demo_pair_plot.py

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.metrics import f1_score
import scattertext as st

newsgroups_train = fetch_20newsgroups(subset='train', remove=('headers', 'footers', 'quotes'))

vectorizer = TfidfVectorizer()
tfidf_X = vectorizer.fit_transform(newsgroups_train.data)

corpus = st.CorpusFromScikit(
	X=CountVectorizer(vocabulary=vectorizer.vocabulary_).fit_transform(newsgroups_train.data),
	y=newsgroups_train.target,
	feature_vocabulary=vectorizer.vocabulary_,
	category_names=newsgroups_train.target_names,
	raw_texts=newsgroups_train.data
).build().get_unigram_corpus()

html = st.produce_pairplot(corpus)
file_name = 'demo_pair_plot.html'
open(file_name, 'wb').write(html.encode('utf-8'))
print('Open ./%s in Chrome.' % (file_name))