Following the correct testing structure

Author: mvargas33

metric_learn/oasis.py

@@ -0,0 +1,64 @@
+from metric_learn.base_metric import BilinearMixin
+import numpy as np
+from numpy.testing import assert_array_almost_equal
+
+class IdentityBilinearMixin(BilinearMixin):
+    """A simple Identity bilinear mixin that returns an identity matrix
+    M as learned. Can change M for a random matrix calling random_M.
+    Class for testing purposes.
+    """
+    def __init__(self, preprocessor=None):
+        super().__init__(preprocessor=preprocessor)
+
+    def fit(self, X, y):
+        X, y = self._prepare_inputs(X, y, ensure_min_samples=2)
+        self.d = np.shape(X[0])[-1]
+        self.components_ = np.identity(self.d)
+        return self
+    
+    def random_M(self):
+        self.components_ = np.random.rand(self.d, self.d)
+
+def test_same_similarity_with_two_methods():
+    d = 100
+    u = np.random.rand(d)
+    v = np.random.rand(d)
+    mixin = IdentityBilinearMixin()
+    mixin.fit([u, v], [0, 0])  # Dummy fit
+    mixin.random_M()
+
+    # 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)]
+
+    assert_array_almost_equal(dist1, dist2)
+
+def test_check_correctness_similarity():
+    d = 100
+    u = np.random.rand(d)
+    v = np.random.rand(d)
+    mixin = IdentityBilinearMixin()
+    mixin.fit([u, v], [0, 0])  # Dummy fit
+    dist1 = mixin.score_pairs([[u, v], [v, u]])
+    u_v = np.dot(np.dot(u.T, np.identity(d)), v)
+    v_u = np.dot(np.dot(v.T, np.identity(d)), u)
+    desired = [u_v, v_u]
+    assert_array_almost_equal(dist1, desired)
+
+def test_check_handmade_example():
+    u = np.array([0, 1, 2])
+    v = np.array([3, 4, 5])
+    mixin = IdentityBilinearMixin()
+    mixin.fit([u, v], [0, 0])
+    c = np.array([[2, 4, 6], [6, 4, 2], [1, 2, 3]])
+    mixin.components_ = c # Force a components_
+    dists = mixin.score_pairs([[u, v], [v, u]])
+    assert_array_almost_equal(dists, [96, 120])
+
+def test_check_handmade_symmetric_example():
+    u = np.array([0, 1, 2])
+    v = np.array([3, 4, 5])
+    mixin = IdentityBilinearMixin()
+    mixin.fit([u, v], [0, 0])
+    dists = mixin.score_pairs([[u, v], [v, u]])
+    assert_array_almost_equal(dists, [14, 14])