def run_sim(self, neural_networks): start_t = time.time() self.sim.gen_sim() if self.initial is True: #if the initial sim, run until the offset time reached self.initial = False self.sim.run_offset(self.offset) print( str(self.idx) + ' train waiting at offset ------------- ' + str(self.offset) + ' at ' + str(get_time_now())) write_to_log(' ACTOR #' + str(self.idx) + ' FINISHED RUNNING OFFSET ' + str(self.offset) + ' to time ' + str(self.sim.t) + ' , WAITING FOR OTHER OFFSETS...') self.barrier.wait() print( str(self.idx) + ' train broken offset =================== ' + str(self.offset) + ' at ' + str(get_time_now())) write_to_log(' ACTOR #' + str(self.idx) + ' BROKEN OFFSET BARRIER...') self.sim.create_tsc(self.rl_stats, self.exp_replays, self.eps, neural_networks) write_to_log('ACTOR #' + str(self.idx) + ' START RUN SIM...') self.sim.run() print('sim finished in ' + str(time.time() - start_t) + ' on proc ' + str(self.idx)) write_to_log('ACTOR #' + str(self.idx) + ' FINISHED SIM...')
def __init__(self, idx, args, barrier, netdata, agent_ids, rl_stats, exp_replay): Process.__init__(self) self.idx = idx self.args = args self.barrier = barrier self.netdata = netdata self.agent_ids = agent_ids self.rl_stats = rl_stats self.exp_replay = exp_replay self.save_t = 0 self.replay_fp = self.args.save_replay + '/' + self.args.tsc + '/' #for saving agent progress if self.idx == 0: path = 'tmp/' check_and_make_dir(path) now = get_time_now() self.updates_path = path + str( self.args.tsc) + '_' + str(now) + '_agent_updates.csv' self.replay_path = path + str( self.args.tsc) + '_' + str(now) + '_agent_replay.csv' self.n_exp_path = path + str( self.args.tsc) + '_' + str(now) + '_agent_nexp.csv' self.tsc_ids = list(sorted(list(self.netdata['inter'].keys()))) #write header line with tsc names write_line_to_file(self.updates_path, 'a+', ','.join([now] + self.tsc_ids)) write_line_to_file(self.replay_path, 'a+', ','.join([now] + self.tsc_ids)) write_line_to_file(self.n_exp_path, 'a+', ','.join([now] + self.tsc_ids))
def main(): start = time.time() args = parse_cl_args() #get hyperparams for command line supplied tsc tsc_str = args.tsc hp_dict = get_hp_dict(tsc_str) hp_order = sorted(list(hp_dict.keys())) hp_list = [hp_dict[hp] for hp in hp_order] #use itertools to produce cartesian product of hyperparams hp_set = list(itertools.product(*hp_list)) print(str(len(hp_set))+' total hyper params') #where to find metrics hp_travel_times = {} metrics_fp = 'metrics/'+tsc_str #where to print hp results path = 'hyperparams/'+tsc_str+'/' check_and_make_dir(path) fname = get_time_now() hp_fp = path+fname+'.csv' write_line_to_file(hp_fp, 'a+', ','.join(hp_order)+',mean,std,mean+std' ) #run each set of hp from cartesian product for hp in hp_set: hp_cmds = create_hp_cmds(args, hp_order, hp) #print(hp_cmds) for cmd in hp_cmds: os.system(cmd) #read travel times, store mean and std for determining best hp set hp_str = ','.join([str(h) for h in hp]) travel_times = get_hp_results(metrics_fp+'/traveltime/') hp_travel_times[hp_str] = {'mean':int(np.mean(travel_times)), 'std':int(np.std(travel_times))} write_temp_hp(hp_str, hp_travel_times[hp_str], hp_fp) #generate_returns(tsc_str, 'metrics/', hp_str) save_hp_performance(travel_times, 'hp/'+tsc_str+'/', hp_str) #remove all metrics for most recent hp shutil.rmtree(metrics_fp) #remove temp hp and write ranked final results os.remove(hp_fp) rank_hp(hp_travel_times, hp_order, tsc_str, hp_fp) print('All hyperparamers performance can be viewed at: '+str(hp_fp)) print('TOTAL HP SEARCH TIME') secs = time.time()-start print(str(int(secs/60.0))+' minutes ')
def write_sim_tsc_metrics(self): #get data dict of all tsc in sim #where each tsc has dict of all metrics tsc_metrics = self.sim.get_tsc_metrics() #create file name and path for writing metrics data #now = datetime.datetime.now() #fname = str(self.idx)+'_'+str(now).replace(" ","-") fname = get_time_now() #write all metrics to correct path #path = 'metrics/'+str(self.args.tsc) path = 'metrics/'+str(self.args.tsc) for tsc in tsc_metrics: for m in tsc_metrics[tsc]: mpath = path + '/'+str(m)+'/'+str(tsc)+'/' check_and_make_dir(mpath) save_data(mpath+fname+'_'+str(self.eps)+'_.p', tsc_metrics[tsc][m]) travel_times = self.sim.get_travel_times() path += '/traveltime/' check_and_make_dir(path) save_data(path+fname+'.p', travel_times)
def write_n_exp_progress(self): n_replay = [str(self.rl_stats[i]['n_exp']) for i in self.tsc_ids] write_line_to_file( self.n_exp_path, 'a+', ','.join([get_time_now()]+n_replay) )
def write_replay_progress(self): n_replay = [str(len(self.exp_replay[i])) for i in self.tsc_ids] write_line_to_file( self.replay_path, 'a+', ','.join([get_time_now()]+n_replay) )
def write_training_progress(self): updates = [str(self.rl_stats[i]['updates']) for i in self.tsc_ids] write_line_to_file( self.updates_path, 'a+', ','.join([get_time_now()]+updates) )