SOTA_xAI_Visual_analytics

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Repository containing code and experiments supplementing "Structured review of state-of-art xAI Python libraries with focus on visual analytics"

Environment

Recreate anaconda environment on Linux using following command

conda env create -f environment_linux.yml

TODO: TABLE WITH LIBRARY VERSIONS

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Repository Structure

      ├── LICENSE
      ├── Makefile           <- Makefile with commands like `make data` or `make train`
      ├── README.md          <- The top-level README for developers using this project.
      ├── data
      │   ├── external       <- Data from third party sources.
      │   ├── interim        <- Intermediate data that has been transformed.
      │   ├── processed      <- The final, canonical data sets for modeling.
      │   └── raw            <- The original, immutable data dump.
      │
      ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
      │
      ├── models             <- Trained and serialized models, model predictions, or model summaries
      │
      ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
      │                         the creator's initials, and a short `-` delimited description, e.g.
      │                         `1.0-jqp-initial-data-exploration`.
      │
      ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
      │
      ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
      │   └── figures        <- Generated graphics and figures to be used in reporting
      │
      ├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
      │                         generated with `pip freeze > requirements.txt`
      │
      ├── setup.py           <- Make this project pip installable with `pip install -e`
      ├── src                <- Source code for use in this project.
      │   ├── __init__.py    <- Makes src a Python module
      │   │
      │   ├── data           <- Scripts to download or generate data
      │   │   └── make_dataset.py
      │   │
      │   ├── features       <- Scripts to turn raw data into features for modeling
      │   │   └── build_features.py
      │   │
      │   ├── models         <- Scripts to train models and then use trained models to make
      │   │   │                 predictions
      │   │   ├── predict_model.py
      │   │   └── train_model.py
      │   │
      │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
      │       └── visualize.py
      │
      └── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io