TensorFlow Model Analysis 0.35.0

Major Features and Improvements

Added support for specifying weighted vs unweighted metrics. The setting is
available in the tfma.MetricsSpec( example_weights=tfma.ExampleWeightOptions(weighted=True, unweighted=True)).
If no options are provided then TFMA will default to weighted provided the
associated tfma.ModelSpec has an example weight key configured, otherwise
unweighted will be used.

Added support for example_weights that are arrays.
Reads baseUrl in JupyterLab to support TFMA rendering:
#112
Fixing couple of issues with CIDerivedMetricComputation:
- no CI derived metric, deriving from private metrics such as
  binary_confusion_matrices, was being computed
- convert_slice_metrics_to_proto method didn't have support for bounded
  values metrics.
Depends on tfx-bsl>=1.4.0,<1.5.0.
Depends on tensorflow-metadata>=1.4.0,<1.5.0.
Depends on apache-beam[gcp]>=2.33,<3.

Confidence intervals for scalar metrics are no longer stored in the
MetricValue.bounded_value. Instead, the confidence interval for a metric
can be found under MetricKeysAndValues.confidence_interval.
MetricKeys now require specifying whether they are weighted (
tfma.metrics.MetricKey(..., example_weighted=True)) or unweighted (the
default). If the weighting is unknown then example_weighted will be None.
Any metric computed outside of a tfma.metrics.MetricConfig setting (i.e.
metrics loaded from a saved model) will have the weighting set to None.
ExampleCount is now weighted based on tfma.MetricsSpec.example_weights
settings. WeightedExampleCount has been deprecated (use ExampleCount
instead). To get unweighted example counts (i.e. the previous implementation
of ExampleCount), ExampleCount must now be added to a MetricsSpec
where example_weights.unweighted is true. To get a weighted example count
(i.e. what was previously WeightedExampleCount), ExampleCount must now
be added to a MetricsSpec where example_weights.weighted is true.