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[MRD] running again, using missing_data classes, more details needed for missing data though

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Max Zwiessele 2014-11-03 15:04:12 +00:00
parent 80b1bbbd64
commit 1a8cae99a5
2 changed files with 32 additions and 41 deletions
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9
GPy/examples/dimensionality_reduction.py
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@ -415,7 +415,6 @@ def mrd_simulation(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw): def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim=True, **kw):
from GPy import kern from GPy import kern
from GPy.models import MRD from GPy.models import MRD
from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5 D1, D2, D3, N, num_inducing, Q = 60, 20, 36, 60, 6, 5
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim) _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
@ -429,12 +428,8 @@ def mrd_simulation_missing_data(optimize=True, verbose=True, plot=True, plot_sim
inanlist.append(inan) inanlist.append(inan)
Y[inan] = _np.nan Y[inan] = _np.nan
imlist = []
for inan in inanlist:
imlist.append(VarDTCMissingData(limit=1, inan=inan))
m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing, m = MRD(Ylist, input_dim=Q, num_inducing=num_inducing,
kernel=k, inference_method=imlist, kernel=k, inference_method=None,
initx="random", initz='permute', **kw) initx="random", initz='permute', **kw)
if optimize: if optimize:
@ -494,7 +489,7 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True): def stick_play(range=None, frame_rate=15, optimize=False, verbose=True, plot=True):
import GPy import GPy
import pods import pods
data = pods.datasets.osu_run1() data = pods.datasets.osu_run1()
# optimize # optimize

64
GPy/models/mrd.py
View file

@ -13,11 +13,11 @@ from ..inference.latent_function_inference import InferenceMethodList
from ..likelihoods import Gaussian from ..likelihoods import Gaussian
from ..util.initialization import initialize_latent from ..util.initialization import initialize_latent
from ..core.sparse_gp import SparseGP, GP from ..core.sparse_gp import SparseGP, GP
from GPy.models.bayesian_gplvm import BayesianGPLVM
from GPy.core.parameterization.variational import VariationalPosterior from GPy.core.parameterization.variational import VariationalPosterior
from GPy.core.sparse_gp_mpi import SparseGP_MPI from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.models.sparse_gp_minibatch import SparseGPMiniBatch
class MRD(BayesianGPLVM): class MRD(BayesianGPLVMMiniBatch):
""" """
!WARNING: This is bleeding edge code and still in development. !WARNING: This is bleeding edge code and still in development.
Functionality may change fundamentally during development! Functionality may change fundamentally during development!
@ -92,7 +92,8 @@ class MRD(BayesianGPLVM):
else: else:
fracs = [X.var(0)]*len(Ylist) fracs = [X.var(0)]*len(Ylist)
self.Z = Param('inducing inputs', self._init_Z(initz, X)) Z = self._init_Z(initz, X)
self.Z = Param('inducing inputs', Z)
self.num_inducing = self.Z.shape[0] # ensure M==N if M>N self.num_inducing = self.Z.shape[0] # ensure M==N if M>N
# sort out the kernels # sort out the kernels
@ -104,6 +105,7 @@ class MRD(BayesianGPLVM):
kernels = [] kernels = []
for i in range(len(Ylist)): for i in range(len(Ylist)):
k = kernel.copy() k = kernel.copy()
print k is kernel, k.observers, k.constraints
kernels.append(k) kernels.append(k)
else: else:
assert len(kernel) == len(Ylist), "need one kernel per output" assert len(kernel) == len(Ylist), "need one kernel per output"
@ -114,7 +116,7 @@ class MRD(BayesianGPLVM):
X_variance = np.random.uniform(0.1, 0.2, X.shape) X_variance = np.random.uniform(0.1, 0.2, X.shape)
self.variational_prior = NormalPrior() self.variational_prior = NormalPrior()
self.X = NormalPosterior(X, X_variance) #self.X = NormalPosterior(X, X_variance)
if likelihoods is None: if likelihoods is None:
likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))] likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
@ -123,48 +125,33 @@ class MRD(BayesianGPLVM):
self.logger.info("adding X and Z") self.logger.info("adding X and Z")
super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing, super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing,
Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(), Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(),
name='bayesian gplvm', mpi_comm=None, normalizer=None, name='manifold relevance determination', normalizer=None,
missing_data=False, stochastic=False, batchsize=1) missing_data=False, stochastic=False, batchsize=1)
import GPy
self._log_marginal_likelihood = 0 self._log_marginal_likelihood = 0
print "------------"
print self.size
print self.constraints[GPy.constraints.Logexp()][-10:]
print "------------"
self.unlink_parameter(self.likelihood) self.unlink_parameter(self.likelihood)
print self.size
print self.constraints[GPy.constraints.Logexp()][-10:]
print "------------"
self.unlink_parameter(self.kern) self.unlink_parameter(self.kern)
print self.size del self.kern
print self.constraints[GPy.constraints.Logexp()][-10:] del self.likelihood
print "------------"
print
print '================='
self.num_data = Ylist[0].shape[0] self.num_data = Ylist[0].shape[0]
if isinstance(batchsize, int): if isinstance(batchsize, int):
batchsize = itertools.repeat(batchsize) batchsize = itertools.repeat(batchsize)
print self.size self.bgplvms = []
print self.constraints[GPy.constraints.Logexp()][-10:]
for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize): for i, n, k, l, Y, im, bs in itertools.izip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another" assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
md = np.isnan(Y).any() md = np.isnan(Y).any()
spgp = SparseGP(self.X, Y, self.Z, k, l, im, n, None, normalizer, md, stochastic, bs) spgp = SparseGPMiniBatch(self.X, Y, Z, k, l, im, n, None, normalizer, md, stochastic, bs)
spgp.unlink_parameter(spgp.Z) spgp.unlink_parameter(spgp.Z)
del spgp.Z
del spgp.X
spgp.Z = self.Z spgp.Z = self.Z
spgp.X = self.X
self.link_parameter(spgp, i+2) self.link_parameter(spgp, i+2)
self.bgplvms.append(spgp)
print self.constraints[GPy.constraints.Logexp()][-10:]
self.link_parameter(self.Z, 2)
print self.size
print self.constraints[GPy.constraints.Logexp()][-10:]
print "==========="
self.posterior = None self.posterior = None
self.logger.info("init done") self.logger.info("init done")
@ -173,7 +160,9 @@ class MRD(BayesianGPLVM):
self._log_marginal_likelihood = 0 self._log_marginal_likelihood = 0
self.Z.gradient[:] = 0. self.Z.gradient[:] = 0.
self.X.gradient[:] = 0. self.X.gradient[:] = 0.
for b, i in itertools.izip(self.parameters[3:], self.inference_method): for b, i in itertools.izip(self.bgplvms, self.inference_method):
self._log_marginal_likelihood += b._log_marginal_likelihood
self.logger.info('working on im <{}>'.format(hex(id(i)))) self.logger.info('working on im <{}>'.format(hex(id(i))))
self.Z.gradient[:] += b.full_values['Zgrad'] self.Z.gradient[:] += b.full_values['Zgrad']
grad_dict = b.grad_dict grad_dict = b.grad_dict
@ -195,6 +184,7 @@ class MRD(BayesianGPLVM):
# update for the KL divergence # update for the KL divergence
self.variational_prior.update_gradients_KL(self.X) self.variational_prior.update_gradients_KL(self.X)
self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X) self._log_marginal_likelihood -= self.variational_prior.KL_divergence(self.X)
pass
def log_likelihood(self): def log_likelihood(self):
return self._log_marginal_likelihood return self._log_marginal_likelihood
@ -268,7 +258,7 @@ class MRD(BayesianGPLVM):
Prediction for data set Yindex[default=0]. Prediction for data set Yindex[default=0].
This predicts the output mean and variance for the dataset given in Ylist[Yindex] This predicts the output mean and variance for the dataset given in Ylist[Yindex]
""" """
b = self.parameters[Yindex+2] b = self.bgplvms[Yindex]
self.posterior = b.posterior self.posterior = b.posterior
self.kern = b.kern self.kern = b.kern
self.likelihood = b.likelihood self.likelihood = b.likelihood
@ -317,16 +307,20 @@ class MRD(BayesianGPLVM):
from ..plotting.matplot_dep import dim_reduction_plots from ..plotting.matplot_dep import dim_reduction_plots
if "Yindex" not in predict_kwargs: if "Yindex" not in predict_kwargs:
predict_kwargs['Yindex'] = 0 predict_kwargs['Yindex'] = 0
Yindex = predict_kwargs['Yindex']
if ax is None: if ax is None:
fig = plt.figure(num=fignum) fig = plt.figure(num=fignum)
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
else: else:
fig = ax.figure fig = ax.figure
self.kern = self.bgplvms[Yindex].kern
self.likelihood = self.bgplvms[Yindex].likelihood
plot = dim_reduction_plots.plot_latent(self, labels, which_indices, plot = dim_reduction_plots.plot_latent(self, labels, which_indices,
resolution, ax, marker, s, resolution, ax, marker, s,
fignum, plot_inducing, legend, fignum, plot_inducing, legend,
plot_limits, aspect, updates, predict_kwargs, imshow_kwargs) plot_limits, aspect, updates, predict_kwargs, imshow_kwargs)
ax.set_title(self.bgplvms[predict_kwargs['Yindex']].name) ax.set_title(self.bgplvms[Yindex].name)
try: try:
fig.tight_layout() fig.tight_layout()
except: except:
@ -336,8 +330,10 @@ class MRD(BayesianGPLVM):
def __getstate__(self): def __getstate__(self):
state = super(MRD, self).__getstate__() state = super(MRD, self).__getstate__()
del state['kern'] if state.has_key('kern'):
del state['likelihood'] del state['kern']
if state.has_key('likelihood'):
del state['likelihood']
return state return state
def __setstate__(self, state): def __setstate__(self, state):