Why do Lars and Glmnet give different solutions for the Lasso problem?

by Andre   Last Updated July 12, 2018 01:19 AM

I want to better understand the R packages Lars and Glmnet, which are used to solve the Lasso problem: $$min_{(\beta_0 \beta) \in R^{p+1}} \left[\frac{1}{2N}\sum_{i=1}^{N}(y_i-\beta_0-x_i^T\beta)^2 + \lambda||\beta ||_{l_{1}} \right]$$ (for $p$ Variables and $N$ samples, see www.stanford.edu/~hastie/Papers/glmnet.pdf on page 3)

Therefore, I applied them both on the same toy dataset. Unfortunately, the two methods do not give the same solutions for the same data input. Does anybody have an idea where the difference comes from?

I obtained the results as follows: After generating some data (8 samples, 12 features, Toeplitz design, everything centered), I computed the whole Lasso path using Lars. Then, I ran Glmnet using the sequence of lambdas computed by Lars (multiplied by 0.5) and hoped to obtain the same solution, but I did not.

One can see that the solutions are similar. But how can I explain the differences? Please find my code below. There is a related question here: GLMNET or LARS for computing LASSO solutions? , but it does not contain the answer to my question.

Setup:

# Load packages.
library(lars)
library(glmnet)
library(MASS)

# Set parameters.
nb.features <- 12
nb.samples <- 8
nb.relevant.indices <- 3
snr <- 1
nb.lambdas <- 10

# Create data, not really important. 
sigma <- matrix(0, nb.features, nb.features)
for (i in (1:nb.features)) {
  for (j in (1:nb.features)) {
    sigma[i, j] <- 0.99 ^ (abs(i - j))
  }
}

x <- mvrnorm(n=nb.samples, rep(0, nb.features), sigma, tol=1e-6, empirical=FALSE)
relevant.indices <- sample(1:nb.features, nb.relevant.indices, replace=FALSE)
x <- scale(x)
beta <- rep(0, times=nb.features)
beta[relevant.indices] <- runif(nb.relevant.indices, 0, 1)
epsilon <- matrix(rnorm(nb.samples),nb.samples, 1)
simulated.snr <-(norm(x %*% beta, type="F")) / (norm(epsilon, type="F"))
epsilon <- epsilon * (simulated.snr / snr)
y <- x %*% beta + epsilon
y <- scale(y)

lars:

la <- lars(x, y, intercept=TRUE, max.steps=1000, use.Gram=FALSE)
co.lars <- as.matrix(coef(la, mode="lambda"))
print(round(co.lars, 4))

#          [,1] [,2] [,3]   [,4]   [,5]   [,6]    [,7]   [,8]    [,9]   [,10]
#  [1,]  0.0000    0    0 0.0000 0.0000 0.0000  0.0000 0.0000  0.0000  0.0000
#  [2,]  0.0000    0    0 0.0000 0.0000 0.1735  0.0000 0.0000  0.0000  0.0000
#  [3,]  0.0000    0    0 0.2503 0.0000 0.4238  0.0000 0.0000  0.0000  0.0000
#  [4,]  0.0000    0    0 0.1383 0.0000 0.7578  0.0000 0.0000  0.0000  0.0000
#  [5,] -0.1175    0    0 0.2532 0.0000 0.8506  0.0000 0.0000  0.0000  0.0000
#  [6,] -0.3502    0    0 0.2676 0.3068 0.9935  0.0000 0.0000  0.0000  0.0000
#  [7,] -0.4579    0    0 0.6270 0.0000 0.9436  0.0000 0.0000  0.0000  0.0000
#  [8,] -0.7848    0    0 0.9970 0.0000 0.9856  0.0000 0.0000  0.0000  0.0000
#  [9,] -0.3175    0    0 0.0000 0.0000 3.4488  0.0000 0.0000 -2.1714  0.0000
# [10,] -0.4842    0    0 0.0000 0.0000 4.7731  0.0000 0.0000 -3.4102  0.0000
# [11,] -0.4685    0    0 0.0000 0.0000 4.7958  0.0000 0.1191 -3.6243  0.0000
# [12,] -0.4364    0    0 0.0000 0.0000 5.0424  0.0000 0.3007 -4.0694 -0.4903
# [13,] -0.4373    0    0 0.0000 0.0000 5.0535  0.0000 0.3213 -4.1012 -0.4996
# [14,] -0.4525    0    0 0.0000 0.0000 5.6876 -1.5467 1.5095 -4.7207  0.0000
# [15,] -0.4593    0    0 0.0000 0.0000 5.7355 -1.6242 1.5684 -4.7440  0.0000
# [16,] -0.4490    0    0 0.0000 0.0000 5.8601 -1.8485 1.7767 -4.9291  0.0000
#         [,11]  [,12]
#  [1,]  0.0000 0.0000
#  [2,]  0.0000 0.0000
#  [3,]  0.0000 0.0000
#  [4,] -0.2279 0.0000
#  [5,] -0.3266 0.0000
#  [6,] -0.5791 0.0000
#  [7,] -0.6724 0.2001
#  [8,] -1.0207 0.4462
#  [9,] -0.4912 0.1635
# [10,] -0.5562 0.2958
# [11,] -0.5267 0.3274
# [12,]  0.0000 0.2858
# [13,]  0.0000 0.2964
# [14,]  0.0000 0.1570
# [15,]  0.0000 0.1571

glmnet with lambda=(lambda_lars / 2):

glm2 <- glmnet(x, y, family="gaussian", lambda=(0.5 * la$lambda), thresh=1e-16)
co.glm2 <- as.matrix(t(coef(glm2, mode="lambda")))
print(round(co.glm2, 4))

#     (Intercept)      V1 V2 V3     V4     V5     V6      V7     V8      V9
# s0            0  0.0000  0  0 0.0000 0.0000 0.0000  0.0000 0.0000  0.0000
# s1            0  0.0000  0  0 0.0000 0.0000 0.0000  0.0000 0.0000  0.0000
# s2            0  0.0000  0  0 0.2385 0.0000 0.4120  0.0000 0.0000  0.0000
# s3            0  0.0000  0  0 0.2441 0.0000 0.4176  0.0000 0.0000  0.0000
# s4            0  0.0000  0  0 0.2466 0.0000 0.4200  0.0000 0.0000  0.0000
# s5            0  0.0000  0  0 0.2275 0.0000 0.4919  0.0000 0.0000  0.0000
# s6            0  0.0000  0  0 0.1868 0.0000 0.6132  0.0000 0.0000  0.0000
# s7            0 -0.2651  0  0 0.2623 0.1946 0.9413  0.0000 0.0000  0.0000
# s8            0 -0.6609  0  0 0.7328 0.0000 1.6384  0.0000 0.0000 -0.5755
# s9            0 -0.4633  0  0 0.0000 0.0000 4.6069  0.0000 0.0000 -3.2547
# s10           0 -0.4819  0  0 0.0000 0.0000 4.7546  0.0000 0.0000 -3.3929
# s11           0 -0.4767  0  0 0.0000 0.0000 4.7839  0.0000 0.0567 -3.5122
# s12           0 -0.4715  0  0 0.0000 0.0000 4.7915  0.0000 0.0965 -3.5836
# s13           0 -0.4510  0  0 0.0000 0.0000 5.6237 -1.3909 1.3898 -4.6583
# s14           0 -0.4552  0  0 0.0000 0.0000 5.7064 -1.5771 1.5326 -4.7298
#         V10     V11    V12
# s0   0.0000  0.0000 0.0000
# s1   0.0000  0.0000 0.0000
# s2   0.0000  0.0000 0.0000
# s3   0.0000  0.0000 0.0000
# s4   0.0000  0.0000 0.0000
# s5   0.0000 -0.0464 0.0000
# s6   0.0000 -0.1293 0.0000
# s7   0.0000 -0.4868 0.0000
# s8   0.0000 -0.8803 0.3712
# s9   0.0000 -0.5481 0.2792
# s10  0.0000 -0.5553 0.2939
# s11  0.0000 -0.5422 0.3108
# s12  0.0000 -0.5323 0.3214
# s13 -0.0503  0.0000 0.1711
# s14  0.0000  0.0000 0.1571


Answers 3


Obviously if the methods use different models you will get different answers. Subtracting off the intercept terms does not lead to the model without the intercept because the best fitting coefficients will change and you do not change them the way you are approaching it. You need to fit the same model with both methods if you want the same or nearly the same answers.

Michael Chernick
Michael Chernick
August 04, 2012 16:01 PM

Finally we were able to produce the same solution with both methods! First issue is that glmnet solves the lasso problem as stated in the question, but lars has a slightly different normalization in the objective function, it replaces $\frac{1}{2N}$by $\frac{1}{2}$. Second, both methods normalize the data differently, so the normalization must be swiched off when calling the methods.

To reproduce that, and see that the same solutions for the lasso problem can be computed using lars and glmnet, the following lines in the code above must be changed:

la <- lars(X,Y,intercept=TRUE, max.steps=1000, use.Gram=FALSE)

to

la <- lars(X,Y,intercept=TRUE, normalize=FALSE, max.steps=1000, use.Gram=FALSE)

and

glm2 <- glmnet(X,Y,family="gaussian",lambda=0.5*la$lambda,thresh=1e-16)

to

glm2 <- glmnet(X,Y,family="gaussian",lambda=1/nbSamples*la$lambda,standardize=FALSE,thresh=1e-16)
Andre
Andre
August 09, 2012 08:48 AM

Results have to be the same. lars package uses by default type="lar", change this value to type="lasso". Just lower the parameter 'thresh=1e-16' for glmnet since coordinate descent is based on convergence.

Marcool Lopez Cruz
Marcool Lopez Cruz
July 12, 2018 00:41 AM

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