def _build_features()

in graphlearn_torch/python/data/dataset.py [0:0]

• 31 lines of code
• 9 McCabe index (conditional complexity)

def _build_features(feature_data, id2idx, split_ratio,
                    device_group_list, device, with_gpu, dtype):
  r""" Build `Feature`s for node/edge feature data.
  """
  if feature_data is not None:
    if isinstance(feature_data, dict):
      # heterogeneous.
      if not isinstance(split_ratio, dict):
        split_ratio = {
          graph_type: float(split_ratio)
          for graph_type in feature_data.keys()
        }

      if id2idx is not None:
        assert isinstance(id2idx, dict)
      else:
        id2idx = {}

      features = {}
      for graph_type, feat in feature_data.items():
        features[graph_type] = Feature(
          feat, id2idx.get(graph_type, None),
          split_ratio.get(graph_type, 0.0),
          device_group_list, device, with_gpu,
          dtype if dtype is not None else feat.dtype
        )
    else:
      # homogeneous.
      features = Feature(
        feature_data, id2idx, float(split_ratio),
        device_group_list, device, with_gpu,
        dtype if dtype is not None else feature_data.dtype
      )
  else:
    features = None

  return features