metrics.py 文件源码

def compute_scale(self, queries, relevance_scores=None):
        ''' 
        Return the ideal DCG value for each query. Optionally, external
        relevance assessments can be used instead of the relevances
        present in the queries.

        Parameters
        ----------
        queries: Queries
            The queries for which the ideal DCG should be computed.

        relevance_scores: array of integers, optional, (default is None)
            The relevance scores that should be used instead of the 
            relevance scores inside queries. Note, this argument is
            experimental.
        '''
        ideal_values = np.empty(queries.query_count(), dtype=np.float64)

        if relevance_scores is not None:
            if queries.document_count() != relevance_scores.shape[0]:
                raise ValueError('number of documents and relevance scores do not match')

            # Need to sort the relevance labels first.
            indices = np.empty(relevance_scores.shape[0], dtype=np.intc)
            relevance_argsort_v1(relevance_scores, indices, relevance_scores.shape[0])
            # Creates a copy.
            relevance_scores = relevance_scores[indices]
        else:
            # Assuming these are sorted.
            relevance_scores = queries.relevance_scores

        self.metric_.evaluate_queries_ideal(queries.query_indptr, relevance_scores, ideal_values)

        return ideal_values