def test_explore_lazy():
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
bo.explore({'x': [f.argmin()]}, eager=False)
assert len(bo.space) == 0
assert len(bo.init_points) == 1
# Note we currently expect lazy explore to override points
# This may not be the case in the future.
bo.explore({'x': [f.argmax()]}, eager=False)
assert len(bo.space) == 0
assert len(bo.init_points) == 1
bo.maximize(init_points=0, n_iter=0, acq='ucb', kappa=5)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
assert max_params['x'] == f.argmax()
assert max_val == f.max()
def test_explore_lazy():
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
bo.explore({'x': [f.argmin()]}, eager=False)
assert len(bo.space) == 0
assert len(bo.init_points) == 1
# Note we currently expect lazy explore to override points
# This may not be the case in the future.
bo.explore({'x': [f.argmax()]}, eager=False)
assert len(bo.space) == 0
assert len(bo.init_points) == 1
bo.maximize(init_points=0, n_iter=0, acq='ucb', kappa=5)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
assert max_params['x'] == f.argmax()
assert max_val == f.max()
def test_bayes_opt_demo():
"""
pytest tests/test_bayesian_optimization.py::test_bayes_opt_demo
See Also
--------
https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation%20vs%20exploration.ipynb
"""
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
gp_params = {'alpha': 1e-5, 'n_restarts_optimizer': 2}
bo.maximize(init_points=10, n_iter=10, acq='ucb', kappa=5, **gp_params)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
assert max_val > 1.2, 'function range is ~.2 - ~1.4, should be above 1.'
assert max_val / f.max() > .9, 'should be better than 90% of max val'
assert max_params['x'] > 300, 'should be in a peak area (around 300)'
assert max_params['x'] < 400, 'should be in a peak area (around 300)'
def test_bayes_opt_demo():
"""
pytest tests/test_bayesian_optimization.py::test_bayes_opt_demo
See Also
--------
https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation%20vs%20exploration.ipynb
"""
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
gp_params = {'alpha': 1e-5, 'n_restarts_optimizer': 2}
# Change aquisition params to speedup optimization for testing purposes
bo._acqkw['n_iter'] = 5
bo._acqkw['n_warmup'] = 1000
bo.maximize(init_points=10, n_iter=5, acq='ucb', kappa=5, **gp_params)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
ratio = max_val / f.max()
assert max_val > 1.1, 'got {}, but should be > 1'.format(max_val)
assert ratio > .9, 'got {}, should be better than 90% of max val'.format(ratio)
assert max_params['x'] > 300, 'should be in a peak area (around 300)'
assert max_params['x'] < 400, 'should be in a peak area (around 300)'
def test_bayes_opt_demo():
"""
pytest tests/test_bayesian_optimization.py::test_bayes_opt_demo
See Also
--------
https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation%20vs%20exploration.ipynb
"""
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
gp_params = {'alpha': 1e-5, 'n_restarts_optimizer': 2}
# Change aquisition params to speedup optimization for testing purposes
bo._acqkw['n_iter'] = 5
bo._acqkw['n_warmup'] = 1000
bo.maximize(init_points=10, n_iter=5, acq='ucb', kappa=5, **gp_params)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
ratio = max_val / f.max()
assert max_val > 1.1, 'got {}, but should be > 1'.format(max_val)
assert ratio > .9, 'got {}, should be better than 90% of max val'.format(
ratio)
assert max_params['x'] > 300, 'should be in a peak area (around 300)'
assert max_params['x'] < 400, 'should be in a peak area (around 300)'
def test_only_random():
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
bo.init(20)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
assert max_val > 1.0, 'function range is ~.2 - ~1.4, should be above 1.'
assert max_val / f.max() > .8, 'should be better than 80% of max val'
assert max_params['x'] > 200, 'should be in a peak area (around 300)'
assert max_params['x'] < 500, 'should be in a peak area (around 300)'
def test_only_random():
random_state = ensure_rng(0)
xs = np.linspace(-2, 10, 1000)
f = np.exp(-(xs - 2)**2) + np.exp(-(xs - 6)**2 / 10) + 1 / (xs**2 + 1)
bo = BayesianOptimization(f=lambda x: f[int(x)],
pbounds={'x': (0, len(f) - 1)},
random_state=random_state,
verbose=0)
bo.init(20)
res = bo.space.max_point()
max_params = res['max_params']
max_val = res['max_val']
assert max_val > 1.0, 'function range is ~.2 - ~1.4, should be above 1.'
assert max_val / f.max() > .8, 'should be better than 80% of max val'
assert max_params['x'] > 200, 'should be in a peak area (around 300)'
assert max_params['x'] < 500, 'should be in a peak area (around 300)'
def test_db_manager_dimension():
"""
pytest tests/test_db_manager.py::test_db_manager
See Also
--------
https://github.com/fmfn/BayesianOptimization/blob/master/examples/database_interactions.ipynb
"""
random_state = ensure_rng(0)
gp_params = {'alpha': 1e-5, 'n_restarts_optimizer': 2}
bo = BayesianOptimization(target,
pbounds={
'x': (-2, 10),
'y': (-2, 10),
'z': (-2, 10)
},
random_state=random_state,
verbose=1)
# Change aquisition params to speedup optimization for testing purposes
bo._acqkw['n_iter'] = 5
bo._acqkw['n_warmup'] = 1000
bo.init_db('sqlite:///target_space.db')
bo.maximize(init_points=2, n_iter=2, acq='ucb', kappa=5, **gp_params)
bo.save()
for i in range(5):
bo = BayesianOptimization(target,
pbounds={
'x': (-2, 10),
'y': (-2, 10),
'z': (-2, 10)
},
random_state=random_state,
verbose=1)
bo._acqkw['n_iter'] = 5
bo._acqkw['n_warmup'] = 1000
bo.init_db('sqlite:///target_space.db')
bo.load()
assert bo.space.Y.shape[0] == 4 + 2 * i, 'should increase by 2'
bo.maximize(init_points=0, n_iter=2, acq='ucb', kappa=5, **gp_params)
bo.save()
bo.print_summary()
def setUp(self, kind='ucb', kappa=1.0, xi=1e-6):
self.util = UtilityFunction(kind=kind, kappa=kappa, xi=xi)
self.episilon = 1e-2
self.y_max = 2.0
self.random_state = ensure_rng(0)
def setUp(self, kind='ucb', kappa=1.0, xi=1e-6):
self.util = UtilityFunction(kind=kind, kappa=kappa, xi=xi)
self.episilon = 1e-2
self.y_max = 2.0
self.random_state = ensure_rng(0)