remove inst/, improve vignette
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hetong
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@ -1,10 +0,0 @@
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require(xgboost)
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require(methods)
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# Directly read in local file
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dtrain <- xgb.DMatrix("agaricus.txt.train")
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history <- xgb.cv( data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
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"max_depth"=3, "eta"=1,
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"objective"="binary:logistic")
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@ -1,153 +0,0 @@
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require(xgboost)
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require(methods)
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# helper function to read libsvm format this is very badly written, load in dense, and convert to sparse
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# use this only for demo purpose adopted from
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# https://github.com/zygmuntz/r-libsvm-format-read-write/blob/master/f_read.libsvm.r
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read.libsvm <- function(fname, maxcol) {
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content <- readLines(fname)
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nline <- length(content)
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label <- numeric(nline)
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mat <- matrix(0, nline, maxcol + 1)
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for (i in 1:nline) {
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arr <- as.vector(strsplit(content[i], " ")[[1]])
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label[i] <- as.numeric(arr[[1]])
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for (j in 2:length(arr)) {
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kv <- strsplit(arr[j], ":")[[1]]
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# to avoid 0 index
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findex <- as.integer(kv[1]) + 1
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fvalue <- as.numeric(kv[2])
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mat[i, findex] <- fvalue
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}
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}
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mat <- as(mat, "sparseMatrix")
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return(list(label = label, data = mat))
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}
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############################ Test xgb.DMatrix with local file, sparse matrix and dense matrix in R.
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# Directly read in local file
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dtrain <- xgb.DMatrix("agaricus.txt.train")
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class(dtrain)
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# read file in R
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csc <- read.libsvm("agaricus.txt.train", 126)
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y <- csc$label
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x <- csc$data
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# x as Sparse Matrix
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class(x)
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dtrain <- xgb.DMatrix(x, label = y)
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# x as dense matrix
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dense.x <- as.matrix(x)
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dtrain <- xgb.DMatrix(dense.x, label = y)
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############################ Test xgboost with local file, sparse matrix and dense matrix in R.
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# Test with DMatrix object
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bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic")
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# Verbose = 0,1,2
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bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic", verbose = 0)
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bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic", verbose = 1)
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bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic", verbose = 2)
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# Test with local file
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bst <- xgboost(data = "agaricus.txt.train", max_depth = 2, eta = 1,nround = 2,
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objective = "binary:logistic")
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# Test with Sparse Matrix
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bst <- xgboost(data = x, label = y, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic")
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# Test with dense Matrix
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bst <- xgboost(data = dense.x, label = y, max_depth = 2, eta = 1, nround = 2,
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objective = "binary:logistic")
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############################ Test predict
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# Prediction with DMatrix object
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dtest <- xgb.DMatrix("agaricus.txt.test")
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pred <- predict(bst, dtest)
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# Prediction with local test file
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pred <- predict(bst, "agaricus.txt.test")
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# Prediction with Sparse Matrix
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csc <- read.libsvm("agaricus.txt.test", 126)
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test.y <- csc$label
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test.x <- csc$data
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pred <- predict(bst, test.x)
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# Extrac label with getinfo
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labels <- getinfo(dtest, "label")
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err <- as.numeric(sum(as.integer(pred > 0.5) != labels))/length(labels)
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print(paste("error=", err))
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############################ Save and load model to hard disk
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# save model to binary local file
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xgb.save(bst, "xgboost.model")
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# load binary model to R
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bst <- xgb.load("xgboost.model")
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pred <- predict(bst, test.x)
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# save model to text file
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xgb.dump(bst, "dump.raw.txt")
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# save model to text file, with feature map
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xgb.dump(bst, "dump.nice.txt", "featmap.txt")
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# save a DMatrix object to hard disk
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xgb.DMatrix.save(dtrain, "dtrain.buffer")
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# load a DMatrix object to R
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dtrain <- xgb.DMatrix("dtrain.buffer")
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############################ More flexible training function xgb.train
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param <- list(max_depth = 2, eta = 1, silent = 1, objective = "binary:logistic")
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watchlist <- list(eval = dtest, train = dtrain)
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# training xgboost model
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bst <- xgb.train(param, dtrain, nround = 2, watchlist = watchlist)
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############################ cutomsized loss function
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param <- list(max_depth = 2, eta = 1, silent = 1)
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# note: for customized objective function, we leave objective as default note: what we are getting is
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# margin value in prediction you must know what you are doing
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# user define objective function, given prediction, return gradient and second order gradient this is
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# loglikelihood loss
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logregobj <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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preds <- 1/(1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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# user defined evaluation function, return a list(metric='metric-name', value='metric-value') NOTE: when
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# you do customized loss function, the default prediction value is margin this may make buildin
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# evalution metric not function properly for example, we are doing logistic loss, the prediction is
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# score before logistic transformation the buildin evaluation error assumes input is after logistic
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# transformation Take this in mind when you use the customization, and maybe you need write customized
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# evaluation function
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
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return(list(metric = "error", value = err))
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}
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# training with customized objective, we can also do step by step training simply look at xgboost.py's
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# implementation of train
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bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
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@ -1,126 +0,0 @@
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0 cap-shape=bell i
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1 cap-shape=conical i
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2 cap-shape=convex i
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3 cap-shape=flat i
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4 cap-shape=knobbed i
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5 cap-shape=sunken i
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6 cap-surface=fibrous i
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7 cap-surface=grooves i
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8 cap-surface=scaly i
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9 cap-surface=smooth i
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10 cap-color=brown i
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11 cap-color=buff i
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12 cap-color=cinnamon i
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13 cap-color=gray i
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14 cap-color=green i
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15 cap-color=pink i
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16 cap-color=purple i
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17 cap-color=red i
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18 cap-color=white i
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19 cap-color=yellow i
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20 bruises?=bruises i
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21 bruises?=no i
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22 odor=almond i
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23 odor=anise i
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24 odor=creosote i
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25 odor=fishy i
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26 odor=foul i
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27 odor=musty i
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28 odor=none i
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29 odor=pungent i
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30 odor=spicy i
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31 gill-attachment=attached i
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32 gill-attachment=descending i
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33 gill-attachment=free i
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34 gill-attachment=notched i
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35 gill-spacing=close i
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36 gill-spacing=crowded i
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37 gill-spacing=distant i
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38 gill-size=broad i
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39 gill-size=narrow i
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40 gill-color=black i
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41 gill-color=brown i
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42 gill-color=buff i
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43 gill-color=chocolate i
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44 gill-color=gray i
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45 gill-color=green i
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46 gill-color=orange i
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47 gill-color=pink i
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48 gill-color=purple i
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49 gill-color=red i
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50 gill-color=white i
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51 gill-color=yellow i
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52 stalk-shape=enlarging i
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53 stalk-shape=tapering i
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54 stalk-root=bulbous i
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55 stalk-root=club i
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56 stalk-root=cup i
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57 stalk-root=equal i
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58 stalk-root=rhizomorphs i
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59 stalk-root=rooted i
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60 stalk-root=missing i
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61 stalk-surface-above-ring=fibrous i
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62 stalk-surface-above-ring=scaly i
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63 stalk-surface-above-ring=silky i
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64 stalk-surface-above-ring=smooth i
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65 stalk-surface-below-ring=fibrous i
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66 stalk-surface-below-ring=scaly i
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67 stalk-surface-below-ring=silky i
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68 stalk-surface-below-ring=smooth i
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69 stalk-color-above-ring=brown i
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70 stalk-color-above-ring=buff i
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71 stalk-color-above-ring=cinnamon i
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72 stalk-color-above-ring=gray i
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73 stalk-color-above-ring=orange i
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74 stalk-color-above-ring=pink i
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75 stalk-color-above-ring=red i
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76 stalk-color-above-ring=white i
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77 stalk-color-above-ring=yellow i
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78 stalk-color-below-ring=brown i
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79 stalk-color-below-ring=buff i
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80 stalk-color-below-ring=cinnamon i
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81 stalk-color-below-ring=gray i
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82 stalk-color-below-ring=orange i
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83 stalk-color-below-ring=pink i
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84 stalk-color-below-ring=red i
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85 stalk-color-below-ring=white i
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86 stalk-color-below-ring=yellow i
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87 veil-type=partial i
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88 veil-type=universal i
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89 veil-color=brown i
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90 veil-color=orange i
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91 veil-color=white i
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92 veil-color=yellow i
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93 ring-number=none i
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94 ring-number=one i
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95 ring-number=two i
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96 ring-type=cobwebby i
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97 ring-type=evanescent i
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98 ring-type=flaring i
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99 ring-type=large i
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100 ring-type=none i
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101 ring-type=pendant i
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102 ring-type=sheathing i
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103 ring-type=zone i
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104 spore-print-color=black i
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105 spore-print-color=brown i
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106 spore-print-color=buff i
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107 spore-print-color=chocolate i
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108 spore-print-color=green i
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109 spore-print-color=orange i
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110 spore-print-color=purple i
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111 spore-print-color=white i
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112 spore-print-color=yellow i
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113 population=abundant i
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114 population=clustered i
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115 population=numerous i
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116 population=scattered i
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117 population=several i
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118 population=solitary i
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119 habitat=grasses i
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120 habitat=leaves i
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121 habitat=meadows i
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122 habitat=paths i
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123 habitat=urban i
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124 habitat=waste i
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125 habitat=woods i
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@ -80,12 +80,15 @@ Mushroom data is cited from UCI Machine Learning Repository. \citep{Bache+Lichma
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<<Training and prediction with iris>>=
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library(xgboost)
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data(iris)
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bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'),
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nrounds = 5)
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1,
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nround = 2, objective = "binary:logistic")
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xgb.save(bst, 'model.save')
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bst = xgb.load('model.save')
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pred <- predict(bst, as.matrix(iris[,1:4]))
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pred <- predict(bst, test$data)
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@
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\verb@xgboost@ is the main function to train a \verb@Booster@, i.e. a model.
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@ -102,17 +105,19 @@ The output looks like
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\begin{verbatim}
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booster[0]:
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0:[f2<2.45] yes=1,no=2,missing=1
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1:leaf=0.147059
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2:[f3<1.65] yes=3,no=4,missing=3
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3:leaf=0.464151
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4:leaf=0.722449
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0:[f28<1.00001] yes=1,no=2,missing=2
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1:[f108<1.00001] yes=3,no=4,missing=4
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3:leaf=1.85965
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4:leaf=-1.94071
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2:[f55<1.00001] yes=5,no=6,missing=6
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5:leaf=-1.70044
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6:leaf=1.71218
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booster[1]:
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0:[f2<2.45] yes=1,no=2,missing=1
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1:leaf=0.103806
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2:[f2<4.85] yes=3,no=4,missing=3
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3:leaf=0.316341
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4:leaf=0.510365
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0:[f59<1.00001] yes=1,no=2,missing=2
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1:leaf=-6.23624
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2:[f28<1.00001] yes=3,no=4,missing=4
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3:leaf=-0.96853
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4:leaf=0.784718
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\end{verbatim}
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It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@.
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@ -121,18 +126,16 @@ training from initial prediction value, weighted training instance.
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We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
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<<xgb.DMatrix>>=
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iris.mat <- as.matrix(iris[,1:4])
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iris.label <- as.numeric(iris[,5]=='setosa')
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diris <- xgb.DMatrix(iris.mat, label = iris.label)
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class(diris)
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getinfo(diris,'label')
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dtrain <- xgb.DMatrix(train$data, label = train$label)
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class(dtrain)
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head(getinfo(dtrain,'label'))
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@
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We can also save the matrix to a binary file. Then load it simply with
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\verb@xgb.DMatrix@
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<<save model>>=
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xgb.DMatrix.save(diris, 'iris.xgb.DMatrix')
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diris = xgb.DMatrix('iris.xgb.DMatrix')
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xgb.DMatrix.save(dtrain, 'xgb.DMatrix')
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dtrain = xgb.DMatrix('xgb.DMatrix')
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@
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\section{Advanced Examples}
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@ -157,11 +160,11 @@ evalerror <- function(preds, dtrain) {
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return(list(metric = "MSE", value = err))
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}
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dtest <- slice(diris,1:100)
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watchlist <- list(eval = dtest, train = diris)
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dtest <- xgb.DMatrix(test$data, label = test$label)
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watchlist <- list(eval = dtest, train = dtrain)
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param <- list(max_depth = 2, eta = 1, silent = 1)
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bst <- xgb.train(param, diris, nround = 2, watchlist, logregobj, evalerror)
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bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
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@
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The gradient and second order gradient is required for the output of customized
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Block a user