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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 Converter: |
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def __init__(self, search_space): |
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self.search_space = search_space |
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self.para_names = list(self.search_space.keys()) |
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def value2position(self, value): |
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position = [] |
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for n, space_dim in enumerate(self.search_space_values): |
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pos = np.abs(value[n] - space_dim).argmin() |
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position.append(pos) |
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return np.array(position).astype(int) |
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def value2para(self, value): |
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para = {} |
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for key, p_ in zip(self.para_names, value): |
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para[key] = p_ |
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return para |
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def position2value(self, position): |
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value = [] |
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for n, space_dim in enumerate(self.search_space_values): |
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value.append(space_dim[position[n]]) |
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return np.array(value) |
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def positions2values(self, positions): |
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values_temp = [] |
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positions_np = np.array(positions) |
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for n, space_dim in enumerate(self.search_space_values): |
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pos_1d = positions_np[:, n] |
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value_ = np.take(space_dim, pos_1d, axis=0) |
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values_temp.append(value_) |
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values = list(np.array(values_temp).T) |
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return values |
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def para2value(self, para): |
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value = [] |
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for para_name in self.para_names: |
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value.append(para[para_name]) |
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return np.array(value) |
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def _memory2dataframe(self, memory_dict): |
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positions = [np.array(pos).astype(int) for pos in list(memory_dict.keys())] |
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scores = list(memory_dict.values()) |
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memory_positions = pd.DataFrame(positions, columns=self.para_names) |
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memory_positions["score"] = scores |
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return memory_positions |
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class HyperGradientTrafo(Converter): |
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def __init__(self, search_space): |
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super().__init__(search_space) |
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self.search_space_values = list(self.search_space.values()) |
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search_space_positions = {} |
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for key in search_space.keys(): |
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search_space_positions[key] = np.array(range(len(search_space[key]))) |
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self.search_space_positions = search_space_positions |
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def trafo_initialize(self, initialize): |
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if "warm_start" in list(initialize.keys()): |
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warm_start = initialize["warm_start"] |
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warm_start_gfo = [] |
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for warm_start_ in warm_start: |
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value = self.para2value(warm_start_) |
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position = self.value2position(value) |
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pos_para = self.value2para(position) |
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warm_start_gfo.append(pos_para) |
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initialize["warm_start"] = warm_start_gfo |
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return initialize |
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def get_list_positions(self, list1_values, search_dim): |
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list_positions = [] |
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for value2 in list1_values: |
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pos_appended = False |
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for value1 in search_dim: |
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if value1 == value2: |
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list_positions.append(search_dim.index(value1)) |
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pos_appended = True |
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break |
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if not pos_appended: |
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list_positions.append(None) |
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return list_positions |
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def trafo_memory_warm_start(self, results): |
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if results is None: |
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return results |
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df_positions_dict = {} |
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for para_name in self.para_names: |
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list1_values = list(results[para_name].values) |
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search_dim = self.search_space[para_name] |
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""" |
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list1_positions = [ |
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search_dim.index(value) if value in search_dim else None |
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for value in list1_values |
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] |
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""" |
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""" |
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list1_positions = [ |
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search_dim.index(value1) |
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for value2 in list1_values |
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for value1 in search_dim |
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if value1 == value2 |
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] |
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""" |
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list1_positions = self.get_list_positions(list1_values, search_dim) |
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# remove None |
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# list1_positions_ = [x for x in list1_positions if x is not None] |
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df_positions_dict[para_name] = list1_positions |
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results_new = pd.DataFrame(df_positions_dict) |
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results_new["score"] = results["score"] |
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results_new.dropna(how="any", inplace=True) |
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return results_new |
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SimonBlanke /
Hyperactive
