def caculate_fitness(x_train, y_train, x_test, y_test):
y_train = y_train.argmax(1)
y_test = y_test.argmax(1)
clf = svm_lpp().fit(x_train, y_train)
y_test_score = clf.decision_function(x_test)
#acc = accuracy_score(y_test, clf.predict(x_test))
#f1 = F1(y_test, clf.predict(x_test))
#acc = accuracy_score(y_test, clf.predict(x_test))
roc_auc = function_lpp_new.draw_roc(y_test, y_test_score)
return roc_auc
def caculate_fitness_plot(x_train, y_train, x_test, y_test):
y_train = y_train.argmax(1)
y_test = y_test.argmax(1)
clf = svm_lpp().fit(x_train, y_train)
train_predict = clf.predict(x_train)
test_predict = clf.predict(x_test)
test_predict_proba = clf.decision_function(x_test)
print(confusion_matrix(y_test, test_predict))
print('train sensitivity:', sensitivity(y_train, train_predict),
'train specifity:', specificity(y_train, train_predict))
print('test sensitivity:', sensitivity(y_test, test_predict),
'test specifity:', specificity(y_test, test_predict))
print('f1_score:', F1(y_test, test_predict))
print('acc:', accuracy_score(y_test, test_predict))
roc_auc = function_lpp_new.draw_roc(y_test, test_predict_proba)
print('roc:', roc_auc)
print('over')
x_train, x_test, index)
result = obtain_flod_predict(x_train_pca, y_train, x_test_pca,
y_test)
save_true.append(result[0])
save_predict.append(result[1])
y_predict_label.append(result[2])
print(str(fold), 'over')
for i in range(5):
result_eva.append([
recall_score(save_true[i], y_predict_label[i]),
specificity(save_true[i], y_predict_label[i]),
precision_score(save_true[i], y_predict_label[i]),
f1_score(save_true[i], y_predict_label[i]),
accuracy_score(save_true[i], y_predict_label[i]),
function_lpp_new.draw_roc(save_true[i], save_predict[i])
])
for i in range(1, 5):
save_true[0] = np.hstack([save_true[0], save_true[i]])
save_predict[0] = np.hstack([save_predict[0], save_predict[i]])
y_predict_label[0] = np.hstack(
[y_predict_label[0], y_predict_label[i]])
save_true = save_true[0]
save_predict = save_predict[0]
y_predict_label = y_predict_label[0]
scio.savemat(
params['path_PCA'] + str(lll) + 'predict_genetic_multi.mat', {
'y_predict': save_predict,
'y_true': save_true