lightgbm: Regression

    # nolint start
    library(mlexperiments)
    library(mllrnrs)

See https://github.com/kapsner/mllrnrs/blob/main/R/learner_lightgbm.R for implementation details.

# Preprocessing

## Import and Prepare Data

    library(mlbench)
    data("BostonHousing")
    dataset <- BostonHousing |>
      data.table::as.data.table() |>
      na.omit()

    feature_cols <- colnames(dataset)[1:13]
    target_col <- "medv"

## General Configurations

    seed <- 123
    if (isTRUE(as.logical(Sys.getenv("_R_CHECK_LIMIT_CORES_")))) {
      # on cran
      ncores <- 2L
    } else {
      ncores <- ifelse(
        test = parallel::detectCores() > 4,
        yes = 4L,
        no = ifelse(
          test = parallel::detectCores() < 2L,
          yes = 1L,
          no = parallel::detectCores()
        )
      )
    }
    options("mlexperiments.bayesian.max_init" = 4L)
    options("mlexperiments.optim.lgb.nrounds" = 20L)
    options("mlexperiments.optim.lgb.early_stopping_rounds" = 5L)

## Generate Training- and Test Data

    data_split <- splitTools::partition(
      y = dataset[, get(target_col)],
      p = c(train = 0.7, test = 0.3),
      type = "stratified",
      seed = seed
    )

    train_x <- model.matrix(
      ~ -1 + .,
      dataset[data_split$train, .SD, .SDcols = feature_cols]
    )
    train_y <- dataset[data_split$train, get(target_col)]

    test_x <- model.matrix(
      ~ -1 + .,
      dataset[data_split$test, .SD, .SDcols = feature_cols]
    )
    test_y <- dataset[data_split$test, get(target_col)]

## Generate Training Data Folds

    fold_list <- splitTools::create_folds(
      y = train_y,
      k = 3,
      type = "stratified",
      seed = seed
    )

# Experiments

## Prepare Experiments

    # required learner arguments, not optimized
    learner_args <- list(
      max_depth = -1L,
      verbose = -1L,
      objective = "regression",
      metric = "l2"
    )

    # set arguments for predict function and performance metric,
    # required for mlexperiments::MLCrossValidation and
    # mlexperiments::MLNestedCV
    predict_args <- NULL
    performance_metric <- metric("rmsle")
    performance_metric_args <- NULL
    return_models <- FALSE

    # required for grid search and initialization of bayesian optimization
    parameter_grid <- expand.grid(
      bagging_fraction = seq(0.6, 1, .2),
      feature_fraction = seq(0.6, 1, .2),
      min_data_in_leaf = seq(2, 10, 2),
      learning_rate = seq(0.1, 0.2, 0.1),
      num_leaves = seq(2, 20, 4)
    )
    # reduce to a maximum of 10 rows
    if (nrow(parameter_grid) > 10) {
      set.seed(123)
      sample_rows <- sample(seq_len(nrow(parameter_grid)), 10, FALSE)
      parameter_grid <- kdry::mlh_subset(parameter_grid, sample_rows)
    }

    # required for bayesian optimization
    parameter_bounds <- list(
      bagging_fraction = c(0.2, 1),
      feature_fraction = c(0.2, 1),
      min_data_in_leaf = c(2L, 12L),
      learning_rate = c(0.1, 0.2),
      num_leaves =  c(2L, 20L)
    )
    optim_args <- list(
      n_iter = ncores,
      kappa = 3.5,
      acq = "ucb"
    )

## Hyperparameter Tuning

### Grid Search

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      ncores = ncores,
      seed = seed
    )

    tuner$parameter_grid <- parameter_grid
    tuner$learner_args <- learner_args
    tuner$split_type <- "stratified"

    tuner$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_grid <- tuner$execute(k = 3)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score 22.450000
    #> [LightGBM] [Info] Start training from score 22.655319
    #> [LightGBM] [Info] Start training from score 22.592704

    head(tuner_results_grid)
    #>    setting_id metric_optim_mean nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth
    #> 1:          1          13.59085      85              0.6              0.6                4           0.2         18        -1
    #> 2:          2          13.75483      55              0.8              1.0               10           0.2          6        -1
    #> 3:          3          21.08526      58              0.8              0.8                4           0.1          2        -1
    #> 4:          4          13.31343      92              1.0              0.8                4           0.1         10        -1
    #> 5:          5          13.86649      80              1.0              0.6                6           0.2         18        -1
    #> 6:          6          14.58646     100              1.0              1.0                8           0.1         14        -1
    #>    verbose  objective metric
    #> 1:      -1 regression     l2
    #> 2:      -1 regression     l2
    #> 3:      -1 regression     l2
    #> 4:      -1 regression     l2
    #> 5:      -1 regression     l2
    #> 6:      -1 regression     l2

### Bayesian Optimization

    tuner <- mlexperiments::MLTuneParameters$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "bayesian",
      ncores = ncores,
      seed = seed
    )

    tuner$parameter_grid <- parameter_grid
    tuner$parameter_bounds <- parameter_bounds

    tuner$learner_args <- learner_args
    tuner$optim_args <- optim_args

    tuner$split_type <- "stratified"

    tuner$set_data(
      x = train_x,
      y = train_y
    )

    tuner_results_bayesian <- tuner$execute(k = 3)
    #>
    #> Registering parallel backend using 4 cores.

    head(tuner_results_bayesian)
    #>    Epoch setting_id bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves gpUtility acqOptimum inBounds
    #> 1:     0          1              0.6              0.6                4           0.2         18        NA      FALSE     TRUE
    #> 2:     0          2              0.8              1.0               10           0.2          6        NA      FALSE     TRUE
    #> 3:     0          3              0.8              0.8                4           0.1          2        NA      FALSE     TRUE
    #> 4:     0          4              1.0              0.8                4           0.1         10        NA      FALSE     TRUE
    #> 5:     0          5              1.0              0.6                6           0.2         18        NA      FALSE     TRUE
    #> 6:     0          6              1.0              1.0                8           0.1         14        NA      FALSE     TRUE
    #>    Elapsed     Score metric_optim_mean nrounds errorMessage max_depth verbose  objective metric
    #> 1:   1.081 -13.59085          13.59085      85           NA        -1      -1 regression     l2
    #> 2:   1.072 -13.75483          13.75483      55           NA        -1      -1 regression     l2
    #> 3:   1.044 -21.08526          21.08526      58           NA        -1      -1 regression     l2
    #> 4:   1.126 -13.31343          13.31343      92           NA        -1      -1 regression     l2
    #> 5:   0.104 -13.86649          13.86649      80           NA        -1      -1 regression     l2
    #> 6:   0.106 -14.58646          14.58646     100           NA        -1      -1 regression     l2

## k-Fold Cross Validation

    validator <- mlexperiments::MLCrossValidation$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      fold_list = fold_list,
      ncores = ncores,
      seed = seed
    )

    validator$learner_args <- tuner$results$best.setting[-1]

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- return_models

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #> CV fold: Fold2
    #>
    #> CV fold: Fold3

    head(validator_results)
    #>     fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
    #> 1: Fold1   0.1572748              0.6              0.8                2           0.2         10      34        -1      -1
    #> 2: Fold2   0.1770563              0.6              0.8                2           0.2         10      34        -1      -1
    #> 3: Fold3   0.1439331              0.6              0.8                2           0.2         10      34        -1      -1
    #>     objective metric
    #> 1: regression     l2
    #> 2: regression     l2
    #> 3: regression     l2

## Nested Cross Validation

### Inner Grid Search

    validator <- mlexperiments::MLNestedCV$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "grid",
      fold_list = fold_list,
      k_tuning = 3L,
      ncores = ncores,
      seed = seed
    )

    validator$parameter_grid <- parameter_grid
    validator$learner_args <- learner_args
    validator$split_type <- "stratified"

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- return_models

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score 22.387821
    #> [LightGBM] [Info] Start training from score 22.485257
    #> [LightGBM] [Info] Start training from score 22.476923
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [=====================================>----------------------------------------------------------] 4/10 ( 40%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [===============================================>------------------------------------------------] 5/10 ( 50%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [=========================================================>--------------------------------------] 6/10 ( 60%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score 22.517722
    #> [LightGBM] [Info] Start training from score 22.641401
    #> [LightGBM] [Info] Start training from score 22.809677
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [==================================================================>-----------------------------] 7/10 ( 70%)
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #>
    #> Parameter settings [============================================================================>-------------------] 8/10 ( 80%)
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
    #>
    #> Parameter settings [=====================================================================================>----------] 9/10 ( 90%)
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846
    #>
    #> Parameter settings [===============================================================================================] 10/10 (100%)
    #> [LightGBM] [Info] Start training from score 22.496129
    #> [LightGBM] [Info] Start training from score 22.728387
    #> [LightGBM] [Info] Start training from score 22.553846

    head(validator_results)
    #>     fold performance nrounds bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves max_depth verbose
    #> 1: Fold1   0.1856914      99              0.8              0.8                4           0.1          2        -1      -1
    #> 2: Fold2   0.1842789      37              0.8              0.6                8           0.1         14        -1      -1
    #> 3: Fold3   0.1516625      17              0.6              0.6                4           0.2         18        -1      -1
    #>     objective metric
    #> 1: regression     l2
    #> 2: regression     l2
    #> 3: regression     l2

### Inner Bayesian Optimization

    validator <- mlexperiments::MLNestedCV$new(
      learner = mllrnrs::LearnerLightgbm$new(
        metric_optimization_higher_better = FALSE
      ),
      strategy = "bayesian",
      fold_list = fold_list,
      k_tuning = 3L,
      ncores = ncores,
      seed = seed
    )

    validator$parameter_grid <- parameter_grid
    validator$learner_args <- learner_args
    validator$split_type <- "stratified"

    validator$parameter_bounds <- parameter_bounds
    validator$optim_args <- optim_args

    validator$predict_args <- predict_args
    validator$performance_metric <- performance_metric
    validator$performance_metric_args <- performance_metric_args
    validator$return_models <- TRUE

    validator$set_data(
      x = train_x,
      y = train_y
    )

    validator_results <- validator$execute()
    #>
    #> CV fold: Fold1
    #>
    #> Registering parallel backend using 4 cores.
    #>
    #> CV fold: Fold2
    #> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
    #>
    #> Registering parallel backend using 4 cores.
    #>
    #> CV fold: Fold3
    #> CV progress [========================================================================================================] 3/3 (100%)
    #>
    #> Registering parallel backend using 4 cores.

    head(validator_results)
    #>     fold performance bagging_fraction feature_fraction min_data_in_leaf learning_rate num_leaves nrounds max_depth verbose
    #> 1: Fold1   0.1972673        1.0000000        0.2000000                7     0.1000000          2     100        -1      -1
    #> 2: Fold2   0.1934754        0.5029800        0.4977050                7     0.1195995          4      52        -1      -1
    #> 3: Fold3   0.1391255        0.8050493        0.5902201                2     0.1458152         20      44        -1      -1
    #>     objective metric
    #> 1: regression     l2
    #> 2: regression     l2
    #> 3: regression     l2

## Holdout Test Dataset Performance

### Predict Outcome in Holdout Test Dataset

    preds_lightgbm <- mlexperiments::predictions(
      object = validator,
      newdata = test_x
    )

### Evaluate Performance on Holdout Test Dataset

    perf_lightgbm <- mlexperiments::performance(
      object = validator,
      prediction_results = preds_lightgbm,
      y_ground_truth = test_y,
      type = "regression"
    )
    perf_lightgbm
    #>     model performance      SSE       MSE     RMSE    MEDSE      SAE      MAE    MEDAE       RSQ    EXPVAR      RRSE       RAE      MAPE
    #>    <char>       <num>    <num>     <num>    <num>    <num>    <num>    <num>    <num>     <num>     <num>     <num>     <num>     <num>
    #> 1:  Fold1   0.1593611 2520.012 16.258140 4.032138 4.017770 418.5148 2.700096 2.004438 0.7945966 0.6911933 0.4532145 0.4109793 0.1230206
    #> 2:  Fold2   0.1720557 1955.299 12.614833 3.551737 3.471434 407.4954 2.629003 1.863178 0.8406257 0.7344607 0.3992171 0.4001583 0.1329465
    #> 3:  Fold3   0.1424064 1498.258  9.666178 3.109048 1.945550 345.1170 2.226562 1.394830 0.8778787 0.7626673 0.3494586 0.3389031 0.1101779
    #>    KendallTau SpearmanRho
    #>         <num>       <num>
    #> 1:  0.7422895   0.9026361
    #> 2:  0.7275269   0.8866550
    #> 3:  0.7836888   0.9280580