向量绝对值最大化预测 (VectorMaxAbsScalerPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.dataproc.vector.VectorMaxAbsScalerPredictStreamOp

Python 类名：VectorMaxAbsScalerPredictStreamOp

功能介绍

vector绝对值最大标准化是对vector数据按照最大值和最小值进行标准化的组件, 将数据归一到-1和1之间。

预测组件使用VectorMaxAbsScalerTrainBatchOp训练生成的模型，处理数据之后生成结果数据。

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
modelFilePath	模型的文件路径	模型的文件路径	String			null
outputCol	输出结果列	输出结果列列名，可选，默认null	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([
    ["a", "10.0, 100"],
    ["b", "-2.5, 9"],
    ["c", "100.2, 1"],
    ["d", "-99.9, 100"],
    ["a", "1.4, 1"],
    ["b", "-2.2, 9"],
    ["c", "100.9, 1"]
])
data = BatchOperator.fromDataframe(df, schemaStr="col string, vec string")
dataStream = StreamOperator.fromDataframe(df, schemaStr="col string, vec string")

trainOp = VectorMaxAbsScalerTrainBatchOp()\
           .setSelectedCol("vec")
model = trainOp.linkFrom(data)

streamPredictOp = VectorMaxAbsScalerPredictStreamOp(model)
streamPredictOp.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.dataproc.vector.VectorMaxAbsScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.vector.VectorMaxAbsScalerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class VectorMaxAbsScalerPredictStreamOpTest {
	@Test
	public void testVectorMaxAbsScalerPredictStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of("a", "10.0, 100"),
			Row.of("b", "-2.5, 9"),
			Row.of("c", "100.2, 1"),
			Row.of("d", "-99.9, 100"),
			Row.of("a", "1.4, 1"),
			Row.of("b", "-2.2, 9"),
			Row.of("c", "100.9, 1")
		);
		BatchOperator <?> data = new MemSourceBatchOp(df, "col string, vec string");
		StreamOperator <?> dataStream = new MemSourceStreamOp(df, "col string, vec string");
		BatchOperator <?> trainOp = new VectorMaxAbsScalerTrainBatchOp()
			.setSelectedCol("vec");
		BatchOperator <?> model = trainOp.linkFrom(data);
		StreamOperator <?> streamPredictOp = new VectorMaxAbsScalerPredictStreamOp(model);
		streamPredictOp.linkFrom(dataStream).print();
		StreamOperator.execute();
	}
}

运行结果

col1	vec
c	1.0,0.01
b	-0.024777006937561942,0.09
d	-0.9900891972249752,1.0
a	0.09910802775024777,1.0
b	-0.02180376610505451,0.09
c	0.9930624380574826,0.01
a	0.013875123885034686,0.01