custom_confusion_matrix.py

def confusion_matrix(y_test, y_pred):
    tp = y_test.dot(y_pred)
    fp = y_pred.sum() - tp
    fn = y_test.sum() - tp
    tn = y_test.shape[0] - tp - fp - fn
    return np.array([[tn, fp], [fn, tp]])

def f(Y_pred_proba, Y_test, mlcm, k, thresholds):
    y_pred_proba = np.array(Y_pred_proba[:,k].todense()).ravel()
    y_test = np.array(Y_test[:,k].todense()).ravel()
    threshold = thresholds[k]

    y_pred = y_pred_proba > threshold
    cmk = confusion_matrix(y_test, y_pred)
    mlcm[k,:,:] = cmk
 
    cm = mlcm.sum(axis=0)
    tn, fp, fn, tp = cm.ravel()
    f1 = tp / ( tp+ (fp+fn) / 2)
    return f1