def test_monotonic_factory_1d(self):
conf = {"monotonic_mean_covar_factory": {"lb": [0], "ub": [1]}}
config = Config(config_dict=conf)
meanfun, covarfun = monotonic_mean_covar_factory(config)
self.assertTrue(covarfun.base_kernel.ard_num_dims == 1)
self.assertTrue(isinstance(meanfun, ConstantMeanPartialObsGrad))
self.assertTrue(isinstance(covarfun, gpytorch.kernels.ScaleKernel))
self.assertTrue(isinstance(covarfun.base_kernel, RBFKernelPartialObsGrad))
self.assertTrue(meanfun.constant.requires_grad)
def test_monotonic_factory_2d(self):
conf = {
"monotonic_mean_covar_factory": {
"lb": [0, 1],
"ub": [1, 70],
"target": 0.89,
}
}
config = Config(config_dict=conf)
meanfun, covarfun = monotonic_mean_covar_factory(config)
self.assertTrue(covarfun.base_kernel.ard_num_dims == 2)
self.assertTrue(isinstance(meanfun, ConstantMeanPartialObsGrad))
self.assertTrue(isinstance(covarfun, gpytorch.kernels.ScaleKernel))
self.assertTrue(isinstance(covarfun.base_kernel, RBFKernelPartialObsGrad))
self.assertTrue(meanfun.constant.requires_grad is False)
self.assertTrue(meanfun.constant == norm.ppf(0.89))
def test_monotonic_factory_args_1d(self):
conf = {
"monotonic_mean_covar_factory": {
"lb": [0],
"ub": [1],
"fixed_mean": True,
"target": 0.88,
}
}
config = Config(config_dict=conf)
meanfun, covarfun = monotonic_mean_covar_factory(config)
self.assertTrue(covarfun.base_kernel.ard_num_dims == 1)
self.assertTrue(isinstance(meanfun, ConstantMeanPartialObsGrad))
self.assertTrue(isinstance(covarfun, gpytorch.kernels.ScaleKernel))
self.assertTrue(isinstance(covarfun.base_kernel, RBFKernelPartialObsGrad))
self.assertFalse(meanfun.constant.requires_grad)
self.assertTrue(np.allclose(meanfun.constant, norm.ppf(0.88)))
def plot_prior_samps_1d():
config = Config(
config_dict={
"common": {
"outcome_type": "single_probit",
"target": 0.75,
"lb": "[-3]",
"ub": "[3]",
},
"default_mean_covar_factory": {},
"song_mean_covar_factory": {},
"monotonic_mean_covar_factory": {"monotonic_idxs": "[0]"},
}
)
lb = torch.Tensor([-3])
ub = torch.Tensor([3])
nsamps = 10
gridsize = 50
grid = _dim_grid(lower=lb, upper=ub, dim=1, gridsize=gridsize)
np.random.seed(global_seed)
torch.random.manual_seed(global_seed)
with gpytorch.settings.prior_mode(True):
rbf_mean, rbf_covar = default_mean_covar_factory(config)
rbf_model = GPClassificationModel(
inducing_min=lb,
inducing_max=ub,
inducing_size=100,
mean_module=rbf_mean,
covar_module=rbf_covar,
)
# add just two samples at high and low
rbf_model.set_train_data(
torch.Tensor([-3, 3])[:, None], torch.LongTensor([0, 1])
)
rbf_samps = rbf_model(grid).sample(torch.Size([nsamps]))
song_mean, song_covar = song_mean_covar_factory(config)
song_model = GPClassificationModel(
inducing_min=lb,
inducing_max=ub,
inducing_size=100,
mean_module=song_mean,
covar_module=song_covar,
)
song_model.set_train_data(
torch.Tensor([-3, 3])[:, None], torch.LongTensor([0, 1])
)
song_samps = song_model(grid).sample(torch.Size([nsamps]))
mono_mean, mono_covar = monotonic_mean_covar_factory(config)
mono_model = MonotonicRejectionGP(
likelihood="probit-bernoulli",
monotonic_idxs=[0],
mean_module=mono_mean,
covar_module=mono_covar,
)
bounds_ = torch.tensor([-3.0, 3.0])[:, None]
# Select inducing points
mono_model.inducing_points = draw_sobol_samples(
bounds=bounds_, n=mono_model.num_induc, q=1
).squeeze(1)
inducing_points_aug = mono_model._augment_with_deriv_index(
mono_model.inducing_points, 0
)
scales = ub - lb
dummy_train_x = mono_model._augment_with_deriv_index(
torch.Tensor([-3, 3])[:, None], 0
)
mono_model.model = MixedDerivativeVariationalGP(
train_x=dummy_train_x,
train_y=torch.LongTensor([0, 1]),
inducing_points=inducing_points_aug,
scales=scales,
fixed_prior_mean=torch.Tensor([0.75]),
covar_module=mono_covar,
mean_module=mono_mean,
)
mono_samps = mono_model.sample(grid, nsamps)
fig, ax = plt.subplots(1, 3, figsize=(7.5, 3))
fig.tight_layout(rect=[0.01, 0.03, 1, 0.9])
fig.suptitle("GP prior samples (probit-transformed)")
ax[0].plot(grid.squeeze(), norm.cdf(song_samps.T), "b")
ax[0].set_ylabel("Response Probability")
ax[0].set_title("Linear kernel")
ax[1].plot(grid.squeeze(), norm.cdf(rbf_samps.T), "b")
ax[1].set_xlabel("Intensity")
ax[1].set_title("RBF kernel (nonmonotonic)")
ax[2].plot(grid.squeeze(), norm.cdf(mono_samps.T), "b")
ax[2].set_title("RBF kernel (monotonic)")
return fig
def plot_prior_samps_2d():
config = Config(
config_dict={
"common": {
"outcome_type": "single_probit",
"target": 0.75,
"lb": "[-3, -3]",
"ub": "[3, 3]",
},
"default_mean_covar_factory": {},
"song_mean_covar_factory": {},
"monotonic_mean_covar_factory": {"monotonic_idxs": "[1]"},
}
)
lb = torch.Tensor([-3, -3])
ub = torch.Tensor([3, 3])
nsamps = 5
gridsize = 30
grid = _dim_grid(lower=lb, upper=ub, dim=2, gridsize=gridsize)
np.random.seed(global_seed)
torch.random.manual_seed(global_seed)
with gpytorch.settings.prior_mode(True):
rbf_mean, rbf_covar = default_mean_covar_factory(config)
rbf_model = GPClassificationModel(
inducing_min=lb,
inducing_max=ub,
inducing_size=100,
mean_module=rbf_mean,
covar_module=rbf_covar,
)
# add just two samples at high and low
rbf_model.set_train_data(torch.Tensor([-3, -3])[:, None], torch.LongTensor([0]))
rbf_samps = rbf_model(grid).sample(torch.Size([nsamps]))
song_mean, song_covar = song_mean_covar_factory(config)
song_model = GPClassificationModel(
inducing_min=lb,
inducing_max=ub,
inducing_size=100,
mean_module=song_mean,
covar_module=song_covar,
)
song_model.set_train_data(
torch.Tensor([-3, -3])[:, None], torch.LongTensor([0])
)
song_samps = song_model(grid).sample(torch.Size([nsamps]))
mono_mean, mono_covar = monotonic_mean_covar_factory(config)
mono_model = MonotonicRejectionGP(
likelihood="probit-bernoulli",
monotonic_idxs=[1],
mean_module=mono_mean,
covar_module=mono_covar,
num_induc=1000,
)
bounds_ = torch.tensor([-3.0, -3.0, 3.0, 3.0]).reshape(2, -1)
# Select inducing points
mono_model.inducing_points = draw_sobol_samples(
bounds=bounds_, n=mono_model.num_induc, q=1
).squeeze(1)
inducing_points_aug = mono_model._augment_with_deriv_index(
mono_model.inducing_points, 0
)
scales = ub - lb
dummy_train_x = mono_model._augment_with_deriv_index(
torch.Tensor([-3, 3])[None, :], 0
)
mono_model.model = MixedDerivativeVariationalGP(
train_x=dummy_train_x,
train_y=torch.LongTensor([0]),
inducing_points=inducing_points_aug,
scales=scales,
fixed_prior_mean=torch.Tensor([0.75]),
covar_module=mono_covar,
mean_module=mono_mean,
)
mono_samps = mono_model.sample(grid, nsamps)
intensity_grid = np.linspace(-3, 3, gridsize)
fig, ax = plt.subplots(1, 3, figsize=(7.5, 3))
fig.tight_layout(rect=[0, 0.03, 1, 0.9])
fig.suptitle("Prior samples")
square_samps = np.array([s.reshape((gridsize,) * 2).numpy() for s in song_samps])
plotsamps = norm.cdf(square_samps[:, ::5, :]).T.reshape(gridsize, -1)
ax[0].plot(intensity_grid, plotsamps, "b")
ax[0].set_title("Linear kernel model")
square_samps = np.array([s.reshape((gridsize,) * 2).numpy() for s in rbf_samps])
plotsamps = norm.cdf(square_samps[:, ::5, :]).T.reshape(gridsize, -1)
ax[1].plot(intensity_grid, plotsamps, "b")
ax[1].set_title("Nonmonotonic RBF kernel model")
square_samps = np.array([s.reshape((gridsize,) * 2).numpy() for s in mono_samps])
plotsamps = norm.cdf(square_samps[:, ::5, :]).T.reshape(gridsize, -1)
ax[2].plot(intensity_grid, plotsamps, "b")
ax[2].set_title("Monotonic RBF kernel model")
return fig