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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 more details.

Usage example

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:/[email protected]";
// 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)

Google IO Presentation

Yggdrasil Decision Forests powers TensorFlow Decision Forests.

<iframe width="560" height="315" src="https://www.youtube.com/embed/5qgk9QJ4rdQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

Documentation & Resources

The following resources are available:

Contributing

Contributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are welcome. If you want to contribute, check the contribution guidelines.

Credits

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)

License

Apache License 2.0



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