NeurIPS 2022 CausalEdu Competition - Task 3
The required environment is as bellow:
- Unix-based system
- Python 3.9+
- PyTorch 1.12.1
- Numpy 1.23.1
- Wandb 0.13.4
- Pandas 1.5.0
- SciPy 1.9.1
- HuggingFace (Transformers) 4.18.0
/data/ - Repository containing the dataset
/serialized_torch/ - Intermediate data from the prepocessed dataset
predict_graph.py - Training Causal KT
construct_solution.py - Creating adjacency matrix for submission
/submissions/final - Contains the model and the adjacency matrix for submission on the private leaderboard.
First install all dependecies of the projects below is the pip methodology. (Similiar approaches exist for conda or other package management systems).
pip3 install -r requirements.txt
Here is an example how to train Deep Causal Knowledge Tracing (DCKT) with a learning_rate of 0.01.
python3 predict_graph.py -L 1e-3
For a list of all possible hyperparameters see:
python3 predict_graph.py -h
The output of this is a model .pt file which contains a learned P and L matrix. By default, this is saved into the saved_models directory.
To construct the construct ordering adjacency matrix from this model we use construct_solution.py. Here is an example how to create a submission file. You do not need to include .pt.
python3 construct_solution.py -f <Your Model File>
The output of this script is a zip file containing the .npy casual order adjency matrix.
Contact: ml4ed @ UMass Amherst
- Jaewook Lee (jaewooklee@umass.edu)
- Hunter McNichols (wmcnichols@umass.edu)
- Nischal Ashok Kumar (nashokkumar@umass.edu)
- Wanyong Feng (wanyongfeng@umass.edu)
- Aritra Ghosh (arighosh@cs.umass.edu)