import numpy as np
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,
IntegerAttribute,
ListAttribute,
LiteralAttribute,
)
from dtaianomaly.windowing import WINDOW_SIZE_TYPE
__all__ = ["ConvolutionalNeuralNetwork"]
[docs]
class ConvolutionalNeuralNetwork(BaseNeuralForecastingDetector):
"""
Use a convolutional neural network to detect anomalies.
The Convolutional Neural Network (CNN) is a neural network consisting
of convolutional layers, each consisting of multiple kernels. Given some
input, the convolutional layer computes the convolution of the input with
each kernel to create an output. The input sequences are fed through
multiple such convolutional layers, and the task is to forecast the
time series. Hence, by computing the difference between the forecasted
value and the actually observed values, the neural network can detect
anomalies.
The architecture of the CNN consists of blocks, in which each block applies
the following operations: convolutional layer :math:`\\rightarrow` batch
normalization :math:`\\rightarrow` activation function :math:`\\rightarrow`
average pooling. The first and final layers of the network has no batch
normalization.
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.
kernel_size : int, default=3
The size of the kernels in the convolutional layers.
hidden_layers : list of ints, default=[64, 32]
The number of kernels in each hidden layer. Must contain at least 1 value.
activation_function : {"linear", "relu", "sigmoid", "tanh"} default="relu"
The activation function to use at the end of each convolutional layer.
batch_normalization : bool = True,
Whether to add batch normalization after each convolutional 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
--------
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 ConvolutionalNeuralNetwork
>>> from dtaianomaly.data import demonstration_time_series
>>> x, y = demonstration_time_series()
>>> cnn = ConvolutionalNeuralNetwork(10, seed=0).fit(x)
>>> cnn.decision_function(x) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE, +SKIP
array([0.07708263, 0.07708263, 0.06242053, ..., 0.1827196 , 0.2396274 ,
0.06390759]...)
"""
kernel_size: int
hidden_layers: list[int]
activation_function: ACTIVATION_FUNCTION_TYPE
batch_normalization: bool
attribute_validation = {
"kernel_size": IntegerAttribute(minimum=1),
"hidden_layers": ListAttribute(IntegerAttribute(minimum=1), minimum_length=1),
"activation_function": LiteralAttribute(ACTIVATION_FUNCTIONS),
"batch_normalization": BoolAttribute(),
}
def __init__(
self,
window_size: WINDOW_SIZE_TYPE,
error_metric: ERROR_METRIC_TYPE = "mean-absolute-error",
forecast_length: int = 1,
kernel_size: int = 3,
hidden_layers: list[int] = (64, 32),
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__(
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.kernel_size = kernel_size
self.hidden_layers = list(hidden_layers)
self.activation_function = activation_function
self.batch_normalization = batch_normalization
def _build_architecture(self, n_attributes: int) -> torch.nn.Module:
# Initialize the CNN
cnn = torch.nn.Sequential()
padding = int((self.kernel_size - 1) / 2)
# Initialize layer inputs and outputs
inputs = [n_attributes, *self.hidden_layers]
outputs = [*self.hidden_layers, n_attributes * self.forecast_length]
for i in range(len(inputs) - 1):
# Add the convolutional layer
cnn.add_module(
f"conv-{i}",
torch.nn.Conv1d(
in_channels=inputs[i],
out_channels=outputs[i],
kernel_size=self.kernel_size,
stride=1,
padding=padding,
),
)
# Add batch normalization
if self.batch_normalization and 0 < i:
cnn.add_module(f"batch-norm-{i}", torch.nn.BatchNorm1d(outputs[i]))
# Add the activation function
cnn.add_module(
f"activation-{i}",
self._build_activation_function(self.activation_function),
)
# Add a pooling layer
cnn.add_module(f"pool-{i}", torch.nn.AvgPool1d(kernel_size=2))
# Add a linear layer
cnn.add_module("flatten", torch.nn.Flatten())
channel_correction_term = int(
np.floor(self.window_size_ / 2 ** len(self.hidden_layers))
)
cnn.add_module(
"linear",
torch.nn.Linear(
in_features=inputs[-1] * channel_correction_term,
out_features=outputs[-1],
),
)
# Return the CNN
return _CNN(n_attributes, cnn)
class _CNN(torch.nn.Module):
n_attributes: int
cnn: torch.nn.Sequential
def __init__(self, n_attributes: int, cnn: torch.nn.Sequential):
super().__init__()
self.n_attributes = n_attributes
self.cnn = cnn
def forward(self, x):
x = x.view(x.shape[0], self.n_attributes, -1)
x = self.cnn(x)
return x