Source code for dtaianomaly.anomaly_detection._Transformer
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._BaseNeuralForecastingDetector import (
ERROR_METRIC_TYPE,
BaseNeuralForecastingDetector,
)
from dtaianomaly.type_validation import (
BoolAttribute,
FloatAttribute,
IntegerAttribute,
LiteralAttribute,
)
from dtaianomaly.windowing import WINDOW_SIZE_TYPE
__all__ = ["Transformer"]
[docs]
class Transformer(BaseNeuralForecastingDetector):
"""
Use a transformer to detect anomalies :cite:`vaswani2017attention`.
A transformer anomaly detector first forecasts the time series, and
then detects anomalies by measuring the deviation from the forecasted
values to the actual observations. A transformer is a neural network
consisting of only attention-layers: all you need is attention. The
forecasting network therefore consists first of a transformer-encoder,
which is connected to a linear layer to forecast the time series.
The architecture of the transformer consists of 2 blocks: (1) a transformer
-decoder consisting of one or more attention-layers, and (2) a single linear
layer.
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.
forecast_length : int default=1
The number of time steps the neural network must forecast.
num_heads : int, default=12
The number of heads in each attention layer.
num_transformer_layers : int, default=1
The number of attention layers.
dimension_feedforward : int, default=32,
The dimension of the linear layer at the end of each attention layer.
bias : bool, default=True
Whether to use bias weights in each layer of the LSTM block.
dropout_rate : float in interval [0, 1[, default=0.0
The dropout rate to put on each attention layer.
activation_function : {"linear", "relu", "sigmoid", "tanh"} default="relu"
The activation function to use at the end of each attention layer.
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
--------
BaseNeuralForecastingDetector: Use a neural network to forecast the time
series, and detect anomalies by measuring the difference with the
actual observations.
Examples
--------
>>> from dtaianomaly.anomaly_detection import Transformer
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> transformer = Transformer(10, seed=0).fit(x)
>>> transformer.decision_function(x) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE, +SKIP
array([0.41845179, 0.41845179, 0.3603762 , ..., 0.46213843, 0.63743933,
0.08675425]...)
"""
num_heads: int
num_transformer_layers: int
dimension_feedforward: int
bias: bool
dropout_rate: float
activation_function: ACTIVATION_FUNCTION_TYPE
attribute_validation = {
"num_heads": IntegerAttribute(minimum=1),
"num_transformer_layers": IntegerAttribute(minimum=1),
"dimension_feedforward": IntegerAttribute(minimum=1),
"dropout_rate": FloatAttribute(0.0, 1.0, inclusive_maximum=False),
"activation_function": LiteralAttribute(ACTIVATION_FUNCTIONS),
"bias": BoolAttribute(),
}
def __init__(
self,
window_size: WINDOW_SIZE_TYPE,
error_metric: ERROR_METRIC_TYPE = "mean-absolute-error",
forecast_length: int = 1,
num_heads: int = 12,
num_transformer_layers: int = 1,
dimension_feedforward: int = 32,
bias: bool = True,
dropout_rate: float = 0.0,
activation_function: ACTIVATION_FUNCTION_TYPE = "relu",
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__(
window_size=window_size,
supervision=Supervision.SEMI_SUPERVISED,
error_metric=error_metric,
forecast_length=forecast_length,
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.num_heads = num_heads
self.num_transformer_layers = num_transformer_layers
self.dimension_feedforward = dimension_feedforward
self.bias = bias
self.dropout_rate = dropout_rate
self.activation_function = activation_function
def _build_architecture(self, n_attributes: int) -> torch.nn.Module:
transformer = torch.nn.Sequential()
transformer.add_module("flatten", torch.nn.Flatten())
d_model = n_attributes * self.window_size_
nhead = _adjust_nhead(d_model, self.num_heads)
transformer.add_module(
"transformer",
torch.nn.TransformerEncoder(
encoder_layer=torch.nn.TransformerEncoderLayer(
d_model=d_model,
nhead=nhead,
dim_feedforward=self.dimension_feedforward,
dropout=self.dropout_rate,
activation=self._build_activation_function(
self.activation_function
),
batch_first=True,
bias=self.bias,
),
num_layers=self.num_transformer_layers,
enable_nested_tensor=(nhead % 2) == 0,
),
)
transformer.add_module(
"linear",
torch.nn.Linear(
in_features=n_attributes * self.window_size_,
out_features=n_attributes * self.forecast_length,
),
)
return transformer
def _adjust_nhead(d_model, nhead) -> int:
"""
Computes a valid nhead for the given parameters, such that the constraint
(d_model // nhead) * nhead == d_model
is satisfied. This is done by finding the value closest to nhead which
satisfies the constraint. This value can thus be larger or smaller. If
the constraint is not satisfied by the given values, and there are
two values equally far from nhead that satisfy the constraint, then the
smaller value is returned (e.g., the constraint is not satisfied if
d_model=100 and nhead=3, but it is satisfied for both 2 and 4, but 2
will be returned since it is lower).
"""
if d_model % nhead == 0:
return nhead # Already valid
# Search for closest valid nhead
lower = nhead - 1
upper = nhead + 1
while lower > 1 and upper <= d_model:
if d_model % lower == 0:
return lower
if d_model % upper == 0:
return upper
lower -= 1
upper += 1
return 1