Found an efficient way to compute Bilinear Sim for n pairs

Author: mvargas33

metric_learn/base_metric.py

@@ -184,12 +184,7 @@ def score_pairs(self, pairs):
       # 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.array([np.dot(np.dot(u.T, self.components_), v)
-                      for u, v in zip(pairs[:, 0, :], pairs[:, 1, :])])
+      return (np.dot(pairs[:, 0, :], self.components_) * pairs[:, 1, :]).sum(-1)
 
   def get_metric(self):
       check_is_fitted(self, 'components_')

test_bilinear.py

@@ -59,7 +59,7 @@ def test_bilinar_properties():
 def test_performace():
 
     features = int(1e4)
-    samples = int(1e3)
+    samples = int(1e4)
 
     a = [np.random.rand(features) for i in range(samples)]
     b = [np.random.rand(features) for i in range(samples)]
@@ -75,6 +75,9 @@ def op_2(pairs, components):
         return np.array([np.dot(np.dot(u.T, components), v)
                          for u, v in zip(pairs[:, 0, :], pairs[:, 1, :])])
 
+    def op_3(pairs, components):
+        return (np.dot(pairs[:, 0, :], components) * pairs[:, 1, :]).sum(-1)
+
     # Test first method
     start = timer()
     op_1(pairs, components)
@@ -86,8 +89,9 @@ def op_2(pairs, components):
     op_2(pairs, components)
     end = timer()
     print(f'Second method took {end - start}')
-
-
-# test_toy_distance()
-# test_bilinar_properties()
-test_performace()
+    
+    # Test second method
+    start = timer()
+    op_3(pairs, components)
+    end = timer()
+    print(f'Third method took {end - start}')