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Commit 0a4a7a86 authored by Nikita Titov's avatar Nikita Titov Committed by Guolin Ke
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fixed cpplint errors about spaces and newlines (#2102)

parent 32ef7603
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Showing with 97 additions and 84 deletions
helpers/parameter_generator.py
View file @ 0a4a7a86
......@@ -313,7 +313,7 @@ def gen_parameter_code(config_hpp, config_out_cpp):
# tails
str_to_write += " return str_buf.str();\n"
str_to_write += "}\n\n"
str_to_write += "}\n"
str_to_write += "} // namespace LightGBM\n"
with open(config_out_cpp, "w") as config_out_cpp_file:
config_out_cpp_file.write(str_to_write)
......
include/LightGBM/c_api.h
View file @ 0a4a7a86
......@@ -168,11 +168,11 @@ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr,
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr,
int num_rows,
int64_t num_col,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
int num_rows,
int64_t num_col,
const char* parameters,
const DatasetHandle reference,
DatasetHandle* out);
/*!
......@@ -253,12 +253,11 @@ LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat,
* \param out subset of data
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(
const DatasetHandle handle,
const int32_t* used_row_indices,
int32_t num_used_row_indices,
const char* parameters,
DatasetHandle* out);
LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle,
const int32_t* used_row_indices,
int32_t num_used_row_indices,
const char* parameters,
DatasetHandle* out);
/*!
* \brief save feature names to Dataset
......@@ -267,10 +266,9 @@ LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(
* \param num_feature_names number of feature names
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(
DatasetHandle handle,
const char** feature_names,
int num_feature_names);
LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle,
const char** feature_names,
int num_feature_names);
/*!
......@@ -280,10 +278,9 @@ LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(
* \param num_feature_names number of feature names
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(
DatasetHandle handle,
char** feature_names,
int* num_feature_names);
LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle,
char** feature_names,
int* num_feature_names);
/*!
......@@ -348,7 +345,8 @@ LIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle,
* \param handle a instance of data matrix
* \param parameters parameters
*/
LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParam(DatasetHandle handle, const char* parameters);
LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParam(DatasetHandle handle,
const char* parameters);
/*!
* \brief get number of data.
......@@ -397,10 +395,9 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data,
* \param out handle of created Booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(
const char* filename,
int* out_num_iterations,
BoosterHandle* out);
LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename,
int* out_num_iterations,
BoosterHandle* out);
/*!
* \brief load an existing boosting from string
......@@ -409,10 +406,9 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(
* \param out handle of created Booster
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(
const char* model_str,
int* out_num_iterations,
BoosterHandle* out);
LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str,
int* out_num_iterations,
BoosterHandle* out);
/*!
* \brief free obj in handle
......@@ -424,7 +420,9 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle);
/*!
* \brief Shuffle Models
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle, int start_iter, int end_iter);
LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle,
int start_iter,
int end_iter);
/*!
* \brief Merge model in two booster to first handle
......@@ -459,7 +457,8 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle,
* \param parameters format: 'key1=value1 key2=value2'
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle, const char* parameters);
LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle,
const char* parameters);
/*!
* \brief Get number of class
......@@ -467,7 +466,8 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle, const cha
* \param out_len number of class
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_len);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle,
int* out_len);
/*!
* \brief update the model in one round
......@@ -475,7 +475,8 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_l
* \param is_finished 1 means finised(cannot split any more)
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* is_finished);
LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle,
int* is_finished);
/*!
* \brief Refit the tree model using the new data (online learning)
......@@ -485,7 +486,10 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* is_fi
* \param ncol number of columns of leaf_preds
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t nrow, int32_t ncol);
LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,
const int32_t* leaf_preds,
int32_t nrow,
int32_t ncol);
/*!
* \brief update the model, by directly specify gradient and second order gradient,
......@@ -513,28 +517,32 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle);
* \param out_iteration iteration of boosting rounds
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle,
int* out_iteration);
/*!
* \brief Get number of tree per iteration
* \param out_tree_per_iteration number of tree per iteration
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration);
LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle,
int* out_tree_per_iteration);
/*!
* \brief Get number of weak sub-models
* \param out_models number of weak sub-models
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models);
LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle,
int* out_models);
/*!
* \brief Get number of eval
* \param out_len total number of eval results
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle,
int* out_len);
/*!
* \brief Get name of eval
......@@ -542,7 +550,9 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_l
* \param out_strs names of eval result, need to pre-allocate memory before call this
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle, int* out_len, char** out_strs);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle,
int* out_len,
char** out_strs);
/*!
* \brief Get name of features
......@@ -550,14 +560,17 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle, int* out_le
* \param out_strs names of features, need to pre-allocate memory before call this
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle, int* out_len, char** out_strs);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle,
int* out_len,
char** out_strs);
/*!
* \brief Get number of features
* \param out_len total number of features
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle, int* out_len);
LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle,
int* out_len);
/*!
* \brief get evaluation for training data and validation data
......@@ -707,19 +720,19 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle,
* \return 0 when succeed, -1 when failure happens
*/
LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle,
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
const void* indptr,
int indptr_type,
const int32_t* indices,
const void* data,
int data_type,
int64_t nindptr,
int64_t nelem,
int64_t num_col,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
......@@ -816,15 +829,15 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle,
* \param out_result used to set a pointer to array, should allocate memory before call this function
* \return 0 when succeed, -1 when failure happens
*/LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle,
const void* data,
int data_type,
int ncol,
int is_row_major,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
const void* data,
int data_type,
int ncol,
int is_row_major,
int predict_type,
int num_iteration,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief make prediction for an new data set
......@@ -856,7 +869,7 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle,
const char* parameter,
int64_t* out_len,
double* out_result);
/*!
* \brief save model into file
* \param handle handle
......
include/LightGBM/config.h
View file @ 0a4a7a86
......@@ -399,7 +399,7 @@ struct Config {
// default = 0,0,...,0
// desc = cost-effective gradient boosting penalty for using a feature
// desc = applied once per forest
std::vector<double> cegb_penalty_feature_coupled;
std::vector<double> cegb_penalty_feature_coupled;
#pragma endregion
......
include/LightGBM/utils/common.h
View file @ 0a4a7a86
......@@ -814,20 +814,20 @@ inline static void ObtainMinMaxSum(const T1 *w, int nw, T1 *mi, T1 *ma, T2 *su)
}
}
inline static std::vector<uint32_t> EmptyBitset(int n){
inline static std::vector<uint32_t> EmptyBitset(int n) {
int size = n / 32;
if(n % 32 != 0) size++;
if (n % 32 != 0) ++size;
return std::vector<uint32_t>(size);
}
template<typename T>
inline static void InsertBitset(std::vector<uint32_t>& vec, const T val){
inline static void InsertBitset(std::vector<uint32_t>& vec, const T val) {
int i1 = val / 32;
int i2 = val % 32;
if (static_cast<int>(vec.size()) < i1 + 1) {
vec.resize(i1 + 1, 0);
}
vec[i1] |= (1 << i2);
vec[i1] |= (1 << i2);
}
template<typename T>
......
src/c_api.cpp
View file @ 0a4a7a86
......@@ -220,9 +220,9 @@ class Booster {
is_raw_score = false;
}
// TODO: config could be optimized away... (maybe using lambda callback?)
// TODO(eisber): config could be optimized away... (maybe using lambda callback?)
single_row_predictor_.reset(new Predictor(boosting_.get(), num_iteration, is_raw_score, is_predict_leaf, predict_contrib,
config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin));
config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin));
single_row_num_pred_in_one_row_ = boosting_->NumPredictOneRow(num_iteration, is_predict_leaf, predict_contrib);
single_row_predict_function_ = single_row_predictor_->GetPredictFunction();
}
......
src/io/config_auto.cpp
View file @ 0a4a7a86
......@@ -644,4 +644,4 @@ std::string Config::SaveMembersToString() const {
return str_buf.str();
}
}
} // namespace LightGBM
src/treelearner/serial_tree_learner.cpp
View file @ 0a4a7a86
......@@ -107,10 +107,10 @@ void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian
feature_used.clear();
feature_used.resize(train_data->num_features());
if(!config_->cegb_penalty_feature_coupled.empty()){
if (!config_->cegb_penalty_feature_coupled.empty()) {
CHECK(config_->cegb_penalty_feature_coupled.size() == static_cast<size_t>(train_data_->num_total_features()));
}
if(!config_->cegb_penalty_feature_lazy.empty()){
if (!config_->cegb_penalty_feature_lazy.empty()) {
CHECK(config_->cegb_penalty_feature_lazy.size() == static_cast<size_t>(train_data_->num_total_features()));
feature_used_in_data = Common::EmptyBitset(train_data->num_features() * num_data_);
}
......@@ -535,10 +535,10 @@ void SerialTreeLearner::FindBestSplitsFromHistograms(const std::vector<int8_t>&
&smaller_split);
smaller_split.feature = real_fidx;
smaller_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_split * smaller_leaf_splits_->num_data_in_leaf();
if(!config_->cegb_penalty_feature_coupled.empty() && !feature_used[feature_index]){
if (!config_->cegb_penalty_feature_coupled.empty() && !feature_used[feature_index]) {
smaller_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_feature_coupled[real_fidx];
}
if(!config_->cegb_penalty_feature_lazy.empty()){
if (!config_->cegb_penalty_feature_lazy.empty()) {
smaller_split.gain -= config_->cegb_tradeoff * CalculateOndemandCosts(real_fidx, smaller_leaf_splits_->LeafIndex());
}
splits_per_leaf_[smaller_leaf_splits_->LeafIndex()*train_data_->num_features() + feature_index] = smaller_split;
......@@ -566,10 +566,10 @@ void SerialTreeLearner::FindBestSplitsFromHistograms(const std::vector<int8_t>&
&larger_split);
larger_split.feature = real_fidx;
larger_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_split * larger_leaf_splits_->num_data_in_leaf();
if(!config_->cegb_penalty_feature_coupled.empty() && !feature_used[feature_index]){
if (!config_->cegb_penalty_feature_coupled.empty() && !feature_used[feature_index]) {
larger_split.gain -= config_->cegb_tradeoff * config_->cegb_penalty_feature_coupled[real_fidx];
}
if(!config_->cegb_penalty_feature_lazy.empty()){
if (!config_->cegb_penalty_feature_lazy.empty()) {
larger_split.gain -= config_->cegb_tradeoff*CalculateOndemandCosts(real_fidx, larger_leaf_splits_->LeafIndex());
}
splits_per_leaf_[larger_leaf_splits_->LeafIndex()*train_data_->num_features() + feature_index] = larger_split;
......@@ -757,18 +757,18 @@ int32_t SerialTreeLearner::ForceSplits(Tree* tree, Json& forced_split_json, int*
void SerialTreeLearner::Split(Tree* tree, int best_leaf, int* left_leaf, int* right_leaf) {
const SplitInfo& best_split_info = best_split_per_leaf_[best_leaf];
const int inner_feature_index = train_data_->InnerFeatureIndex(best_split_info.feature);
if(!config_->cegb_penalty_feature_coupled.empty() && !feature_used[inner_feature_index]){
if (!config_->cegb_penalty_feature_coupled.empty() && !feature_used[inner_feature_index]) {
feature_used[inner_feature_index] = true;
for(int i = 0; i < tree->num_leaves(); ++i){
if(i == best_leaf) continue;
for (int i = 0; i < tree->num_leaves(); ++i) {
if (i == best_leaf) continue;
auto split = &splits_per_leaf_[i*train_data_->num_features() + inner_feature_index];
split->gain += config_->cegb_tradeoff*config_->cegb_penalty_feature_coupled[best_split_info.feature];
if(*split > best_split_per_leaf_[i])
best_split_per_leaf_[i] = *split;
if (*split > best_split_per_leaf_[i])
best_split_per_leaf_[i] = *split;
}
}
if(!config_->cegb_penalty_feature_lazy.empty()){
if (!config_->cegb_penalty_feature_lazy.empty()) {
data_size_t cnt_leaf_data = 0;
auto tmp_idx = data_partition_->GetIndexOnLeaf(best_leaf, &cnt_leaf_data);
for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) {
......
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