src/test/scripts/functions/builtin/decisionTreeRealData2.dml

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F = read($1, data_type="frame", format="csv", header=FALSE);
tfspec = read($2, data_type="scalar", value_type="string");

R = matrix("1 1 1 1 1 1 1 1 1 1 1 1 2", rows=1, cols=13)

[X, meta] = transformencode(target=F, spec=tfspec);
Y = X[,ncol(X)];
X = X[,1:ncol(X)-1];
X = replace(target=X, pattern=NaN, replacement=5); # 1 val

if( $3==1 ) {
  M = decisionTree(X=X, y=Y, ctypes=R, max_features=1, max_values=$4,
                   min_split=10, min_leaf=4, seed=7, verbose=TRUE);
  yhat = decisionTreePredict(X=X, y=Y, ctypes=R, M=M)
}
else {
  sf = 1.0/($3-1);
  M = randomForest(X=X, y=Y, ctypes=R, sample_frac=sf, num_trees=$3-1,
                   max_features=1, max_values=$4,
                   min_split=10, min_leaf=4, seed=7, verbose=TRUE);
  yhat = randomForestPredict(X=X, y=Y, ctypes=R,  M=M)
}

acc = as.matrix(mean(yhat == Y))
err = 1-(acc);
print("accuracy: "+as.scalar(acc))

write(acc, $5);