Fix filter issue

features.py

@@ -507,7 +507,14 @@ def _transform_features_to_numbers(
             remainder="passthrough",
             **common_column_transformer_kwargs,
         )),
-        ("filter", SelectMinNonMostFrequentValues(min_non_most_frequent_values)),
+        ("filter", make_column_transformer(
+            # Pass a separate copy of the same type of transformer to bools because the output bool dtype is otherwise
+            # lost. See https://github.com/scikit-learn/scikit-learn/issues/25560
+            (SelectMinNonMostFrequentValues(min_non_most_frequent_values), make_column_selector(dtype_include=bool)),
+            # `set_output` is not passed to the `remainder` transformer.
+            # See https://github.com/scikit-learn/scikit-learn/issues/26306
+            remainder=SelectMinNonMostFrequentValues(min_non_most_frequent_values).set_output(transform="pandas"),
+            **common_column_transformer_kwargs)),
         ("scaler", make_column_transformer(
             (StandardScaler(), make_column_selector(dtype_exclude=None if standardize_binary_features else bool)),
             remainder="passthrough",
@@ -516,7 +523,9 @@ def _transform_features_to_numbers(
         ("imputer", make_column_transformer(
             (SimpleImputer(strategy="mean"), make_column_selector(dtype_include=np.number)),
             (BoolImputer(strategy="most_frequent"), make_column_selector(dtype_include=bool)),
-            remainder=SimpleImputer(strategy="most_frequent"),
+            # `set_output` is not passed to the `remainder` transformer.
+            # See https://github.com/scikit-learn/scikit-learn/issues/26306
+            remainder=SimpleImputer(strategy="most_frequent").set_output(transform="pandas"),
             **common_column_transformer_kwargs,
         )),
     ], verbose=verbose).set_output(transform="pandas").fit_transform(df)

sklearn_util.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from collections import Counter
-from typing import Any, Callable, Sequence
+from numbers import Real
+from typing import Any, Callable, Iterable, Mapping, Sequence
 
 import numpy as np
 import numpy.typing as npt
@@ -13,17 +14,24 @@
 from sklearn.impute._base import _check_inputs_dtype
 from sklearn.preprocessing import MultiLabelBinarizer
 from sklearn.utils import _is_pandas_na, is_scalar_nan
+from sklearn.utils._param_validation import Interval
 from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
 
 class SelectMinNonMostFrequentValues(SelectorMixin, BaseEstimator):
     """Select features with at least `min_non_most_frequent_values` values different from the most frequent value."""
 
+    _parameter_constraints: Mapping[str, Iterable[Any]] = {
+        "min_non_most_frequent_values": [Interval(Real, 0, None, closed="left")],
+        "leave_at_least_one": ["boolean"],
+    }
+
     def __init__(self, min_non_most_frequent_values: int = 50, leave_at_least_one: bool = True) -> None:
         self.min_non_most_frequent_values = min_non_most_frequent_values
         self.leave_at_least_one = leave_at_least_one
 
     def fit(self, X: np.ndarray, y: np.ndarray | None = None) -> SelectMinNonMostFrequentValues:  # noqa
+        self._validate_params()
         X = self._validate_data(X, ensure_2d=True, force_all_finite="allow-nan")
 
         self.non_most_frequent_counts_ = np.empty(X.shape[1], dtype=np.int64)  # noqa
@@ -41,13 +49,6 @@ def fit(self, X: np.ndarray, y: np.ndarray | None = None) -> SelectMinNonMostFre
 
         return self
 
-    @overrides
-    def transform(self, X: np.ndarray) -> np.ndarray:
-        # We don't change `X` because the data types may be changed.
-        _ = self._validate_data(X, ensure_2d=True, dtype=None, accept_sparse="csr", force_all_finite="allow-nan",
-                                reset=False)
-        return self._transform(X)
-
     @overrides
     def _get_support_mask(self) -> np.ndarray:
         check_is_fitted(self)
@@ -58,6 +59,10 @@ def _get_support_mask(self) -> np.ndarray:
 
         return mask
 
+    @overrides
+    def _more_tags(self) -> Mapping[str, Any]:
+        return {"allow_nan": True}
+
 
 # See https://github.com/scikit-learn/scikit-learn/issues/11309#issuecomment-1528042914
 class MultiHotEncoder(BaseEstimator, TransformerMixin):