trustworthyai 0.35.0

SDK API to explain models, generate counterfactual examples, analyze causal effects and analyze errors in Machine Learning models.

Trustworthy AI Model Analysis SDK for Python

This package has been tested with Python 3.7, 3.8, 3.9 and 3.10

The Trustworthy AI Model Analysis SDK enables users to analyze their machine learning models in one API. Users will be able to analyze errors, explain the most important features, compute counterfactuals and run causal analysis using a single API.

Highlights of the package include:

• explainer.add() explains the model
• counterfactuals.add() computes counterfactuals
• error_analysis.add() runs error analysis
• causal.add() runs causal analysis

Supported scenarios, models and datasets

trustworthyai supports computation of Trustworthy AI insights for scikit-learn models that are ttained on pandas.DataFrame. The trustworthyai accept both models and pipelines as input as long as the model or pipeline implements a predict or predict_proba function that conforms to the scikit-learn convention. If not compatible, you can wrap your model's prediction function into a wrapper class that transforms the output into the format that is supported (predict or predict_proba of scikit-learn), and pass that wrapper class to modules in trustworthyai.

Currently, we support datasets having numerical and categorical features. The following table provides the scenarios supported for each of the four trustworthy AI insights:-

RAI insight	Binary classification	Multi-class classification	Multilabel classification	Regression	Timeseries forecasting	Categorical features	Text features	Image Features	Recommender Systems	Reinforcement Learning
Explainability	Yes	Yes	No	Yes	No	Yes	No	No	No	No
Error Analysis	Yes	Yes	No	Yes	No	Yes	No	No	No	No
Causal Analysis	Yes	No	No	Yes	No	Yes (max 5 features due to expensiveness)	No	No	No	No
Counterfactual	Yes	Yes	No	Yes	No	Yes	No	No	No	No

The source code can be found here: https://github.com/affectlog/trustworthy-ai-toolbox/tree/main/trustworthyai