| import numpy as np |
| import pandas as pd |
| from gensim.corpora import Dictionary, MmCorpus |
| from gensim.models import LdaModel, Word2Vec |
| import matplotlib.pyplot as plt |
| import streamlit as st |
| from pyLDAvis import prepared_data_to_html |
| import pyLDAvis.gensim_models as gensimvis |
|
|
| |
| df = pd.read_csv("./raw_corpus.csv") |
| corpus = MmCorpus('./corpus.mm') |
| dict = Dictionary.load(f'./livedoor_demo.dict') |
| lda = LdaModel.load('./lda_demo.model') |
|
|
| st.caption("生データ一覧") |
| st.dataframe(df.iloc[:100]) |
|
|
| st.caption("記事のカテゴリ") |
| fig, ax = plt.subplots() |
| count = df[["CATEGORY", "DOCUMENT"]].groupby("CATEGORY").count() |
| count.plot.pie(y="DOCUMENT", ax=ax, ylabel="", legend=False) |
| st.pyplot(fig) |
|
|
| |
| vis = gensimvis.prepare(lda, corpus, dict) |
| html_string = prepared_data_to_html(vis) |
| st.components.v1.html(html_string, width=1300, height=800) |
|
|
