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
return html_map