[python-package] pass params of engine.train and engine.cv to Dataset.

python-package/lightgbm/basic.py

@@ -927,6 +927,12 @@ class Dataset(object):
         ret._set_predictor(self._predictor)
         return ret
 
+    def _update_params(self, params):
+        if not self.params:
+            self.params = params
+        else:
+            self.params.update(params)
+
     def construct(self):
         """
         Lazy init

python-package/lightgbm/engine.py

@@ -92,6 +92,7 @@ def train(params, train_set, num_boost_round=100,
     if not isinstance(train_set, Dataset):
         raise TypeError("Traninig only accepts Dataset object")
 
+    train_set._update_params(params)
     train_set._set_predictor(predictor)
     train_set.set_feature_name(feature_name)
     train_set.set_categorical_feature(categorical_feature)
@@ -120,7 +121,8 @@ def train(params, train_set, num_boost_round=100,
                 name_valid_sets.append(valid_names[i])
             else:
                 name_valid_sets.append('valid_'+str(i))
-
+        for valid_data in valid_sets:
+            valid_data._update_params(params)
     """process callbacks"""
     if callbacks is None:
         callbacks = set()
@@ -332,7 +334,7 @@ def cv(params, train_set, num_boost_round=10, nfold=5, stratified=False,
         predictor = init_model._to_predictor()
     else:
         predictor = None
-
+    train_set._update_params(params)
     train_set._set_predictor(predictor)
     train_set.set_feature_name(feature_name)
     train_set.set_categorical_feature(categorical_feature)

src/io/config.cpp

@@ -273,11 +273,11 @@ void TreeConfig::Set(const std::unordered_map<std::string, std::string>& params)
   GetDouble(params, "min_sum_hessian_in_leaf", &min_sum_hessian_in_leaf);
   CHECK(min_sum_hessian_in_leaf > 1.0f || min_data_in_leaf > 0);
   GetDouble(params, "lambda_l1", &lambda_l1);
-  CHECK(lambda_l1 >= 0.0f)
+  CHECK(lambda_l1 >= 0.0f);
   GetDouble(params, "lambda_l2", &lambda_l2);
-  CHECK(lambda_l2 >= 0.0f)
+  CHECK(lambda_l2 >= 0.0f);
   GetDouble(params, "min_gain_to_split", &min_gain_to_split);
-  CHECK(min_gain_to_split >= 0.0f)
+  CHECK(min_gain_to_split >= 0.0f);
   GetInt(params, "num_leaves", &num_leaves);
   CHECK(num_leaves > 1);
   GetInt(params, "feature_fraction_seed", &feature_fraction_seed);