# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.nbeats.ipynb.
# %% auto 0
__all__ = ['NBEATS']
# %% ../../nbs/models.nbeats.ipynb 5
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.nbeats.ipynb 7
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 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.nbeats.ipynb 8
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,
n_theta: int,
mlp_units: list,
basis: nn.Module,
dropout_prob: float,
activation: str,
):
""" """
super().__init__()
self.dropout_prob = dropout_prob
assert activation in ACTIVATIONS, f"{activation} is not in {ACTIVATIONS}"
activ = getattr(nn, activation)()
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:
raise NotImplementedError("dropout")
# 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) -> Tuple[torch.Tensor, torch.Tensor]:
# Compute local projection weights and projection
theta = self.layers(insample_y)
backcast, forecast = self.basis(theta)
return backcast, forecast
# %% ../../nbs/models.nbeats.ipynb 9
class NBEATS(BaseWindows):
"""NBEATS
The Neural Basis Expansion Analysis for Time Series (NBEATS), is a simple and yet
effective architecture, it is built with a deep stack of MLPs with the doubly
residual connections. It has a generic and interpretable architecture depending
on the blocks it uses. Its interpretable architecture is recommended for scarce
data settings, as it regularizes its predictions through projections unto harmonic
and trend basis well-suited for most forecasting tasks.
**Parameters:**
`h`: int, forecast horizon.
`input_size`: int, considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
`n_harmonics`: int, Number of harmonic terms for seasonality stack type. Note that len(n_harmonics) = len(stack_types). Note that it will only be used if a seasonality stack is used.
`n_polynomials`: int, polynomial degree for trend stack. Note that len(n_polynomials) = len(stack_types). Note that it will only be used if a trend stack is used.
`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.
`shared_weights`: bool, If True, all blocks within each stack will share parameters.
`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 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:**
-[Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio (2019).
"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting".](https://arxiv.org/abs/1905.10437)
"""
# Class attributes
SAMPLING_TYPE = "windows"
def __init__(
self,
h,
input_size,
n_harmonics: int = 2,
n_polynomials: int = 2,
stack_types: list = ["identity", "trend", "seasonality"],
n_blocks: list = [1, 1, 1],
mlp_units: list = 3 * [[512, 512]],
dropout_prob_theta: float = 0.0,
activation: str = "ReLU",
shared_weights: bool = 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: int = 32,
valid_batch_size: Optional[int] = None,
windows_batch_size: int = 1024,
inference_windows_batch_size: int = -1,
start_padding_enabled=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(NBEATS, self).__init__(
h=h,
input_size=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,
windows_batch_size=windows_batch_size,
valid_batch_size=valid_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
blocks = self.create_stack(
h=h,
input_size=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)
def create_stack(
self,
stack_types,
n_blocks,
input_size,
h,
mlp_units,
dropout_prob_theta,
activation,
shared_weights,
n_polynomials,
n_harmonics,
):
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,
)
else:
raise ValueError(f"Block type {stack_types[i]} not found!")
nbeats_block = NBEATSBlock(
input_size=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"]
# NBEATS' 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)
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, out_features)
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