cpath 0.0.1

Description:

cpath 0.0.1

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}
}