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XAI Inverse Estimation #337

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XAI Inverse Estimation #337

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@annahedstroem annahedstroem commented Feb 23, 2024

Description

  • Docstring fixes in all metrics and fixed typos across code base
  • Added call_kwargs_che
  • Replaced assertions for attributions with warnings
  • InveseEstimation implementation
    • tests for pixel flipping and localisation
    • implementation next to base.py
    • plotting functionality
  • fixes in metric base class, including adding the call params, making hem available as self.attributes
  • added return_auc_per_sampel to RegionPerturbation

Minimum acceptance criteria

  • Specify what is necessary for the PR to be merged with the main branch.
  • @mentions of the person that is apt to review these changes e.g., @annahedstroem

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OK.

@annahedstroem annahedstroem changed the title XAI inverse estimation XAI Inverse Estimation Feb 23, 2024
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codecov-commenter commented Mar 25, 2024

Codecov Report

Attention: Patch coverage is 74.81481% with 34 lines in your changes are missing coverage. Please review.

Project coverage is 90.58%. Comparing base (6857561) to head (24ec8fa).

Files Patch % Lines
quantus/metrics/inverse_estimation.py 79.26% 17 Missing ⚠️
quantus/helpers/model/pytorch_model.py 12.50% 7 Missing ⚠️
quantus/helpers/warn.py 69.56% 7 Missing ⚠️
quantus/evaluation.py 88.88% 1 Missing ⚠️
...uantus/metrics/faithfulness/region_perturbation.py 80.00% 1 Missing ⚠️
quantus/metrics/faithfulness/road.py 75.00% 1 Missing ⚠️

❗ Your organization needs to install the Codecov GitHub app to enable full functionality.

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #337      +/-   ##
==========================================
- Coverage   91.15%   90.58%   -0.58%     
==========================================
  Files          66       67       +1     
  Lines        3925     4047     +122     
==========================================
+ Hits         3578     3666      +88     
- Misses        347      381      +34     

☔ View full report in Codecov by Sentry.
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first round of review ok

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another review, looks good.

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2 participants