AI开发平台MODELARTS-缺省值填充:组件配置方式
时间：2024-04-30 17:04:13
下载AI开发平台MODELARTS用户手册完整版
复制链接到剪贴板
分享文章到微博
分享文章到朋友圈
组件配置方式
方式一 : 以配置表方式确定填充策略
输入
参数 |
子参数 |
参数说明 |
|---|---|---|
inputs |
dataDF |
inputs为字典类型，dataDF为pyspark中的DataFrame类型对象，输入数据 |
inputs |
paramDF |
inputs为字典类型，paramDF为pyspark中的DataFrame类型对象，待修改字段的配置 |
输出
参数 |
参数说明 |
|---|---|
dataDF |
inputs为字典类型，dataDF为pyspark中的DataFrame类型对象，填充后的数据输出 |
paramDF |
inputs为字典类型，paramDF为pyspark中的DataFrame类型对象，已被修改字段的配置 |
样例
dataDF：
+---+----+-----------+--------+---------+-------+----+
|id |age |job |marital |education|housing|loan|
+---+----+-----------+--------+---------+-------+----+
|0 |59 |admin. |married |secondary|yes |no |
|1 |56 |admin. |married |secondary|no |no |
|2 |41 |technician |married |secondary|yes |no |
|3 |55 |services |married |secondary|yes |no |
|4 |54 |admin. |married |tertiary |no |no |
|5 |null|management |single |tertiary |yes |yes |
|6 |56 |management |married |tertiary |yes |yes |
|7 |60 |retired |divorced|secondary|yes |no |
|8 |39 |technician |single |unknown |yes |no |
|9 |37 |technician |married |secondary|yes |no |
|10 |34 |admin. |married |secondary|no |no |
|11 |null|null |divorced|secondary|yes |no |
|12 |28 |services |single |secondary|yes |no |
|13 |30 |technician |married |secondary|yes |no |
|14 |36 |technician |married |secondary|yes |yes |
|15 |37 |admin. |single |secondary|yes |yes |
|16 |null|blue-collar|married |secondary|yes |no |
|17 |53 |services |divorced|primary |yes |yes |
+---+----+-----------+--------+---------+-------+----+
paramDF
+---------+---------------------------------------------------------------------------------------------------------------------+
|feature |json |
+---------+---------------------------------------------------------------------------------------------------------------------+
|age |{"name":"fillMissingValues","type":"IntegerType","paras":{"missing_value_type":"null","replaced_value":"45.0"}} |
|job |{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"null","replaced_value":"blue-collar"}}|
|education|{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"unknown","replaced_value":"primary"}} |
+---------+---------------------------------------------------------------------------------------------------------------------+
结果：
数据输出结果：
+---+---+-----------+--------+---------+-------+----+
|id |age|job |marital |education|housing|loan|
+---+---+-----------+--------+---------+-------+----+
|0 |59 |admin. |married |secondary|yes |no |
|1 |56 |admin. |married |secondary|no |no |
|2 |41 |technician |married |secondary|yes |no |
|3 |55 |services |married |secondary|yes |no |
|4 |54 |admin. |married |tertiary |no |no |
|5 |45 |management |single |tertiary |yes |yes |
|6 |56 |management |married |tertiary |yes |yes |
|7 |60 |retired |divorced|secondary|yes |no |
|8 |39 |technician |single |primary |yes |no |
|9 |37 |technician |married |secondary|yes |no |
|10 |34 |admin. |married |secondary|no |no |
|11 |45 |blue-collar|divorced|secondary|yes |no |
|12 |28 |services |single |secondary|yes |no |
|13 |30 |technician |married |secondary|yes |no |
|14 |36 |technician |married |secondary|yes |yes |
|15 |37 |admin. |single |secondary|yes |yes |
|16 |45 |blue-collar|married |secondary|yes |no |
|17 |53 |services |divorced|primary |yes |yes |
+---+---+-----------+--------+---------+-------+----+
配置输出结果：
+---------+---------------------------------------------------------------------------------------------------------------------+
|feature |json |
+---------+---------------------------------------------------------------------------------------------------------------------+
|age |{"name":"fillMissingValues","type":"IntegerType","paras":{"missing_value_type":"null","replaced_value":"45.0"}} |
|job |{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"null","replaced_value":"blue-collar"}}|
|education|{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"unknown","replaced_value":"primary"}} |
+---------+---------------------------------------------------------------------------------------------------------------------+
方式二 : 以配置表方式确定填充策略
输入
参数 |
子参数 |
参数说明 |
|---|---|---|
inputs |
dataDF |
inputs为字典类型，dataDF为pyspark中的DataFrame类型对象，输入数据 |
输出
参数 |
参数说明 |
|---|---|
dataDF |
inputs为字典类型，dataDF为pyspark中的DataFrame类型对象，填充后的数据输出 |
paramDF |
inputs为字典类型，paramDF为pyspark中的DataFrame类型对象，已被修改字段的配置 |
参数说明
参数 |
参数说明 |
|---|---|
configs |
第一个是列名1，被填充值1，填充值1；列名2，被填充值2，填充值2 例：col_double,null,mean;col_string,null-empty,str_type_empty |
样例
dataDF：
+---+----+-----------+--------+---------+-------+----+
|id |age |job |marital |education|housing|loan|
+---+----+-----------+--------+---------+-------+----+
|0 |59 |admin. |married |secondary|yes |no |
|1 |56 |admin. |married |secondary|no |no |
|2 |41 |technician |married |secondary|yes |no |
|3 |55 |services |married |secondary|yes |no |
|4 |54 |admin. |married |tertiary |no |no |
|5 |null|management |single |tertiary |yes |yes |
|6 |56 |management |married |tertiary |yes |yes |
|7 |60 |retired |divorced|secondary|yes |no |
|8 |39 |technician |single |unknown |yes |no |
|9 |37 |technician |married |secondary|yes |no |
|10 |34 |admin. |married |secondary|no |no |
|11 |null|null |divorced|secondary|yes |no |
|12 |28 |services |single |secondary|yes |no |
|13 |30 |technician |married |secondary|yes |no |
|14 |36 |technician |married |secondary|yes |yes |
|15 |37 |admin. |single |secondary|yes |yes |
|16 |null|blue-collar|married |secondary|yes |no |
|17 |53 |services |divorced|primary |yes |yes |
+---+----+-----------+--------+---------+-------+----+
configs:
age,null,mean;job,null,blue-collar;education,unknown,primary
结果：
数据输出结果：
+---+---+-----------+--------+---------+-------+----+
|id |age|job |marital |education|housing|loan|
+---+---+-----------+--------+---------+-------+----+
|0 |59 |admin. |married |secondary|yes |no |
|1 |56 |admin. |married |secondary|no |no |
|2 |41 |technician |married |secondary|yes |no |
|3 |55 |services |married |secondary|yes |no |
|4 |54 |admin. |married |tertiary |no |no |
|5 |45 |management |single |tertiary |yes |yes |
|6 |56 |management |married |tertiary |yes |yes |
|7 |60 |retired |divorced|secondary|yes |no |
|8 |39 |technician |single |primary |yes |no |
|9 |37 |technician |married |secondary|yes |no |
|10 |34 |admin. |married |secondary|no |no |
|11 |45 |blue-collar|divorced|secondary|yes |no |
|12 |28 |services |single |secondary|yes |no |
|13 |30 |technician |married |secondary|yes |no |
|14 |36 |technician |married |secondary|yes |yes |
|15 |37 |admin. |single |secondary|yes |yes |
|16 |45 |blue-collar|married |secondary|yes |no |
|17 |53 |services |divorced|primary |yes |yes |
+---+---+-----------+--------+---------+-------+----+
配置输出结果：
+---------+---------------------------------------------------------------------------------------------------------------------+
|feature |json |
+---------+---------------------------------------------------------------------------------------------------------------------+
|age |{"name":"fillMissingValues","type":"IntegerType","paras":{"missing_value_type":"null","replaced_value":"45.0"}} |
|job |{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"null","replaced_value":"blue-collar"}}|
|education|{"name":"fillMissingValues","type":"StringType","paras":{"missing_value_type":"unknown","replaced_value":"primary"}} |
+---------+---------------------------------------------------------------------------------------------------------------------+
support.huaweicloud.com/devtool-modelarts/devtool-modelarts_0248.html
看了此文的人还看了
CDN加速
GaussDB
文字转换成语音
免费的服务器
如何创建网站
域名网站购买
私有云桌面
云主机哪个好
域名怎么备案
手机云电脑
SSL证书申请
云点播服务器
免费OCR是什么
电脑云桌面
域名备案怎么弄
语音转文字
文字图片识别
云桌面是什么
网址安全检测
网站建设搭建
国外CDN加速
SSL免费证书申请
短信批量发送
图片OCR识别
云数据库MySQL
个人域名购买
录音转文字
扫描图片识别文字
OCR图片识别
行驶证识别
虚拟电话号码
电话呼叫中心软件
怎么制作一个网站
Email注册网站
华为VNC
图像文字识别
企业网站制作
个人网站搭建
华为云计算
免费租用云托管
云桌面云服务器
ocr文字识别免费版
HTTPS证书申请
图片文字识别转换
国外域名注册商
使用免费虚拟主机
云电脑主机多少钱
鲲鹏云手机
短信验证码平台
OCR图片文字识别
SSL证书是什么
申请企业邮箱步骤
免费的企业用邮箱
云免流搭建教程
域名价格
推荐文章
