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[source]
Bases:
immuneML.workflows.steps.Step.Step- static run(input_params: Optional[immuneML.workflows.steps.StepParams.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:
immuneML.workflows.steps.StepParams.StepParams- encoder_params: immuneML.encodings.EncoderParams.EncoderParams
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: str, path: pathlib.Path, split_index: int, predictions_path: pathlib.Path, ml_score_path: pathlib.Path)[source]
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: 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)[source]
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]