主成分分析预测 (PcaPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.feature.PcaPredictBatchOp

Python 类名：PcaPredictBatchOp

功能介绍

主成分分析，是考察多个变量间相关性一种多元统计方法，研究如何通过少数几个主成分来揭示多个变量间的内部结构，即从原始变量中导出少数几个主成分，使它们尽可能多地保留原始变量的信息，且彼此间互不相关，作为新的综合指标。详细介绍请见维基百科链接wiki。

主成分分析功能包含主成分分析训练和主成分分析预测（批和流)。

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	✓
modelFilePath	模型的文件路径	模型的文件路径	String			null
reservedCols	算法保留列名	算法保留列	String[]			null
vectorCol	向量列名	向量列对应的列名，默认值是null	String		所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR]	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([
        [0.0,0.0,0.0],
        [0.1,0.2,0.1],
        [0.2,0.2,0.8],
        [9.0,9.5,9.7],
        [9.1,9.1,9.6],
        [9.2,9.3,9.9]
])

# batch source 
inOp = BatchOperator.fromDataframe(df, schemaStr='x1 double, x2 double, x3 double')

trainOp = PcaTrainBatchOp()\
       .setK(2)\
       .setSelectedCols(["x1","x2","x3"])

predictOp = PcaPredictBatchOp()\
        .setPredictionCol("pred")

# batch train
inOp.link(trainOp)

# batch predict
predictOp.linkFrom(trainOp,inOp)

predictOp.print()

# stream predict
inStreamOp = StreamOperator.fromDataframe(df, schemaStr='x1 double, x2 double, x3 double')

predictStreamOp = PcaPredictStreamOp(trainOp)\
        .setPredictionCol("pred")

predictStreamOp.linkFrom(inStreamOp)

predictStreamOp.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.feature.PcaPredictBatchOp;
import com.alibaba.alink.operator.batch.feature.PcaTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.feature.PcaPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class PcaPredictBatchOpTest {
	@Test
	public void testPcaPredictBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(0.0, 0.0, 0.0),
			Row.of(0.1, 0.2, 0.1),
			Row.of(0.2, 0.2, 0.8),
			Row.of(9.0, 9.5, 9.7),
			Row.of(9.1, 9.1, 9.6),
			Row.of(9.2, 9.3, 9.9)
		);
		BatchOperator <?> inOp = new MemSourceBatchOp(df, "x1 double, x2 double, x3 double");
		BatchOperator <?> trainOp = new PcaTrainBatchOp()
			.setK(2)
			.setSelectedCols("x1", "x2", "x3");
		BatchOperator <?> predictOp = new PcaPredictBatchOp()
			.setPredictionCol("pred");
		inOp.link(trainOp);
		predictOp.linkFrom(trainOp, inOp);
		predictOp.print();
		StreamOperator <?> inStreamOp = new MemSourceStreamOp(df, "x1 double, x2 double, x3 double");
		StreamOperator <?> predictStreamOp = new PcaPredictStreamOp(trainOp)
			.setPredictionCol("pred");
		predictStreamOp.linkFrom(inStreamOp);
		predictStreamOp.print();
		StreamOperator.execute();
	}
}

运行结果

x1	x2	x3	pred
9.0	9.5	9.7	3.2280384305400736,1.1516225426477789E-4
0.2	0.2	0.8	0.13565076707329407,0.09003329494282108
9.2	9.3	9.9	3.250783163664603,0.0456526246528135
9.1	9.1	9.6	3.182618319978973,0.027469531992220464
0.1	0.2	0.1	0.045855205015063565,-0.012182917696915518
0.0	0.0	0.0	0.0,0.0