Fomate pep8 and remove redundant test for 'new_labels' error

Hamzeh Alsalhi · Hamzeh Alsalhi · commit 767bea95e68e · 2014-08-30T19:27:51.000-04:00
diff --git a/sklearn/preprocessing/label.py b/sklearn/preprocessing/label.py
@@ -12,7 +12,6 @@
 import array
 import warnings
 
-import operator
 import numpy as np
 import scipy.sparse as sp
 
@@ -108,9 +107,11 @@ class LabelEncoder(BaseEstimator, TransformerMixin):
 
     def __init__(self, new_labels="raise"):
         # Check new_labels parameter
-        if new_labels not in ["update", "raise"] and type(new_labels) is not int:
-            raise ValueError("Value of argument `new_labels`={0} is unknown "
-                             "and not an integer.".format(new_labels))
+        if (new_labels not in ["update", "raise"] and
+            type(new_labels) is not int):
+                raise ValueError("Value of argument `new_labels`={0} is "
+                                 "unknown and not an "
+                                 "integer.".format(new_labels))
         self.new_labels = new_labels
 
     def _check_fitted(self):
@@ -505,7 +506,8 @@ def label_binarize(y, classes, neg_label=0, pos_label=1,
         allow for fitting to classes independently of the transform operation
     """
     if not isinstance(y, list):
-        # XXX Workaround that will be removed when list of list format is dropped
+        # XXX Workaround that will be removed when list of list format is
+        # dropped
         y = check_array(y, accept_sparse='csr', ensure_2d=False)
     if neg_label >= pos_label:
         raise ValueError("neg_label={0} must be strictly less than "
diff --git a/sklearn/preprocessing/tests/test_label.py b/sklearn/preprocessing/tests/test_label.py
@@ -240,11 +240,6 @@ def test_label_encoder_new_label_replace():
     assert_array_equal(le.inverse_transform([3, 1, 0]), ["d", "b", "a"])
 
 
-def test_label_encoder_new_label_arg():
-    """Test LabelEncoder's new_labels argument handling"""
-    assert_raises(ValueError, LabelEncoder, "xyz")
-
-
 def test_label_encoder_fit_transform():
     """Test fit_transform"""
     le = LabelEncoder()
@@ -261,7 +256,7 @@ def test_label_encoder_errors():
     le = LabelEncoder()
     assert_raises(ValueError, le.transform, [])
     assert_raises(ValueError, le.inverse_transform, [])
-    # Fail on unrecognized vlaue for the 'new_label' parameter 
+    # Fail on unrecognized vlaue for the 'new_label' parameter
     assert_raises(ValueError, LabelEncoder, "xyz")
 
 
