cursus.steps.hyperparams.hyperparameters_lightgbmmt

Hyperparameters for LightGBMMT (Multi-Task) model training.

Extends ModelHyperparameters directly with complete LightGBM and multi-task parameters.

class LightGBMMtModelHyperparameters(*, full_field_list, cat_field_list, tab_field_list, id_name, label_name, multiclass_categories, categorical_features_to_encode=<factory>, model_class='lightgbmmt', 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, task_label_names, main_task_index, num_leaves=31, learning_rate=0.1, 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, loss_type='adaptive', loss_epsilon=1e-15, loss_epsilon_norm=0.0, loss_similarity_min_distance=0.0, loss_beta=0.2, loss_main_task_weight=1.0, loss_weight_lr=1.0, loss_patience=100, loss_weight_method=None, loss_weight_update_frequency=10, loss_delta_lr=0.01, loss_normalize_gradients=True, **extra_data)

Bases: ModelHyperparameters

Hyperparameters for LightGBMMT (Multi-Task) model training.

Extends ModelHyperparameters directly (not LightGBMModelHyperparameters). Includes complete LightGBM parameters plus multi-task specific parameters. All loss function parameters are prefixed with ‘loss_’ to avoid naming conflicts.

Follows three-tier hyperparameter pattern: - Tier 1: Essential User Inputs (from ModelHyperparameters + LightGBM essentials) - Tier 2: System Inputs with Defaults (LightGBM + MT-specific parameters) - Tier 3: Derived Fields (enable_kd computed from loss_type, num_tasks from task_label_names)

Design Notes: - No separate LossConfig class - all loss parameters integrated here - Loss functions receive this hyperparameters object directly - Training parameters (max_epochs, batch_size) inherited from base - No TrainingConfig class - only TrainingState for runtime tracking

task_label_names: list[str]

main_task_index: int

model_class: str

num_leaves: int

learning_rate: float

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

loss_type: Literal['fixed', 'adaptive', 'adaptive_kd']

loss_epsilon: float

loss_epsilon_norm: float

loss_similarity_min_distance: float

loss_beta: float

loss_main_task_weight: float

loss_weight_lr: float

loss_patience: int

loss_weight_method: Literal['tenIters', 'sqrt', 'delta', 'ema'] | None

loss_weight_update_frequency: int

loss_delta_lr: float

loss_normalize_gradients: bool

property num_tasks: int

Get number of tasks derived from task_label_names.

property enable_kd: bool

Whether knowledge distillation is enabled (derived from loss_type).

property objective: str

Get objective derived from is_binary.

property metric: list

Get evaluation metrics derived from is_binary.

validate_mt_hyperparameters()

Validate multi-task and LightGBM-specific hyperparameters.

get_public_init_fields()

Override to include MT-specific and LightGBM-specific derived fields.

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].

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