mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-04 01:02:39 +02:00
87 lines
4 KiB
Python
87 lines
4 KiB
Python
# Copyright (c) 2017, Zhenwen Dai
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
|
|
import numpy as np
|
|
from ..core.sparse_gp_mpi import SparseGP_MPI
|
|
from .. import likelihoods
|
|
from .. import kern
|
|
from ..inference.latent_function_inference.vardtc_md import VarDTC_MD
|
|
from GPy.core.parameterization.variational import NormalPosterior
|
|
|
|
class SparseGPRegressionMD(SparseGP_MPI):
|
|
"""Sparse Gaussian Process Regression with Missing Data
|
|
|
|
This model targets at the use case, in which there are multiple
|
|
output dimensions (different dimensions are assumed to be
|
|
independent following the same GP prior) and each output dimension
|
|
is observed at a different set of inputs. The model takes a
|
|
different data format: the inputs and outputs observations of all
|
|
the output dimensions are stacked together correspondingly into
|
|
two matrices. An extra array is used to indicate the index of
|
|
output dimension for each data point. The output dimensions are
|
|
indexed using integers from 0 to D-1 assuming there are D output
|
|
dimensions.
|
|
|
|
:param X: input observations.
|
|
:type X: numpy.ndarray
|
|
:param Y: output observations, each column corresponding to an output dimension.
|
|
:type Y: numpy.ndarray
|
|
:param indexD: the array containing the index of output dimension for each data point
|
|
:type indexD: numpy.ndarray
|
|
:param kernel: a GPy kernel for GP of individual output dimensions ** defaults to RBF **
|
|
:type kernel: GPy.kern.Kern or None
|
|
:param Z: inducing inputs
|
|
:type Z: numpy.ndarray or None
|
|
:param num_inducing: a tuple (M, Mr). M is the number of inducing points for GP of individual output dimensions. Mr is the number of inducing points for the latent space.
|
|
:type num_inducing: (int, int)
|
|
:param boolean individual_Y_noise: whether individual output dimensions have their own noise variance or not, boolean
|
|
:param str name: the name of the model
|
|
|
|
"""
|
|
|
|
def __init__(self, X, Y, indexD, kernel=None, Z=None, num_inducing=10, normalizer=None, mpi_comm=None, individual_Y_noise=False, name='sparse_gp'):
|
|
|
|
assert len(Y.shape)==1 or Y.shape[1]==1
|
|
self.individual_Y_noise = individual_Y_noise
|
|
self.indexD = indexD
|
|
output_dim = int(np.max(indexD))+1
|
|
|
|
num_data, input_dim = X.shape
|
|
|
|
# kern defaults to rbf (plus white for stability)
|
|
if kernel is None:
|
|
kernel = kern.RBF(input_dim)# + kern.white(input_dim, variance=1e-3)
|
|
|
|
# Z defaults to a subset of the data
|
|
if Z is None:
|
|
i = np.random.permutation(num_data)[:min(num_inducing, num_data)]
|
|
Z = X.view(np.ndarray)[i].copy()
|
|
else:
|
|
assert Z.shape[1] == input_dim
|
|
|
|
if individual_Y_noise:
|
|
likelihood = likelihoods.Gaussian(variance=np.array([np.var(Y[indexD==d]) for d in range(output_dim)])*0.01)
|
|
else:
|
|
likelihood = likelihoods.Gaussian(variance=np.var(Y)*0.01)
|
|
|
|
infr = VarDTC_MD()
|
|
|
|
super(SparseGPRegressionMD, self).__init__(X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
|
|
self.output_dim = output_dim
|
|
|
|
def parameters_changed(self):
|
|
|
|
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.indexD, self.output_dim, self.Y_metadata)
|
|
|
|
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'] if self.individual_Y_noise else self.grad_dict['dL_dthetaL'].sum())
|
|
|
|
self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X)
|
|
kerngrad = self.kern.gradient.copy()
|
|
self.kern.update_gradients_full(self.grad_dict['dL_dKnm'], self.X, self.Z)
|
|
kerngrad += self.kern.gradient
|
|
self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z, None)
|
|
self.kern.gradient += kerngrad
|
|
#gradients wrt Z
|
|
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
|
|
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
|