def test_fit_predict_super(self, predict_mock, random_mock, uniform_mock):
"""If random is higher than POU, super.predict is used."""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = GPEiVelocity(tunables, r_minimum=2)
tuner.POU = 0.05
random_mock.random.return_value = 0.1
# Run
X = np.array([
[1., 1],
[1.2, 2],
[1.4, 3],
[1.6, 4],
[1.8, 5]
])
tuner.predict(X)
# assert
predict_mock.assert_called_once_with(X)
uniform_mock.assert_not_called()
def test_fit_predict_uniform(self, predict_mock, random_mock,
uniform_mock):
"""If random is lower than POU, Uniform is used."""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = GPEiVelocity(tunables, r_minimum=2)
tuner.POU = 0.05
random_mock.random.return_value = 0.01
uniform_instance_mock = Mock()
uniform_mock.return_value = uniform_instance_mock
# Run
X = np.array([[1., 1], [1.2, 2], [1.4, 3], [1.6, 4], [1.8, 5]])
tuner.predict(X)
# assert
uniform_mock.assert_called_once_with(tunables)
uniform_instance_mock.predict.assert_called_once_with(X)
predict_mock.assert_not_called()
def test_fit_gt_r_min(self, fit_mock):
"""If the length of X is greater than r_minimum, calculate POU."""
# Set-up
tuner = GPEiVelocity(tuple(), r_minimum=2)
# Run
X = np.array([[1., 1], [1.2, 2], [1.4, 3], [1.6, 4], [1.8, 5]])
y = np.array([0.8, 0.81, 0.9, 0.84, 0.87])
tuner.fit(X, y)
# assert
assert tuner.POU == 0.08208499862389883
def test_fit_lt_r_min(self, fit_mock):
"""If the length of X is smaller than r_minimum, nothing is done."""
# Set-up
tuner = GPEiVelocity(tuple(), r_minimum=5)
# Run
X = np.array([[1., 1], [1.2, 2], [1.4, 4]])
y = np.array([0.5, 0.6, 0.7])
tuner.fit(X, y)
# assert
assert tuner.POU == 0
def test_gpeivelocity(self):
X = [{'a': 1.1, 'b': 0.01, 'c': 3.5}, {'a': 4, 'b': 0.001, 'c': 6.2}]
y = [0.5, 0.6]
c1 = HyperParameter(ParamTypes.INT, [1, 5])
c2 = HyperParameter(ParamTypes.FLOAT_EXP, [0.0001, 0.1])
c3 = HyperParameter(ParamTypes.FLOAT, [2, 8])
tunables = [('a', c1), ('b', c2), ('c', c3)]
u = GPEiVelocity(tunables)
u.add(X, y)
for i in range(100):
proposed = u.propose()
self.assertTrue(proposed['a'] >= 1 and proposed['a'] <= 5)
self.assertTrue(proposed['b'] >= 0.0001 and proposed['b'] <= 0.1)
self.assertTrue(proposed['c'] >= 2 and proposed['c'] <= 8)
def test_fit_lt_r_min(self, fit_mock):
"""If the length of X is smaller than r_minimum, nothing is done."""
# Set-up
tuner = GPEiVelocity(tuple(), r_minimum=5)
# Run
X = np.array([
[1., 1],
[1.2, 2],
[1.4, 4]
])
y = np.array([0.5, 0.6, 0.7])
tuner.fit(X, y)
# assert
assert tuner.POU == 0
def test_fit_gt_r_min(self, fit_mock):
"""If the length of X is greater than r_minimum, calculate POU."""
# Set-up
tuner = GPEiVelocity(tuple(), r_minimum=2)
# Run
X = np.array([
[1., 1],
[1.2, 2],
[1.4, 3],
[1.6, 4],
[1.8, 5]
])
y = np.array([0.8, 0.81, 0.9, 0.84, 0.87])
tuner.fit(X, y)
# assert
assert tuner.POU == 0.08208499862389883