def test_predict_output(self):
d, n = (3, 10)
sx = LHS(
xlimits=np.repeat(np.atleast_2d([0.0, 1.0]), d, axis=0),
criterion="m",
random_state=42,
)
x = sx(n)
sy = LHS(
xlimits=np.repeat(np.atleast_2d([0.0, 1.0]), 1, axis=0),
criterion="m",
random_state=42,
)
y = sy(n)
kriging = KPLS(n_comp=2)
kriging.set_training_values(x, y)
kriging.train()
x_fail_1 = np.asarray([0, 0, 0, 0])
x_fail_2 = np.asarray([0])
self.assertRaises(ValueError, lambda: kriging.predict_values(x_fail_1))
self.assertRaises(ValueError, lambda: kriging.predict_values(x_fail_2))
var = kriging.predict_variances(x)
self.assertEqual(y.shape[0], var.shape[0])
def test_kpls_training_with_zeroed_outputs(self):
# Test scikit-learn 0.24 regression cf. https://github.com/SMTorg/smt/issues/274
x = np.random.rand(50, 3)
y = np.zeros(50)
kpls = KPLS()
kpls.set_training_values(x, y)
kpls.train()
x_test = np.asarray([[0, 0, 0], [0.5, 0.5, 0.5], [1, 1, 1]])
y_test = kpls.predict_values(x_test)
# KPLS training fails anyway but not due to PLS exception StopIteration
self.assertEqual(np.linalg.norm(y_test - np.asarray([[0, 0, 0]])), 0)
def test_predict_output(self):
x = np.random.random((10, 3))
y = np.random.random((10))
kriging = KPLS(n_comp=2)
kriging.set_training_values(x, y)
kriging.train()
x_fail_1 = np.asarray([0, 0, 0, 0])
x_fail_2 = np.asarray([0])
self.assertRaises(ValueError, lambda: kriging.predict_values(x_fail_1))
self.assertRaises(ValueError, lambda: kriging.predict_values(x_fail_2))
var = kriging.predict_variances(x)
self.assertEqual(y.shape[0], var.shape[0])
def test_kpls(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLS
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.9, 1.0])
sm = KPLS(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
# estimated variance
s2 = sm.predict_variances(x)
# to compute the derivative according to the first variable
dydx = sm.predict_derivatives(xt, 0)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
# add a plot with variance
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.fill_between(
np.ravel(x),
np.ravel(y - 3 * np.sqrt(s2)),
np.ravel(y + 3 * np.sqrt(s2)),
color="lightgrey",
)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction", "Confidence Interval 99%"])
plt.show()
def test_kpls(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLS
xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
yt = np.array([0.0, 1.0, 1.5, 0.5, 1.0])
sm = KPLS(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0.0, 4.0, num)
y = sm.predict_values(x)
plt.plot(xt, yt, "o")
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.legend(["Training data", "Prediction"])
plt.show()
def test_kpls(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.surrogate_models import KPLS
xt = np.array([0., 1., 2., 3., 4.])
yt = np.array([0., 1., 1.5, 0.5, 1.0])
sm = KPLS(theta0=[1e-2])
sm.set_training_values(xt, yt)
sm.train()
num = 100
x = np.linspace(0., 4., num)
y = sm.predict_values(x)
plt.plot(xt, yt, 'o')
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.legend(['Training data', 'Prediction'])
plt.show()