1.3.0

Features

• Check learner parameters during the model construction.
• Fix discretized numerical features for regression task.
• Allow for float32 values to be fed as categorical features.
• Add new / improved tutorials for ranking and visualization.
• Compatibility with Tensorflow 2.12.0. Unfortunately, this means dropping
  support for Python 3.7.

Fix

• Fix crashes when using ranking with very large groups.
• Add option to set the port used by YDF in TF-DF distributed training.
• Improve logging robustness.

1.2.0

Features

• Add support for distributed training and distributed hyper-parameter tuning
  in the OSS build. See
  https://www.tensorflow.org/decision_forests/distributed_training
• Setting "subsample" is enough enable random subsampling (to need to also set
  "sampling_method=RANDOM").
• Add "min_vocab_frequency" argument in "FeatureUsage" to control the minimum
  frequency of categorical items.
• Add "override_global_imputation_value" argument in "FeatureUsage" to
  override the value used for global imputation of missing value by the
  global-imputation algorithm.
• The Tuner argument "use_predefined_hps" automatically configures the set of
  hyper-parameters to explore during automatic hyper-parameter tuning.
• Replaces the MEAN_MIN_DEPTH variable importance with INV_MEAN_MIN_DEPTH.
• Add option to forbid model inference with the slow inference engine.

Fix

• Automatic documentation generation for RandomForestModel and other classes.

1.1.0

1.1.0 - 2022-11-18

Features

• Native support for TensorFlow Decision Forests in TensorFlow Serving.
• Add support for zipped Yggdrasil Decision Forests model for
  yggdrasil_model_to_keras_model.
• Added model prediction tutorial.
• Prevent premature stopping of GBT training through new parameter
  early_stopping_initial_iteration.

Fix

• Using loaded datasets with TF-DF no longer fails (Github #131).
• Automatically infer the semantic of int8 values as numerical (was
  categorical before).
• Build script fixed
• Model saving no longer fails when using invalid feature names.
• Added keyword to pandas dataset drop (Github #135).

1.1.0rc2

Features

• Support for Tensorflow Serving APIs.
• Add support for zipped Yggdrasil Decision Forests model for yggdrasil_model_to_keras_model.
• Added model prediction tutorial.
• Prevent premature stopping of GBT training through new parameter early_stopping_initial_iteration.

Fix

• Using loaded datasets with TF-DF no longer fails (Github #131).
• Automatically infer the semantic of int8 values as numerical (was categorical before).
• Build script fixed
• Model saving no longer fails when using invalid feature names.
• Added keyword to pandas dataset drop (Github #135).

TensorFlow Serving 2.11 Nightly

Nightly build of TensorFlow Serving 2.11.
TensorFlow Serving >=2.11 supports natively TensorFlow Decision Forests models.

Build instructions:

git clone https://github.com/tensorflow/serving.git
docker run -it -v ${PWD}/..:/working_dir -w /working_dir/serving tensorflow/serving:nightly-devel bash
bazel build //tensorflow_serving/model_servers:tensorflow_model_server

1.0.1

TensorFlow Decision Forests 1.0.1

With this release, TensorFlow Decision Forests finally reaches its first major release 🥳

With this milestone we want to communicate more broadly that TensorFlow Decision Forests has become a more stable and mature library. In particular, we established more comprehensive testing to make sure that TF-DF is ready for professional environments.

Features

• Add customization of the number of IO threads when using fit_on_dataset_path.

Fix

• Improved documentation
• Improved testing and stability
• Issue in the application of auditwheel

Tensorflow Decision Forests 1.0.1 for MacOS

Experimental TF-DF Release for MacOS

This pre-release is designed to help testing a release for TF-DF 1.0.1 with different MacOS versions.

Make sure you pick a version corresponding to your MacOS version and Python version.

1.0.0rc0

Fix

• Improve documentation

0.2.7

Features

• Multithreading of the oblique splitter for gradient boosted tree models.
• Support for pure serving model i.e. model containing only serving data.
• Add "edit_model" cli tool.

Fix

• Remove bias toward low outcome in uplift modeling.

tensorflow_model_server_linux.zip 0.2.6

TF-DF with TF-Serving binary for Tensorflow 2.9.1