replace whitespaces with underlines in feature name (#426)

* change whitespace to underline in feature names

* add test

* fix bug

* fix bug

* warning -> fatal

include/LightGBM/dataset.h

@@ -495,10 +495,20 @@ public:
 
   inline void set_feature_names(const std::vector<std::string>& feature_names) {
     if (feature_names.size() != static_cast<size_t>(num_total_features_)) {
-      Log::Warning("size of feature_names error, should equal with total number of features");
-      return;
+      Log::Fatal("Size of feature_names error, should equal with total number of features");
     }
     feature_names_ = std::vector<std::string>(feature_names);
+    // replace ' ' in feature_names with '_'
+    bool spaceInFeatureName = false;
+    for (auto& feature_name: feature_names_){
+      if (feature_name.find(' ') != std::string::npos){
+        spaceInFeatureName = true;
+        std::replace(feature_name.begin(), feature_name.end(), ' ', '_');
+      }
+    }
+    if (spaceInFeatureName){
+      Log::Warning("Find whitespaces in feature_names, replace with underlines");
+    }
   }
 
   inline std::vector<std::string> feature_infos() const {

python-package/lightgbm/basic.py

@@ -770,7 +770,7 @@ class Dataset(object):
                     """create valid"""
                     self._lazy_init(self.data, label=self.label, max_bin=self.max_bin, reference=self.reference,
                                     weight=self.weight, group=self.group, predictor=self._predictor,
-                                    silent=self.silent, params=self.params)
+                                    silent=self.silent, feature_name=self.feature_name, params=self.params)
                 else:
                     """construct subset"""
                     used_indices = list_to_1d_numpy(self.used_indices, np.int32, name='used_indices')
@@ -1004,7 +1004,8 @@ class Dataset(object):
         feature_name : list of str
             Feature names
         """
-        self.feature_name = feature_name
+        if feature_name != 'auto':
+            self.feature_name = feature_name
         if self.handle is not None and feature_name is not None and feature_name != 'auto':
             if len(feature_name) != self.num_feature():
                 raise ValueError("Length of feature_name({}) and num_feature({}) don't match".format(len(feature_name), self.num_feature()))

src/io/dataset_loader.cpp

@@ -28,7 +28,7 @@ void DatasetLoader::SetHeader(const char* filename) {
     // get column names
     if (io_config_.has_header) {
       std::string first_line = text_reader.first_line();
-      feature_names_ = Common::Split(first_line.c_str(), "\t ,");
+      feature_names_ = Common::Split(first_line.c_str(), "\t,");
     }
 
     // load label idx first
@@ -509,8 +509,8 @@ Dataset* DatasetLoader::CostructFromSampleData(double** sample_values,
   }
   OMP_THROW_EX();
   auto dataset = std::unique_ptr<Dataset>(new Dataset(num_data));
-  dataset->feature_names_ = feature_names_;
   dataset->Construct(bin_mappers, sample_indices, num_per_col, total_sample_size, io_config_);
+  dataset->set_feature_names(feature_names_);
   return dataset.release();
 }
 
@@ -704,7 +704,7 @@ void DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines,
       feature_names_.push_back(str_buf.str());
     }
   }
-  dataset->feature_names_ = feature_names_;
+  dataset->set_feature_names(feature_names_);
   std::vector<std::unique_ptr<BinMapper>> bin_mappers(sample_values.size());
   const data_size_t filter_cnt = static_cast<data_size_t>(
     static_cast<double>(io_config_.min_data_in_leaf* sample_data.size()) / dataset->num_data_);

tests/python_package_test/test_engine.py

@@ -164,8 +164,12 @@ class TestEngine(unittest.TestCase):
 
     def test_feature_name(self):
         lgb_train, _ = template.test_template(return_data=True)
-        feature_names = ['f' + str(i) for i in range(13)]
-        gbm = lgb.train({'verbose': -1}, lgb_train, num_boost_round=10, feature_name=feature_names)
+        feature_names = ['f_' + str(i) for i in range(13)]
+        gbm = lgb.train({'verbose': -1}, lgb_train, num_boost_round=5, feature_name=feature_names)
+        self.assertListEqual(feature_names, gbm.feature_name())
+        # test feature_names with whitespaces
+        feature_names_with_space = ['f ' + str(i) for i in range(13)]
+        gbm = lgb.train({'verbose': -1}, lgb_train, num_boost_round=5, feature_name=feature_names_with_space)
         self.assertListEqual(feature_names, gbm.feature_name())
 
     def test_save_load_copy_pickle(self):