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Team Bald Unicorns Major Group project

Team members

The members of the team are:

  • Manivannan Prushorth
  • Charlie Bowen
  • Jun Yi Goh
  • Zaid Maraqa
  • Daro Saleh
  • Nilhil Suri
  • Alavro Moreno

Project structure

The project is called bookClub. It currently consists of a single app unibook.

Installation instructions

To install the software and use it in your local development environment, you must first set up and activate a local development environment. From the root of the project:

$ virtualenv venv
$ source venv/bin/activate

Install all required packages:

$ pip3 install -r requirements.txt

Migrate the database:

$ python3 manage.py migrate

Seed the development database with:

$ python3 manage.py seed

Run all tests with:

$ python3 manage.py test

To use spark recommender system, Java 8 needs to be installed and used: Installation of java 8: https://docs.datastax.com/en/jdk-install/doc/jdk-install/installOpenJdkDeb.html pyspark is included in requirements.txt, so running pip3 install -r requirements.txt will have pyspark downloaded. Switching between java versions in ubuntu if multiple versions are installed: https://aboullaite.me/switching-between-java-versions-on-ubuntu-linux/

The above instructions should work in your version of the application. If there are deviations, declare those here in bold. Otherwise, remove this line.

Sources

The packages used by this application are specified in requirements.txt

Declare are other sources here.

Some of the tests written have been reused as well with modifications and extensions of the tests to fit the bookaholics application.

DateField accepting only today and future's date: https://stackoverflow.com/questions/4941974/django-how-to-set-datefield-to-only-accept-today-future-dates/4942284 Disappearing location field with checkbox: https://stackoverflow.com/questions/53319441/when-checking-one-check-box-the-other-check-box-must-disappear

Recommender system and evaluation: Source code - https://sundog-education.com/RecSys/ The source code has been refactored to classes and functions to fit the bookaholics application.

Spark recommender sources Column in dataframe not being numeric - https://stackoverflow.com/questions/27772769/how-to-use-mllib-recommendation-if-the-user-ids-are-string-instead-of-contiguous Getting original column id - https://stackoverflow.com/questions/45787323/pyspark-reversing-stringindexer-in-nested-array