cpath 0.0.1

Explainable AI with counterfactual paths

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}
}