rgf_python

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	12	1

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import cross_val_score
from rgf.sklearn import RGFClassifier

from hyperactive import Hyperactive

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


def model(para, X, y):
    rgf = RGFClassifier(
        max_leaf=para["max_leaf"],
        reg_depth=para["reg_depth"],
        min_samples_leaf=para["min_samples_leaf"],
        algorithm="RGF_Sib",
        test_interval=100,
        verbose=False,
    )
    scores = cross_val_score(rgf, X, y, cv=3)

    return scores.mean()


search_config = {
    model: {
        "max_leaf": range(1000, 10000, 100),
        "reg_depth": range(1, 21),
        "min_samples_leaf": range(1, 21),
    }
}

opt = Hyperactive(search_config, n_iter=5)
opt.search(X, y)