[python] minor fixes in sklearn wrapper (#1572)

* refined random_state description

* use neutral alias and fixed desc of eval_at param, since metric can be not only ndcg

* simplified checks

* consider verbose alias

* fixed declaring function every loop iteration

python-package/lightgbm/sklearn.py

@@ -188,7 +188,7 @@ class LGBMModel(_LGBMModelBase):
             L2 regularization term on weights.
         random_state : int or None, optional (default=None)
             Random number seed.
-            Will use default seeds in c++ code if set to None.
+            If None, default seeds in C++ code will be used.
         n_jobs : int, optional (default=-1)
             Number of parallel threads.
         silent : bool, optional (default=True)
@@ -398,7 +398,7 @@ class LGBMModel(_LGBMModelBase):
         evals_result = {}
         params = self.get_params()
         # user can set verbose with kwargs, it has higher priority
-        if 'verbose' not in params and self.silent:
+        if not any(verbose_alias in params for verbose_alias in ('verbose', 'verbosity')) and self.silent:
             params['verbose'] = 0
         params.pop('silent', None)
         params.pop('importance_type', None)
@@ -407,7 +407,7 @@ class LGBMModel(_LGBMModelBase):
         if self._n_classes is not None and self._n_classes > 2:
             params['num_class'] = self._n_classes
         if hasattr(self, '_eval_at'):
-            params['ndcg_eval_at'] = self._eval_at
+            params['eval_at'] = self._eval_at
         params['objective'] = self._objective
         if self._fobj:
             params['objective'] = 'None'  # objective = nullptr for unknown objective
@@ -440,14 +440,8 @@ class LGBMModel(_LGBMModelBase):
 
         valid_sets = []
         if eval_set is not None:
-            if isinstance(eval_set, tuple):
-                eval_set = [eval_set]
-            for i, valid_data in enumerate(eval_set):
-                # reduce cost for prediction training data
-                if valid_data[0] is X and valid_data[1] is y:
-                    valid_set = train_set
-                else:
-                    def get_meta_data(collection, i):
+
+            def _get_meta_data(collection, i):
                 if collection is None:
                     return None
                 elif isinstance(collection, list):
@@ -456,15 +450,23 @@ class LGBMModel(_LGBMModelBase):
                     return collection.get(i, None)
                 else:
                     raise TypeError('eval_sample_weight, eval_class_weight, eval_init_score, and eval_group should be dict or list')
-                    valid_weight = get_meta_data(eval_sample_weight, i)
-                    if get_meta_data(eval_class_weight, i) is not None:
-                        valid_class_sample_weight = _LGBMComputeSampleWeight(get_meta_data(eval_class_weight, i), valid_data[1])
+
+            if isinstance(eval_set, tuple):
+                eval_set = [eval_set]
+            for i, valid_data in enumerate(eval_set):
+                # reduce cost for prediction training data
+                if valid_data[0] is X and valid_data[1] is y:
+                    valid_set = train_set
+                else:
+                    valid_weight = _get_meta_data(eval_sample_weight, i)
+                    if _get_meta_data(eval_class_weight, i) is not None:
+                        valid_class_sample_weight = _LGBMComputeSampleWeight(_get_meta_data(eval_class_weight, i), valid_data[1])
                         if valid_weight is None or len(valid_weight) == 0:
                             valid_weight = valid_class_sample_weight
                         else:
                             valid_weight = np.multiply(valid_weight, valid_class_sample_weight)
-                    valid_init_score = get_meta_data(eval_init_score, i)
-                    valid_group = get_meta_data(eval_group, i)
+                    valid_init_score = _get_meta_data(eval_init_score, i)
+                    valid_group = _get_meta_data(eval_group, i)
                     valid_set = _construct_dataset(valid_data[0], valid_data[1], valid_weight, valid_init_score, valid_group, params)
                 valid_sets.append(valid_set)
 
@@ -668,14 +670,14 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
             ova_aliases = ("multiclassova", "multiclass_ova", "ova", "ovr")
             if self._objective not in ova_aliases and not callable(self._objective):
                 self._objective = "multiclass"
-            if eval_metric == 'logloss' or eval_metric == 'binary_logloss':
+            if eval_metric in ('logloss', 'binary_logloss'):
                 eval_metric = "multi_logloss"
-            elif eval_metric == 'error' or eval_metric == 'binary_error':
+            elif eval_metric in ('error', 'binary_error'):
                 eval_metric = "multi_error"
         else:
-            if eval_metric == 'logloss' or eval_metric == 'multi_logloss':
+            if eval_metric in ('logloss', 'multi_logloss'):
                 eval_metric = 'binary_logloss'
-            elif eval_metric == 'error' or eval_metric == 'multi_error':
+            elif eval_metric in ('error', 'multi_error'):
                 eval_metric = 'binary_error'
 
         if eval_set is not None:
@@ -809,5 +811,5 @@ class LGBMRanker(LGBMModel):
                    + 'eval_metric : string, list of strings, callable or None, optional (default="ndcg")\n'
                    + _base_doc[_base_doc.find('            If string, it should be a built-in evaluation metric to use.'):_base_doc.find('early_stopping_rounds :')]
                    + 'eval_at : list of int, optional (default=[1])\n'
-                     '            The evaluation positions of NDCG.\n'
+                     '            The evaluation positions of the specified metric.\n'
                    + _base_doc[_base_doc.find('        early_stopping_rounds :'):])