#------------------------------------------------------------------------------
# Test on real data.
# Copyright (C) 2014
#------------------------------------------------------------------------------
library(ParallelForest)
PERFORM_TREE_TESTS = FALSE
PERFORM_FOREST_TESTS = TRUE
N_TESTS = 10
### SETUP ###
# Load real dataset
data(low_high_earners)
if(PERFORM_TREE_TESTS){
# test tree functions
ptm = proc.time() # timing
ftree = grow.tree(Y~., data=low_high_earners, min_node_obs=1, max_depth=20)
print(proc.time() - ptm) # timing
ptm = proc.time() # timing
ftree_samepred = predict(ftree, low_high_earners)
print(proc.time() - ptm) # timing
if(sum(low_high_earners$Y==ftree_samepred)/nrow(low_high_earners) <= 0.95) {
stop("Tree prediction on training data performs worse than 95%.")
}
}
test_realdata_forest = function() {
ptm = proc.time() # timing
fforest = grow.forest(Y~., data=low_high_earners, min_node_obs=1000, max_depth=10,
numsamps=100000, numvars=5, numboots=5)
print(proc.time() - ptm) # timing
ptm = proc.time() # timing
fforest_samepred = predict(fforest, low_high_earners)
print(proc.time() - ptm) # timing
acc = sum(low_high_earners$Y==fforest_samepred)/nrow(low_high_earners)
return(acc)
}
if(PERFORM_FOREST_TESTS){
acc_vec = sapply(X=1:N_TESTS, FUN=function(dummy_input) test_realdata_forest())
acc_avg = mean(acc_vec)
if(acc_avg< 0.65) {
stop("Forest prediction on training data performs worse than threshold.")
}
}
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