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RELEASE.md

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Version 0.25.0

Major Features and Improvements

  • Added support for reading and writing metrics, plots and validation results using Apache Parquet.

  • Updated the FI indicator slicing selection UI.

  • Fixed the problem that slices are refreshed when user selected a new baseline.

  • Add support for slicing on ragged and multidimensional data.

  • Load TFMA correctly in JupyterLabs even if Facets has loaded first.

  • Added support for aggregating metrics using top k values.

  • Added support for padding labels and predictions with -1 to align a batch of inputs for use in tf-ranking metrics computations.

  • Added support for fractional labels.

  • Add metric definitions as tooltips in the Fairness Inidicators metric selector UI

  • Added support for specifying label_key to use with MinLabelPosition metric.

  • From this release TFMA will also be hosting nightly packages on https://pypi-nightly.tensorflow.org. To install the nightly package use the following command:

    pip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-model-analysis

    Note: These nightly packages are unstable and breakages are likely to happen. The fix could often take a week or more depending on the complexity involved for the wheels to be available on the PyPI cloud service. You can always use the stable version of TFMA available on PyPI by running the command pip install tensorflow-model-analysis .

Bug fixes and other changes

  • Fix incorrect calculation with MinLabelPosition when used with weighted examples.
  • Fix issue with using NDCG metric without binarization settings.
  • Fix incorrect computation when example weight is set to zero.
  • Depends on apache-beam[gcp]>=2.25,<3.
  • Depends on tensorflow-metadata>=0.25.0,<0.26.0.
  • Depends on tfx-bsl>=0.25.0,<0.26.0.

Breaking changes

  • AggregationOptions are now independent of BinarizeOptions. In order to compute AggregationOptions.macro_average or AggregationOptions.weighted_macro_average, AggregationOptions.class_weights must now be configured. If AggregationOptions.class_weights are provided, any missing keys now default to 0.0 instead of 1.0.
  • In the UI, aggregation based metrics will now be prefixed with 'micro_', 'macro_', or 'weighted_macro_' depending on the aggregation type.

Deprecations

  • tfma.extractors.FeatureExtractor, tfma.extractors.PredictExtractor, tfma.extractors.InputExtractor, and tfma.evaluators.MetricsAndPlotsEvaluator are deprecated and may be replaced with newer versions in upcoming releases.