All of the examples can be found in Jupyter notebook form here.

Lasso, Ridge and Elastic Net Regressions

This notebook presents a simple implementation of Lasso and elastic net regressions.

Load Packages and Extra Functions

using DelimitedFiles, LinearAlgebra, Statistics, Plots, Convex, SCS

import MathOptInterface
const MOI = MathOptInterface

Loading Data

We use the diabetes data from Efron et al, downloaded from and then converted from a tab to a comma delimited file.

All data series are standardised (see below) to have zero means and unit standard deviation, which improves the numerical stability. (Efron et al do not standardise the scale of the response variable.)

(x,header) = readdlm("aux_files/diabetes.csv",',',header=true)

x = (x .- mean(x,dims=1))./std(x,dims=1)          #standardise

(Y,X) = (x[:,end],x[:,1:end-1]);                  #to get traditional names
xNames = header[1:end-1];

Lasso, Ridge and Elastic Net Regressions

(a) The regression is $Y = Xb + u$, where $Y$ and $u$ are $T \times 1$, $X$ is $T \times K$, and $b$ is the $K$-vector of regression coefficients.

(b) We want to minimize $(Y-Xb)'(Y-Xb) + \gamma \sum |b_i| + \lambda \sum b_i^2$.

(c) We can equally well minimise $b'Qb - 2c'b + \gamma \sum |b_i| + \lambda \sum b_i^2$, where $Q = X'X$ and $c=X'Y$

(d) Lasso: $\gamma>0,\lambda=0$; Ridge: $\gamma=0,\lambda>0$; elastic net: $\gamma>0,\lambda>0$.


Do Lasso (set γ>0,λ=0), ridge (set γ=0,λ>0) or elastic net regression (set γ>0,λ>0).

# Input
- `Y::Vector`:     T-vector with the response (dependent) variable
- `Y::VecOrMat`:   TxK matrix of covariates (regressors)
- `γ::Number`:     penalty on sum(abs.(b))
- `λ::Number`:     penalty on sum(b.^2)

function LassoEN(Y,X,γ,λ=0.0)

    K = size(X,2)

    b_ls = X\Y                    #LS estimate of weights, no restrictions

    Q  = X'X
    c  = X'Y                      #c'b = Y'X*b

    b  = Variable(K)              #define variables to optimize over
    L1 = quadform(b,Q)            #b'Q*b
    L2 = dot(c,b)                 #c'b
    L3 = norm(b,1)                #sum(|b|)
    L4 = sumsquares(b)            #sum(b^2)

    Sol = minimize(L1-2*L2+γ*L3+λ*L4)      #u'u + γ*sum(|b|) + λsum(b^2), where u = Y-Xb
    solve!(Sol,()->SCS.Optimizer(verbose = false))
    Sol.status == MOI.OPTIMAL ? b_i = vec(evaluate(b)) : b_i = NaN

    return b_i, b_ls


The next cell makes a Lasso regression for a single value of γ.

K = size(X,2)
γ = 100

(b,b_ls) = LassoEN(Y,X,γ)

println("OLS and Lasso coeffs (with γ=$γ)")
display([["" "OLS" "Lasso"];xNames b_ls b])
OLS and Lasso coeffs (with γ=100)

Redo the Lasso Regression with Different Gamma Values

We now loop over $\gamma$ values.

Remark: it would be quicker to put this loop inside the LassoEN() function so as to not recreate L1-L4.

nγ = 101
γM = range(0; stop=600, length=nγ)             #different γ values

bLasso   = fill(NaN,size(X,2),nγ)       #results for γM[i] are in bLasso[:,i]
for i = 1:nγ
    bLasso[:,i], = LassoEN(Y,X,γM[i])
     title = "Lasso regression coefficients",
     xlabel = "log(γ)",
     label = permutedims(xNames),
     size = (600,400))

Ridge Regression

We use the same function to do a ridge regression. Alternatively, do b = inv(X'X + λ*I)*X'Y.

nλ = 101
λM = range(0; stop=3000, length=nλ)

bRidge  = fill(NaN,size(X,2),nλ)
for i = 1:nλ
    bRidge[:,i], = LassoEN(Y,X,0,λM[i])
     title = "Ridge regression coefficients",
     xlabel = "log(λ)",
     label = permutedims(xNames),
     size = (600,400))

Elastic Net Regression

λ = 200
println("redo the Lasso regression, but with λ=$λ: an elastic net regression")

bEN   = fill(NaN,size(X,2),nγ)
for i = 1:nγ
    bEN[:,i], = LassoEN(Y,X,γM[i],λ)
redo the Lasso regression, but with λ=200: an elastic net regression
     title = "Elastic Net regression coefficients",
     xlabel = "log(γ)",
     label = permutedims(xNames),
     size = (600,400))

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