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[optimizer] one copy for the optimizer in optimizer_array, use this instead of _set|get_params_transformed
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mzwiessele
parent
43ee8ce614
commit
5a2bc4863b
7 changed files with 158 additions and 79 deletions
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@ -59,7 +59,7 @@ class MRD(SparseGP):
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inference_method=None, likelihoods=None, name='mrd', Ynames=None):
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super(GP, self).__init__(name)
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self.logger = logging.getLogger("MRD <{}>".format(hex(id(self))))
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self.logger = logging.getLogger(self.__class__.__name__)
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self.input_dim = input_dim
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self.num_inducing = num_inducing
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@ -107,16 +107,16 @@ class MRD(SparseGP):
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self.logger.info("building kernels")
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if kernel is None:
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from ..kern import RBF
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self.kernels = [RBF(input_dim, ARD=1, lengthscale=fracs[i]) for i in range(len(Ylist))]
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kernels = [RBF(input_dim, ARD=1, lengthscale=fracs[i]) for i in range(len(Ylist))]
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elif isinstance(kernel, Kern):
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self.kernels = []
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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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self.kernels.append(k)
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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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assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
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self.kernels = kernel
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kernels = kernel
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if X_variance is None:
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X_variance = np.random.uniform(0.1, 0.2, X.shape)
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@ -125,8 +125,8 @@ class MRD(SparseGP):
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self.X = NormalPosterior(X, X_variance)
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if likelihoods is None:
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self.likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
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else: self.likelihoods = likelihoods
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likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
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else: likelihoods = likelihoods
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self.logger.info("adding X and Z")
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self.add_parameters(self.X, self.Z)
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@ -134,9 +134,8 @@ class MRD(SparseGP):
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self.bgplvms = []
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self.num_data = Ylist[0].shape[0]
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for i, n, k, l, Y in itertools.izip(itertools.count(), Ynames, self.kernels, self.likelihoods, self.Ylist):
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for i, n, k, l, Y in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist):
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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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p = Parameterized(name=n)
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p.add_parameter(k)
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p.kern = k
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@ -154,19 +153,18 @@ class MRD(SparseGP):
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self.posteriors = []
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self.Z.gradient[:] = 0.
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self.X.gradient[:] = 0.
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for y, k, l, i in itertools.izip(self.Ylist, self.kernels, self.likelihoods, self.inference_method):
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for y, b, i in itertools.izip(self.Ylist, self.bgplvms, self.inference_method):
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self.logger.info('working on im <{}>'.format(hex(id(i))))
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k, l = b.kern, b.likelihood
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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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# likelihoods gradients
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self.logger.info("likelihood gradients")
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l.update_gradients(grad_dict.pop('dL_dthetaL'))
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#gradients wrt kernel
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self.logger.info("kernel gradients")
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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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@ -174,7 +172,6 @@ class MRD(SparseGP):
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k.gradient += target
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#gradients wrt Z
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self.logger.info("Z gradients")
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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_dpsi0'],
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@ -182,16 +179,15 @@ class MRD(SparseGP):
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grad_dict['dL_dpsi2'],
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Z=self.Z, variational_posterior=self.X)
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self.logger.info("X gradients")
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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.posterior = self.posteriors[0]
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self.kern = self.kernels[0]
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self.likelihood = self.likelihoods[0]
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self.kern = self.bgplvms[0].kern
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self.likelihood = self.bgplvms[0].likelihood
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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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@ -268,8 +264,8 @@ class MRD(SparseGP):
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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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self.posterior = self.posteriors[Yindex]
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self.kern = self.kernels[Yindex]
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self.likelihood = self.likelihoods[Yindex]
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self.kern = self.bgplvms[0].kern
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self.likelihood = self.bgplvms[0].likelihood
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return super(MRD, self).predict(Xnew, full_cov, Y_metadata, kern)
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#===============================================================================
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@ -311,7 +307,7 @@ class MRD(SparseGP):
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"""
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import sys
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assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
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import matplotlib.pyplot as plt
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from matplotlib import pyplot as plt
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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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@ -333,10 +329,7 @@ class MRD(SparseGP):
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return plot
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def __getstate__(self):
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# TODO:
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import copy
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state = copy.copy(self.__dict__)
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del state['kernels']
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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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return state
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@ -344,7 +337,6 @@ class MRD(SparseGP):
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def __setstate__(self, state):
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# TODO:
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super(MRD, self).__setstate__(state)
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self.kernels = [p.kern for p in self.bgplvms]
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self.kern = self.kernels[0]
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self.likelihood = self.likelihoods[0]
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self.kern = self.bgplvms[0].kern
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self.likelihood = self.bgplvms[0].likelihood
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self.parameters_changed()
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