Skater

Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system often needed for real world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction).

The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive "black boxes" both for researchers and practioners. The project is still in beta phase.

Install Skater

pip

    Option 1: without rule lists and without deepinterpreter
    pip install -U skater

    Option 2: without rule lists and with deep-interpreter:
    1. Ubuntu: pip3 install --upgrade tensorflow (follow instructions at https://www.tensorflow.org/install/ for details and best practices)
    2. sudo pip install keras
    3. pip install -U skater==1.1.2

    Option 3: For everything included
    1. conda install gxx_linux-64
    2. Ubuntu: pip3 install --upgrade tensorflow (follow instructions https://www.tensorflow.org/install/ for
       details and best practices)
    3. sudo pip install keras
    4. sudo pip install -U --no-deps --force-reinstall --install-option="--rl=True" skater==1.1.2

To get the latest changes try cloning the repo and use the below mentioned commands to get started,

    1. conda install gxx_linux-64
    2. Ubuntu: pip3 install --upgrade tensorflow (follow instructions https://www.tensorflow.org/install/ for
       details and best practices)
    3. sudo pip install keras
    4. git clone the repo
    5. sudo python setup.py install --ostype=linux-ubuntu --rl=True

Testing

1. If repo is cloned: python skater/tests/all_tests.py
2. If pip installed: python -c "from skater.tests.all_tests import run_tests; run_tests()"

Usage and Examples

See examples folder for usage examples.