estimateTheta <- function(obs, weights, id,
flavor=c("learning", "superLearning", "h2oEnsembleLearning"),
learnTheta,
light=TRUE, SuperLearner.=NULL, ..., verbose=FALSE) {
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## Validate arguments
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## Argument 'obs':
obs <- validateArgumentObs(obs);
## Argument 'weights':
obsWeights <- Arguments$getNumerics(weights);
## Argument 'id':
id <- Arguments$getCharacters(id);
## Argument 'flavor':
flavor <- match.arg(flavor);
learnMode <- switch(flavor,
learning="function",
superLearning="character",
h2oEnsembleLearning="character");
## Argument 'learnTheta'
mode <- mode(learnTheta);
if (mode != learnMode) {
throw("Argument 'learnTheta' should be of mode '", learnMode, "', not '", mode, "' for flavor: ", flavor);
}
## Argument 'SuperLearner.'
if (flavor!="learning") {
if (is.null(SuperLearner.) || mode(SuperLearner.)!="function") {
throw("Argument 'SuperLearner.' should be a function")
}
}
## Argument 'verbose'
verbose <- Arguments$getVerbose(verbose);
if (flavor=="learning") {
theta <- learnTheta(obs, weights=obsWeights, light=light, ...);
} else if (flavor=="superLearning") {
logSL <- as.logical(less(verbose, 10)); ## decrease verbosity in SuperLearner
SL.library.theta <- learnTheta;
obsD <- as.data.frame(obs)
fitTheta <- SuperLearner.(Y=obsD[, "Y"], X=extractXW(obsD), ## obsD[, c("X", "W")]
obsWeights=obsWeights, id=id,
SL.library=SL.library.theta, verbose=logSL,
family=gaussian(), ...)
theta <- function(XW) {
XWd <- as.data.frame(XW)
predict(fitTheta, newdata=XWd)$pred
}
} else if (flavor=="h2oEnsembleLearning") {
EL.library.theta <- learnTheta;
obsD <- as.data.frame(obs)
data <- h2o::as.h2o(attr(SuperLearner., "H2OConnection"), obsD)
##
## CAUTION: provide 'id' as soon as this argument is supported
##
fitTheta <- SuperLearner.(y="Y", x=colnames(extractXW(obsD)),
training_frame=data,
family="gaussian",
learner=EL.library.theta,
weights_column=obsWeights)
theta <- function(XW) {
XWd <- as.data.frame(XW)
newdata <- h2o::as.h2o(attr(SuperLearner., "H2OConnection"), XWd)
predict(fitTheta, newdata=newdata)$pred
}
}
verbose && cat(verbose, "theta(X,W):");
verbose && print(verbose, summary(theta(extractXW(obs)))); ## obs[, c("X", "W")]
theta
}
############################################################################
## HISTORY:
## 2014-02-07
## o Created.
############################################################################
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