  In this paper, we consider the problem of invariant set computation for
black-box switched linear systems using merely a finite set of observations of
system trajectories. In particular, this paper focuses on polyhedral invariant
sets. We propose a data-driven method based on the one step forward reachable
set. For formal verification of the proposed method, we introduce the concepts
of $\lambda$-contractive sets and almost-invariant sets for switched linear
systems. The convexity-preserving property of switched linear systems allows us
to conduct contraction analysis on the computed set and derive a probabilistic
contraction property. In the spirit of non-convex scenario optimization, we
also establish a chance-constrained guarantee on set invariance. The
performance of our method is then illustrated by numerical examples.
