标准化批预测 (StandardScalerPredictBatchOp)

Java 类名：com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp

Python 类名：StandardScalerPredictBatchOp

功能介绍

标准化是对数据进行按正态化处理的组件

使用标准化训练组件训练的模型，对数据做标准化处理

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

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 = StandardScalerTrainBatchOp()\
           .setSelectedCols(selectedColNames)

trainOp.linkFrom(inOp)

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

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class StandardScalerPredictBatchOpTest {
	@Test
	public void testStandardScalerPredictBatchOp() 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 StandardScalerTrainBatchOp()
			.setSelectedCols(selectedColNames);
		trainOp.linkFrom(inOp);
		BatchOperator <?> predictOp = new StandardScalerPredictBatchOp();
		predictOp.linkFrom(trainOp, inOp).print();
	}
}

运行结果

col1	col2	col3
a	-0.0784	1.4596
b	-0.2592	-0.4814
c	1.2270	-0.6521
d	-1.6687	1.4596
a	-0.2028	-0.6521
b	-0.2549	-0.4814
c	1.2371	-0.6521