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lillijo / xai-evaluation-master-thesis

code for experiments of Lilli Joppien's master thesis: Are We Explaining the Data or the Model? Concept-Based Methods and Their Fidelity in Presence of Spurious Features Under a Causal Lense.
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Master Thesis -- Lilli Joppien

Are We Explaining the Data or the Model?

Concept-Based Methods and Their Fidelity in Presence of Spurious Features Under a Causal Lense.

Advisors: Oana-Iuliana Popescu, Simon Bing
First Examiner: Prof. Dr. Jakob Runge
Second Examiner: Prof. Dr. Grégoire Montavon

Documentation

Next to the conda environment (env.yml) some other packages need to be installed to run this experiment

Installation

  1. install conda environment conda env create -n ENVNAME --file env.yml
  2. follow instructions from https://github.com/rachtibat/zennit-crp to install zennit-crp
  3. (optionally) install tigramite from isntructions at https://github.com/jakobrunge/tigramite/

If one desires to rerun the whole experiment, follow the instructions below, else all data necessary for the compare_measures notebook should be here. If one wants to look at further visualizations of what the explanation heatmaps look like etc. The trained models can be requested by making an issue here, or otherwise recomputed as seen below.

Computation

  1. Training Models if access to SLURM based cluster with gpu nodes:

    • copy all files inside experiments folder to cluster
    • do install steps on cluster
    • make sure that correct EXPERIMENT is selected in python3 run_iterations.py
    • run python3 script_parallel_iterations.py
    • if accuracies are not satisfactory, rerun until desired accuracy reached
    • copy jobs over from cluster and extract into file using python3 extract_infos.py else ask for pretrained models

  2. Computing Explanations and Measures

    • run python3 compute_normal_measures.py [1,2] (choose 1 for watermark experiment or 2 for pattern) This computes the relevance and attribution maps measures as well as the region-specific measures (RMA, RRA, PG) note that if this has not previously been done, it will take about 20 minutes to compute all explanations
    • run python3 compute_relmax.py recompute=True
    • optionally run python3 compute_latent_factors_gt.py, this will also take a while when done for the first time. This computes relevances and model ground truth effect for the other latent factors of our causal model: shape, rotation, scale, posX, posY

  3. Explore Measures and Plot Visualizations

    • load and analyze cells in compare_measures.ipynb
    • for further visualizations also explore further_visualizations.ipynb