import numpy as np

import matplotlib.pyplot as plt

from sklearn.datasets import load_boston

from sklearn.ensemble import GradientBoostingRegressor

from sklearn.model_selection import cross_val_score

X,y = load_boston(return_X_y=True)

n_features = X.shape[1]

model = GradientBoostingRegressor(n_estimators=50,random_state=1)

from skopt.space import Real,Integer,Categorical

from skopt.utils import use_named_args

space = [Integer(1,5,name="max_depth"),

         Real(10**-5, 10**0, "log-uniform", name='learning_rate'),

         Integer(1, n_features, name='max_features'),

         Integer(2, 100, name='min_samples_split'),

         Integer(1, 100, name='min_samples_leaf')]

# this decorator allows your objective function to receive a the parameters as

# keyword arguments. This is particularly convenient when you want to set scikit-learn

@use_named_args(space)

def objective(**params):

    model.set_params(**params)

    return -np.mean(cross_val_score(model,X,y,cv=5,n_jobs=-1,scoring="neg_mean_absolute_error"))

from skopt import gp_minimize

result = gp_minimize(objective,space,n_calls=50,random_state=0)

from skopt.plots import plot_convergence

from skopt.plots import plot_evaluations

from skopt.plots import plot_objective

plot_convergence(result)

plot_evaluations(result)

plot_objective(result)

plt.show()