Java 类名：com.alibaba.alink.operator.batch.dataproc.MaxAbsScalerTrainBatchOp
Python 类名：MaxAbsScalerTrainBatchOp

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。
  • 使用绝对值最大标准化预测组件使用生成的模型，转换输入的数据

    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] | |

代码示例

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df = pd.DataFrame([
  8.             ["a", 10.0, 100],
  9.             ["b", -2.5, 9],
  10.             ["c", 100.2, 1],
  11.             ["d", -99.9, 100],
  12.             ["a", 1.4, 1],
  13.             ["b", -2.2, 9],
  14.             ["c", 100.9, 1]
  15. ])
  17. colnames = ["col1", "col2", "col3"]
  18. selectedColNames = ["col2", "col3"]
  21. inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 long')
  24. # train
  25. trainOp = MaxAbsScalerTrainBatchOp()\
  26.            .setSelectedCols(selectedColNames)
  28. trainOp.linkFrom(inOp)
  30. # batch predict
  31. predictOp = MaxAbsScalerPredictBatchOp()
  32. predictOp.linkFrom(trainOp, inOp).print()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.dataproc.MaxAbsScalerPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.dataproc.MaxAbsScalerTrainBatchOp;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import org.junit.Test;
  9. import java.util.Arrays;
  10. import java.util.List;
  12. public class MaxAbsScalerTrainBatchOpTest {
  13.     @Test
  14.     public void testMaxAbsScalerTrainBatchOp() throws Exception {
  15.         List <Row> df = Arrays.asList(
  16.             Row.of("a", 10.0, 100),
  17.             Row.of("b", -2.5, 9),
  18.             Row.of("c", 100.2, 1),
  19.             Row.of("d", -99.9, 100),
  20.             Row.of("a", 1.4, 1),
  21.             Row.of("b", -2.2, 9),
  22.             Row.of("c", 100.9, 1)
  23.         );
  25.         String[] selectedColNames = new String[] {"col2", "col3"};
  26.         BatchOperator <?> inOp = new MemSourceBatchOp(df, "col1 string, col2 double, col3 int");
  27.         BatchOperator <?> trainOp = new MaxAbsScalerTrainBatchOp()
  28.             .setSelectedCols(selectedColNames);
  29.         trainOp.linkFrom(inOp);
  30.         BatchOperator <?> predictOp = new MaxAbsScalerPredictBatchOp();
  31.         predictOp.linkFrom(trainOp, inOp).print();
  32.     }
  33. }

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 |