ALINK(二十):数据处理(六)数值型数据处理(二)标准化 (StandardScalerPredictBatchOp/StandardScalerTrainBatchOp )

标准化训练 (StandardScalerTrainBatchOp)

Java 类名:com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp

Python 类名:StandardScalerTrainBatchOp

功能介绍

标准化是对数据进行按正态化处理的组件

训练过程计算数据的均值和标准差,在预测组件中使用模型结果

参数说明

名称

中文名称

描述

类型

是否必须?

默认值

selectedCols

选择的列名

计算列对应的列名列表

String[]

 

withMean

是否使用均值

是否使用均值,默认使用

Boolean

 

true

withStd

是否使用标准差

是否使用标准差,默认使用

Boolean

 

true

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
            ["a", 10.0, 100],
            ["b", -2.5, 9],
            ["c", 100.2, 1],
            ["d", -99.9, 100],
            ["a", 1.4, 1],
            ["b", -2.2, 9],
            ["c", 100.9, 1]
])
             
colnames = ["col1", "col2", "col3"]
selectedColNames = ["col2", "col3"]
inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
         
# train
trainOp = StandardScalerTrainBatchOp()\
           .setSelectedCols(selectedColNames)
trainOp.linkFrom(inOp)
# batch predict
predictOp = StandardScalerPredictBatchOp()
predictOp.linkFrom(trainOp, inOp).print()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class StandardScalerTrainBatchOpTest {
  @Test
  public void testStandardScalerTrainBatchOp() throws Exception {
    List <Row> df = Arrays.asList(
      Row.of("a", 10.0, 100),
      Row.of("b", -2.5, 9),
      Row.of("c", 100.2, 1),
      Row.of("d", -99.9, 100),
      Row.of("a", 1.4, 1),
      Row.of("b", -2.2, 9),
      Row.of("c", 100.9, 1)
    );
    String[] selectedColNames = new String[] {"col2", "col3"};
    BatchOperator <?> inOp = new MemSourceBatchOp(df, "col1 string, col2 double, col3 int");
    BatchOperator <?> trainOp = new StandardScalerTrainBatchOp()
      .setSelectedCols(selectedColNames);
    trainOp.linkFrom(inOp);
    BatchOperator <?> predictOp = new StandardScalerPredictBatchOp();
    predictOp.linkFrom(trainOp, inOp).print();
  }
}

运行结果

col1

col2

col3

a

-0.0784

1.4596

b

-0.2592

-0.4814

c

1.2270

-0.6521

d

-1.6687

1.4596

a

-0.2028

-0.6521

b

-0.2549

-0.4814

c

1.2371

-0.6521

 

标准化批预测 (StandardScalerPredictBatchOp)

Java 类名:com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp

Python 类名:StandardScalerPredictBatchOp

功能介绍

标准化是对数据进行按正态化处理的组件

使用标准化训练组件训练的模型,对数据做标准化处理

参数说明

名称

中文名称

描述

类型

是否必须?

默认值

outputCols

输出结果列列名数组

输出结果列列名数组,可选,默认null

String[]

 

null

numThreads

组件多线程线程个数

组件多线程线程个数

Integer

 

1

modelStreamFilePath

模型流的文件路径

模型流的文件路径

String

 

null

modelStreamScanInterval

扫描模型路径的时间间隔

描模型路径的时间间隔,单位秒

Integer

 

10

modelStreamStartTime

模型流的起始时间

模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式,详见Timestamp.valueOf(String s)

String

 

null

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
            ["a", 10.0, 100],
            ["b", -2.5, 9],
            ["c", 100.2, 1],
            ["d", -99.9, 100],
            ["a", 1.4, 1],
            ["b", -2.2, 9],
            ["c", 100.9, 1]
])
             
colnames = ["col1", "col2", "col3"]
selectedColNames = ["col2", "col3"]
inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
         
# train
trainOp = StandardScalerTrainBatchOp()\
           .setSelectedCols(selectedColNames)
trainOp.linkFrom(inOp)
# batch predict
predictOp = StandardScalerPredictBatchOp()
predictOp.linkFrom(trainOp, inOp).print()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class StandardScalerPredictBatchOpTest {
  @Test
  public void testStandardScalerPredictBatchOp() throws Exception {
    List <Row> df = Arrays.asList(
      Row.of("a", 10.0, 100),
      Row.of("b", -2.5, 9),
      Row.of("c", 100.2, 1),
      Row.of("d", -99.9, 100),
      Row.of("a", 1.4, 1),
      Row.of("b", -2.2, 9),
      Row.of("c", 100.9, 1)
    );
    String[] selectedColNames = new String[] {"col2", "col3"};
    BatchOperator <?> inOp = new MemSourceBatchOp(df, "col1 string, col2 double, col3 int");
    BatchOperator <?> trainOp = new StandardScalerTrainBatchOp()
      .setSelectedCols(selectedColNames);
    trainOp.linkFrom(inOp);
    BatchOperator <?> predictOp = new StandardScalerPredictBatchOp();
    predictOp.linkFrom(trainOp, inOp).print();
  }
}

运行结果

col1

col2

col3

a

-0.0784

1.4596

b

-0.2592

-0.4814

c

1.2270

-0.6521

d

-1.6687

1.4596

a

-0.2028

-0.6521

b

-0.2549

-0.4814

c

1.2371

-0.6521

 

 

 

上一篇:MYSQL 多行转列、多列合并为JSON


下一篇:leetcode304. 二维区域和检索 (二维前缀)