ALINK(十八)：数据处理（四）缺失值处理(二)缺失值填充训练 (ImputerTrainBatchOp)

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

Python 类名：ImputerTrainBatchOp

功能介绍

数据缺失值模型训练

缺失值填充支持4种策略，最大值、最小值、均值、指定数值。当策略为指定数值时，需要设置参数fillValue。

模型生成后处理其他数据参考ImputerPredictBatchOp

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[]	✓
fillValue	填充缺失值	自定义的填充值。当strategy为value时，读取fillValue的值	String		null
strategy	缺失值填充规则	缺失值填充的规则，支持mean，max，min或者value。选择value时，需要读取fillValue的值	String		"MEAN"

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 ImputerTrainBatchOpTest {
    @Test
    public void testImputerTrainBatchOp() 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