rf-counterfactuals 0.1.1

Random Forest counterfactual explanation

Random forest counterfactuals

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The RFC is a library specialised to be use in the domain of Explainable Artificial Intelligence (XAI).

It allows to search for an explanation for pretrained Random Forest Classifier model (from scikit-learn library) in the form of counterfactual examples.

The library also allow to specify constraints (frozen attribute, monotonnicity) for attributes in the specified dataset.

An algorithm is an extended version of Gabriele Tolomei et al. algorithm (link to github).

An extended version has:

• multiclass classification problem's explainability,

• gives new methods of selecting counterfactual examples,

• provides new distance functions (specially HOEM)

• it is optimized to provide explanation as fast as possible with multiprocessing calculation

This work is done for purpose of my master's thesis.

Installation

This package was tested only on Windows platform. However it should work on Linux platform as well.

To install package:

pip install rf_counterfactuals

Manual installation (for developers)

To build up a pywheel file:

python setup.py bdist_whee

Simple example on iris dataset

from rf_counterfactuals import RandomForestExplainer, visualize



from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split



### Load iris dataset as Pandas Dataframe (other formats aren't supported yet) and split. Then train RF classifier on it

X, y = load_iris(return_X_y=True, as_frame=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.67, random_state=420, stratify=y)

rf = RandomForestClassifier()

rf.fit(X_train, y_train)



### Make an RandomForestExplainer object with input of: RandomForest model, training data

rfe = RandomForestExplainer(rf, X_train, y_train)



### Look for counterfactual examples (max/limit 1) in test data, which lead to change label's value from '0'('setosa') to '2'('virginica')

### Counterfactual examples are selected based on 'hoem' metric's value

X_test_label_0 = X_test[y_test==0]

counterfactuals = rfe.explain_with_single_metric(X_test_label_0, 2, metric='hoem', limit=1)



### Visualize an example row (row_no = 0) of data with its counterfactual

row_index_to_visualize = 0



row = X_test_label_0.iloc[row_index_to_visualize]



# First counterfactual found for row 0th

cf = counterfactuals[row_index_to_visualize].iloc[0]



print(f"row label: {rf.predict(row.to_frame(0).T)[0]} |\t cf label: {rf.predict(cf.to_frame(0).T)[0]}")

print(visualize(rfe, row, cf))

output (it may vary because of used random seed to split dataset):

row label: 0 |	 cf label: 2

                     X     X'  difference constraints

sepal length (cm)  5.8  6.333       0.533            

sepal width (cm)   4.0  4.000       0.000            

petal length (cm)  1.2  5.076       3.876            

petal width (cm)   0.2  0.200       0.000    

Author

Maciej Leszczyk, Poznan University of Technology

maciej.leszczyk98(at)gmail.com