def _generate_walks(self):
        """
        Generates the random walks which will be used as the skip-gram input.
        :return: List of walks. Each walk is a list of nodes.
        """

        flatten = lambda l: [item for sublist in l for item in sublist]

        # Split num_walks for each worker
        num_walks_lists = np.array_split(range(self.num_walks), self.workers)

        walk_results = Parallel(n_jobs=self.workers, temp_folder=self.temp_folder, require=self.require)(
            delayed(parallel_generate_walks)(self.d_graph,
                                             self.walk_length,
                                             len(num_walks),
                                             idx,
                                             self.sampling_strategy,
                                             self.NUM_WALKS_KEY,
                                             self.WALK_LENGTH_KEY,
                                             self.NEIGHBORS_KEY,
                                             self.PROBABILITIES_KEY,
                                             self.FIRST_TRAVEL_KEY,
                                             self.quiet) for
            idx, num_walks
            in enumerate(num_walks_lists, 1))

        walks = flatten(walk_results)

        return walks
def _generate_walks ( self ) : flatten = lambda l : [ item for sublist in l for item in sublist ] num_walks_lists = np . array_split ( range ( self . num_walks ) , self . workers ) walk_results = Parallel ( n_jobs = self . workers , temp_folder = self . temp_folder , require = self . require ) ( delayed ( parallel_generate_walks ) ( self . d_graph , self . walk_length , len ( num_walks ) , idx , self . sampling_strategy , self . NUM_WALKS_KEY , self . WALK_LENGTH_KEY , self . NEIGHBORS_KEY , self . PROBABILITIES_KEY , self . FIRST_TRAVEL_KEY , self . quiet ) for idx , num_walks in enumerate ( num_walks_lists , 1 ) ) walks = flatten ( walk_results ) return walks
Generates the random walks which will be used as the skip-gram input.
        :return: List of walks. Each walk is a list of nodes.