st-dif 0.1.2

A library for crowdflow prediction!

st_dif

A Python library for crowd flow prediction and spatiotemporal modeling. Built on PyTorch, PyTorch Geometric, and PyTorch Geometric Temporal.

This is the Python library developed for Chapter 3 and 4 of the thesis:

V. W. H. Wong, Spatio-temporal Representation Learning: Applications to Manufacturing Planning and Pedestrian Crowd Analysis, Ph.D. Thesis, Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, June 2024.

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Installation Guide

Follow these steps carefully to set up your environment.

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Option 1: Install from PYPI

1. Create a New Virtual Environment

Using conda:

conda create -n st_dif_env python=3.10
conda activate st_dif_env

Or using venv:

python -m venv st_dif_env
st_dif_env\Scripts\activate   # Windows
source st_dif_env/bin/activate # macOS/Linux

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2. Install PyTorch

Install PyTorch matching your hardware (example for CUDA 12.6):

pip install torch torchvision torchaudio

(Refer to the PyTorch Get Started guide if you need specific instructions.)

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3. Install PyTorch Geometric Libraries

Because st_dif depends on CUDA-based libraries, it is necessary to install them manually:

pip install torch-scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-2.5.1+cu121.html
pip install torch-geometric-temporal

Important: Make sure you install from the special https://data.pyg.org wheels to ensure CUDA compatibility.

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4. Install st_dif

Install the st_dif library:

pip install st_dif

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Summary:

• Follow the installation steps carefully to match CUDA and PyTorch versions.
• Manually install torch-scatter, torch-sparse, and torch-geometric from the correct source.
• Then install st_dif.

Example Full Setup Commands

conda create -n st_dif_env python=3.10
conda activate st_dif_env

pip install torch torchvision torchaudio

pip install torch-scatter torch-sparse torch-geometric -f https://data.pyg.org/whl/torch-2.5.1+cu121.html
pip install torch-geometric-temporal

pip install st_dif

At this point, you are ready to use st_dif.

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Notes

• If you encounter [WinError 127] The specified procedure could not be found, it usually indicates that torch-scatter or torch-sparse were installed incorrectly. Reinstall them following the instructions above.
• Ensure that your PyTorch version matches your CUDA version.
• If using a CPU-only version of PyTorch, install CPU-compatible versions of the dependencies.

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Option 2: Install from Source (developer)

git clone https://github.com/vivian-wong/crowd-framework/
cd crowd-framework
pip install -e .[dev]

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Run Installation Test

Assuming pytest has been installed:

python -m pytest

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Example Usage

from st_dif.data_utils import get_pyg_temporal_dataset, get_loaders
from st_dif.models.sten import STEN

Check the examples/ directory for simplified demo notebooks.

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Reproducing paper experiments

To run all experiments as detailed in the thesis, run

bash reproduce_paper_experiments.sh

and generate plots with the jupyter notebook experiments/plot_results.ipynb

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Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss proposed changes.

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License

MIT License

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