Filter invalid SIPP imputation weights

changelog.d/sipp-positive-weights.fixed

@@ -0,0 +1,3 @@
+Filter non-positive SIPP donor weights before fitting source imputation models.
+Interpret SIPP status flags with Census status semantics when filtering observed donor targets.
+Bump policyengine-us to 1.703.1.

policyengine_us_data/calibration/source_impute.py

@@ -82,6 +82,7 @@
 from policyengine_us_data.pipeline_schema import PipelineNode
 from policyengine_us_data.utils.source_quality import (
     cap_training_sample,
+    filter_positive_finite_weight_rows,
     require_columns_present,
     target_observed_source_masks,
 )
@@ -710,6 +711,12 @@ def _impute_sipp(
         "household_weight",
     ]
     tip_train = sipp_df[tip_cols].dropna()
+    tip_train, tip_target_filters = filter_positive_finite_weight_rows(
+        tip_train,
+        weight_col="household_weight",
+        target_filters=tip_target_filters,
+        context_name="SIPP source tip donor",
+    )
     tip_train, tip_target_filters = cap_training_sample(
         tip_train,
         max_train_samples=10_000,
@@ -849,6 +856,12 @@ def _impute_sipp(
             target_source_columns=SIPP_ASSET_TARGET_SOURCE_COLUMNS,
             target_allocation_flag_columns=SIPP_ASSET_TARGET_ALLOCATION_COLUMNS,
         )
+        asset_train, asset_target_filters = filter_positive_finite_weight_rows(
+            asset_train,
+            weight_col="household_weight",
+            target_filters=asset_target_filters,
+            context_name="SIPP source asset donor",
+        )
         asset_train, asset_target_filters = cap_training_sample(
             asset_train,
             max_train_samples=20_000,
@@ -1013,6 +1026,12 @@ def _impute_sipp(
             targets=vehicle_vars,
             target_allocation_flag_columns=SIPP_VEHICLE_TARGET_ALLOCATION_COLUMNS,
         )
+        vehicle_train, vehicle_target_filters = filter_positive_finite_weight_rows(
+            vehicle_train,
+            weight_col="household_weight",
+            target_filters=vehicle_target_filters,
+            context_name="SIPP source vehicle donor",
+        )
         vehicle_train, vehicle_target_filters = cap_training_sample(
             vehicle_train,
             max_train_samples=20_000,

policyengine_us_data/datasets/sipp/sipp.py

@@ -11,6 +11,7 @@
 )
 from policyengine_us_data.utils.source_quality import (
     cap_training_sample,
+    filter_positive_finite_weight_rows,
     filter_observed_source_rows,
     require_columns_present,
     sipp_allocation_flag_for,
@@ -188,6 +189,12 @@ def train_tip_model():
     ]
 
     sipp = sipp[~sipp.isna().any(axis=1)]
+    sipp, tip_target_filters = filter_positive_finite_weight_rows(
+        sipp,
+        weight_col="household_weight",
+        target_filters=tip_target_filters,
+        context_name="SIPP tip donor",
+    )
     sipp, tip_target_filters = cap_training_sample(
         sipp,
         max_train_samples=10_000,
@@ -232,9 +239,40 @@ def get_tip_model() -> QRF:
     "stock_assets": ["TVAL_STMF"],
     "bond_assets": ["TVAL_BOND"],
 }
+SIPP_BANK_ACCOUNT_ASSET_ALLOCATION_COLUMNS = [
+    "AJSSAVVAL",
+    "AJOSAVVAL",
+    "AOSAVVAL",
+    "AJSMMVAL",
+    "AJOMMVAL",
+    "AOMMVAL",
+    "AJSCDVAL",
+    "AJOCDVAL",
+    "AOCDVAL",
+    "AJSCHKVAL",
+    "AJOCHKVAL",
+    "AOCHKVAL",
+]
+SIPP_STOCK_ASSET_ALLOCATION_COLUMNS = [
+    "AJSSTVAL",
+    "AJOSTVAL",
+    "AOSTVAL",
+    "AJSMFVAL",
+    "AJOMFVAL",
+    "AOMFVAL",
+]
+SIPP_BOND_ASSET_ALLOCATION_COLUMNS = [
+    "AJSGOVSVAL",
+    "AJOGOVSVAL",
+    "AOGOVSVAL",
+    "AJSMCBDVAL",
+    "AJOMCBDVAL",
+    "AOMCBDVAL",
+]
 SIPP_ASSET_TARGET_ALLOCATION_COLUMNS = {
-    target: [sipp_allocation_flag_for(column) for column in columns]
-    for target, columns in SIPP_ASSET_TARGET_SOURCE_COLUMNS.items()
+    "bank_account_assets": SIPP_BANK_ACCOUNT_ASSET_ALLOCATION_COLUMNS,
+    "stock_assets": SIPP_STOCK_ASSET_ALLOCATION_COLUMNS,
+    "bond_assets": SIPP_BOND_ASSET_ALLOCATION_COLUMNS,
 }
 SIPP_ASSET_ALLOCATION_COLUMNS = sorted(
     {
@@ -326,7 +364,7 @@ def get_tip_model() -> QRF:
 
 SIPP_VEHICLE_TARGET_ALLOCATION_COLUMNS = {
     "household_vehicles_owned": [sipp_allocation_flag_for("TVEH_NUM")],
-    "household_vehicles_value": [sipp_allocation_flag_for("THVAL_VEH")],
+    "household_vehicles_value": ["AVEH1VAL", "AVEH2VAL", "AVEH3VAL"],
 }
 
 VEHICLE_COLUMNS = [
@@ -347,6 +385,9 @@ def get_tip_model() -> QRF:
     "THVAL_HOME",
     "AVEH_NUM",
     "AHVAL_VEH",
+    "AVEH1VAL",
+    "AVEH2VAL",
+    "AVEH3VAL",
 ]
 
 
@@ -652,6 +693,12 @@ def train_asset_model():
         target_source_columns=SIPP_ASSET_TARGET_SOURCE_COLUMNS,
         target_allocation_flag_columns=SIPP_ASSET_TARGET_ALLOCATION_COLUMNS,
     )
+    sipp, asset_target_filters = filter_positive_finite_weight_rows(
+        sipp,
+        weight_col="household_weight",
+        target_filters=asset_target_filters,
+        context_name="SIPP asset donor",
+    )
     sipp, asset_target_filters = cap_training_sample(
         sipp,
         max_train_samples=20_000,
@@ -799,6 +846,9 @@ def build_vehicle_training_frame() -> pd.DataFrame:
             "household_vehicles_value": grouped["THVAL_VEH"].first().fillna(0),
             "AVEH_NUM": grouped["AVEH_NUM"].max().fillna(0),
             "AHVAL_VEH": grouped["AHVAL_VEH"].first().fillna(0),
+            "AVEH1VAL": grouped["AVEH1VAL"].max().fillna(0),
+            "AVEH2VAL": grouped["AVEH2VAL"].max().fillna(0),
+            "AVEH3VAL": grouped["AVEH3VAL"].max().fillna(0),
             "is_homeowner": (grouped["THVAL_HOME"].first().fillna(0) > 0).astype(
                 np.float32
             ),
@@ -839,6 +889,12 @@ def train_vehicle_model():
         targets=vehicle_vars,
         target_allocation_flag_columns=SIPP_VEHICLE_TARGET_ALLOCATION_COLUMNS,
     )
+    sipp, vehicle_target_filters = filter_positive_finite_weight_rows(
+        sipp,
+        weight_col="household_weight",
+        target_filters=vehicle_target_filters,
+        context_name="SIPP vehicle donor",
+    )
     sipp, vehicle_target_filters = cap_training_sample(
         sipp,
         max_train_samples=20_000,

policyengine_us_data/utils/source_quality.py

@@ -12,6 +12,18 @@
 
 logger = logging.getLogger(__name__)
 
+SIPP_OBSERVED_STATUS_VALUES = frozenset((0, 1, 9))
+SIPP_STATUS_FLAG_PREFIXES = (
+    "AJB",
+    "AJS",
+    "AJO",
+    "AO",
+    "ASSI",
+    "AVAL",
+    "AVEH",
+    "AHVAL",
+)
+
 
 def sipp_allocation_flag_for(source_column: str) -> str:
     """Return the SIPP allocation flag name for a source variable."""
@@ -20,6 +32,11 @@ def sipp_allocation_flag_for(source_column: str) -> str:
     return f"A{source_column[1:]}"
 
 
+def is_sipp_status_flag_column(column: str) -> bool:
+    """Return whether a column name looks like a Census SIPP status flag."""
+    return column.startswith(SIPP_STATUS_FLAG_PREFIXES)
+
+
 def require_columns_present(
     available_columns: Container[str],
     required_columns: Sequence[str],
@@ -47,9 +64,13 @@ def observed_source_mask(
 ) -> pd.Series:
     """Mask rows whose donor source values are observed for one target.
 
-    Source-survey allocation flags conventionally use ``0`` for not allocated
-    and non-zero values for allocated/imputed. Missing flag columns are ignored
-    so callers can use this helper across sources with different flag coverage.
+    Generic allocation flags use ``0`` for not allocated and non-zero values
+    for allocated/imputed. Census SIPP ``A*`` status flags instead encode
+    ``0`` as not in universe, ``1`` as reported, and ``9`` as derivable from
+    component flags; values ``2`` through ``8`` indicate imputation.
+
+    Missing flag columns are ignored so callers can use this helper across
+    sources with different flag coverage.
     """
     mask = pd.Series(True, index=df.index)
 
@@ -62,7 +83,10 @@ def observed_source_mask(
         if column not in df:
             continue
         flag = pd.to_numeric(df[column], errors="coerce").fillna(0)
-        mask &= flag.eq(0)
+        if is_sipp_status_flag_column(column):
+            mask &= flag.isin(SIPP_OBSERVED_STATUS_VALUES)
+        else:
+            mask &= flag.eq(0)
 
     return mask
 
@@ -225,3 +249,52 @@ def cap_training_sample(
         for target, mask in filters.items()
     }
     return sampled_df, sampled_filters
+
+
+def filter_positive_finite_weight_rows(
+    df: pd.DataFrame,
+    *,
+    weight_col: str,
+    target_filters: Mapping[str, pd.Series] | None = None,
+    context_name: str = "donor training frame",
+) -> tuple[pd.DataFrame, dict[str, pd.Series]]:
+    """Drop rows whose fit weight cannot be passed to microimpute."""
+    if weight_col not in df:
+        raise KeyError(f"{context_name} is missing weight column {weight_col!r}")
+
+    filters = {}
+    for target, mask in (target_filters or {}).items():
+        aligned = mask.reindex(df.index)
+        if aligned.isna().any():
+            raise ValueError(f"target_filters[{target!r}] contains missing values")
+        filters[target] = aligned.astype(bool)
+
+    weights = pd.to_numeric(df[weight_col], errors="coerce")
+    valid_weight = np.isfinite(weights) & weights.gt(0)
+    dropped = int((~valid_weight).sum())
+    if dropped:
+        logger.info(
+            "Dropped %d/%d %s rows with non-positive or non-finite %s",
+            dropped,
+            len(df),
+            context_name,
+            weight_col,
+        )
+
+    filtered_df = df.loc[valid_weight].copy().reset_index(drop=True)
+    filtered_filters = {
+        target: pd.Series(
+            mask.loc[valid_weight].to_numpy(dtype=bool),
+            index=filtered_df.index,
+        )
+        for target, mask in filters.items()
+    }
+
+    for target, mask in filtered_filters.items():
+        if not mask.any():
+            raise ValueError(
+                f"No observed donor rows with positive finite {weight_col} "
+                f"available for {target}"
+            )
+
+    return filtered_df, filtered_filters

pyproject.toml

@@ -22,7 +22,7 @@ classifiers = [
     "Programming Language :: Python :: 3.14",
 ]
 dependencies = [
-    "policyengine-us==1.702.1",
+    "policyengine-us==1.703.1",
     # policyengine-core 3.26.1 is the current 3.26.x runtime and includes the fix for
     # PolicyEngine/policyengine-core#482 (user-set ETERNITY inputs lost
     # after _invalidate_all_caches) and is required by policyengine-us 1.682.1+.

tests/unit/calibration/test_source_impute.py

@@ -410,7 +410,7 @@ def test_calibration_sipp_qrf_passes_target_filters(self, monkeypatch):
         for column in source_impute.SIPP_TIP_AMOUNT_COLUMNS:
             tip_columns[column] = [10.0, 5.0, 0.0]
         for column in source_impute.SIPP_TIP_AMOUNT_TO_ALLOCATION_COLUMN.values():
-            tip_columns[column] = [0, 1, 0]
+            tip_columns[column] = [1, 2, 0]
         for column in source_impute.SIPP_JOB_OCCUPATION_COLUMNS:
             tip_columns[column] = [0, 0, 0]
         tip_source = pd.DataFrame(tip_columns)
@@ -437,8 +437,8 @@ def test_calibration_sipp_qrf_passes_target_filters(self, monkeypatch):
             asset_columns[column] = [1_000.0, 2_000.0, 0.0]
         for column in source_impute.SIPP_ASSET_ALLOCATION_COLUMNS:
             asset_columns[column] = [0, 0, 0]
-        asset_columns["AVAL_BANK"] = [0, 1, 0]
-        asset_columns["AVAL_STMF"] = [0, 0, 1]
+        asset_columns["AJSSAVVAL"] = [0, 2, 0]
+        asset_columns["AJSSTVAL"] = [0, 0, 6]
         asset_source = pd.DataFrame(asset_columns)
 
         vehicle_train = pd.DataFrame(
@@ -449,8 +449,10 @@ def test_calibration_sipp_qrf_passes_target_filters(self, monkeypatch):
                 },
                 "household_vehicles_owned": [1.0, 2.0, 3.0],
                 "household_vehicles_value": [5_000.0, 10_000.0, 15_000.0],
-                "AVEH_NUM": [0, 1, 0],
-                "AHVAL_VEH": [0, 0, 1],
+                "AVEH_NUM": [1, 2, 1],
+                "AVEH1VAL": [1, 1, 5],
+                "AVEH2VAL": [0, 0, 0],
+                "AVEH3VAL": [0, 0, 0],
                 "household_weight": [1.0, 1.0, 1.0],
             }
         )

tests/unit/datasets/test_sipp_ssi_disability.py

@@ -74,7 +74,7 @@ def test_ssi_disability_training_usecols_include_label_and_income_columns():
 
 def test_build_ssi_disability_training_frame_excludes_allocated_label_source():
     frame = _base_sipp_frame()
-    frame.loc[0, "ASSI_YRYN"] = 1
+    frame.loc[0, "ASSI_YRYN"] = 3
     frame.loc[1:, "ASSI_YRYN"] = 0
     frame["ASSI_BRSN"] = 0