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()}",
"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": 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(analysis.report_result, 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
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_html_map(state: DatasetExportState, base_path: Path) -> dict:
html_map = {
"css_style":
Util.get_css_content(DatasetExportHTMLBuilder.CSS_PATH),
"name":
state.name,
'immuneML_version':
MLUtil.get_immuneML_version(),
"full_specs":
Util.get_full_specs_path(base_path),
"datasets": [{
"dataset_name":
dataset.name,
"dataset_type":
StringHelper.camel_case_to_word_string(type(dataset).__name__),
"dataset_size":
f"{dataset.get_example_count()} {type(dataset).__name__.replace('Dataset', 's').lower()}",
"labels": [{
"label_name": label
} for label in dataset.get_label_names()],
"formats": [{
"format_name":
format_name,
"dataset_download_link":
os.path.relpath(path=Util.make_downloadable_zip(
state.result_path,
state.paths[dataset.name][format_name]),
start=base_path)
} for format_name in state.formats]
} for dataset in state.datasets]
}
return html_map
def make_html_map(state: DatasetExportState, base_path: Path) -> dict:
html_map = {
"css_style": Util.get_css_content(DatasetExportHTMLBuilder.CSS_PATH),
"name": state.name,
'immuneML_version': MLUtil.get_immuneML_version(),
"full_specs": Util.get_full_specs_path(base_path),
"datasets": [
{
"dataset_name": dataset.name,
"dataset_type": StringHelper.camel_case_to_word_string(type(dataset).__name__),
"dataset_size": f"{dataset.get_example_count()} {type(dataset).__name__.replace('Dataset', 's').lower()}",
"labels": [{"label_name": label} for label in dataset.get_label_names()],
"preprocessing_sequence": [
{
"preprocessing_name": preprocessing.__class__.__name__,
"preprocessing_params": ", ".join([f"{key}: {value}" for key, value in vars(preprocessing).items()])
} for preprocessing in state.preprocessing_sequence
] if state.preprocessing_sequence is not None else [],
"show_preprocessing": state.preprocessing_sequence is not None and len(state.preprocessing_sequence) > 0,
"formats": [
{
"format_name": format_name,
"dataset_download_link": os.path.relpath(path=Util.make_downloadable_zip(state.result_path, state.paths[dataset.name][format_name]),
start=base_path)
} for format_name in state.formats
]
} for dataset in state.datasets
]
}
return html_map
def _make_main_html_map(state: TrainMLModelState, base_path: Path) -> dict:
html_map = {
"css_style":
Util.get_css_content(HPHTMLBuilder.CSS_PATH),
"full_specs":
Util.get_full_specs_path(base_path),
"dataset_name":
state.dataset.name
if state.dataset.name is not None else state.dataset.identifier,
"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(label.values)[1:-1]
} for label in state.label_configuration.get_label_objects()],
"optimization_metric":
state.optimization_metric.name.lower(),
"other_metrics":
str([metric.name.lower()
for metric in state.metrics])[1:-1].replace("'", ""),
"metrics": [{
"name": metric.name.lower()
} for metric in state.metrics],
"assessment_desc":
state.assessment,
"selection_desc":
state.selection,
"show_hp_reports":
bool(state.report_results),
'hp_reports':
Util.to_dict_recursive(state.report_results, base_path)
if state.report_results else None,
"hp_per_label":
HPHTMLBuilder._make_hp_per_label(state),
'models_per_label':
HPHTMLBuilder._make_model_per_label(state, base_path),
'immuneML_version':
MLUtil.get_immuneML_version()
}
return html_map
def make_html_map(state: SimulationState, base_path: Path) -> dict:
html_map = {
"css_style":
Util.get_css_content(SimulationHTMLBuilder.CSS_PATH),
"name":
state.name,
'immuneML_version':
MLUtil.get_immuneML_version(),
"full_specs":
Util.get_full_specs_path(base_path),
"dataset_name":
state.resulting_dataset.name if state.resulting_dataset.name
is not None else state.resulting_dataset.identifier,
"dataset_type":
StringHelper.camel_case_to_word_string(
type(state.resulting_dataset).__name__),
"example_count":
state.resulting_dataset.get_example_count(),
"dataset_size":
f"{state.resulting_dataset.get_example_count()} {type(state.resulting_dataset).__name__.replace('Dataset', 's').lower()}",
"labels": [{
"label_name": label
} for label in state.resulting_dataset.get_label_names()],
"formats": [{
"format_name":
format_name,
"dataset_download_link":
os.path.relpath(path=Util.make_downloadable_zip(
state.result_path,
state.paths[state.resulting_dataset.name][format_name]),
start=base_path)
} for format_name in state.formats],
"implantings": [
Util.to_dict_recursive(implanting, base_path)
for implanting in state.simulation.implantings
]
}
return html_map
def make_html_map(state: SubsamplingState, base_path: Path) -> dict:
html_map = {
"css_style":
Util.get_css_content(SubsamplingHTMLBuilder.CSS_PATH),
"name":
state.name,
'immuneML_version':
MLUtil.get_immuneML_version(),
"full_specs":
Util.get_full_specs_path(base_path),
"dataset_name":
state.dataset.name
if state.dataset.name is not None else state.dataset.identifier,
"labels": [{
"label_name": label
} for label in state.dataset.get_label_names()],
"dataset_type":
StringHelper.camel_case_to_word_string(
type(state.dataset).__name__),
"example_count":
state.dataset.get_example_count(),
"subsampled_datasets": [{
"sub_dataset_iter":
i,
"sub_dataset_name":
dataset.name,
"dataset_size":
f"{dataset.get_example_count()} {type(dataset).__name__.replace('Dataset', 's').lower()}",
"formats": [{
"dataset_download_link": item,
"format_name": key
} for key, item in state.subsampled_dataset_paths[
dataset.name].items()]
} for i, dataset in enumerate(state.subsampled_datasets, 1)]
}
return html_map
def test_camel_case_to_words(self):
self.assertEqual(["Repertoire", "Dataset"],
StringHelper.camel_case_to_words("RepertoireDataset"))
self.assertEqual(["Repertoire", "Dataset"],
StringHelper.camel_case_to_words("repertoireDataset"))