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Commit 292f972e authored by Guolin Ke's avatar Guolin Ke
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decouple num_class in Dataset class

parent 45e0da2c
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Showing with 103 additions and 111 deletions
include/LightGBM/dataset.h
View file @ 292f972e
......@@ -43,9 +43,8 @@ public:
* \brief Initialization will load qurey level informations, since it is need for sampling data
* \param data_filename Filename of data
* \param init_score_filename Filename of initial score
* \param num_class Number of classes
*/
void Init(const char* data_filename, const int num_class);
void Init(const char* data_filename);
/*!
* \brief init as subset
* \param metadata Filename of data
......@@ -64,11 +63,10 @@ public:
/*!
* \brief Initial work, will allocate space for label, weight(if exists) and query(if exists)
* \param num_data Number of training data
* \param num_class Number of classes
* \param weight_idx Index of weight column, < 0 means doesn't exists
* \param query_idx Index of query id column, < 0 means doesn't exists
*/
void Init(data_size_t num_data, int num_class, int weight_idx, int query_idx);
void Init(data_size_t num_data, int weight_idx, int query_idx);
/*!
* \brief Partition label by used indices
......@@ -203,6 +201,11 @@ public:
}
}
/*!
* \brief Get size of initial scores
*/
inline data_size_t num_init_score() const { return num_init_score_; }
/*! \brief Disable copy */
Metadata& operator=(const Metadata&) = delete;
/*! \brief Disable copy */
......@@ -221,8 +224,6 @@ private:
const char* data_filename_;
/*! \brief Number of data */
data_size_t num_data_;
/*! \brief Number of classes */
int num_class_;
/*! \brief Number of weights, used to check correct weight file */
data_size_t num_weights_;
/*! \brief Label data */
......@@ -281,7 +282,7 @@ public:
Dataset();
Dataset(data_size_t num_data, int num_class);
Dataset(data_size_t num_data);
/*! \brief Destructor */
~Dataset();
......@@ -293,9 +294,6 @@ public:
if (num_total_features_ != other.num_total_features_) {
return false;
}
if (num_class_ != other.num_class_) {
return false;
}
if (label_idx_ != other.label_idx_) {
return false;
}
......@@ -408,8 +406,6 @@ private:
int num_total_features_;
/*! \brief Number of total data*/
data_size_t num_data_;
/*! \brief Number of classes*/
int num_class_;
/*! \brief Store some label level data*/
Metadata metadata_;
/*! \brief index of label column */
......
include/LightGBM/dataset_loader.h
View file @ 292f972e
......@@ -8,7 +8,7 @@ namespace LightGBM {
class DatasetLoader {
public:
DatasetLoader(const IOConfig& io_config, const PredictFunction& predict_fun, const char* filename);
DatasetLoader(const IOConfig& io_config, const PredictFunction& predict_fun, int num_class, const char* filename);
~DatasetLoader();
......@@ -57,6 +57,8 @@ private:
Random random_;
/*! \brief prediction function for initial model */
const PredictFunction& predict_fun_;
/*! \brief number of classes */
int num_class_;
/*! \brief index of label column */
int label_idx_;
/*! \brief index of weight column */
......
src/application/application.cpp
View file @ 292f972e
......@@ -120,7 +120,8 @@ void Application::LoadData() {
GlobalSyncUpByMin<int>(config_.io_config.data_random_seed);
}
DatasetLoader dataset_loader(config_.io_config, predict_fun, config_.io_config.data_filename.c_str());
DatasetLoader dataset_loader(config_.io_config, predict_fun,
boosting_->NumberOfClasses(), config_.io_config.data_filename.c_str());
// load Training data
if (config_.is_parallel_find_bin) {
// load data for parallel training
......
src/boosting/score_updater.hpp
View file @ 292f972e
......@@ -27,6 +27,10 @@ public:
const float* init_score = data->metadata().init_score();
// if exists initial score, will start from it
if (init_score != nullptr) {
if ((data->metadata().num_init_score() % num_data_) != 0
|| (data->metadata().num_init_score() / num_data_) != num_class) {
Log::Fatal("number of class for initial score error");
}
for (size_t i = 0; i < total_size; ++i) {
score_[i] = init_score[i];
}
......
src/c_api.cpp
View file @ 292f972e
......@@ -245,7 +245,7 @@ DllExport int LGBM_DatasetCreateFromFile(const char* filename,
auto param = ConfigBase::Str2Map(parameters);
IOConfig io_config;
io_config.Set(param);
DatasetLoader loader(io_config, nullptr, filename);
DatasetLoader loader(io_config, nullptr, 1, filename);
if (reference == nullptr) {
*out = loader.LoadFromFile(filename);
} else {
......@@ -284,10 +284,10 @@ DllExport int LGBM_DatasetCreateFromMat(const void* data,
}
}
}
DatasetLoader loader(io_config, nullptr, nullptr);
DatasetLoader loader(io_config, nullptr, 1, nullptr);
ret.reset(loader.CostructFromSampleData(sample_values, sample_cnt, nrow));
} else {
ret.reset(new Dataset(nrow, io_config.num_class));
ret.reset(new Dataset(nrow));
ret->CopyFeatureMapperFrom(
reinterpret_cast<const Dataset*>(reference),
io_config.is_enable_sparse);
......@@ -346,10 +346,10 @@ DllExport int LGBM_DatasetCreateFromCSR(const void* indptr,
}
}
CHECK(num_col >= static_cast<int>(sample_values.size()));
DatasetLoader loader(io_config, nullptr, nullptr);
DatasetLoader loader(io_config, nullptr, 1, nullptr);
ret.reset(loader.CostructFromSampleData(sample_values, sample_cnt, nrow));
} else {
ret.reset(new Dataset(nrow, io_config.num_class));
ret.reset(new Dataset(nrow));
ret->CopyFeatureMapperFrom(
reinterpret_cast<const Dataset*>(reference),
io_config.is_enable_sparse);
......@@ -396,10 +396,10 @@ DllExport int LGBM_DatasetCreateFromCSC(const void* col_ptr,
auto cur_col = get_col_fun(i);
sample_values[i] = SampleFromOneColumn(cur_col, sample_indices);
}
DatasetLoader loader(io_config, nullptr, nullptr);
DatasetLoader loader(io_config, nullptr, 1, nullptr);
ret.reset(loader.CostructFromSampleData(sample_values, sample_cnt, nrow));
} else {
ret.reset(new Dataset(nrow, io_config.num_class));
ret.reset(new Dataset(nrow));
ret->CopyFeatureMapperFrom(
reinterpret_cast<const Dataset*>(reference),
io_config.is_enable_sparse);
......
src/io/config.cpp
View file @ 292f972e
......@@ -24,7 +24,7 @@ std::unordered_map<std::string, std::string> ConfigBase::Str2Map(const char* par
continue;
}
params[key] = value;
} else if(Common::Trim(arg).size() > 0){
} else if (Common::Trim(arg).size() > 0) {
Log::Warning("Unknown parameter %s", arg.c_str());
}
}
......@@ -62,14 +62,11 @@ void OverallConfig::Set(const std::unordered_map<std::string, std::string>& para
if (io_config.verbosity == 1) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info);
}
else if (io_config.verbosity == 0) {
} else if (io_config.verbosity == 0) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning);
}
else if (io_config.verbosity >= 2) {
} else if (io_config.verbosity >= 2) {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug);
}
else {
} else {
LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal);
}
}
......@@ -141,20 +138,19 @@ void OverallConfig::CheckParamConflict() {
// check if objective_type, metric_type, and num_class match
bool objective_type_multiclass = (objective_type == std::string("multiclass"));
int num_class_check = boosting_config.num_class;
if (objective_type_multiclass){
if (num_class_check <= 1){
Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training");
}
if (objective_type_multiclass) {
if (num_class_check <= 2) {
Log::Fatal("Number of classes should be specified and greater than 2 for multiclass training");
}
else {
if (task_type == TaskType::kTrain && num_class_check != 1){
} else {
if (task_type == TaskType::kTrain && num_class_check != 1) {
Log::Fatal("Number of classes must be 1 for non-multiclass training");
}
}
for (std::string metric_type : metric_types){
bool metric_type_multiclass = ( metric_type == std::string("multi_logloss") || metric_type == std::string("multi_error"));
for (std::string metric_type : metric_types) {
bool metric_type_multiclass = (metric_type == std::string("multi_logloss") || metric_type == std::string("multi_error"));
if ((objective_type_multiclass && !metric_type_multiclass)
|| (!objective_type_multiclass && metric_type_multiclass)){
|| (!objective_type_multiclass && metric_type_multiclass)) {
Log::Fatal("Objective and metrics don't match");
}
}
......
src/io/dataset.cpp
View file @ 292f972e
......@@ -17,14 +17,12 @@ namespace LightGBM {
const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n";
Dataset::Dataset() {
num_class_ = 1;
num_data_ = 0;
}
Dataset::Dataset(data_size_t num_data, int num_class) {
num_class_ = num_class;
Dataset::Dataset(data_size_t num_data) {
num_data_ = num_data;
metadata_.Init(num_data_, num_class_, -1, -1);
metadata_.Init(num_data_, -1, -1);
}
Dataset::~Dataset() {
......@@ -50,7 +48,6 @@ void Dataset::CopyFeatureMapperFrom(const Dataset* dataset, bool is_enable_spars
));
}
features_.shrink_to_fit();
num_class_ = dataset->num_class_;
used_feature_map_ = dataset->used_feature_map_;
num_features_ = static_cast<int>(features_.size());
num_total_features_ = dataset->num_total_features_;
......@@ -59,7 +56,7 @@ void Dataset::CopyFeatureMapperFrom(const Dataset* dataset, bool is_enable_spars
}
Dataset* Dataset::Subset(const data_size_t* used_indices, data_size_t num_used_indices, bool is_enable_sparse) const {
auto ret = std::unique_ptr<Dataset>(new Dataset(num_used_indices, num_class_));
auto ret = std::unique_ptr<Dataset>(new Dataset(num_used_indices));
ret->CopyFeatureMapperFrom(this, is_enable_sparse);
#pragma omp parallel for schedule(guided)
for (int fidx = 0; fidx < num_features_; ++fidx) {
......@@ -169,7 +166,7 @@ void Dataset::SaveBinaryFile(const char* bin_filename) {
size_t size_of_token = std::strlen(binary_file_token);
fwrite(binary_file_token, sizeof(char), size_of_token, file);
// get size of header
size_t size_of_header = sizeof(num_data_) + sizeof(num_class_) + sizeof(num_features_) + sizeof(num_total_features_)
size_t size_of_header = sizeof(num_data_) + sizeof(num_features_) + sizeof(num_total_features_)
+ sizeof(size_t) + sizeof(int) * used_feature_map_.size();
// size of feature names
for (int i = 0; i < num_total_features_; ++i) {
......@@ -178,7 +175,6 @@ void Dataset::SaveBinaryFile(const char* bin_filename) {
fwrite(&size_of_header, sizeof(size_of_header), 1, file);
// write header
fwrite(&num_data_, sizeof(num_data_), 1, file);
fwrite(&num_class_, sizeof(num_class_), 1, file);
fwrite(&num_features_, sizeof(num_features_), 1, file);
fwrite(&num_total_features_, sizeof(num_features_), 1, file);
size_t num_used_feature_map = used_feature_map_.size();
......
src/io/dataset_loader.cpp
View file @ 292f972e
......@@ -8,8 +8,8 @@
namespace LightGBM {
DatasetLoader::DatasetLoader(const IOConfig& io_config, const PredictFunction& predict_fun, const char* filename)
:io_config_(io_config), random_(io_config_.data_random_seed), predict_fun_(predict_fun) {
DatasetLoader::DatasetLoader(const IOConfig& io_config, const PredictFunction& predict_fun, int num_class, const char* filename)
:io_config_(io_config), random_(io_config_.data_random_seed), predict_fun_(predict_fun), num_class_(num_class) {
label_idx_ = 0;
weight_idx_ = NO_SPECIFIC;
group_idx_ = NO_SPECIFIC;
......@@ -177,8 +177,7 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
Log::Fatal("Could not recognize data format of %s", filename);
}
dataset->data_filename_ = filename;
dataset->num_class_ = io_config_.num_class;
dataset->metadata_.Init(filename, dataset->num_class_);
dataset->metadata_.Init(filename);
if (!io_config_.use_two_round_loading) {
// read data to memory
auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, rank, num_machines,&num_global_data, &used_data_indices);
......@@ -188,7 +187,7 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
// construct feature bin mappers
ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get());
// initialize label
dataset->metadata_.Init(dataset->num_data_, io_config_.num_class, weight_idx_, group_idx_);
dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);
// extract features
ExtractFeaturesFromMemory(text_data, parser.get(), dataset.get());
text_data.clear();
......@@ -203,7 +202,7 @@ Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_mac
// construct feature bin mappers
ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get());
// initialize label
dataset->metadata_.Init(dataset->num_data_, dataset->num_class_, weight_idx_, group_idx_);
dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);
// extract features
ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get());
......@@ -232,14 +231,13 @@ Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename,
Log::Fatal("Could not recognize data format of %s", filename);
}
dataset->data_filename_ = filename;
dataset->num_class_ = io_config_.num_class;
dataset->metadata_.Init(filename, dataset->num_class_);
dataset->metadata_.Init(filename);
if (!io_config_.use_two_round_loading) {
// read data in memory
auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, 0, 1, &num_global_data, &used_data_indices);
dataset->num_data_ = static_cast<data_size_t>(text_data.size());
// initialize label
dataset->metadata_.Init(dataset->num_data_, dataset->num_class_, weight_idx_, group_idx_);
dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);
dataset->CopyFeatureMapperFrom(train_data, io_config_.is_enable_sparse);
// extract features
ExtractFeaturesFromMemory(text_data, parser.get(), dataset.get());
......@@ -250,7 +248,7 @@ Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename,
dataset->num_data_ = static_cast<data_size_t>(text_reader.CountLine());
num_global_data = dataset->num_data_;
// initialize label
dataset->metadata_.Init(dataset->num_data_, dataset->num_class_, weight_idx_, group_idx_);
dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);
dataset->CopyFeatureMapperFrom(train_data, io_config_.is_enable_sparse);
// extract features
ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get());
......@@ -316,8 +314,6 @@ Dataset* DatasetLoader::LoadFromBinFile(const char* data_filename, const char* b
const char* mem_ptr = buffer.data();
dataset->num_data_ = *(reinterpret_cast<const data_size_t*>(mem_ptr));
mem_ptr += sizeof(dataset->num_data_);
dataset->num_class_ = *(reinterpret_cast<const int*>(mem_ptr));
mem_ptr += sizeof(dataset->num_class_);
dataset->num_features_ = *(reinterpret_cast<const int*>(mem_ptr));
mem_ptr += sizeof(dataset->num_features_);
dataset->num_total_features_ = *(reinterpret_cast<const int*>(mem_ptr));
......@@ -448,7 +444,6 @@ Dataset* DatasetLoader::CostructFromSampleData(std::vector<std::vector<double>>&
}
auto dataset = std::unique_ptr<Dataset>(new Dataset());
dataset->num_class_ = io_config_.num_class;
dataset->features_.clear();
dataset->num_data_ = num_data;
// -1 means doesn't use this feature
......@@ -482,7 +477,7 @@ Dataset* DatasetLoader::CostructFromSampleData(std::vector<std::vector<double>>&
}
dataset->feature_names_ = feature_names_;
dataset->num_features_ = static_cast<int>(dataset->features_.size());
dataset->metadata_.Init(dataset->num_data_, dataset->num_class_, NO_SPECIFIC, NO_SPECIFIC);
dataset->metadata_.Init(dataset->num_data_, NO_SPECIFIC, NO_SPECIFIC);
return dataset.release();
}
......@@ -798,7 +793,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector<std::string>& text_dat
}
} else {
// if need to prediction with initial model
std::vector<score_t> init_score(dataset->num_data_ * dataset->num_class_);
std::vector<score_t> init_score(dataset->num_data_ * num_class_);
#pragma omp parallel for schedule(guided) private(oneline_features) firstprivate(tmp_label)
for (data_size_t i = 0; i < dataset->num_data_; ++i) {
const int tid = omp_get_thread_num();
......@@ -807,7 +802,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector<std::string>& text_dat
parser->ParseOneLine(text_data[i].c_str(), &oneline_features, &tmp_label);
// set initial score
std::vector<double> oneline_init_score = predict_fun_(oneline_features);
for (int k = 0; k < dataset->num_class_; ++k) {
for (int k = 0; k < num_class_; ++k) {
init_score[k * dataset->num_data_ + i] = static_cast<float>(oneline_init_score[k]);
}
// set label
......@@ -833,7 +828,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector<std::string>& text_dat
}
}
// metadata_ will manage space of init_score
dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * dataset->num_class_);
dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_);
}
dataset->FinishLoad();
// text data can be free after loaded feature values
......@@ -844,7 +839,7 @@ void DatasetLoader::ExtractFeaturesFromMemory(std::vector<std::string>& text_dat
void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser* parser, const std::vector<data_size_t>& used_data_indices, Dataset* dataset) {
std::vector<score_t> init_score;
if (predict_fun_ != nullptr) {
init_score = std::vector<score_t>(dataset->num_data_ * dataset->num_class_);
init_score = std::vector<score_t>(dataset->num_data_ * num_class_);
}
std::function<void(data_size_t, const std::vector<std::string>&)> process_fun =
[this, &init_score, &parser, &dataset]
......@@ -860,7 +855,7 @@ void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser*
// set initial score
if (!init_score.empty()) {
std::vector<double> oneline_init_score = predict_fun_(oneline_features);
for (int k = 0; k < dataset->num_class_; ++k) {
for (int k = 0; k < num_class_; ++k) {
init_score[k * dataset->num_data_ + start_idx + i] = static_cast<float>(oneline_init_score[k]);
}
}
......@@ -894,7 +889,7 @@ void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser*
// metadata_ will manage space of init_score
if (!init_score.empty()) {
dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * dataset->num_class_);
dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_);
}
dataset->FinishLoad();
}
......
src/io/metadata.cpp
View file @ 292f972e
......@@ -10,9 +10,8 @@ namespace LightGBM {
Metadata::Metadata() {
}
void Metadata::Init(const char * data_filename, const int num_class) {
void Metadata::Init(const char * data_filename) {
data_filename_ = data_filename;
num_class_ = num_class;
// for lambdarank, it needs query data for partition data in parallel learning
LoadQueryBoundaries();
LoadWeights();
......@@ -20,15 +19,11 @@ void Metadata::Init(const char * data_filename, const int num_class) {
LoadInitialScore();
}
Metadata::~Metadata() {
}
void Metadata::Init(data_size_t num_data, int num_class, int weight_idx, int query_idx) {
void Metadata::Init(data_size_t num_data, int weight_idx, int query_idx) {
num_data_ = num_data;
num_class_ = num_class;
label_ = std::vector<float>(num_data_);
if (weight_idx >= 0) {
if (!weights_.empty()) {
......@@ -52,7 +47,6 @@ void Metadata::Init(data_size_t num_data, int num_class, int weight_idx, int que
void Metadata::Init(const Metadata& fullset, const data_size_t* used_indices, data_size_t num_used_indices) {
num_data_ = num_used_indices;
num_class_ = fullset.num_class_;
label_ = std::vector<float>(num_used_indices);
for (data_size_t i = 0; i < num_used_indices; i++) {
......@@ -70,10 +64,13 @@ void Metadata::Init(const Metadata& fullset, const data_size_t* used_indices, da
}
if (!fullset.init_score_.empty()) {
init_score_ = std::vector<float>(num_used_indices);
num_init_score_ = num_used_indices;
int num_class = static_cast<int>(fullset.num_init_score_) / fullset.num_data_;
init_score_ = std::vector<float>(num_used_indices*num_class);
num_init_score_ = num_used_indices*num_class;
for (int k = 0; k < num_class; ++k) {
for (data_size_t i = 0; i < num_used_indices; i++) {
init_score_[i] = fullset.init_score_[used_indices[i]];
init_score_[k*num_data_ + i] = fullset.init_score_[k* fullset.num_data_ + used_indices[i]];
}
}
} else {
num_init_score_ = 0;
......@@ -168,7 +165,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
}
// contain initial score file
if (!init_score_.empty() && num_init_score_ != num_data_) {
if (!init_score_.empty() && (num_init_score_ % num_data_) != 0) {
init_score_.clear();
num_init_score_ = 0;
Log::Fatal("Initial score size doesn't match data size");
......@@ -189,7 +186,7 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
}
// contain initial score file
if (!init_score_.empty() && num_init_score_ != num_all_data) {
if (!init_score_.empty() && (num_init_score_ % num_all_data) != 0) {
init_score_.clear();
num_init_score_ = 0;
Log::Fatal("Initial score size doesn't match data size");
......@@ -242,9 +239,10 @@ void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector<data
// get local initial scores
if (!init_score_.empty()) {
auto old_scores = init_score_;
num_init_score_ = num_data_;
init_score_ = std::vector<float>(num_init_score_ * num_class_);
for (int k = 0; k < num_class_; ++k){
int num_class = num_init_score_ / num_all_data;
num_init_score_ = num_data_ * num_class;
init_score_ = std::vector<float>(num_init_score_);
for (int k = 0; k < num_class; ++k){
for (size_t i = 0; i < used_data_indices.size(); ++i) {
init_score_[k * num_data_ + i] = old_scores[k * num_all_data + used_data_indices[i]];
}
......@@ -266,11 +264,11 @@ void Metadata::SetInitScore(const float* init_score, data_size_t len) {
num_init_score_ = 0;
return;
}
if (len != num_data_ * num_class_) {
if ((len % num_data_) != 0) {
Log::Fatal("Initial score size doesn't match data size");
}
if (!init_score_.empty()) { init_score_.clear(); }
num_init_score_ = num_data_;
num_init_score_ = len;
init_score_ = std::vector<float>(len);
for (data_size_t i = 0; i < len; ++i) {
init_score_[i] = init_score[i];
......@@ -410,26 +408,30 @@ void Metadata::LoadInitialScore() {
return;
}
Log::Info("Loading initial scores...");
num_init_score_ = static_cast<data_size_t>(reader.Lines().size());
init_score_ = std::vector<float>(num_init_score_ * num_class_);
// use first line to count number class
int num_class = static_cast<int>(Common::Split(reader.Lines()[0].c_str(), '\t').size());
data_size_t num_line = static_cast<data_size_t>(reader.Lines().size());
num_init_score_ = static_cast<data_size_t>(num_line * num_class);
init_score_ = std::vector<float>(num_init_score_);
double tmp = 0.0f;
if (num_class_ == 1){
for (data_size_t i = 0; i < num_init_score_; ++i) {
if (num_class == 1) {
for (data_size_t i = 0; i < num_line; ++i) {
Common::Atof(reader.Lines()[i].c_str(), &tmp);
init_score_[i] = static_cast<float>(tmp);
}
} else {
std::vector<std::string> oneline_init_score;
for (data_size_t i = 0; i < num_init_score_; ++i) {
for (data_size_t i = 0; i < num_line; ++i) {
oneline_init_score = Common::Split(reader.Lines()[i].c_str(), '\t');
if (static_cast<int>(oneline_init_score.size()) != num_class_){
if (static_cast<int>(oneline_init_score.size()) != num_class) {
Log::Fatal("Invalid initial score file. Redundant or insufficient columns.");
}
for (int k = 0; k < num_class_; ++k) {
for (int k = 0; k < num_class; ++k) {
Common::Atof(oneline_init_score[k].c_str(), &tmp);
init_score_[k * num_init_score_ + i] = static_cast<float>(tmp);
init_score_[k * num_line + i] = static_cast<float>(tmp);
}
}
}
......
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