from sklearn.metrics import f1_score, accuracy_score
ground_truth = [r[1] for r in testing_set]
predictions = {}
f1_scores = {}
f
or clf, listy in classifiers_dict.items():
    # getting predictions for the testing set by looping over each reviews featureset tuple
    # The first elemnt of the tuple is the feature set and the second element is the label
    predictions[clf] = [listy[0].classify(r[0]) for r in testing_set]
    f1_scores[clf] = f1_score(ground_truth, predictions[clf])
    print(f'f1_score {clf}: {f1_scores[clf]}')