888edba03f
Squashed commits: [9109887] Added test for eta decay(+1 squashed commit) Squashed commits: [1336bd4] Added tests for eta decay (+2 squashed commit) Squashed commits: [91aac2d] Added tests for eta decay (+1 squashed commit) Squashed commits: [3ff48e7] Added test for eta decay [6bb1eed] Rewrote Rd files [bf0dec4] Added learning_rates for diff eta in each boosting round
59 lines
1.8 KiB
R
59 lines
1.8 KiB
R
context('Test models with custom objective')
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require(xgboost)
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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test_that("custom objective works", {
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watchlist <- list(eval = dtest, train = dtrain)
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num_round <- 2
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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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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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param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
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objective=logregobj, 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$raw), 1064)
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attr(dtrain, 'label') <- getinfo(dtrain, 'label')
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logregobjattr <- function(preds, dtrain) {
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labels <- attr(dtrain, 'label')
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preds <- 1 / (1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
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objective = logregobjattr, eval_metric = evalerror)
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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expect_equal(class(bst), "xgb.Booster")
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expect_equal(length(bst$raw), 1064)
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})
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test_that("different eta for each boosting round works", {
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num_round <- 2
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watchlist <- list(eval = dtest, train = dtrain)
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param <- list(max.depth=2, eta=1, nthread = 2, silent=1)
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bst <- xgb.train(param, dtrain, num_round, watchlist, learning_rates = c(0.2, 0.3))
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})
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