Algorithm - Fairness Metrics Toolbox for Classification

Description

• The Fairness Metrics Toolbox (FMT) for Classification contains a list of fairness metrics to measure how resources (e.g. opportunities, food, loan, medical help) are allocated among the demographic groups (e.g. married male, married female) given a set of sensitive feature(s) (e.g. gender, marital status). This plugin is developed for classification models.

License

• Licensed under Apache Software License 2.0

Developers:

• AI Verify

Installation

Each test algorithm can now be installed via pip and run individually.

pip install aiverify-fairness-metrics-toolbox-for-classification

Example Usage:

Run the below bash script to execute the plugin

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files"
python -m aiverify_fairness_metrics_toolbox_for_classification \
  --data_path $root_path/data/sample_mc_toxic_data.sav \
  --model_path $root_path/model/sample_mc_toxic_sklearn_linear.LogisticRegression.sav \
  --ground_truth_path $root_path/data/sample_mc_toxic_data.sav \
  --ground_truth toxic \
  --model_type CLASSIFICATION \
  --sensitive_features_list gender

If the algorithm runs successfully, the results of the test will be saved in an output folder.

Develop plugin locally

Assuming aiverify-test-engine has already been installed in the virtual environment, run the following bash script to install the plugin and execute a test:

#!/bin/bash

# setup virtual environment
python3 -m venv .venv
source .venv/bin/activate

# install plugin
cd aiverify/stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification/
pip install .

python -m aiverify_fairness_metrics_toolbox_for_classification --data_path  <data_path> --model_path <model_path> --ground_truth_path <ground_truth_path> --ground_truth <str> --model_type CLASSIFICATION --run_pipeline --sensitive_features_list <list[str]> --annotated_labels_path <annotated_file_path> --file_name_label <str>

Build Plugin

cd aiverify/stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification/
hatch build

Tests

Pytest is used as the testing framework.

Run the following steps to execute the unit and integration tests inside the tests/ folder

cd aiverify/stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification/
pytest .

Run using Docker

In the aiverify root directory, run the below command to build the docker image

docker build -t aiverify-fairness-metrics-toolbox-for-classification -f stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification/Dockerfile .

Run the below bash script to run the algorithm

#!/bin/bash
docker run \
  -v $(pwd)/stock-plugins/user_defined_files:/input \
  -v $(pwd)/stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification/output:/app/aiverify/output \
  aiverify-fairness-metrics-toolbox-for-classification \
  --data_path /input/data/sample_mc_pipeline_toxic_data.sav \
  --model_path /model/sample_mc_toxic_sklearn_linear.LogisticRegression.sav \
  --ground_truth_path /input/data/sample_mc_pipeline_toxic_ytest_data.sav \
  --ground_truth toxic \
  --model_type CLASSIFICATION \
  --sensitive_features_list gender

If the algorithm runs successfully, the results of the test will be saved in an output folder in the algorithm directory.

Tests

Pytest is used as the testing framework.

Run the following steps to execute the unit and integration tests inside the tests/ folder

docker run \
  --entrypoint python3 \
  -w /app/aiverify/stock-plugins/aiverify.stock.fairness-metrics-toolbox-for-classification/algorithms/fairness_metrics_toolbox_for_classification \
  aiverify-fairness-metrics-toolbox-for-classification \
  -m pytest .