def testGetCorrectMatchedRowIndices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_row_indices = [3, 1, 0, 5]
matched_row_indices = match.matched_row_indices()
self.assertEquals(matched_row_indices.dtype, tf.int32)
with self.test_session() as sess:
matched_row_inds = sess.run(matched_row_indices)
self.assertAllEqual(matched_row_inds, expected_row_indices)
def test_get_correct_ignored_column_indices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [6]
ignored_column_indices = match.ignored_column_indices()
self.assertEquals(ignored_column_indices.dtype, tf.int32)
with self.test_session() as sess:
ignored_column_indices = sess.run(ignored_column_indices)
self.assertAllEqual(ignored_column_indices,
expected_column_indices)
def testGetCorrectUnmatchedColumnIndices(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indices = [2, 4]
unmatched_column_indices = match.unmatched_column_indices()
self.assertEquals(unmatched_column_indices.dtype, tf.int32)
with self.test_session() as sess:
unmatched_column_indices = sess.run(unmatched_column_indices)
self.assertAllEqual(unmatched_column_indices,
expected_column_indices)
def test_get_correct_matched_column_indicator(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
expected_column_indicator = [
True, True, False, True, False, True, False
]
matched_column_indicator = match.matched_column_indicator()
self.assertEquals(matched_column_indicator.dtype, tf.bool)
with self.test_session() as sess:
matched_column_indicator = sess.run(matched_column_indicator)
self.assertAllEqual(matched_column_indicator,
expected_column_indicator)
def test_scalar_gather_based_on_match(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
input_tensor = tf.constant([0, 1, 2, 3, 4, 5, 6, 7], dtype=tf.float32)
expected_gathered_tensor = [3, 1, 100, 0, 100, 5, 200]
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(input_tensor,
unmatched_value=100.,
ignored_value=200.)
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session():
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_multidimensional_gather_based_on_match(self):
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0],
[0, 0, 0, 0]]
match = matcher.Match(match_results)
gathered_tensor = match.gather_based_on_match(
input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session():
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_multidimensional_gather_based_on_match_with_matmul_gather_op(
self):
match_results = tf.constant([1, -1, -2])
input_tensor = tf.constant([[0, 0.5, 0, 0.5], [0, 0, 0.5, 0.5]],
dtype=tf.float32)
expected_gathered_tensor = [[0, 0, 0.5, 0.5], [0, 0, 0, 0],
[0, 0, 0, 0]]
match = matcher.Match(match_results, use_matmul_gather=True)
gathered_tensor = match.gather_based_on_match(
input_tensor,
unmatched_value=tf.zeros(4),
ignored_value=tf.zeros(4))
self.assertEquals(gathered_tensor.dtype, tf.float32)
with self.test_session() as sess:
self.assertTrue(
all([
op.name is not 'Gather'
for op in sess.graph.get_operations()
]))
gathered_tensor_out = gathered_tensor.eval()
self.assertAllEqual(expected_gathered_tensor, gathered_tensor_out)
def test_all_columns_accounted_for(self):
# Note: deliberately setting to small number so not always
# all possibilities appear (matched, unmatched, ignored)
num_matches = 10
match_results = tf.random_uniform([num_matches],
minval=-2,
maxval=5,
dtype=tf.int32)
match = matcher.Match(match_results)
matched_column_indices = match.matched_column_indices()
unmatched_column_indices = match.unmatched_column_indices()
ignored_column_indices = match.ignored_column_indices()
with self.test_session() as sess:
matched, unmatched, ignored = sess.run([
matched_column_indices, unmatched_column_indices,
ignored_column_indices
])
all_indices = np.hstack((matched, unmatched, ignored))
all_indices_sorted = np.sort(all_indices)
self.assertAllEqual(all_indices_sorted,
np.arange(num_matches, dtype=np.int32))
def test_get_correct_counts(self):
match_results = tf.constant([3, 1, -1, 0, -1, 5, -2])
match = matcher.Match(match_results)
exp_num_matched_columns = 4
exp_num_unmatched_columns = 2
exp_num_ignored_columns = 1
num_matched_columns = match.num_matched_columns()
num_unmatched_columns = match.num_unmatched_columns()
num_ignored_columns = match.num_ignored_columns()
self.assertEquals(num_matched_columns.dtype, tf.int32)
self.assertEquals(num_unmatched_columns.dtype, tf.int32)
self.assertEquals(num_ignored_columns.dtype, tf.int32)
with self.test_session() as sess:
(num_matched_columns_out, num_unmatched_columns_out,
num_ignored_columns_out) = sess.run([
num_matched_columns, num_unmatched_columns,
num_ignored_columns
])
self.assertAllEqual(num_matched_columns_out,
exp_num_matched_columns)
self.assertAllEqual(num_unmatched_columns_out,
exp_num_unmatched_columns)
self.assertAllEqual(num_ignored_columns_out,
exp_num_ignored_columns)