以Perplexity作为指标写一段代码
时间: 2023-07-10 19:18:31 浏览: 54
以下是一个使用Perplexity度量指标确定LDA模型最佳主题数的Python代码示例:
```python
from gensim.models.ldamodel import LdaModel
from gensim.corpora import Dictionary
import matplotlib.pyplot as plt
# 导入数据并生成词典
texts = [["apple", "banana", "orange", "pear", "peach"], ["dog", "cat", "bird", "fish"], ["red", "yellow", "blue", "green"]]
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
# 定义函数,计算LDA模型的Perplexity值
def compute_perplexity_values(dictionary, corpus, texts, limit, start=2, step=2):
perplexity_values = []
model_list = []
for num_topics in range(start, limit, step):
model = LdaModel(corpus=corpus, num_topics=num_topics, id2word=dictionary)
model_list.append(model)
perplexity_values.append(model.log_perplexity(corpus))
return model_list, perplexity_values
# 调用函数,计算不同主题数下的Perplexity值
model_list, perplexity_values = compute_perplexity_values(dictionary=dictionary, corpus=corpus, texts=texts, start=2, limit=10, step=1)
# 可视化Perplexity值随主题数变化的趋势
x = range(2, 10, 1)
plt.plot(x, perplexity_values)
plt.xlabel("Num Topics")
plt.ylabel("Perplexity score")
plt.legend(("perplexity_values"), loc='best')
plt.show()
```
运行以上代码,可以得到一个Perplexity值随主题数变化的趋势图,从图中可以看出最佳主题数在哪个范围内。