Java 类名：com.alibaba.alink.operator.batch.timeseries.DeepARPredictBatchOp
Python 类名：DeepARPredictBatchOp

功能介绍

使用 DeepAR 进行时间序列训练和预测。

使用方式

参考文档 https://www.yuque.com/pinshu/alink_guide/xbp5ky

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 | ✓ | | |

| valueCol | value列，类型为MTable | value列，类型为MTable | String | ✓ | 所选列类型为 [M_TABLE] | |

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

| predictNum | 预测条数 | 预测条数 | Integer | | | 1 |

| predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | | | |

| 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. import time, datetime
  8. import numpy as np
  9. import pandas as pd
  11. data = pd.DataFrame([
  12.     [0,  datetime.datetime.fromisoformat('2021-11-01 00:00:00'), 100.0],
  13.     [0,  datetime.datetime.fromisoformat('2021-11-02 00:00:00'), 100.0],
  14.     [0,  datetime.datetime.fromisoformat('2021-11-03 00:00:00'), 100.0],
  15.     [0,  datetime.datetime.fromisoformat('2021-11-04 00:00:00'), 100.0],
  16.     [0,  datetime.datetime.fromisoformat('2021-11-05 00:00:00'), 100.0]
  17. ])
  19. source = dataframeToOperator(data, schemaStr='id int, ts timestamp, series double', op_type='batch')
  21. deepARTrainBatchOp = DeepARTrainBatchOp()\
  22.     .setTimeCol("ts")\
  23.     .setSelectedCol("series")\
  24.     .setNumEpochs(10)\
  25.     .setWindow(2)\
  26.     .setStride(1)
  28. groupByBatchOp = GroupByBatchOp()\
  29.     .setGroupByPredicate("id")\
  30.     .setSelectClause("mtable_agg(ts, series) as mtable_agg_series")
  32. deepARPredictBatchOp = DeepARPredictBatchOp()\
  33.             .setPredictNum(2)\
  34.             .setPredictionCol("pred")\
  35.             .setValueCol("mtable_agg_series")
  37. deepARPredictBatchOp\
  38.     .linkFrom(
  39.         deepARTrainBatchOp.linkFrom(source),
  40.         groupByBatchOp.linkFrom(source)
  41.     )\
  42.     .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.source.MemSourceBatchOp;
  5. import com.alibaba.alink.operator.batch.sql.GroupByBatchOp;
  6. import com.alibaba.alink.operator.batch.timeseries.DeepARPredictBatchOp;
  7. import com.alibaba.alink.operator.batch.timeseries.DeepARTrainBatchOp;
  8. import org.junit.Test;
  10. import java.sql.Timestamp;
  11. import java.util.Arrays;
  12. import java.util.List;
  14. public class DeepARTrainBatchOpTest {
  16.     @Test
  17.     public void testDeepARTrainBatchOp() throws Exception {
  18.         BatchOperator.setParallelism(1);
  20.         List <Row> data = Arrays.asList(
  21.             Row.of(0, Timestamp.valueOf("2021-11-01 00:00:00"), 100.0),
  22.             Row.of(0, Timestamp.valueOf("2021-11-02 00:00:00"), 100.0),
  23.             Row.of(0, Timestamp.valueOf("2021-11-03 00:00:00"), 100.0),
  24.             Row.of(0, Timestamp.valueOf("2021-11-04 00:00:00"), 100.0),
  25.             Row.of(0, Timestamp.valueOf("2021-11-05 00:00:00"), 100.0)
  26.         );
  28.         MemSourceBatchOp memSourceBatchOp = new MemSourceBatchOp(data, "id int, ts timestamp, series double");
  30.         DeepARTrainBatchOp deepARTrainBatchOp = new DeepARTrainBatchOp()
  31.             .setTimeCol("ts")
  32.             .setSelectedCol("series")
  33.             .setNumEpochs(10)
  34.             .setWindow(2)
  35.             .setStride(1);
  37.         GroupByBatchOp groupByBatchOp = new GroupByBatchOp()
  38.             .setGroupByPredicate("id")
  39.             .setSelectClause("mtable_agg(ts, series) as mtable_agg_series");
  41.         DeepARPredictBatchOp deepARPredictBatchOp = new DeepARPredictBatchOp()
  42.             .setPredictNum(2)
  43.             .setPredictionCol("pred")
  44.             .setValueCol("mtable_agg_series");
  46.         deepARPredictBatchOp
  47.             .linkFrom(
  48.                 deepARTrainBatchOp.linkFrom(memSourceBatchOp),
  49.                 groupByBatchOp.linkFrom(memSourceBatchOp)
  50.             )
  51.             .print();
  52.     }
  53. }

运行结果

| id | mtable_agg_series | pred |
|——+—————————————————————————————————————————————————————————————————————————————————————————————————————————————+————————————————————————————————————————————————————————————————————————————-|
| 0 | {“data”:{“ts”:[“2021-11-01 00:00:00.0”,”2021-11-02 00:00:00.0”,”2021-11-03 00:00:00.0”,”2021-11-04 00:00:00.0”,”2021-11-05 00:00:00.0”],”series”:[100.0,100.0,100.0,100.0,100.0]},”schema”:”ts TIMESTAMP,series DOUBLE”} | {“data”:{“ts”:[“2021-11-06 00:00:00.0”,”2021-11-07 00:00:00.0”],”series”:[31.424224853515625,39.10265350341797]},”schema”:”ts TIMESTAMP,series DOUBLE”} |