pysurvival | Open source package for Survival Analysis modeling | Machine Learning library

by square HTML Version: Current License: Apache-2.0

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kandi X-RAY | pysurvival Summary

pysurvival is a HTML library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Keras, Numpy applications. pysurvival has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

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.

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Support

pysurvival has a low active ecosystem.

It has 293 star(s) with 93 fork(s). There are 19 watchers for this library.

It had no major release in the last 6 months.

There are 47 open issues and 8 have been closed. On average issues are closed in 43 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pysurvival is current.

Quality

pysurvival has no bugs reported.

Security

pysurvival has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

pysurvival is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pysurvival releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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pysurvival Key Features

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pysurvival Examples and Code Snippets

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Community Discussions

Trending Discussions on pysurvival

Making point predictions using Cox proportional hazard

QUESTION

Making point predictions using Cox proportional hazard

Asked 2021-Apr-19 at 10:20

I am using the pysurvival library to model with the Cox proportional hazard model (CPH). Instead of getting the survival curves, I am interested in getting point predictions. In the library, the function predict_survival returns an array-like representing the prediction of the survival function which I assume that I can use to get the expected values - but I just cant find the right way.

Below I've attached a dummy example.

...

ANSWER

Answered 2021-Apr-19 at 10:20

As mentioned we can use the function predict_survival to get the estimated survival probability. Furthermore, by calling coxPH.times we obtain the time of every estimated survival probability, and thereby for example can plot the individual survival curve for every observation and calculate the area under the curve. By using the auc function from the sklearn.metrics library the following definition gives point predictions for the training and test set given a CPH model, the X_test data and X_train data:

Source https://stackoverflow.com/questions/67120861

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pysurvival

If you have already installed a working version of gcc, the easiest way to install Pysurvival is using pip.
Because of its simple API, Pysurvival has been built to provide to best user experience when it comes to modeling. Here's a quick modeling example to get you started:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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