def match(self, data_1, data_2, threshold=0.5, generator=False):  # pragma: no cover
        """
        Identifies pairs of records that refer to the same entity, returns
        tuples containing a set of record ids and a confidence score as a float
        between 0 and 1. The record_ids within each set should refer to the
        same entity and the confidence score is the estimated probability that
        the records refer to the same entity.

        This method should only used for small to moderately sized datasets
        for larger data, use matchBlocks

        Arguments:
        data_1    -- Dictionary of records from first dataset, where the
                     keys are record_ids and the values are dictionaries
                     with the keys being field names

        data_2    -- Dictionary of records from second dataset, same form
                     as data_1

        threshold -- Number between 0 and 1 (default is .5). We will consider
                     records as potential duplicates if the predicted
                     probability of being a duplicate is above the threshold.

                     Lowering the number will increase recall, raising it
                     will increase precision
        """

        blocked_pairs = self._blockData(data_1, data_2)
        clusters = self.matchBlocks(blocked_pairs, threshold)

        if generator:
            return clusters
        else:
            return list(clusters)
Identifies pairs of records that refer to the same entity, returns
        tuples containing a set of record ids and a confidence score as a float
        between 0 and 1. The record_ids within each set should refer to the
        same entity and the confidence score is the estimated probability that
        the records refer to the same entity.

        This method should only used for small to moderately sized datasets
        for larger data, use matchBlocks

        Arguments:
        data_1    -- Dictionary of records from first dataset, where the
                     keys are record_ids and the values are dictionaries
                     with the keys being field names

        data_2    -- Dictionary of records from second dataset, same form
                     as data_1

        threshold -- Number between 0 and 1 (default is .5). We will consider
                     records as potential duplicates if the predicted
                     probability of being a duplicate is above the threshold.

                     Lowering the number will increase recall, raising it
                     will increase precision