Source code for immuneML.reports.data_reports.SimpleDatasetOverview

from pathlib import Path

from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.data_model.dataset.ReceptorDataset import ReceptorDataset
from immuneML.data_model.dataset.RepertoireDataset import RepertoireDataset
from immuneML.data_model.dataset.SequenceDataset import SequenceDataset
from immuneML.reports.ReportOutput import ReportOutput
from immuneML.reports.ReportResult import ReportResult
from immuneML.reports.data_reports.DataReport import DataReport
from immuneML.util.PathBuilder import PathBuilder

[docs]class SimpleDatasetOverview(DataReport): """ Generates a simple overview of the properties of any dataset, including the dataset name, size, and metadata labels. YAML specification: .. indent with spaces .. code-block:: yaml reports: my_overview: SimpleDatasetOverview """ def __init__(self, dataset: Dataset = None, result_path: Path = None, name: str = None): super().__init__(dataset, result_path, name)
[docs] @classmethod def build_object(cls, **kwargs): return SimpleDatasetOverview(**kwargs)
def _generate(self) -> ReportResult: text_path = self.result_path / "dataset_description.txt" dataset_name = if is not None else self.dataset.identifier output_text = self._get_generic_dataset_text() if isinstance(self.dataset, RepertoireDataset): output_text += self._get_repertoire_dataset_text() elif isinstance(self.dataset, ReceptorDataset): output_text += self._get_receptor_dataset_text() elif isinstance(self.dataset, SequenceDataset): output_text += self._get_sequence_dataset_text() text_path.write_text(output_text) return ReportResult(, output_text=[ReportOutput(text_path, f"Description of dataset {dataset_name}")]) def _get_generic_dataset_text(self): element_name = type(self.dataset).__name__.replace("Dataset", "s").lower() output_text = f"Dataset name: {}\n" \ f"Dataset identifier: {self.dataset.identifier}\n" \ f"Dataset type: {type(self.dataset).__name__}\n" \ f"Dataset size: {self.dataset.get_example_count()} {element_name}\n" \ f"Labels available for classification:" if len(self.dataset.get_label_names()) == 0: output_text += " None" else: for label in self.dataset.get_label_names(): output_text += "\n - " + label return output_text def _get_repertoire_dataset_text(self): output_text = f"\nmetadata file location: {self.dataset.metadata_file}\n" output_text += "\n\nProperties per repertoire:\n" for repertoire in self.dataset.repertoires: output_text += f"- Name: {}\n" output_text += f" Number of sequences: {repertoire.get_element_count()}\n" chains = [chain.value for chain in set(repertoire.get_chains())] if len(chains) == 1: output_text += f" Chain type: {chains[0]}\n" else: output_text += f" Chain types: {','.join(chains)}\n" return output_text def _get_receptor_dataset_text(self): receptor_types = list(set([type(receptor).__name__ for receptor in self.dataset.get_data()])) if len(receptor_types) > 1: output_text = "\nReceptor types: " + ",".join(receptor_types) else: output_text = "\nReceptor type: " + receptor_types[0] return output_text def _get_sequence_dataset_text(self): chains = list(set([sequence.get_attribute("chain").value for sequence in self.dataset.get_data()])) if len(chains) > 1: output_text = "\nChain types: " + ",".join(chains) else: output_text = "\nChain type: " + chains[0] return output_text