1 | from abc import ABC |
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2 | |
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3 | import numpy as np |
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4 | |
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5 | from evolalg.base.frams_step import FramsStep |
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6 | from evolalg.dissimilarity.dissimilarity import Dissimilarity |
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7 | |
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8 | #TODO eliminate overlap with dissimilarity.py |
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9 | |
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10 | |
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11 | class FramsDissimilarity(FramsStep): |
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12 | |
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13 | def __init__(self, frams_lib, reduction="mean", output_field="dissim", knn=None, *args, **kwargs): |
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14 | super(FramsDissimilarity, self).__init__(frams_lib, *args, **kwargs) |
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15 | |
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16 | self.output_field = output_field |
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17 | self.fn_reduce = None |
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18 | self.knn = knn |
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19 | if reduction == "mean": |
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20 | self.fn_reduce = np.mean |
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21 | elif reduction == "max": |
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22 | self.fn_reduce = np.max |
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23 | elif reduction == "min": |
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24 | self.fn_reduce = np.min |
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25 | elif reduction == "sum": |
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26 | self.fn_reduce = np.sum |
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27 | elif reduction == "knn_mean": |
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28 | self.fn_reduce = self.knn_mean |
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29 | elif reduction == "none" or reduction is None: |
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30 | self.fn_reduce = None |
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31 | else: |
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32 | raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, knn_mean, none") |
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33 | |
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34 | def reduce(self, dissim_matrix): |
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35 | if self.fn_reduce is None: |
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36 | return dissim_matrix |
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37 | return self.fn_reduce(dissim_matrix, axis=1) |
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38 | |
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39 | def call(self, population): |
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40 | super(FramsDissimilarity, self).call(population) |
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41 | if len(population) == 0: |
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42 | return [] |
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43 | dissim_matrix = self.frams.dissimilarity([_.genotype for _ in population]) |
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44 | dissim = self.reduce(dissim_matrix) |
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45 | for d,ind in zip(dissim, population): |
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46 | setattr(ind, self.output_field, d) |
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47 | return population |
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48 | |
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49 | def knn_mean(self, dissim_matrix,axis): |
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50 | return np.mean(np.partition(dissim_matrix, self.knn)[:,:self.knn],axis=axis) |
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