AutoEncoder

class dtaianomaly.anomaly_detection.AutoEncoder(window_size: int | Literal['fft', 'acf', 'mwf', 'suss'], error_metric: Literal['mean-absolute-error', 'mean-squared-error'] = 'mean-absolute-error', encoder_dimensions: list[int] = (64,), latent_space_dimension: int = 32, decoder_dimensions: list[int] = (64,), dropout_rate: float = 0.2, activation_function: Literal['linear', 'relu', 'sigmoid', 'tanh'] = 'relu', batch_normalization: bool = True, 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]

Use an auto encoder to detect anomalies [30].

An auto encoder is a neural network that consists of two parts: an encoder and a decoder. The encoder maps the input features to a lower dimensional space, the latent space, while the decoder reconstructs the latent embedding back into the original feature space. Samples that are common during the training phase (i.e., normal behavior) are more easily reconstructed compared to rare observations (i.e., anomalies). Thus, anomalies are detected by reconstructing the time series data and measuring the deviation of the reconstruction from the original data.

The architecture of the autoencoder consists of blocks, in which each block applies the following operations: fully-connected layer \(\rightarrow\) batch normalization \(\rightarrow\) activation function \(\rightarrow\) dropout layer. The first layer of the encoder has no batch normalization, and the final layer of the decoder has no batch normalization nor dropout.

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().

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

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

encoder_dimensionslist of ints, default=[64]

The number of neurons in each layer of the encoder. If an empty list is given, then the input of the encoder is directly connected to the latent space in a fully-connected manner.

latent_space_dimensionint default=32

The dimension of the latent space.

decoder_dimensionslist of ints, default=[64]

The number of neurons in each layer of the decoder. If an empty list is given, then the latent space is directly connected to the output in a fully-connected manner.

dropout_ratefloat in interval [0, 1[, default=0.2

The dropout rate for the dropout layers. If the dropout rate is 0, no dropout layers will be added to the auto encoder.

activation_function{“linear”, “relu”, “sigmoid”, “tanh”} default=”relu”

The activation function to use at the end of each layer.

batch_normalizationbool = True,

Whether to add batch normalization after each layer or not.

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.

See also

BaseNeuralReconstructionDetector: Use a neural network to reconstruct windows in the time series, and detect anomalies as windows that are incorrectly reconstructed.

Examples

>>> from dtaianomaly.anomaly_detection import AutoEncoder
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> auto_encoder = AutoEncoder(10, seed=0).fit(x)
>>> auto_encoder.decision_function(x)
array([0.59210092, 0.56707534, 0.56629006, ..., 0.58380051, 0.5808109 , 0.54450774]...)

check_is_fitted() → None

Raise an error if this object is not fitted.

Check whether this object is fitted, and if it is not fitted, an exception is thrown.

Raises:

NotFittedError: If this object is not fitted.

decision_function(X: ndarray) → array

Compute anomaly scores.

Compute the anomaly scores for the given time series using this detector.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Input time series.

Returns:

array-like of shape (n_samples): The computed anomaly scores.

fit(X: ndarray, y: ndarray = None, **kwargs) → BaseDetector

Fit this detector.

Fit this detector to the given data.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Input time series.
yarray-like, default=None: Ground-truth information.
**kwargs: Additional parameters to be used to fit the anomaly detector.

Returns:

BaseDetector: Returns the instance itself.

is_fitted() → bool

Check whether this object is fitted.

Check whether all the attributes of this object that end with an underscore (‘_’) has been initialized.

Returns:

bool: True if and only if all the attributes of this object ending with ‘_’ are initialized.

predict_confidence(X: ndarray, X_train: ndarray = None, contamination: float = 0.05, decision_scores_given: bool = False)

Predict the confidence of the anomaly scores on the test given test data [26].

This method implements ExCeeD (Example-wise Confidence of anomaly Detectors) to estimate the confidence. ExCeed transforms the predicted decision scores to probability estimates using a Bayesian approach, which enables to assign a confidence score to each prediction which captures the uncertainty of the anomaly detector in that prediction.

Parameters:

Xarray-like of shape (n_samples, n_attributes): The test time series for which the confidence of anomaly scores should be predicted.
X_trainarray-like of shape (n_samples_train, n_attributes), default=None: The training time series, which can be used as reference. If X_train=None, the test set is used as reference set.
contaminationfloat, default=0.05: The (estimated) contamination rate for the data, i.e., the expected percentage of anomalies.
decision_scores_givenbool, default=False: Whether the given X and X_train represent time series data or decision scores. If decision_scores_given=False (default), then the given arrays are interpreted as time series. Otherwise, they are interpreted as decision scores, as computed by decision_function().

Returns:

array-like of shape (n_samples): The confidence of this anomaly detector in each prediction in the given test time series.

predict_proba(X: ndarray) → ndarray

Predict anomaly probabilities.

Estimate the probability of a sample of X being anomalous, based on the anomaly scores obtained from decision_function by rescaling them to the range of [0, 1] via min-max scaling.

Parameters:

Xarray-like of shape (n_samples, n_attributes): Input time series.

Returns:

array-like of shape (n_samples): 1D array with the same length as X, with values in the interval [0, 1], in which a higher value implies that the instance is more likely to be anomalous.

Raises:

ValueError: If scores is not a valid array.
ValueError: If the prediction scores from ‘decision_function’ are constant, but not in the interval [0, 1], because these values can not unambiguously be transformed to an anomaly probability.

requires_fitting() → bool

Check whether this object requires fitting.

Check whether any of the attributes of this object ends with an underscore (‘_’), which indicates that the attribute is set when the object is fitted. Note that this method does not check whether the object is fitted, i.e., whether the attributes have been set.

Returns:

bool: True if and only if this object has attributes that end with ‘_’.

save(path: str | Path) → None

Save this detector.

Save detector to disk as a pickle file with extension .dtai. If the given path consists of multiple subdirectories, then the not existing subdirectories are created.

Parameters:

pathstr or Path: Location where to store the detector.