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)