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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 HyperGradientConv: |
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def __init__(self, s_space): |
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self.s_space = s_space |
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def value2position(self, value: list) -> list: |
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return [ |
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np.abs(v - np.array(space_dim)).argmin() |
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for v, space_dim in zip(value, self.s_space.values_l) |
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] |
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def value2para(self, value: list) -> dict: |
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return {key: p for key, p in zip(self.s_space.dim_keys, value)} |
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def para2value(self, para: dict) -> list: |
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return [para[para_name] for para_name in self.s_space.dim_keys] |
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def position2value(self, position): |
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return [ |
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space_dim[pos] |
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for pos, space_dim in zip(position, self.s_space.values_l) |
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] |
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def para_func2str(self, para): |
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return { |
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dim_key: ( |
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para[dim_key].__name__ |
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if self.s_space.data_types[dim_key] != "number" |
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else para[dim_key] |
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) |
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for dim_key in self.s_space.dim_keys |
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} |
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def value_func2str(self, value): |
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try: |
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return value.__name__ |
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except: |
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return value |
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def conv_para(self, para_hyper): |
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para_gfo = {} |
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for para in self.s_space.dim_keys: |
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value_hyper = para_hyper[para] |
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space_dim = list(self.s_space.func2str[para]) |
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if self.s_space.data_types[para] == "number": |
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value_gfo = np.abs(value_hyper - np.array(space_dim)).argmin() |
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else: |
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value_hyper = self.value_func2str(value_hyper) |
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if value_hyper in space_dim: |
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value_gfo = space_dim.index(value_hyper) |
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else: |
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raise ValueError( |
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f"'{value_hyper}' was not found in '{para}'" |
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) |
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para_gfo[para] = value_gfo |
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return para_gfo |
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def conv_initialize(self, initialize): |
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if "warm_start" in initialize: |
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warm_start_l = initialize["warm_start"] |
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warm_start_gfo = [ |
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self.conv_para(warm_start) for warm_start in warm_start_l |
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] |
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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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return [search_dim.index(value2) for value2 in list1_values] |
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def values2positions(self, values, search_dim): |
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return np.array(search_dim).searchsorted(values) |
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def positions2results(self, positions): |
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results_dict = {} |
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for para_name in self.s_space.dim_keys: |
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values_list = self.s_space[para_name] |
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pos_ = positions[para_name].values |
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values_ = [values_list[idx] for idx in pos_] |
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results_dict[para_name] = values_ |
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results = pd.DataFrame.from_dict(results_dict) |
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diff_list = np.setdiff1d(positions.columns, results.columns) |
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results[diff_list] = positions[diff_list] |
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return results |
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def conv_memory_warm_start(self, results): |
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if results is None: |
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return results |
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results.reset_index(inplace=True, drop=True) |
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df_positions_dict = {} |
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for dim_key in self.s_space.dim_keys: |
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result_dim_values = list(results[dim_key].values) |
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search_dim = self.s_space.func2str[dim_key] |
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if self.s_space.data_types[dim_key] == "object": |
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result_dim_values = [ |
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self.value_func2str(value) for value in result_dim_values |
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] |
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list1_positions = self.get_list_positions( |
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result_dim_values, search_dim |
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) |
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else: |
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list1_positions = self.values2positions( |
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result_dim_values, search_dim |
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) |
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df_positions_dict[dim_key] = 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
