TensorFlow Data Validation 1.4.0

Major Features and Improvements

• Float features can now be analyzed as categorical for the purposes of top-k
  and unique count using experimental sketch based generators.
• Support SQL based slicing in TFDV. This would enable slicing (using SQL) in
  TFX OSS and Dataflow environments. SQL based slicing is currently not
  supported on Windows.

Bug Fixes and Other Changes

• Variance calculations have been updated to be more numerically stable for
  large datasets or large magnitude numeric data.
• When running per-example validation against a schema, output of
  validate_examples_in_tfrecord and validate_examples_in_csv now optionally
  return samples of anomalous examples.
• Changes to source code ensures that it can now work with pyarrow>=3.
• Add load_anomalies_binary utility function.
• Merge two accumulators at a time instead of batching.
• BasicStatsGenerator is now responsible for setting
  FeatureNameStatistics.Type. Previously it was possible for a top-k generator
  and BasicStatsGenerator to set different types for categorical numeric
  features with physical type STRING.
• Depends on pyarrow>=1,<6.
• Depends on tensorflow-metadata>=1.4,<1.5.
• Depends on tfx-bsl>=1.4,<1.5.

Known Issues

• N/A

Breaking Changes

• N/A

Deprecations

• Deprecated python 3.6 support.