标准化训练 (StandardScalerTrainBatchOp)

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

Python 类名：StandardScalerTrainBatchOp

功能介绍

标准化是对数据进行按正态化处理的组件

训练过程计算数据的均值和标准差，在预测组件中使用模型结果

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
selectedCols	选择的列名	计算列对应的列名列表	String[]	✓	所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]
withMean	是否使用均值	是否使用均值，默认使用	Boolean			true
withStd	是否使用标准差	是否使用标准差，默认使用	Boolean			true

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 StandardScalerTrainBatchOpTest {
	@Test
	public void testStandardScalerTrainBatchOp() 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