Compile ACA bronze marketplace targets

packages/populace-build/src/populace/build/us/fiscal_targets.py

@@ -284,6 +284,16 @@
             "count_filter_variable": "is_aca_ptc_eligible",
         },
     ),
+    ("cms_aca", "bronze_aptc_consumers"): (
+        "selected_marketplace_plan_benchmark_ratio",
+        "cms_aca",
+        {
+            "target_role": "aca_bronze_aptc_consumers",
+            "measure_mode": "less_than_count",
+            "count_less_than": "1.0",
+            "count_filter_variable": "assigned_aca_ptc",
+        },
+    ),
     ("cms_medicaid", "total_medicaid_enrollment"): (
         "medicaid_enrolled",
         "cms_medicaid",

packages/populace-build/tests/test_us_fiscal_refresh_builder.py

@@ -234,6 +234,44 @@ def calculate(self, variable, *, period, map_to=None):
     assert np.array_equal(values, np.asarray([1.0, 1.0]))
 
 
+def test_combined_household_values_threshold_count_keeps_domain_filter(
+    small_frame,
+) -> None:
+    builder = _load_builder_module()
+
+    mapped_values = {
+        "selected_marketplace_plan_benchmark_ratio": np.asarray([0.8, 1.2, 0.7]),
+        "assigned_aca_ptc": np.asarray([500.0, 500.0, 0.0]),
+    }
+
+    class FakeSimulation:
+        def calculate(self, variable, *, period, map_to=None):
+            assert period == builder.PERIOD
+            assert map_to is None
+            return mapped_values[variable]
+
+    system = SimpleNamespace(
+        variables={
+            "selected_marketplace_plan_benchmark_ratio": SimpleNamespace(
+                entity=SimpleNamespace(key="tax_unit")
+            ),
+            "assigned_aca_ptc": SimpleNamespace(entity=SimpleNamespace(key="tax_unit")),
+        }
+    )
+
+    values = builder._combined_household_values(
+        frame=small_frame,
+        simulation=FakeSimulation(),
+        system=system,
+        variables=("selected_marketplace_plan_benchmark_ratio",),
+        tax_unit_positions=np.asarray([0, 0, 1], dtype=np.int64),
+        filter_variable="assigned_aca_ptc",
+        less_than=1.0,
+    )
+
+    assert np.array_equal(values, np.asarray([1.0, 0.0]))
+
+
 def test_release_gate_failures_are_not_unconditional() -> None:
     builder = _load_builder_module()
     result = SimpleNamespace(
@@ -527,9 +565,21 @@ def test_fiscal_target_value_basis_keeps_person_counts_separate_from_amounts() -
             "count_map_to": "person",
         },
     )
+    bronze_count = TargetSpec(
+        name="bronze_count",
+        entity="household",
+        value=100.0,
+        source="fixture",
+        metadata={
+            "measure_mode": "less_than_count",
+            "source_measure_id": "bronze_aptc_consumers",
+            "target_role": "aca_bronze_aptc_consumers",
+        },
+    )
 
     assert builder._fiscal_target_value_basis(amount) == "amount"
     assert builder._fiscal_target_value_basis(person_count) == "person_count"
+    assert builder._fiscal_target_value_basis(bronze_count) == "person_count"
 
 
 def test_release_gate_failures_reject_bad_ctc_fit() -> None:

packages/populace-build/tests/test_us_fiscal_targets.py

@@ -465,6 +465,7 @@ def test_cms_aca_references_use_current_annual_aca_variables() -> None:
         "cms_aca.oep2024.state_marketplace.ca.marketplace_enrollment"
     )
     aptc_source_record_id = "cms_aca.oep2024.state_marketplace.ca.aptc_recipients"
+    bronze_source_record_id = "cms_aca.oep2024.state_metal.ca.bronze_aptc_consumers"
     registry = compile_us_fiscal_target_registry(
         [
             *packaged_reference_facts(),
@@ -486,6 +487,15 @@ def test_cms_aca_references_use_current_annual_aca_variables() -> None:
                 geography_id="0400000US06",
                 groupby_value_id="ca",
             ),
+            _dynamic_ledger_fact(
+                source_record_id=bronze_source_record_id,
+                source_name="cms_aca",
+                measure_id="bronze_aptc_consumers",
+                value=1_000_000,
+                geography_level="state",
+                geography_id="0400000US06",
+                groupby_value_id="ca",
+            ),
         ]
     )
 
@@ -509,6 +519,17 @@ def test_cms_aca_references_use_current_annual_aca_variables() -> None:
     assert aptc.metadata["count_filter_variable"] == "is_aca_ptc_eligible"
     assert aptc.metadata["state_fips"] == "06"
 
+    bronze = specs[bronze_source_record_id]
+    assert bronze.family == "cms_aca"
+    assert bronze.metadata["target_role"] == "aca_bronze_aptc_consumers"
+    assert bronze.metadata["measure_mode"] == "less_than_count"
+    assert (
+        bronze.metadata["base_variable"] == "selected_marketplace_plan_benchmark_ratio"
+    )
+    assert bronze.metadata["count_less_than"] == "1.0"
+    assert bronze.metadata["count_filter_variable"] == "assigned_aca_ptc"
+    assert bronze.metadata["state_fips"] == "06"
+
 
 def test_soi_premium_tax_credit_targets_use_annual_assigned_ptc() -> None:
     amount_source_record_id = (

tools/build_us_fiscal_refresh_release.py

@@ -845,6 +845,7 @@ def _household_values(
     positive_indicator: bool = False,
     map_to: str | None = None,
     filter_variable: str | None = None,
+    less_than: float | None = None,
 ) -> np.ndarray:
     entity = _variable_entity(system, variable)
     if entity is None:
@@ -853,16 +854,32 @@ def _household_values(
         entity = map_to
     if entity == "household":
         values = _calculate_array(simulation, variable, map_to=map_to)
+        filter_values = None
         if filter_variable is not None:
             filter_values = _calculate_array(simulation, filter_variable, map_to=map_to)
+        if less_than is not None:
+            indicator = values < less_than
+            if filter_values is not None:
+                indicator &= filter_values > 0
+            return indicator.astype(np.float64)
+        if filter_values is not None:
             values = np.where(filter_values > 0, values, 0)
         return (values > 0).astype(np.float64) if positive_indicator else values
     raw = _calculate_array(simulation, variable, map_to=map_to)
+    filter_values = None
     if filter_variable is not None:
         filter_values = _calculate_array(simulation, filter_variable, map_to=map_to)
-        raw = np.where(filter_values > 0, raw, 0)
-    if positive_indicator:
+    if less_than is not None:
+        indicator = raw < less_than
+        if filter_values is not None:
+            indicator &= filter_values > 0
+        raw = indicator.astype(np.float64)
+    elif positive_indicator:
+        if filter_values is not None:
+            raw = np.where(filter_values > 0, raw, 0)
         raw = (raw > 0).astype(np.float64)
+    elif filter_values is not None:
+        raw = np.where(filter_values > 0, raw, 0)
     if entity == "tax_unit":
         return _collapse_tax_unit(
             raw,
@@ -892,6 +909,16 @@ def _base_variables_from_metadata(metadata: Mapping[str, str]) -> tuple[str, ...
     return (metadata["base_variable"],)
 
 
+def _less_than_from_metadata(metadata: Mapping[str, str]) -> float | None:
+    mode = metadata.get("measure_mode", "sum")
+    threshold = metadata.get("count_less_than")
+    if mode != "less_than_count":
+        return None
+    if threshold is None:
+        raise ValueError("less_than_count targets must set count_less_than metadata.")
+    return float(threshold)
+
+
 def _combined_household_values(
     *,
     frame: Frame,
@@ -902,6 +929,7 @@ def _combined_household_values(
     positive_indicator: bool = False,
     map_to: str | None = None,
     filter_variable: str | None = None,
+    less_than: float | None = None,
 ) -> np.ndarray:
     if len(variables) == 1:
         return _household_values(
@@ -913,6 +941,7 @@ def _combined_household_values(
             positive_indicator=positive_indicator,
             map_to=map_to,
             filter_variable=filter_variable,
+            less_than=less_than,
         )
 
     entities = tuple(_variable_entity(system, variable) for variable in variables)
@@ -942,7 +971,13 @@ def _combined_household_values(
     else:
         raw = np.sum(raw_arrays, axis=0, dtype=np.float64)
 
-    if filter_variable is not None:
+    if less_than is not None:
+        raw = raw < less_than
+        if filter_variable is not None:
+            filter_values = _calculate_array(simulation, filter_variable, map_to=map_to)
+            raw &= filter_values > 0
+        raw = raw.astype(np.float64)
+    elif filter_variable is not None:
         filter_values = _calculate_array(simulation, filter_variable, map_to=map_to)
         raw = np.where(filter_values > 0, raw, 0)
 
@@ -1082,13 +1117,22 @@ def _materialize_target_frame(
         for spec in target_specs
         if spec.metadata.get("materializer") == "policyengine_variable"
     ]
-    direct_value_cache: dict[tuple[tuple[str, ...], str, str, str], np.ndarray] = {}
+    direct_value_cache: dict[
+        tuple[tuple[str, ...], str, str, str, str], np.ndarray
+    ] = {}
     for spec in direct_target_specs:
         base_variables = _base_variables_from_metadata(spec.metadata)
         mode = spec.metadata.get("measure_mode", "sum")
         map_to = spec.metadata.get("count_map_to")
         filter_variable = spec.metadata.get("count_filter_variable")
-        cache_key = (base_variables, mode, map_to or "", filter_variable or "")
+        less_than = _less_than_from_metadata(spec.metadata)
+        cache_key = (
+            base_variables,
+            mode,
+            map_to or "",
+            filter_variable or "",
+            "" if less_than is None else str(less_than),
+        )
         if cache_key not in direct_value_cache:
             variables_to_check = (
                 *base_variables,
@@ -1105,6 +1149,7 @@ def _materialize_target_frame(
                 positive_indicator=mode == "positive_count",
                 map_to=map_to,
                 filter_variable=filter_variable,
+                less_than=less_than,
             )
         values = direct_value_cache[cache_key]
         state_fips = spec.metadata.get("state_fips")
@@ -1358,9 +1403,10 @@ def _fiscal_target_value_basis(spec) -> str:
             if _fiscal_target_is_return_count_measure(source_measure_id)
             else "count"
         )
-    if measure_mode in {"count", "positive_count"}:
+    if measure_mode in {"count", "positive_count", "less_than_count"}:
         if metadata.get("count_map_to") == "person" or target_role in {
             "aca_enrollment",
+            "aca_bronze_aptc_consumers",
             "aca_ptc_recipients",
             "medicaid_enrollment",
             "medicaid_chip_enrollment",