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)