def test_switched_likelihood_regression_valid_num_latent_gps(num_latent_gps):
"""
A Regression test when using Switched likelihood: the number of latent
functions in a GP model must be equal to the number of columns in Y minus
one. The final column of Y is used to index the switch. If the number of
latent functions does not match, an exception will be raised.
"""
x = np.random.randn(100, 1)
y = np.hstack((np.random.randn(100, 1), np.random.randint(0, 3, (100, 1))))
data = x, y
Z = InducingPoints(np.random.randn(num_latent_gps, 1))
likelihoods = [StudentT()] * 3
switched_likelihood = SwitchedLikelihood(likelihoods)
m = gpflow.models.SVGP(
kernel=gpflow.kernels.Matern12(),
inducing_variable=Z,
likelihood=switched_likelihood,
num_latent_gps=num_latent_gps,
)
if num_latent_gps == 1:
_ = m.training_loss(data)
else:
with pytest.raises(tf.errors.InvalidArgumentError):
_ = m.training_loss(data)
def test_switched_likelihood_with_vgp():
"""
Reproduces the bug in https://github.com/GPflow/GPflow/issues/951
"""
X = np.random.randn(12 + 15, 1)
Y = np.random.randn(12 + 15, 1)
idx = np.array([0] * 12 + [1] * 15)
Y_aug = np.c_[Y, idx]
assert Y_aug.shape == (12 + 15, 2)
kernel = gpflow.kernels.Matern32()
likelihood = gpflow.likelihoods.SwitchedLikelihood([StudentT(), StudentT()])
model = gpflow.models.VGP((X, Y_aug), kernel=kernel, likelihood=likelihood)
# without bugfix, optimization errors out
opt = gpflow.optimizers.Scipy()
opt.minimize(model.training_loss, model.trainable_variables, options=dict(maxiter=1))
def test_switched_likelihood_regression_valid_num_latent(X, Y, num_latent):
"""
A Regression test when using Switched likelihood: the number of latent
functions in a GP model must be equal to the number of columns in Y minus
one. The final column of Y is used to index the switch. If the number of
latent functions does not match, an exception will be raised.
"""
Z = InducingPoints(np.random.randn(num_latent, 1))
likelihoods = [StudentT()] * 3
switched_likelihood = SwitchedLikelihood(likelihoods)
m = gpflow.models.SVGP(kernel=gpflow.kernels.Matern12(),
inducing_variable=Z,
likelihood=switched_likelihood,
num_latent=num_latent)
if num_latent == 1:
m.log_likelihood(X, Y)
else:
with pytest.raises(tf.errors.InvalidArgumentError):
m.log_likelihood(X, Y)
class LikelihoodSetup(object):
def __init__(self, likelihood, Y=Datum.Y, rtol=1e-06, atol=0.):
self.likelihood = likelihood
self.Y = Y
self.rtol = rtol
self.atol = atol
def __repr__(self):
name = self.likelihood.__class__.__name__
return f"{name}-rtol={self.rtol}-atol={self.atol}"
likelihood_setups = [
LikelihoodSetup(Gaussian()),
LikelihoodSetup(StudentT()),
LikelihoodSetup(Beta(),
Y=tf.random.uniform(Datum.Yshape, dtype=default_float())),
LikelihoodSetup(Ordinal(np.array([-1, 1])),
Y=tf.random.uniform(Datum.Yshape,
0,
3,
dtype=default_int())),
LikelihoodSetup(Poisson(invlink=tf.square),
Y=tf.random.poisson(Datum.Yshape,
1.0,
dtype=default_float())),
LikelihoodSetup(Exponential(invlink=tf.square),
Y=tf.random.uniform(Datum.Yshape, dtype=default_float())),
LikelihoodSetup(Gamma(invlink=tf.square),
Y=tf.random.uniform(Datum.Yshape, dtype=default_float())),