Information Extraction with Reinforcement Learning
Installation
You will need to install Torch and the python packages in requirements.txt.
You will also need to install the Lua dev library liblua (sudo apt-get install liblua5.2) and the signal package for Torch to deal with SIGPIPE issues in Linux.
(You may need to uninstall the signal-fft package or rename it to avoid conflicts.)
Data Preparation
Create the vectorizers (using a pre-trained model), for example:
python vec_consolidate.py dloads/Shooter/train.extra 5 trained_model2.p consolidated/vec_train.5.p
python vec_consolidate.py dloads/Shooter/dev.extra 5 trained_model2.p consolidated/vec_dev.5.p
Consolidate the articles, for example:
python consolidate.py dloads/Shooter/train.extra 5 trained_model2.p consolidated/train+context.5.p consolidated/vec_train.5.p
python consolidate.py dloads/Shooter/dev.extra 5 trained_model2.p consolidated/dev+context.5.p consolidated/vec_dev.5.p
Make sure you use the correct vec_xyz.p file as input to the consolidate.py script. You can also find pre-consolidated files in pickle format here: link
Running the code
-
Change to the code directory:
cd code/ -
First run the server, for example:
python server.py --port 7000 --trainEntities consolidated/train+context.5.p --testEntities consolidated/dev+context.5.p --outFile outputs/run.out --modelFile trained_model2.p --entity 4 --aggregate always --shooterLenientEval True --delayedReward False --contextType 2 -
In a separate terminal/tab, change to the agent code directory:
cd code/dqn/ -
Then run the agent:
./run_cpu 7000 logs/tmp/
Make sure the port numbers for the server and agent match up.