The Random Survival Forest package provides a python implementation of the survival prediction method originally published by Ishwaran et al. (2008).
Reference: Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860.
$ pip install random-survival-forest>>> from random_survival_forest import RandomSurvivalForest, concordance_index
>>> from lifelines import datasets
>>> from sklearn.model_selection import train_test_split
>>> rossi = datasets.load_rossi()
# Attention: duration column must be index 0, event column index 1 in y
>>> y = rossi.loc[:, ["week", "arrest"]]
>>> X = rossi.drop(["arrest", "week"], axis=1)
>>> X, X_test, y, y_test = train_test_split(X, y, test_size=0.25)
>>> rsf = RandomSurvivalForest(n_estimators=20, n_jobs=-1)
>>> rsf = rsf.fit(X, y)
>>> y_pred = rsf.predict(X_test)
>>> c_val = concordance_index(y_time=y_test["week"], y_pred=y_pred, y_event=y_test["arrest"])
>>> print("C-index", round(c_val, 3))If you are having issues or feedback, please let me know. I am happy to fix some bug or implement feature requests.
This package is completely open-source. If it helped you or you even use it comercially, I would be happy about a little support:
MIT