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from sklearn.model_selection import cross_val_score
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.datasets import load_iris
from hyperactive import Hyperactive
iris_data = load_iris()
X = iris_data.data
y = iris_data.target
def model(para, X, y):
gbc = GradientBoostingClassifier(
n_estimators=para["n_estimators"], max_depth=para["max_depth"]
)
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, 15)}}
"""
The memory will remember previous evaluations done during the optimization process.
Instead of retraining the model, it accesses the memory and uses the saved score/loss.
This shows as a speed up during the optimization process, since the whole search space has been explored.
opt = Hyperactive(X, y)
opt.search(search_config, n_iter=1000, memory=True)
SimonBlanke /
Hyperactive
