687e31d276db17b4ac7bea08b01c1e5401168ca9,tensorflow_transform/schema_inference.py,,infer_feature_schema,#Any#Any#Any#,32

Before Change

        min_value, max_value = session.run(override_min_and_max)
      else:
        min_value, max_value = None, None
      column_schemas[name] = dataset_schema.ColumnSchema(
          dataset_schema.IntDomain(tf.int64, min_value, max_value,
                                   is_categorical=True),
          column_schema.axes,
          column_schema.representation)
    else:
      column_schemas[name] = column_schema

  return dataset_schema.Schema(column_schemas)

After Change

  domains = {}
  for name, tensor in six.iteritems(features):
    values = tensor.values if isinstance(tensor, tf.SparseTensor) else tensor
    if values in tensor_ranges:
      assert values.dtype == tf.int64
      min_value, max_value = tensor_ranges[values]
      domains[name] = schema_pb2.IntDomain(
          min=min_value, max=max_value, is_categorical=True)

  feature_spec = _feature_spec_from_batched_tensors(features)

  return dataset_schema.from_feature_spec(feature_spec, domains)

Instances