topic-wizard 0.5.0

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 0.5.0 🌟

• Enhanced readibility and legibility of graphs.
• Added helper tooltips to help you understand and interpret the graphs.
• Improved stability.
• Negative topic distributions are now supported in documents.

Features

• Investigate complex relations between topics, words, documents and groups/genres/labels
• Easy to use pipelines that can be utilized for downstream tasks
• Sklearn, Gensim and BERTopic compatible :nut_and_bolt:
• Highly interactive web app
• Interactive and composable Plotly figures
• Automatically infer topic names, oooor...
• Name topics manually
• Easy deployment :earth_africa:

Installation

Install from PyPI:

pip install topic-wizard

Pipelines

The main abstraction of topicwizard around a topic model is a topic pipeline, which consists of a vectorizer, that turns texts into bag-of-tokens representations and a topic model which decomposes these representations into vectors of topic importance. topicwizard allows you to use both scikit-learn pipelines or its own TopicPipeline.

Let's build a pipeline. We will use scikit-learns CountVectorizer as our vectorizer component:

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer(min_df=5, max_df=0.8, stop_words="english")

The topic model I will use for this example is Non-negative Matrix Factorization as it is fast and usually finds good topics.

from sklearn.decomposition import NMF

model = NMF(n_components=10)

Then let's put this all together in a pipeline. You can either use sklearn Pipelines...

from sklearn.pipeline import make_pipeline

topic_pipeline = make_pipeline(vectorizer, model)

Or TopicPipeline from topicwizard:

from topicwizard.pipeline import make_topic_pipeline

topic_pipeline = make_topic_pipeline(vectorizer, model, norm_rows=False)

Let's load a corpus that we would like to analyze, in this example I will use 20newsgroups from sklearn.

from sklearn.datasets import fetch_20newsgroups

newsgroups = fetch_20newsgroups(subset="all")
corpus = newsgroups.data

# Sklearn gives the labels back as integers, we have to map them back to
# the actual textual label.
group_labels = [newsgroups.target_names[label] for label in newsgroups.target]

Then let's fit our pipeline to this data:

topic_pipeline.fit(corpus)

The advantages of using a TopicPipeline over a regular pipeline are numerous:

• Output dimensions (topics) are named
• You can set the output to be a pandas dataframe (topic_pipeline.set_output(transform="pandas")) with topics as columns.
• You can treat topic importances as pseudoprobability-distributions (topic_pipeline.norm_row = True)
• You can freeze components so that the pipeline will stay frozen when fitting downstream components (topic_pipeline.freeze = True)

Here's an example of how you can easily display a heatmap over topics in a document using TopicPipelines.

import plotly.express as px

pipeline = make_topic_pipeline(vectorizer, model).set_output(transform="pandas")
texts = [
   "Coronavirus killed 50000 people today.",
   "Donald Trump's presidential campaing is going very well",
   "Protests against police brutality have been going on all around the US.",
]
topic_df = pipeline.transform(texts)
topic_df.index = texts
px.imshow(topic_df).show()

You didn't even have to use topicwizards own visualizations for this!!

You can also use TopicPipelines for downstream tasks, such as unsupervised text labeling with the help of human-learn.

pip install human-learn

from hulearn.classification import FunctionClassifier
from sklearn.pipeline import make_pipeline

topic_pipeline = make_topic_pipeline(vectorizer, model).fit(texts)

# Investigate topics
topicwizard.visualize(topic_pipeline)

# Creating rule for classifying something as a corona document
def corona_rule(df, threshold=0.5):
    is_about_corona = df["11_vaccine_pandemic_virus_coronavirus"] > threshold
    return is_about_corona.astype(int)

# Freezing topic pipeline
topic_pipeline.freeze = True
classifier = FunctionClassifier(corona_rule)
cls_pipeline = make_pipeline(topic_pipeline, classifier)

Web Application

You can launch the topic wizard web application for interactively investigating your topic models. The app is also quite easy to deploy in case you want to create a client-facing interface.

import topicwizard

topicwizard.visualize(corpus, pipeline=topic_pipeline)

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

# 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(corpus, pipeline=topic_pipeline, exclude_pages=["documents"])

Topics	Words	Documents	Groups

Figures

If you want customizable, faster, html-saveable interactive plots, you can use the figures API. Here are a couple of examples:

from topicwizard.figures import word_map, document_topic_timeline, topic_wordclouds, word_association_barchart

Word Map	Timeline of Topics in a Document
word_map(corpus, pipeline=topic_pipeline)	document_topic_timeline( "Joe Biden takes over presidential office from Donald Trump.", pipeline=topic_pipeline)

Wordclouds of Topics	Topic for Word Importance
topic_wordclouds(corpus, pipeline=topic_pipeline)	word_association_barchart(["supreme", "court"], corpus=corpus, pipeline=topic_pipeline)

For more information consult our Documentation