hi_name[j] = "resNet{layer}_{pre}_{e}".format(layer=layer, pre=p_i, e=epoch_i) highest_accur[j][0] = epoch_i highest_accur[j][1] = accuracy if (accuracy >= 0.8): t.save( net.state_dict(), "resNet{layer}_{pre}_{e}.pkl".format(layer=layer, pre=p_i, e=epoch_i)) test_accuracy.append(accuracy * 100) print('Test accuracy:', accuracy) # decay lr models.lr_decay(optimizer) print('END:', time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), '\n') print("{name}: {acc}".format(name=hi_name[j], acc=highest_accur[j][1])) plot.plot_line(TOTAL_EPOCH, train_accuracy, 'train', p_i) plot.plot_line(TOTAL_EPOCH, test_accuracy, 'test', p_i) j += 1 plot.show_result_end(layer)
pair_mid = pair_mid.groupby( pd.Grouper(freq='D')).resample(mid_freq).mean().droplevel(0) pair_mid = pair_mid.resample('D').apply(aux.fill_nan).droplevel(0) if ln: pair_mid = np.log(pair_mid) pair_ask = pair_ask.resample('D').apply(aux.fill_nan).droplevel(0) pair_bid = pair_bid.resample('D').apply(aux.fill_nan).droplevel(0) pair_mid.dropna(inplace=True) pair_ask.dropna(inplace=True) pair_bid.dropna(inplace=True) plot.plot_line(pair_mid, 'Mid Price.html') all_windows = rolling.windows(pair_mid, window_size) residuals = list( map(lambda w: residual.get_resid(w, intercept=intercept, wavelet=wavelet), all_windows)) std = [resid.std() for resid in residuals] residuals = pd.concat(map(lambda r: r.tail(1), residuals)) # get last values std = pd.Series(std, index=residuals.index) residuals = residuals.dropna().reindex(pair_mid.index) std = std.dropna().reindex(pair_mid.index) * threshold plot.plot_signals(residuals, std, signal_func, name) all_signals = signal_func(residuals, std)
def main(): print("#######") print("WARNING: All rewards are clipped or normalized so you need to use a monitor (see envs.py) or visdom plot to get true rewards") print("#######") os.environ['OMP_NUM_THREADS'] = '1' #os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" #os.environ['CUDA_VISIBLE_DEVICES'] = "9" if args.vis: from visdom import Visdom viz = Visdom(port=args.port) win = None envs = [make_env(args.env_name, args.seed, i, args.log_dir, args.add_timestep) for i in range(args.num_processes)] if args.num_processes > 1: envs = SubprocVecEnv(envs) else: envs = DummyVecEnv(envs) if len(envs.observation_space.shape) == 1: envs = VecNormalize(envs) obs_shape = envs.observation_space.shape obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:]) if len(envs.observation_space.shape) == 3: actor_critic = CNNPolicy(obs_shape[0], envs.action_space,args.hid_size, args.feat_size,args.recurrent_policy) else: assert not args.recurrent_policy, \ "Recurrent policy is not implemented for the MLP controller" actor_critic = MLPPolicy(obs_shape[0], envs.action_space) if envs.action_space.__class__.__name__ == "Discrete": action_shape = 1 else: action_shape = envs.action_space.shape[0] if args.use_cell: hs = HistoryCell(obs_shape[0], actor_critic.feat_size, 2*actor_critic.hidden_size, 1) ft = FutureCell(obs_shape[0], actor_critic.feat_size, 2 * actor_critic.hidden_size, 1) else: hs = History(obs_shape[0], actor_critic.feat_size, actor_critic.hidden_size, 2, 1) ft = Future(obs_shape[0], actor_critic.feat_size, actor_critic.hidden_size, 2, 1) if args.cuda: actor_critic=actor_critic.cuda() hs = hs.cuda() ft = ft.cuda() if args.algo == 'a2c': agent = algo.A2C_ACKTR(actor_critic, args.value_loss_coef, args.entropy_coef, lr=args.lr, eps=args.eps, alpha=args.alpha, max_grad_norm=args.max_grad_norm) elif args.algo == 'ppo': agent = algo.PPO(actor_critic, hs,ft,args.clip_param, args.ppo_epoch, args.num_mini_batch, args.value_loss_coef, args.entropy_coef, args.hf_loss_coef,ac_lr=args.lr,hs_lr=args.lr,ft_lr=args.lr, eps=args.eps, max_grad_norm=args.max_grad_norm, num_processes=args.num_processes, num_steps=args.num_steps, use_cell=args.use_cell, lenhs=args.lenhs,lenft=args.lenft, plan=args.plan, ac_intv=args.ac_interval, hs_intv=args.hs_interval, ft_intv=args.ft_interval ) elif args.algo == 'acktr': agent = algo.A2C_ACKTR(actor_critic, args.value_loss_coef, args.entropy_coef, acktr=True) rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape, envs.action_space, actor_critic.state_size, feat_size=512) current_obs = torch.zeros(args.num_processes, *obs_shape) def update_current_obs(obs): shape_dim0 = envs.observation_space.shape[0] obs = torch.from_numpy(obs).float() if args.num_stack > 1: current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:] current_obs[:, -shape_dim0:] = obs obs = envs.reset() update_current_obs(obs) rollouts.observations[0].copy_(current_obs) if args.cuda: current_obs = current_obs.cuda() rollouts.cuda() rec_x = [] rec_y = [] file = open('./rec/' + args.env_name + '_' + args.method_name + '.txt', 'w') hs_info = torch.zeros(args.num_processes, 2 * actor_critic.hidden_size).cuda() hs_ind = torch.IntTensor(args.num_processes, 1).zero_() epinfobuf = deque(maxlen=100) start_time = time.time() for j in range(num_updates): print('begin sample, time {}'.format(time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)))) for step in range(args.num_steps): # Sample actions with torch.no_grad(): rollouts.feat[step]=actor_critic.get_feat(rollouts.observations[step]) if args.use_cell: for i in range(args.num_processes): h = torch.zeros(1, 2 * actor_critic.hid_size).cuda() c = torch.zeros(1, 2 * actor_critic.hid_size).cuda() start_ind = max(hs_ind[i],step+1-args.lenhs) for ind in range(start_ind,step+1): h,c=hs(rollouts.feat[ind,i].unsqueeze(0),h,c) hs_info[i,:]=h.view(1,2*actor_critic.hid_size) del h,c gc.collect() else: for i in range(args.num_processes): start_ind = max(hs_ind[i], step + 1 - args.lenhs) hs_info[i,:]=hs(rollouts.feat[start_ind:step+1,i]) hidden_feat=actor_critic.cat(rollouts.feat[step],hs_info) value, action, action_log_prob, states = actor_critic.act( hidden_feat, rollouts.states[step]) cpu_actions = action.data.squeeze(1).cpu().numpy() # Obser reward and next obs obs, reward, done, infos = envs.step(cpu_actions) for info in infos: maybeepinfo = info.get('episode') if maybeepinfo: epinfobuf.extend([maybeepinfo['r']]) reward = torch.from_numpy(np.expand_dims(np.stack(reward), 1)).float() masks = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done]) hs_ind = ((1-masks)*(step+1)+masks*hs_ind.float()).int() if args.cuda: masks = masks.cuda() if current_obs.dim() == 4: current_obs *= masks.unsqueeze(2).unsqueeze(2) else: current_obs *= masks update_current_obs(obs) rollouts.insert(current_obs, hs_ind,states.data, action.data, action_log_prob.data, value.data, reward, masks) with torch.no_grad(): rollouts.feat[-1] = actor_critic.get_feat(rollouts.observations[-1]) if args.use_cell: for i in range(args.num_processes): h = torch.zeros(1, 2 * actor_critic.hid_size).cuda() c = torch.zeros(1, 2 * actor_critic.hid_size).cuda() start = max(hs_ind[i], step + 1 - args.lenhs) for ind in range(start, step + 1): h, c = hs(rollouts.feat[ind, i].unsqueeze(0), h, c) hs_info[i, :] = h.view(1, 2 * actor_critic.hid_size) del h,c else: for i in range(args.num_processes): start_ind = max(hs_ind[i], step + 1 - args.lenhs) hs_info[i, :] = hs(rollouts.feat[start_ind:step + 1, i]) hidden_feat = actor_critic.cat(rollouts.feat[-1],hs_info) next_value = actor_critic.get_value(hidden_feat).detach() rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau) rollouts.compute_ft_ind() print('begin update, time {}'.format(time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)))) value_loss, action_loss, dist_entropy = agent.update(rollouts) print('end update, time {}'.format(time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)))) rollouts.after_update() if j % args.save_interval == 0 and args.save_dir != "": save_path = os.path.join(args.save_dir, args.algo) try: os.makedirs(save_path) except OSError: pass # A really ugly way to save a model to CPU save_model = actor_critic if args.cuda: save_model = copy.deepcopy(actor_critic).cpu() save_model = [save_model, hasattr(envs, 'ob_rms') and envs.ob_rms or None] torch.save(save_model, os.path.join(save_path, args.env_name + ".pt")) if j % args.log_interval == 0: end = time.time() total_num_steps = (j + 1) * args.num_processes * args.num_steps v_mean,v_median,v_min,v_max = safe(epinfobuf) print("Updates {}, num timesteps {},time {}, FPS {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}". format(j, total_num_steps, time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)), int(total_num_steps / (end - start_time)), v_mean, v_median, v_min, v_max, dist_entropy, value_loss, action_loss)) if not (v_mean==np.nan): rec_x.append(total_num_steps) rec_y.append(v_mean) file.write(str(total_num_steps)) file.write(' ') file.writelines(str(v_mean)) file.write('\n') if args.vis and j % args.vis_interval == 0: try: # Sometimes monitor doesn't properly flush the outputs win = visdom_plot(viz, win, args.log_dir, args.env_name, args.algo, args.num_frames) except IOError: pass plot_line(rec_x, rec_y, './imgs/' + args.env_name + '_' + args.method_name + '.png', args.method_name, args.env_name, args.num_frames) file.close()
def plot_records(self, title, which): to_plot = map(lambda x: np.array(self.records[x]), which) plot.plot_line(to_plot, title, map(lambda w: self.find_label(w), which))
def plot_records(self, title, which): to_plot = map(lambda x: numpy.array(self.records[x]), which) plot.plot_line(to_plot)
pair_mid = pair_mid.groupby( pd.Grouper(freq='D')).resample(mid_freq).mean().droplevel(0) pair_mid = pair_mid.resample('D').apply(aux.fill_nan).droplevel(0) if ln: pair_mid = np.log(pair_mid) pair_ask = pair_ask.resample('D').apply(aux.fill_nan).droplevel(0) pair_bid = pair_bid.resample('D').apply(aux.fill_nan).droplevel(0) pair_mid.dropna(inplace=True) pair_ask.dropna(inplace=True) pair_bid.dropna(inplace=True) plot.plot_line(pair_mid, 'Mid Price') all_windows = rolling.windows(pair_mid, window_size) residuals = list( map(lambda w: residual.get_resid(w, intercept=intercept, wavelet=wavelet), all_windows)) std = [resid.std() for resid in residuals] residuals = pd.concat(map(lambda r: r.tail(1), residuals)) # get last values std = pd.Series(std, index=residuals.index) residuals = residuals.dropna().reindex(pair_mid.index) std = std.dropna().reindex(pair_mid.index) * threshold plot.plot_signals(residuals, std, signals.get_signal, 'Signal V1') plot.plot_signals(residuals, std, signals.get_signal2, 'Signal V2') all_signals = signals.get_signal(residuals, std)