Source code for pytorch_forecasting.models.basic_rnn

Basic RNN model with LSTM or GRU cells
from typing import Dict, List, Tuple, Union

import numpy as np
import torch
from torch import nn
from torch.nn.utils import rnn

from pytorch_forecasting.metrics import MultiHorizonMetric
from pytorch_forecasting.models.base_model import AutoRegressiveBaseModelWithCovariates
from pytorch_forecasting.models.nn import MultiEmbedding, get_rnn
from pytorch_forecasting.utils import to_list

[docs]class LSTMModel(AutoRegressiveBaseModelWithCovariates): """ Basic RNN network. """ def __init__( self, cell_type: str = "LSTM", hidden_size: int = 10, rnn_layers: int = 2, dropout: float = 0.1, static_categoricals: List[str] = [], static_reals: List[str] = [], time_varying_categoricals_encoder: List[str] = [], time_varying_categoricals_decoder: List[str] = [], categorical_groups: Dict[str, List[str]] = {}, time_varying_reals_encoder: List[str] = [], time_varying_reals_decoder: List[str] = [], embedding_sizes: Dict[str, Tuple[int, int]] = {}, embedding_paddings: List[str] = [], embedding_labels: Dict[str, np.ndarray] = {}, x_reals: List[str] = [], x_categoricals: List[str] = [], n_validation_samples: int = None, n_plotting_samples: int = None, target: Union[str, List[str]] = None, loss: MultiHorizonMetric = None, logging_metrics: nn.ModuleList = None, **kwargs, ): """ Args: cell_type (str, optional): Recurrent cell type ["LSTM", "GRU"]. Defaults to "LSTM". hidden_size (int, optional): hidden recurrent size - the most important hyperparameter along with ``rnn_layers``. Defaults to 10. rnn_layers (int, optional): Number of RNN layers - important hyperparameter. Defaults to 2. dropout (float, optional): Dropout in RNN layers. Defaults to 0.1. static_categoricals: integer of positions of static categorical variables static_reals: integer of positions of static continuous variables time_varying_categoricals_encoder: integer of positions of categorical variables for encoder time_varying_categoricals_decoder: integer of positions of categorical variables for decoder time_varying_reals_encoder: integer of positions of continuous variables for encoder time_varying_reals_decoder: integer of positions of continuous variables for decoder categorical_groups: dictionary where values are list of categorical variables that are forming together a new categorical variable which is the key in the dictionary x_reals: order of continuous variables in tensor passed to forward function x_categoricals: order of categorical variables in tensor passed to forward function embedding_sizes: dictionary mapping (string) indices to tuple of number of categorical classes and embedding size embedding_paddings: list of indices for embeddings which transform the zero's embedding to a zero vector embedding_labels: dictionary mapping (string) indices to list of categorical labels n_validation_samples (int, optional): Number of samples to use for calculating validation metrics. Defaults to None, i.e. no sampling at validation stage and using "mean" of distribution for logging metrics calculation. n_plotting_samples (int, optional): Number of samples to generate for plotting predictions during training. Defaults to ``n_validation_samples`` if not None or 100 otherwise. target (str, optional): Target variable or list of target variables. Defaults to None. loss (DistributionLoss, optional): Distribution loss function. Keep in mind that each distribution loss function might have specific requirements for target normalization. Defaults to :py:class:`~pytorch_forecasting.metrics.NormalDistributionLoss`. logging_metrics (nn.ModuleList, optional): Metrics to log during training. Defaults to nn.ModuleList([SMAPE(), MAE(), RMSE(), MAPE(), MASE()]). """ # saves arguments in signature to `.hparams` attribute, mandatory call - do not skip this self.save_hyperparameters() # pass additional arguments to BaseModel.__init__, mandatory call - do not skip this super().__init__(loss=loss, logging_metrics=logging_metrics, **kwargs) assert set(self.encoder_variables) - set(to_list(target)) == set( self.decoder_variables ), "Encoder and decoder variables have to be the same apart from target variable" for targeti in to_list(target): assert ( targeti in time_varying_reals_encoder ), f"target {targeti} has to be real" # todo: remove this restriction self.embeddings = MultiEmbedding( embedding_sizes=embedding_sizes, embedding_paddings=embedding_paddings, categorical_groups=categorical_groups, x_categoricals=x_categoricals, ) time_series_rnn = get_rnn(cell_type) cont_size = len(self.reals) cat_size = sum([size[1] for size in self.hparams.embedding_sizes.values()]) input_size = cont_size + cat_size self.rnn = time_series_rnn( input_size=input_size, hidden_size=self.hparams.hidden_size, num_layers=self.hparams.rnn_layers, dropout=self.hparams.dropout if self.hparams.rnn_layers > 1 else 0, batch_first=True, ) # add linear layers for argument projects if isinstance(target, str): # single target self.output_projector = nn.Linear(self.hparams.hidden_size, 1) else: # multi target self.output_projector = nn.ModuleList([nn.Linear(self.hparams.hidden_size, 1) for _ in target]) @property def target_position(self): # position of target within reals vector: with covariates: self.hparams.x_reals.index( return 0 def encode(self, x: Dict[str, torch.Tensor]): # we need at least one encoding step as because the target needs to be lagged by one time step # as we are lazy, we also require that the encoder length is at least 1, so we can easily generate a # hidden state here. See the DeepAR implementation for how to use a minimal encoder length of 1 max_encoder_length = x["encoder_lengths"].max() assert x["encoder_lengths"].min() > 0 if max_encoder_length > 1: encoder_lengths = x["encoder_lengths"] - 1 rnn_encoder_lengths = encoder_lengths.where(encoder_lengths > 0, torch.ones_like(encoder_lengths)) input_vector = self.construct_input_vector(x["encoder_cat"], x["encoder_cont"]) _, hidden_state = self.rnn( rnn.pack_padded_sequence( input_vector, rnn_encoder_lengths.cpu(), enforce_sorted=False, batch_first=True ) ) # second ouput is not needed (hidden state) # replace hidden cell with initial input if encoder_length is zero to determine correct initial state no_encoding = (encoder_lengths == 0)[None, :, None] # shape: n_lstm_layers x batch_size x hidden_size hidden_state = self.rnn.handle_no_encoding(hidden_state, no_encoding) else: hidden_state = self.rnn.init_hidden_state(x, self.hparam.hidden_size) return hidden_state def decode(self, x: Dict[str, torch.Tensor], hidden_state): # again lag target by one input_vector = x["decoder_cont"].clone() input_vector[..., self.target_position] = torch.roll(input_vector[..., self.target_position], shifts=1, dims=1) # but this time fill in missing target from encoder_cont at the first time step instead of throwing it away last_encoder_target = x["encoder_cont"][ torch.arange(x["encoder_cont"].size(0)), x["encoder_lengths"] - 1, self.target_position ] input_vector[:, 0, self.target_position] = last_encoder_target if # training attribute is provided from PyTorch and indicates if module is in training model packed_decoder = rnn.pack_padded_sequence( input_vector, lengths=x["decoder_lengths"].cpu(), batch_first=True, enforce_sorted=False ) # run through same lstm lstm_output, _ = self.lstm(packed_decoder, hidden_state) # unpack sequence lstm_output, _ = rnn.pad_packed_sequence(lstm_output, batch_first=True) # transform into right shape prediction = self.output_layer(lstm_output) else: # if not training, need to predict in autoregressive manner # predict one by one max_decoder_length = x["decoder_lengths"].max() # initialize previous target and hidden state last_target = last_encoder_target last_hidden_state = hidden_state predictions = [] # for each time step run prediction for i in range(max_decoder_length): current_input_vector = input_vector[:, i].unsqueeze(1) # select time step in decoder current_input_vector[:, 0, self.target_position] = last_target # insert previous target # make lstm prediction lstm_prediction, new_hidden_state = self.lstm(current_input_vector, last_hidden_state) prediction = self.output_layer(lstm_prediction).squeeze(1) # save prediction predictions.append(prediction) # prepare for next time step last_hidden_state = new_hidden_state # Prediction should be passed through transformer and then inversely transformed. # The inverse transformation might be only approximately the inverse of the # forward transformation making this step important. rescaled_prediction = self.transform_output( prediction=prediction, target_scale=x["target_scale"] ) # inverse transform normalized_prediction = self.output_transformer.transform( rescaled_prediction, target_scale=x["target_scale"] ) # transform last_target = normalized_prediction.squeeze(1) # stack all predictions prediction = torch.stack(predictions, dim=1) return prediction
[docs] def forward(self, x: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: hidden_state = self.encode(x) # encode to hidden state prediction = self.decode(x, hidden_state) # decode leveraging hidden state return self.to_network_output(prediction=prediction)