Yggdrasil Decision Forests (YDF) is a collection of state-of-the-art algorithms for the training, serving, and interpretation of Decision Forest models. The library is available in C++, CLI (command-line-interface, i.e. shell commands), in TensorFlow under the name TensorFlow Decision Forests (TF-DF), Go and in Javascript (inference only). YDF is supported on Linux, Windows, macOS, Raspberry, and Arduino (experimental).
See the documentation 📕 for more details.
Train, evaluate, and benchmark the speed of a model in a few shell lines with the CLI interface:
# Download YDF.
wget https://github.com/google/yggdrasil-decision-forests/releases/download/1.0.0/cli_linux.zip
unzip cli_linux.zip
# Training configuration
echo 'label:"my_label" learner:"RANDOM_FOREST" ' > config.pbtxt
# Scan the dataset
infer_dataspec --dataset="csv:train.csv" --output="spec.pbtxt"
# Train a model
train --dataset="csv:train.csv" --dataspec="spec.pbtxt" --config="config.pbtxt" --output="my_model"
# Evaluate the model
evaluate --dataset="csv:test.csv" --model="my_model" > evaluation.txt
# Benchmark the speed of the model
benchmark_inference --dataset="csv:test.csv" --model="my_model" > benchmark.txt(based on examples/beginner.sh)
or use the C++ interface:
auto dataset_path = "csv:/train@10";
// Training configuration
TrainingConfig train_config;
train_config.set_learner("RANDOM_FOREST");
train_config.set_task(Task::CLASSIFICATION);
train_config.set_label("my_label");
// Scan the dataset
DataSpecification spec;
CreateDataSpec(dataset_path, false, {}, &spec);
// Train a model
std::unique_ptr<AbstractLearner> learner;
GetLearner(train_config, &learner);
auto model = learner->Train(dataset_path, spec);
// Export the model
SaveModel("my_model", model.get());(based on examples/beginner.cc)
or use the Keras/Python interface of TensorFlow Decision Forests:
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")
# Convert the dataset into a TensorFlow dataset.
train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="my_label")
# Train the model
model = tfdf.keras.RandomForestModel()
model.fit(train_ds)
# Export a SavedModel.
model.save("project/model")(see TensorFlow Decision Forests)
Yggdrasil Decision Forests powers TensorFlow Decision Forests.
The following resources are available:
If you us Yggdrasil Decision Forests in a scientific publication, please cite the following paper: Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library.
Bibtex
@misc{guillamebert2022yggdrasil,
title={Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library},
author={Mathieu Guillame-Bert and Sebastian Bruch and Richard Stotz and Jan Pfeifer},
year={2022},
eprint={2212.02934},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Raw
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library, Guillame-Bert et al., arXiv, 2022. doi:10.48550/ARXIV.2212.02934
Yggdrasil Decision Forests and TensorFlow Decision Forests are developed by:
- Mathieu Guillame-Bert (gbm AT google DOT com)
- Jan Pfeifer (janpf AT google DOT com)
- Sebastian Bruch (sebastian AT bruch DOT io)
- Richard Stotz (richardstotz AT google DOT com)
- Arvind Srinivasan (arvnd AT google DOT com)
Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are welcome. If you want to contribute, check the contribution guidelines.