Fix multi dimensional GaussianRegressor

Signed-off-by: Xavier Dupre <[email protected]>
onnx · May 28, 2024 · c48511c · c48511c
1 parent 369949a
commit c48511c
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Showing 2 changed files with 13 additions and 12 deletions.
diff --git a/skl2onnx/operator_converters/gaussian_process.py b/skl2onnx/operator_converters/gaussian_process.py
@@ -194,7 +194,9 @@ def convert_gaussian_process_regressor(
 
             if hasattr(op, "_y_train_std"):
                 # y_var = y_var * self._y_train_std**2
-                ys0_var = OnnxMul(var_y**2, ys0_var, op_version=opv)
+                ys0_var = OnnxMul(
+                    ys0_var, (op._y_train_std**2).astype(dtype), op_version=opv
+                )
 
             # var = np.sqrt(ys0_var)
             var = OnnxSqrt(ys0_var, op_version=opv)
@@ -414,4 +416,4 @@ def convert_gaussian_process_classifier(
             "output_class_labels": [False, True],
             "zipmap": [False, True],
         },
-    )
+    )
diff --git a/tests/test_sklearn_gaussian_process_regressor.py b/tests/test_sklearn_gaussian_process_regressor.py
@@ -24,13 +24,7 @@
     WhiteKernel,
 )
 from sklearn.model_selection import train_test_split
-
-try:
-    # scikit-learn >= 0.22
-    from sklearn.utils._testing import ignore_warnings
-except ImportError:
-    # scikit-learn < 0.22
-    from sklearn.utils.testing import ignore_warnings
+from sklearn.utils._testing import ignore_warnings
 from sklearn.exceptions import ConvergenceWarning
 from skl2onnx.common.data_types import FloatTensorType, DoubleTensorType
 from skl2onnx import to_onnx
@@ -1019,6 +1013,9 @@ def test_gpr_rbf_fitted_return_std_exp_sine_squared_false(self):
             model_onnx,
             verbose=False,
             basename="SklearnGaussianProcessExpSineSquaredStdF-Out0-Dec3",
+            # operator MatMul gets replaced by FusedMatMul but onnxruntime does not check
+            # the availability of the kernel for double.
+            disable_optimisation=True,
         )
         self.check_outputs(
             gp,
@@ -1418,6 +1415,7 @@ def test_x_issue_789_cdist(self):
             pipe.predict(vx1.astype(np.float64)).ravel(), pred[0].ravel()
         )
 
+    @ignore_warnings(category=ConvergenceWarning)
     def test_white_kernel_float(self):
         X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
         tx1, vx1, ty1, vy1 = train_test_split(X, y)
@@ -1436,6 +1434,7 @@ def test_white_kernel_float(self):
             gpr.predict(vx1.astype(np.float32)).ravel(), pred[0].ravel(), rtol=1e-3
         )
 
+    @ignore_warnings(category=ConvergenceWarning)
     def test_white_kernel_double(self):
         X, y = make_friedman2(n_samples=500, noise=0, random_state=0)
         tx1, vx1, ty1, vy1 = train_test_split(X, y)
@@ -1497,8 +1496,8 @@ def test_kernel_white_kernel(self):
         m1 = res
         m2 = ker(x, x)
         assert_almost_equal(m2, m1, decimal=5)
-        
-    def test_issue_1073(self):
+
+    def test_issue_1073_multidimension_process(self):
         # multioutput gpr
         n_samples, n_features, n_targets = 1000, 8, 3
         X, y = make_regression(n_samples, n_features, n_targets=n_targets)
@@ -1522,5 +1521,5 @@ def test_issue_1073(self):
     # log.setLevel(logging.DEBUG)
     # logging.basicConfig(level=logging.DEBUG)
     # TestSklearnGaussianProcessRegressor().test_kernel_white_kernel()
-    #TestSklearnGaussianProcessRegressor().test_issue_1073()
+    # TestSklearnGaussianProcessRegressor().test_issue_1073()
     unittest.main(verbosity=2)