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WTTE-RNN (Python Implementation / API)

Build Status

Weibull Time To Event Recurrent Neural Network

A less hacky machine-learning framework for churn- and time to event prediction. Forecasting problems as diverse as server monitoring to earthquake- and churn-prediction can be posed as the problem of predicting the time to an event. WTTE-RNN is an algorithm and a philosophy about how this should be done.

Installation

Install via PyPI. We recommend updating pip/setuptools first.

wtte provides multiple extra dependency sets to install additional dependencies for your environment and purposes.

$ pip install -U pip setuptools
$ pip install wtte[extras]

In extras you may specify the followings:

  • tf: install with TensorFlow (CPU-version)
  • tf_gpu: install with TensorFlow (GPU-version)
  • plot: install matplotlib for additional plotting support
  • build: install additional packages to build your own distribution package
  • test: install additional packages to run test suite
  • dev: install additional packages used for local development (including documentation tools)
  • docs: install additional packages used for documentation builds in readthedocs build-farm servers

Development

Follow the below instructions. Change tf to tf_gpu if your machine has CUDA GPUs.

$ git clone $thisrepo
$ cd ./wtte-rnn/python/
$ pip install -U pip setuptools
$ pip install -r requirements-dev.txt
$ pip install -e .[build,test,dev,tf]

Licensing

  • MIT License

Citation

@MastersThesis{martinsson:Thesis:2016,
    author = {Egil Martinsson},
    title  = {{WTTE-RNN : Weibull Time To Event Recurrent Neural Network}},
    school = {Chalmers University Of Technology},
    year   = {2016},
}

Contributing

Contributions/PR/Comments etc are very welcome! Post an issue if you have any questions and feel free to reach out to egil.martinsson[at]gmail.com.

Contributors (by order of commit)

  • Egil Martinsson
  • Clay Kim
  • Jannik Hoffjann
  • Daniel Klevebring
  • Jeongkyu Shin
  • Joongi Kim
  • Jonghyun Park