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`.