BoostCB

class lightautoml.ml_algo.boost_cb.BoostCB(default_params=None, freeze_defaults=True, timer=None, optimization_search_space={})[source]

Bases: lightautoml.ml_algo.base.TabularMLAlgo, lightautoml.pipelines.selection.base.ImportanceEstimator

Gradient boosting on decision trees from catboost library.

All available parameters listed in CatBoost documentation:

https://catboost.ai/docs/concepts/python-reference_parameters-list.html#python-reference_parameters-list

freeze_defaults:

True : params may be rewritten depending on dataset.

False: params may be changed only manually or with tuning.

timer: Timer instance or None.

init_params_on_input(train_valid_iterator)[source]

Get model parameters depending on input dataset parameters.

Parameters: train_valid_iterator (TrainValidIterator) – Classic cv-iterator.
Return type: dict
Returns: Parameters of model.

fit_predict_single_fold(train, valid)[source]

Implements training and prediction on single fold.

Parameters

train (Union[NumpyDataset, PandasDataset]) – Train Dataset.
valid (Union[NumpyDataset, PandasDataset]) – Validation Dataset.

Return type

Tuple[CatBoost, ndarray]

Returns

Tuple (model, predicted_values).

predict_single_fold(model, dataset)[source]

Predict of target values for dataset.

Parameters

model (CatBoost) – CatBoost object.
dataset (Union[NumpyDataset, PandasDataset]) – Test dataset.

Return type

ndarray

Returns

Predicted target values.

get_features_score()[source]

Computes feature importance.

Computes as mean values of feature importance, provided by CatBoost (PredictionValuesChange), per all models.

Return type: Series
Returns: Series with feature importances.

fit(train_valid)[source]

Just to be compatible with ImportanceEstimator.

Parameters: train_valid (TrainValidIterator) – Classic cv-iterator.