def testUniformGeneratorFixedSpace(self):
generator = UniformGenerator(seed=0)
bounds = self._create_bounds(n_tunable=0, n_fixed=2)
n = 3
generated_points, _ = generator.gen(n=3,
bounds=bounds,
fixed_features={
0: 1,
1: 2
})
expected_points = np.tile(np.array([[1, 2]]), (n, 1))
self.assertTrue(
np.shape(expected_points) == np.shape(generated_points))
self.assertTrue(np.allclose(expected_points, generated_points))
def testUniformGeneratorAllTunable(self):
generator = UniformGenerator(seed=0)
bounds = self._create_bounds(n_tunable=3, n_fixed=0)
generated_points, weights = generator.gen(n=3, bounds=bounds)
expected_points = np.array([
[0.5488135, 0.71518937, 0.60276338],
[0.54488318, 0.4236548, 0.64589411],
[0.43758721, 0.891773, 0.96366276],
])
self.assertTrue(
np.shape(expected_points) == np.shape(generated_points))
self.assertTrue(np.allclose(expected_points, generated_points))
self.assertTrue(np.all(weights == 1.0))
def testUniformGeneratorReseed(self):
# Verify that the generator will return the expected arms if called
# one at a time.
generator = UniformGenerator(seed=0)
n_tunable = fixed_param_index = 3
bounds = self._create_bounds(n_tunable=n_tunable, n_fixed=1)
n = 3
expected_points = np.array([
[0.5488135, 0.71518937, 0.60276338, 1],
[0.54488318, 0.4236548, 0.64589411, 1],
[0.43758721, 0.891773, 0.96366276, 1],
])
for i in range(n):
generated_points, weights = generator.gen(
n=1, bounds=bounds, fixed_features={fixed_param_index: 1})
self.assertEqual(weights, [1])
self.assertTrue(
np.allclose(generated_points, expected_points[i, :]))
def testUniformGeneratorWithLinearConstraints(self):
# Enforce dim_0 <= dim_1 <= dim_2 <= dim_3.
# Enforce both fixed and tunable constraints.
generator = UniformGenerator(seed=0)
n_tunable = fixed_param_index = 3
bounds = self._create_bounds(n_tunable=n_tunable, n_fixed=1)
generated_points, weights = generator.gen(
n=3,
bounds=bounds,
linear_constraints=(
np.array([[1, 1, 0, 0], [0, 1, 1, 0]]),
np.array([1, 1]),
),
fixed_features={fixed_param_index: 1},
)
expected_points = np.array([
[0.0871293, 0.0202184, 0.83261985, 1.0],
[0.11827443, 0.63992102, 0.14335329, 1.0],
[0.56843395, 0.0187898, 0.6176355, 1.0],
])
self.assertTrue(
np.shape(expected_points) == np.shape(generated_points))
self.assertTrue(np.allclose(expected_points, generated_points))
def testUniformGeneratorWithOrderConstraints(self):
# Enforce dim_0 <= dim_1 <= dim_2 <= dim_3.
# Enforce both fixed and tunable constraints.
generator = UniformGenerator(seed=0)
n_tunable = fixed_param_index = 3
bounds = self._create_bounds(n_tunable=n_tunable, n_fixed=1)
generated_points, weights = generator.gen(
n=3,
bounds=bounds,
linear_constraints=(
np.array([[1, -1, 0, 0], [0, 1, -1, 0], [0, 0, 1, -1]]),
np.array([0, 0, 0]),
),
fixed_features={fixed_param_index: 0.5},
)
expected_points = np.array([
[0.13818295, 0.19658236, 0.36872517, 0.5],
[0.0486903, 0.25364252, 0.44613551, 0.5],
[0.09088573, 0.2277595, 0.41030156, 0.5],
])
self.assertTrue(
np.shape(expected_points) == np.shape(generated_points))
self.assertTrue(np.allclose(expected_points, generated_points))
def get_uniform(search_space: SearchSpace,
deduplicate: bool = False,
seed: Optional[int] = None) -> RandomModelBridge:
"""Instantiate uniform generator.
Args:
search_space: Uniform generator search space.
kwargs: Custom args for uniform generator.
Returns:
RandomModelBridge, with UniformGenerator as model.
"""
return RandomModelBridge(
search_space=search_space,
model=UniformGenerator(deduplicate=deduplicate, seed=seed),
transforms=Cont_X_trans,
)
def testUniformGeneratorBadBounds(self):
generator = UniformGenerator()
with self.assertRaises(ValueError):
generated_points, weights = generator.gen(n=1, bounds=[(-1, 1)])