Java 类名：com.alibaba.alink.operator.stream.dataproc.vector.VectorMaxAbsScalerPredictStreamOp
Python 类名：VectorMaxAbsScalerPredictStreamOp

功能介绍

vector绝对值最大标准化是对vector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。
预测组件使用VectorMaxAbsScalerTrainBatchOp训练生成的模型，处理数据之后生成结果数据。

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 | | —- | —- | —- | —- | —- | —- | —- |

| modelFilePath | 模型的文件路径 | 模型的文件路径 | String | | | null |

| outputCol | 输出结果列 | 输出结果列列名，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 |

| modelStreamFilePath | 模型流的文件路径 | 模型流的文件路径 | String | | | null |

| modelStreamScanInterval | 扫描模型路径的时间间隔 | 描模型路径的时间间隔，单位秒 | Integer | | | 10 |

| modelStreamStartTime | 模型流的起始时间 | 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s) | String | | | null |

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. ])
  16. data = BatchOperator.fromDataframe(df, schemaStr="col string, vec string")
  17. dataStream = StreamOperator.fromDataframe(df, schemaStr="col string, vec string")
  19. trainOp = VectorMaxAbsScalerTrainBatchOp()\
  20.            .setSelectedCol("vec")
  21. model = trainOp.linkFrom(data)
  23. streamPredictOp = VectorMaxAbsScalerPredictStreamOp(model)
  24. streamPredictOp.linkFrom(dataStream).print()
  26. StreamOperator.execute()

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.vector.VectorMaxAbsScalerTrainBatchOp;
  5. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  6. import com.alibaba.alink.operator.stream.StreamOperator;
  7. import com.alibaba.alink.operator.stream.dataproc.vector.VectorMaxAbsScalerPredictStreamOp;
  8. import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
  9. import org.junit.Test;
  11. import java.util.Arrays;
  12. import java.util.List;
  14. public class VectorMaxAbsScalerPredictStreamOpTest {
  15.     @Test
  16.     public void testVectorMaxAbsScalerPredictStreamOp() throws Exception {
  17.         List <Row> df = Arrays.asList(
  18.             Row.of("a", "10.0, 100"),
  19.             Row.of("b", "-2.5, 9"),
  20.             Row.of("c", "100.2, 1"),
  21.             Row.of("d", "-99.9, 100"),
  22.             Row.of("a", "1.4, 1"),
  23.             Row.of("b", "-2.2, 9"),
  24.             Row.of("c", "100.9, 1")
  25.         );
  26.         BatchOperator <?> data = new MemSourceBatchOp(df, "col string, vec string");
  27.         StreamOperator <?> dataStream = new MemSourceStreamOp(df, "col string, vec string");
  28.         BatchOperator <?> trainOp = new VectorMaxAbsScalerTrainBatchOp()
  29.             .setSelectedCol("vec");
  30.         BatchOperator <?> model = trainOp.linkFrom(data);
  31.         StreamOperator <?> streamPredictOp = new VectorMaxAbsScalerPredictStreamOp(model);
  32.         streamPredictOp.linkFrom(dataStream).print();
  33.         StreamOperator.execute();
  34.     }
  35. }

运行结果

| col1 | vec | | —- | —- |

| c | 1.0,0.01 |

| b | -0.024777006937561942,0.09 |

| d | -0.9900891972249752,1.0 |

| a | 0.09910802775024777,1.0 |

| b | -0.02180376610505451,0.09 |

| c | 0.9930624380574826,0.01 |

| a | 0.013875123885034686,0.01 |