/*******************************************************************************
* Copyright 2023 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.
*******************************************************************************/
#ifndef ONEDAL_DATA_PARALLEL
#define ONEDAL_DATA_PARALLEL
#endif
#include "oneapi/dal/algo/linear_regression.hpp"
#include "oneapi/dal/io/csv.hpp"
#include "oneapi/dal/exceptions.hpp"
#include "example_util/utils.hpp"
namespace dal = oneapi::dal;
namespace result_options = dal::linear_regression::result_options;
void run(sycl::queue& q) {
const auto train_data_file_name = get_data_path("linear_regression_train_data.csv");
const auto train_response_file_name = get_data_path("linear_regression_train_responses.csv");
const auto test_data_file_name = get_data_path("linear_regression_test_data.csv");
const auto test_response_file_name = get_data_path("linear_regression_test_responses.csv");
const std::int64_t nBlocks = 10;
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 lr_desc = dal::linear_regression::descriptor<>().set_result_options(
result_options::coefficients | result_options::intercept);
dal::linear_regression::partial_train_result<> partial_result;
auto input_table_x = split_table_by_rows<double>(x_train, nBlocks);
auto input_table_y = split_table_by_rows<double>(y_train, nBlocks);
for (std::int64_t i = 0; i < nBlocks; i++) {
partial_result =
dal::partial_train(q, lr_desc, partial_result, input_table_x[i], input_table_y[i]);
}
auto result = dal::finalize_train(q, lr_desc, partial_result);
std::cout << "Coefficients:\n" << result.get_coefficients() << std::endl;
std::cout << "Intercept:\n" << result.get_intercept() << std::endl;
const auto lr_model = result.get_model();
const auto test_result = dal::infer(q, lr_desc, x_test, lr_model);
std::cout << "Test results:\n" << test_result.get_responses() << std::endl;
std::cout << "True responses:\n" << y_test << std::endl;
}
int main(int argc, char const* argv[]) {
for (auto d : list_devices()) {
std::cout << "Running on " << d.get_platform().get_info<sycl::info::platform::name>()
<< ", " << d.get_info<sycl::info::device::name>() << "\n"
<< std::endl;
auto q = sycl::queue{ d };
run(q);
}
return 0;
}
