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

功能介绍

  • 决策树回归组件支持稠密数据格式
  • 支持带样本权重的训练

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 |

| reservedCols | 算法保留列名 | 算法保留列 | 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.     [1.0, "A", 0, 0, 0],
  9.     [2.0, "B", 1, 1, 0],
  10.     [3.0, "C", 2, 2, 1],
  11.     [4.0, "D", 3, 3, 1]
  12. ])
  13. batchSource = BatchOperator.fromDataframe(
  14.     df, schemaStr='f0 double, f1 string, f2 int, f3 int, label int')
  15. streamSource = StreamOperator.fromDataframe(
  16.     df, schemaStr='f0 double, f1 string, f2 int, f3 int, label int')
  18. trainOp = DecisionTreeRegTrainBatchOp()\
  19.     .setLabelCol('label')\
  20.     .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
  21.     .linkFrom(batchSource)
  22. predictBatchOp = DecisionTreeRegPredictBatchOp()\
  23.     .setPredictionCol('pred')
  24. predictStreamOp = DecisionTreeRegPredictStreamOp(trainOp)\
  25.     .setPredictionCol('pred')
  27. predictBatchOp.linkFrom(trainOp, batchSource).print()
  28. predictStreamOp.linkFrom(streamSource).print()
  30. 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.DecisionTreeRegPredictBatchOp;
  5. import com.alibaba.alink.operator.batch.regression.DecisionTreeRegTrainBatchOp;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import com.alibaba.alink.operator.stream.StreamOperator;
  8. import com.alibaba.alink.operator.stream.regression.DecisionTreeRegPredictStreamOp;
  9. import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
  10. import org.junit.Test;
  12. import java.util.Arrays;
  13. import java.util.List;
  15. public class DecisionTreeRegPredictBatchOpTest {
  16.     @Test
  17.     public void testDecisionTreeRegPredictBatchOp() throws Exception {
  18.         List <Row> df = Arrays.asList(
  19.             Row.of(1.0, "A", 0, 0, 0),
  20.             Row.of(2.0, "B", 1, 1, 0),
  21.             Row.of(3.0, "C", 2, 2, 1),
  22.             Row.of(4.0, "D", 3, 3, 1)
  23.         );
  24.         BatchOperator <?> batchSource = new MemSourceBatchOp(
  25.             df, "f0 double, f1 string, f2 int, f3 int, label int");
  26.         StreamOperator <?> streamSource = new MemSourceStreamOp(
  27.             df, "f0 double, f1 string, f2 int, f3 int, label int");
  28.         BatchOperator <?> trainOp = new DecisionTreeRegTrainBatchOp()
  29.             .setLabelCol("label")
  30.             .setFeatureCols("f0", "f1", "f2", "f3")
  31.             .linkFrom(batchSource);
  32.         BatchOperator <?> predictBatchOp = new DecisionTreeRegPredictBatchOp()
  33.             .setPredictionCol("pred");
  34.         StreamOperator <?> predictStreamOp = new DecisionTreeRegPredictStreamOp(trainOp)
  35.             .setPredictionCol("pred");
  36.         predictBatchOp.linkFrom(trainOp, batchSource).print();
  37.         predictStreamOp.linkFrom(streamSource).print();
  38.         StreamOperator.execute();
  39.     }
  40. }

运行结果

批预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

流预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |