import bentoml

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction import DictVectorizer

from catboost import CatBoostClassifier

import numpy as np

import pandas as pd

# model Selection

model = XGBClassifier(random_state=10)

# Preprocessing

df_train = pd.read_csv('lc_clean.csv')

df_full_train, df_test_small = train_test_split(df, test_size=0.1, random_state=1)

# df_test_small see lc_test.csv

df_full_train = df_full_train.reset_index(drop=True)

y_full_train = df_full_train.loan_status.values

del df_full_train['loan_status']

train_full_dict = <http://df_full_train.to|df_full_train.to>_dict(orient='records')

dv = DictVectorizer(sparse=False)

x_full_train = dv.fit_transform(train_full_dict)

# train model

model.fit(x_full_train, y_full_train)

# save model with bentoml

bentoml.sklearn.save_model('loan_approval', final_model,

custom_objects={

'dicVectorizer': dv

},

signatures = {"predict_proba": {"batchable": False}}

)