exponential search space in first example on main page

docs/examples/python/sklearn/svc.py

@@ -4,13 +4,13 @@ import sklearn.svm
 
 # score function: twice iterated 10-fold cross-validated accuracy
 @optunity.cross_validated(x=data, y=labels, num_folds=10, num_iter=2)
-def svm_auc(x_train, y_train, x_test, y_test, C, gamma):
-    model = sklearn.svm.SVC(C=C, gamma=gamma).fit(x_train, y_train)
+def svm_auc(x_train, y_train, x_test, y_test, logC, logGamma):
+    model = sklearn.svm.SVC(C=10 ** logC, gamma=10 ** logGamma).fit(x_train, y_train)
     decision_values = model.decision_function(x_test)
     return optunity.metrics.roc_auc(y_test, decision_values)
 
 # perform tuning
-optimal_pars, _, _ = optunity.maximize(svm_auc, num_evals=200, C=[0, 10], gamma=[0, 1])
+hps, _, _ = optunity.maximize(svm_auc, num_evals=200, logC=[-5, 2], logGamma=[-5, 1])
 
 # train model on the full training set with tuned hyperparameters
-optimal_model = sklearn.svm.SVC(**optimal_pars).fit(data, labels)
+optimal_model = sklearn.svm.SVC(C=10 ** hps['logC'], gamma=10 ** hps['logGamma']).fit(data, labels)

setup.py

@@ -3,7 +3,7 @@ import setuptools
 
 setup(
     name = 'Optunity',
-    version = '1.1.0',
+    version = '1.1.1',
     author = 'Marc Claesen',
     author_email = 'marc.claesen@esat.kuleuven.be',
     packages = ['optunity', 'optunity.tests', 'optunity.solvers'],