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

功能介绍

文本相似度是在字符串相似度的基础上，基于词，计算两两文章或者句子之间的相似度，文章或者句子需要以空格分割的文本，计算方式和字符串相似度类似：支持SimHashHamming，MinHash和Jaccard三种近似相似度计算方式，通过选择metric参数可计算不同的相似度。
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

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df = pd.DataFrame([
  8.     [0, "a b c d e", "a a b c e"],
  9.     [1, "a a c e d w", "a a b b e d"],
  10.     [2, "c d e f a", "b b c e f a"],
  11.     [3, "b d e f h", "d d e a c"],
  12.     [4, "a c e d m", "a e e f b c"]
  13. ])
  15. inOp = BatchOperator.fromDataframe(df, schemaStr='id long, text1 string, text2 string')
  17. train = TextApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1").setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp)
  18. predict = TextApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3).linkFrom(train, inOp)
  19. predict.print()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.similarity.TextApproxNearestNeighborPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.similarity.TextApproxNearestNeighborTrainBatchOp;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import org.junit.Test;
  9. import java.util.Arrays;
  10. import java.util.List;
  12. public class TextApproxNearestNeighborTrainBatchOpTest {
  13.     @Test
  14.     public void testTextApproxNearestNeighborTrainBatchOp() throws Exception {
  15.         List <Row> df = Arrays.asList(
  16.             Row.of(0, "a b c d e", "a a b c e"),
  17.             Row.of(1, "a a c e d w", "a a b b e d"),
  18.             Row.of(2, "c d e f a", "b b c e f a"),
  19.             Row.of(3, "b d e f h", "d d e a c"),
  20.             Row.of(4, "a c e d m", "a e e f b c")
  21.         );
  22.         BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, text1 string, text2 string");
  23.         BatchOperator <?> train = new TextApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("text1")
  24.             .setMetric("SIMHASH_HAMMING_SIM").linkFrom(inOp);
  25.         BatchOperator <?> predict = new TextApproxNearestNeighborPredictBatchOp().setSelectedCol("text2").setTopN(3)
  26.             .linkFrom(train, inOp);
  27.         predict.print();
  28.     }
  29. }

运行结果

| id | text1 | text2 | | —- | —- | —- |

| 0 | a b c d e | {“ID”:”[0,1,2]”,”METRIC”:”[0.953125,0.921875,0.90625]”} |

| 1 | a a c e d w | {“ID”:”[0,1,4]”,”METRIC”:”[0.9375,0.90625,0.859375]”} |

| 2 | c d e f a | {“ID”:”[0,1,4]”,”METRIC”:”[0.890625,0.859375,0.8125]”} |

| 3 | b d e f h | {“ID”:”[4,2,1]”,”METRIC”:”[0.9375,0.90625,0.890625]”} |

| 4 | a c e d m | {“ID”:”[1,0,4]”,”METRIC”:”[0.921875,0.921875,0.90625]”} |