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import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_wine
from gradient_free_optimizers import HillClimbingOptimizer
data = load_wine()
X, y = data.data, data.target
def model(para):
gbc = DecisionTreeClassifier(
min_samples_split=para["min_samples_split"],
min_samples_leaf=para["min_samples_leaf"],
)
scores = cross_val_score(gbc, X, y, cv=5)
return scores.mean()
search_space = {
"min_samples_split": np.arange(2, 25, 1),
"min_samples_leaf": np.arange(1, 25, 1),
}
opt = HillClimbingOptimizer(search_space)
opt.search(model, n_iter=500, memory=False)
print("\n\nMemory activated:")
opt.search(model, n_iter=500, memory=True)
SimonBlanke /
Gradient-Free-Optimizers
