归一化训练 (MinMaxScalerTrainBatchOp)

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

Python 类名：MinMaxScalerTrainBatchOp

功能介绍

数据归一化组件

将数据归一到minValue和maxValue之间，value最终结果为 (value - min) / (max - min) * (maxValue - minValue) + minValue，最终结果的范围为[minValue, maxValue]。

minValue和maxValue由用户指定，默认为0和1。

生成的最大值最小值归一化模型在归一化预处理组件中使用。

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]
max	归一化的上界	归一化的上界	Double			1.0
min	归一化的下界	归一化的下界	Double			0.0

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 = MinMaxScalerTrainBatchOp()\
           .setSelectedCols(selectedColNames)

trainOp.linkFrom(inOp)

# batch predict
predictOp = MinMaxScalerPredictBatchOp()
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.MinMaxScalerPredictBatchOp;
import com.alibaba.alink.operator.batch.dataproc.MinMaxScalerTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class MinMaxScalerTrainBatchOpTest {
	@Test
	public void testMinMaxScalerTrainBatchOp() 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 MinMaxScalerTrainBatchOp()
			.setSelectedCols(selectedColNames);
		trainOp.linkFrom(inOp);
		BatchOperator <?> predictOp = new MinMaxScalerPredictBatchOp();
		predictOp.linkFrom(trainOp, inOp).print();
	}
}

运行结果

col1	col2	col3
a	0.5473	1.0000
b	0.4851	0.0808
c	0.9965	0.0000
d	0.0000	1.0000
a	0.5045	0.0000
b	0.4866	0.0808
c	1.0000	0.0000