154 lines
5.1 KiB
R
154 lines
5.1 KiB
R
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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