def optimize(self, params0):
""" initialize """
self.gp.setParams(params0)
self.cache['params0'] = params0
self.cache['lml0'] = self.gp.LML()
self.cache['lmlGrad0'] = self.gp.LMLgrad()
""" optimize """
gpopt = limix.CGPopt(self.gp)
conv = gpopt.opt()
""" store stuff """
self.cache['lml'] = self.gp.LML()
self.cache['lmlGrad'] = self.gp.LMLgrad()
self.cache['params'] = self.gp.getParams()
self.cache['X'] = self.Ca.getParams().reshape((self.N, self.k),
order='F')
self.cache['K'] = self.Ca.K()
if self.interaction:
self.cache['Ki'] = self.Ca.K() * self.Cb2.K()
else:
self.cache['Ki'] = None
self.cache['var'] = {}
self.cache['var']['K0'] = self.C0.getParams()[0]**2
self.cache['var']['K'] = self.cache['K'].diagonal().mean()
if self.interaction:
self.cache['var']['Ki'] = self.cache['Ki'].diagonal().mean()
self.cache['var']['noise'] = self.ll.getParams()[0]**2
self.cache['K'] /= self.cache['var']['K']
if self.interaction:
self.cache['Ki'] /= self.cache['var']['Ki']
return conv
def test_fit(self):
#create optimization object
self.gpopt = dlimix_legacy.CGPopt(self.gp)
#run
RV = self.gpopt.opt()
RV = self.gpopt.opt()
m = (SP.absolute(self.gp.LMLgrad()['X']).max() +
SP.absolute(self.gp.LMLgrad()['covar']).max() +
SP.absolute(self.gp.LMLgrad()['lik']).max())
np.testing.assert_almost_equal(m, 0., decimal=1)
def train(self, rank=20, Kpop=True, LinearARD=False):
"""train panama module"""
if 0:
covar = limix_legacy.CCovLinearISO(rank)
ll = limix_legacy.CLikNormalIso()
X0 = sp.random.randn(self.N, rank)
X0 = PCA(self.Y, rank)[0]
X0 /= sp.sqrt(rank)
covar_params = sp.array([1.0])
lik_params = sp.array([1.0])
hyperparams = limix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = X0
constrainU = limix_legacy.CGPHyperParams()
constrainL = limix_legacy.CGPHyperParams()
constrainU['covar'] = +5 * sp.ones_like(covar_params)
constrainL['covar'] = 0 * sp.ones_like(covar_params)
constrainU['lik'] = +5 * sp.ones_like(lik_params)
constrainL['lik'] = 0 * sp.ones_like(lik_params)
if 1:
covar = limix_legacy.CSumCF()
if LinearARD:
covar_1 = limix_legacy.CCovLinearARD(rank)
covar_params = []
for d in range(rank):
covar_params.append(1 / sp.sqrt(d + 2))
else:
covar_1 = limix_legacy.CCovLinearISO(rank)
covar_params = [1.0]
covar.addCovariance(covar_1)
for K in self.Ks:
covar.addCovariance(limix_legacy.CFixedCF(K))
covar_params.append(1.0)
ll = limix_legacy.CLikNormalIso()
X0 = PCA(self.Y, rank)[0]
X0 /= sp.sqrt(rank)
covar_params = sp.array(covar_params)
lik_params = sp.array([1.0])
hyperparams = limix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = X0
constrainU = limix_legacy.CGPHyperParams()
constrainL = limix_legacy.CGPHyperParams()
constrainU['covar'] = +5 * sp.ones_like(covar_params)
constrainL['covar'] = -5 * sp.ones_like(covar_params)
constrainU['lik'] = +5 * sp.ones_like(lik_params)
gp = limix_legacy.CGPbase(covar, ll)
gp.setY(self.Y)
gp.setX(X0)
lml0 = gp.LML(hyperparams)
dlml0 = gp.LMLgrad(hyperparams)
gpopt = limix_legacy.CGPopt(gp)
gpopt.setOptBoundLower(constrainL)
gpopt.setOptBoundUpper(constrainU)
t1 = time.time()
gpopt.opt()
t2 = time.time()
#Kpanama
self.Xpanama = covar_1.getX()
if LinearARD:
self.Xpanama /= self.Xpanama.std(0)
self.Kpanama = covar_1.K()
# self.Kpanama/= self.Kpanama.diagonal().mean()
# Ktot
self.Ktot = covar_1.K()
for c_i in range(len(self.Ks)):
self.Ktot += covar.getCovariance(c_i + 1).K()
# self.Ktot/= self.Ktot.diagonal().mean()
#store variances
V = {}
if LinearARD:
V['LinearARD'] = covar_1.getParams()**2 * covar_1.getX().var(0)
else:
V['Kpanama'] = sp.array([covar_1.K().diagonal().mean()])
# if self.use_Kpop:
# V['Ks'] = sp.array([covar.getCovariance(c_i+1).K().diagonal().mean() for c_i in range(len(self.Ks))])
V['Ks'] = sp.array(
[covar.getCovariance(c_i + 1).K() for c_i in range(len(self.Ks))])
V['noise'] = gp.getParams()['lik']**2
self.varianceComps = V
# predictions
Ki = la.inv(ll.K() + covar.K())
self.Ypanama = sp.dot(covar_1.K(), sp.dot(Ki, self.Y))
self.LL = ll.K()