immuneML.hyperparameter_optimization.core package

Submodules

immuneML.hyperparameter_optimization.core.HPAssessment module

class immuneML.hyperparameter_optimization.core.HPAssessment.HPAssessment

Bases: object

static create_assessment_path(state, split_index)

static reeval_on_assessment_split(state: TrainMLModelState, train_val_dataset: Dataset, test_dataset: Dataset, hp_setting: HPSetting, path: Path, label: Label, split_index: int) → TrainMLModelState

retrain model for specific label, assessment split and hp_setting

static run_assessment(state: TrainMLModelState) → TrainMLModelState

static run_assessment_split(state, train_val_dataset, test_dataset, split_index: int, n_splits)

run inner CV loop (selection) and retrain on the full train_val_dataset after optimal model is chosen

static run_assessment_split_per_label(state: TrainMLModelState, split_index: int)

iterate through labels and hp_settings and retrain all models

static update_hp_setting_for_assessment(hp_setting: HPSetting, state: TrainMLModelState, split_index: int, label_name: str)

immuneML.hyperparameter_optimization.core.HPSelection module

class immuneML.hyperparameter_optimization.core.HPSelection.HPSelection

Bases: object

static create_selection_path(state: TrainMLModelState, current_path: Path) → Path

static evaluate_hp_setting(state: TrainMLModelState, hp_setting: HPSetting, train_datasets: list, val_datasets: list, current_path: Path, label: Label, assessment_split_index: int)

static run_selection(state: TrainMLModelState, train_val_dataset, current_path: Path, split_index: int) → TrainMLModelState

static run_setting(state: TrainMLModelState, hp_setting, train_dataset, val_dataset, split_index: int, current_path: Path, label: Label, assessment_index: int)

static update_split_count(state: TrainMLModelState, train_val_dataset)

immuneML.hyperparameter_optimization.core.HPUtil module

class immuneML.hyperparameter_optimization.core.HPUtil.HPUtil

Bases: object

static assess_performance(method, metrics, optimization_metric, dataset, split_index, current_path: Path, test_predictions_path: Path, label: Label, ml_score_path: Path)

static encode_dataset(dataset, hp_setting: HPSetting, path: Path, learn_model: bool, context: dict, number_of_processes: int, label_configuration: LabelConfiguration, encode_labels: bool = True, sequence_type: SequenceType = SequenceType.AMINO_ACID, region_type: RegionType = RegionType.IMGT_CDR3)

static get_average_performance(performances)

static preprocess_dataset(dataset: Dataset, preproc_sequence: list, path: Path, context: dict = None, hp_setting: HPSetting = None, number_of_processes: int = 1) → Dataset

static run_hyperparameter_reports(state: TrainMLModelState, path: Path) → List[ReportResult]

static run_selection_reports(state: TrainMLModelState, dataset, train_datasets: list, val_datasets: list, selection_state: HPSelectionState)

static split_data(dataset: Dataset, split_config: SplitConfig, path: Path, label_config: LabelConfiguration) → tuple

static weight_examples(dataset, weighting_strategy: ExampleWeightingStrategy, path: Path, learn_model: bool, number_of_processes: int)

Module contents