How to get the True Negative Rate from this code?

Question

I want to calculate the TNR. I am in a larger code project and we have this one binary classifier.

The major problem is that I don't find the information about the variables in the code. What is the benign rate, guesses and ad rate, guesses? And to calculate the TNR of that? I am guessing that the TNR is TNR = 2*benign_rate/len(y_hat).

y_hat  = np.array([0, 1])
y_test = np.array([0, 1])

benign_rate = 0
benign_guesses = 0
ad_guesses = 0
ad_rate = 0
for i in range(len(y_hat)):
    if y_hat[i] == 0:
        benign_guesses += 1
        if y_test[i] == 0:
            benign_rate += 1
    else:
        ad_guesses += 1
        if y_test[i] == 1:
            ad_rate += 1

    if y_test[i] == 1:
        if y_hat[i] == 0:

acc = (benign_rate+ad_rate)/len(y_hat)        
TP = 2*ad_rate/len(y_hat)

precision = ad_rate/ad_guesses
recall = round(100*TPR, 2)
TPR = 2 * ad_rate / len(y_hat)

TNR = 2*benign_rate/len(y_hat)

Answer 1

The True Negative Ratio is the fraction of the correctly classified negative samples from all negative samples.

You can compute it using numpy:

correct_negative = np.sum((y_hat == 0) & (y_test == 0))
total_negative = np.sum(y_test == 0)

TNR = correct_negative / total_negative

or you can do it "manually":

correct_negative = 0
total_negative = 0

for i in range(len(y_hat)):
    if y_test[i] == 0:
        total_negative += 1
        if y_hat[i] == 0:
            correct_negative += 1

TNR = correct_negative / total_negative