I'm using the package WordCloud to display words generated by scikit LDA (Latent Dirichlet Allocation). For each topic generated by LDA I'll have a chart. I want to be able to plot all charts in a grid to allow visualization side by side. Essentially I have a function that takes an LDA model as input, along with the LDA topic I want to visualize and then plots a wordcloud:
from wordcloud import WordCloud
import matplotlib.pyplot as plt
SEED=0
def topicWordCloud(model, topicNumber, WCmaxWords,WCwidth, WCheight):
topic = model.components_[topicNumber]
tupleList = [(tf_feature_names[i],int(topic[i]/topic.sum()*10000)) for i in range(len(topic))]
wordcloud = WordCloud(width=WCwidth, height=WCheight, max_words=WCmaxWords, random_state=42).generate_from_frequencies(tupleList)
plt.figure( figsize=(20,10) )
plt.imshow(wordcloud)
plt.axis("off")
topicWordCloud(model=lda, topicNumber=2, WCmaxWords=100,WCwidth=800, WCheight=600)
How do I loop through all my topics (n_topics
) to visualize all the charts in a grid? I was thinking something along the lines of:
fig = plt.figure()
for i in range(n_topics):
plt.subplot(2,1,i+1)
#something here
Return the wordcloud from your function, then call topicWordCloud
from within your for loop. Then, use imshow
on the Axes
that you create with fig.add_subplot
. For example, something like this:
def topicWordCloud(model, topicNumber, WCmaxWords,WCwidth, WCheight):
topic = model.components_[topicNumber]
tupleList = [(tf_feature_names[i],int(topic[i]/topic.sum()*10000)) for i in range(len(topic))]
wordcloud = WordCloud(width=WCwidth, height=WCheight, max_words=WCmaxWords, random_state=42).generate_from_frequencies(tupleList)
return wordcloud
fig = plt.figure()
for i in range(n_topics):
ax = fig.add_subplot(2,1,i+1)
wordcloud = topicWordCloud(...)
ax.imshow(wordcloud)
ax.axis('off')
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