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[pull] main from thuml:main #29

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merged 4 commits into from
Sep 30, 2024
Merged

[pull] main from thuml:main #29

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53184bf
clean
wuhaixu2016 Apr 26, 2024
9f0958c
Update print_args.py
wuhaixu2016 Sep 29, 2024
71963ac
Merge branch 'main' of https://github.com/thuml/Time-Series-Library i…
wuhaixu2016 Sep 29, 2024
2fb5b84
remove output attention
wuhaixu2016 Sep 29, 2024
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2 changes: 1 addition & 1 deletion exp/exp_long_term_forecasting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,7 @@ def test(self, setting, test=0):
dtw_list.append(d)
dtw = np.array(dtw_list).mean()
else:
dtw = -999
dtw = 'not calculated'


mae, mse, rmse, mape, mspe = metric(preds, trues)
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6 changes: 3 additions & 3 deletions layers/SelfAttention_Family.py
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Expand Up @@ -254,11 +254,11 @@ def __init__(self, configs,
super(TwoStageAttentionLayer, self).__init__()
d_ff = d_ff or 4 * d_model
self.time_attention = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), d_model, n_heads)
output_attention=False), d_model, n_heads)
self.dim_sender = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), d_model, n_heads)
output_attention=False), d_model, n_heads)
self.dim_receiver = AttentionLayer(FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), d_model, n_heads)
output_attention=False), d_model, n_heads)
self.router = nn.Parameter(torch.randn(seg_num, factor, d_model))

self.dropout = nn.Dropout(dropout)
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3 changes: 1 addition & 2 deletions models/Autoformer.py
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Expand Up @@ -21,7 +21,6 @@ def __init__(self, configs):
self.seq_len = configs.seq_len
self.label_len = configs.label_len
self.pred_len = configs.pred_len
self.output_attention = configs.output_attention

# Decomp
kernel_size = configs.moving_avg
Expand All @@ -36,7 +35,7 @@ def __init__(self, configs):
EncoderLayer(
AutoCorrelationLayer(
AutoCorrelation(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention),
output_attention=False),
configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
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2 changes: 0 additions & 2 deletions models/FiLM.py
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Expand Up @@ -96,14 +96,12 @@ def __init__(self, configs):
super(Model, self).__init__()
self.task_name = configs.task_name
self.configs = configs
# self.modes = configs.modes
self.seq_len = configs.seq_len
self.label_len = configs.label_len
self.pred_len = configs.seq_len if configs.pred_len == 0 else configs.pred_len

self.seq_len_all = self.seq_len + self.label_len

self.output_attention = configs.output_attention
self.layers = configs.e_layers
self.enc_in = configs.enc_in
self.e_layers = configs.e_layers
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2 changes: 1 addition & 1 deletion models/Informer.py
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Expand Up @@ -30,7 +30,7 @@ def __init__(self, configs):
EncoderLayer(
AttentionLayer(
ProbAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention),
output_attention=False),
configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
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3 changes: 1 addition & 2 deletions models/Nonstationary_Transformer.py
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Expand Up @@ -50,7 +50,6 @@ def __init__(self, configs):
self.pred_len = configs.pred_len
self.seq_len = configs.seq_len
self.label_len = configs.label_len
self.output_attention = configs.output_attention

# Embedding
self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
Expand All @@ -62,7 +61,7 @@ def __init__(self, configs):
EncoderLayer(
AttentionLayer(
DSAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), configs.d_model, configs.n_heads),
output_attention=False), configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
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2 changes: 1 addition & 1 deletion models/PatchTST.py
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Expand Up @@ -54,7 +54,7 @@ def __init__(self, configs, patch_len=16, stride=8):
EncoderLayer(
AttentionLayer(
FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), configs.d_model, configs.n_heads),
output_attention=False), configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
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3 changes: 1 addition & 2 deletions models/Transformer.py
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Expand Up @@ -18,7 +18,6 @@ def __init__(self, configs):
super(Model, self).__init__()
self.task_name = configs.task_name
self.pred_len = configs.pred_len
self.output_attention = configs.output_attention
# Embedding
self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
configs.dropout)
Expand All @@ -28,7 +27,7 @@ def __init__(self, configs):
EncoderLayer(
AttentionLayer(
FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), configs.d_model, configs.n_heads),
output_attention=False), configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
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3 changes: 1 addition & 2 deletions models/iTransformer.py
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Expand Up @@ -17,7 +17,6 @@ def __init__(self, configs):
self.task_name = configs.task_name
self.seq_len = configs.seq_len
self.pred_len = configs.pred_len
self.output_attention = configs.output_attention
# Embedding
self.enc_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq,
configs.dropout)
Expand All @@ -27,7 +26,7 @@ def __init__(self, configs):
EncoderLayer(
AttentionLayer(
FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=configs.output_attention), configs.d_model, configs.n_heads),
output_attention=False), configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
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1 change: 0 additions & 1 deletion utils/print_args.py
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Expand Up @@ -37,7 +37,6 @@ def print_args(args):
print(f' {"Moving Avg:":<20}{args.moving_avg:<20}{"Factor:":<20}{args.factor:<20}')
print(f' {"Distil:":<20}{args.distil:<20}{"Dropout:":<20}{args.dropout:<20}')
print(f' {"Embed:":<20}{args.embed:<20}{"Activation:":<20}{args.activation:<20}')
print(f' {"Output Attention:":<20}{args.output_attention:<20}')
print()

print("\033[1m" + "Run Parameters" + "\033[0m")
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