immuneML.workflows.steps package

Subpackages

Submodules

immuneML.workflows.steps.DataEncoder module

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

Bases: Step

static run(input_params: StepParams = None)[source]

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)[source]

Bases: StepParams

dataset: Dataset
encoder: DatasetEncoder
encoder_params: EncoderParams

immuneML.workflows.steps.MLMethodAssessment module

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

Bases: Step

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

immuneML.workflows.steps.MLMethodAssessmentParams module

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

Bases: StepParams

immuneML.workflows.steps.MLMethodTrainer module

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

Bases: Step

static run(input_params: MLMethodTrainerParams = None)[source]
static store(method: MLMethod, input_params: 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: StepParams

immuneML.workflows.steps.SignalImplanter module

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

Bases: Step

DATASET_NAME = 'simulated_dataset'
static run(simulation_state: 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: StepParams = None)[source]

immuneML.workflows.steps.StepParams module

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

Bases: object

Module contents