seems ok

wrapper/R-example/demo.R

@@ -1,30 +1,78 @@
 # include xgboost library, must set chdir=TRURE
-source('../xgboost.R', chdir=TRUE)
+source("../xgboost.R", chdir=TRUE)
+
+# helper function to read libsvm format
+# this is very badly written, load in dense, and convert to sparse
+# use this only for demo purpose
+read.libsvm <- function(fname, maxcol) {
+  content <- readLines(fname)
+  nline <- length(content)
+  label <- numeric(nline)
+  mat <- matrix(0, nline, maxcol+1)
+  for (i in 1:nline) {
+    arr <- as.vector(strsplit(content[i], " ")[[1]])
+    label[i] <- as.numeric(arr[[1]])
+    for (j in 2:length(arr)) {
+      kv <- strsplit(arr[j], ":")[[1]]
+      # to avoid 0 index
+      findex <- as.integer(kv[1]) + 1
+      fvalue <- as.numeric(kv[2])
+      mat[i,findex] <- fvalue
+    }
+  }
+  mat <- as(mat, "sparseMatrix")
+  return(list(label=label, data=mat))
+}
 
 # test code here
 dtrain <- xgb.DMatrix("agaricus.txt.train")
 dtest <- xgb.DMatrix("agaricus.txt.test")
-param = list('bst:max_depth'=2, 'bst:eta'=1, 'silent'=1, 'objective'='binary:logistic')
-watchlist <- list('train'=dtrain,'test'=dtest)
+param = list("bst:max_depth"=2, "bst:eta"=1, "silent"=1, "objective"="binary:logistic")
+watchlist <- list("eval"=dtest,"train"=dtrain)
 # training xgboost model
-bst <- xgb.train(param, dtrain, nround=3, watchlist=watchlist)
+bst <- xgb.train(param, dtrain, nround=2, watchlist=watchlist)
 # make prediction
 preds <- xgb.predict(bst, dtest)
 labels <- xgb.getinfo(dtest, "label")
 err <- as.real(sum(as.integer(preds > 0.5) != labels)) / length(labels)
 # print error rate
-print(err)
+print(paste("error=",err))
+
+# dump model
+xgb.dump(bst, "dump.raw.txt")
+# dump model with feature map
+xgb.dump(bst, "dump.nice.txt", "featmap.txt")
 
 # save dmatrix into binary buffer
 succ <- xgb.save(dtest, "dtest.buffer")
 # save model into file
 succ <- xgb.save(bst, "xgb.model")
-# load model in
+# load model and data in 
 bst2 <- xgb.Booster(modelfile="xgb.model")
 dtest2 <- xgb.DMatrix("dtest.buffer")
 preds2 <- xgb.predict(bst2, dtest2)
-# print difference
-print(sum(abs(preds2-preds)))
+# assert they are the same
+stopifnot(sum(abs(preds2-preds)) == 0)
+
+###
+# build dmatrix from sparseMatrix
+###
+print ('start running example of build DMatrix from R.sparseMatrix')
+csc <- read.libsvm("agaricus.txt.train", 126)
+label <- csc$label
+data <- csc$data
+dtrain <- xgb.DMatrix(data, info=list(label=label) )
+watchlist <- list("eval"=dtest,"train"=dtrain)
+bst <- xgb.train(param, dtrain, nround=2, watchlist=watchlist)
+
+###
+# build dmatrix from dense matrix
+###
+print ('start running example of build DMatrix from R.Matrix')
+mat = as.matrix(data)
+dtrain <- xgb.DMatrix(mat, info=list(label=label) )
+watchlist <- list("eval"=dtest,"train"=dtrain)
+bst <- xgb.train(param, dtrain, nround=2, watchlist=watchlist)
 
 ###
 # advanced: cutomsized loss function
@@ -33,7 +81,7 @@ print("start running example to used cutomized objective function")
 # note: for customized objective function, we leave objective as default
 # note: what we are getting is margin value in prediction
 # you must know what you are doing
-param <- list('bst:max_depth' = 2, 'bst:eta' = 1, 'silent' =1)
+param <- list("bst:max_depth" = 2, "bst:eta" = 1, "silent" =1)
 # user define objective function, given prediction, return gradient and second order gradient
 # this is loglikelihood loss
 logregobj <- function(preds, dtrain) {
@@ -56,15 +104,15 @@ evalerror <- function(preds, dtrain) {
 }
 
 # training with customized objective, we can also do step by step training
-# simply look at xgboost.py's implementation of train
+# simply look at xgboost.py"s implementation of train
 bst <- xgb.train(param, dtrain, nround=2, watchlist, logregobj, evalerror)
 
 ###
 # advanced: start from a initial base prediction
 #
-print ('start running example to start from a initial prediction')
+print ("start running example to start from a initial prediction")
 # specify parameters via map, definition are same as c++ version
-param = list('bst:max_depth'=2, 'bst:eta'=1, 'silent'=1, 'objective'='binary:logistic')
+param = list("bst:max_depth"=2, "bst:eta"=1, "silent"=1, "objective"="binary:logistic")
 # train xgboost for 1 round
 bst <- xgb.train( param, dtrain, 1, watchlist )
 # Note: we need the margin value instead of transformed prediction in set_base_margin
@@ -73,5 +121,5 @@ ptrain <- xgb.predict(bst, dtrain, outputmargin=TRUE)
 ptest <- xgb.predict(bst, dtest, outputmargin=TRUE)
 succ <- xgb.setinfo(dtrain, "base_margin", ptrain)
 succ <- xgb.setinfo(dtest, "base_margin", ptest)
-print ('this is result of running from initial prediction')
+print ("this is result of running from initial prediction")
 bst <- xgb.train( param, dtrain, 1, watchlist )