# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.rnn.ipynb.
# %% auto 0
__all__ = ['RNN']
# %% ../../nbs/models.rnn.ipynb 6
from typing import Optional
import torch
import torch.nn as nn
from ..losses.pytorch import MAE
from ..common._base_recurrent import BaseRecurrent
from ..common._modules import MLP
# %% ../../nbs/models.rnn.ipynb 7
class RNN(BaseRecurrent):
"""RNN
Multi Layer Elman RNN (RNN), with MLP decoder.
The network has `tanh` or `relu` non-linearities, it is trained using
ADAM stochastic gradient descent. The network accepts static, historic
and future exogenous data.
**Parameters:**
`h`: int, forecast horizon.
`input_size`: int, maximum sequence length for truncated train backpropagation. Default -1 uses all history.
`inference_input_size`: int, maximum sequence length for truncated inference. Default -1 uses all history.
`encoder_n_layers`: int=2, number of layers for the RNN.
`encoder_hidden_size`: int=200, units for the RNN's hidden state size.
`encoder_activation`: str=`tanh`, type of RNN activation from `tanh` or `relu`.
`encoder_bias`: bool=True, whether or not to use biases b_ih, b_hh within RNN units.
`encoder_dropout`: float=0., dropout regularization applied to RNN outputs.
`context_size`: int=10, size of context vector for each timestamp on the forecasting window.
`decoder_hidden_size`: int=200, size of hidden layer for the MLP decoder.
`decoder_layers`: int=2, number of layers for the MLP decoder.
`futr_exog_list`: str list, future exogenous columns.
`hist_exog_list`: str list, historic exogenous columns.
`stat_exog_list`: str list, static exogenous columns.
`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 differentseries in each batch.
`valid_batch_size`: int=None, number of different series in each validation and test batch.
`scaler_type`: str='robust', 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.
`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`.
`alias`: str, optional, Custom name of the model.
`**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).
"""
# Class attributes
SAMPLING_TYPE = "recurrent"
def __init__(
self,
h: int,
input_size: int = -1,
inference_input_size: int = -1,
encoder_n_layers: int = 2,
encoder_hidden_size: int = 200,
encoder_activation: str = "tanh",
encoder_bias: bool = True,
encoder_dropout: float = 0.0,
context_size: int = 10,
decoder_hidden_size: int = 200,
decoder_layers: int = 2,
futr_exog_list=None,
hist_exog_list=None,
stat_exog_list=None,
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=32,
valid_batch_size: Optional[int] = None,
scaler_type: str = "robust",
random_seed=1,
num_workers_loader=0,
drop_last_loader=False,
optimizer=None,
optimizer_kwargs=None,
**trainer_kwargs
):
super(RNN, self).__init__(
h=h,
input_size=input_size,
inference_input_size=inference_input_size,
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,
valid_batch_size=valid_batch_size,
scaler_type=scaler_type,
futr_exog_list=futr_exog_list,
hist_exog_list=hist_exog_list,
stat_exog_list=stat_exog_list,
num_workers_loader=num_workers_loader,
drop_last_loader=drop_last_loader,
random_seed=random_seed,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
**trainer_kwargs
)
# RNN
self.encoder_n_layers = encoder_n_layers
self.encoder_hidden_size = encoder_hidden_size
self.encoder_activation = encoder_activation
self.encoder_bias = encoder_bias
self.encoder_dropout = encoder_dropout
# Context adapter
self.context_size = context_size
# MLP decoder
self.decoder_hidden_size = decoder_hidden_size
self.decoder_layers = decoder_layers
self.futr_exog_size = len(self.futr_exog_list)
self.hist_exog_size = len(self.hist_exog_list)
self.stat_exog_size = len(self.stat_exog_list)
# RNN input size (1 for target variable y)
input_encoder = 1 + self.hist_exog_size + self.stat_exog_size
# Instantiate model
self.hist_encoder = nn.RNN(
input_size=input_encoder,
hidden_size=self.encoder_hidden_size,
num_layers=self.encoder_n_layers,
nonlinearity=self.encoder_activation,
bias=self.encoder_bias,
dropout=self.encoder_dropout,
batch_first=True,
)
# Context adapter
self.context_adapter = nn.Linear(
in_features=self.encoder_hidden_size + self.futr_exog_size * h,
out_features=self.context_size * h,
)
# Decoder MLP
self.mlp_decoder = MLP(
in_features=self.context_size + self.futr_exog_size,
out_features=self.loss.outputsize_multiplier,
hidden_size=self.decoder_hidden_size,
num_layers=self.decoder_layers,
activation="ReLU",
dropout=0.0,
)
def forward(self, windows_batch):
# Parse windows_batch
encoder_input = windows_batch["insample_y"] # [B, seq_len, 1]
futr_exog = windows_batch["futr_exog"]
hist_exog = windows_batch["hist_exog"]
stat_exog = windows_batch["stat_exog"]
# Concatenate y, historic and static inputs
# [B, C, seq_len, 1] -> [B, seq_len, C]
# Contatenate [ Y_t, | X_{t-L},..., X_{t} | S ]
batch_size, seq_len = encoder_input.shape[:2]
if self.hist_exog_size > 0:
hist_exog = hist_exog.permute(0, 2, 1, 3).squeeze(
-1
) # [B, X, seq_len, 1] -> [B, seq_len, X]
encoder_input = torch.cat((encoder_input, hist_exog), dim=2)
if self.stat_exog_size > 0:
stat_exog = stat_exog.unsqueeze(1).repeat(
1, seq_len, 1
) # [B, S] -> [B, seq_len, S]
encoder_input = torch.cat((encoder_input, stat_exog), dim=2)
# RNN forward
hidden_state, _ = self.hist_encoder(
encoder_input
) # [B, seq_len, rnn_hidden_state]
if self.futr_exog_size > 0:
futr_exog = futr_exog.permute(0, 2, 3, 1)[
:, :, 1:, :
] # [B, F, seq_len, 1+H] -> [B, seq_len, H, F]
hidden_state = torch.cat(
(hidden_state, futr_exog.reshape(batch_size, seq_len, -1)), dim=2
)
# Context adapter
context = self.context_adapter(hidden_state)
context = context.reshape(batch_size, seq_len, self.h, self.context_size)
# Residual connection with futr_exog
if self.futr_exog_size > 0:
context = torch.cat((context, futr_exog), dim=-1)
# Final forecast
output = self.mlp_decoder(context)
output = self.loss.domain_map(output)
return output