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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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class Memory: |
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def __init__(self, warm_start: pd.DataFrame, conv: Any, memory: Any = None): |
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self.memory_dict = {} |
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self.memory_dict_new = {} |
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self.conv = conv |
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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( |
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"Memory warm start must be of type pandas.DataFrame" |
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) |
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logging.warning( |
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"Optimization will continue without memory warm start" |
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) |
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return |
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if warm_start.empty: |
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logging.warning( |
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"Memory warm start has no values in current search space" |
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) |
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logging.warning( |
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"Optimization will continue without memory warm start" |
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) |
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return |
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self.memory_dict.update(self.conv.dataframe2memory_dict(warm_start)) |
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def memory( |
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self, objective_function: Callable[[List[float]], float] |
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) -> Callable[[List[float]], float]: |
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def wrapper(para: List[float]) -> float: |
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value = self.conv.para2value(para) |
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position = self.conv.value2position(value) |
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pos_tuple = tuple(position) |
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if pos_tuple in self.memory_dict: |
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return self.memory_dict[pos_tuple] |
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else: |
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score = objective_function(para) |
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self.memory_dict[pos_tuple] = score |
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self.memory_dict_new[pos_tuple] = score |
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return score |
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return wrapper |
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SimonBlanke /
Gradient-Free-Optimizers
