from collections.abc import Callable
from typing import Literal
import torch
from dtaianomaly import utils
from dtaianomaly.anomaly_detection.BaseDetector import Supervision
from dtaianomaly.anomaly_detection.BaseNeuralDetector import (
_ACTIVATION_FUNCTION_TYPE,
_COMPILE_MODE_TYPE,
_MODEL_PARAMETERS_TYPE,
_OPTIMIZER_TYPE,
)
from dtaianomaly.anomaly_detection.BaseNeuralDetector_utils import (
BaseNeuralForecastingDetector,
)
[docs]
class MultilayerPerceptron(BaseNeuralForecastingDetector):
"""
Use a multilayer perceptron to detect anomalies.
The multilayer perceptron is a fully connected neural network which
will detect anomalies based on forecasting. Given a subsequence in the
time series, the network will learn to forecast the future values. Because
anomalies are unexpected events, they are difficult to forecast. Hence,
by computing the difference between the forecasted value and the actually
observed values, the neural network can detect anomalies.
The architecture of the multilayer perceptron 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 and final layers of the network has no batch normalization,
the final layer of the network has no 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.
hidden_layers: list of ints, default=[64, 32]
The number of neurons in each hidden layer. If an empty list is given, then the input
layer is directly connected to the output layer.
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: torch.nn.Module, default=torch.nn.MSELoss()
The loss function to use for updating the weights.
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.
Examples
--------
>>> from dtaianomaly.anomaly_detection import MultilayerPerceptron
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> mlp = MultilayerPerceptron(10, seed=0).fit(x)
>>> mlp.decision_function(x) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE, +SKIP
array([1.8944391 , 1.8944391 , 1.83804671, ..., 0.59621549, 0.54421651,
0.05852008]...)
See also
--------
BaseNeuralForecastingDetector: Use a neural network to forecast the time
series, and detect anomalies by measuring the difference with the
actual observations.
"""
hidden_layers: list[int]
dropout_rate: float
activation_function: _ACTIVATION_FUNCTION_TYPE
batch_normalization: bool
def __init__(
self,
window_size: str | int,
error_metric: Literal[
"mean-absolute-error", "mean-squared-error"
] = "mean-absolute-error",
forecast_length: int = 1,
hidden_layers: list[int] = (64, 32),
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 | Callable[[_MODEL_PARAMETERS_TYPE], torch.optim.Optimizer]
) = "adam",
learning_rate: float = 1e-3,
compile_model: bool = False,
compile_mode: _COMPILE_MODE_TYPE = "default",
n_epochs: int = 10,
loss_function: torch.nn.Module = torch.nn.MSELoss(),
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,
)
if not utils.is_valid_list(hidden_layers, int):
raise TypeError("`hidden_layers` should be a list of integer")
if any(map(lambda x: x <= 0, hidden_layers)):
raise ValueError(
"All values in `hidden_layers` should be strictly positive"
)
if not isinstance(activation_function, str):
raise TypeError("`activation_function` should be a string")
if activation_function not in self._ACTIVATION_FUNCTIONS:
raise ValueError(
f"Unknown `activation_function` '{activation_function}'. Valid options are {list(self._ACTIVATION_FUNCTIONS.keys())}"
)
if not isinstance(batch_normalization, bool):
raise TypeError("`batch_normalization` should be a bool")
if not isinstance(dropout_rate, (float, int)) or isinstance(dropout_rate, bool):
raise TypeError("`dropout_rate` should be a float")
if not 0.0 <= dropout_rate < 1.0:
raise ValueError(f"`dropout_rate` should be in interval [0, 1[.")
self.hidden_layers = hidden_layers
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:
# Initialize the MLP
mlp = torch.nn.Sequential()
mlp.add_module("flatten", torch.nn.Flatten())
# Initialize layer inputs and outputs
inputs = [n_attributes * self.window_size_, *self.hidden_layers]
outputs = [*self.hidden_layers, n_attributes * self.forecast_length]
# Add all the layers
for i in range(len(inputs)):
# Add the linear layer
mlp.add_module(f"linear-{i}", torch.nn.Linear(inputs[i], outputs[i]))
# Add batch normalization
if self.batch_normalization and 0 < i < len(inputs) - 1:
mlp.add_module(f"batch-norm-{i}", torch.nn.BatchNorm1d(outputs[i]))
# Add the activation function
mlp.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:
mlp.add_module(f"dropout-{i}", torch.nn.Dropout(self.dropout_rate))
# Restore the dimensions of the window
mlp.add_module(
"unflatten", torch.nn.Unflatten(1, (self.forecast_length, n_attributes))
)
# Return the MLP
return mlp