cursus.steps.hyperparams.hyperparameters_xgboost

class XGBoostModelHyperparameters(*, full_field_list, cat_field_list, tab_field_list, id_name, label_name, multiclass_categories, categorical_features_to_encode=<factory>, model_class='xgboost', 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_round, max_depth, min_child_weight=1.0, booster='gbtree', eta=0.3, gamma=0.0, max_delta_step=0.0, subsample=1.0, colsample_bytree=1.0, colsample_bylevel=1.0, colsample_bynode=1.0, lambda_xgb=1.0, alpha_xgb=0.0, tree_method='auto', sketch_eps=None, scale_pos_weight=1.0, num_parallel_tree=None, base_score=None, seed=None, early_stopping_rounds=None, **extra_data)

Bases: ModelHyperparameters

Hyperparameters for the XGBoost 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_round: int

max_depth: int

model_class: str

min_child_weight: float

booster: str

eta: float

gamma: float

max_delta_step: float

subsample: float

colsample_bytree: float

colsample_bylevel: float

colsample_bynode: float

lambda_xgb: float

alpha_xgb: float

tree_method: str

sketch_eps: float | None

scale_pos_weight: float

num_parallel_tree: int | None

base_score: float | None

seed: int | None

early_stopping_rounds: int | None

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 eval_metric: List[str]

Get evaluation metrics derived from is_binary.

validate_xgboost_hyperparameters()

Validate XGBoost-specific hyperparameters

get_public_init_fields()

Override get_public_init_fields to include XGBoost-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