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EmilHvitfeldt merged 3 commits into from
Dec 1, 2025
Merged

make work with all versions of xgboost #267

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f59c78d
make xgboost work for all versions
EmilHvitfeldt Dec 1, 2025
953f06d
update tests
EmilHvitfeldt Dec 1, 2025
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add news
EmilHvitfeldt Dec 1, 2025
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3 changes: 3 additions & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,9 @@
# embed (development version)

* Make work with all versions of xgboost. (#267)

# embed 1.2.0

## Improvements

* Adds `step_lencode()` to perform analytical likelihood encoding. (#258)
Expand Down
54 changes: 39 additions & 15 deletions R/discretize_xgb.R
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Expand Up @@ -206,20 +206,38 @@ run_xgboost <- function(
)
}

xgboost::xgb.train(
params = .params,
nrounds = 100,
data = .train,
watchlist = list(
train = .train,
test = .test
),
tree_method = "hist",
early_stopping_rounds = 10,
objective = .objective,
verbose = 0,
nthread = 1
)
if (utils::packageVersion("xgboost") >= "2.0.0.0") {
.params$tree_method = "hist"
.params$nthread = 1
.params$objective = .objective

xgboost::xgb.train(
params = .params,
nrounds = 100,
data = .train,
evals = list(
train = .train,
test = .test
),
early_stopping_rounds = 10,
verbose = 0
)
} else {
xgboost::xgb.train(
params = .params,
nrounds = 100,
data = .train,
watchlist = list(
train = .train,
test = .test
),
tree_method = "hist",
early_stopping_rounds = 10,
objective = .objective,
verbose = 0,
nthread = 1
)
}
}

xgb_binning <- function(
Expand Down Expand Up @@ -373,10 +391,16 @@ xgb_binning <- function(
# inform the user that the dataset is insufficient for this particular case
# https://github.com/dmlc/xgboost/issues/2876
# https://stackoverflow.com/questions/42670033/r-getting-non-tree-model-detected-this-function-can-only-be-used-with-tree-mo
if (utils::packageVersion("xgboost") >= "2.0.0.0") {
best_iteration <- attr(xgb_mdl, "early_stop")$best_iteration
} else {
best_iteration <- xgb_mdl$best_iteration
}

xgb_tree <- try(
xgboost::xgb.model.dt.tree(
model = xgb_mdl,
trees = 1:xgb_mdl$best_iteration,
trees = seq_len(best_iteration),
use_int_id = TRUE
),
silent = TRUE
Expand Down
241 changes: 241 additions & 0 deletions tests/testthat/_snaps/1.7.11.1/discretize_xgb.md
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# run_xgboost for classification

Code
xgboost
Output
##### xgb.Booster
raw: 74.2 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "binary:logistic", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 13
niter: 96
best_iteration : 86
best_ntreelimit : 86
best_score : 0.4421503
best_msg : [86] train-logloss:0.417583 test-logloss:0.442150
nfeatures : 13
evaluation_log:
iter train_logloss test_logloss
<num> <num> <num>
1 0.6279229 0.6303495
2 0.5869984 0.5894989
--- --- ---
95 0.4157892 0.4425857
96 0.4156102 0.4432699

# run_xgboost for multi-classification

Code
xgboost
Output
##### xgb.Booster
raw: 149.7 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", num_class = "6", tree_method = "hist", objective = "multi:softprob", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 30
niter: 33
best_iteration : 23
best_ntreelimit : 23
best_score : 1.246428
best_msg : [23] train-mlogloss:1.178121 test-mlogloss:1.246428
nfeatures : 30
evaluation_log:
iter train_mlogloss test_mlogloss
<num> <num> <num>
1 1.623174 1.631783
2 1.515108 1.531188
--- --- ---
32 1.159813 1.249701
33 1.158088 1.250462

# run_xgboost for regression

Code
xgboost
Output
##### xgb.Booster
raw: 40.2 Kb
call:
xgboost::xgb.train(params = .params, data = .train, nrounds = 100,
watchlist = list(train = .train, test = .test), verbose = 0,
early_stopping_rounds = 10, tree_method = "hist", objective = .objective,
nthread = 1)
params (as set within xgb.train):
eta = "0.3", max_bin = "10", max_depth = "1", min_child_weight = "5", tree_method = "hist", objective = "reg:squarederror", nthread = "1", validate_parameters = "TRUE"
xgb.attributes:
best_iteration, best_msg, best_ntreelimit, best_score, niter
callbacks:
cb.evaluation.log()
cb.early.stop(stopping_rounds = early_stopping_rounds, maximize = maximize,
verbose = verbose)
# of features: 73
niter: 50
best_iteration : 40
best_ntreelimit : 40
best_score : 0.1165337
best_msg : [40] train-rmse:0.064010 test-rmse:0.116534
nfeatures : 73
evaluation_log:
iter train_rmse test_rmse
<num> <num> <num>
1 3.31007782 3.3068878
2 2.31969213 2.3262197
--- --- ---
49 0.06207940 0.1175223
50 0.06191289 0.1188113

# xgb_binning for classification

Code
xgb_binning
Output
[1] 1 2 3 5 6 9 12 15 20

# xgb_binning for multi-classification

Code
xgb_binning
Output
[1] 26 31 35 38

# xgb_binning for regression

Code
xgb_binning
Output
[1] 42.01972 42.02510 42.03122 42.03462 42.03840 42.04638 42.05236 42.05917

# step_discretize_xgb for classification

Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6808, Inf] [0.4208,0.7999) a
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.5749,0.6808) [0.4208,0.7999) b
5 [0.6808, Inf] [0.4208,0.7999) a
6 [0.5749,0.6808) [0.7999, Inf] a
7 [0.5749,0.6808) [-Inf,0.3327) b
8 [0.5749,0.6808) [-Inf,0.3327) a
9 [0.2779,0.3687) [0.4208,0.7999) a
10 [0.3687,0.5749) [0.4208,0.7999) b

---

Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5749,0.6808) [-Inf,0.3327) b
2 [0.5749,0.6808) [0.4208,0.7999) b
3 [0.3687,0.5749) [-Inf,0.3327) b
4 [0.2779,0.3687) [0.4208,0.7999) b
5 [0.6808, Inf] [0.7999, Inf] a
6 [0.3687,0.5749) [-Inf,0.3327) b
7 [0.2779,0.3687) [0.4208,0.7999) a
8 [0.6808, Inf] [0.7999, Inf] a
9 [0.3687,0.5749) [0.7999, Inf] b
10 [0.3687,0.5749) [0.4208,0.7999) b

---

Code
prep(step_discretize_xgb(recipe(class ~ ., data = sim_tr_cls[1:9, ]),
all_predictors(), outcome = "class"))
Condition
Error in `step_discretize_xgb()`:
Caused by error in `prep()`:
! Too few observations in the early stopping validation set.
i Consider increasing the `sample_val` parameter.

---

Code
set.seed(1)
prep(step_discretize_xgb(recipe(Status ~ ., data = credit_data_train), Time,
outcome = "Status"), retain = TRUE)
Condition
Warning:
More than 20 unique training set values are required.
i Predictors 'Time' were not processed; their original values will be used.
Message

-- Recipe ----------------------------------------------------------------------

-- Inputs
Number of variables by role
outcome: 1
predictor: 13

-- Training information
Training data contained 3340 data points and 301 incomplete rows.

-- Operations
* Discretizing variables using xgboost: <none> | Trained

# step_discretize_xgb for multi-classification

Code
xgb_train_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.6879, Inf] [0.3274, Inf] c
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.5863,0.6879) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.5863,0.6879) [0.3274, Inf] c
7 [0.5863,0.6879) [-Inf,0.3274) b
8 [0.5863,0.6879) [-Inf,0.3274) c
9 [-Inf,0.2887) [0.3274, Inf] a
10 [0.3821,0.5863) [0.3274, Inf] b

---

Code
xgb_test_bins[1:10, ]
Output
# A tibble: 10 x 3
x z class
<fct> <fct> <fct>
1 [0.5863,0.6879) [-Inf,0.3274) b
2 [0.5863,0.6879) [0.3274, Inf] b
3 [0.3821,0.5863) [-Inf,0.3274) b
4 [0.2887,0.3821) [0.3274, Inf] b
5 [0.6879, Inf] [0.3274, Inf] c
6 [0.3821,0.5863) [-Inf,0.3274) b
7 [0.2887,0.3821) [0.3274, Inf] a
8 [0.6879, Inf] [0.3274, Inf] c
9 [0.3821,0.5863) [0.3274, Inf] b
10 [0.3821,0.5863) [0.3274, Inf] b

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