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
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
- encoder: DatasetEncoder
- encoder_params: EncoderParams
immuneML.workflows.steps.MLMethodAssessment module
immuneML.workflows.steps.MLMethodAssessmentParams module
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
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]