immuneML.api.galaxy package

Submodules

immuneML.api.galaxy.DataSimulationTool module

class immuneML.api.galaxy.DataSimulationTool.DataSimulationTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

prepare_specs()[source]

immuneML.api.galaxy.DatasetGenerationTool module

class immuneML.api.galaxy.DatasetGenerationTool.DatasetGenerationTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

DatasetGenerationTool is an alternative to running ImmuneMLApp directly. It accepts a path to YAML specification and a path to the output directory and generates the dataset according to the given specification. The created dataset will be located under supplied output directory, under results folder. The main dataset file will have the name of the dataset given in the specification and has an extension .iml_dataset.

This tool is meant to be used as an endpoint for Galaxy tool that will create a Galaxy collection out of a dataset in immuneML format.

Specification for this tool is the same as for the DatasetExportInstruction, except it can create only one dataset with one format at the time.

immuneML.api.galaxy.GalaxyMLApplicationTool module

class immuneML.api.galaxy.GalaxyMLApplicationTool.GalaxyMLApplicationTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

immuneML.api.galaxy.GalaxySimulationTool module

class immuneML.api.galaxy.GalaxySimulationTool.GalaxySimulationTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

GalaxySimulationTool is an alternative to running immuneML with the simulation instruction directly. It accepts a YAML specification file and a path to the output directory. It implants the signals in the dataset that was provided either as an existing dataset with a set of files or in the random dataset as described in the specification file.

This tool is meant to be used as an endpoint for Galaxy tool that will create a Galaxy collection out of a dataset in immuneML format that can be readily used by other immuneML-based Galaxy tools.

The specification supplied for this tool is identical to immuneML specification, except that it can include only one instruction which has to be of type ‘Simulation’:

prepare_specs()[source]

immuneML.api.galaxy.GalaxyTool module

class immuneML.api.galaxy.GalaxyTool.GalaxyTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: object

run()[source]

immuneML.api.galaxy.GalaxyTrainMLModel module

class immuneML.api.galaxy.GalaxyTrainMLModel.GalaxyTrainMLModel(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

immuneML.api.galaxy.GalaxyYamlTool module

class immuneML.api.galaxy.GalaxyYamlTool.GalaxyYamlTool(specification_path: pathlib.Path, result_path: pathlib.Path, **kwargs)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

update_specs()[source]

immuneML.api.galaxy.RepertoireClassificationTool module

class immuneML.api.galaxy.RepertoireClassificationTool.RepertoireClassificationTool(args, result_path: pathlib.Path)[source]

Bases: immuneML.api.galaxy.GalaxyTool.GalaxyTool

immuneML.api.galaxy.Util module

class immuneML.api.galaxy.Util.Util[source]

Bases: object

static check_export_format(specs: dict, tool_name: str, instruction_name: str)[source]
static check_instruction_type(specs: dict, tool_name, expected_instruction) → str[source]
static check_parameters(yaml_path: pathlib.Path, output_dir: pathlib.Path, kwargs, location)[source]
static check_paths(specs: dict, tool_name: str)[source]
static run_tool(yaml_path, result_path)[source]
static update_result_paths(specs: dict, result_path: pathlib.Path, yaml_path: pathlib.Path)[source]

immuneML.api.galaxy.build_from_objects_dataset_yaml module

immuneML.api.galaxy.build_dataset_yaml.build_metadata_column_mapping(columns_str)[source]
immuneML.api.galaxy.build_dataset_yaml.build_specs(args)[source]
immuneML.api.galaxy.build_dataset_yaml.main(args)[source]
immuneML.api.galaxy.build_dataset_yaml.parse_commandline_arguments(args)[source]

immuneML.api.galaxy.build_from_objects_yaml_from_arguments module

immuneML.api.galaxy.build_yaml_from_arguments.build_encodings_specs(args)[source]
immuneML.api.galaxy.build_yaml_from_arguments.build_labels(labels_str)[source]
immuneML.api.galaxy.build_yaml_from_arguments.build_ml_methods_specs(args)[source]
immuneML.api.galaxy.build_yaml_from_arguments.build_settings_specs(enc_names, ml_names)[source]
immuneML.api.galaxy.build_yaml_from_arguments.build_specs(args)[source]
immuneML.api.galaxy.build_yaml_from_arguments.check_arguments(args)[source]
immuneML.api.galaxy.build_yaml_from_arguments.discover_dataset_params()[source]
immuneML.api.galaxy.build_yaml_from_arguments.get_ml_method_spec(ml_method_class, model_selection_n_folds=5)[source]
immuneML.api.galaxy.build_yaml_from_arguments.get_sequence_enc_type(sequence_type, position_type, gap_type)[source]
immuneML.api.galaxy.build_yaml_from_arguments.main(args)[source]
immuneML.api.galaxy.build_yaml_from_arguments.parse_commandline_arguments(args)[source]

Module contents