File size: 4,599 Bytes
2a79cd9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
import torch
import gpytorch
from gpytorch.kernels import MaternKernel, ScaleKernel
from gpytorch.priors import GammaPrior
from gpytorch.constraints import GreaterThan
import numpy as np
tkwargs = {
"dtype": torch.double,
"device": torch.device("cpu"),
}
def build_and_optimize_model(train_x, train_y):
""" Builds surrogate model and optimizes it."""
gp_options = {
'ls_prior1': 2.0, 'ls_prior2': 0.2, 'ls_prior3': 5.0,
'out_prior1': 5.0, 'out_prior2': 0.5, 'out_prior3': 8.0,
'noise_prior1': 1.5, 'noise_prior2': 0.1, 'noise_prior3': 5.0,
'noise_constraint': 1e-5,
}
n_features = np.shape(train_x)[1]
class ExactGPModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood):
super(ExactGPModel, self).__init__(train_x, train_y,
likelihood)
self.mean_module = gpytorch.means.ConstantMean()
kernels = MaternKernel(
ard_num_dims=n_features,
lengthscale_prior=GammaPrior(gp_options['ls_prior1'],
gp_options['ls_prior2'])
)
self.covar_module = ScaleKernel(
kernels,
outputscale_prior=GammaPrior(gp_options['out_prior1'],
gp_options['out_prior2']))
try:
ls_init = gp_options['ls_prior3']
self.covar_module.base_kernel.lengthscale = ls_init
except:
uniform = gp_options['ls_prior3']
ls_init = torch.ones(n_features).to(**tkwargs) * uniform
self.covar_module.base_kernel.lengthscale = ls_init
def forward(self, x):
mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)
# initialize likelihood and model
likelihood = gpytorch.likelihoods.GaussianLikelihood(
GammaPrior(gp_options['noise_prior1'], gp_options['noise_prior2'])
)
likelihood.noise = gp_options['noise_prior3']
model = ExactGPModel(train_x, train_y, likelihood).to(**tkwargs)
model.likelihood.noise_covar.register_constraint(
"raw_noise", GreaterThan(gp_options['noise_constraint'])
)
model.train()
likelihood.train()
optimizer = torch.optim.Adam([
{'params': model.parameters()},
], lr=0.1)
# "Loss" for GPs - the marginal log likelihood
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, model)
training_iter = 1000
for i in range(training_iter):
# Zero gradients from previous iteration
optimizer.zero_grad()
# Output from model
output = model(train_x)
# Calc loss and backprop gradients
loss = -mll(output, train_y.squeeze(-1).to(**tkwargs))
loss.backward()
optimizer.step()
model.eval()
likelihood.eval()
return model, likelihood # Optimized model
def get_covar_matrix(train_x):
"""
Section of the build_and_optimize_model function above to just get the covariance matrix of the prior (needed for the Vendi score).
------------------------------------------------------------------------------------------------------
train_x: tensor
tensor of data points for which the covariance matrix is calculated.
"""
gp_options = {
'ls_prior1': 2.0, 'ls_prior2': 0.2, 'ls_prior3': 5.0,
'out_prior1': 5.0, 'out_prior2': 0.5, 'out_prior3': 8.0,
'noise_prior1': 1.5, 'noise_prior2': 0.1, 'noise_prior3': 5.0,
'noise_constraint': 1e-5,
}
n_features = np.shape(train_x)[1]
kernels = MaternKernel(
ard_num_dims=n_features,
lengthscale_prior=GammaPrior(gp_options['ls_prior1'],
gp_options['ls_prior2'])
)
covar_module = ScaleKernel(
kernels,
outputscale_prior=GammaPrior(gp_options['out_prior1'],
gp_options['out_prior2']))
try:
ls_init = gp_options['ls_prior3']
covar_module.base_kernel.lengthscale = ls_init
except:
uniform = gp_options['ls_prior3']
ls_init = torch.ones(n_features).to(**tkwargs) * uniform
covar_module.base_kernel.lengthscale = ls_init
covar_matrix = covar_module(train_x)
return covar_matrix |