mlrl.testbed_sklearn.experiments.input.dataset.splitters.splitter_bipartition module

Author: Michael Rapp (michael.rapp.ml@gmail.com)

Provides classes for splitting dataset into distinct training and test datasets.

class mlrl.testbed_sklearn.experiments.input.dataset.splitters.splitter_bipartition.BipartitionSplitter(dataset_reader: DatasetReader | None, test_size: float, random_state: int)

Bases: DatasetSplitter

Splits a tabular dataset into distinct training and test datasets.

class DynamicSplit(splitter: BipartitionSplitter, state: ExperimentState)

Bases: Split

A split into a training and a test dataset that has been created dynamically.

class Cache(training_dataset: TabularDataset, test_dataset: TabularDataset)

Bases: object

Caches training and test datasets that been created dynamically.

Attributes:

training_dataset: The training dataset test_dataset: The test dataset

test_dataset: TabularDataset

training_dataset: TabularDataset

get_state(dataset_type: DatasetType) → ExperimentState

See mlrl.testbed.experiments.input.dataset.splitters.splitter.DatasetSplitter.Split.get_state()

class PredefinedSplit(dataset_reader: DatasetReader | None, state: ExperimentState)

Bases: Split

A predefined split into a training and a test dataset.

get_state(dataset_type: DatasetType) → ExperimentState

See mlrl.testbed.experiments.input.dataset.splitters.splitter.DatasetSplitter.Split.get_state()

split(state: ExperimentState) → Generator[Split, None, None]

See mlrl.testbed.experiments.input.dataset.splitters.splitter.DatasetSplitter.split()