我从DARIAH项目网站获得了以下代码，以便在Python中进行主题建模。当我在命令外壳中运行脚本时，它开始读取文件，但始终卡在以下位置：

**reading files ...

Traceback (most recent call last):

File "C:\topmodel.py",line 131,in <module>
    dictionary,corpus,doc_labels = preprocessing(path,columns,pos_tags,doc_size,doc_split,stopwordlist)

  File "C:\topmodel.py",line 64,in preprocessing
    for file in os.listdir(path=path):

OSError: [WinError 123] The filename,directory name,or volume label syntax is incorrect: '[C:\\topmodel.py]'**

感谢您的帮助。预先感谢。

#!/usr/bin/env python

from gensim.corpora import MmCorpus,Dictionary
from gensim.models import LdaMulticore,LdaModel
import pandas as pd
import os
import sys
import csv

#import logging
#logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',level=logging.INFO)


#########################
# CONFIGURATION

# input
columns = ['ParagraphId','TokenId','Lemma','CPOS']   #,'NamedEntity']   # columns to read from csv file
pos_tags = ['ADJ','NN','V']                        # parts-of-speech to include into the model,following dkpro's
                                            # coarse grained tagset: ADJ,ADV,ART,CARD,CONJ,N (NP,NN),O,PP,PR,V,PUNC

# stopwords
stopwordlist = "stopwords.txt"              # path to text file,e.g. stopwords.txt in the same directory as the script

# document size (in words)
#doc_size = 1000000                             # set to arbitrarily large value to use original doc size
doc_size = 1000                                 # the document size for LDA commonly ranges from 500-2000 words
doc_split = 0                                   # set to 1 to use the pipeline's ParagraphId feature instead of doc_size

# model parameters,cf. https://radimrehurek.com/gensim/models/ldamodel.html
no_of_topics = 20                               # no. of topics to be generated
no_of_passes = 100                              # no. of lda iterations - the more the better,but increases computing time

eval = 1                                        # perplexity estimation every n chunks - the smaller the better,but also increases computing time
chunk = 10                                      # documents to process at once

alpha = "auto"                             # "symmetric","asymmetric","auto",or array (default: a symmetric 1.0/num_topics prior)
                                                # affects sparsity of the document-topic (theta) distribution

# custom alpha may increase topic coherence,but may also produce more topics with zero probability
#alpha = np.array([ 0.02,0.02,0.03,0.04,0.05,#                   0.05,0.02])

eta = None                                      # can be a number (int/float),an array,or None
                                                # affects topic-word (lambda) distribution - not necessarily beneficial to topic coherence


#########################
# PRE-PROCESSING

def preprocessing(path,stopwordlist):
    docs = []
    doc_labels = []
    stopwords = ""

    print("reading files ...\n")

    try:
        with open(stopwordlist,'r') as f: stopwords = f.read()
    except OSError:
        pass
    stopwords = sorted(set(stopwords.split("\n")))

    for file in os.listdir(path=path):
        if not file.startswith("."):
            filepath = path+"/"+file
            print(filepath)

            df = pd.read_csv(filepath,sep="\t",quoting=csv.QUOTE_NONE)
            #df = pd.read_csv(filepath)
            df = df[columns]
            df = df.groupby('CPOS')

            doc = pd.DataFrame()
            for p in pos_tags:                          # collect only the specified parts-of-speech
                doc = doc.append(df.get_group(p))

            """
            df = df.groupby('NamedEntity')              # add named entities to stopword list
            names = df.get_group('B-PER')['Lemma'].values.astype(str)
            names += df.get_group('I-PER')['Lemma'].values.astype(str)
            """
            #names = df.get_group('NP')['Lemma'].values.astype(str)
            #stopwords += names.tolist()

            # construct documents
            if doc_split:                               # size according to paragraph id
                doc = doc.groupby('ParagraphId')
                for para_id,para in doc:
                    docs.append(para['Lemma'].values.astype(str))
                    doc_labels.append(file.split(".")[0]+" #"+str(para_id))     # use filename + doc id as plot label
            else:                                       # size according to doc_size
                doc = doc.sort(columns='TokenId')
                i = 1
                while(doc_size < doc.shape[0]):
                    docs.append(doc[:doc_size]['Lemma'].values.astype(str))
                    doc_labels.append(file.split(".")[0]+" #"+str(i))
                    doc = doc.drop(doc.index[:doc_size])        # drop doc_size rows
                    i += 1
                docs.append(doc['Lemma'].values.astype(str))    # add the rest
                doc_labels.append(file.split(".")[0]+" #"+str(i))

    #for doc in docs: print(str(len(doc)))              # display resulting doc sizes
    #print(stopwords)

    print("\nnormalizing and vectorizing ...\n")        # cf. https://radimrehurek.com/gensim/tut1.html

    texts = [[word for word in doc if word not in stopwords] for doc in docs]       # remove stopwords

    all_tokens = sum(texts,[])                                                     # remove words that appear only once
    tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)
    texts = [[word for word in text if word not in tokens_once] for text in texts]

    dictionary = Dictionary(texts)                      # vectorize
    corpus = [dictionary.doc2bow(text) for text in texts]

    return dictionary,doc_labels


#########################
# MAIN

if len(sys.argv) < 2:
    print("usage: {0} [folder containing csv files]\n"
          "parameters are set inside the script.".format(sys.argv[0]))
    sys.exit(1)

path = sys.argv[1]
foldername = path.split("/")[-1]

dictionary,stopwordlist)

print("fitting the model ...\n")

model = LdaModel(corpus=corpus,id2word=dictionary,num_topics=no_of_topics,passes=no_of_passes,eval_every=eval,chunksize=chunk,alpha=alpha,eta=eta)

#model = LdaMulticore(corpus=corpus,#                 eval_every=eval,eta=eta)

print(model,"\n")

topics = model.show_topics(num_topics=no_of_topics)

for item,i in zip(topics,enumerate(topics)):
    print("topic #"+str(i[0])+": "+str(item)+"\n")


print("saving ...\n")

if not os.path.exists("out"): os.makedirs("out")

with open("out/"+foldername+"_doclabels.txt","w") as f:
    for item in doc_labels: f.write(item+"\n")

with open("out/"+foldername+"_topics.txt","w") as f:
    for item,enumerate(topics)):
        f.write("topic #"+str(i[0])+": "+str(item)+"\n")

dictionary.save("out/"+foldername+".dict")
MmCorpus.serialize("out/"+foldername+".mm",corpus)
model.save("out/"+foldername+".lda")