更新时间:2022-09-08 GMT+08:00
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Scikit Learn

训练并保存模型

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import json
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
iris = pd.read_csv('/home/ma-user/work/iris.csv')
X = iris.drop(['variety'],axis=1)
y = iris[['variety']]
# Create a LogisticRegression instance and train model
logisticRegression = LogisticRegression(C=1000.0, random_state=0)
logisticRegression.fit(X,y)
# Save model to local path
joblib.dump(logisticRegression, '/tmp/sklearn.m')

训练前请先下载iris.csv数据集,解压后上传至Notebook本地路径/home/ma-user/work/。iris.csv数据集下载地址:https://gist.github.com/netj/8836201。Notebook上传文件操作请参见上传本地文件至Notebook中

保存完模型后,需要上传到OBS目录才能发布。发布时需要带上“config.json”配置以及“customize_service.py”,定义方式参考模型包规范介绍

推理代码

在模型代码推理文件customize_service.py中,需要添加一个子类,该子类继承对应模型类型的父类,各模型类型的父类名称和导入语句如请参考表1

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# coding:utf-8
import collections
import json
from sklearn.externals import joblib
from model_service.python_model_service import XgSklServingBaseService

class UserService(XgSklServingBaseService):

    # request data preprocess
    def _preprocess(self, data):
        list_data = []
        json_data = json.loads(data, object_pairs_hook=collections.OrderedDict)
        for element in json_data["data"]["req_data"]:
            array = []
            for each in element:
                array.append(element[each])
                list_data.append(array)
        return list_data

    # predict
    def _inference(self, data):
        sk_model = joblib.load(self.model_path)
        pre_result = sk_model.predict(data)
        pre_result = pre_result.tolist()
        return pre_result

    # predict result process
    def _postprocess(self,data):
        resp_data = []
        for element in data:
            resp_data.append({"predictresult": element})
        return resp_data
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