绝对值最大化流预测 (MaxAbsScalerPredictStreamOp)

Java 类名:com.alibaba.alink.operator.stream.dataproc.MaxAbsScalerPredictStreamOp

Python 类名:MaxAbsScalerPredictStreamOp

功能介绍

  • 绝对值最大标准化是对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。
  • 需要读入MaxAbsScalerTrainBatchOp生成的模型

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
outputCols 输出结果列列名数组 输出结果列列名数组,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]
])
             
colnames = ["col1", "col2", "col3"]
selectedColNames = ["col2", "col3"]

inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
         
# train
trainOp = MaxAbsScalerTrainBatchOp()\
           .setSelectedCols(selectedColNames)

trainOp.linkFrom(inOp)

# batch predict
predictOp = MaxAbsScalerPredictBatchOp()
predictOp.linkFrom(trainOp, inOp).print()

# stream predict
sinOp = StreamOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')

predictStreamOp = MaxAbsScalerPredictStreamOp(trainOp)
predictStreamOp.linkFrom(sinOp).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.MaxAbsScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.MaxAbsScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.dataproc.MaxAbsScalerPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class MaxAbsScalerPredictStreamOpTest {
	@Test
	public void testMaxAbsScalerPredictStreamOp() 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)
		);

		String[] selectedColNames = new String[] {"col2", "col3"};
		BatchOperator <?> inOp = new MemSourceBatchOp(df, "col1 string, col2 double, col3 int");
		BatchOperator <?> trainOp = new MaxAbsScalerTrainBatchOp()
			.setSelectedCols(selectedColNames);
		trainOp.linkFrom(inOp);
		BatchOperator <?> predictOp = new MaxAbsScalerPredictBatchOp();
		predictOp.linkFrom(trainOp, inOp).print();
		StreamOperator <?> sinOp = new MemSourceStreamOp(df, "col1 string, col2 double, col3 int");
		StreamOperator <?> predictStreamOp = new MaxAbsScalerPredictStreamOp(trainOp);
		predictStreamOp.linkFrom(sinOp).print();
		StreamOperator.execute();
	}
}

运行结果

col1 col2 col3
a 0.0991 1.0000
b -0.0248 0.0900
c 0.9931 0.0100
d -0.9901 1.0000
a 0.0139 0.0100
b -0.0218 0.0900
c 1.0000 0.0100
col1 col2 col3
b -0.0248 0.0900
d -0.9901 1.0000
a 0.0139 0.0100
c 0.9931 0.0100
c 1.0000 0.0100
a 0.0991 1.0000
b -0.0218 0.0900