TFT时间序列预测
from torch import nn
import math
import torch
import ipdb
class GLU(nn.Module):
#Gated Linear Unit
def __init__(self,input_size):
super(GLU, self).__init__()
self.fc1=nn.Linear(input_size,input_size)
self.fc2=nn.Linear(input_size,input_size)
self.sigmoid=nn.Sigmoid()
def forward(self,x):
sig=self.sigmoid(self.fc1(x))
x=self.fc2(x)
return torch.mul(sig,x)
class TimeDistributed(nn.Module):
## Takes any module and stacks the time dimension with the batch dimenison of inputs before apply the module
## From: https://discuss.pytorch.org/t/any-pytorch-function-can-work-as-keras-timedistributed/1346/4
# 模块化用来改变输入大小,考虑到直接用Linear层对多维数据处理可能出问题，单独处理
def __init__(self, module, batch_first=False):
super(TimeDistributed, self).__init__()
self.module = module
self.batch_first = batch_first
def forward(self, x):
if len(x.size()) <= 2:
return self.module(x)
# Squash samples and timesteps into a single axis
x_reshape = x.contiguous().view(-1, x.size(-1)) # (samples * timesteps, input_size)，view变换原矩阵的大小，需要原矩阵的内存是整块的。
# print(x_reshape.device)
y = self.module(x_reshape)
# We have to reshape Y
if self.batch_first:
y = y.contiguous().view(x.size(0), -1, y.size(-1)) # (samples, timesteps, output_size)
else:
y = y.view(-1, x.size(1), y.size(-1)) # (timesteps, samples, output_size)
return y
class GRN(nn.Module):
# GatedResidualNetwork
def __init__(self,input_size,hidden_state_size,output_size,drop_out,hidden_context_size=None,batch_first=False):
super(GRN, self).__init__()
self.input_size=input_size
self.output_size=output_size
self.hidden_context_size=hidden_context_size
self.hidden_state_size=hidden_state_size
self.drop_out=drop_out
if self.input_size!=self.output_size:
self.skip_layer=TimeDistributed(nn.Linear(self.input_size,self.output_size))
self.fc1=TimeDistributed(nn.Linear(self.input_size,self.hidden_state_size),batch_first=batch_first)
self.elu1=nn.ELU()
if self.hidden_context_size is not None:
# 如果c能够传递的话，将c的大小化为和a的大小一致
self.context=TimeDistributed(nn.Linear(self.hidden_context_size,self.hidden_state_size),batch_first=batch_first)
self.fc2=TimeDistributed(nn.Linear(self.hidden_state_size,self.output_size),batch_first=batch_first)
# self.elu2=nn.ELU()#做不做问题不大
self.dropout=nn.Dropout(self.drop_out)
self.ln=TimeDistributed(nn.LayerNorm(self.output_size),batch_first=batch_first)#层归一化归一化最后k个维度
self.gate=TimeDistributed(GLU(self.output_size),batch_first=batch_first)
def forward(self,x,context=None):
if self.input_size!=self.output_size:
residual=self.skip_layer(x)
else:
residual=x
x=self.fc1(x)
if context is not None:
context=self.context(context)
x=x+context
x=self.elu1(x)
x=self.fc2(x)
x=self.dropout(x)
x=self.gate(x)
x=x+residual
x=self.ln(x)
return x
class PositionalEncoder(nn.Module):##仿照transformer层添加位置编码,有点多此一举
def __init__(self,d_model,max_seq_len=160):
super(PositionalEncoder, self).__init__()
self.d_model=d_model#Embedding大小，输入为(seq_len,batch_size,index)-->(seq_len,batch_size,input_size)
pe=torch.zeros(max_seq_len,d_model)
for pos in range(max_seq_len):
for i in range(0,d_model,2):
pe[pos, i] =
math.sin(pos / (10000 ** ((2 * i) / d_model)))
pe[pos, i + 1] =
math.cos(pos / (10000 ** ((2 * (i + 1)) / d_model)))
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self,x):
with torch.no_grad():
x=x*math.sqrt(self.d_model)
seq_len=x.size(0)
pe=self.pe[:,:seq_len].view(seq_len,1,self.d_model)
x=x+pe
return x
class VSN(nn.Module):
# Variable Selection Network
def __init__(self,input_size,num_inputs,hidden_size,drop_out,context=None):
super(VSN, self).__init__()
self.hidden_size=hidden_size
self.input_size=input_size
self.num_inputs=num_inputs
self.drop_out=drop_out
self.context=context
#num_inputs*input_size的原因是这里将所有的变量摊平了，原来先将num_inputs中的每个变量都做了embedding
self.flattened_grn=GRN(input_size=self.num_inputs*self.input_size,hidden_state_size=self.hidden_size,output_size=self.num_inputs,drop_out=self.drop_out,hidden_context_size=self.context)
self.single_variable_grns=nn.ModuleList()
for i in range(self.num_inputs):
self.single_variable_grns.append(GRN(self.input_size,self.hidden_size,self.hidden_size,self.drop_out))#为每一个展开变量均添加一个GRN
self.softmax=nn.Softmax()
def forward(self,embedding,context=None):
sparse_weights=self.flattened_grn(embedding,context)#将embedding铺平+grn+softmax cat embedding
sparse_weights=self.softmax(sparse_weights).unsqueeze(2)#强制加入第二个维度，【seq,bs,1,num_inputs]
var_outputs=[]#对每一个emb计算GRN并化为列表
for i in range(self.num_inputs):
# 每个对应的输入维度的embedding分别求GRN,这里embedding后的维度为(seq,bs,input_size*num_inputs)
var_outputs.append(self.single_variable_grns[i](embedding[:,:,(i*self.input_size):(i+1)*self.input_size]))
var_outputs=torch.stack(var_outputs,dim=-1)#在最后一个维度上进行堆叠,结果为[seq,bs,input_size,num_inputs]
# print(var_outputs.shape)
# print(sparse_weights.shape)
'''
ggg
ggg
'''
outputs=var_outputs*sparse_weights
outputs=outputs.sum(axis=-1)
return outputs,sparse_weights
class TFT(nn.Module):
def __init__(self,config):#传入config字典
super(TFT, self).__init__()
self.device=config['device']
self.batch_size=config['batch_size']
self.static_variables=config['static_variables']
self.encode_length=config['encode_length']
self.time_varying_categorical_variables=config['time_varying_categorical_variables']#随时间变化的离散型变量(分类型)
self.time_varying_real_variables_encoder=config['time_varying_real_variables_encoder']#encoder中随时间变化的连续型变量
self.time_varying_real_variables_decoder=config['time_varying_real_variables_decoder']#decoder中随时间变化的连续型变量
self.num_input_series_to_mask=config['num_masked_series']
self.hidden_size=config['lstm_hidden_dimension']
self.lstm_layers=config['lstm_layers']
self.drop_out=config['drop_out']
self.embedding_dim=config['embedding_dim']
self.attn_heads=config['attn_heads']
self.num_quantiles=config['num_quantiles']#分位数的个数
self.valid_quantiles=config['valid_quantiles']#有效分位数
self.seq_length=config['seq_length']
self.static_embedding_layers=nn.ModuleList()#对每一个变量分别做embedding,[bs,1]对应着所有bs的第i个变量
for i in range(self.static_variables):
emb=nn.Embedding(config['static_embedding_vocab_sizes'][i],config['embedding_dim']).to(self.device)#有可能static_embedding_vocab_sizes不为1？
self.static_embedding_layers.append(emb)
self.time_varying_embedding_layers=nn.ModuleList()#随时间变化的变量的编码
for i in range(self.time_varying_categorical_variables):
emb=TimeDistributed(nn.Embedding(config['time_varying_embedding_vocab_sizes'][i],config['embedding_dim']))
self.time_varying_embedding_layers.append(emb)
self.time_varying_linear_layers=nn.ModuleList()
for i in range(self.time_varying_real_variables_encoder):
emb=TimeDistributed(nn.Linear(1,config['embedding_dim']),batch_first=True).to(self.device)
self.time_varying_linear_layers.append(emb)
self.encoder_variable_selection=VSN(config['embedding_dim'],
(config['time_varying_real_variables_encoder']+
config['time_varying_categorical_variables']),
self.hidden_size,
self.drop_out,
config['embedding_dim']*config['static_variables'])
self.decoder_variable_selection = VSN(config['embedding_dim'],
(config['time_varying_real_variables_decoder'] + config['time_varying_categorical_variables']),
self.hidden_size,
self.drop_out,
config['embedding_dim']*config['static_variables'])
self.lstm_encoder_input_size = config['embedding_dim']*(config['time_varying_real_variables_encoder'] +
config['time_varying_categorical_variables'] +
config['static_variables'])#输入lstm的变量应当是三个不同变量分类的和
self.lstm_decoder_input_size = config['embedding_dim']*(config['time_varying_real_variables_decoder'] +
config['time_varying_categorical_variables'] +
config['static_variables'])
self.lstm_encoder=nn.LSTM(input_size=self.hidden_size,hidden_size=self.hidden_size,num_layers=self.lstm_layers
,dropout=self.drop_out)
self.lstm_decoder=nn.LSTM(input_size=self.hidden_size,hidden_size=self.hidden_size,num_layers=self.lstm_layers
,dropout=self.drop_out)
self.post_lstm_gate=TimeDistributed(GLU(self.hidden_size))
self.post_lstm_norm=TimeDistributed(nn.LayerNorm(self.hidden_size))
self.static_enrichment=GRN(self.hidden_size,self.hidden_size,self.hidden_size,self.drop_out,config['embedding_dim']*self.static_variables)#最后一项是static_context_size
self.position_encoding=PositionalEncoder(self.hidden_size,self.seq_length)
self.multihead_attn=nn.MultiheadAttention(self.hidden_size,self.attn_heads)#第二项是头的数量
self.post_attn_gate=TimeDistributed(GLU(self.hidden_size))
self.post_attn_norm=TimeDistributed(nn.LayerNorm(self.hidden_size))
self.pos_wise_ff=GRN(self.hidden_size,self.hidden_size,self.hidden_size,self.drop_out)#Position_wise_Feed_forward
self.pre_output_norm=TimeDistributed(nn.LayerNorm(self.hidden_size))
self.pre_output_gate=TimeDistributed(GLU(self.hidden_size))
self. output_layer=TimeDistributed(nn.Linear(self.hidden_size,self.num_quantiles),batch_first=True)
def init_hidden(self):
return torch.zeros(self.lstm_layers,self.batch_size,self.hidden_size,device=self.device)#初始化隐藏层大小
def apply_embedding(self,x,static_embedding,apply_masking):#连续型变量
###x should have dimensions (batch_size, timesteps, input_size)
if apply_masking:#判断是否进行masking
time_varying_real_vectors=[]
for i in range(self.time_varying_real_variables_decoder):
emb=self.time_varying_linear_layers[i+self.num_input_series_to_mask](x[:,:,i+self.num_input_series_to_mask].view(x.size(0), -1, 1))
# print(emb.device)
time_varying_real_vectors.append(emb)
time_varying_real_embedding=torch.cat(time_varying_real_vectors,dim=-1)
else:#正常的进行embedding[bs,time_steps,input_size]-->[bs,time_step,input_size*num_inputs]
time_varying_real_vectors = []
for i in range(self.time_varying_real_variables_encoder):
emb = self.time_varying_linear_layers[i](x[:,:,i].view(x.size(0), -1, 1))
time_varying_real_vectors.append(emb)
time_varying_real_embedding = torch.cat(time_varying_real_vectors, dim=-1)
##Time-varying categorical embeddings (eg:hour),时序离散型变量
time_varying_categorical_vectors=[]
for i in range(self.time_varying_categorical_variables):
# print(x[:,:,self.time_varying_real_variables_encoder+i].view(x.size(0),-1,1).long().device)
emb=self.time_varying_embedding_layers[i](x[:,:,self.time_varying_real_variables_encoder+i].view(x.size(0),-1,1).long())
time_varying_categorical_vectors.append(emb)
time_varying_categorical_embedding=torch.cat(time_varying_categorical_vectors,dim=-1)
# 对static_embedding在时间步上进行扩维
static_embedding = torch.cat(time_varying_categorical_embedding.size(1)*[static_embedding])
static_embedding = static_embedding.view(time_varying_categorical_embedding.size(0),time_varying_categorical_embedding.size(1),-1 )
#连接所有的embeddings
embeddings=torch.cat([static_embedding,time_varying_categorical_embedding,time_varying_real_embedding],dim=-1)
return embeddings.view(-1,x.size(0),embeddings.size(-1))#最后一项返回的是num_inputs*inputs_size的大小
def encode(self, x, hidden=None):
if hidden is None:
hidden = self.init_hidden()
output, (hidden, cell) = self.lstm_encoder(x, (hidden, hidden))
return output, hidden
def decode(self, x, hidden=None):
if hidden is None:
hidden = self.init_hidden()
output, (hidden, cell) = self.lstm_decoder(x, (hidden,hidden))
return output, hidden
def forward(self,x):
#输入的顺序为：static,time_varying_categorical,time_varying_real
embedding_vectors=[]
for i in range(self.static_variables):
#静态变量只需要从第一个时间步获取即可 x:-->[bs,time_step,num_inputs]
emb=self.static_embedding_layers[i](x['identifier'].to(self.device)[:,0,i].long())
embedding_vectors.append(emb)#[bs,inputs]*number_inputs-->[bs,inputs*num_inputs]
# Embedding和variables selection
static_embedding=torch.cat(embedding_vectors,dim=-1).to(self.device)#[bs,inputs*num_inputs]
# print(static_embedding.device)
# print(x['inputs'][:,:self.encode_length,:].float().to(self.device).device)
embeddings_encoder=self.apply_embedding(x['inputs'][:,:self.encode_length,:].float().to(self.device),static_embedding,apply_masking=False)#
embeddings_decoder=self.apply_embedding(x['inputs'][:,self.encode_length:,:].float().to(self.device),static_embedding,apply_masking=True)
embeddings_encoder,encoder_sparse_weights=self.encoder_variable_selection(embeddings_encoder[:,:,:-(self.embedding_dim*self.static_variables)],embeddings_encoder[:,:,-(self.embedding_dim*self.static_variables):])
embeddings_decoder,decoder_sparse_weights=self.decoder_variable_selection(embeddings_decoder[:,:,:-(self.embedding_dim*self.static_variables)],embeddings_decoder[:,:,-(self.embedding_dim*self.static_variables):])
#进行位置编码
pe=self.position_encoding(torch.zeros(self.seq_length,1,embeddings_encoder.size(2)).to(self.device)).to(self.device)
embeddings_encoder=embeddings_encoder+pe[:self.encode_length,:,:]##[seq_len_encoder,bs,num_inputs*inputs_len]
embeddings_decoder=embeddings_decoder+pe[self.encode_length:,:,:]##[seq_len_decoder,bs,num_inputs*inputs_len]
##LSTM
lstm_input=torch.cat([embeddings_encoder,embeddings_decoder],dim=0)#在时间序列上对编码和解码后的数据进行拼接
encoder_output,hidden=self.encode(embeddings_encoder)#对encoder部分的数据进行编码，并传回hidden层的数据给下一步解码
decoder_output,_=self.decode(embeddings_decoder,hidden)
lstm_output=torch.cat([encoder_output,decoder_output],dim=0)#将lstm的输出在序列维度上进行拼接[sq,bs,hidden_len]
#进行残差连接并通过gate(GLU)+Norm
lstm_output=self.post_lstm_norm((self.post_lstm_gate(lstm_output)+lstm_input))
##static enrichment
static_embedding=torch.cat(lstm_output.size(0)*[static_embedding]).view(lstm_output.size(0),lstm_output.size(1),-1)#[bs,inputs*num_static_inputs]-->GRN(inputs,se)-->context
attn_input=self.static_enrichment(lstm_output,static_embedding)
#求一个LN
## Attention层(Multihead Attention)
attn_output,atten_output_weight=self.multihead_attn(attn_input[self.encode_length:,:,:],attn_input[:self.encode_length,:,:],attn_input[:self.encode_length,:,:])#结果大小同查询，也就是Q的大小，weights的大小为：(N, num_heads, L, S),L为Q的大小，S为K,V的大小
## gate
attn_output=self.post_attn_gate(attn_output)+attn_input[self.encode_length:,:,:]
# print(attn_output.shape)
attn_output=self.post_attn_norm(attn_output)
output=self.pos_wise_ff(attn_output) #[self.encode_length:,:,:]
# resurial
output=self.pre_output_gate(output)+lstm_output[self.encode_length:,:,:]
output=self.pre_output_norm(output)
#Final output layers(Dense)
output=self.output_layer(output.view(self.batch_size,-1,self.hidden_size))#这里batch_first=ture
return output,encoder_output,decoder_output,attn_output,atten_output_weight,encoder_sparse_weights,decoder_sparse_weights
#损失：
class QuantileLoss(nn.Module):#--》input:<bs,ts,q>--><ts,q>->,计算损失如下：
## From: https://medium.com/the-artificial-impostor/quantile-regression-part-2-6fdbc26b2629
def __init__(self, quantiles):
##takes a list of quantiles
super().__init__()
self.quantiles = quantiles
def forward(self, preds, target):
assert not target.requires_grad
assert preds.size(0) == target.size(0)#检验程序使用的，如果不满足条件，程序会自动退出
losses = []
for i, q in enumerate(self.quantiles):
errors = target - preds[:, i]
losses.append(
torch.max(
(q-1) * errors,
q * errors
).unsqueeze(1))
loss = torch.mean(
torch.sum(torch.cat(losses, dim=1), dim=1))
return loss
import pandas as pd
from torch.utils.data import Dataset
import numpy as np
构建数据集
#生成演示数据
df=pd.read_csv('LD2011_2014.txt',index_col=0,sep=';',decimal=',')
df.index=pd.to_datetime(df.index)
df.sort_index(inplace=True)#inplace是使用排序后的数据来代替现有数据
#定义时间步
output=df.resample('1h').mean().replace(0,np.nan)
earliest_time=output.index.min()
df_list=[]
for label in output:
print('Processing {}'.format(label))
srs = output[label]
start_date = min(srs.fillna(method='ffill').dropna().index)
end_date = max(srs.fillna(method='bfill').dropna().index)
active_range = (srs.index >= start_date) & (srs.index <= end_date)
srs = srs[active_range].fillna(0.)
tmp = pd.DataFrame({'power_usage': srs})
date = tmp.index
tmp['t'] = (date - earliest_time).seconds / 60 / 60 + (
date - earliest_time).days * 24
tmp['days_from_start'] = (date - earliest_time).days
tmp['categorical_id'] = label
tmp['date'] = date
tmp['id'] = label
tmp['hour'] = date.hour
tmp['day'] = date.day
tmp['day_of_week'] = date.dayofweek
tmp['month'] = date.month
df_list.append(tmp)
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output=pd.concat(df_list,axis=0,join='outer').reset_index(drop=True)
output['categorical_id'] = output['id'].copy()
output['hours_from_start'] = output['t']
output['categorical_day_of_week'] = output['day_of_week'].copy()
output['categorical_hour'] = output['hour'].copy()
# Filter to match range used by other academic papers
output = output[(output['days_from_start'] >= 1096)
& (output['days_from_start'] < 1346)].copy()
##查看数据格式:
import expt_settings.configs
ExperimentConfig = expt_settings.configs.ExperimentConfig
config = ExperimentConfig('electricity', 'outputs')
data_formatter = config.make_data_formatter()
print("*** Training from defined parameters for {} ***".format('electricity'))
data_csv_path = 'hourly_electricity.csv'
print("Loading & splitting data...")
raw_data = pd.read_csv(data_csv_path, index_col=0)
train, valid, test = data_formatter.split_data(raw_data)
train_samples, valid_samples = data_formatter.get_num_samples_for_calibration(
)
*** Training from defined parameters for electricity ***
Loading & splitting data...
Formatting train-valid-test splits.
Setting scalers with training data...
# Sets up default params
fixed_params = data_formatter.get_experiment_params()
params = data_formatter.get_default_model_params()
# train#对部分数据进行了标准化
len(train.id.unique())
369
##定义time_step dataset-->[bs,ts,num_inputs]
class TSDataset(Dataset):#继承Dataset,核心在于__getitem__和__len__
def __init__(self,id_col,static_cols,time_col,input_col,target_col,time_steps,max_samples,input_size,num_encoder_steps,num_static,output_size,data):
self.time_steps=time_steps
self.input_size=input_size
self.output_size=output_size
self.num_encoder_steps=num_encoder_steps
data.sort_values(by=[id_col,time_col],inplace=True)#将数据根据id和时间轴进行排序
valid_sampling_locations=[]
split_data_map={}
for identifier,df in data.groupby(id_col):#group本质上是一个聚合函数，按照需求进行分类，将数据切分成i个df,每个df的聚合指标的值都是相同的
num_entries=len(df)
if num_entries>=self.time_steps:#将数据进行切片
valid_sampling_locations+=[(identifier,self.time_steps+i) for i in range(num_entries-self.time_steps+1)]#第identifier个元组列表，长度为时间轴-时间序列的长度(这里是做项的切割)
split_data_map[identifier]=df#第identifier的字典存放第identifier的项目数据
self.inputs=np.zeros((max_samples,self.time_steps,self.input_size))#大小为samples*ts*input_num
self.outputs=np.zeros((max_samples,self.time_steps,self.output_size))#samples*ts*output_num
self.time=np.empty((max_samples,self.time_steps,1))
self.identifiers=np.empty((max_samples,self.time_steps,num_static))
if max_samples>0 and len(valid_sampling_locations)>max_samples:#基本限制
print(f'Extracting {max_samples} samples')
ranges=[valid_sampling_locations[i] for i in np.random.choice(len(valid_sampling_locations),max_samples,replace=False)]
# 随机在数据集中抽取max_samples个数据
# replace是指允许出现相同的值(拿球后需要放回去),replace为flase是指禁止出现相同的值，此时所选取的数列长度必须要小于数据集合的元素数量
else:
print(f'Max samples ={max_samples} available segments={len(valid_sampling_locations)}')
ranges=valid_sampling_locations
for i,tup in enumerate(ranges):#ranges内为随机切割的(identifier,self.time_step+i)的元组
if ((i+1)%10000)==0:
print(i+1,'of',max_samples,'samples done....')
identifier,start_idx=tup
sliced=split_data_map[identifier].iloc[start_idx-self.time_steps:start_idx]#默认先选第一维
self.inputs[i,:,:]=sliced[input_col]
self.outputs[i,:,:]=sliced[[target_col]]
self.time[i,:,0]=sliced[time_col]
if static_cols:
self.identifiers[i,:,:]=sliced[static_cols]
self.sample_data={
'inputs':self.inputs,
'outputs':self.outputs[:,self.num_encoder_steps:,:],
'active_entries':np.ones_like(self.outputs[:,self.num_encoder_steps:,:]),#np.ones_like函数直接生成某大小的全为1的float数组
'time':self.time,
'identifier':self.identifiers
}
def __getitem__(self,index):
s={
'inputs':self.inputs[index],
'outputs':self.outputs[index,self.num_encoder_steps:,:],
'active_entries': np.ones_like(self.outputs[index, self.num_encoder_steps:, :]),
'time': self.time[index],
'identifier': self.identifiers[index]
}
return s
def __len__(self):
return self.inputs.shape[0]#max_samples
id_col = 'categorical_id'
time_col='hours_from_start'
input_cols =['power_usage', 'hour', 'day_of_week', 'hours_from_start', 'categorical_id']
target_col = 'power_usage'
time_steps=192
num_encoder_steps = 168
output_size = 1
max_samples = 1000
input_size = 5
elect=TSDataset(id_col=id_col,time_col=time_col,input_col=input_cols,target_col=target_col,time_steps=time_steps,max_samples=max_samples,input_size=input_size,num_encoder_steps=num_encoder_steps,output_size=output_size,data=train,static_cols=None,num_static=1)
Extracting 1000 samples
batch_size=128
from torch.utils.data import DataLoader
loader=DataLoader(
elect,
batch_size=batch_size,
)
t=next(iter(loader))
for batch in loader:
break
static_cols = ['meter']
categorical_cols = ['hour']
real_cols = ['power_usage', 'hour', 'day']
config = {}
config['static_variables'] = 1#静态变量的数量
config['time_varying_categorical_variables'] = 1#离散变量的数量
config['time_varying_real_variables_encoder'] = 4#连续变量的数量
config['time_varying_real_variables_decoder'] = 3#解码层连续变量的数量
config['num_masked_series'] = 1#解码层需要mask的变量的数量(4-3)
config['static_embedding_vocab_sizes'] = [369]
config['time_varying_embedding_vocab_sizes'] = [369]
config['embedding_dim'] = 8
config['lstm_hidden_dimension'] = 160
config['lstm_layers'] = 1
config['drop_out'] = 0.05
config['device'] = 'cuda:0'
config['batch_size'] = 128
config['encode_length'] = 168
config['attn_heads'] = 4
config['num_quantiles'] = 3
config['valid_quantiles'] = [0.1, 0.5, 0.9]
config['seq_length']=192#168+24
device=torch.device('cuda:0')
model=TFT(config).to(device)
output,encoder_output,decoder_output,attn_output,atten_output_weight,encoder_sparse_weights,decoder_sparse_weights = model(batch)
D:anaconda3envspytorchlibsite-packagestorchnnmodulesrnn.py:65: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.05 and num_layers=1
"num_layers={}".format(dropout, num_layers))
D:anaconda3envspytorchlibsite-packagesipykernel_launcher.py:134: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
output.shape
torch.Size([128, 24, 3])
q_loss_func=QuantileLoss([0.1,0.5,0.9])
import torch.optim as optim
optimizer = optim.Adam(model.parameters(), lr=0.0001)
model.train()
epochs=100
losses = []
for i in range(epochs):
epoch_loss = []
j=0
for batch in loader:
output, encoder_ouput, decoder_output, attn, attn_weights,encoder_sparse_weights,decoder_sparse_weights = model(batch)
# print(output.device,)
loss= q_loss_func(output[:,:,:].view(-1,3), batch['outputs'][:,:,0].flatten().float().to(device))
loss.backward()
optimizer.step()
epoch_loss.append(loss.item())
j+=1
if j>5:
break
losses.append(np.mean(epoch_loss))
print(np.mean(epoch_loss))
D:anaconda3envspytorchlibsite-packagesipykernel_launcher.py:134: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
0.8006416956583658
0.7999884088834127
0.7668151756127676
0.7319403886795044
0.726747582356135
0.7607065538565317
0.8135423362255096
0.8432016670703888
0.8231969078381857
0.761721005042394
0.6956829130649567
0.6592607200145721
0.6720658640066782
0.7302353382110596
0.7924310863018036
0.8072405358155569
0.7689102192719778
0.7168515821297964
0.6805834372838339
0.6649592419465383
0.6662554542223612
0.678734670082728
0.6953920423984528
0.7107692460219065
0.7225532829761505
0.729248841603597
0.7277947465578715
0.7171043356259664
0.700122614701589
0.6821212271849314
0.6697695453961691
0.6694994668165842
0.6816134452819824
0.7033764322598776
0.725969264904658
0.7419355611006418
0.7459502319494883
0.7388339142004648
0.7245846688747406
0.7101153830687205
0.7013588547706604
0.703824390967687
0.7168124715487162
0.7370895445346832
0.7601349651813507
0.7801720400651296
0.7943577965100607
0.8007777631282806
0.7973537842432658
0.7844546735286713
0.7637931108474731
0.7376755177974701
0.7090499997138977
0.6818542381127676
0.6595257719357809
0.645796130100886
0.6428835491339365
0.6525691449642181
0.6745708882808685
0.7062879502773285
0.7445076505343119
0.7836808959643046
0.8192594250043234
0.8472486337025961
0.8637207349141439
0.8675130009651184
0.8589732150236765
0.8392985363801321
0.8106586337089539
0.7769776284694672
0.7424585918585459
0.7098000744978586
0.6822504798571268
0.661311407883962
0.6473604043324789
0.6410616636276245
0.6426865061124166
0.6523745556672415
0.6679417590300242
0.6892276406288147
0.7145460347334543
0.743001401424408
0.7725268006324768
0.801397830247879
0.8284187217553457
0.8512799839178721
0.868938128153483
0.8796140054861704
0.8833309511343638
0.8803511659304301
0.8718405067920685
0.8586658835411072
0.8424527943134308
0.8235893646876017
0.8035478393236796
0.7830379406611124
0.7635184427102407
0.7448866168657938
0.7281776269276937
0.7138447066148123
output, encoder_ouput, decoder_output, attn, attn_weights,encoder_sparse_weights,decoder_sparse_weights = model(batch)
D:anaconda3envspytorchlibsite-packagesipykernel_launcher.py:134: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.
output.to(torch.device('cpu'))
import matplotlib.pyplot as plt
import numpy as np
ind = np.random.choice(128)
print(ind)
plt.plot(output[ind,:,0].detach().cpu().numpy(), label='pred_1')
plt.plot(output[ind,:,1].detach().cpu().numpy(), label='pred_5')
plt.plot(output[ind,:,2].detach().cpu().numpy(), label='pred_9')
plt.plot(batch['outputs'][ind,:,0], label='true')
plt.legend()
53
<matplotlib.legend.Legend at 0x239bea424c8>
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