xgboost_example

Complexity

Total Complexity

1

Size/Duplication

Total Lines	32
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 xgboost import XGBClassifier
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, y):
    model = XGBClassifier(
        n_estimators=para["n_estimators"],
        max_depth=para["max_depth"],
        learning_rate=para["learning_rate"],
    )
    scores = cross_val_score(model, X, y, cv=3)

    return scores.mean()


search_config = {
    model: {
        "n_estimators": range(10, 200, 10),
        "max_depth": range(2, 12),
        "learning_rate": [1e-3, 1e-2, 1e-1, 0.5, 1.0],
    }
}


opt = Hyperactive(search_config, n_iter=100)
opt.fit(X, y)