def setUp(self):
self.atom_gens = [
lambda: np.random.random(),
]
self.R = Spawner(self.atom_gens)
self.gn = self.R.random_plush_genome(1)
self.individual = Individual(self.gn)
class TestSpawnMethods(unittest.TestCase):
def setUp(self):
self.atom_gens = [
Instruction("noop", lambda s: s, [], 0),
lambda: np.random.random()
]
self.R = Spawner(self.atom_gens)
def test_generate_close_count(self):
c = self.R.generate_close_count()
self.assertIsInstance(c, int)
self.assertTrue(c <= 3)
self.assertTrue(c >= 0)
def test_atom_to_plush_gene(self):
a = self.atom_gens[0]
g = self.R.atom_to_plush_gene(a)
self.assertIsInstance(g, pl.Gene)
def test_random_plush_gene(self):
self.assertIsInstance(self.R.random_plush_gene(), pl.Gene)
def test_random_plush_genome_with_size(self):
gn = self.R.random_plush_genome_with_size(5)
self.assertEqual(len(gn), 5)
self.assertIsInstance(gn[0], pl.Gene)
def test_random_plush_genome(self):
gn = self.R.random_plush_genome(5)
self.assertTrue(len(gn) <= 5)
self.assertTrue(len(gn) > 0)
self.assertIsInstance(gn[0], pl.Gene)
def setUp(self):
self.atom_gens = [lambda: np.random.random()]
self.R = Spawner(self.atom_gens)
self.pop = p.Population()
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
class TestSimplificationFunctions(unittest.TestCase):
def setUp(self):
self.atom_gens = [
lambda: np.random.random(),
]
self.R = Spawner(self.atom_gens)
self.gn = self.R.random_plush_genome(1)
self.individual = Individual(self.gn)
def test_silent_n_random_genes(self):
silent_n_random_genes(self.gn, 1)
self.assertTrue(self.gn[0].is_silent)
def test_noop_n_random_genes(self):
noop_n_random_genes(self.gn, 1)
self.assertTrue(self.gn[0].atom.name == '_exec_noop')
def test_simplify_once(self):
self.gn = simplify_once(self.gn)
a = self.gn[0].is_silent
b = (hasattr(self.gn[0].atom, 'name')
and self.gn[0].atom.name == '_exec_noop')
self.assertTrue(a or b)
def test_simplify_by_dataset(self):
simplify_by_dataset(self.individual, np.array([[0, 0], [0, 0]]),
np.array([0, 0]), 'regression')
def test_simplify_by_function(self):
simplify_by_function(self.individual, lambda s: [0, 0, 0])
class TestEvaluateFunctions(unittest.TestCase):
def setUp(self):
self.atom_gens = [
lambda: np.random.random(),
]
self.R = Spawner(self.atom_gens)
self.gn = self.R.random_plush_genome(1)
self.individual = Individual(self.gn)
def test_evaluate_with_function(self):
def ef(program):
return [1, 2, 3]
i = evaluate_with_function(self.individual, ef)
self.assertTrue(hasattr(i, 'error_vector'))
def test_evaluate_for_regression(self):
X = np.array([[1, 2], [3, 4]])
y = np.array([3, 7])
i = evaluate_for_regression(self.individual, X, y)
self.assertTrue(hasattr(i, 'total_error'))
def evaluate_for_classification(self):
X = np.array([[1, 2], [3, 4]])
y = np.array([3, 7])
i = evaluate_for_classification(self.individual, X, y)
self.assertTrue(hasattr(i, 'total_error'))
class TestAlternationMethods(unittest.TestCase):
def setUp(self):
self.R = Spawner(atom_gens)
self.i1 = p.Individual(self.R.random_plush_genome(5))
self.i2 = p.Individual(self.R.random_plush_genome(5))
def test_produce(self):
al = v.Alternation(rate=0.5)
child = al.produce((self.i1, self.i2))
self.assertFalse(hasattr(child, 'total_error'))
class TestPerturbIntegerMutationMethods(unittest.TestCase):
def setUp(self):
self.R = Spawner(atom_gens)
self.i1 = p.Individual(self.R.random_plush_genome(15))
def test_produce(self):
pim = v.PerturbIntegerMutation(rate=0.9)
child = pim.produce([self.i1], self.R)
self.assertEqual(len(child.genome), len(self.i1.genome))
self.assertFalse(hasattr(child, 'total_error'))
class TestVariationOperatorPipelineMethods(unittest.TestCase):
def setUp(self):
self.R = Spawner(atom_gens)
self.i1 = p.Individual(self.R.random_plush_genome(5))
self.i2 = p.Individual(self.R.random_plush_genome(5))
def test_produce(self):
al = v.VariationOperatorPipeline(
(v.Alternation(rate=0.5), v.TweakStringMutation(rate=0.7))
)
child = al.produce((self.i1, self.i2), self.R)
self.assertFalse(hasattr(child, 'total_error'))
class TestIndividualMethods(unittest.TestCase):
def setUp(self):
self.atom_gens = [
lambda: np.random.random(),
]
self.R = Spawner(self.atom_gens)
self.i = p.Individual(self.R.random_plush_genome_with_size(4))
def test_run_program(self):
inputs = [1, 2, 3]
result = self.i.run_program(inputs, ['_integer'])
self.assertEqual(len(result), 1)
self.assertIsNone(result[0])
def setUp(self):
self.atom_gens = [
Instruction("noop", lambda s: s, [], 0),
lambda: np.random.random()
]
self.R = Spawner(self.atom_gens)
def setUp(self):
self.R = Spawner(atom_gens)
self.i1 = p.Individual(self.R.random_plush_genome(5))
self.i2 = p.Individual(self.R.random_plush_genome(5))
def setUp(self):
self.atom_gens = [
lambda: np.random.random(),
]
self.R = Spawner(self.atom_gens)
self.i = p.Individual(self.R.random_plush_genome_with_size(4))
class TestPopulationMethods(unittest.TestCase):
def setUp(self):
self.atom_gens = [lambda: np.random.random()]
self.R = Spawner(self.atom_gens)
self.pop = p.Population()
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
self.pop.append(p.Individual(self.R.random_plush_genome(5)))
def test_pop_evaluate_by_function(self):
def ef(program):
return [1, 2, 3]
self.pop.evaluate_by_function(ef)
self.assertEqual(self.pop[0].error_vector, [1, 2, 3])
self.assertEqual(self.pop[2].error_vector, [1, 2, 3])
def test_pop_evaluate_by_dataset(self):
X = np.array([[1, 2], [3, 4]])
y = np.array([3, 7])
self.pop.evaluate_by_dataset(X, y, 'regression')
self.assertTrue(hasattr(self.pop[0], 'error_vector'))
self.assertTrue(hasattr(self.pop[1], 'error_vector'))
self.assertTrue(hasattr(self.pop[2], 'error_vector'))
self.assertIsNotNone(self.pop[0].error_vector)
self.assertIsNotNone(self.pop[1].error_vector)
self.assertIsNotNone(self.pop[2].error_vector)
def test_lexicase(self):
def ef(program):
return [1, 2, 3]
self.pop.evaluate_by_function(ef)
parent = self.pop.select(method='lexicase')
self.assertEqual(parent.error_vector, [1, 2, 3])
def test_eplexicase(self):
def ef(program):
return [1, 2, 3]
self.pop.evaluate_by_function(ef)
parent = self.pop.select(method='epsilon_lexicase')
self.assertEqual(parent.error_vector, [1, 2, 3])
def test_tourn(self):
def ef(program):
return [1, 2, 3]
self.pop.evaluate_by_function(ef)
parent = self.pop.select(method='tournament')
self.assertEqual(parent.error_vector, [1, 2, 3])
def test_lowest_error(self):
def ef(program):
return [np.random.random(), np.random.random()]
self.pop.evaluate_by_function(ef)
err = self.pop.lowest_error()
self.assertTrue(err <= self.pop[0].total_error)
self.assertTrue(err <= self.pop[1].total_error)
self.assertTrue(err <= self.pop[2].total_error)
def test_average_error(self):
def ef(program):
return [np.random.random(), np.random.random()]
self.pop.evaluate_by_function(ef)
err = self.pop.average_error()
self.assertTrue(err < 2)
self.assertTrue(err > 0)
def test_unique(self):
def ef(program):
return [np.random.random(), np.random.random()]
self.pop.evaluate_by_function(ef)
unique = self.pop.unique()
self.assertEqual(unique, 3)
def test_best_program(self):
def ef(program):
return [np.random.random(), np.random.random()]
self.pop.evaluate_by_function(ef)
best = self.pop.best_program()
self.assertIsInstance(best, list)
def best_program_error_vector(self):
def ef(program):
return [np.random.random(), np.random.random()]
self.pop.evaluate_by_function(ef)
best_err = self.pop.best_program_error_vector()
self.assertIsInstance(best_err, list)
self.assertEqual(len(best_err), 2)
def setUp(self):
self.balenced = ["_open", 1, "_close", "_open", 2, "_close"]
self.unbal = ["_open", "_open", 1, "_close", "_close", 3, "_close"]
R = Spawner([lambda: 0])
self.gn = R.random_plush_genome_with_size(5)
