BaseNeuralReconstructionDetector

class dtaianomaly.anomaly_detection.BaseNeuralReconstructionDetector(window_size: int | Literal['fft', 'acf', 'mwf', 'suss'], supervision: Supervision = Supervision.SEMI_SUPERVISED, error_metric: Literal['mean-absolute-error', 'mean-squared-error'] = 'mean-absolute-error', stride: int = 1, standard_scaling: bool = True, batch_size: int = 32, data_loader_kwargs: dict[str, any] = None, optimizer: Literal['adam', 'sgd'] = 'adam', learning_rate: float = 0.001, compile_model: bool = False, compile_mode: Literal['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs'] = 'default', n_epochs: int = 10, loss_function: Literal['mse', 'l1', 'huber'] = 'mse', device: str = 'cpu', seed: int = None)[source]

Base class for reconstruction-based neural anomaly detectors.

Reconstruction-based anomaly detection detect anomalies by learning to reconstruct the data. Specifically, the neural network takes as input a sliding window of the time series, and learns to output the exactly same data. Given a normal time series enable to learn the normal behaviors, and as a consequence it is possible to accurately reconstruct the data. However, anomalous subsequences, which were not seen during training, can not be accurately reconstructed, and will have a larger reconstruction error as a consequence.

Parameters:

window_sizeint or str

The window size to use for extracting sliding windows from the time series. This value will be passed to compute_window_size().

supervisionSupervision, default=Supervision.SEMI_SUPERVISED

The type of supervision this anomaly detector requires.

error_metric{“mean-absolute-error”, “mean-squared-error”}, default=”mean-absolute-error”

The error measure between the reconstructed window and the original window.

strideint, default=1

The stride, i.e., the step size for extracting sliding windows from the time series.

standard_scalingbool, default=True

Whether to standard scale each window independently, before feeding it to the network.

batch_sizeint, default=32

The size of the batches to feed to the network.

data_loader_kwargsdictionary, default=None

Additional kwargs to be passed to the data loader. For more information, see: https://docs.pytorch.org/docs/stable/data.html.

optimizer{“adam”, “sgd”} or callable default=”adam”

The optimizer to use for learning the weights. If “adam” is given, then the torch.optim.Adam optimizer will be used. If “sgd” is given, then the torch.optim.SGD optimizer will be used. Otherwise, a callable should be given, which takes as input the network parameters, and then creates an optimizer.

learning_ratefloat, default=1e-3

The learning rate to use for training the network. Has no effect if optimize is a callable.

compile_modelbool, default=False

Whether the network architecture should be compiled or not before training the weights. For more information, see: https://docs.pytorch.org/docs/stable/generated/torch.compile.html.

compile_mode{“default”, “reduce-overhead”, “max-autotune”, “max-autotune-no-cudagraphs”}, default=”default”

Method to compile the architecture. For more information, see: https://docs.pytorch.org/docs/stable/generated/torch.compile.html.

n_epochsint, default=10

The number of epochs for which the neural network should be trained.

loss_function{“mse”, “l1”, “huber} or torch.nn.Module, default=”mse”

The loss function to use for updating the weights. Valid options are:

'mse': Use the Mean Squared Error loss.
'l1': Use the L1-loss or the mean absolute error.
'huber': Use the huber loss, which smoothly combines the MSE-loss with the L1-loss.
torch.nn.Module: a custom torch module to use for the loss function.

devicestr, default=”cpu”

The device on which te neural network should be trained. For more information, see: https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch-device.

seedint, default=None

The seed used for training the model. This seed will update the torch and numpy seed at the beginning of the fit method.

Attributes:

window_size_int: The effectively used window size for this anomaly detector.
optimizer_torch.optim.Optimizer: The optimizer used for learning the weights of the network.
neural_network_torch.nn.Module: The PyTorch network architecture.