我用了scorecardpy包来做一个评分卡模型，现在问题是，我想将它自带的逻辑回归预测模型替换为我自己的autogluon模型，但是报错了，代码是这样的
import pandas as pd
import numpy as np
import scorecardpy as sc
dat=pd.read_csv("dat.csv",index_col=0)
dat
dt_s = sc.var_filter(dat, y="isDefault")
dt_s.info()
train, test = sc.split_df(dt_s,'isDefault',ratio=0.8).values()
bins = sc.woebin(dt_s, y='isDefault',method="chimerge")
sc.woebin_plot(bins)
train_woe = sc.woebin_ply(train, bins)
test_woe = sc.woebin_ply(test, bins)
y_train = train_woe.loc[:,'isDefault']
X_train = train_woe.loc[:,train_woe.columns != 'isDefault']
y_test = test_woe.loc[:,'isDefault']
X_test = test_woe.loc[:,train_woe.columns != 'isDefault']
mport autogluon
from autogluon.tabular import TabularDataset,TabularPredictor
import pandas as pd
import numpy as np
label='isDefault'
train_data=TabularDataset(train_woe )
metric = 'roc_auc'
time_limit=60
predictor=TabularPredictor(label=label,eval_metric=metric).fit(train_data,presets='best_quality',time_limit=time_limit,auto_stack=True)
test_data=TabularDataset(test_woe)
predictor.predict_proba(train_data)
train_proba=predictor.predict_proba(train_data)
train_proba=train_proba.values[:,1]
train_proba = np.array(train_proba).flatten()
train_proba
predictor.predict_proba(test_data)
auto_proba=predictor.predict_proba(test_data)
auto_proba=auto_proba.values[:,1]
auto_proba = np.array(auto_proba).flatten()
auto_proba
import toad
from toad.metrics import KS, AUC
print('Training error')
print('KS:', KS(train_proba,y_train))
print('AUC:', AUC(train_proba,y_train))
print('\nTest error')
print('KS:', KS(auto_proba,y_test))
print('AUC:', AUC(auto_proba,y_test))
card = sc.scorecard(bins,predictor, X_train.columns)
问题出在这个最后一句，我将我的模型predictor替换了scorecard包自带的lr，就显示报错
'TabularPredictor' object has no attribute 'coef_'
这个怎么解决？
谢谢各位了
