[bug fix] fix predict sigmoid; fix bagging bug.

include/LightGBM/bin.h

@@ -129,11 +129,13 @@ public:
   }
   /*!
   * \brief Construct feature value to bin mapper according feature values
-  * \param values (Sampled) values of this feature
+  * \param column_name name of this column
+  * \param values (Sampled) values of this feature, Note: not include zero. 
+  * \param total_sample_cnt number of total sample count, equal with values.size() + num_zeros
   * \param max_bin The maximal number of bin
   * \param bin_type Type of this bin
   */
-  void FindBin(std::vector<double>* values, size_t total_sample_cnt, int max_bin, BinType bin_type);
+  void FindBin(const std::string& column_name, std::vector<double>* values, size_t total_sample_cnt, int max_bin, BinType bin_type);
 
   /*!
   * \brief Use specific number of bin to calculate the size of this class

src/boosting/gbdt.cpp

@@ -25,7 +25,7 @@ GBDT::GBDT()
   early_stopping_round_(0),
   max_feature_idx_(0),
   num_class_(1),
-  sigmoid_(1.0f),
+  sigmoid_(-1.0f),
   num_iteration_for_pred_(0),
   shrinkage_rate_(0.1f),
   num_init_iteration_(0) {
@@ -187,6 +187,9 @@ void GBDT::AddValidDataset(const Dataset* valid_data,
 }
 
 data_size_t GBDT::BaggingHelper(Random& cur_rand, data_size_t start, data_size_t cnt, data_size_t* buffer){
+  if (cnt <= 0) {
+    return 0;
+  }
   data_size_t bag_data_cnt =
     static_cast<data_size_t>(gbdt_config_->bagging_fraction * cnt);
   data_size_t cur_left_cnt = 0;
@@ -492,7 +495,7 @@ void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {
   } else if(sigmoid_ > 0.0f){
 #pragma omp parallel for schedule(static)
     for (data_size_t i = 0; i < num_data; ++i) {
-      out_result[i] = static_cast<double>(1.0f / (1.0f + std::exp(-2.0f * sigmoid_ * raw_scores[i])));
+      out_result[i] = static_cast<double>(1.0f / (1.0f + std::exp(- sigmoid_ * raw_scores[i])));
     }
   } else {
 #pragma omp parallel for schedule(static)
@@ -761,7 +764,7 @@ std::vector<double> GBDT::Predict(const double* value) const {
   }
   // if need sigmoid transform
   if (sigmoid_ > 0 && num_class_ == 1) {
-    ret[0] = 1.0f / (1.0f + std::exp(- 2.0f * sigmoid_ * ret[0]));
+    ret[0] = 1.0f / (1.0f + std::exp(-sigmoid_ * ret[0]));
   } else if (num_class_ > 1) {
     Common::Softmax(&ret);
   }

src/io/bin.cpp

@@ -41,7 +41,7 @@ BinMapper::~BinMapper() {
 
 }
 
-void BinMapper::FindBin(std::vector<double>* values, size_t total_sample_cnt, int max_bin, BinType bin_type) {
+void BinMapper::FindBin(const std::string& column_name, std::vector<double>* values, size_t total_sample_cnt, int max_bin, BinType bin_type) {
   bin_type_ = bin_type;
   std::vector<double>& ref_values = (*values);
   size_t sample_size = total_sample_cnt;
@@ -181,7 +181,7 @@ void BinMapper::FindBin(std::vector<double>* values, size_t total_sample_cnt, in
     }
     if (used_cnt / static_cast<double>(sample_size) < 0.95f) {
       Log::Warning("Too many categoricals are ignored, \
-                   please use bigger max_bin or partition this column ");
+                   please use bigger max_bin or partition column \"%s\" ", column_name.c_str());
     }
     cnt_in_bin0 = static_cast<int>(sample_size) - used_cnt + counts_int[0];
   }

src/io/dataset_loader.cpp

@@ -433,6 +433,14 @@ Dataset* DatasetLoader::LoadFromBinFile(const char* data_filename, const char* b
 
 Dataset* DatasetLoader::CostructFromSampleData(std::vector<std::vector<double>>& sample_values, size_t total_sample_size, data_size_t num_data) {
   std::vector<std::unique_ptr<BinMapper>> bin_mappers(sample_values.size());
+  // fill feature_names_ if not header
+  if (feature_names_.empty()) {
+    for (int i = 0; i < static_cast<int>(sample_values.size()); ++i) {
+      std::stringstream str_buf;
+      str_buf << "Column_" << i;
+      feature_names_.push_back(str_buf.str());
+    }
+  }
 #pragma omp parallel for schedule(guided)
   for (int i = 0; i < static_cast<int>(sample_values.size()); ++i) {
     bin_mappers[i].reset(new BinMapper());
@@ -440,7 +448,7 @@ Dataset* DatasetLoader::CostructFromSampleData(std::vector<std::vector<double>>&
     if (categorical_features_.count(i)) {
       bin_type = BinType::CategoricalBin;
     }
-    bin_mappers[i]->FindBin(&sample_values[i], total_sample_size, io_config_.max_bin, bin_type);
+    bin_mappers[i]->FindBin(feature_names_[i], &sample_values[i], total_sample_size, io_config_.max_bin, bin_type);
   }
 
   auto dataset = std::unique_ptr<Dataset>(new Dataset());
@@ -467,14 +475,6 @@ Dataset* DatasetLoader::CostructFromSampleData(std::vector<std::vector<double>>&
     }
   }
   dataset->features_.shrink_to_fit();
-  // fill feature_names_ if not header
-  if (feature_names_.empty()) {
-    for (int i = 0; i < dataset->num_total_features_; ++i) {
-      std::stringstream str_buf;
-      str_buf << "Column_" << i;
-      feature_names_.push_back(str_buf.str());
-    }
-  }
   dataset->feature_names_ = feature_names_;
   dataset->num_features_ = static_cast<int>(dataset->features_.size());
   dataset->metadata_.Init(dataset->num_data_, NO_SPECIFIC, NO_SPECIFIC);
@@ -668,7 +668,7 @@ void DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines,
       if (categorical_features_.count(i)) {
         bin_type = BinType::CategoricalBin;
       }
-      bin_mappers[i]->FindBin(&sample_values[i], sample_data.size(), io_config_.max_bin, bin_type);
+      bin_mappers[i]->FindBin(feature_names_[i], &sample_values[i], sample_data.size(), io_config_.max_bin, bin_type);
     }
 
     for (size_t i = 0; i < sample_values.size(); ++i) {
@@ -722,7 +722,7 @@ void DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines,
       if (categorical_features_.count(start[rank] + i)) {
         bin_type = BinType::CategoricalBin;
       }
-      bin_mapper.FindBin(&sample_values[start[rank] + i], sample_data.size(), io_config_.max_bin, bin_type);
+      bin_mapper.FindBin(feature_names_[start[rank] + i], &sample_values[start[rank] + i], sample_data.size(), io_config_.max_bin, bin_type);
       bin_mapper.CopyTo(input_buffer.data() + i * type_size);
     }
     // convert to binary size

src/metric/binary_metric.hpp

@@ -63,7 +63,7 @@ public:
 #pragma omp parallel for schedule(static) reduction(+:sum_loss)
       for (data_size_t i = 0; i < num_data_; ++i) {
         // sigmoid transform
-        double prob = 1.0f / (1.0f + std::exp(-2.0f * sigmoid_ * score[i]));
+        double prob = 1.0f / (1.0f + std::exp(-sigmoid_ * score[i]));
         // add loss
         sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob);
       }
@@ -71,7 +71,7 @@ public:
 #pragma omp parallel for schedule(static) reduction(+:sum_loss)
       for (data_size_t i = 0; i < num_data_; ++i) {
         // sigmoid transform
-        double prob = 1.0f / (1.0f + std::exp(-2.0f * sigmoid_ * score[i]));
+        double prob = 1.0f / (1.0f + std::exp(-sigmoid_ * score[i]));
         // add loss
         sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob) * weights_[i];
       }