[python] updated multiclass objective in sklearn (#1218)

* added comment to not forget

* updated LGBMClassifier according to new aliases

python-package/lightgbm/basic.py

@@ -183,7 +183,7 @@ def convert_from_sliced_object(data):
     """fix the memory of multi-dimensional sliced object"""
     if data.base is not None and isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray):
         if not data.flags.c_contiguous:
-            warnings.warn("Use subset(sliced data) of np.ndarray is not recommended due to it will double the peak memory cost in LightGBM.")
+            warnings.warn("Usage subset(sliced data) of np.ndarray is not recommended due to it will double the peak memory cost in LightGBM.")
             return np.copy(data)
     return data
 
@@ -206,7 +206,7 @@ def c_float_array(data):
                             .format(data.dtype))
     else:
         raise TypeError("Unknown type({})".format(type(data).__name__))
-    return (ptr_data, type_data, data)
+    return (ptr_data, type_data, data)  # return `data` to avoid the temporary copy is freed
 
 
 def c_int_array(data):
@@ -227,7 +227,7 @@ def c_int_array(data):
                             .format(data.dtype))
     else:
         raise TypeError("Unknown type({})".format(type(data).__name__))
-    return (ptr_data, type_data, data)
+    return (ptr_data, type_data, data)  # return `data` to avoid the temporary copy is freed
 
 
 PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int',

python-package/lightgbm/sklearn.py

@@ -644,7 +644,8 @@ class LGBMClassifier(LGBMModel, _LGBMClassifierBase):
         self._n_classes = len(self._classes)
         if self._n_classes > 2:
             # Switch to using a multiclass objective in the underlying LGBM instance
-            if self._objective != "multiclassova" and not callable(self._objective):
+            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':
                 eval_metric = "multi_logloss"