85 lines
No EOL
2.5 KiB
Markdown
85 lines
No EOL
2.5 KiB
Markdown
# Project 02: Multi-source code search
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**Claudio Maggioni**
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### About the Project
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This project has the goal of developing a search engine able to query a large Python code repository using multiple
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sources of information.
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It is part of the Knowledge Analysis & Management - 2022 course from the Università della Svizzera italiana.
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In this repository, you can find the following files:
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- tensor flow: a code repository to be used during this project
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- ground-truth-unique: a file containing the references triples necessary to evaluate the search engine (step 3)
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For more information, see the Project-02 slides (available on iCourse)
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Note: Feel free to modify this file according to the project's necessities.
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## Environment setup
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To install the required dependencies make sure `python3` points to a Python 3.10 or 3.11 installation and then run:
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```shell
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python3 -m venv env
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source env/bin/activate
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pip install -r requirements.txt
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```
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## Part 1: data extraction
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To extract the data in file `data.csv` run the command:
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```shell
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python3 extract-data.py
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```
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The script prints the requested counts, which are namely:
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```
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Methods: 5817
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Functions: 4565
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Classes: 1882
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Python Files: 2817
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```
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## Part 2: Training
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In order to train and predict the output of a given query run the command:
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```shell
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python3 search-data.py [method] "[query]"
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```
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where `[method]` is one of `{tfidf,freq,lsi,doc2vec}` or `all` to run all classifiers and `[query]` is the natural
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language query to search. Outputs are printed on stdout, and in case of `doc2vec` the trained model file is saved in
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`./doc2vec_model.dat` and fetched in this path for subsequent executions.
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## Part 3: Evaluation
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To evaluate a model run the command:
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```shell
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python3 search-data.py [method] ./ground-truth-unique.txt
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```
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where `[method]` is one of `{tfidf,freq,lsi,doc2vec}` or `all` to evaluate all classifiers. The script outputs the
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performance of the classifiers in terms of average precision and recall, which are namely:
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| Engine | Average Precision | Average Recall |
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|:---------|:--------------------|:-----------------|
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| tfidf | 90.00% | 90.00% |
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| freq | 93.33% | 100.00% |
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| lsi | 90.00% | 90.00% |
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| doc2vec | 73.33% | 80.00% |
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## Report
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To compile the report run:
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```shell
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cd report
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pdflatex -interaction=nonstopmode -output-directory=. main.tex
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pdflatex -interaction=nonstopmode -output-directory=. main.tex
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``` |