VAE model

This is the accompanying code to the thesis report "Deep Generative Modelling For Population Synthesis in Participatory Climate Simulations".

Similar to Jupyter notebooks, the code follows a literate programming approach. The .org files can be opened and executed in Emacs' org-mode.

• preparation.org: Contains the data preprocessing and feature engineering code. The output is saved as "pickled" Python data structures.
• vae4.org: Contains the code for the baseline VAE model and code blocks to plot the latent space and the visualize the neural network architecture.
• vae5_hyper.org: Contains the code for the hyperparameter search.
• epa_ghg_calculator.py: Contains a modified version of the EPA carbon emissions calculator, and was provided by the external partner. The function calculate_co2 is my own addition, with default values taken from the provided code.
• sample.org: Contains code blocks to sample from the VAE and generate plots for each feature category, marginal distributions and histograms for the GHG emissions distribution.

Dependencies

The following Python packages were used.

matplotlib
pandas
numpy
tensorflow
scipy
pydot
statsmodels
scikit-learn
pymongo
keras-tuner

The .nix files provide a reproducible environment using the Nix package manager.