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Actuarial reserving in Python
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README.md

chainladder (python)

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chainladder - Property and Casualty Loss Reserving in Python

This package is highly inspired by the popular R ChainLadder package and where equivalent procedures exist, has been heavily tested against the R package.

A goal of this package is to be minimalistic in needing its own API. To that end, we've adopted as much of the pandas API for data manipulation and the scikit-learn API for model construction as possible. The idea here is to allow an actuary already versed in these tools to easily pick up this package. We figure an actuary who uses python has reasonable familiarity with pandas and scikit-learn, so they can spend as little mental energy as possible learning yet another API.

Documentation

Please visit the Documentation page for examples, how-tos, and source code documentation.

Tutorials

Tutorial notebooks are available for download here.

Have a question?

Feel free to reach out on Gitter.

Want to contribute?

Check out our contributing guidelines.

Installation

To install using pip: pip install chainladder

Alternatively, install directly from github: pip install git+https://github.com/casact/chainladder-python/

Note: This package requires Python 3.5 and later, numpy 1.12.0 and later, pandas 0.23.0 and later, scikit-learn 0.18.0 and later.

GPU support

New in version 0.5.0 - chainladder now supports CUDA-based GPU computations by way of CuPY. You can now swap array_backend between numpy and cupy to switch between CPU and GPU-based computations.

Array backends can be set globally:

import chainladder as cl
cl.array_backend('cupy')

Alternatively, they can be set per Triangle instance.

cl.Triangle(..., array_backend='cupy')

Note you must have a CUDA-enabled graphics card and CuPY installed to use the GPU backend.

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