https://github.com/neelsoumya/complex_stories_explanations

Complex stories as explanations for machine learning models
https://github.com/neelsoumya/complex_stories_explanations

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Complex stories as explanations for machine learning models

• Host: GitHub
• URL: https://github.com/neelsoumya/complex_stories_explanations
• Owner: neelsoumya
• License: gpl-3.0
• Created: 2023-06-22T09:01:36.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-09T09:44:03.000Z (about 1 year ago)
• Last Synced: 2023-10-09T10:46:25.683Z (about 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 2.31 MB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Class Contrastive Explanations and Generating Complex Stories from Machine Learning Models

In order to provide a better explanability for machine learning models in healthcare are, we provide an implementation for class contrastive analysis for Pima dataset.

Class contrastive analysis is a technique to explain black-box machine learning models [1].

In addition to this approach, Mark W. Craven and Jude W. Shavlik proposed extracting Thee-Structured representations of trained networks in 1993.

We use the TREPAN implementation in "Extracting tree-structured representations of trained networks" : Craven,Shavlik 1993 from github user: @abarthakur.

https://github.com/abarthakur/trepan_python

# Quick Installation

Install Anaconda (Python distribution)

and install all packages by running the following command at the Terminal

```python

pip install -r requirements.txt

```

# Data

Download the data from

https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database

# Files

The script `simple_2D__pima_heatmap.ipynb` does the following things:

1. Trains a simple 3-layer neural network on the pima dataset

2. Provides Data centric explanation like outlier detection

3. Provides double feature class contrastive analysis

Figure 3.1 is generated in `pima_heatmap.ipynb`.

Figure 4.1 is generated from `pima_heatmap.ipynb`.

Figure 4.2 is generated from `2D_pima_heatmap.ipynb`.

Figure 4.3 is generated from `3D_pima_heatmap.ipynb`.

Figure 4.4 is generated from `4D_pima_heatmap.ipynb`.

Figure 4.7 is generated from `2D_pima_heatmap.ipynb`.

Figure 4.8 is generated from `opposite2D_pima_heatmap.ipynb`.

# Manuscript and citation

Banerjee S, Lio P, Jones PB, Cardinal RN (2021) A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness. npj Schizophr 7: 1–13.

https://www.nature.com/articles/s41537-021-00191-y

Generating complex explanations from machine learning models using class-contrastive reasoning

https://www.medrxiv.org/content/10.1101/2023.10.06.23296591v1

# Contact

Yujia Yang and Soumya Banerjee

[email protected]

# References

If you use this work, please cite the following paper:

1. Banerjee S, Lio P, Jones PB, Cardinal RN (2021) A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness. npj Schizophr 7: 1–13.

  https://www.nature.com/articles/s41537-021-00191-y

2. Yujia Yang, Soumya Banerjee, Generating complex explanations from machine learning models using class-contrastive reasoning

https://www.medrxiv.org/content/10.1101/2023.10.06.23296591v1