def test_forward_pass():
npr.seed(1)
N = 15
D = 10
data = 0.5*npr.rand(N,D)
norm = Normalization(3)
norm_inds = [1,3,5]
bw = BetaWarp(2)
bw_inds = [0,2]
lin = Linear(3)
lin_inds = [6,8,9]
t = Transformer(D)
t.add_layer((norm, norm_inds), (bw, bw_inds), (lin, lin_inds))
new_data = t.forward_pass(data)
assert new_data.shape[1] == 9
assert np.all(new_data[:,7:] == data[:,[4,7]])
assert np.linalg.norm(new_data[:,0:3].sum(1) - 1) < 1e-10
bw = BetaWarp(9)
t.add_layer(bw)
def test_backward_pass():
npr.seed(1)
N = 10
D = 3
alpha = Hyperparameter(
initial_value = 2*np.ones(D),
prior = priors.Lognormal(1.5),
name = 'alpha'
)
beta = Hyperparameter(
initial_value = 0.5*np.ones(D),
prior = priors.Lognormal(1.5),
name = 'beta'
)
bw = BetaWarp(D, alpha=alpha, beta=beta)
data = 0.5*np.ones(D)
v = npr.randn(D)
bw.forward_pass(data)
assert np.all(bw.backward_pass(v) == 0.53033008588991071*v)
def test_backward_pass():
npr.seed(1)
eps = 1e-5
N = 15
D = 10
data = 0.5*npr.rand(N,D)
norm = Normalization(3)
norm_inds = [1,3,5]
bw = BetaWarp(2)
bw_inds = [0,2]
lin = Linear(3)
lin_inds = [6,8,9]
t = Transformer(D)
# Add a layer and test the gradient
t.add_layer((norm, norm_inds), (bw, bw_inds), (lin, lin_inds))
new_data = t.forward_pass(data)
loss = np.sum(new_data**2)
V = 2*new_data
dloss = t.backward_pass(V)
dloss_est = np.zeros(dloss.shape)
for i in xrange(N):
for j in xrange(D):
data[i,j] += eps
loss_1 = np.sum(t.forward_pass(data)**2)
data[i,j] -= 2*eps
loss_2 = np.sum(t.forward_pass(data)**2)
data[i,j] += eps
dloss_est[i,j] = ((loss_1 - loss_2) / (2*eps))
assert np.linalg.norm(dloss - dloss_est) < 1e-6
# Add a second layer and test the gradient
t.add_layer(Linear(9))
new_data = t.forward_pass(data)
loss = np.sum(new_data**2)
V = 2*new_data
dloss = t.backward_pass(V)
dloss_est = np.zeros(dloss.shape)
for i in xrange(N):
for j in xrange(D):
data[i,j] += eps
loss_1 = np.sum(t.forward_pass(data)**2)
data[i,j] -= 2*eps
loss_2 = np.sum(t.forward_pass(data)**2)
data[i,j] += eps
dloss_est[i,j] = ((loss_1 - loss_2) / (2*eps))
assert np.linalg.norm(dloss - dloss_est) < 1e-6
def _build(self):
self.params = {}
self.latent_values = None
# Build the transformer
beta_warp = BetaWarp(self.num_dims)
beta_alpha, beta_beta = beta_warp.hypers
self.params['beta_alpha'] = beta_alpha
self.params['beta_beta'] = beta_beta
transformer = Transformer(self.num_dims)
transformer.add_layer(beta_warp)
# Build the component kernels
input_kernel = Matern52(self.num_dims)
ls = input_kernel.hypers
self.params['ls'] = ls
# Now apply the transformation.
transform_kernel = TransformKernel(input_kernel, transformer)
# Add some perturbation for stability
stability_noise = Noise(self.num_dims)
# Finally make a noisy version if necessary
# In a classifier GP the notion of "noise" is really just the scale.
if self.noiseless:
self._kernel = SumKernel(transform_kernel, stability_noise)
else:
scaled_kernel = Scale(transform_kernel)
self._kernel = SumKernel(scaled_kernel, stability_noise)
amp2 = scaled_kernel.hypers
self.params['amp2'] = amp2
# Build the mean function (just a constant mean for now)
self.mean = Hyperparameter(initial_value=0.0,
prior=priors.Gaussian(0.0, 1.0),
name='mean')
self.params['mean'] = self.mean
# Buld the latent values. Empty for now until the GP gets data.
self.latent_values = Hyperparameter(initial_value=np.array([]),
name='latent values')
# Build the samplers
to_sample = [self.mean] if self.noiseless else [self.mean, amp2]
self._samplers.append(
SliceSampler(*to_sample, compwise=False, thinning=self.thinning))
self._samplers.append(
WhitenedPriorSliceSampler(ls,
beta_alpha,
beta_beta,
compwise=True,
thinning=self.thinning))
self.latent_values_sampler = EllipticalSliceSampler(
self.latent_values, thinning=self.ess_thinning)
def test_forward_pass():
npr.seed(1)
N = 10
D = 3
alpha = Hyperparameter(initial_value=2 * np.ones(D),
prior=priors.Lognormal(1.5),
name='alpha')
beta = Hyperparameter(initial_value=0.5 * np.ones(D),
prior=priors.Lognormal(1.5),
name='beta')
bw = BetaWarp(D, alpha=alpha, beta=beta)
data = 0.5 * np.ones(D)
assert np.all(bw.forward_pass(data) == 0.1161165235168156)
def test_backward_pass():
npr.seed(1)
N = 10
D = 3
alpha = Hyperparameter(initial_value=2 * np.ones(D),
prior=priors.Lognormal(1.5),
name='alpha')
beta = Hyperparameter(initial_value=0.5 * np.ones(D),
prior=priors.Lognormal(1.5),
name='beta')
bw = BetaWarp(D, alpha=alpha, beta=beta)
data = 0.5 * np.ones(D)
v = npr.randn(D)
bw.forward_pass(data)
assert np.all(bw.backward_pass(v) == 0.53033008588991071 * v)
def test_validation():
warnings.filterwarnings('error')
npr.seed(1)
N = 10
D = 3
bw = BetaWarp(D)
data = npr.randn(N, D)
assert_raises(UserWarning, bw.forward_pass, data)
def test_forward_pass():
npr.seed(1)
N = 10
D = 3
alpha = Hyperparameter(
initial_value = 2*np.ones(D),
prior = priors.Lognormal(1.5),
name = 'alpha'
)
beta = Hyperparameter(
initial_value = 0.5*np.ones(D),
prior = priors.Lognormal(1.5),
name = 'beta'
)
bw = BetaWarp(D, alpha=alpha, beta=beta)
data = 0.5*np.ones(D)
assert np.all(bw.forward_pass(data) == 0.1161165235168156)
def test_construction():
npr.seed(1)
D = 10
norm = Normalization(3)
norm_inds = [1,3,5]
bw = BetaWarp(2)
bw_inds = [0,2]
lin = Linear(3)
lin_inds = [6,8,9]
t = Transformer(D)
t.add_layer((norm, norm_inds), (bw, bw_inds), (lin, lin_inds))
def test_grad():
npr.seed(1)
eps = 1e-5
N = 10
M = 5
D = 5
beta_warp = BetaWarp(2)
norm = Normalization(2)
lin = Linear(D)
transformer = Transformer(D)
# Each entry is a tuple, (transformation, indices_it_acts_on)
transformer.add_layer(
(beta_warp, [0, 2]),
(norm, [1, 4])) # This is crazy. We would never do this.
# One transformation means apply to all dimensions.
transformer.add_layer(lin)
kernel = TransformKernel(Matern52(lin.num_factors), transformer)
data1 = npr.rand(N, D)
data2 = npr.rand(M, D)
loss = np.sum(kernel.cross_cov(data1, data2))
dloss = kernel.cross_cov_grad_data(data1, data2).sum(0)
dloss_est = np.zeros(dloss.shape)
for i in xrange(M):
for j in xrange(D):
data2[i, j] += eps
loss_1 = np.sum(kernel.cross_cov(data1, data2))
data2[i, j] -= 2 * eps
loss_2 = np.sum(kernel.cross_cov(data1, data2))
data2[i, j] += eps
dloss_est[i, j] = ((loss_1 - loss_2) / (2 * eps))
assert np.linalg.norm(dloss - dloss_est) < 1e-6