0.2.0

Features

• Add advanced option predict_single_probability_for_binary_classification
  to generate prediction tensors of shape [batch_size, 2] for binary
  classification model.
• Add support for weighted training.
• Add support for permutation variable importance in the GBT learner with the
  compute_permutation_variable_importance parameter.
• Support for tf.int8 and tf.int16 values.
• Support for distributed gradient boosted trees learning. Currently, the TF
  ParameterServerStrategy distribution strategy is only available in
  monolithic TF-DF builds. The Yggdrasil Decision Forest GRPC distribute
  strategy can be used instead.
• Support for training from dataset stored on disk in CSV and RecordIO format
  (instead of creating a tensorflow dataset). This option is currently more
  efficient for distributed training (until the ParameterServerStrategy
  support per-worker datasets).
• Add max_vocab_count argument to the model constructor. The existing
  max_vocab_count argument in FeatureUsage objects take precedence.

Fixes

• Missing filtering of unique values in the categorical-set training feature
  accumulator. Was responsible for a small (e.g. ~0.5% on SST2 dataset) drop
  of accuracy compared to the C++ API.
• Fix broken support for max_vocab_count in a FeatureUsage with type
  CATEGORICAL_SET.