<p align="center">
<img src="https://github.com/pievos101/cpath/blob/main/logo.png" width="400">
</p>
# Explainable AI with counterfactual paths
## Usage
Install the Python package cpath via pip
```python
pip install cpath
```
and import
```python
import cpath
```
or from source
```python
pip install ./cpath
import cpath
```
Other imports
```python
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
```python
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
```python
# number of trees
ntrees = 10
clf = RandomForestClassifier(n_estimators=ntrees)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
```
### Explain using cpath
```python
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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"description": "<p align=\"center\">\n<img src=\"https://github.com/pievos101/cpath/blob/main/logo.png\" width=\"400\">\n</p>\n\n# Explainable AI with counterfactual paths\n\n## Usage\n\nInstall the Python package cpath via pip\n\n```python\npip install cpath\n```\n\nand import \n\n```python\nimport cpath\n```\n\nor from source \n\n```python\npip install ./cpath\nimport cpath\n```\n\nOther imports\n\n```python\nfrom imodels.util.data_util import get_clean_dataset\nimport numpy as np\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import balanced_accuracy_score\nfrom sklearn.metrics import roc_auc_score\n\nimport sys\n```\n\nExample data set \n\n```python\nclf_datasets = [\n (\"breast-cancer\", \"breast_cancer\", \"imodels\")\n]\n\n# Read in data set\nX, y, feature_names = get_clean_dataset('breast_cancer', data_source='imodels')\n\n# train-test split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)\n\n```\n\n### Random Forest \n\n```python\n\n# number of trees \nntrees = 10\n\nclf = RandomForestClassifier(n_estimators=ntrees) \nclf.fit(X_train, y_train)\npred = clf.predict(X_test)\n\n```\n\n### Explain using cpath\n\n```python\n\nP = cpath.cpaths(clf, X_test, y_test)\n\nT = cpath.transition(P, X_test, y_test)\n\nIMP = cpath.importance(T)\n\nIMP[\"global\"]\n\n```\n\n## Citation\nIf you find cpath please cite\n\n```\n@misc{pfeifer2023explainable,\n title={Explainable AI with counterfactual paths}, \n author={Bastian Pfeifer and Mateusz Krzyzinski and Hubert Baniecki and Anna Saranti and Andreas Holzinger and Przemyslaw Biecek},\n year={2023},\n eprint={2307.07764},\n archivePrefix={arXiv},\n primaryClass={cs.AI}\n}\n```\n\n\n",
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