def compute(self, collection_size):
        true_positives = float(len(self.positives))
        false_negatives = self.get_false_positives(self.negatives, self.positives)

        precision = true_positives / collection_size
        recall = true_positives / UMath.get_denominator(true_positives + false_negatives)
        f1_score = 2 * UMath.get_denominator(precision + recall) / (precision * recall)
        reliability = np.mean(self.reliabilities)

        print "f1_score: {} (precision: {}, recall: {}), reliability: {}".format(f1_score, precision, recall, reliability)

        self.all_f1_score.append(f1_score)
        self.all_precision.append(precision)
        self.all_recall.append(recall)
        self.all_reliability.append(reliability)

        self.positives = []
        self.negatives = []
        self.reliabilities = []
dataset/ETHPy150Open tonybeltramelli/Deep-Spying/server/analytics/modules/classification/RelevanceAssessment.py/RelevanceAssessment.compute/swapped operands of `/`