scalableExplainabilities

Python package related to scaling lime plots

import scalableExplainabilities 
from scalableExplainabilities import explainability
from pprint import pprint
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split


if __name__ == "__main__":


    DATA_PATH = r"C:\Users\gprak\Downloads\Github Repos\scalableExplainabilities\archive\HousingData.csv"

    df = pd.read_csv(
        DATA_PATH
    )
    df = explainability.automate_imputation(df)
    
    

    feature_names = [
        "LSTAT",
        "RM",
        "NOX",
        "PTRATIO",
        "DIS",
        "AGE"
    ]

    X = df[
        feature_names
    ]


    y = df[
        "MEDV"
    ]

    

    X_train, X_test, y_train, y_test = (
        train_test_split(
            X,
            y,
            test_size=0.3,
            random_state=0
        )
    )

    

    model = RandomForestRegressor(
        max_depth=6,
        random_state=0,
        n_estimators=10,
        n_jobs=1
    )


    instance_indices = [i for i in range(len(X_test))]

    paths = [
        f"lime_explanation_{i}.png"
        for i in instance_indices
    ]

    results = explainability.general_explainabilities(
        X_train=X_train,
        y_train=y_train,
        X_test=X_test,
        model=model,
        paths=paths,
        feature_names=feature_names,
        instance_indices=instance_indices,
        num_features=6,
        progress_bar=True
    )

    print(results)