14ee57f33aa9a07fa6440c049fefd47099dbc5ae,tensorflow_transform/beam/impl_test.py,BeamImplTest,testNumericAnalyzersWithScalarInputs,#BeamImplTest#,410

Before Change

        "size": tf.FixedLenFeature((), tf.int64, None),
        "mean": tf.FixedLenFeature((), tf.float64, None)
    })
    self.assertDatasetsEqual(
        transformed_dataset,
        (expected_transformed_data, expected_transformed_metadata))

  def testNumericAnalyzersWithNDInputs(self):
    def preprocessing_fn(inputs):
      def repeat(in_tensor, value):

After Change

      }

    input_data = [{"a": 4}, {"a": 1}]
    input_metadata = dataset_metadata.DatasetMetadata({
        "a": sch.ColumnSchema(tf.int64, [], sch.FixedColumnRepresentation())
    })
    expected_data = [
        {"min": 1, "max": 4, "sum": 5, "size": 2, "mean": 2.5},
        {"min": 1, "max": 4, "sum": 5, "size": 2, "mean": 2.5}
    ]
    expected_metadata = dataset_metadata.DatasetMetadata({
        "min": sch.ColumnSchema(tf.int64, [], sch.FixedColumnRepresentation()),
        "max": sch.ColumnSchema(tf.int64, [], sch.FixedColumnRepresentation()),
        "sum": sch.ColumnSchema(tf.int64, [], sch.FixedColumnRepresentation()),
        "size": sch.ColumnSchema(tf.int64, [], sch.FixedColumnRepresentation()),
        "mean": sch.ColumnSchema(tf.float64, [],
                                 sch.FixedColumnRepresentation())
    })
    self.assertAnalyzeAndTransformResults(
        input_data, input_metadata, preprocessing_fn, expected_data,
        expected_metadata)

Instances