def test__create_candidates_gt_n_remaining(self, np_random_mock):
"""more than n points remaining"""
# Set-up
mock_context = dict()
def shuffle(array):
np_shuffle(array)
# Store a copy of the array for the assert
mock_context['shuffled_array'] = array.copy()
np_random_mock.shuffle.side_effect = shuffle
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables, gridding=3)
# Insert 5 used vectors into X (4 remaining)
tuner.X = np.array([
[1., 1.],
[1., 3.],
[1., 5.],
[1.5, 1.],
[1.5, 5.],
])
# Run
candidates = tuner._create_candidates(2)
# Assert
expected_candidates = mock_context['shuffled_array'][0:2]
np.testing.assert_array_equal(candidates, expected_candidates)
def test__create_candidates_no_grid(self, np_random_mock):
"""self.grid is False"""
# Set-up
np_random_mock.rand.return_value = np.array([.0, .2, .4, .6, .8])
np_random_mock.randint.return_value = np.array([1, 2, 3, 4, 5])
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
('a_string_param',
HyperParameter(ParamTypes.STRING, ['a', 'b', 'c'])),
)
tuner = BaseTuner(tunables)
# Run
candidates = tuner._create_candidates(5)
# Assert
expected_candidates = np.array([[1.0, 1, 0.], [1.2, 2, 0.2],
[1.4, 3, 0.4], [1.6, 4, 0.6],
[1.8, 5, 0.8]])
np.testing.assert_array_equal(candidates, expected_candidates)
expected_calls = [
call(5),
call(5),
]
np_random_mock.rand.assert_has_calls(expected_calls)
np_random_mock.randint.assert_called_once_with(1, 6, size=5)
def test_propse_n_eq_1(self, predict_mock, create_candidates_mock):
"""n == 1"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.return_value = np.array([
[1.0, 1],
[1.2, 2],
[1.4, 3],
[1.6, 4],
[1.8, 5]
])
predict_mock.return_value = np.array([0.5, 0.6, 0.7, 0.8, 0.9])
# Run
params = tuner.propose(1)
# Assert
expected_params = {'a_float_param': 1.8, 'an_int_param': 5}
assert params == expected_params
def test__create_candidates_no_grid(self, np_random_mock):
"""self.grid is False"""
# Set-up
np_random_mock.rand.return_value = np.array([.0, .2, .4, .6, .8])
np_random_mock.randint.return_value = np.array([1, 2, 3, 4, 5])
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
# Run
candidates = tuner._create_candidates(5)
# Assert
expected_candidates = np.array([
[1.0, 1],
[1.2, 2],
[1.4, 3],
[1.6, 4],
[1.8, 5]
])
np.testing.assert_array_equal(candidates, expected_candidates)
np_random_mock.rand.assert_called_once_with(5)
np_random_mock.randint.assert_called_once_with(1, 6, size=5)
def test_propse_n_gt_1(self, predict_mock, create_candidates_mock):
"""n > 1"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.side_effect = [
np.array([[1.0, 1], [1.4, 3]]),
np.array([[1.2, 2], [1.6, 4]]),
np.array([[1.6, 4], [1.8, 5]]),
]
predict_mock.side_effect = [
np.array([0.5, 0.7]),
np.array([0.6, 0.8]),
np.array([0.7, 0.9]),
]
# Run
params = tuner.propose(3)
# Assert
expected_params = [
{'a_float_param': 1.4, 'an_int_param': 3},
{'a_float_param': 1.6, 'an_int_param': 4},
{'a_float_param': 1.8, 'an_int_param': 5},
]
assert params == expected_params
def test__create_candidates_gt_n_remaining(self, np_random_mock):
"""more than n points remaining"""
# Set-up
mock_context = dict()
def shuffle(array):
np_shuffle(array)
# Store a copy of the array for the assert
mock_context['shuffled_array'] = array.copy()
np_random_mock.shuffle.side_effect = shuffle
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables, gridding=3)
# Insert 5 used vectors into X (4 remaining)
tuner.X = np.array([
[1., 1.],
[1., 3.],
[1., 5.],
[1.5, 1.],
[1.5, 5.],
])
# Run
candidates = tuner._create_candidates(2)
# Assert
expected_candidates = mock_context['shuffled_array'][0:2]
np.testing.assert_array_equal(candidates, expected_candidates)
def test_add_multitype(self):
t = BaseTuner(self.tunables)
t.add(self.X, self.Y)
X = np.array([
[2, 3.0, 3.0, -1.0, 0.5, 0.55, 0.5],
[1, 2.0, 1.9, -1.0, 0.6, 0.55, 0.6],
[3, 1.0, 2.6, -2.0, 0.1, 0.1, 0.1],
], dtype=object)
np.testing.assert_array_equal(t.X, X)
self.assertEqual(t.X.dtype, np.float64)
def test___acquire(self):
"""np.argmax is properly called"""
# Set-up
tuner = BaseTuner(tuple())
# Run
predictions = np.array([0.9, 0.95, 0.8])
idx = tuner._acquire(predictions)
# Assert
assert idx == 1
def test___acquire(self):
"""np.argmax is properly called"""
# Set-up
tuner = BaseTuner(tuple())
# Run
predictions = np.array([0.9, 0.95, 0.8])
idx = tuner._acquire(predictions)
# Assert
assert idx == 1
def test___init__(self):
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
# Run
tuner = BaseTuner(tunables, gridding=5)
# assert
assert tuner.tunables == tunables
assert tuner.grid is True
assert tuner._best_score == -np.inf
assert tuner._best_hyperparams is None
assert tuner.grid_width == 5
assert tuner.X_raw is None
assert tuner.y_raw == []
assert tuner.X.tolist() == []
assert tuner.y.tolist() == []
expected_grid_axes = [
np.array([1., 1.25, 1.5, 1.75, 2.]),
np.array([1., 2., 3., 4., 5.])
]
np.testing.assert_array_equal(tuner._grid_axes, expected_grid_axes)
def test_propse_done(self, predict_mock, create_candidates_mock):
"""gridding is done"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.return_value = None
# Run
params = tuner.propose(1)
# Assert
expected_params = None
assert params == expected_params
def test__create_candidates_every_point_used(self):
"""every point has been used"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables, gridding=5)
# Insert 25 part_vecs into X
tuner.X = np.array(list(product(*tuner._grid_axes)))
# Run
candidates = tuner._create_candidates(5)
# Assert
assert candidates is None
def test__create_candidates_every_point_used(self):
"""every point has been used"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables, gridding=5)
# Insert 25 part_vecs into X
tuner.X = np.array(list(product(*tuner._grid_axes)))
# Run
candidates = tuner._create_candidates(5)
# Assert
assert candidates is None
def test_propse_done(self, predict_mock, create_candidates_mock):
"""gridding is done"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.return_value = None
# Run
params = tuner.propose(1)
# Assert
expected_params = None
assert params == expected_params
def test_propse_n_eq_1(self, predict_mock, create_candidates_mock):
"""n == 1"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.return_value = np.array([[1.0, 1], [1.2, 2],
[1.4, 3], [1.6, 4],
[1.8, 5]])
predict_mock.return_value = np.array([0.5, 0.6, 0.7, 0.8, 0.9])
# Run
params = tuner.propose(1)
# Assert
expected_params = {'a_float_param': 1.8, 'an_int_param': 5}
assert params == expected_params
def test_add(self):
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT_EXP, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
# Run
# first run
X = {'a_float_param': 1., 'an_int_param': 1}
y = 0.5
tuner.add(X, y)
# second run (test append)
X = [
{'a_float_param': 1.2, 'an_int_param': 2},
{'a_float_param': 1.4, 'an_int_param': 3}
]
y = [0.6, 0.7]
tuner.add(X, y)
# Assert
expected_X = np.array([
[0., 1],
[0.07918124604762482, 2],
[0.146128035678238, 3],
])
expected_X_raw = np.array([
[1.0, 1],
[1.2, 2],
[1.4, 3],
])
expected_y = np.array([0.5, 0.6, 0.7])
np.testing.assert_array_equal(tuner.X, expected_X)
np.testing.assert_array_equal(tuner.X_raw, expected_X_raw)
np.testing.assert_array_equal(tuner.y, expected_y)
np.testing.assert_array_equal(tuner.y_raw, expected_y)
def test_propse_n_gt_1(self, predict_mock, create_candidates_mock):
"""n > 1"""
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
create_candidates_mock.side_effect = [
np.array([[1.0, 1], [1.4, 3]]),
np.array([[1.2, 2], [1.6, 4]]),
np.array([[1.6, 4], [1.8, 5]]),
]
predict_mock.side_effect = [
np.array([0.5, 0.7]),
np.array([0.6, 0.8]),
np.array([0.7, 0.9]),
]
# Run
params = tuner.propose(3)
# Assert
expected_params = [
{
'a_float_param': 1.4,
'an_int_param': 3
},
{
'a_float_param': 1.6,
'an_int_param': 4
},
{
'a_float_param': 1.8,
'an_int_param': 5
},
]
assert params == expected_params
def test_fit(self):
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
# Run
X = [
[1., 1],
[1.25, 2],
[1.5, 3],
[1.75, 4],
[2., 5],
]
y = [0.5, 0.6, 0.7, 0.8, 0.9]
tuner.fit(X, y)
# Assert
assert tuner.X == X
assert tuner.y == y
def test_fit(self):
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
# Run
X = [
[1., 1],
[1.25, 2],
[1.5, 3],
[1.75, 4],
[2., 5],
]
y = [0.5, 0.6, 0.7, 0.8, 0.9]
tuner.fit(X, y)
# Assert
assert tuner.X == X
assert tuner.y == y
def test_add(self):
# Set-up
tunables = (
('a_float_param', HyperParameter(ParamTypes.FLOAT_EXP, [1., 2.])),
('an_int_param', HyperParameter(ParamTypes.INT, [1, 5])),
)
tuner = BaseTuner(tunables)
# Run
# first run
X = {'a_float_param': 1., 'an_int_param': 1}
y = 0.5
tuner.add(X, y)
# second run (test append)
X = [{
'a_float_param': 1.2,
'an_int_param': 2
}, {
'a_float_param': 1.4,
'an_int_param': 3
}]
y = [0.6, 0.7]
tuner.add(X, y)
# Assert
expected_X = np.array([
[0., 1],
[0.07918124604762482, 2],
[0.146128035678238, 3],
])
expected_X_raw = np.array([
[1.0, 1],
[1.2, 2],
[1.4, 3],
])
expected_y = np.array([0.5, 0.6, 0.7])
np.testing.assert_array_equal(tuner.X, expected_X)
np.testing.assert_array_equal(tuner.X_raw, expected_X_raw)
np.testing.assert_array_equal(tuner.y, expected_y)
np.testing.assert_array_equal(tuner.y_raw, expected_y)
def test__predict(self):
"""Exception is raised"""
tuner = BaseTuner(tuple())
with pytest.raises(NotImplementedError):
tuner.predict([])
def test__predict(self):
"""Exception is raised"""
tuner = BaseTuner(tuple())
with pytest.raises(NotImplementedError):
tuner.predict([])