高斯混合模型预测 (GmmPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.clustering.GmmPredictBatchOp

Python 类名：GmmPredictBatchOp

功能介绍

高斯混合模型对应的预测组件，基于训练好的高斯混合模型（Gaussian Mixture Model）进行聚类预测。

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
predictionCol	预测结果列名	预测结果列名	String	✓
vectorCol	向量列名	向量列对应的列名	String	✓	所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR]
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

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
    ["-0.6264538 0.1836433"],
    ["-0.8356286 1.5952808"],
    ["0.3295078 -0.8204684"],
    ["0.4874291 0.7383247"],
    ["0.5757814 -0.3053884"],
    ["1.5117812 0.3898432"],
    ["-0.6212406 -2.2146999"],
    ["11.1249309 9.9550664"],
    ["9.9838097 10.9438362"],
    ["10.8212212 10.5939013"],
    ["10.9189774 10.7821363"],
    ["10.0745650 8.0106483"],
    ["10.6198257 9.9438713"],
    ["9.8442045 8.5292476"],
    ["9.5218499 10.4179416"],
])

data = BatchOperator.fromDataframe(df_data, schemaStr='features string')
dataStream = StreamOperator.fromDataframe(df_data, schemaStr='features string')

gmm = GmmTrainBatchOp() \
    .setVectorCol("features") \
    .setEpsilon(0.)

model = gmm.linkFrom(data)

predictor = GmmPredictBatchOp() \
    .setPredictionCol("cluster_id") \
    .setVectorCol("features") \
    .setPredictionDetailCol("cluster_detail")

predictor.linkFrom(model, data).print()

predictorStream = GmmPredictStreamOp(model) \
    .setPredictionCol("cluster_id") \
    .setVectorCol("features") \
    .setPredictionDetailCol("cluster_detail")

predictorStream.linkFrom(dataStream).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.GmmPredictBatchOp;
import com.alibaba.alink.operator.batch.clustering.GmmTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.clustering.GmmPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class GmmPredictBatchOpTest {
	@Test
	public void testGmmPredictBatchOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of("-0.6264538 0.1836433"),
			Row.of("-0.8356286 1.5952808"),
			Row.of("0.3295078 -0.8204684"),
			Row.of("0.4874291 0.7383247"),
			Row.of("0.5757814 -0.3053884"),
			Row.of("1.5117812 0.3898432"),
			Row.of("-0.6212406 -2.2146999"),
			Row.of("11.1249309 9.9550664"),
			Row.of("9.9838097 10.9438362"),
			Row.of("10.8212212 10.5939013"),
			Row.of("10.9189774 10.7821363"),
			Row.of("10.0745650 8.0106483"),
			Row.of("10.6198257 9.9438713"),
			Row.of("9.8442045 8.5292476"),
			Row.of("9.5218499 10.4179416")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df_data, "features string");
		StreamOperator <?> dataStream = new MemSourceStreamOp(df_data, "features string");
		BatchOperator <?> gmm = new GmmTrainBatchOp()
			.setVectorCol("features")
			.setEpsilon(0.);
		BatchOperator <?> model = gmm.linkFrom(data);
		BatchOperator <?> predictor = new GmmPredictBatchOp()
			.setPredictionCol("cluster_id")
			.setVectorCol("features")
			.setPredictionDetailCol("cluster_detail");
		predictor.linkFrom(model, data).print();
		StreamOperator <?> predictorStream = new GmmPredictStreamOp(model)
			.setPredictionCol("cluster_id")
			.setVectorCol("features")
			.setPredictionDetailCol("cluster_detail");
		predictorStream.linkFrom(dataStream).print();
		StreamOperator.execute();
	}
}

运行结果

features	cluster_id	cluster_detail
-0.6264538 0.1836433	0	1.0 4.275273913994647E-92
-0.8356286 1.5952808	0	1.0 1.0260377730322135E-92
0.3295078 -0.8204684	0	1.0 1.0970173367582936E-80
0.4874291 0.7383247	0	1.0 3.30217313232611E-75
0.5757814 -0.3053884	0	1.0 3.163811360527691E-76
1.5117812 0.3898432	0	1.0 2.1018052308786076E-62
-0.6212406 -2.2146999	0	1.0 6.772270268625197E-97
11.1249309 9.9550664	1	3.1567838012477083E-56 1.0
9.9838097 10.9438362	1	1.9024447346702333E-51 1.0
10.8212212 10.5939013	1	2.8009730987296404E-56 1.0
10.9189774 10.7821363	1	1.7209132744891575E-57 1.0
10.0745650 8.0106483	1	2.864269663513225E-43 1.0
10.6198257 9.9438713	1	5.77327399194046E-53 1.0
9.8442045 8.5292476	1	2.5273123050926845E-43 1.0
9.5218499 10.4179416	1	1.7314580596765865E-46 1.0

备注

资源如何预估

• 每个worker的内存大小如何估计？
  • 将模型的大小乘以30，即假设输入数据的大小是1GB，那么每个worker的大小可以设置为30GB。
• 如何设置worker的个数？
  • 一般情况下，随着worker数目的增加，由于通信开销的存在，分布式训练任务会先变快，然后变慢。用户如果观测到worker数目增加之后，速度变慢，那么应该停止增加worker数目。