Run the experiments

Clone the code

after git clone, also do git lfs pull, to pull the large data files in atc/

Setup environment

conda env create -f environment.yml
conda activate mod

For training

python3 train.py  --model siren

The argument --model can be one of ["siren", "time_grid", "fourier"]. In the paper, we use siren as the main model. For the ablation study, we provide two alternative temporal encodings: time grid and Fourier features.

The training runs for 100 epochs, and models are saved in models/distri_gmm_siren. For time_grid and fourier version, trained models are also provided models/distri_gmm_feature_ff_time, and models/distri_gmm_feature_time.

For evaluation

python3 evaluate_NLL.py --model siren

After training the model, we can evaluate it by computing the Negative Log Likelihood (NLL) value. Same here, the arg can be chosen from ["siren", "time_grid", "fourier"]. Detailed NLL results for each test sample will be saved in nll_results/distri_gmm_siren/atc-all.csv.

For querying MoDs

python3 generate_MoD_files.py --model siren

We can query the trained model to generated maps of dynamics for each hour of the ATC dataset. The generated MoDs are saved in MoDs/distri_gmm_siren/<hour>.csv

For plotting

python3 plot_MoD_files.py --model siren --version max

We can also plot the generated MoDs. Two version of plotting are provided.

• Version all shows multimodality by rendering all SWGMM components with transparency proportional to their weights.
• Version max more clearly shows the dominant flow, only displaying the mixture component with the largest weight.

The generated MoD figures are saved in MoDs/distri_gmm_siren/all_png and MoDs/distri_gmm_siren/max_png folders.