reddit.py 文件源码

def make_preprocessing_fn(frequency_threshold):
  """Creates a preprocessing function for criteo.

  Args:
    frequency_threshold: The frequency_threshold used when generating
      vocabularies for categorical and text features.

  Returns:
    A preprocessing function.
  """

  def preprocessing_fn(inputs):
    """User defined preprocessing function for criteo columns.

    Args:
      inputs: dictionary of input `tensorflow_transform.Column`.
    Returns:
      A dictionary of `tensorflow_transform.Column` representing the transformed
          columns.
    """
    # TODO(b/35001605) Make this "passthrough" more DRY.
    result = {'score': inputs['score'], 'toplevel': inputs['toplevel']}

    result['subreddit_id'] = tft.string_to_int(
        inputs['subreddit'], frequency_threshold=frequency_threshold)

    # TODO(b/35318962): Obviate the need for this workaround on Dense features.
    # FeatureColumns expect shape (batch_size, 1), not just (batch_size)
    # All features added to results up to this point are dense and require this
    # workaround. All following features will be sparse.
    result = {
        k: tft.map(lambda x: tf.expand_dims(x, -1), v)
        for k, v in result.items()
    }

    for name in ('author', 'comment_body', 'comment_parent_body'):
      words = tft.map(tf.string_split, inputs[name])
      # TODO(b/33467613) Translate these to bag-of-words style sparse features.
      result[name + '_bow'] = tft.string_to_int(
          words, frequency_threshold=frequency_threshold)

    return result

  return preprocessing_fn