- Replaced keras metrics with TFMA implementations. To use a keras metric in a
tfma.MetricConfigyou must now specify a module (i.e.tf.keras.metrics). - Added FixedSizeSample metric which can be used to extract a random, per-slice, fixed-sized sample of values for a user-configured feature key.
- Updated QueryStatistics to support weighted examples.
- Depends on
apache-beam[gcp]>=2.34,<3. - Depends on
tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3. - Depends on
tfx-bsl>=1.5.0,<1.6.0. - Depends on
tensorflow-metadata>=1.5.0,<1.6.0.
- Removes register_metric from public API, as it is not intended to be public facing. To use a custom metric, provide the module name in which the metric is defined in the MetricConfig message, instead.