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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 abc |
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from tqdm.auto import tqdm |
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from .util import sort_for_best |
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class Verbosity(metaclass=abc.ABCMeta): |
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def __init__(self): |
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pass |
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@abc.abstractmethod |
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def print_start_point(self, _cand_): |
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pass |
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@abc.abstractmethod |
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def print_start_points(self, _cand_): |
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pass |
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def init_p_bar(self, _cand_, _core_): |
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pass |
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def update_p_bar(self, n, _cand_): |
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pass |
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def close_p_bar(self): |
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pass |
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def _tqdm_dict(self, _cand_): |
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pass |
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class VerbosityLVL0(Verbosity): |
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def __init__(self): |
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pass |
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def print_start_point(self, _cand_): |
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return _cand_._get_warm_start() |
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def print_start_points(self, _cand_list, _core_): |
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start_point_list = [] |
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score_best_list = [] |
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model_best_list = [] |
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results = {} |
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for _cand_ in _cand_list: |
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model_best = _cand_.model_best |
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score_best = _cand_.score_best |
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start_point = _cand_._get_warm_start() |
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results[score_best] = start_point |
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start_point_list.append(start_point) |
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score_best_list.append(score_best) |
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model_best_list.append(model_best) |
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start_point_sorted, score_best_sorted = sort_for_best( |
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start_point_list, score_best_list |
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) |
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model_best_sorted, score_best_sorted = sort_for_best( |
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model_best_list, score_best_list |
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) |
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return score_best_sorted, model_best_sorted, results |
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class VerbosityLVL1(VerbosityLVL0): |
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def __init__(self): |
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pass |
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def print_start_point(self, _cand_): |
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start_point = _cand_._get_warm_start() |
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print("\nbest para =", start_point) |
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print("score =", _cand_.score_best) |
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return start_point |
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def print_start_points(self, _cand_list, _core_): |
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start_point_list = [] |
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score_best_list = [] |
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model_best_list = [] |
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results = {} |
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for _cand_ in _cand_list: |
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model_best = _cand_.model_best |
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score_best = _cand_.score_best |
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start_point = _cand_._get_warm_start() |
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results[score_best] = start_point |
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start_point_list.append(start_point) |
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score_best_list.append(score_best) |
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model_best_list.append(model_best) |
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start_point_sorted, score_best_sorted = sort_for_best( |
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start_point_list, score_best_list |
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) |
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model_best_sorted, score_best_sorted = sort_for_best( |
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model_best_list, score_best_list |
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) |
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for i in range(int(_core_.n_jobs / 2)): |
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print("\n") |
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print("\nList of start points (best first):\n") |
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for start_point, score_best in zip(start_point_sorted, score_best_sorted): |
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print("best para =", start_point) |
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print("score =", score_best, "\n") |
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return score_best_sorted, model_best_sorted, results |
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class VerbosityLVL2(VerbosityLVL1): |
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def __init__(self): |
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pass |
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def init_p_bar(self, _cand_, _core_): |
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self.p_bar = tqdm(**self._tqdm_dict(_cand_, _core_)) |
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def update_p_bar(self, n, _cand_): |
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self.p_bar.update(n) |
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self.p_bar.set_postfix(best_score=str(_cand_.score_best)) |
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def close_p_bar(self): |
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self.p_bar.close() |
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def _tqdm_dict(self, _cand_, _core_): |
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"""Generates the parameter dict for tqdm in the iteration-loop of each optimizer""" |
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return { |
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"total": _core_.n_iter, |
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"desc": "Thread " |
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+ str(_cand_.nth_process) |
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+ " -> " |
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+ _cand_._model_.func_.__name__, |
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"position": _cand_.nth_process, |
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"leave": True, |
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} |
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class VerbosityLVL10(VerbosityLVL0): |
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def __init__(self): |
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pass |
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def start_search(self): |
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print("") |
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def get_search_path(self): |
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pass |
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SimonBlanke /
Hyperactive
