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News_Topic_Clustering

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rajistics commited on May 24, 2022

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87e9481

1 Parent(s): 0787c8d

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app.py +88 -0
requirements.txt +127 -0
topic_10000.model +3 -0
topic_10000.par +0 -0
topics +1 -0

app.py ADDED Viewed

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+from bertopic import BERTopic
+import streamlit as st
+import streamlit.components.v1 as components
+#from datasets import load_dataset
+import pandas as pd
+from datasets import load_dataset
+import json
+##Load Dataset
+#dataset = load_dataset("rshah/million-headlines")
+#news = pd.DataFrame.from_dict(dataset["train"])
+news = pd.read_parquet("/Users/rajiv.shah/Downloads/topic_10000.par")
+news['date'] = pd.to_datetime(news['publish_date'], format='%Y%m%d')
+timestamps = news.date.to_list()
+tweets = news.headline_text.to_list()
+with open("/Users/rajiv.shah/Downloads/test", "r") as fp:
+    topics = json.load(fp)
+option_n = 5
+st.set_page_config(page_title="News Topic Clustering")
+st.title("News Topic Clustering")
+st.caption("By Rajiv Shah")
+st.caption("")
+st.caption("This is a simple example of using identifying topics in the [one million ABC news headline dataset](https://huggingface.co/datasets/rshah/million-headlines).  \
+    If you look at the code for this app, you will see how it uses just a few lines of [BertTopic](https://maartengr.github.io/BERTopic/index.html) to \
+         build the topics and create the visualizations")
+st.caption("The preloaded existing model provides the more interesting results.  However, this app can be run live by building a new model, but \
+    is limited to a small number of rows. I also limited topics over time to the existing model.")
+form = st.sidebar.form("Main Settings")
+form.header("Main Settings")
+option = form.selectbox(
+    'What model would you like to run',
+    ('Load existing model', 'Build new model'),index=0)
+option_n = form.number_input(
+    'What topic would you like to get terms for?',
+    min_value=0,max_value=10,value=5)
+submitted = form.form_submit_button(label = 'Select Model')
+if option == 'Load existing model':
+    ##Load existing model
+    topic_model = BERTopic.load("topic_10000.model")
+    #topics, _ = topic_model.transform(tweets)
+else:
+    ##Builds Topic Model
+    #news_sample = news[(news['date'] > '2015-06-01')]
+    news_sample = news[(news['date'] > '2017-01-01') & (news['date'] < '2019-01-01') ]
+    news_sample = news_sample.sample(200,random_state=123)
+    tweets = news_sample.headline_text.to_list()
+    topic_model = BERTopic(min_topic_size=5, verbose=True)
+    topics, _ = topic_model.fit_transform(tweets)
+#Get top topics
+freq = topic_model.get_topic_info()
+freq = freq.iloc[1: , :]   ##drop -1 row
+freq.head(10)
+st.header("The Main Topic Clusters")
+st.write(freq)
+topic_nr = freq.iloc[option_n]["Topic"]  # We select a frequent topic
+st.caption("")
+st.write('Top words in topic cluster: ',option_n)
+#st.caption(option_n)
+mytuple = (topic_model.get_topic(topic_nr))
+for item in mytuple:
+    st.write(str(item[0]))
+st.header("Relationships between clusters ")
+st.plotly_chart(topic_model.visualize_hierarchy())
+if option == 'Load existing model':
+    st.header("Topics over time for Existing Model")
+    topics_over_time = topic_model.topics_over_time(docs=tweets,
+                                                topics=topics,
+                                                timestamps=timestamps,
+                                                global_tuning=True,
+                                                evolution_tuning=True,
+                                                nr_bins=20)
+    st.plotly_chart(topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=20))

requirements.txt ADDED Viewed

	@@ -0,0 +1,127 @@

+aiohttp==3.8.1
+aiosignal==1.2.0
+altair==4.2.0
+appnope @ file:///opt/concourse/worker/volumes/live/4f734db2-9ca8-4d8b-5b29-6ca15b4b4772/volume/appnope_1606859466979/work
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+async-timeout==4.0.2
+asynctest==0.13.0
+attrs==21.4.0
+backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
+backports.zoneinfo==0.2.1
+beautifulsoup4==4.11.1
+bertopic==0.10.0
+bleach==5.0.0
+blinker==1.4
+cachetools==5.1.0
+certifi==2022.5.18.1
+cffi==1.15.0
+charset-normalizer==2.0.12
+click==8.0.4
+Cython==0.29.30
+datasets==2.2.2
+debugpy @ file:///opt/concourse/worker/volumes/live/32b11d06-4d64-4ec8-497a-cf4fc97343d2/volume/debugpy_1637091821874/work
+decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work
+defusedxml==0.7.1
+dill==0.3.4
+entrypoints @ file:///opt/concourse/worker/volumes/live/194c0a28-55ce-4e83-6a87-0d9f2e06ab2c/volume/entrypoints_1649926487944/work
+fastjsonschema==2.15.3
+filelock==3.7.0
+frozenlist==1.3.0
+fsspec==2022.5.0
+gitdb==4.0.9
+GitPython==3.1.27
+hdbscan==0.8.28
+huggingface-hub==0.6.0
+idna==3.3
+importlib-metadata==4.11.4
+importlib-resources==5.7.1
+ipykernel @ file:///opt/concourse/worker/volumes/live/c21734a4-132a-471d-7fe9-76688d5488be/volume/ipykernel_1647000841085/work/dist/ipykernel-6.9.1-py3-none-any.whl
+ipython @ file:///opt/concourse/worker/volumes/live/333da4fb-3db0-439c-4a1b-ee802c44da4f/volume/ipython_1643818165055/work
+ipython-genutils==0.2.0
+ipywidgets==7.7.0
+jedi @ file:///opt/concourse/worker/volumes/live/c9d2fa99-8bc1-4572-41e7-6beba6391441/volume/jedi_1644315238822/work
+Jinja2==3.1.2
+joblib==1.1.0
+jsonschema==4.5.1
+jupyter-client @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_4d0a367d-74c5-447f-831e-3e1fd3984797bfrg56pj/croots/recipe/jupyter_client_1650622206454/work
+jupyter-core @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_8ba802e0-15f6-4a45-a9ee-144034526783uwxtvvew/croots/recipe/jupyter_core_1651671235924/work
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==1.1.0
+llvmlite==0.34.0
+MarkupSafe==2.1.1
+matplotlib-inline @ file:///tmp/build/80754af9/matplotlib-inline_1628242447089/work
+mistune==0.8.4
+multidict==6.0.2
+multiprocess==0.70.12.2
+nbclient==0.6.3
+nbconvert==6.5.0
+nbformat==5.4.0
+nest-asyncio @ file:///opt/concourse/worker/volumes/live/bf4e1da0-0622-4bab-4608-fdb487494886/volume/nest-asyncio_1649847922626/work
+nltk==3.7
+notebook==6.4.11
+numba==0.51.2
+numpy==1.21.6
+packaging==21.3
+pandas==1.3.5
+pandocfilters==1.5.0
+parso @ file:///opt/conda/conda-bld/parso_1641458642106/work
+pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work
+pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
+Pillow==9.1.1
+plotly==5.8.0
+prometheus-client==0.14.1
+prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1633440160888/work
+protobuf==3.20.1
+ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pyarrow==8.0.0
+pycparser==2.21
+pydeck==0.7.1
+Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work
+Pympler==1.0.1
+pynndescent==0.5.7
+pyparsing==3.0.9
+pyrsistent==0.18.1
+python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
+pytz==2022.1
+pytz-deprecation-shim==0.1.0.post0
+PyYAML==5.4.1
+pyzmq @ file:///opt/concourse/worker/volumes/live/7dec6841-64b1-4d95-7226-ce37c6ca8915/volume/pyzmq_1638435000749/work
+regex==2022.4.24
+requests==2.27.1
+responses==0.18.0
+scikit-learn==1.0.2
+scipy==1.7.3
+semver==2.13.0
+Send2Trash==1.8.0
+sentence-transformers==2.2.0
+sentencepiece==0.1.96
+six @ file:///tmp/build/80754af9/six_1644875935023/work
+smmap==5.0.0
+soupsieve==2.3.2.post1
+streamlit==1.9.0
+tenacity==8.0.1
+terminado==0.15.0
+threadpoolctl==3.1.0
+tinycss2==1.1.1
+tokenizers==0.12.1
+toml==0.10.2
+toolz==0.11.2
+torch==1.11.0
+torchvision==0.12.0
+tornado @ file:///opt/concourse/worker/volumes/live/d531d395-893c-4ca1-6a5f-717b318eb08c/volume/tornado_1606942307627/work
+tqdm==4.64.0
+traitlets @ file:///tmp/build/80754af9/traitlets_1636710298902/work
+transformers==4.19.2
+typing_extensions==4.2.0
+tzdata==2022.1
+tzlocal==4.2
+umap-learn==0.5.3
+urllib3==1.26.9
+validators==0.19.0
+wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work
+webencodings==0.5.1
+widgetsnbextension==3.6.0
+xxhash==3.0.0
+yarl==1.7.2
+zipp==3.8.0

topic_10000.model ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:3fcb5b76adace0d35bb26c34a74badb0fd8374e4aad30f8a2da89e8dc48e0127
+size 156714218

topic_10000.par ADDED Viewed

Binary file (514 kB). View file

topics ADDED Viewed

	@@ -0,0 +1 @@

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