Address comments

onedal/linear_model/incremental_linear_model.py

@@ -83,10 +83,11 @@ def partial_fit(self, X, y, queue=None):
             self._dtype = get_dtype(X)
             self._params = self._get_onedal_params(self._dtype)
 
-        y = np.asarray(y).astype(dtype=self._dtype)
-        self._y_ndim_1 = y.ndim == 1
+        y = np.asarray(y, dtype=self._dtype)
 
-        X, y = _check_X_y(X, y, dtype=[np.float64, np.float32], accept_2d_y=True)
+        X, y = _check_X_y(
+            X, y, dtype=[np.float64, np.float32], accept_2d_y=True, force_all_finite=False
+        )
 
         self.n_features_in_ = _num_features(X, fallback_1d=True)
         X_table, y_table = to_table(X, y)
@@ -139,14 +140,10 @@ def finalize_fit(self, queue=None):
 
         packed_coefficients = from_table(result.model.packed_coefficients)
         self.coef_, self.intercept_ = (
-            packed_coefficients[:, 1:],
-            packed_coefficients[:, 0],
+            packed_coefficients[:, 1:].squeeze(),
+            packed_coefficients[:, 0].squeeze(),
         )
 
-        if self.coef_.shape[0] == 1 and self._y_ndim_1:
-            self.coef_ = self.coef_.ravel()
-            self.intercept_ = self.intercept_[0]
-
         return self
 
 
@@ -216,9 +213,11 @@ def partial_fit(self, X, y, queue=None):
             self._dtype = get_dtype(X)
             self._params = self._get_onedal_params(self._dtype)
 
-        y = np.asarray(y).astype(dtype=self._dtype)
+        y = np.asarray(y, dtype=self._dtype)
 
-        X, y = _check_X_y(X, y, dtype=[np.float64, np.float32], accept_2d_y=True)
+        X, y = _check_X_y(
+            X, y, dtype=[np.float64, np.float32], accept_2d_y=True, force_all_finite=False
+        )
 
         self.n_features_in_ = _num_features(X, fallback_1d=True)
         X_table, y_table = to_table(X, y)

onedal/spmd/linear_model/incremental_linear_model.py

@@ -67,10 +67,11 @@ def partial_fit(self, X, y, queue=None):
             self._dtype = get_dtype(X)
             self._params = self._get_onedal_params(self._dtype)
 
-        y = np.asarray(y).astype(dtype=self._dtype)
-        self._y_ndim_1 = y.ndim == 1
+        y = np.asarray(y, dtype=self._dtype)
 
-        X, y = _check_X_y(X, y, dtype=[np.float64, np.float32], accept_2d_y=True)
+        X, y = _check_X_y(
+            X, y, dtype=[np.float64, np.float32], accept_2d_y=True, force_all_finite=False
+        )
 
         self.n_features_in_ = _num_features(X, fallback_1d=True)
         X_table, y_table = to_table(X, y)

sklearnex/linear_model/tests/test_incremental_linear.py

@@ -34,7 +34,7 @@ def test_sklearnex_fit_on_gold_data(dataframe, queue, fit_intercept, macro_block
     X = np.array([[1], [2]])
     X = X.astype(dtype=dtype)
     X_df = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
-    y = np.array([1, 2])
+    y = np.array([[1], [2]])
     y = y.astype(dtype=dtype)
     y_df = _convert_to_dataframe(y, sycl_queue=queue, target_df=dataframe)
 
@@ -185,16 +185,16 @@ def test_sklearnex_partial_fit_on_random_data(
         inclin.partial_fit(X_split_df, y_split_df)
 
     tol = 1e-4 if inclin.coef_.dtype == np.float32 else 1e-7
-    assert_allclose(coef, inclin.coef_.T, atol=tol)
+    assert_allclose(coef.T.squeeze(), inclin.coef_, atol=tol)
 
     if fit_intercept:
         assert_allclose(intercept, inclin.intercept_, atol=tol)
 
     X_test = gen.random(size=(num_samples, num_features), dtype=dtype)
     if fit_intercept:
-        expected_y_pred = X_test @ coef + intercept[np.newaxis, :]
+        expected_y_pred = (X_test @ coef + intercept[np.newaxis, :]).squeeze()
     else:
-        expected_y_pred = X_test @ coef
+        expected_y_pred = (X_test @ coef).squeeze()
 
     X_test_df = _convert_to_dataframe(X_test, sycl_queue=queue, target_df=dataframe)
 

sklearnex/spmd/linear_model/tests/test_incremental_linear_spmd.py

@@ -71,8 +71,9 @@ def test_incremental_linear_regression_fit_spmd_gold(
             [13.0, 32.0],
             [14.0, 64.0],
             [15.0, 128.0],
-        ]
-    ).astype(dtype=dtype)
+        ],
+        dtype=dtype,
+    )
     dpt_X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     local_X = _get_local_tensor(X)
     local_dpt_X = _convert_to_dataframe(local_X, sycl_queue=queue, target_df=dataframe)
@@ -143,8 +144,9 @@ def test_incremental_linear_regression_partial_fit_spmd_gold(
             [13.0, 32.0],
             [14.0, 64.0],
             [15.0, 128.0],
-        ]
-    ).astype(dtype=dtype)
+        ],
+        dtype=dtype,
+    )
     dpt_X = _convert_to_dataframe(X, sycl_queue=queue, target_df=dataframe)
     local_X = _get_local_tensor(X)
     split_local_X = np.array_split(local_X, num_blocks)