[R] various R code maintenance (#1964)
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
This commit is contained in:
Vadim Khotilovich
committed by
Tianqi Chen
Tianqi Chen
parent
a073a2c3d4
commit
2b5b96d760
@@ -107,18 +107,27 @@ test_that("cb.evaluation.log works as expected", {
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param <- list(objective = "binary:logistic", max_depth = 4, nthread = 2)
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test_that("can store evaluation_log without printing", {
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expect_silent(
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bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1, verbose = 0)
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)
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expect_false(is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log$train_error))
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expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
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})
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test_that("cb.reset.parameters works as expected", {
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# fixed eta
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set.seed(111)
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bst0 <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 0.9)
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bst0 <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 0.9, verbose = 0)
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expect_false(is.null(bst0$evaluation_log))
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expect_false(is.null(bst0$evaluation_log$train_error))
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# same eta but re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.9, 0.9))
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bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst1$evaluation_log$train_error))
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expect_equal(bst0$evaluation_log$train_error,
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@@ -127,7 +136,7 @@ test_that("cb.reset.parameters works as expected", {
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# same eta but re-set via a function in the callback
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set.seed(111)
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my_par <- list(eta = function(itr, itr_end) 0.9)
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst2$evaluation_log$train_error))
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expect_equal(bst0$evaluation_log$train_error,
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@@ -136,7 +145,7 @@ test_that("cb.reset.parameters works as expected", {
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# different eta re-set as a vector parameter in the callback
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set.seed(111)
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my_par <- list(eta = c(0.6, 0.5))
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bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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bst3 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bst3$evaluation_log$train_error))
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expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
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@@ -144,18 +153,18 @@ test_that("cb.reset.parameters works as expected", {
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# resetting multiple parameters at the same time runs with no error
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my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
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expect_error(
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bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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bst4 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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, NA) # NA = no error
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# CV works as well
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expect_error(
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bst4 <- xgb.cv(param, dtrain, nfold = 2, nrounds = 2,
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bst4 <- xgb.cv(param, dtrain, nfold = 2, nrounds = 2, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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, NA) # NA = no error
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# expect no learning with 0 learning rate
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my_par <- list(eta = c(0., 0.))
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bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist,
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bstX <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
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callbacks = list(cb.reset.parameters(my_par)))
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expect_false(is.null(bstX$evaluation_log$train_error))
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er <- unique(bstX$evaluation_log$train_error)
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@@ -167,7 +176,7 @@ test_that("cb.save.model works as expected", {
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files <- c('xgboost_01.model', 'xgboost_02.model', 'xgboost.model')
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for (f in files) if (file.exists(f)) file.remove(f)
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1,
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
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save_period = 1, save_name = "xgboost_%02d.model")
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expect_true(file.exists('xgboost_01.model'))
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expect_true(file.exists('xgboost_02.model'))
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@@ -178,7 +187,8 @@ test_that("cb.save.model works as expected", {
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expect_equal(bst$raw, b2$raw)
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# save_period = 0 saves the last iteration's model
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, save_period = 0)
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bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
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save_period = 0)
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expect_true(file.exists('xgboost.model'))
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b2 <- xgb.load('xgboost.model')
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expect_equal(bst$raw, b2$raw)
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@@ -186,16 +196,6 @@ test_that("cb.save.model works as expected", {
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for (f in files) if (file.exists(f)) file.remove(f)
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})
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test_that("can store evaluation_log without printing", {
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expect_silent(
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bst <- xgb.train(param, dtrain, nrounds = 10, watchlist, eta = 1,
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verbose = 0, callbacks = list(cb.evaluation.log()))
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)
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expect_false(is.null(bst$evaluation_log))
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expect_false(is.null(bst$evaluation_log$train_error))
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expect_lt(bst$evaluation_log[, min(train_error)], 0.2)
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})
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test_that("early stopping xgb.train works", {
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set.seed(11)
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expect_output(
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@@ -211,6 +211,13 @@ test_that("early stopping xgb.train works", {
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err_pred <- err(ltest, pred)
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err_log <- bst$evaluation_log[bst$best_iteration, test_error]
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expect_equal(err_log, err_pred, tolerance = 5e-6)
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set.seed(11)
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expect_silent(
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bst0 <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
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early_stopping_rounds = 3, maximize = FALSE, verbose = 0)
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)
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expect_equal(bst$evaluation_log, bst0$evaluation_log)
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})
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test_that("early stopping using a specific metric works", {
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@@ -248,7 +255,7 @@ test_that("early stopping xgb.cv works", {
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test_that("prediction in xgb.cv works", {
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set.seed(11)
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nrounds = 4
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE)
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cv <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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@@ -258,7 +265,7 @@ test_that("prediction in xgb.cv works", {
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# save CV models
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set.seed(11)
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cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE,
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cvx <- xgb.cv(param, dtrain, nfold = 5, eta = 0.5, nrounds = nrounds, prediction = TRUE, verbose = 0,
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callbacks = list(cb.cv.predict(save_models = TRUE)))
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expect_equal(cv$evaluation_log, cvx$evaluation_log)
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expect_length(cvx$models, 5)
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@@ -268,7 +275,7 @@ test_that("prediction in xgb.cv works", {
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test_that("prediction in xgb.cv works for gblinear too", {
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set.seed(11)
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p <- list(booster = 'gblinear', objective = "reg:logistic", nthread = 2)
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cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE)
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cv <- xgb.cv(p, dtrain, nfold = 5, eta = 0.5, nrounds = 2, prediction = TRUE, verbose = 0)
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expect_false(is.null(cv$evaluation_log))
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expect_false(is.null(cv$pred))
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expect_length(cv$pred, nrow(train$data))
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@@ -300,7 +307,7 @@ test_that("prediction in xgb.cv for softprob works", {
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expect_warning(
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cv <- xgb.cv(data = as.matrix(iris[, -5]), label = lb, nfold = 4,
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eta = 0.5, nrounds = 5, max_depth = 3, nthread = 2,
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subsample = 0.8, gamma = 2,
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subsample = 0.8, gamma = 2, verbose = 0,
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prediction = TRUE, objective = "multi:softprob", num_class = 3)
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, NA)
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expect_false(is.null(cv$pred))
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