SubsetSelection is a Julia package that computes sparse L2-regularized estimators. Sparsity is enforced through explicit cardinality constraint or L0-penalty. Supported loss functions for regression are least squares, L1 and L2 SVR; for classification, logistic, L1 and L2 Hinge loss. The algorithm formulates the problem as a mixed-integer saddle-point problem and solves its boolean relaxation using a dual sub-gradient approach.

To install the package:

```
julia> Pkg.install("SubsetSelection")
```

or the have the latest version

```
julia> Pkg.clone("git://github.com/jeanpauphilet/SubsetSelection.jl.git")
```

To fit a basic model:

```
julia> using SubsetSelection, StatsBase
julia> n = 100; p = 10000; k = 10;
julia> indices = sort(sample(1:p, StatsBase.Weights(ones(p)/p), k, replace=false));
julia> w = sample(-1:2:1, k);
julia> X = randn(n,p); Y = X[:,indices]*w;
julia> Sparse_Regressor = subsetSelection(OLS(), Constraint(k), Y, X)
SubsetSelection.SparseEstimator(SubsetSelection.OLS(),SubsetSelection.Constraint(10),10.0,[362,1548,2361,3263,3369,3598,5221,7314,7748,9267],[5.37997,-5.51019,-5.77256,-7.27197,-6.32432,-4.97585,5.94814,4.75648,5.48098,-5.91967],[-0.224588,-1.1446,2.81566,0.582427,-0.923311,4.1153,-2.43833,0.117831,0.0982258,-1.60631 … 0.783925,-1.1055,0.841752,-1.09645,-0.397962,3.48083,-1.33903,1.44676,4.03583,1.05817],0.0,19)
```

The algorithm returns a SparseEstimator object with the following fields: `loss`

(loss function used), `sparsity`

(model to enforce sparsity), `indices`

(features selected), `w`

(value of the estimator on the selected features only), `α`

(values of the associated dual variables), `b`

(bias term), `iter`

(number of iterations required by the algorithm).

For instance, you can access the selected features directly in the `indices`

field:

```
julia> Sparse_Regressor.indices
10-element Array{Int64,1}:
362
1548
2361
3263
3369
3598
5221
7314
7748
9267
```

or compute predictions

```
julia> Y_pred = X[:,Sparse_Regressor.indices]*Sparse_Regressor.w
100-element Array{Float64,1}:
4.62918
8.59952
-16.2796
-5.611
1.62764
-50.4562
37.407
-12.3341
-4.75339
25.122
⋮
-7.98349
11.0327
-8.58172
16.904
-9.04211
-36.5475
17.2558
-22.3915
-57.9727
-6.06553
```

For classification, we use +1/-1 labels and the convention
`P ( Y = y | X = x ) = 1 / (1+e^{- y x^T w})`

.

`subsetSelection`

has four required parameters:

- the loss function to be minimized, to be chosen among least squares (
`OLS()`

), L1SVR (`L1SVR(ɛ)`

), L2SVR (`L2SVR(ɛ)`

), Logistic loss (`LogReg()`

), Hinge Loss (`L1SVM()`

), L2-SVM (`L2SVM()`

). For classification, we recommend using Hinge loss or L2-SVM functions. Indeed, the Fenchel conjugate of the Logistic loss exhibits unbounded gradients, which largely hinders convergence of the algorithm and might require smaller and more steps (see optional parameters). - the model used to enforce sparsity; either by adding a hard constraint of the form "||w||
*0 < k" (`julia-observer-quote-cut-paste-16**work*_work`) to the objective. For tractability issues, we highly recommend using an explicit constraint instead of a penalty, for it ensures the size of the support remains bounded through the algorithm.`) or by adding a penalty of the form "+ λ ||w||_0" (`

julia-observer-quote-cut-paste-17 - the vector of outputs
`Y`

of size`n`

, the sample size. In classification settings,`Y`

should be a vector of ±1s. - the matrix of covariates
`X`

of size`n`

×`p`

, where`n`

and`p`

are the number of samples and features respectively.

In addition, `subsetSelection`

accepts the following optional parameters:

- an initialization for the selected features,
`indInit`

. - an initialization for the dual variables,
`αInit`

. - the value of the ℓ2-regularization parameter
`γ`

, set to 1/√n by default. `intercept`

, a boolean. If true, an intercept/bias term is computed as well. By default, set to false.- the maximum number of iterations in the sub-gradient algorithm,
`maxIter`

. - the value of the gradient stepsize
`δ`

. By default, the stepsize is set to 1e-3, which demonstrates very good empirical performance. However, smaller stepsizes might be needed when dealing with very large datasets or when the Logistic loss is used. - the number of gradient updates of dual variable α performed per update of primal variable s,
`gradUp`

. `anticycling`

a boolean. If true, the algorithm stops as soon as the support is not unchanged from one iteration to another. Empirically, the accuracy of the resulting support is strongly sensitive to noise - to use with caution. By default, set to false.`averaging`

a boolean. If true, the dual solution is averaged over past iterates. By default, set to true.

- Tuning the regularization parameter
`γ`

: By default,`γ`

is set to 1/√n, which is an appropriate scaling in most regression instances. For an optimal performance, and especially in classification or noisy settings, we recommend performing a grid search and using cross-validation to assess out-of-sample performance. The grid search should start with a very low value for`γ`

, such as

`γ = 1.*p / k / n / maximum(sum(X[train,:].^2,2))`

and iteratively increase it by a factor 2. Mean square error or Area Under the Curve (see ROCAnalysis for implementation) are commonly used performance metrics for regression and classification tasks respectively. - Instances where the algorithm fails to converge have been reported. If you occur such cases, try normalize the data matrix
`X`

and relaunch the algorithm. If the algorithm still fails to converge, reduce the stepsize`δ`

by a factor 10 or 100 and increase the number of iterations`maxIter`

by a factor at least 2.

09/01/2017

7 months ago

101 commits