BaseNeuralDetector

class dtaianomaly.anomaly_detection.BaseNeuralDetector(supervision: Supervision, window_size: int | Literal['fft', 'acf', 'mwf', 'suss'], 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 neural anomaly detectors, based on PyTorch.

This class implements the main functionality for training a model and detecting anomalies, including building the data loader, building the optimizer, and implementing the main train and evaluation loops. Extensions of this class should also implement methods to build the data set, the neural architecture, and how to train and evaluate on a single batch.

Parameters:

supervisionSupervision

The type of supervision this anomaly detector requires.

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().

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.