/*******************************************************************************
* Copyright 2020-2021 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
#include "example_util/utils.hpp"
#include "oneapi/dal/algo/decision_forest.hpp"
#include "oneapi/dal/io/csv.hpp"
namespace dal = oneapi::dal;
namespace df = dal::decision_forest;
int main(int argc, char const *argv[]) {
const auto train_data_file_name = get_data_path("df_regression_train_data.csv");
const auto train_response_file_name = get_data_path("df_regression_train_label.csv");
const auto test_data_file_name = get_data_path("df_regression_test_data.csv");
const auto test_response_file_name = get_data_path("df_regression_test_label.csv");
const auto x_train = dal::read<dal::table>(dal::csv::data_source{ train_data_file_name });
const auto y_train = dal::read<dal::table>(dal::csv::data_source{ train_response_file_name });
const auto x_test = dal::read<dal::table>(dal::csv::data_source{ test_data_file_name });
const auto y_test = dal::read<dal::table>(dal::csv::data_source{ test_response_file_name });
const auto df_desc =
df::descriptor<float, df::method::dense, df::task::regression>{}
.set_tree_count(100)
.set_features_per_node(0)
.set_min_observations_in_leaf_node(1)
.set_error_metric_mode(df::error_metric_mode::out_of_bag_error |
df::error_metric_mode::out_of_bag_error_per_observation)
.set_variable_importance_mode(df::variable_importance_mode::mda_raw);
const auto result_train = dal::train(df_desc, x_train, y_train);
std::cout << "Variable importance results:\n" << result_train.get_var_importance() << std::endl;
std::cout << "OOB error: " << result_train.get_oob_err() << std::endl;
std::cout << "OOB error per observation:\n"
<< result_train.get_oob_err_per_observation() << std::endl;
const auto result_infer = dal::infer(df_desc, result_train.get_model(), x_test);
std::cout << "Prediction results:\n" << result_infer.get_responses() << std::endl;
std::cout << "Ground truth:\n" << y_test << std::endl;
return 0;
}
