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metric_break.py
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metric_break.py
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import numpy as np
import scipy.io as sio
import os
from model.metric import *
import torch
import argparse
from utils import Params
def parse_args():
"""
Args:
config: json file with hyperparams and exp settings
"""
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='base', help='model type')
parser.add_argument('--id', type=str, default='13', help='exp id')
args = parser.parse_args()
return args
args = parse_args()
path_root = './experiments/{}/{}'.format(args.model, args.id)
# all_recons = []
# all_inputs = []
# # for i in [0, 1, 2, 3, 5, 6, 7, 8, 11, 13, 14, 15]:
# # for i in [1, 2, 3, 5, 7, 8, 10, 11, 13]:
# for i in [5, 7, 8, 10, 11, 13]:
# data = sio.loadmat('{}/data/qry_{}.mat'.format(path_root, i))
# # data = sio.loadmat('{}/{:02d}/data/test.mat'.format(path_root, i))
# recons = data['recons']
# inputs = data['inputs']
# all_recons.append(recons)
# all_inputs.append(inputs)
# all_recons = np.concatenate(all_recons, axis=0)
# all_inputs = np.concatenate(all_inputs, axis=0)
# recons_torch = torch.Tensor(all_recons)
# inputs_torch = torch.Tensor(all_inputs)
# mse_total = mse(recons_torch, inputs_torch)
# mse_total = mse_total.mean([1, 2, 3])
# mse_total = mse_total.cpu().detach().numpy()
# vpt_total = vpt(recons_torch, inputs_torch)
# vpt_total = vpt_total.cpu().detach().numpy()
# dst_total = dst(all_recons, all_inputs)
# dst_total = dst_total.mean(1)
# vpd_total = vpd(all_recons, all_inputs)
# print('Known dynamics')
# print('mse for seq avg = {}'.format(mse_total.mean()))
# print('mse for seq std = {}'.format(mse_total.std()))
# print('vpt for seq avg = {}'.format(vpt_total.mean()))
# print('vpt for seq std = {}'.format(vpt_total.std()))
# print('dst for seq avg = {}'.format(dst_total.mean()))
# print('dst for seq std = {}'.format(dst_total.std()))
# print('vpd for seq avg = {}'.format(vpd_total.mean()))
# print('vpd for seq std = {}'.format(vpd_total.std()))
# with open('{}/data/metric_break.txt'.format(path_root), 'a+') as f:
# # with open('{}/metric_break.txt'.format(path_root), 'a+') as f:
# f.write('Known dynamics\n')
# f.write('mse for seq avg = {}\n'.format(mse_total.mean()))
# f.write('mse for seq std = {}\n'.format(mse_total.std()))
# f.write('vpt for seq avg = {}\n'.format(vpt_total.mean()))
# f.write('vpt for seq std = {}\n'.format(vpt_total.std()))
# f.write('dst for seq avg = {}\n'.format(dst_total.mean()))
# f.write('dst for seq std = {}\n'.format(dst_total.std()))
# f.write('vpd for seq avg = {}\n'.format(vpd_total.mean()))
# f.write('vpd for seq std = {}\n'.format(vpd_total.std()))
# all_recons = []
# all_inputs = []
# # for i in [4, 9, 10, 12]:
# # data = sio.loadmat('{}/data/unknown_qry_{}.mat'.format(path_root, i))
# # for i in [0, 4, 6, 9, 12, 14]:
# for i in [6, 9, 12, 14]:
# data = sio.loadmat('{}/data/qry_{}.mat'.format(path_root, i))
# # data = sio.loadmat('{}/{:02d}/data/test.mat'.format(path_root, i))
# recons = data['recons']
# inputs = data['inputs']
# all_recons.append(recons)
# all_inputs.append(inputs)
# all_recons = np.concatenate(all_recons, axis=0)
# all_inputs = np.concatenate(all_inputs, axis=0)
# recons_torch = torch.Tensor(all_recons)
# inputs_torch = torch.Tensor(all_inputs)
# mse_total = mse(recons_torch, inputs_torch)
# mse_total = mse_total.mean([1, 2, 3])
# mse_total = mse_total.cpu().detach().numpy()
# vpt_total = vpt(recons_torch, inputs_torch)
# vpt_total = vpt_total.cpu().detach().numpy()
# dst_total = dst(all_recons, all_inputs)
# dst_total = dst_total.mean(1)
# vpd_total = vpd(all_recons, all_inputs)
# print('Unknown dynamics')
# print('mse for seq avg = {}'.format(mse_total.mean()))
# print('mse for seq std = {}'.format(mse_total.std()))
# print('vpt for seq avg = {}'.format(vpt_total.mean()))
# print('vpt for seq std = {}'.format(vpt_total.std()))
# print('dst for seq avg = {}'.format(dst_total.mean()))
# print('dst for seq std = {}'.format(dst_total.std()))
# print('vpd for seq avg = {}'.format(vpd_total.mean()))
# print('vpd for seq std = {}'.format(vpd_total.std()))
# with open('{}/data/metric_break.txt'.format(path_root), 'a+') as f:
# # with open('{}/metric_break.txt'.format(path_root), 'a+') as f:
# f.write('Unknown dynamics\n')
# f.write('mse for seq avg = {}\n'.format(mse_total.mean()))
# f.write('mse for seq std = {}\n'.format(mse_total.std()))
# f.write('vpt for seq avg = {}\n'.format(vpt_total.mean()))
# f.write('vpt for seq std = {}\n'.format(vpt_total.std()))
# f.write('dst for seq avg = {}\n'.format(dst_total.mean()))
# f.write('dst for seq std = {}\n'.format(dst_total.std()))
# f.write('vpd for seq avg = {}\n'.format(vpd_total.mean()))
# f.write('vpd for seq std = {}\n'.format(vpd_total.std()))
all_recons = []
all_inputs = []
# for i in [0, 6, 12]:
for i in [4, 9, 14]:
# for i in [6, 12]:
# for i in [9, 14]:
data = sio.loadmat('{}/data/qry_{}_center.mat'.format(path_root, i))
recons = data['recons']
inputs = data['inputs']
all_recons.append(recons)
all_inputs.append(inputs)
all_recons = np.concatenate(all_recons, axis=0)
all_inputs = np.concatenate(all_inputs, axis=0)
recons_torch = torch.Tensor(all_recons)
inputs_torch = torch.Tensor(all_inputs)
mse_total = mse(recons_torch, inputs_torch)
mse_total = mse_total.mean([1, 2, 3])
mse_total = mse_total.cpu().detach().numpy()
vpt_total = vpt(recons_torch, inputs_torch)
vpt_total = vpt_total.cpu().detach().numpy()
dst_total = dst(all_recons, all_inputs)
dst_total = dst_total.mean(1)
vpd_total = vpd(all_recons, all_inputs)
print('Unknown dynamics 4, 9, 14 center')
# print('Unknown dynamics 0, 6, 12 center')
# print('Unknown dynamics 6, 12')
# print('Unknown dynamics 9, 14')
print('mse for seq avg = {}'.format(mse_total.mean()))
print('mse for seq std = {}'.format(mse_total.std()))
print('vpt for seq avg = {}'.format(vpt_total.mean()))
print('vpt for seq std = {}'.format(vpt_total.std()))
print('dst for seq avg = {}'.format(dst_total.mean()))
print('dst for seq std = {}'.format(dst_total.std()))
print('vpd for seq avg = {}'.format(vpd_total.mean()))
print('vpd for seq std = {}'.format(vpd_total.std()))
with open('{}/data/metric_break.txt'.format(path_root), 'a+') as f:
f.write('Unknown dynamics 4, 9, 14 center\n')
# f.write('Unknown dynamics 0, 6, 12 center\n')
# f.write('Unknown dynamics 6, 12\n')
# f.write('Unknown dynamics 9, 14\n')
f.write('mse for seq avg = {}\n'.format(mse_total.mean()))
f.write('mse for seq std = {}\n'.format(mse_total.std()))
f.write('vpt for seq avg = {}\n'.format(vpt_total.mean()))
f.write('vpt for seq std = {}\n'.format(vpt_total.std()))
f.write('dst for seq avg = {}\n'.format(dst_total.mean()))
f.write('dst for seq std = {}\n'.format(dst_total.std()))
f.write('vpd for seq avg = {}\n'.format(vpd_total.mean()))
f.write('vpd for seq std = {}\n'.format(vpd_total.std()))