incx 0.0.2

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IncX

IncX (Incremental Explanations) is a method for generating saliency maps and explanations incrementally in real-time.

Getting Started

To set up and use this project, you will need pyenv for managing Python versions and poetry for handling dependencies. Follow these instructions to configure your environment.

Prerequisites

1. Install Pyenv
   Pyenv allows you to manage multiple Python versions on your system. Select your operating system and follow the relevant installation instructions:

   • Linux and macOS: Refer to the guide on the pyenv GitHub repository.
   • Windows: Use pyenv-win for installation instructions.

2. Install the Required Python Version
   After installing pyenv, use it to install the Python version specified in the .python-version file located in the root directory of this project. Execute the following command:

   pyenv install
   

3. Install Poetry
   Install poetry by following the instructions on the poetry installation page.

4. Configure Poetry
   Set up poetry to create virtual environments within the project directory. This configuration helps manage isolated environments for different projects:

   poetry config virtualenvs.in-project true
   

5. Install Project Dependencies
   Use poetry to install all required dependencies and create a virtual environment:

   poetry install
   

Running Experiments

To reproduce the experiments comparing D-RISE and IncX, follow these steps using the scripts located in the /experiments directory.

1. Generate Saliency Maps with D-RISE
   Navigate to /experiments/d_rise and execute the get_saliency_maps.py script:

   python get_saliency_maps.py
   

   If you are using a high-performance computing (HPC) system, you can run these tasks in parallel by using batch_experiment_gpu.sh and multiple_experiment_gpu.sh.

2. Generate Saliency Maps with IncX
   Move to /experiments/incx and run get_job_names.py to create a list of jobs:

   python get_job_names.py
   

   Then, execute get_saliency_maps.py to compute the saliency maps. For parallel execution on HPC systems, use batch_experiment_gpu.sh and multiple_experiment_gpu.sh.

3. Compare Saliency Maps
   After generating the saliency maps from both methods, compare them by running:

   python get_blob_names.py
   

   Next, obtain the comparison results with:

   python get_similarity_comparison.py
   

   On HPC systems, expedite this process with batch_similarity_comparison.sh and multiple_batch_similarity.sh.

4. Compute Metrics
   To calculate metrics such as insertion, deletion, EPG, and explanation size, start by running:

   python get_blob_names_metrics.py
   

   Then, execute:

   python get_metrics.py
   

   For HPC systems, speed up this process by using batch_metrics.sh and multiple_batch_metrics.sh.

Running Unit Tests

To ensure that the code functions correctly and is thoroughly tested, run:

poetry run pytest

This command will execute all tests in the tests/ directory, providing feedback on code accuracy and test coverage.

Linting and Formatting

To maintain high code quality and consistency, use the following commands:

1. Linting
   Run ruff to identify and automatically fix code issues:

   poetry run ruff check . --fix
   

2. Formatting
   Format your code according to style guidelines with ruff:

   poetry run ruff format .
   

Installing the Package

The Incx package is available on PyPI. To install the latest version, execute:

pip install incx

Usage

The usage_examples.ipynb provides examples on how to use the python package. For instance, to explain a video the following code will suffice:

from incx import incx, yolo, rt_detr, faster_rcnn, d_rise

video_path = 'PATH_TO_YOUR_VIDEO'

# Choose the model you want to use

model = yolo.Yolo()
# model = rt_detr.RTDETR()
# model = faster_rcnn.FasterRcnn()


explainer = d_rise.DRise(model)
incX = incx.IncX(model, explainer)

frames = incX.explain_video(video_path)