def test_generate_corruptions_for_eval():
X = np.array([['a', 'x', 'b'],
['c', 'x', 'd'],
['e', 'x', 'f'],
['b', 'y', 'h'],
['a', 'y', 'l']])
rel_to_idx, ent_to_idx = create_mappings(X)
X = to_idx(X, ent_to_idx=ent_to_idx, rel_to_idx=rel_to_idx)
with tf.Session() as sess:
all_ent = tf.constant(list(ent_to_idx.values()), dtype=tf.int64)
x = tf.constant(np.array([X[0]]), dtype=tf.int64)
x_n_actual, _ = sess.run(generate_corruptions_for_eval(x, all_ent))
x_n_expected = np.array([[0, 0, 0],
[0, 0, 1],
[0, 0, 2],
[0, 0, 3],
[0, 0, 4],
[0, 0, 5],
[0, 0, 6],
[0, 0, 7],
[0, 0, 1],
[1, 0, 1],
[2, 0, 1],
[3, 0, 1],
[4, 0, 1],
[5, 0, 1],
[6, 0, 1],
[7, 0, 1]])
np.testing.assert_array_equal(x_n_actual, x_n_expected)
def test_to_idx():
X = np.array([['a', 'x', 'b'], ['c', 'y', 'd']])
X_idx_expected = [[0, 0, 1], [2, 1, 3]]
rel_to_idx, ent_to_idx = create_mappings(X)
X_idx = to_idx(X, ent_to_idx=ent_to_idx, rel_to_idx=rel_to_idx)
np.testing.assert_array_equal(X_idx, X_idx_expected)
def test_generate_corruptions_for_fit_curropt_side_s():
tf.reset_default_graph()
X = np.array([['a', 'x', 'b'],
['c', 'x', 'd'],
['e', 'x', 'f'],
['b', 'y', 'h'],
['a', 'y', 'l']])
rel_to_idx, ent_to_idx = create_mappings(X)
X = to_idx(X, ent_to_idx=ent_to_idx, rel_to_idx=rel_to_idx)
eta = 1
with tf.Session() as sess:
all_ent = tf.squeeze(tf.constant(list(ent_to_idx.values()), dtype=tf.int32))
dataset = tf.constant(X, dtype=tf.int32)
X_corr = sess.run(generate_corruptions_for_fit(dataset, eta=eta, corrupt_side='s', entities_size=len(X), rnd=0))
print(X_corr)
# these values occur when seed=0
X_corr_exp = [[1, 0, 1],
[3, 0, 3],
[3, 0, 5],
[0, 1, 6],
[3, 1, 7]]
np.testing.assert_array_equal(X_corr, X_corr_exp)
