pypots/imputation/csai/model.py
"""
The implementation of CSAI
"""
# Created by Linglong Qian, Joseph Arul Raj <linglong.qian@kcl.ac.uk, joseph_arul_raj@kcl.ac.uk>
# License: BSD-3-Clause
from typing import Union, Optional
import numpy as np
import torch
from torch.utils.data import DataLoader
from .core import _BCSAI
from .data import DatasetForCSAI
from ..base import BaseNNImputer
from ...data.checking import key_in_data_set
from ...optim.adam import Adam
from ...optim.base import Optimizer
class CSAI(BaseNNImputer):
"""The PyTorch implementation of the CSAI model :cite:`qian2023csai`.
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 GRU hidden state, also the number of hidden units in the GRU cell.
imputation_weight :
The weight assigned to the reconstruction loss during training.
consistency_weight :
The weight assigned to the consistency loss during training.
removal_percent :
The percentage of data to be removed during training for imputation tasks.
increase_factor :
A scaling factor used to adjust the amount of missing data during training.
compute_intervals :
Whether to compute time intervals between observations for handling irregular time-series.
step_channels :
The number of channels for each step in the sequence.
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.
verbose :
Whether to print out the training logs during the training process.
Notes
-----
CSAI (Consistent Sequential Imputation) is a bidirectional model designed for time-series imputation.
It employs a forward and backward GRU network to handle missing data, using consistency and reconstruction losses
to improve accuracy. The model supports various training configurations, such as interval computations,
early-stopping, and multiple devices for training. Results can be saved based on the specified saving strategy,
and tensorboard files are generated for tracking the model's performance over time.
"""
def __init__(
self,
n_steps: int,
n_features: int,
rnn_hidden_size: int,
imputation_weight: float,
consistency_weight: float,
removal_percent: int,
increase_factor: float,
compute_intervals: bool,
step_channels: int,
batch_size: int,
epochs: int,
patience: Union[int, None] = None,
optimizer: Optional[Optimizer] = Adam(),
num_workers: int = 0,
device: Union[str, torch.device, list, None] = None,
saving_path: str = None,
model_saving_strategy: Union[str, None] = "best",
verbose: bool = True,
):
super().__init__(
batch_size,
epochs,
patience,
num_workers,
device,
saving_path,
model_saving_strategy,
verbose,
)
self.n_steps = n_steps
self.n_features = n_features
self.rnn_hidden_size = rnn_hidden_size
self.imputation_weight = imputation_weight
self.consistency_weight = consistency_weight
self.removal_percent = removal_percent
self.increase_factor = increase_factor
self.step_channels = step_channels
self.compute_intervals = compute_intervals
self.intervals = None
# Initialise model
self.model = _BCSAI(
self.n_steps,
self.n_features,
self.rnn_hidden_size,
self.step_channels,
self.consistency_weight,
self.imputation_weight,
self.intervals,
)
self._send_model_to_given_device()
self._print_model_size()
# set up the optimizer
self.optimizer = optimizer
def _assemble_input_for_training(self, data: list, training=True) -> dict:
# extract data
sample = data["sample"]
(indices, X, missing_mask, deltas, last_obs, back_X, back_missing_mask, back_deltas, back_last_obs) = (
self._send_data_to_given_device(sample)
)
# assemble input data
inputs = {
"indices": indices,
"forward": {
"X": X,
"missing_mask": missing_mask,
"deltas": deltas,
"last_obs": last_obs,
},
"backward": {
"X": back_X,
"missing_mask": back_missing_mask,
"deltas": back_deltas,
"last_obs": back_last_obs,
},
}
return inputs
def _assemble_input_for_validating(self, data: list) -> dict:
# extract data
sample = data["sample"]
(
indices,
X,
missing_mask,
deltas,
last_obs,
back_X,
back_missing_mask,
back_deltas,
back_last_obs,
X_ori,
indicating_mask,
) = self._send_data_to_given_device(sample)
# assemble input data
inputs = {
"indices": indices,
"forward": {
"X": X,
"missing_mask": missing_mask,
"deltas": deltas,
"last_obs": last_obs,
},
"backward": {
"X": back_X,
"missing_mask": back_missing_mask,
"deltas": back_deltas,
"last_obs": back_last_obs,
},
"X_ori": X_ori,
"indicating_mask": indicating_mask,
}
return inputs
def _assemble_input_for_testing(self, data: list) -> dict:
return self._assemble_input_for_validating(data)
def fit(
self,
train_set,
val_set=None,
file_type: str = "hdf5",
) -> None:
self.training_set = DatasetForCSAI(
train_set, False, False, file_type, self.removal_percent, self.increase_factor, self.compute_intervals
)
self.intervals = self.training_set.intervals
self.replacement_probabilities = self.training_set.replacement_probabilities
self.mean_set = self.training_set.mean_set
self.std_set = self.training_set.std_set
training_loader = DataLoader(
self.training_set,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
# collate_fn=collate_fn_bidirectional
)
if val_set is not None:
val_set = DatasetForCSAI(
val_set,
True,
False,
file_type,
self.removal_percent,
self.increase_factor,
self.compute_intervals,
self.replacement_probabilities,
self.mean_set,
self.std_set,
False,
)
val_loader = DataLoader(
val_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
# collate_fn=collate_fn_bidirectional
)
# Reset the model
self.model = _BCSAI(
self.n_steps,
self.n_features,
self.rnn_hidden_size,
self.step_channels,
self.consistency_weight,
self.imputation_weight,
self.intervals,
)
self._send_model_to_given_device()
self._print_model_size()
# set up the optimizer
self.optimizer.init_optimizer(self.model.parameters())
# train the model
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=self.model_saving_strategy == "best")
def predict(
self,
test_set: Union[dict, str],
file_type: str = "hdf5",
) -> dict:
self.model.eval()
test_set = DatasetForCSAI(
test_set,
True,
False,
file_type,
self.removal_percent,
self.increase_factor,
self.compute_intervals,
self.replacement_probabilities,
self.mean_set,
self.std_set,
False,
)
test_loader = DataLoader(
test_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
# collate_fn=collate_fn_bidirectional
)
imputation_collector = []
x_ori_collector = []
indicating_mask_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)
x_ori_collector.append(inputs["X_ori"])
indicating_mask_collector.append(inputs["indicating_mask"])
imputation = torch.cat(imputation_collector).cpu().detach().numpy()
result_dict = {
"imputation": imputation,
"X_ori": torch.cat(x_ori_collector).cpu().detach().numpy(),
"indicating_mask": torch.cat(indicating_mask_collector).cpu().detach().numpy(),
}
return result_dict
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"]