mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-02 16:22:39 +02:00
pca adjustements to lvm models
This commit is contained in:
Max Zwiessele
parent
f9c9e8e1d5
commit
a6725b55e1
3 changed files with 94 additions and 33 deletions
|
|
@ -2,17 +2,18 @@
|
|||
# Licensed under the BSD 3-clause license (see LICENSE.txt)
|
||||
|
||||
import numpy as np
|
||||
import itertools
|
||||
from matplotlib import pyplot
|
||||
|
||||
from ..core.sparse_gp import SparseGP
|
||||
from ..likelihoods import Gaussian
|
||||
from .. import kern
|
||||
import itertools
|
||||
from matplotlib.colors import colorConverter
|
||||
from GPy.inference.optimization import SCG
|
||||
from GPy.util import plot_latent, linalg
|
||||
from .gplvm import GPLVM
|
||||
from GPy.util.plot_latent import most_significant_input_dimensions
|
||||
from matplotlib import pyplot
|
||||
from GPy.core.model import Model
|
||||
from ..inference.optimization import SCG
|
||||
from ..util import plot_latent, linalg
|
||||
from .gplvm import GPLVM, initialise_latent
|
||||
from ..util.plot_latent import most_significant_input_dimensions
|
||||
from ..core.model import Model
|
||||
from ..util.subarray_and_sorting import common_subarrays
|
||||
|
||||
class BayesianGPLVM(SparseGP, GPLVM):
|
||||
"""
|
||||
|
|
@ -34,7 +35,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
|
|||
likelihood = likelihood_or_Y
|
||||
|
||||
if X == None:
|
||||
X = self.initialise_latent(init, input_dim, likelihood.Y)
|
||||
X = initialise_latent(init, input_dim, likelihood.Y)
|
||||
self.init = init
|
||||
|
||||
if X_variance is None:
|
||||
|
|
@ -308,14 +309,36 @@ class BayesianGPLVMWithMissingData(Model):
|
|||
:type init: 'PCA' | 'random'
|
||||
"""
|
||||
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
|
||||
Z=None, kernel=None, missing=np.nan, **kwargs):
|
||||
Z=None, kernel=None, **kwargs):
|
||||
#=======================================================================
|
||||
# Filter Y, such that same missing data is at same positions.
|
||||
# If full rows are missing, delete them entirely!
|
||||
if type(likelihood_or_Y) is np.ndarray:
|
||||
likelihood = Gaussian(likelihood_or_Y)
|
||||
Y = likelihood_or_Y
|
||||
likelihood = Gaussian
|
||||
params = 1.
|
||||
normalize=None
|
||||
else:
|
||||
likelihood = likelihood_or_Y
|
||||
Y = likelihood_or_Y.Y
|
||||
likelihood = likelihood_or_Y.__class__
|
||||
params = likelihood_or_Y._get_params()
|
||||
if isinstance(likelihood_or_Y, Gaussian):
|
||||
normalize = True
|
||||
scale = likelihood_or_Y._scale
|
||||
offset = likelihood_or_Y._offset
|
||||
# Get common subrows
|
||||
filter_ = np.isnan(Y)
|
||||
self.subarray_indices = common_subarrays(filter_,axis=1)
|
||||
likelihoods = [likelihood(Y[~np.array(v,dtype=bool),:][:,ind]) for v,ind in self.subarray_indices.iteritems()]
|
||||
for l in likelihoods:
|
||||
l._set_params(params)
|
||||
if normalize: # get normalization in common
|
||||
l._scale = scale
|
||||
l._offset = offset
|
||||
#=======================================================================
|
||||
|
||||
if X == None:
|
||||
X = self.initialise_latent(init, input_dim, likelihood.Y)
|
||||
X = initialise_latent(init, input_dim, Y[:,np.any(np.isnan(Y),1)])
|
||||
self.init = init
|
||||
|
||||
if X_variance is None:
|
||||
|
|
@ -328,13 +351,52 @@ class BayesianGPLVMWithMissingData(Model):
|
|||
if kernel is None:
|
||||
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
|
||||
|
||||
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, X_variance=X_variance, **kwargs)
|
||||
self.submodels = [BayesianGPLVM(l, input_dim, X, X_variance, init, num_inducing, Z, kernel) for l in likelihoods]
|
||||
self.gref = self.submodels[0]
|
||||
#:type self.gref: BayesianGPLVM
|
||||
self.ensure_default_constraints()
|
||||
|
||||
def log_likelihood(self):
|
||||
ll = -self.gref.KL_divergence()
|
||||
for g in self.submodels:
|
||||
ll += SparseGP.log_likelihood(g)
|
||||
return ll
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
dLdmu, dLdS = reduce(lambda a, b: [a[0] + b[0], a[1] + b[1]], (g.dL_dmuS() for g in self.bgplvms))
|
||||
dKLmu, dKLdS = self.gref.dKL_dmuS()
|
||||
dLdmu -= dKLmu
|
||||
dLdS -= dKLdS
|
||||
dLdmuS = np.hstack((dLdmu.flatten(), dLdS.flatten())).flatten()
|
||||
dldzt1 = reduce(lambda a, b: a + b, (SparseGP._log_likelihood_gradients(g)[:self.gref.num_inducing*self.gref.input_dim] for g in self.submodels))
|
||||
|
||||
return np.hstack((dLdmuS,
|
||||
dldzt1,
|
||||
np.hstack([np.hstack([g.dL_dtheta(),
|
||||
g.likelihood._gradients(\
|
||||
partial=g.partial_for_likelihood)]) \
|
||||
for g in self.submodels])))
|
||||
|
||||
def getstate(self):
|
||||
return Model.getstate(self)+[self.submodels,self.subarray_indices]
|
||||
|
||||
def setstate(self, state):
|
||||
self.subarray_indices = state.pop()
|
||||
self.submodels = state.pop()
|
||||
self.gref = self.submodels[0]
|
||||
Model.setstate(self, state)
|
||||
self._set_params(self._get_params())
|
||||
|
||||
def _get_param_names(self):
|
||||
X_names = sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
|
||||
S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], [])
|
||||
return (X_names + S_names + SparseGP._get_param_names(self))
|
||||
return (X_names + S_names + SparseGP._get_param_names(self.gref))
|
||||
|
||||
def _get_params(self):
|
||||
return self.gref._get_params()
|
||||
def _set_params(self, x):
|
||||
[g._set_params(x) for g in self.submodels]
|
||||
|
||||
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -10,6 +10,13 @@ from ..core import GP
|
|||
from ..likelihoods import Gaussian
|
||||
from .. import util
|
||||
|
||||
def initialise_latent(init, input_dim, Y):
|
||||
Xr = np.random.randn(Y.shape[0], input_dim)
|
||||
if init == 'pca':
|
||||
from ..util.linalg import pca
|
||||
PC = pca(Y, input_dim)[0]
|
||||
Xr[:PC.shape[0], :PC.shape[1]] = PC
|
||||
return Xr
|
||||
|
||||
class GPLVM(GP):
|
||||
"""
|
||||
|
|
@ -20,12 +27,12 @@ class GPLVM(GP):
|
|||
:param input_dim: latent dimensionality
|
||||
:type input_dim: int
|
||||
:param init: initialisation method for the latent space
|
||||
:type init: 'PCA'|'random'
|
||||
:type init: 'pca'|'random'
|
||||
|
||||
"""
|
||||
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False):
|
||||
def __init__(self, Y, input_dim, init='pca', X=None, kernel=None, normalize_Y=False):
|
||||
if X is None:
|
||||
X = self.initialise_latent(init, input_dim, Y)
|
||||
X = initialise_latent(init, input_dim, Y)
|
||||
if kernel is None:
|
||||
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2))
|
||||
likelihood = Gaussian(Y, normalize=normalize_Y, variance=np.exp(-2.))
|
||||
|
|
@ -33,14 +40,6 @@ class GPLVM(GP):
|
|||
self.set_prior('.*X', priors.Gaussian(0, 1))
|
||||
self.ensure_default_constraints()
|
||||
|
||||
def initialise_latent(self, init, input_dim, Y):
|
||||
Xr = np.random.randn(Y.shape[0], input_dim)
|
||||
if init == 'PCA':
|
||||
from ..util.linalg import PCA
|
||||
PC = PCA(Y, input_dim)[0]
|
||||
Xr[:PC.shape[0], :PC.shape[1]] = PC
|
||||
return Xr
|
||||
|
||||
def _get_param_names(self):
|
||||
return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ Created on 10 Apr 2013
|
|||
'''
|
||||
from GPy.core import Model
|
||||
from GPy.core import SparseGP
|
||||
from GPy.util.linalg import PCA
|
||||
from GPy.util.linalg import pca
|
||||
import numpy
|
||||
import itertools
|
||||
import pylab
|
||||
|
|
@ -26,8 +26,8 @@ class MRD(Model):
|
|||
:type input_dim: int
|
||||
:param initx: initialisation method for the latent space :
|
||||
|
||||
* 'concat' - PCA on concatenation of all datasets
|
||||
* 'single' - Concatenation of PCA on datasets, respectively
|
||||
* 'concat' - pca on concatenation of all datasets
|
||||
* 'single' - Concatenation of pca on datasets, respectively
|
||||
* 'random' - Random draw from a normal
|
||||
|
||||
:type initx: ['concat'|'single'|'random']
|
||||
|
|
@ -42,7 +42,7 @@ class MRD(Model):
|
|||
|
||||
"""
|
||||
def __init__(self, likelihood_or_Y_list, input_dim, num_inducing=10, names=None,
|
||||
kernels=None, initx='PCA',
|
||||
kernels=None, initx='pca',
|
||||
initz='permute', _debug=False, **kw):
|
||||
if names is None:
|
||||
self.names = ["{}".format(i) for i in range(len(likelihood_or_Y_list))]
|
||||
|
|
@ -237,7 +237,7 @@ class MRD(Model):
|
|||
partial=g.partial_for_likelihood)]) \
|
||||
for g in self.bgplvms])))
|
||||
|
||||
def _init_X(self, init='PCA', likelihood_list=None):
|
||||
def _init_X(self, init='pca', likelihood_list=None):
|
||||
if likelihood_list is None:
|
||||
likelihood_list = self.likelihood_list
|
||||
Ylist = []
|
||||
|
|
@ -248,11 +248,11 @@ class MRD(Model):
|
|||
Ylist.append(likelihood_or_Y.Y)
|
||||
del likelihood_list
|
||||
if init in "PCA_concat":
|
||||
X = PCA(numpy.hstack(Ylist), self.input_dim)[0]
|
||||
X = pca(numpy.hstack(Ylist), self.input_dim)[0]
|
||||
elif init in "PCA_single":
|
||||
X = numpy.zeros((Ylist[0].shape[0], self.input_dim))
|
||||
for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.input_dim), len(Ylist)), Ylist):
|
||||
X[:, qs] = PCA(Y, len(qs))[0]
|
||||
X[:, qs] = pca(Y, len(qs))[0]
|
||||
else: # init == 'random':
|
||||
X = numpy.random.randn(Ylist[0].shape[0], self.input_dim)
|
||||
self.X = X
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue