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[R] xgb.importance: fix for multiclass gblinear, new 'trees' parameter (#2388)

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This commit is contained in:
Vadim Khotilovich
2017-06-07 13:13:21 -05:00
committed by GitHub
parent 2ae56ca84f
commit c82276386d
4 changed files with 120 additions and 35 deletions
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43
R-package/tests/testthat/test_helpers.R
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@@ -14,6 +14,7 @@ df[,ID := NULL]
sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
label <- df[, ifelse(Improved == "Marked", 1, 0)]
# binary
nrounds <- 12
bst.Tree <- xgboost(data = sparse_matrix, label = label, max_depth = 9,
eta = 1, nthread = 2, nrounds = nrounds, verbose = 0,
@@ -25,6 +26,18 @@ bst.GLM <- xgboost(data = sparse_matrix, label = label,
feature.names <- colnames(sparse_matrix)
# multiclass
mlabel <- as.numeric(iris$Species) - 1
nclass <- 3
mbst.Tree <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
max_depth = 3, eta = 0.5, nthread = 2, nrounds = nrounds,
objective = "multi:softprob", num_class = nclass, base_score = 0)
mbst.GLM <- xgboost(data = as.matrix(iris[, -5]), label = mlabel, verbose = 0,
booster = "gblinear", eta = 0.1, nthread = 1, nrounds = nrounds,
objective = "multi:softprob", num_class = nclass, base_score = 0)
test_that("xgb.dump works", {
expect_length(xgb.dump(bst.Tree), 200)
expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with_stats = T))
@@ -82,12 +95,8 @@ test_that("predict feature contributions works", {
expect_equal(as.numeric(pred_contr), as.numeric(pred_contr_manual), 2e-6)
# gbtree multiclass
lb <- as.numeric(iris$Species) - 1
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, verbose = 0,
max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
objective = "multi:softprob", num_class = 3)
pred <- predict(bst, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
pred_contr <- predict(bst, as.matrix(iris[, -5]), predcontrib = TRUE)
pred <- predict(mbst.Tree, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
pred_contr <- predict(mbst.Tree, as.matrix(iris[, -5]), predcontrib = TRUE)
expect_is(pred_contr, "list")
expect_length(pred_contr, 3)
for (g in seq_along(pred_contr)) {
@@ -96,19 +105,15 @@ test_that("predict feature contributions works", {
}
# gblinear multiclass (set base_score = 0, which is base margin in multiclass)
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb, verbose = 0,
booster = "gblinear", eta = 0.1, nthread = 1, nrounds = 10,
objective = "multi:softprob", num_class = 3, base_score = 0)
pred <- predict(bst, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
pred_contr <- predict(bst, as.matrix(iris[, -5]), predcontrib = TRUE)
pred <- predict(mbst.GLM, as.matrix(iris[, -5]), outputmargin = TRUE, reshape = TRUE)
pred_contr <- predict(mbst.GLM, as.matrix(iris[, -5]), predcontrib = TRUE)
expect_length(pred_contr, 3)
coefs_all <- xgb.dump(bst)[-c(1,2,6)] %>% as.numeric
coefs_all <- xgb.dump(mbst.GLM)[-c(1,2,6)] %>% as.numeric %>% matrix(ncol = 3, byrow = TRUE)
for (g in seq_along(pred_contr)) {
expect_equal(colnames(pred_contr[[g]]), c(colnames(iris[, -5]), "BIAS"))
expect_lt(max(abs(rowSums(pred_contr[[g]]) - pred[, g])), 2e-6)
# manual calculation of linear terms
coefs <- coefs_all[seq(g, length(coefs_all), by = 3)]
coefs <- c(coefs[-1], coefs[1]) # intercept needs to be the last
coefs <- c(coefs_all[-1, g], coefs_all[1, g]) # intercept needs to be the last
pred_contr_manual <- sweep(as.matrix(cbind(iris[,-5], 1)), 2, coefs, FUN="*")
expect_equal(as.numeric(pred_contr[[g]]), as.numeric(pred_contr_manual), 2e-6)
}
@@ -227,6 +232,11 @@ test_that("xgb.importance works with and without feature names", {
imp2plot <- xgb.plot.importance(importance_matrix = importance.Tree)
expect_equal(colnames(imp2plot), c("Feature", "Gain", "Cover", "Frequency", "Importance"))
xgb.ggplot.importance(importance_matrix = importance.Tree)
# for multiclass
imp.Tree <- xgb.importance(model = mbst.Tree)
expect_equal(dim(imp.Tree), c(4, 4))
xgb.importance(model = mbst.Tree, trees = seq(from=0, by=nclass, length.out=nrounds))
})
test_that("xgb.importance works with GLM model", {
@@ -237,6 +247,11 @@ test_that("xgb.importance works with GLM model", {
imp2plot <- xgb.plot.importance(importance.GLM)
expect_equal(colnames(imp2plot), c("Feature", "Weight", "Importance"))
xgb.ggplot.importance(importance.GLM)
# for multiclass
imp.GLM <- xgb.importance(model = mbst.GLM)
expect_equal(dim(imp.GLM), c(12, 3))
expect_equal(imp.GLM$Class, rep(0:2, each=4))
})
test_that("xgb.model.dt.tree and xgb.importance work with a single split model", {