{"action":"created","comment":{"url":"https://api.github.com/repos/cosname/cosx.org/pulls/comments/206139503","pull_request_review_id":141528229,"id":206139503,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwNjEzOTUwMw==","diff_hunk":"@@ -0,0 +1,506 @@\n+---\n+title: R模型部署实战\n+author: 周震宇\n+date: '2018-07-23'\n+slug: r-model-deployment\n+categories:\n+  - R\n+tags:\n+  - model-deployment\n+output: \n+  html_document: \n+    keep_md: yes\n+---\n+\n+## 引言\n+\n+如果此时你对何谓模型部署仍然一无所知的话，不必有任何焦虑的心情，引导你入门并建立模型部署的清晰认识正是本文的目标所在。请相信我，这篇介绍是十分新手友好的。\n+\n+模型部署是商业情景中的统计建模的极其重要的一部分，然而却往往被人忽视。读完本文，你将了解模型部署的基本概念与用途，并且按照教程学会如何在R环境中使用网络服务(web service)这种形式来部署上线一个模型，更多地，你的方法武器库中将会增添几柄利器: opencpu、fiery及plumbeR。话不多说，Let's start! \n+\n+## 平台介绍\n+\n+操作系统:Windows10\n+R版本信息如下：\n+\n+```r\n+version\n+```\n+\n+```\n+##                _                           \n+## platform       x86_64-w64-mingw32          \n+## arch           x86_64                      \n+## os             mingw32                     \n+## system         x86_64, mingw32             \n+## status                                     \n+## major          3                           \n+## minor          5.0                         \n+## year           2018                        \n+## month          04                          \n+## day            23                          \n+## svn rev        74626                       \n+## language       R                           \n+## version.string R version 3.5.0 (2018-04-23)\n+## nickname       Joy in Playing\n+```\n+\n+\n+\n+\n+```r\n+knitr::opts_chunk$set(eval = FALSE)\n+```\n+## 引入模型部署\n+\n+### 什么是模型部署？\n+\n+一句话概括，[模型部署](https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/lifecycle-deployment)是将一个调试好的模型运转在生产或者类生产环境中的操作。我们先来看下面这幅数据科学项目开发流程图。\n+\n+<img src=\"http://chuantu.biz/t6/319/1527346823x-1404775605.png\", height = \"1000px\", width = \"800px\">\n+\n+位于图片左边区域中的紫色圆圈中的文字，相信对大部分读者都不陌生。我们在学校中习得了很多关于数据科学模型、特征选择技巧及模型评价方法的知识，回想一下，在你过去大多数课程作业或是打比赛的经历中，完成了模型评价也就意味着项目宣告结束。然而在商业应用中，模型部署才标志着一个项目开发的阶段性结束。它的价值体现在两点：\n+\n+* 赋予数据科学模型处理实时**流数据**的能力。\n+* 在实际应用场景中完成模型效果与性能的反馈，为模型调整提供依据，实现建模开发流程的闭环。\n+\n+\n+### 模型部署的手段[1]：\n+\n+这里我引述了一些常用的模型部署的手段，接下来我们将会介绍用网络服务这种形式来搭建一个线上的模型。\n+\n+* 在线网站\n+* 电子表格程序\n+* 仪表盘\n+* 业务线应用程序(Line-of-business applications)[2]\n+* 移动终端\n+* 云\n+* ……\n+\n+\n+### 闲话流数据：\n+\n+前文说到模型部署的一大价值便在于它赋予了数据科学模型处理流数据的能力。而在我看来，对于流式数据的陌生也是大部分同学在校期间对模型部署接触不多的一大原因，或者与数据采集环节的遥远导致我们把大部分的精力都投入在如何用模型把面前样本数据中的模式挖掘出来。在这种场景下，数据是固定的，被存放在.csv或者各式各样的文件中。\n+\n+让我们的思路再向前推移一步，用以存放数据的各种数据库或者数据文件是像蓄水池一样逐渐蓄满了被传递进来的数据，而这些数据均是经过一定的技术手段实时采集而来。那么，对于新的观测，一个部署在生产环境中的模型便可以实时地处理预测这些数据：比如互联网金融公司便可根据一个新申请的用户提供的个人信息来预测这名用户的信用评分，垃圾过滤系统也可以获取实时的邮件内容来判断这是否是垃圾邮件。\n+\n+### 网络服务做模型部署\n+\n+用网络服务做模型部署，将划分出线上与线下两个环境。线下环境是单机环境，用以做一些探索性或者验证性的分析，包含了分析建模的每一个步骤：特征工程、模型选择、模型评价、参数调节……  \n+\n+而线上环境则是生产环境，是把线下调整好参数的模型传至线上对新搜集的数据进行实时反馈。其背后的原理是借助get&post方法把特征字段传递给web服务器，服务器将会用封装好的模型预测并返回参数数值。对get&post不熟悉的可以参考[这个教程](http://www.w3school.com.cn/tags/html_ref_httpmethods.asp)。\n+\n+### web service in R\n+\n+几个常用的R web service包：plumber，fiery，opencpu，httpuv\n+\n+## 实战环节\n+\n+得益于Rstudio开发的工具包[httpuv](https://github.com/rstudio/httpuv)，在R语言中处理http以及Websocket请求变成了现实，基于此工具包二次开发的框架opencpu, fiery和plumbeR提供了在线模型部署的R版本的解决方案，下面我们一一介绍：\n+\n+\n+\n+### 场景实例：","path":"content/post/2018-04-09_model_deployment_in_r.md","position":103,"original_position":103,"commit_id":"74b99758fe1b470c4e05ee68c899a934f34cb55b","original_commit_id":"74b99758fe1b470c4e05ee68c899a934f34cb55b","user":{"login":"Lchiffon","id":7221728,"node_id":"MDQ6VXNlcjcyMjE3Mjg=","avatar_url":"https://avatars1.githubusercontent.com/u/7221728?v=4","gravatar_id":"","url":"https://api.github.com/users/Lchiffon","html_url":"https://github.com/Lchiffon","followers_url":"https://api.github.com/users/Lchiffon/followers","following_url":"https://api.github.com/users/Lchiffon/following{/other_user}","gists_url":"https://api.github.com/users/Lchiffon/gists{/gist_id}","starred_url":"https://api.github.com/users/Lchiffon/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Lchiffon/subscriptions","organizations_url":"https://api.github.com/users/Lchiffon/orgs","repos_url":"https://api.github.com/users/Lchiffon/repos","events_url":"https://api.github.com/users/Lchiffon/events{/privacy}","received_events_url":"https://api.github.com/users/Lchiffon/received_events","type":"User","site_admin":false},"body":"后面这部分结构有点乱（可能是因为代码比较多），建议把网络的测试结果也放在前面，后面放三个包不同的实现方式\r\n","created_at":"2018-07-30T13:50:34Z","updated_at":"2018-07-30T13:51:22Z","html_url":"https://github.com/cosname/cosx.org/pull/771#discussion_r206139503","pull_request_url":"https://api.github.com/repos/cosname/cosx.org/pulls/771","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/cosname/cosx.org/pulls/comments/206139503"},"html":{"href":"https://github.com/cosname/cosx.org/pull/771#discussion_r206139503"},"pull_request":{"href":"https://api.github.com/repos/cosname/cosx.org/pulls/771"}}},"pull_request":{"url":"https://api.github.com/repos/cosname/cosx.org/pulls/771","id":180266882,"node_id":"MDExOlB1bGxSZXF1ZXN0MTgwMjY2ODgy","html_url":"https://github.com/cosname/cosx.org/pull/771","diff_url":"https://github.com/cosname/cosx.org/pull/771.diff","patch_url":"https://github.com/cosname/cosx.org/pull/771.patch","issue_url":"https://api.github.com/repos/cosname/cosx.org/issues/771","number":771,"state":"open","locked":false,"title":"投稿《模型部署的R实现》","user":{"login":"LalZzy","id":12784943,"node_id":"MDQ6VXNlcjEyNzg0OTQz","avatar_url":"https://avatars0.githubusercontent.com/u/12784943?v=4","gravatar_id":"","url":"https://api.github.com/users/LalZzy","html_url":"https://github.com/LalZzy","followers_url":"https://api.github.com/users/LalZzy/followers","following_url":"https://api.github.com/users/LalZzy/following{/other_user}","gists_url":"https://api.github.com/users/LalZzy/gists{/gist_id}","starred_url":"https://api.github.com/users/LalZzy/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LalZzy/subscriptions","organizations_url":"https://api.github.com/users/LalZzy/orgs","repos_url":"https://api.github.com/users/LalZzy/repos","events_url":"https://api.github.com/users/LalZzy/events{/privacy}","received_events_url":"https://api.github.com/users/LalZzy/received_events","type":"User","site_admin":false},"body":"投稿《模型部署的R实现》\r\n\r\n非常感谢您的PR, 如果您是在为主站投稿, 请将PR的标题改为\"投稿:标题+作者的形式\",如:\r\n\"投稿: 数据通灵术 杜亚磊\"\r\n并保留下面的内容.\r\n\r\n- 投稿者请注意\r\n  - [ ] 主编审核确认接收\r\n  - [ ] 主编安排审稿人\r\n  - [ ] 审稿、修改\r\n\r\n> 至此，投稿部分的工作结束\r\n\r\n- 编辑部分工作\r\n  - [ ] 文字编辑做校对工作。需校对文章分类，目前的文章分类包括：**COS访谈，R会议，R语言，推荐文章，新闻动态，机器学习，统计之都，统计图形，统计应用，统计模型，统计计算，统计软件，职业事业**。不得擅自增加分类。\r\n  - [ ] 论坛帖子负责人发帖，粘贴链接，隐藏帖子后提供 `forum_id` 加入文章\r\n  - [ ] 微信编辑、发布，阅读原文附主站链接，文末附作者介绍\r\n  - [ ] 合并 PR，发布文章\r\n  - [ ] 论坛帖子负责人帖子取消隐藏\r\n\r\n[投稿指南在这里](https://cosx.org/contribute/)，有任何问题，可以直接在PR留言，其他问题请联系: 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