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This repository contains the source code used for experiments in the following paper:
Cranefield, Stephen and Dhiman, Ashish. Identifying Norms from Observation Using MCMC Sampling, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 118-124, 2021. https://doi.org/10.24963/ijcai.2021/17 (BibTeX)
The project covers the steps as given in the schematic below:
The supplementary material for IJCAI paper can be found at Learning_Norms_with_MCMC_supp_material.pdf
To clone and run this application, you'll need to follow the below-mentioned steps:
# Clone this repository
$ git clone https://github.com/ashish1610dhiman/learning_norms_with_mcmc_from_pcfg_IJCAI21
# Go into the repository
$ cd learning_norms_with_mcmc_from_pcfg_IJCAI21
# Install depenencies using pip
$ pip install -r requirements.txt
# Or install depenencies in a conda env
$ conda create --name <env_name> --file requirements.txt
├── LICENSE
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├── README.md <- The top-level README.
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├── *_supp_material.pdf <- Supplemetary material for paper published in IJCAI-21.
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├── data_nc/* <- Folder with dvc files for various experiments with p_nn > 0
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├── data/* <- Folder with dvc files for various experiments with p_nn = 0
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├── src/
│ ├── mcmc_norm_*/ <- Code files for grammar/Metropolis Hastings Algorithm/convergence
| | and preciscion-recall
│ └── *.py <- Small Helper files
│
├── scripts/ <- Scripts used for variouis instances of the process depicted in
| | schematic above.
│ └── nc_experiments.py <- Binding script used to run various parts of experiment
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├── notebooks/ <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `_` delimited description. The notebooks with tag 1.5 mark
| the files used for experiment shown in paper.
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├── params_nc.yaml <- yaml file detailing parameters for experiments used
│
└── requirements.txt <- The requirements file for reproducing the analysis environment
The project uses dvc for tracking data. There are two data folders in the repository:
exp_nc5.dvc is the file corresponding to experiment presented in paper
Use the following commands to download the data corresponding to a dvc file:
# fetch the data
$ dvc fetch exp_nc5.dvc
# checkout the branch
$ dvc checkout
As outlined in the About The Project above, MCMC Norm learning pipleine involves the following steps:
The above steps are outlined in the binding script nc_experiments.py.
Jupyter notebook is then used as a wrapper over nc_experiments.py, to run different experiment iterations. The naming scheme of notebooks is mentioned above in Project Organsisation.
1.5_nc_exp5.ipynb is the notebook used to run experiment presented in paper
Stephen CranefieldDepartment of Information Science, University of Otago (Professor) Google Scholar
Ashish DhimanDepartment of Aerospace Engineering, Indian Institute of Technology, Kharagpur (B-Tech, 2019)Connect on LinkedIn
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