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0.839 -1.289 -1.518 1.871 0.366 0.986 1.648 -0.735 0.454 0.343 -0.768 -0.742 0.548 -1.308 -0.535 0.398 0.057 1.000 0.078 0.469 -0.653 0.051 0.513 -0.186 -1.679 1.098 +2 -0.010 -0.555 0.570 2.541 -1.295 1.338 -0.411 -0.683 0.818 -0.256 -1.286 -0.024 0.137 -0.040 0.086 -0.396 0.922 0.266 -1.657 -1.771 1.562 0.832 0.263 -0.503 0.389 0.995 0.194 0.654 diff --git a/examples/multiclass_classification/predict.conf b/examples/multiclass_classification/predict.conf new file mode 100644 index 000000000..57a6019c0 --- /dev/null +++ b/examples/multiclass_classification/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = multiclass.test + +input_model= LightGBM_model.txt diff --git a/examples/multiclass_classification/train.conf b/examples/multiclass_classification/train.conf new file mode 100644 index 000000000..3973630cf --- /dev/null +++ b/examples/multiclass_classification/train.conf @@ -0,0 +1,56 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , lambdarank task +# multiclass +# alias: application, app +objective = multiclass + +# eval metrics, support multi metric, delimite by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +# multi_logloss +# multi_error +metric = multi_logloss + +# number of class, for multiclass classification +num_class = 5 + +# frequence for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if exsting weight file, should name to "regression.train.weight" +# alias: train_data, train +data = multiclass.train + +# valid data +valid_data = multiclass.test + +# round for early stopping +early_stopping = 10 + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.05 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 31 diff --git a/include/LightGBM/boosting.h b/include/LightGBM/boosting.h index 321e7e8a0..ac860ca57 100644 --- a/include/LightGBM/boosting.h +++ b/include/LightGBM/boosting.h @@ -80,6 +80,13 @@ public: const float* feature_values, int num_used_model) const = 0; + /*! + * \brief Predtion for multiclass classification + * \param feature_values Feature value on this record + * \return Prediction result, num_class numbers per line + */ + virtual std::vector PredictMulticlass(const float* value, int num_used_model) const = 0; + /*! * \brief save model to file */ @@ -108,7 +115,13 @@ public: * \return Number of weak sub-models */ virtual int NumberOfSubModels() const = 0; - + + /*! + * \brief Get number of classes + * \return Number of classes + */ + virtual int NumberOfClass() const = 0; + /*! * \brief Get Type name of this boosting object */ diff --git a/include/LightGBM/config.h b/include/LightGBM/config.h index 2ea4b8036..6af0aa558 100644 --- a/include/LightGBM/config.h +++ b/include/LightGBM/config.h @@ -128,6 +128,8 @@ public: int max_position = 20; // for binary bool is_unbalance = false; + // for multiclass + int num_class = 1; void Set(const std::unordered_map& params) override; }; @@ -135,6 +137,7 @@ public: struct MetricConfig: public ConfigBase { public: virtual ~MetricConfig() {} + int num_class = 1; float sigmoid = 1.0f; std::vector label_gain; std::vector eval_at; @@ -179,6 +182,7 @@ public: int bagging_seed = 3; int bagging_freq = 0; int early_stopping_round = 0; + int num_class = 1; void Set(const std::unordered_map& params) override; }; diff --git a/include/LightGBM/utils/common.h b/include/LightGBM/utils/common.h index 81bc6733a..e4e5044f6 100644 --- a/include/LightGBM/utils/common.h +++ b/include/LightGBM/utils/common.h @@ -336,6 +336,26 @@ static inline int64_t Pow2RoundUp(int64_t x) { return 0; } +/*! + * \brief Do inplace softmax transformaton on p_rec + * \param p_rec The input/output vector of the values. + */ +inline void Softmax(std::vector* p_rec) { + std::vector &rec = *p_rec; + float wmax = rec[0]; + for (size_t i = 1; i < rec.size(); ++i) { + wmax = std::max(rec[i], wmax); + } + float wsum = 0.0f; + for (size_t i = 0; i < rec.size(); ++i) { + rec[i] = std::exp(rec[i] - wmax); + wsum += rec[i]; + } + for (size_t i = 0; i < rec.size(); ++i) { + rec[i] /= static_cast(wsum); + } +} + } // namespace Common } // namespace LightGBM diff --git a/src/application/predictor.hpp b/src/application/predictor.hpp index 4db3227ff..1873c3c50 100644 --- a/src/application/predictor.hpp +++ b/src/application/predictor.hpp @@ -33,6 +33,7 @@ public: num_used_model_(num_used_model) { boosting_ = boosting; num_features_ = boosting_->MaxFeatureIdx() + 1; + num_class_ = boosting_->NumberOfClass(); #pragma omp parallel #pragma omp master { @@ -87,6 +88,18 @@ public: // get result with sigmoid transform if needed return boosting_->Predict(features_[tid], num_used_model_); } + + /*! + * \brief prediction for multiclass classification + * \param features Feature of this record + * \return Prediction result + */ + std::vector PredictMulticlassOneLine(const std::vector>& features) { + const int tid = PutFeatureValuesToBuffer(features); + // get result with sigmoid transform if needed + return boosting_->PredictMulticlass(features_[tid], num_used_model_); + } + /*! * \brief predicting on data, then saving result to disk * \param data_filename Filename of data @@ -120,17 +133,30 @@ public: }; std::function>&)> predict_fun; - if (is_predict_leaf_index_) { + if (num_class_ > 1) { + predict_fun = [this](const std::vector>& features){ + std::vector prediction = PredictMulticlassOneLine(features); + std::stringstream result_stream_buf; + for (size_t i = 0; i < prediction.size(); ++i){ + if (i > 0) { + result_stream_buf << '\t'; + } + result_stream_buf << prediction[i]; + } + return result_stream_buf.str(); + }; + } + else if (is_predict_leaf_index_) { predict_fun = [this](const std::vector>& features){ std::vector predicted_leaf_index = PredictLeafIndexOneLine(features); - std::stringstream result_ss; + std::stringstream result_stream_buf; for (size_t i = 0; i < predicted_leaf_index.size(); ++i){ if (i > 0) { - result_ss << '\t'; + result_stream_buf << '\t'; } - result_ss << predicted_leaf_index[i]; + result_stream_buf << predicted_leaf_index[i]; } - return result_ss.str(); + return result_stream_buf.str(); }; } else { @@ -189,6 +215,8 @@ private: float** features_; /*! \brief Number of features */ int num_features_; + /*! \brief Number of classes */ + int num_class_; /*! \brief True if need to predict result with sigmoid transform */ bool is_simgoid_; /*! \brief Number of threads */ diff --git a/src/boosting/gbdt.cpp b/src/boosting/gbdt.cpp index e01218c9b..50afde5e7 100644 --- a/src/boosting/gbdt.cpp +++ b/src/boosting/gbdt.cpp @@ -17,13 +17,15 @@ namespace LightGBM { GBDT::GBDT() - : tree_learner_(nullptr), train_score_updater_(nullptr), + : train_score_updater_(nullptr), gradients_(nullptr), hessians_(nullptr), out_of_bag_data_indices_(nullptr), bag_data_indices_(nullptr) { } GBDT::~GBDT() { - if (tree_learner_ != nullptr) { delete tree_learner_; } + for (auto& tree_learner: tree_learner_){ + if (tree_learner != nullptr) { delete tree_learner; } + } if (gradients_ != nullptr) { delete[] gradients_; } if (hessians_ != nullptr) { delete[] hessians_; } if (out_of_bag_data_indices_ != nullptr) { delete[] out_of_bag_data_indices_; } @@ -44,23 +46,27 @@ void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const O max_feature_idx_ = 0; early_stopping_round_ = gbdt_config_->early_stopping_round; train_data_ = train_data; + num_class_ = config->num_class; + tree_learner_ = std::vector(num_class_, nullptr); // create tree learner - tree_learner_ = - TreeLearner::CreateTreeLearner(gbdt_config_->tree_learner_type, gbdt_config_->tree_config); - // init tree learner - tree_learner_->Init(train_data_); + for (int i = 0; i < num_class_; ++i){ + tree_learner_[i] = + TreeLearner::CreateTreeLearner(gbdt_config_->tree_learner_type, gbdt_config_->tree_config); + // init tree learner + tree_learner_[i]->Init(train_data_); + } object_function_ = object_function; // push training metrics for (const auto& metric : training_metrics) { training_metrics_.push_back(metric); } // create score tracker - train_score_updater_ = new ScoreUpdater(train_data_); + train_score_updater_ = new ScoreUpdater(train_data_, num_class_); num_data_ = train_data_->num_data(); // create buffer for gradients and hessians if (object_function_ != nullptr) { - gradients_ = new score_t[num_data_]; - hessians_ = new score_t[num_data_]; + gradients_ = new score_t[num_data_ * num_class_]; + hessians_ = new score_t[num_data_ * num_class_]; } // get max feature index @@ -85,7 +91,7 @@ void GBDT::Init(const BoostingConfig* config, const Dataset* train_data, const O void GBDT::AddDataset(const Dataset* valid_data, const std::vector& valid_metrics) { // for a validation dataset, we need its score and metric - valid_score_updater_.push_back(new ScoreUpdater(valid_data)); + valid_score_updater_.push_back(new ScoreUpdater(valid_data, num_class_)); valid_metrics_.emplace_back(); best_iter_.emplace_back(); best_score_.emplace_back(); @@ -97,7 +103,7 @@ void GBDT::AddDataset(const Dataset* valid_data, } -void GBDT::Bagging(int iter) { +void GBDT::Bagging(int iter, const int curr_class) { // if need bagging if (out_of_bag_data_indices_ != nullptr && iter % gbdt_config_->bagging_freq == 0) { // if doesn't have query data @@ -146,52 +152,59 @@ void GBDT::Bagging(int iter) { } Log::Info("re-bagging, using %d data to train", bag_data_cnt_); // set bagging data to tree learner - tree_learner_->SetBaggingData(bag_data_indices_, bag_data_cnt_); + tree_learner_[curr_class]->SetBaggingData(bag_data_indices_, bag_data_cnt_); } } -void GBDT::UpdateScoreOutOfBag(const Tree* tree) { +void GBDT::UpdateScoreOutOfBag(const Tree* tree, const int curr_class) { // we need to predict out-of-bag socres of data for boosting if (out_of_bag_data_indices_ != nullptr) { train_score_updater_-> - AddScore(tree, out_of_bag_data_indices_, out_of_bag_data_cnt_); + AddScore(tree, out_of_bag_data_indices_, out_of_bag_data_cnt_, curr_class); } } bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is_eval) { - // boosting first - if (gradient == nullptr || hessian == nullptr) { - Boosting(); - gradient = gradients_; - hessian = hessians_; - } - // bagging logic - Bagging(iter_); - // train a new tree - Tree * new_tree = tree_learner_->Train(gradient, hessian); - // if cannot learn a new tree, then stop - if (new_tree->num_leaves() <= 1) { - Log::Info("Can't training anymore, there isn't any leaf meets split requirements."); - return true; - } - // shrinkage by learning rate - new_tree->Shrinkage(gbdt_config_->learning_rate); - // update score - UpdateScore(new_tree); - UpdateScoreOutOfBag(new_tree); + // boosting first + if (gradient == nullptr || hessian == nullptr) { + Boosting(); + gradient = gradients_; + hessian = hessians_; + } + + for (int curr_class = 0; curr_class < num_class_; ++curr_class){ + // bagging logic + Bagging(iter_, curr_class); + + // train a new tree + Tree * new_tree = tree_learner_[curr_class]->Train(gradient + curr_class * num_data_, hessian+ curr_class * num_data_); + // if cannot learn a new tree, then stop + if (new_tree->num_leaves() <= 1) { + Log::Info("Can't training anymore, there isn't any leaf meets split requirements."); + return true; + } + + // shrinkage by learning rate + new_tree->Shrinkage(gbdt_config_->learning_rate); + // update score + UpdateScore(new_tree, curr_class); + UpdateScoreOutOfBag(new_tree, curr_class); + + // add model + models_.push_back(new_tree); + } + bool is_met_early_stopping = false; // print message for metric if (is_eval) { is_met_early_stopping = OutputMetric(iter_ + 1); } - // add model - models_.push_back(new_tree); ++iter_; if (is_met_early_stopping) { Log::Info("Early stopping at iteration %d, the best iteration round is %d", iter_, iter_ - early_stopping_round_); // pop last early_stopping_round_ models - for (int i = 0; i < early_stopping_round_; ++i) { + for (int i = 0; i < early_stopping_round_ * num_class_; ++i) { delete models_.back(); models_.pop_back(); } @@ -200,12 +213,12 @@ bool GBDT::TrainOneIter(const score_t* gradient, const score_t* hessian, bool is } -void GBDT::UpdateScore(const Tree* tree) { +void GBDT::UpdateScore(const Tree* tree, const int curr_class) { // update training score - train_score_updater_->AddScore(tree_learner_); + train_score_updater_->AddScore(tree_learner_[curr_class], curr_class); // update validation score for (auto& score_tracker : valid_score_updater_) { - score_tracker->AddScore(tree); + score_tracker->AddScore(tree, curr_class); } } @@ -298,6 +311,8 @@ void GBDT::SaveModelToFile(bool is_finish, const char* filename) { model_output_file_.open(filename); // output model type model_output_file_ << "gbdt" << std::endl; + // output number of class + model_output_file_ << "num_class=" << num_class_ << std::endl; // output label index model_output_file_ << "label_index=" << label_idx_ << std::endl; // output max_feature_idx @@ -311,7 +326,7 @@ void GBDT::SaveModelToFile(bool is_finish, const char* filename) { if (!model_output_file_.is_open()) { return; } - int rest = static_cast(models_.size()) - early_stopping_round_; + int rest = static_cast(models_.size()) - early_stopping_round_ * num_class_; // output tree models for (int i = saved_model_size_; i < rest; ++i) { model_output_file_ << "Tree=" << i << std::endl; @@ -337,8 +352,26 @@ void GBDT::ModelsFromString(const std::string& model_str) { models_.clear(); std::vector lines = Common::Split(model_str.c_str(), '\n'); size_t i = 0; + + // get number of class + while (i < lines.size()) { + size_t find_pos = lines[i].find("num_class="); + if (find_pos != std::string::npos) { + std::vector strs = Common::Split(lines[i].c_str(), '='); + Common::Atoi(strs[1].c_str(), &num_class_); + ++i; + break; + } else { + ++i; + } + } + if (i == lines.size()) { + Log::Fatal("Model file doesn't contain number of class"); + return; + } // get index of label + i = 0; while (i < lines.size()) { size_t find_pos = lines[i].find("label_index="); if (find_pos != std::string::npos) { @@ -460,6 +493,20 @@ float GBDT::Predict(const float* value, int num_used_model) const { return ret; } +std::vector GBDT::PredictMulticlass(const float* value, int num_used_model) const { + if (num_used_model < 0) { + num_used_model = static_cast(models_.size()) / num_class_; + } + std::vector ret(num_class_, 0.0f); + for (int i = 0; i < num_used_model; ++i) { + for (int j = 0; j < num_class_; ++j){ + ret[j] += models_[i * num_class_ + j] -> Predict(value); + } + } + Common::Softmax(&ret); + return ret; +} + std::vector GBDT::PredictLeafIndex(const float* value, int num_used_model) const { if (num_used_model < 0) { num_used_model = static_cast(models_.size()); diff --git a/src/boosting/gbdt.h b/src/boosting/gbdt.h index 5a1e6321b..3932d89bf 100644 --- a/src/boosting/gbdt.h +++ b/src/boosting/gbdt.h @@ -68,13 +68,20 @@ public: */ float Predict(const float* feature_values, int num_used_model) const override; + /*! + * \brief Predtion for multiclass classification + * \param feature_values Feature value on this record + * \return Prediction result, num_class numbers per line + */ + std::vector PredictMulticlass(const float* value, int num_used_model) const override; + /*! * \brief Predtion for one record with leaf index * \param feature_values Feature value on this record * \param num_used_model Number of used model * \return Predicted leaf index for this record */ - std::vector PredictLeafIndex(const float* value, int num_used_model) const override; + std::vector PredictLeafIndex(const float* value, int num_used_model) const override; /*! * \brief Serialize models by string @@ -103,6 +110,12 @@ public: */ inline int NumberOfSubModels() const override { return static_cast(models_.size()); } + /*! + * \brief Get number of classes + * \return Number of classes + */ + inline int NumberOfClass() const override { return num_class_; } + /*! * \brief Get Type name of this boosting object */ @@ -112,14 +125,16 @@ private: /*! * \brief Implement bagging logic * \param iter Current interation + * \param curr_class Current class for multiclass training */ - void Bagging(int iter); + void Bagging(int iter, const int curr_class); /*! * \brief updating score for out-of-bag data. * Data should be update since we may re-bagging data on training * \param tree Trained tree of this iteration + * \param curr_class Current class for multiclass training */ - void UpdateScoreOutOfBag(const Tree* tree); + void UpdateScoreOutOfBag(const Tree* tree, const int curr_class); /*! * \brief calculate the object function */ @@ -127,8 +142,9 @@ private: /*! * \brief updating score after tree was trained * \param tree Trained tree of this iteration + * \param curr_class Current class for multiclass training */ - void UpdateScore(const Tree* tree); + void UpdateScore(const Tree* tree, const int curr_class); /*! * \brief Print metric result of current iteration * \param iter Current interation @@ -146,7 +162,7 @@ private: /*! \brief Config of gbdt */ const GBDTConfig* gbdt_config_; /*! \brief Tree learner, will use this class to learn trees */ - TreeLearner* tree_learner_; + std::vector tree_learner_; /*! \brief Objective function */ const ObjectiveFunction* object_function_; /*! \brief Store and update training data's score */ @@ -180,6 +196,8 @@ private: data_size_t bag_data_cnt_; /*! \brief Number of traning data */ data_size_t num_data_; + /*! \brief Number of classes */ + int num_class_; /*! \brief Random generator, used for bagging */ Random random_; /*! diff --git a/src/boosting/score_updater.hpp b/src/boosting/score_updater.hpp index 9ad87b924..37a2d1682 100644 --- a/src/boosting/score_updater.hpp +++ b/src/boosting/score_updater.hpp @@ -18,12 +18,12 @@ public: * \brief Constructor, will pass a const pointer of dataset * \param data This class will bind with this data set */ - explicit ScoreUpdater(const Dataset* data) + explicit ScoreUpdater(const Dataset* data, int num_class) :data_(data) { num_data_ = data->num_data(); - score_ = new score_t[num_data_]; + score_ = new score_t[num_data_ * num_class]; // default start score is zero - std::memset(score_, 0, sizeof(score_t)*num_data_); + std::memset(score_, 0, sizeof(score_t) * num_data_ * num_class); const score_t* init_score = data->metadata().init_score(); // if exists initial score, will start from it if (init_score != nullptr) { @@ -41,8 +41,8 @@ public: * Note: this function generally will be used on validation data too. * \param tree Trained tree model */ - inline void AddScore(const Tree* tree) { - tree->AddPredictionToScore(data_, num_data_, score_); + inline void AddScore(const Tree* tree, int curr_class) { + tree->AddPredictionToScore(data_, num_data_, score_ + curr_class * num_data_); } /*! * \brief Adding prediction score, only used for training data. @@ -50,8 +50,8 @@ public: * Based on which We can get prediction quckily. * \param tree_learner */ - inline void AddScore(const TreeLearner* tree_learner) { - tree_learner->AddPredictionToScore(score_); + inline void AddScore(const TreeLearner* tree_learner, int curr_class) { + tree_learner->AddPredictionToScore(score_ + curr_class * num_data_); } /*! * \brief Using tree model to get prediction number, then adding to scores for parts of data @@ -61,8 +61,8 @@ public: * \param data_cnt Number of data that will be proccessed */ inline void AddScore(const Tree* tree, const data_size_t* data_indices, - data_size_t data_cnt) { - tree->AddPredictionToScore(data_, data_indices, data_cnt, score_); + data_size_t data_cnt, int curr_class) { + tree->AddPredictionToScore(data_, data_indices, data_cnt, score_ + curr_class * num_data_); } /*! \brief Pointer of score */ inline const score_t * score() { return score_; } @@ -72,7 +72,7 @@ private: data_size_t num_data_; /*! \brief Pointer of data set */ const Dataset* data_; - /*! \brief scores for data set */ + /*! \brief Scores for data set */ score_t* score_; }; diff --git a/src/io/config.cpp b/src/io/config.cpp index 6cb95b5e2..4599e757f 100644 --- a/src/io/config.cpp +++ b/src/io/config.cpp @@ -46,7 +46,6 @@ void OverallConfig::Set(const std::unordered_map& para boosting_config = new GBDTConfig(); } - // sub-config setup network_config.Set(params); io_config.Set(params); @@ -132,7 +131,29 @@ void OverallConfig::GetTaskType(const std::unordered_map(boosting_config); + GBDTConfig* gbdt_config = dynamic_cast(boosting_config); + + // check if objective_type, metric_type, and num_class match + bool objective_type_multiclass = (objective_type == std::string("multiclass")); + int num_class_check = gbdt_config->num_class; + if (objective_type_multiclass){ + if (num_class_check <= 1){ + Log::Fatal("You should specify number of class(>=2) for multiclass training."); + } + } + else { + if (task_type == TaskType::kTrain && num_class_check != 1){ + Log::Fatal("Number of class 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")); + if ((objective_type_multiclass && !metric_type_multiclass) + || (!objective_type_multiclass && metric_type_multiclass)){ + Log::Fatal("Objective and metrics don't match."); + } + } + if (network_config.num_machines > 1) { is_parallel = true; } else { @@ -196,6 +217,8 @@ void ObjectiveConfig::Set(const std::unordered_map& pa GetFloat(params, "sigmoid", &sigmoid); GetInt(params, "max_position", &max_position); CHECK(max_position > 0); + GetInt(params, "num_class", &num_class); + CHECK(num_class >= 1); std::string tmp_str = ""; if (GetString(params, "label_gain", &tmp_str)) { label_gain = Common::StringToFloatArray(tmp_str, ','); @@ -212,6 +235,8 @@ void ObjectiveConfig::Set(const std::unordered_map& pa void MetricConfig::Set(const std::unordered_map& params) { GetFloat(params, "sigmoid", &sigmoid); + GetInt(params, "num_class", &num_class); + CHECK(num_class >= 1); std::string tmp_str = ""; if (GetString(params, "label_gain", &tmp_str)) { label_gain = Common::StringToFloatArray(tmp_str, ','); @@ -268,6 +293,8 @@ void BoostingConfig::Set(const std::unordered_map& par GetInt(params, "metric_freq", &output_freq); CHECK(output_freq >= 0); GetBool(params, "is_training_metric", &is_provide_training_metric); + GetInt(params, "num_class", &num_class); + CHECK(num_class >= 1); } void GBDTConfig::GetTreeLearnerType(const std::unordered_map& params) { diff --git a/src/metric/metric.cpp b/src/metric/metric.cpp index f4d6c6147..116136610 100644 --- a/src/metric/metric.cpp +++ b/src/metric/metric.cpp @@ -2,6 +2,7 @@ #include "regression_metric.hpp" #include "binary_metric.hpp" #include "rank_metric.hpp" +#include "multiclass_metric.hpp" namespace LightGBM { @@ -18,6 +19,10 @@ Metric* Metric::CreateMetric(const std::string& type, const MetricConfig& config return new AUCMetric(config); } else if (type == "ndcg") { return new NDCGMetric(config); + } else if (type == "multi_logloss"){ + return new MultiLoglossMetric(config); + } else if (type == "multi_error"){ + return new MultiErrorMetric(config); } return nullptr; } diff --git a/src/metric/multiclass_metric.hpp b/src/metric/multiclass_metric.hpp new file mode 100644 index 000000000..73506cbc8 --- /dev/null +++ b/src/metric/multiclass_metric.hpp @@ -0,0 +1,138 @@ +#ifndef LIGHTGBM_METRIC_MULTICLASS_METRIC_HPP_ +#define LIGHTGBM_METRIC_MULTICLASS_METRIC_HPP_ + +#include + +#include + +#include + +namespace LightGBM { +/*! +* \brief Metric for multiclass task. +* Use static class "PointWiseLossCalculator" to calculate loss point-wise +*/ +template +class MulticlassMetric: public Metric { +public: + explicit MulticlassMetric(const MetricConfig& config) { + num_class_ = config.num_class; + } + + virtual ~MulticlassMetric() { + + } + + void Init(const char* test_name, const Metadata& metadata, data_size_t num_data) override { + std::stringstream str_buf; + str_buf << test_name << "'s " << PointWiseLossCalculator::Name(); + name_ = str_buf.str(); + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data_; ++i) { + sum_weights_ += weights_[i]; + } + } + } + + const char* GetName() const override { + return name_.c_str(); + } + + bool is_bigger_better() const override { + return false; + } + + std::vector Eval(const score_t* score) const override { + score_t sum_loss = 0.0; + if (weights_ == nullptr) { + #pragma omp parallel for schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_class_); + for (int k = 0; k < num_class_; ++k) { + rec[k] = score[k * num_data_ + i]; + } + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], rec); + } + } else { + #pragma omp parallel for schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_class_); + for (int k = 0; k < num_class_; ++k) { + rec[k] = score[k * num_data_ + i]; + } + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], rec) * weights_[i]; + } + } + score_t loss = sum_loss / sum_weights_; + return std::vector(1, loss); + } + +private: + /*! \brief Output frequency */ + int output_freq_; + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of classes */ + int num_class_; + /*! \brief Pointer of label */ + const float* label_; + /*! \brief Pointer of weighs */ + const float* weights_; + /*! \brief Sum weights */ + float sum_weights_; + /*! \brief Name of this test set */ + std::string name_; +}; + +/*! \brief L2 loss for multiclass task */ +class MultiErrorMetric: public MulticlassMetric { +public: + explicit MultiErrorMetric(const MetricConfig& config) :MulticlassMetric(config) {} + + inline static score_t LossOnPoint(float label, std::vector score) { + size_t k = static_cast(label); + for (size_t i = 0; i < score.size(); ++i){ + if (i != k && score[i] > score[k]) { + return 0.0f; + } + } + return 1.0f; + } + + inline static const char* Name() { + return "multi error"; + } +}; + +/*! \brief Logloss for multiclass task */ +class MultiLoglossMetric: public MulticlassMetric { +public: + explicit MultiLoglossMetric(const MetricConfig& config) :MulticlassMetric(config) {} + + inline static score_t LossOnPoint(float label, std::vector score) { + size_t k = static_cast(label); + Common::Softmax(&score); + if (score[k] > kEpsilon) { + return -std::log(score[k]); + } else { + return -std::log(kEpsilon); + } + } + + inline static const char* Name() { + return "multi logloss"; + } +}; + +} // namespace LightGBM +#endif // LightGBM_METRIC_MULTICLASS_METRIC_HPP_ diff --git a/src/objective/binary_objective.hpp b/src/objective/binary_objective.hpp index 114340827..a813f5173 100644 --- a/src/objective/binary_objective.hpp +++ b/src/objective/binary_objective.hpp @@ -8,7 +8,7 @@ namespace LightGBM { /*! -* \brief Objective funtion for binary classification +* \brief Objective function for binary classification */ class BinaryLogloss: public ObjectiveFunction { public: diff --git a/src/objective/multiclass_objective.hpp b/src/objective/multiclass_objective.hpp new file mode 100644 index 000000000..d64347c00 --- /dev/null +++ b/src/objective/multiclass_objective.hpp @@ -0,0 +1,95 @@ +#ifndef LIGHTGBM_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ +#define LIGHTGBM_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ + +#include + +#include +#include + +namespace LightGBM { +/*! +* \brief Objective function for multiclass classification +*/ +class MulticlassLogloss: public ObjectiveFunction { +public: + explicit MulticlassLogloss(const ObjectiveConfig& config) + :label_int_(nullptr) { + num_class_ = config.num_class; + } + + ~MulticlassLogloss() { + if (label_int_ != nullptr) { delete[] label_int_; } + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + label_int_ = new int[num_data_]; + for (int i = 0; i < num_data_; ++i){ + label_int_[i] = static_cast(label_[i]); + if (label_int_[i] < 0 || label_int_[i] >= num_class_) { + Log::Fatal("Label must be in [0, %d), but find %d in label", num_class_, label_int_[i]); + } + } + } + + void GetGradients(const score_t* score, score_t* gradients, score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_class_); + for (int k = 0; k < num_class_; ++k){ + rec[k] = score[k * num_data_ + i]; + } + Common::Softmax(&rec); + for (int k = 0; k < num_class_; ++k) { + score_t p = rec[k]; + if (label_int_[i] == k) { + gradients[k * num_data_ + i] = p - 1.0f; + } else { + gradients[k * num_data_ + i] = p; + } + hessians[k * num_data_ + i] = 2.0f * p * (1.0f - p); + } + } + } else { + #pragma omp parallel for schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_class_); + for (int k = 0; k < num_class_; ++k){ + rec[k] = score[k * num_data_ + i]; + } + Common::Softmax(&rec); + for (int k = 0; k < num_class_; ++k) { + float p = rec[k]; + if (label_int_[i] == k) { + gradients[k * num_data_ + i] = (p - 1.0f) * weights_[i]; + } else { + gradients[k * num_data_ + i] = p * weights_[i]; + } + hessians[k * num_data_ + i] = 2.0f * p * (1.0f - p) * weights_[i]; + } + } + } + } + + float GetSigmoid() const override { + return -1.0f; + } + +private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of classes */ + int num_class_; + /*! \brief Pointer of label */ + const float* label_; + /*! \brief Corresponding integers of label_ */ + int* label_int_; + /*! \brief Weights for data */ + const float* weights_; +}; + +} // namespace LightGBM +#endif // LightGBM_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ diff --git a/src/objective/objective_function.cpp b/src/objective/objective_function.cpp index bc7a29993..5ea6fcf3f 100644 --- a/src/objective/objective_function.cpp +++ b/src/objective/objective_function.cpp @@ -2,6 +2,7 @@ #include "regression_objective.hpp" #include "binary_objective.hpp" #include "rank_objective.hpp" +#include "multiclass_objective.hpp" namespace LightGBM { @@ -12,6 +13,8 @@ ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& return new BinaryLogloss(config); } else if (type == "lambdarank") { return new LambdarankNDCG(config); + } else if (type == "multiclass") { + return new MulticlassLogloss(config); } return nullptr; } diff --git a/src/objective/rank_objective.hpp b/src/objective/rank_objective.hpp index c83e329e9..88d932f78 100644 --- a/src/objective/rank_objective.hpp +++ b/src/objective/rank_objective.hpp @@ -14,7 +14,7 @@ namespace LightGBM { /*! -* \brief Objective funtion for Lambdrank with NDCG +* \brief Objective function for Lambdrank with NDCG */ class LambdarankNDCG: public ObjectiveFunction { public: diff --git a/src/objective/regression_objective.hpp b/src/objective/regression_objective.hpp index 1f3ae0c77..07dc07017 100644 --- a/src/objective/regression_objective.hpp +++ b/src/objective/regression_objective.hpp @@ -5,7 +5,7 @@ namespace LightGBM { /*! -* \brief Objective funtion for regression +* \brief Objective function for regression */ class RegressionL2loss: public ObjectiveFunction { public: diff --git a/windows/LightGBM.vcxproj b/windows/LightGBM.vcxproj index 992273aed..83c688bf4 100644 --- a/windows/LightGBM.vcxproj +++ b/windows/LightGBM.vcxproj @@ -185,11 +185,13 @@ + + diff --git a/windows/LightGBM.vcxproj.filters b/windows/LightGBM.vcxproj.filters index f601428e8..574ddf62d 100644 --- a/windows/LightGBM.vcxproj.filters +++ b/windows/LightGBM.vcxproj.filters @@ -75,6 +75,9 @@ src\metric + + src\metric + src\network @@ -87,6 +90,9 @@ src\objective + + src\objective + src\treelearner