• 功能介绍
  • 参数说明
  • 脚本示例
    • 脚本代码
    • 脚本运行结果
      • 模型数据
      • 输出数据

    功能介绍

    根据分词后的文本统计词的TF/IDF信息，将文本转化为稀疏的向量。

    参数说明

    名称 中文名称 描述 类型 是否必须？ 默认值
    maxDF 最大词频 如果一个词出现的文档次数大于maxDF, 这个词不会被包含在字典中。maxDF可以是具体的词频也可以是整体词频的比例，如果minDF在[0,1)区间，会被认为是比例。 Double 1.7976931348623157E308
    selectedCol 选中的列名 计算列对应的列名 String ✓
    minDF 最小文档词频 如果一个词出现的文档次数小于minDF, 这个词不会被包含在字典中。minTF可以是具体的词频也可以是整体词频的比例，如果minDF在[0,1)区间，会被认为是比例。 Double 1.0
    featureType 特征类型 生成特征向量的类型，支持IDF/WORD_COUNT/TF_IDF/Binary/TF String “WORD_COUNT”
    vocabSize 字典库大小 字典库大小，如果总词数目大于这个值，那个文档频率低的词会被过滤掉。 Integer 262144
    minTF 最低词频 最低词频，如果词频小于minTF,这个词会被忽略掉。minTF可以是具体的词频也可以是整体词频的比例，如果minTF在[0,1)区间，会被认为是比例。 Double 1.0

    脚本示例

    脚本代码

    1. import numpy as np
    2. import pandas as pd
    3. data = np.array([
    4.     [0, u'二手旧书:医学电磁成像'],
    5.     [1, u'二手美国文学选读（ 下册 ）李宜燮南开大学出版社 9787310003969'],
    6.     [2, u'二手正版图解象棋入门/谢恩思主编/华龄出版社'],
    7.     [3, u'二手中国糖尿病文献索引'],
    8.     [4, u'二手郁达夫文集（ 国内版 ）全十二册馆藏书']])
    9. df = pd.DataFrame({"id": data[:, 0], "text": data[:, 1]})
    10. inOp1 = BatchOperator.fromDataframe(df, schemaStr='id int, text string')
    11. inOp2 = StreamOperator.fromDataframe(df, schemaStr='id int, text string')
    13. segment = SegmentBatchOp().setSelectedCol("text").linkFrom(inOp1)
    14. train = DocCountVectorizerTrainBatchOp().setSelectedCol("text").linkFrom(segment)
    15. predictBatch = DocCountVectorizerPredictBatchOp().setSelectedCol("text").linkFrom(train, segment)
    16. [model,predict] = collectToDataframes(kmeans, predictBatch)
    17. print(model)
    18. print(predict)
    20. segment = SegmentStreamOp().setSelectedCol("text").linkFrom(inOp2)
    21. predictStream = DocCountVectorizerPredictStreamOp(train).setSelectedCol("text").linkFrom(segment)
    22. predictStream.print(refreshInterval=-1)
    23. StreamOperator.execute()

    脚本运行结果

    模型数据
    1. rowID  model_id                                         model_info
    2. 0          0     {"minTF":"1.0","featureType":"\"WORD_COUNT\""}
    3. 1    1048576                        {"f0":"二手","f1":0.0,"f2":0}
    4. 2    2097152          {"f0":"/","f1":1.0986122886681098,"f2":1}
    5. 3    3145728        {"f0":"出版社","f1":0.6931471805599453,"f2":2}
    6. 4    4194304          {"f0":"（","f1":0.6931471805599453,"f2":3}
    7. 5    5242880          {"f0":"）","f1":0.6931471805599453,"f2":4}
    8. 6    6291456  {"f0":"9787310003969","f1":1.0986122886681098,...
    9. 7    7340032          {"f0":":","f1":1.0986122886681098,"f2":6}
    10. 8    8388608         {"f0":"下册","f1":1.0986122886681098,"f2":7}
    11. 9    9437184         {"f0":"中国","f1":1.0986122886681098,"f2":8}
    12. 10  10485760         {"f0":"主编","f1":1.0986122886681098,"f2":9}
    13. 11  11534336         {"f0":"书","f1":1.0986122886681098,"f2":10}
    14. 12  12582912        {"f0":"入门","f1":1.0986122886681098,"f2":11}
    15. 13  13631488         {"f0":"全","f1":1.0986122886681098,"f2":12}
    16. 14  14680064        {"f0":"医学","f1":1.0986122886681098,"f2":13}
    17. 15  15728640       {"f0":"十二册","f1":1.0986122886681098,"f2":14}
    18. 16  16777216        {"f0":"华龄","f1":1.0986122886681098,"f2":15}
    19. 17  17825792      {"f0":"南开大学","f1":1.0986122886681098,"f2":16}
    20. 18  18874368        {"f0":"国内","f1":1.0986122886681098,"f2":17}
    21. 19  19922944        {"f0":"图解","f1":1.0986122886681098,"f2":18}
    22. 20  20971520         {"f0":"思","f1":1.0986122886681098,"f2":19}
    23. 21  22020096        {"f0":"成像","f1":1.0986122886681098,"f2":20}
    24. 22  23068672        {"f0":"文学","f1":1.0986122886681098,"f2":21}
    25. 23  24117248        {"f0":"文献","f1":1.0986122886681098,"f2":22}
    26. 24  25165824        {"f0":"文集","f1":1.0986122886681098,"f2":23}
    27. 25  26214400        {"f0":"旧书","f1":1.0986122886681098,"f2":24}
    28. 26  27262976       {"f0":"李宜燮","f1":1.0986122886681098,"f2":25}
    29. 27  28311552        {"f0":"正版","f1":1.0986122886681098,"f2":26}
    30. 28  29360128         {"f0":"版","f1":1.0986122886681098,"f2":27}
    31. 29  30408704        {"f0":"电磁","f1":1.0986122886681098,"f2":28}
    32. 30  31457280       {"f0":"糖尿病","f1":1.0986122886681098,"f2":29}
    33. 31  32505856        {"f0":"索引","f1":1.0986122886681098,"f2":30}
    34. 32  33554432        {"f0":"美国","f1":1.0986122886681098,"f2":31}
    35. 33  34603008        {"f0":"谢恩","f1":1.0986122886681098,"f2":32}
    36. 34  35651584        {"f0":"象棋","f1":1.0986122886681098,"f2":33}
    37. 35  36700160        {"f0":"选读","f1":1.0986122886681098,"f2":34}
    38. 36  37748736       {"f0":"郁达夫","f1":1.0986122886681098,"f2":35}
    39. 37  38797312        {"f0":"馆藏","f1":1.0986122886681098,"f2":36}
    输出数据
    1. rowID   id                                               text
    2. 0   0        $37$0:1.0 6:1.0 13:1.0 20:1.0 24:1.0 28:1.0
    3. 1   1  $37$0:1.0 2:1.0 3:1.0 4:1.0 5:1.0 7:1.0 16:1.0...
    4. 2   2  $37$0:1.0 1:2.0 2:1.0 9:1.0 11:1.0 15:1.0 18:1...
    5. 3   3               $37$0:1.0 8:1.0 22:1.0 29:1.0 30:1.0
    6. 4   4  $37$0:1.0 3:1.0 4:1.0 10:1.0 12:1.0 14:1.0 17:...