def run_optunity(self):
cv_decorator = optunity.cross_validated(
x=self.X,
y=self.Y,
)
svm_tuned_auroc = cv_decorator(self.svm_tuned_auroc)
optimal_svm_pars, info, _ = optunity.maximize_structured(
svm_tuned_auroc, self.space, num_evals=150, pmap=optunity.pmap)
print("Optimal parameters" + str(optimal_svm_pars))
print("AUROC of tuned SVM: %1.3f" % info.optimum)
df = optunity.call_log2dataframe(info.call_log)
print(df.sort_values('value', ascending=False))
name = sys.argv[1]
budget = 150
search = {'logC': [-8, 1], 'logGamma': [-8, 1]}
objfun = executable.unpickled['objfun']
def quacking_objfun(**kwargs):
result = objfun(**kwargs)
print(str(kwargs) + ' --> %f' % result)
return result
if __name__ == '__main__':
pars, info, _ = optunity.maximize(quacking_objfun,
num_evals=budget,
pmap=optunity.pmap,
**search)
df = optunity.call_log2dataframe(info.call_log)
with open('results/%s-optunity.pkl' % name, 'w') as f:
log = {
'results': df['value'],
'logC': df['logC'],
'logGamma': df['logGamma']
}
pickle.dump(log, f)
@optunity.cross_validated(x=data, y=labels, num_folds=5, regenerate_folds=True)
def svm_rbf_tuned_auroc(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)
auc = optunity.metrics.roc_auc(y_test, decision_values)
return auc
optimal_rbf_pars, info, _ = optunity.maximize(svm_rbf_tuned_auroc, num_evals=300, logC=[-4, 2], logGamma=[-5, 0])
# when running this outside of IPython we can parallelize via optunity.pmap
# optimal_rbf_pars, _, _ = optunity.maximize(svm_rbf_tuned_auroc, 150, C=[0, 10], gamma=[0, 0.1], pmap=optunity.pmap)
print('**********************************************')
print("Optimal parameters: " + str(optimal_rbf_pars))
print("AUROC of tuned SVM with RBF kernel: %1.3f" % info.optimum)
df = optunity.call_log2dataframe(info.call_log)
################################
cutoff = 0.1
fig3 = plt.figure()
ax3 = fig3.add_subplot(111, projection='3d')
ax3.scatter(xs=df[df.value > cutoff]['logC'],
ys=df[df.value > cutoff]['logGamma'],
zs=df[df.value > cutoff]['value'])
ax3.set_xlabel('logC')
ax3.set_ylabel('logGamma')
ax3.set_zlabel('AUROC')
ax3.set_title('Show all values of AUROC.')
fig3.savefig('AUROCcutoff01')
################################