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Welcome to pysurvival.io

What is PySurvival ?

PySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or predict when an event is likely to happen. It is built upon the most commonly used machine learning packages such NumPy, SciPy and PyTorch.

PySurvival is compatible with Python 2.7-3.7.

Content

PySurvival provides a very easy way to navigate between theoretical knowledge on Survival Analysis and detailed tutorials on how to conduct a full analysis, build and use a model. Indeed, the package contains:

10+ models ranging from the Cox Proportional Hazard model, the Neural Multi-Task Logistic Regression to Random Survival Forest
Summaries of the theory behind each model as well as API descriptions and examples.
Tutorials displaying in great details how to perform exploratory data analysis, survival modeling, cross-validation and prediction, for churn modeling and credit risk to name a few.
Performance metrics to assess the models' abilities like c-index or brier score
Simple ways to load and save models
... and more !

Getting started

Because of its simple API, PySurvival has been built to provide a great user experience when it comes to modeling. Here's a quick modeling example to get you started:

# Loading the modules
from pysurvival.models.semi_parametric import CoxPHModel
from pysurvival.models.multi_task import LinearMultiTaskModel
from pysurvival.datasets import Dataset
from pysurvival.utils.metrics import concordance_index

# Loading and splitting a simple example into train/test sets
X_train, T_train, E_train, X_test, T_test, E_test = \
    Dataset('simple_example').load_train_test()

# Building a CoxPH model
coxph_model = CoxPHModel()
coxph_model.fit(X=X_train, T=T_train, E=E_train, init_method='he_uniform',
                l2_reg = 1e-4, lr = .4, tol = 1e-4)

# Building a MTLR model
mtlr = LinearMultiTaskModel()
mtlr.fit(X=X_train, T=T_train, E=E_train, init_method = 'glorot_uniform',
         optimizer ='adam', lr = 8e-4)

# Checking the model performance
c_index1 = concordance_index(model=coxph_model, X=X_test, T=T_test, E=E_test )
print("CoxPH model c-index = {:.2f}".format(c_index1))

c_index2 = concordance_index(model=mtlr, X=X_test, T=T_test, E=E_test )
print("MTLR model c-index = {:.2f}".format(c_index2))

For additional models and performance metrics, checkout the documentation.

Citation

If you use Pysurvival in your research and we would greatly appreciate if you could use the following:

@Misc{ pysurvival_cite,
  author = {Stephane Fotso and others},
  title = {{PySurvival}: Open source package for Survival Analysis modeling},
  year = {2019--},
  url = "https://www.pysurvival.io/"
}