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#*# --------- demo/demo01cpu.r ---------
#*# This demo shows a simple data mining process (phase 1 of TDMR) for the regression task
#*# CPU (from UCI repository, http://archive.ics.uci.edu/ml/datasets/Computer+Hardware).
#*# The data mining process is in main_cpu.r, which calls tdmRegressLoop and tdmRegress
#*# with Random Forest as the prediction model.
## path is the dir with data and main_*.r file:
path <- paste(find.package("TDMR"), "demo01cpu",sep="/");
#path <- paste("../inst", "demo01cpu",sep="/");
source(paste(path,"main_cpu.r",sep="/")); # needed to define readCmdCpu
controlDM <- function() {
#
# settings for the DM process (former cpu_00.apd file):
# (see ?tdmOptsDefaultsSet for a complete list of all default settings
# and many explanatory comments)
#
opts = list(path = path,
dir.data = "data/",
filename = "cpu.csv",
READ.TrnFn = readTrnCpu, # defined in main_sonar.r
TST.valiFrac=0.2, # set this fraction of data aside for validation (only for DO.CV=F)
TST.SEED = NULL, # [NULL] a seed for the random test set selection
NFOLD = 5, # how many cross validation folds
data.title = "CPU Data",
MOD.method="RF", # ["RF"|"MC.RF"|"SVM"|"NB" ...]
OCUT = 600, # cut records with output > OCUT (may be strong outliers,
# dropping them makes rmse$test and rmse$OOB faster
# converge)
SRF.kind = "ndrop",
SRF.ndrop = 2 , # 0..n: how many variables (those with lowest importance) to drop
RF.ntree = 50,
RF.samp = 1000,
RF.mtry = 3,
fct.postproc="cpu.postproc",
rgain.type="rmae", # ["rmae" (default) |"rmse" ]
gr.log=TRUE, # if =T: log(x+1)-transform for graphics "true vs. predicted"
NRUN = 1, # how many runs with different train & test samples - or -
# how many CV-runs, if TST.kind="cv"
GD.DEVICE="non", # ["pdf"|"win"|"non"]: all graphics to
# [one multi-page PDF | (several) windows (X11) | dev.null]
GD.RESTART=F,
VERBOSE = 2,
SRF.verbose = 1,
logFile=FALSE # no logfile (needed for Sweave/.Rnw only)
);
opts <- setParams(opts, defaultOpts(), keepNotMatching = TRUE);
# defaultOpts() fills in sensible defaults for all other controls
# See tdmOptsDefaults.r for the list of those elements and many
# explanatory comments.
# Keep all elements present in opts, but NULL in defaultOpts().
}
opts <- controlDM();
result <- main_cpu(opts);
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