teslaxhub/decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

TensorFlow Decision Forests

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking.

TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa. This link explains how to do inference of TF-DF models in C++ using Yggdrasil.

Usage example

A minimal end-to-end run looks as follow:

import tensorflow_decision_forests as tfdf
import pandas as pd

# Load the dataset in a Pandas dataframe.
train_df = pd.read_csv("project/train.csv")
test_df = pd.read_csv("project/test.csv")

# Convert the dataset into a TensorFlow dataset.
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="my_label")
test_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label="my_label")

# Train the model
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)

# Look at the model.
model.summary()

# Evaluate the model.
model.evaluate(test_ds)

# Export to a TensorFlow SavedModel.
# Note: the model is compatible with Yggdrasil Decision Forests.
model.save("project/model")

Documentation & Resources

The following resources are available:

• TF-DF on TensorFlow.org (with the API Reference and Tutorials)
• Notebook tutorials (on Github)
• Notebook tutorials (on tensorflow.org)
• Migration guide from Neural Network to Decision Forests
• Issue tracker
• Known issues
• Changelog
• Discuss on TensorFlow.Org
• Yggdrasil documentation (for advanced users and C++ serving)
• Distributed Training
• TF-DF in TensorFlow Serving

Installation

To install TensorFlow Decision Forests, run:

pip3 install tensorflow_decision_forests --upgrade

See the installation page for more details, troubleshooting and alternative installation solutions.

Contributing

Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are welcome. If you want to contribute, make sure to review the developer manual and contribution guidelines.

Credits

TensorFlow Decision Forests was developed by:

• Mathieu Guillame-Bert (gbm AT google DOT com)
• Jan Pfeifer (janpf AT google DOT com)
• Sebastian Bruch (sebastian AT bruch DOT io)
• Arvind Srinivasan (arvnd AT google DOT com)

License

Apache License 2.0