change no_limit to <=0. fix float in is_1d_list.

include/LightGBM/c_api.h

@@ -365,7 +365,7 @@ DllExport int LGBM_BoosterGetPredict(BoosterHandle handle,
 *          0:normal, with transform (if needed)
 *          1:raw score
 *          2:leaf index
-* \param num_iteration number of iteration for prediction, < 0 means no limit
+* \param num_iteration number of iteration for prediction, <= 0 means no limit
 * \param result_filename filename of result file
 * \return 0 when succeed, -1 when failure happens
 */
@@ -391,7 +391,7 @@ DllExport int LGBM_BoosterPredictForFile(BoosterHandle handle,
 *          0:normal, with transform (if needed)
 *          1:raw score
 *          2:leaf index
-* \param num_iteration number of iteration for prediction, < 0 means no limit
+* \param num_iteration number of iteration for prediction, <= 0 means no limit
 * \param out_len len of output result
 * \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
@@ -422,7 +422,7 @@ DllExport int LGBM_BoosterPredictForCSR(BoosterHandle handle,
 *          0:normal, with transform (if needed)
 *          1:raw score
 *          2:leaf index
-* \param num_iteration number of iteration for prediction, < 0 means no limit
+* \param num_iteration number of iteration for prediction, <= 0 means no limit
 * \param out_len len of output result
 * \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
@@ -441,7 +441,7 @@ DllExport int LGBM_BoosterPredictForMat(BoosterHandle handle,
 /*!
 * \brief save model into file
 * \param handle handle
-* \param num_iteration, < 0 means no limit
+* \param num_iteration, <= 0 means save all
 * \param filename file name
 * \return 0 when succeed, -1 when failure happens
 */

include/LightGBM/config.h

@@ -99,7 +99,7 @@ public:
   std::string output_result = "LightGBM_predict_result.txt";
   std::string input_model = "";
   int verbosity = 1;
-  int num_iteration_predict = NO_LIMIT;
+  int num_iteration_predict = -1;
   bool is_pre_partition = false;
   bool is_enable_sparse = true;
   bool use_two_round_loading = false;
@@ -166,12 +166,12 @@ public:
   int feature_fraction_seed = 2;
   double feature_fraction = 1.0f;
   // max cache size(unit:MB) for historical histogram. < 0 means not limit
-  double histogram_pool_size = NO_LIMIT;
+  double histogram_pool_size = -1.0f;
   // max depth of tree model.
   // Still grow tree by leaf-wise, but limit the max depth to avoid over-fitting
   // And the max leaves will be min(num_leaves, pow(2, max_depth - 1))
   // max_depth < 0 means not limit
-  int max_depth = NO_LIMIT;
+  int max_depth = -1;
   void Set(const std::unordered_map<std::string, std::string>& params) override;
 };
 

include/LightGBM/meta.h

@@ -24,7 +24,6 @@ using ReduceFunction = std::function<void(const char*, char*, int)>;
 using PredictFunction =
 std::function<std::vector<double>(const std::vector<std::pair<int, double>>&)>;
 
-#define NO_LIMIT (-1)
 #define NO_SPECIFIC (-1)
 
 }  // namespace LightGBM

python-package/lightgbm/basic.py

@@ -82,7 +82,7 @@ def is_1d_list(data):
     if not isinstance(data, list):
         return False
     if len(data) > 0:
-        if not isinstance(data[0], (int, str, bool) ):
+        if not isinstance(data[0], (int, float, bool) ):
             return False
     return True
 

src/application/application.cpp

@@ -226,9 +226,8 @@ void Application::Train() {
     Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration<double,
       std::milli>(end_time - start_time) * 1e-3, iter + 1);
   }
-  is_finished = true;
   // save model to file
-  boosting_->SaveModelToFile(NO_LIMIT, config_.io_config.output_model.c_str());
+  boosting_->SaveModelToFile(-1, config_.io_config.output_model.c_str());
   Log::Info("Finished training");
 }
 

src/boosting/gbdt.cpp

@@ -414,12 +414,12 @@ void GBDT::SaveModelToFile(int num_iteration, const char* filename) const {
   output_file << std::endl;
 
   int num_used_model = 0;
-  if (num_iteration == NO_LIMIT) {
+  if (num_iteration <= 0) {
     num_used_model = static_cast<int>(models_.size());
   } else {
     num_used_model = num_iteration * num_class_;
   }
-
+  num_used_model = std::min(num_used_model, static_cast<int>(models_.size()));
   // output tree models
   for (int i = 0; i < num_used_model; ++i) {
     output_file << "Tree=" << i << std::endl;