timesead.models.reconstruction.mscred ===================================== .. py:module:: timesead.models.reconstruction.mscred Classes ------- .. autoapisummary:: timesead.models.reconstruction.mscred.MSCRED timesead.models.reconstruction.mscred.MSCREDLoss timesead.models.reconstruction.mscred.MSCREDAnomalyDetector timesead.models.reconstruction.mscred.MSCREDAnomalyDetectorOrig timesead.models.reconstruction.mscred.SignatureMatrixTransform Functions --------- .. autoapisummary:: timesead.models.reconstruction.mscred.compute_signature_matrix Module Contents --------------- .. py:class:: MSCRED(n_features: int, in_channels: int, c_out: int = 256, small_model: bool = False, chi: float = 5.0) Bases: :py:obj:`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. .. py:attribute:: small_model :value: False .. py:attribute:: chi :value: 5.0 .. py:attribute:: enc1 .. py:attribute:: lstm1 .. py:method:: forward(inputs: Tuple[torch.Tensor]) -> torch.Tensor .. py:class:: MSCREDLoss Bases: :py:obj:`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 :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: mse_loss .. py:method:: forward(predictions: Tuple[torch.Tensor, Ellipsis], targets: Tuple[torch.Tensor, Ellipsis], *args, **kwargs) -> torch.Tensor .. py:class:: MSCREDAnomalyDetector(model: MSCRED) Bases: :py:obj:`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 :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool 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 .. py:method:: 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. :param inputs: tuple of input tensors :return: Tensor of shape (B,) that contains the anomaly scores for this batch .. py:method:: compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor :abstractmethod: 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. :param inputs: tuple of input tensors :return: Tensor of shape (N,) that contains the anomaly scores for this batch .. py:class:: MSCREDAnomalyDetectorOrig(model: MSCRED, error_threshold: float = 0.5) Bases: :py:obj:`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 :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Initialize internal Module state, shared by both nn.Module and ScriptModule. .. py:attribute:: model .. py:attribute:: error_threshold :value: 0.5 .. py:method:: 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. :param inputs: tuple of input tensors :return: Tensor of shape (B,) that contains the anomaly scores for this batch .. py:method:: compute_offline_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor :abstractmethod: 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. :param inputs: tuple of input tensors :return: Tensor of shape (N,) that contains the anomaly scores for this batch .. py:method:: fit(dataset: torch.utils.data.DataLoader) -> None Fit this anomaly detector on a dataset. Note that we assume only normal data here. :param dataset: A dataset .. py:method:: 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. :param targets: tuple of target tensors :return: Tensor of shape (B,) that contains the ground truth labels for this batch .. py:function:: compute_signature_matrix(x: torch.Tensor, seg_interval: int, wins: Tuple[int, Ellipsis], h: int) -> torch.Tensor .. py:class:: SignatureMatrixTransform(parent: timesead.data.transforms.Transform, wins: Tuple[int] = (10, 30, 60), seg_interval: int = 10, h: int = 5) Bases: :py:obj:`timesead.data.transforms.WindowTransform` .. py:attribute:: wins :value: (10, 30, 60) .. py:attribute:: seg_interval :value: 10 .. py:attribute:: h :value: 5 .. py:property:: num_features :type: Union[int, Tuple[int, Ellipsis]]