timesead.models.reconstruction.mscred

Classes

`MSCRED`	input is signature matrices of shape (Seq_len, Batch, Channel, Height, Width)
`MSCREDLoss`	Base class for all neural network modules.
`MSCREDAnomalyDetector`	Base class for all neural network modules.
`MSCREDAnomalyDetectorOrig`	Base class for all neural network modules.
`SignatureMatrixTransform`

Functions

compute_signature_matrix(→ torch.Tensor)

Module Contents

class timesead.models.reconstruction.mscred.MSCRED(n_features: int, in_channels: int, c_out: int = 256, small_model: bool = False, chi: float = 5.0)

Bases: timesead.models.BaseModel

input is signature matrices of shape (Seq_len, Batch, Channel, Height, Width)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:

n_features (int)
in_channels (int)
c_out (int)
small_model (bool)
chi (float)

small_model = False

chi = 5.0

enc1

lstm1

forward(inputs: Tuple[torch.Tensor]) → torch.Tensor

Parameters:: inputs (Tuple[torch.Tensor])
Return type:: torch.Tensor

class timesead.models.reconstruction.mscred.MSCREDLoss

Bases: timesead.optim.loss.Loss

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

mse_loss

forward(predictions: Tuple[torch.Tensor, Ellipsis], targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) → torch.Tensor

Parameters:

predictions (Tuple[torch.Tensor, Ellipsis])
targets (Tuple[torch.Tensor, Ellipsis])

Return type:

torch.Tensor

class timesead.models.reconstruction.mscred.MSCREDAnomalyDetector(model: MSCRED)

Bases: timesead.models.common.MSEReconstructionAnomalyDetector

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.
Parameters:: model (MSCRED)

This is what Florian uses, but in the paper they describe sth. completely different. They compute the number of badly reconstructed entries in the signature matrix (i.e., higher than some threshold) and use that count as the anomaly score

compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the online anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors
Returns:: Tensor of shape (B,) that contains the anomaly scores for this batch
Return type:: torch.Tensor

abstract compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the offline anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors
Returns:: Tensor of shape (N,) that contains the anomaly scores for this batch
Return type:: torch.Tensor

class timesead.models.reconstruction.mscred.MSCREDAnomalyDetectorOrig(model: MSCRED, error_threshold: float = 0.5)

Bases: timesead.models.common.AnomalyDetector

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Parameters:

model (MSCRED)
error_threshold (float)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

model

error_threshold = 0.5

compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the online anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors
Returns:: Tensor of shape (B,) that contains the anomaly scores for this batch
Return type:: torch.Tensor

abstract compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Compute the offline anomaly score for a batch of inputs. The output tensor must have the same shape as the output of format_targets when called with the corresponding targets for this batch. This method expects a window (or a batch of windows) as its input and should return a score for the last point in the window.

Parameters:: inputs (Tuple[torch.Tensor, Ellipsis]) – tuple of input tensors
Returns:: Tensor of shape (N,) that contains the anomaly scores for this batch
Return type:: torch.Tensor

fit(dataset: torch.utils.data.DataLoader) → None

Fit this anomaly detector on a dataset. Note that we assume only normal data here.

Parameters:: dataset (torch.utils.data.DataLoader) – A dataset
Return type:: None

format_online_targets(targets: Tuple[torch.Tensor, Ellipsis]) → torch.Tensor

Format the labels for a batch of targets. The output tensor must have the same shape as the output of compute_online_anomaly_score when called with the corresponding inputs for this batch.

Parameters:: targets (Tuple[torch.Tensor, Ellipsis]) – tuple of target tensors
Returns:: Tensor of shape (B,) that contains the ground truth labels for this batch
Return type:: torch.Tensor

timesead.models.reconstruction.mscred.compute_signature_matrix(x: torch.Tensor, seg_interval: int, wins: Tuple[int, Ellipsis], h: int) → torch.Tensor

Parameters:

x (torch.Tensor)
seg_interval (int)
wins (Tuple[int, Ellipsis])
h (int)

Return type:

torch.Tensor

class timesead.models.reconstruction.mscred.SignatureMatrixTransform(parent: timesead.data.transforms.Transform, wins: Tuple[int] = (10, 30, 60), seg_interval: int = 10, h: int = 5)

Bases: timesead.data.transforms.WindowTransform

Parameters:

parent (timesead.data.transforms.Transform)
wins (Tuple[int])
seg_interval (int)
h (int)

wins = (10, 30, 60)

seg_interval = 10

h = 5

property num_features: int | Tuple[int, Ellipsis]

Return type:: Union[int, Tuple[int, Ellipsis]]