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from sklearn.model_selection import cross_val_score
from catboost import CatBoostClassifier
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 = CatBoostClassifier(depth=para["depth"], 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: {"depth": range(2, 22), "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)
SimonBlanke /
Hyperactive
