RLT {RLT} | R Documentation |
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: |
print.summary |
Whether summary should be printed |
use.cores |
Number of cores |
ntrees |
Number of trees, |
mtry |
Number of variables used at each internal node, only for |
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 |
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 |
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, |
muting.percent |
Only for |
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.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 |
VarImp |
Variable importance measures, if |
ObsTrack |
Registration of each observation in each fitted tree |
... |
All the tuning parameters are saved in the fitted |
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)