tensorflow-decision-forests


Nametensorflow-decision-forests JSON
Version 1.9.2 PyPI version JSON
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home_pagehttps://github.com/tensorflow/decision-forests
SummaryCollection of training and inference decision forest algorithms.
upload_time2024-07-07 22:56:10
maintainerNone
docs_urlNone
authorGoogle Inc.
requires_python>=3.9
licenseApache 2.0
keywords tensorflow tensor machine learning decision forests random forest gradient boosted decision trees
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requirements No requirements were recorded.
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            <p align="center">
<img src="documentation/image/logo.png"  />
</p>

**TensorFlow Decision Forests** (**TF-DF**) is a library to train, run and
interpret [decision forest](https://ydf.readthedocs.io/en/latest/intro_df.html)
models (e.g., Random Forests, Gradient Boosted Trees) in TensorFlow. TF-DF
supports classification, regression and ranking.

**TF-DF** is powered by
[Yggdrasil Decision Forest](https://github.com/google/yggdrasil-decision-forests)
(**YDF**, a library to train and use decision forests in C++, JavaScript, CLI,
and Go. TF-DF models are
[compatible](https://ydf.readthedocs.io/en/latest/convert_model.html#convert-a-a-tensorflow-decision-forests-model-to-a-yggdrasil-model)
with YDF' models, and vice versa.

Tensorflow Decision Forests is available on Linux and Mac. Windows users can use
the library through WSL+Linux.

## Usage example

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

```python
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")
```

## Google I/O Presentation

<div align="center">
    <a href="https://youtu.be/5qgk9QJ4rdQ">
        <img src="https://img.youtube.com/vi/5qgk9QJ4rdQ/0.jpg"></img>
    </a>
</div>

## Documentation & Resources

The following resources are available:

-   [TF-DF on TensorFlow.org](https://tensorflow.org/decision_forests) (API
    Reference, Guides and Tutorials)
-   [Tutorials](https://www.tensorflow.org/decision_forests/tutorials) (on
    tensorflow.org)
-   [YDF documentation](https://ydf.readthedocs.io) (also applicable to TF-DF)
-   [Issue tracker](https://github.com/tensorflow/decision-forests/issues)
-   [Known issues](documentation/known_issues.md)
-   [Changelog](CHANGELOG.md)
-   [More examples](documentation/more_examples.md)

## Installation

To install TensorFlow Decision Forests, run:

```shell
pip3 install tensorflow_decision_forests --upgrade
```

See the [installation](documentation/installation.md) 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](documentation/developer_manual.md) and
[contribution guidelines](CONTRIBUTING.md).

## Citation

If you us Tensorflow Decision Forests in a scientific publication, please cite
the following paper:
[Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://doi.org/10.1145/3580305.3599933).

**Bibtex**

```
@inproceedings{GBBSP23,
  author       = {Mathieu Guillame{-}Bert and
                  Sebastian Bruch and
                  Richard Stotz and
                  Jan Pfeifer},
  title        = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests
                  Library},
  booktitle    = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
                  and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
  pages        = {4068--4077},
  year         = {2023},
  url          = {https://doi.org/10.1145/3580305.3599933},
  doi          = {10.1145/3580305.3599933},
}
```

**Raw**

Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library,
Guillame-Bert et al., KDD 2023: 4068-4077. doi:10.1145/3580305.3599933

## Contact

You can contact the core development team at
[decision-forests-contact@google.com](mailto:decision-forests-contact@google.com).

## Credits

TensorFlow Decision Forests was developed by:

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

## License

[Apache License 2.0](LICENSE)

            

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    "description": "<p align=\"center\">\n<img src=\"documentation/image/logo.png\"  />\n</p>\n\n**TensorFlow Decision Forests** (**TF-DF**) is a library to train, run and\ninterpret [decision forest](https://ydf.readthedocs.io/en/latest/intro_df.html)\nmodels (e.g., Random Forests, Gradient Boosted Trees) in TensorFlow. TF-DF\nsupports classification, regression and ranking.\n\n**TF-DF** is powered by\n[Yggdrasil Decision Forest](https://github.com/google/yggdrasil-decision-forests)\n(**YDF**, a library to train and use decision forests in C++, JavaScript, CLI,\nand Go. TF-DF models are\n[compatible](https://ydf.readthedocs.io/en/latest/convert_model.html#convert-a-a-tensorflow-decision-forests-model-to-a-yggdrasil-model)\nwith YDF' models, and vice versa.\n\nTensorflow Decision Forests is available on Linux and Mac. Windows users can use\nthe library through WSL+Linux.\n\n## Usage example\n\nA minimal end-to-end run looks as follows:\n\n```python\nimport tensorflow_decision_forests as tfdf\nimport pandas as pd\n\n# Load the dataset in a Pandas dataframe.\ntrain_df = pd.read_csv(\"project/train.csv\")\ntest_df = pd.read_csv(\"project/test.csv\")\n\n# Convert the dataset into a TensorFlow dataset.\ntrain_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label=\"my_label\")\ntest_ds = tfdf.keras.pd_dataframe_to_tf_dataset(test_df, label=\"my_label\")\n\n# Train the model\nmodel = tfdf.keras.RandomForestModel()\nmodel.fit(train_ds)\n\n# Look at the model.\nmodel.summary()\n\n# Evaluate the model.\nmodel.evaluate(test_ds)\n\n# Export to a TensorFlow SavedModel.\n# Note: the model is compatible with Yggdrasil Decision Forests.\nmodel.save(\"project/model\")\n```\n\n## Google I/O Presentation\n\n<div align=\"center\">\n    <a href=\"https://youtu.be/5qgk9QJ4rdQ\">\n        <img src=\"https://img.youtube.com/vi/5qgk9QJ4rdQ/0.jpg\"></img>\n    </a>\n</div>\n\n## Documentation & Resources\n\nThe following resources are available:\n\n-   [TF-DF on TensorFlow.org](https://tensorflow.org/decision_forests) (API\n    Reference, Guides and Tutorials)\n-   [Tutorials](https://www.tensorflow.org/decision_forests/tutorials) (on\n    tensorflow.org)\n-   [YDF documentation](https://ydf.readthedocs.io) (also applicable to TF-DF)\n-   [Issue tracker](https://github.com/tensorflow/decision-forests/issues)\n-   [Known issues](documentation/known_issues.md)\n-   [Changelog](CHANGELOG.md)\n-   [More examples](documentation/more_examples.md)\n\n## Installation\n\nTo install TensorFlow Decision Forests, run:\n\n```shell\npip3 install tensorflow_decision_forests --upgrade\n```\n\nSee the [installation](documentation/installation.md) page for more details,\ntroubleshooting and alternative installation solutions.\n\n## Contributing\n\nContributions to TensorFlow Decision Forests and Yggdrasil Decision Forests are\nwelcome. If you want to contribute, make sure to review the\n[developer manual](documentation/developer_manual.md) and\n[contribution guidelines](CONTRIBUTING.md).\n\n## Citation\n\nIf you us Tensorflow Decision Forests in a scientific publication, please cite\nthe following paper:\n[Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://doi.org/10.1145/3580305.3599933).\n\n**Bibtex**\n\n```\n@inproceedings{GBBSP23,\n  author       = {Mathieu Guillame{-}Bert and\n                  Sebastian Bruch and\n                  Richard Stotz and\n                  Jan Pfeifer},\n  title        = {Yggdrasil Decision Forests: {A} Fast and Extensible Decision Forests\n                  Library},\n  booktitle    = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery\n                  and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},\n  pages        = {4068--4077},\n  year         = {2023},\n  url          = {https://doi.org/10.1145/3580305.3599933},\n  doi          = {10.1145/3580305.3599933},\n}\n```\n\n**Raw**\n\nYggdrasil Decision Forests: A Fast and Extensible Decision Forests Library,\nGuillame-Bert et al., KDD 2023: 4068-4077. doi:10.1145/3580305.3599933\n\n## Contact\n\nYou can contact the core development team at\n[decision-forests-contact@google.com](mailto:decision-forests-contact@google.com).\n\n## Credits\n\nTensorFlow Decision Forests was developed by:\n\n-   Mathieu Guillame-Bert (gbm AT google DOT com)\n-   Jan Pfeifer (janpf AT google DOT com)\n-   Richard Stotz (richardstotz AT google DOT com)\n-   Sebastian Bruch (sebastian AT bruch DOT io)\n-   Arvind Srinivasan (arvnd AT google DOT com)\n\n## License\n\n[Apache License 2.0](LICENSE)\n",
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