Fix identation for bilinear

Author: mvargas33

metric_learn/base_metric.py

@@ -160,6 +160,7 @@ def transform(self, X):
       Input data transformed to the metric space by :math:`XL^{\\top}`
     """
 
+
 class BilinearMixin(BaseMetricLearner, metaclass=ABCMeta):
 
   def score_pairs(self, pairs):
@@ -180,14 +181,15 @@ def score_pairs(self, pairs):
       pairs = check_input(pairs, type_of_inputs='tuples',
                           preprocessor=self.preprocessor_,
                           estimator=self, tuple_size=2)
-                          
       # Note: For bilinear order matters, dist(a,b) != dist(b,a)
       # We always choose first pair first, then second pair
       # (In contrast with Mahalanobis implementation)
-      
       # I dont know wich implementation performs better
-      return np.diagonal(np.dot(np.dot(pairs[:, 0, :], self.components_), pairs[:, 1, :].T))
-      return [np.dot(np.dot(u.T, self.components_), v) for u,v in zip(pairs[:, 0, :], pairs[:, 1, :])]
+      return np.diagonal(np.dot(
+                      np.dot(pairs[:, 0, :], self.components_),
+                      pairs[:, 1, :].T))
+      return np.array([np.dot(np.dot(u.T, self.components_), v)
+                      for u, v in zip(pairs[:, 0, :], pairs[:, 1, :])])
 
   def get_metric(self):
       check_is_fitted(self, 'components_')
@@ -220,6 +222,7 @@ def get_bilinear_matrix(self):
       check_is_fitted(self, 'components_')
       return self.components_
 
+
 class MahalanobisMixin(BaseMetricLearner, MetricTransformer,
                        metaclass=ABCMeta):
   r"""Mahalanobis metric learning algorithms.

metric_learn/oasis.py

@@ -1,6 +1,7 @@
 from .base_metric import BilinearMixin
 import numpy as np
 
+
 class OASIS(BilinearMixin):
 
     def __init__(self, preprocessor=None):
@@ -16,7 +17,8 @@ def fit(self, X, y):
         y : (n) data labels
         """
         X = self._prepare_inputs(X, y, ensure_min_samples=2)
-        
+
         # Dummy fit
-        self.components_ = np.random.rand(np.shape(X[0])[-1], np.shape(X[0])[-1])
-        return self
+        self.components_ = np.random.rand(
+            np.shape(X[0])[-1], np.shape(X[0])[-1])
+        return self

test_bilinear.py

@@ -2,6 +2,7 @@
 import numpy as np
 from numpy.testing import assert_array_almost_equal
 
+
 def test_toy_distance():
     # Random generalized test for 2 points
     d = 100
@@ -10,33 +11,34 @@ def test_toy_distance():
     v = np.random.rand(d)
 
     mixin = OASIS()
-    mixin.fit([u, v], [0, 0]) # Dummy fit
+    mixin.fit([u, v], [0, 0])  # Dummy fit
 
     # The distances must match, whether calc with get_metric() or score_pairs()
     dist1 = mixin.score_pairs([[u, v], [v, u]])
     dist2 = [mixin.get_metric()(u, v), mixin.get_metric()(v, u)]
-    
+
     u_v = (np.dot(np.dot(u.T, mixin.get_bilinear_matrix()), v))
     v_u = (np.dot(np.dot(v.T, mixin.get_bilinear_matrix()), u))
     desired = [u_v, v_u]
-    
+
     assert_array_almost_equal(dist1, desired)
     assert_array_almost_equal(dist2, desired)
 
     # Handmade example
-    u = np.array([0, 1 ,2])
+    u = np.array([0, 1, 2])
     v = np.array([3, 4, 5])
 
-    mixin.components_= np.array([[2,4,6], [6,4,2], [1, 2, 3]])
+    mixin.components_ = np.array([[2, 4, 6], [6, 4, 2], [1, 2, 3]])
     dists = mixin.score_pairs([[u, v], [v, u]])
     assert_array_almost_equal(dists, [96, 120])
 
     # Symetric example
-    u = np.array([0, 1 ,2])
+    u = np.array([0, 1, 2])
     v = np.array([3, 4, 5])
 
-    mixin.components_= np.identity(3) # Identity matrix
+    mixin.components_ = np.identity(3)  # Identity matrix
     dists = mixin.score_pairs([[u, v], [v, u]])
     assert_array_almost_equal(dists, [14, 14])
 
-test_toy_distance()
+
+test_toy_distance()