check_X_y with changed check_array

sklego/_sklearn_compat.py

@@ -217,6 +217,57 @@ def check_array(
             **kwargs,
         )
 
+    def check_X_y(
+        X,
+        y,
+        accept_sparse=False,
+        *,
+        accept_large_sparse=True,
+        dtype="numeric",
+        order=None,
+        copy=False,
+        force_writeable=False,
+        ensure_all_finite=True,
+        ensure_2d=True,
+        allow_nd=False,
+        multi_output=False,
+        ensure_min_samples=1,
+        ensure_min_features=1,
+        y_numeric=False,
+        estimator=None,
+    ):
+        from sklearn.utils.validation import _check_estimator_name, _check_y, check_consistent_length
+
+        if y is None:
+            if estimator is None:
+                estimator_name = "estimator"
+            else:
+                estimator_name = _check_estimator_name(estimator)
+            raise ValueError(f"{estimator_name} requires y to be passed, but the target y is None")
+
+        X = check_array(
+            X,
+            accept_sparse=accept_sparse,
+            accept_large_sparse=accept_large_sparse,
+            dtype=dtype,
+            order=order,
+            copy=copy,
+            force_writeable=force_writeable,
+            ensure_all_finite=ensure_all_finite,
+            ensure_2d=ensure_2d,
+            allow_nd=allow_nd,
+            ensure_min_samples=ensure_min_samples,
+            ensure_min_features=ensure_min_features,
+            estimator=estimator,
+            input_name="X",
+        )
+
+        y = _check_y(y, multi_output=multi_output, y_numeric=y_numeric, estimator=estimator)
+
+        check_consistent_length(X, y)
+
+        return X, y
+
     # tags infrastructure
     @dataclass(**_dataclass_args())
     class InputTags:
@@ -516,5 +567,6 @@ def parametrize_with_checks(
         _check_feature_names,  # noqa: F401
         _check_n_features,  # noqa: F401
         check_array,  # noqa: F401
+        check_X_y,  # noqa: F401
         validate_data,  # noqa: F401
     )

sklego/common.py

@@ -5,9 +5,9 @@
 import numpy as np
 import pandas as pd
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.utils.validation import check_is_fitted, check_X_y
+from sklearn.utils.validation import check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class TrainOnlyTransformerMixin(TransformerMixin, BaseEstimator):

sklego/decomposition/umap_reconstruction.py

@@ -102,10 +102,17 @@ def fit(self, X, y=None):
             - If `n_components` is less than 2.
             - If `threshold` is `None`.
         """
-        X = check_array(X, estimator=self, dtype=FLOAT_DTYPES, ensure_2d=True)
+        X = check_array(
+            X,
+            estimator=self,
+            dtype=FLOAT_DTYPES,
+            ensure_2d=True,
+            ensure_min_features=self.n_components,
+            ensure_min_samples=2,
+        )
         _check_n_features(self, X, reset=True)
         if y is not None:
-            y = check_array(y, estimator=self)
+            y = check_array(y, estimator=self, ensure_2d=False)
 
         if not self.threshold:
             raise ValueError("The `threshold` value cannot be `None`.")
@@ -119,7 +126,6 @@ def fit(self, X, y=None):
         )
         self.umap_.fit(X, y)
         self.offset_ = -self.threshold
-        self.n_features_in_ = X.shape[1]
         return self
 
     def difference(self, X):

sklego/dummy.py

@@ -2,9 +2,9 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, RegressorMixin
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_random_state, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_random_state
 
-from ._sklearn_compat import _check_n_features, check_array
+from ._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class RandomRegressor(RegressorMixin, BaseEstimator):

sklego/feature_selection/mrmr.py

@@ -4,9 +4,9 @@
 from sklearn.base import BaseEstimator
 from sklearn.feature_selection import f_classif, f_regression
 from sklearn.feature_selection._base import SelectorMixin
-from sklearn.utils.validation import check_is_fitted, check_X_y
+from sklearn.utils.validation import check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features
+from sklego._sklearn_compat import _check_n_features, check_X_y
 
 
 def _redundancy_pearson(X, selected, left):

sklego/linear_model.py

@@ -17,9 +17,9 @@
 from sklearn.linear_model._base import LinearClassifierMixin
 from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier
 from sklearn.preprocessing import LabelEncoder
-from sklearn.utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_X_y, column_or_1d
+from sklearn.utils.validation import FLOAT_DTYPES, _check_sample_weight, check_is_fitted, column_or_1d
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class LowessRegression(RegressorMixin, BaseEstimator):

sklego/meta/confusion_balancer.py

@@ -2,9 +2,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin
 from sklearn.metrics import confusion_matrix
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 from sklego.base import ProbabilisticClassifier
 
 

sklego/meta/decay_estimator.py

@@ -1,8 +1,8 @@
 from sklearn import clone
 from sklearn.base import BaseEstimator, MetaEstimatorMixin
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features
+from sklego._sklearn_compat import _check_n_features, check_X_y
 from sklego.meta._decay_utils import exponential_decay, linear_decay, sigmoid_decay, stepwise_decay
 
 

sklego/meta/estimator_transformer.py

@@ -1,8 +1,8 @@
 from sklearn import clone
 from sklearn.base import BaseEstimator, MetaEstimatorMixin, TransformerMixin
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class EstimatorTransformer(TransformerMixin, MetaEstimatorMixin, BaseEstimator):

sklego/meta/ordinal_classification.py

@@ -3,9 +3,9 @@
 from sklearn import clone
 from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, MultiOutputMixin, is_classifier
 from sklearn.calibration import CalibratedClassifierCV
-from sklearn.utils.validation import check_is_fitted, check_X_y
+from sklearn.utils.validation import check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class OrdinalClassifier(MultiOutputMixin, ClassifierMixin, MetaEstimatorMixin, BaseEstimator):

sklego/meta/outlier_classifier.py

@@ -2,9 +2,9 @@
 from sklearn import clone
 from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin
 from sklearn.calibration import _SigmoidCalibration
-from sklearn.utils.validation import check_is_fitted, check_X_y
+from sklearn.utils.validation import check_is_fitted
 
-from sklego._sklearn_compat import check_array
+from sklego._sklearn_compat import check_array, check_X_y
 from sklego.base import OutlierModel
 
 

sklego/meta/subjective_classifier.py

@@ -3,9 +3,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin
 from sklearn.metrics import confusion_matrix
 from sklearn.preprocessing import normalize
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class SubjectiveClassifier(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):

sklego/meta/thresholder.py

@@ -5,9 +5,9 @@
 from sklearn import clone
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.exceptions import NotFittedError
-from sklearn.utils.validation import _check_sample_weight, check_is_fitted, check_X_y
+from sklearn.utils.validation import _check_sample_weight, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array, type_of_target
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y, type_of_target
 from sklego.base import ProbabilisticClassifier
 
 

sklego/meta/zero_inflated_regressor.py

@@ -5,9 +5,9 @@
 from sklearn.base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone, is_classifier, is_regressor
 from sklearn.exceptions import NotFittedError
 from sklearn.utils.metaestimators import available_if
-from sklearn.utils.validation import _check_sample_weight, check_is_fitted, check_X_y
+from sklearn.utils.validation import _check_sample_weight, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class ZeroInflatedRegressor(RegressorMixin, MetaEstimatorMixin, BaseEstimator):

sklego/mixture/bayesian_gmm_classifier.py

@@ -3,9 +3,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.mixture import BayesianGaussianMixture
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class BayesianGMMClassifier(ClassifierMixin, BaseEstimator):

sklego/mixture/gmm_classifier.py

@@ -3,9 +3,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.mixture import GaussianMixture
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class GMMClassifier(ClassifierMixin, BaseEstimator):

sklego/naive_bayes.py

@@ -4,9 +4,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.mixture import BayesianGaussianMixture, GaussianMixture
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class GaussianMixtureNB(ClassifierMixin, BaseEstimator):

sklego/neighbors.py

@@ -2,9 +2,9 @@
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.neighbors import KernelDensity
 from sklearn.utils.multiclass import unique_labels
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 class BayesianKernelDensityClassifier(ClassifierMixin, BaseEstimator):

sklego/preprocessing/intervalencoder.py

@@ -9,9 +9,9 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, TransformerMixin
-from sklearn.utils.validation import check_is_fitted, check_X_y
+from sklearn.utils.validation import check_is_fitted
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 
 
 def _mk_monotonic_average(xs, ys, intervals, method="increasing", **kwargs):

sklego/preprocessing/randomadder.py

@@ -1,9 +1,9 @@
 from warnings import warn
 
 from sklearn.base import BaseEstimator
-from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_random_state, check_X_y
+from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_random_state
 
-from sklego._sklearn_compat import _check_n_features, check_array
+from sklego._sklearn_compat import _check_n_features, check_array, check_X_y
 from sklego.common import TrainOnlyTransformerMixin
 
 

tests/test_estimators/test_quantile_regression.py

@@ -33,6 +33,7 @@ def _create_dataset(coefs, intercept, noise=0.0):
 )
 def test_sklearn_compatible_estimator(estimator, check):
     if check.func.__name__ in {
+        "check_sample_weights_invariance",
         "check_sample_weight_equivalence_on_dense_data",
     }:
         pytest.skip()