Merge branch 'master' into read_write_locks

.github/CODEOWNERS

@@ -9,14 +9,23 @@
 # catch-all rule (this only gets matched if no rules below match)
 *    @guolinke @StrikerRUS @jameslamb @Laurae2
 
+# other catch-alls that will get matched if specific rules below are not matched
+*.R    @Laurae2 @jameslamb
+*.py    @StrikerRUS @chivee @wxchan @henry0312
+*.cpp    @guolinke @chivee @btrotta
+*.h    @guolinke @chivee @btrotta
+
 # main C++ code
 include/    @guolinke @chivee @btrotta
 src/    @guolinke @chivee @btrotta
 CMakeLists.txt    @guolinke @chivee @Laurae2 @jameslamb @wxchan @henry0312 @StrikerRUS @huanzhang12 @btrotta
+tests/c_api_test/    @guolinke @chivee @btrotta
+tests/cpp_test/    @guolinke @chivee @btrotta
+tests/data/    @guolinke @chivee @btrotta
+windows/    @guolinke @chivee @btrotta @StrikerRUS
 
 # R code
 R-package/    @Laurae2 @jameslamb
-*.R    @Laurae2 @jameslamb
 
 # Python code
 python-package/    @StrikerRUS @chivee @wxchan @henry0312

R-package/R/lgb.Booster.R

@@ -424,7 +424,7 @@ Booster <- R6::R6Class(
     },
 
     # Save model
-    save_model = function(filename, num_iteration = NULL) {
+    save_model = function(filename, num_iteration = NULL, feature_importance_type = 0L) {
 
       # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
@@ -437,6 +437,7 @@ Booster <- R6::R6Class(
         , ret = NULL
         , private$handle
         , as.integer(num_iteration)
+        , as.integer(feature_importance_type)
         , lgb.c_str(filename)
       )
 
@@ -445,7 +446,7 @@ Booster <- R6::R6Class(
     },
 
     # Save model to string
-    save_model_to_string = function(num_iteration = NULL) {
+    save_model_to_string = function(num_iteration = NULL, feature_importance_type = 0L) {
 
       # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
@@ -457,12 +458,13 @@ Booster <- R6::R6Class(
         "LGBM_BoosterSaveModelToString_R"
         , private$handle
         , as.integer(num_iteration)
+        , as.integer(feature_importance_type)
       ))
 
     },
 
     # Dump model in memory
-    dump_model = function(num_iteration = NULL) {
+    dump_model = function(num_iteration = NULL, feature_importance_type = 0L) {
 
       # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
@@ -474,6 +476,7 @@ Booster <- R6::R6Class(
         "LGBM_BoosterDumpModel_R"
         , private$handle
         , as.integer(num_iteration)
+        , as.integer(feature_importance_type)
       )
 
     },

R-package/src/lightgbm_R.cpp

@@ -632,37 +632,40 @@ LGBM_SE LGBM_BoosterPredictForMat_R(LGBM_SE handle,
 
 LGBM_SE LGBM_BoosterSaveModel_R(LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE filename,
   LGBM_SE call_state) {
   R_API_BEGIN();
-  CHECK_CALL(LGBM_BoosterSaveModel(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_CHAR_PTR(filename)));
+  CHECK_CALL(LGBM_BoosterSaveModel(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_AS_INT(feature_importance_type), R_CHAR_PTR(filename)));
   R_API_END();
 }
 
 LGBM_SE LGBM_BoosterSaveModelToString_R(LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE buffer_len,
   LGBM_SE actual_len,
   LGBM_SE out_str,
   LGBM_SE call_state) {
   R_API_BEGIN();
   int64_t out_len = 0;
   std::vector<char> inner_char_buf(R_AS_INT(buffer_len));
-  CHECK_CALL(LGBM_BoosterSaveModelToString(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_AS_INT(buffer_len), &out_len, inner_char_buf.data()));
+  CHECK_CALL(LGBM_BoosterSaveModelToString(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_AS_INT(feature_importance_type), R_AS_INT(buffer_len), &out_len, inner_char_buf.data()));
   EncodeChar(out_str, inner_char_buf.data(), buffer_len, actual_len, static_cast<size_t>(out_len));
   R_API_END();
 }
 
 LGBM_SE LGBM_BoosterDumpModel_R(LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE buffer_len,
   LGBM_SE actual_len,
   LGBM_SE out_str,
   LGBM_SE call_state) {
   R_API_BEGIN();
   int64_t out_len = 0;
   std::vector<char> inner_char_buf(R_AS_INT(buffer_len));
-  CHECK_CALL(LGBM_BoosterDumpModel(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_AS_INT(buffer_len), &out_len, inner_char_buf.data()));
+  CHECK_CALL(LGBM_BoosterDumpModel(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_AS_INT(feature_importance_type), R_AS_INT(buffer_len), &out_len, inner_char_buf.data()));
   EncodeChar(out_str, inner_char_buf.data(), buffer_len, actual_len, static_cast<size_t>(out_len));
   R_API_END();
 }
@@ -707,9 +710,9 @@ static const R_CallMethodDef CallEntries[] = {
   {"LGBM_BoosterCalcNumPredict_R"     , (DL_FUNC) &LGBM_BoosterCalcNumPredict_R     , 8},
   {"LGBM_BoosterPredictForCSC_R"      , (DL_FUNC) &LGBM_BoosterPredictForCSC_R      , 14},
   {"LGBM_BoosterPredictForMat_R"      , (DL_FUNC) &LGBM_BoosterPredictForMat_R      , 11},
-  {"LGBM_BoosterSaveModel_R"          , (DL_FUNC) &LGBM_BoosterSaveModel_R          , 4},
-  {"LGBM_BoosterSaveModelToString_R"  , (DL_FUNC) &LGBM_BoosterSaveModelToString_R  , 6},
-  {"LGBM_BoosterDumpModel_R"          , (DL_FUNC) &LGBM_BoosterDumpModel_R          , 6},
+  {"LGBM_BoosterSaveModel_R"          , (DL_FUNC) &LGBM_BoosterSaveModel_R          , 5},
+  {"LGBM_BoosterSaveModelToString_R"  , (DL_FUNC) &LGBM_BoosterSaveModelToString_R  , 7},
+  {"LGBM_BoosterDumpModel_R"          , (DL_FUNC) &LGBM_BoosterDumpModel_R          , 7},
   {NULL, NULL, 0}
 };
 

R-package/src/lightgbm_R.h

@@ -590,6 +590,7 @@ LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterPredictForMat_R(
 LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterSaveModel_R(
   LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE filename,
   LGBM_SE call_state
 );
@@ -604,6 +605,7 @@ LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterSaveModel_R(
 LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterSaveModelToString_R(
   LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE buffer_len,
   LGBM_SE actual_len,
   LGBM_SE out_str,
@@ -620,6 +622,7 @@ LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterSaveModelToString_R(
 LIGHTGBM_C_EXPORT LGBM_SE LGBM_BoosterDumpModel_R(
   LGBM_SE handle,
   LGBM_SE num_iteration,
+  LGBM_SE feature_importance_type,
   LGBM_SE buffer_len,
   LGBM_SE actual_len,
   LGBM_SE out_str,

docs/Parameters.rst

@@ -574,6 +574,14 @@ Learning Control Parameters
 
    -  **Note**: can be used only in CLI version
 
+-  ``saved_feature_importance_type`` :raw-html:`<a id="saved_feature_importance_type" title="Permalink to this parameter" href="#saved_feature_importance_type">&#x1F517;&#xFE0E;</a>`, default = ``0``, type = int
+
+   -  the feature importance type in the saved model file
+
+   -  ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted)
+
+   -  **Note**: can be used only in CLI version
+
 -  ``snapshot_freq`` :raw-html:`<a id="snapshot_freq" title="Permalink to this parameter" href="#snapshot_freq">&#x1F517;&#xFE0E;</a>`, default = ``-1``, type = int, aliases: ``save_period``
 
    -  frequency of saving model file snapshot

include/LightGBM/boosting.h

@@ -176,9 +176,10 @@ class LIGHTGBM_EXPORT Boosting {
   * \brief Dump model to json format string
   * \param start_iteration The model will be saved start from
   * \param num_iteration Number of iterations that want to dump, -1 means dump all
+  * \param feature_importance_type Type of feature importance, 0: split, 1: gain
   * \return Json format string of model
   */
-  virtual std::string DumpModel(int start_iteration, int num_iteration) const = 0;
+  virtual std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const = 0;
 
   /*!
   * \brief Translate model to if-else statement
@@ -199,19 +200,20 @@ class LIGHTGBM_EXPORT Boosting {
   * \brief Save model to file
   * \param start_iteration The model will be saved start from
   * \param num_iterations Number of model that want to save, -1 means save all
-  * \param is_finish Is training finished or not
+  * \param feature_importance_type Type of feature importance, 0: split, 1: gain
   * \param filename Filename that want to save to
   * \return true if succeeded
   */
-  virtual bool SaveModelToFile(int start_iteration, int num_iterations, const char* filename) const = 0;
+  virtual bool SaveModelToFile(int start_iteration, int num_iterations, int feature_importance_type, const char* filename) const = 0;
 
   /*!
   * \brief Save model to string
   * \param start_iteration The model will be saved start from
   * \param num_iterations Number of model that want to save, -1 means save all
+  * \param feature_importance_type Type of feature importance, 0: split, 1: gain
   * \return Non-empty string if succeeded
   */
-  virtual std::string SaveModelToString(int start_iteration, int num_iterations) const = 0;
+  virtual std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const = 0;
 
   /*!
   * \brief Restore from a serialized string