This repository contains instructions for using deep learning-based approaches that predicts types for Python.
To install required dependencies for the approaches, run the following command:
pip install -r requirements.txt
The TypeWriter is proposed by:
Pradel, M., Gousios, G., Liu, J., & Chandra, S. (2019). TypeWriter: Neural Type Prediction with Search-based Validation. arXiv preprint arXiv:1912.03768.
The re-implementation of this approach consists of two parts:
- Extractor: To process functions and generate data vectors, use the following script:
python TW_extractor.py --o $OUTPUT_FOLDER --d $REPOS --w $THREADS
$OUTPUT_FOLDER: Specify a folder to store the output files. The folder will be created automatically.
$REPOS: The path to Python projects
$THREADS: Number of workers for extracting functions from the projects.
- Model: To learn the neural model and do prediction, run the following script:
python TW_model.py --o $OUTPUT_FOLDER
$OUTPUT_FOLDER: Note that this is the output folder that you specified in the extractor part.
To change the hyper-parameters of the neural model, you can change the values in the file data/tw_model_learning_params.json.
It should be noted that both Extractor and Model scripts save the required files for running the inference script in $OUTPUT_FOLDER/tw_model_files.
To run all the steps of TypeWriter manually with explanation, check out the notebook main_TW.ipynb.
By employing the pre-trained neural model of TypeWriter, you can infer both argument and return types of the methods of a given Python source file using the following script:
python TW_inference.py --s $SRC_FILE --m $TW_MODEL_PATH --t $TOP_K
$SRC_FILE: A Python source file.
$TW_MODEL_PATH: The path to the TypeWriter's pre-trained model and auxiliary files.
$TOP_K: Top K predictions (the default value of K is 3)