fastpolicytree: Construct an optimal policy tree from covariate and reward...

In fastpolicytree: Constructs Policy Trees from Covariate and Reward Data

Construct an optimal policy tree from covariate and reward data

Description

This function accepts almost the same input and generates the same type of output as the policy_tree function in the policytree package. The only difference is that this function has no 'split.step' argument (since it is effectively hard-coded to the value 1).

Usage

fastpolicytree(
  X,
  Gamma,
  depth = 3,
  min.node.size = 1,
  strategy.datatype = 2,
  strategy.find.reward.ub = FALSE,
  strategy.find.dummy.split.reward = FALSE,
  strategy.use.last.rewards = TRUE,
  strategy.use.cutoffs = FALSE,
  strategy.use.cache = TRUE,
  strategy.exploitbinaryvars = TRUE
)

Arguments

X	The covariates used. Dimension N*p where p is the number of features.
Gamma	The rewards for each action. Dimension N*d where d is the number of actions.
depth	The depth of the fitted tree. Default is 3.
min.node.size	An integer indicating the smallest terminal node size permitted. Default is 1.
strategy.datatype	If set to 0 policytree style sorted sets are used to represent datasets during solving. If set to 1 then unsorted sets are used which are sorted 'on demand'. If set to to 2 then the choice of representation is decided automatically. Default is 2 (choice is automatically made).
strategy.find.reward.ub	If TRUE upper bounds on rewards are computed. Default is FALSE
strategy.find.dummy.split.reward	If TRUE then the reward for a dummy split (where the left split has no datapoints) is computed. Default is FALSE.
strategy.use.last.rewards	If TRUE an upper bound on the reward for a split is computed from the reward for the most recent split value for the current covariate. Default is TRUE
strategy.use.cutoffs	If TRUE then tree finding is aborted if it can be deduced that the reward for the tree is beaten by some existing tree. Default is FALSE
strategy.use.cache	If TRUE a cache of optimal trees for (sub-)datasets is used. Default is TRUE
strategy.exploitbinaryvars	If TRUE then covariates with only 2 values are treated specially. Default is TRUE

Value

A policy_tree object.

Examples

X <-  data.frame(
 X1=c(-0.32, 0.16, 0.34, 1.24, 0.22, 0.45, 1.48, 0.65,-0.93,-1.11),
 X2=c(-0.58, 0.90,-0.22, 1.54,-0.57,-1.08,-1.42,-1.98,-0.02, 0.05),
 X3=c(0.70,-1.49, 0.36,-0.05,-0.14, 1.57,-0.18,-1.98,-1.77,-1.25),
 X4=c(0.21, 0.34, 0.60,-0.05,-0.66,-0.69, 0.52, 0.31,-0.03, 1.09),
 X5=c(0.16, 0.96,-1.07,-0.97, 2.02,-0.43,-0.79,-2.08, 1.21, 0.39))
Gamma  <- data.frame(
 control=c(0.8502363,-1.4950411,1.9608062,0.7487925,2.9718517,
  0.8952429,-0.2563680,5.9945581,-1.8485703,-1.2840477),
 treat=c(-2.91607259,-2.25464535, 0.28214637,-0.17284650,-0.09480810,
  1.48786125,2.08600119,-2.05283394,0.72903608,-0.04416392))
tree3  <- fastpolicytree(X,Gamma)
tree3
tree2  <- fastpolicytree(X,Gamma,depth=2)
tree2
# to get a human-readable display of the trees use the
# policytree package...
#library(policytree)
#print(tree3)
#print(tree2)

fastpolicytree documentation built on June 24, 2025, 9:08 a.m.