Developer Guide and Reference

  • 2021.3
  • 06/28/2021
  • Public Content
Contents

df_reg_dense_batch.cpp

/******************************************************************************* * 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_label_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_label_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_label_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_label_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_labels() << std::endl; std::cout << "Ground truth:\n" << y_test << std::endl; return 0; }

Product and Performance Information

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