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
from pyalink.alink import *import pandas as pduseLocalEnv(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 StringApproxNearestNeighborPredictBatchOpTest {@Testpublic void testStringApproxNearestNeighborPredictBatchOp() 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… | 
