Algorithm - SHAP Toolbox

Description

• SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).

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-shap-toolbox

Example Usage:

Run the following bash script to execute the plugin

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files"
python -m aiverify_shap_toolbox \
    --data_path  $root_path/data/sample_bc_credit_data.sav \
    --model_path $root_path/model/sample_bc_credit_sklearn_linear.LogisticRegression.sav \
    --ground_truth_path $root_path/data/sample_bc_credit_data.sav \
    --ground_truth default \
    --model_type CLASSIFICATION \
    --no-run_pipeline \
    --background_path $root_path/data/sample_bc_credit_data.sav \
    --background_samples 25 \
    --data_samples 25 \
    --explain_type global

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
python -m venv .venv
source .venv/bin/activate

# execute plugin
cd aiverify/stock-plugins/aiverify.stock.shap-toolbox/algorithms/shap_toolbox/
# install aiverify-test-engine
pip install -e '.[dev]'

python -m aiverify_shap_toolbox --data_path  <data_path> --model_path <model_path> --ground_truth_path <ground_truth_path> --ground_truth <str> --model_type CLASSIFICATION --run_pipeline --background_path <background_path> --background_samples <number> --data_samples <number> --explain_type <str>

Build Plugin

cd aiverify/stock-plugins/aiverify.stock.shap-toolbox/algorithms/shap_toolbox/
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.shap-toolbox/algorithms/shap_toolbox/
pytest .

Run using Docker

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

docker build -t aiverify-shap-toolbox -f stock-plugins/aiverify.stock.shap-toolbox/algorithms/shap_toolbox/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.shap-toolbox/algorithms/shap_toolbox/output:/app/aiverify/output \
  aiverify-shap-toolbox \
  --data_path /input/data/sample_bc_credit_data.sav \
  --model_path /input/model/sample_bc_credit_sklearn_linear.LogisticRegression.sav \
  --ground_truth_path /input/data/sample_bc_credit_data.sav \
  --ground_truth default \
  --model_type CLASSIFICATION \
  --no-run_pipeline \
  --background_path /input/data/sample_bc_credit_data.sav \
  --background_samples 25 \
  --data_samples 25 \
  --explain_type global

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.shap-toolbox/algorithms/shap_toolbox \
  aiverify-shap-toolbox \
  -m pytest .