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mwis_mlp_test.py
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mwis_mlp_test.py
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# python3
# Make this standard template for testing and training
from __future__ import division
from __future__ import print_function
import sys
import os
import shutil
sys.path.append( '%s/gcn' % os.path.dirname(os.path.realpath(__file__)) )
# add the libary path for graph reduction and local search
# sys.path.append( '%s/kernel' % os.path.dirname(os.path.realpath(__file__)) )
import argparse
import time
import random
import scipy.io as sio
import numpy as np
import scipy.sparse as sp
from multiprocessing import Queue
from copy import deepcopy
# import tensorflow as tf
from collections import deque
# from models import GCN_DQN
import pandas as pd
# import the libary for graph reduction and local search
# from reduce_lib import reducelib
import warnings
warnings.filterwarnings('ignore')
from gcn.utils import *
# Settings (FLAGS)
# from runtime_config import *
from test_utils import *
from heuristics import *
# Arguments
parser = argparse.ArgumentParser()
parser.add_argument("--datapath", default="./data/BA_Graph_Uniform_GEN21_test2", type=str, help="directory of test dataset")
parser.add_argument("--solver", default="optimal", type=str, help="MWIS solver: optimal, mp_greedy.")
args = parser.parse_args()
# test data path
# data_path = FLAGS.datapath
solver = args.solver.lower()
data_path = args.datapath
# data_path = './data/BA_Graph_Uniform_GEN21_test2'
# data_path = './data/ER_Graph_Uniform_GEN21_test2'
# test_datapath = FLAGS.test_datapath
val_mat_names = sorted(os.listdir(data_path))
# test_mat_names = sorted(os.listdir(test_datapath))
if solver=='mp_greedy':
model_origin = "mp_clique_greedy_"+data_path.split('/')[-1]
elif solver=='optimal':
model_origin = "mlp_gurobi_"+data_path.split('/')[-1]
else:
raise NameError('Unsupported MWIS solver')
# plp.pulpTestAll()
# use gpu 0
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
# Initialize session
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
best_IS_vec = []
loss_vec = []
losses = []
cnt = 0
f_ct = 0
q_totals = []
p_ratios = []
outputfile = "./output/{}.csv".format(model_origin)
if os.path.isfile(outputfile):
results = pd.read_csv(outputfile, index_col=0)
# results.set_index('data', inplace=True)
else:
results = pd.DataFrame([], columns=["data","p","runtime","status"])
results['data'] = val_mat_names
results['p'] = 0.0
results.set_index('data', inplace=True)
newtime = time.time()
cnt2solve = len(val_mat_names)
timeout=4800
while cnt2solve:
unsolved = results[results['p']==0.0].index.values
cnt2solve = len(unsolved)
for mat_name in unsolved:
best_IS_num = -1
# print(val_mat_names[id])
mat_contents = sio.loadmat(data_path + '/' + mat_name)
adj_0 = mat_contents['adj']
wts = mat_contents['weights'].transpose()
yy_util = mat_contents['mwis_utility']
_, greedy_util = greedy_search(adj_0, wts)
nn = adj_0.shape[0]
bsf_q = []
q_ct = 0
res_ct = 0
out_id = -1
start_time = time.time()
if solver=='mp_greedy':
solution, ss_util = mp_greedy(adj_0, wts)
status = ''
elif solver=='optimal':
solution, ss_util, status = mlp_gurobi(adj_0, wts, timeout=timeout)
else:
raise NameError('Unsupported solver')
p_ratio = ss_util.flatten()/greedy_util.flatten()
if p_ratio[0]==0:
continue
f_ct += 1
# q_totals.append(q_total)
p_ratios.append(p_ratio[0])
# avg_is_size = np.mean(q_totals)
# print("Epoch: {}".format(epoch), "ID: %05d" % f_ct, "Avg_IS_Size: {:.4f}".format(avg_is_size),
# "Epsilon: {:.6f}".format(dqn_agent.epsilon), "Ratio: {:.6f}".format(p_ratio[0]),
# "Loss: {:.6f}".format(loss), "Epoch_Loss: {:.6f}".format(np.mean(losses)), "Epoch_Ratio: {:.6f}".format(np.mean(p_ratios)), "runtime: {:.3f}".format(runtime))
test_ratio=[]
# best_IS_util,_,_ = solve_mwis(test=False)
# test_ratio.append(best_IS_util[0] / yy_util[0, 0])
runtime = time.time() - start_time
print("ID: %03d" % f_ct,
"File: {}".format(mat_name),
"Ratio: {:.6f}".format(p_ratio[0]),
"Avg_Ratio: {:.6f}".format(np.mean(p_ratios)),
# "Avg_IS_Size: {:.4f}".format(avg_is_size),
"runtime: {:.3f}".format(runtime))
# results = results.append(
# {"data": val_mat_names[id],
# "p": p_ratio[0] ,
# "runtime":runtime},
# ignore_index=True
# )
results.loc[mat_name, 'p'] = p_ratio[0]
results.loc[mat_name, 'runtime'] = runtime
results.loc[mat_name, 'status'] = status
# dqn_agent.save(model_origin)
results.to_csv(outputfile)
# sio.savemat('./%s/%s' % (outputfolder, val_mat_names[id]), {'er_graph': adj_0, 'nIS_vec': best_IS_vec, 'weights': wts, 'best_util': best_IS_util, 'yy_util': yy_util})
cnt2solve -= 1
timeout = timeout*10