def check_result(got):
try:
self.assertEqual(2, len(got), 'got: %s' % got)
for res in got:
self.assertIn('num_interests', res.extracts['fpl'].features)
self.assertEqual(
len(
meta_feature_extractor.get_feature_value(
res.extracts['fpl'], 'interest')),
meta_feature_extractor.get_feature_value(
res.extracts['fpl'], 'num_interests'))
except AssertionError as err:
raise util.BeamAssertException(err)
def check_result(got):
try:
self.assertEqual(2, len(got), 'got: %s' % got)
for res in got:
self.assertIn(
'num_interests',
res[constants.
FEATURES_PREDICTIONS_LABELS_KEY].features)
self.assertEqual(
len(
meta_feature_extractor.get_feature_value(
res[constants.
FEATURES_PREDICTIONS_LABELS_KEY],
'interest')),
meta_feature_extractor.get_feature_value(
res[constants.FEATURES_PREDICTIONS_LABELS_KEY],
'num_interests'))
except AssertionError as err:
raise util.BeamAssertException(err)
def testGetSparseTensorValue(self):
sparse_tensor_value = tf.compat.v1.SparseTensorValue(
indices=[[0, 0, 0], [0, 1, 0], [0, 1, 1]],
values=['', 'one', 'two'],
dense_shape=[1, 2, 2])
fpl_with_sparse_tensor = types.FeaturesPredictionsLabels(
input_ref=0, features={}, predictions={}, labels={})
meta_feature_extractor._set_feature_value(
fpl_with_sparse_tensor.features, 'sparse', sparse_tensor_value)
self.assertEqual(['', 'one', 'two'],
meta_feature_extractor.get_feature_value(
fpl_with_sparse_tensor, 'sparse'))
def get_num_interests(fpl):
interests = meta_feature_extractor.get_feature_value(fpl, 'interest')
new_features = {'num_interests': len(interests)}
return new_features