# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.nbeatsx.ipynb.
# %% auto 0
__all__ = ['NBEATSx']
# %% ../../nbs/models.nbeatsx.ipynb 6
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
from ..losses.pytorch import MAE
from ..common._base_windows import BaseWindows
# %% ../../nbs/models.nbeatsx.ipynb 8
class IdentityBasis(nn.Module):
def __init__(self, backcast_size: int, forecast_size: int, out_features: int = 1):
super().__init__()
self.out_features = out_features
self.forecast_size = forecast_size
self.backcast_size = backcast_size
def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
backcast = theta[:, : self.backcast_size]
forecast = theta[:, self.backcast_size :]
forecast = forecast.reshape(len(forecast), -1, self.out_features)
return backcast, forecast
class TrendBasis(nn.Module):
def __init__(
self,
degree_of_polynomial: int,
backcast_size: int,
forecast_size: int,
out_features: int = 1,
):
super().__init__()
self.out_features = out_features
polynomial_size = degree_of_polynomial + 1
self.backcast_basis = nn.Parameter(
torch.tensor(
np.concatenate(
[
np.power(
np.arange(backcast_size, dtype=float) / backcast_size, i
)[None, :]
for i in range(polynomial_size)
]
),
dtype=torch.float32,
),
requires_grad=False,
)
self.forecast_basis = nn.Parameter(
torch.tensor(
np.concatenate(
[
np.power(
np.arange(forecast_size, dtype=float) / forecast_size, i
)[None, :]
for i in range(polynomial_size)
]
),
dtype=torch.float32,
),
requires_grad=False,
)
def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
polynomial_size = self.forecast_basis.shape[0] # [polynomial_size, L+H]
backcast_theta = theta[:, :polynomial_size]
forecast_theta = theta[:, polynomial_size:]
forecast_theta = forecast_theta.reshape(
len(forecast_theta), polynomial_size, -1
)
backcast = torch.einsum("bp,pt->bt", backcast_theta, self.backcast_basis)
forecast = torch.einsum("bpq,pt->btq", forecast_theta, self.forecast_basis)
return backcast, forecast
class ExogenousBasis(nn.Module):
# Reference: https://github.com/cchallu/nbeatsx
def __init__(self, forecast_size: int):
super().__init__()
self.forecast_size = forecast_size
def forward(
self, theta: torch.Tensor, futr_exog: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
backcast_basis = futr_exog[:, : -self.forecast_size, :].permute(0, 2, 1)
forecast_basis = futr_exog[:, -self.forecast_size :, :].permute(0, 2, 1)
cut_point = forecast_basis.shape[1]
backcast_theta = theta[:, cut_point:]
forecast_theta = theta[:, :cut_point].reshape(len(theta), cut_point, -1)
backcast = torch.einsum("bp,bpt->bt", backcast_theta, backcast_basis)
forecast = torch.einsum("bpq,bpt->btq", forecast_theta, forecast_basis)
return backcast, forecast
class SeasonalityBasis(nn.Module):
def __init__(
self,
harmonics: int,
backcast_size: int,
forecast_size: int,
out_features: int = 1,
):
super().__init__()
self.out_features = out_features
frequency = np.append(
np.zeros(1, dtype=float),
np.arange(harmonics, harmonics / 2 * forecast_size, dtype=float)
/ harmonics,
)[None, :]
backcast_grid = (
-2
* np.pi
* (np.arange(backcast_size, dtype=float)[:, None] / forecast_size)
* frequency
)
forecast_grid = (
2
* np.pi
* (np.arange(forecast_size, dtype=float)[:, None] / forecast_size)
* frequency
)
backcast_cos_template = torch.tensor(
np.transpose(np.cos(backcast_grid)), dtype=torch.float32
)
backcast_sin_template = torch.tensor(
np.transpose(np.sin(backcast_grid)), dtype=torch.float32
)
backcast_template = torch.cat(
[backcast_cos_template, backcast_sin_template], dim=0
)
forecast_cos_template = torch.tensor(
np.transpose(np.cos(forecast_grid)), dtype=torch.float32
)
forecast_sin_template = torch.tensor(
np.transpose(np.sin(forecast_grid)), dtype=torch.float32
)
forecast_template = torch.cat(
[forecast_cos_template, forecast_sin_template], dim=0
)
self.backcast_basis = nn.Parameter(backcast_template, requires_grad=False)
self.forecast_basis = nn.Parameter(forecast_template, requires_grad=False)
def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
harmonic_size = self.forecast_basis.shape[0] # [harmonic_size, L+H]
backcast_theta = theta[:, :harmonic_size]
forecast_theta = theta[:, harmonic_size:]
forecast_theta = forecast_theta.reshape(len(forecast_theta), harmonic_size, -1)
backcast = torch.einsum("bp,pt->bt", backcast_theta, self.backcast_basis)
forecast = torch.einsum("bpq,pt->btq", forecast_theta, self.forecast_basis)
return backcast, forecast
# %% ../../nbs/models.nbeatsx.ipynb 9
ACTIVATIONS = ["ReLU", "Softplus", "Tanh", "SELU", "LeakyReLU", "PReLU", "Sigmoid"]
class NBEATSBlock(nn.Module):
"""
N-BEATS block which takes a basis function as an argument.
"""
def __init__(
self,
input_size: int,
h: int,
futr_input_size: int,
hist_input_size: int,
stat_input_size: int,
n_theta: int,
mlp_units: list,
basis: nn.Module,
dropout_prob: float,
activation: str,
):
""" """
super().__init__()
self.dropout_prob = dropout_prob
self.futr_input_size = futr_input_size
self.hist_input_size = hist_input_size
self.stat_input_size = stat_input_size
assert activation in ACTIVATIONS, f"{activation} is not in {ACTIVATIONS}"
activ = getattr(nn, activation)()
# Input vector for the block is
# y_lags (input_size) + historical exogenous (hist_input_size*input_size) +
# future exogenous (futr_input_size*input_size) + static exogenous (stat_input_size)
# [ Y_[t-L:t], X_[t-L:t], F_[t-L:t+H], S ]
input_size = (
input_size
+ hist_input_size * input_size
+ futr_input_size * (input_size + h)
+ stat_input_size
)
hidden_layers = [
nn.Linear(in_features=input_size, out_features=mlp_units[0][0])
]
for layer in mlp_units:
hidden_layers.append(nn.Linear(in_features=layer[0], out_features=layer[1]))
hidden_layers.append(activ)
if self.dropout_prob > 0:
hidden_layers.append(nn.Dropout(p=self.dropout_prob))
output_layer = [nn.Linear(in_features=mlp_units[-1][1], out_features=n_theta)]
layers = hidden_layers + output_layer
self.layers = nn.Sequential(*layers)
self.basis = basis
def forward(
self,
insample_y: torch.Tensor,
futr_exog: torch.Tensor,
hist_exog: torch.Tensor,
stat_exog: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Flatten MLP inputs [B, L+H, C] -> [B, (L+H)*C]
# Contatenate [ Y_t, | X_{t-L},..., X_{t} | F_{t-L},..., F_{t+H} | S ]
batch_size = len(insample_y)
if self.hist_input_size > 0:
insample_y = torch.cat(
(insample_y, hist_exog.reshape(batch_size, -1)), dim=1
)
if self.futr_input_size > 0:
insample_y = torch.cat(
(insample_y, futr_exog.reshape(batch_size, -1)), dim=1
)
if self.stat_input_size > 0:
insample_y = torch.cat(
(insample_y, stat_exog.reshape(batch_size, -1)), dim=1
)
# Compute local projection weights and projection
theta = self.layers(insample_y)
if isinstance(self.basis, ExogenousBasis):
if self.futr_input_size > 0 and self.stat_input_size > 0:
futr_exog = torch.cat((futr_exog, stat_exog), dim=2)
elif self.futr_input_size > 0:
futr_exog = futr_exog
elif self.stat_input_size > 0:
futr_exog = stat_exog
else:
raise (
ValueError(
"No stats or future exogenous. ExogenousBlock not supported."
)
)
backcast, forecast = self.basis(theta, futr_exog)
return backcast, forecast
else:
backcast, forecast = self.basis(theta)
return backcast, forecast
# %% ../../nbs/models.nbeatsx.ipynb 10
class NBEATSx(BaseWindows):
"""NBEATSx
The Neural Basis Expansion Analysis with Exogenous variables (NBEATSx) is a simple
and effective deep learning architecture. It is built with a deep stack of MLPs with
doubly residual connections. The NBEATSx architecture includes additional exogenous
blocks, extending NBEATS capabilities and interpretability. With its interpretable
version, NBEATSx decomposes its predictions on seasonality, trend, and exogenous effects.
**Parameters:**
`h`: int, Forecast horizon.
`input_size`: int, autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].
`stat_exog_list`: str list, static exogenous columns.
`hist_exog_list`: str list, historic exogenous columns.
`futr_exog_list`: str list, future exogenous columns.
`exclude_insample_y`: bool=False, the model skips the autoregressive features y[t-input_size:t] if True.
`n_harmonics`: int, Number of harmonic oscillations in the SeasonalityBasis [cos(i * t/n_harmonics), sin(i * t/n_harmonics)]. Note that it will only be used if 'seasonality' is in `stack_types`.
`n_polynomials`: int, Number of polynomial terms for TrendBasis [1,t,...,t^n_poly]. Note that it will only be used if 'trend' is in `stack_types`.
`stack_types`: List[str], List of stack types. Subset from ['seasonality', 'trend', 'identity'].
`n_blocks`: List[int], Number of blocks for each stack. Note that len(n_blocks) = len(stack_types).
`mlp_units`: List[List[int]], Structure of hidden layers for each stack type. Each internal list should contain the number of units of each hidden layer. Note that len(n_hidden) = len(stack_types).
`dropout_prob_theta`: float, Float between (0, 1). Dropout for N-BEATS basis.
`activation`: str, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'].
`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=3, 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.
`valid_batch_size`: int=None, number of different series in each validation and test batch, if None uses batch_size.
`windows_batch_size`: int=1024, number of windows to sample in each training batch, default uses all.
`inference_windows_batch_size`: int=-1, number of windows to sample in each inference batch, -1 uses all.
`start_padding_enabled`: bool=False, if True, the model will pad the time series with zeros at the beginning, by input size.
`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, random seed initialization for replicability.
`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:**
-[Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski (2021).
"Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx".](https://arxiv.org/abs/2104.05522)
"""
# Class attributes
SAMPLING_TYPE = "windows"
def __init__(
self,
h,
input_size,
futr_exog_list=None,
hist_exog_list=None,
stat_exog_list=None,
exclude_insample_y=False,
n_harmonics=2,
n_polynomials=2,
stack_types: list = ["identity", "trend", "seasonality"],
n_blocks: list = [1, 1, 1],
mlp_units: list = 3 * [[512, 512]],
dropout_prob_theta=0.0,
activation="ReLU",
shared_weights=False,
loss=MAE(),
valid_loss=None,
max_steps: int = 1000,
learning_rate: float = 1e-3,
num_lr_decays: int = 3,
early_stop_patience_steps: int = -1,
val_check_steps: int = 100,
batch_size=32,
valid_batch_size: Optional[int] = None,
windows_batch_size: int = 1024,
inference_windows_batch_size: int = -1,
start_padding_enabled: bool = False,
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,
):
# Protect horizon collapsed seasonality and trend NBEATSx-i basis
if h == 1 and (("seasonality" in stack_types) or ("trend" in stack_types)):
raise Exception(
"Horizon `h=1` incompatible with `seasonality` or `trend` in stacks"
)
# Inherit BaseWindows class
super(NBEATSx, self).__init__(
h=h,
input_size=input_size,
futr_exog_list=futr_exog_list,
hist_exog_list=hist_exog_list,
stat_exog_list=stat_exog_list,
exclude_insample_y=exclude_insample_y,
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,
windows_batch_size=windows_batch_size,
inference_windows_batch_size=inference_windows_batch_size,
start_padding_enabled=start_padding_enabled,
step_size=step_size,
scaler_type=scaler_type,
num_workers_loader=num_workers_loader,
drop_last_loader=drop_last_loader,
random_seed=random_seed,
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
**trainer_kwargs,
)
# Architecture
self.futr_input_size = len(self.futr_exog_list)
self.hist_input_size = len(self.hist_exog_list)
self.stat_input_size = len(self.stat_exog_list)
blocks = self.create_stack(
h=h,
input_size=input_size,
futr_input_size=self.futr_input_size,
hist_input_size=self.hist_input_size,
stat_input_size=self.stat_input_size,
stack_types=stack_types,
n_blocks=n_blocks,
mlp_units=mlp_units,
dropout_prob_theta=dropout_prob_theta,
activation=activation,
shared_weights=shared_weights,
n_polynomials=n_polynomials,
n_harmonics=n_harmonics,
)
self.blocks = torch.nn.ModuleList(blocks)
# Adapter with Loss dependent dimensions
if self.loss.outputsize_multiplier > 1:
self.out = nn.Linear(
in_features=h, out_features=h * self.loss.outputsize_multiplier
)
def create_stack(
self,
h,
input_size,
stack_types,
n_blocks,
mlp_units,
dropout_prob_theta,
activation,
shared_weights,
n_polynomials,
n_harmonics,
futr_input_size,
hist_input_size,
stat_input_size,
):
block_list = []
for i in range(len(stack_types)):
for block_id in range(n_blocks[i]):
# Shared weights
if shared_weights and block_id > 0:
nbeats_block = block_list[-1]
else:
if stack_types[i] == "seasonality":
n_theta = (
2
* (self.loss.outputsize_multiplier + 1)
* int(np.ceil(n_harmonics / 2 * h) - (n_harmonics - 1))
)
basis = SeasonalityBasis(
harmonics=n_harmonics,
backcast_size=input_size,
forecast_size=h,
out_features=self.loss.outputsize_multiplier,
)
elif stack_types[i] == "trend":
n_theta = (self.loss.outputsize_multiplier + 1) * (
n_polynomials + 1
)
basis = TrendBasis(
degree_of_polynomial=n_polynomials,
backcast_size=input_size,
forecast_size=h,
out_features=self.loss.outputsize_multiplier,
)
elif stack_types[i] == "identity":
n_theta = input_size + self.loss.outputsize_multiplier * h
basis = IdentityBasis(
backcast_size=input_size,
forecast_size=h,
out_features=self.loss.outputsize_multiplier,
)
elif stack_types[i] == "exogenous":
if futr_input_size + stat_input_size > 0:
n_theta = 2 * (futr_input_size + stat_input_size)
basis = ExogenousBasis(forecast_size=h)
else:
raise ValueError(f"Block type {stack_types[i]} not found!")
nbeats_block = NBEATSBlock(
input_size=input_size,
h=h,
futr_input_size=futr_input_size,
hist_input_size=hist_input_size,
stat_input_size=stat_input_size,
n_theta=n_theta,
mlp_units=mlp_units,
basis=basis,
dropout_prob=dropout_prob_theta,
activation=activation,
)
# Select type of evaluation and apply it to all layers of block
block_list.append(nbeats_block)
return block_list
def forward(self, windows_batch):
# Parse windows_batch
insample_y = windows_batch["insample_y"]
insample_mask = windows_batch["insample_mask"]
futr_exog = windows_batch["futr_exog"]
hist_exog = windows_batch["hist_exog"]
stat_exog = windows_batch["stat_exog"]
# NBEATSx' forward
residuals = insample_y.flip(dims=(-1,)) # backcast init
insample_mask = insample_mask.flip(dims=(-1,))
forecast = insample_y[:, -1:, None] # Level with Naive1
block_forecasts = [forecast.repeat(1, self.h, 1)]
for i, block in enumerate(self.blocks):
backcast, block_forecast = block(
insample_y=residuals,
futr_exog=futr_exog,
hist_exog=hist_exog,
stat_exog=stat_exog,
)
residuals = (residuals - backcast) * insample_mask
forecast = forecast + block_forecast
if self.decompose_forecast:
block_forecasts.append(block_forecast)
# Adapting output's domain
forecast = self.loss.domain_map(forecast)
if self.decompose_forecast:
# (n_batch, n_blocks, h)
block_forecasts = torch.stack(block_forecasts)
block_forecasts = block_forecasts.permute(1, 0, 2, 3)
block_forecasts = block_forecasts.squeeze(-1) # univariate output
return block_forecasts
else:
return forecast