lambdamart.py 文件源码

def predict_rankings(self, queries, compact=False, n_jobs=1):
        '''
        Predict rankings of the documents for the given queries.

        If `compact` is set to True then the output will be one
        long 1d array containing the rankings for all the queries
        instead of a list of 1d arrays.

        The compact array can be subsequently index using query
        index pointer array, see `queries.query_indptr`.

        query: Query
            The query whose documents should be ranked.

        compact: bool
            Specify to return rankings in compact format.

         n_jobs: int, optional (default is 1)
            The number of working threads that will be spawned to compute
            the ranking scores. If -1, the current number of CPUs will be used.
        '''
        # Predict the ranking scores for the documents.
        predictions = self.predict(queries, n_jobs)

        rankings = np.zeros(queries.document_count(), dtype=np.intc)

        ranksort_queries(queries.query_indptr, predictions, rankings)

        if compact or len(queries) == 1:
            return rankings
        else:
            return np.array_split(rankings, queries.query_indptr[1:-1])