54 lines
1.7 KiB
R
54 lines
1.7 KiB
R
require(xgboost)
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dtrain <- xgb.DMatrix('../data/agaricus.txt.train')
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dtest <- xgb.DMatrix('../data/agaricus.txt.test')
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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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num_round <- 2
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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preds <- predict(bst, dtest)
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labels <- getinfo(dtest,'label')
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cat('error=', mean(as.numeric(preds>0.5)!=labels),'\n')
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xgb.save(bst, 'xgb.model')
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xgb.dump(bst, 'dump.raw.txt')
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xgb.dump(bst, 'dump.nuce.txt','../data/featmap.txt')
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bst2 <- xgb.load('xgb.model')
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preds2 <- predict(bst2,dtest)
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stopifnot(sum((preds-preds2)^2)==0)
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cat('start running example of build DMatrix from scipy.sparse CSR Matrix\n')
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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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csc <- read.libsvm("../data/agaricus.txt.train", 126)
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y <- csc$label
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x <- csc$data
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class(x)
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dtrain <- xgb.DMatrix(x, label = y)
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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cat('start running example of build DMatrix from numpy array\n')
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x <- as.matrix(x)
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class(x)
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dtrain <- xgb.DMatrix(x, label = y)
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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