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[bgplvm&mrd] missing data greatly improved, still not there yet
This commit is contained in:
Max Zwiessele
parent
b520eb212c
commit
156ba00719
6 changed files with 111 additions and 55 deletions
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@ -2,10 +2,8 @@
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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import itertools
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import pylab
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import itertools, logging
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from ..core import Model
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from ..kern import Kern
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from ..core.parameterization.variational import NormalPosterior, NormalPrior
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from ..core.parameterization import Param, Parameterized
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@ -61,15 +59,18 @@ 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.input_dim = input_dim
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self.num_inducing = num_inducing
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if isinstance(Ylist, dict):
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Ynames, Ylist = zip(*Ylist.items())
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self.logger.debug("creating observable arrays")
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self.Ylist = [ObsAr(Y) for Y in Ylist]
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if Ynames is None:
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self.logger.debug("creating Ynames")
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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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assert len(self.names) == len(self.Ylist), "one name per dataset, or None if Ylist is a dict"
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@ -81,13 +82,15 @@ class MRD(SparseGP):
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inan = np.isnan(y)
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if np.any(inan):
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if not warned:
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print "WARING: NaN values detected, make sure initx method can cope with NaN values or provide starting latent space X"
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self.logger.warn("WARNING: NaN values detected, make sure initx method can cope with NaN values or provide starting latent space X")
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warned = True
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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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self.logger.debug("created inference method <{}>".format(hex(id(self.inference_method[-1]))))
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else:
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if not isinstance(inference_method, InferenceMethodList):
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self.logger.debug("making inference_method an InferenceMethodList")
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inference_method = InferenceMethodList(inference_method)
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self.inference_method = inference_method
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@ -101,6 +104,7 @@ class MRD(SparseGP):
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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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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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@ -124,6 +128,7 @@ class MRD(SparseGP):
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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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self.logger.info("adding X and Z")
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self.add_parameters(self.X, self.Z)
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self.bgplvms = []
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@ -141,6 +146,7 @@ class MRD(SparseGP):
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self.bgplvms.append(p)
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self.posterior = None
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self.logger.info("init done")
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self._in_init_ = False
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def parameters_changed(self):
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@ -148,17 +154,19 @@ 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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self.logger.info('working on im <{}>'.format(hex(id(i))))
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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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@ -166,6 +174,7 @@ 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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@ -173,6 +182,7 @@ 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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@ -219,8 +229,9 @@ class MRD(SparseGP):
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return Z
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def _handle_plotting(self, fignum, axes, plotf, sharex=False, sharey=False):
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import matplotlib.pyplot as plt
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if axes is None:
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fig = pylab.figure(num=fignum)
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fig = plt.figure(num=fignum)
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sharex_ax = None
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sharey_ax = None
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plots = []
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@ -242,8 +253,8 @@ class MRD(SparseGP):
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raise ValueError("Need one axes per latent dimension input_dim")
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plots.append(plotf(i, g, ax))
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if sharey_ax is not None:
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pylab.setp(ax.get_yticklabels(), visible=False)
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pylab.draw()
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plt.setp(ax.get_yticklabels(), visible=False)
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plt.draw()
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if axes is None:
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try:
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fig.tight_layout()
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@ -300,11 +311,12 @@ 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 ..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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if ax is None:
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fig = pylab.figure(num=fignum)
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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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