def run_simulations(args, save_result, local_mode, init_ray=True):
if init_ray:
start_ray(local_mode)
if save_result:
create_result_dir(args)
write_to_log('local_mode == {}'.format(local_mode), args)
start_time = timeit.default_timer()
set_random_seed(args.seed)
n_reps = args.n_reps
alg_param_grid = get_grid(args.param_grid_def)
n_grid = alg_param_grid.shape[0]
config_grid_vals = get_grid(args.config_grid_def)
n_config_grid = len(config_grid_vals)
planing_loss = np.zeros((n_reps, n_config_grid, n_grid))
info_dict = {}
# ----- Run simulation in parrnell process---------------------------------------------#
loss_rep_id_lst = []
for i_rep in range(n_reps):
# returns objects ids:
args_r = deepcopy(args)
planing_loss_rep_id = run_rep.remote(i_rep, alg_param_grid,
args_r.config_grid_def, args_r)
loss_rep_id_lst.append(planing_loss_rep_id)
# end for i_rep
# ----- get the results --------------------------------------------#
for i_rep in range(n_reps):
loss_rep = ray.get(loss_rep_id_lst[i_rep])
if i_rep % max(n_reps // 100, 1) == 0:
time_str = time.strftime(
"%H hours, %M minutes and %S seconds",
time.gmtime(timeit.default_timer() - start_time))
write_to_log(
'Finished: {} out of {} reps, time: {}'.format(
i_rep + 1, n_reps, time_str), args)
# end if
planing_loss[i_rep] = loss_rep
info_dict = {
'planing_loss_avg': planing_loss.mean(axis=0),
'planing_loss_std': planing_loss.std(axis=0),
'alg_param_grid': alg_param_grid,
'n_reps_finished': i_rep + 1
}
if save_result:
save_run_data(args, info_dict, verbose=0)
# end if
# end for i_rep
if save_result:
save_run_data(args, info_dict)
stop_time = timeit.default_timer()
write_to_log(
'Total runtime: ' +
time.strftime("%H hours, %M minutes and %S seconds",
time.gmtime(stop_time - start_time)), args, save_result)
return info_dict
def run_simulation(args, hyper_grid_vals, loss, reg_grids, local_mode):
start_ray(local_mode)
write_to_log('local_mode == {}'.format(local_mode), args)
SetMrpArgs(args)
start_time = timeit.default_timer()
set_random_seed(args.seed)
reg_types = args.reg_types
n_hyper_grid = len(hyper_grid_vals)
n_reps = args.n_reps
results_dict = dict()
write_to_log('***** Starting {} reps'.format(n_reps), args)
for i_rep in range(n_reps):
for i_hyper_grid, hyper_grid_val in enumerate(hyper_grid_vals):
args_run = deepcopy(args)
set_hyper_param(args_run, hyper_grid_val)
# send jobs:
out_ids = {reg_type: [None for _ in range(len(reg_grids[reg_type]))] for reg_type in reg_types}
for reg_type in reg_types:
for i_reg_pram, reg_param in enumerate(reg_grids[reg_type]):
# ray put
if np.isnan(loss[reg_type][i_hyper_grid, i_reg_pram, i_rep]):
out_ids[reg_type][i_reg_pram] = run_exp.remote(i_rep, args_run, reg_type, reg_param)
# end if
# end for i_reg_pram
# end for reg_type
# Gather results:
for reg_type in reg_types:
for i_reg_pram, reg_param in enumerate(reg_grids[reg_type]):
# ray get
if out_ids[reg_type][i_reg_pram] is not None:
out = ray.get(out_ids[reg_type][i_reg_pram])
loss[reg_type][i_hyper_grid, i_reg_pram, i_rep] = out
# end if
# end for i_reg_pram
# end for reg_type
# end for i_hyper_grid
# Save results so far
results_dict = {'hyper_grid_vals': hyper_grid_vals, 'loss': loss, 'reg_grids': reg_grids, 'n_reps_finished': i_rep + 1}
save_run_data(args, results_dict, verbose=0)
time_str = time.strftime("%H hours, %M minutes and %S seconds", time.gmtime(timeit.default_timer() - start_time))
write_to_log('Finished: {} out of {} reps, time: {}'.format(i_rep + 1, n_reps, time_str), args)
# end for i_rep
stop_time = timeit.default_timer()
write_to_log('Total runtime: ' + time.strftime("%H hours, %M minutes and %S seconds", time.gmtime(stop_time - start_time)), args)
return results_dict
def run_simulations(args, save_result, local_mode):
args_def = deepcopy(args)
start_ray(local_mode)
if save_result:
create_result_dir(args)
write_to_log('local_mode == {}'.format(local_mode), args)
start_time = timeit.default_timer()
set_random_seed(args.seed)
n_reps = args.n_reps
param_val_grid = get_grid(args.param_grid_def)
n_grid = param_val_grid.shape[0]
config_grid = get_grid(args.config_grid_def)
n_configs = len(config_grid)
args.n_configs = n_configs
loss_mat = np.zeros((n_reps, n_configs, n_grid))
# ----- Run simulation in parrnell process---------------------------------------------#
loss_rep_id_lst = []
for i_rep in range(n_reps):
# returns objects ids:
loss_mat_rep_id = run_rep.remote(i_rep, param_val_grid, config_grid,
args)
loss_rep_id_lst.append(loss_mat_rep_id)
# ----- get the results --------------------------------------------#
for i_rep in range(n_reps):
loss_rep = ray.get(loss_rep_id_lst[i_rep])
write_to_log('Finished: {} out of {} reps'.format(i_rep + 1, n_reps),
args)
loss_mat[i_rep] = loss_rep
# end for i_rep
info_dict = {
'loss_avg': loss_mat.mean(axis=0),
'loss_std': loss_mat.std(axis=0),
'param_val_grid': param_val_grid,
'config_grid': config_grid
}
if save_result:
save_run_data(args, info_dict)
stop_time = timeit.default_timer()
write_to_log(
'Total runtime: ' +
time.strftime("%H hours, %M minutes and %S seconds",
time.gmtime(stop_time - start_time)), args)
write_to_log(
['-' * 10 + 'Defined args: ',
pretty_print_args(args_def), '-' * 20], args)
return info_dict
def run_simulations(args, local_mode):
start_ray(local_mode)
create_result_dir(args)
write_to_log('local_mode == {}'.format(local_mode), args)
start_time = timeit.default_timer()
create_result_dir(args)
set_random_seed(args.seed)
l2_grid = get_grid(args.l2_grid_def)
gam_grid = get_grid(args.gam_grid_def)
write_to_log('gamma_grid == {}'.format(gam_grid), args)
write_to_log('l2_grid == {}'.format(l2_grid), args)
grid_shape = (len(l2_grid), len(gam_grid))
loss_avg = np.zeros(grid_shape)
loss_std = np.zeros(grid_shape)
run_idx = 0
for i0 in range(grid_shape[0]):
for i1 in range(grid_shape[1]):
args_run = deepcopy(args)
args_run.param_grid_def = {
'type': 'L2_factor',
'spacing': 'list',
'list': [l2_grid[i0]]
}
args_run.default_gamma = gam_grid[i1]
info_dict = run_main_control(args_run,
save_result=False,
plot=False,
init_ray=False)
loss_avg[i0, i1] = info_dict['planing_loss_avg'][0]
loss_std[i0, i1] = info_dict['planing_loss_std'][0]
run_idx += 1
print("Finished {}/{}".format(run_idx, loss_avg.size))
# end for
# end for
grid_results_dict = {
'l2_grid': l2_grid,
'gam_grid': gam_grid,
'loss_avg': loss_avg,
'loss_std': loss_std
}
save_run_data(args, grid_results_dict)
stop_time = timeit.default_timer()
write_to_log(
'Total runtime: ' +
time.strftime("%H hours, %M minutes and %S seconds",
time.gmtime(stop_time - start_time)), args)
return grid_results_dict
n_hyper_grid = len(hyper_grid_vals)
n_reps = args.n_reps
reg_types = args.reg_types
# define search grids for regularization parameters
reg_grids = dict()
for reg_type in reg_types:
reg_param_grid_def = args.search_grid_def[reg_type]
reg_grids[reg_type] = get_grid(reg_param_grid_def)
# init result matrix with nan (no result)
loss = {reg_type: np.full((n_hyper_grid, len(reg_grids[reg_type]), n_reps), np.nan) for reg_type in reg_types}
# run
results_dict = run_simulation(args, hyper_grid_vals, loss, reg_grids, local_mode)
save_run_data(args, results_dict)
# *********************************
elif run_mode == 'Continue':
loaded_args, loaded_results_dict = load_run_data(result_dir_to_load)
args = loaded_args
args.result_dir = result_dir_to_load # update the path, in case the result folder moved
hyper_grid_vals = loaded_results_dict['hyper_grid_vals']
loss = loaded_results_dict['loss']
reg_grids = loaded_results_dict['reg_grids']
results_dict = run_simulation(args, hyper_grid_vals, loss, reg_grids, local_mode)
save_run_data(args, results_dict)
# *********************************
else:
raise AssertionError('Unrecognized run_mode')
# *********************************
result_reward_mat[i_grid, i_rep]):
continue # skip
output, args_run = ray.get(out_id[i_grid][i_rep])
result_reward_mat[i_grid, i_rep] = output
# note: the final reward is an average performance on eval_episodes=10 of final policy
write_to_log(
f'Finished Rep: {i_rep + 1}/{n_reps_per_point[i_grid]} of Grid point {i_grid}/{len(alg_param_grid)}'
f' ({args_run.job_name}), Reward : {output}, Time now: {time_now()}',
args)
# Save results so far:
stop_time = timeit.default_timer()
run_time += stop_time - start_time
start_time = timeit.default_timer()
save_run_data(args, {
'alg_param_grid': alg_param_grid,
'result_reward_mat': result_reward_mat,
'run_time': run_time
},
verbose=1)
# end for i_grid
# end for i_rep
write_to_log(
'Total runtime: ' + time.strftime(
"%H hours, %M minutes and %S seconds", time.gmtime(run_time)),
args)
# --------------------------------------------------------------------------------------------------------------------#
# Plot results
# --------------------------------------------------------------------------------------------------------------------#
mean_reward = []