Shourya Bose
commited on
Commit
β’
4cc7625
1
Parent(s):
881bc20
updated models for L=512
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See raw diff
- README.md +4 -5
- model_kwargs.py +11 -0
- models/Autoformer.py +3 -0
- models/Informer.py +3 -0
- models/LSTM.py +2 -0
- models/LSTNet.py +2 -1
- models/PatchTST.py +382 -0
- models/TimesNet.py +3 -0
- models/Transformer.py +3 -0
- weights/{autoformer_L_96_T_4_HET.pth β Autoformer_L_512_T_48_HET.pth} +2 -2
- weights/{autoformer_L_96_T_4_HOM.pth β Autoformer_L_512_T_48_HOM.pth} +2 -2
- weights/{autoformer_L_96_T_48_HET.pth β Autoformer_L_512_T_4_HET.pth} +1 -1
- weights/{autoformer_L_96_T_48_HOM.pth β Autoformer_L_512_T_4_HOM.pth} +1 -1
- weights/Autoformer_L_512_T_96_HET.pth +3 -0
- weights/Autoformer_L_512_T_96_HOM.pth +3 -0
- weights/Informer_L_512_T_48_HET.pth +3 -0
- weights/Informer_L_512_T_48_HOM.pth +3 -0
- weights/{informer_L_96_T_48_HET.pth β Informer_L_512_T_4_HET.pth} +1 -1
- weights/{informer_L_96_T_48_HOM.pth β Informer_L_512_T_4_HOM.pth} +1 -1
- weights/Informer_L_512_T_96_HET.pth +3 -0
- weights/Informer_L_512_T_96_HOM.pth +3 -0
- weights/{lstm_L_96_T_48_HET.pth β LSTM_L_512_T_48_HET.pth} +2 -2
- weights/{lstm_L_96_T_48_HOM.pth β LSTM_L_512_T_48_HOM.pth} +2 -2
- weights/{lstm_L_96_T_4_HET.pth β LSTM_L_512_T_4_HET.pth} +2 -2
- weights/{lstm_L_96_T_4_HOM.pth β LSTM_L_512_T_4_HOM.pth} +2 -2
- weights/LSTM_L_512_T_96_HET.pth +3 -0
- weights/LSTM_L_512_T_96_HOM.pth +3 -0
- weights/LSTNet_L_512_T_48_HET.pth +3 -0
- weights/LSTNet_L_512_T_48_HOM.pth +3 -0
- weights/LSTNet_L_512_T_4_HET.pth +3 -0
- weights/LSTNet_L_512_T_4_HOM.pth +3 -0
- weights/LSTNet_L_512_T_96_HET.pth +3 -0
- weights/LSTNet_L_512_T_96_HOM.pth +3 -0
- weights/PatchTST_L_512_T_48_HET.pth +3 -0
- weights/PatchTST_L_512_T_48_HOM.pth +3 -0
- weights/PatchTST_L_512_T_4_HET.pth +3 -0
- weights/PatchTST_L_512_T_4_HOM.pth +3 -0
- weights/PatchTST_L_512_T_96_HET.pth +3 -0
- weights/PatchTST_L_512_T_96_HOM.pth +3 -0
- weights/TimesNet_L_512_T_48_HET.pth +3 -0
- weights/TimesNet_L_512_T_48_HOM.pth +3 -0
- weights/TimesNet_L_512_T_4_HET.pth +3 -0
- weights/TimesNet_L_512_T_4_HOM.pth +3 -0
- weights/TimesNet_L_512_T_96_HET.pth +3 -0
- weights/TimesNet_L_512_T_96_HOM.pth +3 -0
- weights/Transformer_L_512_T_48_HET.pth +3 -0
- weights/Transformer_L_512_T_48_HOM.pth +3 -0
- weights/{transformer_L_96_T_48_HET.pth β Transformer_L_512_T_4_HET.pth} +1 -1
- weights/{transformer_L_96_T_48_HOM.pth β Transformer_L_512_T_4_HOM.pth} +1 -1
- weights/Transformer_L_512_T_96_HET.pth +3 -0
README.md
CHANGED
@@ -1,20 +1,19 @@
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---
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license: cc
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---
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-
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## About
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This repository provides model weights to run load forecasting models trained on ComStock datasets. The companion dataset repository is [this](https://huggingface.co/datasets/APPFL/Illinois_load_datasets). The model definitions are present in the `models` directory. The corresponding trained model weights are present in the `weights` directory. The corresponding model keyword arguments (as a function of a provided `lookback` and `lookahead`) can be imported from the file `model_kwargs.py`.
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Note that `lookback` is denoted by `L` and `lookahead` by `T` in the weights directory. We provide weights for the following `(L,T)` pairs: `(
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## Data
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When using the companion [dataset](https://huggingface.co/datasets/APPFL/Illinois_load_datasets), the following points must be noted (see the
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- All models accept normalized inputs and produce normalized outputs, i.e. set `normalize = True` when generating the datasets.
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- For Transformer, Autoformer, Informer, and TimesNet set `transformer = True`, while for LSTM
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## Credits
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Some model definitions have been adapted from the code provided in the [TSLib Library](https://github.com/thuml/Time-Series-Library).
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---
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license: cc
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---
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## About
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This repository provides model weights to run load forecasting models trained on ComStock datasets. The companion dataset repository is [this](https://huggingface.co/datasets/APPFL/Illinois_load_datasets). The model definitions are present in the `models` directory. The corresponding trained model weights are present in the `weights` directory. The corresponding model keyword arguments (as a function of a provided `lookback` and `lookahead`) can be imported from the file `model_kwargs.py`.
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Note that `lookback` is denoted by `L` and `lookahead` by `T` in the weights directory. We provide weights for the following `(L,T)` pairs: `(512,4)`, `(512,48)`, and `(512,96)`, and for `HOM`ogenous and `HET`erogenous datasets.
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## Data
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When using the companion [dataset](https://huggingface.co/datasets/APPFL/Illinois_load_datasets), the following points must be noted (see the page for more information on configuring the data loaders):
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- All models accept normalized inputs and produce normalized outputs, i.e. set `normalize = True` when generating the datasets.
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- For Transformer, Autoformer, Informer, and TimesNet set `transformer = True`, while for LSTM, LSTNet, and PatchTST set `transformer = False`.
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## Credits
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Some model definitions have been adapted from the code provided in the [TSLib Library](https://github.com/thuml/Time-Series-Library).
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model_kwargs.py
CHANGED
@@ -52,4 +52,15 @@ lstnet_kwargs = lambda lookback,lookahead:{
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'conv1_out_channels':8*4,
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'conv1_kernel_height':3*4,
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'recc1_out_channels':32*4
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}
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'conv1_out_channels':8*4,
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'conv1_kernel_height':3*4,
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'recc1_out_channels':32*4
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}
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patchtst_kwargs = lambda lookback,lookahead:{
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'enc_in': 6,
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'dec_in': 2,
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'c_out': 1,
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'pred_len': lookahead,
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'seq_len': lookback,
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'd_model': 32*4,
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'data_idx': [0,3,4,5,6,7],
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'time_idx': [1,2]
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}
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models/Autoformer.py
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@@ -4,6 +4,9 @@ import torch.nn.functional as F
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import math
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import numpy as np
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class AutoCorrelation(nn.Module):
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"""
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AutoCorrelation Mechanism with the following two phases:
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import math
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import numpy as np
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# Modified from: https://github.com/thuml/Time-Series-Library
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# Modified by Shourya Bose, shbose@ucsc.edu
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class AutoCorrelation(nn.Module):
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"""
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AutoCorrelation Mechanism with the following two phases:
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models/Informer.py
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@@ -5,6 +5,9 @@ from math import sqrt
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import numpy as np
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import torch.nn.functional as F
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class ConvLayer(nn.Module):
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def __init__(self, c_in):
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super(ConvLayer, self).__init__()
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import numpy as np
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import torch.nn.functional as F
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# Modified from: https://github.com/thuml/Time-Series-Library
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# Modified by Shourya Bose, shbose@ucsc.edu
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class ConvLayer(nn.Module):
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def __init__(self, c_in):
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super(ConvLayer, self).__init__()
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models/LSTM.py
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@@ -2,6 +2,8 @@ import torch
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import torch.nn as nn
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from typing import Union, List, Tuple
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class LSTM(nn.Module):
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def __init__(
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import torch.nn as nn
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from typing import Union, List, Tuple
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# Written by Shourya Bose, shbose@ucsc.edu
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class LSTM(nn.Module):
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def __init__(
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models/LSTNet.py
CHANGED
@@ -3,7 +3,8 @@ import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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# https://github.com/gokulkarthik/LSTNet.pytorch/blob/master/LSTNet.py
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class LSTNet(nn.Module):
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import torch.nn.functional as F
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import numpy as np
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# Modified from https://github.com/gokulkarthik/LSTNet.pytorch/blob/master/LSTNet.py
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# Modified by Shourya Bose, shbose@ucsc.edu
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class LSTNet(nn.Module):
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models/PatchTST.py
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+
import torch
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+
from torch import nn
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+
import torch.nn.functional as F
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4 |
+
import math
|
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+
from math import sqrt
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+
import numpy as np
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+
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+
# Modified from: https://github.com/thuml/Time-Series-Library
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+
# Modified by Shourya Bose, shbose@ucsc.edu
|
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+
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+
class PositionalEmbedding(nn.Module):
|
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+
def __init__(self, d_model, max_len=5000):
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+
super(PositionalEmbedding, self).__init__()
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+
# Compute the positional encodings once in log space.
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+
pe = torch.zeros(max_len, d_model).float()
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pe.require_grad = False
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+
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position = torch.arange(0, max_len).float().unsqueeze(1)
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div_term = (torch.arange(0, d_model, 2).float()
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+
* -(math.log(10000.0) / d_model)).exp()
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+
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.register_buffer('pe', pe)
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+
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def forward(self, x):
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return self.pe[:, :x.size(1)]
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+
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class PatchEmbedding(nn.Module):
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def __init__(self, d_model, patch_len, stride, padding, dropout):
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super(PatchEmbedding, self).__init__()
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# Patching
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self.patch_len = patch_len
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self.stride = stride
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self.padding_patch_layer = nn.ReplicationPad1d((0, padding))
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# Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
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self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
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# Positional embedding
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self.position_embedding = PositionalEmbedding(d_model)
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+
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# Residual dropout
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self.dropout = nn.Dropout(dropout)
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+
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def forward(self, x):
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# do patching
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n_vars = x.shape[1]
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x = self.padding_patch_layer(x)
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x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
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x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
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# Input encoding
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x = self.value_embedding(x) + self.position_embedding(x)
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return self.dropout(x), n_vars
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+
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+
class AttentionLayer(nn.Module):
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+
def __init__(self, attention, d_model, n_heads, d_keys=None,
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d_values=None):
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+
super(AttentionLayer, self).__init__()
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+
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d_keys = d_keys or (d_model // n_heads)
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d_values = d_values or (d_model // n_heads)
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+
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self.inner_attention = attention
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+
self.query_projection = nn.Linear(d_model, d_keys * n_heads)
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self.key_projection = nn.Linear(d_model, d_keys * n_heads)
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self.value_projection = nn.Linear(d_model, d_values * n_heads)
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self.out_projection = nn.Linear(d_values * n_heads, d_model)
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self.n_heads = n_heads
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+
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+
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
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+
B, L, _ = queries.shape
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+
_, S, _ = keys.shape
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+
H = self.n_heads
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+
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+
queries = self.query_projection(queries).view(B, L, H, -1)
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keys = self.key_projection(keys).view(B, S, H, -1)
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80 |
+
values = self.value_projection(values).view(B, S, H, -1)
|
81 |
+
|
82 |
+
out, attn = self.inner_attention(
|
83 |
+
queries,
|
84 |
+
keys,
|
85 |
+
values,
|
86 |
+
attn_mask,
|
87 |
+
tau=tau,
|
88 |
+
delta=delta
|
89 |
+
)
|
90 |
+
out = out.view(B, L, -1)
|
91 |
+
|
92 |
+
return self.out_projection(out), attn
|
93 |
+
|
94 |
+
class FullAttention(nn.Module):
|
95 |
+
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
|
96 |
+
super(FullAttention, self).__init__()
|
97 |
+
self.scale = scale
|
98 |
+
self.mask_flag = mask_flag
|
99 |
+
self.output_attention = output_attention
|
100 |
+
self.dropout = nn.Dropout(attention_dropout)
|
101 |
+
|
102 |
+
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
|
103 |
+
B, L, H, E = queries.shape
|
104 |
+
_, S, _, D = values.shape
|
105 |
+
scale = self.scale or 1. / sqrt(E)
|
106 |
+
|
107 |
+
scores = torch.einsum("blhe,bshe->bhls", queries, keys)
|
108 |
+
|
109 |
+
if self.mask_flag:
|
110 |
+
if attn_mask is None:
|
111 |
+
attn_mask = TriangularCausalMask(B, L, device=queries.device)
|
112 |
+
|
113 |
+
scores.masked_fill_(attn_mask.mask, -np.inf)
|
114 |
+
|
115 |
+
A = self.dropout(torch.softmax(scale * scores, dim=-1))
|
116 |
+
V = torch.einsum("bhls,bshd->blhd", A, values)
|
117 |
+
|
118 |
+
if self.output_attention:
|
119 |
+
return V.contiguous(), A
|
120 |
+
else:
|
121 |
+
return V.contiguous(), None
|
122 |
+
|
123 |
+
class TriangularCausalMask():
|
124 |
+
def __init__(self, B, L, device="cpu"):
|
125 |
+
mask_shape = [B, 1, L, L]
|
126 |
+
with torch.no_grad():
|
127 |
+
self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device)
|
128 |
+
|
129 |
+
@property
|
130 |
+
def mask(self):
|
131 |
+
return self._mask
|
132 |
+
|
133 |
+
class FullAttention(nn.Module):
|
134 |
+
def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False):
|
135 |
+
super(FullAttention, self).__init__()
|
136 |
+
self.scale = scale
|
137 |
+
self.mask_flag = mask_flag
|
138 |
+
self.output_attention = output_attention
|
139 |
+
self.dropout = nn.Dropout(attention_dropout)
|
140 |
+
|
141 |
+
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
|
142 |
+
B, L, H, E = queries.shape
|
143 |
+
_, S, _, D = values.shape
|
144 |
+
scale = self.scale or 1. / sqrt(E)
|
145 |
+
|
146 |
+
scores = torch.einsum("blhe,bshe->bhls", queries, keys)
|
147 |
+
|
148 |
+
if self.mask_flag:
|
149 |
+
if attn_mask is None:
|
150 |
+
attn_mask = TriangularCausalMask(B, L, device=queries.device)
|
151 |
+
|
152 |
+
scores.masked_fill_(attn_mask.mask, -np.inf)
|
153 |
+
|
154 |
+
A = self.dropout(torch.softmax(scale * scores, dim=-1))
|
155 |
+
V = torch.einsum("bhls,bshd->blhd", A, values)
|
156 |
+
|
157 |
+
if self.output_attention:
|
158 |
+
return V.contiguous(), A
|
159 |
+
else:
|
160 |
+
return V.contiguous(), None
|
161 |
+
|
162 |
+
class AttentionLayer(nn.Module):
|
163 |
+
def __init__(self, attention, d_model, n_heads, d_keys=None,
|
164 |
+
d_values=None):
|
165 |
+
super(AttentionLayer, self).__init__()
|
166 |
+
|
167 |
+
d_keys = d_keys or (d_model // n_heads)
|
168 |
+
d_values = d_values or (d_model // n_heads)
|
169 |
+
|
170 |
+
self.inner_attention = attention
|
171 |
+
self.query_projection = nn.Linear(d_model, d_keys * n_heads)
|
172 |
+
self.key_projection = nn.Linear(d_model, d_keys * n_heads)
|
173 |
+
self.value_projection = nn.Linear(d_model, d_values * n_heads)
|
174 |
+
self.out_projection = nn.Linear(d_values * n_heads, d_model)
|
175 |
+
self.n_heads = n_heads
|
176 |
+
|
177 |
+
def forward(self, queries, keys, values, attn_mask, tau=None, delta=None):
|
178 |
+
B, L, _ = queries.shape
|
179 |
+
_, S, _ = keys.shape
|
180 |
+
H = self.n_heads
|
181 |
+
|
182 |
+
queries = self.query_projection(queries).view(B, L, H, -1)
|
183 |
+
keys = self.key_projection(keys).view(B, S, H, -1)
|
184 |
+
values = self.value_projection(values).view(B, S, H, -1)
|
185 |
+
|
186 |
+
out, attn = self.inner_attention(
|
187 |
+
queries,
|
188 |
+
keys,
|
189 |
+
values,
|
190 |
+
attn_mask,
|
191 |
+
tau=tau,
|
192 |
+
delta=delta
|
193 |
+
)
|
194 |
+
out = out.view(B, L, -1)
|
195 |
+
|
196 |
+
return self.out_projection(out), attn
|
197 |
+
|
198 |
+
class EncoderLayer(nn.Module):
|
199 |
+
def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"):
|
200 |
+
super(EncoderLayer, self).__init__()
|
201 |
+
d_ff = d_ff or 4 * d_model
|
202 |
+
self.attention = attention
|
203 |
+
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
|
204 |
+
self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
|
205 |
+
self.norm1 = nn.LayerNorm(d_model)
|
206 |
+
self.norm2 = nn.LayerNorm(d_model)
|
207 |
+
self.dropout = nn.Dropout(dropout)
|
208 |
+
self.activation = F.relu if activation == "relu" else F.gelu
|
209 |
+
|
210 |
+
def forward(self, x, attn_mask=None, tau=None, delta=None):
|
211 |
+
new_x, attn = self.attention(
|
212 |
+
x, x, x,
|
213 |
+
attn_mask=attn_mask,
|
214 |
+
tau=tau, delta=delta
|
215 |
+
)
|
216 |
+
x = x + self.dropout(new_x)
|
217 |
+
|
218 |
+
y = x = self.norm1(x)
|
219 |
+
y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
|
220 |
+
y = self.dropout(self.conv2(y).transpose(-1, 1))
|
221 |
+
|
222 |
+
return self.norm2(x + y), attn
|
223 |
+
|
224 |
+
|
225 |
+
class Encoder(nn.Module):
|
226 |
+
def __init__(self, attn_layers, conv_layers=None, norm_layer=None):
|
227 |
+
super(Encoder, self).__init__()
|
228 |
+
self.attn_layers = nn.ModuleList(attn_layers)
|
229 |
+
self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None
|
230 |
+
self.norm = norm_layer
|
231 |
+
|
232 |
+
def forward(self, x, attn_mask=None, tau=None, delta=None):
|
233 |
+
# x [B, L, D]
|
234 |
+
attns = []
|
235 |
+
if self.conv_layers is not None:
|
236 |
+
for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)):
|
237 |
+
delta = delta if i == 0 else None
|
238 |
+
x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
|
239 |
+
x = conv_layer(x)
|
240 |
+
attns.append(attn)
|
241 |
+
x, attn = self.attn_layers[-1](x, tau=tau, delta=None)
|
242 |
+
attns.append(attn)
|
243 |
+
else:
|
244 |
+
for attn_layer in self.attn_layers:
|
245 |
+
x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta)
|
246 |
+
attns.append(attn)
|
247 |
+
|
248 |
+
if self.norm is not None:
|
249 |
+
x = self.norm(x)
|
250 |
+
|
251 |
+
return x, attns
|
252 |
+
|
253 |
+
class Transpose(nn.Module):
|
254 |
+
def __init__(self, *dims, contiguous=False):
|
255 |
+
super().__init__()
|
256 |
+
self.dims, self.contiguous = dims, contiguous
|
257 |
+
def forward(self, x):
|
258 |
+
if self.contiguous: return x.transpose(*self.dims).contiguous()
|
259 |
+
else: return x.transpose(*self.dims)
|
260 |
+
|
261 |
+
|
262 |
+
class FlattenHead(nn.Module):
|
263 |
+
def __init__(self, n_vars, nf, target_window, head_dropout=0):
|
264 |
+
super().__init__()
|
265 |
+
self.n_vars = n_vars
|
266 |
+
self.flatten = nn.Flatten(start_dim=-2)
|
267 |
+
self.linear = nn.Linear(nf, target_window)
|
268 |
+
self.dropout = nn.Dropout(head_dropout)
|
269 |
+
|
270 |
+
def forward(self, x): # x: [bs x nvars x d_model x patch_num]
|
271 |
+
x = self.flatten(x)
|
272 |
+
x = self.linear(x)
|
273 |
+
x = self.dropout(x)
|
274 |
+
return x
|
275 |
+
|
276 |
+
|
277 |
+
class PatchTST(nn.Module):
|
278 |
+
"""
|
279 |
+
Paper link: https://arxiv.org/pdf/2211.14730.pdf
|
280 |
+
"""
|
281 |
+
|
282 |
+
def __init__(
|
283 |
+
self,
|
284 |
+
enc_in,
|
285 |
+
dec_in, # unused
|
286 |
+
c_out, # unused
|
287 |
+
pred_len,
|
288 |
+
seq_len,
|
289 |
+
d_model = 64,
|
290 |
+
patch_len = 16,
|
291 |
+
stride = 8,
|
292 |
+
data_idx = [0,3,4,5,6,7],
|
293 |
+
time_idx = [1,2],
|
294 |
+
output_attention = False,
|
295 |
+
factor = 3,
|
296 |
+
n_heads = 4,
|
297 |
+
d_ff = 512,
|
298 |
+
e_layers = 3,
|
299 |
+
activation = 'gelu',
|
300 |
+
dropout = 0.1
|
301 |
+
):
|
302 |
+
|
303 |
+
#(self, configs, patch_len=16, stride=8):
|
304 |
+
"""
|
305 |
+
patch_len: int, patch len for patch_embedding
|
306 |
+
stride: int, stride for patch_embedding
|
307 |
+
"""
|
308 |
+
super().__init__()
|
309 |
+
self.seq_len = seq_len
|
310 |
+
self.pred_len = pred_len
|
311 |
+
self.data_idx = data_idx
|
312 |
+
self.time_idx = time_idx
|
313 |
+
self.dec_in = dec_in
|
314 |
+
padding = stride
|
315 |
+
|
316 |
+
# patching and embedding
|
317 |
+
self.patch_embedding = PatchEmbedding(
|
318 |
+
d_model, patch_len, stride, padding, dropout)
|
319 |
+
|
320 |
+
# Encoder
|
321 |
+
self.encoder = Encoder(
|
322 |
+
[
|
323 |
+
EncoderLayer(
|
324 |
+
AttentionLayer(
|
325 |
+
FullAttention(False, factor, attention_dropout=dropout,
|
326 |
+
output_attention=output_attention), d_model, n_heads),
|
327 |
+
d_model,
|
328 |
+
d_ff,
|
329 |
+
dropout=dropout,
|
330 |
+
activation=activation
|
331 |
+
) for l in range(e_layers)
|
332 |
+
],
|
333 |
+
norm_layer=nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
|
334 |
+
)
|
335 |
+
|
336 |
+
# Prediction Head
|
337 |
+
self.head_nf = d_model * \
|
338 |
+
int((seq_len - patch_len) / stride + 2)
|
339 |
+
self.head = FlattenHead(enc_in, self.head_nf,pred_len,
|
340 |
+
head_dropout=dropout)
|
341 |
+
|
342 |
+
def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
|
343 |
+
# Normalization from Non-stationary Transformer
|
344 |
+
means = x_enc.mean(1, keepdim=True).detach()
|
345 |
+
x_enc = x_enc - means
|
346 |
+
stdev = torch.sqrt(
|
347 |
+
torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
|
348 |
+
x_enc /= stdev
|
349 |
+
|
350 |
+
# do patching and embedding
|
351 |
+
x_enc = x_enc.permute(0, 2, 1)
|
352 |
+
# u: [bs * nvars x patch_num x d_model]
|
353 |
+
enc_out, n_vars = self.patch_embedding(x_enc)
|
354 |
+
|
355 |
+
# Encoder
|
356 |
+
# z: [bs * nvars x patch_num x d_model]
|
357 |
+
enc_out, attns = self.encoder(enc_out)
|
358 |
+
# z: [bs x nvars x patch_num x d_model]
|
359 |
+
enc_out = torch.reshape(
|
360 |
+
enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
|
361 |
+
# z: [bs x nvars x d_model x patch_num]
|
362 |
+
enc_out = enc_out.permute(0, 1, 3, 2)
|
363 |
+
|
364 |
+
# Decoder
|
365 |
+
dec_out = self.head(enc_out) # z: [bs x nvars x target_window]
|
366 |
+
dec_out = dec_out.permute(0, 2, 1)
|
367 |
+
|
368 |
+
# De-Normalization from Non-stationary Transformer
|
369 |
+
dec_out = dec_out * \
|
370 |
+
(stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
|
371 |
+
dec_out = dec_out + \
|
372 |
+
(means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
|
373 |
+
return dec_out
|
374 |
+
|
375 |
+
def forward(self, x, fut_time):
|
376 |
+
|
377 |
+
x_enc = x[:,:,self.data_idx]
|
378 |
+
x_mark_enc = x[:,:,self.time_idx]
|
379 |
+
x_dec = torch.zeros((fut_time.shape[0],fut_time.shape[1],self.dec_in),dtype=fut_time.dtype,device=fut_time.device)
|
380 |
+
x_mark_dec = fut_time
|
381 |
+
|
382 |
+
return self.forecast(x_enc,x_mark_enc,x_dec,x_mark_dec)[:,-1,[0]]
|
models/TimesNet.py
CHANGED
@@ -4,6 +4,9 @@ import torch.nn.functional as F
|
|
4 |
import torch.fft
|
5 |
import math
|
6 |
|
|
|
|
|
|
|
7 |
class Inception_Block_V1(nn.Module):
|
8 |
def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
|
9 |
super(Inception_Block_V1, self).__init__()
|
|
|
4 |
import torch.fft
|
5 |
import math
|
6 |
|
7 |
+
# Modified from: https://github.com/thuml/Time-Series-Library
|
8 |
+
# Modified by Shourya Bose, shbose@ucsc.edu
|
9 |
+
|
10 |
class Inception_Block_V1(nn.Module):
|
11 |
def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
|
12 |
super(Inception_Block_V1, self).__init__()
|
models/Transformer.py
CHANGED
@@ -5,6 +5,9 @@ import numpy as np
|
|
5 |
import math
|
6 |
from math import sqrt
|
7 |
|
|
|
|
|
|
|
8 |
class TriangularCausalMask():
|
9 |
def __init__(self, B, L, device="cpu"):
|
10 |
mask_shape = [B, 1, L, L]
|
|
|
5 |
import math
|
6 |
from math import sqrt
|
7 |
|
8 |
+
# Modified from: https://github.com/thuml/Time-Series-Library
|
9 |
+
# Modified by Shourya Bose, shbose@ucsc.edu
|
10 |
+
|
11 |
class TriangularCausalMask():
|
12 |
def __init__(self, B, L, device="cpu"):
|
13 |
mask_shape = [B, 1, L, L]
|
weights/{autoformer_L_96_T_4_HET.pth β Autoformer_L_512_T_48_HET.pth}
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:45b9ec43695627b47f4d26af2a2bbd9bb0bd2f0555f500285ee8cdb755a67f2e
|
3 |
+
size 5688555
|
weights/{autoformer_L_96_T_4_HOM.pth β Autoformer_L_512_T_48_HOM.pth}
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
-
size
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b29d91943f51b0ef3e21e462a9916a6a98319fa05c980ecca8718c2eaf335cf3
|
3 |
+
size 5688555
|
weights/{autoformer_L_96_T_48_HET.pth β Autoformer_L_512_T_4_HET.pth}
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 5688452
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:467f7080dc13b7b35b480d87bc981fcd9de06c22b0b8462dc5bed7d5a4725b02
|
3 |
size 5688452
|
weights/{autoformer_L_96_T_48_HOM.pth β Autoformer_L_512_T_4_HOM.pth}
RENAMED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 5688452
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a691b1217ad12b7b478d6677d223cbe9ab83582d28d59aabee40634cb308c597
|
3 |
size 5688452
|
weights/Autoformer_L_512_T_96_HET.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
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