future1111/interpretable-ai-book

Code associated with my Interpretable AI Book (https://www.manning.com/books/interpretable-ai)

Interpretable AI - Building Explainable Machine Learning Systems

This repository contains Jupyter notebooks implementing the code samples found in the book Interpretable AI - Building Explainable Machine Learning Systems (Manning Publications). The book features far more content than you will find in these notebooks.

Setup

These notebooks use Python 3.7, scikit-learn 0.21.3 and PyTorch 1.4.0. You can create the conda environment from the environment.yml file as follows.

conda env create -f environment.yml

The environment name is interpretable-ai and it can be activated as follows.

conda activate interpretable-ai

Table of Contents

• Chapter 2: White-Box Models
  • Linear Regression and Decision Trees
  • Generalized Additive Models (GAMs)
  • Illustration of B-Splines
  • Data
  • Models
• Chapter 3: Model Agnostic Methods - Global Interpretability
  • Tree Ensembles and Global Interpretability
    • Tree Ensembles (Random Forest)
    • Partial Dependence Plots (PDPs)
    • Feature Interactions
  • Data
  • Models
• Chapter 4: Model Agnostic Methods - Local Interpretability
  • Deep Neural Networks and Local Interpretability
    • Deep Neural Networks (DNNs)
    • Local Interpretable Model-agnostic Explanations (LIME)
    • Shapley Additive exPlanations (SHAP)
    • Anchors
  • Illustration of Activation Functions
  • Data
  • Models
• Chapter 5: Saliency Mapping
  • Convolutional Neural Networks and Visual Attribution
    • Convolutional Neural Networks (CNNs)
    • Visual Attribution Methods
      • Vanilla backpropagation
      • Guided backpropagation
      • Integrated gradients
      • SmoothGrad
      • Grad-CAM
      • Guided Grad-CAM
  • Data
  • Models
• Chapter 6: Understanding Layers and Units
  • Setup: Refer the readme on how to setup the network dissection framework
  • Results: Refer the readme to download the network dissection results for certain pre-trained models
  • Network Dissection
  • Visualize Network Dissection Results
• Appendix A: PyTorch
  • Intro to PyTorch
• Chapter 7 (work in progress)
• Chapter 8 (work in progress)