from bigraph.predict import aa_predict
from bigraph.preprocessing import import_files, make_graph


def adamic_adar_prediction():
    """
    Link prediction on bipartite networks
    :return: A dictionary containing predicted links
    """

    df, df_nodes = import_files()
    print(df)
    print(f"Graph Nodes: ", df_nodes)
    G = make_graph(df)
    print(G)
    predicted = aa_predict(G)  # Here we have called Adamic Adar method from bigraph module
    return predicted


# Executing the function

if __name__ == '__main__':
    adamic_adar_prediction()

from bigraph.evaluation.evaluation import evaluate
from bigraph.preprocessing import import_files, make_graph


def adamic_adar_evaluation():
    """
    Evaluate Adamic-Adar algorithm using 10-Fold cross-validation 
    :return: A dictionary containing the evaluation results
    """
    df, df_nodes = import_files()
    G = make_graph(df)
    results = evaluate(G, k=10,
                       method='aa')  # Here we have evaluated adamic-adar
    # methods using evaluation module. Methods are 'jc', 'aa', 'pa', 'cn'
    return results


# Executing the function
if __name__ == '__main__':
    adamic_adar_evaluation()

from bigraph.predict import pa_predict, jc_predict, cn_predict
from bigraph.preprocessing import import_files, make_graph


def main():
    """
    Link prediction on bipartite networks
    :return:
    """
    df, df_nodes = import_files()
    G = make_graph(df)
    pa_predict(G)  # Preferential attachment
    jc_predict(G)  # Jaccard coefficient
    cn_predict(G)  # Common neighbors


# Executing the function
if __name__ == '__main__':
    main()