wehub-resource-sync
101 lines
3.8 KiB
C++
101 lines
3.8 KiB
C++
/*!
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* Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.
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* Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.
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* Licensed under the MIT License. See LICENSE file in the project root for license information.
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*/
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#include <LightGBM/objective_function.h>
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#include <LightGBM/prediction_early_stop.h>
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#include <LightGBM/utils/openmp_wrapper.h>
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#include <unordered_map>
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#include "gbdt.h"
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namespace LightGBM {
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void GBDT::PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const {
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int early_stop_round_counter = 0;
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// set zero
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std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);
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const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;
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for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {
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// predict all the trees for one iteration
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for (int k = 0; k < num_tree_per_iteration_; ++k) {
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output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features);
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}
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// check early stopping
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++early_stop_round_counter;
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if (early_stop->round_period == early_stop_round_counter) {
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if (early_stop->callback_function(output, num_tree_per_iteration_)) {
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return;
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}
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early_stop_round_counter = 0;
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}
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}
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}
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void GBDT::PredictRawByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {
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int early_stop_round_counter = 0;
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// set zero
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std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);
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const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;
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for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {
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// predict all the trees for one iteration
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for (int k = 0; k < num_tree_per_iteration_; ++k) {
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output[k] += models_[i * num_tree_per_iteration_ + k]->PredictByMap(features);
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}
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// check early stopping
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++early_stop_round_counter;
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if (early_stop->round_period == early_stop_round_counter) {
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if (early_stop->callback_function(output, num_tree_per_iteration_)) {
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return;
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}
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early_stop_round_counter = 0;
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}
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}
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}
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void GBDT::Predict(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const {
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PredictRaw(features, output, early_stop);
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if (average_output_) {
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for (int k = 0; k < num_tree_per_iteration_; ++k) {
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output[k] /= num_iteration_for_pred_;
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}
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}
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if (objective_function_ != nullptr) {
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objective_function_->ConvertOutput(output, output);
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}
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}
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void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double* output, const PredictionEarlyStopInstance* early_stop) const {
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PredictRawByMap(features, output, early_stop);
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if (average_output_) {
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for (int k = 0; k < num_tree_per_iteration_; ++k) {
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output[k] /= num_iteration_for_pred_;
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}
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}
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if (objective_function_ != nullptr) {
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objective_function_->ConvertOutput(output, output);
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}
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}
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void GBDT::PredictLeafIndex(const double* features, double* output) const {
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int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_;
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int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_;
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const auto* models_ptr = models_.data() + start_tree;
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for (int i = 0; i < num_trees; ++i) {
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output[i] = models_ptr[i]->PredictLeafIndex(features);
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}
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}
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void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features, double* output) const {
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int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_;
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int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_;
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const auto* models_ptr = models_.data() + start_tree;
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for (int i = 0; i < num_trees; ++i) {
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output[i] = models_ptr[i]->PredictLeafIndexByMap(features);
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}
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}
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} // namespace LightGBM
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