[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
@@ -482,6 +486,7 @@ 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
@@ -489,11 +494,13 @@ struct Config {
   // 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
@@ -660,6 +672,7 @@ 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
@@ -667,18 +680,22 @@ struct Config {
   // 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 @@ class Booster(object):
                     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 @@ class Booster(object):
         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 @@ class TestEngine(unittest.TestCase):
         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))