字符串近似最近邻训练 (StringApproxNearestNeighborTrainBatchOp)

Java 类名:com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborTrainBatchOp

Python 类名:StringApproxNearestNeighborTrainBatchOp

功能介绍

该功能由训练和预测组成,支持计算1. 求最近邻topN 2. 求radius范围内的邻居。该功能由预测时候的topN和radius参数控制, 如果填写了topN,则输出最近邻,如果填写了radius,则输出radius范围内的邻居。

SimhashHamming(SimHash_Hamming_Distance)相似度=1-距离/64.0,应选择metric的参数为SIMHASH_HAMMING_SIM。

MinHash应选择metric的参数为MINHASH_SIM。

Jaccard应选择metric的参数为JACCARD_SIM。

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
idCol id列名 id列名 String
selectedCol 选中的列名 计算列对应的列名 String
metric 距离类型 用于计算的距离类型 String “SIMHASH_HAMMING_SIM”, “SIMHASH_HAMMING”, “MINHASH_JACCARD_SIM”, “JACCARD_SIM” “SIMHASH_HAMMING_SIM”
numBucket 分桶个数 分桶个数 Integer 10
numHashTables 哈希表个数 哈希表的数目 Integer 10
seed 采样种子 采样种子 Long 0

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    [0, "abcde", "aabce"],
    [1, "aacedw", "aabbed"],
    [2, "cdefa", "bbcefa"],
    [3, "bdefh", "ddeac"],
    [4, "acedm", "aeefbc"]
])

inOp = BatchOperator.fromDataframe(df, schemaStr='id long, text1 string, text2 string')

train = StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1").setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp)
predict = StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3).linkFrom(train, inOp)
predict.print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborPredictBatchOp;
import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class StringApproxNearestNeighborTrainBatchOpTest {
	@Test
	public void testStringApproxNearestNeighborTrainBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(0, "abcde", "aabce"),
			Row.of(1, "aacedw", "aabbed"),
			Row.of(2, "cdefa", "bbcefa"),
			Row.of(3, "bdefh", "ddeac"),
			Row.of(4, "acedm", "aeefbc")
		);
		BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, text1 string, text2 string");
		BatchOperator <?> train = new StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1")
			.setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp);
		BatchOperator <?> predict = new StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3)
			.linkFrom(train, inOp);
		predict.print();
	}
}

运行结果

id text1 text2
0 abcde {“ID”:“[0,1,2]”,“METRIC”:“[0.953125,0.921875,0…
1 | aacedw | {”ID“:”[0,1,4]“,”METRIC“:”[0.9375,0.90625,0.85…
2 cdefa {“ID”:“[0,1,4]”,“METRIC”:“[0.890625,0.859375,0…
3 | bdefh | {”ID“:”[4,2,1]“,”METRIC“:”[0.9375,0.90625,0.89…
4 acedm {“ID”:“[1,0,4]”,“METRIC”:"[0.921875,0.921875,0…