ALINK(十七)：数据处理（三）缺失值处理(一)缺失值填充批预测

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

Python 类名：ImputerPredictBatchOp

功能介绍

数据缺失值填充处理

运行时需要指定缺失值模型，由ImputerTrainBatchOp产生。缺失值填充的4种策略，即最大值、最小值、均值、指定数值，在生成缺失值模型时指定。

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_data = 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],
            [None, None, None]
])
             
colnames = ["col1", "col2", "col3"]
selectedColNames = ["col2", "col3"]
inOp = BatchOperator.fromDataframe(df_data, schemaStr='col1 string, col2 double, col3 double')
# train
trainOp = ImputerTrainBatchOp()\
           .setSelectedCols(selectedColNames)
model = trainOp.linkFrom(inOp)
# batch predict
predictOp = ImputerPredictBatchOp()
predictOp.linkFrom(model, inOp).print()
# stream predict
sinOp = StreamOperator.fromDataframe(df_data, schemaStr='col1 string, col2 double, col3 double')
predictStreamOp = ImputerPredictStreamOp(model)
predictStreamOp.linkFrom(sinOp).print()
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.ImputerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.ImputerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.ImputerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class ImputerPredictBatchOpTest {
  @Test
  public void testImputerPredictBatchOp() throws Exception {
    List <Row> df_data = 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),
      Row.of(null, null, null)
    );
    String[] selectedColNames = new String[] {"col2", "col3"};
    BatchOperator <?> inOp = new MemSourceBatchOp(df_data, "col1 string, col2 double, col3 int");
    BatchOperator <?> trainOp = new ImputerTrainBatchOp()
      .setSelectedCols(selectedColNames);
    BatchOperator model = trainOp.linkFrom(inOp);
    BatchOperator <?> predictOp = new ImputerPredictBatchOp();
    predictOp.linkFrom(model, inOp).print();
    StreamOperator <?> sinOp = new MemSourceStreamOp(df_data, "col1 string, col2 double, col3 int");
    StreamOperator <?> predictStreamOp = new ImputerPredictStreamOp(model);
    predictStreamOp.linkFrom(sinOp).print();
    StreamOperator.execute();
  }
}

运行结果

col1	col2	col3
a	10.000000	100
b	-2.500000	9
c	100.200000	1
d	-99.900000	100
a	1.400000	1
b	-2.200000	9
c	100.900000	1
null	15.414286	31