def getGPR(self, M, opt=True):
"""create a GP object, optimize hyperparameters for M[0]: x , M[1]: multiple time series y"""
[x, y] = self.M2GPxy(M)
logtheta = self.logtheta0
if self.robust:
gpr = GPREP.GPEP(covar=self.covar,
Nep=3,
likelihood=self.likelihood,
Smean=True,
x=x,
y=y)
pass
else:
gpr = GPR.GP(self.covar, Smean=True, x=x, y=y)
if opt:
Ifilter = S.ones_like(logtheta)
LG.debug("opt")
LG.debug('priors: %s' % str(self.priors))
logtheta = GPR.optHyper(gpr,
logtheta,
Ifilter=Ifilter,
priors=self.priors,
maxiter=self.maxiter)
gpr.logtheta = logtheta
gpr.priors = self.priors
return gpr
def getGPR(self,M,opt=True):
"""create a GP object, optimize hyperparameters for M[0]: x , M[1]: multiple time series y"""
[x,y] = self.M2GPxy(M)
logtheta = self.logtheta0
if self.robust:
gpr = GPREP.GPEP(covar=self.covar,Nep=3,likelihood=self.likelihood,Smean=True,x=x,y=y)
pass
else:
gpr = GPR.GP(self.covar,Smean=True,x=x,y=y)
if opt:
Ifilter = S.ones_like(logtheta)
LG.debug("opt")
LG.debug('priors: %s' % str(self.priors))
logtheta=GPR.optHyper(gpr,logtheta,Ifilter=Ifilter,priors=self.priors,maxiter=self.maxiter)
gpr.logtheta = logtheta
gpr.priors = self.priors
return gpr
def getGPR(self, M, opt=True):
"""create a GP object, optimize hyperparameters for M[0]: x , M[1]: multiple time series y"""
[x, y] = self.M2GPxy(M)
#initialize GP
logtheta = self.logtheta0
priors = self.priors
#adjust priors ?
if 0:
Yexp = 0.5
scale = 20
loc = Yexp / scale
priors[0][1] = [scale, loc]
Lmin = (x[:, 0].max(axis=0) - x[:, 0].min(axis=0)) / 4
scale = 30
loc = Lmin / scale
priors[1][1] = [scale, loc]
sigma = 1.0
scale = 30
sigma = 0.5
scale = 3
loc = sigma / scale
gpr = GPR.GP(self.covar, Smean=True, x=x, y=y)
pydb.set_trace()
if opt:
#opt filter
Ifilter = S.ones_like(logtheta)
logtheta = GPR.optHyper(gpr,
logtheta,
Ifilter=Ifilter,
priors=priors,
maxiter=self.maxiter)
#save optimised hyperparameters
gpr.logtheta = logtheta
return gpr
def getGPR(self,M,opt=True):
"""create a GP object, optimize hyperparameters for M[0]: x , M[1]: multiple time series y"""
[x,y] = self.M2GPxy(M)
#initialize GP
logtheta = self.logtheta0
priors = self.priors
#adjust priors ?
if 0:
Yexp = 0.5
scale = 20
loc = Yexp/scale
priors[0][1] = [scale,loc]
Lmin = (x[:,0].max(axis=0)-x[:,0].min(axis=0))/4
scale = 30
loc = Lmin/scale
priors[1][1] = [scale,loc]
sigma = 1.0
scale = 30
sigma = 0.5
scale = 3
loc = sigma/scale
gpr = GPR.GP(self.covar,Smean=True,x=x,y=y)
pydb.set_trace()
if opt:
#opt filter
Ifilter = S.ones_like(logtheta)
logtheta=GPR.optHyper(gpr,logtheta,Ifilter=Ifilter,priors=priors,maxiter=self.maxiter)
#save optimised hyperparameters
gpr.logtheta = logtheta
return gpr
covar = sederiv.SquaredExponentialCF(dim)
gpr = GPR.GP(covar,Smean=True,x=x,y=y)
if 1:
GPR.DEBUG=2
priors = []
#scale
priors.append([lngammapdf,[1,2]])
for i in range(dim):
priors.append([lngammapdf,[1,1]])
#noise
priors.append([lngammapdf,[1,1]])
I_filter =ones_like(logtheta)
#maybe we should filter optimzing theta
logthetaO=GPR.optHyper(gpr,logtheta,I_filter,priors=priors)
print "optimized hyperparameters:" + str(exp(logthetaO))
else:
logthetaO=logtheta
#predict
[M,S] = gpr.predict(logthetaO,X)
hold(True)
plot(x[:,0], y, 'ro',
X[:,0], M, 'g-',
X[:,0], M+2*sqrt(S), 'b-',
X[:,0], M-2*sqrt(S), 'b-')
#show()
def test(self,M0,M1,verbose=False,opt=None,logtrafo=False,rescale=False):
"""test for differential expression
M0: dataset in condition 0
M1: dataset in condition 1
verbose: create verbose plot
opt: optimise hyper(true)
logtrafo: takes logs first (False)
rescale: rescale to unit varaince (False)
"""
if opt is None:
opt = self.opt
[M0,M1] = self.preprocess(M0,M1,logtrafo=logtrafo,rescale=rescale)
#0. model: both in one bucket:
##changed this here:
#xjoin = S.concatenate((M0[0],M1[0]),axis=1)
#yjoin = S.concatenate((M0[1],M1[1]),axis=1)
#Mjoin = [xjoin,yjoin]
#this version is compatible with lists:
Mjoin = self.Mconcatenate(M0,M1)
MJ = Mjoin
gpr_join = self.getGPR(Mjoin,opt=opt)
#1. both separately:
gpr_0 = self.getGPR(M0,opt=False)
gpr_1 = self.getGPR(M1,opt=False)
gprs = GPR.GroupGP([gpr_0,gpr_1])
if opt:
Ifilter = S.ones_like(gpr_0.logtheta)
# Ifilter[-1] = 0
logtheta=GPR.optHyper(gprs,gpr_0.logtheta,Ifilter=Ifilter,priors=gpr_0.priors,maxiter=self.maxiter)
gpr_0.logtheta=logtheta
gpr_1.logtheta=logtheta
LML_join = -1.0*gpr_join.lMl(gpr_join.logtheta,lml=True,dlml=False)
LML_0 = -1.0*gpr_0.lMl(gpr_0.logtheta,lml=True,dlml=False)
LML_1 = -1.0*gpr_1.lMl(gpr_1.logtheta,lml=True,dlml=False)
#bayes factor
ratio = (LML_0+LML_1-LML_join)
#store result structures in local object
self.gpr_join = gpr_join
self.gpr_s = gprs
self.gpr_0 = gpr_0
self.gpr_1 = gpr_1
if verbose:
LG.debug('theta0: %s' % str(S.exp(gpr_0.logtheta)))
LG.debug('theta1: %s' % str(S.exp(gpr_1.logtheta)))
X = S.linspace(M0[0][0].min(),M0[0][0].max(),100)
#plot
PL.clf()
PL.hold(True)
LG.debug("theta(0):" + str(S.exp(gpr_0.logtheta)))
LG.debug("theta(1):" + str(S.exp(gpr_1.logtheta)))
LG.debug("theta: " + str(S.exp(gpr_join.logtheta)))
#plot the GP erorrbars and means first:
self.plotGPpredict(gpr_0,M0,X,{'alpha':0.1,'facecolor':'r'},{'linewidth':2,'color':'r'})
self.plotGPpredict(gpr_1,M0,X,{'alpha':0.1,'facecolor':'g'},{'linewidth':2,'color':'g'})
self.plotGPpredict(gpr_join,M0,X,{'alpha':0.1,'facecolor':'b'},{'linewidth':2,'color':'b'})
for rep in range(len(M0[0])):
PL.plot(M0[0][rep],M0[1][rep],'r.--')
for rep in range(len(M1[0])):
PL.plot(M1[0][rep],M1[1][rep],'g.--')
PL.xlim((X.min(),X.max()))
PL.title('%s: %.4f' % ('',ratio))
PL.xlabel('Time/hr')
PL.ylabel('Log expression level')
# Ymax = MJ[1].max()
# Ymin = MJ[1].min()
# DY = Ymax-Ymin
# PL.ylim([Ymin-0.1*DY,Ymax+0.1*DY])
return ratio
X = Spca.copy()
#X = SP.random.randn(N,K)
gplvm = GPLVM(covar_func=covariance,x=X,y=Y)
gpr = GPR.GP(covar_func=covariance,x=X,y=Y[:,0])
#construct hyperparams
covar = SP.log([1.0,0.1])
#X are hyperparameters, i.e. we optimize over them also
#1. this is jointly with the latent X
X_ = X.copy()
hyperparams = {'covar': covar, 'x': X_}
#for testing just covar params alone:
#hyperparams = {'covar': covar}
#evaluate log marginal likelihood
lml = gplvm.lMl(hyperparams=hyperparams)
[opt_model_params,opt_lml]=GPR.optHyper(gplvm,hyperparams,gradcheck=False)
Xo = opt_model_params['x']
for k in xrange(K):
print SP.corrcoef(Spca[:,k],S[:,k])
for k in xrange(K):
print SP.corrcoef(Xo[:,k],S[:,k])
X = Spca.copy()
#X = SP.random.randn(N,K)
gplvm = GPLVM(covar_func=covariance, x=X, y=Y)
gpr = GPR.GP(covar_func=covariance, x=X, y=Y[:, 0])
#construct hyperparams
covar = SP.log([1.0, 0.1])
#X are hyperparameters, i.e. we optimize over them also
#1. this is jointly with the latent X
X_ = X.copy()
hyperparams = {'covar': covar, 'x': X_}
#for testing just covar params alone:
#hyperparams = {'covar': covar}
#evaluate log marginal likelihood
lml = gplvm.lMl(hyperparams=hyperparams)
[opt_model_params, opt_lml] = GPR.optHyper(gplvm,
hyperparams,
gradcheck=False)
Xo = opt_model_params['x']
for k in xrange(K):
print SP.corrcoef(Spca[:, k], S[:, k])
for k in xrange(K):
print SP.corrcoef(Xo[:, k], S[:, k])
covar = sederiv.SquaredExponentialCF(dim)
gpr = GPR.GP(covar, Smean=True, x=x, y=y)
if 1:
GPR.DEBUG = 2
priors = []
#scale
priors.append([lngammapdf, [1, 2]])
for i in range(dim):
priors.append([lngammapdf, [1, 1]])
#noise
priors.append([lngammapdf, [1, 1]])
I_filter = ones_like(logtheta)
#maybe we should filter optimzing theta
logthetaO = GPR.optHyper(gpr, logtheta, I_filter, priors=priors)
print "optimized hyperparameters:" + str(exp(logthetaO))
else:
logthetaO = logtheta
#predict
[M, S] = gpr.predict(logthetaO, X)
hold(True)
plot(x[:, 0], y, 'ro', X[:, 0], M, 'g-', X[:, 0], M + 2 * sqrt(S), 'b-',
X[:, 0], M - 2 * sqrt(S), 'b-')
#show()
D = zeros([x.shape[0], x.shape[1] + 1])
D[:, 0:-1] = x
D[:, -1] = y
def test(self,
M0,
M1,
verbose=False,
opt=None,
logtrafo=False,
rescale=False):
"""test for differential expression
M0: dataset in condition 0
M1: dataset in condition 1
verbose: create verbose plot
opt: optimise hyper(true)
logtrafo: takes logs first (False)
rescale: rescale to unit varaince (False)
"""
if opt is None:
opt = self.opt
[M0, M1] = self.preprocess(M0, M1, logtrafo=logtrafo, rescale=rescale)
#0. model: both in one bucket:
##changed this here:
#xjoin = S.concatenate((M0[0],M1[0]),axis=1)
#yjoin = S.concatenate((M0[1],M1[1]),axis=1)
#Mjoin = [xjoin,yjoin]
#this version is compatible with lists:
Mjoin = self.Mconcatenate(M0, M1)
MJ = Mjoin
gpr_join = self.getGPR(Mjoin, opt=opt)
#1. both separately:
gpr_0 = self.getGPR(M0, opt=False)
gpr_1 = self.getGPR(M1, opt=False)
gprs = GPR.GroupGP([gpr_0, gpr_1])
if opt:
Ifilter = S.ones_like(gpr_0.logtheta)
# Ifilter[-1] = 0
logtheta = GPR.optHyper(gprs,
gpr_0.logtheta,
Ifilter=Ifilter,
priors=gpr_0.priors,
maxiter=self.maxiter)
gpr_0.logtheta = logtheta
gpr_1.logtheta = logtheta
LML_join = -1.0 * gpr_join.lMl(gpr_join.logtheta, lml=True, dlml=False)
LML_0 = -1.0 * gpr_0.lMl(gpr_0.logtheta, lml=True, dlml=False)
LML_1 = -1.0 * gpr_1.lMl(gpr_1.logtheta, lml=True, dlml=False)
#bayes factor
ratio = (LML_0 + LML_1 - LML_join)
#store result structures in local object
self.gpr_join = gpr_join
self.gpr_s = gprs
self.gpr_0 = gpr_0
self.gpr_1 = gpr_1
if verbose:
LG.debug('theta0: %s' % str(S.exp(gpr_0.logtheta)))
LG.debug('theta1: %s' % str(S.exp(gpr_1.logtheta)))
X = S.linspace(M0[0][0].min(), M0[0][0].max(), 100)
#plot
PL.clf()
PL.hold(True)
LG.debug("theta(0):" + str(S.exp(gpr_0.logtheta)))
LG.debug("theta(1):" + str(S.exp(gpr_1.logtheta)))
LG.debug("theta: " + str(S.exp(gpr_join.logtheta)))
#plot the GP erorrbars and means first:
self.plotGPpredict(gpr_0, M0, X, {
'alpha': 0.1,
'facecolor': 'r'
}, {
'linewidth': 2,
'color': 'r'
})
self.plotGPpredict(gpr_1, M0, X, {
'alpha': 0.1,
'facecolor': 'g'
}, {
'linewidth': 2,
'color': 'g'
})
self.plotGPpredict(gpr_join, M0, X, {
'alpha': 0.1,
'facecolor': 'b'
}, {
'linewidth': 2,
'color': 'b'
})
for rep in range(len(M0[0])):
PL.plot(M0[0][rep], M0[1][rep], 'r.--')
for rep in range(len(M1[0])):
PL.plot(M1[0][rep], M1[1][rep], 'g.--')
PL.xlim((X.min(), X.max()))
PL.title('%s: %.4f' % ('', ratio))
PL.xlabel('Time/hr')
PL.ylabel('Log expression level')
# Ymax = MJ[1].max()
# Ymin = MJ[1].min()
# DY = Ymax-Ymin
# PL.ylim([Ymin-0.1*DY,Ymax+0.1*DY])
return ratio
