timesead.models.baselines.threshold_ads ======================================= .. py:module:: timesead.models.baselines.threshold_ads Classes ------- .. autoapisummary:: timesead.models.baselines.threshold_ads.Base_ThresholdAD timesead.models.baselines.threshold_ads.OOSAnomalyDetector timesead.models.baselines.threshold_ads.IQRAnomalyDetector Module Contents --------------- .. py:class:: Base_ThresholdAD(first_diffs: bool, cum_method: str, feature_index: Optional[int], input_shape: str = 'btf') 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 A Basis Threshold Anomaly Detector A simlpe anomaly detector that is equals zero for all data within the given Threshold and else equals the distance to the given lower/upper thr of training value. :param first_diffs[bool]: Flag, if instead of raw values first difference should be used. :param cum_method[str]: One of {'mean', 'max'}. Accumulation method over feature dimension. Note that when `feature_index` is not None. Then accumulation method will be ignored. :param feature_index[optional[int]]: Take scores for specific feature. If none, above accumulation rule will be used. .. py:attribute:: first_diffs .. py:attribute:: cum_method .. py:attribute:: feature .. py:attribute:: input_shape :value: 'btf' .. py:attribute:: lower_thresh :value: None .. py:attribute:: upper_thresh :value: None .. 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:: 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:class:: OOSAnomalyDetector(first_diffs: bool = False, cum_method: str = 'mean', feature_index: Optional[int] = None, *args, **kwargs) Bases: :py:obj:`Base_ThresholdAD` 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 Out of Support Anomaly Detector A simlpe anomaly detector that is equals zero for all data within the data support of the training data and else equals the distance to the min/max of training value. :param first_diffs[bool]: Flag, if instead of raw values first difference should be used. Default is False. :param cum_method[str]: One of {'mean', 'max'}. Accumulation method over feature dimension. Note that when `feature_index` is not None. Then accumulation method will be ignored. Default is 'mean'. :param feature_index[optional[int]]: Take scores for specific feature. If none, above accumulation rule will be used. Default is None. .. 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:class:: IQRAnomalyDetector(std_factor: float = 2.58, first_diffs: bool = False, cum_method: str = 'mean', feature_index: Optional[int] = None, *args, **kwargs) Bases: :py:obj:`Base_ThresholdAD` 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 Interquantile Range Anomaly Detector A simlpe anomaly detector that is equals zero for all data within some interquantile range of the normal training data and else equals the distance to nearest quantile border. :param std_factor[float]: float, that gives the width of the IQR. Default is 2.58 (=99.5% normal quantile). :param first_diffs[bool]: Flag, if instead of raw values first difference should be used. Default is False. :param cum_method[str]: One of {'mean', 'max'}. Accumulation method over feature dimension. Note that when `feature_index` is not None. Then accumulation method will be ignored. Default is 'mean'. :param feature_index[optional[int]]: Take scores for specific feature. If none, above accumulation rule will be used. Default is None. .. py:attribute:: std_factor :value: 2.58 .. 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