Splitting

timenet_connectors.splitting ¶source

Generate dataset partitions during connector conversion.

DatasetSplits ¶source

Bases: Mapping[Split, tuple[T, ...]]

Named partitions, accessed with splits[Split.TRAIN].

Iteration yields partition names, not samples. An absent partition raises KeyError; a configured but empty partition contains an empty tuple. Samples retain their original identity and order within each partition.

counts `property` ¶source

counts: Mapping[Split, int]

Actual sample counts, including configured partitions with no samples.

Splitter ¶source

Bases: ABC, Generic[T]

Assign samples to partitions without modifying the samples themselves.

split `abstractmethod` ¶source

split(samples: Sequence[T]) -> DatasetSplits[T]

Partition source samples for a connector to turn into split-tagged tasks.

Returns:

Type	Description
`DatasetSplits[T]`	Named partitions containing every input occurrence exactly once.

StratifiedSplitter ¶source

Bases: Splitter[T]

Balance category counts while assigning whole groups to partitions.

stratify_by returns one category per sample; group_by identifies samples that must stay together (for example, questions about one signal). Without group_by, StratifiedKFold assigns independent input occurrences; otherwise StratifiedGroupKFold keeps related samples together.

train_folds, test_folds, and optional validation_folds are positive integer fold counts. Their sum is the total number of folds: train_folds=4, test_folds=1 uses five folds; train_folds=8, validation_folds=1, test_folds=1 uses ten. Each group stays in one fold. Nonempty input must contain at least as many groups as folds and meet scikit-learn's category-count requirements. Invalid inputs raise TimeFValidationError.

Sample counts and category proportions are approximate for unequal groups. The same ordered input, seed, and scikit-learn version give the same result. This is not a temporal split strategy.

split ¶source

split(samples: Sequence[T]) -> DatasetSplits[T]

Assign every sample once, preserving input order within partitions.