Remove generate_weights

sklearnex/spmd/basic_statistics/tests/test_incremental_basic_statistics_spmd.py

@@ -25,7 +25,6 @@
 )
 from sklearnex.tests._utils_spmd import (
     _generate_statistic_data,
-    _generate_weights,
     _get_local_tensor,
     _mpi_libs_and_gpu_available,
 )
@@ -272,7 +271,7 @@ def test_incremental_basic_statistics_partial_fit_spmd_synthetic(
 
     if weighted:
         # Create weights array containing the weight for each sample in the data
-        weights = _generate_weights(n_samples, dtype=dtype)
+        weights = _generate_statistic_data(n_samples, dtype=dtype)
         local_weights = _get_local_tensor(weights)
         split_local_weights = np.array_split(local_weights, num_blocks)
         split_weights = np.array_split(weights, num_blocks)

sklearnex/tests/_utils_spmd.py

@@ -89,10 +89,16 @@ def _generate_classification_data(
     return X_train, X_test, y_train, y_test
 
 
-def _generate_statistic_data(n_samples, n_features, dtype=np.float64, random_state=42):
+def _generate_statistic_data(
+    n_samples, n_features=None, dtype=np.float64, random_state=42
+):
     # Generates statistical data
     gen = np.random.default_rng(random_state)
-    data = gen.uniform(low=-0.3, high=+0.7, size=(n_samples, n_features)).astype(dtype)
+    data = gen.uniform(
+        low=-0.3,
+        high=+0.7,
+        size=(n_samples, n_features) if n_features is not None else (n_samples,),
+    ).astype(dtype)
     return data
 
 
@@ -111,13 +117,6 @@ def _generate_clustering_data(
     return X_train, X_test
 
 
-def _generate_weights(n_samples, dtype=np.float64, random_state=42):
-    # Generates weights
-    gen = np.random.default_rng(random_state)
-    weights = gen.uniform(low=-0.3, high=+0.7, size=(n_samples)).astype(dtype)
-    return weights
-
-
 def _spmd_assert_allclose(spmd_result, batch_result, **kwargs):
     """Calls assert_allclose on spmd and batch results.