Releases: tensorflow/decision-forests
Releases · tensorflow/decision-forests
v1.9.1
v1.9.0
1.9.0 - 2024-03-12
Fix
- Fix max_depth, early stopping parameter documentation.
- Fix plotting contains conditions.
Features
- Compatibility with TensorFlow 2.16.0.
- Expose new parameter sparse_oblique_max_num_projections.
- Using tf_keras instead tf.keras in examples, documentation.
- Support NAConditions for fast engine.
- Faster model loading for models with many features and dense oblique
conditions.
Documentation
- Clarified documentation of parameters for oblique splits.
v1.9.0rc0
1.9.0rc0 - 2024-02-26
Fix
- Fix max_depth, early stopping parameter documentation.
- Fix plotting contains conditions.
Features
- Compatibility with TensorFlow 2.16.0rc0.
- Compatibility with YDF 1.9.0
- Using tf_keras instead tf.keras in examples, documentation.
- Support NAConditions for fast engine.
1.8.1
v1.8.0
v1.6.0
1.6.0 2023-09-27
Breaking Changes
- TF-DF no longer supports Python 3.8 since Tensorflow dropped its support.
Features
- Compatibility with Tensorflow 2.14.0
- Contrib: Training preprocessing jointly on the input features, labels and
weights
Fix
- Incorrect model predictions for models without features
- Data race for model resources
1.5.0
1.4.0
Features
- Support for multi-task learning.
- New tutorial for TF-DF <--> TF.js
- Support for uplift modeling in the model inspector.
- New tutorial for Uplift modeling.
- Bump Bazel version to 6.1.0.
Fix
- Regex to generate Bazel workspace.
- Remove warning when converting Keras -> YDF.
- Fixed default hyperparameter issue Github #172.
- Various documentation issues fixed.
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.