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IRNet_multivariate.py
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import os
import pickle
import time
import argparse
import pandas as pd
from hyperopt import fmin, tpe, hp, Trials, STATUS_OK
from src.losses.numpy import mae, mse
from src.experiments.utils import hyperopt_tunning
def get_experiment_space(args):
space= {# Architecture parameters
'model':'irnet',
'mode': 'simple',
'n_time_in': hp.choice('n_time_in', [args.horizon_input]),
'n_time_out': hp.choice('n_time_out', [args.horizon]),
'n_x_hidden': hp.choice('n_x_hidden', [0]),
'n_s_hidden': hp.choice('n_s_hidden', [0]),
'shared_weights': hp.choice('shared_weights', [False]),
'activation': hp.choice('activation', ['ReLU']),
'initialization': hp.choice('initialization', ['lecun_normal']),
'stack_types': hp.choice('stack_types', [ 3*['identity'] ]),
'n_blocks': hp.choice('n_blocks', [ 3*[1]]),
'n_layers': hp.choice('n_layers', [ 9*[2] ]),
'n_hidden': hp.choice('n_hidden', [ 512 ]),
#'inner_size':hp.choice('n_hidden', [64,128,256,512]),
'n_pool_kernel_size': hp.choice('n_pool_kernel_size', [ 3*[1], 3*[2], 3*[4], 3*[8], [8, 4, 1], [16, 8, 1] ]),
'n_freq_downsample': hp.choice('n_freq_downsample', [ [168, 24, 1], [24, 12, 1],
[180, 60, 1], [60, 8, 1],
[40, 20, 1]
]),
'pooling_mode': hp.choice('pooling_mode', [ 'max' ]),
'pooling_mode': hp.choice('pooling_mode', [ 'FNO2' ]),
'interpolation_mode': hp.choice('interpolation_mode', ['linear']),
# Regularization and optimization parameters
'batch_normalization': hp.choice('batch_normalization', [False]),
'dropout_prob_theta': hp.choice('dropout_prob_theta', [ 0 ]),
'dropout_prob_exogenous': hp.choice('dropout_prob_exogenous', [0]),
'learning_rate': hp.choice('learning_rate', [0.001]),
'lr_decay': hp.choice('lr_decay', [0.5] ),
'n_lr_decays': hp.choice('n_lr_decays', [3]),
'weight_decay': hp.choice('weight_decay', [0] ),
'max_epochs': hp.choice('max_epochs', [None]),
'max_steps': hp.choice('max_steps', [1_000]),
'early_stop_patience': hp.choice('early_stop_patience', [10]),
'eval_freq': hp.choice('eval_freq', [50]),
'loss_train': hp.choice('loss', ['MAE']),
'loss_hypar': hp.choice('loss_hypar', [0.5]),
'loss_valid': hp.choice('loss_valid', ['MAE']),
'l1_theta': hp.choice('l1_theta', [0]),
# Data parameters
'normalizer_y': hp.choice('normalizer_y', [None]),
'normalizer_x': hp.choice('normalizer_x', [None]),
'complete_windows': hp.choice('complete_windows', [True]),
'frequency': hp.choice('frequency', ['H']),
'seasonality': hp.choice('seasonality', [24]),
'idx_to_sample_freq': hp.choice('idx_to_sample_freq', [1]),
'val_idx_to_sample_freq': hp.choice('val_idx_to_sample_freq', [1]),
'batch_size': hp.choice('batch_size', [2]),
'n_windows': hp.choice('n_windows', [256]),
'random_seed': hp.quniform('random_seed', 1, 10, 1)}
return space
def main(args):
#----------------------------------------------- Load Data -----------------------------------------------#
Y_df = pd.read_csv(f'./data/{args.dataset}/M/df_y.csv')
#Y_df = pd.read_csv(f'./data/{args.dataset}/S/df_y.csv')
X_df = None
S_df = None
print('Y_df: ', Y_df.head())
if args.dataset == 'ETTm2':
len_val = 5760
len_test = 11520
if args.dataset == 'ETTm1':
len_val = 5760
len_test = 11520
if args.dataset == 'ETTh1':
len_val = 1440
len_test = 2880
if args.dataset == 'ETTh2':
len_val = 1440
len_test = 2880
if args.dataset == 'Exchange':
len_val = 760
len_test = 1517
if args.dataset == 'ECL':
len_val = 2632
len_test = 5260
if args.dataset == 'traffic':
len_val = 1756
len_test = 3508
if args.dataset == 'weather':
len_val = 5270
len_test = 10539
if args.dataset == 'ili':
len_val = 97
len_test = 193
space = get_experiment_space(args)
#---------------------------------------------- Directories ----------------------------------------------#
output_dir = f'./results/multivariate/{args.dataset}_{args.horizon}/treeDRNet/'
#output_dir = f'./results/univariate/{args.dataset}_{args.horizon}/treeDRNet/'
os.makedirs(output_dir, exist_ok = True)
assert os.path.exists(output_dir), f'Output dir {output_dir} does not exist'
hyperopt_file = output_dir + f'hyperopt_{args.experiment_id}.p'
if not os.path.isfile(hyperopt_file):
print('Hyperparameter optimization')
#----------------------------------------------- Hyperopt -----------------------------------------------#
trials = hyperopt_tunning(space=space, hyperopt_max_evals=args.hyperopt_max_evals, loss_function_val=mae,
loss_functions_test={'mae':mae, 'mse': mse},
Y_df=Y_df, X_df=X_df, S_df=S_df, f_cols=[],
ds_in_val=len_val, ds_in_test=len_test,
return_forecasts=False,
results_file = hyperopt_file,
save_progress=True,
loss_kwargs={})
with open(hyperopt_file, "wb") as f:
pickle.dump(trials, f)
else:
print('Hyperparameter optimization already done!')
def parse_args():
desc = "Example of hyperparameter tuning"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--hyperopt_max_evals', type=int, help='hyperopt_max_evals')
parser.add_argument('--experiment_id', default=None, required=False, type=str, help='string to identify experiment')
return parser.parse_args()
if __name__ == '__main__':
# parse arguments
args = parse_args()
if args is None:
exit()
# horizons = [96, 192, 336, 720]
# horizons_input =[96,96,96*2,96*2]
# ILI_horizons = [24, 36, 48, 60]
# ILI_horizons_input = [24, 24, 24, 24]
# Exchange_horizons_input = [96,96,96,96]
horizons = [720]
horizons_input =[720]
#datasets = ['ETTm2', 'ECL', 'Exchange', 'traffic', 'weather', 'ili']
#datasets = ['ETTm2', 'ETTm1','ECL','Exchange', 'ETTh2', 'ETTh1']
#datasets = ['traffic', 'weather', 'ili']
datasets=['ETTm2']
for dataset in datasets:
# Horizon
if dataset == 'ili':
horizons_dataset = ILI_horizons
horizons_input = ILI_horizons_input
elif dataset == 'Exchange':
horizons_input = Exchange_horizons_input
else:
horizons_dataset = horizons
for horizon,horizon_input in zip(horizons_dataset,horizons_input):
print(50*'-', dataset, 50*'-')
print(50*'-', horizon, 50*'-')
start = time.time()
args.dataset = dataset
args.horizon = horizon
args.horizon_input=horizon_input
main(args)
print('Time: ', time.time() - start)
main(args)
# source ~/anaconda3/etc/profile.d/conda.sh
# conda activate nixtla
# CUDA_VISIBLE_DEVICES=0 python nhits_multivariate.py --hyperopt_max_evals 10 --experiment_id "test"