Java 类名:com.alibaba.alink.operator.batch.nlp.DocCountVectorizerPredictBatchOp
Python 类名:DocCountVectorizerPredictBatchOp

功能介绍

根据文本中词语的特征信息,将每条文本转化为稀疏向量。

使用方式

该组件是预测组件,需要配合训练组件 DocCountVectorizerTrainBatchOp 使用。

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

| selectedCol | 选中的列名 | 计算列对应的列名 | String | ✓ | 所选列类型为 [STRING] | |

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

| outputCol | 输出结果列 | 输出结果列列名,可选,默认null | 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. df = pd.DataFrame([
  2. [0, u'二手旧书:医学电磁成像'],
  3. [1, u'二手美国文学选读( 下册 )李宜燮南开大学出版社 9787310003969'],
  4. [2, u'二手正版图解象棋入门/谢恩思主编/华龄出版社'],
  5. [3, u'二手中国糖尿病文献索引'],
  6. [4, u'二手郁达夫文集( 国内版 )全十二册馆藏书']
  7. ])
  8. inOp1 = BatchOperator.fromDataframe(df, schemaStr='id int, text string')
  9. segment = SegmentBatchOp().setSelectedCol("text").linkFrom(inOp1)
  10. train = DocCountVectorizerTrainBatchOp().setSelectedCol("text").linkFrom(segment)
  11. predictBatch = DocCountVectorizerPredictBatchOp().setSelectedCol("text").linkFrom(train, segment)
  12. train.lazyPrint(-1)
  13. predictBatch.print()

Java 代码

  1. import org.apache.flink.types.Row;
  2. import com.alibaba.alink.operator.batch.BatchOperator;
  3. import com.alibaba.alink.operator.batch.nlp.DocCountVectorizerPredictBatchOp;
  4. import com.alibaba.alink.operator.batch.nlp.DocCountVectorizerTrainBatchOp;
  5. import com.alibaba.alink.operator.batch.nlp.SegmentBatchOp;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import org.junit.Test;
  8. import java.util.Arrays;
  9. import java.util.List;
  10. public class DocCountVectorizerPredictBatchOpTest {
  11. @Test
  12. public void testDocCountVectorizerPredictBatchOp() throws Exception {
  13. List <Row> df = Arrays.asList(
  14. Row.of(0, "二手旧书:医学电磁成像"),
  15. Row.of(1, "二手美国文学选读( 下册 )李宜燮南开大学出版社 9787310003969"),
  16. Row.of(2, "二手正版图解象棋入门/谢恩思主编/华龄出版社"),
  17. Row.of(3, "二手中国糖尿病文献索引"),
  18. Row.of(4, "二手郁达夫文集( 国内版 )全十二册馆藏书")
  19. );
  20. BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "id int, text string");
  21. BatchOperator <?> segment = new SegmentBatchOp().setSelectedCol("text").linkFrom(inOp1);
  22. BatchOperator <?> train = new DocCountVectorizerTrainBatchOp().setSelectedCol("text").linkFrom(segment);
  23. BatchOperator <?> predictBatch = new DocCountVectorizerPredictBatchOp().setSelectedCol("text").linkFrom(train,
  24. segment);
  25. train.lazyPrint(-1);
  26. predictBatch.print();
  27. }
  28. }

运行结果

模型数据

| model_id | model_info | | —- | —- |

| 0 | {“minTF”:”1.0”,”featureType”:””WORD_COUNT””} |

| 1048576 | {“f0”:”)”,”f1”:0.6931471805599453,”f2”:0} |

| 2097152 | {“f0”:”(”,”f1”:0.6931471805599453,”f2”:1} |

| 3145728 | {“f0”:”馆藏”,”f1”:1.0986122886681098,”f2”:2} |

| 4194304 | {“f0”:”郁达夫”,”f1”:1.0986122886681098,”f2”:3} |

| 5242880 | {“f0”:”选读”,”f1”:1.0986122886681098,”f2”:4} |

| 6291456 | {“f0”:”象棋”,”f1”:1.0986122886681098,”f2”:5} |

| 7340032 | {“f0”:”谢恩”,”f1”:1.0986122886681098,”f2”:6} |

| 8388608 | {“f0”:”美国”,”f1”:1.0986122886681098,”f2”:7} |

| 9437184 | {“f0”:”索引”,”f1”:1.0986122886681098,”f2”:8} |

| 10485760 | {“f0”:”糖尿病”,”f1”:1.0986122886681098,”f2”:9} |

| 11534336 | {“f0”:”电磁”,”f1”:1.0986122886681098,”f2”:10} |

| 12582912 | {“f0”:”版”,”f1”:1.0986122886681098,”f2”:11} |

| 13631488 | {“f0”:”正版”,”f1”:1.0986122886681098,”f2”:12} |

| 14680064 | {“f0”:”李宜燮”,”f1”:1.0986122886681098,”f2”:13} |

| 15728640 | {“f0”:”旧书”,”f1”:1.0986122886681098,”f2”:14} |

| 16777216 | {“f0”:”文集”,”f1”:1.0986122886681098,”f2”:15} |

| 17825792 | {“f0”:”文献”,”f1”:1.0986122886681098,”f2”:16} |

| 18874368 | {“f0”:”文学”,”f1”:1.0986122886681098,”f2”:17} |

| 19922944 | {“f0”:”成像”,”f1”:1.0986122886681098,”f2”:18} |

| 20971520 | {“f0”:”思”,”f1”:1.0986122886681098,”f2”:19} |

| 22020096 | {“f0”:”图解”,”f1”:1.0986122886681098,”f2”:20} |

| 23068672 | {“f0”:”国内”,”f1”:1.0986122886681098,”f2”:21} |

| 24117248 | {“f0”:”南开大学”,”f1”:1.0986122886681098,”f2”:22} |

| 25165824 | {“f0”:”华龄”,”f1”:1.0986122886681098,”f2”:23} |

| 26214400 | {“f0”:”十二册”,”f1”:1.0986122886681098,”f2”:24} |

| 27262976 | {“f0”:”医学”,”f1”:1.0986122886681098,”f2”:25} |

| 28311552 | {“f0”:”出版社”,”f1”:0.6931471805599453,”f2”:26} |

| 29360128 | {“f0”:”全”,”f1”:1.0986122886681098,”f2”:27} |

| 30408704 | {“f0”:”入门”,”f1”:1.0986122886681098,”f2”:28} |

| 31457280 | {“f0”:”二手”,”f1”:0.0,”f2”:29} |

| 32505856 | {“f0”:”书”,”f1”:1.0986122886681098,”f2”:30} |

| 33554432 | {“f0”:”主编”,”f1”:1.0986122886681098,”f2”:31} |

| 34603008 | {“f0”:”中国”,”f1”:1.0986122886681098,”f2”:32} |

| 35651584 | {“f0”:”下册”,”f1”:1.0986122886681098,”f2”:33} |

| 36700160 | {“f0”:”:”,”f1”:1.0986122886681098,”f2”:34} |

| 37748736 | {“f0”:”9787310003969”,”f1”:1.0986122886681098,”f2”:35} |

| 38797312 | {“f0”:”/“,”f1”:1.0986122886681098,”f2”:36} |

批预测结果

| id | text | | —- | —- |

| 0 | $37$10:1.0 14:1.0 18:1.0 25:1.0 29:1.0 34:1.0 |

| 1 | $37$0:1.0 1:1.0 4:1.0 7:1.0 13:1.0 17:1.0 22:1.0 26:1.0 29:1.0 33:1.0 35:1.0 |

| 2 | $37$5:1.0 6:1.0 12:1.0 19:1.0 20:1.0 23:1.0 26:1.0 28:1.0 29:1.0 31:1.0 36:2.0 |

| 3 | $37$8:1.0 9:1.0 16:1.0 29:1.0 32:1.0 |

| 4 | $37$0:1.0 1:1.0 2:1.0 3:1.0 11:1.0 15:1.0 21:1.0 24:1.0 27:1.0 29:1.0 30:1.0 |