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public release 2.2.0 - see README.md and CHANGES.md for details

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muntwiler_m
2020-09-04 16:22:42 +02:00
parent fbd2d4fa8c
commit 7c61eb1b41
67 changed files with 2934 additions and 682 deletions
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60
tests/test_genetic.py
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@ -39,11 +39,11 @@ class TestPopulation(unittest.TestCase):
def setUp(self):
random.seed(0)
self._test_dir = ""
self.domain = mp.Domain()
self.model_space = mp.ModelSpace()
self.domain.add_param('A', 1.5, 1.0, 2.0, 0.1)
self.domain.add_param('B', 2.5, 2.0, 3.0, 0.1)
self.domain.add_param('C', 3.5, 3.0, 4.0, 0.1)
self.model_space.add_param('A', 1.5, 1.0, 2.0, 0.1)
self.model_space.add_param('B', 2.5, 2.0, 3.0, 0.1)
self.model_space.add_param('C', 3.5, 3.0, 4.0, 0.1)
self.expected_names = ('_gen', '_model', '_particle', '_rfac', 'A', 'B', 'C')
self.size = POP_SIZE
@ -114,7 +114,7 @@ class TestPopulation(unittest.TestCase):
return r
def test_setup(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
np.testing.assert_array_equal(np.arange(POP_SIZE), self.pop.pos['_particle'])
@ -131,7 +131,7 @@ class TestPopulation(unittest.TestCase):
def test_setup_with_results(self):
data_dir = os.path.dirname(os.path.abspath(__file__))
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=False)
self.pop.setup(self.size, self.model_space, seed_file=data_file, recalc_seed=False)
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
@ -158,7 +158,7 @@ class TestPopulation(unittest.TestCase):
def test_setup_with_results_recalc(self):
data_dir = os.path.dirname(os.path.abspath(__file__))
data_file = os.path.join(data_dir, "test_swarm.setup_with_results.1.dat")
self.pop.setup(self.size, self.domain, seed_file=data_file, recalc_seed=True)
self.pop.setup(self.size, self.model_space, seed_file=data_file, recalc_seed=True)
self.assertEqual(self.pop.pos.dtype.names, self.expected_names)
self.assertEqual(self.pop.pos.shape, (POP_SIZE,))
@ -183,26 +183,26 @@ class TestPopulation(unittest.TestCase):
self.assertAlmostEqual(3.5, self.pop.pos['C'][0], 3)
def test_pos_gen(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
for index, item in enumerate(self.pop.pos_gen()):
self.assertIsInstance(item, dict)
self.assertEqual(set(item.keys()), set(self.expected_names))
self.assertEqual(item['_particle'], index)
def test_randomize(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
self.pop.randomize()
self.assertTrue(np.all(self.pop.pos['A'] >= self.domain.min['A']))
self.assertTrue(np.all(self.pop.pos['A'] <= self.domain.max['A']))
self.assertGreater(np.std(self.pop.pos['A']), self.domain.step['A'])
self.assertTrue(np.all(self.pop.pos['A'] >= self.model_space.min['A']))
self.assertTrue(np.all(self.pop.pos['A'] <= self.model_space.max['A']))
self.assertGreater(np.std(self.pop.pos['A']), self.model_space.step['A'])
def test_seed(self):
self.pop.setup(self.size, self.domain)
self.pop.seed(self.domain.start)
self.assertAlmostEqual(self.pop.pos['A'][0], self.domain.start['A'], delta=0.001)
self.pop.setup(self.size, self.model_space)
self.pop.seed(self.model_space.start)
self.assertAlmostEqual(self.pop.pos['A'][0], self.model_space.start['A'], delta=0.001)
def test_add_result(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
i_sample = 1
i_result = 0
result = self.pop.pos[i_sample]
@ -212,7 +212,7 @@ class TestPopulation(unittest.TestCase):
self.assertEqual(self.pop.best[i_sample], result)
def test_is_converged(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
self.assertFalse(self.pop.is_converged())
i_sample = 0
result = self.pop.pos[i_sample]
@ -226,12 +226,12 @@ class TestPopulation(unittest.TestCase):
self.assertTrue(self.pop.is_converged())
def test_save_population(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
filename = os.path.join(self.test_dir, "test_save_population.pop")
self.pop.save_population(filename)
def test_save_results(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
i_sample = 1
result = self.pop.pos[i_sample]
self.pop.add_result(result, 1.0)
@ -239,17 +239,17 @@ class TestPopulation(unittest.TestCase):
self.pop.save_results(filename)
def test_save_array(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
filename = os.path.join(self.test_dir, "test_save_array.pos")
self.pop.save_array(filename, self.pop.pos)
def test_load_array(self):
n = 3
filename = os.path.join(self.test_dir, "test_load_array")
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
# expected array
dt_exp = self.pop.get_pop_dtype(self.domain.start)
dt_exp = self.pop.get_pop_dtype(self.model_space.start)
a_exp = np.zeros((n,), dtype=dt_exp)
a_exp['A'] = np.linspace(0, 1, n)
a_exp['B'] = np.linspace(1, 2, n)
@ -276,13 +276,13 @@ class TestPopulation(unittest.TestCase):
np.testing.assert_almost_equal(result[name], a_exp[name], err_msg=name)
def test_mate_parents(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
pos1 = self.pop.pos.copy()
parents = self.pop.mate_parents(pos1)
self.assertEqual(len(parents), pos1.shape[0] / 2)
def test_crossover(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
p1 = self.pop.pos[2].copy()
p2 = self.pop.pos[3].copy()
c1, c2 = self.pop.crossover(p1, p2)
@ -290,11 +290,11 @@ class TestPopulation(unittest.TestCase):
self.assertIsInstance(c2, np.void)
self.assertEqual(c1['_particle'], p1['_particle'])
self.assertEqual(c2['_particle'], p2['_particle'])
for name in self.domain.start:
for name in self.model_space.start:
self.assertAlmostEqual(c1[name] + c2[name], p1[name] + p2[name], msg=name)
def test_mutate_weak(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
p1 = self.pop.pos[3].copy()
c1 = p1.copy()
self.pop.mutate_weak(c1, 1.0)
@ -304,7 +304,7 @@ class TestPopulation(unittest.TestCase):
self.assertNotAlmostEqual(c1['C'], p1['C'])
def test_mutate_strong(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
p1 = self.pop.pos[3].copy()
c1 = p1.copy()
self.pop.mutate_strong(c1, 1.0)
@ -314,7 +314,7 @@ class TestPopulation(unittest.TestCase):
self.assertNotAlmostEqual(c1['C'], p1['C'])
def test_advance_population(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
p1 = {'A': np.linspace(1.0, 2.0, POP_SIZE),
'B': np.linspace(2.0, 3.0, POP_SIZE),
@ -335,7 +335,7 @@ class TestPopulation(unittest.TestCase):
self.assertTrue(np.any(abs(self.pop.pos[name] - value) >= 0.001), msg=name)
def test_convergence_1(self):
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
self.pop.pos['A'] = np.linspace(1.0, 2.0, POP_SIZE)
self.pop.pos['B'] = np.linspace(2.0, 3.0, POP_SIZE)
@ -352,7 +352,7 @@ class TestPopulation(unittest.TestCase):
def optimize_rfactor_2(self, pop_size, iterations):
self.size = pop_size
self.pop.setup(self.size, self.domain)
self.pop.setup(self.size, self.model_space)
for i in range(iterations):
self.pop.advance_population()