Code to accompany AAAI 24 paper. Written by Gabriel S. Agostini, Nikhil Garg, and Emma Pierson.
A .yml file is provided with all required packages and their versions. Please update the d03_src.vars.py file with your path to the repository _path_to_repo to ensure all functions work correctly.
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├── d01_data
│ ├── d01_raw <- Contain raw data files that are publicly available
│ └── d02_processed <- Contain files produced by this code (census demographic covariates)
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├── d02_notebooks <- Jupyter notebooks that produce plots used in the submission
│ ├── 1_preprocessing_311_complaints.ipynb <- Basic EDA of 311 flood reports
│ ├── 2_preprocessing_demographic_data.ipynb <- Shows processing of Census and ACS data into demographic features
│ ├── 3_model_example.ipynb <- Read inference on real world summaries and produce visualizations
│ └── 4_producing_results.ipynb <- Read semi-synthetic simulation summaries and produce visualizations
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├── d03_src <- Source code for use in this project, which can be imported as modules into the notebooks and scripts
│ ├── model_*.py <- Model functions
│ ├── evaluate_*.py <- Misc. functions for reading model results and visualization
│ ├── process_*.py <- Misc. functions for data pre-processing
│ └── vars.py <- Main variables used in other scripts such as paths and flood event dates
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├── d04_scripts <- Full code routines
│ ├── calibrate.py <- Semi-synthetic data experiments to test model calibration
│ ├── generate.py <- Generates data to test priors
│ ├── inference.py <- Empirical routines
│ └── read_inferences.py <- Read job outputs into posterior and summaries
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├── d05_joboutputs <- Outputs should be directed and read from this directory. Not included due to size limits
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├── d06_processed-outputs <- Outputs from the read_inference.py script such as posterior summaries and inferred quantities
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└── d07_plots <- Plots used on paper
The main function we implement is d03_src.model_MCMC.run_MCMC which runs the chain to either sample the variables given fixed parameters (pass the argument fixed_params=True along with a dictionary params) or sample the parameters given observed reports fixed_T=True along with an array T). A NetworkX.Graph must be passed. Other arguments that control the chain hyperparameters (number of iterations, thinning fraction, burn-in period) or the sampling hyperparameters (step-size on the SVEA, priors) are detailed in the signature.
The notebook d02_notebooks/3_model_example.ipynb details the usage of these functions.