绝对值最大化模型 (MaxAbsScalerModel)

Java 类名：com.alibaba.alink.pipeline.dataproc.MaxAbsScalerModel
Python 类名：MaxAbsScalerModel

功能介绍

• 绝对值最大标准化是对数据按照绝对值最大值进行标准化的组件, 将数据归一到-1和1之间。
• 该组件为绝对值最大标准化模型，由MaxAbsScaler生成，用于预处理其他数据

  参数说明

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')
sinOp = StreamOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
model = MaxAbsScaler()\
           .setSelectedCols(selectedColNames)\
           .fit(inOp)
model.transform(inOp).print()
model.transform(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.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import com.alibaba.alink.pipeline.dataproc.MaxAbsScaler;
import com.alibaba.alink.pipeline.dataproc.MaxAbsScalerModel;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class MaxAbsScalerModelTest {
    @Test
    public void testMaxAbsScalerModel() 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");
        StreamOperator <?> sinOp = new MemSourceStreamOp(df, "col1 string, col2 double, col3 int");
        MaxAbsScalerModel model = new MaxAbsScaler()
            .setSelectedCols(selectedColNames)
            .fit(inOp);
        model.transform(inOp).print();
        model.transform(sinOp).print();
        StreamOperator.execute();
    }
}