Changeset 1177 for framspy/FramsticksLib.py
 Timestamp:
 04/26/22 00:52:58 (14 months ago)
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 1 edited
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framspy/FramsticksLib.py
r1170 r1177 57 57 print('OK.') 58 58 if not self.DETERMINISTIC: 59 frams.Math.randomize() ;59 frams.Math.randomize() 60 60 frams.Simulator.expdef = "standardeval" # this expdef (or fully compatible) must be used by EVALUATION_SETTINGS_FILE 61 61 if sim_settings_files is not None: … … 78 78 partially empty and may not have the fields you expected, so handle such cases properly. 79 79 """ 80 assert isinstance(genotype_list, list) # because in python str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity80 assert isinstance(genotype_list, list) # because in python, str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity 81 81 82 82 if not self.PRINT_FRAMSTICKS_OUTPUT: … … 107 107 for g in frams.GenePools[0]: 108 108 serialized_dict = frams.String.serialize(g.data[frams.ExpProperties.evalsavedata._value()]) 109 evaluations = json.loads(serialized_dict._string()) # Framsticks native ExtValue's get converted to native python types such as int, float, list, str.109 evaluations = json.loads(serialized_dict._string()) # Framsticks native ExtValue's get converted to native python types such as int, float, list, str. 110 110 # now, for consistency with FramsticksCLI.py, add "num" and "name" keys that are missing because we got data directly from Genotype, not from the file produced by standardeval.expdef's function printStats(). What we do below is what printStats() does. 111 111 result = {"num": g.num._value(), "name": g.name._value(), "evaluations": evaluations} … … 120 120 The genotype(s) of the mutated source genotype(s). self.GENOTYPE_INVALID for genotypes whose mutation failed (for example because the source genotype was invalid). 121 121 """ 122 assert isinstance(genotype_list, list) # because in python str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity122 assert isinstance(genotype_list, list) # because in python, str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity 123 123 124 124 mutated = [] … … 137 137 138 138 139 def dissimilarity(self, genotype_list: List[str] ) > np.ndarray:140 """ 141 Returns:142 A square array with dissimilarities of each pair of genotypes.143 """ 144 assert isinstance(genotype_list, list) # because in python str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity139 def dissimilarity(self, genotype_list: List[str], method: int) > np.ndarray: 140 """ 141 :param method: 1 = genetic Levenshtein distance; 0, 1, 2 = phenetic dissimilarity (SimilMeasureGreedy, SimilMeasureHungarian, SimilMeasureDistribution) 142 :return: A square array with dissimilarities of each pair of genotypes. 143 """ 144 assert isinstance(genotype_list, list) # because in python, str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity 145 145 146 146 # if you want to override what EVALUATION_SETTINGS_FILE sets, you can do it below: 147 # frams.SimilMeasure.simil_type = 1148 147 # frams.SimilMeasureHungarian.simil_partgeom = 1 149 148 # frams.SimilMeasureHungarian.simil_weightedMDS = 1 … … 151 150 n = len(genotype_list) 152 151 square_matrix = np.zeros((n, n)) 153 genos = [] # prepare an array of Geno objects so that we don't need to convert raw strings to Geno objects all the time in loops 154 for g in genotype_list: 155 genos.append(frams.Geno.newFromString(g)) 156 frams_evaluateDistance = frams.SimilMeasure.evaluateDistance # cache function reference for better performance in loops 157 for i in range(n): 158 for j in range(n): # maybe calculate only one triangle if you really need a 2x speedup 159 square_matrix[i][j] = frams_evaluateDistance(genos[i], genos[j])._double() 152 153 if method in (0, 1, 2): # Framsticks phenetic dissimilarity methods 154 frams.SimilMeasure.simil_type = method 155 genos = [] # prepare an array of Geno objects so that we don't need to convert raw strings to Geno objects all the time in loops 156 for g in genotype_list: 157 genos.append(frams.Geno.newFromString(g)) 158 frams_evaluateDistance = frams.SimilMeasure.evaluateDistance # cache function reference for better performance in loops 159 for i in range(n): 160 for j in range(n): # maybe calculate only one triangle if you really need a 2x speedup 161 square_matrix[i][j] = frams_evaluateDistance(genos[i], genos[j])._double() 162 elif method == 1: 163 import Levenshtein 164 for i in range(n): 165 for j in range(n): # maybe calculate only one triangle if you really need a 2x speedup 166 square_matrix[i][j] = Levenshtein.distance(genotype_list[i], genotype_list[j]) 167 else: 168 raise Exception("Don't know what to do with dissimilarity method = %d" % method) 160 169 161 170 for i in range(n): … … 165 174 if non_symmetric_count > 0: 166 175 non_symmetric_diff_abs = np.abs(non_symmetric_diff) 167 max_pos1d = np.argmax(non_symmetric_diff_abs) # location of largest discrepancy168 max_pos2d_XY = np.unravel_index(max_pos1d, non_symmetric_diff_abs.shape) # 2D coordinates of largest discrepancy169 max_pos2d_YX = max_pos2d_XY[1], max_pos2d_XY[0] # 2D coordinates of largest discrepancy mirror176 max_pos1d = np.argmax(non_symmetric_diff_abs) # location of the largest discrepancy 177 max_pos2d_XY = np.unravel_index(max_pos1d, non_symmetric_diff_abs.shape) # 2D coordinates of the largest discrepancy 178 max_pos2d_YX = max_pos2d_XY[1], max_pos2d_XY[0] # 2D coordinates of the largest discrepancy mirror 170 179 worst_guy_XY = square_matrix[max_pos2d_XY] # this distance and the other below (its mirror) are most different 171 180 worst_guy_YX = square_matrix[max_pos2d_YX] … … 179 188 180 189 def isValid(self, genotype_list: List[str]) > List[bool]: 181 assert isinstance(genotype_list, list) # because in python str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity190 assert isinstance(genotype_list, list) # because in python, str has similar capabilities as list and here it would pretend to work too, so to avoid any ambiguity 182 191 valid = [] 183 192 for g in genotype_list: … … 226 235 offspring = framsLib.crossOver(parent1, parent2) 227 236 print("\tCrossover (Offspring):", offspring) 228 print('\tDissimilarity of Parent1 and Offspring:', framsLib.dissimilarity([parent1, offspring] )[0, 1])237 print('\tDissimilarity of Parent1 and Offspring:', framsLib.dissimilarity([parent1, offspring], 1)[0, 1]) 229 238 print('\tPerformance of Offspring:', framsLib.evaluate([offspring])) 230 239 print('\tValidity of Parent1, Parent 2, and Offspring:', framsLib.isValid([parent1, parent2, offspring]))
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