Question

我在ipython中运行我的代码（运行python版本2.7）。在调用＆＃34; pyLDAvis＆＃34;时发现错误用于可视化我的LDA生成主题的功能。以下错误：

topics_df = pd.DataFrame（主题中元组的[dict（x（y，x）代表x，y））TypeError：＆＃39; int＆＃39;对象不可迭代

输入数据采用表格的形式，该表格有两列＆＃34; complaints_ID＆＃34;和＆＃34; complaints_txt＆＃34;。我正在尝试在每个complaint_txt值

这个问题与Python版本或参数我传递给这个函数？以下是我的代码。

from stop_words import get_stop_words
import pandas as pd
import numpy as np
from nltk import bigrams
from lib.lda import lda, visualizeLDA
from nltk.tokenize import RegexpTokenizer
from gensim import corpora, models
import gensim
import pyLDAvis.gensim

#provide path name here
mypath = " "
allcomplaints = pd.read_csv(mypath)
#combining complaints for each ID
myremarks= allcomplaints.groupby(['complaint_ID'])['complaint_txt'].agg(lambda x: ''.join(x)).values 
#create English stop words list
en_stop = get_stop_words('en')

#including domain specific stop words
my_stopwords = ["xx","xxxx"]
my_stopwords= [i.decode('utf-8') for i in my_stopwords]
en_stop = en_stop +my_stopwords

texts = []
for doc in myremarks:
        raw = doc.lower()
        tokens = bigrams(i for i in tokenizer.tokenize(raw)if not i in en_stop and len(i)>1)
        mergedtokens = [i[0]+" "+i[1] for i in tokens]
        stopped_tokens = [i for i in mergedtokens if not i in en_stop]
        texts.append(stopped_tokens)
dictionary = corpora.Dictionary(texts)
print dictionary
    # convert tokenized documents into a document-term matrix
corpus = [dictionary.doc2bow(text) for text in texts]

    # generate LDA model
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics = 5 , id2word = dictionary, passes = 1)
print(ldamodel.print_topics(num_topics=5))

#     Visualize ldamodel
vis= pyLDAvis.gensim.prepare(ldamodel,corpus,dictionary)
pyLDAvis.display(vis)

＃以下是我用来运行LDA的数据样本：

Complaint_ID| Complaint_txt
------------| --------------
4545        | cust has billing issue
4545        | for $480 
6878        | connct issue for a day ne
6878        | ed immediate resoltn

＆＃39; INT＆＃39; python中不可迭代的错误运行pyldavis时

0 个答案: