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_xgb_classifier(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 0
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_binary_classification(xgb, verbose=True)
def test_xgb_classifier_reglog(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
y[y == 2] = 0
xgb = XGBClassifier(objective='reg:logistic')
xgb.fit(X, y)
conv_model = convert_xgboost(xgb,
initial_types=[
('input',
FloatTensorType(shape=[1, 2]))
])
self.assertTrue(conv_model is not None)
dump_binary_classification(xgb, suffix="RegLog")
def test_gradient_boosting_classifier(self):
model = GradientBoostingClassifier(n_estimators=3)
dump_binary_classification(model)
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)