timesead.models.generative.sis_vae

Classes

`SISVAE`	Base class for all neural network modules.
`SISVAELossWithGeneratedPrior`
`SISVAEAnomalyDetector`	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.sis_vae.SISVAE(input_dim: int, rnn_hidden_dim: int = 200, latent_dim: int = 40, x_hidden_dims: List[int] = [100], z_hidden_dims: List[int] = [100], enc_hidden_dims: List[int] = [100], dec_hidden_dims: List[int] = [100], prior_hidden_dims: List[int] = [100])

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)
rnn_hidden_dim (int)
latent_dim (int)
x_hidden_dims (List[int])
z_hidden_dims (List[int])
enc_hidden_dims (List[int])
dec_hidden_dims (List[int])
prior_hidden_dims (List[int])

Li2021, ist aber im Prinzip nur Chung2015 mit einem extra loss term

Parameters:

input_dim (int)
lstm_hidden_dims
latent_dim (int)
rnn_hidden_dim (int)
x_hidden_dims (List[int])
z_hidden_dims (List[int])
enc_hidden_dims (List[int])
dec_hidden_dims (List[int])
prior_hidden_dims (List[int])

latent_dim = 40

rnn_hidden_dim = 200

x_embed

z_embed

encoder

decoder

prior_decoder

rnn_cell

softplus

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

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

class timesead.models.generative.sis_vae.SISVAELossWithGeneratedPrior(smooth_weight: float = 0.5)

Bases: timesead.models.common.VAELoss

Parameters:: smooth_weight (float)

smooth_weight = 0.5

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.sis_vae.SISVAEAnomalyDetector(model: SISVAE, num_mc_samples: int = 128)

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 (SISVAE)
num_mc_samples (int)

model

num_mc_samples = 128

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