timesead.models.generative.donut

Classes

`Donut`	Base class for all neural network modules.
`MaskedVAELoss`
`DonutAnomalyDetector`	We decided not to include the reconstruction step from the paper here, since we don't have missing data.

Module Contents

class timesead.models.generative.donut.Donut(input_dim: int, hidden_dims: List[int] = [100, 100], latent_dim: int = 20, mask_prob: float = 0.01)

Bases: 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 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:

input_dim (int)
hidden_dims (List[int])
latent_dim (int)
mask_prob (float)

Xu2018

Parameters:

input_dim (int) – Should be window_size * features
hidden_dims (List[int])
latent_dim (int)
mask_prob (float)

latent_dim = 20

mask_prob = 0.01

vae

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

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

class timesead.models.generative.donut.MaskedVAELoss(size_average=None, reduce=None, reduction: str = 'mean')

Bases: timesead.models.common.VAELoss

Parameters:: reduction (str)

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.generative.donut.DonutAnomalyDetector(model: Donut, num_mc_samples: int = 1024)

Bases: timesead.models.common.AnomalyDetector

We decided not to include the reconstruction step from the paper here, since we don’t have missing data.

Parameters:

model (Donut)
num_mc_samples (int)

model

num_mc_samples = 1024

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

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

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

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

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

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

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

Parameters:: targets (Tuple[torch.Tensor, Ellipsis])
Return type:: torch.Tensor