Source code for immuneML.presentation.html.HPHTMLBuilder

import io
import os
import statistics
from pathlib import Path

import pandas as pd

from immuneML.environment.Constants import Constants
from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.hyperparameter_optimization.config.SplitType import SplitType
from immuneML.hyperparameter_optimization.states.HPAssessmentState import HPAssessmentState
from immuneML.hyperparameter_optimization.states.HPItem import HPItem
from immuneML.hyperparameter_optimization.states.HPLabelState import HPLabelState
from immuneML.hyperparameter_optimization.states.HPSelectionState import HPSelectionState
from immuneML.hyperparameter_optimization.states.TrainMLModelState import TrainMLModelState
from immuneML.ml_methods.util.Util import Util as MLUtil
from immuneML.ml_metrics.Metric import Metric
from immuneML.presentation.TemplateParser import TemplateParser
from immuneML.presentation.html.Util import Util
from immuneML.reports.ReportResult import ReportResult
from immuneML.util.PathBuilder import PathBuilder
from immuneML.util.StringHelper import StringHelper


[docs]class HPHTMLBuilder: """ A class that will make HTML file(s) out of TrainMLModelState object to show what analysis took place in the TrainMLModel. """ CSS_PATH = EnvironmentSettings.html_templates_path / "css/custom.css" NUM_DIGITS = 3
[docs] @staticmethod def build(state: TrainMLModelState = None) -> Path: """ Function that builds the HTML files based on the HPOptimization state. Arguments: state: HPOptimizationState object with all details on the optimization Returns: path to the main HTML file (index.html which is located under state.result_path) """ base_path = PathBuilder.build(state.path / "../HTML_output/") state = HPHTMLBuilder._move_reports_recursive(state, base_path) html_map = HPHTMLBuilder._make_main_html_map(state, base_path) result_file = base_path / f"TrainMLModelReport_{state.name}.html" TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "HPOptimization.html", template_map=html_map, result_path=result_file) for label_name in state.label_configuration.get_labels_by_name(): for index, item in enumerate(HPHTMLBuilder._make_assessment_pages(state, base_path, label_name)): TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "AssessmentSplitDetails.html", template_map=item, result_path=base_path / HPHTMLBuilder._make_assessment_split_path(index, state.name, label_name)) for label_name in state.label_configuration.get_labels_by_name(): for assessment_index in range(state.assessment.split_count): TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "SelectionDetails.html", template_map=HPHTMLBuilder._make_selection(state, assessment_index, label_name, base_path), result_path=base_path / HPHTMLBuilder._make_selection_split_path(assessment_index, label_name, state.name)) return result_file
@staticmethod def _make_assessment_split_path(split_index: int, state_name: str, label_name: str) -> Path: return Path(f"{state_name}_CV_assessment_split_{split_index + 1}_{label_name}.html") @staticmethod def _make_selection_split_path(assessment_index: int, label_name: str, state_name: str) -> Path: return Path(f"{state_name}_selection_details_{label_name}_split_{assessment_index + 1}.html") @staticmethod def _make_selection(state: TrainMLModelState, assessment_index: int, label_name: str, base_path): selection_state = state.assessment_states[assessment_index].label_states[label_name].selection_state hp_settings = [] optimal = selection_state.optimal_hp_setting.get_key() for hp_setting, hp_items in selection_state.hp_items.items(): hp_splits = [] for hp_item in hp_items: hp_splits.append(HPHTMLBuilder._print_metric(hp_item.performance, state.optimization_metric)) hp_settings.append({ "hp_setting": hp_setting, "hp_splits": hp_splits, "optimal": hp_setting == optimal }) if len(hp_splits) > 1: hp_settings[-1]["average"] = round(statistics.mean(perf for perf in hp_splits if [isinstance(perf, float)]), HPHTMLBuilder.NUM_DIGITS) hp_settings[-1]["show_average"] = True else: hp_settings[-1]["average"] = None hp_settings[-1]["show_average"] = False hp_settings[-1]["hp_splits"] = [{"optimization_metric_val": val} for val in hp_settings[-1]["hp_splits"]] has_other_metrics = len([metric for metric in state.metrics if metric != state.optimization_metric]) > 0 and \ not (state.selection.split_strategy == SplitType.RANDOM and state.selection.training_percentage == 1) return { "css_style": Util.get_css_content(HPHTMLBuilder.CSS_PATH), "label": label_name, "assessment_split": assessment_index + 1, "splits": [{"split_index": i} for i in range(1, state.selection.split_count + 1)], "split_count": state.selection.split_count, "optimization_metric": state.optimization_metric.name.lower(), "has_other_metrics": has_other_metrics, "metrics": [{"performance": HPHTMLBuilder._extract_selection_performance_per_metric(selection_state, metric, state.selection.split_count), "metric": HPHTMLBuilder._get_heading_metric_name(metric.name.lower())} for metric in state.metrics if metric != state.optimization_metric] if has_other_metrics else None, "hp_settings": hp_settings, "show_average": any(hps["show_average"] for hps in hp_settings), "data_split_reports": [ {'split_index': index + 1, 'train': Util.to_dict_recursive(selection_state.train_data_reports[index], base_path) if len(selection_state.train_data_reports) == state.selection.split_count else None, 'test': Util.to_dict_recursive(selection_state.val_data_reports[index], base_path) if len(selection_state.train_data_reports) == state.selection.split_count else None} for index in range(state.selection.split_count)] if len(state.selection.reports.data_split_reports) > 0 else None, "has_data_split_reports": len(state.selection.reports.data_split_reports) > 0, "has_reports_per_setting": len(state.selection.reports.encoding_reports) + len(state.selection.reports.model_reports) > 0, "reports_per_setting": [{ "hp_setting": hp_setting, "reports": HPHTMLBuilder._make_selection_reports_for_item_list(hp_items, base_path) } for hp_setting, hp_items in selection_state.hp_items.items()] } @staticmethod def _make_selection_reports_for_item_list(hp_items: list, base_path) -> list: result = [] for split_index, hp_item in enumerate(hp_items): result.append({ "split_index": split_index + 1, "has_encoding_train_reports": len(hp_item.encoding_train_results) > 0, "has_encoding_test_reports": len(hp_item.encoding_test_results) > 0, "has_ml_reports": len(hp_item.model_report_results) > 0, "encoding_train_reports": Util.to_dict_recursive(hp_item.encoding_train_results, base_path) if len( hp_item.encoding_train_results) > 0 else None, "encoding_test_reports": Util.to_dict_recursive(hp_item.encoding_test_results, base_path) if len( hp_item.encoding_test_results) > 0 else None, "ml_reports": Util.to_dict_recursive(hp_item.model_report_results, base_path) if len(hp_item.model_report_results) > 0 else None, }) return result if len(result) > 0 else None @staticmethod def _make_assessment_pages(state: TrainMLModelState, base_path: Path, label_name: 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_name for hp_setting, item in assessment_state.label_states[label_name].assessment_items.items(): optimal = str(assessment_state.label_states[label_name].optimal_hp_setting.get_key()) reports_path = HPHTMLBuilder._make_assessment_reports(state, i, hp_setting, assessment_state, label_name, 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_name, state.name) assessment_item['performances_per_metric'] = HPHTMLBuilder._extract_assessment_performances_per_metric(state, assessment_state, label_name) assessment_list.append(assessment_item) return assessment_list @staticmethod def _extract_assessment_performances_per_metric(state: TrainMLModelState, assessment_state: HPAssessmentState, label_name: str) -> str: performance_metric = {"setting": [], **{metric.name.lower(): [] for metric in state.metrics}} for hp_setting, hp_item in assessment_state.label_states[label_name].assessment_items.items(): performance_metric['setting'].append(str(hp_setting)) for metric in sorted(state.metrics, key=lambda metric: metric.name.lower()): performance_metric[metric.name.lower()].append(HPHTMLBuilder._print_metric(hp_item.performance, metric)) s = io.StringIO() pd.DataFrame(performance_metric).rename(columns={"setting": 'Hyperparameter settings (preprocessing, encoding, ML method)'})\ .to_csv(s, sep="\t", index=False) return Util.get_table_string_from_csv_string(s.getvalue(), separator="\t") @staticmethod def _make_assessment_reports(state, i, hp_setting_key, assessment_state, label_name: str, base_path: Path): path = base_path / f"{state.name}_{label_name}_{hp_setting_key}_assessment_reports_split_{i + 1}.html" hp_item = assessment_state.label_states[label_name].assessment_items[hp_setting_key] data = { "split_index": i + 1, "hp_setting": hp_setting_key, "label": label_name, "css_style": Util.get_css_content(HPHTMLBuilder.CSS_PATH), "has_encoding_reports": len(hp_item.encoding_train_results) > 0 or len(hp_item.encoding_test_results) > 0, "has_ml_reports": len(hp_item.model_report_results) > 0, "encoding_train_reports": Util.to_dict_recursive(hp_item.encoding_train_results, base_path) if len( hp_item.encoding_train_results) > 0 else None, "encoding_test_reports": Util.to_dict_recursive(hp_item.encoding_test_results, base_path) if len( hp_item.encoding_test_results) > 0 else None, "ml_reports": Util.to_dict_recursive(hp_item.model_report_results, base_path) if len( hp_item.model_report_results) > 0 else None } if data["has_ml_reports"] or data["has_encoding_reports"]: TemplateParser.parse(template_path=EnvironmentSettings.html_templates_path / "Reports.html", template_map=data, result_path=path) return path.name else: return None @staticmethod def _make_hp_per_label(state: TrainMLModelState): mapping = [] for label_name in state.label_configuration.get_labels_by_name(): results = [] for i, assessment_state in enumerate(state.assessment_states): results.append({ "index": assessment_state.split_index + 1, "hp_setting": assessment_state.label_states[label_name].optimal_assessment_item.hp_setting, "optimization_metric_val": HPHTMLBuilder._print_metric(assessment_state.label_states[label_name].optimal_assessment_item.performance, state.optimization_metric), "split_details_path": HPHTMLBuilder._make_assessment_split_path(assessment_state.split_index, state.name, label_name) }) mapping.append({"label": label_name, "assessment_results": results}) return mapping @staticmethod def _print_metric(performance: dict, metric: Metric): if performance is not None and metric.name.lower() in performance: if isinstance(performance[metric.name.lower()], float): return round(performance[metric.name.lower()], HPHTMLBuilder.NUM_DIGITS) else: return performance[metric.name.lower()] else: return Constants.NOT_COMPUTED @staticmethod def _make_model_per_label(state: TrainMLModelState, base_path: Path) -> list: mapping = [] for label_name in state.label_configuration.get_labels_by_name(): mapping.append({ "label": label_name, "model_path": Path(os.path.relpath(path=str(state.optimal_hp_item_paths[label_name]), start=str(base_path))) }) return mapping @staticmethod 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 @staticmethod def _move_reports_recursive(obj, path: Path): for attribute in (vars(obj) if not isinstance(obj, dict) else obj): attribute_value = getattr(obj, attribute) if not isinstance(obj, dict) else obj[attribute] if isinstance(attribute_value, list) and all(isinstance(item, ReportResult) for item in attribute_value): new_attribute_values = [] for report_result in attribute_value: new_attribute_values.append(Util.update_report_paths(report_result, path)) setattr(obj, attribute, new_attribute_values) elif isinstance(attribute_value, list) and all(isinstance(item, HPAssessmentState) for item in attribute_value): obj = HPHTMLBuilder._process_list_recursively(obj, attribute, attribute_value, path) elif isinstance(attribute_value, dict) and all( isinstance(item, HPLabelState) or isinstance(item, HPItem) for item in attribute_value.values()): obj = HPHTMLBuilder._process_dict_recursive(obj, attribute, attribute_value, path) elif isinstance(attribute_value, dict) and all(isinstance(item, list) for item in attribute_value.values()) and all( all(isinstance(item, HPItem) for item in item_list) for item_list in attribute_value.values()): obj = HPHTMLBuilder._process_hp_items(obj, attribute, attribute_value, path) elif isinstance(attribute_value, HPSelectionState): setattr(obj, attribute, HPHTMLBuilder._move_reports_recursive(attribute_value, path)) return obj @staticmethod def _process_hp_items(obj, attribute, attribute_value, path: Path): new_attribute_value = {} for hp_setting, hp_item_list in attribute_value.items(): new_hp_item_list = [] for hp_item in hp_item_list: new_hp_item_list.append(HPHTMLBuilder._move_reports_recursive(hp_item, path)) new_attribute_value[hp_setting] = new_hp_item_list setattr(obj, attribute, new_attribute_value) return obj @staticmethod def _process_dict_recursive(obj, attribute, attribute_value, path: Path): for key, value in attribute_value.items(): attribute_value[key] = HPHTMLBuilder._move_reports_recursive(value, path) setattr(obj, attribute, attribute_value) return obj @staticmethod def _process_list_recursively(obj, attribute, attribute_value, path: Path): new_attribute_values = [] for item in attribute_value: new_attribute_values.append(HPHTMLBuilder._move_reports_recursive(item, path)) setattr(obj, attribute, new_attribute_values) return obj @staticmethod def _extract_selection_performance_per_metric(selection_state: HPSelectionState, metric: Metric, split_count): performance = {"setting": [], **{f"split {i + 1}": [] for i in range(split_count)}} for hp_setting, hp_item_list in selection_state.hp_items.items(): performance['setting'].append(str(hp_setting)) for index, hp_item in enumerate(hp_item_list): performance[f'split {index + 1}'].append(HPHTMLBuilder._print_metric(hp_item.performance, metric)) s = io.StringIO() pd.DataFrame(performance).rename(columns={"setting": 'Hyperparameter settings (preprocessing, encoding, ML method)'}).to_csv(s, sep="\t", index=False) return Util.get_table_string_from_csv_string(s.getvalue(), separator="\t") @staticmethod def _get_heading_metric_name(metric: str): if metric != "auc": return " ".join(metric.split("_")).title() else: return metric.upper()