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hub / github.com/BorealisAI/scaleformer / main

Function main

run.py:18–221  ·  view source on GitHub ↗
()

Source from the content-addressed store, hash-verified

16import numpy as np
17
18def main():
19 fix_seed = 2021
20 random.seed(fix_seed)
21 torch.manual_seed(fix_seed)
22 np.random.seed(fix_seed)
23
24 parser = argparse.ArgumentParser(description='Autoformer & Transformer family for Time Series Forecasting')
25
26 # basic config
27 parser.add_argument('--is_training', type=int, default=1, help='status')
28 parser.add_argument('--use_multi_scale', action='store_true', help='using mult-scale')
29 parser.add_argument('--prob_forecasting', action='store_true', help='using probabilistic forecasting')
30 parser.add_argument('--scales', default=[16, 8, 4, 2, 1], help='scales in mult-scale')
31 parser.add_argument('--scale_factor', type=int, default=2, help='scale factor for upsample')
32 parser.add_argument('--model', type=str, required=True, default='Autoformer',
33 help='model name, options: [Autoformer, Informer, Transformer, Reformer, FEDformer] and their MS versions: [AutoformerMS, InformerMS, etc]')
34
35 # data loader
36 parser.add_argument('--data', type=str, default='custom', help='dataset type')
37 parser.add_argument('--root_path', type=str, default='./data/ETT/', help='root path of the data file')
38 parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
39 parser.add_argument('--features', type=str, default='M',
40 help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
41 parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
42 parser.add_argument('--freq', type=str, default='h',
43 help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
44 parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
45
46 # forecasting task
47 parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
48 parser.add_argument('--label_len', type=int, default=48, help='start token length')
49 parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
50
51 # supplementary config for FiLM model
52 parser.add_argument('--modes1', type=int, default=64, help='modes to be selected random 64')
53 parser.add_argument('--mode_type',type=int,default=0)
54
55 # supplementary config for FEDformer model
56 parser.add_argument('--version', type=str, default='Wavelets',
57 help='for FEDformer, there are two versions to choose, options: [Fourier, Wavelets]')
58 parser.add_argument('--mode_select', type=str, default='low',
59 help='for FEDformer, there are two mode selection method, options: [random, low]')
60 parser.add_argument('--modes', type=int, default=64, help='modes to be selected random 64')
61 parser.add_argument('--L', type=int, default=3, help='ignore level')
62 parser.add_argument('--base', type=str, default='legendre', help='mwt base')
63 parser.add_argument('--cross_activation', type=str, default='tanh',
64 help='mwt cross atention activation function tanh or softmax')
65
66 # supplementary config for Reformer model
67 parser.add_argument('--bucket_size', type=int, default=4, help='for Reformer')
68 parser.add_argument('--n_hashes', type=int, default=4, help='for Reformer')
69 parser.add_argument('--film_ours', default=True, action='store_true')
70 parser.add_argument('--ab', type=int, default=2, help='ablation version')
71 parser.add_argument('--ratio', type=float, default=0.5, help='dropout')
72 parser.add_argument('--film_version', type=int, default=0, help='compression')
73
74 # model define
75 parser.add_argument('--enc_in', type=int, default=7, help='encoder input size')

Callers 1

run.pyFile · 0.85

Calls 3

predictMethod · 0.80
trainMethod · 0.45
testMethod · 0.45

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