LDA主题分析及可视化

Question

最近在学LDA主题分析，写了一个小demo。在最后可视化的时候，运行到这里vis_data = gensimvis.prepare(lda_model, corpus, dictionary)，出现了报错A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.

```python

import gensim
from gensim import corpora
from gensim.models import LdaModel
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pyLDAvis.gensim_models as gensimvis
import pyLDAvis

# 导入必要的库


# 假设你有一些文档集合，每个文档都是一个词列表
documents = [
    ["apple", "banana", "orange", "fruit", "juice"],
    ["car", "vehicle", "drive", "road", "traffic"],
    ["python", "programming", "code", "language", "computer"],
    ["car","apple","computer","orange","apple"],
    ["apple","car","orange","car","computer"]
    # 添加更多文档...
]

# 创建字典（词袋模型）
dictionary = corpora.Dictionary(documents)

# 创建文档-词频矩阵
corpus = [dictionary.doc2bow(doc) for doc in documents]

# 训练LDA模型
num_topics = 3  # 指定主题数量
lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=15)

# 打印主题和对应的词
topics = lda_model.print_topics(num_words=5)
for topic in topics:
    print(topic)

# 计算主题一致性得分（可选）
coherence_model = CoherenceModel(model=lda_model, texts=documents, dictionary=dictionary, coherence='c_v')
coherence_score = coherence_model.get_coherence()
print("Coherence Score:", coherence_score)

# 可视化主题


# 转换为pyLDAvis格式并可视化
vis_data = gensimvis.prepare(lda_model, corpus, dictionary)
pyLDAvis.display(vis_data)

这是为什么啊？数据集也不多啊，电脑就运行了一个jupyter，请各位专业人士帮忙解答一下，困扰我好几天了

Answer 1

更新或降低joblib库的版本，使其与pyLDAvis库相匹配