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',
n_estimators=10, verbose=0)
dump_single_regression(catboost_model)
catboost_model.fit(X.astype(numpy.float32), y)
catboost_onnx = convert_catboost(catboost_model, name='CatBoostRegression',
doc_string='test regression')
self.assertTrue(catboost_onnx is not None)
dump_data_and_model(X.astype(numpy.float32), catboost_model, catboost_onnx, basename="CatBoostReg-Dec4")
def test_xgb_regressor(self):
iris = load_iris()
X = iris.data[:, :2]
y = iris.target
xgb = XGBRegressor()
xgb.fit(X, y)
conv_model = convert_xgboost(xgb,
initial_types=[
('input',
FloatTensorType(shape=[1, 'None']))
])
self.assertTrue(conv_model is not None)
dump_single_regression(xgb, suffix="-Dec4")
def test_lightgbm_regressor2(self):
model = LGBMRegressor(n_estimators=2, max_depth=1, min_child_samples=1)
dump_single_regression(model, suffix="2")
def test_lightgbm_regressor(self):
model = LGBMRegressor(n_estimators=3, min_child_samples=1)
dump_single_regression(model)
def test_gradient_boosting_regressor(self):
model = GradientBoostingRegressor(n_estimators=3)
dump_single_regression(model)
def test_extra_trees_regressor(self):
model = ExtraTreesRegressor(n_estimators=3)
dump_single_regression(model)
dump_multiple_regression(model)
def test_random_forest_regressor(self):
model = RandomForestRegressor(n_estimators=3)
dump_single_regression(model)
dump_multiple_regression(model)
def test_decision_tree_regressor(self):
model = DecisionTreeRegressor()
dump_single_regression(model)
dump_multiple_regression(model)
