tfdv.DetectFeatureSkew

API for detecting feature skew between training and serving examples.

tfdv.DetectFeatureSkew(
    identifier_features: List[types.FeatureName],
    features_to_ignore: Optional[List[types.FeatureName]] = None,
    sample_size: int = 0,
    float_round_ndigits: Optional[int] = None,
    allow_duplicate_identifiers: bool = False
) -> None

Example:

  with beam.Pipeline(runner=...) as p:
     training_examples = p | 'ReadTrainingData' >>
       beam.io.ReadFromTFRecord(
          training_filepaths, coder=beam.coders.ProtoCoder(tf.train.Example))
     serving_examples = p | 'ReadServingData' >>
       beam.io.ReadFromTFRecord(
          serving_filepaths, coder=beam.coders.ProtoCoder(tf.train.Example))
     _ = ((training_examples, serving_examples) | 'DetectFeatureSkew' >>
       DetectFeatureSkew(identifier_features=['id1'], sample_size=5)
     | 'WriteFeatureSkewResultsOutput' >>
       tfdv.WriteFeatureSkewResultsToTFRecord(output_path)
     | 'WriteFeatureSkwePairsOutput' >>
     tfdv.WriteFeatureSkewPairsToTFRecord(output_path))

See the documentation for DetectFeatureSkewImpl for more detail about feature skew detection.

identifier_features Names of features to use as identifiers.
features_to_ignore Names of features for which no feature skew detection is done.
sample_size Size of the sample of training-serving example pairs that exhibit skew to include in the skew results.
float_round_ndigits Number of digits precision after the decimal point to which to round float values before comparing them.
allow_duplicate_identifiers If set, skew detection will be done on examples for which there are duplicate identifier feature values. In this case, the counts in the FeatureSkew result are based on each training-serving example pair analyzed. Examples with given identifier feature values must all fit in memory.

pipeline None