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.
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
- If repo is cloned:
python skater/tests/all_tests.py - If pip installed:
python -c "from skater.tests.all_tests import run_tests; run_tests()"
See examples folder for usage examples.