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Testing stop_criteria #241

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Nov 2, 2023
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Testing stop_criteria #241

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84 changes: 71 additions & 13 deletions pygad/pygad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1171,23 +1171,40 @@ def __init__(self,
# reach_{target_fitness}: Stop if the target fitness value is reached.
# saturate_{num_generations}: Stop if the fitness value does not change (saturates) for the given number of generations.
criterion = stop_criteria.split("_")
if len(criterion) == 2:
stop_word = criterion[0]
number = criterion[1]

if stop_word in self.supported_stop_words:
pass
else:
self.valid_parameters = False
raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.")
stop_word = criterion[0]
# criterion[1] might be a single or multiple numbers.
number = criterion[1:]
if stop_word in self.supported_stop_words:
pass
else:
self.valid_parameters = False
raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are '{self.supported_stop_words}' but '{stop_word}' found.")

if len(criterion) == 2:
# There is only a single number.
number = number[0]
if number.replace(".", "").isnumeric():
number = float(number)
else:
self.valid_parameters = False
raise ValueError(f"The value following the stop word in the 'stop_criteria' parameter must be a number but the value ({number}) of type {type(number)} found.")

self.stop_criteria.append([stop_word, number])
elif len(criterion) > 2:
if stop_word == 'reach':
pass
else:
self.valid_parameters = False
raise ValueError(f"Passing multiple numbers following the keyword in the 'stop_criteria' parameter is expected only with the 'reach' keyword but the keyword ({stop_word}) found.")

for idx, num in enumerate(number):
if num.replace(".", "").isnumeric():
number[idx] = float(num)
else:
self.valid_parameters = False
raise ValueError(f"The value(s) following the stop word in the 'stop_criteria' parameter must be numeric but the value ({num}) of type {type(num)} found.")

self.stop_criteria.append([stop_word] + number)

else:
self.valid_parameters = False
Expand Down Expand Up @@ -2133,15 +2150,56 @@ def run(self):
if not self.stop_criteria is None:
for criterion in self.stop_criteria:
if criterion[0] == "reach":
if max(self.last_generation_fitness) >= criterion[1]:
# Single-objective problem.
if type(self.last_generation_fitness[0]) in GA.supported_int_float_types:
if max(self.last_generation_fitness) >= criterion[1]:
stop_run = True
break
# Multi-objective problem.
elif type(self.last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
# Validate the value passed to the criterion.
if len(criterion[1:]) == 1:
# There is a single value used across all the objectives.
pass
elif len(criterion[1:]) > 1:
# There are multiple values. The number of values must be equal to the number of objectives.
if len(criterion[1:]) == len(self.last_generation_fitness[0]):
pass
else:
self.valid_parameters = False
raise ValueError(f"When the the 'reach' keyword is used with the 'stop_criteria' parameter for solving a multi-objective problem, then the number of numeric values following the keyword can be:\n1) A single numeric value to be used across all the objective functions.\n2) A number of numeric values equal to the number of objective functions.\nBut the value {criterion} found with {len(criterion)-1} numeric values which is not equal to the number of objective functions {len(self.last_generation_fitness[0])}.")

stop_run = True
break
for obj_idx in range(len(self.last_generation_fitness[0])):
# Use the objective index to return the proper value for the criterion.

if len(criterion[1:]) == len(self.last_generation_fitness[0]):
reach_fitness_value = criterion[obj_idx + 1]
elif len(criterion[1:]) == 1:
reach_fitness_value = criterion[1]

if max(self.last_generation_fitness[:, obj_idx]) >= reach_fitness_value:
pass
else:
stop_run = False
break
elif criterion[0] == "saturate":
criterion[1] = int(criterion[1])
if (self.generations_completed >= criterion[1]):
if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0:
# Single-objective problem.
if type(self.last_generation_fitness[0]) in GA.supported_int_float_types:
if (self.best_solutions_fitness[self.generations_completed - criterion[1]] - self.best_solutions_fitness[self.generations_completed - 1]) == 0:
stop_run = True
break
# Multi-objective problem.
elif type(self.last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
stop_run = True
break
for obj_idx in range(len(self.last_generation_fitness[0])):
if (self.best_solutions_fitness[self.generations_completed - criterion[1]][obj_idx] - self.best_solutions_fitness[self.generations_completed - 1][obj_idx]) == 0:
pass
else:
stop_run = False
break

if stop_run:
break
Expand Down
215 changes: 215 additions & 0 deletions tests/test_stop_criteria.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,215 @@
import pygad
import numpy

actual_num_fitness_calls_default_keep = 0
actual_num_fitness_calls_no_keep = 0
actual_num_fitness_calls_keep_elitism = 0
actual_num_fitness_calls_keep_parents = 0

num_generations = 100
sol_per_pop = 10
num_parents_mating = 5

def multi_objective_problem(keep_elitism=1,
keep_parents=-1,
fitness_batch_size=None,
stop_criteria=None,
parent_selection_type='sss',
mutation_type="random",
mutation_percent_genes="default",
multi_objective=False):

function_inputs1 = [4,-2,3.5,5,-11,-4.7] # Function 1 inputs.
function_inputs2 = [-2,0.7,-9,1.4,3,5] # Function 2 inputs.
desired_output1 = 50 # Function 1 output.
desired_output2 = 30 # Function 2 output.

def fitness_func_batch_multi(ga_instance, solution, solution_idx):
f = []
for sol in solution:
output1 = numpy.sum(sol*function_inputs1)
output2 = numpy.sum(sol*function_inputs2)
fitness1 = 1.0 / (numpy.abs(output1 - desired_output1) + 0.000001)
fitness2 = 1.0 / (numpy.abs(output2 - desired_output2) + 0.000001)
f.append([fitness1, fitness2])
return f

def fitness_func_no_batch_multi(ga_instance, solution, solution_idx):
output1 = numpy.sum(solution*function_inputs1)
output2 = numpy.sum(solution*function_inputs2)
fitness1 = 1.0 / (numpy.abs(output1 - desired_output1) + 0.000001)
fitness2 = 1.0 / (numpy.abs(output2 - desired_output2) + 0.000001)
return [fitness1, fitness2]

def fitness_func_batch_single(ga_instance, solution, solution_idx):
f = []
for sol in solution:
output = numpy.sum(solution*function_inputs1)
fitness = 1.0 / (numpy.abs(output - desired_output1) + 0.000001)
f.append(fitness)
return f

def fitness_func_no_batch_single(ga_instance, solution, solution_idx):
output = numpy.sum(solution*function_inputs1)
fitness = 1.0 / (numpy.abs(output - desired_output1) + 0.000001)
return fitness

if fitness_batch_size is None or (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size == 1):
if multi_objective == True:
fitness_func = fitness_func_no_batch_multi
else:
fitness_func = fitness_func_no_batch_single
elif (type(fitness_batch_size) in pygad.GA.supported_int_types and fitness_batch_size > 1):
if multi_objective == True:
fitness_func = fitness_func_batch_multi
else:
fitness_func = fitness_func_batch_single

ga_optimizer = pygad.GA(num_generations=num_generations,
sol_per_pop=sol_per_pop,
num_genes=6,
num_parents_mating=num_parents_mating,
fitness_func=fitness_func,
fitness_batch_size=fitness_batch_size,
mutation_type=mutation_type,
mutation_percent_genes=mutation_percent_genes,
keep_elitism=keep_elitism,
keep_parents=keep_parents,
stop_criteria=stop_criteria,
parent_selection_type=parent_selection_type,
suppress_warnings=True)

ga_optimizer.run()

return ga_optimizer.generations_completed, ga_optimizer.best_solutions_fitness, ga_optimizer.last_generation_fitness, stop_criteria

def test_number_calls_fitness_function_default_keep():
multi_objective_problem()

def test_number_calls_fitness_function_stop_criteria_reach(multi_objective=False,
fitness_batch_size=None,
num=10):
generations_completed, best_solutions_fitness, last_generation_fitness, stop_criteria = multi_objective_problem(multi_objective=multi_objective,
fitness_batch_size=fitness_batch_size,
stop_criteria=f"reach_{num}")
# Verify that the GA stops when meeting the criterion.
criterion = stop_criteria.split('_')
stop_word = criterion[0]
if generations_completed < num_generations:
if stop_word == 'reach':
if len(criterion) > 2:
# multi-objective problem.
for idx, num in enumerate(criterion[1:]):
criterion[idx + 1] = float(num)
else:
criterion[1] = float(criterion[1])

# Single-objective
if type(last_generation_fitness[0]) in pygad.GA.supported_int_float_types:
assert max(last_generation_fitness) >= criterion[1]
# Multi-objective
elif type(last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
# Validate the value passed to the criterion.
if len(criterion[1:]) == 1:
# There is a single value used across all the objectives.
pass
elif len(criterion[1:]) > 1:
# There are multiple values. The number of values must be equal to the number of objectives.
if len(criterion[1:]) == len(last_generation_fitness[0]):
pass
else:
raise ValueError("Error")

for obj_idx in range(len(last_generation_fitness[0])):
# Use the objective index to return the proper value for the criterion.
if len(criterion[1:]) == len(last_generation_fitness[0]):
reach_fitness_value = criterion[obj_idx + 1]
elif len(criterion[1:]) == 1:
reach_fitness_value = criterion[1]

assert max(last_generation_fitness[:, obj_idx]) >= reach_fitness_value

def test_number_calls_fitness_function_stop_criteria_saturate(multi_objective=False,
fitness_batch_size=None,
num=5):
generations_completed, best_solutions_fitness, last_generation_fitness, stop_criteria = multi_objective_problem(multi_objective=multi_objective,
fitness_batch_size=fitness_batch_size,
stop_criteria=f"saturate_{num}")
# Verify that the GA stops when meeting the criterion.
criterion = stop_criteria.split('_')
stop_word = criterion[0]
number = criterion[1]
if generations_completed < num_generations:
if stop_word == 'saturate':
number = int(number)
if type(last_generation_fitness[0]) in pygad.GA.supported_int_float_types:
assert best_solutions_fitness[generations_completed - number] == best_solutions_fitness[generations_completed - 1]
elif type(last_generation_fitness[0]) in [list, tuple, numpy.ndarray]:
for obj_idx in range(len(best_solutions_fitness[0])):
assert best_solutions_fitness[generations_completed - number][obj_idx] == best_solutions_fitness[generations_completed - 1][obj_idx]

if __name__ == "__main__":
#### Single-objective problem with a single numeric value with stop_criteria.
print()
test_number_calls_fitness_function_default_keep()
print()
test_number_calls_fitness_function_stop_criteria_reach()
print()
test_number_calls_fitness_function_stop_criteria_reach(num=2)
print()
test_number_calls_fitness_function_stop_criteria_saturate()
print()
test_number_calls_fitness_function_stop_criteria_saturate(num=2)
print()
test_number_calls_fitness_function_stop_criteria_reach(fitness_batch_size=4)
print()
test_number_calls_fitness_function_stop_criteria_reach(fitness_batch_size=4,
num=2)
print()
test_number_calls_fitness_function_stop_criteria_saturate(fitness_batch_size=4)
print()
test_number_calls_fitness_function_stop_criteria_saturate(fitness_batch_size=4,
num=2)
print()


#### Multi-objective problem with a single numeric value with stop_criteria.
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True)
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
num=2)
print()
test_number_calls_fitness_function_stop_criteria_saturate(multi_objective=True)
print()
test_number_calls_fitness_function_stop_criteria_saturate(multi_objective=True,
num=2)
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
fitness_batch_size=4)
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
fitness_batch_size=4,
num=2)
print()
test_number_calls_fitness_function_stop_criteria_saturate(multi_objective=True,
fitness_batch_size=4)
print()
test_number_calls_fitness_function_stop_criteria_saturate(multi_objective=True,
fitness_batch_size=4,
num=50)
print()


#### Multi-objective problem with multiple numeric values with stop_criteria.
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True)
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
num="2_5")
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
fitness_batch_size=4)
print()
test_number_calls_fitness_function_stop_criteria_reach(multi_objective=True,
fitness_batch_size=4,
num="10_20")