def call()

in tensorflow_ranking/python/keras/feature.py [0:0]

• 37 lines of code
• 8 McCabe index (conditional complexity)

  def call(self, inputs, training=None):
    """Transforms the features into dense context features and example features.

    This is the Keras equivalent of `tfr.feature.encode_listwise_features`.

    Args:
      inputs: (dict) Features with a mix of context (2D) and example features
        (3D).
      training: (bool) whether in train or inference mode.

    Returns:
      context_features: (dict) context feature names to dense 2D tensors of
        shape [batch_size, feature_dims].
      example_features: (dict) example feature names to dense 3D tensors of
        shape [batch_size, list_size, feature_dims].
    """
    features = inputs
    context_features = {}
    if self._context_feature_columns:
      context_cols_to_tensors = {}
      self._context_dense_layer(
          features,
          training=training,
          cols_to_output_tensors=context_cols_to_tensors)
      context_features = {
          name: context_cols_to_tensors[col]
          for name, col in six.iteritems(self.context_feature_columns)
      }
    example_features = {}
    if self._example_feature_columns:
      # Compute example_features. Note that the key in `example_feature_columns`
      # dict can be different from the key in the `features` dict. We only need
      # to reshape the per-example tensors in `features`. To obtain the keys for
      # per-example features, we use the parsing feature specs.
      example_specs = tf.feature_column.make_parse_example_spec(
          list(six.itervalues(self._example_feature_columns)))
      example_name = next(six.iterkeys(example_specs))
      batch_size = tf.shape(input=features[example_name])[0]
      list_size = tf.shape(input=features[example_name])[1]
      reshaped_example_features = {}
      for name in example_specs:
        if name not in features:
          continue
        reshaped_example_features[name] = utils.reshape_first_ndims(
            features[name], 2, [batch_size * list_size])

      example_cols_to_tensors = {}
      self._example_dense_layer(
          reshaped_example_features,
          training=training,
          cols_to_output_tensors=example_cols_to_tensors)
      example_features = {
          name: utils.reshape_first_ndims(example_cols_to_tensors[col], 1,
                                          [batch_size, list_size])
          for name, col in six.iteritems(self._example_feature_columns)
      }
    return context_features, example_features