timesead.models.generative.gru_gmm_vae

Classes

`RNNVAECategoricalEncoder`	Base class for all neural network modules.
`GRUGMMVAE`	Base class for all neural network modules.
`GMMVAELoss`
`GMMVAEAnomalyDetector`	Use sampled log likelihood of data

Module Contents

class timesead.models.generative.gru_gmm_vae.RNNVAECategoricalEncoder(input_dim: int, rnn_type: str = 'lstm', rnn_hidden_dims: List[int] = [60], categories: int = 10, bidirectional: bool = False, mode: str = 's2s')

Bases: 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 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_type (str)
rnn_hidden_dims (List[int])
categories (int)
bidirectional (bool)
mode (str)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

rnn

linear

forward(x: torch.Tensor) → torch.Tensor

Parameters:: x (torch.Tensor)
Return type:: torch.Tensor

class timesead.models.generative.gru_gmm_vae.GRUGMMVAE(input_dim: int, gru_hidden_dims: List[int] = [60], latent_dim: int = 8, gmm_components: int = 2)

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)
gru_hidden_dims (List[int])
latent_dim (int)
gmm_components (int)

Guo2018 (more or less)

Parameters:

input_dim (int)
gru_hidden_dims (List[int])
latent_dim (int)
gmm_components (int)

latent_dim = 8

gmm_components = 2

encoder_rnn

encoder_component

vae

prior_means

prior_std

softplus

get_prior(batch_size: int, seq_len: int) → Tuple[torch.Tensor | None, torch.Tensor | None]

Parameters:

batch_size (int)
seq_len (int)

Return type:

Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]

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

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

class timesead.models.generative.gru_gmm_vae.GMMVAELoss

Bases: timesead.models.common.VAELoss

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.gru_gmm_vae.GMMVAEAnomalyDetector(model: GRUGMMVAE, num_mc_samples: int = 1)

Bases: timesead.models.common.AnomalyDetector

Use sampled log likelihood of data

Parameters:

model (GRUGMMVAE)
num_mc_samples (int)

model

num_mc_samples = 1

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