https://github.com/crflynn/skgrf

scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library
https://github.com/crflynn/skgrf

generalized-random-forest machine-learning random-forest scikit-learn

Last synced: about 5 hours ago
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scikit-learn compatible Python bindings for grf (generalized random forests) C++ random forest library

• Host: GitHub
• URL: https://github.com/crflynn/skgrf
• Owner: crflynn
• License: gpl-3.0
• Created: 2020-11-20T04:36:24.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-05-07T00:10:30.000Z (over 2 years ago)
• Last Synced: 2024-10-01T07:48:29.005Z (about 1 month ago)
• Topics: generalized-random-forest, machine-learning, random-forest, scikit-learn
• Language: Python
• Homepage: https://skgrf.readthedocs.io/en/stable/
• Size: 413 KB
• Stars: 30
• Watchers: 3
• Forks: 6
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE.txt

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README

        
skgrf

=====

|build| |wheels| |rtd| |pypi| |pyversions|

.. |build| image:: https://github.com/crflynn/skgrf/workflows/build_and_test/badge.svg

    :target: https://github.com/crflynn/skgrf/actions

.. |wheels| image:: https://github.com/crflynn/skgrf-wheels/workflows/release/badge.svg

    :target: https://github.com/crflynn/skgrf/actions

.. |rtd| image:: https://img.shields.io/readthedocs/skgrf.svg

    :target: http://skgrf.readthedocs.io/en/latest/

.. |pypi| image:: https://img.shields.io/pypi/v/skgrf.svg

    :target: https://pypi.python.org/pypi/skgrf

.. |pyversions| image:: https://img.shields.io/pypi/pyversions/skgrf.svg

    :target: https://pypi.python.org/pypi/skgrf

``skgrf`` provides `scikit-learn `__ compatible Python bindings to the C++ random forest implementation, `grf `__, using `Cython `__.

The latest release of ``skgrf`` uses version `2.1.0 `__ of ``grf``.

``skgrf`` is still in development. Please create issues for any discrepancies or errors. PRs welcome.

Documentation

-------------

* `stable `__

* `latest `__

Installation

------------

``skgrf`` is available on `pypi `__ and can be installed via pip:

.. code-block:: bash

    pip install skgrf

Estimators

----------

* GRFForestCausalRegressor

* GRFForestInstrumentalRegressor

* GRFForestLocalLinearRegressor

* GRFForestQuantileRegressor

* GRFForestRegressor

* GRFBoostedForestRegressor

* GRFForestSurvival

Usage

-----

GRFForestRegressor

~~~~~~~~~~~~~~~~~~

The ``GRFForestRegressor`` predictor uses ``grf``'s RegressionPredictionStrategy class.

.. code-block:: python

    from sklearn.datasets import load_boston

    from sklearn.model_selection import train_test_split

    from skgrf.ensemble import GRFForestRegressor

    

    X, y = load_boston(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y)

    

    forest = GRFForestRegressor()

    forest.fit(X_train, y_train)

    

    predictions = forest.predict(X_test)

    print(predictions)

    # [31.81349144 32.2734354  16.51560285 11.90284392 39.69744341 21.30367911

    #  19.52732937 15.82126562 26.49528961 11.27220097 16.02447197 20.01224404

    #  ...

    #  20.70674263 17.09041289 12.89671205 20.79787926 21.18317924 25.45553279

    #  20.82455595]

GRFForestQuantileRegressor

~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``GRFForestQuantileRegressor`` predictor uses ``grf``'s QuantilePredictionStrategy class.

.. code-block:: python

    from sklearn.datasets import load_boston

    from sklearn.model_selection import train_test_split

    from skgrf.ensemble import GRFForestQuantileRegressor

    

    X, y = load_boston(return_X_y=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y)

    

    forest = GRFForestQuantileRegressor(quantiles=[0.1, 0.9])

    forest.fit(X_train, y_train)

    

    predictions = forest.predict(X_test)

    print(predictions)

    # [[21.9 50. ]

    # [ 8.5 24.5]

    # ...

    # [ 8.4 18.6]

    # [ 8.1 20. ]]

License

-------

``skgrf`` is licensed under `GPLv3 `__.

Development

-----------

To develop locally, it is recommended to have ``asdf``, ``make`` and a C++ compiler already installed. After cloning, run ``make setup``. This will setup the grf submodule, install python and poetry from ``.tool-versions``, install dependencies using poetry, copy the grf source code into skgrf, and then build and install skgrf in the local virtualenv.

To format code, run ``make fmt``. This will run isort and black against the .py files.

To run tests and inspect coverage, run ``make test`` or ``make xtest`` for testing in parallel.

To rebuild in place after making changes, run ``make build``.

To create python package artifacts, run ``make dist``.

To build and view documentation, run ``make docs``.