def test_tune_pr_matrix_dual_annealing(self):
np.random.seed(42)
fm = FeatureMap(distr='beta',
bias=None,
input_dim=2,
n_features=5,
params=[0.1, 0.3],
sigma_f=0.1)
func = lambda x: np.sin(x[0] + x[1])
bounds = (slice(3, 4, 0.25), slice(1, 2, 0.25))
fm.tune_pr_matrix(func, bounds, args=(), method='dual_annealing')
true_value = np.array([[0.96136567,
0.68442507], [0.27881283, 0.69151686],
[0.57626534, 0.6273798], [0.3170462, 0.8636379],
[0.97294359, 0.99518304]])
np.testing.assert_array_almost_equal(true_value, fm.pr_matrix)
def test_bso(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.zeros(1),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(dim=1, feature_map=fm)
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
best = fm.tune_pr_matrix(func=average_rrmse,
bounds=[slice(-2, 1, 0.2) for i in range(1)],
args=(csv, ),
method='bso',
maxiter=10,
save_file=False)[1]
true = np.array([[14.9646475, 4.2713126], [11.28870881, 8.33313971],
[1.16475035, 9.92216877]])
np.testing.assert_array_almost_equal(true, best)
def test_dual_annealing(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.zeros(1),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(dim=1, feature_map=fm)
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
best = fm.tune_pr_matrix(func=average_rrmse,
bounds=[slice(-2, 1, 0.2) for i in range(1)],
args=(csv, ),
method='dual_annealing',
maxiter=5,
save_file=False)[1]
true = np.array([[0.21895786, 0.52423581], [-5.3186504, 14.9646475],
[4.2713126, 11.28870881]])
np.testing.assert_array_almost_equal(true, best)
def test_bso(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.zeros(1),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(feature_map=fm)
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
best = fm.tune_pr_matrix(func=average_rrmse,
bounds=[slice(-2, 1, 0.2) for i in range(1)],
args=(csv, ),
method='bso',
maxiter=10,
save_file=False)[1]
true = np.array([[2.13951417, -9.18658594], [1.16340082, 0.5677881],
[8.7675616, -1.1558472]])
np.testing.assert_array_almost_equal(true, best)
def test_tune_pr_matrix_brute(self):
np.random.seed(42)
fm = FeatureMap(distr='uniform',
bias=None,
input_dim=2,
n_features=5,
params=[0.1, 0.3],
sigma_f=0.1)
func = lambda x: np.sin(x[0] + x[1])
bounds = (slice(-np.pi, 0, 0.25), slice(1, 2, 0.25))
fm.tune_pr_matrix(func, bounds, args=(), method='brute')
true_value = np.array([[-1.40312999, 1.27123589],
[0.25602505, -0.36286383],
[-2.41741768, -2.41752963],
[-2.87199219, 0.87884418],
[-0.35146163, 0.14499182]])
np.testing.assert_array_almost_equal(true_value, fm.pr_matrix)
def test_tune_pr_matrix_none_02(self):
np.random.seed(42)
fm = FeatureMap(distr='multivariate_normal',
bias=None,
input_dim=3,
n_features=5,
params=[0.1, 0.3, 0.1],
sigma_f=0.1)
func = lambda x: np.sin(x[0] + x[1] + x[2])
bounds = (slice(-np.pi, 0, 0.25), slice(1, 2, 0.25), slice(3, 4, 0.25))
fm.tune_pr_matrix(func, bounds, args=(), method=None)
true_value = np.array([[-0.61157462, -0.18290648, 0.96188282],
[0.22108191, -0.3097558, 2.94933463],
[0.44329736, 1.01522072, 3.05813247],
[0.43976152, -0.61304398, 1.05066301],
[1.62873959, -2.53103186, 0.46855792]])
np.testing.assert_array_almost_equal(true_value, fm.pr_matrix)
def test_tune_pr_matrix_none_01(self):
np.random.seed(42)
fm = FeatureMap(distr='multivariate_normal',
bias=None,
input_dim=2,
n_features=5,
params=[0.1, 0.3],
sigma_f=0.1)
func = lambda x: np.sin(x[0] + x[1])
bounds = (slice(-np.pi, 0, 0.25), slice(1, 2, 0.25))
fm.tune_pr_matrix(func, bounds, args=(), method=None)
true_value = np.array([[-0.88040291, -0.16933849],
[-1.14799804, 1.86532301],
[0.41502605, -0.28675804],
[-2.79908184, 0.93991175],
[0.83212168, 0.66449763]])
np.testing.assert_array_almost_equal(true_value, fm.pr_matrix)
def test_tune_pr_matrix_none_03(self):
np.random.seed(42)
fm = FeatureMap(distr='multivariate_normal',
bias=None,
input_dim=4,
n_features=5,
params=[0.1, 0.3, 0.1, 0.3],
sigma_f=0.1)
func = lambda x: np.sin(x[0] + x[1] + x[2] + x[3])
bounds = (slice(-np.pi, 0, 0.25), slice(1, 2, 0.25), slice(3, 4, 0.25),
slice(3, 4, 0.25))
fm.tune_pr_matrix(func, bounds, args=(), method=None)
true_value = np.array(
[[1.79243416, 0.96579734, 1.49638769, -1.17085025],
[1.92976509, -0.97102892, 0.9688167, 0.30584914],
[-0.76036359, -1.4831584, -1.66068389, 1.06297975],
[-0.90661282, 0.67872439, -2.15831277, 1.50962728],
[-0.42752186, -0.58457006, 1.01920618, -1.24015295]])
np.testing.assert_array_almost_equal(true_value, fm.pr_matrix)
def test_brute(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.array([5.34265038]),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(feature_map=fm)
csv = CrossValidation(inputs=inputs, outputs=outputs, gradients=gradients, folds=2, subspace=ss)
best = fm.tune_pr_matrix(func=average_rrmse,
bounds=[slice(-1, 1.2, 0.2) for i in range(1)],
args=(csv, ),
maxiter=10,
save_file=False)[1]
true = np.array([[0.03857183, -0.45825228], [-1.06057884, 0.9981594],
[1.01812996, 0.19529565]])
np.testing.assert_array_almost_equal(true, best)
def test_brute(self):
np.random.seed(42)
inputs = np.random.uniform(-1, 1, 10).reshape(5, 2)
outputs = np.random.uniform(0, 5, 10).reshape(5, 2)
gradients = np.random.uniform(-1, 1, 20).reshape(5, 2, 2)
fm = FeatureMap(distr='laplace',
bias=np.random.uniform(-1, 1, 3),
input_dim=2,
n_features=3,
params=np.array([5.34265038]),
sigma_f=outputs.var())
ss = KernelActiveSubspaces(dim=1, feature_map=fm)
csv = CrossValidation(inputs=inputs,
outputs=outputs,
gradients=gradients,
folds=2,
subspace=ss)
best = fm.tune_pr_matrix(func=average_rrmse,
bounds=[slice(-2, 1, 0.2) for i in range(1)],
fn_args={'csv':csv},
maxiter=10,
save_file=False)[1]
true = np.array([[0., 0.], [0., 0.], [0., 0.]])
np.testing.assert_array_almost_equal(true, best)