How to get the True Negative Rate from this code?

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

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])

nr_not_detect_adv = 0
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:
            nr_not_detect_adv +=1

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

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

Answers:

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Method 1

Looking at the for loop, I’d guess that meaning of the variables is:

• y_hat: predicted labels
• y_test: ground truth
• beningn_rate: TN (true negatives)
• benign_guesses: TN + FN (false negatives)
• ad_rate: TP (true positives)
• ad_guesses: TP + FP (false positives)
• nr_not_detect_adv: FN (yes, it’s redundant)

These lines should be fixed (definitions):

TP = ad_rate
TNR = benign_rate/benign_guesses

precision = ad_rate/ad_guesses
recall = ad_rate / (ad_rate+nr_not_detect_adv)
TPR = recall

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0