..
   survivalpredict documentation master file, created by
   sphinx-quickstart on Mon Apr 13 17:46:34 2026.
   You can adapt this file completely to your liking, but it should at least
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###############################
 survivalpredict documentation
###############################

Survivalpredict is a Python package for survival analysis with a
statistical learning lens.

Survival analysis is a branch of statistics that frames its modeling on
the assumption that all observations are being pushed towards failure as
time moves forward. Each observation is assumed to have either
**failed** at a given point in time or has yet to fail at the last known
interval in time, and is hence **censored**. Survival curves are the
estimated probability of failure at different intervals of time.
Survival analysis was originally developed in the context of clinical
trials but has applications in customer churn, lead conversion,
financial defaults, operational failures, and other areas.

SurvivalPredict has a singular focus on **survival curves** and being
able to tune models using statistical learning methodology. The ability
to easily perform cross-validation, hyperparameter tuning, and feature
selection on survival models is core to SurvivalPredict.

Installation
============

You can easily install Survivalpredict via the pip command.

	``pip install survivalpredict``



Core features of Survivalpredict are:

-  Vectorised code base, with some necessary parts written in numba.
   Resulting in performat code.

-  Scikit-learn inspired api. All estimators return survival curves on
   **predict**. Survivalpredict also has tooling to interlope with
   Scikit-learn directly.

-  Ability to easily cross-validate estimators with Brier scores. Making
   tuning directly influenced by the performance of survival curves.

-  First-class support for left-censorship and model stratification.

Survivalpredict makes some assumptions:

-  The **times** array, the last known observed time of an individual
   before the event or censorship, is to be encoded as an integer. It is
   assumed that **time** starts with 1, and each interval of time is
   equally important. It is advised to engineer time to max out at a few
   thousand; large values in the **time** array can trigger expensive
   computation on several estimators.

-  The **events** array should be of boolean type, **True** if the
   individual experiences an event (e.g., charged, death, conversion,
   etc.), and **False** otherwise.

-  If the data is left-censored, in cases of time-varying effects or
   recurrent events, the smallest value of the **times_start** array
   should be 0, and the **times_start** array should be smaller than the
   **times** array.

-  When calling **predict** on an estimator, columns of the output will
   correspond to all times till max time, starting at time 1.

.. toctree::
    :maxdepth: 2
    :titlesonly:
    :caption: Walkthroughs:

    General Walkthrough<_collections/notebooks/survivalpredict_walkthrough_demo.ipynb>
    Interfacing With Scikit-learn<_collections/notebooks/demo_sklearn_interface.ipynb>

.. toctree::
    :maxdepth: 2
    :titlesonly:
    :caption: API:

    api/datasets
    api/estimators
    api/metrics
    api/model_selection
    api/pipeline
    api/strata_preprocessing
    api/validation