acv00
ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.
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Download Latest Version (.zip)- build_publish.yml
- install_test.yml
- __init__.py
- _colorconv.py
- _colors.py
- __init__.py
- app.py
- plots.py
- sdp_app.py
- sv_app.py
- __init__.py
- data_loader.py
- telco_churn.csv
- _cext.cc
- acv_tree.h
- cyext_acv.cpp
- cyext_acv.pyx
- cyext_acv_cache.cpp
- cyext_acv_cache.pyx
- cyext_acv_nopa.cpp
- cyext_acv_nopa.pyx
- __init__.py
- acv_agnosticX.py
- acv_tree.py
- base_agnostree.py
- base_tree.py
- counterfactual_rules.py
- py_acv.py
- setup.py
- utils.py
- utils_cr.py
- utils_exp.py
- utils_sdp.py
- __init__.py
- exp_linear.py
- exp_linear_gmm.py
- exp_linear_gmm_ohe.py
- exp_syn.py
- shap_linear.py
- utils.py
- lucas0_train.csv
- demo_acv_tree_explainers.ipynb
- demo_agnostic_acxplainer.ipynb
- demo_global_explainer_model.ipynb
- demo_swings_sdp.ipynb
- 4 Focusing on influential variables with Same Decision Probabilities.ipynb
- coalition_or_sum_adult.ipynb
- coalition_or_sum_toy_model.ipynb
- comparisons_of_the_different_estimators.ipynb
- Figure 1 - Comparison coalition and sum.ipynb
- Figure 2 - Comparisons of the differents estimators on continuous variables.ipynb
- Figure 2 - Comparisons of the differents estimators on discretized variables.ipynb
- Runtime.ipynb
- ACV_vs_SHAP_TELCO_Churn.ipynb
- telco_churn.csv
- AccurateAndRobustShapleyValuesForExplainingPredictionsAndFocusingOnLocalImportantVariables.pdf
- ImportantVariablesAreGameChangersRevisitingShapleyValuesForExplainingBlackBoxModels.pdf
- TheShapleyValueofCoalitionOfVariablesProvidesBetterExplanations.pdf
- ConsistentSufficientExplanationsAndMinimalLocalRulesForExplainingRegressionAndClassificationModels.pdf
- test_acx.py
- test_cyext.py
- test_models_acv.py
- .gitignore
- __init__.py
- LICENSE
- Makefile
- MANIFEST.in
- pyproject.toml
- README.md
- requirements.txt
- setup.py
# Installation Guide
1. Get the code
git clone https://github.com/salimamoukou/acv00
Downloads the entire project code from GitHub to your computer.
cd acv00
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
$ pip install acv-exp
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
3. Make
MediumPrerequisites
- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
make
Compiles the code based on the generated build configuration to produce an executable.
If it finishes without errors, it worked. Try running the generated executable directly.
// repository documentation
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