some new features

.venv/lib/python3.12/site-packages/sklearn/cluster/_feature_agglomeration.py

@@ -0,0 +1,92 @@
+"""
+Feature agglomeration. Base classes and functions for performing feature
+agglomeration.
+"""
+
+# Author: V. Michel, A. Gramfort
+# License: BSD 3 clause
+
+
+import numpy as np
+from scipy.sparse import issparse
+
+from ..base import TransformerMixin
+from ..utils import metadata_routing
+from ..utils.deprecation import _deprecate_Xt_in_inverse_transform
+from ..utils.validation import check_is_fitted
+
+###############################################################################
+# Mixin class for feature agglomeration.
+
+
+class AgglomerationTransform(TransformerMixin):
+    """
+    A class for feature agglomeration via the transform interface.
+    """
+
+    # This prevents ``set_split_inverse_transform`` to be generated for the
+    # non-standard ``Xt`` arg on ``inverse_transform``.
+    # TODO(1.7): remove when Xt is removed for inverse_transform.
+    __metadata_request__inverse_transform = {"Xt": metadata_routing.UNUSED}
+
+    def transform(self, X):
+        """
+        Transform a new matrix using the built clustering.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features) or \
+                (n_samples, n_samples)
+            A M by N array of M observations in N dimensions or a length
+            M array of M one-dimensional observations.
+
+        Returns
+        -------
+        Y : ndarray of shape (n_samples, n_clusters) or (n_clusters,)
+            The pooled values for each feature cluster.
+        """
+        check_is_fitted(self)
+
+        X = self._validate_data(X, reset=False)
+        if self.pooling_func == np.mean and not issparse(X):
+            size = np.bincount(self.labels_)
+            n_samples = X.shape[0]
+            # a fast way to compute the mean of grouped features
+            nX = np.array(
+                [np.bincount(self.labels_, X[i, :]) / size for i in range(n_samples)]
+            )
+        else:
+            nX = [
+                self.pooling_func(X[:, self.labels_ == l], axis=1)
+                for l in np.unique(self.labels_)
+            ]
+            nX = np.array(nX).T
+        return nX
+
+    def inverse_transform(self, X=None, *, Xt=None):
+        """
+        Inverse the transformation and return a vector of size `n_features`.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples.
+
+        Xt : array-like of shape (n_samples, n_clusters) or (n_clusters,)
+            The values to be assigned to each cluster of samples.
+
+            .. deprecated:: 1.5
+                `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead.
+
+        Returns
+        -------
+        X : ndarray of shape (n_samples, n_features) or (n_features,)
+            A vector of size `n_samples` with the values of `Xred` assigned to
+            each of the cluster of samples.
+        """
+        X = _deprecate_Xt_in_inverse_transform(X, Xt)
+
+        check_is_fitted(self)
+
+        unil, inverse = np.unique(self.labels_, return_inverse=True)
+        return X[..., inverse]