immuneML.workflows.steps package

Submodules

immuneML.workflows.steps.DataEncoder module

class immuneML.workflows.steps.DataEncoder.DataEncoder[source]

Bases: immuneML.workflows.steps.Step.Step

static run(input_params: Optional[immuneML.workflows.steps.StepParams.StepParams] = None)[source]

immuneML.workflows.steps.MLMethodAssessment module

class immuneML.workflows.steps.MLMethodAssessment.MLMethodAssessment[source]

Bases: immuneML.workflows.steps.Step.Step

fieldnames = ['run', 'optimal_method_params', 'method', 'encoding_params', 'encoding', 'evaluated_on']
static run(input_params: Optional[immuneML.workflows.steps.MLMethodAssessmentParams.MLMethodAssessmentParams] = None)[source]

immuneML.workflows.steps.MLMethodAssessmentParams module

class immuneML.workflows.steps.MLMethodAssessmentParams.MLMethodAssessmentParams(method: immuneML.ml_methods.MLMethod.MLMethod, dataset: immuneML.data_model.dataset.Dataset.Dataset, metrics: set, optimization_metric: immuneML.ml_metrics.Metric.Metric, label: immuneML.environment.Label.Label, path: pathlib.Path, split_index: int, predictions_path: pathlib.Path, ml_score_path: pathlib.Path)[source]

Bases: immuneML.workflows.steps.StepParams.StepParams

immuneML.workflows.steps.MLMethodTrainer module

class immuneML.workflows.steps.MLMethodTrainer.MLMethodTrainer[source]

Bases: immuneML.workflows.steps.Step.Step

static run(input_params: Optional[immuneML.workflows.steps.MLMethodTrainerParams.MLMethodTrainerParams] = None)[source]
static store(method: immuneML.ml_methods.MLMethod.MLMethod, input_params: immuneML.workflows.steps.MLMethodTrainerParams.MLMethodTrainerParams)[source]

immuneML.workflows.steps.MLMethodTrainerParams module

class immuneML.workflows.steps.MLMethodTrainerParams.MLMethodTrainerParams(method: immuneML.ml_methods.MLMethod.MLMethod, dataset: immuneML.data_model.dataset.Dataset.Dataset, result_path: pathlib.Path, label: <module 'immuneML.environment.Label' from '/Users/milenpa/PycharmProjects/BMIImmuneML/immuneML/environment/Label.py'>, model_selection_cv: bool, model_selection_n_folds: int, cores_for_training: int, train_predictions_path: pathlib.Path, ml_details_path: pathlib.Path, optimization_metric: str)[source]

Bases: immuneML.workflows.steps.StepParams.StepParams

immuneML.workflows.steps.SignalImplanter module

class immuneML.workflows.steps.SignalImplanter.SignalImplanter[source]

Bases: immuneML.workflows.steps.Step.Step

DATASET_NAME = 'simulated_dataset'
static run(simulation_state: Optional[immuneML.simulation.SimulationState.SimulationState] = None)[source]

immuneML.workflows.steps.Step module

class immuneML.workflows.steps.Step.Step[source]

Bases: object

This class encapsulates steps in the analysis which will likely be often used, such as:
  • dataset encoding

  • training of machine learning models

  • signal implanting in repertoires without any signals etc.

For a custom analysis which is not likely to be repeated for different settings (e.g. such as with a different encoding), create a custom class inheriting AbstractProcess from workflows.processes package.

abstract static run(input_params: Optional[immuneML.workflows.steps.StepParams.StepParams] = None)[source]

immuneML.workflows.steps.StepParams module

class immuneML.workflows.steps.StepParams.StepParams[source]

Bases: object

Module contents