credit.ensemble.bred_vector

credit.ensemble.bred_vector#

Attributes#

logger

Functions#

generate_bred_vectors(x_batch, model[, num_cycles, ...])

Generate bred vectors and initialize initial conditions for the given batch.

generate_bred_vectors_cycle(initial_condition, ...[, ...])

Generate bred vectors and initialize initial conditions for the given batch.

clone_dataset(dataset)

Clones a PyTorch Dataset by creating a deep copy.

adjust_start_times(time_ranges[, hours])

Adjusts the start times by subtracting 24 hours.

Module Contents#

credit.ensemble.bred_vector.generate_bred_vectors(x_batch, model, num_cycles=5, perturbation_std=0.15, epsilon=1.0, flag_clamp=False, clamp_min=None, clamp_max=None)#

Generate bred vectors and initialize initial conditions for the given batch.

Parameters:

  • x_batch (torch.Tensor) – The input batch.

  • batch (dict) – A dictionary containing additional batch data.

  • model (nn.Module) – The model used for predictions.

  • num_cycles (int) – Number of perturbation cycles.

  • perturbation_std (float) – Magnitude of initial perturbations.

  • epsilon (float) – Scaling factor for bred vectors.

  • flag_clamp (bool, optional) – Whether to clamp inputs. Defaults to False.

  • clamp_min (float, optional) – Minimum clamp value. Required if flag_clamp is True.

  • clamp_max (float, optional) – Maximum clamp value. Required if flag_clamp is True.

Returns:

List of initial conditions generated using bred vectors.

Return type:

list[torch.Tensor]

credit.ensemble.bred_vector.generate_bred_vectors_cycle(initial_condition, dataset, model, num_cycles=5, perturbation_std=0.15, epsilon=1.0, flag_clamp=False, clamp_min=None, clamp_max=None, device='cuda', history_len=1, varnum_diag=None, static_dim_size=None, post_conf={})#

Generate bred vectors and initialize initial conditions for the given batch.

Parameters:

  • x_batch (torch.Tensor) – The input batch.

  • batch (dict) – A dictionary containing additional batch data.

  • model (nn.Module) – The model used for predictions.

  • num_cycles (int) – Number of perturbation cycles.

  • perturbation_std (float) – Magnitude of initial perturbations.

  • epsilon (float) – Scaling factor for bred vectors.

  • flag_clamp (bool, optional) – Whether to clamp inputs. Defaults to False.

  • clamp_min (float, optional) – Minimum clamp value. Required if flag_clamp is True.

  • clamp_max (float, optional) – Maximum clamp value. Required if flag_clamp is True.

Returns:

List of initial conditions generated using bred vectors.

Return type:

list[torch.Tensor]

credit.ensemble.bred_vector.clone_dataset(dataset)#

Clones a PyTorch Dataset by creating a deep copy.

Parameters:

dataset (torch.utils.data.Dataset) – The original dataset.

Returns:

A cloned dataset.

Return type:

torch.utils.data.Dataset

credit.ensemble.bred_vector.adjust_start_times(time_ranges, hours=24)#

Adjusts the start times by subtracting 24 hours.

Parameters:

time_ranges (list of lists) – Each sublist contains [start_time, end_time] as strings.

Returns:

Adjusted time ranges [[start_time - 24hrs, start_time], …]

Return type:

list of lists

credit.ensemble.bred_vector.logger#
Contents