# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.tsmixer.ipynb.
# %% auto 0
__all__ = ['TSMixer']
# %% ../../nbs/models.tsmixer.ipynb 5
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..losses.pytorch import MAE
from ..common._base_multivariate import BaseMultivariate
# %% ../../nbs/models.tsmixer.ipynb 8
class TemporalMixing(nn.Module):
def __init__(self, n_series, input_size, dropout):
super().__init__()
self.temporal_norm = nn.BatchNorm1d(
num_features=n_series * input_size, eps=0.001, momentum=0.01
)
self.temporal_lin = nn.Linear(input_size, input_size)
self.temporal_drop = nn.Dropout(dropout)
def forward(self, input):
# Get shapes
batch_size = input.shape[0]
input_size = input.shape[1]
n_series = input.shape[2]
# Temporal MLP
x = input.permute(0, 2, 1) # [B, L, N] -> [B, N, L]
x = x.reshape(batch_size, -1) # [B, N, L] -> [B, N * L]
x = self.temporal_norm(x) # [B, N * L] -> [B, N * L]
x = x.reshape(batch_size, n_series, input_size) # [B, N * L] -> [B, N, L]
x = F.relu(self.temporal_lin(x)) # [B, N, L] -> [B, N, L]
x = x.permute(0, 2, 1) # [B, N, L] -> [B, L, N]
x = self.temporal_drop(x) # [B, L, N] -> [B, L, N]
return x + input
class FeatureMixing(nn.Module):
def __init__(self, n_series, input_size, dropout, ff_dim):
super().__init__()
self.feature_norm = nn.BatchNorm1d(
num_features=n_series * input_size, eps=0.001, momentum=0.01
)
self.feature_lin_1 = nn.Linear(n_series, ff_dim)
self.feature_lin_2 = nn.Linear(ff_dim, n_series)
self.feature_drop_1 = nn.Dropout(dropout)
self.feature_drop_2 = nn.Dropout(dropout)
def forward(self, input):
# Get shapes
batch_size = input.shape[0]
input_size = input.shape[1]
n_series = input.shape[2]
# Feature MLP
x = input.reshape(batch_size, -1) # [B, L, N] -> [B, L * N]
x = self.feature_norm(x) # [B, L * N] -> [B, L * N]
x = x.reshape(batch_size, input_size, n_series) # [B, L * N] -> [B, L, N]
x = F.relu(self.feature_lin_1(x)) # [B, L, N] -> [B, L, ff_dim]
x = self.feature_drop_1(x) # [B, L, ff_dim] -> [B, L, ff_dim]
x = self.feature_lin_2(x) # [B, L, ff_dim] -> [B, L, N]
x = self.feature_drop_2(x) # [B, L, N] -> [B, L, N]
return x + input
class MixingLayer(nn.Module):
def __init__(self, n_series, input_size, dropout, ff_dim):
super().__init__()
# Mixing layer consists of a temporal and feature mixer
self.temporal_mixer = TemporalMixing(n_series, input_size, dropout)
self.feature_mixer = FeatureMixing(n_series, input_size, dropout, ff_dim)
def forward(self, input):
x = self.temporal_mixer(input)
x = self.feature_mixer(x)
return x
# %% ../../nbs/models.tsmixer.ipynb 10
class ReversibleInstanceNorm1d(nn.Module):
def __init__(self, n_series, eps=1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones((1, 1, n_series)))
self.bias = nn.Parameter(torch.zeros((1, 1, n_series)))
self.eps = eps
def forward(self, x):
# Batch statistics
self.batch_mean = torch.mean(x, axis=1, keepdim=True).detach()
self.batch_std = torch.sqrt(
torch.var(x, axis=1, keepdim=True, unbiased=False) + self.eps
).detach()
# Instance normalization
x = x - self.batch_mean
x = x / self.batch_std
x = x * self.weight
x = x + self.bias
return x
def reverse(self, x):
# Reverse the normalization
x = x - self.bias
x = x / self.weight
x = x * self.batch_std
x = x + self.batch_mean
return x
# %% ../../nbs/models.tsmixer.ipynb 12
class TSMixer(BaseMultivariate):
"""TSMixer
Time-Series Mixer (`TSMixer`) is a MLP-based multivariate time-series forecasting model. `TSMixer` jointly learns temporal and cross-sectional representations of the time-series by repeatedly combining time- and feature information using stacked mixing layers. A mixing layer consists of a sequential time- and feature Multi Layer Perceptron (`MLP`).
**Parameters:**
`h`: int, forecast horizon.
`input_size`: int, considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
`n_series`: int, number of time-series.
`futr_exog_list`: str list, future exogenous columns.
`hist_exog_list`: str list, historic exogenous columns.
`stat_exog_list`: str list, static exogenous columns.
`n_block`: int=2, number of mixing layers in the model.
`ff_dim`: int=64, number of units for the second feed-forward layer in the feature MLP.
`dropout`: float=0.9, dropout rate between (0, 1) .
`revin`: bool=True, if True uses Reverse Instance Normalization to process inputs and outputs.
`loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`valid_loss`: PyTorch module=`loss`, instantiated valid loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`max_steps`: int=1000, maximum number of training steps.
`learning_rate`: float=1e-3, Learning rate between (0, 1).
`num_lr_decays`: int=-1, Number of learning rate decays, evenly distributed across max_steps.
`early_stop_patience_steps`: int=-1, Number of validation iterations before early stopping.
`val_check_steps`: int=100, Number of training steps between every validation loss check.
`batch_size`: int=32, number of different series in each batch.
`step_size`: int=1, step size between each window of temporal data.
`scaler_type`: str='identity', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).
`random_seed`: int=1, random_seed for pytorch initializer and numpy generators.
`num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.
`drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.
`alias`: str, optional, Custom name of the model.
`optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).
`optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.
`**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
**References:**
- [Chen, Si-An, Chun-Liang Li, Nate Yoder, Sercan O. Arik, and Tomas Pfister (2023). "TSMixer: An All-MLP Architecture for Time Series Forecasting."](http://arxiv.org/abs/2303.06053)
"""
# Class attributes
SAMPLING_TYPE = "multivariate"
def __init__(
self,
h,
input_size,
n_series,
futr_exog_list=None,
hist_exog_list=None,
stat_exog_list=None,
n_block=2,
ff_dim=64,
dropout=0.9,
revin=True,
loss=MAE(),
valid_loss=None,
max_steps: int = 1000,
learning_rate: float = 1e-3,
num_lr_decays: int = -1,
early_stop_patience_steps: int = -1,
val_check_steps: int = 100,
batch_size: int = 32,
step_size: int = 1,
scaler_type: str = "identity",
random_seed: int = 1,
num_workers_loader: int = 0,
drop_last_loader: bool = False,
optimizer=None,
optimizer_kwargs=None,
**trainer_kwargs
):
# Inherit BaseMultivariate class
super(TSMixer, self).__init__(
h=h,
input_size=input_size,
n_series=n_series,
futr_exog_list=futr_exog_list,
hist_exog_list=hist_exog_list,
stat_exog_list=stat_exog_list,
loss=loss,
valid_loss=valid_loss,
max_steps=max_steps,
learning_rate=learning_rate,
num_lr_decays=num_lr_decays,
early_stop_patience_steps=early_stop_patience_steps,
val_check_steps=val_check_steps,
batch_size=batch_size,
step_size=step_size,
scaler_type=scaler_type,
random_seed=random_seed,
num_workers_loader=num_workers_loader,
drop_last_loader=drop_last_loader,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
**trainer_kwargs
)
# Asserts
if stat_exog_list is not None:
raise Exception(
"TSMixer does not support static exogenous variables. Use TSMixerx if you want to use static exogenous variables."
)
if futr_exog_list is not None:
raise Exception(
"TSMixer does not support future exogenous variables. Use TSMixerx if you want to use future exogenous variables."
)
if hist_exog_list is not None:
raise Exception(
"TSMixer does not support historical exogenous variables. Use TSMixerx if you want to use historical exogenous variables."
)
# Reversible InstanceNormalization layer
self.revin = revin
if self.revin:
self.norm = ReversibleInstanceNorm1d(n_series=n_series)
# Mixing layers
mixing_layers = [
MixingLayer(
n_series=n_series, input_size=input_size, dropout=dropout, ff_dim=ff_dim
)
for _ in range(n_block)
]
self.mixing_layers = nn.Sequential(*mixing_layers)
# Linear output with Loss dependent dimensions
self.out = nn.Linear(
in_features=input_size, out_features=h * self.loss.outputsize_multiplier
)
def forward(self, windows_batch):
# Parse batch
x = windows_batch["insample_y"] # x: [batch_size, input_size, n_series]
batch_size = x.shape[0]
# TSMixer: InstanceNorm + Mixing layers + Dense output layer + ReverseInstanceNorm
if self.revin:
x = self.norm(x)
x = self.mixing_layers(x)
x = x.permute(0, 2, 1)
x = self.out(x)
x = x.permute(0, 2, 1)
if self.revin:
x = self.norm.reverse(x)
x = x.reshape(
batch_size, self.h, self.loss.outputsize_multiplier * self.n_series
)
forecast = self.loss.domain_map(x)
# domain_map might have squeezed the last dimension in case n_series == 1
# Note that this fails in case of a tuple loss, but Multivariate does not support tuple losses yet.
if forecast.ndim == 2:
return forecast.unsqueeze(-1)
else:
return forecast