How to make sklearn.metrics.confusion_matrix() to always return TP, TN, FP, FN?

Question

I am using sklearn.metrics.confusion_matrix(y_actual, y_predict) to extract tn, fp, fn, tp and most of the time it works perfectly.

from sklearn.metrics import confusion_matrix

y_actual, y_predict = [1,1,1,1], [0,0,0,0]
tn, fp, fn, tp = confusion_matrix(y_actual, y_predict).ravel()
>>> [0 0 4 0]   # ok

y_actual, y_predict = [1,1,1,1],[0,1,0,1]
tn, fp, fn, tp = confusion_matrix(y_actual, y_predict).ravel()
>>> [0 0 2 2]   # ok

However, in some cases the confusion_matrix() doesn't always return those info and I would get ValueError as shown below.

from sklearn.metrics import confusion_matrix

y_actual, y_predict = [0,0,0,0],[0,0,0,0]
tn, fp, fn, tp = confusion_matrix(y_actual, y_predict).ravel()
>>> [4]    # ValueError: not enough values to unpack (expected 4, got 1)

y_actual, y_predict = [1,1,1,1],[1,1,1,1]
tn, fp, fn, tp = confusion_matrix(y_actual, y_predict).ravel()
>>> [4]    # ValueError: not enough values to unpack (expected 4, got 1)

My temporary solution is to write my own function to extract those info. Is there any way I can force the confusion_matrix() to always return the tn, fp, fn, tp output?

Thanks

Answer 1

This issue has to do with the number of unique labels that are included in your input matrices. In your second block of examples, it is (correctly) building a confusion matrix with just one class, either 0 or 1, respectively.

To force it to output both classes even when one of them is not predicted, use the label attribute.

y_actual, y_predict = [0,0,0,0],[0,0,0,0]
tn, fp, fn, tp = confusion_matrix(y_actual, y_predict, labels=[0,1]).ravel()
>> array([[4, 0],
          [0, 0]])