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[mrd] plotting, init, inference etc.
This commit is contained in:
Max Zwiessele
parent
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commit
94c84a23a3
8 changed files with 236 additions and 72 deletions
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@ -83,7 +83,7 @@ class BayesianGPLVM(SparseGP):
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resolution=50, ax=None, marker='o', s=40,
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fignum=None, plot_inducing=True, legend=True,
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plot_limits=None,
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aspect='auto', updates=False, **kwargs):
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aspect='auto', updates=False, predict_kwargs={}, imshow_kwargs={}):
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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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from ..plotting.matplot_dep import dim_reduction_plots
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@ -91,7 +91,7 @@ class BayesianGPLVM(SparseGP):
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return 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, **kwargs)
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plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
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def do_test_latents(self, Y):
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"""
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@ -11,9 +11,11 @@ from ..core.parameterization.variational import NormalPosterior, NormalPrior
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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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from GPy.util.initialization import initialize_latent
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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 ..inference.latent_function_inference import InferenceMethodList
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class MRD(Model):
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class MRD(SparseGP):
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"""
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Apply MRD to all given datasets Y in Ylist.
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@ -39,17 +41,18 @@ class MRD(Model):
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:param num_inducing: number of inducing inputs to use
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:param Z: initial inducing inputs
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:param kernel: list of kernels or kernel to copy for each output
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:type kernel: [GPy.kern.kern] | GPy.kern.kern | None (default)
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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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:type kernel: [GPy.kernels.kernels] | GPy.kernels.kernels | None (default)
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:param :class:`~GPy.inference.latent_function_inference inference_method:
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InferenceMethodList of inferences, or one inference method for all
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:param :class:`~GPy.likelihoodss.likelihoods.likelihoods` likelihoods: the likelihoods 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', Ynames=None):
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super(MRD, self).__init__(name)
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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.input_dim = input_dim
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self.num_inducing = num_inducing
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@ -63,16 +66,16 @@ class MRD(Model):
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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, lengthscale=fracs[i], name='rbf'.format(i)) for i in range(len(Ylist))]
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self.kernels = [RBF(input_dim, ARD=1, lengthscale=fracs[i], name='rbf'.format(i)) for i in range(len(Ylist))]
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elif isinstance(kernel, Kern):
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self.kern = []
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self.kernels = []
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for i in range(len(Ylist)):
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k = kernel.copy()
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self.kern.append(k)
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self.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.kern = kernel
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self.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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@ -80,32 +83,44 @@ class MRD(Model):
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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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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 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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if inference_method is None:
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self.inference_method= []
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self.inference_method= InferenceMethodList()
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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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inan = np.isnan(y)
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if np.any(inan):
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self.inference_method.append(VarDTCMissingData(limit=1, inan=inan))
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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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if not isinstance(inference_method, InferenceMethodList):
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inference_method = InferenceMethodList(inference_method)
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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.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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self.names = Ynames
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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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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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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.kern = k
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p.add_parameter(l)
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setattr(self, 'Y{}'.format(i), p)
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p.likelihood = l
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self.add_parameter(p)
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self.bgplvms.append(p)
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self.posterior = None
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self._in_init_ = False
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def parameters_changed(self):
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@ -114,13 +129,13 @@ class MRD(Model):
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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.kern, self.likelihood, self.inference_method):
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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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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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# likelihoods gradients
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l.update_gradients(grad_dict.pop('dL_dthetaL'))
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#gradients wrt kernel
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@ -133,13 +148,20 @@ class MRD(Model):
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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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grad_dict['dL_dpsi0'],
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grad_dict['dL_dpsi1'],
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grad_dict['dL_dpsi2'],
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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.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.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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@ -207,16 +229,25 @@ class MRD(Model):
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else:
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return pylab.gcf()
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def plot_X(self, fignum=None, ax=None):
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fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
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return fig
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def predict(self, Xnew, full_cov=False, Y_metadata=None, kern=None, Yindex=0):
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"""
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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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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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return super(MRD, self).predict(Xnew, full_cov, Y_metadata, kern)
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def plot_predict(self, fignum=None, ax=None, sharex=False, sharey=False, **kwargs):
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fig = self._handle_plotting(fignum,
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ax,
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lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs),
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sharex=sharex, sharey=sharey)
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return fig
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#===============================================================================
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# TODO: Predict! Maybe even change to several bgplvms, which share an X?
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#===============================================================================
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# def plot_predict(self, fignum=None, ax=None, sharex=False, sharey=False, **kwargs):
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# fig = self._handle_plotting(fignum,
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# ax,
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# lambda i, g, ax: ax.imshow(g.predict(g.X)[0], **kwargs),
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# sharex=sharex, sharey=sharey)
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# return fig
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def plot_scales(self, fignum=None, ax=None, titles=None, sharex=False, sharey=True, *args, **kwargs):
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"""
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@ -228,16 +259,28 @@ class MRD(Model):
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"""
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if titles is None:
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titles = [r'${}$'.format(name) for name in self.names]
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ymax = reduce(max, [np.ceil(max(g.input_sensitivity())) for g in self.bgplvms])
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ymax = reduce(max, [np.ceil(max(g.kernels.input_sensitivity())) for g in self.bgplvms])
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def plotf(i, g, ax):
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ax.set_ylim([0,ymax])
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g.kern.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
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g.kernels.plot_ARD(ax=ax, title=titles[i], *args, **kwargs)
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fig = self._handle_plotting(fignum, ax, plotf, sharex=sharex, sharey=sharey)
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return fig
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def plot_latent(self, fignum=None, ax=None, *args, **kwargs):
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fig = self.gref.plot_latent(fignum=fignum, ax=ax, *args, **kwargs) # self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
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return fig
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def plot_latent(self, labels=None, which_indices=None,
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resolution=50, ax=None, marker='o', s=40,
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fignum=None, plot_inducing=True, legend=True,
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plot_limits=None,
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aspect='auto', updates=False, predict_kwargs=dict(Yindex=0), imshow_kwargs={}):
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"""
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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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from ..plotting.matplot_dep import dim_reduction_plots
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return 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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def _debug_plot(self):
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self.plot_X_1d()
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@ -252,4 +295,19 @@ class MRD(Model):
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pylab.draw()
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fig.tight_layout()
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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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del state['kern']
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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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# 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.parameters_changed()
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