def TestNewData(NewDataCsv, model_folder, result_save_path=''):
'''
:param NewDataCsv: New radiomics feature matrix csv file path
:param model_folder:The trained model path
:return:classification result
'''
train_info = LoadTrainInfo(model_folder)
new_data_container = DataContainer()
#Normlization
new_data_container.Load(NewDataCsv)
# feature_selector = FeatureSelector()
# feature_selector.SelectFeatureByName(new_data_container, train_info['selected_features'], is_replace=True)
new_data_container = train_info['normalizer'].Transform(new_data_container)
# data_frame = new_data_container.GetFrame()
# data_frame = data_frame[train_info['selected_features']]
# new_data_container.SetFrame(data_frame)
# new_data_container.UpdateDataByFrame()
##Model
train_info['classifier'].SetDataContainer(new_data_container)
model = train_info['classifier'].GetModel()
predict = model.predict_proba(new_data_container.GetArray())[:, 1]
label = new_data_container.GetLabel()
case_name = new_data_container.GetCaseName()
test_result_info = [['CaseName', 'Pred', 'Label']]
for index in range(len(label)):
test_result_info.append(
[case_name[index], predict[index], label[index]])
metric = EstimateMetirc(predict, label)
info = {}
info.update(metric)
cv = CrossValidation()
print(metric)
print('\t')
if result_save_path:
cv.SaveResult(info, result_save_path)
np.save(os.path.join(result_save_path, 'test_predict.npy'), predict)
np.save(os.path.join(result_save_path, 'test_label.npy'), label)
with open(os.path.join(result_save_path, 'test_info.csv'),
'w',
newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(test_result_info)
return metric
return "To Remove the unbalance of the training data set, we applied an Tomek link after the " \
"Synthetic Minority Oversampling TEchnique (SMOTE) to make positive/negative samples balance. "
def Run(self, data_container, store_path=''):
data, label, feature_name, label_name = data_container.GetData()
data_resampled, label_resampled = self._model.fit_sample(data, label)
new_case_name = [
'Generate' + str(index) for index in range(data_resampled.shape[0])
]
new_data_container = DataContainer(data_resampled, label_resampled,
data_container.GetFeatureName(),
new_case_name)
if store_path != '':
if os.path.isdir(store_path):
new_data_container.Save(
os.path.join(store_path,
'{}_features.csv'.format(self._name)))
else:
new_data_container.Save(store_path)
return new_data_container
if __name__ == '__main__':
dc = DataContainer()
dc.Load(r'..\..\Example\numeric_feature.csv')
print(dc.GetArray().shape, np.sum(dc.GetLabel()))
b = SmoteTomekSampling()
new = b.Run(dc)
print(new.GetArray().shape, np.sum(new.GetLabel()))
