def test_neighbor_erasing_dataset_2(self):
x = np.linspace(0, 1, 100, dtype=np.float32).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
r = 0.0125
gp = MaternGP(x,
y,
noise_constraint=(0, 1e-3),
dataset_type='neighborerasing',
dataset_params={'radius': r})
x_ = np.linspace(0, 1, 20, dtype=np.float32).reshape((-1, 1))
y_ = np.exp(-x_**2).reshape(-1)
forgettable = [False] * len(y_)
gp.append_data(x_, y_, forgettable=forgettable)
x__ = np.linspace(0, 1, 21, dtype=np.float32).reshape((-1, 1))
y__ = np.exp(-x_**2).reshape(-1)
gp.append_data(x__, y__)
all_present_x_ = all(
x_ex.tolist() in gp.train_x.cpu().numpy().tolist() for x_ex in x_)
all_present_x__ = all(
x__ex.tolist() in gp.train_x.cpu().numpy().tolist()
for x__ex in x__)
self.assertTrue(all_present_x_ and all_present_x__)
def test_neighbor_erasing_dataset_1(self):
x = np.linspace(0, 1, 100, dtype=np.float32).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
r = 0.0125
gp = MaternGP(x,
y,
noise_constraint=(0, 1e-3),
dataset_type='neighborerasing',
dataset_params={'radius': r})
x_ = np.linspace(0, 1, 20, dtype=np.float32).reshape((-1, 1))
y_ = np.exp(-x_**2).reshape(-1)
gp.append_data(x_, y_)
distances = np.abs(gp.train_x.cpu().numpy().reshape((-1, 1)) -
x_.squeeze())
self.assertTrue(np.all(distances[:-len(x_), :] >= r))
self.assertTrue((gp.train_x.cpu().numpy()[-len(x_):, :] == x_).all())
# Set to True to plot
if False:
import matplotlib.pyplot as plt
plt.figure()
plt.scatter(
gp.train_x.cpu().numpy()[-len(x_):],
gp.train_x.cpu().numpy()[-len(x_):],
color='r',
)
plt.scatter(gp.train_x.cpu().numpy()[:-len(x_)],
gp.train_x.cpu().numpy()[:-len(x_)],
color='b')
def test_data_manipulation(self):
tol = 1e-1
x = np.linspace(0, 1, 101).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
x_ = np.linspace(1.5, 2, 51).reshape((-1, 1))
y_ = 1 + np.exp(-x_**2).reshape(-1)
gp = MaternGP(x, y, noise_prior=(0.1, 0.1))
tmp = gp.empty_data()
self.assertEqual(tmp, gp)
self.assertTrue(tuple(gp.train_x.shape), (0, 1))
# GPyTorch fails when predicting with an empty dataset, so the following line fails if uncommented
# gp.predict(x)
gp.set_data(x, y)
self.assertEqual(tuple(gp.train_x.shape), (len(x), 1))
gp.optimize_hyperparameters(epochs=10)
gp_pred = gp.predict(x_).mean.cpu().numpy()
self.assertFalse(np.all(np.abs(gp_pred - y_) < tol))
tmp = gp.append_data(x_, y_)
# self.assertTrue(gp != tmp)
# self.assertEqual(tuple(gp.train_x.shape), (len(x), 1))
self.assertEqual(tuple(tmp.train_x.shape), (len(x) + len(x_), 1))
tmp.optimize_hyperparameters(epochs=10)
tmp_pred = tmp.predict(x_).mean.cpu().numpy()
self.assertTrue(np.all(np.abs(tmp_pred - y_) < tol))
def test_timeforgetting_dataset(self):
x = np.linspace(0, 1, 100, dtype=np.float32).reshape((-1, 1))
y = np.exp(-x**2).reshape(-1)
gp = MaternGP(x,
y,
noise_constraint=(0, 1e-3),
dataset_type='timeforgetting',
dataset_params={'keep': 50})
self.assertTrue((gp.train_x.cpu().numpy() == x[-50:]).all())
self.assertTrue((gp.train_y.cpu().numpy() == y[-50:]).all())
gp.append_data(x[:10], y[:10])
self.assertTrue((gp.train_x.cpu().numpy() == np.vstack(
(x[-40:], x[:10]))).all())
self.assertTrue((gp.train_y.cpu().numpy() == np.hstack(
(y[-40:], y[:10]))).all())
gp.set_data(x[:75], y[:75])
self.assertTrue((gp.train_x.cpu().numpy() == x[25:75]).all())
self.assertTrue((gp.train_y.cpu().numpy() == y[25:75]).all())