def test_lightgbm_classifier(self):
model = LGBMClassifier(n_estimators=3, min_child_samples=1)
dump_binary_classification(
model,
allow_failure=
"StrictVersion(onnx.__version__) < StrictVersion('1.3.0')")
dump_multiple_classification(
model,
allow_failure=
"StrictVersion(onnx.__version__) < StrictVersion('1.3.0')")
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',
n_estimators=100, verbose=0)
dump_multiple_classification(catboost_model)
catboost_model.fit(X.astype(numpy.float32), y)
catboost_onnx = convert_catboost(catboost_model, name='CatBoostMultiClassification',
doc_string='test multiclass classification')
self.assertTrue(catboost_onnx is not None)
dump_data_and_model(X.astype(numpy.float32), catboost_model, catboost_onnx, basename="CatBoostMultiClass")
def test_xgb_classifier_multi(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
xgb = XGBClassifier()
xgb.fit(X, y)
conv_model = convert_xgboost(xgb,
initial_types=[
('input',
FloatTensorType(shape=[1, 'None']))
])
self.assertTrue(conv_model is not None)
dump_multiple_classification(
xgb,
allow_failure=
"StrictVersion(onnx.__version__) < StrictVersion('1.3.0')")
def test_extra_trees_classifier(self):
model = ExtraTreesClassifier(n_estimators=3)
dump_one_class_classification(model)
dump_binary_classification(model)
dump_multiple_classification(model)
def test_random_forest_classifier(self):
model = RandomForestClassifier(n_estimators=3)
dump_one_class_classification(model)
dump_binary_classification(model)
dump_multiple_classification(model)
def test_decision_tree_classifier(self):
model = DecisionTreeClassifier()
dump_one_class_classification(model)
dump_binary_classification(model)
dump_multiple_classification(model)