def test_dot(self):
# Four users, two topics, and slate size is two.
similarity_model = affinities.TargetPointSimilarity((4, ), 2, 'dot')
user_interests = ed.Deterministic(loc=np.array(
[[-4, 3], [-4, -3], [4, 3], [4, -3]], dtype=np.float32))
slate_docs = ed.Deterministic(
loc=np.array([[[-8, 6], [0, 0]], [[-8, -6], [0, 0]],
[[8, 6], [0, 0]], [[8, -6], [0, 0]]],
dtype=np.float32))
expected = ed.Deterministic(loc=np.array(
[[50, 0.], [50, 0.], [50, 0.], [50, 0.]], dtype=np.float32))
actual = similarity_model.affinities(user_interests, slate_docs)
self.assertAllClose(self.evaluate(expected),
self.evaluate(actual.get('affinities')))
def test_negative_cosine(self):
# Four users, two topics, and five documents.
similarity_model = affinities.TargetPointSimilarity((4, ), 5,
'negative_cosine')
user_interests = ed.Deterministic(loc=np.array(
[[-4, 3], [-4, -3], [4, 3], [4, -3]], dtype=np.float32))
documents = ed.Deterministic(loc=np.array(
[[-8, 6], [-8, -6], [8, 6], [8, -6], [0, 0]], dtype=np.float32))
expected = ed.Deterministic(loc=np.array(
[[1., 0.28, -0.28, -1., 0.], [0.28, 1., -1., -0.28, 0.],
[-0.28, -1., 1., 0.28, 0.], [-1., -0.28, 0.28, 1., 0.]],
dtype=np.float32))
actual = similarity_model.affinities(user_interests, documents)
self.assertAllClose(self.evaluate(expected),
self.evaluate(actual.get('affinities')))
def test_single_peaked(self):
similarity_model = affinities.TargetPointSimilarity((4, ), 2,
'single_peaked')
user_interests = ed.Deterministic(loc=np.array(
[[-4, 3], [-4, -3], [4, 3], [4, -3]], dtype=np.float32))
slate_docs = ed.Deterministic(
loc=np.array([[[-8, 6], [0, 0]], [[-8, -6], [0, 0]],
[[8, 6], [0, 0]], [[8, -6], [0, 0]]],
dtype=np.float32))
expected = ed.Deterministic(loc=np.array(
[[50, 0.], [14, 0.], [-14, 0.], [-50, 0.]], dtype=np.float32))
actual = similarity_model.affinities(user_interests,
slate_docs,
affinity_peaks=tf.constant(
[[32., 32.], [32., 0.],
[0., 32.], [0., 0.]]))
self.assertAllClose(self.evaluate(expected),
self.evaluate(actual.get('affinities')))
def test_inverse_euclidean(self):
# Four users, two topics, and five documents.
similarity_model = affinities.TargetPointSimilarity(
(4, ), 5, 'inverse_euclidean')
user_interests = ed.Deterministic(loc=np.array(
[[-4, 3], [-4, -3], [4, 3], [4, -3]], dtype=np.float32))
documents = ed.Deterministic(loc=np.array(
[[-8, 6], [-8, -6], [8, 6], [8, -6], [0, 0]], dtype=np.float32))
expected = ed.Deterministic(loc=np.array(
[[.2, np.sqrt(1. / 97),
np.sqrt(1. / 153), 1. /
15, .2], [np.sqrt(1. / 97), .2, 1. / 15,
np.sqrt(1. / 153), .2],
[np.sqrt(1. / 153), 1. /
15, .2, np.sqrt(1. / 97), .2],
[1. / 15, np.sqrt(1. / 153),
np.sqrt(1. / 97), .2, .2]],
dtype=np.float32))
actual = similarity_model.affinities(user_interests, documents)
self.assertAllClose(self.evaluate(expected),
self.evaluate(actual.get('affinities')))
def test_specs(self):
similarity_model = affinities.TargetPointSimilarity((4, ), 2, 'dot')
specs = similarity_model.specs()
self.assertAllEqual(specs.get('affinities').space.shape, (4, 2))