Remove manual support sign controls from donor imputation

pyproject.toml

@@ -32,7 +32,7 @@ r2 = [
     "boto3>=1.34",
 ]
 policyengine = [
-    "microimpute==1.15.1 ; python_full_version >= '3.12' and python_full_version < '3.15'",
+    "microimpute @ git+https://github.com/PolicyEngine/microimpute.git@27d23090dcf04c2c30ae369b0a209e27eb3659f0 ; python_full_version >= '3.12' and python_full_version < '3.15'",
     "policyengine-us==1.715.2; python_version >= '3.11' and python_version < '3.15'",
     "spm-calculator>=0.3.1",
     # Standalone tax-unit construction engine (the extraction of eCPS's

src/microplex_us/pipelines/donor_imputers.py

@@ -168,19 +168,13 @@ def __init__(
         condition_vars: list[str],
         target_vars: list[str],
         n_estimators: int = 100,
-        nonnegative_vars: set[str] | None = None,
         classifier_type: str = "hist_gb",
-        min_class_count: int = 10,
-        min_class_fraction: float = 0.01,
         seed: int = 42,
     ) -> None:
         self.condition_vars = list(condition_vars)
         self.target_vars = list(target_vars)
         self.n_estimators = int(n_estimators)
-        self.nonnegative_vars = set(nonnegative_vars or ())
         self.classifier_type = str(classifier_type)
-        self.min_class_count = int(min_class_count)
-        self.min_class_fraction = float(min_class_fraction)
         self.seed = int(seed)
         self._fitted: dict[str, Any] = {}
         self._regimes: dict[str, str] = {}
@@ -204,8 +198,7 @@ def fit(
             )
         if importlib.util.find_spec("quantile_forest") is None:
             raise ImportError(
-                "quantile-forest is required for the RegimeAwareDonorImputer "
-                "base QRF."
+                "quantile-forest is required for the RegimeAwareDonorImputer base QRF."
             )
 
         from microimpute.models.qrf import QRF
@@ -224,8 +217,6 @@ def fit(
             wrapper = ZeroInflatedImputer(
                 base_imputer_class=QRF,
                 base_imputer_kwargs={},
-                min_class_count=self.min_class_count,
-                min_class_fraction=self.min_class_fraction,
                 classifier_type=self.classifier_type,
                 seed=self.seed,
             )
@@ -256,10 +247,7 @@ def generate(
             )
             self._reset_prediction_rngs(fitted, seed=column_seed)
             preds = fitted.predict(synthetic[self.condition_vars])
-            values = preds[column].to_numpy(dtype=float)
-            if column in self.nonnegative_vars:
-                values = np.maximum(values, 0.0)
-            synthetic[column] = values
+            synthetic[column] = preds[column].to_numpy(dtype=float)
         return synthetic
 
     def _reset_prediction_rngs(

src/microplex_us/pipelines/us.py

@@ -5291,18 +5291,13 @@ def _build_donor_imputer(
         nonnegative_vars = {
             variable
             for variable, support_family in support_families.items()
-            if support_family
-            in {
-                VariableSupportFamily.ZERO_INFLATED_POSITIVE,
-                VariableSupportFamily.BOUNDED_SHARE,
-            }
+            if support_family is VariableSupportFamily.BOUNDED_SHARE
         }
         if backend == "regime_aware":
             return RegimeAwareDonorImputer(
                 condition_vars=condition_vars,
                 target_vars=list(target_vars),
                 n_estimators=self.config.donor_imputer_qrf_n_estimators,
-                nonnegative_vars=nonnegative_vars,
                 seed=self.config.random_seed,
             )
         zero_inflated_vars = (
@@ -5311,8 +5306,7 @@ def _build_donor_imputer(
                 for variable, support_family in support_families.items()
                 if support_family
                 in {
-                    VariableSupportFamily.ZERO_INFLATED_POSITIVE,
-                    VariableSupportFamily.ZERO_INFLATED_SIGNED,
+                    VariableSupportFamily.SUPPORT_SENSITIVE,
                 }
             }
             if backend == "zi_qrf"
@@ -7147,7 +7141,7 @@ def _rank_match_donor_values(
             donor_weight_array = donor_weights.to_numpy(dtype=float)
             donor_weight_array = np.clip(donor_weight_array, a_min=0.0, a_max=None)
 
-        if strategy is DonorMatchStrategy.ZERO_INFLATED_POSITIVE or (
+        if (
             strategy is DonorMatchStrategy.RANK
             and self._is_zero_inflated_positive_distribution(donor_array)
         ):