Version 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.

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.