Java 类名：com.alibaba.alink.operator.batch.regression.IsotonicRegPredictBatchOp
Python 类名：IsotonicRegPredictBatchOp

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 |

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]
  23. ])
  25. data = BatchOperator.fromDataframe(df, schemaStr="feature double, label double")
  27. trainOp = IsotonicRegTrainBatchOp()\
  28.             .setFeatureCol("feature")\
  29.             .setLabelCol("label")
  31. model = trainOp.linkFrom(data)
  33. predictOp = IsotonicRegPredictBatchOp()\
  34.             .setPredictionCol("result")
  36. predictOp.linkFrom(model, data).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.regression.IsotonicRegPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.regression.IsotonicRegTrainBatchOp;
  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 IsotonicRegPredictBatchOpTest {
  13.     @Test
  14.     public void testIsotonicRegPredictBatchOp() throws Exception {
  15.         List <Row> df = Arrays.asList(
  16.             Row.of(0.35, 1.0),
  17.             Row.of(0.6, 1.0),
  18.             Row.of(0.55, 1.0),
  19.             Row.of(0.5, 1.0),
  20.             Row.of(0.18, 0.0),
  21.             Row.of(0.1, 1.0),
  22.             Row.of(0.8, 1.0),
  23.             Row.of(0.45, 0.0),
  24.             Row.of(0.4, 1.0),
  25.             Row.of(0.7, 0.0),
  26.             Row.of(0.02, 1.0),
  27.             Row.of(0.3, 0.0),
  28.             Row.of(0.27, 1.0),
  29.             Row.of(0.2, 0.0),
  30.             Row.of(0.9, 1.0)
  31.         );
  32.         BatchOperator <?> data = new MemSourceBatchOp(df, "feature double, label double");
  33.         BatchOperator <?> trainOp = new IsotonicRegTrainBatchOp()
  34.             .setFeatureCol("feature")
  35.             .setLabelCol("label");
  36.         BatchOperator model = trainOp.linkFrom(data);
  37.         BatchOperator <?> predictOp = new IsotonicRegPredictBatchOp()
  38.             .setPredictionCol("result");
  39.         predictOp.linkFrom(model, data).print();
  40.     }
  41. }

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 |