Re-implementation of the paper Mapping Language to Code in Programmatic Context
This is not the official implementation of the paper.
Create a virtual environment with python3 (optional).
python3 -m venv langcode
Activate the virtual environment
source langcode/bin/activate
Install the requirements
pip install -r requirements.txt
You are ready to go.
If you have a directory containing some .proto files, you can extract all the data recursively from the root dir. Alternatively, if you want to use the CONCODE dataset you can skip the next command.
python transform.py -root_dir=path/to/root/of/proto/files
This will create train.json, test.json, valid.json files in the main project directory.
Once the json files are created (or downloaded from CONCODE), run:
python build.py -train_file train.json -valid_file valid.json -test_file test.json -output_folder data
This will create the corresponding .dataset files in the data directory. You can inspect those if you wish.
Next, we need to preprocess data. Run:
python preprocess.py -train data/train.dataset -valid data/valid.dataset -save_data data/processed
Finally, to train, run:
python train.py -dropout 0.5 -data data/processed -save_model data/processed/ -epochs 30 -learning_rate 0.001 -seed 1123 -enc_layers 2 -dec_layers 2 -batch_size 20 -src_word_vec_size 512 -tgt_word_vec_size 512 -rnn_size 512 -decoder_rnn_size 1024
The command above includes the same hyperparameters as in the original paper. This will result in trained models
being saved in data/processed/. Alternatively, already trained models can be put in this directory and tested (see next step).
To evaluate the model run:
ipython predict.ipy -- -start 2 -end 2 -beam 3 -models_dir data/processed/ -test_file data/valid.dataset -tgt_len 500
This will evaluate the model on the weights of the 2nd epoch (that model should exist in data/processed/).
Tweak start and end if needed to evaluate on more epochs, or other ones.
Predictions that the model outputs will be saved in data/processed/preds.
Concode dataset can be downloaded from: https://drive.google.com/drive/folders/1kC6fe7JgOmEHhVFaXjzOmKeatTJy1I1W