import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF
from sklearn.model_selection import RandomizedSearchCV

X = np.random.rand(100, 2)
y = np.sin(X[:, 0]) + np.cos(X[:, 1])
alpha = 0.1 * np.ones_like(y)

kernel = RBF(length_scale=1.)

param_dist = {
    'kernel__length_scale': (1e-3, 1e3),
}


gp = GaussianProcessRegressor(kernel=kernel, alpha=alpha)
rs = RandomizedSearchCV(gp, param_distributions=param_dist, cv=5, n_iter=50)

rs.fit(X, y)

print("Best hyperparameters: ", rs.best_params_)
print("Best score: ", rs.best_score_)

ValueError: alpha must be a scalar or an array with same number of entries as y. (100 != 80)