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changed likelihood and kernel handling
This commit is contained in:
Max Zwiessele
parent
db58239063
commit
9e83d8162e
1 changed files with 31 additions and 90 deletions
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@ -11,6 +11,7 @@ from scipy import linalg
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import numpy
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import itertools
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import pylab
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from GPy.kern.kern import kern
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class MRD(model):
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"""
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@ -19,7 +20,7 @@ class MRD(model):
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N must be shared across datasets though.
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:param likelihood_list...: likelihoods of observed datasets
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:type likelihood_list: [GPy.likelihood]
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:type likelihood_list: [GPy.likelihood] | [Y1..Yy]
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:param names: names for different gplvm models
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:type names: [str]
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:param Q: latent dimensionality (will raise
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@ -38,85 +39,32 @@ class MRD(model):
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number of inducing inputs to use
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:param Z:
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initial inducing inputs
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:param kernel:
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kernel 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_list,Q,M=10,names=None,kernels=None,initX='PCA',initz='permute',_debug=False, **kwargs):
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def __init__(self, likelihood_or_Y_list, Q, M=10, names=None,
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kernels=None, initx='PCA',
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initz='permute', _debug=False, **kwargs):
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if names is None:
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self.names = ["{}".format(i + 1) for i in range(len(likelihood_list))]
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self.names = ["{}".format(i + 1) 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_list)
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elif isinstance(kernels,kern.kern):
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kernels = [kernels.copy() for i in range(len(likelihood_list))]
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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_list), "need one kernel per output"
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assert all([isinstance(k, kern.kern) for k in kernels]), "invalid kernel object detected!"
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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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self.Q = Q
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self.M = M
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self.N = self.gref.N
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self.NQ = self.N * self.Q
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self.MQ = self.M * self.Q
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self._debug = _debug
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self._init = True
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X = self._init_X(initx, likelihood_list)
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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.bgplvms = [Bayesian_GPLVM(l, k, X=X, Z=Z, M=self.M, **kwargs) for l,k in zip(likelihood_list,kernels)]
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del self._init
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self.gref = self.bgplvms[0]
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nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
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self.nparams = nparams.cumsum()
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model.__init__(self) # @UndefinedVariable
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def __init__(self, *likelihood_list, **kwargs):
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if kwargs.has_key("_debug"):
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self._debug = kwargs['_debug']
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del kwargs['_debug']
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else:
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self._debug = False
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if kwargs.has_key("names"):
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self.names = kwargs['names']
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del kwargs['names']
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else:
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self.names = ["{}".format(i + 1) for i in range(len(likelihood_list))]
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if kwargs.has_key('kernel'):
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kernel = kwargs['kernel']
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k = lambda: kernel.copy()
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del kwargs['kernel']
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else:
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k = lambda: None
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if kwargs.has_key('initx'):
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initx = kwargs['initx']
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del kwargs['initx']
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else:
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initx = "PCA"
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if kwargs.has_key('initz'):
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initz = kwargs['initz']
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del kwargs['initz']
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else:
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initz = "permute"
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try:
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self.Q = kwargs["Q"]
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except KeyError:
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raise ValueError("Need Q for MRD")
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try:
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self.M = kwargs["M"]
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del kwargs["M"]
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except KeyError:
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self.M = 10
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self._init = True
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X = self._init_X(initx, likelihood_list)
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Z = self._init_Z(initz, X)
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self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), X=X, Z=Z, M=self.M, **kwargs) for Y in likelihood_list]
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self.bgplvms = [Bayesian_GPLVM(l, k, X=X, Z=Z, M=self.M, **kwargs) for l, k in zip(likelihood_or_Y_list, kernels)]
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del self._init
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self.gref = self.bgplvms[0]
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@ -212,13 +160,6 @@ class MRD(model):
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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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if self._debug:
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for g in self.bgplvms:
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assert numpy.allclose(g.X.ravel(), X)
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assert numpy.allclose(g.X_variance.ravel(), X_var)
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assert numpy.allclose(g.Z.ravel(), Z)
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thetas = [sparse_GP._get_params(g)[g.Z.size:] for g in self.bgplvms]
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params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
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return params
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@ -241,12 +182,6 @@ class MRD(model):
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Z = x[start:end]
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thetas = x[end:]
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if self._debug:
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for g in self.bgplvms:
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assert numpy.allclose(g.X, self.gref.X)
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assert numpy.allclose(g.X_variance, self.gref.X_variance)
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assert numpy.allclose(g.Z, self.gref.Z)
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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(numpy.hstack([X, X_var, Z, thetas[s:e]]))
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@ -287,15 +222,21 @@ class MRD(model):
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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 numpy.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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if init in "PCA_single":
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X = numpy.zeros((likelihood_list[0].Y.shape[0], self.Q))
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for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.Q), len(likelihood_list)), likelihood_list):
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X[:, qs] = PCA(Y.Y, len(qs))[0]
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X = numpy.zeros((Ylist[0].shape[0], self.Q))
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for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.Q), len(Ylist)), Ylist):
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X[:, qs] = PCA(Y, len(qs))[0]
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elif init in "PCA_concat":
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X = PCA(numpy.hstack([l.Y for l in likelihood_list]), self.Q)[0]
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#X = PCA(numpy.hstack(likelihood_list), self.Q)[0]
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X = PCA(numpy.hstack(Ylist), self.Q)[0]
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else: # init == 'random':
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X = numpy.random.randn(likelihood_list[0].Y.shape[0], self.Q)
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X = numpy.random.randn(Ylist[0].shape[0], self.Q)
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self.X = X
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return X
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