def make_html_map(state: MLApplicationState, base_path: Path) -> dict:
return {
"css_style":
Util.get_css_content(MLApplicationHTMLBuilder.CSS_PATH),
"hp_setting":
state.hp_setting.get_key(),
'immuneML_version':
MLUtil.get_immuneML_version(),
"label":
state.label_config.get_labels_by_name()[0],
"dataset_name":
state.dataset.name,
"dataset_type":
StringHelper.camel_case_to_word_string(
type(state.dataset).__name__),
"example_count":
state.dataset.get_example_count(),
"dataset_size":
f"{state.dataset.get_example_count()} {type(state.dataset).__name__.replace('Dataset', 's').lower()}",
"labels": [{
"name":
label_name,
"values":
str(state.label_config.get_label_values(label_name))[1:-1]
} for label_name in state.label_config.get_labels_by_name()],
"predictions":
Util.get_table_string_from_csv(state.predictions_path),
"predictions_download_link":
os.path.relpath(state.predictions_path, base_path)
}
def _make_assessment_pages(state: TrainMLModelState, base_path: Path, label: str):
assessment_list = []
for i, assessment_state in enumerate(state.assessment_states):
assessment_item = {"css_style": Util.get_css_content(HPHTMLBuilder.CSS_PATH),
"optimization_metric": state.optimization_metric.name.lower(),
"split_index": assessment_state.split_index + 1,
"hp_settings": [],
"has_reports": len(state.assessment.reports.model_reports) + len(state.assessment.reports.encoding_reports) > 0,
"train_data_reports": Util.to_dict_recursive(assessment_state.train_val_data_reports, base_path),
"test_data_reports": Util.to_dict_recursive(assessment_state.test_data_reports, base_path),
"show_data_reports": len(assessment_state.train_val_data_reports) > 0 or len(assessment_state.test_data_reports) > 0}
if hasattr(assessment_state.train_val_dataset, "metadata_file") and assessment_state.train_val_dataset.metadata_file is not None:
assessment_item["train_metadata_path"] = os.path.relpath(str(assessment_state.train_val_dataset.metadata_file), str(base_path))
assessment_item["train_metadata"] = Util.get_table_string_from_csv(assessment_state.train_val_dataset.metadata_file)
else:
assessment_item["train_metadata_path"] = None
if hasattr(assessment_state.test_dataset, "metadata_file") and assessment_state.test_dataset.metadata_file is not None:
assessment_item['test_metadata_path'] = os.path.relpath(assessment_state.test_dataset.metadata_file, base_path)
assessment_item["test_metadata"] = Util.get_table_string_from_csv(assessment_state.test_dataset.metadata_file)
else:
assessment_item["test_metadata_path"] = None
assessment_item["label"] = label
for hp_setting, item in assessment_state.label_states[label].assessment_items.items():
optimal = str(assessment_state.label_states[label].optimal_hp_setting.get_key())
reports_path = HPHTMLBuilder._make_assessment_reports(state, i, hp_setting, assessment_state, label, base_path)
assessment_item["hp_settings"].append({
"optimal": str(hp_setting) == optimal,
"hp_setting": str(hp_setting),
"optimization_metric_val": HPHTMLBuilder._print_metric(item.performance, state.optimization_metric),
"reports_path": reports_path
})
assessment_item["show_non_optimal"] = len(assessment_item["hp_settings"]) > 1
assessment_item["selection_path"] = HPHTMLBuilder._make_selection_split_path(i, label, state.name)
assessment_item['performances_per_metric'] = HPHTMLBuilder._extract_assessment_performances_per_metric(state, assessment_state, label)
assessment_list.append(assessment_item)
return assessment_list