• 功能介绍
  • 参数说明
  • 脚本示例
    • 脚本代码
    • 脚本运行结果
      • 模型结果
      • 预测结果

    功能介绍

    LDA是一种文档主题生成模型。LDA是一种非监督机器学习技术,可以用来识别大规模文档集(document collection)或语料库(corpus)中潜藏的主题信息。它采用了词袋(bag of words)的方法,这种方法将每一篇文档视为一个词频向量,从而将文本信息转化为了易于建模的数字信息。但是词袋方法没有考虑词与词之间的顺序,这简化了问题的复杂性,同时也为模型的改进提供了契机。每一篇文档代表了一些主题所构成的一个概率分布,而每一个主题又代表了很多单词所构成的一个概率分布。

    参数说明

    名称 中文名称 描述 类型 是否必须? 默认值
    topicNum 主题个数 主题个数 Integer
    alpha 文章的超参 文章的超参 Double -1.0
    beta 词的超参 词的超参 Double -1.0
    method 优化方法 优化方法, 包含”em”和”online”两种。 String “em”
    onlineLearningOffset 偏移量 偏移量 Double 1024.0
    learningDecay 衰减率 衰减率 Double 0.51
    subsamplingRate 采样率 采样率 Double 0.05
    optimizeDocConcentration 是否优化alpha 是否优化alpha Boolean true
    numIter 迭代次数 迭代次数,默认为10 Integer 10
    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
    selectedCol 选中的列名 计算列对应的列名 String

    脚本示例

    脚本代码

    1. data = np.array(["a b b c c c c c c e e f f f g h k k k", \
    2. "a b b b d e e e h h k", \
    3. "a b b b b c f f f f g g g g g g g g g i j j", \
    4. "a a b d d d g g g g g i i j j j k k k k k k k k k", \
    5. "a a a b c d d d d d d d d d e e e g g j k k k", \
    6. "a a a a b b d d d e e e e f f f f f g h i j j j j", \
    7. "a a b d d d g g g g g i i j j k k k k k k k k k", \
    8. "a b c d d d d d d d d d e e f g g j k k k", \
    9. "a a a a b b b b d d d e e e e f f g h h h", \
    10. "a a b b b b b b b b c c e e e g g i i j j j j j j j k k", \
    11. "a b c d d d d d d d d d f f g g j j j k k k", \
    12. "a a a a b e e e e f f f f f g h h h j"])
    13. df = pd.DataFrame({"doc" : data})
    14. inOp = dataframe_to_operator(df, schema_str="doc string")
    15. ldaTrain = LdaTrainBatchOp()\
    16. .setSelectedCol("doc")\
    17. .setTopicNum(6)\
    18. .setMethod("em")\
    19. .setSubsamplingRate(1.0)\
    20. .setOptimizeDocConcentration(True)\
    21. .setNumIter(50)
    22. ldaPredict = LdaPredictBatchOp().setPredictionCol("pred").setSelectedCol("doc")
    23. model = ldaTrain.linkFrom(inOp)
    24. ldaPredict.linkFrom(model, inOp).collect_to_dataframe()

    脚本运行结果

    模型结果
    model_id model_info
    0 {“logPerplexity”:”22.332946259667825”,”betaArray”:”[0.2,0.2,0.2,0.2,0.2]”,”logLikelihood”:”-915.6507966463809”,”method”:”\”online\””,”alphaArray”:”[0.16926092344987234,0.17828690973899627,0.17282213771078062,0.18555794554097874,0.15898463316059516]”,”topicNum”:”5”,”vocabularySize”:”11”}
    1048576 {“m”:5,”n”:11,”data”:[6135.5227952852865,7454.918734235136,6569.887273287071,7647.590029783137,7459.37196542985,6689.783286316853,8396.842418256507,7771.120258275389,7497.94247894282,7983.617922597562,7975.470848777338,7114.413879475893,8420.381073064213,6747.377398176922,6959.728145538011,7368.902852508116,7635.5968635989275,6734.522904998126,6792.566021565353,6487.885790775943,8086.932892160501,8443.888239756887,7227.0417299467745,7561.023252667202,6264.97808011349,6964.080980387547,8234.247108608217,8263.190977757107,7872.088651923572,7725.669369347696,7591.453097717432,7733.627117746213,6595.2753568320295,8158.346230399092,7765.777648163369,6456.891859572009,6814.768507000475,6612.17371610521,6506.877213010642,7166.140342089344,7588.370517354863,7645.016947338933,8929.620632942893,6855.855247335312,7263.088264847597,7993.009126022237,7302.794183756114,6074.524636118613,6386.578740892538,8465.84700774072,7242.276290933901,7257.474039179472,7676.72445702261,6733.70550536632,7577.265607033211]}
    2097152 {“f0”:”d”,”f1”:0.36772478012531734,”f2”:0}
    3145728 {“f0”:”k”,”f1”:0.36772478012531734,”f2”:1}
    4194304 {“f0”:”g”,”f1”:0.08004270767353636,”f2”:2}
    5242880 {“f0”:”b”,”f1”:0.0,”f2”:3}
    6291456 {“f0”:”a”,”f1”:0.0,”f2”:4}
    7340032 {“f0”:”e”,”f1”:0.36772478012531734,”f2”:5}
    8388608 {“f0”:”j”,”f1”:0.26236426446749106,”f2”:6}
    9437184 {“f0”:”f”,”f1”:0.4855078157817008,”f2”:7}
    10485760 {“f0”:”c”,”f1”:0.6190392084062235,”f2”:8}
    11534336 {“f0”:”h”,”f1”:0.7731898882334817,”f2”:9}
    12582912 {“f0”:”i”,”f1”:0.7731898882334817,”f2”:10}
    预测结果
    doc pred
    a b b b d e e e h h k 1
    a a b d d d g g g g g i i j j j k k k k k k k k k 3
    a a a a b b d d d e e e e f f f f f g h i j j j j 3
    a a b d d d g g g g g i i j j k k k k k k k k k 1
    a a a a b b b b d d d e e e e f f g h h h 3
    a b c d d d d d d d d d f f g g j j j k k k 3
    a b b c c c c c c e e f f f g h k k k 2
    a b b b b c f f f f g g g g g g g g g i j j 0
    a a a b c d d d d d d d d d e e e g g j k k k 3
    a b c d d d d d d d d d e e f g g j k k k 3
    a a b b b b b b b b c c e e e g g i i j j j j j j j k k 3
    a a a a b e e e e f f f f f g h h h j 0