# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.nhits.ipynb.
# %% auto 0
__all__ = ['NHITS']
# %% ../../nbs/models.nhits.ipynb 5
from typing import Tuple, Optional
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..losses.pytorch import MAE
from ..common._base_windows import BaseWindows
# %% ../../nbs/models.nhits.ipynb 8
class _IdentityBasis(nn.Module):
def __init__(
self,
backcast_size: int,
forecast_size: int,
interpolation_mode: str,
out_features: int = 1,
):
super().__init__()
assert (interpolation_mode in ["linear", "nearest"]) or (
"cubic" in interpolation_mode
)
self.forecast_size = forecast_size
self.backcast_size = backcast_size
self.interpolation_mode = interpolation_mode
self.out_features = out_features
def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
backcast = theta[:, : self.backcast_size]
knots = theta[:, self.backcast_size :]
# Interpolation is performed on default dim=-1 := H
knots = knots.reshape(len(knots), self.out_features, -1)
if self.interpolation_mode in ["nearest", "linear"]:
# knots = knots[:,None,:]
forecast = F.interpolate(
knots, size=self.forecast_size, mode=self.interpolation_mode
)
# forecast = forecast[:,0,:]
elif "cubic" in self.interpolation_mode:
if self.out_features > 1:
raise Exception(
"Cubic interpolation not available with multiple outputs."
)
batch_size = len(backcast)
knots = knots[:, None, :, :]
forecast = torch.zeros(
(len(knots), self.forecast_size), device=knots.device
)
n_batches = int(np.ceil(len(knots) / batch_size))
for i in range(n_batches):
forecast_i = F.interpolate(
knots[i * batch_size : (i + 1) * batch_size],
size=self.forecast_size,
mode="bicubic",
)
forecast[i * batch_size : (i + 1) * batch_size] += forecast_i[
:, 0, 0, :
] # [B,None,H,H] -> [B,H]
forecast = forecast[:, None, :] # [B,H] -> [B,None,H]
# [B,Q,H] -> [B,H,Q]
forecast = forecast.permute(0, 2, 1)
return backcast, forecast
# %% ../../nbs/models.nhits.ipynb 9
ACTIVATIONS = ["ReLU", "Softplus", "Tanh", "SELU", "LeakyReLU", "PReLU", "Sigmoid"]
POOLING = ["MaxPool1d", "AvgPool1d"]
class NHITSBlock(nn.Module):
"""
NHITS block which takes a basis function as an argument.
"""
def __init__(
self,
input_size: int,
h: int,
n_theta: int,
mlp_units: list,
basis: nn.Module,
futr_input_size: int,
hist_input_size: int,
stat_input_size: int,
n_pool_kernel_size: int,
pooling_mode: str,
dropout_prob: float,
activation: str,
):
super().__init__()
pooled_hist_size = int(np.ceil(input_size / n_pool_kernel_size))
pooled_futr_size = int(np.ceil((input_size + h) / n_pool_kernel_size))
input_size = (
pooled_hist_size
+ hist_input_size * pooled_hist_size
+ futr_input_size * pooled_futr_size
+ stat_input_size
)
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}"
assert pooling_mode in POOLING, f"{pooling_mode} is not in {POOLING}"
activ = getattr(nn, activation)()
self.pooling_layer = getattr(nn, pooling_mode)(
kernel_size=n_pool_kernel_size, stride=n_pool_kernel_size, ceil_mode=True
)
# Block MLPs
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,
futr_exog: torch.Tensor,
hist_exog: torch.Tensor,
stat_exog: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
# Pooling
# Pool1d needs 3D input, (B,C,L), adding C dimension
insample_y = insample_y.unsqueeze(1)
insample_y = self.pooling_layer(insample_y)
insample_y = insample_y.squeeze(1)
# 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:
hist_exog = hist_exog.permute(0, 2, 1) # [B, L, C] -> [B, C, L]
hist_exog = self.pooling_layer(hist_exog)
hist_exog = hist_exog.permute(0, 2, 1) # [B, C, L] -> [B, L, C]
insample_y = torch.cat(
(insample_y, hist_exog.reshape(batch_size, -1)), dim=1
)
if self.futr_input_size > 0:
futr_exog = futr_exog.permute(0, 2, 1) # [B, L, C] -> [B, C, L]
futr_exog = self.pooling_layer(futr_exog)
futr_exog = futr_exog.permute(0, 2, 1) # [B, C, L] -> [B, L, C]
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)
backcast, forecast = self.basis(theta)
return backcast, forecast
# %% ../../nbs/models.nhits.ipynb 10
class NHITS(BaseWindows):
"""NHITS
The Neural Hierarchical Interpolation for Time Series (NHITS), is an MLP-based deep
neural architecture with backward and forward residual links. NHITS tackles volatility and
memory complexity challenges, by locally specializing its sequential predictions into
the signals frequencies with hierarchical interpolation and pooling.
**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.
`activation`: str, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'].
`stack_types`: List[str], stacks list in the form N * ['identity'], to be deprecated in favor of `n_stacks`. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
`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).
`n_freq_downsample`: List[int], list with the stack's coefficients (inverse expressivity ratios). Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
`interpolation_mode`: str='linear', interpolation basis from ['linear', 'nearest', 'cubic'].
`n_pool_kernel_size`: List[int], list with the size of the windows to take a max/avg over. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
`pooling_mode`: str, input pooling module from ['MaxPool1d', 'AvgPool1d'].
`dropout_prob_theta`: float, Float between (0, 1). Dropout for NHITS basis.
`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.
`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:**
-[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza,
Max Mergenthaler-Canseco, Artur Dubrawski (2023). "NHITS: Neural Hierarchical Interpolation for Time Series Forecasting".
Accepted at the Thirty-Seventh AAAI Conference on Artificial Intelligence.](https://arxiv.org/abs/2201.12886)
"""
# 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,
stack_types: list = ["identity", "identity", "identity"],
n_blocks: list = [1, 1, 1],
mlp_units: list = 3 * [[512, 512]],
n_pool_kernel_size: list = [2, 2, 1],
n_freq_downsample: list = [4, 2, 1],
pooling_mode: str = "MaxPool1d",
interpolation_mode: str = "linear",
dropout_prob_theta=0.0,
activation="ReLU",
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=0,
drop_last_loader=False,
optimizer=None,
optimizer_kwargs=None,
**trainer_kwargs,
):
# Inherit BaseWindows class
super(NHITS, 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,
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
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,
stack_types=stack_types,
futr_input_size=self.futr_input_size,
hist_input_size=self.hist_input_size,
stat_input_size=self.stat_input_size,
n_blocks=n_blocks,
mlp_units=mlp_units,
n_pool_kernel_size=n_pool_kernel_size,
n_freq_downsample=n_freq_downsample,
pooling_mode=pooling_mode,
interpolation_mode=interpolation_mode,
dropout_prob_theta=dropout_prob_theta,
activation=activation,
)
self.blocks = torch.nn.ModuleList(blocks)
def create_stack(
self,
h,
input_size,
stack_types,
n_blocks,
mlp_units,
n_pool_kernel_size,
n_freq_downsample,
pooling_mode,
interpolation_mode,
dropout_prob_theta,
activation,
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]):
assert (
stack_types[i] == "identity"
), f"Block type {stack_types[i]} not found!"
n_theta = input_size + self.loss.outputsize_multiplier * max(
h // n_freq_downsample[i], 1
)
basis = _IdentityBasis(
backcast_size=input_size,
forecast_size=h,
out_features=self.loss.outputsize_multiplier,
interpolation_mode=interpolation_mode,
)
nbeats_block = NHITSBlock(
h=h,
input_size=input_size,
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,
n_pool_kernel_size=n_pool_kernel_size[i],
pooling_mode=pooling_mode,
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"]
# insample
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, output_size)
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