from skopt.learning.gaussian_process import gpr, kernels  #gpr is the package with the bug
import numpy as np
np.random.seed(0) # for reproduction

gp = gpr.GaussianProcessRegressor(kernel=kernels.RBF(), alpha=1e-7, noise=0.01)
train_inputs = np.array([0, 0.1, 0.2]).reshape(-1, 1)
train_targets = np.array([1, 1.5, 0.6])
for i in range(10):
    gp.fit(train_inputs, train_targets)
    test_inputs = np.arange(0, 0.25, 0.05).reshape(-1, 1)
    gp.predict(test_inputs)
    print(str(gp.get_params()['kernel__k1']).count("WhiteKernel")) # number of WhiteKernels in self.kernel increases with each .fit
    train_inputs = np.vstack([train_inputs, np.random.rand()])
    train_targets = np.append(train_targets, np.random.rand())

        if isinstance(self.noise, str) and self.noise != "gaussian":
            raise ValueError("expected noise to be 'gaussian', got %s"
                             % self.noise)
        if self.noise == "gaussian":
            kernel = kernel + WhiteKernel()
        elif self.noise:
            kernel = kernel + WhiteKernel(
                noise_level=self.noise, noise_level_bounds="fixed"
            )