随机森林回归预测 (RandomForestRegPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.regression.RandomForestRegPredictBatchOp

Python 类名：RandomForestRegPredictBatchOp

功能介绍

• 随机森林回归是一种常用的树模型，由于bagging的过程，可以避免过拟合

• 随机森林回归组件支持稠密数据格式

• 支持带样本权重的训练

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
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([
        [1.0, "A", 0, 0, 0],
        [2.0, "B", 1, 1, 0],
        [3.0, "C", 2, 2, 1],
        [4.0, "D", 3, 3, 1]
    ])

batchSource = BatchOperator.fromDataframe(df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')
streamSource = StreamOperator.fromDataframe(df, schemaStr=' f0 double, f1 string, f2 int, f3 int, label int')

trainOp = RandomForestRegTrainBatchOp()\
            .setLabelCol('label')\
            .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
            .linkFrom(batchSource)

RandomForestRegPredictBatchOp()\
     .setPredictionCol('pred')\
     .linkFrom(trainOp, batchSource).print()

RandomForestRegPredictStreamOp(trainOp)\
    .setPredictionCol('pred')\
    .linkFrom(streamSource)\
    .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.regression.RandomForestRegPredictBatchOp;
import com.alibaba.alink.operator.batch.regression.RandomForestRegTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.regression.RandomForestRegPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class RandomForestRegPredictBatchOpTest {
	@Test
	public void testRandomForestRegPredictBatchOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(1.0, "A", 0, 0, 0),
			Row.of(2.0, "B", 1, 1, 0),
			Row.of(3.0, "C", 2, 2, 1),
			Row.of(4.0, "D", 3, 3, 1)
		);
		BatchOperator <?> batchSource = new MemSourceBatchOp(df, " f0 double, f1 string, f2 int, f3 int, label int");
		StreamOperator <?> streamSource = new MemSourceStreamOp(df, " f0 double, f1 string, f2 int, f3 int, label "
			+ "int");
		BatchOperator <?> trainOp = new RandomForestRegTrainBatchOp()
			.setLabelCol("label")
			.setFeatureCols("f0", "f1", "f2", "f3")
			.linkFrom(batchSource);
		new RandomForestRegPredictBatchOp()
			.setPredictionCol("pred")
			.linkFrom(trainOp, batchSource).print();
		new RandomForestRegPredictStreamOp(trainOp)
			.setPredictionCol("pred")
			.linkFrom(streamSource)
			.print();
		StreamOperator.execute();
	}
}

运行结果

批预测结果

f0	f1	f2	f3	label	pred
1.0000	A	0	0	0	0.0000
2.0000	B	1	1	0	0.0000
3.0000	C	2	2	1	1.0000
4.0000	D	3	3	1	1.0000

流预测结果

f0	f1	f2	f3	label	pred
1.0000	A	0	0	0	0.0000
4.0000	D	3	3	1	1.0000
2.0000	B	1	1	0	0.0000
3.0000	C	2	2	1	1.0000