14ee57f33aa9a07fa6440c049fefd47099dbc5ae,tensorflow_transform/beam/impl_test.py,BeamImplTest,testNumericAnalyzersWithNDInputs,#BeamImplTest#,446

Before Change

        {"a": [[4, 5], [6, 7]]},
        {"a": [[1, 2], [3, 4]]}
    ]
    input_metadata = self.toMetadata(
        {"a": tf.FixedLenFeature((2, 2), tf.int64)})
    with beam_impl.Context(temp_dir=self.get_temp_dir()):
      transformed_dataset, _ = (
          (input_data, input_metadata) |
          beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))

    expected_transformed_data = [
        {"min": 1, "max": 7, "sum": 32, "size": 8, "mean": 4.0},
        {"min": 1, "max": 7, "sum": 32, "size": 8, "mean": 4.0}]
    expected_transformed_metadata = self.toMetadata({
        "min": tf.FixedLenFeature((), tf.int64, None),
        "max": tf.FixedLenFeature((), tf.int64, None),
        "sum": tf.FixedLenFeature((), tf.int64, None),
        "size": tf.FixedLenFeature((), tf.int64, None),
        "mean": tf.FixedLenFeature((), tf.float64, None)
    })
    self.assertDatasetsEqual(
        transformed_dataset,
        (expected_transformed_data, expected_transformed_metadata))

After Change

        "mean": sch.ColumnSchema(tf.float64, [],
                                 sch.FixedColumnRepresentation())
    })
    self.assertAnalyzeAndTransformResults(
        input_data, input_metadata, preprocessing_fn, expected_data,
        expected_metadata)

  def testNumericAnalyzersWithSparseInputs(self):
    def repeat(in_tensor, value):
      batch_size = tf.shape(in_tensor)[0]

Instances