https://github.com/charmlab/mace

Model Agnostic Counterfactual Explanations
https://github.com/charmlab/mace

counterfactual-explanations explainable-ai explainable-ml interpretable-machine-learning machine-learning xai

Last synced: 2 months ago
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Model Agnostic Counterfactual Explanations

• Host: GitHub
• URL: https://github.com/charmlab/mace
• Owner: amirhk
• Created: 2019-06-20T12:17:26.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-09-30T18:53:58.000Z (almost 2 years ago)
• Last Synced: 2023-08-12T10:19:59.198Z (11 months ago)
• Topics: counterfactual-explanations, explainable-ai, explainable-ml, interpretable-machine-learning, machine-learning, xai
• Language: Python
• Size: 3 MB
• Stars: 77
• Watchers: 4
• Forks: 12
• Open Issues: 8
• Metadata Files:
  • Readme: README.md

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• awesome-counterfactual-explanations - https://github.com/amirhk/mace - Learn,  (Github Repos)

README

        
# General

This repository provides code and examples for generating nearest counterfactual explanations and minimal consequential interventions. The following papers are supported:

- [2017.06 Feature Tweaking](https://arxiv.org/abs/1706.06691) (4c691b4 @ https://github.com/upura/featureTweakPy)

- [2019.01 Actionable Recourse](https://arxiv.org/pdf/1809.06514) (9387e6c @ https://github.com/ustunb/actionable-recourse)

- [2019.07 Minimum  Observable](https://arxiv.org/abs/1907.04135)

- [2019.05 MACE](https://arxiv.org/abs/1905.11190)

- [2020.02 MINT](https://arxiv.org/abs/2002.06278)

# Code Pre-requisites

First,

```console

$ git clone https://github.com/amirhk/mace.git

$ pip install virtualenv

$ cd mace

$ virtualenv -p python3 _venv

$ source _venv/bin/activate

$ pip install -r pip_requirements.txt

$ pysmt-install --z3 --confirm-agreement

```

Then refer to

```console

$ python batchTest.py  --help

```

and run as follows

```console

$ python batchTest.py -d *dataset* -m *model* -n *norm* -a *approach* -b 0 -s *numSamples*

```

For instance, you may run

```console

$ python batchTest.py -d adult -m lr -n zero_norm -a AR -b 0 -s 1

$ python batchTest.py -d credit -m mlp -n one_norm -a MACE_eps_1e-3 -b 0 -s 1

$ python batchTest.py -d german -m tree -n two_norm -a MINT__eps_1e-3 -b 0 -s 1

$ python batchTest.py -d mortgage -m forest -n infty_norm -a MINT__eps_1e-3 -b 0 -s 1

```

Finally, view the results under the _experiments folder.

# Specific considerations for _minimal interventions_

For mortgage data, where a causal structure governs the world, AND all variables

are actionable and mutable, we should expect to see `int_dist <= ? >= cfe_dist`,

but `cfe_dist <= scf_dist`. You can assert this by running the following:

```console

$ python batchTest.py -d mortgage -m lr -n one_norm -a MINT_eps_1e-5 MACE_eps_1e-5 -b 0 -s 10

```

Then you can compare the distances resulting fron MACE and MINT as outputted in the console. Do make sure to run `batchTest.py` with `loadData.loadDataset(load_from_cache = True)` so that MACE and MINT use the same data and the resulting comparison is fair.

# Using git-hooks script for sanity checking

There is a `pre-push` script under `_hooks/` which can be used to check MACE under different setups.

Specifically, it checks for successfully running of the code and the closeness of the generated CFEs

to the previously-saved (approximately) optimal ones. You can either manually call the script from MACE root directory by

`_hooks/pre-push` or place it under your local `.git/hooks/` directory to run automatically before every push.

In this case, please remember to give it the required permissions:

```console

$ chmod +x .git/hooks/pre-push

```