def test_reduce_batch_vocabulary(self, x, weights, expected_analyzer_inputs):
x = tf.constant(x)
if weights is not None:
weights = tf.constant(weights)
with tf.Session() as sess:
results = sess.run(tf_utils.reduce_batch_vocabulary(x, weights))
self.assertAllEqual(results,
np.array(expected_analyzer_inputs, dtype=np.object))
def test_reduce_batch_vocabulary(self, x, weights, labels,
expected_analyzer_inputs):
x = tf.constant(x)
vocab_ordering_type = (tf_utils.VocabOrderingType.FREQUENCY)
if weights is not None:
weights = tf.constant(weights)
vocab_ordering_type = (
tf_utils.VocabOrderingType.WEIGHTED_FREQUENCY)
if labels is not None:
labels = tf.constant(labels, dtype=tf.int64)
vocab_ordering_type = (
tf_utils.VocabOrderingType.WEIGHTED_MUTUAL_INFORMATION)
x, sum_weights, sum_positive, counts = tf_utils.reduce_batch_vocabulary(
x, vocab_ordering_type, weights, labels)
with tf.Session() as sess:
results = sess.run([
a for a in [x, sum_weights, sum_positive, counts]
if a is not None
])
for result, expected in zip(results, expected_analyzer_inputs):
self.assertAllEqual(result, np.array(expected))