kddcup_searchers.py 文件源码

def search(self, selected_affils, conf_name, year, exclude_papers=[], rtype="affil", force=False):
        """
        Checks if the graph model already exists, otherwise creates one and
        runs the ranking on the nodes.
        """
        graph = build_graph(conf_name,
                            year,
                            self.params['H'],
                            self.params['min_topic_lift'],
                            self.params['min_ngram_lift'],
                            exclude_papers, force, load=True, save=self.save)

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

        # Rank nodes using subgraph
        scores = rank_nodes(graph, 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=selected_affils, return_type=rtype)

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

        return results