def setUp(self):
np.random.seed(1)
# define phenotype
N = 10
P = 3
Y = sp.randn(N,P)
# pheno with missing data
Ym = Y.copy()
Im = sp.rand(N, P)<0.2
Ym[Im] = sp.nan
# define fixed effects
F = []; A = []
F.append(1.*(sp.rand(N,2)<0.5))
A.append(sp.eye(P))
mean = MeanKronSum(Y, F=F, A=A)
mean_m = MeanKronSum(Ym, F=F, A=A)
# define row caoriance
f = 10
X = 1.*(sp.rand(N, f)<0.2)
R = covar_rescale(sp.dot(X,X.T))
R+= 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
Cn = FreeFormCov(P)
Cg.setRandomParams()
Cn.setRandomParams()
# define covariance matrices
covar1 = KronCov(Cg, R)
covar2 = KronCov(Cn, sp.eye(N))
covar = SumCov(covar1,covar2)
# define covariance matrice with missing data
Iok = (~Im).reshape(N * P, order='F')
covar1_m = KronCov(copy.copy(Cg), R, Iok=Iok)
covar2_m = KronCov(copy.copy(Cn), sp.eye(N), Iok=Iok)
covar_m = SumCov(covar1_m,covar2_m)
# define gp
self._gp = GP(covar=covar, mean=mean)
self._gpm = GP(covar=covar_m, mean=mean_m)
self._gp2ks = GP2KronSum(Y=Y, F=F, A=A, Cg=Cg, Cn=Cn, R=R)
from limix.core.gp import GP3KronSumLR
from limix.core.gp import GP
from limix.utils.preprocess import covar_rescale
import time
import copy
import pdb
if __name__ == '__main__':
# define region and bg terms
N = 500
f = 10
P = 3
G = 1. * (sp.rand(N, f) < 0.2)
X = 1. * (sp.rand(N, f) < 0.2)
R = covar_rescale(sp.dot(X, X.T))
R += 1e-4 * sp.eye(N)
# define col covariances
Cg = FreeFormCov(P)
Cn = FreeFormCov(P)
Cg.setRandomParams()
Cn.setRandomParams()
# define pheno
Y = sp.randn(N, P)
# define GP and optimize
gp = GP3KronSumLR(Y=Y, Cg=Cg, Cn=Cn, R=R, G=G, rank=1)
gp.optimize()
F.append(1.*(sp.rand(N,2)<0.5))
#F.append(1.*(sp.rand(N,1)<0.5))
A.append(sp.eye(P))
#A.append(sp.ones((1,P)))
# define row caoriance
f = 10
X = 1.*(sp.rand(N, f)<0.2)
R = covar_rescale(sp.dot(X,X.T))
R+= 1e-4 * sp.eye(N)
S_R, U_R = la.eigh(R)
# define col covariances
Cg = FreeFormCov(P)
Cn = FreeFormCov(P)
Cg.setRandomParams()
Cn.setRandomParams()
# define gp
gp = GP2KronSum(Y=Y, F=F, A=A, Cg=Cg, Cn=Cn, S_R=S_R, U_R=U_R)
pdb.set_trace()
print gp.covar.Sr()
print gp.LML()
print gp.covar.Sr()
print Cg.setRandomParams()
t0 = time.time()
print 'GP2KronSum.LML():', gp.LML()
print 'Time elapsed:', time.time() - t0
# compare with normal gp
G = 1.*(sp.rand(N, f)<0.2)
X = 1.*(sp.rand(N, f)<0.2)
R = covar_rescale(sp.dot(X,X.T))
R+= 1e-4 * sp.eye(N)
S, U = la.eigh(R)
Y = sp.randn(N, P)
return Y, S, U, G
if __name__=='__main__':
P = 4
# define col covariances
Cg = FreeFormCov(P, jitter=0)
Cn = FreeFormCov(P)
Cg.setRandomParams()
Cn.setRandomParams()
out_file = './times.hdf5'
if not os.path.exists(out_file) or 'recalc' in sys.argv:
Ns = sp.array([100,150,200,300,500,800,1200,1600,2000,3000,4000,5000])
n_rips = 5
t = sp.zeros((Ns.shape[0], n_rips))
t0 = sp.zeros((Ns.shape[0], n_rips))
r = sp.zeros((Ns.shape[0], n_rips))
for ni, n in enumerate(Ns):
for ri in range(n_rips):
print '.. %d individuals - rip %d' % (n, ri)
print ' .. generating data'
Y, S, U, G = gen_data(N=n, P=P)
if __name__=='__main__':
P = 4
pdb.set_trace()
# define col covariances
C = FreeFormCov(P, jitter=0)
C0 = covariance.freeform(P)
t1 = 0
t0 = 0
for ti in range(1000):
C.setRandomParams()
C0.setParams(C.getParams())
for i in range(int(0.5*P*(P+1))):
_t0 = time.time()
C0.Kgrad_param(i)
_t1 = time.time()
C.K_grad_i(i)
_t2 = time.time()
t0 += _t1-_t0
t1 += _t2-_t1
print 'old:', t0
print 'new:', t1
