<jupyter_start><jupyter_text>Visualizing GraphLab's LDA TopicModel with pyLDAvisThis is an example of how to use [`pyLDAvis`](https://github.com/bmabey/pyLDAvis) [helper functions](https://pyldavis.readthedocs.org/en/latest/modules/API.htmlmodule-pyLDAvis.graphlab) to visualize a GraphLab Create Topic Model model. For our example model we will be extending the example provided by [GraphLab's own documenation](https://dato.com/products/create/docs/generated/graphlab.topic_model.create.html).<jupyter_code>import graphlab as gl
import pyLDAvis
import pyLDAvis.graphlab
# turn on automatic rendering of visualizations
pyLDAvis.enable_notebook()
docs = gl.SArray('http://s3.amazonaws.com/GraphLab-Datasets/nytimes')
gl.topic_model.create?
%%time
topic_model = gl.topic_model.create(docs, num_topics=15, num_iterations=3000)
pyLDAvis.graphlab.prepare(topic_model, docs)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1067853170189099, 'fraction_numerical': 0.00778642936596218, 'mean_word_length': 7.108108108108108, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '125240', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 312, 'n_tokens_neox': 300, 'n_words': 63, 'path': 'notebooks/GraphLab.ipynb', 'repo_name': 'lymph12138/pyLDAvis', 'size': '899'}