You're Invited:Meet the Socket Team at BlackHat and DEF CON in Las Vegas, Aug 7-8.RSVP
Socket
Socket
Sign inDemoInstall

skranger

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Install Socket

Detect and block malicious and high-risk dependencies

Install

skranger

Python bindings for C++ ranger random forests


Maintainers
1

Readme

skranger

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

.. |build| image:: https://github.com/crflynn/skranger/actions/workflows/build_and_test.yml/badge.svg :target: https://github.com/crflynn/skranger/actions

.. |wheels| image:: https://github.com/crflynn/skranger/actions/workflows/release.yml/badge.svg :target: https://github.com/crflynn/skranger/actions

.. |rtd| image:: https://img.shields.io/readthedocs/skranger.svg :target: http://skranger.readthedocs.io/en/latest/

.. |pypi| image:: https://img.shields.io/pypi/v/skranger.svg :target: https://pypi.python.org/pypi/skranger

.. |pyversions| image:: https://img.shields.io/pypi/pyversions/skranger.svg :target: https://pypi.python.org/pypi/skranger

skranger provides scikit-learn <https://scikit-learn.org/stable/index.html>__ compatible Python bindings to the C++ random forest implementation, ranger <https://github.com/imbs-hl/ranger>, using Cython <https://cython.readthedocs.io/en/latest/>.

The latest release of skranger uses version 0.12.1 <https://github.com/imbs-hl/ranger/releases/tag/0.12.1>__ of ranger.

Installation

skranger is available on pypi <https://pypi.org/project/skranger>__ and can be installed via pip:

.. code-block:: bash

pip install skranger

Usage

There are two sklearn compatible classes, RangerForestClassifier and RangerForestRegressor. There is also the RangerForestSurvival class, which aims to be compatible with the scikit-survival <https://github.com/sebp/scikit-survival>__ API.

RangerForestClassifier


The ``RangerForestClassifier`` predictor uses ``ranger``'s ForestProbability class to enable both ``predict`` and ``predict_proba`` methods.

.. code-block:: python

    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from skranger.ensemble import RangerForestClassifier

    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    rfc = RangerForestClassifier()
    rfc.fit(X_train, y_train)

    predictions = rfc.predict(X_test)
    print(predictions)
    # [1 2 0 0 0 0 1 2 1 1 2 2 2 1 1 0 1 1 0 1 1 1 0 2 1 0 0 1 2 2 0 1 2 2 0 2 0 0]

    probabilities = rfc.predict_proba(X_test)
    print(probabilities)
    # [[0.01333333 0.98666667 0.        ]
    #  [0.         0.         1.        ]
    #  ...
    #  [0.98746032 0.01253968 0.        ]
    #  [0.99       0.01       0.        ]]


RangerForestRegressor
~~~~~~~~~~~~~~~~~~~~~

The ``RangerForestRegressor`` predictor uses ``ranger``'s ForestRegression class. It also supports quantile regression using the ``predict_quantiles`` method.

.. code-block:: python

    from sklearn.datasets import load_boston
    from sklearn.model_selection import train_test_split
    from skranger.ensemble import RangerForestRegressor

    X, y = load_boston(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    rfr = RangerForestRegressor()
    rfr.fit(X_train, y_train)

    predictions = rfr.predict(X_test)
    print(predictions)
    # [26.27401667  8.96549989 24.82981667 27.92506667 28.04606667 45.4693
    #  21.89681787 40.30345    11.53959613 19.13675    15.88567273 16.69713567
    #  ...
    #  20.29025364 26.21245833 23.79643333 14.03546362 21.24893333 34.8825
    #  21.22463333]

    # enable quantile regression on instantiation
    rfr = RangerForestRegressor(quantiles=True)
    rfr.fit(X_train, y_train)

    quantile_lower = rfr.predict_quantiles(X_test, quantiles=[0.1])
    print(quantile_lower)
    # [22.    5.   21.88 23.08 23.1  35.89 10.85 31.5   7.04 14.5  11.7  10.9
    #   8.1  28.38  7.2  19.6  29.1  13.1  24.94 21.09 15.6  11.7  10.41 14.5
    #  ...
    #  18.9  21.4   9.43  8.7  26.46 18.99  7.2  19.27 18.5  21.19 18.99 18.88
    #  14.07 21.87 22.18  9.43 17.28 29.6  18.2 ]
    quantile_upper = rfr.predict_quantiles(X_test, quantiles=[0.9])
    print(quantile_upper)
    # [30.83 12.85 29.01 33.1  33.1  50.   29.75 50.   15.   23.   19.96 21.4
    #  20.53 50.   13.35 25.   48.5  19.6  46.   26.6  23.7  20.1  17.8  21.4
    #  ...
    #  26.78 28.1  17.86 27.5  46.25 24.4  16.74 24.4  28.7  29.1  24.4  25.
    #  25.   31.51 28.   20.8  26.7  42.13 24.24]


RangerForestSurvival
~~~~~~~~~~~~~~~~~~~~

The ``RangerForestSurvival`` predictor uses ``ranger``'s ForestSurvival class, and has an interface similar to the RandomSurvivalForest found in the ``scikit-survival`` package.

.. code-block:: python

    from sksurv.datasets import load_veterans_lung_cancer
    from sklearn.model_selection import train_test_split
    from skranger.ensemble import RangerForestSurvival

    X, y = load_veterans_lung_cancer()
    # select the numeric columns as features
    X = X[["Age_in_years", "Karnofsky_score", "Months_from_Diagnosis"]]
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    rfs = RangerForestSurvival()
    rfs.fit(X_train, y_train)

    predictions = rfs.predict(X_test)
    print(predictions)
    # [107.99634921  47.41235714  88.39933333  91.23566667  61.82104762
    #   61.15052381  90.29888492  47.88706349  21.25111508  85.5768254
    #   ...
    #   56.85498016  53.98227381  48.88464683  95.58649206  48.9142619
    #   57.68516667  71.96549206 101.79123016  58.95402381  98.36299206]

    chf = rfs.predict_cumulative_hazard_function(X_test)
    print(chf)
    # [[0.04233333 0.0605     0.24305556 ... 1.6216627  1.6216627  1.6216627 ]
    #  [0.00583333 0.00583333 0.00583333 ... 1.55410714 1.56410714 1.58410714]
    #  ...
    #  [0.12933333 0.14766667 0.14766667 ... 1.64342857 1.64342857 1.65342857]
    #  [0.00983333 0.0112619  0.04815079 ... 1.79304365 1.79304365 1.79304365]]

    survival = rfs.predict_survival_function(X_test)
    print(survival)
    # [[0.95855021 0.94129377 0.78422794 ... 0.19756993 0.19756993 0.19756993]
    #  [0.99418365 0.99418365 0.99418365 ... 0.21137803 0.20927478 0.20513086]
    #  ...
    #  [0.87868102 0.86271864 0.86271864 ... 0.19331611 0.19331611 0.19139258]
    #  [0.99021486 0.98880127 0.95299007 ... 0.16645277 0.16645277 0.16645277]]


License
-------

``skranger`` is licensed under `GPLv3 <https://github.com/crflynn/skranger/blob/master/LICENSE.txt>`__.

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 ranger submodule, install python and poetry from ``.tool-versions``, install dependencies using poetry, copy the ranger source code into skranger, and then build and install skranger 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``.

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``.

Keywords

FAQs


Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc