resnet_regressor.py 文件源码

def train_xgboost():
    df = pd.read_csv('survival_data.csv', index_col=0, encoding = 'UTF-7')
    p = np.array([np.mean(np.load('training/%s_flair.nii.gz.npy' % str(id)), axis=0) for id in folder_names_train])
    q = np.array([np.mean(np.load('training/%s_t1.nii.gz.npy' % str(id)), axis=0) for id in folder_names_train])
    r = np.array([np.mean(np.load('training/%s_t1ce.nii.gz.npy' % str(id)), axis=0) for id in folder_names_train])
    s = np.array([np.mean(np.load('training/%s_t2.nii.gz.npy' % str(id)), axis=0) for id in folder_names_train])

    y=np.array([])
    t=0
    z=np.array([])
    for ind in range(len(folder_names_train)):
        try:
            temp = df.get_value(str(folder_names_train[ind]),'Survival')
            y=np.append(y,temp)
            temp = df.get_value(str(folder_names_train[ind]),'Age')
            z=np.append(z,np.array([temp]))
        except Exception as e:
            t+=1 
            print (t,str(e),"Label Not found, deleting entry")
            y=np.append(y,0)

    z=np.array([[v] for v in z])

    t=np.concatenate((p,q),axis=1)
    u=np.concatenate((r,s),axis=1)
    x=np.concatenate((t,u),axis=1) 
    #print(x.shape)
    #print (x)
    #print (x.shape,z.shape)
    x=np.concatenate((x,z),axis=1)
    #print (x)
    #clf=linear_model.LogisticRegression(C=1e5)
    #clf = RandomForestRegressor()
    clf = xgb.XGBRegressor()
    clf.fit(x,y)
    return clf