14ee57f33aa9a07fa6440c049fefd47099dbc5ae,tensorflow_transform/beam/impl_test.py,BeamImplTest,testUniquesAnalyzerWithTopK,#BeamImplTest#,624

Before Change

    input_data = [{"a": "hello hello world"},
                  {"a": "hello goodbye world"},
                  {"a": "hello goodbye foo"}]
    input_schema = self.toMetadata({
        "a": tf.FixedLenFeature((), tf.string, ""),
    })

    with beam_impl.Context(temp_dir=self.get_temp_dir()):
      transformed_dataset, _ = (
          (input_data, input_schema)
          | beam_impl.AnalyzeAndTransformDataset(preprocessing_fn))

    // Generated vocab (ordered by frequency, then value) should be:
    // ["hello", "world", "goodbye", "foo"]. After applying top_k=2, this becomes
    // ["hello", "world"].
    expected_transformed_data = [{
        "index1": [0, 0, 1],
        "index2": [0, 0, 1]
    }, {
        "index1": [0, -99, 1],
        "index2": [0, -9, 1]
    }, {
        "index1": [0, -99, -99],
        "index2": [0, -9, -9]
    }]
    expected_transformed_schema = self.toMetadata({
        "index1": tf.VarLenFeature(tf.int64),
        "index2": tf.VarLenFeature(tf.int64)
    })
    self.assertDatasetsEqual(
        transformed_dataset,
        (expected_transformed_data, expected_transformed_schema))

After Change

        "index2": sch.ColumnSchema(tf.int64, [None],
                                   sch.ListColumnRepresentation())
    })
    self.assertAnalyzeAndTransformResults(
        input_data, input_metadata, preprocessing_fn, expected_data,
        expected_metadata)

  def testUniquesAnalyzerWithFrequencyThreshold(self):
    def preprocessing_fn(inputs):
      return {

Instances