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#*# Perform high-quality training & eval on all data for a reloaded parameter configuration.
#*# This script is the second part of Lesson 8 in TDMR-tutorial.pdf,
#*# see there for further details.
## path should point to a dir with subdir data/, main_*.r, and .Rdata file:
##
path <- paste(find.package("TDMR"), "examples/ex-winequality",sep="/");
#path <- paste("../..", "examples/ex-winequality",sep="/");
tdm=list(mainFile="main_wine.r"
,path=path
,filenameEnvT="wine_01.RData" # reload envT from <path>/wine_01.RData"
,umode="TST"
,TST.valiFrac=0
,U.saveModel=T
,optsVerbosity=1
,nrun=2
);
source(paste(path,tdm$mainFile,sep="/"));
#
# re-use prior tuning result from envT: do only tdmEnvTReport and unbiased eval on
# best tuning result. But do so by training a model on all training data
# (80% of 4898 =3919 records: white-wine-train.csv) and testing it on all test data
# (20% of 4898 = 979 records: white-wine-tst.csv).
#
tdm <- tdmDefaultsFill(tdm)
envT<- tdmEnvTLoad(tdm$filenameEnvT,path); # loads envT
envT<- tdmEnvTUpdate(envT,tdm);
# update the re-loaded envT$tdm with new elements given in tdm
opts <- tdmEnvTGetOpts(envT);
opts$READ.NROW=-1; # read *all* records in winequality-white-*.csv
opts$RF.samp=5000;
opts$READ.TstFn = readTstWine
opts$VERBOSE=1;
# read 'new' data (both from opts$filename and opts$filetest):
dataObj <- tdmReadAndSplit(opts,envT$tdm);
envT <- tdmEnvTSetOpts(envT,opts);
envT$tdm$nrun=2; # =0: no unbiasedRun,
# >0: perform unbiasedRun with opts$NRUN=envT$tdm$nrun
envT <- tdmEnvTReport(envT,1);
if (!is.null(envT$theFinals)) print(envT$theFinals);
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