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:
PathBuilder.build(self.result_path)
data_long_format = DataReshaper.reshape(self.dataset, self.dataset.get_label_names())
table_result = self._write_results_table(data_long_format)
report_output_fig = self._safe_plot(data_long_format=data_long_format)
output_figures = None if report_output_fig is None else [report_output_fig]
return ReportResult(name=self.name, output_figures=output_figures, output_tables=[table_result])
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