[bug] fix sample_weight check for IncrementalBasicStatistics (#1799)

* Update incremental_basic_statistics.py

* formatting'

* Update incremental_basic_statistics.py

* Update test_incremental_basic_statistics.py

* Update incremental_basic_statistics.py

* Update incremental_basic_statistics.py

* Update incremental_basic_statistics.py

* Update incremental_basic_statistics.py

* Update incremental_basic_statistics.py

* Update test_incremental_basic_statistics.py

sklearnex/basic_statistics/incremental_basic_statistics.py

@@ -17,6 +17,7 @@
 import numpy as np
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_array, gen_batches
+from sklearn.utils.validation import _check_sample_weight
 
 from daal4py.sklearn._n_jobs_support import control_n_jobs
 from daal4py.sklearn._utils import sklearn_check_version
@@ -139,7 +140,7 @@ def _onedal_finalize_fit(self):
         self._onedal_estimator.finalize_fit()
         self._need_to_finalize = False
 
-    def _onedal_partial_fit(self, X, weights, queue):
+    def _onedal_partial_fit(self, X, sample_weight=None, queue=None):
         first_pass = not hasattr(self, "n_samples_seen_") or self.n_samples_seen_ == 0
 
         if sklearn_check_version("1.0"):
@@ -152,9 +153,11 @@ def _onedal_partial_fit(self, X, weights, queue):
             X = check_array(
                 X,
                 dtype=[np.float64, np.float32],
-                copy=self.copy_X,
             )
 
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
         if first_pass:
             self.n_samples_seen_ = X.shape[0]
             self.n_features_in_ = X.shape[1]
@@ -168,15 +171,18 @@ def _onedal_partial_fit(self, X, weights, queue):
             self._onedal_estimator = self._onedal_incremental_basic_statistics(
                 **onedal_params
             )
-        self._onedal_estimator.partial_fit(X, weights, queue)
+        self._onedal_estimator.partial_fit(X, sample_weight, queue)
         self._need_to_finalize = True
 
-    def _onedal_fit(self, X, weights, queue=None):
+    def _onedal_fit(self, X, sample_weight=None, queue=None):
         if sklearn_check_version("1.0"):
             X = self._validate_data(X, dtype=[np.float64, np.float32])
         else:
             X = check_array(X, dtype=[np.float64, np.float32])
 
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
         n_samples, n_features = X.shape
         if self.batch_size is None:
             self.batch_size_ = 5 * n_features
@@ -189,7 +195,7 @@ def _onedal_fit(self, X, weights, queue=None):
 
         for batch in gen_batches(X.shape[0], self.batch_size_):
             X_batch = X[batch]
-            weights_batch = weights[batch] if weights is not None else None
+            weights_batch = sample_weight[batch] if sample_weight is not None else None
             self._onedal_partial_fit(X_batch, weights_batch, queue=queue)
 
         if sklearn_check_version("1.2"):
@@ -217,7 +223,7 @@ def __getattr__(self, attr):
             f"'{self.__class__.__name__}' object has no attribute '{attr}'"
         )
 
-    def partial_fit(self, X, weights=None):
+    def partial_fit(self, X, sample_weight=None):
         """Incremental fit with X. All of X is processed as a single batch.
 
         Parameters
@@ -226,7 +232,10 @@ def partial_fit(self, X, weights=None):
             Data for compute, where `n_samples` is the number of samples and
             `n_features` is the number of features.
 
-        weights : array-like of shape (n_samples,)
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        sample_weight : array-like of shape (n_samples,), default=None
             Weights for compute weighted statistics, where `n_samples` is the number of samples.
 
         Returns
@@ -242,11 +251,11 @@ def partial_fit(self, X, weights=None):
                 "sklearn": None,
             },
             X,
-            weights,
+            sample_weight,
         )
         return self
 
-    def fit(self, X, weights=None):
+    def fit(self, X, y=None, sample_weight=None):
         """Compute statistics with X, using minibatches of size batch_size.
 
         Parameters
@@ -255,7 +264,10 @@ def fit(self, X, weights=None):
             Data for compute, where `n_samples` is the number of samples and
             `n_features` is the number of features.
 
-        weights : array-like of shape (n_samples,)
+        y : Ignored
+            Not used, present for API consistency by convention.
+
+        sample_weight : array-like of shape (n_samples,), default=None
             Weights for compute weighted statistics, where `n_samples` is the number of samples.
 
         Returns
@@ -271,6 +283,6 @@ def fit(self, X, weights=None):
                 "sklearn": None,
             },
             X,
-            weights,
+            sample_weight,
         )
         return self

sklearnex/basic_statistics/tests/test_incremental_basic_statistics.py

@@ -52,7 +52,7 @@ def test_partial_fit_multiple_options_on_gold_data(dataframe, queue, weighted, d
             weights_split_df = _convert_to_dataframe(
                 weights_split[i], sycl_queue=queue, target_df=dataframe
             )
-            result = incbs.partial_fit(X_split_df, weights_split_df)
+            result = incbs.partial_fit(X_split_df, sample_weight=weights_split_df)
         else:
             result = incbs.partial_fit(X_split_df)
 
@@ -103,7 +103,7 @@ def test_partial_fit_single_option_on_random_data(
             weights_split_df = _convert_to_dataframe(
                 weights_split[i], sycl_queue=queue, target_df=dataframe
             )
-            result = incbs.partial_fit(X_split_df, weights_split_df)
+            result = incbs.partial_fit(X_split_df, sample_weight=weights_split_df)
         else:
             result = incbs.partial_fit(X_split_df)
 
@@ -146,7 +146,7 @@ def test_partial_fit_multiple_options_on_random_data(
             weights_split_df = _convert_to_dataframe(
                 weights_split[i], sycl_queue=queue, target_df=dataframe
             )
-            result = incbs.partial_fit(X_split_df, weights_split_df)
+            result = incbs.partial_fit(X_split_df, sample_weight=weights_split_df)
         else:
             result = incbs.partial_fit(X_split_df)
 
@@ -199,7 +199,7 @@ def test_partial_fit_all_option_on_random_data(
             weights_split_df = _convert_to_dataframe(
                 weights_split[i], sycl_queue=queue, target_df=dataframe
             )
-            result = incbs.partial_fit(X_split_df, weights_split_df)
+            result = incbs.partial_fit(X_split_df, sample_weight=weights_split_df)
         else:
             result = incbs.partial_fit(X_split_df)
 
@@ -233,7 +233,7 @@ def test_fit_multiple_options_on_gold_data(dataframe, queue, weighted, dtype):
     incbs = IncrementalBasicStatistics(batch_size=1)
 
     if weighted:
-        result = incbs.fit(X_df, weights_df)
+        result = incbs.fit(X_df, sample_weight=weights_df)
     else:
         result = incbs.fit(X_df)
 
@@ -272,15 +272,15 @@ def test_fit_single_option_on_random_data(
     X = X.astype(dtype=dtype)
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     if weighted:
-        weights = gen.uniform(low=-0.5, high=+1.0, size=row_count)
+        weights = gen.uniform(low=-0.5, high=1.0, size=row_count)
         weights = weights.astype(dtype=dtype)
         weights_df = _convert_to_dataframe(weights, sycl_queue=queue, target_df=dataframe)
     incbs = IncrementalBasicStatistics(
         result_options=result_option, batch_size=batch_size
     )
 
     if weighted:
-        result = incbs.fit(X_df, weights_df)
+        result = incbs.fit(X_df, sample_weight=weights_df)
     else:
         result = incbs.fit(X_df)
 
@@ -311,15 +311,15 @@ def test_partial_fit_multiple_options_on_random_data(
     X = X.astype(dtype=dtype)
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     if weighted:
-        weights = gen.uniform(low=-0.5, high=+1.0, size=row_count)
+        weights = gen.uniform(low=-0.5, high=1.0, size=row_count)
         weights = weights.astype(dtype=dtype)
         weights_df = _convert_to_dataframe(weights, sycl_queue=queue, target_df=dataframe)
     incbs = IncrementalBasicStatistics(
         result_options=["mean", "max", "sum"], batch_size=batch_size
     )
 
     if weighted:
-        result = incbs.fit(X_df, weights_df)
+        result = incbs.fit(X_df, sample_weight=weights_df)
     else:
         result = incbs.fit(X_df)
 
@@ -366,7 +366,7 @@ def test_fit_all_option_on_random_data(
     incbs = IncrementalBasicStatistics(result_options="all", batch_size=batch_size)
 
     if weighted:
-        result = incbs.fit(X_df, weights_df)
+        result = incbs.fit(X_df, sample_weight=weights_df)
     else:
         result = incbs.fit(X_df)