Replace #507 + fix bug with XGBoost converter when base_score is None (…

…#510)

* Replace #507
* fix xgboost converter with xgboost 1.5.0
* fix nan values
* update catboost test
* update ci

Signed-off-by: xavier dupré <xavier.dupre@gmail.com>

Co-authored-by: xavier dupré <xavier.dupre@gmail.com>
Co-authored-by: Karthikeyan Singaravelan <tir.karthi@gmail.com>

.azure-pipelines/win32-conda-CI.yml

@@ -77,6 +77,11 @@ jobs:
       python -m pip install -r requirements-dev.txt
     displayName: 'Install dependencies-dev'
 
+  - script: |
+      call activate py$(python.version)
+      python -m pip install --upgrade scikit-learn
+    displayName: 'Install scikit-learn'
+
   - script: |
       call activate py$(python.version)
       python -m pip install %COREML_PATH%

README.md

@@ -76,7 +76,7 @@ input2 = Input(shape=(D,))
 mapped1_2 = sub_model1(input1)
 mapped2_2 = sub_model2(input2)
 sub_sum = Add()([mapped1_2, mapped2_2])
-keras_model = Model(inputs=[input1, input2], output=sub_sum)
+keras_model = Model(inputs=[input1, input2], outputs=sub_sum)
 
 # Convert it! The target_opset parameter is optional.
 onnx_model = onnxmltools.convert_keras(keras_model, target_opset=7)

onnxmltools/convert/xgboost/operator_converters/XGBoost.py

@@ -33,6 +33,8 @@ def common_members(xgb_node, inputs):
         params = XGBConverter.get_xgb_params(xgb_node)
         objective = params["objective"]
         base_score = params["base_score"]
+        if base_score is None:
+            base_score = 0.5
         booster = xgb_node.get_booster()
         # The json format was available in October 2017.
         # XGBoost 0.7 was the first version released with it.

tests/catboost/test_CatBoost_converter.py

@@ -4,16 +4,25 @@
 Tests for CatBoostRegressor and CatBoostClassifier converter.
 """
 import unittest
-import numpy
 import warnings
-import catboost
-
-from sklearn.datasets import make_regression, make_classification
+from distutils.version import StrictVersion
+import numpy
+try:
+    import sklearn
+    from sklearn.datasets import make_regression, make_classification
+except (ImportError, FileNotFoundError):
+    sklearn = None
+try:
+    import catboost
+except (ImportError, FileNotFoundError):
+    catboost = None
 from onnxmltools.convert import convert_catboost
 from onnxmltools.utils import dump_data_and_model, dump_single_regression, dump_multiple_classification
 
 
 class TestCatBoost(unittest.TestCase):
+
+    @unittest.skipIf(catboost is None or sklearn is None, reason="catboost not imported")
     def test_catboost_regressor(self):
         X, y = make_regression(n_samples=100, n_features=4, random_state=0)
         catboost_model = catboost.CatBoostRegressor(task_type='CPU', loss_function='RMSE',
@@ -26,11 +35,11 @@ def test_catboost_regressor(self):
         self.assertTrue(catboost_onnx is not None)
         dump_data_and_model(X.astype(numpy.float32), catboost_model, catboost_onnx, basename="CatBoostReg-Dec4")
 
+    @unittest.skipIf(catboost is None or sklearn is None, reason="catboost not imported")
     def test_catboost_bin_classifier(self):
         import onnxruntime
-        from distutils.version import StrictVersion
 
-        if StrictVersion(onnxruntime.__version__) >= StrictVersion('1.3.0'):
+        if StrictVersion('.'.join(onnxruntime.__version__.split('.')[:2])) >= StrictVersion('1.3.0'):
             X, y = make_classification(n_samples=100, n_features=4, random_state=0)
             catboost_model = catboost.CatBoostClassifier(task_type='CPU', loss_function='CrossEntropy',
                                                          n_estimators=10, verbose=0)
@@ -45,6 +54,7 @@ def test_catboost_bin_classifier(self):
             warnings.warn('Converted CatBoost models for binary classification work with onnxruntime version 1.3.0 or '
                           'a newer one')
 
+    @unittest.skipIf(catboost is None or sklearn is None, reason="catboost not imported")
     def test_catboost_multi_classifier(self):
         X, y = make_classification(n_samples=10, n_informative=8, n_classes=3, random_state=0)
         catboost_model = catboost.CatBoostClassifier(task_type='CPU', loss_function='MultiClass',

tests/xgboost/test_xgboost_converters.py

@@ -53,13 +53,8 @@ def test_xgb_regressor(self):
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
             x_test.astype("float32"),
-            xgb,
-            conv_model,
-            basename="SklearnXGBRegressor-Dec3",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            xgb, conv_model,
+            basename="SklearnXGBRegressor-Dec3")
 
     def test_xgb_classifier(self):
         xgb, x_test = _fit_classification_model(XGBClassifier(), 2)
@@ -68,14 +63,8 @@ def test_xgb_classifier(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifier",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifier")
 
     def test_xgb_classifier_uint8(self):
         xgb, x_test = _fit_classification_model(
@@ -85,14 +74,8 @@ def test_xgb_classifier_uint8(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifier",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifier")
 
     def test_xgb_classifier_multi(self):
         xgb, x_test = _fit_classification_model(XGBClassifier(), 3)
@@ -101,14 +84,8 @@ def test_xgb_classifier_multi(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifierMulti",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifierMulti")
 
     def test_xgb_classifier_multi_reglog(self):
         xgb, x_test = _fit_classification_model(
@@ -118,14 +95,8 @@ def test_xgb_classifier_multi_reglog(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifierMultiRegLog",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifierMultiRegLog")
 
     def test_xgb_classifier_reglog(self):
         xgb, x_test = _fit_classification_model(
@@ -135,14 +106,8 @@ def test_xgb_classifier_reglog(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifierRegLog",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifierRegLog")
 
     def test_xgb_classifier_multi_str_labels(self):
         xgb, x_test = _fit_classification_model(
@@ -152,14 +117,8 @@ def test_xgb_classifier_multi_str_labels(self):
             target_opset=TARGET_OPSET)
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
-            x_test,
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifierMultiStrLabels",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            x_test, xgb, conv_model,
+            basename="SklearnXGBClassifierMultiStrLabels")
 
     def test_xgb_classifier_multi_discrete_int_labels(self):
         iris = load_iris()
@@ -180,13 +139,8 @@ def test_xgb_classifier_multi_discrete_int_labels(self):
         self.assertTrue(conv_model is not None)
         dump_data_and_model(
             x_test.astype("float32"),
-            xgb,
-            conv_model,
-            basename="SklearnXGBClassifierMultiDiscreteIntLabels",
-            allow_failure="StrictVersion("
-            "onnx.__version__)"
-            "< StrictVersion('1.3.0')",
-        )
+            xgb, conv_model,
+            basename="SklearnXGBClassifierMultiDiscreteIntLabels")
 
     def test_xgboost_booster_classifier_bin(self):
         x, y = make_classification(n_classes=2, n_features=5,
@@ -202,9 +156,7 @@ def test_xgboost_booster_classifier_bin(self):
                                      [('input', FloatTensorType([None, x.shape[1]]))],
                                      target_opset=TARGET_OPSET)
         dump_data_and_model(x_test.astype(np.float32),
-                            model, model_onnx,
-                            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
-                            basename="XGBBoosterMCl")
+                            model, model_onnx, basename="XGBBoosterMCl")
 
     def test_xgboost_booster_classifier_multiclass_softprob(self):
         x, y = make_classification(n_classes=3, n_features=5,
@@ -221,9 +173,7 @@ def test_xgboost_booster_classifier_multiclass_softprob(self):
                                      [('input', FloatTensorType([None, x.shape[1]]))],
                                      target_opset=TARGET_OPSET)
         dump_data_and_model(x_test.astype(np.float32),
-                            model, model_onnx,
-                            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
-                            basename="XGBBoosterMClSoftProb")
+                            model, model_onnx, basename="XGBBoosterMClSoftProb")
 
     def test_xgboost_booster_classifier_multiclass_softmax(self):
         x, y = make_classification(n_classes=3, n_features=5,
@@ -240,9 +190,7 @@ def test_xgboost_booster_classifier_multiclass_softmax(self):
                                      [('input', FloatTensorType([None, x.shape[1]]))],
                                      target_opset=TARGET_OPSET)
         dump_data_and_model(x_test.astype(np.float32),
-                            model, model_onnx,
-                            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
-                            basename="XGBBoosterMClSoftMax")
+                            model, model_onnx, basename="XGBBoosterMClSoftMax")
 
     def test_xgboost_booster_classifier_reg(self):
         x, y = make_classification(n_classes=2, n_features=5,
@@ -259,9 +207,7 @@ def test_xgboost_booster_classifier_reg(self):
                                      [('input', FloatTensorType([None, x.shape[1]]))],
                                      target_opset=TARGET_OPSET)
         dump_data_and_model(x_test.astype(np.float32),
-                            model, model_onnx,
-                            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
-                            basename="XGBBoosterReg")
+                            model, model_onnx, basename="XGBBoosterReg")
 
     def test_xgboost_10(self):
         this = os.path.abspath(os.path.dirname(__file__))
@@ -279,9 +225,9 @@ def test_xgboost_10(self):
         }
 
         train_df = pandas.read_csv(train)
-        X_train, y_train = train_df.drop('label', axis=1).values, train_df['label'].values
+        X_train, y_train = train_df.drop('label', axis=1).values, train_df['label'].fillna(0).values
         test_df = pandas.read_csv(test)
-        X_test, y_test = test_df.drop('label', axis=1).values, test_df['label'].values
+        X_test, y_test = test_df.drop('label', axis=1).values, test_df['label'].fillna(0).values
 
         regressor = XGBRegressor(verbose=0, objective='reg:squarederror', **param_distributions)
         regressor.fit(X_train, y_train)
@@ -292,9 +238,7 @@ def test_xgboost_10(self):
             target_opset=TARGET_OPSET)
 
         dump_data_and_model(
-            X_test.astype(np.float32),
-            regressor, model_onnx,
-            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
+            X_test.astype(np.float32), regressor, model_onnx,
             basename="XGBBoosterRegBug")
 
     def test_xgboost_classifier_i5450(self):
@@ -315,9 +259,7 @@ def test_xgboost_classifier_i5450(self):
         bst = clr.get_booster()
         bst.dump_model('dump.raw.txt')
         dump_data_and_model(
-            X_test.astype(np.float32) + 1e-5,
-            clr, onx,
-            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
+            X_test.astype(np.float32) + 1e-5, clr, onx,
             basename="XGBClassifierIris")
 
     def test_xgboost_example_mnist(self):
@@ -342,7 +284,6 @@ def test_xgboost_example_mnist(self):
 
         dump_data_and_model(
             X_test.astype(np.float32), clf, onnx_model,
-            allow_failure="StrictVersion(onnx.__version__) < StrictVersion('1.3.0')",
             basename="XGBoostExample")
 
     def test_xgb_empty_tree(self):