mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-06-05 14:55:15 +02:00
removed a lot of unnecessary code in sparse GP
This commit is contained in:
James Hensman
parent
739e8d4058
commit
9b3795fd8c
2 changed files with 41 additions and 280 deletions
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@ -6,6 +6,5 @@ from parameterization import priors
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from parameterization.parameterized import *
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from gp import GP
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from sparse_gp import SparseGP
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from ..inference.latent_function_inference.fitc import FITC
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from svigp import SVIGP
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from mapping import *
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@ -4,12 +4,12 @@
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import numpy as np
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import pylab as pb
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from ..util.linalg import mdot, tdot, symmetrify, backsub_both_sides, chol_inv, dtrtrs, dpotrs, dpotri
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from gp_base import GPBase
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from GPy.core import Param
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from gp import GP
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from parameterization.param import Param
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class SparseGP(GPBase):
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class SparseGP(GP):
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"""
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Variational sparse GP model
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A general purpose Sparse GP model
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:param X: inputs
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:type X: np.ndarray (num_data x input_dim)
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@ -19,17 +19,25 @@ class SparseGP(GPBase):
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:type kernel: a GPy.kern.kern instance
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:param X_variance: The uncertainty in the measurements of X (Gaussian variance)
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:type X_variance: np.ndarray (num_data x input_dim) | None
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:param Z: inducing inputs (optional, see note)
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:type Z: np.ndarray (num_inducing x input_dim) | None
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:param Z: inducing inputs
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:type Z: np.ndarray (num_inducing x input_dim)
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:param num_inducing: Number of inducing points (optional, default 10. Ignored if Z is not None)
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:type num_inducing: int
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:param normalize_(X|Y): whether to normalize the data before computing (predictions will be in original scales)
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:type normalize_(X|Y): bool
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"""
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def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False, name='sparse gp'):
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GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X, name=name)
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def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, X_variance=None, name='sparse gp'):
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#pick a sensible inference method
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if inference_method is None:
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if isinstance(likelihood, likelihoods.Gaussian):
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inference_method = varDTC.Gaussian_inference()
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else:
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#inference_method = ??
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raise NotImplementedError, "what to do what to do?"
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print "defaulting to ", inference_method, "for latent function inference"
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GP.__init__(self, X, Y, likelihood, inference_method, kernel, name)
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self.Z = Z
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self.num_inducing = Z.shape[0]
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@ -42,39 +50,13 @@ class SparseGP(GPBase):
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self.has_uncertain_inputs = True
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self.X_variance = X_variance
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if normalize_X:
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self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
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# normalize X uncertainty also
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if self.has_uncertain_inputs:
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self.X_variance /= np.square(self._Xscale)
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self._const_jitter = None
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self.Z = Param('inducing inputs', self.Z)
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self.add_parameter(self.Z, gradient=self.dL_dZ, index=0)
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self.add_parameter(self.kern, gradient=self.dL_dtheta)
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self.add_parameter(self.likelihood, gradient=lambda:self.likelihood._gradients(partial=self.partial_for_likelihood))
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#self.Z.add_observer(self, lambda Z: self._compute_kernel_matrices() or self._computations())
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def getstate(self):
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"""
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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"""
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return GPBase.getstate(self) + [self.Z,
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self.num_inducing,
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self.has_uncertain_inputs,
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self.X_variance]
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def setstate(self, state):
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self.X_variance = state.pop()
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self.has_uncertain_inputs = state.pop()
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self.num_inducing = state.pop()
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self.Z = state.pop()
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GPBase.setstate(self, state)
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def _compute_kernel_matrices(self):
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def parameters_changed(self):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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if self.has_uncertain_inputs:
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@ -85,35 +67,11 @@ class SparseGP(GPBase):
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self.psi0 = self.kern.Kdiag(self.X)
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self.psi1 = self.kern.K(self.X, self.Z)
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self.psi2 = None
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def parameters_changed(self):
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self._compute_kernel_matrices()
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self._computations()
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self.Cpsi1V = None
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self.dL_dK = self.dL_dKmm
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self.posterior = self.inference_method.inference(??)
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super(SparseGP, self).parameters_changed()
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def update_likelihood_approximation(self, **kwargs):
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"""
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Approximates a non-gaussian likelihood using Expectation Propagation
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For a Gaussian likelihood, no iteration is required:
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this function does nothing
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"""
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if not isinstance(self.likelihood, Gaussian): # Updates not needed for Gaussian likelihood
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self.likelihood.restart()
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if self.has_uncertain_inputs:
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Lmi = chol_inv(self._Lm)
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Kmmi = tdot(Lmi.T)
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diag_tr_psi2Kmmi = np.array([np.trace(psi2_Kmmi) for psi2_Kmmi in np.dot(self.psi2, Kmmi)])
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self.likelihood.fit_FITC(self.Kmm, self.psi1.T, diag_tr_psi2Kmmi, **kwargs) # This uses the fit_FITC code, but does not perfomr a FITC-EP.#TODO solve potential confusion
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# raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
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else:
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self.likelihood.fit_DTC(self.Kmm, self.psi1.T, **kwargs)
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# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
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self._set_params(self._get_params()) # update the GP
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def dL_dtheta(self):
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"""
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Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
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@ -143,82 +101,14 @@ class SparseGP(GPBase):
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def _raw_predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False):
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"""
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Internal helper function for making predictions, does not account for
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normalization or likelihood function
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Make a prediction for the latent function values
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"""
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Bi, _ = dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
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symmetrify(Bi)
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Kmmi_LmiBLmi = backsub_both_sides(self._Lm, np.eye(self.num_inducing) - Bi)
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if self.Cpsi1V is None:
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psi1V = np.dot(self.psi1.T, self.likelihood.V)
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tmp, _ = dtrtrs(self._Lm, np.asfortranarray(psi1V), lower=1, trans=0)
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tmp, _ = dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, _ = dtrtrs(self._Lm, tmp, lower=1, trans=1)
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if X_variance_new is None:
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Kx = self.kern.K(self.Z, Xnew, which_parts=which_parts)
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mu = np.dot(Kx.T, self.Cpsi1V)
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if full_cov:
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Kxx = self.kern.K(Xnew, which_parts=which_parts)
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var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
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else:
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Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
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var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
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else:
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# assert which_parts=='all', "swithching out parts of variational kernels is not implemented"
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Kx = self.kern.psi1(self.Z, Xnew, X_variance_new) # , which_parts=which_parts) TODO: which_parts
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mu = np.dot(Kx, self.Cpsi1V)
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if full_cov:
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raise NotImplementedError, "TODO"
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else:
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Kxx = self.kern.psi0(self.Z, Xnew, X_variance_new)
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psi2 = self.kern.psi2(self.Z, Xnew, X_variance_new)
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var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
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return mu, var[:, None]
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def predict(self, Xnew, X_variance_new=None, which_parts='all', full_cov=False, **likelihood_args):
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"""
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Predict the function(s) at the new point(s) Xnew.
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**Arguments**
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param X_variance_new: The uncertainty in the prediction points
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:type X_variance_new: np.ndarray, Nnew x self.input_dim
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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:type full_cov: bool
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:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
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:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
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:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
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If full_cov and self.input_dim > 1, the return shape of var is Nnew x Nnew x self.input_dim. If self.input_dim == 1, the return shape is Nnew x Nnew.
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This is to allow for different normalizations of the output dimensions.
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"""
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# normalize X values
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Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
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if X_variance_new is not None:
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X_variance_new = X_variance_new / self._Xscale ** 2
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# here's the actual prediction by the GP model
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mu, var = self._raw_predict(Xnew, X_variance_new, full_cov=full_cov, which_parts=which_parts)
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# now push through likelihood
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, **likelihood_args)
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return mean, var, _025pm, _975pm
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#TODO!!!
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def plot_f(self, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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"""
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Plot the GP's view of the world, where the data is normalized and the
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Plot the belief in the latent function, the "GP's view of the world"
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- In one dimension, the function is plotted with a shaded region identifying two standard deviations.
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- In two dimsensions, a contour-plot shows the mean predicted function
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- Not implemented in higher dimensions
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@ -249,12 +139,11 @@ class SparseGP(GPBase):
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_f(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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GP.plot_f(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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ax.errorbar(self.X[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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@ -264,7 +153,6 @@ class SparseGP(GPBase):
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Zu = self.Z * self._Xscale + self._Xoffset
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ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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@ -277,12 +165,11 @@ class SparseGP(GPBase):
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax)
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GP.plot(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax)
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if self.X.shape[1] == 1:
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if self.has_uncertain_inputs:
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Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
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ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
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ax.errorbar(self.X[which_data, 0], self.likelihood.data[which_data, 0],
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xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
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ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
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Zu = self.Z * self._Xscale + self._Xoffset
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@ -296,145 +183,20 @@ class SparseGP(GPBase):
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else:
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raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
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def predict_single_output(self, Xnew, output=0, which_parts='all', full_cov=False):
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def getstate(self):
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"""
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For a specific output, predict the function at the new point(s) Xnew.
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param output: output to predict
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:type output: integer in {0,..., num_outputs-1}
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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:type full_cov: bool
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:rtype: posterior mean, a Numpy array, Nnew x self.input_dim
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:rtype: posterior variance, a Numpy array, Nnew x 1 if full_cov=False, Nnew x Nnew otherwise
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:rtype: lower and upper boundaries of the 95% confidence intervals, Numpy arrays, Nnew x self.input_dim
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.. Note:: For multiple output models only
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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"""
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return GP.getstate(self) + [self.Z,
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self.num_inducing,
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self.has_uncertain_inputs,
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self.X_variance]
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assert hasattr(self,'multioutput')
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index = np.ones_like(Xnew)*output
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Xnew = np.hstack((Xnew,index))
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def setstate(self, state):
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self.X_variance = state.pop()
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self.has_uncertain_inputs = state.pop()
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self.num_inducing = state.pop()
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self.Z = state.pop()
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GP.setstate(self, state)
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# normalize X values
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Xnew = (Xnew.copy() - self._Xoffset) / self._Xscale
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mu, var = self._raw_predict(Xnew, full_cov=full_cov, which_parts=which_parts)
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# now push through likelihood
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mean, var, _025pm, _975pm = self.likelihood.predictive_values(mu, var, full_cov, noise_model = output)
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return mean, var, _025pm, _975pm
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def _raw_predict_single_output(self, _Xnew, output=0, X_variance_new=None, which_parts='all', full_cov=False,stop=False):
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"""
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Internal helper function for making predictions for a specific output,
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does not account for normalization or likelihood
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---------
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:param Xnew: The points at which to make a prediction
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:type Xnew: np.ndarray, Nnew x self.input_dim
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:param output: output to predict
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:type output: integer in {0,..., num_outputs-1}
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:param which_parts: specifies which outputs kernel(s) to use in prediction
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:type which_parts: ('all', list of bools)
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:param full_cov: whether to return the full covariance matrix, or just the diagonal
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.. Note:: For multiple output models only
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"""
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Bi, _ = dpotri(self.LB, lower=0) # WTH? this lower switch should be 1, but that doesn't work!
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symmetrify(Bi)
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Kmmi_LmiBLmi = backsub_both_sides(self._Lm, np.eye(self.num_inducing) - Bi)
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if self.Cpsi1V is None:
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psi1V = np.dot(self.psi1.T,self.likelihood.V)
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tmp, _ = dtrtrs(self._Lm, np.asfortranarray(psi1V), lower=1, trans=0)
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tmp, _ = dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, _ = dtrtrs(self._Lm, tmp, lower=1, trans=1)
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assert hasattr(self,'multioutput')
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index = np.ones_like(_Xnew)*output
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_Xnew = np.hstack((_Xnew,index))
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if X_variance_new is None:
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Kx = self.kern.K(self.Z, _Xnew, which_parts=which_parts)
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mu = np.dot(Kx.T, self.Cpsi1V)
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if full_cov:
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Kxx = self.kern.K(_Xnew, which_parts=which_parts)
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var = Kxx - mdot(Kx.T, Kmmi_LmiBLmi, Kx) # NOTE this won't work for plotting
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else:
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Kxx = self.kern.Kdiag(_Xnew, which_parts=which_parts)
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var = Kxx - np.sum(Kx * np.dot(Kmmi_LmiBLmi, Kx), 0)
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else:
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Kx = self.kern.psi1(self.Z, _Xnew, X_variance_new)
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mu = np.dot(Kx, self.Cpsi1V)
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if full_cov:
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raise NotImplementedError, "TODO"
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else:
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Kxx = self.kern.psi0(self.Z, _Xnew, X_variance_new)
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psi2 = self.kern.psi2(self.Z, _Xnew, X_variance_new)
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var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
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return mu, var[:, None]
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def plot_single_output_f(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, full_cov=False, fignum=None, ax=None):
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if ax is None:
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fig = pb.figure(num=fignum)
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ax = fig.add_subplot(111)
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if fignum is None and ax is None:
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fignum = fig.num
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if which_data is 'all':
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which_data = slice(None)
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GPBase.plot_single_output_f(self, output=output, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, full_cov=full_cov, fignum=fignum, ax=ax)
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||||
|
||||
if self.X.shape[1] == 2:
|
||||
if self.has_uncertain_inputs:
|
||||
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
|
||||
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
|
||||
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
|
||||
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
|
||||
Zu = self.Z * self._Xscale + self._Xoffset
|
||||
Zu = Zu[Zu[:,1]==output,0:1]
|
||||
ax.plot(Zu[:,0], np.zeros_like(Zu[:,0]) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
|
||||
|
||||
elif self.X.shape[1] == 2:
|
||||
Zu = self.Z * self._Xscale + self._Xoffset
|
||||
Zu = Zu[Zu[:,1]==output,0:2]
|
||||
ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
|
||||
|
||||
|
||||
else:
|
||||
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
|
||||
|
||||
def plot_single_output(self, output=None, samples=0, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, fignum=None, ax=None):
|
||||
if ax is None:
|
||||
fig = pb.figure(num=fignum)
|
||||
ax = fig.add_subplot(111)
|
||||
if fignum is None and ax is None:
|
||||
fignum = fig.num
|
||||
if which_data is 'all':
|
||||
which_data = slice(None)
|
||||
|
||||
GPBase.plot_single_output(self, samples=samples, plot_limits=plot_limits, which_data='all', which_parts='all', resolution=resolution, levels=20, fignum=fignum, ax=ax, output=output)
|
||||
|
||||
if self.X.shape[1] == 2:
|
||||
if self.has_uncertain_inputs:
|
||||
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
|
||||
ax.errorbar(Xu[which_data, 0], self.likelihood.data[which_data, 0],
|
||||
xerr=2 * np.sqrt(self.X_variance[which_data, 0]),
|
||||
ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
|
||||
Zu = self.Z * self._Xscale + self._Xoffset
|
||||
Zu = Zu[Zu[:,1]==output,0:1]
|
||||
ax.plot(Zu, np.zeros_like(Zu) + ax.get_ylim()[0], 'r|', mew=1.5, markersize=12)
|
||||
|
||||
elif self.X.shape[1] == 3:
|
||||
Zu = self.Z * self._Xscale + self._Xoffset
|
||||
Zu = Zu[Zu[:,1]==output,0:1]
|
||||
ax.plot(Zu[:, 0], Zu[:, 1], 'wo')
|
||||
|
||||
else:
|
||||
raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue