change higgs script, remove R wrapper
This commit is contained in:
tqchen
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File diff suppressed because it is too large
Load Diff
@@ -1,126 +0,0 @@
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# include xgboost library, must set chdir=TRURE
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source("../xgboost.R", chdir=TRUE)
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# helper function to read libsvm format
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# this is very badly written, load in dense, and convert to sparse
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# use this only for demo purpose
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# adopted from 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 code here
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dtrain <- xgb.DMatrix("agaricus.txt.train")
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dtest <- xgb.DMatrix("agaricus.txt.test")
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param = list("bst:max_depth"=2, "bst: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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# make prediction
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preds <- xgb.predict(bst, dtest)
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labels <- xgb.getinfo(dtest, "label")
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err <- as.numeric(sum(as.integer(preds > 0.5) != labels)) / length(labels)
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# print error rate
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print(paste("error=",err))
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# dump model
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xgb.dump(bst, "dump.raw.txt")
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# dump model with feature map
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xgb.dump(bst, "dump.nice.txt", "featmap.txt")
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# save dmatrix into binary buffer
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succ <- xgb.save(dtest, "dtest.buffer")
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# save model into file
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succ <- xgb.save(bst, "xgb.model")
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# load model and data in
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bst2 <- xgb.Booster(modelfile="xgb.model")
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dtest2 <- xgb.DMatrix("dtest.buffer")
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preds2 <- xgb.predict(bst2, dtest2)
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# assert they are the same
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stopifnot(sum(abs(preds2-preds)) == 0)
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###
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# build dmatrix from sparseMatrix
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###
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print ('start running example of build DMatrix from R.sparseMatrix')
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csc <- read.libsvm("agaricus.txt.train", 126)
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label <- csc$label
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data <- csc$data
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dtrain <- xgb.DMatrix(data, info=list(label=label) )
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watchlist <- list("eval"=dtest,"train"=dtrain)
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bst <- xgb.train(param, dtrain, nround=2, watchlist=watchlist)
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###
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# build dmatrix from dense matrix
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###
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print ('start running example of build DMatrix from R.Matrix')
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mat = as.matrix(data)
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dtrain <- xgb.DMatrix(mat, info=list(label=label) )
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watchlist <- list("eval"=dtest,"train"=dtrain)
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bst <- xgb.train(param, dtrain, nround=2, watchlist=watchlist)
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###
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# advanced: cutomsized loss function
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#
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print("start running example to used cutomized objective function")
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# note: for customized objective function, we leave objective as default
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# note: what we are getting is margin value in prediction
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# you must know what you are doing
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param <- list("bst:max_depth" = 2, "bst:eta" = 1, "silent" =1)
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# user define objective function, given prediction, return gradient and second order gradient
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# this is loglikelihood loss
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logregobj <- function(preds, dtrain) {
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labels <- xgb.getinfo(dtrain, "label")
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preds <- 1.0 / (1.0 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1.0-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")
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# NOTE: when you do customized loss function, the default prediction value is margin
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# this may make buildin evalution metric not function properly
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# for example, we are doing logistic loss, the prediction is score before logistic transformation
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# the buildin evaluation error assumes input is after logistic transformation
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# Take this in mind when you use the customization, and maybe you need write customized evaluation function
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evalerror <- function(preds, dtrain) {
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labels <- xgb.getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0.0))) / 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
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# simply look at xgboost.py"s implementation of train
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bst <- xgb.train(param, dtrain, nround=2, watchlist, logregobj, evalerror)
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###
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# advanced: start from a initial base prediction
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#
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print ("start running example to start from a initial prediction")
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# specify parameters via map, definition are same as c++ version
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param = list("bst:max_depth"=2, "bst:eta"=1, "silent"=1, "objective"="binary:logistic")
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# train xgboost for 1 round
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bst <- xgb.train( param, dtrain, 1, watchlist )
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# Note: we need the margin value instead of transformed prediction in set_base_margin
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# do predict with output_margin=True, will always give you margin values before logistic transformation
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ptrain <- xgb.predict(bst, dtrain, outputmargin=TRUE)
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ptest <- xgb.predict(bst, dtest, outputmargin=TRUE)
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succ <- xgb.setinfo(dtrain, "base_margin", ptrain)
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succ <- xgb.setinfo(dtest, "base_margin", ptest)
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print ("this is result of running from initial prediction")
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bst <- xgb.train( param, dtrain, 1, watchlist )
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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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@@ -10,6 +10,4 @@ Python
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R
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=====
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* To make the R wrapper, type ```make R``` in the root directory of project
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* R module need Rinternals.h, find the path in your system and add it to CPLUS_INCLUDE_PATH in Makefile
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* Refer to the walk through example in [R-example/demo.R](R-example/demo.R)
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* See ../R-package
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@@ -1,222 +0,0 @@
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# depends on matrix
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succ <- require("Matrix")
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if (!succ) {
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stop("xgboost depends on Matrix library")
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}
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# load in library
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dyn.load("./libxgboostR.so")
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# constructing DMatrix
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xgb.DMatrix <- function(data, info=list(), missing=0.0) {
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if (typeof(data) == "character") {
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handle <- .Call("XGDMatrixCreateFromFile_R", data, as.integer(FALSE))
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} else if(is.matrix(data)) {
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handle <- .Call("XGDMatrixCreateFromMat_R", data, missing)
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} else if(class(data) == "dgCMatrix") {
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handle <- .Call("XGDMatrixCreateFromCSC_R", data@p, data@i, data@x)
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} else {
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stop(paste("xgb.DMatrix: does not support to construct from ", typeof(data)))
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}
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dmat <- structure(handle, class="xgb.DMatrix")
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if (length(info) != 0) {
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for (i in 1:length(info)) {
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p <- info[i]
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xgb.setinfo(dmat, names(p), p[[1]])
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}
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}
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return(dmat)
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}
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# get information from dmatrix
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xgb.getinfo <- function(dmat, name) {
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if (typeof(name) != "character") {
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stop("xgb.getinfo: name must be character")
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}
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if (class(dmat) != "xgb.DMatrix") {
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stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix");
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}
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if (name != "label" &&
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name != "weight" &&
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name != "base_margin" ) {
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stop(paste("xgb.getinfo: unknown info name", name))
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}
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ret <- .Call("XGDMatrixGetInfo_R", dmat, name)
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return(ret)
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}
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# set information into dmatrix, this mutate dmatrix
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xgb.setinfo <- function(dmat, name, info) {
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if (class(dmat) != "xgb.DMatrix") {
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stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix");
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}
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if (name == "label") {
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info))
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return(TRUE)
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}
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if (name == "weight") {
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info))
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return(TRUE)
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}
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if (name == "base_margin") {
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info))
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return(TRUE)
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}
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if (name == "group") {
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.Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info))
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return(TRUE)
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}
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stop(pase("xgb.setinfo: unknown info name", name))
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return(FALSE)
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}
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# construct a Booster from cachelist
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xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
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if (typeof(cachelist) != "list") {
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stop("xgb.Booster: only accepts list of DMatrix as cachelist")
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}
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for (dm in cachelist) {
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if (class(dm) != "xgb.DMatrix") {
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stop("xgb.Booster: only accepts list of DMatrix as cachelist")
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}
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}
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handle <- .Call("XGBoosterCreate_R", cachelist)
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.Call("XGBoosterSetParam_R", handle, "seed", "0")
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if (length(params) != 0) {
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for (i in 1:length(params)) {
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p <- params[i]
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.Call("XGBoosterSetParam_R", handle, names(p), as.character(p))
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}
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}
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if (!is.null(modelfile)) {
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if (typeof(modelfile) != "character"){
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stop("xgb.Booster: modelfile must be character");
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}
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.Call("XGBoosterLoadModel_R", handle, modelfile)
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}
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return(structure(handle, class="xgb.Booster"))
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}
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# train a model using given parameters
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xgb.train <- function(params, dtrain, nrounds=10, watchlist=list(), obj=NULL, feval=NULL) {
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if (typeof(params) != "list") {
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stop("xgb.train: first argument params must be list");
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}
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if (class(dtrain) != "xgb.DMatrix") {
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stop("xgb.train: second argument dtrain must be xgb.DMatrix");
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}
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bst <- xgb.Booster(params, append(watchlist,dtrain))
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for (i in 1:nrounds) {
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if (is.null(obj)) {
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succ <- xgb.iter.update(bst, dtrain, i-1)
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} else {
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pred <- xgb.predict(bst, dtrain)
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gpair <- obj(pred, dtrain)
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succ <- xgb.iter.boost(bst, dtrain, gpair)
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}
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if (length(watchlist) != 0) {
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if (is.null(feval)) {
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msg <- xgb.iter.eval(bst, watchlist, i-1)
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cat(msg); cat("\n")
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} else {
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cat("["); cat(i); cat("]");
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for (j in 1:length(watchlist)) {
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w <- watchlist[j]
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if (length(names(w)) == 0) {
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stop("xgb.eval: name tag must be presented for every elements in watchlist")
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}
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ret <- feval(xgb.predict(bst, w[[1]]), w[[1]])
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cat("\t"); cat(names(w)); cat("-"); cat(ret$metric);
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cat(":"); cat(ret$value)
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}
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cat("\n")
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}
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}
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}
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return(bst)
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}
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# save model or DMatrix to file
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xgb.save <- function(handle, fname) {
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if (typeof(fname) != "character") {
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stop("xgb.save: fname must be character");
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}
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if (class(handle) == "xgb.Booster") {
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.Call("XGBoosterSaveModel_R", handle, fname);
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return(TRUE)
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}
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if (class(handle) == "xgb.DMatrix") {
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.Call("XGDMatrixSaveBinary_R", handle, fname, as.integer(FALSE))
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return(TRUE)
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}
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stop("xgb.save: the input must be either xgb.DMatrix or xgb.Booster")
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return(FALSE)
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}
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# predict
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xgb.predict <- function(booster, dmat, outputmargin = FALSE) {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.predict: first argument must be type xgb.Booster")
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}
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if (class(dmat) != "xgb.DMatrix") {
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stop("xgb.predict: second argument must be type xgb.DMatrix")
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}
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ret <- .Call("XGBoosterPredict_R", booster, dmat, as.integer(outputmargin))
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return(ret)
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}
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# dump model
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xgb.dump <- function(booster, fname, fmap = "") {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.dump: first argument must be type xgb.Booster")
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}
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if (typeof(fname) != "character"){
|
||||
stop("xgb.dump: second argument must be type character")
|
||||
}
|
||||
.Call("XGBoosterDumpModel_R", booster, fname, fmap)
|
||||
return(TRUE)
|
||||
}
|
||||
##--------------------------------------
|
||||
# the following are low level iteratively function, not needed
|
||||
# if you do not want to use them
|
||||
#---------------------------------------
|
||||
# iteratively update booster with dtrain
|
||||
xgb.iter.update <- function(booster, dtrain, iter) {
|
||||
if (class(booster) != "xgb.Booster") {
|
||||
stop("xgb.iter.update: first argument must be type xgb.Booster")
|
||||
}
|
||||
if (class(dtrain) != "xgb.DMatrix") {
|
||||
stop("xgb.iter.update: second argument must be type xgb.DMatrix")
|
||||
}
|
||||
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain)
|
||||
return(TRUE)
|
||||
}
|
||||
# iteratively update booster with customized statistics
|
||||
xgb.iter.boost <- function(booster, dtrain, gpair) {
|
||||
if (class(booster) != "xgb.Booster") {
|
||||
stop("xgb.iter.update: first argument must be type xgb.Booster")
|
||||
}
|
||||
if (class(dtrain) != "xgb.DMatrix") {
|
||||
stop("xgb.iter.update: second argument must be type xgb.DMatrix")
|
||||
}
|
||||
.Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess)
|
||||
return(TRUE)
|
||||
}
|
||||
# iteratively evaluate one iteration
|
||||
xgb.iter.eval <- function(booster, watchlist, iter) {
|
||||
if (class(booster) != "xgb.Booster") {
|
||||
stop("xgb.eval: first argument must be type xgb.Booster")
|
||||
}
|
||||
if (typeof(watchlist) != "list") {
|
||||
stop("xgb.eval: only accepts list of DMatrix as watchlist")
|
||||
}
|
||||
for (w in watchlist) {
|
||||
if (class(w) != "xgb.DMatrix") {
|
||||
stop("xgb.eval: watch list can only contain xgb.DMatrix")
|
||||
}
|
||||
}
|
||||
evnames <- list()
|
||||
if (length(watchlist) != 0) {
|
||||
for (i in 1:length(watchlist)) {
|
||||
w <- watchlist[i]
|
||||
if (length(names(w)) == 0) {
|
||||
stop("xgb.eval: name tag must be presented for every elements in watchlist")
|
||||
}
|
||||
evnames <- append(evnames, names(w))
|
||||
}
|
||||
}
|
||||
msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist, evnames)
|
||||
return(msg)
|
||||
}
|
||||
@@ -1,221 +0,0 @@
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <utility>
|
||||
#include <cstring>
|
||||
#include "xgboost_R.h"
|
||||
#include "xgboost_wrapper.h"
|
||||
#include "../src/utils/utils.h"
|
||||
#include "../src/utils/omp.h"
|
||||
#include "../src/utils/matrix_csr.h"
|
||||
|
||||
using namespace xgboost;
|
||||
// implements error handling
|
||||
namespace xgboost {
|
||||
namespace utils {
|
||||
void HandleAssertError(const char *msg) {
|
||||
error("%s", msg);
|
||||
}
|
||||
void HandleCheckError(const char *msg) {
|
||||
error("%s", msg);
|
||||
}
|
||||
} // namespace utils
|
||||
} // namespace xgboost
|
||||
|
||||
extern "C" {
|
||||
void _DMatrixFinalizer(SEXP ext) {
|
||||
if (R_ExternalPtrAddr(ext) == NULL) return;
|
||||
XGDMatrixFree(R_ExternalPtrAddr(ext));
|
||||
R_ClearExternalPtr(ext);
|
||||
}
|
||||
SEXP XGDMatrixCreateFromFile_R(SEXP fname, SEXP silent) {
|
||||
void *handle = XGDMatrixCreateFromFile(CHAR(asChar(fname)), asInteger(silent));
|
||||
SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
|
||||
R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
SEXP XGDMatrixCreateFromMat_R(SEXP mat,
|
||||
SEXP missing) {
|
||||
SEXP dim = getAttrib(mat, R_DimSymbol);
|
||||
int nrow = INTEGER(dim)[0];
|
||||
int ncol = INTEGER(dim)[1];
|
||||
double *din = REAL(mat);
|
||||
std::vector<float> data(nrow * ncol);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (int i = 0; i < nrow; ++i) {
|
||||
for (int j = 0; j < ncol; ++j) {
|
||||
data[i * ncol +j] = din[i + nrow * j];
|
||||
}
|
||||
}
|
||||
void *handle = XGDMatrixCreateFromMat(&data[0], nrow, ncol, asReal(missing));
|
||||
SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
|
||||
R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
SEXP XGDMatrixCreateFromCSC_R(SEXP indptr,
|
||||
SEXP indices,
|
||||
SEXP data) {
|
||||
const int *col_ptr = INTEGER(indptr);
|
||||
const int *row_index = INTEGER(indices);
|
||||
const double *col_data = REAL(data);
|
||||
int ncol = length(indptr) - 1;
|
||||
int ndata = length(data);
|
||||
// transform into CSR format
|
||||
std::vector<bst_ulong> row_ptr;
|
||||
std::vector< std::pair<unsigned, float> > csr_data;
|
||||
utils::SparseCSRMBuilder<std::pair<unsigned,float>, false, bst_ulong> builder(row_ptr, csr_data);
|
||||
builder.InitBudget();
|
||||
for (int i = 0; i < ncol; ++i) {
|
||||
for (int j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
|
||||
builder.AddBudget(row_index[j]);
|
||||
}
|
||||
}
|
||||
builder.InitStorage();
|
||||
for (int i = 0; i < ncol; ++i) {
|
||||
for (int j = col_ptr[i]; j < col_ptr[i+1]; ++j) {
|
||||
builder.PushElem(row_index[j], std::make_pair(i, col_data[j]));
|
||||
}
|
||||
}
|
||||
utils::Assert(csr_data.size() == static_cast<size_t>(ndata), "BUG CreateFromCSC");
|
||||
std::vector<float> row_data(ndata);
|
||||
std::vector<unsigned> col_index(ndata);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (int i = 0; i < ndata; ++i) {
|
||||
col_index[i] = csr_data[i].first;
|
||||
row_data[i] = csr_data[i].second;
|
||||
}
|
||||
void *handle = XGDMatrixCreateFromCSR(&row_ptr[0], &col_index[0], &row_data[0], row_ptr.size(), ndata );
|
||||
SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
|
||||
R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
void XGDMatrixSaveBinary_R(SEXP handle, SEXP fname, SEXP silent) {
|
||||
XGDMatrixSaveBinary(R_ExternalPtrAddr(handle),
|
||||
CHAR(asChar(fname)), asInteger(silent));
|
||||
}
|
||||
void XGDMatrixSetInfo_R(SEXP handle, SEXP field, SEXP array) {
|
||||
int len = length(array);
|
||||
const char *name = CHAR(asChar(field));
|
||||
if (!strcmp("group", name)) {
|
||||
std::vector<unsigned> vec(len);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (int i = 0; i < len; ++i) {
|
||||
vec[i] = static_cast<unsigned>(INTEGER(array)[i]);
|
||||
}
|
||||
XGDMatrixSetGroup(R_ExternalPtrAddr(handle), &vec[0], len);
|
||||
return;
|
||||
}
|
||||
{
|
||||
std::vector<float> vec(len);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (int i = 0; i < len; ++i) {
|
||||
vec[i] = REAL(array)[i];
|
||||
}
|
||||
XGDMatrixSetFloatInfo(R_ExternalPtrAddr(handle),
|
||||
CHAR(asChar(field)),
|
||||
&vec[0], len);
|
||||
}
|
||||
}
|
||||
SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field) {
|
||||
bst_ulong olen;
|
||||
const float *res = XGDMatrixGetFloatInfo(R_ExternalPtrAddr(handle),
|
||||
CHAR(asChar(field)), &olen);
|
||||
SEXP ret = PROTECT(allocVector(REALSXP, olen));
|
||||
for (size_t i = 0; i < olen; ++i) {
|
||||
REAL(ret)[i] = res[i];
|
||||
}
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
// functions related to booster
|
||||
void _BoosterFinalizer(SEXP ext) {
|
||||
if (R_ExternalPtrAddr(ext) == NULL) return;
|
||||
XGBoosterFree(R_ExternalPtrAddr(ext));
|
||||
R_ClearExternalPtr(ext);
|
||||
}
|
||||
SEXP XGBoosterCreate_R(SEXP dmats) {
|
||||
int len = length(dmats);
|
||||
std::vector<void*> dvec;
|
||||
for (int i = 0; i < len; ++i){
|
||||
dvec.push_back(R_ExternalPtrAddr(VECTOR_ELT(dmats, i)));
|
||||
}
|
||||
void *handle = XGBoosterCreate(&dvec[0], dvec.size());
|
||||
SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
|
||||
R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
void XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val) {
|
||||
XGBoosterSetParam(R_ExternalPtrAddr(handle),
|
||||
CHAR(asChar(name)),
|
||||
CHAR(asChar(val)));
|
||||
}
|
||||
void XGBoosterUpdateOneIter_R(SEXP handle, SEXP iter, SEXP dtrain) {
|
||||
XGBoosterUpdateOneIter(R_ExternalPtrAddr(handle),
|
||||
asInteger(iter),
|
||||
R_ExternalPtrAddr(dtrain));
|
||||
}
|
||||
void XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP hess) {
|
||||
utils::Check(length(grad) == length(hess), "gradient and hess must have same length");
|
||||
int len = length(grad);
|
||||
std::vector<float> tgrad(len), thess(len);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (int j = 0; j < len; ++j) {
|
||||
tgrad[j] = REAL(grad)[j];
|
||||
thess[j] = REAL(hess)[j];
|
||||
}
|
||||
XGBoosterBoostOneIter(R_ExternalPtrAddr(handle),
|
||||
R_ExternalPtrAddr(dtrain),
|
||||
&tgrad[0], &thess[0], len);
|
||||
}
|
||||
SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames) {
|
||||
utils::Check(length(dmats) == length(evnames), "dmats and evnams must have same length");
|
||||
int len = length(dmats);
|
||||
std::vector<void*> vec_dmats;
|
||||
std::vector<std::string> vec_names;
|
||||
std::vector<const char*> vec_sptr;
|
||||
for (int i = 0; i < len; ++i) {
|
||||
vec_dmats.push_back(R_ExternalPtrAddr(VECTOR_ELT(dmats, i)));
|
||||
vec_names.push_back(std::string(CHAR(asChar(VECTOR_ELT(evnames, i)))));
|
||||
}
|
||||
for (int i = 0; i < len; ++i) {
|
||||
vec_sptr.push_back(vec_names[i].c_str());
|
||||
}
|
||||
return mkString(XGBoosterEvalOneIter(R_ExternalPtrAddr(handle),
|
||||
asInteger(iter),
|
||||
&vec_dmats[0], &vec_sptr[0], len));
|
||||
}
|
||||
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP output_margin) {
|
||||
bst_ulong olen;
|
||||
const float *res = XGBoosterPredict(R_ExternalPtrAddr(handle),
|
||||
R_ExternalPtrAddr(dmat),
|
||||
asInteger(output_margin),
|
||||
&olen);
|
||||
SEXP ret = PROTECT(allocVector(REALSXP, olen));
|
||||
for (size_t i = 0; i < olen; ++i) {
|
||||
REAL(ret)[i] = res[i];
|
||||
}
|
||||
UNPROTECT(1);
|
||||
return ret;
|
||||
}
|
||||
void XGBoosterLoadModel_R(SEXP handle, SEXP fname) {
|
||||
XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
|
||||
}
|
||||
void XGBoosterSaveModel_R(SEXP handle, SEXP fname) {
|
||||
XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
|
||||
}
|
||||
void XGBoosterDumpModel_R(SEXP handle, SEXP fname, SEXP fmap) {
|
||||
bst_ulong olen;
|
||||
const char **res = XGBoosterDumpModel(R_ExternalPtrAddr(handle),
|
||||
CHAR(asChar(fmap)),
|
||||
&olen);
|
||||
FILE *fo = utils::FopenCheck(CHAR(asChar(fname)), "w");
|
||||
for (size_t i = 0; i < olen; ++i) {
|
||||
fprintf(fo, "booster[%u]:\n", static_cast<unsigned>(i));
|
||||
fprintf(fo, "%s", res[i]);
|
||||
}
|
||||
fclose(fo);
|
||||
}
|
||||
}
|
||||
@@ -1,124 +0,0 @@
|
||||
#ifndef XGBOOST_WRAPPER_R_H_
|
||||
#define XGBOOST_WRAPPER_R_H_
|
||||
/*!
|
||||
* \file xgboost_wrapper_R.h
|
||||
* \author Tianqi Chen
|
||||
* \brief R wrapper of xgboost
|
||||
*/
|
||||
extern "C" {
|
||||
#include <Rinternals.h>
|
||||
}
|
||||
|
||||
extern "C" {
|
||||
/*!
|
||||
* \brief load a data matrix
|
||||
* \param fname name of the content
|
||||
* \param silent whether print messages
|
||||
* \return a loaded data matrix
|
||||
*/
|
||||
SEXP XGDMatrixCreateFromFile_R(SEXP fname, SEXP silent);
|
||||
/*!
|
||||
* \brief create matrix content from dense matrix
|
||||
* This assumes the matrix is stored in column major format
|
||||
* \param data R Matrix object
|
||||
* \param missing which value to represent missing value
|
||||
* \return created dmatrix
|
||||
*/
|
||||
SEXP XGDMatrixCreateFromMat_R(SEXP mat,
|
||||
SEXP missing);
|
||||
/*!
|
||||
* \brief create a matrix content from CSC format
|
||||
* \param indptr pointer to column headers
|
||||
* \param indices row indices
|
||||
* \param data content of the data
|
||||
* \return created dmatrix
|
||||
*/
|
||||
SEXP XGDMatrixCreateFromCSC_R(SEXP indptr,
|
||||
SEXP indices,
|
||||
SEXP data);
|
||||
/*!
|
||||
* \brief load a data matrix into binary file
|
||||
* \param handle a instance of data matrix
|
||||
* \param fname file name
|
||||
* \param silent print statistics when saving
|
||||
*/
|
||||
void XGDMatrixSaveBinary_R(SEXP handle, SEXP fname, SEXP silent);
|
||||
/*!
|
||||
* \brief set information to dmatrix
|
||||
* \param handle a instance of data matrix
|
||||
* \param field field name, can be label, weight
|
||||
* \param array pointer to float vector
|
||||
*/
|
||||
void XGDMatrixSetInfo_R(SEXP handle, SEXP field, SEXP array);
|
||||
/*!
|
||||
* \brief get info vector from matrix
|
||||
* \param handle a instance of data matrix
|
||||
* \param field field name
|
||||
* \return info vector
|
||||
*/
|
||||
SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field);
|
||||
/*!
|
||||
* \brief create xgboost learner
|
||||
* \param dmats a list of dmatrix handles that will be cached
|
||||
*/
|
||||
SEXP XGBoosterCreate_R(SEXP dmats);
|
||||
/*!
|
||||
* \brief set parameters
|
||||
* \param handle handle
|
||||
* \param name parameter name
|
||||
* \param val value of parameter
|
||||
*/
|
||||
void XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val);
|
||||
/*!
|
||||
* \brief update the model in one round using dtrain
|
||||
* \param handle handle
|
||||
* \param iter current iteration rounds
|
||||
* \param dtrain training data
|
||||
*/
|
||||
void XGBoosterUpdateOneIter_R(SEXP ext, SEXP iter, SEXP dtrain);
|
||||
/*!
|
||||
* \brief update the model, by directly specify gradient and second order gradient,
|
||||
* this can be used to replace UpdateOneIter, to support customized loss function
|
||||
* \param handle handle
|
||||
* \param dtrain training data
|
||||
* \param grad gradient statistics
|
||||
* \param hess second order gradient statistics
|
||||
*/
|
||||
void XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP hess);
|
||||
/*!
|
||||
* \brief get evaluation statistics for xgboost
|
||||
* \param handle handle
|
||||
* \param iter current iteration rounds
|
||||
* \param dmats list of handles to dmatrices
|
||||
* \param evname name of evaluation
|
||||
* \return the string containing evaluation stati
|
||||
*/
|
||||
SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames);
|
||||
/*!
|
||||
* \brief make prediction based on dmat
|
||||
* \param handle handle
|
||||
* \param dmat data matrix
|
||||
* \param output_margin whether only output raw margin value
|
||||
*/
|
||||
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP output_margin);
|
||||
/*!
|
||||
* \brief load model from existing file
|
||||
* \param handle handle
|
||||
* \param fname file name
|
||||
*/
|
||||
void XGBoosterLoadModel_R(SEXP handle, SEXP fname);
|
||||
/*!
|
||||
* \brief save model into existing file
|
||||
* \param handle handle
|
||||
* \param fname file name
|
||||
*/
|
||||
void XGBoosterSaveModel_R(SEXP handle, SEXP fname);
|
||||
/*!
|
||||
* \brief dump model into text file
|
||||
* \param handle handle
|
||||
* \param fname file name of model that can be dumped into
|
||||
* \param fmap name to fmap can be empty string
|
||||
*/
|
||||
void XGBoosterDumpModel_R(SEXP handle, SEXP fname, SEXP fmap);
|
||||
};
|
||||
#endif // XGBOOST_WRAPPER_R_H_
|
||||
Reference in New Issue
Block a user