This is our implementation for the paper:

Haoji Hu and Xiangnan He (2019). Sets2Sets: Learning from Sequential Sets with Neural Networks. In the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’19), Anchorage, AK, USA

Please cite our paper if you use our codes and datasets. Thanks!

@inproceedings{hu2019sets2sets,
  title={Sets2Sets: Learning from Sequential Sets with Neural Networks},
  author={Hu, Haoji and He, Xiangnan},
  booktitle={Proceedings of the 25th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={1491--1499},
  year={2019},
  organization={ACM}
}

Author: Haoji Hu

We use pytorch to implement our method.

• Torch version: '1.0.1'
• Python version: '3.6.8'

Training:

python Sets2Sets.py ./data/TaFang_history.csv ./data/TaFang_future.csv TaFang 2 1 

The above command will train our model based on 4 folds of the Ta-Feng data set. The three parameters in the command tail are the model name, the number of subsequent sets in the training instances, and the flag for mode. Our example data can only support the number of subsequent sets no more than 3, which is the same as the results reported in our paper. Note that our method can handle variable length of subsequent sets due to the RNN. We fix this for experimental goal. The flag is set to 1 for training mode and 0 for test mode. The models learned from different epochs are saved under the folder './models/' (Our code will create this folder). We use a default number of max epochs 20 for demonstration. You can change this if you need more epochs.

Test:

python Sets2Sets.py ./data/TaFang_history.csv ./data/TaFang_future.csv TaFang 2 0 

The above command will test the learned model on the left 1 fold data. We just need to change the mode flag from 1 to 0. The test performance of the model giving best performance on the validation set will be printed out.

If you want to try our method on Dunnhumby data set, please visit the offical website. View the 'Let's Get Sort-of-Real'. Download the the data for randomly selected sample of 50,000 customers. We provide our script to transfer their data into the formate our method needs. After extracting all the files in the zip file and put them under a folder (e.g. ./dunnhumby_50k/), please remember to delete a file named time.csv which is not needed in our method. Then, put our script and the folder './dunnhumby_50k/' at the same level. Run our script by following command:

python Dunnhumby_data_preprocessing.py ./dunnhumby_50k/ past.csv future.csv

The data will be generated under the current folder. You can just replace the two files (TaFang_history.csv and TaFang_future.csv) with these two generated files to apply our method on Dunnhumby data set as before.

We update the training loss view as previous version is not easy for observing the training loss at each epoch. The model selection is also added for test step in this version.

Last Update Date: Oct. 1, 2019