TypeError: compute_class_weight() takes 1 positional argument but 3 were given

 

 

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_14395/3700018132.py in <module>
      5 class_weights = class_weight.compute_class_weight('balanced',
      6                                                   np.unique(y_train),
----> 7                                                   y_train.flatten())
      8 
      9 end =  time.time()

TypeError: compute_class_weight() takes 1 positional argument but 3 were given
===================================

 调用sklearn的compute_class_weight提示错误”compute_class_weight() takes 1 positional argument but 3 were given“，解决办法为函数里加上参数名：

 

 

from sklearn.utils.class_weight import compute_class_weight

label = [0] * 9 + [1] * 1 + [2, 2]
classes = [0, 1, 2]
weight = compute_class_weight(class_weight='balanced', classes=classes, y=label)
print(weight)

 =============================================

 为函数里加上参数名【可行】

 

After spending a lot of time, this is how I fixed it. I still don't know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue.

class_weights = compute_class_weight(
                                        class_weight = "balanced",
                                        classes = np.unique(train_classes),
                                        y = train_classes                                                    
                                    )
class_weights = dict(zip(np.unique(train_classes), class_weights))
class_weights

=========================

后面发现是传入y的参数的时候，label是2维的，label的维度是(1000,1)要把它变成(1000,)就可以。

    labels = np.zeros((200,1))
    labels[0:2][0] = 1
    classes = [0, 1]
    weight = compute_class_weight(class_weight='balanced', classes=classes, y=label.reshape(-1)
    print(weight)
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链接：https://blog.csdn.net/qq_34845880/article/details/122318233