kbc's Introduction

Knowledge Base Completion (kbc)

This code reproduces results in Canonical Tensor Decomposition for Knowledge Base Completion (ICML 2018).

Installation

Create a conda environment with pytorch cython and scikit-learn :

conda create --name kbc_env python=3.7
source activate kbc_env
conda install --file requirements.txt -c pytorch

Then install the kbc package to this environment

python setup.py install

Datasets

To download the datasets, go to the kbc/scripts folder and run:

chmod +x download_data.sh
./download_data.sh

Once the datasets are download, add them to the package data folder by running :

python kbc/process_datasets.py

This will create the files required to compute the filtered metrics.

Update notes

In moving from cpp to pytorch, a hidden regularization weight constant was removed. This changes the settings required to reproduce the results in the paper. As a rule of thumb, and before I re-run a grid-search to find optimal parameters, multiplying the regularization strength in the table below by a factor of two should give reasonable results.

The "reciprocal" parameter was also lost in a temporary effort to simplify the code.

Reproducing results (only valid before pytorch update)

To reproduce results, use learning.learn as follows

python kbc/learn.py --dataset FB15K --model ComplEx --rank 2000 --optimizer Adagrad --learning_rate 1e-2 --batch_size 100 --regularizer N3 --reg 5e-3 --reciprocals 1 --max_epochs 10 --valid 1

To reproduce results in this setting, use the following hyper-parameters (model ComplEx, optimizer Adagrad, regularizer N3, reciprocals 1):

Dataset	rank	lr	reg	batch_size	Time
WN18	2000	1e-1	1e-1	100	150s/epoch
WN18RR	2000	1e-1	1e-1	100	93s/epoch
FB15K	2000	1e-2	5e-3	100	225s/epoch
FB15K-237	2000	1e-1	1e-1	100	115s/epoch
YAGO3-10	1000	1e-1	1e-2	1500	485s/epoch