Hash Cross特征 (HashCrossFeatureBatchOp)

Java 类名：com.alibaba.alink.operator.batch.feature.HashCrossFeatureBatchOp
Python 类名：HashCrossFeatureBatchOp

功能介绍

将选定的离散列组合成单列的向量类型的数据。

算法原理

将选定列的数据的字符串形式以逗号为分隔符拼接起来，然后使用 murmur3_32 函数得到哈希值，并将哈希值通过平移的方式转换至
[0, 特征数)之间。

使用方式

使用需要设置选取列的列名（selectCols）和输出列名（outputCol），特征数通过参数 numFeatures 设置，特征数也是
输出列中向量的长度。

参数说明

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    ["1.0", "1.0", 1.0, 1],
    ["1.0", "1.0", 0.0, 1],
    ["1.0", "0.0", 1.0, 1],
    ["1.0", "0.0", 1.0, 1],
    ["2.0", "3.0", None, 0],
    ["2.0", "3.0", 1.0, 0],
    ["0.0", "1.0", 2.0, 0],
    ["0.0", "1.0", 1.0, 0]])
data = BatchOperator.fromDataframe(df, schemaStr="f0 string, f1 string, f2 double, label bigint")
cross = HashCrossFeatureBatchOp().setSelectedCols(['f0', 'f1', 'f2']).setOutputCol('cross').setNumFeatures(4)
print(cross.linkFrom(data))

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.feature.HashCrossFeatureBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class HashCrossFeatureBatchOpTest {
    @Test
    public void testHashCrossFeatureBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of("1.0", "1.0", 1.0, 1),
            Row.of("1.0", "1.0", 0.0, 1),
            Row.of("1.0", "0.0", 1.0, 1),
            Row.of("1.0", "0.0", 1.0, 1),
            Row.of("2.0", "3.0", null, 0),
            Row.of("2.0", "3.0", 1.0, 0),
            Row.of("0.0", "1.0", 2.0, 0)
        );
        BatchOperator <?> data = new MemSourceBatchOp(df, "f0 string, f1 string, f2 double, label bigint");
        BatchOperator <?> cross = new HashCrossFeatureBatchOp().setSelectedCols("f0", "f1", "f2").setOutputCol("cross")
            .setNumFeatures(4);
        System.out.print(cross.linkFrom(data));
    }
}

运行结果

| f0 | f1 | f2 | label | cross | | —- | —- | —- | —- | —- |

| 1.0 | 1.0 | 0.0000 | 1 | $36$33:1.0 |

| 1.0 | 1.0 | 1.0000 | 1 | $36$15:1.0 |

| 1.0 | 0.0 | 1.0000 | 1 | $36$33:1.0 |

| 1.0 | 0.0 | 1.0000 | 1 | $36$33:1.0 |

| 2.0 | 3.0 | 1.0000 | 0 | $36$20:1.0 |

| 2.0 | 3.0 | None | 0 | |

| 0.0 | 1.0 | 1.0000 | 0 | $36$28:1.0 |

| 0.0 | 1.0 | 2.0000 | 0 | $36$33:1.0 |