You can use functions of sklearn library or a simple python script to caculate true positive, true negative, false positive, and false negative. Here are two approaches; you can try one of these:
Approach 1:
>>> y_true = [1, 0, 1, 0, 0, 1, 1, 0, 1]
>>> y_pred = [1, 1, 0, 0, 1, 1, 1, 1, 0]
>>> from sklearn.metrics import confusion_matrix
>>> confusion_matrix(y_true, y_pred)
array([[1, 3],
[2, 3]], dtype=int64)
>>> confusion_matrix(y_true, y_pred).ravel()
array([1, 3, 2, 3], dtype=int64)
>>> tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
>>> tn, fp, fn, tp
(1, 3, 2, 3)
Approach 2:
>>> y_true = [1, 0, 1, 0, 0, 1, 1, 0, 1]
>>> y_pred = [1, 1, 0, 0, 1, 1, 1, 1, 0]
>>> (tn, fp, fn, tp) = (0,0,0,0)
>>> for i in range(len(y_true)):
... if (y_true[i]==1 and y_pred[i] ==1):
... tp+=1
... elif (y_true[i]==1 and y_pred[i] ==0):
... fn+=1
... elif (y_true[i]==0 and y_pred[i] ==1):
... fp+=1
... elif (y_true[i]==0 and y_pred[i] == 0):
... tn+=1
...
>>> tn, fp, fn, tp
(1, 3, 2, 3)