K均值聚类预测 (KMeansPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.clustering.KMeansPredictStreamOp

Python 类名：KMeansPredictStreamOp

功能介绍

KMeans 是一个经典的聚类算法。

基本思想是：以空间中k个点为中心进行聚类，对最靠近他们的对象归类。通过迭代的方法，逐次更新各聚类中心的值，直至得到最好的聚类结果。

Alink上KMeans算法包括KMeans，KMeans批量预测, KMeans流式预测。

参数说明

名称	中文名称	描述	类型	是否必须？	默认值
predictionCol	预测结果列名	预测结果列名	String	✓
modelFilePath	模型的文件路径	模型的文件路径	String		null
predictionDetailCol	预测详细信息列名	预测详细信息列名	String
predictionDistanceCol	预测距离列名	预测距离列名	String
reservedCols	算法保留列名	算法保留列	String[]		null
numThreads	组件多线程线程个数	组件多线程线程个数	Integer		1
modelStreamFilePath	模型流的文件路径	模型流的文件路径	String		null
modelStreamScanInterval	扫描模型路径的时间间隔	描模型路径的时间间隔，单位秒	Integer		10
modelStreamStartTime	模型流的起始时间	模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s)	String		null

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, "0.1,0.1,0.1"],
    [2, "0.2,0.2,0.2"],
    [3, "9 9 9"],
    [4, "9.1 9.1 9.1"],
    [5, "9.2 9.2 9.2"]
])

inOp1 = BatchOperator.fromDataframe(df, schemaStr='id int, vec string')
inOp2 = StreamOperator.fromDataframe(df, schemaStr='id int, vec string')

kmeans = KMeansTrainBatchOp()\
    .setVectorCol("vec")\
    .setK(2)\
    .linkFrom(inOp1)
kmeans.lazyPrint(10)

predictBatch = KMeansPredictBatchOp()\
    .setPredictionCol("pred")\
    .linkFrom(kmeans, inOp1)
predictBatch.print()

predictStream = KMeansPredictStreamOp(kmeans)\
    .setPredictionCol("pred")\
    .linkFrom(inOp2)
predictStream.print()

StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.clustering.KMeansPredictBatchOp;
import com.alibaba.alink.operator.batch.clustering.KMeansTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.clustering.KMeansPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class KMeansPredictBatchOpTest {
	@Test
	public void testKMeansPredictBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(0, "0 0 0"),
			Row.of(1, "0.1,0.1,0.1"),
			Row.of(2, "0.2,0.2,0.2"),
			Row.of(3, "9 9 9"),
			Row.of(4, "9.1 9.1 9.1"),
			Row.of(5, "9.2 9.2 9.2")
		);
		BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "id int, vec string");
		StreamOperator <?> inOp2 = new MemSourceStreamOp(df, "id int, vec string");
		BatchOperator <?> kmeans = new KMeansTrainBatchOp()
			.setVectorCol("vec")
			.setK(2)
            .linkFrom(inOp1);
		kmeans.lazyPrint(10);

		BatchOperator <?> predictBatch = new KMeansPredictBatchOp()
			.setPredictionCol("pred")
            .linkFrom(kmeans, inOp1);
		predictBatch.print();

		StreamOperator <?> predictStream = new KMeansPredictStreamOp(kmeans)
			.setPredictionCol("pred")
            .linkFrom(inOp2);
		predictStream.print();
		StreamOperator.execute();
	}
}

运行结果

模型结果

model_id	model_info
0	{“vectorCol”:“"vec"”,“latitudeCol”:null,“longitudeCol”:null,“distanceType”:“"EUCLIDEAN"”,“k”:“2”,“vectorSize”:“3”}
1048576	{“clusterId”:0,“weight”:3.0,“vec”:{“data”:[9.099999999999998,9.099999999999998,9.099999999999998]}}
2097152	{“clusterId”:1,“weight”:3.0,“vec”:{“data”:[0.1,0.1,0.1]}}

预测结果

id	vec	pred
0	0 0 0	1
1	0.1,0.1,0.1	1
2	0.2,0.2,0.2	1
3	9 9 9	0
4	9.1 9.1 9.1	0
5	9.2 9.2 9.2	0

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