2.3 KiB
Project 02: Multi-source code search
About the Project
This project has the goal of developing a search engine able to query a large Python code repository using multiple sources of information. It is part of the Knowledge Analysis & Management - 2022 course from the Università della Svizzera italiana.
In this repository, you can find the following files:
- tensor flow: a code repository to be used during this project
- ground-truth-unique: a file containing the references triples necessary to evaluate the search engine (step 3)
For more information, see the Project-02 slides (available on iCourse)
Note: Feel free to modify this file according to the project's necessities.
Environment setup
To install the required dependencies make sure python3 points to a Python 3.10 or 3.11 installation and then run:
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
Part 1: data extraction
To extract the data in file data.csv run the command:
python3 extract-data.py
The script prints the requested counts, which are namely:
Methods: 5817
Functions: 4565
Classes: 1882
Python Files: 2817
Part 2: Training
In order to train and predict the output of a given query run the command:
python3 search-data.py [method] "[query]"
where [method] is one of {tfidf,freq,lsi,doc2vec} or all to run all classifiers and [query] is the natural
language query to search. Outputs are printed on stdout, and in case of doc2vec the trained model file is saved in
./doc2vec_model.dat and fetched in this path for subsequent executions.
Part 3: Evaluation
To evaluate a model run the command:
python3 search-data.py [method] ./ground-truth-unique.txt
where [method] is one of {tfidf,freq,lsi,doc2vec} or all to evaluate all classifiers. The script outputs the
performance of the classifiers in terms of average precision and recall, which are namely:
| Engine | Average Precision | Average Recall |
|---|---|---|
| tfidf | 20.00% | 20.00% |
| freq | 27.00% | 40.00% |
| lsi | 4.00% | 20.00% |
| doc2vec | 10.00% | 10.00% |