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_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_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_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_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_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_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_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_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(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 setUp(self):
random = Random()
factory = CandidateFactory(random)
factory.create_candidate = MagicMock(side_effect=lambda: Candidate())
operator = Operator()
fitness_evaluator = FitnessEvaluator()
fitness_evaluator.get_fitness = MagicMock(
side_effect=lambda c, p: Fitness(5))
selection_strategy = SelectionStrategy()
self.engine = EvolutionEngine()
self.engine.create(factory, operator, fitness_evaluator,
selection_strategy)
def test_validate(self):
crossover_operator = StringCrossover(Probability(1), Random(), 1)
candidate1 = Candidate()
candidate1.data = 'AAAA'
candidate2 = Candidate()
candidate2.data = 'BBBB'
crossover_operator.validate_parents(candidate1, candidate2)
candidate2.data = 'BBBBB'
with self.assertRaises(ValidationException):
crossover_operator.validate_parents(candidate1, candidate2)
def test_validate(self):
crossover_operator = ListCrossover(Probability(1), Random(), 1)
candidate1 = Candidate()
candidate1.data = [1, 2, 3, 4]
candidate2 = Candidate()
candidate2.data = [5, 6, 7, 8]
crossover_operator.validate_parents(candidate1, candidate2)
candidate2.data = [1, 2, 3, 4, 5]
with self.assertRaises(ValidationException):
crossover_operator.validate_parents(candidate1, candidate2)
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_apply(self, strings, random, points):
candidate1 = Candidate()
candidate1.data = strings[0][0]
candidate2 = Candidate()
candidate2.data = strings[0][1]
crossover_points = points
crossover_operator = ListCrossover(Probability(1), random, crossover_points)
result = crossover_operator.mate(candidate1, candidate2)
self.assertEqual(len(result), 2)
self.assertEqual(result[0].data, strings[1][0])
self.assertEqual(result[1].data, strings[1][1])
def test_apply(self):
candidate1 = Candidate()
candidate1.data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
candidate2 = Candidate()
candidate2.data = [9, 3, 7, 8, 2, 6, 5, 1, 4]
random = Random()
random.int = MagicMock(side_effect=[3, 7])
crossover_operator = ListOrderCrossover(Probability(1), random)
result = crossover_operator.mate(candidate1, candidate2)
self.assertEqual(len(result), 2)
self.assertEqual(result[0].data, [1, 7, 3, 8, 2, 6, 5, 4, 9])
self.assertEqual(result[1].data, [9, 3, 2, 4, 5, 6, 7, 1, 8])
def create_candidate(self, fitness=None, is_natural=True):
candidate = Candidate()
candidate.fitness = Fitness(fitness, is_natural=is_natural)
return candidate
def create_population(self, size):
population = Population()
for _ in range(size):
population.append(Candidate())
return population
def test_init(self):
candidate = Candidate()
self.assertTrue(str(candidate))