def threshold(self, data_1, data_2, recall_weight=1.5):  # pragma: no cover
        """
        Returns the threshold that maximizes the expected F score,
        a weighted average of precision and recall for a sample of
        data.

        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

        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.
        """

        blocked_pairs = self._blockData(data_1, data_2)
        return self.thresholdBlocks(blocked_pairs, recall_weight)
def threshold ( self , data_1 , data_2 , recall_weight = 1.5 ) : blocked_pairs = self . _blockData ( data_1 , data_2 ) return self . thresholdBlocks ( blocked_pairs , recall_weight )
Returns the threshold that maximizes the expected F score,
        a weighted average of precision and recall for a sample of
        data.

        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

        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.