Classification LightGBM Learner
Source:R/learner_lightgbm_classif_lightgbm.R
mlr_learners_classif.lightgbm.Rd
Gradient boosting algorithm.
Calls lightgbm::lightgbm()
from lightgbm.
The list of parameters can be found here
and in the documentation of lightgbm::lgb.train()
.
Meta Information
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, lightgbm
Parameters
Id | Type | Default | Levels | Range |
objective | character | - | binary, multiclass, multiclassova | - |
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 | numeric | -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 | \([0, 1]\) | |
pos_bagging_fraction | numeric | 1 | \([0, 1]\) | |
neg_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 | "" | - | |
is_unbalance | logical | FALSE | TRUE, FALSE | - |
scale_pos_weight | numeric | 1 | \([0, \infty)\) | |
sigmoid | numeric | 1 | \([0, \infty)\) | |
boost_from_average | logical | TRUE | TRUE, FALSE | - |
eval_at | untyped | 1:5 | - | |
multi_error_top_k | integer | 1 | \([1, \infty)\) | |
auc_mu_weights | untyped | NULL | - | |
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
Initial 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.
objective
:Depends on the task: if binary classification, then this parameter is set to
"binary"
, otherwise"multiclasss"
and cannot be changed.
Custom mlr3 parameters
num_class
: This parameter is automatically inferred for multiclass tasks and does not have to be set.
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.
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
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.
Super classes
mlr3::Learner
-> mlr3::LearnerClassif
-> LearnerClassifLightGBM
Active bindings
internal_valid_scores
The last observation of the validation scores for all metrics. Extracted from
model$evaluation_log
internal_tuned_values
Returns the early stopped iterations if
early_stopping_rounds
was set during training.validate
How to construct the internal validation data. This parameter can be either
NULL
, a ratio,"test"
, or"predefined"
.
Methods
Inherited methods
mlr3::Learner$base_learner()
mlr3::Learner$configure()
mlr3::Learner$encapsulate()
mlr3::Learner$format()
mlr3::Learner$help()
mlr3::Learner$predict()
mlr3::Learner$predict_newdata()
mlr3::Learner$print()
mlr3::Learner$reset()
mlr3::Learner$selected_features()
mlr3::Learner$train()
mlr3::LearnerClassif$predict_newdata_fast()
Method importance()
The importance scores are extracted from lbg.importance
.
Returns
Named numeric()
.
Examples
# Define the Learner
learner = lrn("classif.lightgbm")
print(learner)
#>
#> ── <LearnerClassifLightGBM> (classif.lightgbm): Gradient Boosting ──────────────
#> • Model: -
#> • Parameters: verbose=-1, num_threads=1
#> • Validate: NULL
#> • Packages: mlr3, mlr3extralearners, and lightgbm
#> • Predict Types: response and [prob]
#> • Feature Types: logical, integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: hotstart_forward, importance, internal_tuning, missings,
#> multiclass, twoclass, validation, and weights
#> • Other settings: use_weights = 'use'
# Define a Task
task = tsk("sonar")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> LightGBM Model (100 trees)
#> Objective: binary
#> Fitted to dataset with 60 columns
print(learner$importance())
#> V11 V37 V45 V4 V51 V15
#> 2.416894e-01 8.538884e-02 8.186565e-02 7.156489e-02 6.090663e-02 5.701630e-02
#> V9 V10 V12 V59 V31 V17
#> 4.704740e-02 3.751652e-02 3.288443e-02 2.676176e-02 2.658705e-02 2.435144e-02
#> V5 V36 V55 V52 V48 V43
#> 2.219662e-02 2.218765e-02 1.519124e-02 1.301400e-02 1.216444e-02 1.161529e-02
#> V46 V23 V33 V49 V27 V58
#> 1.068823e-02 7.645732e-03 6.968656e-03 6.466168e-03 6.421010e-03 6.060496e-03
#> V26 V53 V29 V22 V54 V28
#> 5.984530e-03 4.519241e-03 3.834720e-03 3.763258e-03 3.667614e-03 3.639763e-03
#> V60 V18 V21 V41 V7 V38
#> 3.181974e-03 3.118338e-03 2.892898e-03 2.812983e-03 2.783880e-03 2.757828e-03
#> V3 V19 V32 V6 V20 V30
#> 2.589533e-03 2.497672e-03 1.964779e-03 1.953540e-03 1.950562e-03 1.812939e-03
#> V13 V57 V8 V42 V56 V1
#> 1.734668e-03 1.456457e-03 1.413904e-03 1.068682e-03 1.002933e-03 7.831284e-04
#> V44 V40 V16 V34 V25 V47
#> 7.168064e-04 6.890650e-04 5.448858e-04 5.298341e-04 1.229608e-04 9.105071e-06
#> V39
#> 1.652324e-06
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> classif.ce
#> 0.2028986