complete R example

wrapper/R-example/demo.R

@@ -6,9 +6,72 @@ 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)
-bst <- xgb.train(param, dtrain, watchlist=watchlist, nround=3)
+# training xgboost model
+bst <- xgb.train(param, dtrain, nround=3, 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)
 
-succ <- xgb.save(bst, "iter.model")
-print('finsih save model')
-bst2 <- xgb.Booster(modelfile="iter.model")
-pred = xgb.predict(bst2, dtest)
+# save dmatrix into binary buffer
+succ <- xgb.save(dtest, "dtest.buffer")
+# save model into file
+succ <- xgb.save(bst, "xgb.model")
+# load model in
+bst2 <- xgb.Booster(modelfile="xgb.model")
+dtest2 <- xgb.DMatrix("dtest.buffer")
+preds2 <- xgb.predict(bst2, dtest2)
+# print difference
+print(sum(abs(preds2-preds)))
+
+###
+# advanced: cutomsized loss function
+# 
+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)
+# user define objective function, given prediction, return gradient and second order gradient
+# this is loglikelihood loss
+logregobj <- function(preds, dtrain) {
+  labels <- xgb.getinfo(dtrain, "label")
+  preds <- 1.0 / (1.0 + exp(-preds))
+  grad <- preds - labels
+  hess <- preds * (1.0-preds)
+  return(list(grad=grad, hess=hess))
+}
+# user defined evaluation function, return a list(metric="metric-name", value="metric-value")
+# NOTE: when you do customized loss function, the default prediction value is margin
+# this may make buildin evalution metric not function properly
+# for example, we are doing logistic loss, the prediction is score before logistic transformation
+# the buildin evaluation error assumes input is after logistic transformation
+# Take this in mind when you use the customization, and maybe you need write customized evaluation function
+evalerror <- function(preds, dtrain) {
+  labels <- xgb.getinfo(dtrain, "label")
+  err <- as.real(sum(labels != (preds > 0.0))) / length(labels)
+  return(list(metric="error", value=err))
+}
+
+# training with customized objective, we can also do step by step training
+# 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')
+# specify parameters via map, definition are same as c++ version
+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
+# do predict with output_margin=True, will always give you margin values before logistic transformation
+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')
+bst <- xgb.train( param, dtrain, 1, watchlist )