前言
- A Time Series is Worth 64 Words论文下载地址，Github项目地址，论文解读系列
- 本文针对PatchTST模型参数与模型架构开源代码进行讲解，本人水平有限，若出现解读错误，欢迎指出
- 开源代码中分别实现了监督学习(
PatchTST_supervised)与自监督学习(PatchTST_self_supervised)框架，本文仅针对监督学习框架进行讲解。
参数设定模块(run_longExp)
- 首先打开
run_longExp.py文件保证在不修改任何参数的情况下，代码可以跑通，这里windows系统需要将代码中--is_training、--model_id、--model、--data参数中required=True选项删除，否则会报错。--num_workers参数需要置为0。 - 其次需要在项目文件夹下新建子文件夹
data用来存放训练数据，可以使用ETTh1数据，这里提供下载地址 - 运行
run_longExp.py训练完成不报错就成功了
参数含义
- 下面是各参数含义（注释）
parser = argparse.ArgumentParser(description='Autoformer & Transformer family for Time Series Forecasting')
# 随机数种子
parser.add_argument('--random_seed', type=int, default=2021, help='random seed')
# basic config
parser.add_argument('--is_training', type=int, default=1, help='status')
parser.add_argument('--model_id', type=str, default='test', help='model id')
parser.add_argument('--model', type=str, default='PatchTST',
help='model name, options: [Autoformer, Informer, Transformer]')
# 数据名称
parser.add_argument('--data', type=str, default='ETTh1', help='dataset type')
# 数据所在文件夹
parser.add_argument('--root_path', type=str, default='./data/', help='root path of the data file')
# 数据文件全称
parser.add_argument('--data_path', type=str, default='ETTh1.csv', help='data file')
# 时间特征处理方式
parser.add_argument('--features', type=str, default='M',
help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
# 目标列列名
parser.add_argument('--target', type=str, default='OT', help='target feature in S or MS task')
# 时间采集粒度
parser.add_argument('--freq', type=str, default='h',
help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
# 模型保存文件夹
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
# 回视窗口
parser.add_argument('--seq_len', type=int, default=96, help='input sequence length')
# 先验序列长度
parser.add_argument('--label_len', type=int, default=48, help='start token length')
# 预测窗口长度
parser.add_argument('--pred_len', type=int, default=96, help='prediction sequence length')
# DLinear
#parser.add_argument('--individual', action='store_true', default=False, help='DLinear: a linear layer for each variate(channel) individually')
# PatchTST
# 全连接层的dropout率
parser.add_argument('--fc_dropout', type=float, default=0.05, help='fully connected dropout')
# 多头注意力机制的dropout率
parser.add_argument('--head_dropout', type=float, default=0.0, help='head dropout')
# patch的长度
parser.add_argument('--patch_len', type=int, default=16, help='patch length')
# 核的步长
parser.add_argument('--stride', type=int, default=8, help='stride')
# padding方式
parser.add_argument('--padding_patch', default='end', help='None: None; end: padding on the end')
# 是否要进行实例归一化(instancenorm1d)
parser.add_argument('--revin', type=int, default=1, help='RevIN; True 1 False 0')
# 是否要学习仿生参数
parser.add_argument('--affine', type=int, default=0, help='RevIN-affine; True 1 False 0')
parser.add_argument('--subtract_last', type=int, default=0, help='0: subtract mean; 1: subtract last')
# 是否做趋势分解
parser.add_argument('--decomposition', type=int, default=0, help='decomposition; True 1 False 0')
# 趋势分解所用kerner_size
parser.add_argument('--kernel_size', type=int, default=25, help='decomposition-kernel')
parser.add_argument('--individual', type=int, default=0, help='individual head; True 1 False 0')
# embedding方式
parser.add_argument('--embed_type', type=int, default=0, help='0: default 1: value embedding + temporal embedding + positional embedding 2: value embedding + temporal embedding 3: value embedding + positional embedding 4: value embedding')
# encoder输入特征数
parser.add_argument('--enc_in', type=int, default=5, help='encoder input size') # DLinear with --individual, use this hyperparameter as the number of channels
# decoder输入特征数
parser.add_argument('--dec_in', type=int, default=5, help='decoder input size')
# 输出通道数
parser.add_argument('--c_out', type=int, default=5, help='output size')
# 线性层隐含神经元个数
parser.add_argument('--d_model', type=int, default=512, help='dimension of model')
# 多头注意力机制
parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
# encoder层数
parser.add_argument('--e_layers', type=int, default=2, help='num of encoder layers')
# decoder层数
parser.add_argument('--d_layers', type=int, default=1, help='num of decoder layers')
# FFN层隐含神经元个数
parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
# 滑动窗口长度
parser.add_argument('--moving_avg', type=int, default=25, help='window size of moving average')
# 对Q进行采样，对Q采样的因子数
parser.add_argument('--factor', type=int, default=1, help='attn factor')
# 是否下采样操作pooling
parser.add_argument('--distil', action='store_false',
help='whether to use distilling in encoder, using this argument means not using distilling',
default=True)
# dropout率
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
# 时间特征嵌入方式
parser.add_argument('--embed', type=str, default='timeF',
help='time features encoding, options:[timeF, fixed, learned]')
# 激活函数类型
parser.add_argument('--activation', type=str, default='gelu', help='activation')
# 是否输出attention矩阵
parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
# 是否进行预测
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
# 并行核心数
parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
# 实验轮数
parser.add_argument('--itr', type=int, default=1, help='experiments times')
# 训练迭代次数
parser.add_argument('--train_epochs', type=int, default=100, help='train epochs')
# batch size大小
parser.add_argument('--batch_size', type=int, default=128, help='batch size of train input data')
# early stopping机制容忍次数
parser.add_argument('--patience', type=int, default=100, help='early stopping patience')
# 学习率
parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test', help='exp description')
# 损失函数
parser.add_argument('--loss', type=str, default='mse', help='loss function')
# 学习率下降策略
parser.add_argument('--lradj', type=str, default='type3', help='adjust learning rate')
parser.add_argument('--pct_start', type=float, default=0.3, help='pct_start')
# 使用混合精度训练
parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
# GPU
parser.add_argument('--use_gpu', type=bool, default=False, help='use gpu')
parser.add_argument('--gpu', type=int, default=0, help='gpu')
parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
parser.add_argument('--devices', type=str, default='0,1,2,3', help='device ids of multile gpus')
parser.add_argument('--test_flop', action='store_true', default=False, help='See utils/tools for usage')
我们在exp.train(setting)行打上断点跳到训练主函数exp_main.py
数据处理模块
在_get_data中找到数据处理函数data_factory.py点击进入，可以看到各标准数据集处理方法：
data_dict = {
'ETTh1': Dataset_ETT_hour,
'ETTh2': Dataset_ETT_hour,
'ETTm1': Dataset_ETT_minute,
'ETTm2': Dataset_ETT_minute,
'power data': Dataset_Custom,
'custom': Dataset_Custom,
}
- 由于我们的数据集是
ETTh1，那么数据处理的方式为Dataset_ETT_hour，我们进入data_loader.py文件，找到Dataset_ETT_hour类 __init__主要用于传各类参数，这里不过多赘述，主要对__read_data__进行说明
def __read_data__(self):
# 数据标准化实例
self.scaler = StandardScaler()
# 读取数据
df_raw = pd.read_csv(os.path.join(self.root_path,
self.data_path))
# 计算数据起始点
border1s = [0, 12 * 30 * 24 - self.seq_len, 12 * 30 * 24 + 4 * 30 * 24 - self.seq_len]
border2s = [12 * 30 * 24, 12 * 30 * 24 + 4 * 30 * 24, 12 * 30 * 24 + 8 * 30 * 24]
border1 = border1s[self.set_type]
border2 = border2s[self.set_type]
# 如果预测对象为多变量预测或多元预测单变量
if self.features == 'M' or self.features == 'MS':
# 取除日期列的其他所有列
cols_data = df_raw.columns[1:]
df_data = df_raw[cols_data]
# 若预测类型为S(单特征预测单特征)
elif self.features == 'S':
# 取特征列
df_data = df_raw[[self.target]]
# 将数据进行归一化
if self.scale:
train_data = df_data[border1s[0]:border2s[0]]
self.scaler.fit(train_data.values)
data = self.scaler.transform(df_data.values)
else:
data = df_data.values
# 取日期列
df_stamp = df_raw[['date']][border1:border2]
# 利用pandas将数据转换为日期格式
df_stamp['date'] = pd.to_datetime(df_stamp.date)
# 构建时间特征
if self.timeenc == 0:
df_stamp['month'] = df_stamp.date.apply(lambda row: row.month, 1)
df_stamp['day'] = df_stamp.date.apply(lambda row: row.day, 1)
df_stamp['weekday'] = df_stamp.date.apply(lambda row: row.weekday(), 1)
df_stamp['hour'] = df_stamp.date.apply(lambda row: row.hour, 1)
data_stamp = df_stamp.drop(['date'], 1).values
elif self.timeenc == 1:
# 时间特征构造函数
data_stamp = time_features(pd.to_datetime(df_stamp['date'].values), freq=self.freq)
# 转置
data_stamp = data_stamp.transpose(1, 0)
# 取数据特征列
self.data_x = data[border1:border2]
self.data_y = data[border1:border2]
self.data_stamp = data_stamp
- 需要注意的是
time_features函数，用来提取日期特征，比如't':['month','day','weekday','hour','minute']，表示提取月，天，周，小时，分钟。可以打开timefeatures.py文件进行查阅，同样后期也可以加一些日期编码进去。 - 同样的，对
__getitem__进行说明
def __getitem__(self, index):
# 随机取得标签
s_begin = index
# 训练区间
s_end = s_begin + self.seq_len
# 有标签区间+无标签区间(预测时间步长)
r_begin = s_end - self.label_len
r_end = r_begin + self.label_len + self.pred_len
# 取训练数据
seq_x = self.data_x[s_begin:s_end]
seq_y = self.data_y[r_begin:r_end]
# 取训练数据对应时间特征
seq_x_mark = self.data_stamp[s_begin:s_end]
# 取有标签区间+无标签区间(预测时间步长)对应时间特征
seq_y_mark = self.data_stamp[r_begin:r_end]
return seq_x, seq_y, seq_x_mark, seq_y_mark
- 关于这部分数据处理可能有些绕，开源看我在SCInet代码讲解中数据处理那一部分，绘制了数据集划分图。
网络架构
- 这里将模型框架示意图展示出来，方便后续讲解。
- 打开
PatchTST.py文件，可以看到Model类中实例化了骨干网络PatchTST_backbone
PatchTST_backbone
- 可以看到
PatchTST_backbone类，我们直接看该类forward方法。 - 首先将输入进行
revin归一化，然后对数据进行padding操作，使用unfold方法通过滑窗得到不同patch。然后将数据输入TSTiEncoder中。得到输出，通过FNNhead输出结果，再反归一化Revin。 - 代码解析如下所示
class PatchTST_backbone(nn.Module):
def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024,
n_layers:int=3, d_model=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None,
d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str="gelu", key_padding_mask:bool='auto',
padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False,
pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0., head_dropout = 0, padding_patch = None,
pretrain_head:bool=False, head_type = 'flatten', individual = False, revin = True, affine = True, subtract_last = False,
verbose:bool=False, **kwargs):
super().__init__()
# RevIn
self.revin = revin
if self.revin: self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last)
# Patching
self.patch_len = patch_len
self.stride = stride
self.padding_patch = padding_patch
patch_num = int((context_window - patch_len)/stride + 1)
if padding_patch == 'end': # can be modified to general case
self.padding_patch_layer = nn.ReplicationPad1d((0, stride))
patch_num += 1
# Backbone
self.backbone = TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len, max_seq_len=max_seq_len,
n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff,
attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var,
attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn,
pe=pe, learn_pe=learn_pe, verbose=verbose, **kwargs)
# Head
self.head_nf = d_model * patch_num
self.n_vars = c_in
self.pretrain_head = pretrain_head
self.head_type = head_type
self.individual = individual
if self.pretrain_head:
self.head = self.create_pretrain_head(self.head_nf, c_in, fc_dropout) # custom head passed as a partial func with all its kwargs
elif head_type == 'flatten':
self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window, head_dropout=head_dropout)
def forward(self, z):
# z:[batch,feature,seq_len]
# norm
if self.revin:
z = z.permute(0,2,1)
z = self.revin_layer(z, 'norm')
z = z.permute(0,2,1)
# do patching
if self.padding_patch == 'end':
# padding操作
z = self.padding_patch_layer(z)
# 从一个分批输入的张量中提取滑动的局部块
# z:[batch,feature,patch_num,patch_len]
z = z.unfold(dimension=-1, size=self.patch_len, step=self.stride)
# 维度交换z:[batch,feature,patch_len,patch_num]
z = z.permute(0,1,3,2)
# 进入骨干网络,输出维度[batch, feature, d_model, patch_num]
z = self.backbone(z)
z = self.head(z) # z: [bs x nvars x target_window]
# 反归一化
if self.revin:
z = z.permute(0,2,1)
z = self.revin_layer(z, 'denorm')
z = z.permute(0,2,1)
return z
TSTiEncoder
- 首先将数据进行维度转换，放入位置编码
position_encoding函数，初始化为均匀分布[-0.02,0.02]区间
def positional_encoding(pe, learn_pe, q_len, d_model):
# Positional encoding
if pe == None:
W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe
nn.init.uniform_(W_pos, -0.02, 0.02)
learn_pe = False
elif pe == 'zero':
W_pos = torch.empty((q_len, 1))
nn.init.uniform_(W_pos, -0.02, 0.02)
elif pe == 'zeros':
W_pos = torch.empty((q_len, d_model))
nn.init.uniform_(W_pos, -0.02, 0.02)
elif pe == 'normal' or pe == 'gauss':
W_pos = torch.zeros((q_len, 1))
torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)
elif pe == 'uniform':
W_pos = torch.zeros((q_len, 1))
nn.init.uniform_(W_pos, a=0.0, b=0.1)
elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True)
elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True)
elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True)
elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True)
elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True)
else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \
'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)")
# 设定为可训练参数
return nn.Parameter(W_pos, requires_grad=learn_pe)
- 然后进入dropout --> Encoder
class TSTiEncoder(nn.Module): #i means channel-independent
def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024,
n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None,
d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act="gelu", store_attn=False,
key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False,
pe='zeros', learn_pe=True, verbose=False, **kwargs):
super().__init__()
self.patch_num = patch_num
self.patch_len = patch_len
# Input encoding
q_len = patch_num
self.W_P = nn.Linear(patch_len, d_model) # Eq 1: projection of feature vectors onto a d-dim vector space
self.seq_len = q_len
# Positional encoding
self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model)
# Residual dropout
self.dropout = nn.Dropout(dropout)
# Encoder
self.encoder = TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout,
pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn)
def forward(self, x) -> Tensor:
# 输入x维度:[batch,feature,patch_len,patch_num]
# 取feature数量
n_vars = x.shape[1]
# 调换维度,变为:[batch, feature, patch_num, patch_len]
x = x.permute(0,1,3,2)
# 进入全连接层，输出为[batch, feature, patch_num, d_model]
x = self.W_P(x)
# 重置维度为[batch * feature, patch_nums, d_model]
u = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3]))
# 进入位置编码后共同进入dropout层[batch * feature,patch_nums,d_model]
u = self.dropout(u + self.W_pos)
# 进入encoder层后z的维度[batch * feature, patch_num, d_model]
z = self.encoder(u)
# 重置维度为[batch, feature, patch_num, d_model]
z = torch.reshape(z, (-1,n_vars,z.shape[-2],z.shape[-1]))
# 再度交换维度为[batch, feature, d_model, patch_num]
z = z.permute(0,1,3,2)
return z
TSTEncoderLayer
class TSTEncoderLayer(nn.Module):
def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False,
norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation="gelu", res_attention=False, pre_norm=False):
super().__init__()
assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})"
d_k = d_model // n_heads if d_k is None else d_k
d_v = d_model // n_heads if d_v is None else d_v
# Multi-Head attention
self.res_attention = res_attention
self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention)
# Add & Norm
self.dropout_attn = nn.Dropout(dropout)
if "batch" in norm.lower():
self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
else:
self.norm_attn = nn.LayerNorm(d_model)
# Position-wise Feed-Forward
self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias),
get_activation_fn(activation),
nn.Dropout(dropout),
nn.Linear(d_ff, d_model, bias=bias))
# Add & Norm
self.dropout_ffn = nn.Dropout(dropout)
if "batch" in norm.lower():
self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2))
else:
self.norm_ffn = nn.LayerNorm(d_model)
self.pre_norm = pre_norm
self.store_attn = store_attn
def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor:
# Multi-Head attention sublayer
if self.pre_norm:
src = self.norm_attn(src)
## Multi-Head attention
if self.res_attention:
src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
else:
src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask)
if self.store_attn:
self.attn = attn
## Add & Norm
src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout
if not self.pre_norm:
src = self.norm_attn(src)
# Feed-forward sublayer
if self.pre_norm:
src = self.norm_ffn(src)
## Position-wise Feed-Forward
src2 = self.ff(src)
## Add & Norm
src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout
if not self.pre_norm:
src = self.norm_ffn(src)
if self.res_attention:
return src, scores
else:
return src
Flatten层
class Flatten_Head(nn.Module):
def __init__(self, individual, n_vars, nf, target_window, head_dropout=0):
super().__init__()
self.individual = individual
self.n_vars = n_vars
if self.individual:
# 对每个特征进行展平,然后进入线性层和dropout层
self.linears = nn.ModuleList()
self.dropouts = nn.ModuleList()
self.flattens = nn.ModuleList()
for i in range(self.n_vars):
self.flattens.append(nn.Flatten(start_dim=-2))
self.linears.append(nn.Linear(nf, target_window))
self.dropouts.append(nn.Dropout(head_dropout))
else:
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]
if self.individual:
x_out = []
for i in range(self.n_vars):
z = self.flattens[i](x[:,i,:,:]) # z: [bs x d_model * patch_num]
z = self.linears[i](z) # z: [bs x target_window]
z = self.dropouts[i](z)
x_out.append(z)
x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window]
else:
# 输出x为[batch,target_window]
x = self.flatten(x)
x = self.linear(x)
x = self.dropout(x)
return x
