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mrd for new parameterize
This commit is contained in:
Max Zwiessele
parent
e7b601b424
commit
4ce2eb2ac6
1 changed files with 79 additions and 282 deletions
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@ -5,15 +5,15 @@ import numpy as np
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import itertools
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import pylab
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from ..core import Model, SparseGP
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from ..core import Model
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from ..util.linalg import PCA
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from ..kern import Kern
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from bayesian_gplvm import BayesianGPLVM
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from ..core.parameterization.variational import NormalPosterior, NormalPrior
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from ..inference.latent_function_inference.var_dtc import VarDTCMissingData
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from ..likelihoods.gaussian import Gaussian
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from ..core.parameterization import Param, Parameterized
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from ..inference.latent_function_inference.var_dtc import VarDTCMissingData, VarDTC
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from ..likelihoods import Gaussian
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class MRD2(Model):
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class MRD(Model):
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"""
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Apply MRD to all given datasets Y in Ylist.
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@ -43,61 +43,110 @@ class MRD2(Model):
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:param :class:`~GPy.inference.latent_function_inference inference_method: the inference method to use
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:param :class:`~GPy.likelihoods.likelihood.Likelihood` likelihood: the likelihood to use
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:param str name: the name of this model
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:param [str] Ynames: the names for the datasets given, must be of equal length as Ylist or None
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"""
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def __init__(self, Ylist, input_dim, X=None, X_variance=None,
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initx = 'PCA', initz = 'permute',
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num_inducing=10, Z=None, kernel=None,
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inference_method=None, likelihood=None, name='mrd'):
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super(MRD2, self).__init__(name)
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inference_method=None, likelihood=None, name='mrd', Ynames=None):
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super(MRD, self).__init__(name)
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# sort out the kernels
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if kernel is None:
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from ..kern import RBF
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self.kern = [RBF(input_dim, ARD=1, name='Y_{}'.format(i)) for i in range(len(Ylist))]
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self.kern = [RBF(input_dim, ARD=1, name='rbf'.format(i)) for i in range(len(Ylist))]
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elif isinstance(kernel, Kern):
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self.kern = [kernel.copy(name='Y_{}'.format(i)) for i in range(len(Ylist))]
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self.kern = [kernel.copy(name='{}'.format(kernel.name, i)) for i in range(len(Ylist))]
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else:
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assert len(kernel) == len(Ylist), "need one kernel per output"
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assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
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self.kern = kernel
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self.input_dim = input_dim
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self.num_inducing = num_inducing
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self.Ylist = Ylist
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self._in_init_ = True
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X = self._init_X(initx, Ylist)
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self.Z = self._init_Z(initz, X)
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self.Z = Param('inducing inputs', self._init_Z(initz, X))
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self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
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if X_variance is None:
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X_variance = np.random.uniform(0,.2,X.shape)
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X_variance = np.random.uniform(0, .2, X.shape)
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self.variational_prior = NormalPrior()
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self.X = NormalPosterior(X, X_variance)
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if likelihood is None:
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likelihood = Gaussian()
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self.likelihood = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
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else: self.likelihood = likelihood
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if inference_method is None:
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if any(np.any(np.isnan(y)) for y in Ylist):
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self.inference_method = VarDTCMissingData(limit=len(Ylist))
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self.inference_method= []
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for y in Ylist:
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if np.any(np.isnan(y)):
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self.inference_method.append(VarDTCMissingData(limit=1))
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else:
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self.inference_method.append(VarDTC(limit=1))
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else:
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self.inference_method = inference_method
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self.inference_method.set_limit(len(Ylist))
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self.Ylist = Ylist
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self.add_parameters(self.X, self.Z)
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if Ynames is None:
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Ynames = ['Y{}'.format(i) for i in range(len(Ylist))]
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for i, n, k, l in itertools.izip(itertools.count(), Ynames, self.kern, self.likelihood):
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p = Parameterized(name=n)
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p.add_parameter(k)
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p.add_parameter(l)
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setattr(self, 'Y{}'.format(i), p)
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self.add_parameter(p)
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self._in_init_ = False
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def parameters_changed(self):
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for y in self.Ylist:
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pass
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self._log_marginal_likelihood = 0
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self.posteriors = []
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self.Z.gradient = 0.
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self.X.mean.gradient = 0.
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self.X.variance.gradient = 0.
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for y, k, l, i in itertools.izip(self.Ylist, self.kern, self.likelihood, self.inference_method):
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posterior, lml, grad_dict = i.inference(k, self.X, self.Z, l, y)
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self.posteriors.append(posterior)
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self._log_marginal_likelihood += lml
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# likelihood gradients
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l.update_gradients(grad_dict.pop('partial_for_likelihood'))
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#gradients wrt kernel
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dL_dKmm = grad_dict.pop('dL_dKmm')
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k.update_gradients_full(dL_dKmm, self.Z, None)
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target = k.gradient.copy()
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k.update_gradients_expectations(variational_posterior=self.X, Z=self.Z, **grad_dict)
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k.gradient += target
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def _init_X(self, init='PCA', likelihood_list=None):
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if likelihood_list is None:
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likelihood_list = self.likelihood_list
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Ylist = []
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for likelihood_or_Y in likelihood_list:
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if type(likelihood_or_Y) is np.ndarray:
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Ylist.append(likelihood_or_Y)
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else:
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Ylist.append(likelihood_or_Y.Y)
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del likelihood_list
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#gradients wrt Z
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self.Z.gradient += k.gradients_X(dL_dKmm, self.Z)
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self.Z.gradient += k.gradients_Z_expectations(
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grad_dict['dL_dpsi1'], grad_dict['dL_dpsi2'], Z=self.Z, variational_posterior=self.X)
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dL_dmean, dL_dS = k.gradients_qX_expectations(variational_posterior=self.X, Z=self.Z, **grad_dict)
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self.X.mean.gradient += dL_dmean
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self.X.variance.gradient += dL_dS
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# update for the KL divergence
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self.variational_prior.update_gradients_KL(self.X)
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self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
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def log_likelihood(self):
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return self._log_marginal_likelihood
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def _init_X(self, init='PCA', Ylist=None):
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if Ylist is None:
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Ylist = self.Ylist
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if init in "PCA_concat":
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X = PCA(np.hstack(Ylist), self.input_dim)[0]
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elif init in "PCA_single":
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@ -106,7 +155,6 @@ class MRD2(Model):
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X[:, qs] = PCA(Y, len(qs))[0]
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else: # init == 'random':
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X = np.random.randn(Ylist[0].shape[0], self.input_dim)
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self.X = X
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return X
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def _init_Z(self, init="permute", X=None):
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@ -116,259 +164,8 @@ class MRD2(Model):
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Z = np.random.permutation(X.copy())[:self.num_inducing]
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elif init in "random":
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Z = np.random.randn(self.num_inducing, self.input_dim) * X.var()
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self.Z = Z
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return Z
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class MRD(Model):
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"""
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Do MRD on given Datasets in Ylist.
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All Ys in likelihood_list are in [N x Dn], where Dn can be different per Yn,
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N must be shared across datasets though.
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:param likelihood_list: list of observed datasets (:py:class:`~GPy.likelihoods.gaussian.Gaussian` if not supplied directly)
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:type likelihood_list: [:py:class:`~GPy.likelihoods.likelihood.likelihood` | :py:class:`ndarray`]
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:param names: names for different gplvm models
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:type names: [str]
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:param input_dim: latent dimensionality
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:type input_dim: int
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:param initx: initialisation method for the latent space :
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* 'concat' - PCA on concatenation of all datasets
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* 'single' - Concatenation of PCA on datasets, respectively
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* 'random' - Random draw from a normal
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:type initx: ['concat'|'single'|'random']
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:param initz: initialisation method for inducing inputs
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:type initz: 'permute'|'random'
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:param X: Initial latent space
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:param X_variance: Initial latent space variance
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:param Z: initial inducing inputs
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:param num_inducing: number of inducing inputs to use
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:param kernels: list of kernels or kernel shared for all BGPLVMS
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:type kernels: [GPy.kern.kern] | GPy.kern.kern | None (default)
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"""
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def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
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kernels=None, initx='PCA',
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initz='permute', _debug=False, **kw):
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if names is None:
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self.names = ["{}".format(i) for i in range(len(likelihood_or_Y_list))]
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# sort out the kernels
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if kernels is None:
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kernels = [None] * len(likelihood_or_Y_list)
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elif isinstance(kernels, Kern):
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kernels = [kernels.copy() for i in range(len(likelihood_or_Y_list))]
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else:
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assert len(kernels) == len(likelihood_or_Y_list), "need one kernel per output"
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assert all([isinstance(k, Kern) for k in kernels]), "invalid kernel object detected!"
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assert not ('kernel' in kw), "pass kernels through `kernels` argument"
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self.input_dim = input_dim
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self._debug = _debug
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self.num_inducing = num_inducing
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self._in_init_ = True
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X = self._init_X(initx, likelihood_or_Y_list)
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Z = self._init_Z(initz, X)
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self.num_inducing = Z.shape[0] # ensure M==N if M>N
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self.bgplvms = [BayesianGPLVM(l, input_dim=input_dim, kernel=k, X=X, Z=Z, num_inducing=self.num_inducing, **kw) for l, k in zip(likelihood_or_Y_list, kernels)]
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del self._in_init_
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self.gref = self.bgplvms[0]
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nparams = np.array([0] + [SparseGP._get_params(g).size - g.Z.size for g in self.bgplvms])
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self.nparams = nparams.cumsum()
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self.num_data = self.gref.num_data
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self.NQ = self.num_data * self.input_dim
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self.MQ = self.num_inducing * self.input_dim
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Model.__init__(self)
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self.ensure_default_constraints()
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def _getstate(self):
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return Model._getstate(self) + [self.names,
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self.bgplvms,
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self.gref,
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self.nparams,
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self.input_dim,
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self.num_inducing,
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self.num_data,
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self.NQ,
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self.MQ]
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def _setstate(self, state):
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self.MQ = state.pop()
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self.NQ = state.pop()
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self.num_data = state.pop()
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self.num_inducing = state.pop()
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self.input_dim = state.pop()
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self.nparams = state.pop()
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self.gref = state.pop()
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self.bgplvms = state.pop()
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self.names = state.pop()
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Model._setstate(self, state)
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@property
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def X(self):
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return self.gref.X
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@X.setter
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def X(self, X):
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try:
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self.propagate_param(X=X)
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except AttributeError:
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if not self._in_init_:
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raise AttributeError("bgplvm list not initialized")
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@property
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def Z(self):
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return self.gref.Z
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@Z.setter
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def Z(self, Z):
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try:
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self.propagate_param(Z=Z)
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except AttributeError:
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if not self._in_init_:
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raise AttributeError("bgplvm list not initialized")
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@property
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def X_variance(self):
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return self.gref.X_variance
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@X_variance.setter
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def X_variance(self, X_var):
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try:
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self.propagate_param(X_variance=X_var)
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except AttributeError:
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if not self._in_init_:
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raise AttributeError("bgplvm list not initialized")
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@property
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def likelihood_list(self):
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return [g.likelihood.Y for g in self.bgplvms]
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@likelihood_list.setter
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def likelihood_list(self, likelihood_list):
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for g, Y in itertools.izip(self.bgplvms, likelihood_list):
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g.likelihood.Y = Y
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@property
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def auto_scale_factor(self):
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"""
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set auto_scale_factor for all gplvms
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:param b: auto_scale_factor
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:type b:
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"""
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return self.gref.auto_scale_factor
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@auto_scale_factor.setter
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def auto_scale_factor(self, b):
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self.propagate_param(auto_scale_factor=b)
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def propagate_param(self, **kwargs):
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for key, val in kwargs.iteritems():
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for g in self.bgplvms:
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g.__setattr__(key, val)
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def randomize(self, initx='concat', initz='permute', *args, **kw):
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super(MRD, self).randomize(*args, **kw)
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self._init_X(initx, self.likelihood_list)
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self._init_Z(initz, self.X)
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#def _get_latent_param_names(self):
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def _get_param_names(self):
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n1 = self.gref._get_param_names()
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n1var = n1[:self.NQ * 2 + self.MQ]
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# return n1var
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#
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#def _get_kernel_names(self):
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map_names = lambda ns, name: map(lambda x: "{1}_{0}".format(*x),
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itertools.izip(ns,
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itertools.repeat(name)))
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return list(itertools.chain(n1var, *(map_names(\
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SparseGP._get_param_names(g)[self.MQ:], n) \
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for g, n in zip(self.bgplvms, self.names))))
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# kernel_names = (map_names(SparseGP._get_param_names(g)[self.MQ:], n) for g, n in zip(self.bgplvms, self.names))
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# return kernel_names
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#def _get_param_names(self):
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# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
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# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
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# n1var = self._get_latent_param_names()
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# kernel_names = self._get_kernel_names()
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# return list(itertools.chain(n1var, *kernel_names))
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#def _get_print_names(self):
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# return list(itertools.chain(*self._get_kernel_names()))
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def _get_params(self):
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"""
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return parameter list containing private and shared parameters as follows:
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=================================================================
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| mu | S | Z || theta1 | theta2 | .. | thetaN |
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=================================================================
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"""
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X = self.gref.X.ravel()
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X_var = self.gref.X_variance.ravel()
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Z = self.gref.Z.ravel()
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thetas = [SparseGP._get_params(g)[g.Z.size:] for g in self.bgplvms]
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params = np.hstack([X, X_var, Z, np.hstack(thetas)])
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return params
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# def _set_var_params(self, g, X, X_var, Z):
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# g.X = X.reshape(self.num_data, self.input_dim)
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# g.X_variance = X_var.reshape(self.num_data, self.input_dim)
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# g.Z = Z.reshape(self.num_inducing, self.input_dim)
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#
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# def _set_kern_params(self, g, p):
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# g.kern._set_params(p[:g.kern.num_params])
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# g.likelihood._set_params(p[g.kern.num_params:])
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def _set_params(self, x):
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start = 0; end = self.NQ
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X = x[start:end]
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start = end; end += start
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X_var = x[start:end]
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start = end; end += self.MQ
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Z = x[start:end]
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thetas = x[end:]
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# set params for all:
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for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]):
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g._set_params(np.hstack([X, X_var, Z, thetas[s:e]]))
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# self._set_var_params(g, X, X_var, Z)
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# self._set_kern_params(g, thetas[s:e].copy())
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# g._compute_kernel_matrices()
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# if self.auto_scale_factor:
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# g.scale_factor = np.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision)
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# # self.scale_factor = np.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
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# g._computations()
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def update_likelihood_approximation(self): # TODO: object oriented vs script base
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for bgplvm in self.bgplvms:
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bgplvm.update_likelihood_approximation()
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def log_likelihood(self):
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ll = -self.gref.KL_divergence()
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for g in self.bgplvms:
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ll += SparseGP.log_likelihood(g)
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return ll
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def _log_likelihood_gradients(self):
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dLdmu, dLdS = reduce(lambda a, b: [a[0] + b[0], a[1] + b[1]], (g.dL_dmuS() for g in self.bgplvms))
|
||||
dKLmu, dKLdS = self.gref.dKL_dmuS()
|
||||
dLdmu -= dKLmu
|
||||
dLdS -= dKLdS
|
||||
dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
|
||||
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.MQ] for g in self.bgplvms))
|
||||
|
||||
return np.hstack((dLdmuS,
|
||||
dldzt1,
|
||||
np.hstack([np.hstack([g.dL_dtheta(),
|
||||
g.likelihood._gradients(\
|
||||
partial=g.partial_for_likelihood)]) \
|
||||
for g in self.bgplvms])))
|
||||
|
||||
|
||||
|
||||
def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
|
||||
if axes is None:
|
||||
fig = pylab.figure(num=fignum)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue