Source code for immuneML.reports.encoding_reports.FeatureReport

import abc
import warnings
from pathlib import Path

from immuneML.analysis.data_manipulation.DataReshaper import DataReshaper
from immuneML.data_model.dataset.Dataset import Dataset
from immuneML.reports.ReportOutput import ReportOutput
from immuneML.reports.ReportResult import ReportResult
from immuneML.reports.encoding_reports.EncodingReport import EncodingReport
from immuneML.util.PathBuilder import PathBuilder

[docs] class FeatureReport(EncodingReport): """ Base class for reports that plot something about the reshaped feature values of any dataset. """ def __init__(self, dataset: Dataset = None, result_path: Path = None, color_grouping_label: str = None, row_grouping_label=None, column_grouping_label=None, name: str = None, number_of_processes: int = 1): super().__init__(dataset=dataset, result_path=result_path, name=name, number_of_processes=number_of_processes) self.x = "feature" self.color = color_grouping_label self.facet_row = row_grouping_label self.facet_column = column_grouping_label def _generate_report_result(self) -> ReportResult: data_long_format = DataReshaper.reshape(self.dataset, self.dataset.get_label_names()) table_result = self._write_results_table(data_long_format) report_output = self._safe_plot(data_long_format=data_long_format) output_tables = [table_result] if report_output is None: output_figures = None elif isinstance(report_output, tuple): output_figures = report_output[0] if isinstance(report_output[0], list) else [report_output[0]] output_tables = report_output[1] else: output_figures = report_output if isinstance(report_output, list) else [report_output] return ReportResult(, output_figures=output_figures, output_tables=output_tables) def _write_results_table(self, data) -> ReportOutput: table_path = self.result_path / f"feature_values.csv" data.to_csv(table_path, index=False) return ReportOutput(table_path, "feature values")
[docs] def std(self, x): return x.std(ddof=0)
@abc.abstractmethod def _plot(self, data_long_format) -> ReportOutput: pass
[docs] def check_prerequisites(self): location = self.__class__.__name__ run_report = True if self.dataset.encoded_data is None or self.dataset.encoded_data.examples is None: warnings.warn( f"{location}: this report can only be created for an encoded dataset. {location} report will not be created.") run_report = False elif len(self.dataset.encoded_data.examples.shape) != 2: warnings.warn( f"{location}: this report can only be created for a 2-dimensional encoded dataset. {location} report will not be created.") run_report = False else: legal_labels = list(self.dataset.get_label_names()) labels = [self.color, self.facet_row, self.facet_column] for label_param in labels: if label_param is not None: if label_param not in legal_labels: warnings.warn( f"{location}: undefined label '{label_param}'. Legal options are: {legal_labels}. {location} report will not be created.") run_report = False return run_report