Skip to main content

AI Verify implementation of Partial Dependence Plot (PDP) that explains how each feature and its feature value contribute to the predictions.

Project description

Algorithm - Partial Dependence Plot

Description

  • A Partial Dependence Plot (PDP) explains how each feature and its feature value contribute to the predictions.

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-partial-dependence-plot

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_partial_dependence_plot \
  --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 REGRESSION \
  --no-run_pipeline

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.partial-dependence-plot/algorithms/partial_dependence_plot/
pip install .

python -m aiverify_partial_dependence_plot --data_path  <data_path> --model_path <model_path> --ground_truth_path <ground_truth_path> --ground_truth <str> --model_type CLASSIFICATION --run_pipeline

Build Plugin

cd aiverify/stock-plugins/aiverify.stock.partial-dependence-plot/algorithms/partial_dependence_plot/
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.partial-dependence-plot/algorithms/partial_dependence_plot/
pytest .

Run using Docker

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

docker build -t aiverify-partial-dependence-plot -f stock-plugins/aiverify.stock.partial-dependence-plot/algorithms/partial_dependence_plot/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.partial-dependence-plot/algorithms/partial_dependence_plot/output:/app/aiverify/output \
  aiverify-partial-dependence-plot \
  --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 REGRESSION \
  --no-run_pipeline

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 aiverify-partial-dependence-plot -m pytest .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aiverify_partial_dependence_plot-2.0.1.tar.gz (22.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file aiverify_partial_dependence_plot-2.0.1.tar.gz.

File metadata

File hashes

Hashes for aiverify_partial_dependence_plot-2.0.1.tar.gz
Algorithm Hash digest
SHA256 be0f1758a6916e9c6c056bb768ee1d51fa492a22db071d4fa37c8b19b2475886
MD5 2883b22304c4a9a086c5e4fd6c8fb441
BLAKE2b-256 97e683de9412577dd0fe29b6a826cc4cf5297f849f1a209b2fed5b5e69a9e8e8

See more details on using hashes here.

File details

Details for the file aiverify_partial_dependence_plot-2.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for aiverify_partial_dependence_plot-2.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 8cabe6e92b9ad3e49cdcc22c9c2ee892bc4227ebfa0ed52155193a2009088e0d
MD5 21f1aac0580443ec6244c2adb9044a9d
BLAKE2b-256 d5609a381ab7e46814733a91b45c0aa59842cd863601ab4401f1999899c31244

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page