diff --git a/README.md b/README.md index 44e4b8f61ff29f760371ce9b760f325bb869d99f..a4a97be3c1109d872f8ef27341824523dfb3cc3c 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,19 @@ --- license: cc --- - ## About 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`. -Note that `lookback` is denoted by `L` and `lookahead` by `T` in the weights directory. We provide weights for the following `(L,T)` pairs: `(96,4)`, `(96,48)`, and `(96,96)`. +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. ## Data -When using the companion [dataset](https://huggingface.co/datasets/APPFL/Illinois_load_datasets), the following points must be noted (see the dataset for more information on configuring the data loaders): +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): - All models accept normalized inputs and produce normalized outputs, i.e. set `normalize = True` when generating the datasets. -- For Transformer, Autoformer, Informer, and TimesNet set `transformer = True`, while for LSTM and LSTNet, set `transformer = False`. +- For Transformer, Autoformer, Informer, and TimesNet set `transformer = True`, while for LSTM, LSTNet, and PatchTST set `transformer = False`. ## Credits -Some model definitions have been adapted from the code provided in the [TSLib Library](https://github.com/thuml/Time-Series-Library). \ No newline at end of file +Some model definitions have been adapted from the code provided in the [TSLib Library](https://github.com/thuml/Time-Series-Library). diff --git a/model_kwargs.py b/model_kwargs.py index a8afd437713922bc082257ac092235b16d293c55..f2b528d3a2cce9ef725d2be9b6f0bbe016109eb4 100644 --- a/model_kwargs.py +++ b/model_kwargs.py @@ -52,4 +52,15 @@ lstnet_kwargs = lambda lookback,lookahead:{ 'conv1_out_channels':8*4, 'conv1_kernel_height':3*4, 'recc1_out_channels':32*4 +} + +patchtst_kwargs = lambda lookback,lookahead:{ + 'enc_in': 6, + 'dec_in': 2, + 'c_out': 1, + 'pred_len': lookahead, + 'seq_len': lookback, + 'd_model': 32*4, + 'data_idx': [0,3,4,5,6,7], + 'time_idx': [1,2] } \ No newline at end of file diff --git a/models/Autoformer.py b/models/Autoformer.py index cbe6de81f01b633fc881b4f23dce7e104610bc93..d87a3205e82c2eb226a108acdcf0fba440ebd913 100644 --- a/models/Autoformer.py +++ b/models/Autoformer.py @@ -4,6 +4,9 @@ import torch.nn.functional as F import math import numpy as np +# Modified from: https://github.com/thuml/Time-Series-Library +# Modified by Shourya Bose, shbose@ucsc.edu + class AutoCorrelation(nn.Module): """ AutoCorrelation Mechanism with the following two phases: diff --git a/models/Informer.py b/models/Informer.py index 4bf31714e2c6ee629b218e18b2311e4176b133aa..d95e2752baf46b0e9ea9a800a37226f5f166640b 100644 --- a/models/Informer.py +++ b/models/Informer.py @@ -5,6 +5,9 @@ from math import sqrt import numpy as np import torch.nn.functional as F +# Modified from: https://github.com/thuml/Time-Series-Library +# Modified by Shourya Bose, shbose@ucsc.edu + class ConvLayer(nn.Module): def __init__(self, c_in): super(ConvLayer, self).__init__() diff --git a/models/LSTM.py b/models/LSTM.py index 7e69e264254b012561ddd9aeb87fe4c44e7a9d48..fee669bfd7823f44eb6bdefec8b83dd579a4fcfc 100644 --- a/models/LSTM.py +++ b/models/LSTM.py @@ -2,6 +2,8 @@ import torch import torch.nn as nn from typing import Union, List, Tuple +# Written by Shourya Bose, shbose@ucsc.edu + class LSTM(nn.Module): def __init__( diff --git a/models/LSTNet.py b/models/LSTNet.py index 107c95c32e123fa5f68049ac5594b58231462368..d3539b0dc99f0fe0e345d10013b5111d34a550f2 100644 --- a/models/LSTNet.py +++ b/models/LSTNet.py @@ -3,7 +3,8 @@ import torch.nn as nn import torch.nn.functional as F import numpy as np -# https://github.com/gokulkarthik/LSTNet.pytorch/blob/master/LSTNet.py +# Modified from https://github.com/gokulkarthik/LSTNet.pytorch/blob/master/LSTNet.py +# Modified by Shourya Bose, shbose@ucsc.edu class LSTNet(nn.Module): diff --git a/models/PatchTST.py b/models/PatchTST.py new file mode 100644 index 0000000000000000000000000000000000000000..19e666cc26e39c4e0520dd8c443b235d4862c7e7 --- /dev/null +++ b/models/PatchTST.py @@ -0,0 +1,382 @@ +import torch +from torch import nn +import torch.nn.functional as F +import math +from math import sqrt +import numpy as np + +# Modified from: https://github.com/thuml/Time-Series-Library +# Modified by Shourya Bose, shbose@ucsc.edu + +class PositionalEmbedding(nn.Module): + def __init__(self, d_model, max_len=5000): + super(PositionalEmbedding, self).__init__() + # Compute the positional encodings once in log space. + pe = torch.zeros(max_len, d_model).float() + pe.require_grad = False + + position = torch.arange(0, max_len).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() + * -(math.log(10000.0) / d_model)).exp() + + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, x): + return self.pe[:, :x.size(1)] + +class PatchEmbedding(nn.Module): + def __init__(self, d_model, patch_len, stride, padding, dropout): + super(PatchEmbedding, self).__init__() + # Patching + self.patch_len = patch_len + self.stride = stride + self.padding_patch_layer = nn.ReplicationPad1d((0, padding)) + + # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space + self.value_embedding = nn.Linear(patch_len, d_model, bias=False) + + # Positional embedding + self.position_embedding = PositionalEmbedding(d_model) + + # Residual dropout + self.dropout = nn.Dropout(dropout) + + def forward(self, x): + # do patching + n_vars = x.shape[1] + x = self.padding_patch_layer(x) + x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride) + x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) + # Input encoding + x = self.value_embedding(x) + self.position_embedding(x) + return self.dropout(x), n_vars + +class AttentionLayer(nn.Module): + def __init__(self, attention, d_model, n_heads, d_keys=None, + d_values=None): + super(AttentionLayer, self).__init__() + + d_keys = d_keys or (d_model // n_heads) + d_values = d_values or (d_model // n_heads) + + self.inner_attention = attention + self.query_projection = nn.Linear(d_model, d_keys * n_heads) + self.key_projection = nn.Linear(d_model, d_keys * n_heads) + self.value_projection = nn.Linear(d_model, d_values * n_heads) + self.out_projection = nn.Linear(d_values * n_heads, d_model) + self.n_heads = n_heads + + def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): + B, L, _ = queries.shape + _, S, _ = keys.shape + H = self.n_heads + + queries = self.query_projection(queries).view(B, L, H, -1) + keys = self.key_projection(keys).view(B, S, H, -1) + values = self.value_projection(values).view(B, S, H, -1) + + out, attn = self.inner_attention( + queries, + keys, + values, + attn_mask, + tau=tau, + delta=delta + ) + out = out.view(B, L, -1) + + return self.out_projection(out), attn + +class FullAttention(nn.Module): + def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): + super(FullAttention, self).__init__() + self.scale = scale + self.mask_flag = mask_flag + self.output_attention = output_attention + self.dropout = nn.Dropout(attention_dropout) + + def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): + B, L, H, E = queries.shape + _, S, _, D = values.shape + scale = self.scale or 1. / sqrt(E) + + scores = torch.einsum("blhe,bshe->bhls", queries, keys) + + if self.mask_flag: + if attn_mask is None: + attn_mask = TriangularCausalMask(B, L, device=queries.device) + + scores.masked_fill_(attn_mask.mask, -np.inf) + + A = self.dropout(torch.softmax(scale * scores, dim=-1)) + V = torch.einsum("bhls,bshd->blhd", A, values) + + if self.output_attention: + return V.contiguous(), A + else: + return V.contiguous(), None + +class TriangularCausalMask(): + def __init__(self, B, L, device="cpu"): + mask_shape = [B, 1, L, L] + with torch.no_grad(): + self._mask = torch.triu(torch.ones(mask_shape, dtype=torch.bool), diagonal=1).to(device) + + @property + def mask(self): + return self._mask + +class FullAttention(nn.Module): + def __init__(self, mask_flag=True, factor=5, scale=None, attention_dropout=0.1, output_attention=False): + super(FullAttention, self).__init__() + self.scale = scale + self.mask_flag = mask_flag + self.output_attention = output_attention + self.dropout = nn.Dropout(attention_dropout) + + def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): + B, L, H, E = queries.shape + _, S, _, D = values.shape + scale = self.scale or 1. / sqrt(E) + + scores = torch.einsum("blhe,bshe->bhls", queries, keys) + + if self.mask_flag: + if attn_mask is None: + attn_mask = TriangularCausalMask(B, L, device=queries.device) + + scores.masked_fill_(attn_mask.mask, -np.inf) + + A = self.dropout(torch.softmax(scale * scores, dim=-1)) + V = torch.einsum("bhls,bshd->blhd", A, values) + + if self.output_attention: + return V.contiguous(), A + else: + return V.contiguous(), None + +class AttentionLayer(nn.Module): + def __init__(self, attention, d_model, n_heads, d_keys=None, + d_values=None): + super(AttentionLayer, self).__init__() + + d_keys = d_keys or (d_model // n_heads) + d_values = d_values or (d_model // n_heads) + + self.inner_attention = attention + self.query_projection = nn.Linear(d_model, d_keys * n_heads) + self.key_projection = nn.Linear(d_model, d_keys * n_heads) + self.value_projection = nn.Linear(d_model, d_values * n_heads) + self.out_projection = nn.Linear(d_values * n_heads, d_model) + self.n_heads = n_heads + + def forward(self, queries, keys, values, attn_mask, tau=None, delta=None): + B, L, _ = queries.shape + _, S, _ = keys.shape + H = self.n_heads + + queries = self.query_projection(queries).view(B, L, H, -1) + keys = self.key_projection(keys).view(B, S, H, -1) + values = self.value_projection(values).view(B, S, H, -1) + + out, attn = self.inner_attention( + queries, + keys, + values, + attn_mask, + tau=tau, + delta=delta + ) + out = out.view(B, L, -1) + + return self.out_projection(out), attn + +class EncoderLayer(nn.Module): + def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu"): + super(EncoderLayer, self).__init__() + d_ff = d_ff or 4 * d_model + self.attention = attention + self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1) + self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1) + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.dropout = nn.Dropout(dropout) + self.activation = F.relu if activation == "relu" else F.gelu + + def forward(self, x, attn_mask=None, tau=None, delta=None): + new_x, attn = self.attention( + x, x, x, + attn_mask=attn_mask, + tau=tau, delta=delta + ) + x = x + self.dropout(new_x) + + y = x = self.norm1(x) + y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1)))) + y = self.dropout(self.conv2(y).transpose(-1, 1)) + + return self.norm2(x + y), attn + + +class Encoder(nn.Module): + def __init__(self, attn_layers, conv_layers=None, norm_layer=None): + super(Encoder, self).__init__() + self.attn_layers = nn.ModuleList(attn_layers) + self.conv_layers = nn.ModuleList(conv_layers) if conv_layers is not None else None + self.norm = norm_layer + + def forward(self, x, attn_mask=None, tau=None, delta=None): + # x [B, L, D] + attns = [] + if self.conv_layers is not None: + for i, (attn_layer, conv_layer) in enumerate(zip(self.attn_layers, self.conv_layers)): + delta = delta if i == 0 else None + x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta) + x = conv_layer(x) + attns.append(attn) + x, attn = self.attn_layers[-1](x, tau=tau, delta=None) + attns.append(attn) + else: + for attn_layer in self.attn_layers: + x, attn = attn_layer(x, attn_mask=attn_mask, tau=tau, delta=delta) + attns.append(attn) + + if self.norm is not None: + x = self.norm(x) + + return x, attns + +class Transpose(nn.Module): + def __init__(self, *dims, contiguous=False): + super().__init__() + self.dims, self.contiguous = dims, contiguous + def forward(self, x): + if self.contiguous: return x.transpose(*self.dims).contiguous() + else: return x.transpose(*self.dims) + + +class FlattenHead(nn.Module): + def __init__(self, n_vars, nf, target_window, head_dropout=0): + super().__init__() + self.n_vars = n_vars + self.flatten = nn.Flatten(start_dim=-2) + self.linear = nn.Linear(nf, target_window) + self.dropout = nn.Dropout(head_dropout) + + def forward(self, x): # x: [bs x nvars x d_model x patch_num] + x = self.flatten(x) + x = self.linear(x) + x = self.dropout(x) + return x + + +class PatchTST(nn.Module): + """ + Paper link: https://arxiv.org/pdf/2211.14730.pdf + """ + + def __init__( + self, + enc_in, + dec_in, # unused + c_out, # unused + pred_len, + seq_len, + d_model = 64, + patch_len = 16, + stride = 8, + data_idx = [0,3,4,5,6,7], + time_idx = [1,2], + output_attention = False, + factor = 3, + n_heads = 4, + d_ff = 512, + e_layers = 3, + activation = 'gelu', + dropout = 0.1 + ): + + #(self, configs, patch_len=16, stride=8): + """ + patch_len: int, patch len for patch_embedding + stride: int, stride for patch_embedding + """ + super().__init__() + self.seq_len = seq_len + self.pred_len = pred_len + self.data_idx = data_idx + self.time_idx = time_idx + self.dec_in = dec_in + padding = stride + + # patching and embedding + self.patch_embedding = PatchEmbedding( + d_model, patch_len, stride, padding, dropout) + + # Encoder + self.encoder = Encoder( + [ + EncoderLayer( + AttentionLayer( + FullAttention(False, factor, attention_dropout=dropout, + output_attention=output_attention), d_model, n_heads), + d_model, + d_ff, + dropout=dropout, + activation=activation + ) for l in range(e_layers) + ], + norm_layer=nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2)) + ) + + # Prediction Head + self.head_nf = d_model * \ + int((seq_len - patch_len) / stride + 2) + self.head = FlattenHead(enc_in, self.head_nf,pred_len, + head_dropout=dropout) + + def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): + # Normalization from Non-stationary Transformer + means = x_enc.mean(1, keepdim=True).detach() + x_enc = x_enc - means + stdev = torch.sqrt( + torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) + x_enc /= stdev + + # do patching and embedding + x_enc = x_enc.permute(0, 2, 1) + # u: [bs * nvars x patch_num x d_model] + enc_out, n_vars = self.patch_embedding(x_enc) + + # Encoder + # z: [bs * nvars x patch_num x d_model] + enc_out, attns = self.encoder(enc_out) + # z: [bs x nvars x patch_num x d_model] + enc_out = torch.reshape( + enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])) + # z: [bs x nvars x d_model x patch_num] + enc_out = enc_out.permute(0, 1, 3, 2) + + # Decoder + dec_out = self.head(enc_out) # z: [bs x nvars x target_window] + dec_out = dec_out.permute(0, 2, 1) + + # De-Normalization from Non-stationary Transformer + dec_out = dec_out * \ + (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) + dec_out = dec_out + \ + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) + return dec_out + + def forward(self, x, fut_time): + + x_enc = x[:,:,self.data_idx] + x_mark_enc = x[:,:,self.time_idx] + x_dec = torch.zeros((fut_time.shape[0],fut_time.shape[1],self.dec_in),dtype=fut_time.dtype,device=fut_time.device) + x_mark_dec = fut_time + + return self.forecast(x_enc,x_mark_enc,x_dec,x_mark_dec)[:,-1,[0]] \ No newline at end of file diff --git a/models/TimesNet.py b/models/TimesNet.py index b3bba98f2d57682f3f4dbadedeeed0eb3b27644d..fb24dcf60ddd1bbd96786a88132e50ff4d07505c 100644 --- a/models/TimesNet.py +++ b/models/TimesNet.py @@ -4,6 +4,9 @@ import torch.nn.functional as F import torch.fft import math +# Modified from: https://github.com/thuml/Time-Series-Library +# Modified by Shourya Bose, shbose@ucsc.edu + class Inception_Block_V1(nn.Module): def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True): super(Inception_Block_V1, self).__init__() diff --git a/models/Transformer.py b/models/Transformer.py index e37e9d80c3434ad0cfcf3d6812eb6192c4080857..4e1ab2cc8a11557ab776b3d8eba7ba1f5f807578 100644 --- a/models/Transformer.py +++ b/models/Transformer.py @@ -5,6 +5,9 @@ import numpy as np import math from math import sqrt +# Modified from: https://github.com/thuml/Time-Series-Library +# Modified by Shourya Bose, shbose@ucsc.edu + class TriangularCausalMask(): def __init__(self, B, L, device="cpu"): mask_shape = [B, 1, L, L] diff --git a/weights/Autoformer_L_512_T_48_HET.pth b/weights/Autoformer_L_512_T_48_HET.pth new file mode 100644 index 0000000000000000000000000000000000000000..87a93b81b8faa1ca20161b3804c2be27803901dc --- /dev/null +++ b/weights/Autoformer_L_512_T_48_HET.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45b9ec43695627b47f4d26af2a2bbd9bb0bd2f0555f500285ee8cdb755a67f2e +size 5688555 diff --git a/weights/Autoformer_L_512_T_48_HOM.pth b/weights/Autoformer_L_512_T_48_HOM.pth new file mode 100644 index 0000000000000000000000000000000000000000..77a782070f519a9d27b0a6fd683d50dc0bc6c2f3 --- /dev/null +++ b/weights/Autoformer_L_512_T_48_HOM.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b29d91943f51b0ef3e21e462a9916a6a98319fa05c980ecca8718c2eaf335cf3 +size 5688555 diff --git a/weights/Autoformer_L_512_T_4_HET.pth b/weights/Autoformer_L_512_T_4_HET.pth new file mode 100644 index 0000000000000000000000000000000000000000..9f487a5d2e242ebc3fd635c93536d2e6d8ca0eb8 --- /dev/null +++ b/weights/Autoformer_L_512_T_4_HET.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:467f7080dc13b7b35b480d87bc981fcd9de06c22b0b8462dc5bed7d5a4725b02 +size 5688452 diff --git a/weights/Autoformer_L_512_T_4_HOM.pth b/weights/Autoformer_L_512_T_4_HOM.pth new file mode 100644 index 0000000000000000000000000000000000000000..537c09fb8ea5cfbb1cfa2572d1242c64222aad19 --- /dev/null +++ b/weights/Autoformer_L_512_T_4_HOM.pth @@ -0,0 +1,3 @@ +version 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