diff --git a/changelog.d/frs-claimant-or-partner.added.md b/changelog.d/frs-claimant-or-partner.added.md new file mode 100644 index 00000000..b20eb833 --- /dev/null +++ b/changelog.d/frs-claimant-or-partner.added.md @@ -0,0 +1 @@ +Supply `is_claimant_or_partner`, each benefit unit's single adult or couple, from FRS adult-table membership, so policyengine-uk reads the survey's roles instead of inferring them from ages. SPI records and the public transfer dataset (`enhanced_cps_2025.h5`, column added in place) carry it too, and stacking refuses to combine a person table that has it with one that does not. diff --git a/policyengine_uk_data/datasets/enhanced_cps.py b/policyengine_uk_data/datasets/enhanced_cps.py index 490f89ca..b4e038e3 100644 --- a/policyengine_uk_data/datasets/enhanced_cps.py +++ b/policyengine_uk_data/datasets/enhanced_cps.py @@ -559,6 +559,15 @@ def _build_base_dataset( adults = scenario["adults"] children = scenario["children"] people = adults + children + # Each source household is one tax unit with its head listed first and, + # for joint filers, the spouse second: the benefit unit's claimant and + # partner. + claimants_or_partners = 2 if row["filing_status"] == "joint" else 1 + if len(adults) < claimants_or_partners: + raise ValueError( + f"Source household {source_household_id} files " + f"{row['filing_status']} but lists {len(adults)} adult(s)." + ) has_mortgage = any( "deductible_mortgage_interest" in person.get("inputs", {}) @@ -663,6 +672,7 @@ def _build_base_dataset( "person_id": person_id, "person_household_id": household_id, "person_benunit_id": benunit_id, + "is_claimant_or_partner": person_index <= claimants_or_partners, "age": int(person["age"]), "gender": "MALE" if (household_id + person_index) % 2 else "FEMALE", "employment_income_before_lsr": _gbp( diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py index e440d486..01f2435e 100644 --- a/policyengine_uk_data/datasets/frs.py +++ b/policyengine_uk_data/datasets/frs.py @@ -392,6 +392,32 @@ def derive_is_parent_from_frs_microdata( return is_adult_record & has_dependent_children +def derive_is_claimant_or_partner_from_frs_microdata( + person_ids, + person_benunit_ids, + adult_person_ids, +) -> np.ndarray: + """Identify each FRS benefit unit's single adult or couple. + + An FRS benefit unit is one adult or a couple plus any dependent children. + The adult table holds the head (UPERSON 1) and any partner (UPERSON 2); + the child table holds the dependent children. Any other adult in the + household, such as a grown-up son or daughter, forms and heads their own + benefit unit. So adult-table membership is policyengine-uk's + `is_claimant_or_partner`, which the model otherwise infers from ages. + """ + + is_adult_record = np.isin(np.asarray(person_ids), np.asarray(adult_person_ids)) + per_benunit = pd.Series(is_adult_record).groupby(np.asarray(person_benunit_ids)) + counts = per_benunit.sum() + if not counts.between(1, 2).all(): + raise ValueError( + "Every FRS benefit unit needs one or two adult-table records; " + f"{int((~counts.between(1, 2)).sum())} benefit units do not." + ) + return is_adult_record + + def _as_non_negative_array(values) -> np.ndarray: values = np.asarray(values, dtype=float) return np.maximum(np.nan_to_num(values, nan=0.0), 0.0) @@ -728,6 +754,13 @@ def create_frs( benunit_ids=pe_benunit.benunit_id, dependent_children=dependent_children, ) + pe_person["is_claimant_or_partner"] = ( + derive_is_claimant_or_partner_from_frs_microdata( + person_ids=pe_person.person_id, + person_benunit_ids=pe_person.person_benunit_id, + adult_person_ids=frs["adult"].person_id, + ) + ) MARITAL = [ "MARRIED", "SINGLE", diff --git a/policyengine_uk_data/datasets/spi.py b/policyengine_uk_data/datasets/spi.py index e22be675..41de9641 100644 --- a/policyengine_uk_data/datasets/spi.py +++ b/policyengine_uk_data/datasets/spi.py @@ -112,6 +112,8 @@ def create_spi( person["person_id"] = df.SREF person["person_household_id"] = df.SREF person["person_benunit_id"] = df.SREF + # Each SPI taxpayer is their own one-person benefit unit, so its claimant. + person["is_claimant_or_partner"] = np.ones(len(df), dtype=bool) benunit["benunit_id"] = df.SREF household["household_id"] = df.SREF diff --git a/policyengine_uk_data/storage/enhanced_cps_2025.h5 b/policyengine_uk_data/storage/enhanced_cps_2025.h5 index 77bf31c5..633cff57 100644 Binary files a/policyengine_uk_data/storage/enhanced_cps_2025.h5 and b/policyengine_uk_data/storage/enhanced_cps_2025.h5 differ diff --git a/policyengine_uk_data/tests/test_claimant_or_partner.py b/policyengine_uk_data/tests/test_claimant_or_partner.py new file mode 100644 index 00000000..6c19317a --- /dev/null +++ b/policyengine_uk_data/tests/test_claimant_or_partner.py @@ -0,0 +1,181 @@ +"""The claimant-or-partner role: FRS derivation, stacking, and built datasets.""" + +import numpy as np +import pandas as pd +import pytest +from hypothesis import given, settings +from hypothesis import strategies as st +from policyengine_uk.data import UKSingleYearDataset + +from policyengine_uk_data.datasets.frs import ( + derive_is_claimant_or_partner_from_frs_microdata, + derive_is_parent_from_frs_microdata, +) +from policyengine_uk_data.utils.stack import stack_datasets + + +def test_grown_up_child_heads_their_own_benefit_unit(): + # Lone parent and dependent child in one benefit unit; grown-up son in his own. + result = derive_is_claimant_or_partner_from_frs_microdata( + person_ids=np.array([1_001, 1_002, 1_003]), + person_benunit_ids=np.array([101, 102, 101]), + adult_person_ids=np.array([1_001, 1_002]), + ) + assert result.tolist() == [True, True, False] + + +def test_couple_with_children(): + result = derive_is_claimant_or_partner_from_frs_microdata( + person_ids=np.array([2_003, 2_001, 2_002]), + person_benunit_ids=np.array([201, 201, 201]), + adult_person_ids=np.array([2_002, 2_001]), + ) + assert result.tolist() == [False, True, True] + + +@pytest.mark.parametrize("adults", [[], [3_001, 3_002, 3_003]]) +def test_benefit_unit_without_one_or_two_adults_is_rejected(adults): + with pytest.raises(ValueError, match="one or two adult-table records"): + derive_is_claimant_or_partner_from_frs_microdata( + person_ids=np.array([3_001, 3_002, 3_003]), + person_benunit_ids=np.array([301, 301, 301]), + adult_person_ids=np.array(adults, dtype=int), + ) + + +@st.composite +def frs_households(draw): + """FRS-shaped records: benefit units of one or two adults plus children.""" + rows = [] + for household in range(1, draw(st.integers(1, 6)) + 1): + person = 0 + for benunit in range(1, draw(st.integers(1, 3)) + 1): + n_adults = draw(st.integers(1, 2)) + n_children = draw(st.integers(0, 4)) + for index in range(n_adults + n_children): + person += 1 + rows.append( + ( + household * 1_000 + person, + household * 100 + benunit, + index < n_adults, + ) + ) + order = draw(st.permutations(range(len(rows)))) + frame = pd.DataFrame(rows, columns=["person_id", "benunit_id", "adult"]) + return frame.iloc[list(order)].reset_index(drop=True) + + +def _derive(frame, adult_mask=None): + adult = frame.adult if adult_mask is None else adult_mask + return derive_is_claimant_or_partner_from_frs_microdata( + person_ids=frame.person_id, + person_benunit_ids=frame.benunit_id, + adult_person_ids=frame.person_id[adult], + ) + + +@settings(max_examples=300, deadline=None) +@given(frs_households()) +def test_every_benefit_unit_has_a_claimant_and_at_most_one_partner(frame): + result = _derive(frame) + assert (result == frame.adult.to_numpy()).all() + counts = pd.Series(result).groupby(frame.benunit_id.to_numpy()).sum() + assert counts.between(1, 2).all() + + +@settings(max_examples=200, deadline=None) +@given(frs_households(), st.randoms(use_true_random=False)) +def test_record_order_does_not_change_anyones_role(frame, rng): + order = list(range(len(frame))) + rng.shuffle(order) + shuffled = frame.iloc[order].reset_index(drop=True) + by_person = dict(zip(frame.person_id, _derive(frame))) + assert [by_person[p] for p in shuffled.person_id] == _derive(shuffled).tolist() + + +@settings(max_examples=200, deadline=None) +@given(frs_households(), st.data()) +def test_parents_are_always_claimant_or_partner(frame, data): + benunits = np.sort(frame.benunit_id.unique()) + children = data.draw( + st.lists(st.integers(0, 4), min_size=len(benunits), max_size=len(benunits)) + ) + is_parent = derive_is_parent_from_frs_microdata( + person_ids=frame.person_id, + person_benunit_ids=frame.benunit_id, + adult_person_ids=frame.person_id[frame.adult], + benunit_ids=benunits, + dependent_children=np.array(children), + ) + assert not (is_parent & ~_derive(frame)).any() + + +@settings(max_examples=200, deadline=None) +@given(frs_households(), st.data()) +def test_a_benefit_unit_with_no_adult_or_a_third_adult_is_rejected(frame, data): + benunit = data.draw(st.sampled_from(sorted(frame.benunit_id.unique()))) + members = frame.benunit_id == benunit + if data.draw(st.booleans()): + adult = frame.adult & ~members + else: + n_extra = 3 - int(frame.adult[members].sum()) + start = frame.person_id.max() + 1 + extra = pd.DataFrame( + { + "person_id": range(start, start + n_extra), + "benunit_id": benunit, + "adult": True, + } + ) + frame = pd.concat([frame, extra], ignore_index=True) + adult = frame.adult + with pytest.raises(ValueError, match="one or two adult-table records"): + _derive(frame, adult) + + +def _tiny_dataset(roles=True): + person = pd.DataFrame( + { + "person_id": [1, 2, 3], + "person_benunit_id": [1, 1, 1], + "person_household_id": [1, 1, 1], + "age": [40, 38, 10], + } + ) + if roles: + person["is_claimant_or_partner"] = [True, True, False] + return UKSingleYearDataset( + person=person, + benunit=pd.DataFrame({"benunit_id": [1]}), + household=pd.DataFrame({"household_id": [1], "household_weight": [1.0]}), + fiscal_year=2024, + ) + + +def test_stacking_keeps_roles(): + stacked = stack_datasets(_tiny_dataset(), _tiny_dataset()) + stacked.validate() + assert stacked.person.is_claimant_or_partner.dtype == bool + assert stacked.person.is_claimant_or_partner.tolist() == [True, True, False] * 2 + + +@pytest.mark.parametrize("roles", [(True, False), (False, True)]) +def test_stacking_refuses_a_table_without_roles(roles): + with pytest.raises(ValueError, match="is_claimant_or_partner"): + stack_datasets(_tiny_dataset(roles[0]), _tiny_dataset(roles[1])) + + +@pytest.mark.parametrize("fixture", ["frs", "enhanced_frs"]) +def test_built_dataset_roles(fixture, request): + person = request.getfixturevalue(fixture).person + role = person.is_claimant_or_partner + assert role.dtype == bool + counts = role.groupby(person.person_benunit_id).sum() + assert counts.between(1, 2).all() + assert not (person.is_benunit_head & ~role).any() + assert not (person.is_parent & ~role).any() + assert (person.age[role] >= 16).all() + # FRS dependent children are 19 at most, so everyone older is in the + # adult table: a claimant or partner. + assert role[person.age >= 20].all() diff --git a/policyengine_uk_data/tests/test_clone_and_assign.py b/policyengine_uk_data/tests/test_clone_and_assign.py index a6cf1563..a3c8c0e1 100644 --- a/policyengine_uk_data/tests/test_clone_and_assign.py +++ b/policyengine_uk_data/tests/test_clone_and_assign.py @@ -256,6 +256,20 @@ def test_data_preserved_across_clones(self, toy_dataset, small_crosswalk): original_regions = toy_dataset.household["region"].values np.testing.assert_array_equal(regions, original_regions) + def test_claimant_or_partner_follows_each_clone(self, toy_dataset, small_crosswalk): + roles = np.tile([True, False], len(toy_dataset.household)) + toy_dataset.person["is_claimant_or_partner"] = roles + result = clone_and_assign( + toy_dataset, + n_clones=3, + crosswalk_path=str(small_crosswalk), + ) + + assert result.person["is_claimant_or_partner"].dtype == bool + np.testing.assert_array_equal( + result.person["is_claimant_or_partner"].values, np.tile(roles, 3) + ) + def test_single_clone_is_near_identity(self, toy_dataset, small_crosswalk): """With n_clones=1, output should match input dimensions.""" result = clone_and_assign( diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py index 5f1acd85..c0007560 100644 --- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py +++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py @@ -548,8 +548,10 @@ def fake_read_csv(path, *args, **kwargs): "age_started_or_accepted_current_education_or_training", "is_before_universal_credit_qualifying_young_person_terminal_date", "is_parent", + "is_claimant_or_partner", }.issubset(dataset.person.columns) assert not dataset.person["is_parent"].iloc[0] + assert dataset.person["is_claimant_or_partner"].iloc[0] assert not dataset.person["is_in_non_advanced_education"].iloc[0] assert not dataset.person["is_in_approved_training"].iloc[0] assert ( diff --git a/policyengine_uk_data/tests/test_policybench_transfer.py b/policyengine_uk_data/tests/test_policybench_transfer.py index 900b73d1..2f966bcc 100644 --- a/policyengine_uk_data/tests/test_policybench_transfer.py +++ b/policyengine_uk_data/tests/test_policybench_transfer.py @@ -26,6 +26,10 @@ "pip_dl_category", "pip_m_category", } +# Benefit-unit roles the source records, which policyengine-uk infers by +# formula when a dataset leaves them out (is_claimant_or_partner from +# policyengine-uk#1896). Releases that predate a role ignore its column. +DATASET_SUPPLIED_ROLES = {"is_claimant_or_partner"} def _subset_source(tmp_path: Path, rows: int) -> Path: @@ -81,11 +85,15 @@ def test_policybench_transfer_writes_only_valid_leaf_inputs(tmp_path: Path): invalid_columns = [ column for column in frame.columns - if column not in system.variables - or system.variables[column].entity.key != entity + if (column not in system.variables and column not in DATASET_SUPPLIED_ROLES) or ( - not system.variables[column].is_input_variable() - and column not in ALLOWED_COMPATIBILITY_INPUTS + column in system.variables + and system.variables[column].entity.key != entity + ) + or ( + column in system.variables + and not system.variables[column].is_input_variable() + and column not in ALLOWED_COMPATIBILITY_INPUTS | DATASET_SUPPLIED_ROLES ) ] assert invalid_columns == [] @@ -228,6 +236,38 @@ def test_policybench_transfer_family_structure_matches_person_membership( assert observed == expected +def test_policybench_transfer_marks_head_and_joint_spouse_as_claimant_or_partner( + tmp_path: Path, +): + source_file_path = _subset_source(tmp_path, 200) + source = pd.read_csv(source_file_path) + dataset = create_enhanced_cps(source_file_path=source_file_path, calibrate=False) + person = dataset.person + + counts = person.groupby("person_benunit_id").is_claimant_or_partner.sum() + expected = np.where(source.filing_status == "joint", 2, 1) + np.testing.assert_array_equal(counts.sort_index().to_numpy(), expected) + # The head and any spouse are listed before every other member. + first = person.groupby( + "person_benunit_id" + ).cumcount() < person.person_benunit_id.map(counts) + assert (person.is_claimant_or_partner == first).all() + + +def test_checked_in_enhanced_cps_h5_roles_match_the_builder(tmp_path: Path): + checked_in = UKSingleYearDataset(file_path=str(ENHANCED_CPS_FILE)).person + built = create_enhanced_cps( + source_file_path=_subset_source(tmp_path, 500), + calibrate=False, + ).person + head = checked_in.iloc[: len(built)] + + for column in ("person_id", "person_benunit_id", "age", "is_claimant_or_partner"): + np.testing.assert_array_equal(head[column].to_numpy(), built[column].to_numpy()) + counts = checked_in.groupby("person_benunit_id").is_claimant_or_partner.sum() + assert counts.between(1, 2).all() + + def test_assign_council_tax_bands_handles_upper_percentile_edge(): households = pd.DataFrame( { diff --git a/policyengine_uk_data/tests/test_spi_build.py b/policyengine_uk_data/tests/test_spi_build.py index efb37f81..31d3c979 100644 --- a/policyengine_uk_data/tests/test_spi_build.py +++ b/policyengine_uk_data/tests/test_spi_build.py @@ -467,3 +467,15 @@ def test_income_model_cache_accepts_current_spi_release(tmp_path, monkeypatch): ) assert income_module.create_income_model().metadata == current_metadata + + +def test_create_spi_marks_every_taxpayer_as_their_benefit_units_claimant(tmp_path): + from policyengine_uk_data.datasets.spi import create_spi + + tab = tmp_path / "spi.tab" + _write_fake_spi(tab) + + person = create_spi(tab, 2020, seed=0).person + assert person["is_claimant_or_partner"].dtype == bool + assert person["is_claimant_or_partner"].all() + assert person["person_benunit_id"].is_unique diff --git a/policyengine_uk_data/utils/stack.py b/policyengine_uk_data/utils/stack.py index 2fe9df82..d722d347 100644 --- a/policyengine_uk_data/utils/stack.py +++ b/policyengine_uk_data/utils/stack.py @@ -1,10 +1,17 @@ from policyengine_uk.data import UKSingleYearDataset import pandas as pd +# Benefit-unit roles taken from the survey, which policyengine-uk otherwise +# infers from ages. A stacked table without them would leave missing roles. +SURVEY_ROLE_COLUMNS = ("is_claimant_or_partner",) + def stack_datasets( data_1: UKSingleYearDataset, data_2: UKSingleYearDataset ) -> UKSingleYearDataset: + for column in SURVEY_ROLE_COLUMNS: + if (column in data_1.person.columns) != (column in data_2.person.columns): + raise ValueError(f"Only one of the stacked datasets has {column}.") person_id_offset = data_1.person.person_id.max() + 1 benunit_id_offset = data_1.benunit.benunit_id.max() + 1 household_id_offset = data_1.household.household_id.max() + 1 diff --git a/pyproject.toml b/pyproject.toml index beff7f1a..7973343c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,8 +45,9 @@ dev = [ "yaml-changelog>=0.1.7", "itables", "quantile-forest", - "build", "towncrier>=24.8.0", - + "build", + "towncrier>=24.8.0", + "hypothesis>=6.168.3", ] [tool.setuptools] diff --git a/uv.lock b/uv.lock index a3e9f44d..672737ea 100644 --- a/uv.lock +++ b/uv.lock @@ -577,6 +577,75 @@ wheels = 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