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This GitHub repository contains the code implementation for the dynamic co-clustering approach proposed in the article "A Deep Dynamic Latent Block Model for the Co-clustering of Zero-Inflated Data Matrices”.

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Zip-dLBM

This GitHub repository contains the code implementation for the dynamic co-clustering approach proposed in the article "A Deep Dynamic Latent Block Model for the Co-clustering of Zero-Inflated Data Matrices”.

Authors: Giulia Marchello, Benjamin Navet, Marco Corneli, Charles Bouveyron.

Start project

  • conda create -n Zip-dLBM
  • conda activate Zip-dLBM
  • conda remove mkl (Only for MacOs users)
  • conda install nomkl (Only for MacOs users)
  • conda install numpy
  • conda install scipy scikit-learn matplotlib pandas
  • conda install pytorch torchvision torchaudio -c pytorch (alternatively: pip3 install torch)
  • conda install -c conda-forge r-base r-slam r-rlab r-reticulate
  • pip install line-profiler
  • conda install pathos
  • Rscript Script_Exp1.R (or Script-LondonBikes.R for London's Bike data)

Execution:

To run the first experiment do:

  • Rscript Script_Exp1.R

To run the experiment on London Bikes' data do:

  • conda install -c conda-forge r-ggmap r-leaflet r-mapdata
  • Rscript Script-LondonBikes The folder "data" containes the data used in the paper, downloaded from: https://cycling.data.tfl.gov.uk

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This GitHub repository contains the code implementation for the dynamic co-clustering approach proposed in the article "A Deep Dynamic Latent Block Model for the Co-clustering of Zero-Inflated Data Matrices”.

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