def testSequenceEditDistanceMetric(self):
predictions = np.array([[3, 4, 5, 1, 0, 0], [2, 1, 3, 4, 0, 0],
[2, 1, 3, 4, 0, 0]])
# Targets are just a bit different:
# - first sequence has a different prediction
# - second sequence has a different prediction and one extra step
# - third sequence is identical
targets = np.array([[5, 4, 5, 1, 0, 0], [2, 5, 3, 4, 1, 0],
[2, 1, 3, 4, 0, 0]])
# Reshape to match expected input format by metric fns.
predictions = np.reshape(predictions, [3, 6, 1, 1])
targets = np.reshape(targets, [3, 6, 1, 1])
with self.test_session() as session:
scores, weight = metrics.sequence_edit_distance(
tf.one_hot(predictions, depth=6, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32))
session.run(tf.global_variables_initializer())
actual_scores, actual_weight = session.run([scores, weight])
self.assertAlmostEqual(actual_scores, 3.0 / 13)
self.assertEqual(actual_weight, 13)
def testSequenceEditDistanceMetric(self):
predictions = np.array([[3, 4, 5, 1, 0, 0],
[2, 1, 3, 4, 0, 0],
[2, 1, 3, 4, 0, 0]])
# Targets are just a bit different:
# - first sequence has a different prediction
# - second sequence has a different prediction and one extra step
# - third sequence is identical
targets = np.array([[5, 4, 5, 1, 0, 0],
[2, 5, 3, 4, 1, 0],
[2, 1, 3, 4, 0, 0]])
# Reshape to match expected input format by metric fns.
predictions = np.reshape(predictions, [3, 6, 1, 1])
targets = np.reshape(targets, [3, 6, 1, 1])
with self.test_session() as session:
scores, weight = metrics.sequence_edit_distance(
tf.one_hot(predictions, depth=6, dtype=tf.float32),
tf.constant(targets, dtype=tf.int32))
session.run(tf.global_variables_initializer())
actual_scores, actual_weight = session.run([scores, weight])
self.assertAlmostEqual(actual_scores, 3.0 / 13)
self.assertEqual(actual_weight, 13)
