Update sklearnex estimators to support sklearn 1.5 (#1794)

* Update TSNE and PCA to support sklearn 1.5pre-release

* Update PCA solver branching

* Add PCA parameter in test_n_jobs_support.py

* Fix n_iter check in TSNE for sklearn<1.2

* Apply isort

* Move to sklearnex.utils.get_namespace

* Fix PCA.fit_transform

* Update PCA solver selection

* Update PCA algorithm doc

* Fix solver name in tests

* Fix svd solver contraint

* Fix PCA solver constraint

* Fix check_feature_names warning and deselect RF feature importance tests

* Change KMeans estimator doc strings

* Update solver selection

* Update solver selection

* Revert test de-deselection

Author: Alexsandruss

daal4py/sklearn/cluster/k_means.py

@@ -258,23 +258,6 @@ def is_string(s, target_str):
 
 
 def _fit(self, X, y=None, sample_weight=None):
-    """Compute k-means clustering.
-
-    Parameters
-    ----------
-    X : array-like or sparse matrix, shape=(n_samples, n_features)
-        Training instances to cluster. It must be noted that the data
-        will be converted to C ordering, which will cause a memory
-        copy if the given data is not C-contiguous.
-
-    y : Ignored
-        not used, present here for API consistency by convention.
-
-    sample_weight : array-like, shape (n_samples,), optional
-        The weights for each observation in X. If None, all observations
-        are assigned equal weight (default: None)
-
-    """
     init = self.init
     if sklearn_check_version("1.1"):
         if sklearn_check_version("1.2"):
@@ -447,26 +430,6 @@ def _daal4py_check_test_data(self, X):
 
 
 def _predict(self, X, sample_weight=None):
-    """Predict the closest cluster each sample in X belongs to.
-
-    In the vector quantization literature, `cluster_centers_` is called
-    the code book and each value returned by `predict` is the index of
-    the closest code in the code book.
-
-    Parameters
-    ----------
-    X : {array-like, sparse matrix}, shape = [n_samples, n_features]
-       New data to predict.
-
-    sample_weight : array-like, shape (n_samples,), optional
-        The weights for each observation in X. If None, all observations
-        are assigned equal weight (default: None)
-
-    Returns
-    -------
-    labels : array, shape [n_samples,]
-        Index of the cluster each sample belongs to.
-    """
     check_is_fitted(self)
 
     X = _daal4py_check_test_data(self, X)
@@ -614,86 +577,29 @@ def __init__(
 
     @support_usm_ndarray()
     def fit(self, X, y=None, sample_weight=None):
-        """
-        Compute k-means clustering.
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            Training instances to cluster. It must be noted that the data
-            will be converted to C ordering, which will cause a memory
-            copy if the given data is not C-contiguous.
-            If a sparse matrix is passed, a copy will be made if it's not in
-            CSR format.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-            .. versionadded:: 0.20
-
-        Returns
-        -------
-        self : object
-            Fitted estimator.
-        """
         return _fit(self, X, y=y, sample_weight=sample_weight)
 
-    @support_usm_ndarray()
-    def predict(
-        self, X, sample_weight="deprecated" if sklearn_check_version("1.3") else None
-    ):
-        """
-        Predict the closest cluster each sample in X belongs to.
+    if sklearn_check_version("1.5"):
 
-        In the vector quantization literature, `cluster_centers_` is called
-        the code book and each value returned by `predict` is the index of
-        the closest code in the code book.
+        @support_usm_ndarray()
+        def predict(self, X):
+            return _predict(self, X)
 
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to predict.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
+    else:
 
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
-        return _predict(self, X, sample_weight=sample_weight)
+        @support_usm_ndarray()
+        def predict(
+            self, X, sample_weight="deprecated" if sklearn_check_version("1.3") else None
+        ):
+            return _predict(self, X, sample_weight=sample_weight)
 
     @support_usm_ndarray()
     def fit_predict(self, X, y=None, sample_weight=None):
-        """
-        Compute cluster centers and predict cluster index for each sample.
-
-        Convenience method; equivalent to calling fit(X) followed by
-        predict(X).
-
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-            New data to transform.
-
-        y : Ignored
-            Not used, present here for API consistency by convention.
-
-        sample_weight : array-like of shape (n_samples,), default=None
-            The weights for each observation in X. If None, all observations
-            are assigned equal weight.
-
-        Returns
-        -------
-        labels : ndarray of shape (n_samples,)
-            Index of the cluster each sample belongs to.
-        """
         return super().fit_predict(X, y, sample_weight)
 
     score = support_usm_ndarray()(KMeans_original.score)
+
+    fit.__doc__ = KMeans_original.fit.__doc__
+    predict.__doc__ = KMeans_original.predict.__doc__
+    fit_predict.__doc__ = KMeans_original.fit_predict.__doc__
+    score.__doc__ = KMeans_original.score.__doc__

daal4py/sklearn/manifold/_t_sne.py

@@ -44,52 +44,15 @@
 class TSNE(BaseTSNE):
     __doc__ = BaseTSNE.__doc__
 
+    if sklearn_check_version("1.2"):
+        _parameter_constraints: dict = {**BaseTSNE._parameter_constraints}
+
     @support_usm_ndarray()
     def fit_transform(self, X, y=None):
-        """
-        Fit X into an embedded space and return that transformed output.
-
-        Parameters
-        ----------
-        X : ndarray of shape (n_samples, n_features) or (n_samples, n_samples)
-            If the metric is 'precomputed' X must be a square distance
-            matrix. Otherwise it contains a sample per row. If the method
-            is 'exact', X may be a sparse matrix of type 'csr', 'csc'
-            or 'coo'. If the method is 'barnes_hut' and the metric is
-            'precomputed', X may be a precomputed sparse graph.
-
-        y : None
-            Ignored.
-
-        Returns
-        -------
-        X_new : ndarray of shape (n_samples, n_components)
-            Embedding of the training data in low-dimensional space.
-        """
         return super().fit_transform(X, y)
 
     @support_usm_ndarray()
     def fit(self, X, y=None):
-        """
-        Fit X into an embedded space.
-
-        Parameters
-        ----------
-        X : ndarray of shape (n_samples, n_features) or (n_samples, n_samples)
-            If the metric is 'precomputed' X must be a square distance
-            matrix. Otherwise it contains a sample per row. If the method
-            is 'exact', X may be a sparse matrix of type 'csr', 'csc'
-            or 'coo'. If the method is 'barnes_hut' and the metric is
-            'precomputed', X may be a precomputed sparse graph.
-
-        y : None
-            Ignored.
-
-        Returns
-        -------
-        X_new : array of shape (n_samples, n_components)
-            Embedding of the training data in low-dimensional space.
-        """
         return super().fit(X, y)
 
     def _daal_tsne(self, P, n_samples, X_embedded):
@@ -101,11 +64,27 @@ def _daal_tsne(self, P, n_samples, X_embedded):
         # * final optimization with momentum at 0.8
 
         # N, nnz, n_iter_without_progress, n_iter
-        size_iter = [[n_samples], [P.nnz], [self.n_iter_without_progress], [self.n_iter]]
+        size_iter = [
+            [n_samples],
+            [P.nnz],
+            [self.n_iter_without_progress],
+            [self._max_iter if sklearn_check_version("1.5") else self.n_iter],
+        ]
 
         # Pass params to daal4py backend
         if daal_check_version((2023, "P", 1)):
-            size_iter.extend([[self._EXPLORATION_N_ITER], [self._N_ITER_CHECK]])
+            size_iter.extend(
+                [
+                    [
+                        (
+                            self._EXPLORATION_MAX_ITER
+                            if sklearn_check_version("1.5")
+                            else self._EXPLORATION_N_ITER
+                        )
+                    ],
+                    [self._N_ITER_CHECK],
+                ]
+            )
 
         size_iter = np.array(size_iter, dtype=P.dtype)
 
@@ -255,8 +234,9 @@ def _fit(self, X, skip_num_points=0):
                 )
             )
 
-        if self.n_iter < 250:
-            raise ValueError("n_iter should be at least 250")
+        if not sklearn_check_version("1.2"):
+            if self.n_iter < 250:
+                raise ValueError("n_iter should be at least 250")
 
         n_samples = X.shape[0]
 
@@ -423,3 +403,6 @@ def _fit(self, X, skip_num_points=0):
             neighbors=neighbors_nn,
             skip_num_points=skip_num_points,
         )
+
+    fit.__doc__ = BaseTSNE.fit.__doc__
+    fit_transform.__doc__ = BaseTSNE.fit_transform.__doc__

deselected_tests.yaml

@@ -60,6 +60,14 @@ deselected_tests:
   - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est2] >=0.23 darwin
   - inspection/tests/test_partial_dependence.py::test_partial_dependence_easy_target[2-est3] >=0.23 darwin
 
+  # Sklearnex RandomForestClassifier RNG is different from scikit-learn and daal4py
+  # resulting in different feature importances for small number of trees (10).
+  # Issue dissappears with bigger number of trees (>=20)
+  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[0.5-1]
+  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[0.5-2]
+  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[1.0-1]
+  - inspection/tests/test_permutation_importance.py::test_permutation_importance_correlated_feature_regression_pandas[1.0-2]
+
   # Random forest classifier selects a different most-important feature
   # Feature importances:
   # scikit-learn-intelex  [0.             0.00553064     0.71323666     0.2812327 ]

doc/sources/algorithms.rst

@@ -157,7 +157,7 @@ Dimensionality reduction
    * - `PCA`
      - All parameters are supported except:
 
-       - ``svd_solver`` != `'full'`
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
      - Sparse data is not supported
    * - `TSNE`
      - All parameters are supported except:
@@ -340,7 +340,7 @@ Dimensionality reduction
    * - `PCA`
      - All parameters are supported except:
 
-       - ``svd_solver`` != `'full'`
+       - ``svd_solver`` not in [`'full'`, `'covariance_eigh'`]
      - Sparse data is not supported
 
 Nearest Neighbors