add start_iteration in model saving (#1565)

* add start_iteration in model saving

* fix test

* shuffle models ability

* fix bug

* update document

* refine

* Update engine.py

* Update basic.py

* fix comments

* fix comment

include/LightGBM/boosting.h

@@ -44,6 +44,11 @@ public:
   */
   virtual void MergeFrom(const Boosting* other) = 0;
 
+  /*!
+  * \brief Shuffle Existing Models
+  */
+  virtual void ShuffleModels() = 0;
+
   virtual void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,
                                  const std::vector<const Metric*>& training_metrics) = 0;
 
@@ -163,10 +168,11 @@ public:
 
   /*!
   * \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
   * \return Json format string of model
   */
-  virtual std::string DumpModel(int num_iteration) const = 0;
+  virtual std::string DumpModel(int start_iteration, int num_iteration) const = 0;
 
   /*!
   * \brief Translate model to if-else statement
@@ -185,19 +191,21 @@ public:
 
   /*!
   * \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 filename Filename that want to save to
   * \return true if succeeded
   */
-  virtual bool SaveModelToFile(int num_iterations, const char* filename) const = 0;
+  virtual bool SaveModelToFile(int start_iteration, int num_iterations, 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
   * \return Non-empty string if succeeded
   */
-  virtual std::string SaveModelToString(int num_iterations) const = 0;
+  virtual std::string SaveModelToString(int start_iteration, int num_iterations) const = 0;
 
   /*!
   * \brief Restore from a serialized string

include/LightGBM/c_api.h

@@ -371,6 +371,11 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(
 */
 LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle);
 
+/*!
+* \brief Shuffle Models
+*/
+LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle);
+
 /*!
 * \brief Merge model in two booster to first handle
 * \param handle handle, will merge other handle to this
@@ -682,6 +687,7 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle,
 * \return 0 when succeed, -1 when failure happens
 */
 LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle,
+                                            int start_iteration,
                                             int num_iteration,
                                             const char* filename);
 
@@ -695,6 +701,7 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle,
 * \return 0 when succeed, -1 when failure happens
 */
 LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle,
+                                                    int start_iteration,
                                                     int num_iteration,
                                                     int64_t buffer_len,
                                                     int64_t* out_len,
@@ -710,6 +717,7 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle,
 * \return 0 when succeed, -1 when failure happens
 */
 LIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle,
+                                            int start_iteration,
                                             int num_iteration,
                                             int64_t buffer_len,
                                             int64_t* out_len,

python-package/lightgbm/basic.py

@@ -1401,7 +1401,7 @@ class Booster(object):
             self.__num_class = out_num_class.value
             self.pandas_categorical = _load_pandas_categorical(model_file)
         elif 'model_str' in params:
-            self._load_model_from_string(params['model_str'])
+            self.model_from_string(params['model_str'])
         else:
             raise TypeError('Need at least one training dataset or model file to create booster instance')
 
@@ -1421,7 +1421,7 @@ class Booster(object):
         return self.__deepcopy__(None)
 
     def __deepcopy__(self, _):
-        model_str = self._save_model_to_string(num_iteration=-1)
+        model_str = self.model_to_string(num_iteration=-1)
         booster = Booster({'model_str': model_str})
         booster.pandas_categorical = self.pandas_categorical
         return booster
@@ -1432,7 +1432,7 @@ class Booster(object):
         this.pop('train_set', None)
         this.pop('valid_sets', None)
         if handle is not None:
-            this["handle"] = self._save_model_to_string(num_iteration=-1)
+            this["handle"] = self.model_to_string(num_iteration=-1)
         return this
 
     def __setstate__(self, state):
@@ -1710,7 +1710,7 @@ class Booster(object):
         return [item for i in range_(1, self.__num_dataset)
                 for item in self.__inner_eval(self.name_valid_sets[i - 1], i, feval)]
 
-    def save_model(self, filename, num_iteration=None):
+    def save_model(self, filename, num_iteration=None, start_iteration=0):
         """Save Booster to file.
 
         Parameters
@@ -1721,17 +1721,38 @@ class Booster(object):
             Index of the iteration that should be saved.
             If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.
             If <= 0, all iterations are saved.
+        start_iteration: int, optional (default=0)
+            Start index of the iteration that should be saved.
         """
         if num_iteration is None:
             num_iteration = self.best_iteration
         _safe_call(_LIB.LGBM_BoosterSaveModel(
             self.handle,
+            ctypes.c_int(start_iteration),
             ctypes.c_int(num_iteration),
             c_str(filename)))
         _save_pandas_categorical(filename, self.pandas_categorical)
 
-    def _load_model_from_string(self, model_str, verbose=True):
-        """[Private] Load model from string"""
+    def shuffle_models(self):
+        """Shuffle models.
+        """
+        _safe_call(_LIB.LGBM_BoosterShuffleModels(self.handle))
+
+    def model_from_string(self, model_str, verbose=True):
+        """Load Booster from a string.
+
+        Parameters
+        ----------
+        model_str: string
+            Model will be loaded from this string.
+        verbose: bool, optional (default=True)
+            Set to False to disable log when loading model.
+
+        Returns
+        -------
+        result: Booster
+            Loaded Booster object.
+        """
         if self.handle is not None:
             _safe_call(_LIB.LGBM_BoosterFree(self.handle))
         self._free_buffer()
@@ -1748,9 +1769,25 @@ class Booster(object):
         if verbose:
             print('Finished loading model, total used %d iterations' % (int(out_num_iterations.value)))
         self.__num_class = out_num_class.value
+        return self
+
+    def model_to_string(self, num_iteration=None, start_iteration=0):
+        """Save Booster to string.
 
-    def _save_model_to_string(self, num_iteration=None):
-        """[Private] Save model to string"""
+        Parameters
+        ----------
+        num_iteration : int or None, optional (default=None)
+            Index of the iteration that should be saved.
+            If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.
+            If <= 0, all iterations are saved.
+        start_iteration: int, optional (default=0)
+            Start index of the iteration that should be saved.
+
+        Returns
+        -------
+        result: string
+            String representation of Booster.
+        """
         if num_iteration is None:
             num_iteration = self.best_iteration
         buffer_len = 1 << 20
@@ -1759,6 +1796,7 @@ class Booster(object):
         ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
         _safe_call(_LIB.LGBM_BoosterSaveModelToString(
             self.handle,
+            ctypes.c_int(start_iteration),
             ctypes.c_int(num_iteration),
             ctypes.c_int64(buffer_len),
             ctypes.byref(tmp_out_len),
@@ -1770,13 +1808,14 @@ class Booster(object):
             ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
             _safe_call(_LIB.LGBM_BoosterSaveModelToString(
                 self.handle,
+                ctypes.c_int(start_iteration),
                 ctypes.c_int(num_iteration),
                 ctypes.c_int64(actual_len),
                 ctypes.byref(tmp_out_len),
                 ptr_string_buffer))
         return string_buffer.value.decode()
 
-    def dump_model(self, num_iteration=None):
+    def dump_model(self, num_iteration=None, start_iteration=0):
         """Dump Booster to json format.
 
         Parameters
@@ -1785,6 +1824,8 @@ class Booster(object):
             Index of the iteration that should be dumped.
             If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped.
             If <= 0, all iterations are dumped.
+        start_iteration: int, optional (default=0)
+            Start index of the iteration that should be dumped.
 
         Returns
         -------
@@ -1799,6 +1840,7 @@ class Booster(object):
         ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
         _safe_call(_LIB.LGBM_BoosterDumpModel(
             self.handle,
+            ctypes.c_int(start_iteration),
             ctypes.c_int(num_iteration),
             ctypes.c_int64(buffer_len),
             ctypes.byref(tmp_out_len),
@@ -1810,6 +1852,7 @@ class Booster(object):
             ptr_string_buffer = ctypes.c_char_p(*[ctypes.addressof(string_buffer)])
             _safe_call(_LIB.LGBM_BoosterDumpModel(
                 self.handle,
+                ctypes.c_int(start_iteration),
                 ctypes.c_int(num_iteration),
                 ctypes.c_int64(actual_len),
                 ctypes.byref(tmp_out_len),

python-package/lightgbm/engine.py

@@ -230,7 +230,7 @@ def train(params, train_set, num_boost_round=100,
     for dataset_name, eval_name, score, _ in evaluation_result_list:
         booster.best_score[dataset_name][eval_name] = score
     if not keep_training_booster:
-        booster._load_model_from_string(booster._save_model_to_string(), False)
+        booster.model_from_string(booster.model_to_string(), False)
         booster.free_dataset()
     return booster
 

src/application/application.cpp

@@ -203,7 +203,7 @@ void Application::InitTrain() {
 void Application::Train() {
   Log::Info("Started training...");
   boosting_->Train(config_.snapshot_freq, config_.output_model);
-  boosting_->SaveModelToFile(-1, config_.output_model.c_str());
+  boosting_->SaveModelToFile(0, -1, config_.output_model.c_str());
   // convert model to if-else statement code
   if (config_.convert_model_language == std::string("cpp")) {
     boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str());
@@ -237,7 +237,7 @@ void Application::Predict() {
     boosting_->Init(&config_, train_data_.get(), objective_fun_.get(),
                     Common::ConstPtrInVectorWrapper<Metric>(train_metric_));
     boosting_->RefitTree(pred_leaf);
-    boosting_->SaveModelToFile(-1, config_.output_model.c_str());
+    boosting_->SaveModelToFile(0, -1, config_.output_model.c_str());
     Log::Info("Finished RefitTree");
   } else {
     // create predictor

src/boosting/gbdt.cpp

@@ -330,7 +330,7 @@ void GBDT::Train(int snapshot_freq, const std::string& model_output_path) {
     if (snapshot_freq > 0
         && (iter + 1) % snapshot_freq == 0) {
       std::string snapshot_out = model_output_path + ".snapshot_iter_" + std::to_string(iter + 1);
-      SaveModelToFile(-1, snapshot_out.c_str());
+      SaveModelToFile(0, -1, snapshot_out.c_str());
     }
   }
 }

src/boosting/gbdt.h

@@ -70,6 +70,28 @@ public:
     num_iteration_for_pred_ = static_cast<int>(models_.size()) / num_tree_per_iteration_;
   }
 
+  void ShuffleModels() override {
+    int total_iter = static_cast<int>(models_.size()) / num_tree_per_iteration_;
+    auto original_models = std::move(models_);
+    std::vector<int> indices(total_iter);
+    for (int i = 0; i < total_iter; ++i) {
+      indices[i] = i;
+    }
+    Random tmp_rand(17);
+    for (int i = 0; i < total_iter - 1; ++i) {
+      int j = tmp_rand.NextShort(i + 1, total_iter);
+      std::swap(indices[i], indices[j]);
+    }
+    models_ = std::vector<std::unique_ptr<Tree>>();
+    for (int i = 0; i < total_iter; ++i) {
+      for (int j = 0; j < num_tree_per_iteration_; ++j) {
+        int tree_idx = indices[i] * num_tree_per_iteration_ + j;
+        auto new_tree = std::unique_ptr<Tree>(new Tree(*(original_models[tree_idx].get())));
+        models_.push_back(std::move(new_tree));
+      }
+    }
+  }
+
   /*!
   * \brief Reset the training data
   * \param train_data New Training data
@@ -211,10 +233,11 @@ public:
 
   /*!
   * \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
   * \return Json format string of model
   */
-  std::string DumpModel(int num_iteration) const override;
+  std::string DumpModel(int start_iteration, int num_iteration) const override;
 
   /*!
   * \brief Translate model to if-else statement
@@ -233,18 +256,20 @@ public:
 
   /*!
   * \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 filename Filename that want to save to
   * \return is_finish Is training finished or not
   */
-  virtual bool SaveModelToFile(int num_iterations, const char* filename) const override;
+  virtual bool SaveModelToFile(int start_iteration, int num_iterations, const char* filename) const override;
 
   /*!
   * \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
   * \return Non-empty string if succeeded
   */
-  virtual std::string SaveModelToString(int num_iterations) const override;
+  virtual std::string SaveModelToString(int start_iteration, int num_iterations) const override;
 
   /*!
   * \brief Restore from a serialized buffer

src/boosting/gbdt_model_text.cpp

@@ -12,7 +12,7 @@ namespace LightGBM {
 
 const std::string kModelVersion = "v2";
 
-std::string GBDT::DumpModel(int num_iteration) const {
+std::string GBDT::DumpModel(int start_iteration, int num_iteration) const {
   std::stringstream str_buf;
 
   str_buf << "{";
@@ -29,11 +29,16 @@ std::string GBDT::DumpModel(int num_iteration) const {
 
   str_buf << "\"tree_info\":[";
   int num_used_model = static_cast<int>(models_.size());
+  int total_iteration = num_used_model / num_tree_per_iteration_;
+  start_iteration = std::max(start_iteration, 0);
+  start_iteration = std::min(start_iteration, total_iteration);
   if (num_iteration > 0) {
-    num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model);
+    int end_iteration = start_iteration + num_iteration;
+    num_used_model = std::min(end_iteration * num_tree_per_iteration_ , num_used_model);
   }
-  for (int i = 0; i < num_used_model; ++i) {
-    if (i > 0) {
+  int start_model = start_iteration * num_tree_per_iteration_;
+  for (int i = start_model; i < num_used_model; ++i) {
+    if (i > start_model) {
       str_buf << ",";
     }
     str_buf << "{";
@@ -232,7 +237,7 @@ bool GBDT::SaveModelToIfElse(int num_iteration, const char* filename) const {
   return (bool)output_file;
 }
 
-std::string GBDT::SaveModelToString(int num_iteration) const {
+std::string GBDT::SaveModelToString(int start_iteration, int num_iteration) const {
   std::stringstream ss;
 
   // output model type
@@ -259,24 +264,31 @@ std::string GBDT::SaveModelToString(int num_iteration) const {
   ss << "feature_infos=" << Common::Join(feature_infos_, " ") << '\n';
 
   int num_used_model = static_cast<int>(models_.size());
+  int total_iteration = num_used_model / num_tree_per_iteration_;
+  start_iteration = std::max(start_iteration, 0);
+  start_iteration = std::min(start_iteration, total_iteration);
   if (num_iteration > 0) {
-    num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model);
+    int end_iteration = start_iteration + num_iteration;
+    num_used_model = std::min(end_iteration * num_tree_per_iteration_, num_used_model);
   }
 
-  std::vector<std::string> tree_strs(num_used_model);
-  std::vector<size_t> tree_sizes(num_used_model);
+  int start_model = start_iteration * num_tree_per_iteration_;
+
+  std::vector<std::string> tree_strs(num_used_model - start_model);
+  std::vector<size_t> tree_sizes(num_used_model - start_model);
   // output tree models
   #pragma omp parallel for schedule(static)
-  for (int i = 0; i < num_used_model; ++i) {
-    tree_strs[i] = "Tree=" + std::to_string(i) + '\n';
-    tree_strs[i] += models_[i]->ToString() + '\n';
-    tree_sizes[i] = tree_strs[i].size();
+  for (int i = start_model; i < num_used_model; ++i) {
+    const int idx = i - start_model;
+    tree_strs[idx] = "Tree=" + std::to_string(idx) + '\n';
+    tree_strs[idx] += models_[i]->ToString() + '\n';
+    tree_sizes[idx] = tree_strs[idx].size();
   }
 
   ss << "tree_sizes=" << Common::Join(tree_sizes, " ") << '\n';
   ss << '\n';
 
-  for (int i = 0; i < num_used_model; ++i) {
+  for (int i = 0; i < num_used_model - start_model; ++i) {
     ss << tree_strs[i];
     tree_strs[i].clear();
   }
@@ -313,11 +325,11 @@ std::string GBDT::SaveModelToString(int num_iteration) const {
   return ss.str();
 }
 
-bool GBDT::SaveModelToFile(int num_iteration, const char* filename) const {
+bool GBDT::SaveModelToFile(int start_iteration, int num_iteration, const char* filename) const {
   /*! \brief File to write models */
   std::ofstream output_file;
   output_file.open(filename, std::ios::out | std::ios::binary);
-  std::string str_to_write = SaveModelToString(num_iteration);
+  std::string str_to_write = SaveModelToString(start_iteration, num_iteration);
   output_file.write(str_to_write.c_str(), str_to_write.size());
   output_file.close();
 

src/c_api.cpp

@@ -253,8 +253,8 @@ public:
     boosting_->GetPredictAt(data_idx, out_result, out_len);
   }
 
-  void SaveModelToFile(int num_iteration, const char* filename) {
-    boosting_->SaveModelToFile(num_iteration, filename);
+  void SaveModelToFile(int start_iteration, int num_iteration, const char* filename) {
+    boosting_->SaveModelToFile(start_iteration, num_iteration, filename);
   }
 
   void LoadModelFromString(const char* model_str) {
@@ -262,12 +262,12 @@ public:
     boosting_->LoadModelFromString(model_str, len);
   }
 
-  std::string SaveModelToString(int num_iteration) {
-    return boosting_->SaveModelToString(num_iteration);
+  std::string SaveModelToString(int start_iteration, int num_iteration) {
+    return boosting_->SaveModelToString(start_iteration, num_iteration);
   }
 
-  std::string DumpModel(int num_iteration) {
-    return boosting_->DumpModel(num_iteration);
+  std::string DumpModel(int start_iteration,int num_iteration) {
+    return boosting_->DumpModel(start_iteration, num_iteration);
   }
 
   std::vector<double> FeatureImportance(int num_iteration, int importance_type) {
@@ -283,6 +283,11 @@ public:
     dynamic_cast<GBDTBase*>(boosting_.get())->SetLeafValue(tree_idx, leaf_idx, val);
   }
 
+  void ShuffleModels() {
+    std::lock_guard<std::mutex> lock(mutex_);
+    boosting_->ShuffleModels();
+  }
+
   int GetEvalCounts() const {
     int ret = 0;
     for (const auto& metric : train_metric_) {
@@ -903,6 +908,13 @@ int LGBM_BoosterFree(BoosterHandle handle) {
   API_END();
 }
 
+int LGBM_BoosterShuffleModels(BoosterHandle handle) {
+  API_BEGIN();
+  Booster* ref_booster = reinterpret_cast<Booster*>(handle);
+  ref_booster->ShuffleModels();
+  API_END();
+}
+
 int LGBM_BoosterMerge(BoosterHandle handle,
                       BoosterHandle other_handle) {
   API_BEGIN();
@@ -1188,22 +1200,24 @@ int LGBM_BoosterPredictForMat(BoosterHandle handle,
 }
 
 int LGBM_BoosterSaveModel(BoosterHandle handle,
+                          int start_iteration,
                           int num_iteration,
                           const char* filename) {
   API_BEGIN();
   Booster* ref_booster = reinterpret_cast<Booster*>(handle);
-  ref_booster->SaveModelToFile(num_iteration, filename);
+  ref_booster->SaveModelToFile(start_iteration, num_iteration, filename);
   API_END();
 }
 
 int LGBM_BoosterSaveModelToString(BoosterHandle handle,
+                                  int start_iteration,
                                   int num_iteration,
                                   int64_t buffer_len, 
                                   int64_t* out_len,
                                   char* out_str) {
   API_BEGIN();
   Booster* ref_booster = reinterpret_cast<Booster*>(handle);
-  std::string model = ref_booster->SaveModelToString(num_iteration);
+  std::string model = ref_booster->SaveModelToString(start_iteration, num_iteration);
   *out_len = static_cast<int64_t>(model.size()) + 1;
   if (*out_len <= buffer_len) {
     std::memcpy(out_str, model.c_str(), *out_len);
@@ -1212,13 +1226,14 @@ int LGBM_BoosterSaveModelToString(BoosterHandle handle,
 }
 
 int LGBM_BoosterDumpModel(BoosterHandle handle,
+                          int start_iteration,
                           int num_iteration,
                           int64_t buffer_len,
                           int64_t* out_len,
                           char* out_str) {
   API_BEGIN();
   Booster* ref_booster = reinterpret_cast<Booster*>(handle);
-  std::string model = ref_booster->DumpModel(num_iteration);
+  std::string model = ref_booster->DumpModel(start_iteration, num_iteration);
   *out_len = static_cast<int64_t>(model.size()) + 1;
   if (*out_len <= buffer_len) {
     std::memcpy(out_str, model.c_str(), *out_len);

src/lightgbm_R.cpp

@@ -597,7 +597,7 @@ LGBM_SE LGBM_BoosterSaveModel_R(LGBM_SE handle,
   LGBM_SE filename,
   LGBM_SE call_state) {
   R_API_BEGIN();
-  CHECK_CALL(LGBM_BoosterSaveModel(R_GET_PTR(handle), R_AS_INT(num_iteration), R_CHAR_PTR(filename)));
+  CHECK_CALL(LGBM_BoosterSaveModel(R_GET_PTR(handle), 0, R_AS_INT(num_iteration), R_CHAR_PTR(filename)));
   R_API_END();
 }
 
@@ -610,7 +610,7 @@ LGBM_SE LGBM_BoosterSaveModelToString_R(LGBM_SE handle,
   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), 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(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();
 }
@@ -624,7 +624,7 @@ LGBM_SE LGBM_BoosterDumpModel_R(LGBM_SE handle,
   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), 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(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();
 }

tests/c_api_test/test_.py

@@ -225,7 +225,7 @@ def test_booster():
         LIB.LGBM_BoosterGetEval(booster, 0, ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
         if i % 10 == 0:
             print('%d Iteration test AUC %f' % (i, result[0]))
-    LIB.LGBM_BoosterSaveModel(booster, -1, c_str('model.txt'))
+    LIB.LGBM_BoosterSaveModel(booster, 0, -1, c_str('model.txt'))
     LIB.LGBM_BoosterFree(booster)
     test_free_dataset(train)
     test_free_dataset(test)