def test_reduce_batch_coocurrences(self, x, weights, y, expected_results):
x = tf.constant(x)
y = tf.constant(y, dtype=tf.int64)
if weights is not None:
weights = tf.constant(weights)
returned_tensors = (tf_utils.reduce_batch_weighted_cooccurrences(
x, y, weights))
with tf.compat.v1.Session() as sess:
results = sess.run(returned_tensors)
for result, expected in zip(results, expected_results):
self.assertAllEqual(result, np.array(expected))
def test_reduce_batch_coocurrences_sparse_tensor(self):
x = tf.SparseTensor(indices=[(0, 0), (2, 1)],
values=['a', 'b'],
dense_shape=[4, 2])
y = tf.constant([0, 1, 0, 0], dtype=tf.int64)
expected_results = [[b'a', b'b', b'global_y_count_sentinel'],
[1, 1, 4], [[1, 0], [1, 0], [3, 1]], [1, 1, 4]]
returned_tensors = (tf_utils.reduce_batch_weighted_cooccurrences(
x, y, None))
with tf.compat.v1.Session() as sess:
results = sess.run(returned_tensors)
for result, expected in zip(results, expected_results):
self.assertAllEqual(result, np.array(expected))
def _reduce_batch_weighted_cooccurrences_no_weights(x, y):
return tf_utils.reduce_batch_weighted_cooccurrences(x, y)
