This package gets inpiration from the popular R ChainLadder package.
A goal of this package is to be minimalistic in needing its own API. Think in pandas for data manipulation and scikit-learn for model construction. The idea here is to allow an actuary already versed in these tools to pick up this package with ease. Save your mental energy for actuarial work.
Please visit the Documentation page for examples, how-tos, and source code documentation.
Tutorial notebooks are available for download here.
- Working with Triangles
- Selecting Development Patterns
- Extending Development Patterns with Tails
- Applying Deterministic Methods
- Applying Stochastic Methods
Feel free to reach out on Gitter.
Check out our contributing guidelines.
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.
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.