def crossvalidate(self, y, alphas, n_splits=10):
"""
lmmlasso cross-validation to get optimal alpha
alphas = list of alphas to perform cross-validation over
y = phenotype
"""
lasso = lmmlasso.LmmLasso(warm_start=True,
fit_intercept=False,
tol=0.5)
X = self.E
K = self.K
assert K is not None, 'no kinship matrix defined'
MSE_train, MSE_test, W_nonzero, rsquared = lmmlasso.runCrossValidation(
lasso, self.E, y, alphas, n_splits=n_splits, K=K, verbose=True)
train_inter = sp.interpolate.UnivariateSpline(
x=alphas, y=(MSE_train.mean(axis=0))).derivative(
n=2) #Interpolating the values for alphas within the range
test_inter = sp.interpolate.UnivariateSpline(
x=alphas, y=(MSE_test.mean(axis=0))).derivative(n=2)
alphas_inter = (sp.linspace(min(alphas), max(alphas), 100))
idx_train = sp.argmin(train_inter(alphas_inter)) # :/
idx_test = sp.argmin(test_inter(alphas_inter)) # :/
alpha_cv = (float(alphas_inter[idx_train]) +
float(alphas_inter[idx_test])) / 2
self.alpha = alpha_cv
return self.alpha
n_splits=10
N = X.shape[0]
kf = KFold(n_splits,shuffle=True,random_state=None)
n_alphas = len(alphas)
MSE_train = sp.zeros((n_splits,n_alphas))
MSE_test = sp.zeros((n_splits,n_alphas))
W_nonzero = sp.zeros((n_splits,n_alphas))
kf.get_n_splits(X)
os.chdir("/srv/uom-data1-q.unimelb.edu.au/6300-afournier/home/student.unimelb.edu.au/andhikap/Clim_GWAS/Clim_GWAS_2")
import lmmlasso
lasso = lmmlasso.LmmLasso(warm_start=True,fit_intercept=False,tol=0.5) #note the tolerance value
MSE_train,MSE_test,W_nonzero = lmmlasso.runCrossValidation(lasso,X,y,alphas,n_splits=10,K=K,verbose=True)
MSE_train_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_train.mean(axis=0))).derivative(n=2) #something about the rotation here is different from the original script
MSE_test_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_test.mean(axis=0))).derivative(n=2)
alphas_inter = 2.**(sp.linspace(2,12,100))
idx_train = sp.argmin(MSE_train_inter(alphas_inter))
idx_test = sp.argmin(MSE_test_inter(alphas_inter))
alpha_cv = (float(alphas_inter[idx_train])+float(alphas_inter[idx_test]))/2
import pylab as pl
pl.figure(figsize=[20,4])
pls = pl.subplot(1,3,1)
pls.plot(sp.log2(alphas),MSE_train.mean(axis=0),linewidth=2)
pl.axvline(sp.log2(alpha_cv),color='r')
pl.xlabel('log alpha')
pl.ylabel('training error')
n_splits=10
N = X.shape[0]
kf = KFold(n_splits,shuffle=True,random_state=None)
n_alphas = len(alphas)
MSE_train = sp.zeros((n_splits,n_alphas))
MSE_test = sp.zeros((n_splits,n_alphas))
W_nonzero = sp.zeros((n_splits,n_alphas))
rsquared = sp.zeros((n_splits,n_alphas))
os.chdir("/srv/uom-data1-q.unimelb.edu.au/6300-afournier/home/student.unimelb.edu.au/andhikap/Clim_GWAS/Clim_GWAS_2/Temp_Files")
import lmmlasso
'''Pth = 0'''
lasso = lmmlasso.LmmLasso(warm_start=True,fit_intercept=False,tol=0.5) #note the tolerance value
MSE_train,MSE_test,W_nonzero, rsquared = lmmlasso.runCrossValidation(lasso,X0,y,alphas,n_splits=10,K=K,verbose=True)
MSE_train_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_train.mean(axis=0))).derivative(n=2)#Interpolating the values for alphas within -2 to 10...
MSE_test_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_test.mean(axis=0))).derivative(n=2)
alphas_inter = 2.**(sp.linspace(2,10,100))
idx_train = sp.argmin(MSE_train_inter(alphas_inter))
idx_test = sp.argmin(MSE_test_inter(alphas_inter))
alpha_cv = (float(alphas_inter[idx_train])+float(alphas_inter[idx_test]))/2
os.chdir("/srv/uom-data1-q.unimelb.edu.au/6300-afournier/home/student.unimelb.edu.au/andhikap/Clim_GWAS/Clim_GWAS_2")
kf12 = KFold(n_splits,shuffle=True,random_state=12) #12 because the random state is 12
#Model fitting step
N = X.shape[0]
#kf = KFold(n_splits,shuffle=True,random_state=12)
MSE_train_final = sp.zeros((n_splits,))
MSE_test_final = sp.zeros((n_splits,))
W_nonzero_final = sp.zeros((n_splits,))
rsquared_final = sp.zeros((n_splits,))
plt.xlabel('SNPs')
plt.title('GWAS using LMM; F-test; Unique SNPs only')
plt.show()
#Running LMM lasso (100k SNPs + covariance matrix)
import lmmlasso
#Cross validation to get the optimal parameters
alphas = 2.**(sp.linspace(2, 12, 10))
alphas = alphas[::-1]
from lmmlasso import runCrossValidation
lasso = lmmlasso.LmmLasso(
) #No need to set parameters because these will be decided through cross-validation [may need to set tolerance higher, use tol=0.05 as a baseline]
MSE_train, MSE_test, W_nonzero = lmmlasso.runCrossValidation(lasso,
SNP_data,
Pheno_data,
alphas,
n_splits=10,
K=K_data,
verbose=True)
#Then from Alex's code..
import pylab as pl
MSE_train_inter = sp.interpolate.UnivariateSpline(x=np.flip(alphas, axis=0),
y=np.flip(
MSE_train.mean(axis=0),
axis=0)).derivative(n=2)
MSE_test_inter = sp.interpolate.UnivariateSpline(x=np.flip(alphas, axis=0),
y=np.flip(
MSE_test.mean(axis=0),
axis=0)).derivative(n=2)
N = X.shape[0]
kf = KFold(n_splits,shuffle=True,random_state=None)
n_alphas = len(alphas)
MSE_train = sp.zeros((n_splits,n_alphas))
MSE_test = sp.zeros((n_splits,n_alphas))
W_nonzero = sp.zeros((n_splits,n_alphas))
rsquared = sp.zeros((n_splits,n_alphas))
kf.get_n_splits(X)
os.chdir("/srv/uom-data1-q.unimelb.edu.au/6300-afournier/home/student.unimelb.edu.au/andhikap/Clim_GWAS/Clim_GWAS_2")
import lmmlasso
lasso = lmmlasso.LmmLasso(warm_start=True,fit_intercept=False,tol=0.5) #note the tolerance value
MSE_train,MSE_test,W_nonzero, rsquared = lmmlasso.runCrossValidation(lasso,X,y,alphas,n_splits=10,K=K,verbose=True)
MSE_train_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_train.mean(axis=0))).derivative(n=2)#Interpolating the values for alphas within -2 to 10...
MSE_test_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_test.mean(axis=0))).derivative(n=2)
alphas_inter = 2.**(sp.linspace(2,10,100))
idx_train = sp.argmin(MSE_train_inter(alphas_inter))
idx_test = sp.argmin(MSE_test_inter(alphas_inter))
##idx_train=sp.argmin
alpha_cv = (float(alphas_inter[idx_train])+float(alphas_inter[idx_test]))/2
import pylab as pl
pl.figure(figsize=[20,4])
pls = pl.subplot(1,3,1)
pls.plot(sp.log2(alphas),MSE_train.mean(axis=0),linewidth=2)
pl.axvline(sp.log2(alpha_cv),color='r')
lasso = lmmlasso.LmmLasso(warm_start=True,fit_intercept=False,tol=0.5) #note the tolerance value
#Recalculate alpha for each SNP or not?
alphas = 2.**(sp.linspace(-10,10,10)) #list of alphas to test
snp_weights=np.empty((1,snps.shape[1]))
snp_errors=np.empty((1,snps.shape[1]))
for i in range(snps.shape[1]):
X=snps[:,i]
X=np.reshape(X,(-1,1))
MSE_train,MSE_test,W_nonzero, rsquared = lmmlasso.runCrossValidation(lasso,X,y,alphas,n_splits=10,K=K,verbose=True) #X needs to have dimension (5623, 1)
MSE_train_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_train.mean(axis=0))).derivative(n=2)#Interpolating the values for alphas within -2 to 10...
MSE_test_inter=sp.interpolate.UnivariateSpline(x=alphas, y=(MSE_test.mean(axis=0))).derivative(n=2)
alphas_inter = 2.**(sp.linspace(-10,10,100))
idx_train = sp.argmin(MSE_train_inter(alphas_inter))
idx_test = sp.argmin(MSE_test_inter(alphas_inter))
alpha_cv = (float(alphas_inter[idx_train])+float(alphas_inter[idx_test]))/2
os.chdir("/srv/uom-data1-q.unimelb.edu.au/6300-afournier/home/student.unimelb.edu.au/andhikap/Clim_GWAS/Clim_GWAS_2")
kf12 = KFold(n_splits,shuffle=True,random_state=12) #12 because the random state is 12
lasso.set_params(alpha=alpha_cv)
lasso = lasso.fit(X,y,K=K)
weights = lasso.coef_
snp_weights[1,i]=weights[0]
predictions=lasso.predict(X,K)
residuals=y-predictions
residuals=residuals**2
