aiverify-partial-dependence-plot 2.2.0
AI Verify implementation of Partial Dependence Plot (PDP) that explains how each feature and its feature value contribute to the predictions.
Requires Python: <3.13,>=3.10

# 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.

```sh
pip install aiverify-partial-dependence-plot
```

## Example Usage:

Run the following bash script to execute the plugin

```sh
#!/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:

```sh
#!/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

```sh
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

```sh
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

```sh
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

```sh
#!/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

```sh
docker run \
  --entrypoint python3 \
  -w /app/aiverify/stock-plugins/aiverify.stock.partial-dependence-plot/algorithms/partial_dependence_plot \
  aiverify-partial-dependence-plot \
  -m pytest .
```