"""
The implementation of TimeMixerPP for the partially-observed time-series anomaly detection task.
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from typing import Union, Optional
import torch
from torch.utils.data import DataLoader
from ..base import BaseNNDetector
from ...data.checking import key_in_data_set
from ...imputation.saits.data import DatasetForSAITS
from ...imputation.timemixerpp.core import _TimeMixerPP
from ...nn.modules.loss import Criterion, MAE, MSE
from ...optim.adam import Adam
from ...optim.base import Optimizer
[docs]
class TimeMixerPP(BaseNNDetector):
"""The PyTorch implementation of the TimeMixer++ model :cite:`wang2025timemixerpp` on the anomaly detection task.
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.
anomaly_rate :
The rate of anomalies in the data, should be in the range (0, 1).
n_layers :
The number of layers in the TimeMixer++ model.
d_model :
The dimension of the model.
d_ffn :
The dimension of the feed-forward network.
top_k :
The number of top-k amplitude values to be selected to obtain the most significant frequencies.
n_heads:
The head number of full attention in the model.
Only work if channel_mixing is True.
n_kernels:
num_kernels for Inception module.
dropout :
The dropout rate for the model.
channel_mixing :
Whether to apply channel mixing in the model.
channel_independence :
Whether to use channel independence in the model.
downsampling_layers :
The number of downsampling layers in the model.
downsampling_window :
The window size for downsampling.
apply_nonstationary_norm :
Whether to apply non-stationary normalization to the input data for TimeMixer++.
Please refer to :cite:`liu2022nonstationary` for details about non-stationary normalization,
which is not the idea of the original TimeMixer++ paper. Hence, we make it optional and default not to use here.
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,
anomaly_rate: float,
n_layers: int,
d_model: int,
d_ffn: int,
top_k: int,
n_heads: int,
n_kernels: int,
dropout: float = 0,
channel_mixing: bool = True,
channel_independence: bool = True,
downsampling_layers: int = 3,
downsampling_window: int = 2,
apply_nonstationary_norm: bool = False,
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: str = None,
model_saving_strategy: Optional[str] = "best",
verbose: bool = True,
):
super().__init__(
anomaly_rate=anomaly_rate,
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_model = d_model
self.d_ffn = d_ffn
self.n_heads = n_heads
self.dropout = dropout
self.n_kernels = n_kernels
self.top_k = top_k
self.channel_mixing = channel_mixing
self.channel_independence = channel_independence
self.downsampling_layers = downsampling_layers
self.downsampling_window = downsampling_window
self.apply_nonstationary_norm = apply_nonstationary_norm
# set up the model
self.model = _TimeMixerPP(
n_steps=self.n_steps,
n_features=self.n_features,
n_layers=self.n_layers,
d_model=self.d_model,
d_ffn=self.d_ffn,
n_heads=self.n_heads,
dropout=self.dropout,
top_k=self.top_k,
n_kernels=self.n_kernels,
channel_mixing=self.channel_mixing,
channel_independence=self.channel_independence,
downsampling_layers=self.downsampling_layers,
downsampling_window=self.downsampling_window,
apply_nonstationary_norm=self.apply_nonstationary_norm,
training_loss=self.training_loss,
validation_metric=self.validation_metric,
task_name="anomaly_detection",
)
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:
self.train_set = train_set
# 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")