This repository contains a library with popular SQL operators implemented in Python to test efficiency and use of data provenance features, ML explainability libraries and also performance metric dashboards

Topics Resources

Pre-requisites for running queries:

1. Python (3.7+)
2. Pytest
3. Ray

Input Data

The task queries expect two space-delimited text files (similar to CSV files).

The first file (friends) must include records of the form:

UID1 (int)	UID2 (int)
1	2342
231	3
...	...

The second file (ratings) must include records of the form:

UID (int)	MID (int)	RATING (int)
1	10	4
231	54	2
...	...	...

Running queries

You can run queries as shown below:

$ python engine.py --task [task_number] --friends [path_to_friends_file.txt] --ratings [path_to_ratings_file.txt] --uid [user_id] --mid [movie_id]

For example, the following command runs the 'likeness prediction' query of the first task for user id 1 and movie id 1:

$ python3 skeleton/engine.py task1 --friends 'data/friends.txt' --ratings 'data/movie_ratings.txt' --uid '1' --mid '1'

The 'recommendation' query of the second task does not require a movie id. Provide a limit instead.

$ python3 skeleton/assignment_12.py task2 --friends 'data/friends.txt' --ratings 'data/movie_ratings.txt' --uid '1' --limit '1'

$ python3 skeleton/engine.py task3 --friends 'data/friends.txt' --ratings 'data/movie_ratings.txt' --uid '1' --mid '1'

Running queries of Assignment 2

TODO