[python] save all param values into model file (#2589)

* save all param values into model file

* revert storing predict params

* do not save params for predict and convert tasks

* fixed test: 10 is found successfully for default 100

* specify more params as no-save

helpers/parameter_generator.py

@@ -315,7 +315,7 @@ def gen_parameter_code(config_hpp, config_out_cpp):
     str_to_write += "  std::stringstream str_buf;\n"
     for x in infos:
         for y in x:
-            if "[doc-only]" in y:
+            if "[doc-only]" in y or "[no-save]" in y:
                 continue
             param_type = y["inner_type"][0]
             name = y["name"][0]

include/LightGBM/config.h

@@ -3,8 +3,10 @@
  * Licensed under the MIT License. See LICENSE file in the project root for license information.
  *
  * \note
- * desc and descl2 fields must be written in reStructuredText format;
- * nested sections can be placed only at the bottom of parent's section
+ * - desc and descl2 fields must be written in reStructuredText format;
+ * - nested sections can be placed only at the bottom of parent's section;
+ * - [doc-only] tag indicates that only documentation for this param should be generated and all other actions are performed manually;
+ * - [no-save] tag indicates that this param should not be saved into a model text representation.
  */
 #ifndef LIGHTGBM_CONFIG_H_
 #define LIGHTGBM_CONFIG_H_
@@ -83,12 +85,14 @@ struct Config {
 
   #pragma region Core Parameters
 
+  // [no-save]
   // [doc-only]
   // alias = config_file
   // desc = path of config file
   // desc = **Note**: can be used only in CLI version
   std::string config = "";
 
+  // [no-save]
   // [doc-only]
   // type = enum
   // default = train
@@ -482,18 +486,21 @@ struct Config {
   // desc = ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug
   int verbosity = 1;
 
+  // [no-save]
   // alias = model_input, model_in
   // desc = filename of input model
   // desc = for ``prediction`` task, this model will be applied to prediction data
   // desc = for ``train`` task, training will be continued from this model
   // desc = **Note**: can be used only in CLI version
   std::string input_model = "";
 
+  // [no-save]
   // alias = model_output, model_out
   // desc = filename of output model in training
   // desc = **Note**: can be used only in CLI version
   std::string output_model = "LightGBM_model.txt";
 
+  // [no-save]
   // alias = save_period
   // desc = frequency of saving model file snapshot
   // desc = set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1``
@@ -626,6 +633,7 @@ struct Config {
   // desc = see `this file <https://github.com/microsoft/LightGBM/tree/master/examples/regression/forced_bins.json>`__ as an example
   std::string forcedbins_filename = "";
 
+  // [no-save]
   // alias = is_save_binary, is_save_binary_file
   // desc = if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time
   // desc = **Note**: ``init_score`` is not saved in binary file
@@ -636,22 +644,26 @@ struct Config {
 
   #pragma region Predict Parameters
 
+  // [no-save]
   // desc = used only in ``prediction`` task
   // desc = used to specify how many trained iterations will be used in prediction
   // desc = ``<= 0`` means no limit
   int num_iteration_predict = -1;
 
+  // [no-save]
   // alias = is_predict_raw_score, predict_rawscore, raw_score
   // desc = used only in ``prediction`` task
   // desc = set this to ``true`` to predict only the raw scores
   // desc = set this to ``false`` to predict transformed scores
   bool predict_raw_score = false;
 
+  // [no-save]
   // alias = is_predict_leaf_index, leaf_index
   // desc = used only in ``prediction`` task
   // desc = set this to ``true`` to predict with leaf index of all trees
   bool predict_leaf_index = false;
 
+  // [no-save]
   // alias = is_predict_contrib, contrib
   // desc = used only in ``prediction`` task
   // desc = set this to ``true`` to estimate `SHAP values <https://arxiv.org/abs/1706.06060>`__, which represent how each feature contributes to each prediction
@@ -660,25 +672,30 @@ struct Config {
   // desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value
   bool predict_contrib = false;
 
+  // [no-save]
   // desc = used only in ``prediction`` task
   // desc = control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data
   // desc = if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training
   // desc = if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model
   // desc = **Note**: be very careful setting this parameter to ``true``
   bool predict_disable_shape_check = false;
 
+  // [no-save]
   // desc = used only in ``prediction`` task
   // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy
   bool pred_early_stop = false;
 
+  // [no-save]
   // desc = used only in ``prediction`` task
   // desc = the frequency of checking early-stopping prediction
   int pred_early_stop_freq = 10;
 
+  // [no-save]
   // desc = used only in ``prediction`` task
   // desc = the threshold of margin in early-stopping prediction
   double pred_early_stop_margin = 10.0;
 
+  // [no-save]
   // alias = predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred
   // desc = used only in ``prediction`` task
   // desc = filename of prediction result
@@ -689,12 +706,14 @@ struct Config {
 
   #pragma region Convert Parameters
 
+  // [no-save]
   // desc = used only in ``convert_model`` task
   // desc = only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen <https://github.com/BayesWitnesses/m2cgen>`__ utility
   // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted
   // desc = **Note**: can be used only in CLI version
   std::string convert_model_language = "";
 
+  // [no-save]
   // alias = convert_model_file
   // desc = used only in ``convert_model`` task
   // desc = output filename of converted model
@@ -820,12 +839,14 @@ struct Config {
   // desc = support multiple metrics, separated by ``,``
   std::vector<std::string> metric;
 
+  // [no-save]
   // check = >0
   // alias = output_freq
   // desc = frequency for metric output
   // desc = **Note**: can be used only in CLI version
   int metric_freq = 1;
 
+  // [no-save]
   // alias = training_metric, is_training_metric, train_metric
   // desc = set this to ``true`` to output metric result over training dataset
   // desc = **Note**: can be used only in CLI version

python-package/lightgbm/basic.py

@@ -1751,7 +1751,7 @@ def __init__(self, params=None, train_set=None, model_file=None, model_str=None,
                     self.set_network(machines,
                                      local_listen_port=params.get("local_listen_port", 12400),
                                      listen_time_out=params.get("listen_time_out", 120),
-                                     num_machines=params.get("num_machines", num_machines))
+                                     num_machines=params.setdefault("num_machines", num_machines))
                     break
             # construct booster object
             train_set.construct()
@@ -2641,7 +2641,7 @@ def refit(self, data, label, decay_rate=0.9, **kwargs):
         train_set = Dataset(data, label, silent=True)
         new_params = copy.deepcopy(self.params)
         new_params['refit_decay_rate'] = decay_rate
-        new_booster = Booster(new_params, train_set, silent=True)
+        new_booster = Booster(new_params, train_set)
         # Copy models
         _safe_call(_LIB.LGBM_BoosterMerge(
             new_booster.handle,

python-package/lightgbm/engine.py

@@ -146,13 +146,13 @@ def train(params, train_set, num_boost_round=100,
         if alias in params:
             num_boost_round = params.pop(alias)
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
-            break
+    params["num_iterations"] = num_boost_round
     for alias in _ConfigAliases.get("early_stopping_round"):
         if alias in params:
             early_stopping_rounds = params.pop(alias)
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
-            break
-    first_metric_only = params.pop('first_metric_only', False)
+    params["early_stopping_round"] = early_stopping_rounds
+    first_metric_only = params.get('first_metric_only', False)
 
     if num_boost_round <= 0:
         raise ValueError("num_boost_round should be greater than zero.")
@@ -504,13 +504,13 @@ def cv(params, train_set, num_boost_round=100,
         if alias in params:
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             num_boost_round = params.pop(alias)
-            break
+    params["num_iterations"] = num_boost_round
     for alias in _ConfigAliases.get("early_stopping_round"):
         if alias in params:
             warnings.warn("Found `{}` in params. Will use it instead of argument".format(alias))
             early_stopping_rounds = params.pop(alias)
-            break
-    first_metric_only = params.pop('first_metric_only', False)
+    params["early_stopping_round"] = early_stopping_rounds
+    first_metric_only = params.get('first_metric_only', False)
 
     if num_boost_round <= 0:
         raise ValueError("num_boost_round should be greater than zero.")

src/io/config_auto.cpp

@@ -641,9 +641,6 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[cegb_penalty_feature_lazy: " << Common::Join(cegb_penalty_feature_lazy, ",") << "]\n";
   str_buf << "[cegb_penalty_feature_coupled: " << Common::Join(cegb_penalty_feature_coupled, ",") << "]\n";
   str_buf << "[verbosity: " << verbosity << "]\n";
-  str_buf << "[input_model: " << input_model << "]\n";
-  str_buf << "[output_model: " << output_model << "]\n";
-  str_buf << "[snapshot_freq: " << snapshot_freq << "]\n";
   str_buf << "[max_bin: " << max_bin << "]\n";
   str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n";
   str_buf << "[min_data_in_bin: " << min_data_in_bin << "]\n";
@@ -663,18 +660,6 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[ignore_column: " << ignore_column << "]\n";
   str_buf << "[categorical_feature: " << categorical_feature << "]\n";
   str_buf << "[forcedbins_filename: " << forcedbins_filename << "]\n";
-  str_buf << "[save_binary: " << save_binary << "]\n";
-  str_buf << "[num_iteration_predict: " << num_iteration_predict << "]\n";
-  str_buf << "[predict_raw_score: " << predict_raw_score << "]\n";
-  str_buf << "[predict_leaf_index: " << predict_leaf_index << "]\n";
-  str_buf << "[predict_contrib: " << predict_contrib << "]\n";
-  str_buf << "[predict_disable_shape_check: " << predict_disable_shape_check << "]\n";
-  str_buf << "[pred_early_stop: " << pred_early_stop << "]\n";
-  str_buf << "[pred_early_stop_freq: " << pred_early_stop_freq << "]\n";
-  str_buf << "[pred_early_stop_margin: " << pred_early_stop_margin << "]\n";
-  str_buf << "[output_result: " << output_result << "]\n";
-  str_buf << "[convert_model_language: " << convert_model_language << "]\n";
-  str_buf << "[convert_model: " << convert_model << "]\n";
   str_buf << "[objective_seed: " << objective_seed << "]\n";
   str_buf << "[num_class: " << num_class << "]\n";
   str_buf << "[is_unbalance: " << is_unbalance << "]\n";
@@ -689,8 +674,6 @@ std::string Config::SaveMembersToString() const {
   str_buf << "[lambdarank_truncation_level: " << lambdarank_truncation_level << "]\n";
   str_buf << "[lambdarank_norm: " << lambdarank_norm << "]\n";
   str_buf << "[label_gain: " << Common::Join(label_gain, ",") << "]\n";
-  str_buf << "[metric_freq: " << metric_freq << "]\n";
-  str_buf << "[is_provide_training_metric: " << is_provide_training_metric << "]\n";
   str_buf << "[eval_at: " << Common::Join(eval_at, ",") << "]\n";
   str_buf << "[multi_error_top_k: " << multi_error_top_k << "]\n";
   str_buf << "[auc_mu_weights: " << Common::Join(auc_mu_weights, ",") << "]\n";

tests/python_package_test/test_engine.py

@@ -747,6 +747,8 @@ def train_and_predict(init_model=None, return_model=False):
         ret_origin = train_and_predict(init_model=gbm)
         other_ret = []
         gbm.save_model('lgb.model')
+        with open('lgb.model') as f:  # check all params are logged into model file correctly
+            self.assertNotEqual(f.read().find("[num_iterations: 10]"), -1)
         other_ret.append(train_and_predict(init_model='lgb.model'))
         gbm_load = lgb.Booster(model_file='lgb.model')
         other_ret.append(train_and_predict(init_model=gbm_load))