Fix rent and childcare export inputs

src/microplex_us/data_sources/cps.py

@@ -38,7 +38,7 @@
 
 # Default cache directory
 DEFAULT_CACHE_DIR = Path.home() / ".cache" / "microplex"
-CPS_ASEC_PROCESSED_CACHE_VERSION = "20260601_ecps_age80_84"
+CPS_ASEC_PROCESSED_CACHE_VERSION = "20260602_childcare_input"
 
 # CPS ASEC data URLs by year
 CPS_URLS = {
@@ -244,6 +244,7 @@
     "social_security_survivors",
     "social_security_dependents",
     "receives_wic",
+    "spm_unit_pre_subsidy_childcare_expenses",
 )
 
 PERSON_CPS_DISABILITY_COLUMNS = (

src/microplex_us/pipelines/us.py

@@ -10208,6 +10208,12 @@ def has_any(*columns: str) -> bool:
             else:
                 result["is_hispanic"] = hispanic.fillna(0).astype(int).ne(0)
 
+        if has_any("pre_subsidy_rent", "rent"):
+            result["pre_subsidy_rent"] = first_nonzero_or_present(
+                "pre_subsidy_rent",
+                "rent",
+            ).clip(lower=0.0)
+
         marital_status = (
             pd.to_numeric(result["marital_status"], errors="coerce")
             if "marital_status" in result.columns

src/microplex_us/policyengine/us.py

@@ -446,6 +446,11 @@ class PolicyEngineUSVariableMaterializationResult:
     # the persisted leaf input when no explicit last-week field is present.
     "hours_worked": "hours_worked_last_week",
     "race": "cps_race",
+    # PE-US computes ``rent`` from the persisted source input
+    # ``pre_subsidy_rent``. Microplex's ACS donor block restores the
+    # user-facing ``rent`` column, so export it through the storable leaf input
+    # rather than dropping it as formula-owned.
+    "rent": "pre_subsidy_rent",
 }
 
 POLICYENGINE_US_STRUCTURAL_EXPORT_COLUMNS: frozenset[str] = frozenset(

tests/pipelines/test_us.py

@@ -4790,6 +4790,27 @@ def test_augment_policyengine_person_inputs_materializes_non_sch_d_capital_gains
 
         assert augmented["non_sch_d_capital_gains"].tolist() == [250.0]
 
+    def test_augment_policyengine_person_inputs_aliases_rent_to_pre_subsidy_rent(
+        self,
+    ):
+        pipeline = USMicroplexPipeline(USMicroplexBuildConfig())
+        persons = pd.DataFrame(
+            {
+                "rent": [14_400.0, 0.0, 9_600.0],
+                "pre_subsidy_rent": [0.0, 7_200.0, None],
+                "age": [45, 70, 12],
+                "sex": [1, 2, 1],
+            }
+        )
+
+        augmented = pipeline._augment_policyengine_person_inputs(persons)
+
+        assert augmented["pre_subsidy_rent"].tolist() == [
+            14_400.0,
+            7_200.0,
+            9_600.0,
+        ]
+
     def test_augment_policyengine_person_inputs_zeros_part_b_without_medicare(
         self,
     ):

tests/policyengine/test_us.py

@@ -2644,6 +2644,55 @@ class FakeSystem:
             "is_hispanic": "is_hispanic",
         }.items() <= export_maps["person"].items()
 
+    def test_build_policyengine_us_export_variable_maps_aliases_rent_to_pre_subsidy_rent(
+        self,
+    ):
+        class FakeEntity:
+            def __init__(self, key):
+                self.key = key
+
+        class FakeVariable:
+            def __init__(self, entity, formulas=None):
+                self.entity = FakeEntity(entity)
+                self.formulas = formulas or {}
+
+        class FakeSystem:
+            variables = {
+                "pre_subsidy_rent": FakeVariable("person"),
+                "rent": FakeVariable("person", formulas={"2024": object()}),
+            }
+
+        tables = PolicyEngineUSEntityTableBundle(
+            households=pd.DataFrame(
+                {
+                    "household_id": [10, 20],
+                    "household_weight": [1.0, 1.0],
+                }
+            ),
+            persons=pd.DataFrame(
+                {
+                    "person_id": [1, 2],
+                    "household_id": [10, 20],
+                    "rent": [14_400.0, 0.0],
+                }
+            ),
+        )
+
+        export_maps = build_policyengine_us_export_variable_maps(
+            tables,
+            tax_benefit_system=FakeSystem(),
+        )
+        arrays = build_policyengine_us_time_period_arrays(
+            tables,
+            period=2024,
+            person_variable_map=export_maps["person"],
+        )
+
+        assert export_maps["person"]["rent"] == "pre_subsidy_rent"
+        assert "pre_subsidy_rent" not in export_maps["person"]
+        assert "rent" not in export_maps["person"].values()
+        assert arrays["pre_subsidy_rent"]["2024"].tolist() == [14_400.0, 0.0]
+
     def test_build_policyengine_us_export_variable_maps_includes_absent_export_defaults(
         self,
     ):

tests/test_cps_source_provider.py

@@ -47,7 +47,11 @@ def test_cps_parquet_source_provider_loads_observation_frame(tmp_path):
 
     assert isinstance(provider, SourceProvider)
     assert set(frame.tables) == {EntityType.HOUSEHOLD, EntityType.PERSON}
-    assert frame.tables[EntityType.PERSON]["person_id"].tolist() == ["1:1", "1:2", "2:1"]
+    assert frame.tables[EntityType.PERSON]["person_id"].tolist() == [
+        "1:1",
+        "1:2",
+        "2:1",
+    ]
     assert frame.tables[EntityType.HOUSEHOLD]["year"].tolist() == [2024, 2024]
     assert frame.source.archetype is SourceArchetype.HOUSEHOLD_INCOME
 
@@ -110,7 +114,11 @@ def test_cps_parquet_source_provider_derives_tax_unit_roles_from_tax_id(tmp_path
 
     provider = CPSASECParquetSourceProvider(data_dir=tmp_path, year=2024)
     frame = provider.load_frame(SourceQuery(period=2024))
-    result = frame.tables[EntityType.PERSON].sort_values("person_number").reset_index(drop=True)
+    result = (
+        frame.tables[EntityType.PERSON]
+        .sort_values("person_number")
+        .reset_index(drop=True)
+    )
 
     assert result["tax_unit_id"].tolist() == [100, 100, 100, 101]
     assert result["tax_unit_is_joint"].tolist() == [1.0, 1.0, 1.0, 0.0]
@@ -271,7 +279,9 @@ def test_cps_parquet_source_provider_sampling_respects_household_weights(tmp_pat
     assert frame.tables[EntityType.PERSON]["household_id"].tolist() == [3]
 
 
-def test_cps_parquet_source_provider_applies_generic_atomic_variable_semantics(tmp_path):
+def test_cps_parquet_source_provider_applies_generic_atomic_variable_semantics(
+    tmp_path,
+):
     households = pd.DataFrame(
         {
             "household_id": [1],
@@ -391,6 +401,7 @@ def test_load_cps_asec_caches_household_geography_on_persons(tmp_path):
     assert "social_security_survivors" in first.persons.columns
     assert "social_security_dependents" in first.persons.columns
     assert "receives_wic" in first.persons.columns
+    assert "spm_unit_pre_subsidy_childcare_expenses" in first.persons.columns
     assert "has_marketplace_health_coverage" in first.persons.columns
     assert "has_esi" in first.persons.columns
     assert "tax_unit_id" in first.persons.columns
@@ -409,7 +420,16 @@ def test_load_cps_asec_caches_household_geography_on_persons(tmp_path):
     assert cached_persons["social_security_survivors"].to_list() == [0.0, 0.0, 0.0]
     assert cached_persons["social_security_dependents"].to_list() == [0.0, 0.0, 0.0]
     assert cached_persons["receives_wic"].to_list() == [True, False, False]
-    assert cached_persons["has_marketplace_health_coverage"].to_list() == [True, False, False]
+    assert cached_persons["spm_unit_pre_subsidy_childcare_expenses"].to_list() == [
+        0.0,
+        0.0,
+        0.0,
+    ]
+    assert cached_persons["has_marketplace_health_coverage"].to_list() == [
+        True,
+        False,
+        False,
+    ]
     assert cached_persons["has_esi"].to_list() == [False, True, False]
     assert cached_persons["tax_unit_id"].to_list() == [100, 100, 200]
     assert cached_persons["spm_unit_id"].to_list() == [10, 10, 20]
@@ -458,7 +478,9 @@ def test_load_cps_asec_derives_policyengine_value_inputs(tmp_path):
         archive.writestr("pppub23.csv", person_rows.to_csv(index=False))
 
     dataset = load_cps_asec(year=2023, cache_dir=tmp_path, download=False)
-    persons = dataset.persons.to_pandas().sort_values("person_number").reset_index(drop=True)
+    persons = (
+        dataset.persons.to_pandas().sort_values("person_number").reset_index(drop=True)
+    )
 
     assert persons["alimony_income"].tolist() == [1200, 0]
     assert persons["child_support_received"].tolist() == [300, 0]
@@ -476,11 +498,15 @@ def test_load_cps_asec_derives_policyengine_value_inputs(tmp_path):
     assert persons["takes_up_housing_assistance_if_eligible"].tolist() == [True, False]
     assert persons["spm_unit_energy_subsidy"].tolist() == [90, 0]
     assert persons["spm_unit_pre_subsidy_childcare_expenses"].tolist() == [1500, 0]
-    assert persons["health_insurance_premiums_without_medicare_part_b"].tolist() == [900, 0]
+    assert persons["health_insurance_premiums_without_medicare_part_b"].tolist() == [
+        900,
+        0,
+    ]
     assert persons["over_the_counter_health_expenses"].tolist() == [120, 0]
     assert persons["other_medical_expenses"].tolist() == [450, 0]
     assert persons["medicare_part_b_premiums"].tolist() == [600, 0]
 
+
 def test_load_cps_asec_derives_survivor_and_dependent_social_security(tmp_path):
     person_rows = pd.DataFrame(
         {
@@ -497,7 +523,9 @@ def test_load_cps_asec_derives_survivor_and_dependent_social_security(tmp_path):
         archive.writestr("pppub23.csv", person_rows.to_csv(index=False))
 
     dataset = load_cps_asec(year=2023, cache_dir=tmp_path, download=False)
-    persons = dataset.persons.to_pandas().sort_values("person_number").reset_index(drop=True)
+    persons = (
+        dataset.persons.to_pandas().sort_values("person_number").reset_index(drop=True)
+    )
 
     assert persons["social_security_survivors"].tolist() == [1000.0, 1100.0, 0.0, 0.0]
     assert persons["social_security_dependents"].tolist() == [0.0, 0.0, 1200.0, 1300.0]
@@ -535,6 +563,7 @@ def test_cps_source_provider_repeat_loads_are_deterministic_for_cached_processed
             "other_medical_expenses": [0.0] * 5,
             "over_the_counter_health_expenses": [0.0] * 5,
             "medicare_part_b_premiums": [0.0] * 5,
+            "spm_unit_pre_subsidy_childcare_expenses": [0.0] * 5,
             "year": [2023, 2023, 2023, 2023, 2023],
         }
     )
@@ -553,13 +582,21 @@ def test_cps_source_provider_repeat_loads_are_deterministic_for_cached_processed
     first_persons = first.tables[EntityType.PERSON]
     second_persons = second.tables[EntityType.PERSON]
 
-    assert first_households["household_id"].tolist() == second_households["household_id"].tolist()
+    assert (
+        first_households["household_id"].tolist()
+        == second_households["household_id"].tolist()
+    )
     assert first_persons["person_id"].tolist() == second_persons["person_id"].tolist()
-    assert first_households["household_weight"].tolist() == second_households["household_weight"].tolist()
+    assert (
+        first_households["household_weight"].tolist()
+        == second_households["household_weight"].tolist()
+    )
     assert first_persons["weight"].tolist() == second_persons["weight"].tolist()
 
 
-def test_load_cps_asec_rebuilds_stale_processed_cache_without_pe_presim_inputs(tmp_path):
+def test_load_cps_asec_rebuilds_stale_processed_cache_without_pe_presim_inputs(
+    tmp_path,
+):
     stale_processed = pl.DataFrame(
         {
             "household_id": [1, 1, 2],
@@ -623,16 +660,19 @@ def test_load_cps_asec_rebuilds_stale_processed_cache_without_pe_presim_inputs(t
     assert dataset.persons["cps_race"].to_list() == [4, 4, 1]
     assert dataset.persons["is_hispanic"].to_list() == [False, True, False]
     assert dataset.persons["is_disabled"].to_list() == [False, True, False]
-    assert dataset.persons["has_marketplace_health_coverage"].to_list() == [True, False, False]
+    assert dataset.persons["has_marketplace_health_coverage"].to_list() == [
+        True,
+        False,
+        False,
+    ]
     assert dataset.persons["has_esi"].to_list() == [False, True, False]
     assert dataset.persons["alimony_income"].to_list() == [1200, 0, 0]
     assert dataset.persons["child_support_received"].to_list() == [300, 0, 0]
     assert dataset.persons["child_support_expense"].to_list() == [700, 0, 0]
     assert dataset.persons["disability_benefits"].to_list() == [550, 0, 0]
-    assert (
-        dataset.persons["health_insurance_premiums_without_medicare_part_b"].to_list()
-        == [900, 0, 0]
-    )
+    assert dataset.persons[
+        "health_insurance_premiums_without_medicare_part_b"
+    ].to_list() == [900, 0, 0]
     assert dataset.persons["other_medical_expenses"].to_list() == [450, 0, 0]
     assert dataset.persons["over_the_counter_health_expenses"].to_list() == [120, 0, 0]
     assert dataset.persons["medicare_part_b_premiums"].to_list() == [600, 0, 0]
@@ -661,9 +701,7 @@ def test_cps_sampling_falls_back_to_uniform_when_weighted_sampling_is_infeasible
 
     def flaky_sample(self, *args, **kwargs):
         if kwargs.get("weights") is not None:
-            raise ValueError(
-                "Weighted sampling cannot be achieved with replace=False."
-            )
+            raise ValueError("Weighted sampling cannot be achieved with replace=False.")
         return original_sample(self, *args, **kwargs)
 
     monkeypatch.setattr(pd.DataFrame, "sample", flaky_sample)
@@ -710,10 +748,14 @@ def test_sample_households_and_persons_state_floor_preserves_state_coverage() ->
 
     assert len(sampled_households) == 3
     assert sampled_households["state_fips"].nunique() == 3
-    assert set(sampled_persons["household_id"]) == set(sampled_households["household_id"])
+    assert set(sampled_persons["household_id"]) == set(
+        sampled_households["household_id"]
+    )
 
 
-def test_sample_households_and_persons_state_age_floor_preserves_age_band_coverage() -> None:
+def test_sample_households_and_persons_state_age_floor_preserves_age_band_coverage() -> (
+    None
+):
     households = pd.DataFrame(
         {
             "household_id": [1, 2, 3, 4, 5, 6],
@@ -759,4 +801,6 @@ def test_sample_households_and_persons_state_age_floor_preserves_age_band_covera
 
     assert len(sampled_households) == 4
     assert observed_keys.issubset(sampled_keys)
-    assert set(sampled_persons["household_id"]) == set(sampled_households["household_id"])
+    assert set(sampled_persons["household_id"]) == set(
+        sampled_households["household_id"]
+    )