MAINT compatibility sklearn 1.6.0

benchmarks/bench_minhash_batch_number.py

@@ -132,6 +132,11 @@ def _more_tags(self):
         """
         return {"X_types": ["categorical"]}
 
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.categorical = True
+        return tags
+
     def _get_murmur_hash(self, string):
         """
         Encode a string using murmur hashing function.

skrub/_dataframe/_common.py

@@ -106,6 +106,8 @@
     "total_seconds",
 ]
 
+pandas_version = parse_version(parse_version(pd.__version__).base_version)
+
 #
 # Inspecting containers' type and module
 # ======================================
@@ -330,7 +332,8 @@ def _concat_horizontal_pandas(*dataframes):
     init_index = dataframes[0].index
     dataframes = [df.reset_index(drop=True) for df in dataframes]
     dataframes = _join_utils.make_column_names_unique(*dataframes)
-    result = pd.concat(dataframes, axis=1, copy=False)
+    kwargs = {"copy": False} if pandas_version < parse_version("3.0") else {}
+    result = pd.concat(dataframes, axis=1, **kwargs)
     result.index = init_index
     return result
 

skrub/_datetime_encoder.py

@@ -11,6 +11,7 @@
 from . import _dataframe as sbd
 from ._dispatch import dispatch
 from ._on_each_column import RejectColumn, SingleColumnTransformer
+from ._sklearn_compat import TransformerTags
 
 __all__ = ["DatetimeEncoder"]
 
@@ -323,3 +324,11 @@ def _check_params(self):
             raise ValueError(
                 f"'resolution' options are {allowed}, got {self.resolution!r}."
             )
+
+    def _more_tags(self):
+        return {"preserves_dtype": []}
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.transformer_tags = TransformerTags(preserves_dtype=[])
+        return tags

skrub/_interpolation_joiner.py

@@ -7,12 +7,12 @@
     HistGradientBoostingClassifier,
     HistGradientBoostingRegressor,
 )
-from sklearn.utils._tags import _safe_tags
 
 from . import _dataframe as sbd
 from . import _join_utils, _utils
 from . import _selectors as s
 from ._minhash_encoder import MinHashEncoder
+from ._sklearn_compat import get_tags
 from ._table_vectorizer import TableVectorizer
 
 DEFAULT_REGRESSOR = HistGradientBoostingRegressor()
@@ -403,7 +403,7 @@ def _get_assignments_for_estimator(table, estimator):
 
 
 def _handles_multioutput(estimator):
-    return _safe_tags(estimator).get("multioutput", False)
+    return get_tags(estimator).target_tags.multi_output
 
 
 def _fit(key_values, target_table, estimator, propagate_exceptions):

skrub/_similarity_encoder.py

@@ -14,6 +14,7 @@
 from sklearn.utils.fixes import parse_version
 from sklearn.utils.validation import check_is_fitted
 
+from ._sklearn_compat import _check_n_features
 from ._string_distances import get_ngram_count, preprocess
 
 # Ignore lines too long, first docstring lines can't be cut
@@ -334,7 +335,7 @@ def fit(self, X, y=None):
                     X[mask] = self.handle_missing
 
         Xlist, n_samples, n_features = self._check_X(X)
-        self._check_n_features(X, reset=True)
+        _check_n_features(self, X, reset=True)
 
         if self.handle_unknown not in ["error", "ignore"]:
             raise ValueError(
@@ -453,7 +454,7 @@ def transform(self, X, fast=True):
                     X[mask] = self.handle_missing
 
         Xlist, n_samples, n_features = self._check_X(X)
-        self._check_n_features(X, reset=False)
+        _check_n_features(self, X, reset=False)
 
         for i in range(n_features):
             Xi = Xlist[i]
@@ -562,3 +563,10 @@ def _more_tags(self):
                 "check_estimators_dtypes": "We only support string dtypes.",
             },
         }
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.categorical = True
+        tags.input_tags.string = True
+        tags.transformer_tags.preserves_dtype = []
+        return tags