Scikit-learn: How to obtain True Positive, True Ne

Question

I am new in machine learning and in scikit-learn.

My problem:

(Please, correct any type of missconception)

I have a dataset which is a BIG JSON, I retrieve it and store it in a trainList variable.

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

Once I have done that, I start the classification:

1. I use kfold cross validation method in order to obtain the mean accuracy and I train a classifier.
2. I make the predicctions and I obtain the accuracy and 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 would use these paramters to obtain the Sensitivity and the specificity and I would them and the total of the TPs to a HTML in order to show a chart with the TPs of each label.

Code:

The variables I have for the moment:

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 

Most part of the method:

#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

Answer 1

if you have more than one classes in your classifier, you might want to use pandas-ml at that part. Confusion Matrix of pandas-ml give more detailed information. check that

Answer 2

Here's a fix to invoketheshell's buggy code (which currently appears as the accepted answer):

def performance_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] == 0:
            FP += 1
        if y_hat[i] == y_actual[i] == 0:
            TN +=1
        if y_hat[i] == 0 and y_actual[i] == 1:
            FN +=1

    return(TP, FP, TN, FN)

Answer 3

For the multi-class case, everything you need can be found from the confusion matrix. For example, if your confusion matrix looks like this:

Then what you're looking for, per class, can be found like this:

Using pandas/numpy, you can do this for all classes at once like so:

FP = confusion_matrix.sum(axis=0) - np.diag(confusion_matrix)  
FN = confusion_matrix.sum(axis=1) - np.diag(confusion_matrix)
TP = np.diag(confusion_matrix)
TN = confusion_matrix.values.sum() - (FP + FN + TP)

# Sensitivity, hit rate, recall, or true positive rate
TPR = TP/(TP+FN)
# Specificity or true negative rate
TNR = TN/(TN+FP) 
# Precision or positive predictive value
PPV = TP/(TP+FP)
# Negative predictive value
NPV = TN/(TN+FN)
# Fall out or false positive rate
FPR = FP/(FP+TN)
# False negative rate
FNR = FN/(TP+FN)
# False discovery rate
FDR = FP/(TP+FP)

# Overall accuracy
ACC = (TP+TN)/(TP+FP+FN+TN)

Answer 4

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)

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

Answer 5

According to scikit-learn documentation,

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix

By definition a confusion matrix C is such that C[i, j] is equal to the number of observations known to be in group i but predicted to be in group j.

Thus in binary classification, the count of true negatives is C[0,0], false negatives is C[1,0], true positives is C[1,1] and false positives is C[0,1].

CM = confusion_matrix(y_true, y_pred)

TN = CM[0][0]
FN = CM[1][0]
TP = CM[1][1]
FP = CM[0][1]