topics

Material for ** link

See analyses to follow all the steps to reproduce the analyses

Environment

The easiest way to run all the notebooks of this repository with all dependencies handled correctly is using Docker.

You can enable jupyter lab with docker-compose

docker-compose up -d

or you can pull the docker image

docker run -v $PWD:/home/jovyan/work -p 8888:8888 --rm -it --name topic_models docker.pkg.github.com/fvalle1/topics/topicmodel:latest

then point your browser to localhost

The data processed in our analysis when not available trough git ca ìn be accessed via DataVersionControl

dvc pull

A Python package with used functions can be downloaded from https://test.pypi.org/project/topicpy/

pip install -i https://test.pypi.org/simple/ topicpy

This package is still in beta

Name		Name	Last commit message	Last commit date
Latest commit History 176 Commits
.github		.github
colab		colab
datasets		datasets
hSBM_Topicmodel @ 886ee49		hSBM_Topicmodel @ 886ee49
tree_plotter @ b3a2f7c		tree_plotter @ b3a2f7c
unified		unified
.dvcignore		.dvcignore
.gitignore		.gitignore
.gitmodules		.gitmodules
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
WGCNA.ipynb		WGCNA.ipynb
alternative_clustering.ipynb		alternative_clustering.ipynb
analyses.md		analyses.md
bipartite_network.png		bipartite_network.png
docker-compose.yml		docker-compose.yml
hSBM_clusters.ipynb		hSBM_clusters.ipynb
hSBM_topic-dist.ipynb		hSBM_topic-dist.ipynb
hSBM_topics.ipynb		hSBM_topics.ipynb
hSBM_word-dist.ipynb		hSBM_word-dist.ipynb
install_pkgs.R		install_pkgs.R
latent_distances.ipynb		latent_distances.ipynb
predictor.ipynb		predictor.ipynb
requirements.txt		requirements.txt
run_graph.ipynb		run_graph.ipynb