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.

Plots

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.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

tmplot-0.1.1.tar.gz (18.1 kB view details)

Uploaded Oct 27, 2023 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

tmplot-0.1.1-py3-none-any.whl (18.2 kB view details)

Uploaded Oct 27, 2023 Python 3

File details

Details for the file tmplot-0.1.1.tar.gz.

File metadata

Download URL: tmplot-0.1.1.tar.gz
Upload date: Oct 27, 2023
Size: 18.1 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for tmplot-0.1.1.tar.gz
Algorithm	Hash digest
SHA256	`578d73ab5b6ad54cdc3da24d286088f4302a1cd39b4b42dd4501f77cf3a930f6`
MD5	`aedebfd81ed4052ca9aa40dd96cee540`
BLAKE2b-256	`11f176ab8aaf7d71f7093835315701fda5ba32ffb68a59fcbda74d5fab238899`

See more details on using hashes here.

File details

Details for the file tmplot-0.1.1-py3-none-any.whl.

File metadata

Download URL: tmplot-0.1.1-py3-none-any.whl
Upload date: Oct 27, 2023
Size: 18.2 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.12.0

File hashes

Hashes for tmplot-0.1.1-py3-none-any.whl
Algorithm	Hash digest
SHA256	`87a545a8382b6ef42dd367e063dc238e1590518655c8c038bf7d20f4e5b03632`
MD5	`04ef7920aeb8c46571037a407f943200`
BLAKE2b-256	`6d6687a3e823d6b24811fe8fc714748587afa1321f5361f251d00b5e2f5c5048`