Version: 3.2.6
Date: 2023-04-28
Title: Reinforcement Learning Trees
Suggests: randomForest, survival
Description: Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable, based on Zhu, Zeng & Kosorok (2015) <doi:10.1080/01621459.2015.1036994>, subject weight, variable weight, tracking subjects used in each tree, etc.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://cran.r-project.org/package=RLT
NeedsCompilation: yes
Repository: CRAN
Encoding: UTF-8
RoxygenNote: 7.2.3
Packaged: 2023-04-28 09:24:48 UTC; zrq
Author: Ruoqing Zhu ORCID iD [aut, cre, cph]
Maintainer: Ruoqing Zhu <teazrq@gmail.com>
Date/Publication: 2023-04-28 10:50:02 UTC

Muting rate

Description

Get the muting rate based on sample size N and dimension P. This is an experimental feature. When P is too small, this is not recommended.

Usage

MuteRate(N, P, speed = NULL, info = FALSE)

Arguments

N

sample size

P

dimension

speed

Muting speed: moderate or aggressive

info

Whether to output detailed information

Value

A suggested muting rate

Examples

MuteRate(500, 100, speed = "aggressive")

Reinforcement Learning Trees

Description

Fit models for regression, classification and survival analysis using reinforced splitting rules

Usage

RLT(
  x,
  y,
  censor = NULL,
  model = "regression",
  print.summary = 0,
  use.cores = 1,
  ntrees = if (reinforcement) 100 else 500,
  mtry = max(1, as.integer(ncol(x)/3)),
  nmin = max(1, as.integer(log(nrow(x)))),
  alpha = 0.4,
  split.gen = "random",
  nsplit = 1,
  resample.prob = 0.9,
  replacement = TRUE,
  npermute = 1,
  select.method = "var",
  subject.weight = NULL,
  variable.weight = NULL,
  track.obs = FALSE,
  importance = TRUE,
  reinforcement = FALSE,
  muting = -1,
  muting.percent = if (reinforcement) MuteRate(nrow(x), ncol(x), speed = "aggressive",
    info = FALSE) else 0,
  protect = as.integer(log(ncol(x))),
  combsplit = 1,
  combsplit.th = 0.25,
  random.select = 0,
  embed.n.th = 4 * nmin,
  embed.ntrees = max(1, -atan(0.01 * (ncol(x) - 500))/pi * 100 + 50),
  embed.resample.prob = 0.8,
  embed.mtry = 1/2,
  embed.nmin = as.integer(nrow(x)^(1/3)),
  embed.split.gen = "random",
  embed.nsplit = 1
)

Arguments

x

A matrix or data.frame for features

y

Response variable, a numeric/factor vector or a Surv object

censor

The censoring indicator if survival model is used

model

The model type: regression, classification or survival

print.summary

Whether summary should be printed

use.cores

Number of cores

ntrees

Number of trees, ntrees = 100 if use reinforcement, ntrees = 1000 otherwise

mtry

Number of variables used at each internal node, only for reinforcement = FALSE

nmin

Minimum number of observations required in an internal node to perform a split. Set this to twice of the desired terminal node size.

alpha

Minimum number of observations required for each child node as a portion of the parent node. Must be within (0, 0.5].

split.gen

How the cutting points are generated

nsplit

Number of random cutting points to compare for each variable at an internal node

resample.prob

Proportion of in-bag samples

replacement

Whether the in-bag samples are sampled with replacement

npermute

Number of imputations (currently not implemented, saved for future use)

select.method

Method to compare different splits

subject.weight

Subject weights

variable.weight

Variable weights when randomly sample mtry to select the splitting rule

track.obs

Track which terminal node the observation belongs to

importance

Should importance measures be calculated

reinforcement

If reinforcement splitting rules should be used. There are default values for all tuning parameters under this feature.

muting

Muting method, -1 for muting by proportion, positive for muting by count

muting.percent

Only for muting = -1 the proportion of muting

protect

Number of protected variables that will not be muted. These variables are adaptively selected for each tree.

combsplit

Number of variables used in a combination split. combsplit = 1 gives regular binary split; combsplit > 1 produces linear combination splits.

combsplit.th

The minimum threshold (as a relative measurement compared to the best variable) for a variable to be used in the combination split.

random.select

Randomly select a variable from the top variable in the linear combination as the splitting rule.

embed.n.th

Number of observations to stop the embedded model and choose randomly from the current protected variables.

embed.ntrees

Number of embedded trees

embed.resample.prob

Proportion of in-bag samples for embedded trees

embed.mtry

Number of variables used for embedded trees, as proportion

embed.nmin

Terminal node size for embedded trees

embed.split.gen

How the cutting points are generated in the embedded trees

embed.nsplit

Number of random cutting points for embedded trees

Value

A RLT object; a list consisting of

FittedTrees

Fitted tree structure

FittedSurv, timepoints

Terminal node survival estimation and all time points, if survival model is used

AllError

All out-of-bag errors, if importance = TRUE

VarImp

Variable importance measures, if importance = TRUE

ObsTrack

Registration of each observation in each fitted tree

...

All the tuning parameters are saved in the fitted RLT object

References

Zhu, R., Zeng, D., & Kosorok, M. R. (2015) "Reinforcement Learning Trees." Journal of the American Statistical Association. 110(512), 1770-1784.

Zhu, R., & Kosorok, M. R. (2012). Recursively imputed survival trees. Journal of the American Statistical Association, 107(497), 331-340.

Examples


N = 600
P = 100

X = matrix(runif(N*P), N, P)
Y = rowSums(X[,1:5]) + rnorm(N)

trainx = X[1:200,]
trainy = Y[1:200]
testx = X[-c(1:200),]
testy = Y[-c(1:200)]

# Regular ensemble trees (Extremely Randomized Trees, Geurts, et. al., 2006)

RLT.fit = RLT(trainx, trainy, model = "regression", use.cores = 6)

barplot(RLT.fit$VarImp)
RLT.pred = predict(RLT.fit, testx)
mean((RLT.pred$Prediction - testy)^2)

# Reinforcement Learning Trees, using an embedded model to find the splitting rule

Mark0 = proc.time()
RLT.fit = RLT(trainx, trainy, model = "regression", use.cores = 6, ntrees = 100,
              importance = TRUE, reinforcement = TRUE, combsplit = 3, embed.ntrees = 25)
proc.time() - Mark0

barplot(RLT.fit$VarImp)
RLT.pred = predict(RLT.fit, testx)
mean((RLT.pred$Prediction - testy)^2)

Prediction function for reinforcement learning trees

Description

Predict future subjects with a fitted RLT model

Usage

## S3 method for class 'RLT'
predict(object, testx, ...)

Arguments

object

A fitted RLT object

testx

Testing data

...

...

Value

The predicted values. For survival model, it returns the fitted survival functions

Examples

x = matrix(rnorm(100), ncol = 10)
y = rowMeans(x)
fit = RLT(x, y, ntrees = 5)
predict(fit, x)

Print a RLT object

Description

Print a RLT object

Usage

## S3 method for class 'RLT'
print(x, ...)

Arguments

x

A fitted RLT object

...

...

Value

No return value

Examples

x = matrix(rnorm(100), ncol = 10)
y = rowMeans(x)
fit = RLT(x, y, ntrees = 5)
fit