Add missing dependency onnxconverter_common, fix multi regression wit…

…h xgboost (onnx#679)

* add missing dependency onnxconverter_common

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

* issue 676

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

* issue 676

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

* fix issue 676

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

* fix shape calcultator

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

* fix new name for sparse arguement

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

* changelogs

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

* improves stability

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

---------

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

CHANGELOGS.md

@@ -1,5 +1,12 @@
 # Change Logs
 
+## 1.13.0 (development)
+
+* Add missing dependency onnxconverter_common, fix multi regression with xgboost,
+  [#679](https://github.com/onnx/onnxmltools/pull/679),
+  fixes issues [No module named 'onnxconverter_common'](https://github.com/onnx/onnxmltools/issues/673),
+  [onnx converted : xgboostRegressor multioutput model predicts 1 dimension instead of original 210 dimensions.](https://github.com/onnx/onnxmltools/issues/676)
+
 ## 1.12.0
 
 * Fix early stopping for XGBClassifier and xgboost > 2

onnxmltools/__init__.py

@@ -5,7 +5,7 @@
 This framework converts any machine learned model into onnx format
 which is a common language to describe any machine learned model.
 """
-__version__ = "1.12.0"
+__version__ = "1.13.0"
 __author__ = "ONNX"
 __producer__ = "OnnxMLTools"
 __producer_version__ = __version__

onnxmltools/convert/xgboost/common.py

@@ -32,6 +32,12 @@ def get_xgb_params(xgb_node):
         bs = float(config["learner"]["learner_model_param"]["base_score"])
         # xgboost >= 2.0
         params["base_score"] = bs
+    if "num_target" in config["learner"]["learner_model_param"]:
+        params["n_targets"] = int(
+            config["learner"]["learner_model_param"]["num_target"]
+        )
+    else:
+        params["n_targets"] = 1
 
     bst = xgb_node.get_booster()
     if hasattr(bst, "best_ntree_limit"):

onnxmltools/convert/xgboost/operator_converters/XGBoost.py

@@ -254,6 +254,9 @@ def convert(scope, operator, container):
             js_trees, attr_pairs, [1 for _ in js_trees], False
         )
 
+        params = XGBConverter.get_xgb_params(xgb_node)
+        attr_pairs["n_targets"] = params["n_targets"]
+
         # add nodes
         if objective == "count:poisson":
             names = [scope.get_unique_variable_name("tree")]

requirements.txt

@@ -1,2 +1,3 @@
 numpy
 onnx
+onnxconverter_common

tests/xgboost/test_xgboost_converters_base_score.py

@@ -103,7 +103,7 @@ def test_xgbclassifier_sparse_base_score(self):
         assert_almost_equal(expected.reshape((-1, 2)), got, decimal=4)
 
     def test_xgbclassifier_sparse_no_base_score(self):
-        X, y = make_regression(n_samples=200, n_features=10, random_state=0)
+        X, y = make_regression(n_samples=400, n_features=10, random_state=0)
         mask = np.random.randint(0, 50, size=(X.shape)) != 0
         X[mask] = 0
         y = (y + mask.sum(axis=1, keepdims=0)).astype(np.float32)

tests/xgboost/test_xgboost_issues.py

@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+
+
+class TestXGBoostIssues(unittest.TestCase):
+    def test_issue_676(self):
+        import json
+        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 onnxmltools.convert.xgboost.operator_converters.XGBoost import (
+            convert_xgboost,
+        )
+
+        def xgbregressor_shape_calculator(operator):
+            config = json.loads(operator.raw_operator.get_booster().save_config())
+            n_targets = int(config["learner"]["learner_model_param"]["num_target"])
+            operator.outputs[0].type.shape = [None, n_targets]
+
+        update_registered_converter(
+            xgboost.XGBRegressor,
+            "XGBoostXGBRegressor",
+            xgbregressor_shape_calculator,
+            convert_xgboost,
+        )
+        # Your data and labels
+        X = np.random.rand(100, 10)
+        y = np.random.rand(100, 2)
+
+        # Train XGBoost regressor
+        model = xgboost.XGBRegressor(
+            objective="reg:squarederror", n_estimators=2, maxdepth=2
+        )
+        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)
+        self.assertIn("dim_value: 2", str(onnx_model.graph.output))
+
+        sess = onnxruntime.InferenceSession(
+            onnx_model.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        got = sess.run(None, {"float_input": X.astype(np.float32)})
+        self.assertEqual(got[0].shape, (100, 2))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/xgboost/test_xgboost_pipeline.py

@@ -50,7 +50,7 @@ def transformer_for_column(column):
             if column.dtype in ["bool"]:
                 return "passthrough"
             if column.dtype in ["O"]:
-                return OneHotEncoder(sparse=False)
+                return OneHotEncoder(sparse_output=False)
             raise ValueError()
 
         return ColumnTransformer(