cb7c20f6ad714f0b016a874b2b06a9fbed4e0a59,tensorflow_transform/test_case.py,TransformTestCase,AssertVocabularyContents,#TransformTestCase#Any#Any#,290

Before Change

          // per-key analyzers.
          values = list(map(float, frequency.strip(b"\n").split(b",")))
          word_and_frequency_list.append(
              (word.strip(b"\n"), values[0] if len(values) == 1 else values))

        expected_words, expected_frequency = zip(*word_and_frequency_list)
        actual_words, actual_frequency = zip(*file_contents)

After Change

          tf_utils.read_tfrecord_vocabulary_dataset(
              vocab_file_path).as_numpy_iterator())
    else:
      with tf.io.gfile.GFile(vocab_file_path, "rb") as f:
        file_lines = f.read().splitlines()

    // Store frequency case.
    if isinstance(file_contents[0], tuple):
      word_and_frequency_list = []
      for content in file_lines:
        frequency, word = content.split(b" ", 1)

Instances