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test_bo.py
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test_bo.py
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# test_bo
# author: Jungtaek Kim (jtkim@postech.ac.kr)
# last updated: June 23, 2018
import numpy as np
import pytest
from bayeso import bo
TEST_EPSILON = 1e-5
def test_load_bo():
# legitimate cases
arr_range_1 = np.array([
[0.0, 10.0],
[-2.0, 2.0],
[-5.0, 5.0],
])
arr_range_2 = np.array([
[0.0, 10.0],
[2.0, 2.0],
[5.0, 5.0],
])
# wrong cases
arr_range_3 = np.array([
[20.0, 10.0],
[-2.0, 2.0],
[-5.0, 5.0],
])
arr_range_4 = np.array([
[20.0, 10.0],
[4.0, 2.0],
[10.0, 5.0],
])
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(1)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(np.arange(0, 10))
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_3)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_4)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, str_cov=1)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, str_cov='abc')
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, str_acq=1)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, str_acq='abc')
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, is_ard=1)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, prior_mu=1)
with pytest.raises(AssertionError) as error:
model_bo = bo.BO(arr_range_1, debug=1)
model_bo = bo.BO(arr_range_1)
model_bo = bo.BO(arr_range_2)
def test_get_initial():
np.random.seed(42)
arr_range = np.array([
[0.0, 10.0],
[-2.0, 2.0],
[-5.0, 5.0],
])
dim_X = arr_range.shape[0]
num_X = 5
X = np.random.randn(num_X, dim_X)
Y = np.random.randn(num_X, 1)
fun_objective = lambda X: np.sum(X)
model_bo = bo.BO(arr_range)
with pytest.raises(AssertionError) as error:
model_bo.get_initial(1)
with pytest.raises(AssertionError) as error:
model_bo.get_initial('grid', fun_objective=None)
with pytest.raises(AssertionError) as error:
model_bo.get_initial('uniform', fun_objective=1)
with pytest.raises(AssertionError) as error:
model_bo.get_initial('uniform', int_samples='abc')
with pytest.raises(AssertionError) as error:
model_bo.get_initial('uniform', int_seed='abc')
with pytest.raises(NotImplementedError) as error:
model_bo.get_initial('abc')
arr_initials = model_bo.get_initial('grid', fun_objective=fun_objective)
truth_arr_initials = np.array([
[0.0, -2.0, -5.0],
])
assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
arr_initials = model_bo.get_initial('sobol', int_samples=3)
arr_initials = model_bo.get_initial('sobol', int_samples=3, int_seed=42)
truth_arr_initials = np.array([
[5.0, 0.0, 0.0],
[7.5, -1.0, 2.5],
[2.5, 1.0, -2.5],
])
assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
arr_initials = model_bo.get_initial('uniform', int_samples=3, int_seed=42)
truth_arr_initials = np.array([
[3.74540119, 1.80285723, 2.31993942],
[5.98658484, -1.37592544, -3.4400548],
[0.58083612, 1.46470458, 1.01115012],
])
assert (np.abs(arr_initials - truth_arr_initials) < TEST_EPSILON).all()
def test_optimize():
np.random.seed(42)
arr_range_1 = np.array([
[0.0, 10.0],
[-2.0, 2.0],
[-5.0, 5.0],
])
dim_X = arr_range_1.shape[0]
num_X = 5
X = np.random.randn(num_X, dim_X)
Y = np.random.randn(num_X, 1)
model_bo = bo.BO(arr_range_1)
with pytest.raises(AssertionError) as error:
model_bo.optimize(1, Y)
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, 1)
with pytest.raises(AssertionError) as error:
model_bo.optimize(np.random.randn(num_X), Y)
with pytest.raises(AssertionError) as error:
model_bo.optimize(np.random.randn(num_X, 1), Y)
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, np.random.randn(num_X))
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, np.random.randn(num_X, 2))
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, np.random.randn(3, 1))
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, Y, str_initial_method=1)
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, Y, str_initial_method='abc')
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, Y, int_samples='abc')
with pytest.raises(AssertionError) as error:
model_bo.optimize(X, Y, is_normalized='abc')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]
model_bo = bo.BO(arr_range_1, str_acq='pi')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]
model_bo = bo.BO(arr_range_1, str_acq='ucb')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]
model_bo = bo.BO(arr_range_1, str_acq='aei')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]
model_bo = bo.BO(arr_range_1, str_acq='pure_exploit')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]
model_bo = bo.BO(arr_range_1, str_acq='pure_explore')
next_point, next_points, acquisitions, cov_X_X, inv_cov_X_X, hyps = model_bo.optimize(X, Y)
assert isinstance(next_point, np.ndarray)
assert isinstance(next_points, np.ndarray)
assert isinstance(acquisitions, np.ndarray)
assert isinstance(cov_X_X, np.ndarray)
assert isinstance(inv_cov_X_X, np.ndarray)
assert isinstance(hyps, dict)
assert len(next_point.shape) == 1
assert len(next_points.shape) == 2
assert len(acquisitions.shape) == 1
assert next_point.shape[0] == dim_X
assert next_points.shape[1] == dim_X
assert next_points.shape[0] == acquisitions.shape[0]