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

功能介绍

填充缺失值模型，由Imputer生成，可以对数据执行缺失值填充操作

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
overwriteSink 是否覆写已有数据 是否覆写已有数据 Boolean false
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.             [None, None, None]
  16. ])
  18. colnames = ["col1", "col2", "col3"]
  19. selectedColNames = ["col2", "col3"]
  21. inOp = BatchOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 double')
  22. sinOp = StreamOperator.fromDataframe(df, schemaStr='col1 string, col2 double, col3 double')
  24. model = Imputer()\
  25.            .setSelectedCols(selectedColNames)\
  26.            .fit(inOp)
  28. model.transform(inOp).print()
  30. model.transform(sinOp).print()
  32. 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.pipeline.dataproc.Imputer;
  5. import com.alibaba.alink.pipeline.dataproc.ImputerModel;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import com.alibaba.alink.operator.stream.StreamOperator;
  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 ImputerTrainBatchOpTest {
  15.     @Test
  16.     public void testImputerTrainBatchOp() throws Exception {
  17.         List <Row> df_data = 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.             Row.of(null, null, null)
  26.         );
  28.         String[] selectedColNames = new String[] {"col2", "col3"};
  29.         BatchOperator <?> inOp = new MemSourceBatchOp(df_data, "col1 string, col2 double, col3 int");
  30.         ImputerModel imputerModel = new Imputer()
  31.             .setSelectedCols(selectedColNames)
  32.             .fit(inOp);
  33.         imputerModel.transform(inOp).print();
  34.         StreamOperator <?> sinOp = new MemSourceStreamOp(df_data, "col1 string, col2 double, col3 int");
  35.         imputerModel.transform(inOp).print();
  36.         StreamOperator.execute();
  37.     }
  38. }

运行结果

| col1 | col2 | col3 | | —- | —- | —- |

| a | 10.000000 | 100 |

| b | -2.500000 | 9 |

| c | 100.200000 | 1 |

| d | -99.900000 | 100 |

| a | 1.400000 | 1 |

| b | -2.200000 | 9 |

| c | 100.900000 | 1 |

| null | 15.414286 | 31 |