evaluate_node_classification.py 文件源码

def evaluateNodeClassification(X, Y, test_ratio):
    X_train, X_test, Y_train, Y_test = sk_ms.train_test_split(
        X,
        Y,
        test_size=test_ratio
    )
    try:
        top_k_list = list(Y_test.toarray().sum(axis=1))
    except:
        top_k_list = list(Y_test.sum(axis=1))
    classif2 = TopKRanker(lr())
    classif2.fit(X_train, Y_train)
    prediction = classif2.predict(X_test, top_k_list)
    micro = f1_score(Y_test, prediction, average='micro')
    macro = f1_score(Y_test, prediction, average='macro')
    return (micro, macro)