0.2.2

Features

• Surface the validation_interval_in_trees,
  keep_non_leaf_label_distribution and 'random_seed' hyper-parameters.
• Add the batch_size argument in the pd_dataframe_to_tf_dataset utility.
• Automatically determine the number of threads if num_threads=None.
• Add constructor argument try_resume_training to facilitate resuming
  training.
• Check that the training dataset is well configured for TF-DF e.g. no repeat
  operation, has a large enough batch size, etc. The check can be disabled
  with check_dataset=False.
• When a model is created manually with the model builder, and if the dataspec
  is not provided, tries to adapt the dataspec so that the model looks as if
  it was trained with the global imputation strategy for missing values (i.e.
  missing_value_policy: GLOBAL_IMPUTATION). This makes manually created models
  more likely to be compatible with the fast inference engines.
• TF-DF models fit method now passes the validation_data to the Yggdrasil
  learners. This is used for example for early stopping in the case of GBT
  model.
• Add the "loss" parameter of the GBT model directly in the model constructor.
• Control the amount of training logs displayed in the notebook (if using
  notebook) or in the console with the verbose constructor argument and
  fit parameter of the model.

Fixes

• num_candidate_attributes is not ignored anymore when
  num_candidate_attributes_ratio=-1.
• Use the median bucket split value strategy in the discretized numerical
  splitters (local and distributed).
• Surface the max_num_scanned_rows_to_accumulate_statistics parameter to
  control how many examples are scanned to determine the feature statistics
  when training from a file dataset with fit_on_dataset_path.