<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def thresholdBlocks(self, blocks, recall_weight=1.5):
 # pragma: nocover """ Returns the threshold that maximizes the expected F score, a weighted average of precision and recall for a sample of blocked data. Arguments: blocks -- Sequence of tuples of records, where each tuple is a set of records covered by a blocking predicate recall_weight -- Sets the tradeoff between precision and recall. I.e. if you care twice as much about recall as you do precision, set recall_weight to 2. """

        candidate_records = itertools.chain.from_iterable(self._blockedPairs(blocks))

        probability = core.scoreDuplicates(candidate_records,
                                           self.data_model,
                                           self.classifier,
                                           self.num_cores)['score']

        probability = probability.copy()
        probability.sort()
        probability = probability[::-1]

        expected_dupes = numpy.cumsum(probability)

        recall = expected_dupes / expected_dupes[-1]
        precision = expected_dupes / numpy.arange(1, len(expected_dupes) + 1)

        score = recall * precision / (recall + recall_weight ** 2 * precision)

        i = numpy.argmax(score)

        logger.info('Maximum expected recall and precision')
        logger.info('recall: %2.3f', recall[i])
        logger.info('precision: %2.3f', precision[i])
        logger.info('With threshold: %2.3f', probability[i])

        return probability[i]