[scikit-learn] understanding lasso performance ?

[Yoel Sanchez Araujo]

Hi all,

Today I updated to the latest release of scikit-learn, and I went to test out the LassoCV module in linear_model. I've tried both approaches below, and my accuracy seems very poor, while using the same exact data with glmnet in R for example will give me ~ 75% accuracy:



from sklearn import linear_model
from sklearn.model_selection import StratifiedKFold,  train_test_split

lassocv1 = linear_model.LassoCV(cv=10, max_iter=10000, n_alphas=10000)

xtrain, xtest, ytrain, ytest = train_test_split(
    endo_Xv, endo_y, test_size = .25, random_state = 1
)

lassocv1.fit(xtrain, ytrain)
lassocv1.score(xtest, ytest)

from this, lassocv1.coef_ returns all zero coefficients

I've also tried this:

k_fold_S = StratifiedKFold(n_splits=10, shuffle=False)
lasso_cv = linear_model.LassoCV()
alphas=[]
scores=[]
coefs=[]
ks=[]

for k, (train, test) in enumerate(k_fold_S.split(endo_Xv, endo_y)):
    lasso_cv.fit(endo_Xv[train], endo_y[train])
    scores.append(lasso_cv.score(endo_Xv[test], endo_y[test]))
    alphas.append(lasso_cv.alpha_)
    coefs.append(lasso_cv.coef_)
    ks.append(k)

for all k, the coef_ arrays are all zero and the scores array for example:


[-1.3295256159340241e-05,
  -1.3295256159562285e-05,
  -1.3295256159784328e-05,
  -1.3295256159562285e-05,
  -1.3295256159562285e-05,
  -1.3295256159340241e-05,
  -6.4162287406910323e-05,
  -6.4162287406910323e-05,
  -6.4162287406910323e-05,
  -3.8436343168246623e-06])


Any insights would be greatly appreciated, not sure if this has anything to do with the update, but yesterday(unupdated) I was getting better performance.
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