Learning interaction rules from multi-animal trajectories via augmented behavioral models (ABM)

Author

Keisuke Fujii - https://sites.google.com/view/keisuke1986en/

Reference

Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara,
Learning interaction rules from multi-animal trajectories via augmented behavioral models, Advances in Neural Information Processing Systems (NeurIPS'21), 34, 2021 Link

Requirements

• Python 3.8
• To install requirements:

pip install -r requirements.txt

• For Python 3.6, see requirements36.txt

Preprocessing

• The synthetic, sula, flies, and peregrine datasets are stored in the folder ./datasets.
• These can be preprocessed by the code in the folder ./datasets.
• The output file **_data.npy includes the data in the form such that [files][agents, xy(z), timestamps].
• Other animal data can be set in the folder ./datasets/GC_**.
• We addtionally analyzed peregrine data obtained at https://doi.org/10.5061/dryad.md268.

Main analysis

• See run.sh for commands using various datasets.
• The output file is in the folder ./weights.
• Further details are documented within the code.

Post analysis

• The post analysis was performed by matlab code in the folder ./matlab_post_analysis.
• (2023/11) The post analysis code by python is released as post_analysis.py. Currently, mice and flies data can be used (and videos are not generated). For example, run python post_analysis.py --experiment mice --model gvar --K 3 --test_samples 2 (see also run.sh).

Note for using your own data

• The performance of this method will be better in relatively fewer and sparse agents with than those in more and dense agents.

References

Codes for the baseline models are available in the following repositories:

• GVAR: https://openreview.net/forum?id=DEa4JdMWRHp
• eSRU: https://github.com/sakhanna/SRU_for_GCI
• ACD: https://github.com/loeweX/AmortizedCausalDiscovery
• Linear GC and Local TF: https://github.com/tailintalent/causal