Source code for pypots.imputation.imputeformer.model
"""
The package of the partially-observed time-series imputation model ImputeFormer.
"""
# Created by Tong Nie <nietong@tongji.edu.cn> and Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from typing import Union, Optional
import torch
from torch.utils.data import DataLoader
from .core import _ImputeFormer
from ..base import BaseNNImputer
from ..saits.data import DatasetForSAITS
from ...data.checking import key_in_data_set
from ...nn.modules.loss import Criterion, MAE, MSE
from ...optim.adam import Adam
from ...optim.base import Optimizer
[docs]
class ImputeFormer(BaseNNImputer):
"""The PyTorch implementation of the ImputeFormer model.
ImputeFormer is originally proposed by Nie et al. in KDD'24: cite:`nie2024imputeformer`.
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.
n_layers :
The number of layers in the ImputeFormer model.
d_input_embed :
The dimension of the input embedding.
It is the input dimension of the input embedding layer.
d_learnable_embed :
The dimension of the learnable node embedding.
It is the dimension of the learnable node embedding (spatial positional embedding)
used in spatial attention layers.
d_proj :
The dimension of the learnable projector.
It is the dimension of the learnable projector
used in temporal attention layers.
d_ffn :
The dimension of the layer in the Feed-Forward Networks (FFN).
dropout :
The dropout rate for all fully-connected layers in the model.
n_temporal_heads :
The number of attention heads in temporal attention layers.
input_dim :
The dimension of the input feature dimension, default is 1.
output_dim :
The dimension of the output feature dimension, default is 1.
ORT_weight :
The weight for the ORT loss.
MIT_weight :
The weight for the MIT loss.
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.
training_loss:
The customized loss function designed by users for training the model.
If not given, will use the default loss as claimed in the original paper.
validation_metric:
The customized metric function designed by users for validating the model.
If not given, will use the default MSE metric.
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.
"""
def __init__(
self,
n_steps: int,
n_features: int,
n_layers: int,
d_input_embed: int,
d_learnable_embed: int,
d_proj: int,
d_ffn: int,
n_temporal_heads: int,
dropout: float = 0.0,
input_dim: int = 1,
output_dim: int = 1,
ORT_weight: float = 1,
MIT_weight: float = 1,
batch_size: int = 32,
epochs: int = 100,
patience: Optional[int] = None,
training_loss: Union[Criterion, type] = MAE,
validation_metric: Union[Criterion, type] = MSE,
optimizer: Union[Optimizer, type] = Adam,
num_workers: int = 0,
device: Optional[Union[str, torch.device, list]] = None,
saving_path: Optional[str] = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
training_loss=training_loss,
validation_metric=validation_metric,
batch_size=batch_size,
epochs=epochs,
patience=patience,
num_workers=num_workers,
device=device,
saving_path=saving_path,
model_saving_strategy=model_saving_strategy,
verbose=verbose,
)
self.n_steps = n_steps
self.n_features = n_features
# model hyperparameters
self.n_layers = n_layers
self.d_input_embed = d_input_embed
self.d_learnable_embed = d_learnable_embed
self.d_proj = d_proj
self.d_ffn = d_ffn
self.n_temporal_heads = n_temporal_heads
self.dropout = dropout
self.input_dim = input_dim
self.output_dim = output_dim
self.ORT_weight = ORT_weight
self.MIT_weight = MIT_weight
# set up the model
self.model = _ImputeFormer(
n_steps=self.n_steps,
n_features=self.n_features,
n_layers=self.n_layers,
d_input_embed=self.d_input_embed,
d_learnable_embed=self.d_learnable_embed,
d_proj=self.d_proj,
d_ffn=self.d_ffn,
n_temporal_heads=self.n_temporal_heads,
dropout=self.dropout,
input_dim=self.input_dim,
output_dim=self.output_dim,
ORT_weight=self.ORT_weight,
MIT_weight=self.MIT_weight,
training_loss=self.training_loss,
validation_metric=self.validation_metric,
)
self._send_model_to_given_device()
self._print_model_size()
# set up the optimizer
if isinstance(optimizer, Optimizer):
self.optimizer = optimizer
else:
self.optimizer = optimizer() # instantiate the optimizer if it is a class
assert isinstance(self.optimizer, Optimizer)
self.optimizer.init_optimizer(self.model.parameters())
def _assemble_input_for_training(self, data: list) -> dict:
(
indices,
X,
missing_mask,
X_ori,
indicating_mask,
) = self._send_data_to_given_device(data)
inputs = {
"X": X,
"missing_mask": missing_mask,
"X_ori": X_ori,
"indicating_mask": indicating_mask,
}
return inputs
def _assemble_input_for_validating(self, data: list) -> dict:
return self._assemble_input_for_training(data)
def _assemble_input_for_testing(self, data: list) -> dict:
indices, X, missing_mask = self._send_data_to_given_device(data)
inputs = {
"X": X,
"missing_mask": missing_mask,
}
return inputs
[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
train_dataset = DatasetForSAITS(train_set, return_X_ori=False, return_y=False, file_type=file_type)
train_dataloader = DataLoader(
train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
)
val_dataloader = None
if val_set is not None:
if not key_in_data_set("X_ori", val_set):
raise ValueError("val_set must contain 'X_ori' for model validation.")
val_dataset = DatasetForSAITS(val_set, return_X_ori=True, return_y=False, file_type=file_type)
val_dataloader = DataLoader(
val_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
)
# Step 2: train the model and freeze it
self._train_model(train_dataloader, val_dataloader)
self.model.load_state_dict(self.best_model_dict)
# Step 3: save the model if necessary
self._auto_save_model_if_necessary(confirm_saving=self.model_saving_strategy == "best")