Setup

First, create a virtual environment with conda: conda create -n cre python=3.7 and activate it: conda activate cre.

Then, install all dependencies with pip install -r requirements.txt

Data

Download data from https://drive.google.com/file/d/12rcJdauoluBCUV-iPX9lCDYaNcaUZa7f/view?usp=sharing Then unzip it and move all files under CRE/data

Training & Prediction

Open run/train_and_predict.py and scroll to the bottom. Comment out any models that you don't want to run for (in other words, leave only the models you want to run uncommented). For example, if you want to run cnn+TransE and transformer+ComplEx, the bottom of run/train_and_predict.py should look like:

# train_and_predict(weston_runner_supplier, weston_runner_trainer, 'weston')
# train_and_predict(han_runner_supplier, han_runner_trainer, 'han')
train_and_predict(cnn_transe_runner_supplier, cnn_transe_runner_trainer, 'cnn_transe')
# train_and_predict(cnn_complex_runner_supplier, cnn_complex_runner_trainer, 'cnn_complex')
# train_and_predict(lstm_transe_runner_supplier, lstm_transe_runner_trainer, 'lstm_transe')
# train_and_predict(lstm_complex_runner_supplier, lstm_complex_runner_trainer, 'lstm_complex')
# train_and_predict(transformer_transe_runner_supplier, transformer_transe_runner_trainer, 'transformer_transe')
train_and_predict(transformer_complex_runner_supplier, transformer_complex_runner_trainer, 'transformer_complex')

Then, do the following to train & predict:

cd run
python train_and_predict.py

All trained models will be stored in trained_models/, and all prediction results will be stored in run/

Visualization

Once prediction results are made, you can use helper functions in run/visualize.py to view plots for the results. Specifically, compare_results_across_datasets plot results for top-1, top-3, and top-10, within each of which are the prediction results of your selected model on 3 different datasets. compare_results_within_dataset, on the other hand, plot results for top-1, top-3, and top-10 of your selected model on the same dataset, but across 3 different training runs. Suppose you want to compare across datasets for Weston's model, and compare across training runs for cnn+TransE, the bottom of run/visualize.py should look like:

compare_results_across_datasets('weston')
compare_results_within_dataset('cnn_transe')

Then, do the following to show plot:

cd run
python visualize.py