[python] avoid to set all weight to 1. (#1152)

* avoid to set all weight to 1.

* fix valid_class_weight

python-package/lightgbm/basic.py

@@ -1105,6 +1105,8 @@ class Dataset(object):
         weight : list, numpy array or None
             Weight to be set for each data point.
         """
+        if weight is not None and np.all(weight == 1):
+            weight = None
         self.weight = weight
         if self.handle is not None and weight is not None:
             weight = list_to_1d_numpy(weight, name='weight')

python-package/lightgbm/sklearn.py

@@ -418,11 +418,12 @@ class LGBMModel(_LGBMModelBase):
             X, y = _LGBMCheckXY(X, y, accept_sparse=True, force_all_finite=False, ensure_min_samples=2)
             _LGBMCheckConsistentLength(X, y, sample_weight)
 
-        class_sample_weight = _LGBMComputeSampleWeight(self.class_weight, y)
-        if sample_weight is None or len(sample_weight) == 0:
-            sample_weight = class_sample_weight
-        else:
-            sample_weight = np.multiply(sample_weight, class_sample_weight)
+        if self.class_weight is not None:
+            class_sample_weight = _LGBMComputeSampleWeight(self.class_weight, y)
+            if sample_weight is None or len(sample_weight) == 0:
+                sample_weight = class_sample_weight
+            else:
+                sample_weight = np.multiply(sample_weight, class_sample_weight)
 
         self._n_features = X.shape[1]
 
@@ -452,11 +453,12 @@ class LGBMModel(_LGBMModelBase):
                         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)
-                    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)
+                    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_set = _construct_dataset(valid_data[0], valid_data[1], valid_weight, valid_init_score, valid_group, params)