export fewer functions to user and optimize parameter setting

R-package/inst/examples/demo-Rinterface.R

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+require(xgboost)
+
+# helper function to read libsvm format
+# this is very badly written, load in dense, and convert to sparse
+# use this only for demo purpose
+# adopted from https://github.com/zygmuntz/r-libsvm-format-read-write/blob/master/f_read.libsvm.r
+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))
+}
+
+# Parameter setting
+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("eval"=dtest,"train"=dtrain)
+
+###########################
+# Train from local file
+###########################
+
+# Training
+bst = xgboost(file='agaricus.txt.train',params=param,watchlist=watchlist)
+# Prediction
+pred = predict(bst, 'agaricus.txt.test')
+# Performance
+labels = xgb.getinfo(dtest, "label")
+err = as.numeric(sum(as.integer(pred > 0.5) != labels)) / length(labels)
+print(paste("error=",err))
+
+###########################
+# Train from R object
+###########################
+
+csc = read.libsvm("agaricus.txt.train", 126)
+y = csc$label
+x = csc$data
+# x as Sparse Matrix
+class(x)
+
+# Training
+bst = xgboost(x,y,params=param,watchlist=watchlist)
+# Prediction
+pred = predict(bst, 'agaricus.txt.test')
+# Performance
+labels = xgb.getinfo(dtest, "label")
+err = as.numeric(sum(as.integer(pred > 0.5) != labels)) / length(labels)
+print(paste("error=",err))
+
+# Training with dense matrix
+x = as.matrix(x)
+bst = xgboost(x,y,params=param,watchlist=watchlist)
+
+###########################
+# Train with customization
+###########################
+
+# 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.numeric(sum(labels != (preds > 0.0))) / length(labels)
+    return(list(metric="error", value=err))
+}
+
+bst = xgboost(x,y,params=param,watchlist=watchlist,obj=logregobj, feval=evalerror)
+
+############################
+# Train with previous result
+############################
+
+bst = xgboost(x,y,params=param,watchlist=watchlist)
+pred = predict(bst, 'agaricus.txt.train', outputmargin=TRUE)
+bst2 = xgboost(x,y,params=param,watchlist=watchlist,margin=pred)