Source code for immuneML.presentation.html.ExploratoryAnalysisHTMLBuilder

from pathlib import Path

from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.ml_methods.util.Util import Util as MLUtil
from immuneML.presentation.TemplateParser import TemplateParser
from immuneML.presentation.html.Util import Util
from immuneML.util.PathBuilder import PathBuilder
from immuneML.util.StringHelper import StringHelper
from immuneML.workflows.instructions.exploratory_analysis.ExploratoryAnalysisState import ExploratoryAnalysisState


[docs] class ExploratoryAnalysisHTMLBuilder: """ A class that will make a HTML file(s) out of ExploratoryAnalysisState object to show what analysis took place in the ExploratoryAnalysisInstruction. """ CSS_PATH = EnvironmentSettings.html_templates_path / "css/custom.css"
[docs] @staticmethod def build(state: ExploratoryAnalysisState) -> Path: """ Function that builds the HTML files based on the ExploratoryAnalysis state. Arguments: state: ExploratoryAnalysisState object with details and results of the instruction Returns: path to the main HTML file (which is located under state.result_path) """ base_path = PathBuilder.build(state.result_path / "../HTML_output/") html_map = ExploratoryAnalysisHTMLBuilder.make_html_map(state, base_path) result_file = base_path / f"ExploratoryAnalysis_{state.name}.html" TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "ExploratoryAnalysis.html", template_map=html_map, result_path=result_file) return result_file
[docs] @staticmethod def make_html_map(state: ExploratoryAnalysisState, base_path: Path) -> dict: html_map = { "css_style": Util.get_css_content(ExploratoryAnalysisHTMLBuilder.CSS_PATH), "full_specs": Util.get_full_specs_path(base_path), 'immuneML_version': MLUtil.get_immuneML_version(), "analyses": [{ "name": name, "dataset_name": analysis.dataset.name if analysis.dataset.name is not None else analysis.dataset.identifier, "dataset_type": StringHelper.camel_case_to_word_string(type(analysis.dataset).__name__), "example_count": analysis.dataset.get_example_count(), "dataset_size": f"{analysis.dataset.get_example_count()} {type(analysis.dataset).__name__.replace('Dataset', 's').lower()}", "preprocessing_sequence": [ { "preprocessing_name": preprocessing.__class__.__name__, "preprocessing_params": ", ".join( [f"{key}: {value}" for key, value in vars(preprocessing).items()]) } for preprocessing in analysis.preprocessing_sequence ] if analysis.preprocessing_sequence is not None else [], "show_preprocessing": analysis.preprocessing_sequence is not None and len(analysis.preprocessing_sequence) > 0, "show_labels": analysis.label_config is not None and len(analysis.label_config.get_labels_by_name()) > 0, "labels": [{"name": label.name, "values": str(label.values)[1:-1]} for label in analysis.label_config.get_label_objects()] if analysis.label_config else None, "encoding_key": analysis.encoder.name if analysis.encoder is not None else None, "encoding_name": StringHelper.camel_case_to_word_string(type(analysis.encoder).__name__) if analysis.encoder is not None else None, "encoding_params": [{"param_name": key, "param_value": str(value)} for key, value in vars(analysis.encoder).items()] if analysis.encoder is not None else None, "show_encoding": analysis.encoder is not None, "report": Util.to_dict_recursive(Util.update_report_paths(analysis.report_result, base_path), base_path) } for name, analysis in state.exploratory_analysis_units.items()] } for analysis in html_map["analyses"]: analysis["show_tables"] = len(analysis["report"]["output_tables"]) > 0 if "output_tables" in analysis["report"] else False analysis["show_text"] = len(analysis["report"]["output_text"]) > 0 if "output_text" in analysis["report"] else False analysis["show_info"] = analysis["report"]["info"] is not None and len(analysis["report"]["info"]) > 0 if "info" in analysis["report"] else False return html_map