tmplot 0.0.8

Visualization of Topic Modeling Results

tmplot

tmplot is a Python package for analysis and visualization of topic modeling results. It provides the interactive report interface that borrows much from LDAvis/pyLDAvis and builds upon it offering a number of metrics for calculating topic distances and a number of algorithms for calculating scatter coordinates of topics. It can be used to select closest and stable topics across multiple models.

Features

• Supported models:

  • tomotopy: LDAModel, LLDAModel, CTModel, DMRModel, HDPModel, PTModel, SLDAModel, GDMRModel
  • gensim: LdaModel, LdaMulticore
  • bitermplus: BTM

• Supported distance metrics:

  • Kullback-Leibler (symmetric and non-symmetric) divergence
  • Jenson-Shannon divergence
  • Jeffrey's divergence
  • Hellinger distance
  • Bhattacharyya distance
  • Total variation distance
  • Jaccard inversed index

• Supported algorithms for calculating topics scatter coordinates:

  • t-SNE
  • SpectralEmbedding
  • MDS
  • LocallyLinearEmbedding
  • Isomap

Installation

The package can be installed from PyPi:

pip install tmplot

Or directly from this repository:

pip install git+https://github.com/maximtrp/tmplot.git

Dependencies

• numpy
• scipy
• scikit-learn
• pandas
• altair
• ipywidgets
• tomotopy, gensim, and bitermplus (optional)

Quick example

# Importing packages
import tmplot as tmp
import pickle as pkl
import pandas as pd

# Reading a model from a file
with open('data/model.pkl', 'rb') as file:
    model = pkl.load(file)

# Reading documents from a file
docs = pd.read_csv('data/docs.txt.gz', header=None).values.ravel()

# Plotting topics as a scatter plot
topics_coords = tmp.prepare_coords(model)
tmp.plot_scatter_topics(topics_coords, size_col='size', label_col='label')

# Plotting terms probabilities
terms_probs = tmp.calc_terms_probs_ratio(phi, topic=0, lambda_=1)
tmp.plot_terms(terms_probs)

# Running report interface
tmp.report(model, docs=docs, width=250)

You can find more examples in the tutorial.