def test_add_to_initial_population_successful(self):
class SpyFitnessFunc:
def __init__(self):
self.fitness_func_executions = 0
def fitness_func(self, bitstring):
self.fitness_func_executions += 1
return 69.0
unused_spy_fitness_function_holder = SpyFitnessFunc()
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome(
(5, ), unused_spy_fitness_function_holder.fitness_func))
annealing_schedule = PassThroughAnnealingSchedule(0, True)
optimizer_builder = BasicAnnealerOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, annealing_schedule,
unused_spy_fitness_function_holder.fitness_func)
spy_fitness_function_holder = SpyFitnessFunc()
population_genome = Genome(spy_fitness_function_holder.fitness_func,
(0, 0, 0, 0, 0))
optimizer = optimizer_builder \
.add_to_initial_population(population_genome) \
.build()
self.assertAlmostEqual(optimizer.best_genome.fitness, 69.0)
self.assertEqual(spy_fitness_function_holder.fitness_func_executions,
1)
self.assertIsInstance(optimizer, BasicAnnealingOptimizer)
def test_multiple_population_add(self):
class SpyFitnessFunc:
def __init__(self):
self.fitness_func_executions = 0
def fitness_func(self, bitstring):
self.fitness_func_executions += 1
return 69.0
unused_spy_fitness_function_holder = SpyFitnessFunc()
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome(
(5, ), unused_spy_fitness_function_holder.fitness_func))
convergence_criterion = PassThroughConvergenceCriterion(True)
optimizer_builder = BasicOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, convergence_criterion,
unused_spy_fitness_function_holder.fitness_func)
spy_fitness_function_holder = SpyFitnessFunc()
population_genome = Genome(spy_fitness_function_holder.fitness_func,
(0, 0, 0, 0, 0))
optimizer = optimizer_builder \
.add_to_initial_population(population_genome) \
.build()
self.assertAlmostEqual(optimizer.best_genome.fitness, 69.0)
self.assertEqual(spy_fitness_function_holder.fitness_func_executions,
1)
self.assertIsInstance(optimizer, BasicOptimizer)
with self.assertRaises(InitialPopulationUndefinedException):
optimizer_builder.clear().build()
def test_convergence(self):
def fitness_func_zero(bitstring):
return 0
initial_candidate = Genome(fitness_func_zero, (0,))
convergence_criterion = PassThroughConvergenceCriterion(True)
genome_builder = PassThroughGenomeFactory(Genome(fitness_func_zero, (1,)))
optimizer = BasicOptimizer(initial_candidate, genome_builder, convergence_criterion)
self.assertFalse(optimizer.converged)
optimizer.next()
self.assertTrue(optimizer.converged)
def test_build_raises_exception_with_no_population(self):
def fitness_func(bitstring):
self.fail()
return 0.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
annealing_schedule = PassThroughAnnealingSchedule(0, True)
optimizer_builder = BasicAnnealerOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, annealing_schedule,
fitness_func)
with self.assertRaises(InitialPopulationUndefinedException):
optimizer_builder.build()
def test_build_raises_exception_with_no_population(self):
def fitness_func(bitstring):
self.fail()
return 0.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
convergence_criterion = PassThroughConvergenceCriterion(True)
optimizer_builder = BasicOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, convergence_criterion,
fitness_func)
with self.assertRaises(InitialPopulationUndefinedException):
optimizer_builder.build()
def test_add_to_initial_population_returns_self(self):
def fitness_func(bitstring):
self.fail()
return 0.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
annealing_schedule = PassThroughAnnealingSchedule(0, True)
optimizer_builder = BasicAnnealerOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, annealing_schedule,
fitness_func)
returned_object = optimizer_builder.add_to_initial_population(
Genome.new_default_genome((5, ), fitness_func))
self.assertEqual(optimizer_builder, returned_object)
def test_add_to_initial_population_returns_self(self):
def fitness_func(bitstring):
self.fail()
return 0.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
convergence_criterion = PassThroughConvergenceCriterion(True)
optimizer_builder = BasicOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, convergence_criterion,
fitness_func)
returned_object = optimizer_builder.add_to_initial_population(
Genome.new_default_genome((5, ), fitness_func))
self.assertEqual(optimizer_builder, returned_object)
def test_next_generation_improvement(self):
def fitness_func_zero(bitstring):
return 0
def fitness_func_one(bitstring):
return 1
initial_candidate = Genome(fitness_func_zero, (0,))
convergence_criterion = PassThroughConvergenceCriterion(True)
genome_builder = PassThroughGenomeFactory(Genome(fitness_func_one, (1,)))
optimizer = BasicOptimizer(initial_candidate, genome_builder, convergence_criterion)
optimizer.next()
self.assertEqual(1, optimizer.best_genome.fitness)
def test_add_to_initial_population_from_factory_successful(self):
def fitness_func(bitstring):
return 69.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
annealing_schedule = PassThroughAnnealingSchedule(0, True)
optimizer_builder = BasicAnnealerOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, annealing_schedule,
fitness_func)
optimizer = optimizer_builder \
.add_to_initial_population_from_factory(genome_factory, 1) \
.build()
self.assertAlmostEqual(optimizer.best_genome.fitness, 69.0)
self.assertIsInstance(optimizer, BasicAnnealingOptimizer)
def test_convergence(self):
def fitness_func_zero(bitstring):
return 0
initial_population = [Genome(fitness_func_zero, (0, ))]
for genome in initial_population:
genome.run()
convergence_criterion = PassThroughConvergenceCriterion(True)
genome_builder = PassThroughGenomeFactory(
Genome(fitness_func_zero, (1, )))
optimizer = BasicGeneticOptimizer(initial_population, genome_builder,
convergence_criterion)
self.assertFalse(optimizer.converged)
optimizer.next()
self.assertTrue(optimizer.converged)
def test_add_to_initial_population_from_factory_successful(self):
def fitness_func(bitstring):
return 69.0
genome_factory = PassThroughGenomeFactory(
Genome.new_default_genome((5, ), fitness_func))
convergence_criterion = PassThroughConvergenceCriterion(True)
optimizer_builder = BasicOptimizerBuilder(
(5, ), NumpyRNG(), genome_factory, convergence_criterion,
fitness_func)
optimizer = optimizer_builder \
.add_to_initial_population_from_factory(genome_factory, 1) \
.build()
self.assertAlmostEqual(optimizer.best_genome.fitness, 69.0)
self.assertIsInstance(optimizer, BasicOptimizer)
def test_convergence(self):
def fitness_func_zero(bitstring):
return 0
initial_candidate = Genome(fitness_func_zero, (0, ))
initial_candidate.run()
annealing_schedule = PassThroughAnnealingSchedule(0.9, True)
genome_builder = PassThroughGenomeFactory(
Genome(fitness_func_zero, (1, )))
rng = NumpyCustomRandom(0.0, 1)
optimizer = BasicAnnealingOptimizer(initial_candidate, genome_builder,
annealing_schedule, rng)
self.assertFalse(optimizer.converged)
self.assertAlmostEqual(0.9, optimizer.temperature)
optimizer.next()
self.assertTrue(optimizer.converged)
self.assertAlmostEqual(0.9, optimizer.temperature)
def test_worse_solution_acceptance(self):
def fitness_func_zero(bitstring):
return 0
def fitness_func_one(bitstring):
return 1
initial_candidate = Genome(fitness_func_one, (0, ))
initial_candidate.run()
annealing_schedule = PassThroughAnnealingSchedule(0.9, True)
genome_builder = PassThroughGenomeFactory(
Genome(fitness_func_zero, (1, )))
rng = NumpyCustomRandom(0.0, 1)
optimizer = BasicAnnealingOptimizer(initial_candidate, genome_builder,
annealing_schedule, rng)
optimizer.next()
self.assertEqual(0, optimizer.best_genome.fitness)
self.assertAlmostEqual(0.9, optimizer.temperature)