mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-02 08:12:39 +02:00
105 lines
4.5 KiB
Python
105 lines
4.5 KiB
Python
# Copyright (c) 2014, James Hensman, Alex Matthews
|
|
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
|
|
|
import numpy as np
|
|
from ..util import choleskies
|
|
from .sparse_gp import SparseGP
|
|
from .parameterization.param import Param
|
|
from ..inference.latent_function_inference.svgp import SVGP as svgp_inf
|
|
|
|
|
|
class SVGP(SparseGP):
|
|
def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, name='SVGP', Y_metadata=None, batchsize=None, num_latent_functions=None):
|
|
"""
|
|
Stochastic Variational GP.
|
|
|
|
For Gaussian Likelihoods, this implements
|
|
|
|
Gaussian Processes for Big data, Hensman, Fusi and Lawrence, UAI 2013,
|
|
|
|
But without natural gradients. We'll use the lower-triangluar
|
|
representation of the covariance matrix to ensure
|
|
positive-definiteness.
|
|
|
|
For Non Gaussian Likelihoods, this implements
|
|
|
|
Hensman, Matthews and Ghahramani, Scalable Variational GP Classification, ArXiv 1411.2005
|
|
"""
|
|
self.batchsize = batchsize
|
|
self.X_all, self.Y_all = X, Y
|
|
if batchsize is None:
|
|
X_batch, Y_batch = X, Y
|
|
else:
|
|
import climin.util
|
|
#Make a climin slicer to make drawing minibatches much quicker
|
|
self.slicer = climin.util.draw_mini_slices(self.X_all.shape[0], self.batchsize)
|
|
X_batch, Y_batch = self.new_batch()
|
|
|
|
#create the SVI inference method
|
|
inf_method = svgp_inf()
|
|
|
|
super(SVGP, self).__init__(X_batch, Y_batch, Z, kernel, likelihood, mean_function=mean_function, inference_method=inf_method,
|
|
name=name, Y_metadata=Y_metadata, normalizer=False)
|
|
|
|
#assume the number of latent functions is one per col of Y unless specified
|
|
if num_latent_functions is None:
|
|
num_latent_functions = Y.shape[1]
|
|
|
|
self.m = Param('q_u_mean', np.zeros((self.num_inducing, num_latent_functions)))
|
|
chol = choleskies.triang_to_flat(np.tile(np.eye(self.num_inducing)[None,:,:], (num_latent_functions, 1,1)))
|
|
self.chol = Param('q_u_chol', chol)
|
|
self.link_parameter(self.chol)
|
|
self.link_parameter(self.m)
|
|
|
|
def parameters_changed(self):
|
|
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.q_u_mean, self.q_u_chol, self.kern, self.X, self.Z, self.likelihood, self.Y, self.mean_function, self.Y_metadata, KL_scale=1.0, batch_scale=float(self.X_all.shape[0])/float(self.X.shape[0]))
|
|
|
|
#update the kernel gradients
|
|
self.kern.update_gradients_full(self.grad_dict['dL_dKmm'], self.Z)
|
|
grad = self.kern.gradient.copy()
|
|
self.kern.update_gradients_full(self.grad_dict['dL_dKmn'], self.Z, self.X)
|
|
grad += self.kern.gradient.copy()
|
|
self.kern.update_gradients_diag(self.grad_dict['dL_dKdiag'], self.X)
|
|
self.kern.gradient += grad
|
|
if not self.Z.is_fixed:# only compute these expensive gradients if we need them
|
|
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z) + self.kern.gradients_X(self.grad_dict['dL_dKmn'], self.Z, self.X)
|
|
|
|
|
|
self.likelihood.update_gradients(self.grad_dict['dL_dthetaL'])
|
|
#update the variational parameter gradients:
|
|
self.m.gradient = self.grad_dict['dL_dm']
|
|
self.chol.gradient = self.grad_dict['dL_dchol']
|
|
|
|
if self.mean_function is not None:
|
|
self.mean_function.update_gradients(self.grad_dict['dL_dmfX'], self.X)
|
|
g = self.mean_function.gradient[:].copy()
|
|
self.mean_function.update_gradients(self.grad_dict['dL_dmfZ'], self.Z)
|
|
self.mean_function.gradient[:] += g
|
|
self.Z.gradient[:] += self.mean_function.gradients_X(self.grad_dict['dL_dmfZ'], self.Z)
|
|
|
|
def set_data(self, X, Y):
|
|
"""
|
|
Set the data without calling parameters_changed to avoid wasted computation
|
|
If this is called by the stochastic_grad function this will immediately update the gradients
|
|
"""
|
|
assert X.shape[1]==self.Z.shape[1]
|
|
self.X, self.Y = X, Y
|
|
|
|
def new_batch(self):
|
|
"""
|
|
Return a new batch of X and Y by taking a chunk of data from the complete X and Y
|
|
"""
|
|
i = next(self.slicer)
|
|
return self.X_all[i], self.Y_all[i]
|
|
|
|
def stochastic_grad(self, parameters):
|
|
self.set_data(*self.new_batch())
|
|
return self._grads(parameters)
|
|
|
|
def optimizeWithFreezingZ(self):
|
|
self.Z.fix()
|
|
self.kern.fix()
|
|
self.optimize('bfgs')
|
|
self.Z.unfix()
|
|
self.kern.constrain_positive()
|
|
self.optimize('bfgs')
|