- Timestamp:
- 04/26/22 00:52:58 (3 years ago)
- File:
-
- 1 edited
-
framspy/FramsticksLib.py (modified) (9 diffs)
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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 = "standard-eval" # 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 standard-eval.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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