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working sergio version with lf sim and hyperparams
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156
experiments/lf_hf_transfer_exp/sweep_regression_exp_num_data_sergio.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,156 @@ | ||
| from experiments.util import (generate_run_commands, generate_base_command, RESULT_DIR, sample_param_flags, hash_dict) | ||
| from experiments.data_provider import DATASET_CONFIGS | ||
|
|
||
| import experiments.lf_hf_transfer_exp.run_regression_exp | ||
| from experiments.lf_hf_transfer_exp.run_regression_exp import OUTPUTSCALES_RCCAR | ||
| import numpy as np | ||
| import datetime | ||
| import itertools | ||
| import argparse | ||
| import os | ||
| import jax.numpy as jnp | ||
|
|
||
| MODEL_SPECIFIC_CONFIG = { | ||
| 'BNN_SVGD': { | ||
| 'bandwidth_svgd': {'values': [10.]}, | ||
| 'min_train_steps': {'values': [5_000]}, | ||
| 'num_epochs': {'values': [200]}, | ||
| 'lr': {'values': [1e-3]}, | ||
| # 'likelihood_reg': {'values': [10.0]}, | ||
| }, | ||
| 'BNN_FSVGD': { | ||
| 'bandwidth_svgd': {'values': [2.0]}, | ||
| 'bandwidth_gp_prior': {'values': [0.4]}, | ||
| 'min_train_steps': {'values': [5_000]}, | ||
| 'num_epochs': {'values': [200]}, | ||
| 'num_measurement_points': {'values': [128]}, | ||
| 'lr': {'values': [1e-3]}, | ||
| 'likelihood_reg': {'values': [10.0]}, | ||
| }, | ||
|
|
||
| 'BNN_FSVGD_SimPrior_gp': { | ||
| 'bandwidth_svgd': {'values': [2.0]}, | ||
| 'min_train_steps': {'values': [5_000]}, | ||
| 'num_epochs': {'values': [200]}, | ||
| 'num_measurement_points': {'values': [128]}, | ||
| 'num_f_samples': {'values': [1028]}, | ||
| 'added_gp_lengthscale': {'values': [2.]}, | ||
| 'added_gp_outputscale': {'values': [2.0]}, | ||
| 'lr': {'values': [1e-3]}, | ||
| 'likelihood_reg': {'values': [10.0]}, | ||
| }, | ||
|
|
||
| 'BNN_FSVGD_SimPrior_no_add_gp': { | ||
| 'bandwidth_svgd': {'values': [2.0]}, | ||
| 'min_train_steps': {'values': [5_000]}, | ||
| 'num_epochs': {'values': [200]}, | ||
| 'num_measurement_points': {'values': [128]}, | ||
| 'num_f_samples': {'values': [1028]}, | ||
| 'added_gp_lengthscale': {'values': [2.]}, | ||
| 'added_gp_outputscale': {'values': [2.0]}, | ||
| 'lr': {'values': [1e-3]}, | ||
| 'likelihood_reg': {'values': [10.0]}, | ||
| }, | ||
|
|
||
| 'SysID': { | ||
| }, | ||
| 'GreyBox': { | ||
| 'bandwidth_svgd': {'values': [2.0]}, | ||
| 'bandwidth_gp_prior': {'values': [0.4]}, | ||
| 'min_train_steps': {'values': [5_000]}, | ||
| 'num_epochs': {'values': [200]}, | ||
| 'num_measurement_points': {'values': [128]}, | ||
| 'lr': {'values': [1e-3]}, | ||
| 'likelihood_reg': {'values': [10.0]}, | ||
| }, | ||
| } | ||
|
|
||
|
|
||
| def main(args): | ||
| # setup random seeds | ||
| rds = np.random.RandomState(args.seed) | ||
| model_seeds = list(rds.randint(0, 10 ** 6, size=(100,))) | ||
| data_seeds = list(rds.randint(0, 10 ** 6, size=(100,))) | ||
|
|
||
| sweep_config = { | ||
| 'data_source': {'value': args.data_source}, | ||
| # 'num_samples_train': DATASET_CONFIGS[args.data_source]['num_samples_train'], | ||
| 'model': {'value': args.model}, | ||
| 'learn_likelihood_std': {'value': args.learn_likelihood_std}, | ||
| # 'likelihood_std': {'value': None}, | ||
| 'num_particles': {'value': 20}, | ||
| 'data_batch_size': {'value': 8}, | ||
| 'pred_diff': {'value': args.pred_diff}, | ||
| 'max_train_steps': {'value': 300_000}, | ||
| 'num_sim_model_train_steps': {'value': 5_000}, | ||
| } | ||
| # update with model specific sweep ranges | ||
| model_name = args.model.replace('_no_add_gp', '') | ||
| model_name = model_name.replace('_hf', '') | ||
| assert model_name in MODEL_SPECIFIC_CONFIG | ||
| sweep_config.update(MODEL_SPECIFIC_CONFIG[model_name]) | ||
|
|
||
| # determine name of experiment | ||
| exp_base_path = os.path.join(RESULT_DIR, args.exp_name) | ||
| exp_path = os.path.join(exp_base_path, f'{args.data_source}_{args.model}') | ||
|
|
||
| if args.data_source == 'racecar_hf': | ||
| N_SAMPLES_LIST = [50, 100, 200, 400, 800, 1600, 3200, 6400] | ||
| elif args.data_source == 'pendulum_hf': | ||
| N_SAMPLES_LIST = [10, 20, 40, 80, 160, 320, 640, 1280] | ||
| elif args.data_source == 'real_racecar_v3': | ||
| N_SAMPLES_LIST = [50, 100, 200, 400, 800, 1600, 3200, 6400] | ||
| elif args.data_source == 'Sergio_hf': | ||
| N_SAMPLES_LIST = [200, 400, 800, 1600, 3200, 4800, 6400, 12800] | ||
| else: | ||
| raise NotImplementedError(f'Unknown data source {args.data_source}.') | ||
|
|
||
| command_list = [] | ||
| output_file_list = [] | ||
| for _ in range(args.num_hparam_samples): | ||
| flags = sample_param_flags(sweep_config) | ||
| exp_hash = hash_dict(flags) | ||
| for num_samples_train in N_SAMPLES_LIST: | ||
| exp_result_folder = os.path.join(exp_path, f'{exp_hash}_{num_samples_train}') | ||
| flags['exp_result_folder'] = exp_result_folder | ||
|
|
||
| for model_seed, data_seed in itertools.product(model_seeds[:args.num_model_seeds], | ||
| data_seeds[:args.num_data_seeds]): | ||
| cmd = generate_base_command(experiments.lf_hf_transfer_exp.run_regression_exp, | ||
| flags=dict(**flags, **{'model_seed': model_seed, 'data_seed': data_seed, | ||
| 'num_samples_train': num_samples_train, | ||
| })) | ||
| command_list.append(cmd) | ||
| output_file_list.append(os.path.join(exp_result_folder, f'{model_seed}_{data_seed}.out')) | ||
|
|
||
| generate_run_commands(command_list, output_file_list, num_cpus=args.num_cpus, | ||
| num_gpus=1 if args.gpu else 0, mode=args.run_mode, prompt=not args.yes) | ||
|
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||
|
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||
| if __name__ == '__main__': | ||
| current_date = datetime.datetime.now().strftime("%b%d").lower() | ||
| parser = argparse.ArgumentParser(description='Meta-BO run') | ||
|
|
||
| # sweep args | ||
| parser.add_argument('--num_hparam_samples', type=int, default=1) | ||
| parser.add_argument('--num_model_seeds', type=int, default=5, help='number of model seeds per hparam') | ||
| parser.add_argument('--num_data_seeds', type=int, default=5, help='number of model seeds per hparam') | ||
| parser.add_argument('--num_cpus', type=int, default=4, help='number of cpus to use') | ||
| parser.add_argument('--run_mode', type=str, default='euler') | ||
|
|
||
| # general args | ||
| parser.add_argument('--exp_name', type=str, default=f'test_{current_date}') | ||
| parser.add_argument('--seed', type=int, default=94563) | ||
| parser.add_argument('--gpu', default=True, action='store_true') | ||
| parser.add_argument('--yes', default=False, action='store_true') | ||
|
|
||
| # data parameters | ||
| parser.add_argument('--data_source', type=str, default='pendulum_hf') | ||
| parser.add_argument('--pred_diff', type=int, default=0) | ||
|
|
||
| # # standard BNN parameters | ||
| parser.add_argument('--model', type=str, default='BNN_SVGD') | ||
| parser.add_argument('--learn_likelihood_std', type=int, default=0) | ||
|
|
||
| args = parser.parse_args() | ||
| main(args) |
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