issue 676

Signed-off-by: Xavier Dupre <xadupre@microsoft.com>

tests/xgboost/test_xgboost_issues.py

@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+
+
+class TestXGBoostIssues(unittest.TestCase):
+    def test_issue_676(self):
+        import onnxruntime
+        import xgboost
+        import numpy as np
+        from skl2onnx import convert_sklearn
+        from skl2onnx.common.data_types import FloatTensorType
+        from skl2onnx import update_registered_converter
+        from skl2onnx.common.shape_calculator import (
+            calculate_linear_regressor_output_shapes,
+        )
+        from onnxmltools.convert.xgboost.operator_converters.XGBoost import (
+            convert_xgboost,
+        )
+
+        update_registered_converter(
+            xgboost.XGBRegressor,
+            "XGBoostXGBRegressor",
+            calculate_linear_regressor_output_shapes,
+            convert_xgboost,
+        )
+        # Your data and labels
+        X = np.random.rand(100, 10)
+        y = np.random.rand(100, 210)
+
+        # Train XGBoost regressor
+        model = xgboost.XGBRegressor(objective="reg:squarederror", n_estimators=100)
+        model.fit(X, y)
+
+        # Define input type (adjust shape according to your input)
+        initial_type = [("float_input", FloatTensorType([None, X.shape[1]]))]
+
+        # Convert XGBoost model to ONNX
+        onnx_model = convert_sklearn(model, initial_types=initial_type, target_opset=12)
+
+        sess = onnxruntime.InferenceSession(
+            onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        got = sess.run(None, {"float_input": X.astype(np.float32)})
+        self.assertEqual(got[0].shape, (100, 1))
+
+
+if __name__ == "__main__":
+    unittest.main()