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[MRD] running again, using missing_data classes, more details needed for missing data though
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Max Zwiessele
parent
80b1bbbd64
commit
1a8cae99a5
2 changed files with 32 additions and 41 deletions
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@ -415,7 +415,6 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
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from GPy import kern
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from GPy.models import MRD
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from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
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D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
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_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
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@ -429,12 +428,8 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
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inanlist.append(inan)
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Y[inan] = _np.nan
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imlist = []
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for inan in inanlist:
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imlist.append(VarDTCMissingData(limit=1, inan=inan))
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m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing,
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kernel=k, inference_method=imlist,
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kernel=k, inference_method=None,
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initx="random", initz='permute', **kw)
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if optimize:
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@ -13,11 +13,11 @@ from ..inference.latent_function_inference import InferenceMethodList
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from ..likelihoods import Gaussian
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from ..util.initialization import initialize_latent
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from ..core.sparse_gp import SparseGP, GP
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from GPy.models.bayesian_gplvm import BayesianGPLVM
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from GPy.core.parameterization.variational import VariationalPosterior
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from GPy.core.sparse_gp_mpi import SparseGP_MPI
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from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
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from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
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class MRD(BayesianGPLVM):
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class MRD(BayesianGPLVMMiniBatch):
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"""
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!WARNING: This is bleeding edge code and still in development.
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Functionality may change fundamentally during development!
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@ -92,7 +92,8 @@ class MRD(BayesianGPLVM):
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else:
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fracs = [X.var(0)]*len(Ylist)
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self.Z = Param('inducing inputs', self._init_Z(initz, X))
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Z = self._init_Z(initz, X)
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self.Z = Param('inducing inputs', Z)
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self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
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# sort out the kernels
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@ -104,6 +105,7 @@ class MRD(BayesianGPLVM):
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kernels = []
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for i in range(len(Ylist)):
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k = kernel.copy()
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print k is kernel, k.observers, k.constraints
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kernels.append(k)
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else:
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assert len(kernel) == len(Ylist), "need one kernel per output"
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@ -114,7 +116,7 @@ class MRD(BayesianGPLVM):
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X_variance = np.random.uniform(0.1, 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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#self.X = NormalPosterior(X, X_variance)
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if likelihoods is None:
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likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
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@ -123,48 +125,33 @@ class MRD(BayesianGPLVM):
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self.logger.info("adding X and Z")
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super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing,
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Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(),
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name='bayesian gplvm', mpi_comm=None, normalizer=None,
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name='manifold relevance determination', normalizer=None,
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missing_data=False, stochastic=False, batchsize=1)
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import GPy
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self._log_marginal_likelihood = 0
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print "------------"
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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self.unlink_parameter(self.likelihood)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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self.unlink_parameter(self.kern)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "------------"
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print
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print '================='
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del self.kern
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del self.likelihood
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self.num_data = Ylist[0].shape[0]
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if isinstance(batchsize, int):
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batchsize = itertools.repeat(batchsize)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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self.bgplvms = []
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for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
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assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
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md = np.isnan(Y).any()
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spgp = SparseGP(self.X, Y, self.Z, k, l, im, n, None, normalizer, md, stochastic, bs)
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spgp = SparseGPMiniBatch(self.X, Y, Z, k, l, im, n, None, normalizer, md, stochastic, bs)
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spgp.unlink_parameter(spgp.Z)
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del spgp.Z
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del spgp.X
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spgp.Z = self.Z
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spgp.X = self.X
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self.link_parameter(spgp, i+2)
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print self.constraints[GPy.constraints.Logexp()][-10:]
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self.link_parameter(self.Z, 2)
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print self.size
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print self.constraints[GPy.constraints.Logexp()][-10:]
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print "==========="
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self.bgplvms.append(spgp)
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self.posterior = None
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self.logger.info("init done")
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@ -173,7 +160,9 @@ class MRD(BayesianGPLVM):
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self._log_marginal_likelihood = 0
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self.Z.gradient[:] = 0.
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self.X.gradient[:] = 0.
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for b, i in itertools.izip(self.parameters[3:], self.inference_method):
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for b, i in itertools.izip(self.bgplvms, self.inference_method):
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self._log_marginal_likelihood += b._log_marginal_likelihood
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self.logger.info('working on im <{}>'.format(hex(id(i))))
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self.Z.gradient[:] += b.full_values['Zgrad']
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grad_dict = b.grad_dict
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@ -195,6 +184,7 @@ class MRD(BayesianGPLVM):
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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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pass
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def log_likelihood(self):
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return self._log_marginal_likelihood
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@ -268,7 +258,7 @@ class MRD(BayesianGPLVM):
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Prediction for data set Yindex[default=0].
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This predicts the output mean and variance for the dataset given in Ylist[Yindex]
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"""
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b = self.parameters[Yindex+2]
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b = self.bgplvms[Yindex]
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self.posterior = b.posterior
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self.kern = b.kern
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self.likelihood = b.likelihood
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@ -317,16 +307,20 @@ class MRD(BayesianGPLVM):
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from ..plotting.matplot_dep import dim_reduction_plots
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if "Yindex" not in predict_kwargs:
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predict_kwargs['Yindex'] = 0
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Yindex = predict_kwargs['Yindex']
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if ax is None:
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fig = plt.figure(num=fignum)
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ax = fig.add_subplot(111)
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else:
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fig = ax.figure
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self.kern = self.bgplvms[Yindex].kern
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self.likelihood = self.bgplvms[Yindex].likelihood
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plot = dim_reduction_plots.plot_latent(self, labels, which_indices,
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resolution, ax, marker, s,
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fignum, plot_inducing, legend,
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plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
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ax.set_title(self.bgplvms[predict_kwargs['Yindex']].name)
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ax.set_title(self.bgplvms[Yindex].name)
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try:
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fig.tight_layout()
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except:
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@ -336,8 +330,10 @@ class MRD(BayesianGPLVM):
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def __getstate__(self):
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state = super(MRD, self).__getstate__()
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del state['kern']
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del state['likelihood']
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if state.has_key('kern'):
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del state['kern']
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if state.has_key('likelihood'):
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del state['likelihood']
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return state
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def __setstate__(self, state):
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