immuneML.workflows.steps package

Subpackages

• immuneML.workflows.steps.data_splitter package
  • Submodules
  • immuneML.workflows.steps.data_splitter.DataSplitter module
  • immuneML.workflows.steps.data_splitter.DataSplitterParams module
  • immuneML.workflows.steps.data_splitter.LeaveOneOutSplitter module
  • immuneML.workflows.steps.data_splitter.ManualSplitter module
  • immuneML.workflows.steps.data_splitter.Util module
  • Module contents

Submodules

immuneML.workflows.steps.DataEncoder module

class immuneML.workflows.steps.DataEncoder.DataEncoder

Bases: immuneML.workflows.steps.Step.Step

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

immuneML.workflows.steps.DataEncoderParams module

class immuneML.workflows.steps.DataEncoderParams.DataEncoderParams(dataset: immuneML.data_model.dataset.Dataset.Dataset, encoder: immuneML.encodings.DatasetEncoder.DatasetEncoder, encoder_params: immuneML.encodings.EncoderParams.EncoderParams, store_encoded_data: bool)

Bases: immuneML.workflows.steps.StepParams.StepParams

dataset: immuneML.data_model.dataset.Dataset.Dataset

encoder: immuneML.encodings.DatasetEncoder.DatasetEncoder

encoder_params: immuneML.encodings.EncoderParams.EncoderParams

store_encoded_data: bool

immuneML.workflows.steps.MLMethodAssessment module

class immuneML.workflows.steps.MLMethodAssessment.MLMethodAssessment

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)

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.environment.Metric.Metric, label: str, path: pathlib.Path, split_index: int, predictions_path: pathlib.Path, ml_score_path: pathlib.Path)

Bases: immuneML.workflows.steps.StepParams.StepParams

immuneML.workflows.steps.MLMethodTrainer module

class immuneML.workflows.steps.MLMethodTrainer.MLMethodTrainer

Bases: immuneML.workflows.steps.Step.Step

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

static store(method: immuneML.ml_methods.MLMethod.MLMethod, input_params: immuneML.workflows.steps.MLMethodTrainerParams.MLMethodTrainerParams)

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: str, 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)

Bases: immuneML.workflows.steps.StepParams.StepParams

immuneML.workflows.steps.SignalImplanter module

class immuneML.workflows.steps.SignalImplanter.SignalImplanter

Bases: immuneML.workflows.steps.Step.Step

DATASET_NAME = 'simulated_dataset'

static run(simulation_state: Optional[immuneML.simulation.SimulationState.SimulationState] = None)

immuneML.workflows.steps.Step module

class immuneML.workflows.steps.Step.Step

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)

immuneML.workflows.steps.StepParams module

class immuneML.workflows.steps.StepParams.StepParams

Bases: object

Module contents