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Ahmed Gad
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Add sample_size parameter
1 parent dede266 commit 8a34063

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+81
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‎.DS_Store

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‎example.py

Lines changed: 33 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,33 @@
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import pygad
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import numpy
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function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
5+
desired_output = 44 # Function output.
6+
7+
def fitness_func(ga_instance, solution, solution_idx):
8+
output = numpy.sum(solution*function_inputs)
9+
fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
10+
return fitness
11+
12+
num_generations = 100 # Number of generations.
13+
num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool.
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15+
sol_per_pop = 20 # Number of solutions in the population.
16+
num_genes = len(function_inputs)
17+
18+
ga_instance = pygad.GA(num_generations=num_generations,
19+
num_parents_mating=num_parents_mating,
20+
sol_per_pop=sol_per_pop,
21+
num_genes=num_genes,
22+
fitness_func=fitness_func,
23+
gene_type=int,
24+
gene_space=range(1000),
25+
mutation_by_replacement=True,
26+
sample_size=1000000,
27+
gene_constraint=[lambda x: x[0]>5, lambda x: x[0]>5, lambda x: x[0]>5, lambda x: x[0]>5, lambda x: x[0]>5, lambda x: x[0]>5],
28+
allow_duplicate_genes=False)
29+
30+
# Running the GA to optimize the parameters of the function.
31+
ga_instance.run()
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33+
# ga_instance.plot_fitness()

‎pygad/__pycache__/__init__.cpython-310.pyc

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‎pygad/__pycache__/pygad.cpython-310.pyc

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‎pygad/helper/__pycache__/__init__.cpython-310.pyc

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‎pygad/helper/__pycache__/misc.cpython-310.pyc

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‎pygad/helper/__pycache__/unique.cpython-310.pyc

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‎pygad/helper/misc.py

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Original file line numberDiff line numberDiff line change
@@ -507,6 +507,8 @@ def get_valid_gene_constraint_values(self,
507507
-An array with number of values equal to sample_size if sample_size>1. Or
508508
-None if no value found that satisfies the constraint.
509509
"""
510+
511+
print("AAAA", sample_size)
510512
# Either generate the values randomly or from the gene space.
511513
values = self.generate_gene_value(range_min=range_min,
512514
range_max=range_max,

‎pygad/pygad.py

Lines changed: 30 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -51,6 +51,7 @@ def __init__(self,
5151
random_mutation_max_val=1.0,
5252
gene_space=None,
5353
gene_constraint=None,
54+
sample_size=100,
5455
allow_duplicate_genes=True,
5556
on_start=None,
5657
on_fitness=None,
@@ -107,6 +108,7 @@ def __init__(self,
107108
gene_space: It accepts a list of all possible values of the gene. This list is used in the mutation step. Should be used only if the gene space is a set of discrete values. No need for the 2 parameters (random_mutation_min_val and random_mutation_max_val) if the parameter gene_space exists. Added in PyGAD 2.5.0. In PyGAD 2.11.0, the gene_space can be assigned a dict.
108109
109110
gene_constraint: It accepts a list of constraints for the genes. Each constraint is a Python function. Added in PyGAD 3.5.0.
111+
sample_size: To select a gene value that respects a constraint, this variable defines the size of the sample from which a value is selected. Useful if either allow_duplicate_genes or gene_constraint is used. Added in PyGAD 3.5.0.
110112
111113
on_start: Accepts a function/method to be called only once before the genetic algorithm starts its evolution. If functioned, then it must accept a single parameter representing the instance of the genetic algorithm. If method, then it must accept 2 parameters where the second one refers to the method's object. Added in PyGAD 2.6.0.
112114
on_fitness: Accepts a function/method to be called after calculating the fitness values of all solutions in the population. If functioned, then it must accept 2 parameters: 1) a list of all solutions' fitness values 2) the instance of the genetic algorithm. If method, then it must accept 3 parameters where the third one refers to the method's object. Added in PyGAD 2.6.0.
@@ -189,6 +191,19 @@ def __init__(self,
189191

190192
self.mutation_by_replacement = mutation_by_replacement
191193

194+
# Validate the sample_size parameter.
195+
if type(sample_size) in GA.supported_int_types:
196+
if sample_size > 0:
197+
pass
198+
else:
199+
self.valid_parameters = False
200+
raise ValueError(f"The value of the sample_size parameter must be > 0 but the value ({sample_size}) found.")
201+
else:
202+
self.valid_parameters = False
203+
raise TypeError(f"The type of the sample_size parameter must be integer but the value ({sample_size}) of type ({type(sample_size)}) found.")
204+
205+
self.sample_size = sample_size
206+
192207
# Validate allow_duplicate_genes
193208
if not (type(allow_duplicate_genes) is bool):
194209
self.valid_parameters = False
@@ -266,18 +281,6 @@ def __init__(self,
266281

267282
self.gene_space = gene_space
268283

269-
# Validate init_range_low and init_range_high
270-
# if type(init_range_low) in GA.supported_int_float_types:
271-
# if type(init_range_high) in GA.supported_int_float_types:
272-
# self.init_range_low = init_range_low
273-
# self.init_range_high = init_range_high
274-
# else:
275-
# self.valid_parameters = False
276-
# raise ValueError(f"The value passed to the 'init_range_high' parameter must be either integer or floating-point number but the value ({init_range_high}) of type {type(init_range_high)} found.")
277-
# else:
278-
# self.valid_parameters = False
279-
# raise ValueError(f"The value passed to the 'init_range_low' parameter must be either integer or floating-point number but the value ({init_range_low}) of type {type(init_range_low)} found.")
280-
281284
# Validate init_range_low and init_range_high
282285
if type(init_range_low) in GA.supported_int_float_types:
283286
if type(init_range_high) in GA.supported_int_float_types:
@@ -460,11 +463,11 @@ def __init__(self,
460463
max_val=self.init_range_high,
461464
mutation_by_replacement=True,
462465
gene_type=self.gene_type,
463-
sample_size=100)
466+
sample_size=self.sample_size)
464467
else:
465468
self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution,
466469
gene_type=self.gene_type,
467-
sample_size=100,
470+
sample_size=self.sample_size,
468471
mutation_by_replacement=True,
469472
build_initial_pop=True)
470473

@@ -887,13 +890,17 @@ def __init__(self,
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888891
# For tournament selection, validate the K value.
889892
if parent_selection_type == "tournament":
890-
if K_tournament > self.sol_per_pop:
891-
K_tournament = self.sol_per_pop
892-
if not self.suppress_warnings:
893-
warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n")
894-
elif K_tournament <= 0:
893+
if type(K_tournament) in GA.supported_int_types:
894+
if K_tournament > self.sol_per_pop:
895+
K_tournament = self.sol_per_pop
896+
if not self.suppress_warnings:
897+
warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n")
898+
elif K_tournament <= 0:
899+
self.valid_parameters = False
900+
raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n")
901+
else:
895902
self.valid_parameters = False
896-
raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n")
903+
raise ValueError(f"The type of K of the tournament selection must be integer but the value ({K_tournament}) of type ({type(K_tournament)}) found.")
897904

898905
self.K_tournament = K_tournament
899906

@@ -1429,7 +1436,7 @@ def initialize_population(self,
14291436
gene_idx=gene_idx,
14301437
mutation_by_replacement=True,
14311438
solution=solution,
1432-
sample_size=100)
1439+
sample_size=self.sample_size)
14331440
if values_filtered is None:
14341441
if not self.suppress_warnings:
14351442
warnings.warn(f"No value satisfied the constraint for the gene at index {gene_idx} with value {solution[gene_idx]} while creating the initial population.")
@@ -1445,11 +1452,11 @@ def initialize_population(self,
14451452
max_val=self.init_range_high,
14461453
mutation_by_replacement=True,
14471454
gene_type=gene_type,
1448-
sample_size=100)
1455+
sample_size=self.sample_size)
14491456
else:
14501457
self.population[solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=self.population[solution_idx].copy(),
14511458
gene_type=self.gene_type,
1452-
sample_size=100,
1459+
sample_size=self.sample_size,
14531460
mutation_by_replacement=True,
14541461
build_initial_pop=True)
14551462

‎pygad/utils/__pycache__/__init__.cpython-310.pyc

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‎pygad/utils/__pycache__/crossover.cpython-310.pyc

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‎pygad/utils/__pycache__/mutation.cpython-310.pyc

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‎pygad/utils/__pycache__/nsga2.cpython-310.pyc

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‎pygad/utils/__pycache__/parent_selection.cpython-310.pyc

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‎pygad/utils/crossover.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -70,11 +70,11 @@ def single_point_crossover(self, parents, offspring_size):
7070
max_val=self.random_mutation_max_val,
7171
mutation_by_replacement=self.mutation_by_replacement,
7272
gene_type=self.gene_type,
73-
sample_size=100)
73+
sample_size=self.sample_size)
7474
else:
7575
offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k],
7676
gene_type=self.gene_type,
77-
sample_size=100,
77+
sample_size=self.sample_size,
7878
mutation_by_replacement=self.mutation_by_replacement,
7979
build_initial_pop=False)
8080

@@ -144,11 +144,11 @@ def two_points_crossover(self, parents, offspring_size):
144144
max_val=self.random_mutation_max_val,
145145
mutation_by_replacement=self.mutation_by_replacement,
146146
gene_type=self.gene_type,
147-
sample_size=100)
147+
sample_size=self.sample_size)
148148
else:
149149
offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k],
150150
gene_type=self.gene_type,
151-
sample_size=100,
151+
sample_size=self.sample_size,
152152
mutation_by_replacement=self.mutation_by_replacement,
153153
build_initial_pop=False)
154154
return offspring
@@ -213,11 +213,11 @@ def uniform_crossover(self, parents, offspring_size):
213213
max_val=self.random_mutation_max_val,
214214
mutation_by_replacement=self.mutation_by_replacement,
215215
gene_type=self.gene_type,
216-
sample_size=100)
216+
sample_size=self.sample_size)
217217
else:
218218
offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k],
219219
gene_type=self.gene_type,
220-
sample_size=100,
220+
sample_size=self.sample_size,
221221
mutation_by_replacement=self.mutation_by_replacement,
222222
build_initial_pop=False)
223223

@@ -279,11 +279,11 @@ def scattered_crossover(self, parents, offspring_size):
279279
max_val=self.random_mutation_max_val,
280280
mutation_by_replacement=self.mutation_by_replacement,
281281
gene_type=self.gene_type,
282-
sample_size=100)
282+
sample_size=self.sample_size)
283283
else:
284284
offspring[k], _, _ = self.solve_duplicate_genes_by_space(solution=offspring[k],
285285
gene_type=self.gene_type,
286-
sample_size=100,
286+
sample_size=self.sample_size,
287287
mutation_by_replacement=self.mutation_by_replacement,
288288
build_initial_pop=False)
289289
return offspring

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