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from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from hyperactive import Hyperactive
from hyperactive.optimizers import RepulsingHillClimbingOptimizer
data = load_iris()
X, y = data.data, data.target
def model(opt):
knr = KNeighborsClassifier(n_neighbors=opt["n_neighbors"])
scores = cross_val_score(knr, X, y, cv=5)
score = scores.mean()
return score
search_space = {
"n_neighbors": list(range(1, 100)),
}
optimizer = RepulsingHillClimbingOptimizer(
epsilon=0.1,
distribution="laplace",
n_neighbours=4,
repulsion_factor=5,
rand_rest_p=0.1,
)
hyper = Hyperactive()
hyper.add_search(model, search_space, optimizer=optimizer, n_iter=100)
hyper.run()
SimonBlanke /
Hyperactive
