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X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state = 2020, stratify=y)   

X_train_user = X_train[y_train == 'ji2hwh']
X_train_attacker = X_train[y_train != 'ji2hwh']

outlier_prop = len(X_train_user) / len(X_train_attacker)

svm = OneClassSVM(kernel='rbf', nu=outlier_prop, gamma=0.000001) 
svm.fit(X_train_user)

pred = svm.predict(X_test)

y_test[y_test == 'ji2hwh'] = 1

y_test[y_test != 1] = -1

print(accuracy_score(y_test, pred))

我在上面的代码中得到分类指标无法处理未知目标和二进制目标的混合错误。'ji2hwh' 只是一个用户 ID，我将其视为我的一类分类的目标用户，并将其余用户视为攻击者。x 是特征向量，y 包含用户 ID。我无法弄清楚为什么会出现此错误，因为变量 pred 返回一个带有 [-1,1] 值的 ndarray，并且 y_test 似乎正确分配了适当的值，也在同一组值 [-1,1] 中。我能做些什么来克服这个编译错误？

整个错误信息：

  File "C:\Users\User\Desktop\MobileUserAuth\data_exploration.py", line 94, in <module>
    print(accuracy_score(y_test, pred))

  File "C:\Users\User\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f
    return f(**kwargs)

  File "C:\Users\User\anaconda3\lib\site-packages\sklearn\metrics\_classification.py", line 187, in accuracy_score
    y_type, y_true, y_pred = _check_targets(y_true, y_pred)

  File "C:\Users\User\anaconda3\lib\site-packages\sklearn\metrics\_classification.py", line 90, in _check_targets
    raise ValueError("Classification metrics can't handle a mix of {0} "

ValueError: Classification metrics can't handle a mix of unknown and binary targets
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1 回答 1

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我发布了几天后发现的问题的一种解决方案，也许它可以帮助面临同样问题的人，因为我没有得到解决方案。

只需简单地键入 cast y_testto 即可int解决有关未知目标的错误。所以在调用accuracy_score类型之前：

y_test = y_test.astype('int')

于 2021-01-07T16:20:50.500 回答