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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import numpy as np |
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import pandas as pd |
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class HyperactiveResults: |
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def __init__(*args, **kwargs): |
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pass |
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def _sort_results_objFunc(self, objective_function): |
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best_score = -np.inf |
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best_para = None |
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search_data = None |
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results_list = [] |
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for results_ in self.results_list: |
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nth_process = results_["nth_process"] |
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process_infos = self.process_infos[nth_process] |
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objective_function_ = process_infos["objective_function"] |
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if objective_function_ != objective_function: |
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continue |
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if results_["best_score"] > best_score: |
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best_score = results_["best_score"] |
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best_para = results_["best_para"] |
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results_list.append(results_["results"]) |
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if len(results_list) > 0: |
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search_data = pd.concat(results_list) |
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self.objFunc2results[objective_function] = { |
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"best_para": best_para, |
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"best_score": best_score, |
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"search_data": search_data, |
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} |
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def _sort_results_search_id(self, search_id): |
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for results_ in self.results_list: |
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nth_process = results_["nth_process"] |
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search_id_ = self.process_infos[nth_process]["search_id"] |
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if search_id_ != search_id: |
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continue |
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best_score = results_["best_score"] |
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best_para = results_["best_para"] |
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search_data = results_["results"] |
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self.search_id2results[search_id] = { |
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"best_para": best_para, |
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"best_score": best_score, |
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"search_data": search_data, |
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} |
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def _get_one_result(self, id_, result_name): |
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if isinstance(id_, str): |
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if id_ not in self.search_id2results: |
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self._sort_results_search_id(id_) |
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return self.search_id2results[id_][result_name] |
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else: |
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if id_ not in self.objFunc2results: |
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self._sort_results_objFunc(id_) |
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return self.objFunc2results[id_][result_name] |
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def best_para(self, id_): |
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return self._get_one_result(id_, "best_para") |
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def best_score(self, id_): |
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return self._get_one_result(id_, "best_score") |
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def results(self, id_): |
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return self._get_one_result(id_, "search_data") |
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SimonBlanke /
Hyperactive
