mlr_learners_regr.lightgbm: Regression LightGBM Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Regression LightGBM Learner

Description

Gradient boosting algorithm. Calls lightgbm::lightgbm() from lightgbm. The list of parameters can be found here and in the documentation of lightgbm::lgb.train().

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.lightgbm")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “logical”, “integer”, “numeric”, “factor”

• Required Packages: mlr3, mlr3extralearners, lightgbm

Parameters

Id	Type	Default	Levels	Range
objective	character	regression	regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie	-
eval	untyped	-		-
verbose	integer	1		(-\infty, \infty)
record	logical	TRUE	TRUE, FALSE	-
eval_freq	integer	1		[1, \infty)
callbacks	untyped	-		-
reset_data	logical	FALSE	TRUE, FALSE	-
boosting	character	gbdt	gbdt, rf, dart, goss	-
linear_tree	logical	FALSE	TRUE, FALSE	-
learning_rate	numeric	0.1		[0, \infty)
num_leaves	integer	31		[1, 131072]
tree_learner	character	serial	serial, feature, data, voting	-
num_threads	integer	0		[0, \infty)
device_type	character	cpu	cpu, gpu	-
seed	integer	-		(-\infty, \infty)
deterministic	logical	FALSE	TRUE, FALSE	-
data_sample_strategy	character	bagging	bagging, goss	-
force_col_wise	logical	FALSE	TRUE, FALSE	-
force_row_wise	logical	FALSE	TRUE, FALSE	-
histogram_pool_size	integer	-1		(-\infty, \infty)
max_depth	integer	-1		(-\infty, \infty)
min_data_in_leaf	integer	20		[0, \infty)
min_sum_hessian_in_leaf	numeric	0.001		[0, \infty)
bagging_fraction	numeric	1		[0, 1]
bagging_freq	integer	0		[0, \infty)
bagging_seed	integer	3		(-\infty, \infty)
bagging_by_query	logical	FALSE	TRUE, FALSE	-
feature_fraction	numeric	1		[0, 1]
feature_fraction_bynode	numeric	1		[0, 1]
feature_fraction_seed	integer	2		(-\infty, \infty)
extra_trees	logical	FALSE	TRUE, FALSE	-
extra_seed	integer	6		(-\infty, \infty)
max_delta_step	numeric	0		(-\infty, \infty)
lambda_l1	numeric	0		[0, \infty)
lambda_l2	numeric	0		[0, \infty)
linear_lambda	numeric	0		[0, \infty)
min_gain_to_split	numeric	0		[0, \infty)
drop_rate	numeric	0.1		[0, 1]
max_drop	integer	50		(-\infty, \infty)
skip_drop	numeric	0.5		[0, 1]
xgboost_dart_mode	logical	FALSE	TRUE, FALSE	-
uniform_drop	logical	FALSE	TRUE, FALSE	-
drop_seed	integer	4		(-\infty, \infty)
top_rate	numeric	0.2		[0, 1]
other_rate	numeric	0.1		[0, 1]
min_data_per_group	integer	100		[1, \infty)
max_cat_threshold	integer	32		[1, \infty)
cat_l2	numeric	10		[0, \infty)
cat_smooth	numeric	10		[0, \infty)
max_cat_to_onehot	integer	4		[1, \infty)
top_k	integer	20		[1, \infty)
monotone_constraints	untyped	NULL		-
monotone_constraints_method	character	basic	basic, intermediate, advanced	-
monotone_penalty	numeric	0		[0, \infty)
feature_contri	untyped	NULL		-
forcedsplits_filename	untyped	""		-
refit_decay_rate	numeric	0.9		[0, 1]
cegb_tradeoff	numeric	1		[0, \infty)
cegb_penalty_split	numeric	0		[0, \infty)
cegb_penalty_feature_lazy	untyped	-		-
cegb_penalty_feature_coupled	untyped	-		-
path_smooth	numeric	0		[0, \infty)
interaction_constraints	untyped	-		-
use_quantized_grad	logical	TRUE	TRUE, FALSE	-
num_grad_quant_bins	integer	4		(-\infty, \infty)
quant_train_renew_leaf	logical	FALSE	TRUE, FALSE	-
stochastic_rounding	logical	TRUE	TRUE, FALSE	-
serializable	logical	TRUE	TRUE, FALSE	-
max_bin	integer	255		[2, \infty)
max_bin_by_feature	untyped	NULL		-
min_data_in_bin	integer	3		[1, \infty)
bin_construct_sample_cnt	integer	200000		[1, \infty)
data_random_seed	integer	1		(-\infty, \infty)
is_enable_sparse	logical	TRUE	TRUE, FALSE	-
enable_bundle	logical	TRUE	TRUE, FALSE	-
use_missing	logical	TRUE	TRUE, FALSE	-
zero_as_missing	logical	FALSE	TRUE, FALSE	-
feature_pre_filter	logical	TRUE	TRUE, FALSE	-
pre_partition	logical	FALSE	TRUE, FALSE	-
two_round	logical	FALSE	TRUE, FALSE	-
forcedbins_filename	untyped	""		-
boost_from_average	logical	TRUE	TRUE, FALSE	-
reg_sqrt	logical	FALSE	TRUE, FALSE	-
alpha	numeric	0.9		[0, \infty)
fair_c	numeric	1		[0, \infty)
poisson_max_delta_step	numeric	0.7		[0, \infty)
tweedie_variance_power	numeric	1.5		[1, 2]
metric_freq	integer	1		[1, \infty)
num_machines	integer	1		[1, \infty)
local_listen_port	integer	12400		[1, \infty)
time_out	integer	120		[1, \infty)
machines	untyped	""		-
gpu_platform_id	integer	-1		(-\infty, \infty)
gpu_device_id	integer	-1		(-\infty, \infty)
gpu_use_dp	logical	FALSE	TRUE, FALSE	-
num_gpu	integer	1		[1, \infty)
start_iteration_predict	integer	0		(-\infty, \infty)
num_iteration_predict	integer	-1		(-\infty, \infty)
pred_early_stop	logical	FALSE	TRUE, FALSE	-
pred_early_stop_freq	integer	10		(-\infty, \infty)
pred_early_stop_margin	numeric	10		(-\infty, \infty)
num_iterations	integer	100		[1, \infty)
early_stopping_rounds	integer	-		[1, \infty)
early_stopping_min_delta	numeric	-		[0, \infty)
first_metric_only	logical	FALSE	TRUE, FALSE	-

Initial parameter values

• num_threads:

  • Actual default: 0L

  • Initital value: 1L

  • Reason for change: Prevents accidental conflicts with future.

• verbose:

  • Actual default: 1L

  • Initial value: -1L

  • Reason for change: Prevents accidental conflicts with mlr messaging system.

Early Stopping and Validation

Early stopping can be used to find the optimal number of boosting rounds. Set early_stopping_rounds to an integer value to monitor the performance of the model on the validation set while training. For information on how to configure the validation set, see the Validation section of mlr3::Learner. The internal validation measure can be set the eval parameter which should be a list of mlr3::Measures, functions, or strings for the internal lightgbm measures. If first_metric_only = FALSE (default), the learner stops when any metric fails to improve.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLightGBM

Active bindings

internal_valid_scores

The last observation of the validation scores for all metrics. Extracted from model$evaluation_log

internal_tuned_values

Returns the early stopped iterations if early_stopping_rounds was set during training.

validate

How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

• LearnerRegrLightGBM$new()

• LearnerRegrLightGBM$importance()

• LearnerRegrLightGBM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrLightGBM$new()

---

Method importance()

The importance scores are extracted from lbg.importance.

Usage

LearnerRegrLightGBM$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrLightGBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

kapsner

References

Ke, Guolin, Meng, Qi, Finley, Thomas, Wang, Taifeng, Chen, Wei, Ma, Weidong, Ye, Qiwei, Liu, Tie-Yan (2017). “Lightgbm: A highly efficient gradient boosting decision tree.” Advances in neural information processing systems, 30.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

# Define the Learner
learner = mlr3::lrn("regr.lightgbm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.