Release 0.12.0

Release 0.12.0

Major Features and Improvements

• Python 3.5 readiness complete (all tests pass). Full Python 3.5
  compatibility is expected to be available with the next version of Model
  Analysis (after Apache Beam 2.11 is released).
• Added support for customizing the pipeline (via extractors, evaluators, and
  writers). See architecture for more details.
• Added support for excluding the default metrics from the saved model graph
  during evaluation.
• Added a mechanism for performing evaluations via post_export_metrics without
  access to a Tensorflow EvalSavedModel.
• Added support for computing metrics with confidence intervals using the
  Poisson bootstrap technique.
  To use, set the num_bootstrap_samples to a number greater than 1--20 is
  recommended for confidence intervals.

Bug fixes and other changes

• Fixed bugs where TFMA was incorrectly modifying elements in DoFns, which
  violates the Beam API.
• Fixed correctness issue stemming from TFMA incorrectly relying on evaluation
  ordering that TF doesn't guarantee.
• We now store feature and label Tensor information in SignatureDef inputs
  instead of Collections in anticipation of Collections being deprecated in TF
  2.0.
• Add support for fractional labels in AUC, AUPRC and confusion matrix at
  thresholds. Previously the labels were being passed directly to TensorFlow,
  which would cast them to bool, which meant that all non-zero labels were
  treated as positive examples. Now we treat a fractional label l in [0, 1] as two examples, a positive example with weight l and a negative
  example with weight 1 - l.
• Depends on numpy>=1.14.5,<2.
• Depends on scipy==0.19.1.
• Depends on protobuf==3.7.0rc2.
• Chicago Taxi example is moved to tfx repo (https://github.com/tensorflow/tfx/tree/master/examples/chicago_taxi)

Breaking changes

• Moved tfma.SingleSliceSpec to tfma.slicer.SingleSliceSpec.

Deprecations