source: framspy/FramsticksLib.py @ 1114

Last change on this file since 1114 was 1114, checked in by Maciej Komosinski, 2 years ago

Cosmetic

File size: 10.9 KB
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1from typing import List  # to be able to specify a type hint of list(something)
2import json
3import sys, os
4import argparse
5import numpy as np
6import frams
7
8
9class FramsticksLib:
10        """Communicates directly with Framsticks library (.dll or .so).
11        You can perform basic operations like mutation, crossover, and evaluation of genotypes.
12        This way you can perform evolution controlled by python as well as access and manipulate genotypes.
13        You can even design and use in evolution your own genetic representation implemented entirely in python,
14        or access and control the simulation and simulated creatures step by step.
15
16        Should you want to modify or extend this class, first see and test the examples in frams-test.py.
17
18        You need to provide one or two parameters when you run this class: the path to Framsticks where .dll/.so resides
19        and, optionally, the name of the Framsticks dll/so (if it is non-standard). See::
20                FramsticksLib.py -h"""
21
22        PRINT_FRAMSTICKS_OUTPUT: bool = False  # set to True for debugging
23        DETERMINISTIC: bool = False  # set to True to have the same results in each run
24
25        GENOTYPE_INVALID = "/*invalid*/"  # this is how genotype invalidity is represented in Framsticks
26        EVALUATION_SETTINGS_FILE = "eval-allcriteria.sim"  # MUST be compatible with the standard-eval expdef
27
28
29        # This function is not needed because in python, "For efficiency reasons, each module is only imported once per interpreter session."
30        # @staticmethod
31        # def getFramsModuleInstance():
32        #       """If some other party needs access to the frams module to directly access or modify Framsticks objects,
33        #       use this function to avoid importing the "frams" module multiple times and avoid potentially initializing
34        #       it many times."""
35        #       return frams
36
37        def __init__(self, frams_path, frams_lib_name, simsettings):
38                if frams_lib_name is None:
39                        frams.init(frams_path)  # could add support for setting alternative directories using -D and -d
40                else:
41                        frams.init(frams_path, "-L" + frams_lib_name)  # could add support for setting alternative directories using -D and -d
42
43                print('Available objects:', dir(frams))
44                print()
45
46                print('Performing a basic test 1/2... ', end='')
47                simplest = self.getSimplest("1")
48                assert simplest == "X" and type(simplest) is str
49                print('OK.')
50                print('Performing a basic test 2/2... ', end='')
51                assert self.isValid(["X[0:0],", "X[0:0]", "X[1:0]"]) == [False, True, False]
52                print('OK.')
53                if not self.DETERMINISTIC:
54                        frams.Math.randomize();
55                frams.Simulator.expdef = "standard-eval"  # this expdef (or fully compatible) must be used by EVALUATION_SETTINGS_FILE
56                if simsettings is not None:
57                        self.EVALUATION_SETTINGS_FILE = simsettings
58                frams.Simulator.ximport(self.EVALUATION_SETTINGS_FILE, 4 + 8 + 16)
59
60
61        def getSimplest(self, genetic_format) -> str:
62                return frams.GenMan.getSimplest(genetic_format).genotype._string()
63
64
65        def evaluate(self, genotype_list: List[str]):
66                """
67                Returns:
68                        List of dictionaries containing the performance of genotypes evaluated using self.EVALUATION_SETTINGS_FILE.
69                        Note that for whatever reason (e.g. incorrect genotype), the dictionaries you will get may be empty or
70                        partially empty and may not have the fields you expected, so handle such cases properly.
71                """
72                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
73
74                if not self.PRINT_FRAMSTICKS_OUTPUT:
75                        ec = frams.MessageCatcher.new()  # mute potential errors, warnings, messages
76
77                frams.GenePools[0].clear()
78                for g in genotype_list:
79                        frams.GenePools[0].add(g)
80                frams.ExpProperties.evalsavefile = ""  # no need to store results in a file - we will get evaluations directly from Genotype's "data" field
81                frams.Simulator.init()
82                frams.Simulator.start()
83                step = frams.Simulator.step  # cache reference to avoid repeated lookup in the loop (just for performance)
84                while frams.Simulator.running._int():  # standard-eval.expdef sets running to 0 when the evaluation is complete
85                        step()
86
87                if not self.PRINT_FRAMSTICKS_OUTPUT:
88                        if ec.error_count._value() > 0:  # errors are important and should not be ignored, at least display how many
89                                print("[ERROR]", ec.error_count, "error(s) and", ec.warning_count, "warning(s) while evaluating", len(genotype_list), "genotype(s)")
90                        ec.close()
91
92                results = []
93                for g in frams.GenePools[0]:
94                        serialized_dict = frams.String.serialize(g.data[frams.ExpProperties.evalsavedata._value()])
95                        evaluations = json.loads(serialized_dict._string())
96                        # 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.
97                        result = {"num": g.num._value(), "name": g.name._value(), "evaluations": evaluations}
98                        results.append(result)
99
100                return results
101
102
103        def mutate(self, genotype_list: List[str]) -> List[str]:
104                """
105                Returns:
106                        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).
107                """
108                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
109
110                mutated = []
111                for g in genotype_list:
112                        mutated.append(frams.GenMan.mutate(frams.Geno.newFromString(g)).genotype._string())
113                assert len(genotype_list) == len(mutated), "Submitted %d genotypes, received %d validity values" % (len(genotype_list), len(mutated))
114                return mutated
115
116
117        def crossOver(self, genotype_parent1: str, genotype_parent2: str) -> str:
118                """
119                Returns:
120                        The genotype of the offspring. self.GENOTYPE_INVALID if the crossing over failed.
121                """
122                return frams.GenMan.crossOver(frams.Geno.newFromString(genotype_parent1), frams.Geno.newFromString(genotype_parent2)).genotype._string()
123
124
125        def dissimilarity(self, genotype_list: List[str]) -> np.ndarray:
126                """
127                Returns:
128                        A square array with dissimilarities of each pair of genotypes.
129                """
130                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
131
132                # if you want to override what EVALUATION_SETTINGS_FILE sets, you can do it below:
133                # frams.SimilMeasure.simil_type = 1
134                # frams.SimilMeasureHungarian.simil_partgeom = 1
135                # frams.SimilMeasureHungarian.simil_weightedMDS = 1
136
137                n = len(genotype_list)
138                square_matrix = np.zeros((n, n))
139                genos = []  # prepare an array of Geno objects so we don't need to convert raw strings to Geno objects all the time
140                for g in genotype_list:
141                        genos.append(frams.Geno.newFromString(g))
142                for i in range(n):
143                        for j in range(n):  # maybe calculate only one triangle if you really need a 2x speedup
144                                square_matrix[i][j] = frams.SimilMeasure.evaluateDistance(genos[i], genos[j])._double()
145
146                for i in range(n):
147                        assert square_matrix[i][i] == 0, "Not a correct dissimilarity matrix, diagonal expected to be 0"
148                non_symmetric_diff = square_matrix - square_matrix.T
149                non_symmetric_count = np.count_nonzero(non_symmetric_diff)
150                if non_symmetric_count > 0:
151                        non_symmetric_diff_abs = np.abs(non_symmetric_diff)
152                        max_pos1d = np.argmax(non_symmetric_diff_abs)  # location of largest discrepancy
153                        max_pos2d_XY = np.unravel_index(max_pos1d, non_symmetric_diff_abs.shape)  # 2D coordinates of largest discrepancy
154                        max_pos2d_YX = max_pos2d_XY[1], max_pos2d_XY[0]  # 2D coordinates of largest discrepancy mirror
155                        worst_guy_XY = square_matrix[max_pos2d_XY]  # this distance and the other below (its mirror) are most different
156                        worst_guy_YX = square_matrix[max_pos2d_YX]
157                        print("[WARN] Dissimilarity matrix: expecting symmetry, but %g out of %d pairs were asymmetrical, max difference was %g (%g %%)" %
158                              (non_symmetric_count / 2,
159                               n * (n - 1) / 2,
160                               non_symmetric_diff_abs[max_pos2d_XY],
161                               non_symmetric_diff_abs[max_pos2d_XY] * 100 / ((worst_guy_XY + worst_guy_YX) / 2)))  # max diff is not necessarily max %
162                return square_matrix
163
164
165        def isValid(self, genotype_list: List[str]) -> List[bool]:
166                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
167                valid = []
168                for g in genotype_list:
169                        valid.append(frams.Geno.newFromString(g).is_valid._int() == 1)
170                assert len(genotype_list) == len(valid), "Tested %d genotypes, received %d validity values" % (len(genotype_list), len(valid))
171                return valid
172
173
174def parseArguments():
175        parser = argparse.ArgumentParser(description='Run this program with "python -u %s" if you want to disable buffering of its output.' % sys.argv[0])
176        parser.add_argument('-path', type=ensureDir, required=True, help='Path to the Framsticks library (.dll or .so) without trailing slash.')
177        parser.add_argument('-lib', required=False, help='Library name. If not given, "frams-objects.dll" or "frams-objects.so" is assumed depending on the platform.')
178        parser.add_argument('-simsettings', required=False, help='The name of the .sim file with settings for evaluation, mutation, crossover, and similarity estimation. If not given, "eval-allcriteria.sim" is assumed by default. Must be compatible with the "standard-eval" expdef.')
179        parser.add_argument('-genformat', required=False, help='Genetic format for the demo run, for example 4, 9, or S. If not given, f1 is assumed.')
180        return parser.parse_args()
181
182
183def ensureDir(string):
184        if os.path.isdir(string):
185                return string
186        else:
187                raise NotADirectoryError(string)
188
189
190if __name__ == "__main__":
191        # A demo run.
192
193        # TODO ideas:
194        # - check_validity with three levels (invalid, corrected, valid)
195        # - a pool of binaries running simultaneously, balance load - in particular evaluation
196
197        parsed_args = parseArguments()
198        framsLib = FramsticksLib(parsed_args.path, parsed_args.lib, parsed_args.simsettings)
199
200        print("Sending a direct command to Framsticks library that calculates \"4\"+2 yields", frams.Simulator.eval("return \"4\"+2;"))
201
202        simplest = framsLib.getSimplest('1' if parsed_args.genformat is None else parsed_args.genformat)
203        print("\tSimplest genotype:", simplest)
204        parent1 = framsLib.mutate([simplest])[0]
205        parent2 = parent1
206        MUTATE_COUNT = 10
207        for x in range(MUTATE_COUNT):  # example of a chain of 10 mutations
208                parent2 = framsLib.mutate([parent2])[0]
209        print("\tParent1 (mutated simplest):", parent1)
210        print("\tParent2 (Parent1 mutated %d times):" % MUTATE_COUNT, parent2)
211        offspring = framsLib.crossOver(parent1, parent2)
212        print("\tCrossover (Offspring):", offspring)
213        print('\tDissimilarity of Parent1 and Offspring:', framsLib.dissimilarity([parent1, offspring])[0, 1])
214        print('\tPerformance of Offspring:', framsLib.evaluate([offspring]))
215        print('\tValidity of Parent1, Parent 2, and Offspring:', framsLib.isValid([parent1, parent2, offspring]))
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