二分K均值聚类预测 (BisectingKMeansPredictStreamOp)

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

Python 类名：BisectingKMeansPredictStreamOp

功能介绍

二分k均值算法是k-means聚类算法的一个变体，主要是为了改进k-means算法随机选择初始质心的随机性造成聚类结果不确定性的问题.

Alink上算法包括二分K均值聚类训练，二分K均值聚类预测, 二分K均值聚类流式预测。

参数说明

训练

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
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"]
])

inBatch = BatchOperator.fromDataframe(df, schemaStr='id int, vec string')
inStream = StreamOperator.fromDataframe(df, schemaStr='id int, vec string')

kmeansTrain = BisectingKMeansTrainBatchOp()\
    .setVectorCol("vec")\
    .setK(2)\
    .linkFrom(inBatch)
kmeansTrain.lazyPrint(10)

predictBatch = BisectingKMeansPredictBatchOp()\
    .setPredictionCol("pred")\
    .linkFrom(kmeansTrain, inBatch)
predictBatch.print()

predictStream = BisectingKMeansPredictStreamOp(kmeansTrain)\
    .setPredictionCol("pred")\
    .linkFrom(inStream)
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.BisectingKMeansPredictBatchOp;
import com.alibaba.alink.operator.batch.clustering.BisectingKMeansTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.clustering.BisectingKMeansPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class BisectingKMeansPredictStreamOpTest {
	@Test
	public void testBisectingKMeansPredictStreamOp() 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 <?> inBatch = new MemSourceBatchOp(df, "id int, vec string");
		StreamOperator <?> inStream = new MemSourceStreamOp(df, "id int, vec string");
		BatchOperator <?> kmeansTrain = new BisectingKMeansTrainBatchOp()
			.setVectorCol("vec")
			.setK(2)
            .linkFrom(inBatch);
		kmeansTrain.lazyPrint(10);

		BatchOperator <?> predictBatch = new BisectingKMeansPredictBatchOp()
			.setPredictionCol("pred")
		    .linkFrom(kmeansTrain, inBatch);
		predictBatch.print();

		StreamOperator <?> predictStream = new BisectingKMeansPredictStreamOp(kmeansTrain)
			.setPredictionCol("pred")
            .linkFrom(inStream);
		predictStream.print();
		StreamOperator.execute();
	}
}

运行结果

模型结果

model_id	model_info
0	{“vectorCol”:“"vec"”,“distanceType”:“"EUCLIDEAN"”,“k”:“2”,“vectorSize”:“3”}
1048576	{“clusterId”:1,“size”:6,“center”:{“data”:[4.6,4.6,4.6]},“cost”:364.61999999999995}
2097152	{“clusterId”:2,“size”:3,“center”:{“data”:[0.1,0.1,0.1]},“cost”:0.06}
3145728	{“clusterId”:3,“size”:3,“center”:{“data”:[9.099999999999998,9.099999999999998,9.099999999999998]},“cost”:0.060000000000172804}

预测结果

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

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