model.utils.cross_validation

Cross-validation helpers for entity-first ADELM datasets.

All ADELM tensors are assumed to use entity as the first dimension. Time-varying tensors additionally use time as the second dimension.

Module Contents

Classes

CVFold	One cross-validation fold over a entity-first dataset.
CrossValidation	Cross-validation interface for ADELM datasets.

API

class model.utils.cross_validation.CVFold

One cross-validation fold over a entity-first dataset.

index: int

None

name: str

None

train_entity_mask: torch.Tensor

None

valid_entity_mask: torch.Tensor

None

train_time_mask: torch.Tensor

None

valid_time_mask: torch.Tensor

None

valid_entities: tuple

()

valid_time_range: tuple

()

target_mask(split='train')

Return a 2D [entity, time] mask for the requested split.

mask_tensor(tensor, split='train', fill_value=torch.nan)

Keep only the requested split and fill the remainder.

class model.utils.cross_validation.CrossValidation(mode, folds, entity_ids, time_values=None, metadata=None)

Bases: object

Cross-validation interface for ADELM datasets.

Use folds to iterate over prepared splits, then apply the split to a single target tensor or an entire dict[str, Tensor] collection.

Initialization

__len__()

__iter__()

split_targets(targets, fold, fill_value=torch.nan)

Return (train_targets, valid_targets) for one fold.

mask_targets(targets, fold, split='train', fill_value=torch.nan)

Mask one tensor or a dict of tensors by the requested fold split.

classmethod spatial_random(entity_ids, n_folds, shuffle=True, seed=0)

Random spatial CV by entity.

classmethod spatial_predefined(entity_ids, fold_definition)

Spatial CV from a user-defined fold specification.

classmethod temporal_block(entity_ids, time_values, n_folds, train_start, train_end)

Temporal CV by contiguous time blocks within the training window.

classmethod from_config(config, entity_ids, cv_cfg=None, scheme=None, time_values=None, n_folds=None, fold_definition=None, shuffle=True, seed=0)

Build a cross-validator over the given entities.

cv_cfg is the active head’s cross_validation block (site_level or gauge_level); entity_ids are that head’s entities (flux sites or gauges). The time window for temporal CV is read from the shared learning.time.