topic-wizard 0.3.0

Pretty and opinionated topic model visualization in Python.

topicwizard

Pretty and opinionated topic model visualization in Python.

https://user-images.githubusercontent.com/13087737/234209888-0d20ede9-2ea1-4d6e-b69b-71b863287cc9.mp4

New in version 0.3.0 🌟 🌟

• Exclude pages, that are not needed :bird:
• Self-contained interactive figures :gift:
• Topic name inference is now default behavior and is done implicitly.

Features

• Investigate complex relations between topics, words and documents
• Highly interactive
• Automatically infer topic names
• Name topics manually
• Pretty :art:
• Intuitive :cow:
• Clean API :candy:
• Sklearn, Gensim and BERTopic compatible :nut_and_bolt:
• Easy deployment :earth_africa:

Installation

Install from PyPI:

pip install topic-wizard

Usage (documentation)

Step 1:

Train a scikit-learn compatible topic model. (If you want to use non-scikit-learn topic models, check compatibility)

from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import make_pipeline

# Create topic pipeline
topic_pipeline = make_pipeline(
    CountVectorizer(),
    NMF(n_components=10),
)

# Then fit it on the given texts
topic_pipeline.fit(texts)

Step 2a:

Visualize with the topicwizard webapp :bulb:

import topicwizard

topicwizard.visualize(pipeline=topic_pipeline, corpus=texts)

From version 0.3.0 you can also disable pages you do not wish to display thereby sparing a lot of time for yourself:

import topicwizard

# A large corpus takes a looong time to compute 2D projections for so
# so you can speed up preprocessing by disabling it alltogether.
topicwizard.visualize(pipeline=topic_pipeline, corpus=texts, exclude_pages=["documents"])

Ooooor...

Step 2b:

Produce high quality self-contained HTML plots and create your own dashboards/reports :strawberry:

Map of words

from topicwizard.figures import word_map

word_map(corpus=texts, pipeline=pipeline)

Timelines of topic distributions

from topicwizard.figures import document_topic_timeline

document_topic_timeline(
    "Joe Biden takes over presidential office from Donald Trump.",
    pipeline=pipeline,
)

Wordclouds of your topics :cloud:

from topicwizard.figures import topic_wordclouds

topic_wordclouds(corpus=texts, pipeline=pipeline)

And much more (documentation)