[Draft] ENH: Enable linear regression on GPU with non-PSD systems

deselected_tests.yaml

@@ -462,8 +462,3 @@ gpu:
   # RuntimeError: Device support is not implemented, failing as result of fallback to cpu false
   - svm/tests/test_svm.py::test_unfitted
   - tests/test_common.py::test_estimators[SVC()-check_estimators_unfitted]
-
-  # Failed on the onedal's LinearRegression call.
-  # RuntimeError: oneapi::mkl::lapack::potrf: computation error: info = 2: Leading principal minor of order
-  # 2 is not positive, and the factorization could not be completed.
-  - ensemble/tests/test_stacking.py::test_stacking_prefit[StackingRegressor-DummyRegressor-predict-final_estimator1-X1-y1]

doc/sources/algorithms.rst

@@ -304,7 +304,7 @@ Regression
 
        - ``normalize`` != `False`
        - ``sample_weight`` != `None`
-     - Only dense data is supported, `#observations` should be >= `#features` and there should be no linearly dependent features in the data.
+     - Only dense data is supported.
 
 Clustering
 **********
@@ -456,7 +456,7 @@ Regression
 
        - ``normalize`` != `False`
        - ``sample_weight`` != `None`
-     - Only dense data is supported, `#observations` should be >= `#features` and there should be no linearly dependent features in the data.
+     - Only dense data is supported.
 
 Clustering
 **********

onedal/linear_model/tests/test_linear_regression.py

@@ -150,14 +150,16 @@ def test_reconstruct_model(queue, dtype):
     assert_allclose(gtr, res, rtol=tol)
 
 
-@pytest.mark.parametrize("queue", get_queues("cpu"))
+@pytest.mark.parametrize("queue", get_queues())
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
 @pytest.mark.parametrize("fit_intercept", [False, True])
 @pytest.mark.skipif(
     not daal_check_version((2025, "P", 1)),
     reason="Functionality introduced in later versions",
 )
 def test_overdetermined_system(queue, dtype, fit_intercept):
+    if queue and queue.sycl_device.is_gpu and not daal_check_version((2025, "P", 200)):
+        pytest.skip("Functionality introduced in later versions")
     gen = np.random.default_rng(seed=123)
     X = gen.standard_normal(size=(10, 20))
     y = gen.standard_normal(size=X.shape[0])
@@ -176,14 +178,16 @@ def test_overdetermined_system(queue, dtype, fit_intercept):
     assert np.all(np.abs(residual) < 1e-6)
 
 
-@pytest.mark.parametrize("queue", get_queues("cpu"))
+@pytest.mark.parametrize("queue", get_queues())
 @pytest.mark.parametrize("dtype", [np.float32, np.float64])
 @pytest.mark.parametrize("fit_intercept", [False, True])
 @pytest.mark.skipif(
     not daal_check_version((2025, "P", 1)),
     reason="Functionality introduced in later versions",
 )
 def test_singular_matrix(queue, dtype, fit_intercept):
+    if queue and queue.sycl_device.is_gpu and not daal_check_version((2025, "P", 200)):
+        pytest.skip("Functionality introduced in later versions")
     gen = np.random.default_rng(seed=123)
     X = gen.standard_normal(size=(20, 4))
     X[:, 2] = X[:, 3]
@@ -212,8 +216,13 @@ def test_singular_matrix(queue, dtype, fit_intercept):
     reason="Functionality introduced in the versions >= 2025.0",
 )
 def test_multioutput_regression(queue, dtype, fit_intercept, problem_type):
-    if problem_type != "regular" and queue and queue.sycl_device.is_gpu:
-        pytest.skip("Not yet implemented on GPU")
+    if (
+        problem_type != "regular"
+        and queue
+        and queue.sycl_device.is_gpu
+        and not daal_check_version((2025, "P", 200))
+    ):
+        pytest.skip("Functionality introduced in later versions")
     gen = np.random.default_rng(seed=123)
     if problem_type == "regular":
         X = gen.standard_normal(size=(20, 5))

sklearnex/linear_model/linear.py

@@ -148,7 +148,7 @@ def _onedal_gpu_supported(self, method_name, *data):
             f"sklearn.linear_model.{self.__class__.__name__}.{method_name}"
         )
 
-        if method_name == "fit":
+        if method_name == "fit" and not daal_check_version((2025, "P", 200)):
             n_samples = _num_samples(data[0])
             n_features = _num_features(data[0], fallback_1d=True)
             is_underdetermined = n_samples < (n_features + int(self.fit_intercept))

sklearnex/linear_model/tests/test_linear.py

@@ -42,9 +42,14 @@ def test_sklearnex_import_linear(
     dataframe, queue, dtype, macro_block, overdetermined, multi_output
 ):
     if (overdetermined or multi_output) and not daal_check_version((2025, "P", 1)):
-        pytest.skip()
-    if overdetermined and queue and queue.sycl_device.is_gpu:
-        pytest.skip()
+        pytest.skip("Functionality introduced in later versions")
+    if (
+        overdetermined
+        and queue
+        and queue.sycl_device.is_gpu
+        and not daal_check_version((2025, "P", 200))
+    ):
+        pytest.skip("Functionality introduced in later versions")
 
     from sklearnex.linear_model import LinearRegression