Expand get_feature_names_out support

python/cuml/cuml/_thirdparty/sklearn/preprocessing/_data.py

@@ -33,6 +33,7 @@
 from cupyx.scipy import sparse
 from scipy import optimize, stats
 from scipy.special import boxcox
+from sklearn.base import OneToOneFeatureMixin, ClassNamePrefixFeaturesOutMixin
 
 from cuml.common.sparse import csr_row_normalize_l1, csr_row_normalize_l2
 from cuml.internals.interop import InteropMixin
@@ -214,9 +215,12 @@ def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
     return X
 
 
-class MinMaxScaler(TransformerMixin,
-                   BaseEstimator,
-                   AllowNaNTagMixin):
+class MinMaxScaler(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Transform features by scaling each feature to a given range.
 
     This estimator scales and translates each feature individually such
@@ -524,11 +528,14 @@ def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True):
         return X
 
 
-class StandardScaler(TransformerMixin,
-                     AllowNaNTagMixin,
-                     SparseInputTagMixin,
-                     BaseEstimator,
-                     InteropMixin):
+class StandardScaler(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    SparseInputTagMixin,
+    InteropMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Standardize features by removing the mean and scaling to unit variance
 
     The standard score of a sample `x` is calculated as:
@@ -946,10 +953,13 @@ def inverse_transform(self, X, copy=None):
         return X
 
 
-class MaxAbsScaler(TransformerMixin,
-                   BaseEstimator,
-                   AllowNaNTagMixin,
-                   SparseInputTagMixin):
+class MaxAbsScaler(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    SparseInputTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Scale each feature by its maximum absolute value.
 
     This estimator scales and translates each feature individually such
@@ -1196,10 +1206,13 @@ def maxabs_scale(X, *, axis=0, copy=True):
         return X
 
 
-class RobustScaler(TransformerMixin,
-                   BaseEstimator,
-                   AllowNaNTagMixin,
-                   SparseInputTagMixin):
+class RobustScaler(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    SparseInputTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Scale features using statistics that are robust to outliers.
 
     This Scaler removes the median and scales the data according to the
@@ -1867,9 +1880,12 @@ def normalize(X, norm='l2', *, axis=1, copy=True, return_norm=False):
         return X
 
 
-class Normalizer(TransformerMixin,
-                 SparseInputTagMixin,
-                 BaseEstimator):
+class Normalizer(
+    TransformerMixin,
+    SparseInputTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Normalize samples individually to unit norm.
 
     Each sample (i.e. each row of the data matrix) with at least one
@@ -2002,9 +2018,12 @@ def binarize(X, *, threshold=0.0, copy=True):
     return X
 
 
-class Binarizer(TransformerMixin,
-                SparseInputTagMixin,
-                BaseEstimator):
+class Binarizer(
+    TransformerMixin,
+    SparseInputTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Binarize data (set feature values to 0 or 1) according to a threshold
 
     Values greater than the threshold map to 1, while values less than
@@ -2165,7 +2184,11 @@ def add_dummy_feature(X, value=1.0):
         return X
 
 
-class KernelCenterer(TransformerMixin, BaseEstimator):
+class KernelCenterer(
+    TransformerMixin,
+    ClassNamePrefixFeaturesOutMixin,
+    BaseEstimator,
+):
     """Center a kernel matrix
 
     Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a
@@ -2208,6 +2231,15 @@ def __init__(self):
         # Needed for backported inspect.signature compatibility with PyPy
         pass
 
+    @property
+    def _n_features_out(self):
+        return self.n_features_in_
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.pairwise = True
+        return tags
+
     @mlfunc(set_input_type=True)
     def fit(self, K, y=None) -> 'KernelCenterer':
         """Fit KernelCenterer
@@ -2263,14 +2295,13 @@ def transform(self, K, copy=True):
 
         return K
 
-    @property
-    def _pairwise(self):
-        return True
-
 
-class QuantileTransformer(TransformerMixin,
-                          BaseEstimator,
-                          AllowNaNTagMixin):
+class QuantileTransformer(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Transform features using quantiles information.
 
     This method transforms the features to follow a uniform or a normal
@@ -2804,9 +2835,12 @@ def quantile_transform(X, *, axis=0, n_quantiles=1000,
                          " axis={}".format(axis))
 
 
-class PowerTransformer(TransformerMixin,
-                       BaseEstimator,
-                       AllowNaNTagMixin):
+class PowerTransformer(
+    TransformerMixin,
+    AllowNaNTagMixin,
+    OneToOneFeatureMixin,
+    BaseEstimator,
+):
     """Apply a power transform featurewise to make data more Gaussian-like.
 
     Power transforms are a family of parametric, monotonic transformations