bayes_loop.py

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_val_score

from skopt.plots import plot_convergence
from skopt.utils import use_named_args
from skopt.space import Real, Integer
from skopt import gp_minimize

import matplotlib.pyplot as plt

plt.rcParams["figure.dpi"] = 100
plt.rcParams["figure.figsize"] = [10,4]

def bayes_loop_example():
    model = GradientBoostingClassifier(random_state=0, n_estimators=50)

    space = [
        Real(10**-5, 10**0, "log-uniform", name='learning_rate'),
        Integer(20, 50, name='n_estimators'),
        Integer(2, 8, name='min_samples_split')
    ]

    @use_named_args(space)
    def objective(**params):
        model.set_params(**params)

        return -np.mean(cross_val_score(model, X_train, y_train, cv=3, n_jobs=-1,
                                        scoring="neg_mean_absolute_error")) 

    res_gp = gp_minimize(objective, space, n_calls=20, random_state=0)
    plot_convergence(res_gp)

    print("Best Parameters", res_gp.x)
    print("Best Score", res_gp.fun)