import torch
from dtaianomaly.anomaly_detection._BaseDetector import Supervision
from dtaianomaly.anomaly_detection._BaseNeuralDetector import (
ACTIVATION_FUNCTION_TYPE,
ACTIVATION_FUNCTIONS,
COMPILE_MODE_TYPE,
LOSS_TYPE,
OPTIMIZER_TYPE,
)
from dtaianomaly.anomaly_detection._BaseNeuralReconstructionDetector import (
ERROR_METRIC_TYPE,
BaseNeuralReconstructionDetector,
)
from dtaianomaly.type_validation import (
BoolAttribute,
FloatAttribute,
IntegerAttribute,
ListAttribute,
LiteralAttribute,
)
from dtaianomaly.windowing import WINDOW_SIZE_TYPE
__all__ = ["AutoEncoder"]
[docs]
class AutoEncoder(BaseNeuralReconstructionDetector):
"""
Use an auto encoder to detect anomalies :cite:`sakurada2014anomaly`.
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 :math:`\\rightarrow`
batch normalization :math:`\\rightarrow` activation function :math:`\\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_size : int or str
The window size to use for extracting sliding windows from the time series. This
value will be passed to :py:meth:`~dtaianomaly.anomaly_detection.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_dimensions : list 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_dimension : int default=32
The dimension of the latent space.
decoder_dimensions : list 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_rate : float 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_normalization : bool = True,
Whether to add batch normalization after each layer or not.
stride : int, default=1
The stride, i.e., the step size for extracting sliding windows from the time series.
standard_scaling : bool, default=True
Whether to standard scale each window independently, before feeding it to the network.
batch_size : int, default=32
The size of the batches to feed to the network.
data_loader_kwargs : dictionary, 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_rate : float, default=1e-3
The learning rate to use for training the network. Has no effect
if optimize is a callable.
compile_model : bool, 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_epochs : int, 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.
device : str, 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.
seed : int, 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) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE, +SKIP
array([0.59210092, 0.56707534, 0.56629006, ..., 0.58380051, 0.5808109 , 0.54450774]...)
"""
encoder_dimensions: list[int]
latent_space_dimension: int
decoder_dimensions: list[int]
dropout_rate: float
activation_function: ACTIVATION_FUNCTION_TYPE
batch_normalization: bool
attribute_validation = {
"encoder_dimensions": ListAttribute(IntegerAttribute(minimum=1)),
"latent_space_dimension": IntegerAttribute(minimum=1),
"decoder_dimensions": ListAttribute(IntegerAttribute(minimum=1)),
"dropout_rate": FloatAttribute(0.0, 1.0, inclusive_maximum=False),
"activation_function": LiteralAttribute(ACTIVATION_FUNCTIONS),
"batch_normalization": BoolAttribute(),
}
def __init__(
self,
window_size: WINDOW_SIZE_TYPE,
error_metric: ERROR_METRIC_TYPE = "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: ACTIVATION_FUNCTION_TYPE = "relu",
batch_normalization: bool = True,
stride: int = 1,
standard_scaling: bool = True,
batch_size: int = 32,
data_loader_kwargs: dict[str, any] = None,
optimizer: OPTIMIZER_TYPE = "adam",
learning_rate: float = 1e-3,
compile_model: bool = False,
compile_mode: COMPILE_MODE_TYPE = "default",
n_epochs: int = 10,
loss_function: LOSS_TYPE = "mse",
device: str = "cpu",
seed: int = None,
):
super().__init__(
supervision=Supervision.SEMI_SUPERVISED,
error_metric=error_metric,
window_size=window_size,
stride=stride,
standard_scaling=standard_scaling,
batch_size=batch_size,
data_loader_kwargs=data_loader_kwargs,
optimizer=optimizer,
learning_rate=learning_rate,
compile_model=compile_model,
compile_mode=compile_mode,
n_epochs=n_epochs,
loss_function=loss_function,
device=device,
seed=seed,
)
self.encoder_dimensions = list(encoder_dimensions)
self.latent_space_dimension = latent_space_dimension
self.decoder_dimensions = list(decoder_dimensions)
self.dropout_rate = dropout_rate
self.activation_function = activation_function
self.batch_normalization = batch_normalization
def _build_architecture(self, n_attributes: int) -> torch.nn.Module:
return _AutoEncoderArchitecture(
encoder=self._build_encoder(n_attributes),
decoder=self._build_decoder(n_attributes),
)
def _build_encoder(self, n_attributes: int) -> torch.nn.Module:
# Initialize the encoder
encoder = torch.nn.Sequential()
encoder.add_module("flatten", torch.nn.Flatten())
# Initialize layer inputs and outputs
inputs = [n_attributes * self.window_size_, *self.encoder_dimensions]
outputs = [*self.encoder_dimensions, self.latent_space_dimension]
# Add all the layers
for i in range(len(inputs)):
# Add the linear layer
encoder.add_module(f"linear-{i}", torch.nn.Linear(inputs[i], outputs[i]))
# Add batch normalization
if self.batch_normalization and i > 0:
encoder.add_module(f"batch-norm-{i}", torch.nn.BatchNorm1d(outputs[i]))
# Add the activation function
encoder.add_module(
f"activation-{i}",
self._build_activation_function(self.activation_function),
)
# Add the dropout layer
if self.dropout_rate > 0:
encoder.add_module(f"dropout-{i}", torch.nn.Dropout(self.dropout_rate))
# Return the encoder
return encoder
def _build_decoder(self, n_attributes: int) -> torch.nn.Module:
# Initialize the decoder
decoder = torch.nn.Sequential()
# Initialize layer inputs and outputs
inputs = [self.latent_space_dimension, *self.decoder_dimensions]
outputs = [*self.decoder_dimensions, n_attributes * self.window_size_]
# Add all the layers
for i in range(len(inputs)):
# Add the linear layer
decoder.add_module(f"linear-{i}", torch.nn.Linear(inputs[i], outputs[i]))
# Add batch normalization
if self.batch_normalization and i < len(inputs) - 1:
decoder.add_module(f"batch-norm-{i}", torch.nn.BatchNorm1d(outputs[i]))
# Add the activation function
decoder.add_module(
f"activation-{i}",
self._build_activation_function(self.activation_function),
)
# Add the dropout layer
if self.dropout_rate > 0 and i < len(inputs) - 1:
decoder.add_module(f"dropout-{i}", torch.nn.Dropout(self.dropout_rate))
# Restore the dimensions of the window
decoder.add_module(
"unflatten", torch.nn.Unflatten(1, (self.window_size_, n_attributes))
)
# Return the decoder
return decoder
class _AutoEncoderArchitecture(torch.nn.Module):
encoder: torch.nn.Module
decoder: torch.nn.Module
def __init__(self, encoder: torch.nn.Module, decoder: torch.nn.Module):
super().__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, x):
return self.decoder(self.encoder(x)).reshape(x.shape)