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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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def intersection(lst1, lst2): |
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inter = [value for value in lst1 if value in lst2] |
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return inter |
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def convert_dataframe(dataframe1, search_space1, search_space2): |
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dataframe2 = dataframe1.copy() |
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for para1 in search_space1: |
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if para1 not in search_space2: |
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dataframe2.drop(para1, axis=1, inplace=True) |
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continue |
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search_elements1 = search_space1[para1] |
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search_elements2 = search_space2[para1] |
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inter = intersection(search_elements1, search_elements2) |
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indices_A = [search_elements1.index(x) for x in inter] |
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indices_B = [search_elements2.index(x) for x in inter] |
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conv_dict = dict(zip(indices_A, indices_B)) |
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col = dataframe2[para1] |
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col_conv = col.map(conv_dict) |
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col_conv = col_conv.dropna(how="any") |
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col_conv = col_conv.astype(int) |
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dataframe2[para1] = col_conv |
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dataframe2 = dataframe2.dropna(how="any", axis=0) |
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return dataframe2 |
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def memory_dict2dataframe(memory_dict, search_space): |
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columns = list(search_space.keys()) |
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if not bool(memory_dict): |
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return pd.DataFrame([], columns=columns) |
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pos_tuple_list = list(memory_dict.keys()) |
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result_list = list(memory_dict.values()) |
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results_df = pd.DataFrame(result_list) |
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np_pos = np.array(pos_tuple_list) |
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pd_pos = pd.DataFrame(np_pos, columns=columns) |
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dataframe = pd.concat([pd_pos, results_df], axis=1) |
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return dataframe |
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def dataframe2memory_dict(dataframe, search_space): |
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columns = list(search_space.keys()) |
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if dataframe.empty: |
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return {} |
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positions = dataframe[columns] |
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scores = dataframe.drop(columns, axis=1) |
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scores = scores.to_dict("records") |
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positions_list = positions.values.tolist() |
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# list of lists into list of tuples |
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pos_tuple_list = list(map(tuple, positions_list)) |
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memory_dict = dict(zip(pos_tuple_list, scores)) |
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return memory_dict |
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SimonBlanke /
Optimization-Metadata
