向量近似最近邻预测 (VectorApproxNearestNeighborPredictBatchOp)

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

Python 类名：VectorApproxNearestNeighborPredictBatchOp

功能介绍

该功能由训练和预测组成，该组件为预测功能

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。

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		x >= 1	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 pd

useLocalEnv(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 = VectorApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("vec").linkFrom(inOp)
predict = VectorApproxNearestNeighborPredictBatchOp().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.VectorApproxNearestNeighborPredictBatchOp;
import com.alibaba.alink.operator.batch.similarity.VectorApproxNearestNeighborTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class VectorApproxNearestNeighborPredictBatchOpTest {
	@Test
	public void testVectorApproxNearestNeighborPredictBatchOp() 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 VectorApproxNearestNeighborTrainBatchOp().setIdCol("id").setSelectedCol("vec")
			.linkFrom(inOp);
		BatchOperator <?> predict = new VectorApproxNearestNeighborPredictBatchOp().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]”}