Python - Get FP/TP from Confusion Matrix using a List
Looks like you're using scikit-learn. So why not explore a bit more? Scikit has a metrics module, that can be of use for your problem. Essentially, what you need is to have two separate arrays - one with real labels and another with predicted labels. And then you're good to go, you could call metrics.classification_report or metrics.confusion_matrix or metrics.accuracy_score, all of them use the real labels and the predicted labels.

There's nothing wrong with using clf_rf.score(test_features, test_target), but it will only give you a single value. If you look at the source code, what happens is that the score method calls a predict method with test_feature for prediction of labels, which occurs behind the scenes. 

It's better to actually capture those predicted labels, so that you can reuse them.

clf_rf.fit(training_features, training_target)
predicted_target = clf_rf.predict(test_features)
accuracy = sklearn.metrics.accuracy_score(test_target, predicted_target)
cnf_matrix = sklearn.metrics.confusion_matrix(test_target, predicted_target)
class_report = sklearn.metrics.classification_report(test_target, predicted_target)


And then you could do whichever you like with the calculated metrics, print, plot, etc. 
Have a look at the examples that are included for each model/metrics you're using 1, 2, 3, 4, etc.