Description:
interpretcommunity 0.31.0
Microsoft Interpret Community SDK for Python
This package has been tested with Python 3.7, 3.8 and 3.9
The Interpret Community SDK builds on Interpret, an open source python package from Microsoft Research for training interpretable models, and helps to explain blackbox systems by adding additional extensions from the community to interpret ML models.
Interpret-Community is an experimental repository that hosts a wide range of community developed machine learning interpretability techniques. This repository makes it easy for anyone involved in the development of a machine learning system to improve transparency around their machine learning models. Data scientists, machine learning engineers, and researchers can easily add their own interpretability techniques via the set of extension hooks built into the peer repository, Interpret, and expand this repository to include their custom-made interpretability techniques.
Highlights of the package include:
The TabularExplainer can be used to give local and global feature importances
The best explainer is automatically chosen for the user based on the model
Local feature importances are for each evaluation row
Global feature importances summarize the most importance features at the model-level
The API supports both dense (numpy or pandas) and sparse (scipy) datasets
There are utilities provided to convert engineered explanations, based on preprocessed data before training a model, to raw explanations on the original dataset
For more advanced users, individual explainers can be used
The KernelExplainer, GPUKernelExplainer, PFIExplainer and MimicExplainer are for BlackBox models
The MimicExplainer is faster but less accurate than the KernelExplainer, and supports various surrogate model types
The TreeExplainer is for tree-based models
The LinearExplainer is for linear models
The DeepExplainer is for DNN tensorflow or pytorch models
The PFIExplainer can quickly compute global importance values
LIMEExplainer builds local linear approximations of the model's behavior by perturbing each instance
GPUKernelExplainer is GPU-accelerated implementation of SHAP's KernelExplainer as a part of RAPIDS's cuML library, and is optimized for GPU models, like those in cuML. It can be used with CPU-based estimators too.
An adapter to convert any feature importance values to an interpret-community style explanation
Please see the github website for the documentation and sample notebooks:
https://github.com/interpretml/interpret-community
Auto-generated sphinx API documentation can be found here:
https://interpret-community.readthedocs.io/en/latest/index.html
More information on the ExplanationDashboard can be found here:
https://github.com/microsoft/responsible-ai-toolbox
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
