text_html_output_1.png application_vnd.jupyter.stderr_output_1.png
from utils.tools import dotdict

import torch

args = dotdict()
args.model = 'informer'
args.data = 'custom'
args.root_path = '/kaggle/working/'
args.data_path = 'converted.csv'
args.features = 'S'
args.target = 'sales'
args.freq = 't'
args.checkpoints = './informer_checkpoints'
args.seq_len = 96
args.label_len = 48
args.pred_len = 24
args.enc_in = 7
args.dec_in = 7
args.c_out = 7
args.factor = 5
args.d_model = 512
args.n_heads = 8
args.e_layers = 3
args.d_layers = 2
args.d_ff = 2048
args.dropout = 0.05
args.attn = 'prob'
args.embed = 'timeF'
args.activation = 'gelu'
args.distil = True
args.output_attention = False
args.mix = True
args.padding = 0
args.freq = 't'
args.batch_size = 32
args.learning_rate = 0.0001
args.loss = 'mse'
args.lradj = 'type1'
args.use_amp = False
args.num_workers = 0
args.itr = 1
args.train_epochs = 6
args.patience = 3
args.des = 'exp'
args.use_gpu = True if torch.cuda.is_available() else False
args.gpu = 0
args.use_multi_gpu = False
args.devices = '0,1,2,3'

args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False
if args.use_gpu and args.use_multi_gpu:
    args.devices = args.devices.replace(' ', '')
    device_ids = args.devices.split(',')
    args.device_ids = [int(id_) for id_ in device_ids]
    args.gpu = args.device_ids[0]

data_parser = {'custom': {'data': 'converted.csv', 'T': 'sales', 'M': [1, 1, 1], 'S': [1, 1, 1], 'MS': [1, 1, 1]}}
if args.data in data_parser.keys():
    data_info = data_parser[args.data]
    args.data_path = data_info['data']
    args.target = data_info['T']
    args.enc_in, args.dec_in, args.c_out = data_info[args.features]

args.detail_freq = args.freq
args.freq = args.freq[-1:]

print('Args in experiment:')
print(args)