def test_next_evolution_step(self):
population = Population()
for i in range(5):
population.append(Candidate())
result = self.engine.next_evolution_step(population, 3)
self.assertEqual(len(result), len(population))
self.assertEqual(self.engine.selection_strategy.validate.call_count, 1)
self.assertEqual(self.engine.selection_strategy.select.call_count, 1)
self.assertEqual(self.engine.evolutionary_operator.apply.call_count, 1)
def test_get_fitness(self):
candidate = Candidate()
population = Population()
population.append(candidate)
callback = MagicMock(return_value=2)
fitness_evaluator = CallbackFitnessEvaluator(callback)
result = fitness_evaluator.get_fitness(candidate, population)
self.assertEqual(callback.call_count, 1)
self.assertEqual(result, 2)
def _test_get_fitness(self, target, inputs, score):
candidate = Candidate()
candidate.data = inputs
population = Population()
population.append(candidate)
fitness_evaluator = StringFitnessEvaluator(target)
result = fitness_evaluator.get_fitness(candidate, population)
self.assertIsInstance(result, Fitness)
self.assertEqual(result, score)
def test_apply_empty(self):
candidate = Candidate()
population = Population()
population.append(candidate)
population.shuffle = MagicMock()
sample_operator = SampleOperator()
pipeline_operator = PipelineOperator()
pipeline_operator.append_operator(sample_operator)
result = pipeline_operator.apply(population)
self.assertEqual(len(result), len(population))
def test_should_terminate(self):
candidate = Candidate()
candidate.fitness = Fitness(50)
population = Population()
population.append(candidate)
termination_condition = TargetFitness(100)
self.assertEqual(termination_condition.should_terminate(population),
False)
candidate.fitness = Fitness(150)
self.assertEqual(termination_condition.should_terminate(population),
True)
def test_should_terminate_not_natural(self):
candidate = Candidate()
candidate.fitness = Fitness(-50, is_natural=False)
population = Population()
population.append(candidate)
termination_condition = TargetFitness(-100)
self.assertEqual(termination_condition.should_terminate(population),
False)
candidate.fitness = Fitness(-150, is_natural=False)
self.assertEqual(termination_condition.should_terminate(population),
True)
def test_apply_zero(self):
inputs, outputs = ('aaaa', 'aaaa')
candidate = Candidate()
candidate.data = inputs
population = Population()
population.append(candidate)
probability = Probability(0)
random = Random()
mutation_operator = BitStringMutation(probability, random)
result = mutation_operator.apply(population)
self.assertEqual(len(result), len(population))
self.assertEqual(result[0].data, outputs)
def test_apply_one(self):
inputs, outputs = ('0110', '1010')
candidate = Candidate()
candidate.data = inputs
population = Population()
population.append(candidate)
probability = Probability(1)
random = Random()
random.float = MagicMock(side_effect=[0.6, 0.1, 0.9, 0.4])
mutation_operator = BitStringMutation(probability, random)
result = mutation_operator.apply(population)
self.assertEqual(len(result), len(population))
self.assertEqual(result[0].data, outputs)
def test_apply_zero(self):
alphabet = 'abcd'
inputs, outputs = ('aaaa', 'aaaa')
candidate = Candidate()
candidate.data = inputs
population = Population()
population.append(candidate)
probability = Probability(0)
random = Random()
crossover_operator = StringMutation(probability, random, alphabet)
result = crossover_operator.apply(population)
self.assertEqual(len(result), len(population))
self.assertEqual(result[0].data, outputs)
def test_apply_one(self):
alphabet = 'abcd'
inputs, outputs = ('aaaa', 'abcd')
candidate = Candidate()
candidate.data = inputs
population = Population()
population.append(candidate)
probability = Probability(1)
random = Random()
random.choice = MagicMock(side_effect=['a', 'b', 'c', 'd'])
crossover_operator = StringMutation(probability, random, alphabet)
result = crossover_operator.apply(population)
self.assertEqual(len(result), len(population))
self.assertEqual(result[0].data, outputs)
def test_logical_or(self):
population = Population()
population.append(Candidate())
tc1 = TerminationCondition()
tc1.should_terminate = MagicMock(return_value=True)
tc2 = TerminationCondition()
tc2.should_terminate = MagicMock(return_value=False)
termination_condition = Multicondition(logic=Multicondition.LOGIC_OR)
termination_condition.add(tc1)
termination_condition.add(tc2)
self.assertTrue(termination_condition.should_terminate(population))
tc1.should_terminate = MagicMock(return_value=False)
self.assertFalse(termination_condition.should_terminate(population))
def test_should_terminate(self):
population = Population()
termination_condition = TimeLimit(0.2)
self.assertEqual(termination_condition.should_terminate(population),
False)
sleep(0.4)
self.assertEqual(termination_condition.should_terminate(population),
True)
def test_should_terminate(self):
population = Population()
engine = EvolutionEngine()
engine.generation = 99
termination_condition = TargetGeneration(100, engine)
self.assertEqual(termination_condition.should_terminate(population), False)
engine.generation = 100
self.assertEqual(termination_condition.should_terminate(population), True)
engine.generation = 101
self.assertEqual(termination_condition.should_terminate(population), True)
def test_should_terminate(self):
candidate = Candidate()
candidate.fitness = Fitness(1)
population = Population()
population.append(candidate)
termination_condition = Stagnation(3)
self.assertEqual(termination_condition.should_terminate(population),
False)
self.assertEqual(termination_condition.should_terminate(population),
False)
self.assertEqual(termination_condition.should_terminate(population),
True)
candidate.fitness = Fitness(2)
self.assertEqual(termination_condition.should_terminate(population),
False)
self.assertEqual(termination_condition.should_terminate(population),
False)
self.assertEqual(termination_condition.should_terminate(population),
True)
def test_PopulationSelection(self):
"""Testing Population selection"""
pop = Population(32, DummyIndividual)
# print sorted(pop.individuals)
pop.rank()
hist = {}
N = len(pop)
for i in range(N):
indie = pop.select()
try:
hist[indie.fitness] += 1
except:
hist[indie.fitness] = 1
p_obs = []
p_exp = []
for k,v in hist.iteritems():
p_exp.append(k/pop.sumaptitude)
p_obs.append(v/float(N))
chisq, p_value = chisquare(np.array(p_obs), np.array(p_exp))
self.assertTrue( p_value > 0.99 )
def test_apply(self):
candidate = Candidate()
candidate.data = [1, 2, 3, 4]
population = Population([candidate])
probability = Probability(1)
random = Random()
random.int = MagicMock(side_effect=[1, 3])
crossover_operator = ListOrderMutation(probability, random, 1)
result = crossover_operator.apply(population)
self.assertEqual(len(result), len(population))
self.assertEqual(result[0].data, [1, 4, 3, 2])
def test_select_fitness_zero(self):
population = Population()
population.append(self.create_candidate(fitness=0))
population.append(self.create_candidate(fitness=0))
population.append(self.create_candidate(fitness=0))
population.append(self.create_candidate(fitness=0))
results = self.obj.select(population, 4)
self.assertEqual(len(results), 4)
def test_select_result_size(self):
population = Population()
population.append(self.create_candidate(fitness=1))
population.append(self.create_candidate(fitness=2))
population.append(self.create_candidate(fitness=3))
population.append(self.create_candidate(fitness=4))
for selection_size in range(len(population)):
results = self.obj.select(population, selection_size+1)
self.assertEqual(selection_size+1, len(results))
def test_select_proper_items(self):
population = Population()
population.append(self.create_candidate(fitness=1))
population.append(self.create_candidate(fitness=2))
population.append(self.create_candidate(fitness=3))
population.append(self.create_candidate(fitness=4))
selection_size = 2
results = self.obj.select(population, selection_size)
self.assertEqual(results[0].fitness, 4)
self.assertEqual(results[1].fitness, 3)
def test_select_proper_items_natural_false(self):
population = Population()
population.append(self.create_candidate(fitness=-4))
population.append(self.create_candidate(fitness=-3))
population.append(self.create_candidate(fitness=-2))
population.append(self.create_candidate(fitness=-1))
selection_size = 2
results = self.obj.select(population, selection_size)
self.assertEqual(results[0].fitness, -1)
self.assertEqual(results[1].fitness, -2)
def test_trigger(self):
candidate = Candidate()
candidate.fitness = Fitness(1)
population = Population([candidate])
observer = ConsoleObserver()
observer.trigger(Event(Event.INITIALIZE, {'population': population}))
observer.trigger(
Event(Event.EVALUATED_POPULATION, {
'generation': 1,
'population': population
}))
observer.trigger(
Event(Event.TERMINATE, {
'generation': 1,
'population': population
}))
def test_select_proper_items_natural_false(self):
population = Population()
population.append(self.create_candidate(fitness=-4, is_natural=False))
population.append(self.create_candidate(fitness=-3, is_natural=False))
population.append(self.create_candidate(fitness=-2, is_natural=False))
population.append(self.create_candidate(fitness=-1, is_natural=False))
selection_size = 2
self.random.float = MagicMock(side_effect=[0.5, 0.9])
results = self.obj.select(population, selection_size)
self.assertEqual(results[0].fitness, -2)
self.assertEqual(results[1].fitness, -1)
def test_select_proper_items(self):
population = Population()
population.append(self.create_candidate(fitness=1))
population.append(self.create_candidate(fitness=2))
population.append(self.create_candidate(fitness=3))
population.append(self.create_candidate(fitness=4))
selection_size = 3
self.random.float = MagicMock(return_value=0.99)
results = self.obj.select(population, selection_size)
for candidate in results:
self.assertEqual(candidate.fitness, 4)
def test_select_proper_items(self):
population = Population()
population.append(self.create_candidate(fitness=1))
population.append(self.create_candidate(fitness=2))
population.append(self.create_candidate(fitness=3))
population.append(self.create_candidate(fitness=4))
selection_size = 2
self.random.float = MagicMock(side_effect=[0.5, 0.9])
results = self.obj.select(population, selection_size)
self.assertEqual(results[0].fitness, 3)
self.assertEqual(results[1].fitness, 4)
def test_select_proper_items_natural_false(self):
population = Population()
population.append(self.create_candidate(fitness=-4, is_natural=False))
population.append(self.create_candidate(fitness=-3, is_natural=False))
population.append(self.create_candidate(fitness=-2, is_natural=False))
population.append(self.create_candidate(fitness=-1, is_natural=False))
selection_size = 3
self.random.float = MagicMock(return_value=0.99)
results = self.obj.select(population, selection_size)
for candidate in results:
self.assertEqual(candidate.fitness, -1)
def test_select_proper_items_natural_false(self):
population = Population()
population.append(self.create_candidate(fitness=-4))
population.append(self.create_candidate(fitness=-3))
population.append(self.create_candidate(fitness=-2))
population.append(self.create_candidate(fitness=-1))
selection_size = 2
self.random.sample = MagicMock(
side_effect=[[population[3], population[2]],
[population[2], population[1]]])
results = self.obj.select(population, selection_size)
self.assertEqual(results[0].fitness, -1)
self.assertEqual(results[1].fitness, -2)
def test_next_evolution_step_invalid(self):
with self.assertRaises(RuntimeError):
self.engine.next_evolution_step(Population(), 1)
def test_select_result_type(self):
population = Population()
population.append(self.create_candidate(fitness=1))
results = self.obj.select(population, 1)
self.assertIsInstance(results, Population)
def test_validation_population_size(self):
with self.assertRaises(ValidationException):
self.obj.validate(Population(), 5)
def create_population(self, size):
population = Population()
for _ in range(size):
population.append(Candidate())
return population
