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
vif_dict[exog] = vif
# calculate tolerance
tolerance = 1 - r_squared
tolerance_dict[exog] = tolerance
# return VIF DataFrame
df_vif = pd.DataFrame({'VIF': vif_dict, 'Tolerance': tolerance_dict})
return df_vif
if __name__ == '__main__':
data_path = r'..\..\Demo\train_numeric_feature.csv'
from BC.DataContainer.DataContainer import DataContainer
from BC.FeatureAnalysis.Normalizer import NormalizerZeroCenter
pca = DimensionReductionByPCA()
dc = DataContainer()
dc.Load(data_path)
dc = NormalizerZeroCenter.Run(dc)
# dc = pca.Run(dc)
df = pd.DataFrame(dc.GetArray(),
index=dc.GetCaseName(),
columns=dc.GetFeatureName())
dr = DimensionReductionByVIF()
new_df = dr.CalculateVIF(df)
print(dc.GetArray().shape, new_df.shape)
