sklearn log_loss报Unknown label type错误的解决方法咨询

问题场景 #

执行sklearn的log损失计算时触发ValueError，代码逻辑为：用LabelEncoder拟合浮点型类别数组，再调用log_loss函数传入真实标签、类别概率及labels参数。

复现代码 #

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit([2.5, 3.0, 3.5, 3.8, 4.0, 4.5, 5.0, 5.5, 6.0])
from sklearn.metrics import log_loss
le.classes_
log_loss([6.0], [[0.,         0.,         0.,         0.,         0.28571429, 0.14285714,  0.,         0.57142857, 0.        ]], labels=list(le.classes_))

报错信息 #

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
C:\Users\PRANAV~1\AppData\Local\Temp/ipykernel_25368/2311544075.py in <module>
----> 1 log_loss([6.0], [[0.,         0.,         0.,         0.,         0.28571429, 0.14285714,
      2   0.,         0.57142857, 0.        ]], labels=list(le.classes_))

~\AppData\Roaming\Python\Python39\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---&gt; 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0

~\AppData\Roaming\Python\Python39\site-packages\sklearn\metrics\_classification.py in log_loss(y_true, y_pred, eps, normalize, sample_weight, labels)
   2233 
   2234     if labels is not None:
-&gt; 2235         lb.fit(labels)
   2236     else:
   2237         lb.fit(y_true)

~\AppData\Roaming\Python\Python39\site-packages\sklearn\preprocessing\_label.py in fit(self, y)
    295 
    296         self.sparse_input_ = sp.issparse(y)
--&gt; 297         self.classes_ = unique_labels(y)
    298         return self
    299 

~\AppData\Roaming\Python\Python39\site-packages\sklearn\utils\multiclass.py in unique_labels(*ys)
     96     _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
     97     if not _unique_labels:
---&gt; 98         raise ValueError("Unknown label type: %s" % repr(ys))
     99 
    100     ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys))

ValueError: Unknown label type: ([2.5, 3.0, 3.5, 3.8, 4.0, 4.5, 5.0, 5.5, 6.0],)

原因分析 #

log_loss用于分类任务，要求标签为离散类型。但传入的labels是浮点型列表，sklearn将其误判为连续型标签，导致unique_labels函数无法识别标签类型。

解决方法 #

方法一：将真实标签转为LabelEncoder编码后的整数 #

利用LabelEncoder把原始浮点标签转为整数编码，此时无需指定labels参数，log_loss可直接匹配概率数组：

from sklearn import preprocessing
from sklearn.metrics import log_loss

le = preprocessing.LabelEncoder()
le.fit([2.5, 3.0, 3.5, 3.8, 4.0, 4.5, 5.0, 5.5, 6.0])
# 转换真实标签为整数编码
y_true_encoded = le.transform([6.0])
# 计算log损失
loss = log_loss(y_true_encoded, [[0., 0., 0., 0., 0.28571429, 0.14285714, 0., 0.57142857, 0.]])
print(loss)

方法二：将labels参数转为numpy数组 #

将labels从列表转为numpy数组，明确告知sklearn这是离散分类标签：

import numpy as np
from sklearn import preprocessing
from sklearn.metrics import log_loss

le = preprocessing.LabelEncoder()
le.fit([2.5, 3.0, 3.5, 3.8, 4.0, 4.5, 5.0, 5.5, 6.0])
# 用numpy数组传入labels参数
loss = log_loss([6.0], [[0., 0., 0., 0., 0.28571429, 0.14285714, 0., 0.57142857, 0.]], labels=np.array(le.classes_))
print(loss)

内容的提问来源于stack exchange，提问作者pranav nerurkar