Java 类名：com.alibaba.alink.operator.stream.regression.IsotonicRegPredictStreamOp
Python 类名：IsotonicRegPredictStreamOp

功能介绍

保序回归在观念上是寻找一组非递减的片段连续线性函数（piecewise linear continuous functions），即保序函数，使其与样本尽可能的接近。

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

| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | |

| modelFilePath | 模型的文件路径 | 模型的文件路径 | 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.     [0.35, 1],
  9.     [0.6, 1],
  10.     [0.55, 1],
  11.     [0.5, 1],
  12.     [0.18, 0],
  13.     [0.1, 1],
  14.     [0.8, 1],
  15.     [0.45, 0],
  16.     [0.4, 1],
  17.     [0.7, 0],
  18.     [0.02, 1],
  19.     [0.3, 0],
  20.     [0.27, 1],
  21.     [0.2, 0],
  22.     [0.9, 1]])
  24. data = BatchOperator.fromDataframe(df, schemaStr="label double, feature double")
  25. dataStream = StreamOperator.fromDataframe(df, schemaStr="label double, feature double")
  27. trainOp = IsotonicRegTrainBatchOp()\
  28.             .setFeatureCol("feature")\
  29.             .setLabelCol("label")
  31. model = trainOp.linkFrom(data)
  33. predictOp = IsotonicRegPredictStreamOp(model)\
  34.         .setPredictionCol("result")
  36. predictOp.linkFrom(dataStream).print()
  38. 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.regression.IsotonicRegTrainBatchOp;
  5. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  6. import com.alibaba.alink.operator.stream.StreamOperator;
  7. import com.alibaba.alink.operator.stream.regression.IsotonicRegPredictStreamOp;
  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 IsotonicRegPredictStreamOpTest {
  15.     @Test
  16.     public void testIsotonicRegPredictStreamOp() throws Exception {
  17.         List <Row> df = Arrays.asList(
  18.             Row.of(0.35, 1.0),
  19.             Row.of(0.6, 1.0),
  20.             Row.of(0.55, 1.0),
  21.             Row.of(0.5, 1.0),
  22.             Row.of(0.18, 0.0),
  23.             Row.of(0.1, 1.0),
  24.             Row.of(0.8, 1.0),
  25.             Row.of(0.45, 0.0),
  26.             Row.of(0.4, 1.0),
  27.             Row.of(0.7, 0.0),
  28.             Row.of(0.02, 1.0),
  29.             Row.of(0.3, 0.0),
  30.             Row.of(0.27, 1.0),
  31.             Row.of(0.2, 0.0),
  32.             Row.of(0.9, 1.0)
  33.         );
  34.         BatchOperator <?> data = new MemSourceBatchOp(df, "feature double, label double");
  35.         StreamOperator <?> dataStream = new MemSourceStreamOp(df, "feature double, label double");
  36.         BatchOperator <?> trainOp = new IsotonicRegTrainBatchOp()
  37.             .setFeatureCol("feature")
  38.             .setLabelCol("label");
  39.         BatchOperator <?> model = trainOp.linkFrom(data);
  40.         StreamOperator <?> predictOp = new IsotonicRegPredictStreamOp(model)
  41.             .setPredictionCol("result");
  42.         predictOp.linkFrom(dataStream).print();
  43.         StreamOperator.execute();
  44.     }
  45. }

运行结果

模型结果

| model_id | model_info | | —- | —- |

| 0 | {“vectorCol”:””col2””,”featureIndex”:”0”,”featureCol”:null} |

| 1048576 | [0.02,0.3,0.35,0.45,0.5,0.7] |

| 2097152 | [0.5,0.5,0.6666666865348816,0.6666666865348816,0.75,0.75] |

预测结果

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

| 1.0 | 0.9 | 1.0 | 0.75 |

| 0.0 | 0.7 | 1.0 | 0.75 |

| 1.0 | 0.35 | 1.0 | 0.6666666865348816 |

| 1.0 | 0.02 | 1.0 | 0.5 |

| 1.0 | 0.27 | 1.0 | 0.5 |

| 1.0 | 0.5 | 1.0 | 0.75 |

| 0.0 | 0.18 | 1.0 | 0.5 |

| 0.0 | 0.45 | 1.0 | 0.6666666865348816 |

| 1.0 | 0.8 | 1.0 | 0.75 |

| 1.0 | 0.6 | 1.0 | 0.75 |

| 1.0 | 0.4 | 1.0 | 0.6666666865348816 |

| 0.0 | 0.3 | 1.0 | 0.5 |

| 1.0 | 0.55 | 1.0 | 0.75 |

| 0.0 | 0.2 | 1.0 | 0.5 |

| 1.0 | 0.1 | 1.0 | 0.5 |