Explainable AI with counterfactual paths
Project description
Explainable AI with counterfactual paths
Usage
Install the Python package cpath via pip
pip install cpath
and import
import cpath
or from source
pip install ./cpath
import cpath
Other imports
from imodels.util.data_util import get_clean_dataset
import numpy as np
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import balanced_accuracy_score
from sklearn.metrics import roc_auc_score
import sys
Example data set
clf_datasets = [
("breast-cancer", "breast_cancer", "imodels")
]
# Read in data set
X, y, feature_names = get_clean_dataset('breast_cancer', data_source='imodels')
# train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
Random Forest
# number of trees
ntrees = 10
clf = RandomForestClassifier(n_estimators=ntrees)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
Explain using cpath
P = cpath.cpaths(clf, X_test, y_test)
T = cpath.transition(P, X_test, y_test)
IMP = cpath.importance(T)
IMP["global"]
Citation
If you find cpath please cite
@misc{pfeifer2023explainable,
title={Explainable AI with counterfactual paths},
author={Bastian Pfeifer and Mateusz Krzyzinski and Hubert Baniecki and Anna Saranti and Andreas Holzinger and Przemyslaw Biecek},
year={2023},
eprint={2307.07764},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
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