Lint fix on infix operators
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@@ -18,7 +18,7 @@ test_that("early stopping", {
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res <- xgb.cv(data = train$data, label = train$label, max.depth = 2, nfold = 5,
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eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
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early.stop.round = 3, maximize = FALSE)
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expect_true(nrow(res)<20)
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expect_true(nrow(res) < 20)
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bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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eta = 0.3, nthread = 2, nround = 20, objective = "binary:logistic",
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early.stop.round = 3, maximize = FALSE)
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@@ -13,14 +13,14 @@ test_that("custom objective works", {
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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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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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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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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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@@ -34,13 +34,13 @@ test_that("custom objective works", {
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logregobjattr <- function(preds, dtrain) {
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labels <- attr(dtrain, 'label')
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preds <- 1/(1 + exp(-preds))
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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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param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
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objective=logregobjattr, eval_metric=evalerror)
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param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
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objective = logregobjattr, eval_metric = evalerror)
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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expect_equal(class(bst), "xgb.Booster")
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expect_equal(length(bst$raw), 1064)
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@@ -8,11 +8,11 @@ require(vcd)
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data(Arthritis)
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data(agaricus.train, package='xgboost')
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df <- data.table(Arthritis, keep.rownames = F)
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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[,ID:=NULL]
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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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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[,ID := NULL]
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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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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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@@ -23,5 +23,5 @@ test_that("Code Lint", {
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trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
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trailing_whitespace_linter=lintr::trailing_whitespace_linter
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)
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# lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
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lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
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})
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@@ -4,10 +4,10 @@ require(xgboost)
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test_that("poisson regression works", {
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data(mtcars)
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bst = xgboost(data=as.matrix(mtcars[,-11]),label=mtcars[,11],
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objective='count:poisson',nrounds=5)
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bst <- xgboost(data = as.matrix(mtcars[,-11]),label = mtcars[,11],
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objective = 'count:poisson', nrounds=5)
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expect_equal(class(bst), "xgb.Booster")
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pred = predict(bst,as.matrix(mtcars[,-11]))
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pred <- predict(bst,as.matrix(mtcars[, -11]))
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expect_equal(length(pred), 32)
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sqrt(mean((pred-mtcars[,11])^2))
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sqrt(mean((pred - mtcars[,11]) ^ 2))
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})
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