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experiments/meta_learning_exp/sweep_regression_exp_num_data.py
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from experiments.util import (generate_run_commands, generate_base_command, RESULT_DIR, sample_param_flags, hash_dict) | ||
from experiments.data_provider import DATASET_CONFIGS | ||
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import experiments.meta_learning_exp.run_meta_learning_exp | ||
import numpy as np | ||
import datetime | ||
import itertools | ||
import argparse | ||
import os | ||
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MODEL_SPECIFIC_CONFIG = { | ||
'PACOH': { | ||
'prior_weight': {'values': [0.1]}, | ||
'hyper_prior_weight': {'values': [1e-3]}, | ||
'num_iter_meta_train': {'values': [40000]}, | ||
'meta_batch_size': {'values': [4]}, | ||
'bandwidth': {'values': [10.]}, | ||
'lr': {'values': [5e-4]}, | ||
}, | ||
'NP': { | ||
'num_iter_meta_train': {'values': [50000]}, | ||
'latent_dim': {'values': [256]}, | ||
'hidden_dim': {'values': [128]}, | ||
'lr': {'values': [5e-4]}, | ||
}, | ||
} | ||
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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,))) | ||
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sweep_config = { | ||
'learn_likelihood_std': {'value': args.learn_likelihood_std}, | ||
'data_source': {'value': args.data_source}, | ||
'pred_diff': {'value': args.pred_diff}, | ||
'model': {'value': args.model}, | ||
} | ||
# update with model specific sweep ranges | ||
assert args.model in MODEL_SPECIFIC_CONFIG | ||
sweep_config.update(MODEL_SPECIFIC_CONFIG[args.model]) | ||
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# 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}') | ||
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if args.data_source == 'racecar': | ||
N_SAMPLES_LIST = [50, 100, 200, 400, 800, 1600, 3200] | ||
elif args.data_source == 'pendulum': | ||
N_SAMPLES_LIST = [10, 20, 40, 80, 160, 320, 640] | ||
else: | ||
raise NotImplementedError(f'Unknown data source {args.data_source}.') | ||
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command_list = [] | ||
output_file_list = [] | ||
for _ in range(args.num_hparam_samples): | ||
flags = sample_param_flags(sweep_config) | ||
exp_hash = hash_dict(flags) | ||
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for num_samples_train in N_SAMPLES_LIST: | ||
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exp_result_folder = os.path.join(exp_path, f'{exp_hash}_{num_samples_train}') | ||
flags['exp_result_folder'] = exp_result_folder | ||
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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.meta_learning_exp.run_meta_learning_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')) | ||
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generate_run_commands(command_list, output_file_list, num_cpus=args.num_cpus, mem=8*1024, | ||
duration='11:59:00' if args.long else '3:59:00', | ||
num_gpus=1 if args.gpu else 0, mode=args.run_mode, prompt=not args.yes) | ||
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if __name__ == '__main__': | ||
current_date = datetime.datetime.now().strftime("%b%d").lower() | ||
parser = argparse.ArgumentParser(description='Meta-BO run') | ||
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# 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=1, help='number of cpus to use') | ||
parser.add_argument('--run_mode', type=str, default='euler') | ||
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# 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=False, action='store_true') | ||
parser.add_argument('--yes', default=False, action='store_true') | ||
parser.add_argument('--long', default=False, action='store_true') | ||
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# data parameters | ||
parser.add_argument('--data_source', type=str, default='racecar') | ||
parser.add_argument('--pred_diff', type=int, default=1) | ||
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# # standard BNN parameters | ||
parser.add_argument('--model', type=str, default='BNN_SVGD') | ||
parser.add_argument('--learn_likelihood_std', type=int, default=0) | ||
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args = parser.parse_args() | ||
main(args) |