Tensorflow-decision-forests

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`.

Features

- Disable tree pruning in the CART algorithm if the validation dataset is
empty (i.e. `validation_ratio=0`).
- Migration to Tensorflow 2.6. You will see an `undefined symbol` error if you
install this version with a TensorFlow version different than 2.6. Previous
versions were compiled for TF 2.5.

Fixes

- Fix failure from
[Github Issue 45](https://github.com/tensorflow/decision-forests/issues/45)
where the wrong field was accessed for leaf node distributions.
- Fix saving of categorical features specification in the Builder.

Features

- Model can be composed with the functional Keras API before being trained.
- Makes all the Yggdrasil structural variable importances available.
- Makes getting the variable importance instantaneous.
- Surface the `name` argument in the model classes constructors.
- Add a `postprocessing` model constructor argument to easy apply
post-processing on the model predictions without relying on the Keras
Functional API.
- Add `extract_all_trees` method in the model inspector to efficiently exact
all the trees.
- Add `num_threads` constructor argument to control the number of training
threads without using the advanced configuration.
- By default, remove the temporary directory used to train the model when the
model python object is garbage collected.
- Add the `import_dataspec` constructor argument to the model builder to
import the feature definition and dictionaries (instead of relying on
automatic discovery).

Changes

- When saving a model in a directory already containing a model, only the
`assets` directory is entirely removed before the export (instead of the
entire model directory).

Fixes

- Wrong label shape in the model inspector's objective field for
pre-integerized labels.

Features

- Add more of characters to the non-recommended list of feature name
characters.
- Make the inference op multi-thread compatible.
- Print an explicit error and some instructions when training a model with a
Pandas dataframe.
- `pd_dataframe_to_tf_dataset` can automatically rename feature to make them
compatible with SavedModel export signatures.
- `model.save(...)` can override an existing model.
- The link function of GBT model can be removed. For example, a binary
classification GBT model trained with apply_link_function=False will output
logits.

Features

- Add hyper-parameter `sorting_strategy` to disable the computation of the
pre-sorted index (slower to train, but consumes less memory).
- Format wrapper code for colab help display.
- Raises an error when a feature name is not compatible (e.g. contains a
space).

Features

- Raise an error of the number of classes is greater than 100 (can be
disabled).
- Raise an error if the model's task does not match the
`pd_dataframe_to_tf_dataset`'s task.

Bug fix

- Fix failure when input feature contains commas.