def fitPairwiseModel(Y,XX=None,S_XX=None,U_XX=None,verbose=False):
N,P = Y.shape
""" initilizes parameters """
RV = fitSingleTraitModel(Y,XX=XX,S_XX=S_XX,U_XX=U_XX,verbose=verbose)
Cg = covariance.freeform(2)
Cn = covariance.freeform(2)
gp = gp2kronSum(mean(Y[:,0:2]),Cg,Cn,XX=XX,S_XX=S_XX,U_XX=U_XX)
conv2 = SP.ones((P,P),dtype=bool)
rho_g = SP.ones((P,P))
rho_n = SP.ones((P,P))
for p1 in range(P):
for p2 in range(p1):
if verbose:
print '.. fitting correlation (%d,%d)'%(p1,p2)
gp.setY(Y[:,[p1,p2]])
Cg_params0 = SP.array([SP.sqrt(RV['varST'][p1,0]),1e-6*SP.randn(),SP.sqrt(RV['varST'][p2,0])])
Cn_params0 = SP.array([SP.sqrt(RV['varST'][p1,1]),1e-6*SP.randn(),SP.sqrt(RV['varST'][p2,1])])
params0 = {'Cg':Cg_params0,'Cn':Cn_params0}
conv2[p1,p2],info = OPT.opt_hyper(gp,params0,factr=1e3)
rho_g[p1,p2] = Cg.K()[0,1]/SP.sqrt(Cg.K().diagonal().prod())
rho_n[p1,p2] = Cn.K()[0,1]/SP.sqrt(Cn.K().diagonal().prod())
conv2[p2,p1] = conv2[p1,p2]; rho_g[p2,p1] = rho_g[p1,p2]; rho_n[p2,p1] = rho_n[p1,p2]
RV['Cg0'] = rho_g*SP.dot(SP.sqrt(RV['varST'][:,0:1]),SP.sqrt(RV['varST'][:,0:1].T))
RV['Cn0'] = rho_n*SP.dot(SP.sqrt(RV['varST'][:,1:2]),SP.sqrt(RV['varST'][:,1:2].T))
RV['conv2'] = conv2
#3. regularizes covariance matrices
offset_g = abs(SP.minimum(LA.eigh(RV['Cg0'])[0].min(),0))+1e-4
offset_n = abs(SP.minimum(LA.eigh(RV['Cn0'])[0].min(),0))+1e-4
RV['Cg0_reg'] = RV['Cg0']+offset_g*SP.eye(P)
RV['Cn0_reg'] = RV['Cn0']+offset_n*SP.eye(P)
RV['params0_Cg']=LA.cholesky(RV['Cg0_reg'])[SP.tril_indices(P)]
RV['params0_Cn']=LA.cholesky(RV['Cn0_reg'])[SP.tril_indices(P)]
return RV
def fitNull(self,verbose=True,cache=False,out_dir='./cache',fname=None,rewrite=False,seed=None,n_times=10,factr=1e3,init_method=None):
"""
Fit null model
"""
if seed is not None: SP.random.seed(seed)
read_from_file = False
if cache:
assert fname is not None, 'MultiTraitSetTest:: specify fname'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(out_dir,fname)
read_from_file = os.path.exists(out_file) and not rewrite
RV = {}
if read_from_file:
f = h5py.File(out_file,'r')
for key in f.keys():
RV[key] = f[key][:]
f.close()
self.setNull(RV)
else:
start = TIME.time()
if self.bgRE:
self.gpNull = gp2kronSum(self.mean,self.Cg,self.Cn,XX=self.XX,S_XX=self.S_XX,U_XX=self.U_XX)
else:
self.gpNull = gp2kronSumLR(self.Y,self.Cn,Xr=SP.ones((self.N,1)),F=self.F)
for i in range(n_times):
params0,Ifilter=self._initParams(init_method=init_method)
conv,info = OPT.opt_hyper(self.gpNull,params0,Ifilter=Ifilter,factr=factr)
if conv: break
if not conv: warnings.warn("not converged")
LMLgrad = SP.concatenate([self.gpNull.LMLgrad()[key]**2 for key in self.gpNull.LMLgrad().keys()]).mean()
LML = self.gpNull.LML()
if 'mean' in params0.keys():
RV['params_mean'] = self.gpNull.mean.getParams()
RV['params0_g'] = self.Cg.getParams()
RV['params0_n'] = self.Cn.getParams()
RV['Cg'] = self.Cg.K()
RV['Cn'] = self.Cn.K()
RV['conv'] = SP.array([conv])
RV['time'] = SP.array([TIME.time()-start])
RV['NLL0'] = SP.array([LML])
RV['LMLgrad'] = SP.array([LMLgrad])
RV['nit'] = SP.array([info['nit']])
RV['funcalls'] = SP.array([info['funcalls']])
if self.bgRE:
RV['h2'] = self.gpNull.h2()
RV['h2_ste'] = self.gpNull.h2_ste()
RV['Cg_ste'] = self.gpNull.ste('Cg')
RV['Cn_ste'] = self.gpNull.ste('Cn')
self.null = RV
if cache:
f = h5py.File(out_file,'w')
dumpDictHdf5(RV,f)
f.close()
return RV
def fitPairwiseModel(Y, XX=None, S_XX=None, U_XX=None, verbose=False):
N, P = Y.shape
""" initilizes parameters """
RV = fitSingleTraitModel(Y, XX=XX, S_XX=S_XX, U_XX=U_XX, verbose=verbose)
Cg = covariance.freeform(2)
Cn = covariance.freeform(2)
gp = gp2kronSum(mean(Y[:, 0:2]), Cg, Cn, XX=XX, S_XX=S_XX, U_XX=U_XX)
conv2 = SP.ones((P, P), dtype=bool)
rho_g = SP.ones((P, P))
rho_n = SP.ones((P, P))
for p1 in range(P):
for p2 in range(p1):
if verbose:
print '.. fitting correlation (%d,%d)' % (p1, p2)
gp.setY(Y[:, [p1, p2]])
Cg_params0 = SP.array([
SP.sqrt(RV['varST'][p1, 0]), 1e-6 * SP.randn(),
SP.sqrt(RV['varST'][p2, 0])
])
Cn_params0 = SP.array([
SP.sqrt(RV['varST'][p1, 1]), 1e-6 * SP.randn(),
SP.sqrt(RV['varST'][p2, 1])
])
params0 = {'Cg': Cg_params0, 'Cn': Cn_params0}
conv2[p1, p2], info = OPT.opt_hyper(gp, params0, factr=1e3)
rho_g[p1, p2] = Cg.K()[0, 1] / SP.sqrt(Cg.K().diagonal().prod())
rho_n[p1, p2] = Cn.K()[0, 1] / SP.sqrt(Cn.K().diagonal().prod())
conv2[p2, p1] = conv2[p1, p2]
rho_g[p2, p1] = rho_g[p1, p2]
rho_n[p2, p1] = rho_n[p1, p2]
RV['Cg0'] = rho_g * SP.dot(SP.sqrt(RV['varST'][:, 0:1]),
SP.sqrt(RV['varST'][:, 0:1].T))
RV['Cn0'] = rho_n * SP.dot(SP.sqrt(RV['varST'][:, 1:2]),
SP.sqrt(RV['varST'][:, 1:2].T))
RV['conv2'] = conv2
#3. regularizes covariance matrices
offset_g = abs(SP.minimum(LA.eigh(RV['Cg0'])[0].min(), 0)) + 1e-4
offset_n = abs(SP.minimum(LA.eigh(RV['Cn0'])[0].min(), 0)) + 1e-4
RV['Cg0_reg'] = RV['Cg0'] + offset_g * SP.eye(P)
RV['Cn0_reg'] = RV['Cn0'] + offset_n * SP.eye(P)
RV['params0_Cg'] = LA.cholesky(RV['Cg0_reg'])[SP.tril_indices(P)]
RV['params0_Cn'] = LA.cholesky(RV['Cn0_reg'])[SP.tril_indices(P)]
return RV
def fitSingleTraitModel(Y,XX=None,S_XX=None,U_XX=None,verbose=False):
""" fit single trait model """
N,P = Y.shape
RV = {}
Cg = covariance.lowrank(1)
Cn = covariance.lowrank(1)
gp = gp2kronSum(mean(Y[:,0:1]),Cg,Cn,XX=XX,S_XX=S_XX,U_XX=U_XX)
params0 = {'Cg':SP.sqrt(0.5)*SP.ones(1),'Cn':SP.sqrt(0.5)*SP.ones(1)}
var = SP.zeros((P,2))
conv1 = SP.zeros(P,dtype=bool)
for p in range(P):
if verbose:
print '.. fitting variance trait %d'%p
gp.setY(Y[:,p:p+1])
conv1[p],info = OPT.opt_hyper(gp,params0,factr=1e3)
var[p,0] = Cg.K()[0,0]
var[p,1] = Cn.K()[0,0]
RV['conv1'] = conv1
RV['varST'] = var
return RV
def fitSingleTraitModel(Y, XX=None, S_XX=None, U_XX=None, verbose=False):
""" fit single trait model """
N, P = Y.shape
RV = {}
Cg = covariance.lowrank(1)
Cn = covariance.lowrank(1)
gp = gp2kronSum(mean(Y[:, 0:1]), Cg, Cn, XX=XX, S_XX=S_XX, U_XX=U_XX)
params0 = {
'Cg': SP.sqrt(0.5) * SP.ones(1),
'Cn': SP.sqrt(0.5) * SP.ones(1)
}
var = SP.zeros((P, 2))
conv1 = SP.zeros(P, dtype=bool)
for p in range(P):
if verbose:
print '.. fitting variance trait %d' % p
gp.setY(Y[:, p:p + 1])
conv1[p], info = OPT.opt_hyper(gp, params0, factr=1e3)
var[p, 0] = Cg.K()[0, 0]
var[p, 1] = Cn.K()[0, 0]
RV['conv1'] = conv1
RV['varST'] = var
return RV
# add first fixed effect
F = 1.*(SP.rand(N,3)<0.2); A = SP.ones((1,P))
mean.addFixedEffect(F=F,A=A)
# define covariance matrices
Cg = limix.CFreeFormCF(P)
Cn = limix.CFreeFormCF(P)
if 0:
# generate parameters
params = {}
params['Cg'] = SP.randn(int(0.5*P*(P+1)))
params['Cn'] = SP.randn(int(0.5*P*(P+1)))
params['mean'] = 1e-2*SP.randn(mean.getParams().shape[0])
print "check gradient with gp2kronSum"
gp = gp2kronSum(mean,Cg,Cn,XX)
gp.setParams(params)
gp.checkGradient()
print "test optimization"
conv,info = OPT.opt_hyper(gp,params,factr=1e3)
print conv
ipdb.set_trace()
if 1:
# generate parameters
params = {}
params['Cr'] = SP.randn(P)
params['Cg'] = SP.randn(int(0.5*P*(P+1)))
params['Cn'] = SP.randn(int(0.5*P*(P+1)))
params['mean'] = 1e-2*SP.randn(mean.getParams().shape[0])
print "check gradient with gp3kronSum"
def fitNull(self,
verbose=True,
cache=False,
out_dir='./cache',
fname=None,
rewrite=False,
seed=None,
n_times=10,
factr=1e3,
init_method=None):
"""
Fit null model
"""
if seed is not None: SP.random.seed(seed)
read_from_file = False
if cache:
assert fname is not None, 'MultiTraitSetTest:: specify fname'
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(out_dir, fname)
read_from_file = os.path.exists(out_file) and not rewrite
RV = {}
if read_from_file:
f = h5py.File(out_file, 'r')
for key in f.keys():
RV[key] = f[key][:]
f.close()
self.setNull(RV)
else:
start = TIME.time()
if self.bgRE:
self.gpNull = gp2kronSum(self.mean,
self.Cg,
self.Cn,
XX=self.XX,
S_XX=self.S_XX,
U_XX=self.U_XX)
else:
self.gpNull = gp2kronSumLR(self.Y,
self.Cn,
Xr=SP.ones((self.N, 1)),
F=self.F)
for i in range(n_times):
params0, Ifilter = self._initParams(init_method=init_method)
conv, info = OPT.opt_hyper(self.gpNull,
params0,
Ifilter=Ifilter,
factr=factr)
if conv: break
if not conv: warnings.warn("not converged")
LMLgrad = SP.concatenate([
self.gpNull.LMLgrad()[key]**2
for key in self.gpNull.LMLgrad().keys()
]).mean()
LML = self.gpNull.LML()
if 'mean' in params0.keys():
RV['params_mean'] = self.gpNull.mean.getParams()
RV['params0_g'] = self.Cg.getParams()
RV['params0_n'] = self.Cn.getParams()
RV['Cg'] = self.Cg.K()
RV['Cn'] = self.Cn.K()
RV['conv'] = SP.array([conv])
RV['time'] = SP.array([TIME.time() - start])
RV['NLL0'] = SP.array([LML])
RV['LMLgrad'] = SP.array([LMLgrad])
RV['nit'] = SP.array([info['nit']])
RV['funcalls'] = SP.array([info['funcalls']])
if self.bgRE:
RV['h2'] = self.gpNull.h2()
RV['h2_ste'] = self.gpNull.h2_ste()
RV['Cg_ste'] = self.gpNull.ste('Cg')
RV['Cn_ste'] = self.gpNull.ste('Cn')
self.null = RV
if cache:
f = h5py.File(out_file, 'w')
dumpDictHdf5(RV, f)
f.close()
return RV
