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AutoExplainer

A python package for automated selection of explanation method for CNNs.

Reports from demo datasets can be found here: CXR dataset, Imagenette dataset, Kandinsky Patterns dataset.

Installation

Prerequisites: installed python 3.9.

With poetry

To install all dependencies from poetry.toml file using poetry run:

git clone https://github.com/MI2DataLab/autoexplainer.git
cd autoexplainer
poetry config virtualenvs.in-project true
poetry shell # if you want create dedicated .venv inside autoexplainer
poetry install

To use created enviroment, activate it with poetry shell.

With pip

To install dependencies the regular way you can use pip:

git clone https://github.com/MI2DataLab/autoexplainer.git
cd autoexplainer
pip install .

To update the environment

After pulled changes in dependencies, you can update dependencies with:

poetry update

Installing torch 12.1 with other CUDA version

In order to use Pytorch with GPU, you must match torch CUDA version with CUDA driver on your machine. e.g. to install torch 12.1 with CUDA 11.6 support, run:

pip uninstall torch torchvision
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116

To create PDF report with function to_pdf

The function to_pdf creates both .tex and .pdf report versions. Due to this fact, additional features have to be installed to render PDF report properly:

  • install LaTeX eg. MiKTeX and add to PATH
    • in MiKTeX enable automatically installing missing packages
  • add dependency with pip:
pip install pylatex

Usage

See sample notebook in development/notebooks/auto_explainer_usage.ipynb.

Selection process

The most time consuming part. First, all methods are evaluated on provided data. Then, the best method is selected by aggregating raw results. Finally, a report is generated.

from autoexplainer import AutoExplainer

auto_explainer = AutoExplainer(model, data, targets)

# compute all metric values and see not aggregated results (very long)
auto_explainer.evaluate()
auto_explainer.raw_results

# aggregate metric scores and see aggregated results (almost instant)
auto_explainer.aggregate()
auto_explainer.first_aggregation_results  # single value per (method, metric) pair
auto_explainer.second_aggregation_results  # single value per method

# produce a pdf report
auto_explainer.to_html('examples/example_report.html')

Using results

Later, the selected explanation method can be extracted and used right away to explain more data.

best_explanation = auto_explainer.get_best_explanation()
new_attributions = best_explanation.explain(model, data, targets)

This best_explanation object contains all the information about the selected method, including the name of the method, the parameters used, and the attributions of data used during explanation method selection.

best_explanation.name
best_explanation.parameters
best_explanation.attributions

It is also possible to calculate metric values for methods used during selection process but on other data. Values can be either raw (1 value per data point) or aggregated (1 value only, as in auto_explainer.second_aggregation_results).

raw_metric_scores = best_explanation.evaluate(model, data, targets, new_attributions)
aggregated_metric_scores = best_explanation.evaluate(model, data, targets, attributions, aggregate=True)

Development

Running tests

To run test (-n tests in parallel):

pytest tests -n auto

or 1 test at a time:

pytest tests

or a selected test a time:

pytest tests/test_autoexplainer.py

or print output during tests:

pytest -s tests

Running pre-commit hooks

To check formatting, linting, and other checks before commiting, run:

pre-commit run --all-files

Generating documentation

To generate documentation, run:

mkdocs build

The documentation will be generated in site/ directory.

To generate documentation and serve it locally, run:

mkdocs serve

The documentation will be available at http://127.0.0.1:8000/.

If You didn't activate poetry shell, precede commands above with poetry run.

Authors

This repository contains all code used for our bachelor thesis written at the Faculty of Mathematics and Information Science, Warsaw University of Technology.

This project was generated using the wolt-python-package-cookiecutter template.