Fix travis build (+1 squashed commit)
Squashed commits: [9240d5f] Fix Travis build
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@ -1,4 +1,4 @@
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#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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#' @import methods
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#' @import methods
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# depends on matrix
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# depends on matrix
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@ -160,6 +160,8 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
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PACKAGE = "xgboost")
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PACKAGE = "xgboost")
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} else {
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} else {
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pred <- predict(booster, dtrain)
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pred <- predict(booster, dtrain)
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gpair <- obj(pred, dtrain)
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succ <- xgb.iter.boost(booster, dtrain, gpair)
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}
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}
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return(TRUE)
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return(TRUE)
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}
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}
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@ -181,6 +181,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
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msg <- list()
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msg <- list()
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for (k in 1:nfold) {
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for (k in 1:nfold) {
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fd <- xgb_folds[[k]]
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fd <- xgb_folds[[k]]
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succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
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msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
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msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
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}
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}
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ret <- xgb.cv.aggcv(msg, showsd)
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ret <- xgb.cv.aggcv(msg, showsd)
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@ -125,7 +125,7 @@ treeDump <- function(feature_names, text, keepDetail){
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}
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}
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linearDump <- function(feature_names, text){
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linearDump <- function(feature_names, text){
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which(text == "weight:") %>% {a <- . + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
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which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
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}
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}
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# Avoid error messages during CRAN check.
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# Avoid error messages during CRAN check.
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@ -186,6 +186,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
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bestScore <- Inf
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bestScore <- Inf
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}
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}
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bestInd <- 0
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bestInd <- 0
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earlyStopflag = FALSE
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if (length(watchlist) > 1)
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if (length(watchlist) > 1)
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warning('Only the first data set in watchlist is used for early stopping process.')
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warning('Only the first data set in watchlist is used for early stopping process.')
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@ -195,6 +196,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
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bst <- xgb.handleToBooster(handle)
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bst <- xgb.handleToBooster(handle)
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print.every.n <- max( as.integer(print.every.n), 1L)
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print.every.n <- max( as.integer(print.every.n), 1L)
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for (i in 1:nrounds) {
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for (i in 1:nrounds) {
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succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
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if (length(watchlist) != 0) {
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if (length(watchlist) != 0) {
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msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
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msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
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if (0 == ( (i - 1) %% print.every.n))
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if (0 == ( (i - 1) %% print.every.n))
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@ -207,6 +209,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
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bestScore <- score
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bestScore <- score
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bestInd <- i
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bestInd <- i
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} else {
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} else {
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earlyStopflag = TRUE
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if (i - bestInd >= early.stop.round) {
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if (i - bestInd >= early.stop.round) {
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cat('Stopping. Best iteration:',bestInd)
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cat('Stopping. Best iteration:',bestInd)
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break
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break
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@ -11,7 +11,7 @@ df <- data.table(Arthritis, keep.rownames = F)
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df[,AgeDiscret := as.factor(round(Age / 10,0))]
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df[,AgeDiscret := as.factor(round(Age / 10,0))]
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df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
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df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
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df[,ID := NULL]
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df[,ID := NULL]
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sparse_matrix <- sparse.model.matrix(Improved ~ . -1, data = df)
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sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
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output_vector <- df[,Y := 0][Improved == "Marked",Y := 1][,Y]
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output_vector <- df[,Y := 0][Improved == "Marked",Y := 1][,Y]
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bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
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bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
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eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
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eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
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