Release 0.9.1

Major Features and Improvements

Requires pre-installed TensorFlow >=1.10,<2.
Updated ExampleCount to use the batch dimension as the example count. It also now tries a few fallbacks if none of the standard keys are found in the predictions dictionary: the first key in sorted order in the predictions dictionary, or failing that, the first key in sorted order in the labels dictionary, or failing that, it defaults to zero.
Fix bug where we were mutating an element in a DoFn - this is prohibited in the Beam model and can cause subtle bugs.

Add a TFMA unit test library for unit testing your the exported model and associated metrics computations.
Add tfma.export.make_export_strategy which is analogous to tf.contrib.learn.make_export_strategy.
Add tfma.exporter.FinalExporter and tfma.exporter.LatestExporter which are analogous to tf.estimator.FinalExporter and tf.estimator.LastExporter.
Add tfma.export.build_parsing_eval_input_receiver_fn which is analogous to tf.estimator.export.build_parsing_serving_input_receiver_fn.
Add integration testing for DNN-based estimators.
Add new post export metrics:
- AUC (tfma.post_export_metrics.post_export_metrics.auc)
- Precision/Recall at K (tfma.post_export_metrics.post_export_metrics.precision_recall_at_k)
- Confusion matrix at thresholds (tfma.post_export_metrics.post_export_metrics.confusion_matrix_at_thresholds)

Peak memory usage for large DataFlow jobs should be lower with a fix in when we compact batches of metrics during the combine phase of metrics computation.
Remove batch size override in chicago_taxi example.
Added dependency on protobuf>=3.6.0<4 for protocol buffers.
Updated SparseTensor code to work with SparseTensors of any dimension. Previously on SparseTensors with dimension 2 (batch_size x values) were supported in the features dictionary.
Updated code to work with SparseTensors and dense Tensors of variable lengths across batches.

EvalSavedModels produced by TFMA 0.6.0 will not be compatible with later versions due to the following changes:
- EvalSavedModels are now written out with a custom "eval_saved_model" tag, as opposed to the "serving" tag before.
- EvalSavedModels now include version metadata about the TFMA version that they were exported with.
Metrics and plot outputs now are converted into proto and serialized. Metrics and plots produced by TFMA 0.6.0 will not be compatible with later versions.
Requires pre-installed TensorFlow >=1.9,<2.
TFMA now uses the TensorFlow Estimator functionality for exporting models of different modes behind the scenes. There are no user-facing changes API-wise, but EvalSavedModels produced by earlier versions of TFMA will not be compatible with this version of TFMA.
tf.contrib.learn Estimators are no longer supported by TFMA. Only tf.estimator Estimators are supported.
Metrics and plot outputs now include version metadata about the TFMA version that they were exported with. Metrics and plots produced by earlier versions of TFMA will not be compatible with this version of TFMA.