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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.
skranger is available on pypi <https://pypi.org/project/skranger>__ and can be installed via pip:
.. code-block:: bash
pip install skranger
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``.
FAQs
Python bindings for C++ ranger random forests
We found that skranger demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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