80 lines
3.0 KiB
R
80 lines
3.0 KiB
R
context('Test helper functions')
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require(xgboost)
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require(data.table)
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require(Matrix)
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require(vcd)
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set.seed(1982)
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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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bst.Tree <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
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eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gbtree")
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bst.GLM <- xgboost(data = sparse_matrix, label = output_vector,
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eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
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feature.names <- agaricus.train$data@Dimnames[[2]]
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test_that("xgb.dump works", {
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capture.output(print(xgb.dump(bst.Tree)))
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capture.output(print(xgb.dump(bst.GLM)))
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expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with.stats = T))
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})
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test_that("xgb.attr", {
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val <- "my attribute value"
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expect_error(xgb.attr(bst.Tree, NULL))
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expect_error(xgb.attr(val, val))
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xgb.attr(bst.Tree, "my_attr") <- val
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expect_equal(xgb.attr(bst.Tree, "my_attr"), val)
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xgb.save(bst.Tree, 'xgb.model')
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bst1 <- xgb.load('xgb.model')
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expect_equal(xgb.attr(bst1, "my_attr"), val)
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})
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test_that("xgb.model.dt.tree works with and without feature names", {
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names.dt.trees <- c("ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover",
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"Tree", "Yes.Feature", "Yes.Cover", "Yes.Quality", "No.Feature", "No.Cover", "No.Quality")
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dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
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expect_equal(names.dt.trees, names(dt.tree))
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expect_equal(dim(dt.tree), c(162, 15))
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xgb.model.dt.tree(model = bst.Tree)
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})
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test_that("xgb.importance works with and without feature names", {
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importance.Tree <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst.Tree)
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expect_equal(dim(importance.Tree), c(7, 4))
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expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
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xgb.importance(model = bst.Tree)
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xgb.plot.importance(importance_matrix = importance.Tree)
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})
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test_that("xgb.importance works with GLM model", {
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importance.GLM <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst.GLM)
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expect_equal(dim(importance.GLM), c(10, 2))
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expect_equal(colnames(importance.GLM), c("Feature", "Weight"))
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xgb.importance(model = bst.GLM)
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xgb.plot.importance(importance.GLM)
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})
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test_that("xgb.plot.tree works with and without feature names", {
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xgb.plot.tree(feature_names = feature.names, model = bst.Tree)
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xgb.plot.tree(model = bst.Tree)
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})
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test_that("xgb.plot.multi.trees works with and without feature names", {
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xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features.keep = 3)
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xgb.plot.multi.trees(model = bst.Tree, features.keep = 3)
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})
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test_that("xgb.plot.deepness works", {
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xgb.plot.deepness(model = bst.Tree)
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})
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