def test_sum():
# define rbf and dot product custom kernels, and set hyperparameters
custom_rbf_kernel = generate_kernel(rbf, rbf_grad)
custom_dot_prod_kernel = generate_kernel(dot_prod, dot_prod_grad)
sum_custom_kernel = Sum(custom_dot_prod_kernel, custom_rbf_kernel)
model = GaussianProcessRegressor(kernel=sum_custom_kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
sum_kernel = Sum(DotProduct(), RBF())
model = GaussianProcessRegressor(kernel=sum_kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
assert (np.all(np.isclose(preds1, preds2)))
def test_kernel_no_params():
# test a kernel with no parameters
def cov(x, y):
return np.exp(-np.linalg.norm(x - y))
kernel = generate_kernel(cov)
# predict with a model using the above kernel
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds = model.predict(X_test)
assert (len(preds) == 10)
def test_product():
# define rbf and dot product custom kernels, and set hyperparameters
custom_rbf_kernel = generate_kernel(rbf,
rbf_grad,
length_scale=1,
length_scale_bounds=(1e-5, 1e5))
custom_dot_prod_kernel = generate_kernel(dot_prod,
dot_prod_grad,
sigma=1,
sigma_bounds=(1e-5, 1e5))
prod_custom_kernel = Product(custom_dot_prod_kernel, custom_rbf_kernel)
model = GaussianProcessRegressor(kernel=prod_custom_kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
prod_kernel = Product(DotProduct(), RBF())
model = GaussianProcessRegressor(kernel=prod_kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
assert (np.all(np.isclose(preds1, preds2)))
def test_generate_kernel():
kernel = generate_kernel(rbf,
rbf_grad,
length_scale=1,
length_scale_bounds=(1e-5, 1e5),
metric=distance.jaccard)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
print(model.predict(X_test))
kernel = generate_kernel(rbf,
rbf_grad,
length_scale_bounds=(1e-5, 1e5),
metric=distance.jaccard)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
print(model.predict(X_test))
kernel = generate_kernel(rbf,
rbf_grad,
length_scale=1,
metric=distance.sqeuclidean)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
print(model.predict(X_test))
kernel = generate_kernel(rbf, rbf_grad, length_scale=1)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
print(model.predict(X_test))
kernel = generate_kernel(rbf, rbf_grad)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
print(model.predict(X_test))
def test_exponentiation():
# define an rbf custom kernel, and set hyperparameters
custom_rbf_kernel = generate_kernel(rbf,
rbf_grad,
length_scale=1,
length_scale_bounds=(1e-5, 1e5))
exp_custom_kernel = Exponentiation(custom_rbf_kernel, 5)
model = GaussianProcessRegressor(kernel=exp_custom_kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
exp_kernel = Exponentiation(RBF(), 5)
model = GaussianProcessRegressor(kernel=exp_kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
assert (np.all(np.isclose(preds1, preds2)))
def test_constant():
# define a custom kernel with the constant covariance function, and set hyperparameters
kernel = generate_kernel(constant,
constant_grad,
const=1,
const_bounds=(1e-5, 1e5))
# create a GPR with the above kernel and use it on the data
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
# do the same with a ConstantKernel, with the same parameters
kernel = ConstantKernel()
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
# assert that the two give almost the same predictions
assert (np.all(np.isclose(preds1, preds2)))
def test_exp_sin_sq():
# define a custom kernel with the exponential sin squared covariance function
kernel = generate_kernel(exp_sin_sq,
exp_sin_sq_grad,
length_scale=1,
length_scale_bounds=(1e-5, 1e5),
periodicity=1,
periodicity_bounds=(1e-5, 1e5))
# create a GPR with the above kernel and use it on the data
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
# do the same with an RBF, with the same length_scale and bounds
kernel = ExpSineSquared()
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
def test_dot_prod():
# define a custom kernel with the dot product covariance function, and set hyperparameters
kernel = generate_kernel(dot_prod,
dot_prod_grad,
sigma=1,
sigma_bounds=(1e-5, 1e5))
# create a GPR with the above kernel and use it on the data
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)
# do the same with an RBF, with the same length_scale and bounds
kernel = DotProduct()
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds2 = model.predict(X_test)
# assert that the two give almost the same predictions
assert (np.all(np.isclose(preds1, preds2)))
def test_sandbox():
kernel = generate_kernel(rbf, rbf_grad)
model = GaussianProcessRegressor(kernel=kernel, random_state=0)
model.fit(X_train, Y_train)
preds1 = model.predict(X_test)