OfflineBootstrappedReplayBandit {contextual} | R Documentation |
Policy for the evaluation of policies with offline data through replay with bootstrapping.
The key assumption of the method is that that the original logging policy chose i.i.d. arms uniformly at random.
Take care: if the original logging policy does not change over trials, data may be used more efficiently via propensity scoring (Langford et al., 2008; Strehl et al., 2011) and related techniques like doubly robust estimation (Dudik et al., 2011).
bandit <- OfflineBootstrappedReplayBandit(formula, data, k = NULL, d = NULL, unique = NULL, shared = NULL, randomize = TRUE, replacement = TRUE, jitter = TRUE, arm_multiply = TRUE)
formula
formula (required). Format: y.context ~ z.choice | x1.context + x2.xontext + ...
By default, adds an intercept to the context model. Exclude the intercept, by adding "0" or "-1" to
the list of contextual features, as in: y.context ~ z.choice | x1.context + x2.xontext -1
data
data.table or data.frame; offline data source (required)
k
integer; number of arms (optional). Optionally used to reformat the formula defined x.context vector
as a k x d
matrix. When making use of such matrix formatted contexts, you need to define custom
intercept(s) when and where needed in data.table or data.frame.
d
integer; number of contextual features (optional) Optionally used to reformat the formula defined
x.context vector as a k x d
matrix. When making use of such matrix formatted contexts, you need
to define custom intercept(s) when and where needed in data.table or data.frame.
randomize
logical; randomize rows of data stream per simulation (optional, default: TRUE)
replacement
logical; sample with replacement (optional, default: TRUE)
jitter
logical; add jitter to contextual features (optional, default: TRUE)
arm_multiply
logical; multiply the horizon by the number of arms (optional, default: TRUE)
multiplier
integer; replicate the dataset multiplier
times before randomization. When
arm_multiply
has been set to TRUE, the number of replications is the number of arms times
this integer. Can be used when Simulator's policy_time_loop has been set to TRUE, otherwise a
simulation might run out of pre-indexed data.
unique
integer vector; index of disjoint features (optional)
shared
integer vector; index of shared features (optional)
new(formula, data, k = NULL, d = NULL, unique = NULL, shared = NULL, randomize = TRUE,
replacement = TRUE, jitter = TRUE, arm_multiply = TRUE)
generates
and instantializes a new OfflineBootstrappedReplayBandit
instance.
get_context(t)
argument:
t
: integer, time step t
.
returns a named list
containing the current d x k
dimensional matrix context$X
,
the number of arms context$k
and the number of features context$d
.
get_reward(t, context, action)
arguments:
t
: integer, time step t
.
context
: list, containing the current context$X
(d x k context matrix),
context$k
(number of arms) and context$d
(number of context features)
(as set by bandit
).
action
: list, containing action$choice
(as set by policy
).
returns a named list
containing reward$reward
and, where computable,
reward$optimal
(used by "oracle" policies and to calculate regret).
post_initialization()
Randomize offline data by shuffling the offline data.table before the start of each individual simulation when self$randomize is TRUE (default)
Mary, J., Preux, P., & Nicol, O. (2014, January). Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques. In International Conference on Machine Learning (pp. 172-180).
Core contextual classes: Bandit
, Policy
, Simulator
,
Agent
, History
, Plot
Bandit subclass examples: BasicBernoulliBandit
, ContextualLogitBandit
,
OfflineBootstrappedReplayBandit
Policy subclass examples: EpsilonGreedyPolicy
, ContextualLinTSPolicy
## Not run: library(contextual) library(data.table) # Import personalization data-set url <- "http://d1ie9wlkzugsxr.cloudfront.net/data_cmab_basic/dataset.txt" datafile <- fread(url) simulations <- 1 horizon <- nrow(datafile) bandit <- OfflineReplayEvaluatorBandit$new(formula = V2 ~ V1 | . - V1, data = datafile) # Define agents. agents <- list(Agent$new(LinUCBDisjointOptimizedPolicy$new(0.01), bandit, "alpha = 0.01"), Agent$new(LinUCBDisjointOptimizedPolicy$new(0.05), bandit, "alpha = 0.05"), Agent$new(LinUCBDisjointOptimizedPolicy$new(0.1), bandit, "alpha = 0.1"), Agent$new(LinUCBDisjointOptimizedPolicy$new(1.0), bandit, "alpha = 1.0")) # Initialize the simulation. simulation <- Simulator$new(agents = agents, simulations = simulations, horizon = horizon, do_parallel = FALSE, save_context = TRUE) # Run the simulation. sim <- simulation$run() # plot the results plot(sim, type = "cumulative", regret = FALSE, rate = TRUE, legend_position = "bottomright", ylim = c(0,1)) ## End(Not run)