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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 logging |
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import pandas as pd |
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from typing import Callable, Any, List |
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from multiprocessing.managers import DictProxy |
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from ._objective_adapter import ObjectiveAdapter |
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from ._result import Result |
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class CachedObjectiveAdapter(ObjectiveAdapter): |
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def __init__(self, conv, objective): |
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super().__init__(conv, objective) |
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self.memory_dict = {} |
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self.memory_dict_new = {} |
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def memory(self, warm_start: pd.DataFrame, memory: Any = None): |
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if isinstance(memory, DictProxy): |
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self.memory_dict = memory |
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if warm_start is None: |
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return |
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if not isinstance(warm_start, pd.DataFrame): |
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logging.warning("Memory warm start must be of type pandas.DataFrame") |
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logging.warning("Optimization will continue without memory warm start") |
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return |
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if warm_start.empty: |
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logging.warning("Memory warm start has no values in current search space") |
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logging.warning("Optimization will continue without memory warm start") |
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return |
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self.memory_dict.update(self._conv.dataframe2memory_dict(warm_start)) |
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def __call__(self, pos): |
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pos_t = tuple(pos) |
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if pos_t in self.memory_dict: |
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params = self._conv.value2para(self._conv.position2value(pos)) |
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return self.memory_dict[pos_t], params |
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else: |
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result, params = self._call_objective(pos) |
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self.memory_dict[pos_t] = result |
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self.memory_dict_new[pos_t] = result |
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return result, params |
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SimonBlanke /
Gradient-Free-Optimizers
