Welcome to stability-selection’s documentation!

This project contains an implementation of the stability selection algorithm.

Stability selection is a technique that aims to enhance and improve existing feature selection algorithms. For a generic feature selection algorithm, we have a tuning parameter \(\lambda \in \Lambda\) that controls the amount of regularisation. Examples of such algorithms are:

  1. \(\ell_1\)-penalized regression (penalization parameter \(\lambda\)).
  2. Orthogonal matching pursuit (number of steps in forward selection).
  3. Boosting (\(\ell_1\) penalty)

These structure learning algorithms have in common is a parameter \(\lambda \in \Lambda\) that controls the amount of regularisation. For every value of \(\lambda\), we obtain a structure estimate \(S^\lambda = \{1, \ldots, p\}\), which indicates which variables to select. We are interested to determine whether there exists a \(\lambda\) such that \(S^\lambda\) is identical to \(S\) with high probability, and how to achieve the right amount of regularisation.

Sability selection works as follows:

  1. Define a candidate set of regularization parameters \(\Lambda\) and a subsample number \(N\).

  2. For each value \(\lambda \in \Lambda\) do:

    1. For each \(i\) in \(\{1, \ldots, N\}\), do:

      1. Generate a bootstrap sample of the original data \(X^{n \times p}\) of size \(\frac{n}{2}\).
      2. Run the selection algorithm (LASSO) on the bootstrap sample with regularization parameter \(\lambda\).
    2. Given the selection sets from each subsample, calculate the empirical selection probability for each model component:

      \(\hat{\Pi}^\lambda_k = \mathbb{P}[k \in \hat{S}^\lambda] = \frac{1}{N} \sum_{i = 1}^N \mathbb{I}_{\{k \in \hat{S}_i^\lambda\}}.\)

    3. The selection probability for component \(k\) is its probability of being selected by the algorithm.

  3. Given the selection probabilities for each component and for each value of \(\lambda\), construct the stable set according to the following definition:

    \(\hat{S}^{\text{stable}} = \{k : \max_{\lambda \in \Lambda} \hat{\Pi}_k^\lambda \geq \pi_\text{thr}\}.\)

    where \(\pi_\text{thr}\) is a predefined threshold.

This algorithm identifies a set of “stable” variables that are selected with high probability.

See the README for more information.

Indices and tables