Source code for immuneML.api.galaxy.DatasetGenerationTool

import shutil
from pathlib import Path

import yaml

from immuneML.api.galaxy.GalaxyTool import GalaxyTool
from immuneML.api.galaxy.Util import Util
from immuneML.app.ImmuneMLApp import ImmuneMLApp
from immuneML.util.ParameterValidator import ParameterValidator
from immuneML.util.PathBuilder import PathBuilder
from immuneML.workflows.instructions.dataset_generation.DatasetExportInstruction import DatasetExportInstruction


class DatasetGenerationTool(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.

    """

    def __init__(self, specification_path: Path, result_path: Path, **kwargs):
        Util.check_parameters(specification_path, result_path, kwargs, "Dataset generation tool")
        super().__init__(specification_path, result_path, **kwargs)

    def _run(self):
        PathBuilder.build(self.result_path)
        self._update_specs()
        state = ImmuneMLApp(self.yaml_path, self.result_path).run()[0]
        shutil.copytree(list(list(state.paths.values())[0].values())[0], self.result_path / "result/")
        print("Exported dataset.")

    def _update_specs(self):
        with self.yaml_path.open('r') as file:
            specs = yaml.safe_load(file)

        ParameterValidator.assert_keys_present(specs.keys(), ["definitions", "instructions"], DatasetGenerationTool.__name__, "YAML specification")
        ParameterValidator.assert_all_in_valid_list(specs.keys(), ["definitions", "instructions", "output"], DatasetGenerationTool.__name__, "YAML specification")

        self._check_dataset(specs)
        self._check_instruction(specs)

        Util.check_paths(specs, DatasetGenerationTool.__name__)
        Util.update_result_paths(specs, self.result_path, self.yaml_path)

    def _check_dataset(self, specs):
        ParameterValidator.assert_keys_present(specs["definitions"].keys(), ['datasets'], DatasetGenerationTool.__name__, 'definitions')
        assert len(specs['definitions']['datasets'].keys()) == 1, \
            f"{DatasetGenerationTool.__name__}: only one dataset can be defined with this Galaxy tool, got these " \
            f"instead: {list(specs['definitions']['datasets'].keys())}."

        assert len(specs['instructions'].keys()) == 1, \
            f"{DatasetGenerationTool.__name__}: only one instruction of type DatasetExport can be defined with this Galaxy tool, got these " \
            f"instructions instead: {list(specs['instructions'].keys())}."

    def _check_instruction(self, specs):
        instruction_name = Util.check_instruction_type(specs, DatasetGenerationTool.__name__, DatasetExportInstruction.__name__[:-11])

        for key in ['datasets', 'export_formats']:
            ParameterValidator.assert_keys_present(list(specs['instructions'][instruction_name].keys()), [key], DatasetGenerationTool.__name__,
                                                   instruction_name)
            ParameterValidator.assert_type_and_value(specs["instructions"][instruction_name][key], list, DatasetGenerationTool.__name__,
                                                     f"{instruction_name}/{key}")

            assert len(specs['instructions'][instruction_name][key]) == 1, \
                f"{DatasetGenerationTool.__name__}: this tool accepts only one item under {key}, got {specs['instructions'][instruction_name][key]} " \
                f"instead."