Python 如何计算scikit学习ML模型每个样本的二进制日志损失

我试图将二进制日志丢失应用于我创建的朴素贝叶斯ML模型。我生成了一个分类预测数据集（yNew）和一个概率数据集（probabilityYes），但无法在日志丢失函数中成功运行它们

简单的sklearn.metrics函数提供单个日志丢失结果-不确定如何解释此结果

from sklearn.metrics import log_loss
ll = log_loss(yNew, probabilityYes, eps=1e-15)
print(ll)
.0819....

def logloss(yNew,probabilityYes):
epsilon = 1e-15
probabilityYes = sp.maximum(epsilon, probabilityYes)
probabilityYes = sp.minimum(1-epsilon, probabilityYes)

#compute logloss function (vectorised)
ll = sum(yNew*sp.log(probabilityYes) +
            sp.subtract(1,yNew)*sp.log(sp.subtract(1,probabilityYes)))
ll = ll * -1.0/len(yNew)
return ll

print(logloss(yNew,probabilityYes))
2.55352047 2.55352047 2.50358354 2.55352047 2.50358354 2.55352047 .....

更复杂的函数为每个NO返回2.55的值，为每个YES返回2.50的值（总共90列）-同样，不知道如何解释这一点

from sklearn.metrics import log_loss
ll = log_loss(yNew, probabilityYes, eps=1e-15)
print(ll)
.0819....

def logloss(yNew,probabilityYes):
epsilon = 1e-15
probabilityYes = sp.maximum(epsilon, probabilityYes)
probabilityYes = sp.minimum(1-epsilon, probabilityYes)

#compute logloss function (vectorised)
ll = sum(yNew*sp.log(probabilityYes) +
            sp.subtract(1,yNew)*sp.log(sp.subtract(1,probabilityYes)))
ll = ll * -1.0/len(yNew)
return ll

print(logloss(yNew,probabilityYes))
2.55352047 2.55352047 2.50358354 2.55352047 2.50358354 2.55352047 .....

---

以下是如何计算每个样本的损失：

import numpy as np

def logloss(true_label, predicted, eps=1e-15):
  p = np.clip(predicted, eps, 1 - eps)
  if true_label == 1:
    return -np.log(p)
  else:
    return -np.log(1 - p)

让我们用一些虚拟数据检查一下（我们实际上不需要模型）：

从上面的数组中，您应该能够说服自己，预测离相应的真实标签越远，损失就越大，正如我们直观地预期的那样

让我们确认上述计算与scikit learn返回的总（平均）损失一致：

from sklearn.metrics import log_loss

ll_sk = log_loss(targets, predictions)
ll_sk
# 0.4917494284709932

np.mean(ll)
# 0.4917494284709932

np.mean(ll) == ll_sk
# True

代码改编自[链接现已失效]