data-raw/lgbparams.R
In kapsner/mlr3learners.lightgbm: mlr3: LightGBM Learner
lgbparams = function() {
# parameter set using the paradox package
ps = ParamSet$new(
# https://lightgbm.readthedocs.io/en/latest/Parameters.html#
params = list(
#######################################
# Arguments of lgb.train/lgb.cv
ParamUty$new(
id = "custom_eval",
default = NULL,
tags = c("args", "train")),
ParamLgl$new(
id = "nrounds_by_cv",
default = TRUE,
tags = c("args", "train")),
ParamInt$new(
id = "nfolds",
default = 5L,
lower = 3L,
tags = c("args", "train")),
ParamUty$new(
id = "init_model",
default = NULL,
tags = c("args", "train")),
#######################################
#######################################
# Regression only
ParamFct$new(
id = "objective",
default = "regression",
levels = c(
"regression",
"regression_l1",
"huber",
"fair",
"poisson",
"quantile",
"mape",
"gamma",
"tweedie"),
tags = "train"),
ParamLgl$new(
id = "reg_sqrt",
default = FALSE,
tags = c(
"train",
"regression")),
# % constraints: alpha > 0.0
ParamDbl$new(
id = "alpha",
default = 0.9,
lower = 0.0,
tags = c(
"train",
"huber",
"quantile")),
# % constraints: fair_c > 0.0
ParamDbl$new(
id = "fair_c",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"fair")),
# % constraints: poisson_max_delta_step > 0.0
ParamDbl$new(
id = "poisson_max_delta_step",
default = 0.7,
lower = 0.0,
tags = c(
"train",
"poisson")),
# % constraints: 1.0 <= tweedie_variance_power < 2.0
ParamDbl$new(
id = "tweedie_variance_power",
default = 1.5,
lower = 1.0,
upper = 2.0,
tags = c(
"train",
"tweedie")),
# Metric Parameters
ParamFct$new(
id = "metric",
default = "",
levels = c(
"", "None",
"l1", "mean_absolute_error",
"mae", "regression_l1",
"l2", "mean_squared_error",
"mse", "regression_l2",
"regression", "rmse",
"root_mean_squared_error", "l2_root",
"quantile", "lambdarank",
"mean_absolute_percentage_error",
"mean_average_precision", "mape",
"huber", "fair",
"poisson", "gamma",
"gamma_deviance", "tweedie"),
tags = "train"),
#######################################
#######################################
# Classification only
ParamFct$new(
id = "objective",
default = "binary",
levels = c(
"binary",
"multiclass",
"multiclassova",
"cross_entropy",
"cross_entropy_lambda",
"rank_xendcg",
"lambdarank"),
tags = "train"),
# Objective Parameters
# % constraints: num_class > 0
ParamInt$new(
id = "num_class",
default = 1L,
lower = 1L,
tags = c(
"train",
"multi-class")),
ParamLgl$new(
id = "is_unbalance",
default = FALSE,
tags = c(
"train",
"binary",
"multiclassova")),
# % constraints: scale_pos_weight > 0.0
ParamDbl$new(
id = "scale_pos_weight",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"binary",
"multiclassova")),
# % constraints: sigmoid > 0.0
ParamDbl$new(
id = "sigmoid",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"binary",
"multiclassova",
"lambdarank")),
ParamInt$new(
id = "lambdarank_truncation_level",
default = 20L,
lower = 1L,
tags = c(
"train",
"lambdarank")),
ParamLgl$new(
id = "lambdarank_norm",
default = TRUE,
tags = c(
"train",
"lambdarank")),
# Metric Parameters
ParamFct$new(
id = "metric",
default = "",
levels = c(
"", "None",
"ndcg", "lambdarank",
"rank_xendcg", "xendcg",
"xe_ndcg", "xe_ndcg_mart",
"xendcg_mart", "map",
"mean_average_precision",
"cross_entropy",
"cross_entropy_lambda",
"kullback_leibler",
"xentropy", "xentlambda",
"kldiv", "multiclass",
"softmax", "multiclassova",
"multiclass_ova", "ova",
"ovr", "binary",
"binary_logloss",
"binary_error", "auc_mu",
"multi_logloss", "auc",
"multi_error"),
tags = "train"),
# % constraints: multi_error_top_k > 0
ParamInt$new(
id = "multi_error_top_k",
default = 1L,
lower = 1L,
tags = "train"),
#######################################
#######################################
# Core Parameters
ParamFct$new(
id = "boosting",
default = "gbdt",
levels = c(
"gbdt",
"rf",
"dart",
"goss"),
tags = "train"),
# % constraints: num_iterations >= 0
# Note: internally, LightGBM constructs
# num_class * num_iterations
# trees for multi-class classification problems
ParamInt$new(
id = "num_iterations",
default = 100L,
lower = 0L,
tags = "train"),
# % constraints: learning_rate > 0.0
ParamDbl$new(
id = "learning_rate",
default = 0.1,
lower = 0.0,
tags = "train"),
# % constraints: 1 < num_leaves <= 131072
ParamInt$new(
id = "num_leaves",
default = 31L,
lower = 1L,
upper = 131072L,
tags = "train"),
ParamFct$new(
id = "tree_learner",
default = "serial",
levels = c(
"serial",
"feature",
"data",
"voting"),
tags = "train"),
ParamInt$new(
id = "num_threads",
default = 0L,
lower = 0L,
tags = "train"),
ParamFct$new(
id = "device_type",
default = "cpu",
levels = c("cpu", "gpu"),
tags = "train"),
ParamUty$new(
id = "seed",
default = "None",
tags = "train"),
#######################################
# Learning Control Parameters
ParamLgl$new(
id = "force_col_wise",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "force_row_wise",
default = FALSE,
tags = "train"),
ParamDbl$new(
id = "histogram_pool_size",
default = -1.0,
tags = "train"),
# % <= 0 means no limit
ParamInt$new(
id = "max_depth",
default = -1L,
tags = "train"),
# % constraints: min_data_in_leaf >= 0
ParamInt$new(
id = "min_data_in_leaf",
default = 20L,
lower = 0L,
tags = "train"),
# % constraints: min_sum_hessian_in_leaf >= 0.0
# Note: to enable bagging, bagging_freq
# should be set to a non
# zero value as well
ParamDbl$new(
id = "min_sum_hessian_in_leaf",
default = 1e-3,
lower = 0,
tags = "train"),
# % constraints: 0.0 < bagging_fraction <= 1.0
ParamDbl$new(
id = "bagging_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < pos_bagging_fraction <= 1.0
# Note: to enable this, you need to set bagging_freq and
# neg_bagging_fraction as well
# Note: if both pos_bagging_fraction and
# neg_bagging_fraction
# are set to 1.0, balanced bagging is disabled
# Note: if balanced bagging is enabled,
# bagging_fraction will be ignored
ParamDbl$new(
id = "pos_bagging_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < neg_bagging_fraction <= 1.0
ParamDbl$new(
id = "neg_bagging_fraction",
default = 1.0,
lower = 0,
upper = 1.0,
tags = "train"),
# Note: to enable bagging, bagging_fraction
# should be set to value
# smaller than 1.0 as well
ParamInt$new(
id = "bagging_freq",
default = 0L,
lower = 0L,
tags = "train"),
ParamInt$new(
id = "bagging_seed",
default = 3L,
tags = "train"),
# % constraints: 0.0 < feature_fraction <= 1.0
ParamDbl$new(
id = "feature_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < feature_fraction_bynode <= 1.0
# Note: unlike feature_fraction, this cannot
# speed up training
# Note: if both feature_fraction and
# feature_fraction_bynode are
# smaller than 1.0, the final fraction of
# each node is
# % feature_fraction * feature_fraction_bynode
ParamDbl$new(
id = "feature_fraction_bynode",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
ParamInt$new(
id = "feature_fraction_seed",
default = 2L,
tags = "train"),
ParamLgl$new(
id = "extra_trees",
default = FALSE,
tags = "train"),
ParamInt$new(
id = "extra_seed",
default = 6L,
tags = "train"),
# <= 0 means disable
ParamInt$new(
id = "early_stopping_round",
default = 0L,
tags = "train"),
ParamLgl$new(
id = "first_metric_only",
default = FALSE,
tags = "train"),
# <= 0 means no constraint
ParamDbl$new(
id = "max_delta_step",
default = 0.0,
tags = "train"),
# % constraints: lambda_l1 >= 0.0
ParamDbl$new(
id = "lambda_l1",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: lambda_l2 >= 0.0
ParamDbl$new(
id = "lambda_l2",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: min_gain_to_split >= 0.0
ParamDbl$new(
id = "min_gain_to_split",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: 0.0 <= drop_rate <= 1.0
ParamDbl$new(
id = "drop_rate",
default = 0.1,
lower = 0.0,
upper = 1.0,
tags = c("train", "dart")),
# <=0 means no limit
ParamInt$new(
id = "max_drop",
default = 50L,
tags = c("train", "dart")),
# % constraints: 0.0 <= skip_drop <= 1.0
ParamDbl$new(
id = "skip_drop",
default = 0.5,
lower = 0.0,
upper = 1.0,
tags = c("train", "dart")),
ParamLgl$new(
id = "xgboost_dart_mode",
default = FALSE,
tags = c("train", "dart")),
ParamLgl$new(
id = "uniform_drop",
default = FALSE,
tags = c("train", "dart")),
ParamInt$new(
id = "drop_seed",
default = 4L,
tags = c("train", "dart")),
# % constraints: 0.0 <= top_rate <= 1.0
ParamDbl$new(
id = "top_rate",
default = 0.2,
lower = 0.0,
upper = 1.0,
tags = c("train", "goss")),
# % constraints: 0.0 <= other_rate <= 1.0
ParamDbl$new(
id = "other_rate",
default = 0.1,
lower = 0.0,
upper = 1.0,
tags = c("train", "goss")),
# % constraints: min_data_per_group > 0
ParamInt$new(
id = "min_data_per_group",
default = 100L,
lower = 1L,
tags = "train"),
# % constraints: max_cat_threshold > 0
ParamInt$new(
id = "max_cat_threshold",
default = 32L,
lower = 1L,
tags = "train"),
# % constraints: cat_l2 >= 0.0
ParamDbl$new(
id = "cat_l2",
default = 10.0,
lower = 0.0,
tags = "train"),
# % constraints: cat_smooth >= 0.0
ParamDbl$new(
id = "cat_smooth",
default = 10.0,
lower = 0.0,
tags = "train"),
# % constraints: max_cat_to_onehot > 0
ParamInt$new(
id = "max_cat_to_onehot",
default = 4L,
lower = 1L,
tags = "train"),
# % constraints: top_k > 0
ParamInt$new(
id = "top_k",
default = 20L,
lower = 1L,
tags = "train"),
# % constraints: cegb_tradeoff >= 0.0
ParamDbl$new(
id = "cegb_tradeoff",
default = 1.0,
lower = 0.0,
tags = "train"),
# % constraints: cegb_penalty_split >= 0.0
ParamDbl$new(
id = "cegb_penalty_split",
default = 0.0,
lower = 0.0,
tags = "train"),
#######################################
# IO Parameters
ParamInt$new(
id = "verbose",
default = 1L,
tags = "train"),
ParamUty$new(
id = "input_model",
default = "",
tags = "train"),
ParamUty$new(
id = "output_model",
default = "LightGBM_model.txt",
tags = "train"),
ParamInt$new(
id = "snapshot_freq",
default = -1L,
tags = "train"),
# % constraints: max_bin > 1
ParamInt$new(
id = "max_bin",
default = 255L,
lower = 2L,
tags = "train"),
# % constraints: min_data_in_bin > 0
ParamInt$new(
id = "min_data_in_bin",
default = 3L,
lower = 1L,
tags = "train"),
# % constraints: bin_construct_sample_cnt > 0
ParamInt$new(
id = "bin_construct_sample_cnt",
default = 200000L,
lower = 1L,
tags = "train"),
ParamInt$new(
id = "data_random_seed",
default = 1L,
tags = "train"),
ParamLgl$new(
id = "is_enable_sparse",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "enable_bundle",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "use_missing",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "zero_as_missing",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "feature_pre_filter",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "pre_partition",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "two_round",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "header",
default = FALSE,
tags = "train"),
ParamUty$new(
id = "group_column",
default = "",
tags = "train"),
ParamUty$new(
id = "ignore_column",
default = "",
tags = "train"),
ParamUty$new(
id = "categorical_feature",
default = "",
tags = "train"),
#######################################
#######################################
# Predict Parameters TODO are they needed?
# Convert Parameters TODO are they needed?
#######################################
#######################################
# Objective Parameters
ParamInt$new(
id = "objective_seed",
default = 5L,
tags = c("train", "rank_xendcg")),
# moved num_class up to classification part
# moved is_unbalance up to classification part
# moved scale_pos_weight up to classification part
# moved sigmoid up to classification part
ParamLgl$new(
id = "boost_from_average",
default = TRUE,
tags = c(
"train", "regression", "binary",
"multiclassova", "cross-entropy")),
# moved req_sqrt up to regression part
# moved alpha up to regression part
# moved fair_c up to regression part
# moved poisson_max_delta_step up to regression part
# moved tweedie_variance_power up to regression part
# moved lambdarank_truncation_level up to classification part
# moved lambdarank_norm up to classification part
# moved label_gain up to classification part
#######################################
# Metric Parameters
# % constraints: metric_freq > 0
ParamInt$new(
id = "metric_freq",
default = 1L,
lower = 1L,
tags = "train"),
ParamLgl$new(
id = "is_provide_training_metric",
default = FALSE,
tags = "train")
)
)
return(ps)
}
kapsner/mlr3learners.lightgbm documentation built on Feb. 17, 2021, 5:53 p.m.
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lgbparams = function() {
# parameter set using the paradox package
ps = ParamSet$new(
# https://lightgbm.readthedocs.io/en/latest/Parameters.html#
params = list(
#######################################
# Arguments of lgb.train/lgb.cv
ParamUty$new(
id = "custom_eval",
default = NULL,
tags = c("args", "train")),
ParamLgl$new(
id = "nrounds_by_cv",
default = TRUE,
tags = c("args", "train")),
ParamInt$new(
id = "nfolds",
default = 5L,
lower = 3L,
tags = c("args", "train")),
ParamUty$new(
id = "init_model",
default = NULL,
tags = c("args", "train")),
#######################################
#######################################
# Regression only
ParamFct$new(
id = "objective",
default = "regression",
levels = c(
"regression",
"regression_l1",
"huber",
"fair",
"poisson",
"quantile",
"mape",
"gamma",
"tweedie"),
tags = "train"),
ParamLgl$new(
id = "reg_sqrt",
default = FALSE,
tags = c(
"train",
"regression")),
# % constraints: alpha > 0.0
ParamDbl$new(
id = "alpha",
default = 0.9,
lower = 0.0,
tags = c(
"train",
"huber",
"quantile")),
# % constraints: fair_c > 0.0
ParamDbl$new(
id = "fair_c",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"fair")),
# % constraints: poisson_max_delta_step > 0.0
ParamDbl$new(
id = "poisson_max_delta_step",
default = 0.7,
lower = 0.0,
tags = c(
"train",
"poisson")),
# % constraints: 1.0 <= tweedie_variance_power < 2.0
ParamDbl$new(
id = "tweedie_variance_power",
default = 1.5,
lower = 1.0,
upper = 2.0,
tags = c(
"train",
"tweedie")),
# Metric Parameters
ParamFct$new(
id = "metric",
default = "",
levels = c(
"", "None",
"l1", "mean_absolute_error",
"mae", "regression_l1",
"l2", "mean_squared_error",
"mse", "regression_l2",
"regression", "rmse",
"root_mean_squared_error", "l2_root",
"quantile", "lambdarank",
"mean_absolute_percentage_error",
"mean_average_precision", "mape",
"huber", "fair",
"poisson", "gamma",
"gamma_deviance", "tweedie"),
tags = "train"),
#######################################
#######################################
# Classification only
ParamFct$new(
id = "objective",
default = "binary",
levels = c(
"binary",
"multiclass",
"multiclassova",
"cross_entropy",
"cross_entropy_lambda",
"rank_xendcg",
"lambdarank"),
tags = "train"),
# Objective Parameters
# % constraints: num_class > 0
ParamInt$new(
id = "num_class",
default = 1L,
lower = 1L,
tags = c(
"train",
"multi-class")),
ParamLgl$new(
id = "is_unbalance",
default = FALSE,
tags = c(
"train",
"binary",
"multiclassova")),
# % constraints: scale_pos_weight > 0.0
ParamDbl$new(
id = "scale_pos_weight",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"binary",
"multiclassova")),
# % constraints: sigmoid > 0.0
ParamDbl$new(
id = "sigmoid",
default = 1.0,
lower = 0.0,
tags = c(
"train",
"binary",
"multiclassova",
"lambdarank")),
ParamInt$new(
id = "lambdarank_truncation_level",
default = 20L,
lower = 1L,
tags = c(
"train",
"lambdarank")),
ParamLgl$new(
id = "lambdarank_norm",
default = TRUE,
tags = c(
"train",
"lambdarank")),
# Metric Parameters
ParamFct$new(
id = "metric",
default = "",
levels = c(
"", "None",
"ndcg", "lambdarank",
"rank_xendcg", "xendcg",
"xe_ndcg", "xe_ndcg_mart",
"xendcg_mart", "map",
"mean_average_precision",
"cross_entropy",
"cross_entropy_lambda",
"kullback_leibler",
"xentropy", "xentlambda",
"kldiv", "multiclass",
"softmax", "multiclassova",
"multiclass_ova", "ova",
"ovr", "binary",
"binary_logloss",
"binary_error", "auc_mu",
"multi_logloss", "auc",
"multi_error"),
tags = "train"),
# % constraints: multi_error_top_k > 0
ParamInt$new(
id = "multi_error_top_k",
default = 1L,
lower = 1L,
tags = "train"),
#######################################
#######################################
# Core Parameters
ParamFct$new(
id = "boosting",
default = "gbdt",
levels = c(
"gbdt",
"rf",
"dart",
"goss"),
tags = "train"),
# % constraints: num_iterations >= 0
# Note: internally, LightGBM constructs
# num_class * num_iterations
# trees for multi-class classification problems
ParamInt$new(
id = "num_iterations",
default = 100L,
lower = 0L,
tags = "train"),
# % constraints: learning_rate > 0.0
ParamDbl$new(
id = "learning_rate",
default = 0.1,
lower = 0.0,
tags = "train"),
# % constraints: 1 < num_leaves <= 131072
ParamInt$new(
id = "num_leaves",
default = 31L,
lower = 1L,
upper = 131072L,
tags = "train"),
ParamFct$new(
id = "tree_learner",
default = "serial",
levels = c(
"serial",
"feature",
"data",
"voting"),
tags = "train"),
ParamInt$new(
id = "num_threads",
default = 0L,
lower = 0L,
tags = "train"),
ParamFct$new(
id = "device_type",
default = "cpu",
levels = c("cpu", "gpu"),
tags = "train"),
ParamUty$new(
id = "seed",
default = "None",
tags = "train"),
#######################################
# Learning Control Parameters
ParamLgl$new(
id = "force_col_wise",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "force_row_wise",
default = FALSE,
tags = "train"),
ParamDbl$new(
id = "histogram_pool_size",
default = -1.0,
tags = "train"),
# % <= 0 means no limit
ParamInt$new(
id = "max_depth",
default = -1L,
tags = "train"),
# % constraints: min_data_in_leaf >= 0
ParamInt$new(
id = "min_data_in_leaf",
default = 20L,
lower = 0L,
tags = "train"),
# % constraints: min_sum_hessian_in_leaf >= 0.0
# Note: to enable bagging, bagging_freq
# should be set to a non
# zero value as well
ParamDbl$new(
id = "min_sum_hessian_in_leaf",
default = 1e-3,
lower = 0,
tags = "train"),
# % constraints: 0.0 < bagging_fraction <= 1.0
ParamDbl$new(
id = "bagging_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < pos_bagging_fraction <= 1.0
# Note: to enable this, you need to set bagging_freq and
# neg_bagging_fraction as well
# Note: if both pos_bagging_fraction and
# neg_bagging_fraction
# are set to 1.0, balanced bagging is disabled
# Note: if balanced bagging is enabled,
# bagging_fraction will be ignored
ParamDbl$new(
id = "pos_bagging_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < neg_bagging_fraction <= 1.0
ParamDbl$new(
id = "neg_bagging_fraction",
default = 1.0,
lower = 0,
upper = 1.0,
tags = "train"),
# Note: to enable bagging, bagging_fraction
# should be set to value
# smaller than 1.0 as well
ParamInt$new(
id = "bagging_freq",
default = 0L,
lower = 0L,
tags = "train"),
ParamInt$new(
id = "bagging_seed",
default = 3L,
tags = "train"),
# % constraints: 0.0 < feature_fraction <= 1.0
ParamDbl$new(
id = "feature_fraction",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
# % constraints: 0.0 < feature_fraction_bynode <= 1.0
# Note: unlike feature_fraction, this cannot
# speed up training
# Note: if both feature_fraction and
# feature_fraction_bynode are
# smaller than 1.0, the final fraction of
# each node is
# % feature_fraction * feature_fraction_bynode
ParamDbl$new(
id = "feature_fraction_bynode",
default = 1.0,
lower = 0.0,
upper = 1.0,
tags = "train"),
ParamInt$new(
id = "feature_fraction_seed",
default = 2L,
tags = "train"),
ParamLgl$new(
id = "extra_trees",
default = FALSE,
tags = "train"),
ParamInt$new(
id = "extra_seed",
default = 6L,
tags = "train"),
# <= 0 means disable
ParamInt$new(
id = "early_stopping_round",
default = 0L,
tags = "train"),
ParamLgl$new(
id = "first_metric_only",
default = FALSE,
tags = "train"),
# <= 0 means no constraint
ParamDbl$new(
id = "max_delta_step",
default = 0.0,
tags = "train"),
# % constraints: lambda_l1 >= 0.0
ParamDbl$new(
id = "lambda_l1",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: lambda_l2 >= 0.0
ParamDbl$new(
id = "lambda_l2",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: min_gain_to_split >= 0.0
ParamDbl$new(
id = "min_gain_to_split",
default = 0.0,
lower = 0.0,
tags = "train"),
# % constraints: 0.0 <= drop_rate <= 1.0
ParamDbl$new(
id = "drop_rate",
default = 0.1,
lower = 0.0,
upper = 1.0,
tags = c("train", "dart")),
# <=0 means no limit
ParamInt$new(
id = "max_drop",
default = 50L,
tags = c("train", "dart")),
# % constraints: 0.0 <= skip_drop <= 1.0
ParamDbl$new(
id = "skip_drop",
default = 0.5,
lower = 0.0,
upper = 1.0,
tags = c("train", "dart")),
ParamLgl$new(
id = "xgboost_dart_mode",
default = FALSE,
tags = c("train", "dart")),
ParamLgl$new(
id = "uniform_drop",
default = FALSE,
tags = c("train", "dart")),
ParamInt$new(
id = "drop_seed",
default = 4L,
tags = c("train", "dart")),
# % constraints: 0.0 <= top_rate <= 1.0
ParamDbl$new(
id = "top_rate",
default = 0.2,
lower = 0.0,
upper = 1.0,
tags = c("train", "goss")),
# % constraints: 0.0 <= other_rate <= 1.0
ParamDbl$new(
id = "other_rate",
default = 0.1,
lower = 0.0,
upper = 1.0,
tags = c("train", "goss")),
# % constraints: min_data_per_group > 0
ParamInt$new(
id = "min_data_per_group",
default = 100L,
lower = 1L,
tags = "train"),
# % constraints: max_cat_threshold > 0
ParamInt$new(
id = "max_cat_threshold",
default = 32L,
lower = 1L,
tags = "train"),
# % constraints: cat_l2 >= 0.0
ParamDbl$new(
id = "cat_l2",
default = 10.0,
lower = 0.0,
tags = "train"),
# % constraints: cat_smooth >= 0.0
ParamDbl$new(
id = "cat_smooth",
default = 10.0,
lower = 0.0,
tags = "train"),
# % constraints: max_cat_to_onehot > 0
ParamInt$new(
id = "max_cat_to_onehot",
default = 4L,
lower = 1L,
tags = "train"),
# % constraints: top_k > 0
ParamInt$new(
id = "top_k",
default = 20L,
lower = 1L,
tags = "train"),
# % constraints: cegb_tradeoff >= 0.0
ParamDbl$new(
id = "cegb_tradeoff",
default = 1.0,
lower = 0.0,
tags = "train"),
# % constraints: cegb_penalty_split >= 0.0
ParamDbl$new(
id = "cegb_penalty_split",
default = 0.0,
lower = 0.0,
tags = "train"),
#######################################
# IO Parameters
ParamInt$new(
id = "verbose",
default = 1L,
tags = "train"),
ParamUty$new(
id = "input_model",
default = "",
tags = "train"),
ParamUty$new(
id = "output_model",
default = "LightGBM_model.txt",
tags = "train"),
ParamInt$new(
id = "snapshot_freq",
default = -1L,
tags = "train"),
# % constraints: max_bin > 1
ParamInt$new(
id = "max_bin",
default = 255L,
lower = 2L,
tags = "train"),
# % constraints: min_data_in_bin > 0
ParamInt$new(
id = "min_data_in_bin",
default = 3L,
lower = 1L,
tags = "train"),
# % constraints: bin_construct_sample_cnt > 0
ParamInt$new(
id = "bin_construct_sample_cnt",
default = 200000L,
lower = 1L,
tags = "train"),
ParamInt$new(
id = "data_random_seed",
default = 1L,
tags = "train"),
ParamLgl$new(
id = "is_enable_sparse",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "enable_bundle",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "use_missing",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "zero_as_missing",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "feature_pre_filter",
default = TRUE,
tags = "train"),
ParamLgl$new(
id = "pre_partition",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "two_round",
default = FALSE,
tags = "train"),
ParamLgl$new(
id = "header",
default = FALSE,
tags = "train"),
ParamUty$new(
id = "group_column",
default = "",
tags = "train"),
ParamUty$new(
id = "ignore_column",
default = "",
tags = "train"),
ParamUty$new(
id = "categorical_feature",
default = "",
tags = "train"),
#######################################
#######################################
# Predict Parameters TODO are they needed?
# Convert Parameters TODO are they needed?
#######################################
#######################################
# Objective Parameters
ParamInt$new(
id = "objective_seed",
default = 5L,
tags = c("train", "rank_xendcg")),
# moved num_class up to classification part
# moved is_unbalance up to classification part
# moved scale_pos_weight up to classification part
# moved sigmoid up to classification part
ParamLgl$new(
id = "boost_from_average",
default = TRUE,
tags = c(
"train", "regression", "binary",
"multiclassova", "cross-entropy")),
# moved req_sqrt up to regression part
# moved alpha up to regression part
# moved fair_c up to regression part
# moved poisson_max_delta_step up to regression part
# moved tweedie_variance_power up to regression part
# moved lambdarank_truncation_level up to classification part
# moved lambdarank_norm up to classification part
# moved label_gain up to classification part
#######################################
# Metric Parameters
# % constraints: metric_freq > 0
ParamInt$new(
id = "metric_freq",
default = 1L,
lower = 1L,
tags = "train"),
ParamLgl$new(
id = "is_provide_training_metric",
default = FALSE,
tags = "train")
)
)
return(ps)
}
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