gaussian_process_classifier.py

# grid search kernel for gaussian process classifier
from sklearn.datasets import make_classification
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.gaussian_process.kernels import DotProduct
from sklearn.gaussian_process.kernels import Matern
from sklearn.gaussian_process.kernels import RationalQuadratic
from sklearn.gaussian_process.kernels import WhiteKernel
# define dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=15, n_redundant=5, random_state=1)
# define model
model = GaussianProcessClassifier()
# define model evaluation method
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# define grid
grid = dict()
grid['kernel'] = [1*RBF(), 1*DotProduct(), 1*Matern(),  1*RationalQuadratic(), 1*WhiteKernel()]
# define search
search = GridSearchCV(model, grid, scoring='accuracy', cv=cv, n_jobs=-1)
# perform the search
results = search.fit(X, y)
# summarize best
print('Best Mean Accuracy: %.3f' % results.best_score_)
print('Best Config: %s' % results.best_params_)
# summarize all
means = results.cv_results_['mean_test_score']
params = results.cv_results_['params']
for mean, param in zip(means, params):
    print(">%.3f with: %r" % (mean, param))