metrics.py 文件源码

def evaluate(self, ranking=None, labels=None, ranked_labels=None, scales=None):
        '''
        Evaluate NDCG metric on the specified ranked list of document relevance scores.

        The function input can be either ranked list of relevance labels (`ranked_labels`),
        which is most convenient from the computational point of view, or it can be in
        the form of ranked list of documents (`ranking`) and corresponding relevance scores
        (`labels`), from which the ranked document relevance labels are computed.

        Parameters:
        -----------
        ranking: array, shape = (n_documents,)
            Specify list of ranked documents.

        labels: array: shape = (n_documents,)
            Specify relevance score for each document.

        ranked_labels: array, shape = (n_documents,)
            Relevance scores of the ranked documents. If not given, then
            `ranking` and `labels` must not be None, `ranked_labels` will
            be than inferred from them.

        scales: float, optional (default is None)
            The ideal DCG value on the given documents. If None is given
            it will be computed from the document relevance scores.
        '''
        if ranked_labels is not None:
            return self.get_score_from_labels_list(ranked_labels)
        elif ranking is not None and labels is not None:
            if ranking.shape[0] != labels.shape[0]:
                raise ValueError('number of ranked documents != number of relevance labels (%d, %d)' \
                                  % (ranking.shape[0], labels.shape[0]))
            ranked_labels = np.array(sorted(labels, key=dict(zip(labels,ranking)).get, reverse=True), dtype=np.intc)
            return self.get_score_from_labels_list(ranked_labels)