特征哈希 (FeatureHasherStreamOp)

Java 类名：com.alibaba.alink.operator.stream.feature.FeatureHasherStreamOp
Python 类名：FeatureHasherStreamOp

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？ | 取值范围 | 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 FeatureHasherStreamOpTest {
    @Test
    public void testFeatureHasherStreamOp() 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

批预测结果

| f_string | f_long | f_double | | —- | —- | —- |

| a | 2 | 0 |

| b | 2 | 0 |

| c | 4 | 0 |

| d | 0 | 4 |

| a | 2 | 0 |

| b | 2 | 0 |

| c | 4 | 0 |

| d | 0 | 4 |

流预测结果

| f_string | f_long | f_double | | —- | —- | —- |

| c | 4 | 0 |

| a | 2 | 0 |

| d | 0 | 4 |

| d | 0 | 4 |

| b | 2 | 0 |

| c | 4 | 0 |

| a | 2 | 0 |

| b | 2 | 0 |