Fix conversion for XGBClassifier and XGBRegressor (#1157)

Signed-off-by: xadupre <[email protected]>

docs/tutorial/plot_gexternal_xgboost.py

@@ -32,6 +32,7 @@
     calculate_linear_classifier_output_shapes,
     calculate_linear_regressor_output_shapes,
 )
+from skl2onnx.convert import may_switch_bases_classes_order
 from onnxmltools.convert.xgboost.operator_converters.XGBoost import convert_xgboost
 from onnxmltools.convert import convert_xgboost as convert_xgboost_booster
 
@@ -93,12 +94,15 @@
 # Convert again
 # +++++++++++++
 
-model_onnx = convert_sklearn(
-    pipe,
-    "pipeline_xgboost",
-    [("input", FloatTensorType([None, 2]))],
-    target_opset={"": 12, "ai.onnx.ml": 2},
-)
+with may_switch_bases_classes_order(XGBClassifier):
+    # This context should not be needed anymore once this issue
+    # is fixed in XGBoost.
+    model_onnx = convert_sklearn(
+        pipe,
+        "pipeline_xgboost",
+        [("input", FloatTensorType([None, 2]))],
+        target_opset={"": 12, "ai.onnx.ml": 2},
+    )
 
 # And save.
 with open("pipeline_xgboost.onnx", "wb") as f:
@@ -110,8 +114,9 @@
 #
 # Predictions with XGBoost.
 
-print("predict", pipe.predict(X[:5]))
-print("predict_proba", pipe.predict_proba(X[:1]))
+with may_switch_bases_classes_order(XGBClassifier):
+    print("predict", pipe.predict(X[:5]))
+    print("predict_proba", pipe.predict_proba(X[:1]))
 
 ##########################
 # Predictions with onnxruntime.
@@ -141,14 +146,16 @@
 pipe = Pipeline([("scaler", StandardScaler()), ("xgb", XGBRegressor(n_estimators=3))])
 pipe.fit(X_train, y_train)
 
-print("predict", pipe.predict(X_test[:5]))
+with may_switch_bases_classes_order(XGBRegressor):
+    print("predict", pipe.predict(X_test[:5]))
 
 #############################
 # ONNX
 
-onx = to_onnx(
-    pipe, X_train.astype(numpy.float32), target_opset={"": 12, "ai.onnx.ml": 2}
-)
+with may_switch_bases_classes_order(XGBRegressor):
+    onx = to_onnx(
+        pipe, X_train.astype(numpy.float32), target_opset={"": 12, "ai.onnx.ml": 2}
+    )
 
 sess = rt.InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])
 pred_onx = sess.run(None, {"X": X_test[:5].astype(numpy.float32)})

skl2onnx/convert.py

@@ -2,6 +2,7 @@
 
 import warnings
 from uuid import uuid4
+from contextlib import contextmanager
 from typing import Callable, Dict, List, Optional, Sequence, Set, Tuple, Union
 import numpy as np
 import sklearn.base
@@ -357,3 +358,27 @@ def wrap_as_onnx_mixin(model, target_opset=None):
     obj.__setstate__(state)
     obj.op_version = target_opset
     return obj
+
+
+@contextmanager
+def may_switch_bases_classes_order(cls):
+    """
+    XGBClassifier and XGBRegressor do not inherit from those classes
+    in the right order. We change it when it is registered to avoid
+    ``AttributeError: 'super' object has no attribute '__sklearn_tags__'``
+    to be raised.
+    """
+    bases = cls.__bases__
+    if len(bases) == 2 and bases[1] in (
+        sklearn.base.ClassifierMixin,
+        sklearn.base.RegressorMixin,
+    ):
+        cls.__bases__ = bases[1], bases[0]
+        revert = True
+    else:
+        revert = False
+    try:
+        yield
+    finally:
+        if revert:
+            cls.__bases__ = bases