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gen_plot_data_genetic.py
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gen_plot_data_genetic.py
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import os, sys, shutil
import json
import torch
import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
from copy import deepcopy
import pandas as pd
from joint_main_genetic import jointController
from joint_arguments import get_args
np.set_printoptions(suppress=True, precision=4)
def extract_ckpts(args):
ckpts_path = os.path.join(args.root_save_dir, 'run_'+str(args.load_run))
all_ckpts = np.sort([int(ckpt[5:-3]) for ckpt in os.listdir(ckpts_path) if ckpt.endswith('.pt')])
min_ckpt, max_ckpt = all_ckpts[0], all_ckpts[-1]
ckpts = np.linspace(min_ckpt, max_ckpt, args.num_cross_eval_ckpts).astype(int)
return ckpts
def viz_rewards(task_rewards, privacy_rewards):
plt.subplot(1,2,1)
plt.imshow(task_rewards)
plt.grid(False)
plt.colorbar()
plt.title('Task Rewards')
plt.xlabel('Adversary Checkpoint')
plt.ylabel('Swarm Checkpoint')
plt.subplot(1,2,2)
plt.imshow(privacy_rewards)
plt.grid(False)
plt.colorbar()
plt.title('Privacy Rewards')
plt.xlabel('Adversary Checkpoint')
plt.ylabel('Swarm Checkpoint')
plt.savefig(os.path.join(args.out_dir, 'cross_evaluation.png'), dpi=300)
if __name__ == '__main__':
args = get_args()
if args.seed is None:
args.seed = np.random.randint(0,10000)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
myController = jointController(args)
pprint(vars(args))
ckpts = extract_ckpts(args)
print(ckpts)
metrics_dict = {'Method': [], 'No. of environment steps': [], 'Task reward': [], 'Privacy reward': []}
# -------- loading pretrained models -------- #
swarm_checkpoint, adversary_checkpoint = None, None
ckpts_path = os.path.join(args.root_save_dir, 'run_'+str(args.load_run))
# task_rewards = np.zeros((args.num_cross_eval_ckpts, args.num_cross_eval_ckpts))
# privacy_rewards = np.zeros((args.num_cross_eval_ckpts, args.num_cross_eval_ckpts))
for i,ckpt in enumerate(ckpts):
swarm_load_path = os.path.join(ckpts_path, 'ckpt_{}.pt'.format(ckpt))
swarm_checkpoint = torch.load(swarm_load_path)['swarm']
adversary_load_path = os.path.join(ckpts_path, 'ckpt_{}.pt'.format(ckpt))
adversary_checkpoint = torch.load(adversary_load_path)['adversary']
metrics = myController.evaluate(np.random.randint(0,10000), swarm_checkpoint, adversary_checkpoint)
print(ckpts_path, ckpt)
task_rewards = metrics['episode_task_rewards'] # num_episodesxnum_agents
privacy_rewards = metrics['episode_privacy_rewards'] # num_episodesxnum_agents
for team_task_rew, team_priv_reward in zip(task_rewards, privacy_rewards):
metrics_dict['Method'].append('Zheng et al.')
metrics_dict['No. of environment steps'].append(ckpt*args.save_interval)
metrics_dict['Task reward'].append(team_task_rew.mean()/args.num_steps_episode)
metrics_dict['Privacy reward'].append(team_priv_reward.mean())
df = pd.DataFrame(metrics_dict)
df.to_csv('Plotting_data_229.csv')
# print(task_rewards)
# print(privacy_rewards)
# viz_rewards(task_rewards, privacy_rewards)
# print('Metrics \n{}'.format(metrics))
# , 'ckpt_{}.pt'.format(args.load_ckpt))
# if args.load_mode == 'joint':
# print('args.load_path', args.load_path)
#
# adversary_checkpoint = checkpoint['adversary'] if args.use_adversary else None
# elif args.load_mode == 'individual':
# if not args.swarm_load_path is None:
# swarm_checkpoint = torch.load(args.swarm_load_path)
# if 'swarm' in swarm_checkpoint.keys():
# swarm_checkpoint = swarm_checkpoint['swarm']
# if args.use_adversary:
# adversary_checkpoint = torch.load(args.adversary_load_path)
# print('args.adversary_load_path', args.adversary_load_path)
# if 'adversary' in adversary_checkpoint.keys():
# adversary_checkpoint = adversary_checkpoint['adversary']
# else:
# adversary_checkpoint = None
# print('loaded swarm and adversary', 'joint_main.py')
# # Save configs
# if not args.out_dir is None:
# with open(os.path.join(args.out_dir, 'params.json'), 'w') as f:
# params = deepcopy(vars(args))
# params.pop('device')
# json.dump(params, f)
# # save to common file
# with open(os.path.join('output','all_config.txt'),'a') as f:
# f.write('\n\n'+str(params))
# metrics = myController.evaluate(args.seed, swarm_checkpoint, adversary_checkpoint)
# print('Metrics \n{}'.format(metrics))
#