lightgbm_regression

Complexity

Total Complexity

1

Size/Duplication

Total Lines	35
Duplicated Lines	0 %

Importance

Changes

0

1 Function

Rating	Name	Duplication	Size	Complexity
A	model()	0	9	1

from sklearn.model_selection import cross_val_score
from lightgbm import LGBMRegressor
from sklearn.datasets import load_breast_cancer
from hyperactive import Hyperactive

data = load_breast_cancer()
X, y = data.data, data.target


def model(para, X_train, y_train):
    model = LGBMRegressor(
        num_leaves=para["num_leaves"],
        bagging_freq=para["bagging_freq"],
        learning_rate=para["learning_rate"],
    )
    scores = cross_val_score(model, X_train, y_train, cv=3)

    return scores.mean(), model


# this defines the model and hyperparameter search space
search_config = {
    model: {
        "num_leaves": range(2, 20),
        "bagging_freq": range(2, 12),
        "learning_rate": [1e-3, 1e-2, 1e-1, 0.5, 1.0],
    }
}


opt = Hyperactive(search_config, n_iter=100, n_jobs=2)

# search best hyperparameter for given data
opt.fit(X, y)