from __future__ import print_function

from __future__ import division

from sklearn.datasets import make_classification

from sklearn.cross_validation import cross_val_score

from sklearn.ensemble import RandomForestClassifier as RFC

from sklearn.svm import SVC

from bayes_opt import BayesianOptimization

# Load data set and target values

data, target = make_classification(

n_samples=1000,

n_features=45,

n_informative=12,

n_redundant=7

)

def svccv(C, gamma):

val = cross_val_score(

SVC(C=C, gamma=gamma, random_state=2),

data, target, 'f1', cv=2

).mean()

return val

def rfccv(n_estimators, min_samples_split, max_features):

val = cross_val_score(

RFC(n_estimators=int(n_estimators),

min_samples_split=int(min_samples_split),

max_features=min(max_features, 0.999),

random_state=2

),

data, target, 'f1', cv=2

).mean()

return val

if __name__ == "__main__":

gp_params = {"alpha": 1e-5}

svcBO = BayesianOptimization(svccv,

{'C': (0.001, 100), 'gamma': (0.0001, 0.1)})

svcBO.explore({'C': [0.001, 0.01, 0.1], 'gamma': [0.001, 0.01, 0.1]})

rfcBO = BayesianOptimization(

rfccv,

{'n_estimators': (10, 250),

'min_samples_split': (2, 25),

'max_features': (0.1, 0.999)}

)

svcBO.maximize(n_iter=10, **gp_params)

print('-' * 53)

rfcBO.maximize(n_iter=10, **gp_params)

print('-' * 53)

print('Final Results')

print('SVC: %f' % svcBO.res['max']['max_val'])

print('RFC: %f' % rfcBO.res['max']['max_val'])