Generalized toy tests

Author: mvargas33

metric_learn/oasis.py

@@ -16,5 +16,11 @@ def fit(self, X, y):
         y : (n) data labels
         """
         X = self._prepare_inputs(X, y, ensure_min_samples=2)
-        self.components_ = np.identity(np.shape(X[0])[-1]) # Identity matrix
+        
+        # Handmade dummy fit
+        #self.components_ = np.identity(np.shape(X[0])[-1]) # Identity matrix
+        #self.components_ = np.array([[2,4,6], [6,4,2], [1, 2, 3]])
+
+        # Dummy fit
+        self.components_ = np.random.rand(np.shape(X[0])[-1], np.shape(X[0])[-1])
         return self

test_bilinear.py

@@ -1,15 +1,25 @@
 from metric_learn.oasis import OASIS
 import numpy as np
+from numpy.testing import assert_array_almost_equal
 
 def test_toy_distance():
-    u = np.array([0, 1, 2])
-    v = np.array([3, 4, 5])
+    d = 100
+
+    u = np.random.rand(d)
+    v = np.random.rand(d)
 
     mixin = OASIS()
-    mixin.fit([u, v], [0, 0])
-    #mixin.components_ = np.array([[1, 0, 0],[0, 1, 0],[0, 0, 1]])
+    mixin.fit([u, v], [0, 0]) # Dummy fit
 
-    dist = mixin.score_pairs([[u, v],[v, u]])
-    print(dist)
+    # 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)
 
 test_toy_distance()