Cross特征预测 (CrossFeaturePredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.feature.CrossFeaturePredictStreamOp

Python 类名：CrossFeaturePredictStreamOp

功能介绍

将选定的特征列组合成单个向量类型的特征。

使用方式

该组件是预测组件，需要配合预测组件 CrossFeatureTrainBatchOp 使用。

使用中指定输出列名（outputCol）即可。

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
outputCol	输出结果列列名	输出结果列列名，必选	String	✓
modelFilePath	模型的文件路径	模型的文件路径	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([
["1.0", "1.0", 1.0, 1],
["1.0", "1.0", 0.0, 1],
["1.0", "0.0", 1.0, 1],
["1.0", "0.0", 1.0, 1],
["2.0", "3.0", None, 0],
["2.0", "3.0", 1.0, 0],
["0.0", "1.0", 2.0, 0],
["0.0", "1.0", 1.0, 0]])
batchData = BatchOperator.fromDataframe(df, schemaStr="f0 string, f1 string, f2 double, label bigint")
streamData = StreamOperator.fromDataframe(df, schemaStr="f0 string, f1 string, f2 double, label bigint")
train = CrossFeatureTrainBatchOp().setSelectedCols(['f0','f1','f2']).linkFrom(batchData)
CrossFeaturePredictStreamOp(train).setOutputCol("cross").linkFrom(streamData).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.CrossFeaturePredictBatchOp;
import com.alibaba.alink.operator.batch.feature.CrossFeatureTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class CrossFeaturePredictStreamOpTest {
	@Test
	public void testCrossFeaturePredictStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("1.0", "1.0", 1.0, 1),
			Row.of("1.0", "1.0", 0.0, 1),
			Row.of("1.0", "0.0", 1.0, 1),
			Row.of("1.0", "0.0", 1.0, 1),
			Row.of("2.0", "3.0", null, 0),
			Row.of("2.0", "3.0", 1.0, 0),
			Row.of("0.0", "1.0", 2.0, 0)
		);
		BatchOperator <?> batchData = new MemSourceBatchOp(df, "f0 string, f1 string, f2 double, label int");
		StreamOperator<?> streamData = new MemSourceStreamOp(df, "f0 string, f1 string, f2 double, label int");
		BatchOperator <?> train = new CrossFeatureTrainBatchOp().setSelectedCols("f0", "f1", "f2").linkFrom(batchData);
		new CrossFeaturePredictStreamOp(train).setOutputCol("cross").linkFrom(streamData).print();
		StreamOperator.execute();
	}
}

运行结果

f0	f1	f2	label	cross
2.0	3.0	1.0000	0	$36$7:1.0
0.0	1.0	2.0000	0	$36$32:1.0
1.0	1.0	0.0000	1	$36$12:1.0
1.0	1.0	1.0000	1	$36$3:1.0
1.0	0.0	1.0000	1	$36$0:1.0
1.0	0.0	1.0000	1	$36$0:1.0
2.0	3.0	null	0	$36$25:1.0