97 lines
3.9 KiB
R
97 lines
3.9 KiB
R
context("Models from previous versions of XGBoost can be loaded")
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metadata <- list(
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kRounds = 2,
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kRows = 1000,
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kCols = 4,
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kForests = 2,
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kMaxDepth = 2,
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kClasses = 3
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)
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run_model_param_check <- function(config) {
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testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')
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testthat::expect_equal(config$learner$learner_train_param$booster, 'gbtree')
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}
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get_num_tree <- function(booster) {
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dump <- xgb.dump(booster)
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m <- regexec('booster\\[[0-9]+\\]', dump, perl = TRUE)
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m <- regmatches(dump, m)
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num_tree <- Reduce('+', lapply(m, length))
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return(num_tree)
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}
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run_booster_check <- function(booster, name) {
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config <- xgb.config(booster)
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run_model_param_check(config)
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if (name == 'cls') {
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testthat::expect_equal(get_num_tree(booster),
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metadata$kForests * metadata$kRounds * metadata$kClasses)
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testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
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testthat::expect_equal(config$learner$learner_train_param$objective, 'multi:softmax')
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testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class),
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metadata$kClasses)
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} else if (name == 'logitraw') {
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testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
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testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
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testthat::expect_equal(config$learner$learner_train_param$objective, 'binary:logitraw')
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} else if (name == 'logit') {
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testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
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testthat::expect_equal(as.numeric(config$learner$learner_model_param$num_class), 0)
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testthat::expect_equal(config$learner$learner_train_param$objective, 'binary:logistic')
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} else if (name == 'ltr') {
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testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
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testthat::expect_equal(config$learner$learner_train_param$objective, 'rank:ndcg')
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} else {
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testthat::expect_equal(name, 'reg')
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testthat::expect_equal(get_num_tree(booster), metadata$kForests * metadata$kRounds)
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testthat::expect_equal(as.numeric(config$learner$learner_model_param$base_score), 0.5)
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testthat::expect_equal(config$learner$learner_train_param$objective, 'reg:squarederror')
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}
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}
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test_that("Models from previous versions of XGBoost can be loaded", {
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bucket <- 'xgboost-ci-jenkins-artifacts'
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region <- 'us-west-2'
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file_name <- 'xgboost_r_model_compatibility_test.zip'
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zipfile <- tempfile(fileext = ".zip")
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extract_dir <- tempdir()
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download.file(paste('https://', bucket, '.s3-', region, '.amazonaws.com/', file_name, sep = ''),
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destfile = zipfile, mode = 'wb', quiet = TRUE)
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unzip(zipfile, exdir = extract_dir, overwrite = TRUE)
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model_dir <- file.path(extract_dir, 'models')
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pred_data <- xgb.DMatrix(matrix(c(0, 0, 0, 0), nrow = 1, ncol = 4), nthread = 2)
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lapply(list.files(model_dir), function(x) {
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model_file <- file.path(model_dir, x)
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m <- regexec("xgboost-([0-9\\.]+)\\.([a-z]+)\\.[a-z]+", model_file, perl = TRUE)
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m <- regmatches(model_file, m)[[1]]
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model_xgb_ver <- m[2]
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name <- m[3]
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is_rds <- endsWith(model_file, '.rds')
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is_json <- endsWith(model_file, '.json')
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# TODO: update this test for new RDS format
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if (is_rds) {
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return(NULL)
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}
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# Expect an R warning when a model is loaded from RDS and it was generated by version < 1.1.x
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if (is_rds && compareVersion(model_xgb_ver, '1.1.1.1') < 0) {
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booster <- readRDS(model_file)
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expect_warning(predict(booster, newdata = pred_data))
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booster <- readRDS(model_file)
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expect_warning(run_booster_check(booster, name))
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} else {
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if (is_rds) {
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booster <- readRDS(model_file)
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} else {
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booster <- xgb.load(model_file)
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xgb.parameters(booster) <- list(nthread = 2)
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}
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predict(booster, newdata = pred_data)
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run_booster_check(booster, name)
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}
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})
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})
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