summarize_result.py

import os
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import torch.nn.functional as F
import networkx as nx
import datetime

import glob

parser = argparse.ArgumentParser('summarize the results in N file.results')
parser.add_argument('--dir', type=str)
parser.add_argument('--type', type=str)

args = parser.parse_args()

v_abs_error = []
v_rel_error = []
v_abs_error2 = []
v_rel_error2 = []
has_2 = False
for filename in glob.glob(args.dir + r'/*.' + args.type):
    results_dict = torch.load(filename)
    v_iter = results_dict['v_iter']
    abs_error = results_dict['abs_error']
    rel_error = results_dict['rel_error']
    v_abs_error.append(abs_error[-1])
    v_rel_error.append(rel_error[-1])
    if 'abs_error2' in results_dict and len(results_dict['abs_error2']) > 0:
        has_2 = True
        abs_error2 = results_dict['abs_error2']
        rel_error2 = results_dict['rel_error2']
        v_abs_error2.append(abs_error2[-1])
        v_rel_error2.append(rel_error2[-1])

v_abs_error = np.array(v_abs_error)
v_rel_error = np.array(v_rel_error)

print('abs_error:')
print('{} \pm {}'.format(v_abs_error.mean(), v_abs_error.std()))
print('{:.3f} \pm {:.3f}'.format(v_abs_error.mean(), v_abs_error.std()))
print('rel_error:')
print('{} \pm {}'.format(v_rel_error.mean() , v_rel_error.std()))
print('{:.1f} \pm {:.1f} %'.format(v_rel_error.mean() * 100, v_rel_error.std() *100))
if has_2:
    v_abs_error2 = np.array(v_abs_error2)
    v_rel_error2 = np.array(v_rel_error2)
    print('abs_error2 interpolation:')
    print('{} \pm {}'.format(v_abs_error2.mean(), v_abs_error2.std()))
    print('{:.3f} \pm {:.3f}'.format(v_abs_error2.mean(), v_abs_error2.std()))
    print('rel_error2 interpolation:')
    print('{} \pm {}'.format(v_rel_error2.mean(), v_rel_error2.std()))
    print('{:.1f} \pm {:.1f} %'.format(v_rel_error2.mean() * 100, v_rel_error2.std() * 100))

# --dir results/mutualistic/grid  --type grid_our