Java 类名:com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborPredictBatchOp
Python 类名:StringApproxNearestNeighborPredictBatchOp

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
selectedCol 选中的列名 计算列对应的列名 String ✓
modelFilePath 模型的文件路径 模型的文件路径 String null
outputCol 输出结果列 输出结果列列名,可选,默认null String null
radius radius值 radius值 Double null
reservedCols 算法保留列名 算法保留列 String[] null
topN TopN的值 TopN的值 Integer [1, +inf) 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

  1. from pyalink.alink import *
  2. import pandas as pd
  3. useLocalEnv(1)
  4. df = pd.DataFrame([
  5. [0, "abcde", "aabce"],
  6. [1, "aacedw", "aabbed"],
  7. [2, "cdefa", "bbcefa"],
  8. [3, "bdefh", "ddeac"],
  9. [4, "acedm", "aeefbc"]
  10. ])
  11. inOp = BatchOperator.fromDataframe(df, schemaStr='id long, text1 string, text2 string')
  12. train = StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1").setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp)
  13. predict = StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3).linkFrom(train, inOp)
  14. predict.print()

Java 代码

  1. import org.apache.flink.types.Row;
  2. import com.alibaba.alink.operator.batch.BatchOperator;
  3. import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborPredictBatchOp;
  4. import com.alibaba.alink.operator.batch.similarity.StringApproxNearestNeighborTrainBatchOp;
  5. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  6. import org.junit.Test;
  7. import java.util.Arrays;
  8. import java.util.List;
  9. public class StringApproxNearestNeighborPredictBatchOpTest {
  10. @Test
  11. public void testStringApproxNearestNeighborPredictBatchOp() throws Exception {
  12. List <Row> df = Arrays.asList(
  13. Row.of(0, "abcde", "aabce"),
  14. Row.of(1, "aacedw", "aabbed"),
  15. Row.of(2, "cdefa", "bbcefa"),
  16. Row.of(3, "bdefh", "ddeac"),
  17. Row.of(4, "acedm", "aeefbc")
  18. );
  19. BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, text1 string, text2 string");
  20. BatchOperator <?> train = new StringApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1")
  21. .setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp);
  22. BatchOperator <?> predict = new StringApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3)
  23. .linkFrom(train, inOp);
  24. predict.print();
  25. }
  26. }

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…