generate_theoric_random_graph.py

# coding = utf-8
import itertools
import os

import networkx as nx
import argparse
import numpy as np
import pandas as pd
import random
import copy
from tqdm import tqdm
import lib.random as ra

# COMMAND PARSING
parser = argparse.ArgumentParser()
parser.add_argument("output_dir")
args = parser.parse_args()


GRAPH_SIZE = [100,150,200]
EDGE_SIZE = [300,500]
OUTPUT_DIR = args.output_dir
if not os.path.exists(OUTPUT_DIR):
    raise FileExistsError("Output directory does not exists !")


parameters = {
    "stochastic_block_model_graph": {
        "nb_nodes":GRAPH_SIZE,
        "nb_edges":EDGE_SIZE,
        "nb_com" :[2,5],
        "percentage_edge_betw":[0.1,0.01]
    },
    "ER_graph": {
        "nb_nodes":GRAPH_SIZE,
        "nb_edges":EDGE_SIZE
    },
    "powerlaw_graph": {  # configuration_model
        "nb_nodes":GRAPH_SIZE,
        "nb_edges":EDGE_SIZE,
        "exponent":[2,3]
    },
    "spatial_graph":{
        "nb_nodes":[100,150],
        "nb_edges":EDGE_SIZE,
        "coords":["random","country"],
    }
}


#getattr(nx,"geographical_threshold_graph")(**dict(n=20,theta=0.4))



def get_params(inp):
    return (dict(zip(inp.keys(), values)) for values in itertools.product(*inp.values()))



pbar = tqdm(parameters.items(),total=len(parameters))
for method,args in pbar:
    pbar.set_description("Generating graphs using : " + method)
    list_of_params = get_params(parameters[method])
    func = getattr(ra,method)
    for ix,params in enumerate(list_of_params):
        # try:
        G = func(**params)
        G.graph.update(params)
        nx.write_gml(G, OUTPUT_DIR+"/graph_{method}_{ix}.gml".format(method=method,ix=ix),stringizer=str)
        # except Exception as e:
        #     print(e)