/ChainsofReasoning

Forked CVSM code in Torch from rajarshd

Primary LanguageLua

ChainsofReasoning

This repo is forked from Rajarshi Das's code. The original repo is no longer maintained. Modifications have been made to ensure the code runs smoothly. Below is a brief description of the code from the original repo:

Code for paper Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

##Important Notes:

  1. The original code interchanges class labels (1 and 0) of examples by adding one in data/int2torch.lua then multiplying -1 and adding 2 in model/batcher/Batcher.lua. When negative examples are more than positive examples, by flipping the the class labels, the MAP calculated for positive examples will be higher.

  2. Training accuracy can be evaluated by changing input files in eval/test_from_checkpoint.lua around line 55.

##Dependencies

Quick Start

  1. Get the data from here.

  2. The data need to be formatted as torch tensors to pass to the network. Running the following to prepare the data:

    cd data
/bin/bash make_data_format.sh <path_to_input_data> <output_dir>

    For example you can run,

    cd data
bash make_data_format.sh examples/data_small_input examples/data_small_output

    It's important to not put / after directory name.

  3. Specify parameters in run_scripts/config.sh. This file has detailed descriptions for all parameters. For parameters in 2. Features section, see data/make_data_format.sh to find out the dimension of input features. To set paramters in 3. Vocabs section, see files in vocab directory.

  4. Start training:

    cd run_sripts
bash train.sh config.sh

  5. Use the trained model for evaluation. Specify paramters in eval/config.sh. Notice an epoch file is needed. It specifies the model from which iteration will be used. For example, this file can be simply created by running echo "model-20" > test_models.txt

    cd eval
bash get_accuracy_and_trec.sh config.sh

  6. After evaluation, the results can be found in the folder specified in eval/config.sh. There will be an accuracy.txt showing the percentage of correctly predicting whether the target relation exists between entity pairs. There will be another file showing the predicted score and the ground truth label for every entity pair. Each line has the format of "[target_relation]\t[entity_pair_counter]\t[predicted_score]\t[ground_truth_label]\n". This file can be used to compute MAP and MRR for the relation.

Understand the Model

To help understand the network structure, model/model_structure.txt is provided. Most of the content is in standard torch model format, a little bit is in pseudo-code as the original code uses a custom MapReduce class to change the dimension of the input and output for mini-batch training. model/test_network.lua is also there to help you test out different components of the model and see the dimension of the data.