Java 类名:com.alibaba.alink.operator.batch.similarity.VectorNearestNeighborTrainBatchOp
Python 类名:VectorNearestNeighborTrainBatchOp
功能介绍
该组件为向量最近邻的训练过程,在计算时与 VectorNearestNeighborPredictBatchOp 配合使用。
支持的距离计算方式包含EUCLIDEAN,COSINE,INNERPRODUCT(内积),CITYBLOCK(曼哈顿距离),JACCARD,PEARSON
默认距离EUCLIDEAN
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 | ✓ | 所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR] | |
| metric | 距离度量方式 | 聚类使用的距离类型 | String | | “EUCLIDEAN”, “COSINE”, “INNERPRODUCT”, “CITYBLOCK”, “JACCARD”, “PEARSON” | “EUCLIDEAN” |
代码示例
Python 代码
from pyalink.alink import *import pandas as pduseLocalEnv(1)df = pd.DataFrame([[0, "0 0 0"],[1, "1 1 1"],[2, "2 2 2"]])inOp = BatchOperator.fromDataframe(df, schemaStr='id int, vec string')train = VectorNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("vec").linkFrom(inOp)predict = VectorNearestNeighborPredictBatchOp().setSelectedCol("vec").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.VectorNearestNeighborPredictBatchOp;import com.alibaba.alink.operator.batch.similarity.VectorNearestNeighborTrainBatchOp;import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;import org.junit.Test;import java.util.Arrays;import java.util.List;public class VectorNearestNeighborTrainBatchOpTest {@Testpublic void testVectorNearestNeighborTrainBatchOp() throws Exception {List <Row> df = Arrays.asList(Row.of(0, "0 0 0"),Row.of(1, "1 1 1"),Row.of(2, "2 2 2"));BatchOperator <?> inOp = new MemSourceBatchOp(df, "id int, vec string");BatchOperator <?> train =new VectorNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("vec").linkFrom(inOp);BatchOperator <?> predict =new VectorNearestNeighborPredictBatchOp().setSelectedCol("vec").setTopN(3).linkFrom(train, inOp);predict.print();}}
运行结果
| id | vec | | —- | —- |
| 0 | {“ID”:”[0,1,2]”,”METRIC”:”[0.0,1.7320508075688772,3.4641016151377544]”} |
| 1 | {“ID”:”[1,2,0]”,”METRIC”:”[0.0,1.7320508075688772,1.7320508075688772]”} |
| 2 | {“ID”:”[2,1,0]”,”METRIC”:”[0.0,1.7320508075688772,3.4641016151377544]”} |
