Pretrained Embeddings

If you do not want to train the model to generate seed embeddings, and want to use the pretrained vocabulary, please use the vocabulary given here and skip the following.

Currently we support three different embedding dimensions (75, 100, 300). If you want to use embeddings of different dimensions, you can follow the below steps and copy the resultant vocabulary to the vocabulary directory with the following naming convention. seedEmbeddingVocab<DIM>D.txt. Such vocabularies would be automatically used during the build process of IR2Vec.

Generation of Seed Embedding Vocabulary

This directory helps in generating seed embedding vocabulary in 3 steps.

1. Building ir2vec
2. Generating Triplets
3. Training TransE to generate seed embedding vocabulary

Step 1: Building ir2vec

If you have not done make, follow the following steps to build ir2vec binary.

• Go to the build directory (cd ../build)
• make

Step 2: Generating Triplets

Steps to collect the triplets

Run triplets.sh script with the required parameters Usage: bash triplets.sh <build dir> <No of opt> <llFile list> <output FileName>

• buildDir points to the path of IR2Vec's build folder
• numOpt is an integer between 1 and 6
  • Determines number of optimization sequences to apply on each file.
  • Optimization sequence can be one of the standard sequences O[0-3sz] selected at random
• llFileList is a file containing the path of all the ll files. Use find <ll_dir> -type f > files_path.txt
• outputFileName is the file where the triplets would be written
  • New file would be created if the file with given name does not exist.
  • If file exists with the given name, the triplets would be appended on to the same file.

Example Usage:

‍bash triplets.sh ../build 2 files_path.txt triplets.txt

Files used to generate Seed Embedding Vocabulary

We generated ll files from Boost libraries and spec cpu 2017 benchmarks to generate triplets.

Dataset	Source
Boost	https://www.boost.org/
SPEC17 CPU	https://www.spec.org/cpu2017/

Step 3: Training TransE to generate seed embedding vocabulary

The OpenKE directory is a modified version of OpenKE repository (https://github.com/thunlp/OpenKE/tree/OpenKE-PyTorch) with the necessary changes for training seed embedding vocabulary.

Please see OpenKE/README.md for further information on OpenKE.

Requirements

Create conda environment and install the packages given in openKE.yaml

• conda create -f ./OpenKE/openKE.yaml
• conda activate openKE

Preprocessing the triplets

We preprocess the generated triplets from the previous step in a form suitable for training TransE.

• cd OpenKE
• python preprocess.py --tripletFile=<tripletsFilePath>
  • --tripletFile points to the location of the outputFileName generated in the previous step
  • The processed files entity2id.txt, train2id.txt and relation2id.txt will be generated in the same directory as that of tripletsFilePath.

Training TransE to generate embeddings

Run python generate_embedding_ray.py Possible Arguments: All the arguments have default values unless provided:

• --index_dir: Specifies the directory containing the processed files generated from preprocessing the triplets.
• --epoch: Sets the number of epochs. Default is 1000.
• --is_analogy: Boolean flag to report analogy scores, calculated every 10 epochs using analogies.txt. Default is False.
• --link_pred: Boolean flag to report link prediction scores. Requires testing files (test2id.txt,valid2id.txt) in the `--index_dir. Link prediction scores include hit@1, hit@3, hit@10, mean rank (MR), and mean reciprocal rank (MRR). Default is False.
• --nbatches: Specifies the batch size. Default is 100.
• --margin: Specifies the margin size for training. Default is 1.0.

Example Command

To train a model with analogy scoring enabled and a batch size of 200, you can run:

python generate_embedding_ray.py --index_dir "../seed_embeddings/preprocessed/" --epoch 1500 --is_analogy True --use_gpu true

TensorBoard Tracking

Once training begins, you can monitor the progress using TensorBoard by running the following command:

tensorboard --logdir=~/ray_results

ASHA Scheduler for Hyperparameter Optimization

We employ the ASHA Scheduler to efficiently optimize hyperparameters and terminate suboptimal trials. This scheduler tracks key metrics, which are determined by the following conditions:

• If --is_analogy is set to True, the AnalogyScore will be the key metric.
• If --link_pred is set to True, the hit@1 will be the key metric.
• If neither flag is set, the default loss will be used as the key metric.

Results

Once the training completes, the best model will be saved in the specified index_dir with the filename format:

seedEmbedding_{}E_{}D_{}batches_{}margin.ckpt

In addition, the entity embeddings will be stored in the index_dir/embeddings subdirectory in the following format:

embeddings/seedEmbedding_{}E_{}D_{}batches_{}margin.txt