linear_dynamic_system

Classes

LDS(emission_coeff, transition_coeff, ...)

Class which implements Linear Dynamical System (LDS) as a distribution.
class LDS(emission_coeff: torch.Tensor, transition_coeff: torch.Tensor, innovation_coeff: torch.Tensor, noise_std: torch.Tensor, prior_mean: torch.Tensor, prior_cov: torch.Tensor, offset: torch.Tensor, seq_length: int, latent_dim: int)[source]

Class which implements Linear Dynamical System (LDS) as a distribution.

Create instance of LDS.

Parameters

  • emission_coeff (torch.Tensor) – Emission coefficient matrix with shape (batch_size, seq_length, latent_dim).

  • transition_coeff (torch.Tensor) – Transition coefficient matrix with shape (latent_dim, latent_dim).

  • innovation_coeff (torch.Tensor) – Innovation coefficient matrix with shape (batch_size, seq_length, latent_dim).

  • noise_std (torch.Tensor) – Noise standard deviation for targets with shape (batch_size, seq_length, 1).

  • prior_mean (torch.Tensor) – Prior mean for latent state with shape (batch_size, latent_dim)

  • prior_cov (torch.Tensor) – Prior covariance matrix for latent state with shape (batch_size, latent_dim, latent_dim)

  • offset (torch.Tensor) – Offset for the target with shape (batch_size, seq_length, 1)

  • seq_length (int) – Length of the sequence.

  • latent_dim (int) – Dimension of the latent space.

kalman_filter(targets: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor, torch.Tensor][source]

Perform Kalman filtering of given observations.

Parameters

targets (torch.Tensor) – Tensor with observations with shape (batch_size, seq_length, 1)

Returns

  • Log probabilities with shape shape (batch_size, seq_length)

  • Mean of p(l_T | l_{T-1}), where T is seq_length, with shape (batch_size, latent_dim)

  • Covariance of p(l_T | l_{T-1}), where T is seq_length, with shape (batch_size, latent_dim, latent_dim)

Return type

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

kalman_filter_step(target: torch.Tensor, noise_std: torch.Tensor, prior_mean: torch.Tensor, prior_cov: torch.Tensor, emission_coeff: torch.Tensor, offset: torch.Tensor)[source]

One step of the Kalman filter.

This function computes the filtered state (mean and covariance) given the LDS coefficients in the prior state (mean and variance) and observations.

Parameters

  • target (torch.Tensor) – Observations of the system with shape (batch_size, 1)

  • noise_std (torch.Tensor) – Standard deviation of the observations noise with shape (batch_size, 1)

  • prior_mean (torch.Tensor) – Prior mean of the latent state with shape (batch_size, latent_dim)

  • prior_cov (torch.Tensor) – Prior covariance of the latent state with shape (batch_size, latent_dim, latent_dim)

  • emission_coeff (torch.Tensor) – Emission coefficient with shape (batch_size, latent_dim)

  • offset (torch.Tensor) – Offset for the observations with shape (batch_size, 1)

Returns

  • Log probability with shape (batch_size, 1)

  • Filtered_mean with shape (batch_size, latent_dim, 1)

  • Filtered_covariance with shape (batch_size, latent_dim, latent_dim)

log_likelihood(targets: torch.Tensor) → torch.Tensor[source]

Compute the log-likelihood of the target.

Parameters

targets (torch.Tensor) – Tensor with targets of shape (batch_size, seq_length, 1)

Returns

Tensor with log-likelihoods of target of shape (batch_size, seq_length)

Return type

torch.Tensor

sample(n_samples: int) → torch.Tensor[source]

Sample the trajectories of targets from the current LDS.

Parameters

n_samples (int) – Number of trajectories to sample.

Returns

Tensor with trajectories with shape (n_samples, batch_size, seq_length, 1).

Return type

torch.Tensor

set_params(**params: dict) → etna.core.mixins.TMixin

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters

  • **params – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()

Collect all information about etna object in dict.