RECOVERY_TOLERANCE] + for alpha in _reduced_basis(given_back): + if rests_below_floor(alpha): + return found(alpha) + return None + + +def _reduced_basis(basis: np.ndarray) -> list[np.ndarray]: + """The reduced row echelon basis of a span given by its columns. + + A vector in the span is fixed by its entries on the pivots, so separate + relations come out as separate vectors. Each column is scaled to a largest + entry of one first, so the rounding share means the same for all. + """ + + reduced = basis.T.copy() + largest = np.abs(reduced).max(axis=1, initial=0.0) + reduced = reduced[largest > 0] / largest[largest > 0, None] + pivot = 0 + for column in range(reduced.shape[1]): + if pivot == reduced.shape[0]: + break + best = pivot + int(np.argmax(np.abs(reduced[pivot:, column]))) + if abs(reduced[best, column]) < _RECOVERY_ROUNDING: + continue + reduced[[pivot, best]] = reduced[[best, pivot]] + reduced[pivot] /= reduced[pivot, column] + for other in range(reduced.shape[0]): + if other != pivot: + reduced[other] -= reduced[other, column] * reduced[pivot] + pivot += 1 + reduced[np.abs(reduced) < _RECOVERY_ROUNDING] = 0.0 + return [vector for vector in reduced[:pivot] if vector.any()] + + +def uk_withheld_rows( + problem: Any, + weights: Sequence[float] | np.ndarray, + frame: Frame, + target_rows: Sequence[Mapping[str, object]], + geography_levels: Mapping[str, object] | None = None, +) -> set[str]: + """Every row whose model figures stay in the build: :func:`uk_withheld_row_sets`.""" + + thin, complementary = uk_withheld_row_sets( + problem, weights, frame, target_rows, geography_levels + ) + return thin | complementary + + +def uk_withheld_brma_rows( + problem: Any, + weights: Sequence[float] | np.ndarray, + frame: Frame, + target_rows: Sequence[Mapping[str, object]], + geography_levels: Mapping[str, str], +) -> set[str]: + """The BRMA rows among :func:`uk_thin_rows` (their ``carrier_floor_met`` flag).""" + + brma = [ + row for row in target_rows if geography_levels.get(str(row["name"])) == "brma" + ] + return ( + uk_thin_rows(problem, weights, frame, brma, geography_levels) if brma else set() + ) def uk_withheld_flag(value: object) -> bool: @@ -1117,7 +1395,8 @@ def walk(value: object, path: str) -> None: _WITHHELD_TOTALS_NOTE = ( "A target row resting on fewer than ten distinct source households keeps " - "its place without its model figures. initial_loss, final_loss, " + "its place without its model figures, and so does a row that, with the " + "published rows, would give such a row back. initial_loss, final_loss, " "fraction_within_10pct and past_cap_census are restated over the published " "rows, on the solve's own loss weights, scales and cap (initial_loss at the " "input weights, the rows' initial estimates); loss_trajectory, the solve's " @@ -1192,12 +1471,27 @@ def published_loss(estimates: Sequence[float]) -> float | None: ) return { "targets": len(withheld), - "min_source_households": UK_BRMA_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS, + "min_source_households": UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS, "totals_basis": "published_rows", "note": _WITHHELD_TOTALS_NOTE, } +def uk_published_row_losses( + result: CalibrationResult, withheld: frozenset[str] +) -> tuple[float | None, float | None]: + """The initial and final loss over the published rows (microcosm#1262). + + For a manifest that would otherwise carry the solve's loss over every row, + which against the published rows' contributions gives a withheld row back + (María's ruling of 2026-10-18); ``None`` where the result cannot restate. + """ + + totals: dict[str, object] = {} + _restate_totals_over_published_rows(totals, result, frozenset(withheld)) + return totals["initial_loss"], totals["final_loss"] + + def uk_published_options(options: Mapping[str, object]) -> dict[str, object]: """A payload's solve options while any row is withheld (microcosm#1262). @@ -1300,7 +1594,7 @@ def uk_calibration_diagnostics_payload( unknown mapping aborts instead of silently shrinking a scoreboard level. ``withheld`` names the rows whose model figures stay in the build - (:func:`uk_withheld_brma_rows`); the gate kernel passes the set its gate + (:func:`uk_withheld_rows`); the gate kernel passes the set its gate evidence carries, and by default it is counted here on the same rule. """ @@ -1331,13 +1625,20 @@ def uk_calibration_diagnostics_payload( declared, _, skipped = _declared_target_partition(result, target_rows, registry) geography = _geography_mapping(declared, target_geography_levels) weights = uk_weight_summary(result_weights) - withheld = frozenset( - uk_withheld_brma_rows( + if withheld is None: + thin, complementary = uk_withheld_row_sets( result.problem, result.weights, frame, target_rows, geography ) - if withheld is None - else withheld - ) + withheld = frozenset(thin | complementary) + else: + withheld = frozenset(withheld) + # The floor's own verdict, so a row the caller withholds beside a + # thin one is told apart from the thin row itself. + thin = ( + uk_thin_rows(result.problem, result.weights, frame, target_rows, geography) + if withheld + else set() + ) for row in target_rows: name = str(row["name"]) if geography.get(name) == "brma": @@ -1346,7 +1647,7 @@ def uk_calibration_diagnostics_payload( # 2026-10-19 on the fourth review of #1305: it warns). row["metadata"] = { **dict(row.get("metadata") or {}), - "carrier_floor_met": name not in withheld, + "carrier_floor_met": name not in thin, } if name in withheld: for field in _MODEL_FIGURE_FIELDS: @@ -1354,7 +1655,10 @@ def uk_calibration_diagnostics_payload( row[field] = None row["metadata"] = { **dict(row.get("metadata") or {}), - "model_figures_withheld": _WITHHELD_NOTE, + "model_figures_withheld": ( + _WITHHELD_NOTE if name in thin else _COMPLEMENT_NOTE + ), + **({} if name in thin else {"complementary_withholding": True}), } pass_rates = _target_pass_rates(target_rows, skipped, geography, withheld) withheld_block = ( @@ -1380,13 +1684,13 @@ def uk_calibration_diagnostics_payload( }, } if local_area_support is not None: - # A withheld row stays out of every rollup, so that no family total - # can be differenced against the published rows to recover it. - uk_diagnostics["weakest_families"] = uk_weakest_families( - [row for row in target_rows if str(row["name"]) not in withheld] - ) + # A withheld row stays out of every rollup, so that no family or area + # total can be differenced against the published rows to recover it, + # and no rollup reads a figure the row no longer carries. + published = [row for row in target_rows if str(row["name"]) not in withheld] + uk_diagnostics["weakest_families"] = uk_weakest_families(published) uk_diagnostics["weakest_areas_by_fit"] = uk_weakest_areas_by_fit( - target_rows, + published, local_area_support, ) if rotated_holdout is not None: @@ -1414,8 +1718,13 @@ def write_uk_calibration_diagnostics( build: dict[str, Any] | None = None, local_area_support: pd.DataFrame | None = None, rotated_holdout: Mapping[str, object] | None = None, + withheld: Collection[str] | None = None, ) -> DiagnosticsWriteOutcome: - """Validate and write shared-plus-UK diagnostics through the canonical writer.""" + """Validate and write shared-plus-UK diagnostics through the canonical writer. + + ``withheld`` is the payload's: the rows whose model figures stay in the + build, counted on the payload's rule when not given. + """ output = Path(path) try: @@ -1428,6 +1737,7 @@ def write_uk_calibration_diagnostics( build=build, local_area_support=local_area_support, rotated_holdout=rotated_holdout, + withheld=withheld, ) diagnostics = CalibrationDiagnosticsV8.model_validate(payload) except ValidationError as error: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/employer_pension_contributions.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/employer_pension_contributions.py index 1d9293c39..57aaf8ca9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/employer_pension_contributions.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/employer_pension_contributions.py @@ -18,6 +18,14 @@ sit just under 30%), and the contribution is that rate times the person's employment income. Salary sacrifice stays its own column; ASHE and DWP count it as an employer contribution, so the bound DWP employer total adds it back. + +The defined-benefit or defined-contribution draw is not written to +policyengine-uk's defined_benefit_pension_share (2.142.0). The engine counts a +defined-benefit arrangement's pension input as its contributions times +gov.simulation.pensions.defined_benefit_input_per_contribution, which is 1, so +the share would change the annual allowance only for net-pay contributions at +75 and over, which the money-purchase path leaves out. Writing it becomes +worthwhile if that ratio moves away from 1. """ from __future__ import annotations 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 9ce3ffb08..9181cebea 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 @@ -289,6 +289,7 @@ "esa_income_reported", "esa_includes_support_component", "bsp_reported", + "war_widows_pension_reported", "benunit_id", "frs_benunit_capital", "is_married", @@ -997,7 +998,14 @@ def _add_benefits( benefits, person_id, person["person_id"] ) pe_person["bsp_reported"] = ( - _sum_to_entity(amount * benefit.isin((6, 9)), person_id, person["person_id"]) + _sum_to_entity(amount * (benefit == 6), person_id, person["person_id"]) + * WEEKS_IN_YEAR + ) + # FRS code 9 is War Widow's or Widower's Pension, which rises with CPI while + # Bereavement Support Payment's rates are frozen. policyengine-uk 2.135.7 + # gives it its own input (pe-uk#2382), so it leaves bsp_reported (#1222). + pe_person["war_widows_pension_reported"] = ( + _sum_to_entity(amount * (benefit == 9), person_id, person["person_id"]) * WEEKS_IN_YEAR ) pe_person["winter_fuel_allowance_reported"] = ( 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 ab5f4ba6e..7ea3444ed 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 @@ -49,7 +49,7 @@ } 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 2.134.4's ``is_uc_eligible`` +# partner meets its age conditions: policyengine-uk 2.145.0'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 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 2e4b35b85..f52acf28a 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 @@ -1135,21 +1135,19 @@ def _stderr_progress(line: str) -> None: print(line, file=sys.stderr, flush=True) -def _binds_brma_rows(args: argparse.Namespace) -> bool: - """Whether the request can bind Scotland's BRMA rent rows. - - Only the dense role binds local rows, on area BRMAs when the selected - geographies include them (microcosm#1262), and a thin one's model figures - stay in the build. The national role binds none, so its staged epochs - keep their loss (the fourth review of #1305). +def _may_withhold_rows(args: argparse.Namespace) -> bool: + """Whether the request can bind a row whose model figures stay in the build. + + Every request can: María's rule of 2026-10-21 binds a national row however + few source households carry it (the UC payment and benefit-cap bands), as + the dense role's area BRMAs have been bound since microcosm#1262, and a + thin row's figures stay in the build either way. So no role's staged + epochs keep their loss (the fourth review of #1305 kept it for the + national role while that role bound no thin row). """ - levels = getattr(args, "target_geographies", None) - return ( - getattr(args, "release_role", "dense") == "dense" - and getattr(args, "brma_localisation", "area") == "area" - and (levels is None or "brma" in levels) - ) + del args + return True def _solve_observer(args: argparse.Namespace, telemetry): @@ -1158,13 +1156,13 @@ def _solve_observer(args: argparse.Namespace, telemetry): The dense solve, the informed size search and the refit share this one observer; the size events carry the ``phase`` the stderr line names and the staging row records. An epoch's loss covers every row, so a request - that can bind BRMA rows forwards its epochs without it: the staging rows + that can bind a thin row forwards its epochs without it: the staging rows and the emitter carry null, and only the local stderr line keeps it (the third review of #1305). """ from .solve_progress import uk_solve_progress_callback - withhold_loss = _binds_brma_rows(args) + withhold_loss = _may_withhold_rows(args) sinks = [uk_solve_progress_callback(_stderr_progress)] sinks.append( thinned_epochs( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py index a27a9f39e..a1ae4eebb 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py @@ -23,7 +23,7 @@ uk_cgt_projection_artifact, ) from microcosm.build.uk_runtime.cgt_projection import UK_CGT_PROJECTION_ARTIFACT_KEY -from microcosm.build.uk_runtime.diagnostics import uk_withheld_brma_rows +from microcosm.build.uk_runtime.diagnostics import uk_withheld_rows from microcosm.build.uk_runtime.graph_evidence import uk_spine_gate_artifacts from microcosm.build.uk_runtime.national_frame import uk_release_export_frame from microcosm.build.uk_runtime.parity_reference import load_efrs_parity_reference @@ -236,12 +236,14 @@ def build_full_gate_context( local_rows.append(row) else: national_errors[target.row_name] = float(solved_rows[index].relative_error) - # A BRMA row's model figures leave the build only when ten distinct source - # households carry them (María's ruling of 2026-10-17, microcosm#1262). - # The fit gates decide on the published rows and count the withheld ones - # (María's ruling of 2026-10-19); the flag tells the bindings and the - # writers which rows those are. - withheld = uk_withheld_brma_rows( + # A row's model figures leave the build only when ten distinct source + # households carry them: María's ruling of 2026-10-17 for BRMA rows + # (microcosm#1262), extended to national and regional rows by her rule of + # 2026-10-21, which binds a thin row rather than leave it out (local rows + # keep the area-support rule). The fit gates decide on the published rows + # and count the withheld ones (her ruling of 2026-10-19); the flag tells + # the bindings and the writers which rows those are. + withheld = uk_withheld_rows( problem, solution.weights, frame, @@ -308,6 +310,7 @@ def build_full_gate_context( candidate_targets=set(national_errors), reference_targets=national_reference, target_relative_errors=national_errors, + withheld_targets=frozenset(withheld), ), "local_target_diagnostics": local_rows, "target_diagnostics": diagnostics, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py index a995a9290..0788e7e92 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -367,6 +367,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "universal_credit_reported", "esa_contrib_reported", "esa_income_reported", @@ -985,6 +986,7 @@ def _cells( "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", ), ), *_cells( @@ -1119,6 +1121,7 @@ def _cells( "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", 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 a40e54edd..c3e093c2b 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 @@ -101,7 +101,12 @@ ) from .cgt_projection import UK_CGT_PROJECTION_ARTIFACT_KEY from .content_identity import uk_frame_content_identity -from .diagnostics import uk_target_geography_levels +from .diagnostics import ( + uk_published_row_losses, + uk_refuse_named_withheld_rows, + uk_target_geography_levels, + uk_withheld_rows, +) from .full_targets import CHRONICLE_SOURCE_CODEC, load_uk_national_target_inputs from .graph_build import SPINE_PROVENANCE_TYPE from .graph_calibration import ( @@ -1035,12 +1040,41 @@ def run(self, context: KernelContext) -> KernelResult: manifest_block = national_calibration_manifest( result, frame, problem=problem, doctrine=doctrine ) + # María's rule of 2026-10-21 binds a row however few source households + # carry it. Under ten, its model figures stay in the build, as a BRMA + # row's do (her rulings of 2026-10-17 to 2026-10-19): blank in the + # evidence rows, outside the fit gate's verdict, and the manifest's + # losses restated over the published rows. + withheld = frozenset( + uk_withheld_rows( + result.problem, + result.weights, + frame, + [{"name": row.name} for row in result.diagnostics], + ) + ) + if withheld: + initial_loss, final_loss = uk_published_row_losses(result, withheld) + manifest_block = { + **manifest_block, + "loss": final_loss, + "solve": { + **dict(manifest_block["solve"]), + "initial_loss": initial_loss, + "final_loss": final_loss, + }, + "totals_basis": "published_rows", + "model_figures_withheld": len(withheld), + } diagnostics_rows = [ { "name": row.name, - "estimate": row.final_estimate, + "estimate": None if row.name in withheld else row.final_estimate, "target": row.target, - "relative_error": row.relative_error, + "relative_error": None if row.name in withheld else row.relative_error, + **( + {"model_figures_withheld": row.name in withheld} if withheld else {} + ), } for row in result.diagnostics ] @@ -1054,7 +1088,9 @@ def run(self, context: KernelContext) -> KernelResult: target_relative_errors={ str(row["name"]): float(row["relative_error"]) for row in diagnostics_rows - } + if str(row["name"]) not in withheld + }, + withheld_targets=withheld, ), "aggregate_admin": admin_totals, UK_CGT_PROJECTION_ARTIFACT_KEY: projection, @@ -1070,6 +1106,10 @@ def run(self, context: KernelContext) -> KernelResult: registry=UK_GATE_REGISTRY, ) blocking = report.blocking_outcomes(release_candidate=False) + report_payload = gate_phase_report_payload(report, gates=gates) + # The gates decide on the published rows and count the withheld ones; + # a report that names one stops the build before it leaves. + uk_refuse_named_withheld_rows(report_payload, names=withheld, keys=frozenset()) payload = { "schema_version": 1, "kind": "uk_national_gate_report", @@ -1077,7 +1117,7 @@ def run(self, context: KernelContext) -> KernelResult: "policy_suffix": str(context.params["gate_policy_suffix"]), "scope": list(scope), "scope_exclusions": dict(UK_CALIBRATION_GATE_SCOPE_EXCLUSIONS), - "report": gate_phase_report_payload(report, gates=gates), + "report": report_payload, "aggregate_admin_measurement": _json_safe(admin_receipt), "blocking": [outcome.entry.id for outcome in blocking], "artifact_permitted": not blocking, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py index a4f33ea1d..2cc39c6f4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py @@ -437,8 +437,8 @@ def assert_installed_engine_is_pinned() -> str: jobs and the H2 parity run them on the lock. Three things must hold. The distribution's version is the pinned one, - exactly, so a post-release or a local build of it (``2.134.4.post1``, - ``2.134.4+local``) is refused. ``policyengine_uk`` imports from that + exactly, so a post-release or a local build of it (``2.145.0.post1``, + ``2.145.0+local``) is refused. ``policyengine_uk`` imports from that distribution's own files, so a copy earlier on ``sys.path`` or an editable install is refused. And its ``__init__.py`` matches the distribution's RECORD, so a stale record beside other files is refused where the files diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 40df39f35..455b5b1d2 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -7,6 +7,7 @@ import math from collections.abc import Callable, Iterable, Mapping, Sequence from dataclasses import dataclass, replace +from datetime import date from functools import lru_cache from importlib import resources as importlib_resources from types import MappingProxyType @@ -543,21 +544,49 @@ def compile_uk_target_registry( ) -#: DfT BUS05ai fare receipts are aligned from the publisher's year-ending-March -#: period to the calibration year with the BUS0415 local bus fares index (a -#: ruling of 2026-09-10: calendar-year basis). The contract declares the index -#: on the fare-receipt rows (``uprating_index``); net support declares none and -#: is never aligned. The index series is read from the vendored resource -#: ``dft_bus_value_anchors.json`` (hash-pinned to the same Chronicle feed as -#: the references), so the factor is reproducible without the licensed feed. +#: DfT BUS05ai fare receipts are aligned from the publisher's April-to-March +#: year to the calibration year on policyengine-uk's own window (María's ruling +#: of 2026-10-21 on microcosm#1331, which replaced the calendar-year basis of +#: 2026-09-10): the engine's model year t runs from 6 April t to 5 April t+1 +#: and day-weights the BUS0415a quarter-end index over it, each value in force +#: from the start of the quarter it ends (``gov.dft.bus.local_bus_fares_index``, +#: ``fiscal_year_blend``, PolicyEngine/policyengine-uk#2546). London and +#: England outside London move by their own series, and England's receipts are +#: their sum, as the engine prices each area by its own index. The contract +#: declares the index on the fare-receipt rows (``uprating_index``); net +#: support declares none and is never aligned. The index and the receipts are +#: read from the vendored resource ``dft_bus_value_anchors.json`` (hash-pinned +#: to the same Chronicle feed as the references), so the factor is +#: reproducible without the licensed feed. UK_DFT_BUS_FARE_RECEIPTS_CONCEPT = "dft.local_bus_passenger_fare_receipts" UK_DFT_BUS_FARES_INDEX_CONCEPT = "dft.local_bus_fares_index" UK_DFT_BUS_FARES_INDEX_RESOURCE = "dft_bus_value_anchors.json" UK_DFT_BUS_FARE_INDEX_BASIS = ( - "mean of the quarter-end BUS0415 index (March, June, September, December) " - "inside the calibration calendar year over the mean of the four quarter-ends " - "inside the receipts' April-to-March fiscal year" + "the BUS0415a quarter-end index, each value in force from the start of the " + "quarter it ends, day-weighted over 6 April to 5 April of the calibration " + "year over the same in the receipts' fiscal year (policyengine-uk's " + "fiscal_year_blend); England's receipts move as London's and England " + "outside London's, each by its own series" ) +UK_DFT_BUS_LONDON_ID = "E12000007" +UK_DFT_BUS_ENGLAND_OUTSIDE_LONDON_ID = "dft:england_outside_london" +UK_DFT_BUS_ENGLAND_ID = "E92000001" +#: The areas England's fare receipts are the sum of, each moved by its own series. +UK_DFT_BUS_ENGLAND_PARTS: tuple[str, ...] = ( + UK_DFT_BUS_LONDON_ID, + UK_DFT_BUS_ENGLAND_OUTSIDE_LONDON_ID, +) +#: policyengine-uk's ``local_bus_fares_index`` area for each series the +#: receipts move by (the parity check reads the engine's ratio by these). +UK_ENGINE_BUS_INDEX_AREA_BY_SERIES: Mapping[str, str] = MappingProxyType( + { + UK_DFT_BUS_LONDON_ID: "LONDON", + UK_DFT_BUS_ENGLAND_OUTSIDE_LONDON_ID: "ENGLAND_OUTSIDE_LONDON", + } +) +#: How far London's and England outside London's receipts may sum from +#: England's before the split refuses (the vendored values are unrounded). +_UK_DFT_BUS_ENGLAND_SUM_TOLERANCE = 1e-9 def _vendored_fares_index_rows() -> list[Mapping[str, Any]]: @@ -570,6 +599,100 @@ def _vendored_fares_index_rows() -> list[Mapping[str, Any]]: ) +def _vendored_fare_receipt_rows(fiscal_start_year: int) -> list[Mapping[str, Any]]: + """BUS05ai fare receipts for the fiscal year opening in April of a year.""" + + return vendored_rows( + UK_DFT_BUS_FARES_INDEX_RESOURCE, + concept=UK_DFT_BUS_FARE_RECEIPTS_CONCEPT, + fiscal_start=f"{int(fiscal_start_year):04d}-04-01", + ) + + +def _fare_receipts(rows: Iterable[Mapping[str, Any]], geography_id: str) -> float: + values = { + float(row["value"]) + for row in rows + if isinstance(row.get("geography"), Mapping) + and row["geography"].get("id") == geography_id + and row.get("concept") == UK_DFT_BUS_FARE_RECEIPTS_CONCEPT + and isinstance(row.get("value"), (int, float)) + and not isinstance(row.get("value"), bool) + } + if len(values) != 1: + raise ValueError( + f"BUS05ai fare receipts for {geography_id!r} must be one value, " + f"found {sorted(values)}." + ) + return values.pop() + + +def _quarter_start(month: str) -> date: + """The first day of the quarter a quarter-end month (``YYYY-MM``) closes.""" + + year, number = (int(part) for part in month.split("-")) + if number not in (3, 6, 9, 12): + raise ValueError(f"BUS0415 month {month!r} does not end a quarter.") + return date(year, number - 2, 1) + + +def uk_bus_fares_index_fiscal_year_blend( + series: Mapping[str, tuple[float, str]], year: int +) -> tuple[float, tuple[str, ...]]: + """One BUS0415 series over policyengine-uk's model year ``year``. + + The day-weighted mean over 6 April ``year`` to 5 April ``year + 1`` of the + value in force each day, where a quarter-end value is in force from the + start of the quarter it closes until the next one: how the engine dates + ``gov.dft.bus.local_bus_fares_index`` and blends it (``fiscal_year_blend``, + PolicyEngine/policyengine-uk#2546). The last published value holds after + its quarter, as the engine's does until its uprating adds a value. Returns + the blend and the quarter-end months it read; refuses when no value is in + force on 6 April. + """ + + start, end = date(int(year), 4, 6), date(int(year) + 1, 4, 6) + dated = sorted( + (_quarter_start(month), float(value), month) + for month, (value, _record) in series.items() + ) + if not dated or dated[0][0] > start: + raise ValueError( + f"BUS0415 series has no value in force on {start.isoformat()}." + ) + boundaries = [start, *(when for when, _, _ in dated if start < when < end), end] + total = 0.0 + months: list[str] = [] + for first, last in zip(boundaries, boundaries[1:], strict=False): + _when, value, month = next(item for item in reversed(dated) if item[0] <= first) + total += value * (last - first).days + months.append(month) + return total / (end - start).days, tuple(months) + + +def uk_bus_fare_alignment_factors( + from_year: int, + to_year: int, + *, + index_rows: Iterable[Mapping[str, Any]] | None = None, +) -> dict[str, float]: + """The receipts' alignment factor by engine area, from one model year to another. + + The BUS0415 series the fare receipts move by, keyed by the engine's + ``local_bus_fares_index`` area: what the parity check compares with the + engine's own index ratio. + """ + + rows = list(index_rows) if index_rows is not None else _vendored_fares_index_rows() + factors = {} + for series_id, engine_area in UK_ENGINE_BUS_INDEX_AREA_BY_SERIES.items(): + series = _fares_index_series(rows, series_id) + from_blend, _ = uk_bus_fares_index_fiscal_year_blend(series, from_year) + to_blend, _ = uk_bus_fares_index_fiscal_year_blend(series, to_year) + factors[engine_area] = to_blend / from_blend + return factors + + def _fares_index_series( rows: Iterable[Mapping[str, Any]], geography_id: str ) -> dict[str, tuple[float, str]]: @@ -598,37 +721,25 @@ def _fares_index_series( return series -def _quarter_end_months(start_year: int, start_month: int) -> tuple[str, ...]: - months = [] - year, month = start_year, start_month - for _ in range(4): - # advance to the next quarter-end month (3, 6, 9, 12) on or after - # (year, month), then step past it - while month not in (3, 6, 9, 12): - month += 1 - if month > 12: - month, year = 1, year + 1 - months.append(f"{year:04d}-{month:02d}") - month += 1 - if month > 12: - month, year = 1, year + 1 - return tuple(months) - - def align_dft_bus_fare_receipts_to_period( reference: LedgerTargetReference, registry: TargetRegistry, *, index_rows: Iterable[Mapping[str, Any]] | None = None, + receipt_rows: Iterable[Mapping[str, Any]] | None = None, ) -> TargetRegistry: """Transport BUS05ai fare receipts to the reference period with BUS0415. Applies to references that declare ``uprating_index`` equal to the BUS0415 - concept; any other reference passes through untouched. The generator and - the runtime both call this on the compiled registry, so the committed - membership, the parity receipts and the run manifest carry one value. The - factor is declared on the spec's metadata with the index concept, the - series geography, the months used, their source record ids and the + concept; any other reference passes through untouched. The reference + period is policyengine-uk's model year (6 April to 5 April), and each + area moves by its own series blended over that window + (:func:`uk_bus_fares_index_fiscal_year_blend`); England's receipts move as + London's plus England outside London's. The generator and the runtime + both call this on the compiled registry, so the committed membership, the + parity receipts and the run manifest carry one value. The factor is + declared on the spec's metadata with the index concept, each area's + series, blends, months, source record ids and receipt share, and the pre-alignment value. """ @@ -645,7 +756,7 @@ def align_dft_bus_fare_receipts_to_period( if reference.period is None or not str(reference.period).isdigit(): raise ValueError( f"UK reference {reference.name!r}: BUS0415 alignment needs a " - "calendar-year reference period." + "model-year reference period." ) target_period = int(str(reference.period)) rows = list(index_rows) if index_rows is not None else _vendored_fares_index_rows() @@ -659,37 +770,93 @@ def align_dft_bus_fare_receipts_to_period( "resolved Ledger geography and fiscal start year." ) start_year = int(fact_period) - from_months = _quarter_end_months(start_year, 4) - to_months = _quarter_end_months(target_period, 1) - series = _fares_index_series(rows, geography_id) - missing = [m for m in (*from_months, *to_months) if m not in series] - if missing: - raise ValueError( - f"UK target {spec.name!r}: BUS0415 series {geography_id!r} lacks " - f"quarter-end month(s) {missing}; refusing to align fare receipts." + parts = ( + UK_DFT_BUS_ENGLAND_PARTS + if geography_id == UK_DFT_BUS_ENGLAND_ID + else (geography_id,) + ) + areas: dict[str, dict[str, Any]] = {} + record_ids: list[str] = [] + for part in parts: + series = _fares_index_series(rows, part) + try: + from_blend, from_months = uk_bus_fares_index_fiscal_year_blend( + series, start_year + ) + to_blend, to_months = uk_bus_fares_index_fiscal_year_blend( + series, target_period + ) + except ValueError as error: + raise ValueError( + f"UK target {spec.name!r}: BUS0415 series {part!r}: {error} " + "Refusing to align fare receipts." + ) from error + if from_blend <= 0 or to_blend <= 0: + raise ValueError( + f"UK target {spec.name!r}: BUS0415 blends must be positive." + ) + areas[part] = { + "from_blend": from_blend, + "to_blend": to_blend, + "factor": to_blend / from_blend, + "from_months": from_months, + "to_months": to_months, + } + record_ids.extend( + series[month][1] for month in dict.fromkeys((*from_months, *to_months)) ) - from_mean = sum(series[m][0] for m in from_months) / len(from_months) - to_mean = sum(series[m][0] for m in to_months) / len(to_months) - if from_mean <= 0 or to_mean <= 0: - raise ValueError( - f"UK target {spec.name!r}: BUS0415 index means must be positive." + if len(parts) == 1: + areas[parts[0]]["receipts_share"] = 1.0 + factor = areas[parts[0]]["factor"] + else: + receipts = ( + list(receipt_rows) + if receipt_rows is not None + else _vendored_fare_receipt_rows(start_year) + ) + values = {part: _fare_receipts(receipts, part) for part in parts} + total = math.fsum(values.values()) + if not total > 0 or ( + abs(total / spec.value - 1.0) > _UK_DFT_BUS_ENGLAND_SUM_TOLERANCE + ): + raise ValueError( + f"UK target {spec.name!r}: London's and England outside " + "London's fare receipts do not sum to England's; refusing " + "to align them area by area." + ) + for part in parts: + areas[part]["receipts_share"] = values[part] / total + factor = ( + math.fsum(values[part] * areas[part]["factor"] for part in parts) + / total ) - factor = to_mean / from_mean - record_ids = ",".join(series[m][1] for m in (*from_months, *to_months)) metadata = { **spec.metadata, "uprating_index": UK_DFT_BUS_FARES_INDEX_CONCEPT, "uprating_index_basis": UK_DFT_BUS_FARE_INDEX_BASIS, "uprating_index_resource": UK_DFT_BUS_FARES_INDEX_RESOURCE, - "uprating_index_series_geography_id": geography_id, - "uprating_index_from_months": ",".join(from_months), - "uprating_index_to_months": ",".join(to_months), - "uprating_index_from_mean": f"{from_mean:.15g}", - "uprating_index_to_mean": f"{to_mean:.15g}", - "uprating_index_source_record_ids": record_ids, + "uprating_index_series_geography_id": ",".join(parts), + "uprating_index_areas": json.dumps( + { + part: { + "from_blend": f"{area['from_blend']:.15g}", + "to_blend": f"{area['to_blend']:.15g}", + "factor": f"{area['factor']:.15g}", + "receipts_share": f"{area['receipts_share']:.15g}", + "from_months": list(area["from_months"]), + "to_months": list(area["to_months"]), + } + for part, area in areas.items() + }, + sort_keys=True, + ), + "uprating_index_source_record_ids": ",".join(record_ids), "uprating_factor": f"{factor:.15g}", "ledger_value_before_alignment": f"{spec.value:.15g}", - "uprating_adjudication": "microcosm#890 (ruling 2026-09-10: calendar-year basis)", + "uprating_adjudication": ( + "microcosm#1331 (María's ruling of 2026-10-21: policyengine-uk's " + "April-to-March window, London and outside London apart)" + ), } aligned.append(replace(spec, value=spec.value * factor, metadata=metadata)) return TargetRegistry(aligned, country="uk") 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 2aa5a5adb..d96584ee0 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 @@ -707,32 +707,51 @@ def _engine_scratch_frame( return engine_frame, dropped -def engine_bus_pricing_parameters( - tax_benefit_system: Any, year: int -) -> dict[str, dict[str, float]]: +def engine_bus_pricing_parameters(tax_benefit_system: Any, year: int) -> dict[str, Any]: """policyengine-uk's bus boardings per trip and yield per boarding by area. Read at ``year`` in the resolver's own period form (the engine resolves a year to its 1 January instant) for every area the stage prices (``UK_ENGINE_BUS_AREA_BY_STAGE_AREA``); raises AttributeError when the - engine carries no such parameters. + engine carries no such parameters. An engine that moves the benchmark + yield by the published fares index adds its ``benchmark_year`` and + ``local_bus_fares_index_ratio``, the index in ``year`` over the benchmark + year's, by the areas the fare receipts move by. """ from microcosm.build.uk_runtime.bus_fare_pricing import ( UK_ENGINE_BUS_AREA_BY_STAGE_AREA, ) + from microcosm.build.uk_runtime.ledger_targets import ( + UK_ENGINE_BUS_INDEX_AREA_BY_SERIES, + ) parameters = getattr(tax_benefit_system, "parameters", None) if parameters is None: raise AttributeError("the engine exposes no parameters") bus = parameters(str(int(year))).gov.dft.bus - return { + engine: dict[str, Any] = { name: { area: float(getattr(getattr(bus, name), area)) for area in UK_ENGINE_BUS_AREA_BY_STAGE_AREA.values() } for name in ("boardings_per_trip", "yield_per_boarding") } + # From policyengine-uk 2.145.0 (PolicyEngine/policyengine-uk#2546) the + # benchmark yield moves with each area's fares index, read as the engine + # reads it: the model year's blend over the benchmark year's. + index = getattr(bus, "local_bus_fares_index", None) + benchmark_year = getattr(bus, "benchmark_year", None) + if index is not None and benchmark_year is not None: + benchmark = parameters( + f"{int(benchmark_year)}-01-01" + ).gov.dft.bus.local_bus_fares_index + engine["benchmark_year"] = int(benchmark_year) + engine["local_bus_fares_index_ratio"] = { + area: float(getattr(index, area)) / float(getattr(benchmark, area)) + for area in UK_ENGINE_BUS_INDEX_AREA_BY_SERIES.values() + } + return engine class UKMeasureResolver: @@ -1222,9 +1241,20 @@ def _bus_engine_parity(self) -> dict[str, Any]: "reason": str(error), } else: + from microcosm.build.uk_runtime.ledger_targets import ( + uk_bus_fare_alignment_factors, + ) + parity = bus_engine_parity( bus_fare_prices(declared, allowed_resources=UK_LCFS_VENDORED_RESOURCES), engine, + alignment_factors=( + uk_bus_fare_alignment_factors( + int(engine["benchmark_year"]), int(self.year) + ) + if "benchmark_year" in engine + else None + ), ) self._bus_parity = parity return parity diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_role.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_role.py index 5ffc89ce7..91206d19e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_role.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_role.py @@ -195,6 +195,10 @@ def national_dry_run( _national_dry_run = national_dry_run +def _optional_float(value: object) -> float | None: + return None if value is None else float(value) + + def national_fit_by_family( diagnostics: Mapping[str, Any], registry: TargetRegistry ) -> list[dict[str, object]]: @@ -319,8 +323,10 @@ def output(role: str) -> dict[str, Any]: }, "n_households": int(solve["n_households"]), "pool_households": int(solve["n_households"]), - "initial_loss": float(solve["initial_loss"]), - "final_loss": float(solve["final_loss"]), + # Restated over the published rows when a row is withheld, and + # null where the solve cannot restate (microcosm#1262). + "initial_loss": _optional_float(solve["initial_loss"]), + "final_loss": _optional_float(solve["final_loss"]), "n_nonzero": int(solve["n_nonzero"]), "max_abs_relative_error": ( float(abs_errors.max()) if abs_errors.size else None diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/nts_bus_travel.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/nts_bus_travel.py index c7b0eadaf..34fb29412 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/nts_bus_travel.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/nts_bus_travel.py @@ -192,6 +192,14 @@ def boarding_class_column(series: str, boarding_class: str) -> str: RESIDENCE_GROUPS: tuple[str, str] = (LONDON_GROUP, REST_OF_ENGLAND_GROUP) FREQUENCY_SOURCE_INTERVIEW = "interview_band" FREQUENCY_SOURCE_PUBLISHED_SHARES = "published_shares" +#: The disclosure floor of the fare-mix receipt lines (UK Data Service End User +#: Licence CD137 clause 8 and CD171 section 5.2.1, whose working floor is 10 +#: cases): a line, and its complement, must rest on at least this many +#: unweighted boardings and this many distinct households, because one +#: respondent makes many boardings; otherwise it reads ``"<10"``. +UK_NTS_RECEIPT_MINIMUM_BOARDINGS = 10 +UK_NTS_RECEIPT_MINIMUM_HOUSEHOLDS = 10 +UK_NTS_RECEIPT_SUPPRESSED = "<10" class NTSBusTravelError(ValueError): @@ -412,6 +420,57 @@ def from_parameters(cls, parameters: Mapping[str, Any]) -> NTSColumns: ) +@dataclass(frozen=True) +class FareCapRegime: + """The declared fare-cap regime of the donor's survey years (microcosm#1331). + + England's per-journey cap outside London (``cap_fare``) covers the whole of + each of ``cap_years`` (an NTS survey year is a calendar year) and none of + ``pre_cap_years``. The single-fare share is read on the cap years, because + most of the base year sits under the same cap: nine months of FRS 2024-25 + under England's £2 cap, the last three under the £3 cap, which no survey + year of the extract observes. The pre-cap years show the uncapped fare + mix. A fare paid within ``cap_tolerance`` of the cap pays the cap. + """ + + cap_fare: float + cap_years: tuple[int, ...] + pre_cap_years: tuple[int, ...] + cap_tolerance: float + + @classmethod + def from_parameters( + cls, parameters: Mapping[str, Any], *, survey_years: Sequence[int] + ) -> FareCapRegime: + regime = _mapping(parameters.get("fare_cap_regime"), "derive.fare_cap_regime") + declared = {int(year) for year in survey_years} + cap_years = tuple(int(year) for year in regime.get("cap_years", ())) + pre_cap_years = tuple(int(year) for year in regime.get("pre_cap_years", ())) + if not cap_years or not set(cap_years) <= declared: + raise NTSBusTravelError( + "derive.fare_cap_regime.cap_years must name declared survey years " + f"{sorted(declared)}." + ) + if not set(pre_cap_years) <= declared or set(pre_cap_years) & set(cap_years): + raise NTSBusTravelError( + "derive.fare_cap_regime.pre_cap_years must be declared survey years " + "outside the cap years." + ) + cap_fare = float(regime.get("cap_fare", 0.0)) + cap_tolerance = float(regime.get("cap_tolerance", 0.0)) + if not cap_fare > 0.0 or not 0.0 <= cap_tolerance < cap_fare: + raise NTSBusTravelError( + "derive.fare_cap_regime needs a positive cap_fare and a " + "cap_tolerance from 0 up to below it." + ) + return cls( + cap_fare=cap_fare, + cap_years=cap_years, + pre_cap_years=pre_cap_years, + cap_tolerance=cap_tolerance, + ) + + def _mapping(value: object, label: str) -> Mapping[str, Any]: if not isinstance(value, Mapping): raise NTSBusTravelError(f"{label} must be a mapping.") @@ -466,6 +525,7 @@ def clean_nts_travel_tables( *, columns: NTSColumns, cars_clip: tuple[int, int] = (0, 5), + fare_cap: FareCapRegime | None = None, ) -> NTSDonor: """One row per NTS individual of the declared survey years, with predictors. @@ -481,7 +541,9 @@ def clean_nts_travel_tables( positive boarding cost), a non-concessionary season ticket or pass, a concessionary pass (the declared ticket codes), free with no ticket, or unknown (no cost recorded); the share of fare-paying boardings paid at the - point of use is the single-fare share a per-journey cap binds on. + point of use is the single-fare share a per-journey cap binds on. With + ``fare_cap`` the ticket receipt also carries the fare-mix lines of + England outside London before and under the cap. """ hh = _lower(household) @@ -716,7 +778,9 @@ def clean_nts_travel_tables( .to_numpy(dtype=float) * WEEKS_IN_YEAR ) - ticket_receipt = _ticket_receipt(stg, person, hh_keep, columns) + ticket_receipt = _ticket_receipt( + stg, person, hh_keep, columns, fare_cap=fare_cap + ) person = person.rename( columns={ columns.individual_id: "individual_id", @@ -781,13 +845,16 @@ def _ticket_receipt( person: pd.DataFrame, hh_keep: pd.DataFrame, columns: NTSColumns, + *, + fare_cap: FareCapRegime | None = None, ) -> dict[str, Any]: """Boarding payment classes, fares paid and boardings per trip on the diary. - Weighted by the trip factor (the trip weight on the diary household - weight basis); by residence group and survey year, so the fare paid at the - point of use can be read before and after a fare cap. Disclosure-safe - aggregates only. + Weighted by the full trip weight (the diary household weight W2 times the + within-household trip factor, W5 x JJXSC on the NTS basis), as the + single-fare share is; by residence group and survey year, so the fare paid + at the point of use can be read before and after a fare cap. + Disclosure-safe aggregates only. """ person_group = person.set_index(columns.individual_id)["residence_group"] @@ -796,7 +863,16 @@ def _ticket_receipt( year = pd.to_numeric( stg[columns.stage_individual_id].map(person_year), errors="coerce" ) - w = stg["weighted_boardings"].to_numpy(dtype=float) + # The stage's trip factor has W2 divided out for the person sums the + # W2-weighted shares read; a boarding-level aggregate multiplies it back, + # or every diary household would count alike. + household_weight = ( + stg[columns.stage_individual_id] + .map(person.set_index(columns.individual_id)["weight"]) + .to_numpy(dtype=float) + ) + w = stg["weighted_boardings"].to_numpy(dtype=float) * household_weight + trip_weight = stg["trip_factor"].to_numpy(dtype=float) * household_weight out: dict[str, Any] = {"by_group_and_year": {}, "by_series": {}} for g in RESIDENCE_GROUPS: for y in sorted(int(v) for v in year.dropna().unique()): @@ -842,9 +918,9 @@ def _ticket_receipt( ), "boardings_per_bus_trip": ( float( - stg.loc[cell, "weighted_boardings"].sum() - / stg.loc[cell] - .groupby(columns.stage_trip_id)["trip_factor"] + w[cell.to_numpy()].sum() + / pd.Series(trip_weight[cell.to_numpy()]) + .groupby(stg.loc[cell, columns.stage_trip_id].to_numpy()) .first() .sum() ) @@ -854,13 +930,105 @@ def _ticket_receipt( } out["basis"] = ( "bus stages of the diary sample, each stage a boarding weighted by its " - "trip's weight; fare paid at the point of use = no ticket record and a " + "trip's full weight (W2 x the within-household trip factor); fare paid at the point of use = no ticket record and a " "positive boarding cost; season_or_pass = a non-concessionary ticket " "record; concessionary_pass = the declared concessionary ticket codes" ) + if fare_cap is not None: + household = stg[columns.stage_individual_id].map( + person.set_index(columns.individual_id)[columns.household_id] + ) + out["fare_mix"] = _fare_mix_receipt( + stg, + group.to_numpy(), + year.to_numpy(dtype=float), + household.to_numpy(), + w, + fare_cap, + ) return out +def _fare_mix_receipt( + stg: pd.DataFrame, + group: np.ndarray, + year: np.ndarray, + household: np.ndarray, + w: np.ndarray, + fare_cap: FareCapRegime, +) -> dict[str, Any]: + """Fare-mix lines for England outside London, before and under the cap (microcosm#1331). + + Point-of-use boardings only (singles, returns and day tickets bought for + the journey), weighted as the boarding classes are. Before the cap: the + share paid above the cap, by more than the tolerance, and their mean fare, + at that year's prices. Under the cap: the share paid at the cap, within + the tolerance. A line reads ``"<10"`` unless it and its complement each + rest on ``UK_NTS_RECEIPT_MINIMUM_BOARDINGS`` unweighted boardings and + ``UK_NTS_RECEIPT_MINIMUM_HOUSEHOLDS`` distinct households. Shares are + rounded to two decimals and fares to the penny; no minimum, maximum or + percentile is reported. + """ + + fare = stg["fare_per_boarding"].to_numpy(dtype=float) + paid = ( + (group == REST_OF_ENGLAND_GROUP) + & (stg["boarding_class"] == BOARDING_FARE_PAID).to_numpy() + & np.isfinite(fare) + & (w > 0) + ) + + def supported(mask: np.ndarray) -> bool: + return ( + int(mask.sum()) >= UK_NTS_RECEIPT_MINIMUM_BOARDINGS + and int(pd.Series(household[mask]).nunique()) + >= UK_NTS_RECEIPT_MINIMUM_HOUSEHOLDS + ) + + def share(base: np.ndarray, part: np.ndarray) -> float | str: + if not (supported(part) and supported(base & ~part)): + return UK_NTS_RECEIPT_SUPPRESSED + return round(float(w[part].sum() / w[base].sum()), 2) + + def mean_fare(part: np.ndarray) -> float | str: + if not supported(part): + return UK_NTS_RECEIPT_SUPPRESSED + return round(float(np.average(fare[part], weights=w[part])), 2) + + finite = np.where(np.isfinite(fare), fare, 0.0) + above_cap = paid & (finite > fare_cap.cap_fare + fare_cap.cap_tolerance) + at_cap = paid & (np.abs(finite - fare_cap.cap_fare) <= fare_cap.cap_tolerance) + before_cap = {} + for y in fare_cap.pre_cap_years: + base = paid & (year == y) + before_cap[str(y)] = { + "share_above_cap": share(base, base & above_cap), + "mean_fare_above_cap": mean_fare(base & above_cap), + } + under_cap = {} + for y in fare_cap.cap_years: + base = paid & (year == y) + under_cap[str(y)] = {"share_at_cap": share(base, base & at_cap)} + return { + "residence_group": REST_OF_ENGLAND_GROUP, + "cap_fare": fare_cap.cap_fare, + "cap_tolerance": fare_cap.cap_tolerance, + "before_cap": before_cap, + "under_cap": under_cap, + "minimum_cell": { + "boardings": UK_NTS_RECEIPT_MINIMUM_BOARDINGS, + "households": UK_NTS_RECEIPT_MINIMUM_HOUSEHOLDS, + }, + "basis": ( + "point-of-use boardings (singles, returns and day tickets bought for " + "the journey) of residents of England outside London; the fare is the " + "price paid, so it is capped in the cap years and the uncapped mix " + "shows before the cap only, at that year's prices (uprate by the " + "BUS0415a index before comparing it with fares at other prices)" + ), + } + + def nts_support_ranges(donor: pd.DataFrame) -> dict[str, tuple[float, float]]: """Per-column donor min/max of the support-clip columns (the E6 bounds input). @@ -1133,15 +1301,29 @@ def draw_band_from_published_shares( def single_fare_shares_from_donor( donor: pd.DataFrame, + *, + years: Sequence[int] | None = None, ) -> tuple[dict[str, dict[str, dict[int, float]]], dict[str, Any]]: """Share of fare-paying boardings paid at the point of use, per series, group and band. - Weighted by the diary household weight over the cell's boardings. A cell - with no fare-paying boardings takes its group's all-band share, then the - all-England share, receipted; a series with no fare-paying boardings at - all is refused. + Weighted by the diary household weight over the cell's boardings. + ``years`` keeps the donors of those survey years only: the stage passes + the declared cap years, so the share a per-journey cap binds on is read + under a cap, as most of the base year's is (microcosm#1331); ``None`` pools every + donor year. A cell with no fare-paying boardings takes its group's + all-band share, then the all-England share, receipted; a series with no + fare-paying boardings at all is refused. """ + donor_years = pd.to_numeric(donor["survey_year"], errors="coerce") + if years is not None: + keep = donor_years.isin([int(year) for year in years]).to_numpy() + donor = donor.loc[keep] + donor_years = donor_years.loc[keep] + if donor.empty: + raise NTSBusTravelError( + f"no donor persons in the single-fare share's survey years {list(years)}." + ) weights = donor["weight"].to_numpy(dtype=float) bands = donor[BAND_COLUMN].to_numpy() if BAND_COLUMN in donor else None groups = donor["residence_group"].to_numpy().astype(str) @@ -1183,6 +1365,7 @@ def share( shares[series][group][band] = float(value) receipt = { "source": "donor_diary_boarding_classes", + "survey_years": sorted(int(year) for year in donor_years.dropna().unique()), "all_england_single_share": overall, "shares": { series: { @@ -1598,6 +1781,9 @@ def __call__(self, frame: Frame) -> Frame: assert_rules_engine_country(self.engine, "uk") derive = _operation(self.stage, CLEAN_NTS_TRAVEL_TABLES_KIND) columns = NTSColumns.from_parameters(derive) + fare_cap = FareCapRegime.from_parameters( + derive, survey_years=columns.survey_years + ) cars_clip = tuple(int(v) for v in derive.get("cars_clip", (0, 5))) donor = clean_nts_travel_tables( self._table( @@ -1617,6 +1803,7 @@ def __call__(self, frame: Frame) -> Frame: ), columns=columns, cars_clip=(cars_clip[0], cars_clip[1]), + fare_cap=fare_cap, ) band_parameters = _operation(self.stage, IMPUTE_BUS_USE_BAND_KIND) income_edges = [float(v) for v in band_parameters["income_band_edges"]] @@ -1653,7 +1840,7 @@ def __call__(self, frame: Frame) -> Frame: ) means, means_receipt = band_means_from_donor(donor.person) single_fare_shares, shares_receipt = single_fare_shares_from_donor( - donor.person + donor.person, years=fare_cap.cap_years ) else: imputation = draw_band_from_published_shares( @@ -1667,7 +1854,7 @@ def __call__(self, frame: Frame) -> Frame: self.stage, band_shares=shares ) single_fare_shares, shares_receipt = single_fare_shares_from_donor( - donor.person + donor.person, years=fare_cap.cap_years ) band = imputation.band trips = assign_trips_from_band_means( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/orrery_contract.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/orrery_contract.py index 2d6081181..62f0895e8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/orrery_contract.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/orrery_contract.py @@ -31,17 +31,6 @@ from .graph_targets import TARGET_SELECTION_TYPE, registry_from_payload from .graph_terminal import FULL_GATE_REPORT_TYPE - -def _summary_level(metadata) -> str: - """A target's geography level as the diagnostics read it: ``la`` and - ``statistical_scope`` are the local-authority and BRMA levels.""" - - from .diagnostics import _GEOGRAPHY_LEVEL_ALIASES - - level = str(metadata.get("geography_level", "")).strip().lower() - return _GEOGRAPHY_LEVEL_ALIASES.get(level, level) - - #: A summary row's fields that the result's weights produce. _SUMMARY_MODEL_FIELDS = ( "initial", @@ -53,24 +42,26 @@ def _summary_level(metadata) -> str: def _withhold_summary_rows(data: dict, result, ordered) -> None: - """Keep a thin BRMA row's figures out of the published summary (#1262). + """Keep a thin row's figures out of the published summary (#1262). María's ruling of 2026-10-17: a BRMA row's model figures leave a build only when ten distinct source households carry them, here counted on this - result's own weights. A withheld row keeps its target and its place, and - the final loss covers the published rows (her ruling of 2026-10-18). + result's own weights; her rule of 2026-10-21 extends it to national and + regional rows (local rows keep the area-support rule). A withheld row + keeps its target and its place, and the final loss covers the published + rows (her ruling of 2026-10-18). """ - from .diagnostics import _restate_totals_over_published_rows, uk_withheld_brma_rows + from .diagnostics import _restate_totals_over_published_rows, uk_withheld_rows rows = data["tables"]["targets"] - withheld = uk_withheld_brma_rows( + withheld = uk_withheld_rows( ordered.problem, result.weights, result.frame, rows, { - target.row_name: _summary_level(metadata) + target.row_name: metadata.get("geography_level", "") for target, metadata in zip( ordered.problem.targets, ordered.target_metadata, strict=True ) @@ -193,13 +184,13 @@ def _size_summary(context: ArtifactSummaryContext) -> dict: "pi_hi", ) summary = {key: value[key] for key in fields if key in value} - # Totals over every row: with a BRMA row withheld they would give it back - # by difference (microcosm#1262). Without the weights behind them this - # summary cannot tell, so a problem that binds BRMA rows publishes none. - if any(_summary_level(metadata) == "brma" for metadata in problem.target_metadata): - for key in ("dense_loss", "compact_loss", "max_target_scaled_change"): - if key in summary: - summary[key] = None + # Totals over every row: with a row withheld they would give it back by + # difference (microcosm#1262). Without the weights behind them this + # summary cannot tell which rows are thin, and since María's rule of + # 2026-10-21 any problem may bind one, so it publishes none. + for key in ("dense_loss", "compact_loss", "max_target_scaled_change"): + if key in summary: + summary[key] = None summary["problem_sha256"] = problem.sha256 for key in ("support", "protected", "household_ids", "pool_row_indices"): if key in value: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/release_certification.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/release_certification.py index 7bece76f0..aa75456f8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/release_certification.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/release_certification.py @@ -238,6 +238,7 @@ def uk_release_parity_evidence( """ target_relative_errors: dict[str, float] = {} + withheld: set[str] = set() for row in diagnostics_targets: name = str(row["name"]) if "@" not in name: @@ -246,6 +247,13 @@ def uk_release_parity_evidence( "grain; the reference side is keyed name@period, so a " "bare-name row would silently fall out of the comparison." ) + metadata = row.get("metadata") + if isinstance(metadata, Mapping) and metadata.get("model_figures_withheld"): + # Fewer than ten source households carry it (María's rule of + # 2026-10-21): bound, published without its figures, and outside + # the fit gate's verdict. + withheld.add(name) + continue target_relative_errors[name] = float(row["relative_error"]) return SimpleNamespace( candidate_columns=uk_export_candidate_columns(frame), @@ -253,11 +261,12 @@ def uk_release_parity_evidence( f"{entity}.{name}" for name, entity in parity_reference.input_entities.items() }, - candidate_targets=set(target_relative_errors), + candidate_targets=set(target_relative_errors) | withheld, reference_targets={ f"{spec.name}@{spec.period}" for spec in reference_registry.specs }, target_relative_errors=target_relative_errors, + withheld_targets=frozenset(withheld), ) @@ -371,6 +380,16 @@ def run_uk_release_cut_battery( battery.run_phase( "terminal", EvidenceContext(frame=frame, artifacts=terminal_artifacts) ) + from microcosm.build.uk_runtime.diagnostics import uk_refuse_named_withheld_rows + + # The gates decide on the published rows and count the withheld ones + # (María's rule of 2026-10-21 binds a thin national row); a report that + # names one stops before the certification uses it. + uk_refuse_named_withheld_rows( + battery.report_payload(), + names=frozenset(parity_evidence.withheld_targets), + keys=frozenset(), + ) battery.enforce("terminal", mode=BlockingMode.BLOCKS_ARTIFACT) payload = battery.report_payload() finalize_uk_scoped_gate_report( 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 32cc952bc..93dfb7a49 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 @@ -1,4 +1,12 @@ -"""UK salary-sacrifice QRF and headcount support conversion.""" +"""UK salary-sacrifice QRF and headcount support conversion. + +policyengine-uk's pension_contributions_via_salary_sacrifice_pre_2015 stays at +its default of nil: the FRS records no start date for an arrangement, so all +salary sacrifice counts in threshold income for the tapered annual allowance +(FA 2004 s. 228ZA(6)). For arrangements made before 9 July 2015 that +overstates threshold income, and so the taper and the number of people the +annual allowance charge reaches, in any comparison with HMRC. +""" from __future__ import annotations diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py index 16f0da3ba..7b20158b8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py @@ -120,7 +120,12 @@ ) #: Reports that follow an injury, service or a death, not income: the SPI row #: keeps the FRS twin's value. -SPI_RESTORED_REPORT_COLUMNS = ("iidb_reported", "afcs_reported", "bsp_reported") +SPI_RESTORED_REPORT_COLUMNS = ( + "iidb_reported", + "afcs_reported", + "bsp_reported", + "war_widows_pension_reported", +) SPI_RESTORED_REPORT_REASON = "follows an injury, service or a death, not income" SPI_TWIN_LINKAGE = "person_source_id -> FRS-channel person_id" SPI_BENEFITS_IN_OWN_RIGHT_OUTPUT = "receives_benefits_in_own_right" diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py index 606e30767..410e6cdc9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py @@ -238,6 +238,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py index eae1d0fcc..c973dca09 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py @@ -182,6 +182,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "incapacity_benefit_reported", "maternity_allowance_reported", "winter_fuel_allowance_reported", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py index 65628c9c6..bcd76673d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py @@ -1,18 +1,22 @@ -"""The sparse-band support floor (microcosm#1095; María's ruling of 2026-10-16). - -A target row the spine supports with only a few source households cannot be -fitted honestly: the solve reaches it by stretching those few weights, and a -fit that rests on fewer than ten source households is the kind of cell the -release must never report. The UC payment-distribution bands at the edges of -each family are such rows, and before this floor each was held out by a -per-row exclusion with its own expiry (uk-data#452, microcosm#736). +"""The zero-support floor (microcosm#1095; María's rulings of 2026-10-16 and 2026-10-21). + +A target row that no source household carries cannot be fitted by any +weights: the solver's initialiser refuses a nonzero target with no support. A +row that some households carry stays bound however few they are: a cell that +rests on fewer than ten records is no reason to leave a row out, and only a +measured distortion of the calibration is (María's rule of 2026-10-21, which +replaced the floor of ten she signed on 2026-10-16). What the licence asks of +such a row is that its model figures stay in the build: the diagnostics, the +gate evidence and every written surface withhold the figures of a row that +fewer than ten distinct source households carry +(:func:`microcosm.build.uk_runtime.diagnostics.uk_withheld_rows`). The floor is declared once, in the ``support_floor`` block of -``uk/calibration_measure_exclusions.json`` (schema 3), and signed there. On a -built frame, a row in its scope whose matrix row reaches fewer than ``floor`` -distinct source households (``household_source_id``) with a positive prior -weight is dropped, not pooled; each target's dropped published total is -reported. The run refuses outside the block's approval window, when the +``uk/calibration_measure_exclusions.json`` (schema 3), and signed there; its +``floor`` is 1, the only value it may take. On a built frame, a row in its +scope whose matrix row reaches no source household (``household_source_id``) +with a positive prior weight is dropped, not pooled; each target's dropped +published total is reported. The run refuses outside the block's approval window, when the dropped rows pass either declared ceiling, and when a per-row exclusion names a row the floor would drop (the floor covers it, so the entry must retire). The dropped set joins the calibration's measure exclusions as @@ -37,6 +41,10 @@ from microcosm.calibrate import TargetRegistry UK_SUPPORT_FLOOR_KEY = "support_floor" +#: The one floor the register may sign: a row that any source household +#: carries stays bound (María's rule of 2026-10-21), so the floor leaves out +#: only the rows no weights can reach. +UK_SUPPORT_FLOOR_VALUE = 1 UK_SUPPORT_FLOOR_KIND = "support_floor" UK_SUPPORT_FLOOR_METRIC = "distinct_source_households_with_positive_prior_weight" #: The spine household each row was built from; every clone shares it. @@ -137,9 +145,15 @@ def uk_support_floor_from_payload(block: Mapping[str, Any]) -> UKSupportFloor: floor = block["floor"] rows = block["max_dropped_rows"] share = block["max_dropped_published_share"] - if not isinstance(floor, int) or isinstance(floor, bool) or floor < 3: + if ( + not isinstance(floor, int) + or isinstance(floor, bool) + or floor != UK_SUPPORT_FLOOR_VALUE + ): raise ValueError( - "UK support floor must be an integer of at least 3, CD171's floor." + f"UK support floor must be {UK_SUPPORT_FLOOR_VALUE}: a row that any source " + "household carries stays bound however few they are (María's rule of " + "2026-10-21), and the figures of a thin row are withheld, not the row." ) if not isinstance(rows, int) or isinstance(rows, bool) or rows < 0: raise ValueError("UK support floor max_dropped_rows must be a count.") 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 68668a957..6f8d9cdce 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 @@ -29,6 +29,7 @@ uk_time_period, validate_uk_national_frame, ) +from microcosm.build.uk_runtime.receipt_counts import rounded_count from microcosm.build.uk_runtime.spi_support import ( BASE_FRS_SUPPORT_CHANNEL, SPI_SYNTHETIC_SUPPORT_CHANNEL, @@ -74,14 +75,24 @@ _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 2.134.4 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 -#: ``corporate_wealth``). A recorded figure of 0 or more replaces every source, -#: and the engine takes it as already countable, but TOTCAPB4 counts accounts -#: and assets and no property. So the recorded figures add the household's -#: property for the unit that holds it (uk-data#495, microcosm#1095). +#: policyengine-uk 2.145.0 counts these household property values as Universal +#: Credit capital (``property_sources`` under +#: ``gov.dwp.universal_credit.means_test.capital``, at 90% of value less any +#: secured debt since 2.136.1) and as Pension Credit capital (the ``sources`` +#: list under ``gov.dwp.pension_credit.income.capital``, beside ``savings`` and +#: ``corporate_wealth``). A recorded figure of 0 or more is a unit's complete +#: capital: it replaces every source, property included, and the engine takes +#: it as already countable, but TOTCAPB4 counts accounts and assets and no +#: property. So the recorded figures add the household's property for the unit +#: that holds it (uk-data#495, microcosm#1095). +#: +#: Both recorded figures project to later years as financial capital does, by +#: GDP per head (the engine's ``data/uprating_indices.yaml``), while from +#: 2.139.1 the engine projects other residential property by house prices; +#: non-residential property and owned land stay on GDP per head. So the other +#: residential property in a recorded figure grows faster than the engine's +#: own projection of it: by the OBR indices the engine carries, 1.5% faster by +#: 2025 and 2.0% by 2026 from a 2024 base. UC_PROPERTY_CAPITAL_SOURCES = ( "other_residential_property_value", "non_residential_property_value", @@ -92,14 +103,16 @@ #: 2013 reg 18; SPCA 2002 s.5). The household reference person, in whose name #: the home is owned or rented, is the best single reading of the owner, so #: that person's benefit unit holds all of it and no other unit records a -#: share (María's ruling of 2026-10-12, microcosm#1095). policyengine-uk'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. Such a unit takes the gross value, -#: with no sale costs. Beside a recording unit, the Universal Credit proxy -#: shares only what that unit did not record. The Pension Credit proxy -#: subtracts nothing, so a pension-age unit on the sentinel is assessed a -#: share of property the reference person's unit also records. +#: share (María's ruling of 2026-10-12, microcosm#1095). policyengine-uk shares +#: household property over every claimant or partner instead, which puts a +#: parent's property on an adult child's claim; a recorded figure consumes its +#: own unit's share, so the engine's shares reach only a unit on the +#: unavailable sentinel. Since 2.136.1 such a unit takes its share of the +#: property at 90%, whatever the other units record, and the Pension Credit +#: proxy, which subtracts nothing, takes a share of the gross value: beside the +#: reference person's recording unit, both count property that unit already +#: records. The receipt counts any unit left there +#: (``sentinel_units_beside_recorded_property``). #: #: A consequence of the ruling: where the reference person is of working age, #: the property of a pension-age unit in the household is recorded on the @@ -324,7 +337,7 @@ def cohere_uc_capital( # uk-data#513): 0 or more replaces every capital source in the assessable # capital, so the household reference person's unit adds the household's # property to its financial carrier. The unavailable sentinel -1 is the - # engine's own default, so the household proxy applies there and the + # engine's own default, so the engine's own shares apply there and the # sentinel passes through unchanged. A unit that reports Universal Credit # keeps its receipt and records no UC share. uc_unreported_share = uc_property_capital_share(person, benunit, household) @@ -351,6 +364,26 @@ def cohere_uc_capital( }, uc_unreported_share=uc_unreported_share, ) + # From policyengine-uk 2.136.1 a unit on the sentinel takes its own share + # of its household's property, which a recording unit beside it already + # holds, so the property counts twice there. + benunit_household = ( + person.drop_duplicates("person_benunit_id") + .set_index("person_benunit_id")["person_household_id"] + .reindex(benunit["benunit_id"]) + .to_numpy() + ) + # Either scheme: a unit that reports Universal Credit records no UC share + # but may still record the household's Pension Credit property. + recording = (np.asarray(capital) >= 0.0) & ( + (uc_share > 0.0) | (pension_credit_share > 0.0) + ) + sentinel_beside = (np.asarray(capital) == UC_CAPITAL_UNAVAILABLE) & np.isin( + benunit_household, benunit_household[recording] + ) + property_receipt["sentinel_units_beside_recorded_property"] = rounded_count( + int(sentinel_beside.sum()) + ) result_frame = uk_national_frame( person=person, 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 9b7b2f4e0..ff1d9f91a 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 = ( - "751721b231ab8cc6b3d10164f61ca429f5171661951494503fb0873e3f67b117" + "ceffa14029b2f4818fa1749684361a6de4d38358c27d3173cc2b0d47721ef3a4" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_bus_engine_parity.py b/packages/microcosm-build/tests/engine/uk/test_uk_bus_engine_parity.py index 95967bd48..10315cc84 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_bus_engine_parity.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_bus_engine_parity.py @@ -15,6 +15,7 @@ UK_LCFS_VENDORED_RESOURCES, bus_pricing_operation, ) +from microcosm.build.uk_runtime.ledger_targets import uk_bus_fare_alignment_factors from microcosm.build.uk_runtime.measure_simulation import ( engine_bus_pricing_parameters, ) @@ -32,7 +33,8 @@ def test_engine_bus_parameters_stand_beside_the_stages() -> None: prices = bus_fare_prices( bus_pricing_operation(stage), allowed_resources=UK_LCFS_VENDORED_RESOURCES ) - parity = bus_engine_parity(prices, engine) + factors = uk_bus_fare_alignment_factors(int(engine["benchmark_year"]), 2025) + parity = bus_engine_parity(prices, engine, alignment_factors=factors) # The engine's values come from the same published derivation (#954): a # re-pin or a restatement can open a small gap, which the receipt marks; a # gap of a whole per cent would mean the two derive different things. @@ -41,3 +43,8 @@ def test_engine_bus_parameters_stand_beside_the_stages() -> None: assert abs(entry[name]["relative_gap"]) < 0.01, (name, entry) assert isinstance(parity["within_tolerance"], bool) assert parity["tolerance"] == pytest.approx(0.001) + # The fare receipts move on the engine's index window (microcosm#1331), so + # at the locked engine each area's factor ties to its index ratio. + assert set(parity["fares_index_ratio"]) == set(factors) + for area, check in parity["fares_index_ratio"].items(): + assert check["within_tolerance"], (area, check) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py b/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py index 05f789e22..d665c6ca3 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py @@ -3,7 +3,10 @@ The root stage writes ``esa_includes_support_component`` from the FRS ESA group (ESAGROUP, VAR3). A stored value replaces the engine's default, which assumes the support group for anyone with an ESA award of their own, so the -benefit cap's ESA exemption follows the survey's answer. +benefit cap's ESA exemption follows the survey's answer. Since +policyengine-uk 2.135.0 each scheme applies its own cap: this tenant on +income-related ESA meets Housing Benefit's, whose specified-benefit exemption +reads the stored group. """ from __future__ import annotations @@ -15,7 +18,10 @@ from microcosm.frame import WeightKind from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine -_READ = ["esa_includes_support_component", "is_benefit_cap_exempt_health_disability"] +_READ = [ + "esa_includes_support_component", + "is_housing_benefit_benefit_cap_exempt_specified_benefit", +] def _frame(in_support_group: bool): @@ -58,5 +64,6 @@ def test_the_benefit_cap_exemption_follows_the_stored_esa_group() -> None: assert bool(support["esa_includes_support_component"][0]) assert not bool(work_related["esa_includes_support_component"][0]) - assert bool(support["is_benefit_cap_exempt_health_disability"][0]) - assert not bool(work_related["is_benefit_cap_exempt_health_disability"][0]) + exemption = "is_housing_benefit_benefit_cap_exempt_specified_benefit" + assert bool(support[exemption][0]) + assert not bool(work_related[exemption][0]) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py index 364e4e174..f198b13f6 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py @@ -114,9 +114,9 @@ def _assessable_capital(frame): def test_the_engine_assesses_the_recorded_property_and_no_other_unit_takes_it() -> None: """The household reference person's unit records the household's property at 90% of its value, and policyengine-uk takes each recorded figure as the - unit's assessable capital. Its own proxies, which apply only to a unit on - the sentinel, would put a third of the parents' property on their child's - unit (microcosm#1095).""" + unit's complete assessable capital. Its own property share, which reaches + only a unit on the sentinel, would put a third of the parents' property on + their child's unit (microcosm#1095).""" unavailable = [-1.0, -1.0, -1.0] proxied = _capital_frame(unavailable, unavailable) @@ -149,12 +149,11 @@ def test_the_engine_assesses_the_recorded_property_and_no_other_unit_takes_it() pension_credit_recorded, [pension_credit_share[0], 0.0, 0.0], rtol=1e-6 ) - # The engine's UC proxy shares household 1's gross property over its three + # Since policyengine-uk 2.136.1 the engine values property apart, at 90% + # of its value less secured debt, and shares it over the household's # claimants and partners, so the child's unit would be assessed a third. uc_proxy, _ = _assessable_capital(proxied) - np.testing.assert_allclose( - uc_proxy, [200_000.0 / 3, 100_000.0 / 3, 50_000.0], rtol=1e-6 - ) + np.testing.assert_allclose(uc_proxy, [60_000.0, 30_000.0, 45_000.0], rtol=1e-6) # A unit that reports Universal Credit keeps its receipt: it records the # carrier alone, the engine assesses it no property, and its child's unit, @@ -170,11 +169,13 @@ def test_the_engine_assesses_the_recorded_property_and_no_other_unit_takes_it() def test_a_unit_on_the_sentinel_beside_a_recording_unit_takes_the_engines_proxy() -> ( None ): - """A unit on the unavailable sentinel stays on policyengine-uk's proxies, - which share the household's gross property. Beside a recording reference - person's unit, the Universal Credit proxy leaves it the 10% that unit did - not record, and the Pension Credit proxy, which subtracts nothing, counts - the property a second time (microcosm#1095).""" + """A unit on the unavailable sentinel stays on policyengine-uk's own rules. + Since 2.136.1 Universal Credit gives it its claimant and partner share of + the household's property at 90%, whatever the other units record, so + beside a recording reference person's unit it counts that property a + second time; the Pension Credit proxy, which subtracts nothing, does the + same. The build records every unit, so no unit is on the sentinel + (microcosm#1095).""" unavailable = [-1.0, -1.0, -1.0] tables = { @@ -188,16 +189,16 @@ def test_a_unit_on_the_sentinel_beside_a_recording_unit_takes_the_engines_proxy( tables["person"], tables["benunit"], tables["household"] ) - # The parents record and their child's unit is on the sentinel: the - # Universal Credit residual gives the child the 10,000 of sale costs the - # parents' 90,000 left out of household 1's 100,000. + # The parents record and their child's unit is on the sentinel: the child + # takes a third of household 1's property at 90%, which the parents' + # recorded 90,000 already holds. uc_mixed, pension_credit_mixed = _assessable_capital( _capital_frame( [uc_share[0], -1.0, uc_share[2]], [pension_credit_share[0], -1.0, pension_credit_share[2]], ) ) - np.testing.assert_allclose(uc_mixed, [90_000.0, 10_000.0, 45_000.0], rtol=1e-6) + np.testing.assert_allclose(uc_mixed, [90_000.0, 30_000.0, 45_000.0], rtol=1e-6) np.testing.assert_allclose(pension_credit_mixed, [117_000.0, 0.0, 0.0], rtol=1e-6) @@ -208,8 +209,8 @@ def test_property_held_by_a_working_age_reference_person_leaves_pension_credit() child's unit records the household's property and the parents record none. policyengine-uk reads Pension Credit capital only for a unit with a member at the qualifying age, so no Pension Credit assessment counts it. Parents - on the sentinel take the engine's gross proxy instead, beside the child's - recorded figure (microcosm#1095).""" + on the sentinel take the engine's own property share instead, beside the + child's recorded figure (microcosm#1095).""" unavailable = [-1.0, -1.0, -1.0] tables = { @@ -246,5 +247,6 @@ def test_property_held_by_a_working_age_reference_person_leaves_pension_credit() child_heads=True, ) ) - np.testing.assert_allclose(uc_mixed, [10_000.0, 90_000.0, 45_000.0], rtol=1e-6) + # Universal Credit gives the parents two thirds of the property at 90%. + np.testing.assert_allclose(uc_mixed, [60_000.0, 90_000.0, 45_000.0], rtol=1e-6) np.testing.assert_allclose(pension_credit_mixed, [130_000.0, 0.0, 0.0], rtol=1e-6) 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 d5af44b78..ae9b149b0 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 @@ -33,7 +33,7 @@ #: policyengine-core its own floor brings. UK_ENGINE = { "policyengine-core": "3.33.3", - "policyengine-uk": "2.134.4", + "policyengine-uk": "2.145.0", } ENGINE_PACKAGES = ("microcosm-build", "microcosm-data", "microcosm-frame") diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py index 522e04f76..caca7a8b0 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py @@ -170,6 +170,7 @@ def _parity(**overrides) -> SimpleNamespace: "candidate_targets": {"ons/population"}, "reference_targets": {"ons/population"}, "target_relative_errors": {"ons/population": 0.01}, + "withheld_targets": frozenset(), } fields.update(overrides) return SimpleNamespace(**fields) @@ -530,6 +531,54 @@ def test_uc_column_implication_binding_checks_both_capitals_with_property( for column in ("uc_reported_capital", "pension_credit_reported_capital"): assert columns[f"benunit.{column}"]["same_source_mismatch_count"] == 1 + @pytest.mark.parametrize( + ("reference_reports_uc", "expected"), + [ + (False, {"uc_reported_capital": 1, "pension_credit_reported_capital": 1}), + (True, {"uc_reported_capital": 0, "pension_credit_reported_capital": 1}), + ], + ids=["both_schemes", "pension_credit_only"], + ) + def test_uc_column_implication_binding_refuses_a_sentinel_unit_beside_property( + self, reference_reports_uc: bool, expected: dict[str, int] + ) -> None: + # From policyengine-uk 2.136.1 a unit on the sentinel takes its own + # share of its household's property, which the recording unit beside + # it already holds (Vahid's review of microcosm#1333). Unit 204 joins + # household 2 on the sentinel, beside the reference person's unit 202, + # which records the property: both schemes' shares, or Pension + # Credit's alone where unit 202 reports Universal Credit. + person, benunit, household = _tables() + person.loc[3, "person_household_id"] = 2 + person.loc[3, "is_household_head"] = False + for column in ( + "frs_benunit_capital", + "uc_reported_capital", + "pension_credit_reported_capital", + ): + benunit.loc[3, column] = -1.0 + if reference_reports_uc: + person.loc[1, "universal_credit_reported"] = 10.0 + benunit.loc[1, "would_claim_uc"] = True + benunit.loc[1, "uc_reported_capital"] = benunit.loc[ + 1, "frs_benunit_capital" + ] + frame = uk_national_frame( + person=person, + benunit=benunit, + household=household.iloc[:3].copy(), + time_period="2023", + ) + entry = self._capital_gate() + result = UK_GATE_REGISTRY["column_implication"].evaluate( + EvidenceContext(frame=frame), entry.parameters + ) + + assert not result.passed + assert result.details["sentinel_units_beside_recorded_property"] == expected + beside = [failure for failure in result.failures if "would count" in failure] + assert len(beside) == sum(expected.values()) + def test_uc_column_implication_binding_keeps_a_reporters_receipt( self, ) -> None: @@ -1037,6 +1086,37 @@ def test_seeded_defects_fail_the_expected_gates(self) -> None: "uk_target_fit", } <= failed + def test_the_fit_gate_decides_on_the_published_national_rows(self) -> None: + """María's rule of 2026-10-21: a thin national row is bound, its figures kept. + + The fit gate neither checks nor names a withheld row, and counts it; a + published row's breach still fails the gate. + """ + + thin = "dwp.uc.payment_distribution_single_thin@2025" + withheld = _parity( + candidate_targets={"ons/population", thin}, + reference_targets={"ons/population", thin}, + target_relative_errors={"ons/population": 0.01, thin: -0.60}, + withheld_targets=frozenset({thin}), + ) + battery = _run_battery(_tables(), parity=withheld) + fit = {o.entry.id: o for o in battery.outcomes}["uk_target_fit"] + assert fit.status is GateStatus.PASSED, fit.result.failures + assert fit.result.details["withheld_targets"] == 1 + assert thin not in str(fit.result.details) + breached = _parity( + candidate_targets={"ons/population", thin}, + reference_targets={"ons/population", thin}, + target_relative_errors={"ons/population": -0.40, thin: -0.60}, + withheld_targets=frozenset({thin}), + ) + battery = _run_battery(_tables(), parity=breached) + fit = {o.entry.id: o for o in battery.outcomes}["uk_target_fit"] + assert fit.status is GateStatus.FAILED + assert fit.result.failures + assert all(thin not in failure for failure in fit.result.failures) + class TestUnevidencedArms: """Missing evidence is explicit; it blocks release candidates, plus any diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_fare_pricing.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_fare_pricing.py index 6d015722a..f39eb709b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_fare_pricing.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_fare_pricing.py @@ -449,6 +449,47 @@ def test_bus_engine_parity_marks_each_area_against_the_engine() -> None: assert ni["within_tolerance"] is False and drifted["within_tolerance"] is False +def test_bus_engine_parity_ties_the_receipts_factor_to_the_engines_index() -> None: + """microcosm#1331: the fare receipts move on the engine's index, so they tie.""" + + from microcosm.build.uk_runtime.bus_fare_pricing import ( + UK_ENGINE_BUS_AREA_BY_STAGE_AREA, + bus_engine_parity, + ) + from microcosm.build.uk_runtime.ledger_targets import ( + uk_bus_fare_alignment_factors, + ) + + prices = bus_fare_prices(_declared(), allowed_resources=UK_LCFS_VENDORED_RESOURCES) + areas = { + prices.london.label: prices.london, + **{area.label: area for area in prices.other_by_region.values()}, + } + factors = uk_bus_fare_alignment_factors(2024, 2025) + engine = { + "boardings_per_trip": { + UK_ENGINE_BUS_AREA_BY_STAGE_AREA[label]: area.boardings_per_trip + for label, area in areas.items() + }, + "yield_per_boarding": { + UK_ENGINE_BUS_AREA_BY_STAGE_AREA[label]: area.yield_per_fare_paying_boarding + for label, area in areas.items() + }, + "benchmark_year": 2024, + "local_bus_fares_index_ratio": dict(factors), + } + parity = bus_engine_parity(prices, engine, alignment_factors=factors) + assert set(parity["fares_index_ratio"]) == {"LONDON", "ENGLAND_OUTSIDE_LONDON"} + assert parity["within_tolerance"] is True + # An engine still on the calendar factor outside London misses by 1.3%. + engine["local_bus_fares_index_ratio"]["ENGLAND_OUTSIDE_LONDON"] = 1.112 + drifted = bus_engine_parity(prices, engine, alignment_factors=factors) + check = drifted["fares_index_ratio"]["ENGLAND_OUTSIDE_LONDON"] + assert check["within_tolerance"] is False and drifted["within_tolerance"] is False + # Without factors, or an engine without the index, there is nothing to tie. + assert "fares_index_ratio" not in bus_engine_parity(prices, engine) + + def _journey_cell(area: str, fact_period: str) -> TargetSpec: return TargetSpec( name=f"dft.bus_journeys.{area}", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_targets.py index e26358bee..3bc587769 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_bus_targets.py @@ -28,6 +28,28 @@ } +#: The June 2026 BUS0415a release blended over policyengine-uk's model years +#: (6 April to 5 April, each quarter-end value in force from the start of its +#: quarter): 2024-25 is 86 days at June 2024, 92 at September, 92 at +#: December, 90 at March 2025 and 5 at June 2025; 2025-26 is 86, 92 and 92 +#: days at June, September and December 2025 and 95 at March 2026, the last +#: published quarter (microcosm#1331). +_OUTSIDE_LONDON_FACTOR = ( + 210.5 * 86 + 213.6 * 92 + 214.3 * 92 + 217.95569364121366 * 95 +) / (181.6 * 86 + 185.7 * 92 + 185.9 * 92 + 206.4 * 90 + 210.5 * 5) +# London's fares were held; DfT's unrounded March 2026 value lifts it a little. +_LONDON_FACTOR = (196.1 * 270 + 196.10649454164795 * 95) / 365 / 196.1 +#: BUS05ai fare receipts for 2024-25: London and England outside London sum +#: to England's, and England's receipts move as the two parts. +_LONDON_RECEIPTS = 1_347_434_943.01459 +_OUTSIDE_LONDON_RECEIPTS = 2_069_953_713.42079 +_ENGLAND_RECEIPTS = 3_417_388_656.43538 +_ENGLAND_FACTOR = ( + _LONDON_RECEIPTS * _LONDON_FACTOR + + _OUTSIDE_LONDON_RECEIPTS * _OUTSIDE_LONDON_FACTOR +) / _ENGLAND_RECEIPTS + + def _resource(name: str) -> dict: return json.loads( importlib_resources.files("microcosm.build.uk").joinpath(name).read_text() @@ -114,9 +136,11 @@ def test_bus_targets_are_active_or_signed_excluded_as_declared() -> None: assert set(holds[target_id]) == {"name", "from", "to"} (candidate,) = membership["targets"][target_id]["candidates"] assert "uprating" not in candidate - assert float(holds["dft.bus_fare_receipts.london"]["factor"]) == pytest.approx(1.0) + assert float(holds["dft.bus_fare_receipts.london"]["factor"]) == pytest.approx( + _LONDON_FACTOR + ) assert float(holds["dft.bus_fare_receipts.england"]["factor"]) == pytest.approx( - 204.125 / 193.125 + _ENGLAND_FACTOR ) # The London rows pin the publisher's region-stamped fact, not a country # row, and the England rows keep the country pin. @@ -143,7 +167,11 @@ def test_london_bus_rows_resolve_the_publisher_partition() -> None: ("dft.bus_net_support.london", "dft.local_bus_total_estimated_net_support"), ): resolved = membership[target_id]["candidates"][0]["resolved_value"] - assert resolved == pytest.approx(published[(concept, "london")], abs=1.0) + # Fare receipts move by London's index; net support is never aligned. + factor = _LONDON_FACTOR if target_id.startswith("dft.bus_fare") else 1.0 + assert resolved == pytest.approx( + published[(concept, "london")] * factor, abs=1.0 + ) def test_bus_selectors_do_not_consume_nts_or_bus0415() -> None: @@ -277,11 +305,43 @@ def _index_facts(geography: dict, values: dict[str, float]) -> list[dict]: ] -def test_quarter_end_months_follow_the_fiscal_and_calendar_windows() -> None: - from microcosm.build.uk_runtime.ledger_targets import _quarter_end_months +def test_the_fiscal_year_blend_follows_the_engines_window() -> None: + """policyengine-uk's fiscal_year_blend (PolicyEngine/policyengine-uk#2546). - assert _quarter_end_months(2024, 4) == ("2024-06", "2024-09", "2024-12", "2025-03") - assert _quarter_end_months(2025, 1) == ("2025-03", "2025-06", "2025-09", "2025-12") + A quarter-end value is in force from the start of the quarter it closes, + and the model year runs from 6 April to 5 April. + """ + from microcosm.build.uk_runtime.ledger_targets import ( + uk_bus_fare_alignment_factors, + uk_bus_fares_index_fiscal_year_blend, + ) + + series = { + "2024-03": (100.0, "a"), + "2024-06": (100.0, "b"), + "2024-09": (100.0, "c"), + "2024-12": (100.0, "d"), + "2025-03": (120.0, "e"), + "2025-06": (120.0, "f"), + } + blend, months = uk_bus_fares_index_fiscal_year_blend(series, 2024) + # 6 April to 31 December 2024 at 100, then 1 January to 5 April 2025 at + # 120 (the June quarter's value is in force from 1 April). + assert blend == pytest.approx((100.0 * 270 + 120.0 * 95) / 365) + assert months == ("2024-06", "2024-09", "2024-12", "2025-03", "2025-06") + # The last published value holds after its quarter. + held, _ = uk_bus_fares_index_fiscal_year_blend(series, 2025) + assert held == pytest.approx(120.0) + with pytest.raises(ValueError, match="in force"): + uk_bus_fares_index_fiscal_year_blend({"2024-09": (100.0, "c")}, 2024) + with pytest.raises(ValueError, match="does not end a quarter"): + uk_bus_fares_index_fiscal_year_blend({"2024-05": (100.0, "x")}, 2024) + # On the vendored June 2026 BUS0415a release, the factors from 2024-25 to + # 2025-26 match the engine's index (#2546: +12.6% outside London on the + # April-to-March basis; London's fares held). + factors = uk_bus_fare_alignment_factors(2024, 2025) + assert round(factors["ENGLAND_OUTSIDE_LONDON"], 3) == 1.126 + assert round(factors["LONDON"], 4) == 1.0 def _vendored_index_rows(geography: dict, values: dict[str, float]) -> list[dict]: @@ -301,7 +361,29 @@ def _vendored_index_rows(geography: dict, values: dict[str, float]) -> list[dict ] +def _vendored_receipt_row(geography: dict, value: float) -> dict: + """A BUS05ai receipts row in the vendored-resource shape (2024-25).""" + + return { + "concept": "dft.local_bus_passenger_fare_receipts", + "geography": geography, + "period": {"type": "fiscal_year", "value": 2025}, + "period_coverage": { + "start_date": "2024-04-01", + "end_date": "2025-03-31", + "basis": "fiscal", + }, + "value": value, + "source_record_id": f"probe.dft.receipts.{geography['id']}", + } + + def test_fare_receipts_align_with_the_bus0415_series_and_support_does_not() -> None: + """María's ruling of 2026-10-21 (microcosm#1331): the engine's window, area by area. + + London's receipts move by London's series and England's as London's plus + England outside London's, each blended over 6 April to 5 April. + """ from microcosm.build.ledger_targets import ( LedgerTargetReference, compile_ledger_target_references, @@ -312,14 +394,20 @@ def test_fare_receipts_align_with_the_bus0415_series_and_support_does_not() -> N ) england = {"id": "E92000001", "level": "country", "vintage": "current"} + london = {"id": "E12000007", "level": "region", "vintage": "current"} + outside = {"id": "dft:england_outside_london", "level": "country"} coverage = {"start_date": "2024-04-01", "end_date": "2025-03-31", "basis": "fiscal"} - fare = _fare_fact( - "dft.local_bus_passenger_fare_receipts", - england, - {"type": "fiscal_year", "value": 2025}, - 1_000.0, - coverage=coverage, - ) + + def receipts(geography: dict, value: float) -> dict: + return _fare_fact( + "dft.local_bus_passenger_fare_receipts", + geography, + {"type": "fiscal_year", "value": 2025}, + value, + coverage=coverage, + ) + + fare = receipts(england, 1_000.0) support = _fare_fact( "dft.local_bus_total_estimated_net_support", england, @@ -327,29 +415,28 @@ def test_fare_receipts_align_with_the_bus0415_series_and_support_does_not() -> N 500.0, coverage=coverage, ) - index = _vendored_index_rows( - england, - { - "2024-03": 100.0, - "2024-06": 100.0, - "2024-09": 100.0, - "2024-12": 100.0, - "2025-03": 120.0, - "2025-06": 120.0, - "2025-09": 120.0, - "2025-12": 120.0, - }, + flat = dict.fromkeys( + ("2024-03", "2024-06", "2024-09", "2024-12", "2025-03"), 100.0 + ) | dict.fromkeys(("2025-06", "2025-09", "2025-12", "2026-03"), 100.0) + rising = dict.fromkeys(("2024-03", "2024-06", "2024-09", "2024-12"), 100.0) | ( + dict.fromkeys(("2025-03", "2025-06", "2025-09", "2025-12", "2026-03"), 120.0) ) + index = _vendored_index_rows(london, flat) + _vendored_index_rows(outside, rising) + split = [ + _vendored_receipt_row(london, 400.0), + _vendored_receipt_row(outside, 600.0), + ] def reference(name: str, concept: str, **extra: object) -> LedgerTargetReference: + geography = extra.pop("geography", england) return LedgerTargetReference( name=name, ledger_selector={ "source_name": "dft", "source_concept": concept, "period_type": "fiscal_year", - "geography_level": "country", - "geography_id": "E92000001", + "geography_level": geography["level"], + "geography_id": geography["id"], }, entity="household", measure=name, @@ -375,25 +462,53 @@ def reference(name: str, concept: str, **extra: object) -> LedgerTargetReference assert fares.specs[0].metadata["uprating_index"] == "dft.local_bus_fares_index" assert fares.specs[0].value == 1_000.0 aligned = align_dft_bus_fare_receipts_to_period( - fare_reference, fares, index_rows=index + fare_reference, fares, index_rows=index, receipt_rows=split ) (spec,) = aligned.specs - # from-window mean = (100+100+100+120)/4 = 105; to-window mean = 120 - assert spec.value == pytest.approx(1_000.0 * 120.0 / 105.0) - assert spec.metadata["uprating_index"] == "dft.local_bus_fares_index" + # Outside London 2024-25 blends 270 days at 100 and 95 at 120, and 2025-26 + # is 120 throughout; London's series is flat. England's receipts are 400 + # London and 600 outside London. + outside_factor = 120.0 / ((100.0 * 270 + 120.0 * 95) / 365) + assert outside_factor == pytest.approx(1.140625) + assert spec.value == pytest.approx(400.0 + 600.0 * outside_factor) + assert spec.metadata["uprating_factor"] == f"{spec.value / 1_000.0:.15g}" assert spec.metadata["uprating_index_resource"] == "dft_bus_value_anchors.json" assert ( - spec.metadata["uprating_index_from_months"] == "2024-06,2024-09,2024-12,2025-03" + spec.metadata["uprating_index_series_geography_id"] + == "E12000007,dft:england_outside_london" ) - assert ( - spec.metadata["uprating_index_to_months"] == "2025-03,2025-06,2025-09,2025-12" + areas = json.loads(spec.metadata["uprating_index_areas"]) + assert float(areas["E12000007"]["receipts_share"]) == pytest.approx(0.4) + assert float(areas["dft:england_outside_london"]["factor"]) == pytest.approx( + outside_factor ) + assert areas["dft:england_outside_london"]["to_months"] == [ + "2025-06", + "2025-09", + "2025-12", + "2026-03", + ] assert spec.metadata["ledger_value_before_alignment"] == "1000" - assert spec.metadata["uprating_factor"] == f"{120.0 / 105.0:.15g}" + assert "microcosm#1331" in spec.metadata["uprating_adjudication"] assert ( - "probe.dft.local_bus_fares_index.E92000001.2025-12" - in (spec.metadata["uprating_index_source_record_ids"]) + "probe.dft.local_bus_fares_index.dft:england_outside_london.2026-03" + in spec.metadata["uprating_index_source_record_ids"] + ) + # London alone moves by its own series. + london_reference = reference( + "london_fares", + "dft.local_bus_passenger_fare_receipts", + uprating_index="dft.local_bus_fares_index", + geography=london, ) + london_fares = compile_ledger_target_references( + [receipts(london, 400.0)], [london_reference], country="uk" + ) + (london_spec,) = align_dft_bus_fare_receipts_to_period( + london_reference, london_fares, index_rows=index + ).specs + assert london_spec.value == pytest.approx(400.0) + assert london_spec.metadata["uprating_index_series_geography_id"] == "E12000007" supports = compile_ledger_target_references( [support], [support_reference], country="uk" @@ -406,9 +521,23 @@ def reference(name: str, concept: str, **extra: object) -> LedgerTargetReference assert "uprating_factor" not in untouched.specs[0].metadata assert apply_declared_uk_uprating(support_reference, supports) is supports - with pytest.raises(ValueError, match="lacks quarter-end month"): + # The parts must add up to England, and each series must be in force. + with pytest.raises(ValueError, match="do not sum to England"): align_dft_bus_fare_receipts_to_period( - fare_reference, fares, index_rows=index[:-1] + fare_reference, + fares, + index_rows=index, + receipt_rows=[ + _vendored_receipt_row(london, 400.0), + _vendored_receipt_row(outside, 601.0), + ], + ) + late = _vendored_index_rows(london, flat) + _vendored_index_rows( + outside, {month: value for month, value in rising.items() if month > "2024-06"} + ) + with pytest.raises(ValueError, match="in force"): + align_dft_bus_fare_receipts_to_period( + fare_reference, fares, index_rows=late, receipt_rows=split ) # The index transports fare receipts only; declaring it on another # concept is a contract error, as is an index no UK applier implements. @@ -472,7 +601,7 @@ def test_bus0415_alignment_from_the_vendored_series() -> None: if reference.family == "dft_local_bus" } membership = _resource("target_reference_membership.json") - expected_factor = {"E92000001": 204.125 / 193.125, "E12000007": 1.0} + expected_factor = {"E92000001": _ENGLAND_FACTOR, "E12000007": _LONDON_FACTOR} for name, geography_id, groupby in ( ("dft.bus_fare_receipts.england", "E92000001", "england"), ("dft.bus_fare_receipts.london", "E12000007", "london"), @@ -536,12 +665,10 @@ def test_bus0415_alignment_on_the_pinned_feed() -> None: ) england = specs["dft.bus_fare_receipts.england"] london = specs["dft.bus_fare_receipts.london"] - assert float(england.metadata["uprating_factor"]) == pytest.approx( - 204.125 / 193.125 - ) - assert england.value == pytest.approx(3_417_388_656.43538 * 204.125 / 193.125) - assert float(london.metadata["uprating_factor"]) == pytest.approx(1.0) - assert london.value == pytest.approx(1_347_434_943.01459) + assert float(england.metadata["uprating_factor"]) == pytest.approx(_ENGLAND_FACTOR) + assert england.value == pytest.approx(_ENGLAND_RECEIPTS * _ENGLAND_FACTOR) + assert float(london.metadata["uprating_factor"]) == pytest.approx(_LONDON_FACTOR) + assert london.value == pytest.approx(_LONDON_RECEIPTS * _LONDON_FACTOR) assert "uprating_factor" not in specs["dft.bus_net_support.england"].metadata assert "uprating_factor" not in specs["dft.bus_net_support.london"].metadata diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py index a7ef17e6f..526b2a900 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py @@ -317,6 +317,13 @@ def _consumption_columns(n: int) -> dict[str, np.ndarray]: def _diagnostics_case(*, with_skipped: bool = False, with_consumption: bool = False): + """One row per geography level, each carried by one household. + + Every row rests on fewer than ten source households, so a test that is not + about withholding passes an empty withheld set; the withheld path has its + own tests (María's rule of 2026-10-21 withholds a thin row's figures). + """ + levels_and_weights = ( ("national", 11.0), ("region", 12.0), @@ -559,6 +566,7 @@ def test_payload_preserves_common_schema_and_adds_versioned_uk_evidence() -> Non frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), build={"release_id": "fixture"}, ) @@ -751,6 +759,7 @@ def test_payload_carries_the_consumption_drift_when_the_frame_has_it() -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) drift = payload["uk_diagnostics"]["consumption_drift"] @@ -780,6 +789,7 @@ def test_uk_payload_shared_layer_matches_shared_diagnostics_format() -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), build=build, ) @@ -836,6 +846,7 @@ def test_skipped_target_counts_as_a_geography_non_pass() -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) assert len(payload["skipped"]) == 1 @@ -863,6 +874,7 @@ def test_skipped_target_counts_as_a_geography_non_pass() -> None: frame, target_geography_levels=missing_skipped, target_registry=registry, + withheld=frozenset(), ) @@ -935,6 +947,7 @@ def test_writer_round_trips_strict_json(tmp_path: Path) -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) assert isinstance(outcome, DiagnosticsWriteSuccess) @@ -945,6 +958,7 @@ def test_writer_round_trips_strict_json(tmp_path: Path) -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) ) invalid = write_uk_calibration_diagnostics( @@ -953,6 +967,7 @@ def test_writer_round_trips_strict_json(tmp_path: Path) -> None: frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), build={"not_json": object()}, ) assert isinstance(invalid, DiagnosticsWriteFailure) @@ -986,6 +1001,7 @@ def test_written_uk_diagnostics_match_the_published_json_schema( target_geography_levels=geography, target_registry=registry, build={"release_id": "fixture"}, + withheld=frozenset(), ) assert isinstance(outcome, DiagnosticsWriteSuccess) @@ -1011,6 +1027,7 @@ def test_writer_rejects_nested_non_finite_build_provenance( frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), build={"nested": [{"value": value}]}, ) @@ -1039,6 +1056,7 @@ def fail_replace(_temporary, _output): frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) assert isinstance(outcome, DiagnosticsWriteFailure) @@ -1079,6 +1097,7 @@ def canonical_writer( frame, target_geography_levels=geography, target_registry=registry, + withheld=frozenset(), ) assert isinstance(outcome, DiagnosticsWriteSuccess) @@ -1290,6 +1309,335 @@ def test_a_thin_brma_row_keeps_its_place_but_not_its_model_figures() -> None: assert family["n_targets"] == 1 and family["worst_target"] == wide["name"] +def test_a_thin_national_row_keeps_its_place_but_not_its_model_figures() -> None: + """María's rule of 2026-10-21: a national row is bound however thin it is. + + The BRMA rule now covers national rows. One national row rests on three source + households and stays bound, its model figures null and noted; the other, + on eleven, publishes. The withheld block restates the totals over the + published row, and no row is told it met a BRMA floor. + """ + + n = 14 + sources = [0, 0, 1, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10] + household = pd.DataFrame( + { + "household_id": np.arange(n, dtype=np.int64), + "source_household_id": np.asarray(sources, dtype=np.int64), + _SPI_COLUMN: [False] * n, + _CG_COLUMN: [False] * n, + "thin_measure": [1.0] * 6 + [0.0] * 8, + "wide_measure": [1.0] * n, + } + ) + specs = [ + TargetSpec( + name=name, + entity="household", + value=10.0, + measure=measure, + period=2023, + source="Synthetic UK diagnostics fixture", + family="dwp_universal_credit", + hierarchy=_fixture_hierarchy(name, "national"), + ) + for name, measure in ( + ("thin_payment_band", "thin_measure"), + ("wide_payment_band", "wide_measure"), + ) + ] + geography = {f"{spec.name}@2023": "national" for spec in specs} + weights = np.ones(n) + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": np.arange(n, dtype=np.int64), + "person_household_id": np.arange(n, dtype=np.int64), + } + ), + "household": household, + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(weights, WeightKind.DESIGN)}, + ) + registry = TargetRegistry(specs, country="uk") + result = score_targets(frame, registry.to_target_set(), weights=weights) + payload = uk_calibration_diagnostics_payload( + result, + frame, + target_geography_levels=geography, + target_registry=registry, + ) + rows = {row["target_name"]: row for row in payload["targets"]} + thin, wide = rows["thin_payment_band"], rows["wide_payment_band"] + assert thin["final_estimate"] is None and thin["relative_error"] is None + assert "model_figures_withheld" in thin["metadata"] + assert "carrier_floor_met" not in (thin.get("metadata") or {}) + assert wide["final_estimate"] == pytest.approx(14.0) + (national,) = [ + row + for row in payload["uk_diagnostics"]["target_pass_rates_by_geography_level"] + if row["geography_level"] == "national" + ] + assert (national["n_targets"], national["n_scored"]) == (2, 2) + block = payload["uk_diagnostics"]["model_figures_withheld"] + assert block["targets"] == 1 and block["totals_basis"] == "published_rows" + assert payload["loss_trajectory"] == [] + + +def test_a_thin_local_area_row_stays_with_the_area_support_rule() -> None: + """Constituency and authority rows keep the local line's area-support rule. + + María's rule of 2026-10-21 on thin rows reaches national, regional and + BRMA rows; whether it reaches the local line is still hers to rule, and + the report guard matches a withheld local row by its bare area code. + """ + + from types import SimpleNamespace + + from scipy import sparse + + from microcosm.build.uk_runtime.diagnostics import uk_withheld_rows + + n = 12 + matrix = sparse.csr_array( + (np.ones(3), (np.zeros(3, dtype=int), np.arange(3))), shape=(1, n) + ) + problem = SimpleNamespace(matrix=matrix, names=["thin_row@2025"]) + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": np.arange(n, dtype=np.int64), + "person_household_id": np.arange(n, dtype=np.int64), + } + ), + "household": pd.DataFrame( + { + "household_id": np.arange(n, dtype=np.int64), + "source_household_id": np.arange(n, dtype=np.int64), + } + ), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(np.ones(n), WeightKind.DESIGN)}, + ) + rows = [{"name": "thin_row@2025"}] + for level, withheld in ( + ("national", True), + ("region", True), + ("brma", True), + ("statistical_scope", True), + ("constituency", False), + ("local_authority", False), + ("la", False), + ): + found = uk_withheld_rows( + problem, np.ones(n), frame, rows, {"thin_row@2025": level} + ) + assert (found == {"thin_row@2025"}) is withheld, level + + +def _withheld_sets( + rows: dict[str, list[int]], n: int, values: dict[str, float] | None = None +) -> tuple[set[str], set[str]]: + """``uk_withheld_row_sets`` over rows given by the households that carry them. + + Every household is its own source household and carries weight one. + """ + + from types import SimpleNamespace + + from scipy import sparse + + from microcosm.build.uk_runtime.diagnostics import uk_withheld_row_sets + + names = list(rows) + dense = np.zeros((len(names), n)) + for position, name in enumerate(names): + dense[position, rows[name]] = (values or {}).get(name, 1.0) + problem = SimpleNamespace(matrix=sparse.csr_array(dense), names=names) + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": np.arange(n, dtype=np.int64), + "person_household_id": np.arange(n, dtype=np.int64), + } + ), + "household": pd.DataFrame( + { + "household_id": np.arange(n, dtype=np.int64), + "source_household_id": np.arange(n, dtype=np.int64), + } + ), + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(np.ones(n), WeightKind.DESIGN)}, + ) + return uk_withheld_row_sets( + problem, np.ones(n), frame, [{"name": name} for name in names] + ) + + +def test_a_band_its_total_gives_back_withholds_the_next_smallest_band() -> None: + """Vahid's second review of #1333, item 10, and María's hard rule. + + The total less the published bands gives the thin band back, so the + next-smallest band is withheld beside it, not the total. The two together + rest on fifteen source households, which the published rows may give back. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": [0, 1, 2], + "band_b": list(range(3, 15)), + "band_c": list(range(15, 30)), + }, + 30, + ) + assert (thin, beside) == ({"band_a"}, {"band_b"}) + + +def test_a_row_given_back_two_ways_loses_a_giver_from_each() -> None: + """Two relations give the thin band back, so one giver of each is withheld. + + While both stand, every giver has a stand-in and the smallest goes first + (the other total's rest); then the first total's bands are the only way + back, and the next-smallest of them goes. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": [0, 1, 2], + "band_b": list(range(3, 15)), + "band_c": list(range(15, 30)), + "other_total": [0, 1, 2, *range(30, 40)], + "other_rest": list(range(30, 40)), + }, + 40, + ) + assert (thin, beside) == ({"band_a"}, {"other_rest", "band_b"}) + + +def test_proportional_thin_rows_are_no_recovery() -> None: + """A thin count and an amount on the same households differ by nothing. + + The amount is twice the count everywhere, so their difference is zero and + no published row gives anything back by it; the total's relation alone + asks for one more band. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a_count": [0, 1, 2], + "band_a_amount": [0, 1, 2], + "band_b": list(range(3, 15)), + "band_c": list(range(15, 30)), + }, + 30, + values={"band_a_amount": 2.0}, + ) + assert (thin, beside) == ({"band_a_count", "band_a_amount"}, {"band_b"}) + + +def test_a_thin_row_no_published_row_gives_back_withholds_nothing_more() -> None: + thin, beside = _withheld_sets( + {"band_a": [0, 1, 2], "band_b": list(range(3, 15))}, 15 + ) + assert (thin, beside) == ({"band_a"}, set()) + + +def test_a_row_withheld_beside_a_thin_one_says_so_and_meets_the_schema( + tmp_path: Path, +) -> None: + """The written diagnostics tell a row withheld beside a thin one from it. + + BRMA rows: the thin band fails the ten-household floor; the band withheld + beside it meets the floor and is flagged ``complementary_withholding``, as + the release contract reads it. The payload, with its null figures, its + notes and its restated totals, is valid against the published schema. + """ + + n = 30 + members = { + "total": range(30), + "band_a": range(3), + "band_b": range(3, 15), + "band_c": range(15, 30), + } + household = pd.DataFrame( + { + "household_id": np.arange(n, dtype=np.int64), + "source_household_id": np.arange(n, dtype=np.int64), + _SPI_COLUMN: [False] * n, + _CG_COLUMN: [False] * n, + } + ) + for name, carriers in members.items(): + household[f"{name}_measure"] = 0.0 + household.loc[list(carriers), f"{name}_measure"] = 1.0 + specs = [ + TargetSpec( + name=name, + entity="household", + value=float(len(carriers)), + measure=f"{name}_measure", + period=2023, + source="Synthetic UK diagnostics fixture", + family="fixture", + hierarchy=_fixture_hierarchy(name, "brma"), + ) + for name, carriers in members.items() + ] + geography = {f"{spec.name}@2023": "brma" for spec in specs} + weights = np.ones(n) + frame = Frame( + { + "person": pd.DataFrame( + { + "person_id": np.arange(n, dtype=np.int64), + "person_household_id": np.arange(n, dtype=np.int64), + } + ), + "household": household, + }, + EntitySchema(group_entities=("household",)), + {"household": Weights(weights, WeightKind.DESIGN)}, + ) + registry = TargetRegistry(specs, country="uk") + result = score_targets(frame, registry.to_target_set(), weights=weights) + path = tmp_path / "calibration_diagnostics.json" + + outcome = write_uk_calibration_diagnostics( + result, + path, + frame, + target_geography_levels=geography, + target_registry=registry, + ) + + assert isinstance(outcome, DiagnosticsWriteSuccess) + document = load_strict_json(path.read_bytes()) + assert published_json_schema_errors(document) == [] + rows = {row["target_name"]: row for row in document["targets"]} + thin, beside = rows["band_a"]["metadata"], rows["band_b"]["metadata"] + assert thin["carrier_floor_met"] is False + assert "complementary_withholding" not in thin + assert beside["carrier_floor_met"] is True + assert beside["complementary_withholding"] is True + assert beside["model_figures_withheld"].startswith( + "model figures withheld: with the published rows" + ) + assert rows["band_b"]["final_estimate"] is None + assert rows["total"]["final_estimate"] == pytest.approx(30.0) + assert document["uk_diagnostics"]["model_figures_withheld"]["targets"] == 2 + + def _thin_and_wide_brma_payload( thin_values=(1.0,) * 6 + (0.0,) * 8, extra_options=None, thin_target=10.0 ): 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 c2b1d1db5..d648577b8 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 @@ -1846,6 +1846,37 @@ def test_benefit_code_splits_are_ported(tmp_path: Path) -> None: assert adult["bsp_reported"] == pytest.approx(6 * WEEKS_IN_YEAR) +def test_war_widows_pension_leaves_bereavement_support(tmp_path: Path) -> None: + """FRS code 9, War Widow's or Widower's Pension, goes to its own input + (pe-uk#2382), and Bereavement Support Payment keeps code 6 alone (#1222).""" + + tables = _fixture_tables() + tables["benefits"].append( + { + "SERNUM": 1, + "BENUNIT": 1, + "PERSON": 1, + "BENEFIT": 9, + "VAR2": 0, + "BENAMT": 8.0, + "UCSTART": "", + "UCHOUSEL": "", + } + ) + stage = _write_fixture(tmp_path, tables) + person = ( + build_uk_frs_spine_frame(tmp_path, stage=stage) + .table("person") + .set_index("person_id") + ) + + assert person.loc[1001, "bsp_reported"] == pytest.approx(6 * WEEKS_IN_YEAR) + assert person.loc[1001, "war_widows_pension_reported"] == pytest.approx( + 8 * WEEKS_IN_YEAR + ) + assert person.drop(index=1001)["war_widows_pension_reported"].eq(0.0).all() + + def test_scottish_disability_payments_land_in_the_pip_and_dla_columns() -> None: """ADP and CDP rows map to the columns of the benefits they replace (uk-data#500). 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 f9e328f87..de7e62e12 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 2.134.4's is_uc_eligible reads the age of the unit's UC + policyengine-uk 2.145.0'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_build_cli.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_build_cli.py index 98db163ce..d9c074bd6 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_build_cli.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_build_cli.py @@ -1414,17 +1414,16 @@ def walk(value): return rows -@pytest.mark.parametrize( - "extra,withheld", [((), True), (("--target-geographies", "la"), False)] -) -def test_a_request_that_can_bind_brma_rows_stages_its_epochs_without_loss( - tmp_path, monkeypatch, capsys, extra, withheld +@pytest.mark.parametrize("extra", [(), ("--target-geographies", "la")]) +def test_every_request_stages_its_epochs_without_loss( + tmp_path, monkeypatch, capsys, extra ): """The third review of #1305: an epoch's loss covers every row. - A request that can bind BRMA rows stages its epochs, and sends them to - the emitter, with a null loss; only the local stderr line keeps it. A - request that binds none keeps the loss in the staged rows. + Every request can bind a thin row now (María's rule of 2026-10-21 binds + thin national rows as well as BRMA rows), so every request stages its + epochs, and sends them to the emitter, with a null loss; only the local + stderr line keeps it. """ pytest.importorskip("tables") emitted: list[dict] = [] @@ -1442,7 +1441,7 @@ def test_a_request_that_can_bind_brma_rows_stages_its_epochs_without_loss( rows = _staged_epoch_rows(out) assert rows and emitted losses = {row["loss"] for row in rows} | {event["loss"] for event in emitted} - assert (losses == {None}) is withheld + assert losses == {None} # The local stderr line keeps the last epoch's loss either way. assert "loss 0.25" in capsys.readouterr().err @@ -1464,42 +1463,43 @@ def test_the_thinned_epochs_null_the_loss_on_request(): @pytest.mark.parametrize( - "role,localisation,levels,binds", + "role,localisation,levels", [ - ("dense", "area", None, True), - ("dense", "area", ("la", "brma"), True), - ("dense", "area", ("la",), False), - ("dense", "none", None, False), - ("national", "area", None, False), + ("dense", "area", None), + ("dense", "area", ("la", "brma")), + ("dense", "area", ("la",)), + ("dense", "none", None), + ("national", "area", None), ], ) -def test_a_request_binds_brma_rows_only_on_area_brmas_it_selects( - role, localisation, levels, binds -): +def test_every_request_may_withhold_a_row(role, localisation, levels): + """María's rule of 2026-10-21 binds thin national rows as well as BRMA rows.""" + from types import SimpleNamespace args = SimpleNamespace( release_role=role, brma_localisation=localisation, target_geographies=levels ) - assert cli._binds_brma_rows(args) is binds + assert cli._may_withhold_rows(args) is True -def test_a_national_request_stages_its_epochs_with_their_loss(tmp_path, monkeypatch): - """The fourth review of #1305: a national run staged a null loss. +def test_a_national_request_stages_its_epochs_without_their_loss(tmp_path, monkeypatch): + """A national run binds thin UC payment and benefit-cap bands now. - The national role shares the dense role's parser defaults, but binds no - BRMA row, so its staged epochs keep their loss. + An epoch's loss covers every row, so against the published rows it would + give a withheld row back: the staged epochs carry null, as the dense + role's have since the third review of #1305. """ args = cli.parse_args(_national_argv(tmp_path)) - assert cli._binds_brma_rows(args) is False + assert cli._may_withhold_rows(args) is True emitted: list[dict] = [] monkeypatch.setattr( cli, "emit_calibration_progress", lambda event: emitted.append(dict(event)) ) observer = cli._solve_observer(args, None) observer({"kind": "calibration_epoch", "epoch": 1, "epochs": 1, "loss": 0.25}) - assert [event["loss"] for event in emitted] == [0.25] + assert [event["loss"] for event in emitted] == [None] def test_telemetry_content_refusal_never_aborts_the_solve( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_calibration_graph.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_calibration_graph.py index f00a0f516..500d26c1c 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_calibration_graph.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_calibration_graph.py @@ -47,6 +47,27 @@ from test_support.microcosm_build.uk_local_rowwise import _clone_frame +@pytest.fixture(autouse=True) +def _every_toy_row_published(monkeypatch): + """Publish every row of the three-household toy graph. + + A row's model figures leave a build only when ten distinct source + households carry it (María's rule of 2026-10-21 binds a thin row and + withholds its figures). The toy graph declares its columns exactly and + carries no source lineage to count, so these graph tests publish every + row; the withheld path has its own tests in test_uk_diagnostics.py and + test_uk_orrery_contract.py. + """ + + from microcosm.build.uk_runtime import diagnostics, full_gates + + def none_withheld(*args, **kwargs): + return set() + + monkeypatch.setattr(diagnostics, "uk_withheld_rows", none_withheld) + monkeypatch.setattr(full_gates, "uk_withheld_rows", none_withheld) + + def source_frame(): frame = _clone_frame() tables = {entity: frame.table(entity).copy() for entity in frame.entities} 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 b294e2308..872e109d7 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 @@ -123,7 +123,9 @@ def evidence(monkeypatch): {"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]}), + household=pd.DataFrame( + {"household_id": ids, "source_household_id": ids, "income": [1.0, 1.0]} + ), time_period="2024", weight_kind=weights.kind, household_weights=weights.values, 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 06b1014fd..822050019 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 @@ -951,6 +951,23 @@ def test_both_gate_kernels_are_registered(): assert type(registry.get(UKFullHoldoutKernel.ref)) is UKFullHoldoutKernel +@pytest.fixture(autouse=True) +def _toy_national_rows_published(monkeypatch): + """Publish the two-household toy frame's national rows. + + A national row's model figures leave a build only when ten distinct + source households carry it (María's rule of 2026-10-21), and no row here + rests on more than two. These kernel tests lower that floor to one; the + BRMA floor stays at ten, so the withheld BRMA row keeps its own test, and + the national withheld path has its tests in test_uk_diagnostics.py and + test_uk_national_graph.py. + """ + + from microcosm.build.uk_runtime import diagnostics + + monkeypatch.setattr(diagnostics, "UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS", 1) + + def _run_final_gate_kernel(monkeypatch, *, brma_row: bool = False, solved=None): """Run the terminal gate kernel on a two-household fixture. @@ -961,7 +978,9 @@ def _run_final_gate_kernel(monkeypatch, *, brma_row: bool = False, solved=None): ``brma_row`` adds a Scottish BRMA rent row far from its target: its two carriers sit under the ten-household floor, so its figures are withheld - (microcosm#1262). + (microcosm#1262). Its rent differs between the two households, so the + national count, which both carry once, does not give it back (Vahid's + second review of #1333: a row that did would be withheld beside it). """ from dataclasses import replace from types import SimpleNamespace @@ -1097,7 +1116,7 @@ def _run_final_gate_kernel(monkeypatch, *, brma_row: bool = False, solved=None): replace(specs[0].to_target(), measure=lambda f: np.ones(2)), replace(specs[1].to_target(), measure=lambda f: np.array([1.0, 0.0])), *( - [replace(specs[2].to_target(), measure=lambda f: np.ones(2))] + [replace(specs[2].to_target(), measure=lambda f: np.array([1.0, 0.5]))] if brma_row else [] ), 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 46cdadb5a..f79fd63b5 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 @@ -636,8 +636,11 @@ def test_exclusion_applier_warns_within_week_of_expiry(): # re-adjudicated the same day on the mechanism receipts. "dwp.hb.": 3, # Two amount bands (200.01-300, 400.01-500) retired 2026-09-21 on the - # v20 measurement (-10.1 % and -3.8 % unbound); the tail bands stay out. - "dwp.benefit_cap.capped_households_": 11, + # v20 measurement (-10.1 % and -3.8 % unbound); the eleven tail bands + # retired 2026-10-21 under María's rule that thin support alone leaves no + # row out: the zero-support floor drops any band no household carries, + # and the rest are bound and measured. + "dwp.benefit_cap.capped_households_": 0, # microcosm#1113 and #1123 item 3 (2026-10-08): rows whose level the # engine, a published-source residual or a scope the frame doesn't hold # sets, held out and measured with tools/diagnose_uk_consumption_taxes.py. @@ -681,8 +684,11 @@ def test_packaged_exclusions_load(): names = [entry["name"] for entry in exclusions] # 65 less the nine payment bands the support floor covers (microcosm#1095 U3), # plus the employer NICs diagnostic (microcosm#1301) and the rail-support - # hold-out (microcosm#1278). - assert len(names) == len(set(names)) == 58 + # hold-out (microcosm#1278), less the thirteen rows bound again under + # María's rule of 2026-10-21 (the eleven benefit-cap amount bands and the + # HMRC £500k to £1m dividend and property-income-count rows). + assert len(names) == len(set(names)) == 45 + assert not [name for name in names if name.startswith("dwp.benefit_cap.")] band_h_region_cells = [ entry for entry in exclusions @@ -703,16 +709,15 @@ def test_packaged_exclusions_load(): # The sparse HMRC band cells are whatever remains: hmrc/ band cells # that are not the 1m+ channel cells. After the #807 revision three # genuinely thin 500k-1m cells stayed excluded; the self-employment one - # retired 2026-09-21 on the v20 measurement (+0.0 % unbound). + # retired 2026-09-21 on the v20 measurement (+0.0 % unbound), and the + # dividend and property-income-count ones on 2026-10-21, bound again + # under María's rule that thin support alone leaves no row out. sparse = [ name for name in names if name.startswith("hmrc/") and "_1_000_000_to_inf" not in name ] - assert sorted(sparse) == [ - "hmrc/dividend_income_income_band_500_000_to_1_000_000", - "hmrc/property_income_count_income_band_500_000_to_1_000_000", - ], sparse + assert sparse == [], sparse # The 2026-08-26 tranche carries the uk_target_fit disposition # adjudication and a uniform three-month window. Its three ONS @@ -723,9 +728,10 @@ def test_packaged_exclusions_load(): # the obr.fuel_duties entry (2026-09-11): the all-road-users row is # signed out of the reference surface and the cars receipts bind instead. tranche = [e for e in exclusions if e["approved_on"] == "2026-08-26"] - # Three 2026-08-26 rows retired 2026-09-21 on the v20 measurement, and - # three payment bands retired 2026-10-16 under the support floor. - assert len(tranche) == 29 + # Three 2026-08-26 rows retired 2026-09-21 on the v20 measurement, three + # payment bands retired 2026-10-16 under the support floor, and the two + # thin HMRC 500k-1m cells retired 2026-10-21, bound again. + assert len(tranche) == 27 for entry in tranche: assert "5427936411" in entry["adjudication"], entry["name"] assert entry["expires_on"] == "2026-11-26", entry["name"] @@ -867,15 +873,17 @@ def test_packaged_exclusions_load(): assert "hmrc.spi.savings_interest_income" in entry["reason"], name assert "never fitted" in entry["reason"], name - # The 2026-09-16 tranche also carries the Housing Benefit caseload rows + # The 2026-09-16 tranche also carried the Housing Benefit caseload rows # (tracked on #867 like the spend row) and the benefit-cap amount bands - # outside the bound (tracked on #882); every entry names its tool. + # outside the bound (tracked on #882); every entry names its tool. The + # bands retired 2026-10-21, bound again (María's rule of that day), so + # the caseload rows remain. repairs = [ e for e in exclusions if e["approved_on"] == "2026-09-16" and e["name"] not in _A16_READJUDICATED_ROWS ] - assert len(repairs) == 14 + assert len(repairs) == 3 for entry in repairs: assert entry["expires_on"] == "2026-12-08", entry["name"] # Every entry of the tranche points at the ruling that exists (the diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py index f054dcb71..63eb90c0a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py @@ -65,6 +65,7 @@ from test_support.microcosm_build import uk_calibration_run as seam from test_support.microcosm_build.uk_calibration_run import ( # noqa: F401 _cgt_projection, + _every_seam_row_published, _signing_key, ) @@ -510,6 +511,35 @@ def test_national_gates_evaluate_the_seam_scope_and_record_the_evidence( } +def test_national_gates_withhold_a_thin_row_and_judge_only_the_published( + tmp_path, monkeypatch +): + """María's rule of 2026-10-21 on the seam: the fixture's one row is thin. + + Two source households carry it, so its figures stay in the build: the + evidence row is blank and flagged, the manifest's losses are read over the + published rows, and the fit gate, with no published row to judge, blocks + without naming the row. + """ + from microcosm.build.uk_runtime import diagnostics + + monkeypatch.setattr(diagnostics, "UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS", 10) + national, manifest, store, frame, registry = _run(tmp_path, monkeypatch) + row_name = registry.specs[0].to_target().row_name + evidence = json.loads( + _payload(manifest, store, NATIONAL_GATES_NODE, "calibration_evidence") + ) + (row,) = evidence["diagnostics"] + assert row["name"] == row_name and row["model_figures_withheld"] is True + assert row["estimate"] is None and row["relative_error"] is None + calibration = evidence["calibration"] + assert calibration["totals_basis"] == "published_rows" + assert calibration["model_figures_withheld"] == 1 + report = json.loads(_payload(manifest, store, NATIONAL_GATES_NODE, "gate_report")) + assert report["blocking"] == ["uk_target_fit"] + assert row_name.split("@")[0] not in json.dumps(report["report"]) + + def test_national_problem_refuses_an_empty_register(tmp_path, monkeypatch): from microcosm.calibrate import TargetRegistry diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py index 5fac5c4b4..773f80599 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py @@ -28,7 +28,9 @@ SERIES_TRIP_COLUMNS, UK_NTS_BUS_TRAVEL_OUTPUT_COLUMNS, UK_NTS_PERSON_OUTPUT_COLUMNS, + UK_NTS_RECEIPT_SUPPRESSED, WEEKS_IN_YEAR, + FareCapRegime, NTSBusTravelError, NTSColumns, UKNTSBusTravelStageTransform, @@ -78,7 +80,9 @@ def _synthetic_nts_with_tickets( household = pd.DataFrame( { "HouseholdID": 1000 + rows, - "SurveyYear": 2022 + rows % 3, + # Every region in every survey year, so the cap years hold + # London and rest-of-England donors alike. + "SurveyYear": 2022 + (rows // 9) % 3, "HHoldGOR_B02ID": 1 + rows % 9, "HHIncome2002_B02ID": 1 + rows % 3, "NumCarVan": rows % 4, @@ -210,6 +214,21 @@ def test_declared_columns_and_codes_parse_from_the_committed_stage() -> None: bad["codes"] = {**bad["codes"], "main_mode": {"bus_in_london": 7}} with pytest.raises(NTSBusTravelError, match="main_mode"): NTSColumns.from_parameters(bad) + # England's £2 cap covers 2023 and 2024 whole; 2022 is before it. + derive = _operation(_committed_stage(), "clean_nts_travel_tables") + regime = FareCapRegime.from_parameters(derive, survey_years=columns.survey_years) + assert regime.cap_fare == 2.0 and regime.cap_tolerance == 0.01 + assert regime.cap_years == (2023, 2024) and regime.pre_cap_years == (2022,) + stray = {**derive["fare_cap_regime"], "cap_years": [2023, 2025]} + with pytest.raises(NTSBusTravelError, match="cap_years"): + FareCapRegime.from_parameters( + {"fare_cap_regime": stray}, survey_years=columns.survey_years + ) + overlap = {**derive["fare_cap_regime"], "pre_cap_years": [2022, 2023]} + with pytest.raises(NTSBusTravelError, match="pre_cap_years"): + FareCapRegime.from_parameters( + {"fare_cap_regime": overlap}, survey_years=columns.survey_years + ) def test_cleaning_annualises_diary_trips_per_series_and_maps_the_codebook() -> None: @@ -313,6 +332,177 @@ def test_stage_and_ticket_tables_class_boardings_and_give_the_single_fare_share( NTSColumns.from_parameters(bad) +def test_ticket_receipt_weights_boardings_by_the_full_trip_weight() -> None: + """W5 carries the household weight W2: the receipt must not divide it out.""" + + household, individual, trip, stage, ticket = _synthetic_nts_with_tickets() + # Season-ticket households stand for five times as many households. + season = set(ticket.loc[ticket["SpecialTicket_B01ID"] == 1, "IndividualID"]) + heavy = set(individual.loc[individual["IndividualID"].isin(season), "HouseholdID"]) + household.loc[household["HouseholdID"].isin(heavy), "W2"] *= 5.0 + trip.loc[trip["HouseholdID"].isin(heavy), "W5"] *= 5.0 + donor = clean_nts_travel_tables( + household, individual, trip, stage, ticket, columns=_columns() + ) + by_series = donor.receipt["boarding_classes"]["by_series"] + trip_weight = stage["TripID"].map(trip.set_index("TripID")["W5"]) + weight = trip_weight * stage["NumBoardings"] + special = stage["IndTicketID"].map( + ticket.set_index("IndTicketID")["SpecialTicket_B01ID"] + ) + paid = stage["IndTicketID"].isna() & (stage["StageFareCost"] > 0) + passes = special.notna() & ~special.isin([7, 8, 9, 10, 11]) + for series, mode in ((BUS_IN_LONDON, 7), (OTHER_LOCAL_BUS, 8)): + cell = stage["StageMode_B04ID"] == mode + single = weight[cell & paid].sum() + assert by_series[series][ + "single_share_of_fare_paying_boardings" + ] == pytest.approx(single / (single + weight[cell & passes].sum())) + # One boarding stage per synthetic trip. + assert by_series[series]["boardings_per_bus_trip"] == pytest.approx( + weight[cell].sum() / trip_weight[cell].sum() + ) + + +def _single_fare_donor() -> pd.DataFrame: + """One band-3 person per survey year and group, with chosen boardings.""" + + rows = [] + for year, group, paid, passes in ( + (2022, REST_OF_ENGLAND_GROUP, 0.0, 10.0), + (2023, REST_OF_ENGLAND_GROUP, 8.0, 2.0), + (2024, REST_OF_ENGLAND_GROUP, 6.0, 4.0), + (2022, LONDON_GROUP, 1.0, 9.0), + (2023, LONDON_GROUP, 5.0, 5.0), + (2024, LONDON_GROUP, 3.0, 7.0), + ): + own = BUS_IN_LONDON if group == LONDON_GROUP else OTHER_LOCAL_BUS + row = { + "survey_year": year, + "residence_group": group, + "weight": 1.0, + "local_bus_use_band": 3, + } + for series in (BUS_IN_LONDON, OTHER_LOCAL_BUS): + row[boarding_class_column(series, BOARDING_FARE_PAID)] = ( + paid if series == own else 0.0 + ) + row[boarding_class_column(series, BOARDING_PASS)] = ( + passes if series == own else 0.0 + ) + rows.append(row) + return pd.DataFrame(rows) + + +def test_single_fare_share_reads_the_cap_years_only() -> None: + """2022, before the cap, would pull the pooled share down (microcosm#1331).""" + + donor = _single_fare_donor() + pooled, pooled_receipt = single_fare_shares_from_donor(donor) + capped, capped_receipt = single_fare_shares_from_donor(donor, years=(2023, 2024)) + assert pooled[OTHER_LOCAL_BUS][REST_OF_ENGLAND_GROUP][3] == pytest.approx(14 / 30) + assert capped[OTHER_LOCAL_BUS][REST_OF_ENGLAND_GROUP][3] == pytest.approx(14 / 20) + assert pooled[BUS_IN_LONDON][LONDON_GROUP][3] == pytest.approx(9 / 30) + assert capped[BUS_IN_LONDON][LONDON_GROUP][3] == pytest.approx(8 / 20) + assert pooled_receipt["survey_years"] == [2022, 2023, 2024] + assert capped_receipt["survey_years"] == [2023, 2024] + with pytest.raises(NTSBusTravelError, match="survey years"): + single_fare_shares_from_donor(donor, years=(2021,)) + + +def _fare_mix( + stage: pd.DataFrame, + household: pd.DataFrame, + individual: pd.DataFrame, + trip: pd.DataFrame, + ticket: pd.DataFrame, +) -> dict: + regime = FareCapRegime.from_parameters( + _operation(_committed_stage(), "clean_nts_travel_tables"), + survey_years=_columns().survey_years, + ) + donor = clean_nts_travel_tables( + household, individual, trip, stage, ticket, columns=_columns(), fare_cap=regime + ) + return donor.receipt["boarding_classes"]["fare_mix"] + + +def test_fare_mix_lines_report_aggregates_and_withhold_thin_cells() -> None: + """Before the cap: share above it and their mean fare; under it: share at it.""" + + household, individual, trip, stage, ticket = _synthetic_nts_with_tickets(180) + year = stage["HouseholdID"].map(household.set_index("HouseholdID")["SurveyYear"]) + region = stage["HouseholdID"].map( + household.set_index("HouseholdID")["HHoldGOR_B02ID"] + ) + rest = (region != 7).to_numpy() + point_of_use = ( + stage["IndTicketID"].isna() & (stage["StageFareCost"] > 0) + ).to_numpy() + # Before the cap, the full single fares sit above £2. + raised = stage.copy() + full = ( + rest + & point_of_use + & (year == 2022).to_numpy() + & (stage["StageFareCost"] == 2.0).to_numpy() + ) + raised.loc[full, "StageFareCost"] = 2.75 + lines = _fare_mix(raised, household, individual, trip, ticket) + # The expected values, on the full trip weight (W5 x JJXSC, which carries + # the household weight W2) times the boardings, and the fare per boarding. + weight = ( + raised["TripID"].map(trip.set_index("TripID")["W5"]) * raised["NumBoardings"] + ).to_numpy() + fare = (raised["StageFareCost"] / raised["NumBoardings"]).to_numpy() + base = rest & point_of_use & (year == 2022).to_numpy() + above = base & (fare > 2.01) + assert lines["before_cap"]["2022"]["share_above_cap"] == round( + weight[above].sum() / weight[base].sum(), 2 + ) + assert lines["before_cap"]["2022"]["mean_fare_above_cap"] == round( + float(np.average(fare[above], weights=weight[above])), 2 + ) + for y in (2023, 2024): + base = rest & point_of_use & (year == y).to_numpy() + at = base & (np.abs(fare - 2.0) <= 0.01) + assert lines["under_cap"][str(y)]["share_at_cap"] == round( + weight[at].sum() / weight[base].sum(), 2 + ) + assert set(lines["before_cap"]["2022"]) == { + "share_above_cap", + "mean_fare_above_cap", + } + assert set(lines["under_cap"]["2023"]) == {"share_at_cap"} + assert lines["minimum_cell"] == {"boardings": 10, "households": 10} + # Above the cap on three households only: both 2022 lines are withheld, + # however many boardings those households make. + thin = stage.copy() + few = ( + stage["HouseholdID"] + .isin(sorted(set(stage.loc[full, "HouseholdID"]))[:3]) + .to_numpy() + ) + thin.loc[full & few, "StageFareCost"] = 2.75 + lines = _fare_mix(thin, household, individual, trip, ticket) + assert lines["before_cap"]["2022"] == { + "share_above_cap": UK_NTS_RECEIPT_SUPPRESSED, + "mean_fare_above_cap": UK_NTS_RECEIPT_SUPPRESSED, + } + # Under the cap, a share whose complement rests on two households is + # withheld too: one minus it would give that thin cell back. + capped = stage.copy() + in_2023 = rest & point_of_use & (year == 2023).to_numpy() + capped.loc[in_2023, "StageFareCost"] = 2.0 * capped.loc[in_2023, "NumBoardings"] + off_cap = sorted(set(stage.loc[in_2023, "HouseholdID"]))[:2] + capped.loc[ + in_2023 & stage["HouseholdID"].isin(off_cap).to_numpy(), "StageFareCost" + ] = 1.5 + lines = _fare_mix(capped, household, individual, trip, ticket) + assert lines["under_cap"]["2023"]["share_at_cap"] == UK_NTS_RECEIPT_SUPPRESSED + assert lines["under_cap"]["2024"]["share_at_cap"] != UK_NTS_RECEIPT_SUPPRESSED + + def test_cleaning_handles_the_nts_sentinels_and_the_diary_sample() -> None: """-9 is a declared non-user, -8 and unknown age codes drop, W2 = 0 leaves.""" @@ -663,6 +853,11 @@ def test_stage_transform_end_to_end_on_synthetic_inputs() -> None: evidence["eligibility"]["donor_eligible_trip_share"][OTHER_LOCAL_BUS] is not None ) + shares = evidence["band_means"]["single_fare_shares"] + assert shares["survey_years"] == [2023, 2024] + fare_mix = evidence["donor"]["boarding_classes"]["fare_mix"] + assert set(fare_mix["before_cap"]) == {"2022"} + assert set(fare_mix["under_cap"]) == {"2023", "2024"} clip = evidence["support_clip"]["columns"] assert clip["local_bus_use_band"]["clipped_high_rows"] == 0 assert clip["local_bus_trips"]["clipped_low_rows"] == 0 diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_orrery_contract.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_orrery_contract.py index f2453d1ea..984c27746 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_orrery_contract.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_orrery_contract.py @@ -270,9 +270,13 @@ def test_unavailable_checkpoint_bytes_are_named_not_linked(tmp_path): assert "gone.json" not in saved.read_text() -def _brma_problem(n: int = 4, level: str = "brma"): - """An ordered problem over ``n`` households binding one Scottish BRMA row, - its level spelt ``level``.""" +def _brma_problem(n: int = 12, level: str = "brma"): + """An ordered problem over ``n`` households binding one constituency row on + all of them and one Scottish BRMA row on four, its level spelt ``level``. + + Every row with fewer than ten source households is withheld (María's + rule of 2026-10-21), so the constituency row needs the twelve to publish. + """ import numpy as np import pandas as pd @@ -301,7 +305,12 @@ def _brma_problem(n: int = 4, level: str = "brma"): targets = TargetSet( [ Target("households@E14000001", "household", lambda f: np.ones(n), 4.0), - Target("rent@S33000001", "household", lambda f: np.ones(n), 40.0), + Target( + "rent@S33000001", + "household", + lambda f: (np.arange(n) < 4).astype(float), + 40.0, + ), ] ) payload = encode_problem( @@ -341,7 +350,7 @@ def row(name, target, estimate): ) result = SimpleNamespace( - weights=np.ones(4), + weights=np.ones(12), frame=frame, diagnostics=[ row(ordered.problem.names[0], 4.0, 4.0), @@ -412,6 +421,36 @@ def test_a_size_summary_over_brma_rows_publishes_no_all_row_total(level): ) +def test_a_size_summary_over_any_problem_publishes_no_all_row_total(): + """Since María's rule of 2026-10-21 any problem may bind a thin row. + + The summary cannot count a row's carriers without the weights, so a + problem with no BRMA row withholds its all-row totals too. + """ + from types import SimpleNamespace + + from microcosm.build.uk_runtime.orrery_contract import _size_summary + + _, ordered, payload = _brma_problem(level="constituency") + receipt = { + "method": "full_pool", + "problem_sha256": ordered.sha256, + "dense_loss": 0.2, + "compact_loss": 0.3, + "max_target_scaled_change": 0.4, + "requested_households": 4, + } + context = SimpleNamespace( + payload=json.dumps(receipt).encode(), + input_payload=lambda name: payload, + ) + overview = _size_summary(context)["overview"] + assert all( + overview[field] is None + for field in ("dense_loss", "compact_loss", "max_target_scaled_change") + ) + + @pytest.mark.parametrize("level", ["brma", "statistical_scope"]) def test_the_registered_result_provider_withholds_a_thin_brma_row(level, monkeypatch): """The third review of #1305: through the provider, not the helper alone. @@ -439,8 +478,8 @@ def row(name, target, estimate): ) result = SimpleNamespace( - weights=np.ones(4), - initial_weights=np.ones(4), + weights=np.ones(12), + initial_weights=np.ones(12), frame=SimpleNamespace( table=frame.table, weights_for=lambda entity: SimpleNamespace( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_certification.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_certification.py index 83615ec15..eb6000378 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_certification.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_certification.py @@ -8,6 +8,7 @@ import json from datetime import date from pathlib import Path +from types import SimpleNamespace import pytest @@ -141,6 +142,27 @@ class _Reference: parity_reference=_Reference(), ) assert evidence.candidate_targets == {"dwp.uc.households@2025"} + assert evidence.withheld_targets == frozenset() + # A row that fewer than ten source households carry is published without + # its figures (María's rule of 2026-10-21): stated, kept on the candidate + # side, and outside the fit gate's errors. + thin = "dwp.uc.payment_distribution_single_thin@2025" + evidence = uk_release_parity_evidence( + _Frame(), + diagnostics_targets=[ + {"name": "dwp.uc.households@2025", "relative_error": 0.1}, + { + "name": thin, + "relative_error": None, + "metadata": {"model_figures_withheld": "model figures withheld"}, + }, + ], + reference_registry=_Registry(), + parity_reference=_Reference(), + ) + assert evidence.target_relative_errors == {"dwp.uc.households@2025": 0.1} + assert evidence.withheld_targets == {thin} + assert evidence.candidate_targets == {"dwp.uc.households@2025", thin} def test_compose_green_certification(green_certification_inputs): @@ -541,7 +563,8 @@ def test_release_cut_battery_runs_and_signs(tmp_path: Path, monkeypatch): local_ledger_registries={2025: object()}, ledger_unsupported={2025: ()}, local_ledger_unsupported={2025: ()}, - parity_evidence=object(), + # Every producer states its withheld rows (María's rule of 2026-10-21). + parity_evidence=SimpleNamespace(withheld_targets=frozenset()), fit_weight_records=None, input_mass_reference={}, exclusions_evaluated_on=date(2026, 8, 27), 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 e684c94f3..f65a09640 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 @@ -65,6 +65,7 @@ from test_support.microcosm_build import uk_rowwise_candidate as candidate from test_support.microcosm_build.uk_calibration_run import ( # noqa: F401 _cgt_projection, + _every_seam_row_published, _signing_key, ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py index 4256c2923..9db226e7f 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py @@ -1,4 +1,4 @@ -"""The sparse-band support floor (microcosm#1095 U3).""" +"""The zero-support floor (microcosm#1095 U3; María's rule of 2026-10-21).""" from __future__ import annotations @@ -33,7 +33,7 @@ def _block(**overrides): return { "contract_target_ids": [SCOPE], "metric": "distinct_source_households_with_positive_prior_weight", - "floor": 3, + "floor": 1, "max_dropped_rows": 2, "max_dropped_published_share": 0.5, "reason": "Sparse bands are dropped, not pooled.", @@ -128,25 +128,28 @@ def test_support_counts_distinct_source_households_with_a_positive_prior(): assert support.tolist() == [3, 1, 0] -def test_the_floor_drops_thin_rows_in_scope_and_reports_published_totals(): +def test_the_floor_drops_only_rows_no_household_carries(): + """A row on one source household stays bound; a row on none leaves.""" + registry = _registry( _spec("low", "band_low", 40.0), _spec("thin", "band_thin", 30.0), + _spec("zero_weight", "band_zero_weight", 5.0), _spec("other", "other_thin", 7.0, target="dwp.uc.households"), ) result = _apply(registry) - assert [spec.name for spec in result.registry.specs] == ["low", "other"] - assert result.problem.names == ("low@2025", "other@2025") - assert result.problem.target_vector.tolist() == [40.0, 7.0] - assert result.problem.matrix.shape == (2, 6) - assert set(result.exclusions) == {"thin"} - assert result.exclusions["thin"]["kind"] == UK_SUPPORT_FLOOR_KIND - assert result.exclusions["thin"]["evaluated_on"] == "2026-10-20" + assert [spec.name for spec in result.registry.specs] == ["low", "thin", "other"] + assert result.problem.names == ("low@2025", "thin@2025", "other@2025") + assert result.problem.target_vector.tolist() == [40.0, 30.0, 7.0] + assert result.problem.matrix.shape == (3, 6) + assert set(result.exclusions) == {"zero_weight"} + assert result.exclusions["zero_weight"]["kind"] == UK_SUPPORT_FLOOR_KIND + assert result.exclusions["zero_weight"]["evaluated_on"] == "2026-10-20" receipt = result.receipt - assert receipt["dropped"] == ["thin"] - assert receipt["dropped_by_target"] == {SCOPE: {"rows": 1, "published_total": 30.0}} - assert receipt["dropped_published_share"] == pytest.approx(30.0 / 70.0) + assert receipt["dropped"] == ["zero_weight"] + assert receipt["dropped_by_target"] == {SCOPE: {"rows": 1, "published_total": 5.0}} + assert receipt["dropped_published_share"] == pytest.approx(5.0 / 75.0) assert receipt["solved_register_sha256"] == result.registry.version # The receipt carries no row's support: support counts stay local. assert set(receipt) == { @@ -171,6 +174,7 @@ def test_the_floor_refuses_outside_its_window_and_past_its_ceilings(): _spec("low", "band_low", 40.0), _spec("thin", "band_thin", 30.0), _spec("zero_weight", "band_zero_weight", 5.0), + _spec("zero_weight_too", "band_zero_weight", 5.0), ) with pytest.raises(ValueError, match="outside its reviewed window"): _apply(registry, evaluated_on=date(2026, 10, 15)) @@ -179,19 +183,23 @@ def test_the_floor_refuses_outside_its_window_and_past_its_ceilings(): with pytest.raises(ValueError, match="drops 2 rows"): _apply(registry, block={"max_dropped_rows": 1}) with pytest.raises(ValueError, match="published total"): - _apply(registry, block={"max_dropped_published_share": 0.4}) + _apply(registry, block={"max_dropped_published_share": 0.1}) def test_a_per_row_exclusion_the_floor_would_cover_refuses(): registry = _registry( _spec("low", "band_low", 40.0), - _spec("thin", "band_thin", 30.0), + _spec("zero_weight", "band_zero_weight", 5.0), ) - with pytest.raises(ValueError, match=r"retire those entries .*\['thin'\]"): - _apply(registry, row_excluded={"thin"}) - # A per-row entry above the floor is measured and leaves the problem. - result = _apply(_registry(_spec("low", "band_low", 40.0)), row_excluded={"low"}) - assert result.registry.specs == () + with pytest.raises(ValueError, match=r"retire those entries .*\['zero_weight'\]"): + _apply(registry, row_excluded={"zero_weight"}) + # A per-row entry on a row some household carries, however few, is + # measured and leaves the problem. + result = _apply( + _registry(_spec("low", "band_low", 40.0), _spec("thin", "band_thin", 30.0)), + row_excluded={"thin"}, + ) + assert [spec.name for spec in result.registry.specs] == ["low"] assert result.exclusions == {} assert result.receipt["probed_row_exclusions"] == 1 with pytest.raises(ValueError, match="name no compiled row"): @@ -225,8 +233,10 @@ def test_lineage_is_read_only_when_a_row_in_scope_needs_it(): ("override", "message"), [ ({"metric": "rows"}, "metric"), - ({"floor": 2}, "at least 3"), - ({"floor": True}, "at least 3"), + ({"floor": 0}, "must be 1"), + ({"floor": 3}, "must be 1"), + ({"floor": 10}, "must be 1"), + ({"floor": True}, "must be 1"), ({"max_dropped_rows": -1}, "count"), ({"max_dropped_published_share": 1.0}, r"\[0, 1\)"), ({"contract_target_ids": [SCOPE, SCOPE]}, "distinct"), @@ -241,24 +251,41 @@ def test_the_declaration_is_validated_closed_world(override, message): uk_support_floor_from_payload(_block(**override)) -def test_the_packaged_floor_is_signed_and_scoped_to_the_payment_bands(): +def test_the_packaged_floor_drops_only_unsupported_bands_and_binds_thin_ones(): + """María's rule of 2026-10-21: fewer than ten records is no reason to leave a row out.""" + declaration = load_uk_support_floor_declaration() floor = uk_support_floor_from_payload(declaration) + bands = ( + "up_to_100", + *(f"{low}_01_to_{low + 100}" for low in range(100, 1300, 100)), + "1300_01_and_above", + ) assert floor.contract_target_ids == { - f"dwp.uc.payment_distribution_{family}" - for family in ( - "single", - "lone_parent", - "couple_no_children", - "couple_with_children", - ) + *( + f"dwp.uc.payment_distribution_{family}" + for family in ( + "single", + "lone_parent", + "couple_no_children", + "couple_with_children", + ) + ), + *(f"dwp.benefit_cap.capped_households_{band}" for band in bands), } - assert floor.floor == 10 + assert floor.floor == 1 assert (floor.approved_by, floor.approved_on, floor.expires_on) == ( "juaristi22", - date(2026, 10, 16), + date(2026, 10, 21), date(2027, 1, 8), ) + # The rows once left out for thin support are bound again. + named = {entry["name"] for entry in load_uk_calibration_measure_exclusions()} + assert not {name for name in named if name.startswith("dwp.benefit_cap.")} + assert not named & { + "hmrc/dividend_income_income_band_500_000_to_1_000_000", + "hmrc/property_income_count_income_band_500_000_to_1_000_000", + } def test_only_schema_3_registers_carry_a_floor(tmp_path): @@ -358,28 +385,32 @@ def _national_spec(name, measure, value, target=SCOPE): ) -def test_the_national_problem_drops_thin_rows_before_weights_and_metadata( +def test_the_national_problem_drops_unsupported_rows_and_binds_thin_ones( monkeypatch, ): registry = _registry( _national_spec("low", "band_low", 40.0), _national_spec("thin", "band_thin", 30.0), + _national_spec("zero_weight", "band_zero_weight", 5.0), _national_spec("other", "other_thin", 7.0, target="dwp.uc.households"), ) problem, materialized = _encode(monkeypatch, registry) - assert materialized == [["low", "thin", "other"]] - assert problem.problem.names == ("low@2025", "other@2025") + assert materialized == [["low", "thin", "zero_weight", "other"]] + assert problem.problem.names == ("low@2025", "thin@2025", "other@2025") assert [row["contract_target_id"] for row in problem.target_metadata] == [ + SCOPE, SCOPE, "dwp.uc.households", ] bindings = problem.bindings - assert len(bindings["target_loss_weights"]) == 2 - assert bindings["activated_reference_count"] == 2 - assert bindings["resolved_reference_count"] == 2 - assert bindings["measure_exclusions"]["thin"]["kind"] == UK_SUPPORT_FLOOR_KIND - assert bindings["support_floor"]["dropped"] == ["thin"] + assert len(bindings["target_loss_weights"]) == 3 + assert bindings["activated_reference_count"] == 3 + assert bindings["resolved_reference_count"] == 3 + assert ( + bindings["measure_exclusions"]["zero_weight"]["kind"] == UK_SUPPORT_FLOOR_KIND + ) + assert bindings["support_floor"]["dropped"] == ["zero_weight"] # The register the targets node froze stays the approved one. assert bindings["register_sha256"] == registry.version @@ -388,8 +419,8 @@ def test_the_national_problem_measures_per_row_exclusions_in_scope(monkeypatch): approved = _registry(_national_spec("low", "band_low", 40.0)) band_edges = _registry( _national_spec("low", "band_low", 40.0), - _national_spec("thin", "band_thin", 30.0), - _national_spec("high", "band_low", 9.0), + _national_spec("zero_weight", "band_zero_weight", 30.0), + _national_spec("high", "band_thin", 9.0), ) record = { "reason": "reviewed", @@ -400,17 +431,17 @@ def test_the_national_problem_measures_per_row_exclusions_in_scope(monkeypatch): "expires_on": "2026-11-26", "evaluated_on": "2026-10-20", } - with pytest.raises(ValueError, match=r"retire those entries .*\['thin'\]"): + with pytest.raises(ValueError, match=r"retire those entries .*\['zero_weight'\]"): _encode( monkeypatch, approved, band_edge=band_edges, - exclusions={"thin": record, "high": record}, + exclusions={"zero_weight": record, "high": record}, ) problem, materialized = _encode( monkeypatch, approved, band_edge=band_edges, exclusions={"high": record} ) - # The entry above the floor was compiled to be measured and left again. + # The entry on a thin row was compiled to be measured and left again. assert materialized == [["low", "high"]] assert problem.problem.names == ("low@2025",) assert problem.bindings["measure_exclusions"] == {"high": record} diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py index c094e76ec..987b9742f 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py @@ -475,6 +475,7 @@ def test_qrf_columns_check_every_declared_output_regardless_of_density() -> None "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "jsa_contrib_reported", "jsa_income_reported", "esa_income_reported", diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index d036f9227..03c19dea7 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>=2.134.4"] +uk = ["policyengine-uk>=2.145.0"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 1ec393929..6e7e9d22f 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -451,13 +451,13 @@ 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 = ( - "a326d26c249b8b359284f739fe4327180a7c86b9c21b8994cf270b9c0e27787f" + "3e9896aa61e9d9340a3cf6ce4a7b85626288b4312f59b795a7aef77fb4c111d6" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "5bed933d2e261e3c4f0bfaa3ae949e9623a5a70c968eaaf4194e70b72df0b728" + "2054ed4194fac38952f51832c7a30ca81abfec177dff0f22c3d34acf4e63a779" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "2009911edabae63ab2b31d9d50588754fc40129aac952d4a2be6f17e1a8a736b" + "a6d9b3f1dcaeea839af81fda2388e60736fd41c1c7b2e9d23af9b9dbca4e8886" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -931,10 +931,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: _UK_CERTIFICATION_PART_DIGESTS: Mapping[str, Mapping[str, str]] = { "spine": { "gates_manifest_sha256": ( - "a52d74be5d8a8af05cb37b6d3643038de8d8491761a79d7bdbea1048aaee710d" + "39295b40f70be77bb59e2aa70c9429be512e5af4ea78b0189f15687fb8ef3e36" ), "policy_sha256": ( - "e258e8712b7ada5796f7e7e446698a5ddabc6c793f7633093d07bd5d3197631b" + "53a7eb6d9e52eb54fe5d123e1f4393535d0e218d5cf5d9383b9ebf50d367cd1e" ), }, "calibration_seam": { @@ -947,7 +947,7 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "3c876c1d8927a5b31b1a3373ee6ea09c005322838efff967d7148531caf05594" + "da420e28423aa685d82d65ff3367401b63be7dd37edcc4574bdd7d38faac5c19" ), "policy_sha256": ( "939309226c43641ded9402ab47b7b12b0c46fa5165f73da5ced9d97a38985d02" @@ -6427,10 +6427,12 @@ def _uk_brma_floor_verdict_warnings(diagnostics: Mapping) -> list[str]: """BRMA rows whose ten-household verdict is missing or disagrees. microcosm#1262: every BRMA row carries ``metadata.carrier_floor_met``, and - a row withholds its model figures exactly when the verdict is false, so - the contract can see a thin row the build never flagged. María's ruling of - 2026-10-19 on the fourth review of #1305: this is warned, not refused. The - warnings give counts only, never a row. + a row withholds its model figures exactly when the verdict is false or the + row is withheld beside a thin one (``metadata.complementary_withholding``, + Vahid's second review of #1333), so the contract can see a thin row the + build never flagged. María's ruling of 2026-10-19 on the fourth review of + #1305: this is warned, not refused. The warnings give counts only, never a + row. """ missing = disagreeing = 0 @@ -6439,9 +6441,13 @@ def _uk_brma_floor_verdict_warnings(diagnostics: Mapping) -> list[str]: if not isinstance(metadata, Mapping) or not _uk_brma_row(metadata): continue verdict = metadata.get("carrier_floor_met") + withheld = bool(metadata.get("model_figures_withheld")) + complementary = metadata.get("complementary_withholding") is True if not isinstance(verdict, bool): missing += 1 - elif verdict == bool(metadata.get("model_figures_withheld")): + elif (not verdict and (complementary or not withheld)) or ( + verdict and withheld != complementary + ): disagreeing += 1 warnings = [] if missing: 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 42dfcdc7d..b648a84da 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -213,13 +213,13 @@ 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 = ( - "a326d26c249b8b359284f739fe4327180a7c86b9c21b8994cf270b9c0e27787f" + "3e9896aa61e9d9340a3cf6ce4a7b85626288b4312f59b795a7aef77fb4c111d6" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "5bed933d2e261e3c4f0bfaa3ae949e9623a5a70c968eaaf4194e70b72df0b728" + "2054ed4194fac38952f51832c7a30ca81abfec177dff0f22c3d34acf4e63a779" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "2009911edabae63ab2b31d9d50588754fc40129aac952d4a2be6f17e1a8a736b" + "a6d9b3f1dcaeea839af81fda2388e60736fd41c1c7b2e9d23af9b9dbca4e8886" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6a179222c6b6058ad9caacfd347f7bdb8cb98a62237b50d1fc1136df09337251" diff --git a/packages/microcosm-data/tests/engine_free/uk/test_uk_dense_release_contract.py b/packages/microcosm-data/tests/engine_free/uk/test_uk_dense_release_contract.py index fb4c320de..76a2bca8f 100644 --- a/packages/microcosm-data/tests/engine_free/uk/test_uk_dense_release_contract.py +++ b/packages/microcosm-data/tests/engine_free/uk/test_uk_dense_release_contract.py @@ -720,6 +720,30 @@ def test_a_brma_row_without_its_carrier_verdict_is_warned_not_refused() -> None: assert failures == [] +def test_a_brma_row_withheld_beside_a_thin_one_agrees_with_its_verdict() -> None: + """Vahid's second review of #1333, item 10: a row withheld beside a thin + one meets the floor and says so (``metadata.complementary_withholding``).""" + + beside = _withheld_diagnostics() + beside["targets"][1]["metadata"].update( + carrier_floor_met=True, complementary_withholding=True + ) + assert dc._uk_brma_floor_verdict_warnings(beside) == [] + # Flagged as withheld beside a thin row, yet published. + published = _withheld_diagnostics() + published["targets"][1]["metadata"].update( + carrier_floor_met=True, complementary_withholding=True + ) + del published["targets"][1]["metadata"]["model_figures_withheld"] + (warning,) = dc._uk_brma_floor_verdict_warnings(published) + assert "disagrees with their withheld flag" in warning + # A thin row is withheld for itself, never only beside another. + thin = _withheld_diagnostics() + thin["targets"][1]["metadata"]["complementary_withholding"] = True + (warning,) = dc._uk_brma_floor_verdict_warnings(thin) + assert "disagrees with their withheld flag" in warning + + def test_the_dense_validator_runs_the_withheld_checks(tmp_path, monkeypatch) -> None: """The candidate cross-check and the verdict warnings run on a release.""" diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 05e5a2eb6..149476141 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>=2.134.4"] +uk = ["policyengine-uk>=2.145.0"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py index 93c722bef..0197d133e 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 @@ -32,7 +32,7 @@ # --------------------------------------------------------------------------- # # Which PolicyEngine-UK inputs each engine-neutral concept feeds -# (microcosm.frame.concepts), reviewed against policyengine-uk 2.134.4. Every +# (microcosm.frame.concepts), reviewed against policyengine-uk 2.145.0. 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``); @@ -206,7 +206,7 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: POLICYENGINE_UK_CONCEPT_MAPPING = ConceptMapping( engine="policyengine-uk", - engine_version="2.134.4", + engine_version="2.145.0", entity_correspondence={"person": "person", "household": "household"}, input_declaration=InputDeclaration.ENGINE_TYPED, bindings=( @@ -581,16 +581,19 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: "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." + "Credit capital; since policyengine-uk 2.136.1 a value of zero or " + "more set directly is the unit's complete countable capital, in " + "place of every source, property included. benunit_reported_capital, " + "a benefit-unit input ('Savings and investments of the benefit " + "unit's adults (the claimant and any partner) as recorded'), " + "replaces the financial sources only: the household pool it " + "otherwise sums from the household sources, net of what other " + "benefit units report or override, and apportions by claimant and " + "partner count, and the person-level sources of the unit's " + "claimant and partner. Property is valued separately " + "(uc_property_capital) and shared across every claimant and " + "partner in the household. Feeding either 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 " 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 be1742475..d125ff9e7 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 @@ -122,11 +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 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. + # uc_reported_capital defaults to -1 since policyengine-uk 2.136.1: set to + # zero or more it is a unit's complete capital. benunit_reported_capital, + # a benefit-unit input unset at -1, replaces the household financial proxy + # apportioned by claimant and partner count and the person-level sources; + # property is valued apart, in uc_property_capital. inputs = set(engine.variables()) person_stocks = { name @@ -155,12 +155,14 @@ def test_the_liquid_asset_reason_holds(engine) -> None: 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 "return -1" 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 - assert 'add(benunit, period, ["is_uc_claimant"])' in source - assert "p.capital.person_sources" in source + assert 'benunit("benunit_reported_capital", period)' in source + assert 'benunit.members("is_uc_claimant", period)' in source + assert "p.person_sources" in source + assert 'benunit("uc_property_capital", period)' in source assert "loan" in engine._variable("student_loan_balance").label.lower() duty = engine._variable("car_vehicle_excise_duty").get_formula() assert 'person("car_list_price", period)' in inspect.getsource(duty) @@ -182,7 +184,7 @@ def test_the_liquid_asset_reason_names_the_household_wealth_inputs(engine) -> No person_sources = universal_credit.means_test.capital.person_sources("2026-01-01") assert list(person_sources) == ["lifetime_isa_countable_capital"] source = inspect.getsource(engine._variable("uc_assessable_capital").get_formula()) - assert "p.capital.sources" in source + assert "for source in p.sources" in source for name in ( "savings", "corporate_wealth", 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 540c2de74..ddc5b6eb5 100644 --- a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json +++ b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json @@ -413,8 +413,8 @@ } ], "engine": "policyengine-uk", - "engine_version": "2.134.4", - "input_count": 302, + "engine_version": "2.145.0", + "input_count": 311, "schema_sha256": "5a55669d0447a686692911a6327ae298f19741dde29559f2654a75807878a141", "structural_inputs": [ { @@ -731,6 +731,10 @@ "entity": "person", "name": "deficiency_relief" }, + { + "entity": "person", + "name": "defined_benefit_pension_share" + }, { "entity": "household", "name": "dfe_education_spending" @@ -931,6 +935,10 @@ "entity": "person", "name": "is_engaged_in_owned_company_trade" }, + { + "entity": "person", + "name": "is_full_year_category_a_director" + }, { "entity": "person", "name": "is_in_approved_training" @@ -967,6 +975,10 @@ "entity": "household", "name": "local_authority" }, + { + "entity": "person", + "name": "local_bus_uncapped_single_fare" + }, { "entity": "person", "name": "loss_relief" @@ -1055,6 +1067,10 @@ "entity": "household", "name": "non_residential_property_purchased" }, + { + "entity": "household", + "name": "non_residential_property_secured_debt" + }, { "entity": "household", "name": "non_residential_property_value" @@ -1091,6 +1107,10 @@ "entity": "person", "name": "other_local_bus_trips" }, + { + "entity": "household", + "name": "other_residential_property_secured_debt" + }, { "entity": "household", "name": "other_residential_property_value" @@ -1147,10 +1167,18 @@ "entity": "household", "name": "owned_land" }, + { + "entity": "person", + "name": "pension_annual_allowance_taxable_lump_sum_death_benefits" + }, { "entity": "person", "name": "pension_contributions_via_salary_sacrifice" }, + { + "entity": "person", + "name": "pension_contributions_via_salary_sacrifice_pre_2015" + }, { "entity": "person", "name": "pension_credit_reported" @@ -1177,11 +1205,11 @@ }, { "entity": "person", - "name": "pip_dl_category" + "name": "pip_dl_points" }, { "entity": "person", - "name": "pip_m_category" + "name": "pip_m_points" }, { "entity": "household", @@ -1243,6 +1271,10 @@ "entity": "person", "name": "role" }, + { + "entity": "person", + "name": "salary_sacrifice_returned_to_income" + }, { "entity": "household", "name": "savings" @@ -1431,6 +1463,10 @@ "entity": "household", "name": "uses_energy_prepayment_meter" }, + { + "entity": "person", + "name": "war_widows_pension_reported" + }, { "entity": "household", "name": "water_and_sewerage_charges" @@ -1465,7 +1501,7 @@ "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. 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.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; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either 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/fixture.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json index 889a18636..f86286e49 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 @@ -288,7 +288,7 @@ "cgt_residential_split": { "artifacts": [ { - "dependency": "policyengine-uk>=2.134.4 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.145.0 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", @@ -2221,6 +2221,7 @@ "esa_income_reported", "esa_includes_support_component", "bsp_reported", + "war_widows_pension_reported", "benunit_id", "frs_benunit_capital", "is_married", @@ -2256,7 +2257,7 @@ "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 2.134.4'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).", + "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 2.145.0'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": { @@ -2414,7 +2415,7 @@ "hmrc_cgt_asset_type_spine": { "artifacts": [ { - "dependency": "policyengine-uk>=2.134.4 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.145.0 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", @@ -3217,6 +3218,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", @@ -3601,6 +3603,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "winter_fuel_allowance_reported", "council_tax_benefit_reported", "jsa_contrib_reported", @@ -4460,7 +4463,7 @@ "local_bus_trips", "household_local_bus_trips" ], - "notes": "Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, Mar\u00eda's ask of 2026-09-23). The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed.", + "notes": "Imputes local-bus travel per person from the National Travel Survey microdata (microcosm#930; the structural fix Maria ruled on 2026-09-15 for the rake-then-bind bus fares of microcosm#890). The donor is the NTS End User Licence extract (England residents only since 2013) pooled over the declared survey years for the conditional fit; the artifact pins are the 19th-edition (November 2025) tabs, verified at runtime, and the H2 fixture drives the stage with synthetic tabs in the same codebook. One QRF target, the interview frequency-of-use band (the variable behind NTS0313; 0 is the non-user band), conditioned on age band, sex, cars (the was_wealth draw on the recipient), household size, the three-band gross household income (on the recipient its HBAI net income with the deductions policyengine-uk takes to reach it added back, gross cash income as the NTS band measures it; the engine's household_gross_income adds capital gains and imputed NHS, education and transport spending, which put a fifth of households a band too high; microcosm#1164) and region, drawn from identity-keyed uniforms and rounded to the nearest band; devolved recipients take a declared proxy region for prediction only. Each person's annual local-bus trips are the diary's weighted mean for their band and residence group (London or the rest of England), split into the bus-in-London and other-local-bus series by main mode, W5 x JJXSC over W2 persons annualised by 52.14 weeks; a seven-day diary annualised per person would be right in aggregate and wrong in distribution, so it supplies band means, not per-person counts. If the extract carries no frequency column the band is drawn from the vendored NTS0313/NTS0621 shares and declared band midpoints are scaled to the vendored NTS0303 rate (the receipt names the path). Concessionary eligibility is the declared statutory age rule of the person's area, emitted for the lcfs pricing step to zero-price eligible persons' trips; the donor's eligible share of trips per series is receipted beside the publisher's concessionary boarding share. The stage and ticket tables class every diary bus boarding by how it was paid for (at the point of use, a season ticket or pass, a concessionary pass, free) and give each person the share of fare-paying boardings paid at the point of use for their band and residence group (local_bus_single_fare_share), the boardings a per-journey fare cap binds on, with the fare paid per boarding by residence group and survey year receipted beside it (microcosm#930 C13, Mar\u00eda's ask of 2026-09-23). The share is read on the cap years declared in fare_cap_regime only (England's \u00a32 single-fare cap outside London ran from 1 January 2023 to 31 December 2024, and an NTS survey year is a calendar year), because nine months of the FRS 2024-25 base year (April to December 2024) sit under that cap and the last three under the \u00a33 cap that followed it; 2022, before it, stays in the band and trip fit. The share under the \u00a33 cap or with no cap is not observed, because the extract ends in 2024 (microcosm#1331). For England outside London the receipt also carries fare-mix lines, aggregates only. Before the cap they give the share of point-of-use boardings paid above the cap and their mean fare, at that year's prices; under it, the share paid at the cap, within the declared tolerance. A line that rests on fewer than 10 unweighted boardings or 10 distinct households, or whose complement does, reads <10. The stage records the frame's band shares against NTS0313/NTS0621, its trips per person against NTS0303 and by age band against NTS0601, and the frame's car availability against NTS0205, all from vendored rows; nothing derived from the licensed rows is committed.", "operations": [ { "age_band_edges": [ @@ -4572,6 +4575,17 @@ "trip_id": "TripID", "trip_weight": "W5" }, + "fare_cap_regime": { + "cap_fare": 2, + "cap_tolerance": 0.01, + "cap_years": [ + 2023, + 2024 + ], + "pre_cap_years": [ + 2022 + ] + }, "kind": "clean_nts_travel_tables", "lowercase_columns": true, "survey_years": [ @@ -5318,7 +5332,8 @@ "columns": [ "iidb_reported", "afcs_reported", - "bsp_reported" + "bsp_reported", + "war_widows_pension_reported" ], "kind": "restore_spi_channel_reports_from_source", "linkage": "person_source_id -> FRS-channel person_id", @@ -5375,6 +5390,7 @@ "iidb_reported", "afcs_reported", "bsp_reported", + "war_widows_pension_reported", "is_disabled_for_benefits", "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits", @@ -5752,7 +5768,7 @@ "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 2.134.4 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 2.145.0 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 shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). 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. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base.", "operations": [ { "aggregates": { diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/nts_household.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/nts_household.csv index 73940fc3a..79cd24f33 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/nts_household.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/nts_household.csv @@ -1,65 +1,65 @@ HouseholdID,SurveyYear,HHoldGOR_B02ID,HHIncome2002_B02ID,NumCarVan,HHoldNumPeople,W2 1000,2022,1,1,0,1,1 -1001,2023,2,2,1,2,1.1000000000000001 -1002,2024,3,3,2,3,1.2 +1001,2022,2,2,1,2,1.1000000000000001 +1002,2022,3,3,2,3,1.2 1003,2022,4,1,3,4,1.3 -1004,2023,5,2,0,1,1.3999999999999999 -1005,2024,6,3,1,2,1.5 +1004,2022,5,2,0,1,1.3999999999999999 +1005,2022,6,3,1,2,1.5 1006,2022,7,1,2,3,1.6000000000000001 -1007,2023,8,2,3,4,1 -1008,2024,9,3,0,1,1.1000000000000001 -1009,2022,1,1,1,2,1.2 +1007,2022,8,2,3,4,1 +1008,2022,9,3,0,1,1.1000000000000001 +1009,2023,1,1,1,2,1.2 1010,2023,2,2,2,3,1.3 -1011,2024,3,3,3,4,1.3999999999999999 -1012,2022,4,1,0,1,1.5 +1011,2023,3,3,3,4,1.3999999999999999 +1012,2023,4,1,0,1,1.5 1013,2023,5,2,1,2,1.6000000000000001 -1014,2024,6,3,2,3,1 -1015,2022,7,1,3,4,1.1000000000000001 +1014,2023,6,3,2,3,1 +1015,2023,7,1,3,4,1.1000000000000001 1016,2023,8,2,0,1,1.2 -1017,2024,9,3,1,2,1.3 -1018,2022,1,1,2,3,1.3999999999999999 -1019,2023,2,2,3,4,1.5 +1017,2023,9,3,1,2,1.3 +1018,2024,1,1,2,3,1.3999999999999999 +1019,2024,2,2,3,4,1.5 1020,2024,3,3,0,1,1.6000000000000001 -1021,2022,4,1,1,2,1 -1022,2023,5,2,2,3,1.1000000000000001 +1021,2024,4,1,1,2,1 +1022,2024,5,2,2,3,1.1000000000000001 1023,2024,6,3,3,4,1.2 -1024,2022,7,1,0,1,1.3 -1025,2023,8,2,1,2,1.3999999999999999 +1024,2024,7,1,0,1,1.3 +1025,2024,8,2,1,2,1.3999999999999999 1026,2024,9,3,2,3,1.5 1027,2022,1,1,3,4,1.6000000000000001 -1028,2023,2,2,0,1,1 -1029,2024,3,3,1,2,1.1000000000000001 +1028,2022,2,2,0,1,1 +1029,2022,3,3,1,2,1.1000000000000001 1030,2022,4,1,2,3,1.2 -1031,2023,5,2,3,4,1.3 -1032,2024,6,3,0,1,1.3999999999999999 +1031,2022,5,2,3,4,1.3 +1032,2022,6,3,0,1,1.3999999999999999 1033,2022,7,1,1,2,1.5 -1034,2023,8,2,2,3,1.6000000000000001 -1035,2024,9,3,3,4,1 -1036,2022,1,1,0,1,1.1000000000000001 +1034,2022,8,2,2,3,1.6000000000000001 +1035,2022,9,3,3,4,1 +1036,2023,1,1,0,1,1.1000000000000001 1037,2023,2,2,1,2,1.2 -1038,2024,3,3,2,3,1.3 -1039,2022,4,1,3,4,1.3999999999999999 +1038,2023,3,3,2,3,1.3 +1039,2023,4,1,3,4,1.3999999999999999 1040,2023,5,2,0,1,1.5 -1041,2024,6,3,1,2,1.6000000000000001 -1042,2022,7,1,2,3,1 +1041,2023,6,3,1,2,1.6000000000000001 +1042,2023,7,1,2,3,1 1043,2023,8,2,3,4,1.1000000000000001 -1044,2024,9,3,0,1,1.2 -1045,2022,1,1,1,2,1.3 -1046,2023,2,2,2,3,1.3999999999999999 +1044,2023,9,3,0,1,1.2 +1045,2024,1,1,1,2,1.3 +1046,2024,2,2,2,3,1.3999999999999999 1047,2024,3,3,3,4,1.5 -1048,2022,4,1,0,1,1.6000000000000001 -1049,2023,5,2,1,2,1 +1048,2024,4,1,0,1,1.6000000000000001 +1049,2024,5,2,1,2,1 1050,2024,6,3,2,3,1.1000000000000001 -1051,2022,7,1,3,4,1.2 -1052,2023,8,2,0,1,1.3 +1051,2024,7,1,3,4,1.2 +1052,2024,8,2,0,1,1.3 1053,2024,9,3,1,2,1.3999999999999999 1054,2022,1,1,2,3,1.5 -1055,2023,2,2,3,4,1.6000000000000001 -1056,2024,3,3,0,1,1 +1055,2022,2,2,3,4,1.6000000000000001 +1056,2022,3,3,0,1,1 1057,2022,4,1,1,2,1.1000000000000001 -1058,2023,5,2,2,3,1.2 -1059,2024,6,3,3,4,1.3 +1058,2022,5,2,2,3,1.2 +1059,2022,6,3,3,4,1.3 1060,2022,7,1,0,1,1.3999999999999999 -1061,2023,8,2,1,2,1.5 -1062,2024,9,3,2,3,1.6000000000000001 -1063,2022,1,1,3,4,1 +1061,2022,8,2,1,2,1.5 +1062,2022,9,3,2,3,1.6000000000000001 +1063,2023,1,1,3,4,1 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 dcd949158..9f07056db 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,months_since_last_birthday,marital_status,hours_worked,care_hours,is_household_head,is_benunit_head,is_parent,is_blind,employment_income,self_employment_income,trading_loss,loss_relief,ni_class_4_losses_brought_forward,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,stands_as_sole_owner_or_partner_of_company,is_engaged_in_owned_company_trade,owned_company_is_main_employment,owned_company_carries_on_trade,owned_company_carries_on_property_business,owned_company_income_share,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,statutory_paternity_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,esa_includes_support_component,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,5.9_9999999999921663e+00,SINGLE,1.0_9575000000000000e+03,3.5_0000000000000000e+01,True,True,False,False,5.2_7003571428571468e+03,5.2_1785714285714306e+01,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,7.8_2678571428571445e+02,5.2_1785714285714306e+01,1.8_2625000000000000e+02,0.0_0000000000000000e+00,False,False,False,False,False,0.0_0000000000000000e+00,1.0_4357142857142861e+02,True,1.0_4357142857142861e+02,2.1_3932142857142844e+03,2.6_6110714285714312e+03,2.0_0000000000000000e+01,1.0_4357142857142861e+02,5.2_1785714285714306e+01,2.6_0892857142857153e+01,1.3_0446428571428577e+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+00,4.2_7342500000000018e+03,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,5.2_0000000000000000e+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,True,3.1_3071428571428555e+02,True,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,1.0_0509554140127424e+01,SINGLE,1.1_4792857142857156e+03,0.0_0000000000000000e+00,True,True,False,False,5.3_2221428571428623e+03,1.0_4357142857142861e+02,0.0_0000000000000000e+00,0.0_0000000000000000e+00,0.0_0000000000000000e+00,7.8_2678571428571445e+02,5.2_1785714285714306e+01,1.8_2625000000000000e+02,0.0_0000000000000000e+00,False,False,False,False,False,0.0_0000000000000000e+00,1.0_4357142857142861e+02,True,1.0_4357142857142861e+02,2.1_3932142857142844e+03,2.6_6110714285714312e+03,2.0_0000000000000000e+01,1.0_4357142857142861e+02,5.2_1785714285714306e+01,2.6_0892857142857153e+01,1.3_0446428571428577e+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_0000000000000000e+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,False,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 4e0c91f4c..a093457cb 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 @@ -113,6 +113,7 @@ "uc_is_in_gainful_self_employment": "bool", "uc_is_in_startup_period": "bool", "universal_credit_reported": "float64", + "war_widows_pension_reported": "float64", "winter_fuel_allowance_reported": "float64", "working_tax_credit_reported": "float64", "would_claim_carers_allowance": "bool" diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json index 12d556f13..8fdddd886 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json @@ -1 +1 @@ -{"country":"uk","nodes":[{"artifact_outputs":[{"name":"stage_evidence","type":{"name":"microcosm.stage-evidence","schema_version":1}}],"base":null,"citation":"","description":"Load the source-bound UK FRS root 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UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/uk_calibration_run.py b/test_support/microcosm_build/uk_calibration_run.py index 8ae2235c1..713044b62 100644 --- a/test_support/microcosm_build/uk_calibration_run.py +++ b/test_support/microcosm_build/uk_calibration_run.py @@ -102,6 +102,23 @@ def _fake_cgt_projection(base_year: int = 2023, horizon_year: int = 2030): ) +@pytest.fixture(autouse=True) +def _every_seam_row_published(monkeypatch): + """Publish every row of the four-household seam fixture. + + A row's model figures leave a build only when ten distinct source + households carry it (María's rule of 2026-10-21 binds a thin row and + withholds its figures), and no row here rests on more than four, so the + fit gate would see no published row. The pipeline tests lower the count + to one to read the solve's figures; the withheld path has its own tests + in test_uk_diagnostics.py and test_uk_battery_bindings.py. + """ + + from microcosm.build.uk_runtime import diagnostics + + monkeypatch.setattr(diagnostics, "UK_DIAGNOSTIC_MIN_SOURCE_HOUSEHOLDS", 1) + + @pytest.fixture(autouse=True) def _cgt_projection(monkeypatch): """The seam reads the projection from the engine; tests supply a flat one.""" @@ -131,6 +148,7 @@ def _frame(): household=pd.DataFrame( { "household_id": ids, + "source_household_id": ids, "region": "LONDON", "household_weight": [10.0, 10.0, 10.0, 10.0], "household_is_spi_synthetic": [False, False, False, False], diff --git a/tools/diagnose_uk_uc_relationships.py b/tools/diagnose_uk_uc_relationships.py index b79547ff3..f84f590be 100644 --- a/tools/diagnose_uk_uc_relationships.py +++ b/tools/diagnose_uk_uc_relationships.py @@ -58,7 +58,10 @@ "uc_carer_element", "uc_deductions", "benefit_cap_reduction", - "is_benefit_cap_exempt", + # policyengine-uk 2.135.0 gives Universal Credit and Housing Benefit a cap + # each; the single exemption flag is gone. + "is_uc_benefit_cap_exempt", + "is_housing_benefit_benefit_cap_exempt", "is_uc_eligible", "family_type", "relation_type", diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index c18225b62..99861d431 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -1002,7 +1002,9 @@ def _nts_donors() -> tuple[ household = pd.DataFrame( { "HouseholdID": 1000 + rows, - "SurveyYear": 2022 + rows % 3, + # Every region in every survey year: the single-fare share reads + # the cap years only, which must hold London donors too. + "SurveyYear": 2022 + (rows // 9) % 3, "HHoldGOR_B02ID": 1 + rows % 9, "HHIncome2002_B02ID": 1 + rows % 3, "NumCarVan": rows % 4, diff --git a/uv.lock b/uv.lock index 1a6ffe763..e9497a138 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 = ">=2.134.4" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.145.0" }, { 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" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.134.4" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.145.0" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, ] provides-extras = ["us", "uk"] @@ -1055,7 +1055,7 @@ requires-dist = [ { name = "numpy", specifier = ">=2" }, { name = "pandas", specifier = ">=2.3" }, { name = "policyengine-core", marker = "extra == 'policyengine'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.134.4" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.145.0" }, { name = "policyengine-us", marker = "extra == 'policyengine'", specifier = ">=2.2.1,<3" }, { name = "tables", marker = "extra == 'axiom'", specifier = ">=3" }, ] @@ -1675,7 +1675,7 @@ wheels = [ [[package]] name = "policyengine-uk" -version = "2.134.4" +version = "2.145.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "microdf-python" }, @@ -1683,9 +1683,9 @@ dependencies = [ { name = "pydantic" }, { name = "tables" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/79/f3/f31485e455c543371b50c29486d658f9f95d2c47f73db311dd8fdc67f394/policyengine_uk-2.134.4.tar.gz", hash = "sha256:2c4fdbe62e3c84681f39d658b2194b4df9fd6cbedf474f169a553c63c4293a79", size = 2604960, upload-time = "2026-10-16T09:18:16.301Z" } +sdist = { url = "https://files.pythonhosted.org/packages/fc/69/9b71ed5706bb1ea2744673acb76ca7bc17641ee7acd0bff3747dcf939602/policyengine_uk-2.145.0.tar.gz", hash = "sha256:4cfdf97f60a5f95114e7748bad277c4a7b99e5ef8edc775fc65160347213e73c", size = 3074821, upload-time = "2026-10-21T20:22:35.157Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/49/f8/4d09f4986ff929d53c0d8fc5400ee7a5cc79ea60aca73a626b3938fd1288/policyengine_uk-2.134.4-py3-none-any.whl", hash = "sha256:0df7a10dd89cd5604b78c9ed7ffebade27ab621275a4a6dd6d19d789e17dffa9", size = 3935778, upload-time = "2026-10-16T09:18:14.198Z" }, + { url = "https://files.pythonhosted.org/packages/bb/21/3d226b95a5ffc555b1d1ee2a6b910de2e10b70aee71ffecc86466044375b/policyengine_uk-2.145.0-py3-none-any.whl", hash = "sha256:996d9d95cb2deea0d6595a2a6524804cacbc0943f53d45fced0b3ecebbe78331", size = 4557152, upload-time = "2026-10-21T20:22:32.55Z" }, ] [[package]]