scxpand.lightgbm.lightgbm_params

Classes

BoostingType(*values)	Enumeration of supported LightGBM boosting types.
LightGBMParams([use_log_transform, ...])	Parameters for LightGBM model.
MetricType(*values)	Enumeration of supported LightGBM metric types.
ObjectiveType(*values)	Enumeration of supported LightGBM objective types.

class scxpand.lightgbm.lightgbm_params.BoostingType(*values)

Enumeration of supported LightGBM boosting types.

DART = 'dart'

GBDT = 'gbdt'

GOSS = 'goss'

RF = 'rf'

class scxpand.lightgbm.lightgbm_params.LightGBMParams(use_log_transform=True, use_zscore_norm=True, num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, min_child_samples=20, reg_alpha=0.0, reg_lambda=0.0, random_seed=42, force_col_wise=True, deterministic=True, class_weight='balanced', n_jobs=-1, feature_fraction=1.0, bagging_fraction=1.0, min_split_gain=0.0, min_child_weight=0.001, boosting_type=BoostingType.GBDT, objective=ObjectiveType.BINARY, metric=MetricType.BINARY_LOGLOSS, verbose=-1)

Parameters for LightGBM model.

Note: When max_depth > 0, num_leaves should be <= 2^max_depth to avoid overfitting.

classmethod get_model_type()

Return the model type identifier for this parameter class.

Return type:

ModelType

__init__(use_log_transform=True, use_zscore_norm=True, num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, min_child_samples=20, reg_alpha=0.0, reg_lambda=0.0, random_seed=42, force_col_wise=True, deterministic=True, class_weight='balanced', n_jobs=-1, feature_fraction=1.0, bagging_fraction=1.0, min_split_gain=0.0, min_child_weight=0.001, boosting_type=BoostingType.GBDT, objective=ObjectiveType.BINARY, metric=MetricType.BINARY_LOGLOSS, verbose=-1)

bagging_fraction: float = 1.0

boosting_type: BoostingType = 'gbdt'

class_weight: str | None = 'balanced'

deterministic: bool = True

feature_fraction: float = 1.0

force_col_wise: bool = True

learning_rate: float = 0.1

max_depth: int = -1

metric: MetricType = 'binary_logloss'

min_child_samples: int = 20

min_child_weight: float = 0.001

min_split_gain: float = 0.0

n_estimators: int = 100

n_jobs: int = -1

num_leaves: int = 31

objective: ObjectiveType = 'binary'

random_seed: int = 42

reg_alpha: float = 0.0

reg_lambda: float = 0.0

use_log_transform: bool = True

use_zscore_norm: bool = True

verbose: int = -1

class scxpand.lightgbm.lightgbm_params.MetricType(*values)

Enumeration of supported LightGBM metric types.

AUC = 'auc'

BINARY_LOGLOSS = 'binary_logloss'

MAE = 'mae'

MULTICLASS_LOGLOSS = 'multi_logloss'

NDCG = 'ndcg'

RMSE = 'rmse'

class scxpand.lightgbm.lightgbm_params.ObjectiveType(*values)

Enumeration of supported LightGBM objective types.

BINARY = 'binary'

LAMBDARANK = 'lambdarank'

MULTICLASS = 'multiclass'

REGRESSION = 'regression'