cursus.steps.hyperparams.hyperparameters_lightgbm

class LightGBMModelHyperparameters(*, full_field_list, cat_field_list, tab_field_list, id_name, label_name, multiclass_categories, categorical_features_to_encode=<factory>, model_class='lightgbm', device=-1, header=0, lr=3e-05, batch_size=2, eval_batch_size_multiplier=2.0, max_epochs=3, metric_choices=['f1_score', 'auroc'], optimizer='SGD', class_weights=None, num_leaves, learning_rate, boosting_type='gbdt', num_iterations=100, max_depth=-1, min_data_in_leaf=20, min_sum_hessian_in_leaf=0.001, feature_fraction=1.0, bagging_fraction=1.0, bagging_freq=0, lambda_l1=0.0, lambda_l2=0.0, min_gain_to_split=0.0, categorical_feature=None, early_stopping_rounds=None, seed=None, min_data_per_group=100, cat_smooth=10.0, max_cat_threshold=32, use_native_categorical=True, **extra_data)[source]

Bases: ModelHyperparameters

Hyperparameters for the LightGBM model training, extending the base ModelHyperparameters.

Fields are organized into three tiers: 1. Tier 1: Essential User Inputs - fields that users must explicitly provide 2. Tier 2: System Inputs with Defaults - fields with reasonable defaults that can be overridden 3. Tier 3: Derived Fields - fields calculated from other fields (private attributes with properties)

num_leaves: int
learning_rate: float
model_class: str
boosting_type: str
num_iterations: int
max_depth: int
min_data_in_leaf: int
min_sum_hessian_in_leaf: float
feature_fraction: float
bagging_fraction: float
bagging_freq: int
lambda_l1: float
lambda_l2: float
min_gain_to_split: float
categorical_feature: str | None
early_stopping_rounds: int | None
seed: int | None
min_data_per_group: int
cat_smooth: float
max_cat_threshold: int
use_native_categorical: bool
model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'allow', 'protected_namespaces': (), 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property objective: str

Get objective derived from is_binary.

property metric: List[str]

Get evaluation metrics derived from is_binary.

validate_lightgbm_hyperparameters()[source]

Validate LightGBM-specific hyperparameters

get_public_init_fields()[source]

Override get_public_init_fields to include LightGBM-specific fields. Gets a dictionary of public fields suitable for initializing a child config.

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialize private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) – The BaseModel instance.

  • context (Any) – The context.

full_field_list: List[str]
cat_field_list: List[str]
tab_field_list: List[str]
id_name: str
label_name: str
multiclass_categories: List[int | str]
categorical_features_to_encode: List[str]
device: int
header: int
lr: float
batch_size: int
eval_batch_size_multiplier: float
max_epochs: int
metric_choices: List[str]
optimizer: str
class_weights: List[float] | None