• ALINK(三十三):特征工程(十二)特征编码(三)特征哈希 (FeatureHasherBatchOp)


    Java 类名:com.alibaba.alink.operator.batch.feature.FeatureHasherBatchOp

    Python 类名:FeatureHasherBatchOp

    功能介绍

    将多个特征组合成一个特征向量。

    参数说明

    名称

    中文名称

    描述

    类型

    是否必须?

    默认值

    outputCol

    输出结果列列名

    输出结果列列名,必选

    String

     

    selectedCols

    选择的列名

    计算列对应的列名列表

    String[]

     

    categoricalCols

    离散特征列名

    离散特征列名

    String[]

       

    numFeatures

    向量维度

    生成向量长度

    Integer

     

    262144

    reservedCols

    算法保留列名

    算法保留列

    String[]

     

    null

    numThreads

    组件多线程线程个数

    组件多线程线程个数

    Integer

     

    1

    代码示例

    Python 代码

    from pyalink.alink import *
    import pandas as pd
    useLocalEnv(1)
    df = pd.DataFrame([
        [1.1, True, "2", "A"],
        [1.1, False, "2", "B"],
        [1.1, True, "1", "B"],
        [2.2, True, "1", "A"]
    ])
    inOp1 = BatchOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
    inOp2 = StreamOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
    hasher = FeatureHasherBatchOp().setSelectedCols(["double", "bool", "number", "str"]).setOutputCol("output").setNumFeatures(200)
    hasher.linkFrom(inOp1).print()
    hasher = FeatureHasherStreamOp().setSelectedCols(["double", "bool", "number", "str"]).setOutputCol("output").setNumFeatures(200)
    hasher.linkFrom(inOp2).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.feature.FeatureHasherBatchOp;
    import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
    import com.alibaba.alink.operator.stream.StreamOperator;
    import com.alibaba.alink.operator.stream.feature.FeatureHasherStreamOp;
    import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
    import org.junit.Test;
    import java.util.Arrays;
    import java.util.List;
    public class FeatureHasherBatchOpTest {
      @Test
      public void testFeatureHasherBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
          Row.of(1.1, true, 2, "A"),
          Row.of(1.1, false, 2, "B"),
          Row.of(1.1, true, 1, "B"),
          Row.of(2.2, true, 1, "A")
        );
        BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "double double, bool boolean, number int, str string");
        StreamOperator <?> inOp2 = new MemSourceStreamOp(df, "double double, bool boolean, number int, str string");
        BatchOperator <?> hasher = new FeatureHasherBatchOp().setSelectedCols("double", "bool", "number", "str")
          .setOutputCol("output").setNumFeatures(200);
        hasher.linkFrom(inOp1).print();
        StreamOperator <?> hasher2 = new FeatureHasherStreamOp().setSelectedCols("double", "bool", "number", "str")
          .setOutputCol("output").setNumFeatures(200);
        hasher2.linkFrom(inOp2).print();
        StreamOperator.execute();
      }
    }

    运行结果

    输出数据

    double

    bool

    number

    str

    output

    1.1000

    true

    2

    A

    $200$13:2.0 38:1.1 45:1.0 195:1.0

    1.1000

    false

    2

    B

    $200$13:2.0 30:1.0 38:1.1 76:1.0

    1.1000

    true

    1

    B

    $200$13:1.0 38:1.1 76:1.0 195:1.0

    2.2000

    true

    1

    A

    $200$13:1.0 38:2.2 45:1.0 195:1.0

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  • 原文地址:https://www.cnblogs.com/qiu-hua/p/14901612.html
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