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

  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 = StandardScalerTrainBatchOp()\
  26.            .setSelectedCols(selectedColNames)
  28. trainOp.linkFrom(inOp)
  30. # batch predict
  31. predictOp = StandardScalerPredictBatchOp()
  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.StandardScalerPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.dataproc.StandardScalerTrainBatchOp;
  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 StandardScalerPredictBatchOpTest {
  13.     @Test
  14.     public void testStandardScalerPredictBatchOp() 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 StandardScalerTrainBatchOp()
  28.             .setSelectedCols(selectedColNames);
  29.         trainOp.linkFrom(inOp);
  30.         BatchOperator <?> predictOp = new StandardScalerPredictBatchOp();
  31.         predictOp.linkFrom(trainOp, inOp).print();
  32.     }
  33. }

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 |