SequentialNet#

class pytorch_forecasting.metrics._mqf2_utils.SequentialNet(networks: list[Module])[source]#

Bases: SequentialFlow

Class that combines a list of DeepConvexNet and ActNorm layers and provides energy score computation This class is based on SequentialFlow of the CP-Flow repo (CW-Huang/CP-Flow)

Parameters:

networks – list of DeepConvexNet and/or ActNorm instances

Inherited-members:

Methods

energy_score

es_sample

forward

energy_score(z: Tensor, hidden_state: Tensor, es_num_samples: int = 50, beta: float = 1.0) → Tensor[source]#

Computes the (approximated) energy score sum_i ES(g,z_i), where ES(g,z_i) = -1/(2*es_num_samples^2) * sum_{w,w’} ||w-w’||_2^beta + 1/es_num_samples * sum_{w’’} ||w’’-z_i||_2^beta, w’s are samples drawn from the quantile function g(., h_i) (gradient of picnn), h_i is the hidden state associated with z_i, and es_num_samples is the number of samples drawn for each of w, w’, w’’ in energy score approximation

Parameters:

  • z – Observations (numel_batch, dimension)

  • hidden_state – Hidden state (numel_batch, hidden_size)

  • es_num_samples – Number of samples drawn for each of w, w’, w’’ in energy score approximation

  • beta – Hyperparameter of the energy score, see the formula above

Returns:

energy score (numel_batch)

Return type:

loss

es_sample(hidden_state: Tensor, dimension: int) → Tensor[source]#

Auxiliary function for energy score computation Drawing samples conditioned on the hidden state

Parameters:

  • hidden_state – hidden_state which the samples conditioned on (num_samples, hidden_size)

  • dimension – dimension of the input

Returns:

samples drawn (num_samples, dimension)

Return type:

samples

forward(x: Tensor, context: Tensor | None = None) → Tensor[source]#

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