content: 
  
  
  __all__ = ['DeepNPTS']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  import neuralforecast.losses.pytorch as losses
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class DeepNPTS(BaseModel):
      """DeepNPTS
  
      Deep Non-Parametric Time Series Forecaster (`DeepNPTS`) is a baseline model for time-series forecasting. This model generates predictions by (weighted) sampling from the empirical distribution according to a learnable strategy. The strategy is learned by exploiting the information across multiple related time series.
  
      Args:
          h (int): Forecast horizon.
          input_size (int): autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].
          hidden_size (int): hidden size of dense layers.
          batch_norm (bool): if True, applies Batch Normalization after each dense layer in the network.
          dropout (float): dropout.
          n_layers (int): number of dense layers.
          stat_exog_list (list): static exogenous columns.
          hist_exog_list (list): historic exogenous columns.
          futr_exog_list (list): future exogenous columns.
          exclude_insample_y (bool): the model skips the autoregressive features y[t-input_size:t] if True.
          loss (PyTorch module): instantiated train loss class from [losses collection](./losses.pytorch).
          valid_loss (PyTorch module): instantiated valid loss class from [losses collection](./losses.pytorch).
          max_steps (int): maximum number of training steps.
          learning_rate (float): Learning rate between (0, 1).
          num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps.
          early_stop_patience_steps (int): Number of validation iterations before early stopping.
          val_check_steps (int): Number of training steps between every validation loss check.
          batch_size (int): number of different series in each batch.
          valid_batch_size (int): number of different series in each validation and test batch, if None uses batch_size.
          windows_batch_size (int): number of windows to sample in each training batch, default uses all.
          inference_windows_batch_size (int): number of windows to sample in each inference batch, -1 uses all.
          start_padding_enabled (bool): if True, the model will pad the time series with zeros at the beginning, by input size.
          training_data_availability_threshold (Union[float, List[float]]): minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies [insample_fraction, outsample_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior).
          step_size (int): step size between each window of temporal data.
          scaler_type (str): type of scaler for temporal inputs normalization see [temporal scalers](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_scalers.py).
          random_seed (int): random_seed for pytorch initializer and numpy generators.
          drop_last_loader (bool): 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`.
          lr_scheduler (Subclass of 'torch.optim.lr_scheduler.LRScheduler'): optional, user specified lr_scheduler instead of the default choice (StepLR).
          lr_scheduler_kwargs (dict): optional, list of parameters used by the user specified `lr_scheduler`.
          dataloader_kwargs (dict): optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`.
          **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:
          - [Rangapuram, Syama Sundar, Jan Gasthaus, Lorenzo Stella, Valentin Flunkert, David Salinas, Yuyang Wang, and Tim Januschowski (2023). "Deep Non-Parametric Time Series Forecaster". arXiv.](https://arxiv.org/abs/2312.14657)
  
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      MULTIVARIATE = False  # If the model produces multivariate forecasts (True) or univariate (False)
      RECURRENT = (
          False  # If the model produces forecasts recursively (True) or direct (False)
      )
  
      def __init__(
          self,
          h,
          input_size: int,
          hidden_size: int = 32,
          batch_norm: bool = True,
          dropout: float = 0.1,
          n_layers: int = 2,
          stat_exog_list=None,
          hist_exog_list=None,
          futr_exog_list=None,
          exclude_insample_y=False,
          loss=MAE(),
          valid_loss=MAE(),
          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 = 1024,
          start_padding_enabled=False,
          training_data_availability_threshold=0.0,
          step_size: int = 1,
          scaler_type: str = "standard",
          random_seed: int = 1,
          drop_last_loader=False,
          alias: Optional[str] = None,
          optimizer=None,
          optimizer_kwargs=None,
          lr_scheduler=None,
          lr_scheduler_kwargs=None,
          dataloader_kwargs=None,
          **trainer_kwargs
      ):
  
          if exclude_insample_y:
              raise Exception("DeepNPTS has no possibility for excluding y.")
  
          if loss.outputsize_multiplier > 1:
              raise Exception(
                  "DeepNPTS only supports point loss functions (MAE, MSE, etc) as loss function."
              )
  
          if valid_loss is not None and not isinstance(valid_loss, losses.BasePointLoss):
              raise Exception(
                  "DeepNPTS only supports point loss functions (MAE, MSE, etc) as valid loss function."
              )
  
          # Inherit BaseWindows class
          super(DeepNPTS, self).__init__(
              h=h,
              input_size=input_size,
              stat_exog_list=stat_exog_list,
              hist_exog_list=hist_exog_list,
              futr_exog_list=futr_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,
              training_data_availability_threshold=training_data_availability_threshold,
              step_size=step_size,
              scaler_type=scaler_type,
              random_seed=random_seed,
              drop_last_loader=drop_last_loader,
              alias=alias,
              optimizer=optimizer,
              optimizer_kwargs=optimizer_kwargs,
              lr_scheduler=lr_scheduler,
              lr_scheduler_kwargs=lr_scheduler_kwargs,
              dataloader_kwargs=dataloader_kwargs,
              **trainer_kwargs
          )
  
          self.h = h
          self.hidden_size = hidden_size
          self.dropout = dropout
  
          input_dim = (
              input_size * (1 + self.futr_exog_size + self.hist_exog_size)
              + self.stat_exog_size
              + self.h * self.futr_exog_size
          )
  
          # Create DeepNPTSNetwork
          modules = []
          for i in range(n_layers):
              modules.append(nn.Linear(input_dim if i == 0 else hidden_size, hidden_size))
              modules.append(nn.ReLU())
              if batch_norm:
                  modules.append(nn.BatchNorm1d(hidden_size))
              if dropout > 0.0:
                  modules.append(nn.Dropout(dropout))
  
          modules.append(nn.Linear(hidden_size, input_size * self.h))
          self.deepnptsnetwork = nn.Sequential(*modules)
  
      def forward(self, windows_batch):
          # Parse windows_batch
          x = windows_batch["insample_y"]  #   [B, L, 1]
          hist_exog = windows_batch["hist_exog"]  #   [B, L, X]
          futr_exog = windows_batch["futr_exog"]  #   [B, L + h, F]
          stat_exog = windows_batch["stat_exog"]  #   [B, S]
  
          batch_size, seq_len = x.shape[:2]  #   B = batch_size, L = seq_len
          insample_y = windows_batch["insample_y"]
  
          # Concatenate x_t with future exogenous of input
          if self.futr_exog_size > 0:
              x = torch.cat(
                  (x, futr_exog[:, :seq_len]), dim=2
              )  #   [B, L, 1] + [B, L, F] -> [B, L, 1 + F]
  
          # Concatenate x_t with historic exogenous
          if self.hist_exog_size > 0:
              x = torch.cat(
                  (x, hist_exog), dim=2
              )  #   [B, L, 1 + F] + [B, L, X] -> [B, L, 1 + F + X]
  
          x = x.reshape(batch_size, -1)  #   [B, L, 1 + F + X] -> [B, L * (1 + F + X)]
  
          # Concatenate x with static exogenous
          if self.stat_exog_size > 0:
              x = torch.cat(
                  (x, stat_exog), dim=1
              )  #   [B, L * (1 + F + X)] + [B, S] -> [B, L * (1 + F + X) + S]
  
          # Concatenate x_t with future exogenous of horizon
          if self.futr_exog_size > 0:
              futr_exog = futr_exog[:, seq_len:]  #   [B, L + h, F] -> [B, h, F]
              futr_exog = futr_exog.reshape(
                  batch_size, -1
              )  #   [B, L + h, F] -> [B, h * F]
              x = torch.cat(
                  (x, futr_exog), dim=1
              )  #   [B, L * (1 + F + X) + S] + [B, h * F] -> [B, L * (1 + F + X) + S + h * F]
  
          # Run through DeepNPTSNetwork
          weights = self.deepnptsnetwork(
              x
          )  #   [B, L * (1 + F + X) + S + h * F]  -> [B, L * h]
  
          # Apply softmax for weighted input predictions
          weights = weights.reshape(batch_size, seq_len, -1)  #   [B, L * h] -> [B, L, h]
          x = (
              F.softmax(weights, dim=1) * insample_y
          )  #   [B, L, h] * [B, L, 1] = [B, L, h]
          forecast = torch.sum(x, dim=1).unsqueeze(-1)  #   [B, L, h] -> [B, h, 1]
  
          return forecast
  