Scatter-initialization

Complexity

Total Complexity

1

Size/Duplication

Total Lines	49
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 sklearn.ensemble import GradientBoostingClassifier
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):
    gbc = GradientBoostingClassifier(
        n_estimators=para["n_estimators"],
        max_depth=para["max_depth"],
        min_samples_split=para["min_samples_split"],
    )
    scores = cross_val_score(gbc, X, y, cv=3)

    return scores.mean()


search_config = {
    model: {
        "n_estimators": range(10, 200, 10),
        "max_depth": range(2, 12),
        "min_samples_split": range(2, 12),
    }
}

# Without scatter initialization
opt = Hyperactive(X, y)
opt.search(
    search_config,
    optimizer="HillClimbing",
    n_iter=10,
    random_state=0,
    init_config=False,
)

init_config = {"scatter_init": 10}

# With scatter initialization
opt = Hyperactive(X, y)
opt.search(
    search_config,
    optimizer="HillClimbing",
    n_iter=10,
    random_state=0,
    init_config=init_config,
)