immuneML.hyperparameter_optimization.clustering package

Submodules

immuneML.hyperparameter_optimization.clustering.StabilityLange module

class immuneML.hyperparameter_optimization.clustering.StabilityLange.ClItems(cl_setting: immuneML.workflows.instructions.clustering.clustering_run_model.ClusteringSetting, discovery: List[immuneML.workflows.instructions.clustering.clustering_run_model.ClusteringItem] = <factory>, tuning: List[immuneML.workflows.instructions.clustering.clustering_run_model.ClusteringItem] = <factory>)

Bases: object

cl_setting: ClusteringSetting

discovery: List[ClusteringItem]

tuning: List[ClusteringItem]

class immuneML.hyperparameter_optimization.clustering.StabilityLange.StabilityLange(discovery_datasets: List[Dataset], tuning_datasets: List[Dataset], clustering_settings: List[ClusteringSetting], result_path: Path, number_of_processes: int, sequence_type: SequenceType = SequenceType.AMINO_ACID, region_type: RegionType = RegionType.IMGT_CDR3, clustering_items: Dict[str, ClItems] = None)

Bases: object

Class to run stability-based hyperparameter assessment for clustering algorithms, based on Lange et al. 2004.

Reference:

Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-Based Validation of Clustering Solutions. Neural Computation, 16(6), 1299–1323. https://doi.org/10.1162/089976604773717621

clustering_items: Dict[str, ClItems] = None

clustering_settings: List[ClusteringSetting]

discovery_datasets: List[Dataset]

make_figure(df: DataFrame) → ReportOutput

number_of_processes: int

region_type: RegionType = 'cdr3'

result_path: Path

run()

sequence_type: SequenceType = 'sequence_aa'

transfer_clustering(cl_item: ClusteringItem, dataset: Dataset, path: Path, run_id: int) → ClusteringItem

tuning_datasets: List[Dataset]

Module contents