301 lines
12 KiB
R
301 lines
12 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, quietly = TRUE)
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set.seed(1982)
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data(Arthritis)
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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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label <- df[, ifelse(Improved == "Marked", 1, 0)]
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# binary
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nrounds <- 12
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bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
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eta = 1, nthread = 2, nrounds = nrounds, verbose = 0,
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objective = "binary:logistic", booster = "gbtree")
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bst.GLM <- xgboost(data = sparse_matrix, label = label,
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eta = 1, nthread = 1, nrounds = nrounds, verbose = 0,
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objective = "binary:logistic", booster = "gblinear")
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feature.names <- colnames(sparse_matrix)
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# multiclass
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mlabel <- as.numeric(iris$Species) - 1
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nclass <- 3
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mbst.Tree <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = nrounds,
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objective = "multi:softprob", num_class = nclass, base_score = 0)
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mbst.GLM <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
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booster = "gblinear", eta = 0.1, nthread = 1, nrounds = nrounds,
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objective = "multi:softprob", num_class = nclass, base_score = 0)
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test_that("xgb.dump works", {
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expect_length(xgb.dump(bst.Tree), 200)
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expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with_stats = T))
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expect_true(file.exists('xgb.model.dump'))
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expect_gt(file.size('xgb.model.dump'), 8000)
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# JSON format
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dmp <- xgb.dump(bst.Tree, dump_format = "json")
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expect_length(dmp, 1)
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expect_length(grep('nodeid', strsplit(dmp, '\n')[[1]]), 188)
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})
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test_that("xgb.dump works for gblinear", {
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expect_length(xgb.dump(bst.GLM), 14)
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# also make sure that it works properly for a sparse model where some coefficients
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# are 0 from setting large L1 regularization:
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bst.GLM.sp <- xgboost(data = sparse_matrix, label = label, eta = 1, nthread = 2, nrounds = 1,
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alpha=2, objective = "binary:logistic", booster = "gblinear")
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d.sp <- xgb.dump(bst.GLM.sp)
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expect_length(d.sp, 14)
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expect_gt(sum(d.sp == "0"), 0)
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# JSON format
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dmp <- xgb.dump(bst.GLM.sp, dump_format = "json")
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expect_length(dmp, 1)
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expect_length(grep('\\d', strsplit(dmp, '\n')[[1]]), 11)
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})
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test_that("predict leafs works", {
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# no error for gbtree
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expect_error(pred_leaf <- predict(bst.Tree, sparse_matrix, predleaf = TRUE), regexp = NA)
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expect_equal(dim(pred_leaf), c(nrow(sparse_matrix), nrounds))
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# error for gblinear
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expect_error(predict(bst.GLM, sparse_matrix, predleaf = TRUE))
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})
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test_that("predict feature contributions works", {
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# gbtree binary classifier
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expect_error(pred_contr <- predict(bst.Tree, sparse_matrix, predcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.Tree, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 1e-6)
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# gblinear binary classifier
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expect_error(pred_contr <- predict(bst.GLM, sparse_matrix, predcontrib = TRUE), regexp = NA)
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expect_equal(dim(pred_contr), c(nrow(sparse_matrix), ncol(sparse_matrix) + 1))
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expect_equal(colnames(pred_contr), c(colnames(sparse_matrix), "BIAS"))
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pred <- predict(bst.GLM, sparse_matrix, outputmargin = TRUE)
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expect_lt(max(abs(rowSums(pred_contr) - pred)), 2e-6)
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# manual calculation of linear terms
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coefs <- xgb.dump(bst.GLM)[-c(1,2,4)] %>% as.numeric
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coefs <- c(coefs[-1], coefs[1]) # intercept must be the last
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pred_contr_manual <- sweep(cbind(sparse_matrix, 1), 2, coefs, FUN="*")
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expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 2e-6)
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# gbtree multiclass
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pred <- predict(mbst.Tree, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
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pred_contr <- predict(mbst.Tree, as.matrix(iris[, -5]), predcontrib = TRUE)
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expect_is(pred_contr, "list")
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expect_length(pred_contr, 3)
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for (g in seq_along(pred_contr)) {
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expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
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}
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# gblinear multiclass (set base_score = 0, which is base margin in multiclass)
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pred <- predict(mbst.GLM, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
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pred_contr <- predict(mbst.GLM, as.matrix(iris[, -5]), predcontrib = TRUE)
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expect_length(pred_contr, 3)
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coefs_all <- xgb.dump(mbst.GLM)[-c(1,2,6)] %>% as.numeric %>% matrix(ncol = 3, byrow = TRUE)
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for (g in seq_along(pred_contr)) {
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expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
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expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
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# manual calculation of linear terms
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coefs <- c(coefs_all[-1, g], coefs_all[1, g]) # intercept needs to be the last
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pred_contr_manual <- sweep(as.matrix(cbind(iris[,-5], 1)), 2, coefs, FUN="*")
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expect_equal(as.numeric(pred_contr[[g]]), as.numeric(pred_contr_manual), 2e-6)
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}
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})
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test_that("xgb-attribute functionality", {
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val <- "my attribute value"
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list.val <- list(my_attr=val, a=123, b='ok')
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list.ch <- list.val[order(names(list.val))]
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list.ch <- lapply(list.ch, as.character)
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# note: iter is 0-index in xgb attributes
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list.default <- list(niter = as.character(nrounds - 1))
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list.ch <- c(list.ch, list.default)
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# proper input:
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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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# set & get:
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expect_null(xgb.attr(bst.Tree, "asdf"))
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expect_equal(xgb.attributes(bst.Tree), list.default)
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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.attributes(bst.Tree) <- list.val
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expect_equal(xgb.attributes(bst.Tree), list.ch)
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# serializing:
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xgb.save(bst.Tree, 'xgb.model')
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bst <- xgb.load('xgb.model')
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expect_equal(xgb.attr(bst, "my_attr"), val)
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expect_equal(xgb.attributes(bst), list.ch)
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# deletion:
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xgb.attr(bst, "my_attr") <- NULL
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expect_null(xgb.attr(bst, "my_attr"))
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expect_equal(xgb.attributes(bst), list.ch[c("a", "b", "niter")])
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xgb.attributes(bst) <- list(a=NULL, b=NULL)
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expect_equal(xgb.attributes(bst), list.default)
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xgb.attributes(bst) <- list(niter=NULL)
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expect_null(xgb.attributes(bst))
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})
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if (grepl('Windows', Sys.info()[['sysname']]) ||
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grepl('Linux', Sys.info()[['sysname']]) ||
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grepl('Darwin', Sys.info()[['sysname']])) {
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test_that("xgb-attribute numeric precision", {
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# check that lossless conversion works with 17 digits
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# numeric -> character -> numeric
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X <- 10^runif(100, -20, 20)
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X2X <- as.numeric(format(X, digits = 17))
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expect_identical(X, X2X)
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# retrieved attributes to be the same as written
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for (x in X) {
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xgb.attr(bst.Tree, "x") <- x
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expect_identical(as.numeric(xgb.attr(bst.Tree, "x")), x)
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xgb.attributes(bst.Tree) <- list(a = "A", b = x)
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expect_identical(as.numeric(xgb.attr(bst.Tree, "b")), x)
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}
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})
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}
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test_that("xgb.Booster serializing as R object works", {
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saveRDS(bst.Tree, 'xgb.model.rds')
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bst <- readRDS('xgb.model.rds')
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dtrain <- xgb.DMatrix(sparse_matrix, label = label)
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expect_equal(predict(bst.Tree, dtrain), predict(bst, dtrain))
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expect_equal(xgb.dump(bst.Tree), xgb.dump(bst))
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xgb.save(bst, 'xgb.model')
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nil_ptr <- new("externalptr")
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class(nil_ptr) <- "xgb.Booster.handle"
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expect_true(identical(bst$handle, nil_ptr))
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bst <- xgb.Booster.complete(bst)
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expect_true(!identical(bst$handle, nil_ptr))
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expect_equal(predict(bst.Tree, dtrain), predict(bst, dtrain))
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})
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test_that("xgb.model.dt.tree works with and without feature names", {
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names.dt.trees <- c("Tree", "Node", "ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover")
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dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
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expect_equal(names.dt.trees, names(dt.tree))
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expect_equal(dim(dt.tree), c(188, 10))
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expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
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dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
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expect_equal(dt.tree, dt.tree.0)
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# when model contains no feature names:
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bst.Tree.x <- bst.Tree
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bst.Tree.x$feature_names <- NULL
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dt.tree.x <- xgb.model.dt.tree(model = bst.Tree.x)
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expect_output(str(dt.tree.x), 'Feature.*\\"3\\"')
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expect_equal(dt.tree[, -4, with=FALSE], dt.tree.x[, -4, with=FALSE])
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# using integer node ID instead of character
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dt.tree.int <- xgb.model.dt.tree(model = bst.Tree, use_int_id = TRUE)
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expect_equal(as.integer(tstrsplit(dt.tree$Yes, '-')[[2]]), dt.tree.int$Yes)
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expect_equal(as.integer(tstrsplit(dt.tree$No, '-')[[2]]), dt.tree.int$No)
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expect_equal(as.integer(tstrsplit(dt.tree$Missing, '-')[[2]]), dt.tree.int$Missing)
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})
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test_that("xgb.model.dt.tree throws error for gblinear", {
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expect_error(xgb.model.dt.tree(model = bst.GLM))
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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 = feature.names, 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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expect_output(str(importance.Tree), 'Feature.*\\"Age\\"')
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importance.Tree.0 <- xgb.importance(model = bst.Tree)
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expect_equal(importance.Tree, importance.Tree.0)
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# when model contains no feature names:
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bst.Tree.x <- bst.Tree
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bst.Tree.x$feature_names <- NULL
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importance.Tree.x <- xgb.importance(model = bst.Tree)
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expect_equal(importance.Tree[, -1, with=FALSE], importance.Tree.x[, -1, with=FALSE])
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imp2plot <- xgb.plot.importance(importance_matrix = importance.Tree)
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expect_equal(colnames(imp2plot), c("Feature", "Gain", "Cover", "Frequency", "Importance"))
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xgb.ggplot.importance(importance_matrix = importance.Tree)
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# for multiclass
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imp.Tree <- xgb.importance(model = mbst.Tree)
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expect_equal(dim(imp.Tree), c(4, 4))
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xgb.importance(model = mbst.Tree, trees = seq(from=0, by=nclass, length.out=nrounds))
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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 = feature.names, 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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imp2plot <- xgb.plot.importance(importance.GLM)
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expect_equal(colnames(imp2plot), c("Feature", "Weight", "Importance"))
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xgb.ggplot.importance(importance.GLM)
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# for multiclass
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imp.GLM <- xgb.importance(model = mbst.GLM)
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expect_equal(dim(imp.GLM), c(12, 3))
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expect_equal(imp.GLM$Class, rep(0:2, each=4))
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})
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test_that("xgb.model.dt.tree and xgb.importance work with a single split model", {
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bst1 <- xgboost(data = sparse_matrix, label = label, max_depth = 1,
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eta = 1, nthread = 2, nrounds = 1, verbose = 0,
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objective = "binary:logistic")
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expect_error(dt <- xgb.model.dt.tree(model = bst1), regexp = NA) # no error
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expect_equal(nrow(dt), 3)
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expect_error(imp <- xgb.importance(model = bst1), regexp = NA) # no error
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expect_equal(nrow(imp), 1)
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expect_equal(imp$Gain, 1)
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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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d2p <- xgb.plot.deepness(model = bst.Tree)
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expect_equal(colnames(d2p), c("ID", "Tree", "Depth", "Cover", "Weight"))
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xgb.plot.deepness(model = bst.Tree, which = "med.depth")
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xgb.ggplot.deepness(model = bst.Tree)
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})
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test_that("check.deprecation works", {
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ttt <- function(a = NNULL, DUMMY=NULL, ...) {
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check.deprecation(...)
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as.list((environment()))
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}
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res <- ttt(a = 1, DUMMY = 2, z = 3)
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expect_equal(res, list(a = 1, DUMMY = 2))
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expect_warning(
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res <- ttt(a = 1, dummy = 22, z = 3)
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, "\'dummy\' is deprecated")
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expect_equal(res, list(a = 1, DUMMY = 22))
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expect_warning(
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res <- ttt(a = 1, dumm = 22, z = 3)
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, "\'dumm\' was partially matched to \'dummy\'")
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expect_equal(res, list(a = 1, DUMMY = 22))
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})
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