searchers.py 文件源码

def search(self, query, exclude=[], limit=20, rtype="paper", force=False):
        """
        Checks if the graph model already exists, otherwise creates one and
        runs the ranking on the nodes.
        """
        graph = build_graph(query,
                            self.params['K'],
                            self.params['H'],
                            self.params['min_topic_lift'],
                            self.params['min_ngram_lift'],
                            exclude, force, load=True, save=self.save)

        # Store number of nodes for checking later
        self.nnodes = graph.number_of_nodes()

        # Rank nodes using subgraph
        scores = ranker.rank_nodes(graph, limit=limit, return_type=rtype, **self.params)

        # Adds the score to the nodes and writes to disk. A stupid cast
        # is required because write_gexf can't handle np.float64
        scores = {nid: float(score) for nid, score in scores.items()}
        nx.set_node_attributes(graph, "score", scores)

        # nx.write_gexf(graph, utils.get_graph_file_name(model_folder, query))

        # Returns the top values of the type of node of interest
        results = get_top_nodes(graph, scores.items(), limit=limit, return_type=rtype)

        # Add to class object for future access
        self.graph = graph

        return [str(pub_id) for _nid, pub_id, _score in results]