timesead.models.other.mtad_gat ============================== .. py:module:: timesead.models.other.mtad_gat Classes ------- .. autoapisummary:: timesead.models.other.mtad_gat.GAT timesead.models.other.mtad_gat.MTAD_GAT timesead.models.other.mtad_gat.MTAD_GATLoss timesead.models.other.mtad_gat.MTAD_GATAnomalyDetector Module Contents --------------- .. py:class:: GAT(num_nodes: int, node_size: int, initializer_range: float = 0.02) Bases: :py:obj:`torch.nn.Module` 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:: weight .. py:attribute:: leaky_relu .. py:attribute:: layer_norm .. py:attribute:: softmax .. py:method:: forward(x: torch.Tensor) -> torch.Tensor .. py:class:: MTAD_GAT(input_features: int, window_size: int = 100, gru_hidden_dim: int = 300, gru_dropout_prob: float = 0.0, mlp_hidden_dim: Union[int, Sequence[int]] = (300, 300, 300), vae_hidden_dim: int = 300) Bases: :py:obj:`timesead.models.BaseModel` 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:: conv_layer .. py:attribute:: feature_gat .. py:attribute:: temporal_gat .. py:attribute:: gru_layer .. py:attribute:: forecast_MLP .. py:attribute:: vae_encoder .. py:attribute:: vae_decoder .. py:attribute:: vae .. py:method:: forward(inputs: Tuple[torch.Tensor, Ellipsis]) -> Tuple[torch.Tensor, Ellipsis] .. py:class:: MTAD_GATLoss(size_average=None, reduce=None, reduction: str = 'mean') Bases: :py:obj:`timesead.models.common.VAELoss` 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:: MTAD_GATAnomalyDetector(model: MTAD_GAT, gamma: float = 0.8) 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:: gamma :value: 0.8 .. py:method:: compute_vae_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> torch.Tensor :staticmethod: .. py:method:: compute_online_anomaly_score(inputs: Tuple[torch.Tensor, Ellipsis]) -> Tuple[torch.Tensor, 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:: get_labels_and_scores(dataset: torch.utils.data.DataLoader) -> Tuple[torch.Tensor, torch.Tensor] .. py:method:: 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. :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