WenjieDu/PyPOTS

"""

"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

from typing import Tuple

import torch
import torch.nn as nn

from .layers import TemporalDecay


class BackboneGRUD(nn.Module):
    def __init__(
        self,
        n_steps: int,
        n_features: int,
        rnn_hidden_size: int,
    ):
        super().__init__()
        self.n_steps = n_steps
        self.n_features = n_features
        self.rnn_hidden_size = rnn_hidden_size

        # create models
        self.rnn_cell = nn.GRUCell(self.n_features * 2 + self.rnn_hidden_size, self.rnn_hidden_size)
        self.temp_decay_h = TemporalDecay(input_size=self.n_features, output_size=self.rnn_hidden_size, diag=False)
        self.temp_decay_x = TemporalDecay(input_size=self.n_features, output_size=self.n_features, diag=True)

    def forward(self, X, missing_mask, deltas, empirical_mean, X_filledLOCF) -> Tuple[torch.Tensor, ...]:
        """Forward processing of GRU-D.

        Parameters
        ----------
        X:

        missing_mask:

        deltas:

        empirical_mean:

        X_filledLOCF:

        Returns
        -------
        classification_pred:

        logits:


        """

        hidden_state = torch.zeros((X.size()[0], self.rnn_hidden_size), device=X.device)

        representation_collector = []
        for t in range(self.n_steps):
            # for data, [batch, time, features]
            x = X[:, t, :]  # values
            m = missing_mask[:, t, :]  # mask
            d = deltas[:, t, :]  # delta, time gap
            x_filledLOCF = X_filledLOCF[:, t, :]

            gamma_h = self.temp_decay_h(d)
            gamma_x = self.temp_decay_x(d)
            hidden_state = hidden_state * gamma_h
            representation_collector.append(hidden_state)

            x_h = gamma_x * x_filledLOCF + (1 - gamma_x) * empirical_mean
            x_replaced = m * x + (1 - m) * x_h
            data_input = torch.cat([x_replaced, hidden_state, m], dim=1)
            hidden_state = self.rnn_cell(data_input, hidden_state)

        representation_collector = torch.stack(representation_collector, dim=1)

        return representation_collector, hidden_state