class TestCovarianceMatrixAdaption(unittest.TestCase):
def setUp(self):
self.n_envs = 4
self.evaluator = Evaluator(TEST_CONFIG, self.n_envs, store=True)
self.domain = UnitSimplex(7)
def test_cma_es(self):
mu, obj, prolif = cma_es(self.evaluator,
self.domain,
MAX_ITER,
verbose=True,
seed=23)
self.assertTrue(np.abs(obj - np.average(prolif)) < EPS)
self.assertTrue(self.domain.contains(mu))
# compare objective with sequential computation
treatment = prepare_dict(mu.flatten(),
max_dosage=TEST_CONFIG["max_dosage"],
scale=TEST_CONFIG["scale"])
print(treatment)
prolifs = []
for line in TEST_CONFIG["cell_lines"]:
simulator = Simulator()
simulator.initialize(line)
prolifs.append(simulator.apply_treatment(treatment))
o = TEST_CONFIG["objective"].eval(prolifs, treatment)
self.assertTrue(np.abs(obj - o) <= EPS)
def tearDown(self):
# performs internal check if all environments terminate
self.evaluator.terminate()
class TestCrossEntropy(unittest.TestCase):
def setUp(self):
self.n_envs = 4
self.evaluator = Evaluator(TEST_CONFIG, self.n_envs, store=True)
self.domain = UnitSimplex(7)
def test_cross_entropy(self):
mu, obj, prolif = cross_entropy_method(self.evaluator, self.domain, MAX_ITER, 20, 10, 7, verbose=True, seed=23)
self.assertTrue(np.abs(obj - np.average(prolif)) < EPS)
self.assertTrue(self.domain.contains(mu))
def tearDown(self):
# performs internal check if all environments terminate
self.evaluator.terminate()