Scikit学习:如何获得真肯定，真否定，假肯定和假否定 [英] Scikit-learn: How to obtain True Positive, True Negative, False Positive and False Negative

问题描述

我的问题:

我有一个数据集，它是一个大的JSON文件.我将其读取并存储在trainList变量中.

I have a dataset which is a large JSON file. I read it and store it in the trainList variable.

接下来，我对其进行预处理-为了能够使用它.

Next, I pre-process it - in order to be able to work with it.

完成分类后，就开始了

1. 我使用kfold交叉验证方法来获取均值 准确性并训练分类器.
2. 进行预测并获得准确度&折叠的混淆矩阵.
3. 此后，我想获取True Positive(TP)，True Negative(TN)，False Positive(FP)和False Negative(FN)值.我将使用这些参数来获取灵敏度和特异性.

1. I use the kfold cross validation method in order to obtain the mean accuracy and train a classifier.
2. I make the predictions and obtain the accuracy & confusion matrix of that fold.
3. After this, I would like to obtain the True Positive(TP), True Negative(TN), False Positive(FP) and False Negative(FN) values. I'll use these parameters to obtain the Sensitivity and Specificity.

最后，我将使用它来放入HTML，以便显示带有每个标签TP的图表.

Finally, I would use this to put in HTML in order to show a chart with the TPs of each label.

代码:

我目前拥有的变量:

trainList #It is a list with all the data of my dataset in JSON form
labelList #It is a list with all the labels of my data 

该方法的大部分:

#I transform the data from JSON form to a numerical one
X=vec.fit_transform(trainList)

#I scale the matrix (don't know why but without it, it makes an error)
X=preprocessing.scale(X.toarray())

#I generate a KFold in order to make cross validation
kf = KFold(len(X), n_folds=10, indices=True, shuffle=True, random_state=1)

#I start the cross validation
for train_indices, test_indices in kf:
    X_train=[X[ii] for ii in train_indices]
    X_test=[X[ii] for ii in test_indices]
    y_train=[listaLabels[ii] for ii in train_indices]
    y_test=[listaLabels[ii] for ii in test_indices]

    #I train the classifier
    trained=qda.fit(X_train,y_train)

    #I make the predictions
    predicted=qda.predict(X_test)

    #I obtain the accuracy of this fold
    ac=accuracy_score(predicted,y_test)

    #I obtain the confusion matrix
    cm=confusion_matrix(y_test, predicted)

    #I should calculate the TP,TN, FP and FN 
    #I don't know how to continue

推荐答案

如果您有两个包含预测值和实际值的列表；看上去就像您所做的那样，您可以将它们传递给一个函数，该函数将使用以下方法来计算TP，FP，TN，FN:

If you have two lists that have the predicted and actual values; as it appears you do, you can pass them to a function that will calculate TP, FP, TN, FN with something like this:

def perf_measure(y_actual, y_hat):
    TP = 0
    FP = 0
    TN = 0
    FN = 0

    for i in range(len(y_hat)): 
        if y_actual[i]==y_hat[i]==1:
           TP += 1
        if y_hat[i]==1 and y_actual[i]!=y_hat[i]:
           FP += 1
        if y_actual[i]==y_hat[i]==0:
           TN += 1
        if y_hat[i]==0 and y_actual[i]!=y_hat[i]:
           FN += 1

    return(TP, FP, TN, FN)

从这里开始，我认为您将能够计算出您感兴趣的利率以及其他性能指标，例如特异性和敏感性.

From here I think you will be able to calculate rates of interest to you, and other performance measure like specificity and sensitivity.

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