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c_api.cpp
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c_api.cpp
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/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#include <LightGBM/c_api.h>
#include <LightGBM/boosting.h>
#include <LightGBM/config.h>
#include <LightGBM/dataset.h>
#include <LightGBM/dataset_loader.h>
#include <LightGBM/metric.h>
#include <LightGBM/network.h>
#include <LightGBM/objective_function.h>
#include <LightGBM/prediction_early_stop.h>
#include <LightGBM/utils/byte_buffer.h>
#include <LightGBM/utils/common.h>
#include <LightGBM/utils/log.h>
#include <LightGBM/utils/openmp_wrapper.h>
#include <LightGBM/utils/random.h>
#include <LightGBM/utils/threading.h>
#include <string>
#include <cstdio>
#include <functional>
#include <memory>
#include <mutex>
#include <stdexcept>
#include <vector>
#include "application/predictor.hpp"
#include <LightGBM/utils/yamc/alternate_shared_mutex.hpp>
#include <LightGBM/utils/yamc/yamc_shared_lock.hpp>
namespace LightGBM {
inline int LGBM_APIHandleException(const std::exception& ex) {
LGBM_SetLastError(ex.what());
return -1;
}
inline int LGBM_APIHandleException(const std::string& ex) {
LGBM_SetLastError(ex.c_str());
return -1;
}
#define API_BEGIN() try {
#define API_END() } \
catch(std::exception& ex) { return LGBM_APIHandleException(ex); } \
catch(std::string& ex) { return LGBM_APIHandleException(ex); } \
catch(...) { return LGBM_APIHandleException("unknown exception"); } \
return 0;
#define UNIQUE_LOCK(mtx) \
std::unique_lock<yamc::alternate::shared_mutex> lock(mtx);
#define SHARED_LOCK(mtx) \
yamc::shared_lock<yamc::alternate::shared_mutex> lock(&mtx);
const int PREDICTOR_TYPES = 4;
// Single row predictor to abstract away caching logic
class SingleRowPredictorInner {
public:
PredictFunction predict_function;
int64_t num_pred_in_one_row;
SingleRowPredictorInner(int predict_type, Boosting* boosting, const Config& config, int start_iter, int num_iter) {
bool is_predict_leaf = false;
bool is_raw_score = false;
bool predict_contrib = false;
if (predict_type == C_API_PREDICT_LEAF_INDEX) {
is_predict_leaf = true;
} else if (predict_type == C_API_PREDICT_RAW_SCORE) {
is_raw_score = true;
} else if (predict_type == C_API_PREDICT_CONTRIB) {
predict_contrib = true;
}
early_stop_ = config.pred_early_stop;
early_stop_freq_ = config.pred_early_stop_freq;
early_stop_margin_ = config.pred_early_stop_margin;
iter_ = num_iter;
predictor_.reset(new Predictor(boosting, start_iter, iter_, is_raw_score, is_predict_leaf, predict_contrib,
early_stop_, early_stop_freq_, early_stop_margin_));
num_pred_in_one_row = boosting->NumPredictOneRow(start_iter, iter_, is_predict_leaf, predict_contrib);
predict_function = predictor_->GetPredictFunction();
num_total_model_ = boosting->NumberOfTotalModel();
}
~SingleRowPredictorInner() {}
bool IsPredictorEqual(const Config& config, int iter, Boosting* boosting) {
return early_stop_ == config.pred_early_stop &&
early_stop_freq_ == config.pred_early_stop_freq &&
early_stop_margin_ == config.pred_early_stop_margin &&
iter_ == iter &&
num_total_model_ == boosting->NumberOfTotalModel();
}
private:
std::unique_ptr<Predictor> predictor_;
bool early_stop_;
int early_stop_freq_;
double early_stop_margin_;
int iter_;
int num_total_model_;
};
/*!
* \brief Object to store resources meant for single-row Fast Predict methods.
*
* For legacy reasons this is called `FastConfig` in the public C API.
*
* Meant to be used by the *Fast* predict methods only.
* It stores the configuration and prediction resources for reuse across predictions.
*/
struct SingleRowPredictor {
public:
SingleRowPredictor(yamc::alternate::shared_mutex *booster_mutex,
const char *parameters,
const int data_type,
const int32_t num_cols,
int predict_type,
Boosting *boosting,
int start_iter,
int num_iter) : config(Config::Str2Map(parameters)), data_type(data_type), num_cols(num_cols), single_row_predictor_inner(predict_type, boosting, config, start_iter, num_iter), booster_mutex(booster_mutex) {
if (!config.predict_disable_shape_check && num_cols != boosting->MaxFeatureIdx() + 1) {
Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n"\
"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", num_cols, boosting->MaxFeatureIdx() + 1);
}
}
void Predict(std::function<std::vector<std::pair<int, double>>(int row_idx)> get_row_fun,
double* out_result, int64_t* out_len) const {
UNIQUE_LOCK(single_row_predictor_mutex)
yamc::shared_lock<yamc::alternate::shared_mutex> booster_shared_lock(booster_mutex);
auto one_row = get_row_fun(0);
single_row_predictor_inner.predict_function(one_row, out_result);
*out_len = single_row_predictor_inner.num_pred_in_one_row;
}
public:
Config config;
const int data_type;
const int32_t num_cols;
private:
SingleRowPredictorInner single_row_predictor_inner;
// Prevent the booster from being modified while we have a predictor relying on it during prediction
yamc::alternate::shared_mutex *booster_mutex;
// If several threads try to predict at the same time using the same SingleRowPredictor
// we want them to still provide correct values, so the mutex is necessary due to the shared
// resources in the predictor.
// However the recommended approach is to instantiate one SingleRowPredictor per thread,
// to avoid contention here.
mutable yamc::alternate::shared_mutex single_row_predictor_mutex;
};
class Booster {
public:
explicit Booster(const char* filename) {
boosting_.reset(Boosting::CreateBoosting("gbdt", filename));
}
Booster(const Dataset* train_data,
const char* parameters) {
auto param = Config::Str2Map(parameters);
config_.Set(param);
OMP_SET_NUM_THREADS(config_.num_threads);
// create boosting
if (config_.input_model.size() > 0) {
Log::Warning("Continued train from model is not supported for c_api,\n"
"please use continued train with input score");
}
boosting_.reset(Boosting::CreateBoosting(config_.boosting, nullptr));
train_data_ = train_data;
CreateObjectiveAndMetrics();
// initialize the boosting
if (config_.tree_learner == std::string("feature")) {
Log::Fatal("Do not support feature parallel in c api");
}
if (Network::num_machines() == 1 && config_.tree_learner != std::string("serial")) {
Log::Warning("Only find one worker, will switch to serial tree learner");
config_.tree_learner = "serial";
}
boosting_->Init(&config_, train_data_, objective_fun_.get(),
Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
}
void MergeFrom(const Booster* other) {
UNIQUE_LOCK(mutex_)
boosting_->MergeFrom(other->boosting_.get());
}
~Booster() {
}
void CreateObjectiveAndMetrics() {
// create objective function
objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,
config_));
if (objective_fun_ == nullptr) {
Log::Info("Using self-defined objective function");
}
// initialize the objective function
if (objective_fun_ != nullptr) {
objective_fun_->Init(train_data_->metadata(), train_data_->num_data());
}
// create training metric
train_metric_.clear();
for (auto metric_type : config_.metric) {
auto metric = std::unique_ptr<Metric>(
Metric::CreateMetric(metric_type, config_));
if (metric == nullptr) { continue; }
metric->Init(train_data_->metadata(), train_data_->num_data());
train_metric_.push_back(std::move(metric));
}
train_metric_.shrink_to_fit();
}
void ResetTrainingData(const Dataset* train_data) {
if (train_data != train_data_) {
UNIQUE_LOCK(mutex_)
train_data_ = train_data;
CreateObjectiveAndMetrics();
// reset the boosting
boosting_->ResetTrainingData(train_data_,
objective_fun_.get(), Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
}
}
static void CheckDatasetResetConfig(
const Config& old_config,
const std::unordered_map<std::string, std::string>& new_param) {
Config new_config;
new_config.Set(new_param);
if (new_param.count("data_random_seed") &&
new_config.data_random_seed != old_config.data_random_seed) {
Log::Fatal("Cannot change data_random_seed after constructed Dataset handle.");
}
if (new_param.count("max_bin") &&
new_config.max_bin != old_config.max_bin) {
Log::Fatal("Cannot change max_bin after constructed Dataset handle.");
}
if (new_param.count("max_bin_by_feature") &&
new_config.max_bin_by_feature != old_config.max_bin_by_feature) {
Log::Fatal(
"Cannot change max_bin_by_feature after constructed Dataset handle.");
}
if (new_param.count("bin_construct_sample_cnt") &&
new_config.bin_construct_sample_cnt !=
old_config.bin_construct_sample_cnt) {
Log::Fatal(
"Cannot change bin_construct_sample_cnt after constructed Dataset "
"handle.");
}
if (new_param.count("min_data_in_bin") &&
new_config.min_data_in_bin != old_config.min_data_in_bin) {
Log::Fatal(
"Cannot change min_data_in_bin after constructed Dataset handle.");
}
if (new_param.count("use_missing") &&
new_config.use_missing != old_config.use_missing) {
Log::Fatal("Cannot change use_missing after constructed Dataset handle.");
}
if (new_param.count("zero_as_missing") &&
new_config.zero_as_missing != old_config.zero_as_missing) {
Log::Fatal(
"Cannot change zero_as_missing after constructed Dataset handle.");
}
if (new_param.count("categorical_feature") &&
new_config.categorical_feature != old_config.categorical_feature) {
Log::Fatal(
"Cannot change categorical_feature after constructed Dataset "
"handle.");
}
if (new_param.count("feature_pre_filter") &&
new_config.feature_pre_filter != old_config.feature_pre_filter) {
Log::Fatal(
"Cannot change feature_pre_filter after constructed Dataset handle.");
}
if (new_param.count("is_enable_sparse") &&
new_config.is_enable_sparse != old_config.is_enable_sparse) {
Log::Fatal(
"Cannot change is_enable_sparse after constructed Dataset handle.");
}
if (new_param.count("pre_partition") &&
new_config.pre_partition != old_config.pre_partition) {
Log::Fatal(
"Cannot change pre_partition after constructed Dataset handle.");
}
if (new_param.count("enable_bundle") &&
new_config.enable_bundle != old_config.enable_bundle) {
Log::Fatal(
"Cannot change enable_bundle after constructed Dataset handle.");
}
if (new_param.count("header") && new_config.header != old_config.header) {
Log::Fatal("Cannot change header after constructed Dataset handle.");
}
if (new_param.count("two_round") &&
new_config.two_round != old_config.two_round) {
Log::Fatal("Cannot change two_round after constructed Dataset handle.");
}
if (new_param.count("label_column") &&
new_config.label_column != old_config.label_column) {
Log::Fatal(
"Cannot change label_column after constructed Dataset handle.");
}
if (new_param.count("weight_column") &&
new_config.weight_column != old_config.weight_column) {
Log::Fatal(
"Cannot change weight_column after constructed Dataset handle.");
}
if (new_param.count("group_column") &&
new_config.group_column != old_config.group_column) {
Log::Fatal(
"Cannot change group_column after constructed Dataset handle.");
}
if (new_param.count("ignore_column") &&
new_config.ignore_column != old_config.ignore_column) {
Log::Fatal(
"Cannot change ignore_column after constructed Dataset handle.");
}
if (new_param.count("forcedbins_filename")) {
Log::Fatal("Cannot change forced bins after constructed Dataset handle.");
}
if (new_param.count("min_data_in_leaf") &&
new_config.min_data_in_leaf < old_config.min_data_in_leaf &&
old_config.feature_pre_filter) {
Log::Fatal(
"Reducing `min_data_in_leaf` with `feature_pre_filter=true` may "
"cause unexpected behaviour "
"for features that were pre-filtered by the larger "
"`min_data_in_leaf`.\n"
"You need to set `feature_pre_filter=false` to dynamically change "
"the `min_data_in_leaf`.");
}
if (new_param.count("linear_tree") && new_config.linear_tree != old_config.linear_tree) {
Log::Fatal("Cannot change linear_tree after constructed Dataset handle.");
}
if (new_param.count("precise_float_parser") &&
new_config.precise_float_parser != old_config.precise_float_parser) {
Log::Fatal("Cannot change precise_float_parser after constructed Dataset handle.");
}
}
void ResetConfig(const char* parameters) {
UNIQUE_LOCK(mutex_)
auto param = Config::Str2Map(parameters);
Config new_config;
new_config.Set(param);
if (param.count("num_class") && new_config.num_class != config_.num_class) {
Log::Fatal("Cannot change num_class during training");
}
if (param.count("boosting") && new_config.boosting != config_.boosting) {
Log::Fatal("Cannot change boosting during training");
}
if (param.count("metric") && new_config.metric != config_.metric) {
Log::Fatal("Cannot change metric during training");
}
CheckDatasetResetConfig(config_, param);
config_.Set(param);
OMP_SET_NUM_THREADS(config_.num_threads);
if (param.count("objective")) {
// create objective function
objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,
config_));
if (objective_fun_ == nullptr) {
Log::Info("Using self-defined objective function");
}
// initialize the objective function
if (objective_fun_ != nullptr) {
objective_fun_->Init(train_data_->metadata(), train_data_->num_data());
}
boosting_->ResetTrainingData(train_data_,
objective_fun_.get(), Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
}
boosting_->ResetConfig(&config_);
}
void AddValidData(const Dataset* valid_data) {
UNIQUE_LOCK(mutex_)
valid_metrics_.emplace_back();
for (auto metric_type : config_.metric) {
auto metric = std::unique_ptr<Metric>(Metric::CreateMetric(metric_type, config_));
if (metric == nullptr) { continue; }
metric->Init(valid_data->metadata(), valid_data->num_data());
valid_metrics_.back().push_back(std::move(metric));
}
valid_metrics_.back().shrink_to_fit();
boosting_->AddValidDataset(valid_data,
Common::ConstPtrInVectorWrapper<Metric>(valid_metrics_.back()));
}
bool TrainOneIter() {
UNIQUE_LOCK(mutex_)
return boosting_->TrainOneIter(nullptr, nullptr);
}
void Refit(const int32_t* leaf_preds, int32_t nrow, int32_t ncol) {
UNIQUE_LOCK(mutex_)
std::vector<std::vector<int32_t>> v_leaf_preds(nrow, std::vector<int32_t>(ncol, 0));
for (int i = 0; i < nrow; ++i) {
for (int j = 0; j < ncol; ++j) {
v_leaf_preds[i][j] = leaf_preds[static_cast<size_t>(i) * static_cast<size_t>(ncol) + static_cast<size_t>(j)];
}
}
boosting_->RefitTree(v_leaf_preds);
}
bool TrainOneIter(const score_t* gradients, const score_t* hessians) {
UNIQUE_LOCK(mutex_)
return boosting_->TrainOneIter(gradients, hessians);
}
void RollbackOneIter() {
UNIQUE_LOCK(mutex_)
boosting_->RollbackOneIter();
}
void SetSingleRowPredictorInner(int start_iteration, int num_iteration, int predict_type, const Config& config) {
UNIQUE_LOCK(mutex_)
if (single_row_predictor_[predict_type].get() == nullptr ||
!single_row_predictor_[predict_type]->IsPredictorEqual(config, num_iteration, boosting_.get())) {
single_row_predictor_[predict_type].reset(new SingleRowPredictorInner(predict_type, boosting_.get(),
config, start_iteration, num_iteration));
}
}
std::unique_ptr<SingleRowPredictor> InitSingleRowPredictor(int predict_type, int start_iteration, int num_iteration, int data_type, int32_t num_cols, const char *parameters) {
// Workaround https://github.com/microsoft/LightGBM/issues/6142 by locking here
// This is only a workaround because if predictors are initialized differently it may still behave incorrectly,
// and because multiple racing Predictor initializations through LGBM_BoosterPredictForMat suffers from that same issue of Predictor init writing things in the booster.
// Once #6142 is fixed (predictor doesn't write in the Booster as should have been the case since 1c35c3b9ede9adab8ccc5fd7b4b2b6af188a79f0), this line can be removed.
UNIQUE_LOCK(mutex_)
return std::unique_ptr<SingleRowPredictor>(new SingleRowPredictor(
&mutex_, parameters, data_type, num_cols, predict_type, boosting_.get(), start_iteration, num_iteration));
}
void PredictSingleRow(int predict_type, int ncol,
std::function<std::vector<std::pair<int, double>>(int row_idx)> get_row_fun,
const Config& config,
double* out_result, int64_t* out_len) const {
if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) {
Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n"\
"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", ncol, boosting_->MaxFeatureIdx() + 1);
}
UNIQUE_LOCK(mutex_)
const auto& single_row_predictor = single_row_predictor_[predict_type];
auto one_row = get_row_fun(0);
auto pred_wrt_ptr = out_result;
single_row_predictor->predict_function(one_row, pred_wrt_ptr);
*out_len = single_row_predictor->num_pred_in_one_row;
}
Predictor CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const {
if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) {
Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n" \
"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", ncol, boosting_->MaxFeatureIdx() + 1);
}
bool is_predict_leaf = false;
bool is_raw_score = false;
bool predict_contrib = false;
if (predict_type == C_API_PREDICT_LEAF_INDEX) {
is_predict_leaf = true;
} else if (predict_type == C_API_PREDICT_RAW_SCORE) {
is_raw_score = true;
} else if (predict_type == C_API_PREDICT_CONTRIB) {
predict_contrib = true;
} else {
is_raw_score = false;
}
return Predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);
}
void Predict(int start_iteration, int num_iteration, int predict_type, int nrow, int ncol,
std::function<std::vector<std::pair<int, double>>(int row_idx)> get_row_fun,
const Config& config,
double* out_result, int64_t* out_len) const {
SHARED_LOCK(mutex_);
auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);
bool is_predict_leaf = false;
bool predict_contrib = false;
if (predict_type == C_API_PREDICT_LEAF_INDEX) {
is_predict_leaf = true;
} else if (predict_type == C_API_PREDICT_CONTRIB) {
predict_contrib = true;
}
int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(start_iteration, num_iteration, is_predict_leaf, predict_contrib);
auto pred_fun = predictor.GetPredictFunction();
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int i = 0; i < nrow; ++i) {
OMP_LOOP_EX_BEGIN();
auto one_row = get_row_fun(i);
auto pred_wrt_ptr = out_result + static_cast<size_t>(num_pred_in_one_row) * i;
pred_fun(one_row, pred_wrt_ptr);
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
*out_len = num_pred_in_one_row * nrow;
}
void PredictSparse(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,
std::function<std::vector<std::pair<int, double>>(int64_t row_idx)> get_row_fun,
const Config& config, int64_t* out_elements_size,
std::vector<std::vector<std::unordered_map<int, double>>>* agg_ptr,
int32_t** out_indices, void** out_data, int data_type,
bool* is_data_float32_ptr, int num_matrices) const {
auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);
auto pred_sparse_fun = predictor.GetPredictSparseFunction();
std::vector<std::vector<std::unordered_map<int, double>>>& agg = *agg_ptr;
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int64_t i = 0; i < nrow; ++i) {
OMP_LOOP_EX_BEGIN();
auto one_row = get_row_fun(i);
agg[i] = std::vector<std::unordered_map<int, double>>(num_matrices);
pred_sparse_fun(one_row, &agg[i]);
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
// calculate the nonzero data and indices size
int64_t elements_size = 0;
for (int64_t i = 0; i < static_cast<int64_t>(agg.size()); ++i) {
auto row_vector = agg[i];
for (int j = 0; j < static_cast<int>(row_vector.size()); ++j) {
elements_size += static_cast<int64_t>(row_vector[j].size());
}
}
*out_elements_size = elements_size;
*is_data_float32_ptr = false;
// allocate data and indices arrays
if (data_type == C_API_DTYPE_FLOAT32) {
*out_data = new float[elements_size];
*is_data_float32_ptr = true;
} else if (data_type == C_API_DTYPE_FLOAT64) {
*out_data = new double[elements_size];
} else {
Log::Fatal("Unknown data type in PredictSparse");
return;
}
*out_indices = new int32_t[elements_size];
}
void PredictSparseCSR(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,
std::function<std::vector<std::pair<int, double>>(int64_t row_idx)> get_row_fun,
const Config& config,
int64_t* out_len, void** out_indptr, int indptr_type,
int32_t** out_indices, void** out_data, int data_type) const {
SHARED_LOCK(mutex_);
// Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices)
int num_matrices = boosting_->NumModelPerIteration();
bool is_indptr_int32 = false;
bool is_data_float32 = false;
int64_t indptr_size = (nrow + 1) * num_matrices;
if (indptr_type == C_API_DTYPE_INT32) {
*out_indptr = new int32_t[indptr_size];
is_indptr_int32 = true;
} else if (indptr_type == C_API_DTYPE_INT64) {
*out_indptr = new int64_t[indptr_size];
} else {
Log::Fatal("Unknown indptr type in PredictSparseCSR");
return;
}
// aggregated per row feature contribution results
std::vector<std::vector<std::unordered_map<int, double>>> agg(nrow);
int64_t elements_size = 0;
PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg,
out_indices, out_data, data_type, &is_data_float32, num_matrices);
std::vector<int> row_sizes(num_matrices * nrow);
std::vector<int64_t> row_matrix_offsets(num_matrices * nrow);
std::vector<int64_t> matrix_offsets(num_matrices);
int64_t row_vector_cnt = 0;
for (int m = 0; m < num_matrices; ++m) {
for (int64_t i = 0; i < static_cast<int64_t>(agg.size()); ++i) {
auto row_vector = agg[i];
auto row_vector_size = row_vector[m].size();
// keep track of the row_vector sizes for parallelization
row_sizes[row_vector_cnt] = static_cast<int>(row_vector_size);
if (i == 0) {
row_matrix_offsets[row_vector_cnt] = 0;
} else {
row_matrix_offsets[row_vector_cnt] = static_cast<int64_t>(row_sizes[row_vector_cnt - 1] + row_matrix_offsets[row_vector_cnt - 1]);
}
row_vector_cnt++;
}
if (m == 0) {
matrix_offsets[m] = 0;
}
if (m + 1 < num_matrices) {
matrix_offsets[m + 1] = static_cast<int64_t>(matrix_offsets[m] + row_matrix_offsets[row_vector_cnt - 1] + row_sizes[row_vector_cnt - 1]);
}
}
// copy vector results to output for each row
int64_t indptr_index = 0;
for (int m = 0; m < num_matrices; ++m) {
if (is_indptr_int32) {
(reinterpret_cast<int32_t*>(*out_indptr))[indptr_index] = 0;
} else {
(reinterpret_cast<int64_t*>(*out_indptr))[indptr_index] = 0;
}
indptr_index++;
int64_t matrix_start_index = m * static_cast<int64_t>(agg.size());
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int64_t i = 0; i < static_cast<int64_t>(agg.size()); ++i) {
OMP_LOOP_EX_BEGIN();
auto row_vector = agg[i];
int64_t row_start_index = matrix_start_index + i;
int64_t element_index = row_matrix_offsets[row_start_index] + matrix_offsets[m];
int64_t indptr_loop_index = indptr_index + i;
for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {
(*out_indices)[element_index] = it->first;
if (is_data_float32) {
(reinterpret_cast<float*>(*out_data))[element_index] = static_cast<float>(it->second);
} else {
(reinterpret_cast<double*>(*out_data))[element_index] = it->second;
}
element_index++;
}
int64_t indptr_value = row_matrix_offsets[row_start_index] + row_sizes[row_start_index];
if (is_indptr_int32) {
(reinterpret_cast<int32_t*>(*out_indptr))[indptr_loop_index] = static_cast<int32_t>(indptr_value);
} else {
(reinterpret_cast<int64_t*>(*out_indptr))[indptr_loop_index] = indptr_value;
}
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
indptr_index += static_cast<int64_t>(agg.size());
}
out_len[0] = elements_size;
out_len[1] = indptr_size;
}
void PredictSparseCSC(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,
std::function<std::vector<std::pair<int, double>>(int64_t row_idx)> get_row_fun,
const Config& config,
int64_t* out_len, void** out_col_ptr, int col_ptr_type,
int32_t** out_indices, void** out_data, int data_type) const {
SHARED_LOCK(mutex_);
// Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices)
int num_matrices = boosting_->NumModelPerIteration();
auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);
auto pred_sparse_fun = predictor.GetPredictSparseFunction();
bool is_col_ptr_int32 = false;
bool is_data_float32 = false;
int num_output_cols = ncol + 1;
int col_ptr_size = (num_output_cols + 1) * num_matrices;
if (col_ptr_type == C_API_DTYPE_INT32) {
*out_col_ptr = new int32_t[col_ptr_size];
is_col_ptr_int32 = true;
} else if (col_ptr_type == C_API_DTYPE_INT64) {
*out_col_ptr = new int64_t[col_ptr_size];
} else {
Log::Fatal("Unknown col_ptr type in PredictSparseCSC");
return;
}
// aggregated per row feature contribution results
std::vector<std::vector<std::unordered_map<int, double>>> agg(nrow);
int64_t elements_size = 0;
PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg,
out_indices, out_data, data_type, &is_data_float32, num_matrices);
// calculate number of elements per column to construct
// the CSC matrix with random access
std::vector<std::vector<int64_t>> column_sizes(num_matrices);
for (int m = 0; m < num_matrices; ++m) {
column_sizes[m] = std::vector<int64_t>(num_output_cols, 0);
for (int64_t i = 0; i < static_cast<int64_t>(agg.size()); ++i) {
auto row_vector = agg[i];
for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {
column_sizes[m][it->first] += 1;
}
}
}
// keep track of column counts
std::vector<std::vector<int64_t>> column_counts(num_matrices);
// keep track of beginning index for each column
std::vector<std::vector<int64_t>> column_start_indices(num_matrices);
// keep track of beginning index for each matrix
std::vector<int64_t> matrix_start_indices(num_matrices, 0);
int col_ptr_index = 0;
for (int m = 0; m < num_matrices; ++m) {
int64_t col_ptr_value = 0;
column_start_indices[m] = std::vector<int64_t>(num_output_cols, 0);
column_counts[m] = std::vector<int64_t>(num_output_cols, 0);
if (is_col_ptr_int32) {
(reinterpret_cast<int32_t*>(*out_col_ptr))[col_ptr_index] = static_cast<int32_t>(col_ptr_value);
} else {
(reinterpret_cast<int64_t*>(*out_col_ptr))[col_ptr_index] = col_ptr_value;
}
col_ptr_index++;
for (int64_t i = 1; i < static_cast<int64_t>(column_sizes[m].size()); ++i) {
column_start_indices[m][i] = column_sizes[m][i - 1] + column_start_indices[m][i - 1];
if (is_col_ptr_int32) {
(reinterpret_cast<int32_t*>(*out_col_ptr))[col_ptr_index] = static_cast<int32_t>(column_start_indices[m][i]);
} else {
(reinterpret_cast<int64_t*>(*out_col_ptr))[col_ptr_index] = column_start_indices[m][i];
}
col_ptr_index++;
}
int64_t last_elem_index = static_cast<int64_t>(column_sizes[m].size()) - 1;
int64_t last_column_start_index = column_start_indices[m][last_elem_index];
int64_t last_column_size = column_sizes[m][last_elem_index];
if (is_col_ptr_int32) {
(reinterpret_cast<int32_t*>(*out_col_ptr))[col_ptr_index] = static_cast<int32_t>(last_column_start_index + last_column_size);
} else {
(reinterpret_cast<int64_t*>(*out_col_ptr))[col_ptr_index] = last_column_start_index + last_column_size;
}
if (m + 1 < num_matrices) {
matrix_start_indices[m + 1] = matrix_start_indices[m] + last_column_start_index + last_column_size;
}
col_ptr_index++;
}
// Note: we parallelize across matrices instead of rows because of the column_counts[m][col_idx] increment inside the loop
OMP_INIT_EX();
#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)
for (int m = 0; m < num_matrices; ++m) {
OMP_LOOP_EX_BEGIN();
for (int64_t i = 0; i < static_cast<int64_t>(agg.size()); ++i) {
auto row_vector = agg[i];
for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {
int64_t col_idx = it->first;
int64_t element_index = column_start_indices[m][col_idx] +
matrix_start_indices[m] +
column_counts[m][col_idx];
// store the row index
(*out_indices)[element_index] = static_cast<int32_t>(i);
// update column count
column_counts[m][col_idx]++;
if (is_data_float32) {
(reinterpret_cast<float*>(*out_data))[element_index] = static_cast<float>(it->second);
} else {
(reinterpret_cast<double*>(*out_data))[element_index] = it->second;
}
}
}
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
out_len[0] = elements_size;
out_len[1] = col_ptr_size;
}
void Predict(int start_iteration, int num_iteration, int predict_type, const char* data_filename,
int data_has_header, const Config& config,
const char* result_filename) const {
SHARED_LOCK(mutex_)
bool is_predict_leaf = false;
bool is_raw_score = false;
bool predict_contrib = false;
if (predict_type == C_API_PREDICT_LEAF_INDEX) {
is_predict_leaf = true;
} else if (predict_type == C_API_PREDICT_RAW_SCORE) {
is_raw_score = true;
} else if (predict_type == C_API_PREDICT_CONTRIB) {
predict_contrib = true;
} else {
is_raw_score = false;
}
Predictor predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);
bool bool_data_has_header = data_has_header > 0 ? true : false;
predictor.Predict(data_filename, result_filename, bool_data_has_header, config.predict_disable_shape_check,
config.precise_float_parser);
}
void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) const {
boosting_->GetPredictAt(data_idx, out_result, out_len);
}
void SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const {
boosting_->SaveModelToFile(start_iteration, num_iteration, feature_importance_type, filename);
}
void LoadModelFromString(const char* model_str) {
size_t len = std::strlen(model_str);
boosting_->LoadModelFromString(model_str, len);
}
std::string SaveModelToString(int start_iteration, int num_iteration,
int feature_importance_type) const {
return boosting_->SaveModelToString(start_iteration,
num_iteration, feature_importance_type);
}
std::string DumpModel(int start_iteration, int num_iteration,
int feature_importance_type) const {
return boosting_->DumpModel(start_iteration, num_iteration,
feature_importance_type);
}
std::vector<double> FeatureImportance(int num_iteration, int importance_type) const {
return boosting_->FeatureImportance(num_iteration, importance_type);
}
double UpperBoundValue() const {
SHARED_LOCK(mutex_)
return boosting_->GetUpperBoundValue();
}
double LowerBoundValue() const {
SHARED_LOCK(mutex_)
return boosting_->GetLowerBoundValue();
}
double GetLeafValue(int tree_idx, int leaf_idx) const {
SHARED_LOCK(mutex_)
return dynamic_cast<GBDTBase*>(boosting_.get())->GetLeafValue(tree_idx, leaf_idx);
}
void SetLeafValue(int tree_idx, int leaf_idx, double val) {
UNIQUE_LOCK(mutex_)
dynamic_cast<GBDTBase*>(boosting_.get())->SetLeafValue(tree_idx, leaf_idx, val);
}
void ShuffleModels(int start_iter, int end_iter) {
UNIQUE_LOCK(mutex_)
boosting_->ShuffleModels(start_iter, end_iter);
}
int GetEvalCounts() const {
SHARED_LOCK(mutex_)
int ret = 0;
for (const auto& metric : train_metric_) {
ret += static_cast<int>(metric->GetName().size());
}
return ret;
}
int GetEvalNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const {
SHARED_LOCK(mutex_)
*out_buffer_len = 0;
int idx = 0;
for (const auto& metric : train_metric_) {
for (const auto& name : metric->GetName()) {
if (idx < len) {
std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len));
out_strs[idx][buffer_len - 1] = '\0';
}
*out_buffer_len = std::max(name.size() + 1, *out_buffer_len);
++idx;
}
}
return idx;
}
int GetFeatureNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const {
SHARED_LOCK(mutex_)
*out_buffer_len = 0;
int idx = 0;
for (const auto& name : boosting_->FeatureNames()) {
if (idx < len) {
std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len));
out_strs[idx][buffer_len - 1] = '\0';
}
*out_buffer_len = std::max(name.size() + 1, *out_buffer_len);
++idx;
}
return idx;
}
const Boosting* GetBoosting() const { return boosting_.get(); }
private:
const Dataset* train_data_;
std::unique_ptr<Boosting> boosting_;
std::unique_ptr<SingleRowPredictorInner> single_row_predictor_[PREDICTOR_TYPES];
/*! \brief All configs */
Config config_;
/*! \brief Metric for training data */
std::vector<std::unique_ptr<Metric>> train_metric_;
/*! \brief Metrics for validation data */
std::vector<std::vector<std::unique_ptr<Metric>>> valid_metrics_;
/*! \brief Training objective function */
std::unique_ptr<ObjectiveFunction> objective_fun_;
/*! \brief mutex for threading safe call */
mutable yamc::alternate::shared_mutex mutex_;
};
} // namespace LightGBM
// explicitly declare symbols from LightGBM namespace
using LightGBM::AllgatherFunction;
using LightGBM::Booster;
using LightGBM::Common::CheckElementsIntervalClosed;
using LightGBM::Common::RemoveQuotationSymbol;
using LightGBM::Common::Vector2Ptr;
using LightGBM::Common::VectorSize;
using LightGBM::Config;
using LightGBM::data_size_t;
using LightGBM::Dataset;
using LightGBM::DatasetLoader;
using LightGBM::kZeroThreshold;
using LightGBM::LGBM_APIHandleException;
using LightGBM::Log;
using LightGBM::Network;
using LightGBM::Random;
using LightGBM::ReduceScatterFunction;
using LightGBM::SingleRowPredictor;
// some help functions used to convert data
std::function<std::vector<double>(int row_idx)>
RowFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major);
std::function<std::vector<std::pair<int, double>>(int row_idx)>
RowPairFunctionFromDenseMatric(const void* data, int num_row, int num_col, int data_type, int is_row_major);
std::function<std::vector<std::pair<int, double>>(int row_idx)>
RowPairFunctionFromDenseRows(const void** data, int num_col, int data_type);
template<typename T>
std::function<std::vector<std::pair<int, double>>(T idx)>
RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
const void* data, int data_type, int64_t nindptr, int64_t nelem);
// Row iterator of on column for CSC matrix
class CSC_RowIterator {
public:
CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices,
const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx);
~CSC_RowIterator() {}
// return value at idx, only can access by ascent order
double Get(int idx);
// return next non-zero pair, if index < 0, means no more data
std::pair<int, double> NextNonZero();
private:
int nonzero_idx_ = 0;
int cur_idx_ = -1;
double cur_val_ = 0.0f;
bool is_end_ = false;
std::function<std::pair<int, double>(int idx)> iter_fun_;
};
// start of c_api functions
const char* LGBM_GetLastError() {
return LastErrorMsg();
}
int LGBM_DumpParamAliases(int64_t buffer_len,
int64_t* out_len,
char* out_str) {
API_BEGIN();
std::string aliases = Config::DumpAliases();
*out_len = static_cast<int64_t>(aliases.size()) + 1;
if (*out_len <= buffer_len) {
std::memcpy(out_str, aliases.c_str(), *out_len);
}
API_END();
}
int LGBM_RegisterLogCallback(void (*callback)(const char*)) {
API_BEGIN();
Log::ResetCallBack(callback);
API_END();
}
static inline int SampleCount(int32_t total_nrow, const Config& config) {
return static_cast<int>(total_nrow < config.bin_construct_sample_cnt ? total_nrow : config.bin_construct_sample_cnt);
}
static inline std::vector<int32_t> CreateSampleIndices(int32_t total_nrow, const Config& config) {
Random rand(config.data_random_seed);
int sample_cnt = SampleCount(total_nrow, config);
return rand.Sample(total_nrow, sample_cnt);
}
int LGBM_GetSampleCount(int32_t num_total_row,
const char* parameters,
int* out) {
API_BEGIN();
if (out == nullptr) {
Log::Fatal("LGBM_GetSampleCount output is nullptr");
}
auto param = Config::Str2Map(parameters);
Config config;
config.Set(param);
*out = SampleCount(num_total_row, config);
API_END();
}