def test_log_space_conversion(self):
lower, upper = 1e-5, 1e5
hyper = UniformFloatHyperparameter('test', lower=lower, upper=upper, log=True)
self.assertTrue(hyper.is_legal(hyper._transform(1.)))
lower, upper = 1e-10, 1e10
hyper = UniformFloatHyperparameter('test', lower=lower, upper=upper, log=True)
self.assertTrue(hyper.is_legal(hyper._transform(1.)))
def test_log_space_conversion(self):
lower, upper = 1e-5, 1e5
hyper = UniformFloatHyperparameter('test', lower=lower, upper=upper, log=True)
self.assertTrue(hyper.is_legal(hyper._transform(1.)))
lower, upper = 1e-10, 1e10
hyper = UniformFloatHyperparameter('test', lower=lower, upper=upper, log=True)
self.assertTrue(hyper.is_legal(hyper._transform(1.)))
def test_quantization_UniformFloatHyperparameter_decimal_numbers_negative(self):
hp = UniformFloatHyperparameter("ufhp", -1.2, 1.2, q=0.2)
rs = np.random.RandomState()
sample_one = hp._sample(rs=rs, size=1)
self.assertIsInstance(obj=sample_one, cls=np.ndarray)
self.assertEqual(1, sample_one.size)
try:
self.assertAlmostEqual(float(hp._transform(sample_one) + 1.2) % 0.2, 0.0)
except:
self.assertAlmostEqual(float(hp._transform(sample_one) + 1.2) % 0.2, 0.2)
self.assertGreaterEqual(hp._transform(sample_one), -1.2)
self.assertLessEqual(hp._transform(sample_one), 1.2)
def test_quantization_UniformFloatHyperparameter(self):
hp = UniformFloatHyperparameter("ufhp", 1, 100, q=3)
rs = np.random.RandomState(1)
sample_one = hp._sample(rs=rs, size=1)
self.assertIsInstance(obj=sample_one, cls=np.ndarray)
self.assertEqual(1, sample_one.size)
self.assertTrue(hp._transform(sample_one) % 3 == 0)
sample_hundred = hp._sample(rs=rs, size=100)
self.assertIsInstance(obj=sample_hundred, cls=np.ndarray)
self.assertEqual(100, sample_hundred.size)
self.assertTrue(
np.all(hp._transform(val) % 3 == 0 for val in sample_hundred))
def test_quantization_UniformFloatHyperparameter(self):
hp = UniformFloatHyperparameter("ufhp", 1, 100, q=3)
rs = np.random.RandomState()
sample_one = hp._sample(rs=rs, size=1)
self.assertIsInstance(obj=sample_one, cls=np.ndarray)
self.assertEqual(1, sample_one.size)
self.assertEqual((hp._transform(sample_one) - 1) % 3, 0)
self.assertGreaterEqual(hp._transform(sample_one), 1)
self.assertLessEqual(hp._transform(sample_one), 100)
sample_hundred = hp._sample(rs=rs, size=100)
self.assertIsInstance(obj=sample_hundred, cls=np.ndarray)
self.assertEqual(100, sample_hundred.size)
np.testing.assert_array_equal(
[(hp._transform(val) - 1) % 3 for val in sample_hundred],
np.zeros((100,), dtype=int),
)
samples_in_original_space = hp._transform(sample_hundred)
for i in range(100):
self.assertGreaterEqual(samples_in_original_space[i], 1)
self.assertLessEqual(samples_in_original_space[i], 100)