Policies.SparseWrapper module¶

The SparseWrapper policy, designed to tackle sparse stochastic bandit problems:

• This means that only a small subset of size s of the K arms has non-zero means.

• The SparseWrapper algorithm requires to known exactly the value of s.

• This SparseWrapper is a very generic version, and can use any index policy for both the decision in the UCB phase and the construction of the sets $$\mathcal{J}(t)$$ and $$\mathcal{K}(t)$$.

• The usual UCB indexes can be used for the set $$\mathcal{K}(t)$$ by setting the flag use_ucb_for_set_K to true (but usually the indexes from the underlying policy can be used efficiently for set $$\mathcal{K}(t)$$), and for the set $$\mathcal{J}(t)$$ by setting the flag use_ucb_for_set_J to true (as its formula is less easily generalized).

• If used with Policy.UCBalpha or Policy.klUCB, it should be better to use directly Policy.SparseUCB or Policy.SparseklUCB.

• Reference: [[“Sparse Stochastic Bandits”, by J. Kwon, V. Perchet & C. Vernade, COLT 2017](https://arxiv.org/abs/1706.01383)] who introduced SparseUCB.

Warning

This is very EXPERIMENTAL! I have no proof yet! But it works fine!!

Policies.SparseWrapper.default_index_policy
class Policies.SparseWrapper.Phase

Bases: enum.Enum

Different states during the SparseWrapper algorithm.

• RoundRobin means all are sampled once.

• ForceLog uniformly explores arms that are in the set $$\mathcal{J}(t) \setminus \mathcal{K}(t)$$.

• UCB is the phase that the algorithm should converge to, when a normal UCB selection is done only on the “good” arms, i.e., $$\mathcal{K}(t)$$.

ForceLog = 2
RoundRobin = 1
UCB = 3
__module__ = 'Policies.SparseWrapper'
Policies.SparseWrapper.USE_UCB_FOR_SET_K = False

Default value for the flag controlling whether the usual UCB indexes are used for the set $$\mathcal{K}(t)$$. Default it to use the indexes of the underlying policy, which could be more efficient.

Policies.SparseWrapper.USE_UCB_FOR_SET_J = False

Default value for the flag controlling whether the usual UCB indexes are used for the set $$\mathcal{J}(t)$$. Default it to use the UCB indexes as there is no clean and generic formula to obtain the indexes for $$\mathcal{J}(t)$$ from the indexes of the underlying policy. Note that I found a formula, it’s just durty. See below.

Policies.SparseWrapper.ALPHA = 1

Default parameter for $$\alpha$$ for the UCB indexes.

class Policies.SparseWrapper.SparseWrapper(nbArms, sparsity=None, use_ucb_for_set_K=False, use_ucb_for_set_J=False, alpha=1, policy=<class 'Policies.UCBalpha.UCBalpha'>, lower=0.0, amplitude=1.0, *args, **kwargs)[source]

The SparseWrapper policy, designed to tackle sparse stochastic bandit problems.

• By default, assume sparsity = nbArms.

__init__(nbArms, sparsity=None, use_ucb_for_set_K=False, use_ucb_for_set_J=False, alpha=1, policy=<class 'Policies.UCBalpha.UCBalpha'>, lower=0.0, amplitude=1.0, *args, **kwargs)[source]

New policy.

sparsity = None

Known value of the sparsity of the current problem.

use_ucb_for_set_K = None

Whether the usual UCB indexes are used for the set $$\mathcal{K}(t)$$.

use_ucb_for_set_J = None

Whether the usual UCB indexes are used for the set $$\mathcal{J}(t)$$.

alpha = None

Parameter $$\alpha$$ for the UCB indexes for the two sets, if not using the indexes of the underlying policy.

phase = None

Current phase of the algorithm.

force_to_see = None

Binary array for the set $$\mathcal{J}(t)$$.

goods = None

Binary array for the set $$\mathcal{K}(t)$$.

offset = None

Next arm to sample, for the Round-Robin phase

__str__()[source]

-> str

startGame()[source]

Initialize the policy for a new game.

update_j()[source]

Recompute the set $$\mathcal{J}(t)$$:

$\begin{split}\hat{\mu}_k(t) &= \frac{X_k(t)}{N_k(t)}, \\ U^{\mathcal{K}}_k(t) &= I_k^{P}(t) - \hat{\mu}_k(t),\\ U^{\mathcal{J}}_k(t) &= U^{\mathcal{K}}_k(t) \times \sqrt{\frac{\log(N_k(t))}{\log(t)}},\\ \mathcal{J}(t) &= \left\{ k \in [1,...,K]\;, \hat{\mu}_k(t) \geq U^{\mathcal{J}}_k(t) - \hat{\mu}_k(t) \right\}.\end{split}$
• Yes, this is a nothing but a hack, as there is no generic formula to retrieve the indexes used in the set $$\mathcal{J}(t)$$ from the indexes $$I_k^{P}(t)$$ of the underlying index policy $$P$$.

• If use_ucb_for_set_J is True, the same formula from Policies.SparseUCB is used.

Warning

FIXME rewrite the above with LCB and UCB instead of this weird U - mean.

__module__ = 'Policies.SparseWrapper'
update_k()[source]

Recompute the set $$\mathcal{K}(t)$$:

$\begin{split}\hat{\mu}_k(t) &= \frac{X_k(t)}{N_k(t)}, \\ U^{\mathcal{K}}_k(t) &= I_k^{P}(t) - \hat{\mu}_k(t),\\ \mathcal{K}(t) &= \left\{ k \in [1,...,K]\;, \hat{\mu}_k(t) \geq U^{\mathcal{K}}_k(t) - \hat{\mu}_k(t) \right\}.\end{split}$
choice()[source]

Choose the next arm to play:

• If still in a Round-Robin phase, play the next arm,

• Otherwise, recompute the set $$\mathcal{J}(t)$$,

• If it is too small, if $$\mathcal{J}(t) < s$$:
• Start a new Round-Robin phase from arm 0.

• Otherwise, recompute the second set $$\mathcal{K}(t)$$,

• If it is too small, if $$\mathcal{K}(t) < s$$:
• Play a Force-Log step by choosing an arm uniformly at random from the set $$\mathcal{J}(t) \setminus K(t)$$.

• Otherwise,
• Play a UCB step by choosing an arm with highest index (from the underlying policy) from the set $$\mathcal{K}(t)$$.