Source code for pypots.imputation.grud.model

"""
The implementation of GRU-D for the partially-observed time-series imputation task.

"""

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


from typing import Union, Optional

import numpy as np
import torch
from torch.utils.data import DataLoader

from .core import _GRUD
from .data import DatasetForGRUD
from ..base import BaseNNImputer
from ...optim.adam import Adam
from ...optim.base import Optimizer


[docs] class GRUD(BaseNNImputer): """The PyTorch implementation of the GRU-D model :cite:`che2018GRUD`. Parameters ---------- n_steps : The number of time steps in the time-series data sample. n_features : The number of features in the time-series data sample. rnn_hidden_size : The size of the RNN hidden state. batch_size : The batch size for training and evaluating the model. epochs : The number of epochs for training the model. patience : The patience for the early-stopping mechanism. Given a positive integer, the training process will be stopped when the model does not perform better after that number of epochs. Leaving it default as None will disable the early-stopping. optimizer : The optimizer for model training. If not given, will use a default Adam optimizer. num_workers : The number of subprocesses to use for data loading. `0` means data loading will be in the main process, i.e. there won't be subprocesses. device : The device for the model to run on. It can be a string, a :class:`torch.device` object, or a list of them. If not given, will try to use CUDA devices first (will use the default CUDA device if there are multiple), then CPUs, considering CUDA and CPU are so far the main devices for people to train ML models. If given a list of devices, e.g. ['cuda:0', 'cuda:1'], or [torch.device('cuda:0'), torch.device('cuda:1')] , the model will be parallely trained on the multiple devices (so far only support parallel training on CUDA devices). Other devices like Google TPU and Apple Silicon accelerator MPS may be added in the future. saving_path : The path for automatically saving model checkpoints and tensorboard files (i.e. loss values recorded during training into a tensorboard file). Will not save if not given. model_saving_strategy : The strategy to save model checkpoints. It has to be one of [None, "best", "better", "all"]. No model will be saved when it is set as None. The "best" strategy will only automatically save the best model after the training finished. The "better" strategy will automatically save the model during training whenever the model performs better than in previous epochs. The "all" strategy will save every model after each epoch training. """ def __init__( self, n_steps: int, n_features: int, rnn_hidden_size: int, batch_size: int = 32, epochs: int = 100, patience: Optional[int] = None, optimizer: Optional[Optimizer] = Adam(), num_workers: int = 0, device: Optional[Union[str, torch.device, list]] = None, saving_path: str = None, model_saving_strategy: Optional[str] = "best", ): super().__init__( batch_size, epochs, patience, num_workers, device, saving_path, model_saving_strategy, ) self.n_steps = n_steps self.n_features = n_features self.rnn_hidden_size = rnn_hidden_size # set up the model self.model = _GRUD( self.n_steps, self.n_features, self.rnn_hidden_size, ) self._send_model_to_given_device() self._print_model_size() # set up the optimizer self.optimizer = optimizer self.optimizer.init_optimizer(self.model.parameters()) def _assemble_input_for_training(self, data: list) -> dict: # fetch data ( indices, X, X_filledLOCF, missing_mask, deltas, empirical_mean, ) = self._send_data_to_given_device(data) # assemble input data inputs = { "indices": indices, "X": X, "X_filledLOCF": X_filledLOCF, "missing_mask": missing_mask, "deltas": deltas, "empirical_mean": empirical_mean, } return inputs def _assemble_input_for_validating(self, data: list) -> dict: # fetch data ( indices, X, X_filledLOCF, missing_mask, deltas, empirical_mean, X_ori, indicating_mask, ) = self._send_data_to_given_device(data) # assemble input data inputs = { "indices": indices, "X": X, "X_filledLOCF": X_filledLOCF, "missing_mask": missing_mask, "deltas": deltas, "empirical_mean": empirical_mean, "X_ori": X_ori, "indicating_mask": indicating_mask, } return inputs def _assemble_input_for_testing(self, data: list) -> dict: return self._assemble_input_for_training(data)
[docs] def fit( self, train_set: Union[dict, str], val_set: Optional[Union[dict, str]] = None, file_type: str = "hdf5", ) -> None: # Step 1: wrap the input data with classes Dataset and DataLoader training_set = DatasetForGRUD( train_set, return_X_ori=False, file_type=file_type ) training_loader = DataLoader( training_set, batch_size=self.batch_size, shuffle=True, num_workers=self.num_workers, ) val_loader = None if val_set is not None: val_set = DatasetForGRUD(val_set, return_X_ori=True, file_type=file_type) val_loader = DataLoader( val_set, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, ) # Step 2: train the model and freeze it self._train_model(training_loader, val_loader) self.model.load_state_dict(self.best_model_dict) self.model.eval() # set the model as eval status to freeze it. # Step 3: save the model if necessary self._auto_save_model_if_necessary(confirm_saving=True)
[docs] def predict( self, test_set: Union[dict, str], file_type: str = "hdf5", ) -> dict: self.model.eval() # set the model as eval status to freeze it. test_set = DatasetForGRUD(test_set, return_X_ori=False, file_type=file_type) test_loader = DataLoader( test_set, batch_size=self.batch_size, shuffle=False, num_workers=self.num_workers, ) imputation_collector = [] with torch.no_grad(): for idx, data in enumerate(test_loader): inputs = self._assemble_input_for_testing(data) results = self.model.forward(inputs, training=False) imputed_data = results["imputed_data"] imputation_collector.append(imputed_data) imputation = torch.cat(imputation_collector).cpu().detach().numpy() result_dict = { "imputation": imputation, } return result_dict
[docs] def impute( self, test_set: Union[dict, str], file_type: str = "hdf5", ) -> np.ndarray: """Impute missing values in the given data with the trained model. Parameters ---------- test_set : The data samples for testing, should be array-like of shape [n_samples, sequence length (n_steps), n_features], or a path string locating a data file, e.g. h5 file. file_type : The type of the given file if X is a path string. Returns ------- array-like, shape [n_samples, sequence length (n_steps), n_features], Imputed data. """ result_dict = self.predict(test_set, file_type=file_type) return result_dict["imputation"]