Datasets:

macrolens
/

MacroLens

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.nn.utils import weight_norm
	import math


	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 TokenEmbedding(nn.Module):
	def __init__(self, c_in, d_model):
	super(TokenEmbedding, self).__init__()
	padding = 1 if torch.__version__ >= '1.5.0' else 2
	self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
	kernel_size=3, padding=padding, padding_mode='circular', bias=False)
	for m in self.modules():
	if isinstance(m, nn.Conv1d):
	nn.init.kaiming_normal_(
	m.weight, mode='fan_in', nonlinearity='leaky_relu')

	def forward(self, x):
	x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
	return x


	class FixedEmbedding(nn.Module):
	def __init__(self, c_in, d_model):
	super(FixedEmbedding, self).__init__()

	w = torch.zeros(c_in, d_model).float()
	w.require_grad = False

	position = torch.arange(0, c_in).float().unsqueeze(1)
	div_term = (torch.arange(0, d_model, 2).float()
	* -(math.log(10000.0) / d_model)).exp()

	w[:, 0::2] = torch.sin(position * div_term)
	w[:, 1::2] = torch.cos(position * div_term)

	self.emb = nn.Embedding(c_in, d_model)
	self.emb.weight = nn.Parameter(w, requires_grad=False)

	def forward(self, x):
	return self.emb(x).detach()


	class TemporalEmbedding(nn.Module):
	def __init__(self, d_model, embed_type='fixed', freq='h'):
	super(TemporalEmbedding, self).__init__()

	minute_size = 4
	hour_size = 24
	weekday_size = 7
	day_size = 32
	month_size = 13

	Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding
	if freq == 't':
	self.minute_embed = Embed(minute_size, d_model)
	self.hour_embed = Embed(hour_size, d_model)
	self.weekday_embed = Embed(weekday_size, d_model)
	self.day_embed = Embed(day_size, d_model)
	self.month_embed = Embed(month_size, d_model)

	def forward(self, x):
	x = x.long()
	minute_x = self.minute_embed(x[:, :, 4]) if hasattr(
	self, 'minute_embed') else 0.
	hour_x = self.hour_embed(x[:, :, 3])
	weekday_x = self.weekday_embed(x[:, :, 2])
	day_x = self.day_embed(x[:, :, 1])
	month_x = self.month_embed(x[:, :, 0])

	return hour_x + weekday_x + day_x + month_x + minute_x


	class TimeFeatureEmbedding(nn.Module):
	def __init__(self, d_model, embed_type='timeF', freq='h'):
	super(TimeFeatureEmbedding, self).__init__()

	freq_map = {'h': 4, 't': 5, 's': 6,
	'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3}
	d_inp = freq_map[freq]
	self.embed = nn.Linear(d_inp, d_model, bias=False)

	def forward(self, x):
	return self.embed(x)


	class DataEmbedding(nn.Module):
	def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
	super(DataEmbedding, self).__init__()

	self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
	self.position_embedding = PositionalEmbedding(d_model=d_model)
	self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
	freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
	d_model=d_model, embed_type=embed_type, freq=freq)
	self.dropout = nn.Dropout(p=dropout)

	def forward(self, x, x_mark):
	if x_mark is None:
	x = self.value_embedding(x) + self.position_embedding(x)
	else:
	x = self.value_embedding(
	x) + self.temporal_embedding(x_mark) + self.position_embedding(x)
	return self.dropout(x)


	class DataEmbedding_inverted(nn.Module):
	def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
	super(DataEmbedding_inverted, self).__init__()
	self.value_embedding = nn.Linear(c_in, d_model)
	self.dropout = nn.Dropout(p=dropout)

	def forward(self, x, x_mark):
	x = x.permute(0, 2, 1)
	# x: [Batch Variate Time]
	if x_mark is None:
	x = self.value_embedding(x)
	else:
	x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
	# x: [Batch Variate d_model]
	return self.dropout(x)


	class DataEmbedding_wo_pos(nn.Module):
	def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1):
	super(DataEmbedding_wo_pos, self).__init__()

	self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
	self.position_embedding = PositionalEmbedding(d_model=d_model)
	self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type,
	freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding(
	d_model=d_model, embed_type=embed_type, freq=freq)
	self.dropout = nn.Dropout(p=dropout)

	def forward(self, x, x_mark):
	if x_mark is None:
	x = self.value_embedding(x)
	else:
	x = self.value_embedding(x) + self.temporal_embedding(x_mark)
	return self.dropout(x)


	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