Question

我正在pyspark（spark 2.1.1）的客户评论数据集中训练一个LDA模型。现在，基于该模型，我想预测看不见的新文本中的主题。

我正在使用以下代码制作模型

from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext, Row
from pyspark.ml.feature import CountVectorizer
from pyspark.ml.feature import HashingTF, IDF, Tokenizer, CountVectorizer, StopWordsRemover
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.ml.clustering import DistributedLDAModel, LocalLDAModel
from pyspark.mllib.linalg import Vector, Vectors
from pyspark.sql.functions import *
import pyspark.sql.functions as F


path = "D:/sparkdata/sample_text_LDA.txt"
sc = SparkContext("local[*]", "review")
spark = SparkSession.builder.appName('Basics').getOrCreate()
df = spark.read.csv("D:/sparkdata/customers_data.csv", header=True, inferSchema=True)

data = df.select("Reviews").rdd.map(list).map(lambda x: x[0]).zipWithIndex().map(lambda words: Row(idd= words[1], words = words[0].split(" "))).collect()

docDF = spark.createDataFrame(data)
remover = StopWordsRemover(inputCol="words",
outputCol="stopWordsRemoved")
stopWordsRemoved_df = remover.transform(docDF).cache()
Vector = CountVectorizer(inputCol="stopWordsRemoved", outputCol="vectors")
model = Vector.fit(stopWordsRemoved_df)
result = model.transform(stopWordsRemoved_df)
corpus = result.select("idd", "vectors").rdd.map(lambda x: [x[0],Vectors.fromML(x[1])]).cache()

# Cluster the documents topics using LDA
ldaModel = LDA.train(corpus, k=3,maxIterations=100,optimizer='online')
topics = ldaModel.topicsMatrix()
vocabArray = model.vocabulary
print(ldaModel.describeTopics())
wordNumbers = 10  # number of words per topic
topicIndices = sc.parallelize(ldaModel.describeTopics(maxTermsPerTopic = wordNumbers))
def topic_render(topic):  # specify vector id of words to actual words
   terms = topic[0]
   result = []
   for i in range(wordNumbers):
       term = vocabArray[terms[i]]
       result.append(term)
   return result

topics_final = topicIndices.map(lambda topic: topic_render(topic)).collect()

for topic in range(len(topics_final)):
   print("Topic" + str(topic) + ":")
   for term in topics_final[topic]:
       print (term)
   print ('\n')

现在，我有一个带有新客户评论的列的数据框，并且我想预测它们属于哪个主题集群。我已经搜索了答案，大多数情况下建议采用以下方式，例如Spark MLlib LDA, how to infer the topics distribution of a new unseen document?。

newDocuments: RDD[(Long, Vector)] = ...
topicDistributions = distLDA.toLocal.topicDistributions(newDocuments)

但是，出现以下错误：

'LDAModel'对象没有属性'toLocal'。它也没有topicDistribution属性。

那么Spark 2.1.1不支持这些属性吗？

还有其他方法可以从看不见的数据中推断出话题吗？

Answer 1

您将需要预处理新数据：

# import a new data set to be passed through the pre-trained LDA

data_new = pd.read_csv('YourNew.csv', encoding = "ISO-8859-1");
data_new = data_new.dropna()
data_text_new = data_new[['Your Target Column']]
data_text_new['index'] = data_text_new.index

documents_new = data_text_new
#documents_new = documents.dropna(subset=['Preprocessed Document'])

# process the new data set through the lemmatization, and stopwork functions
processed_docs_new = documents_new['Preprocessed Document'].map(preprocess)

# create a dictionary of individual words and filter the dictionary
dictionary_new = gensim.corpora.Dictionary(processed_docs_new[:])
dictionary_new.filter_extremes(no_below=15, no_above=0.5, keep_n=100000)

# define the bow_corpus
bow_corpus_new = [dictionary_new.doc2bow(doc) for doc in processed_docs_new]

然后，您可以将其作为功能通过训练有素的LDA。您需要的只是bow_corpus：

ldamodel[bow_corpus_new[:len(bow_corpus_new)]]

如果要在csv中使用它，请尝试以下操作：

a = ldamodel[bow_corpus_new[:len(bow_corpus_new)]]
b = data_text_new

topic_0=[]
topic_1=[]
topic_2=[]

for i in a:
    topic_0.append(i[0][1])
    topic_1.append(i[1][1])
    topic_2.append(i[2][1])
    
d = {'Your Target Column': b['Your Target Column'].tolist(),
     'topic_0': topic_0,
     'topic_1': topic_1,
     'topic_2': topic_2}
     
df = pd.DataFrame(data=d)
df.to_csv("YourAllocated.csv", index=True, mode = 'a')

我希望这会有所帮助:)

Spark 2.1.1：如何在Spark 2.1.1中已经训练有素的LDA模型上预测看不见的文档中的主题？

1 个答案: