lapluviosilla / cs373-netflix Goto Github PK

An average of at least 3 unit tests for each function, in the files TestNetflix.py and TestNetflix.out, that tests corner cases and failure cases.
A clone of the public test repo with a copy of your unit tests in the files -TestNetflix.py and -TestNetflix.out.

A private Git repository at GitHub, named cs373-netflix, with the grader invited as collaborator and at least 5 commits, one for each bug or feature.
If you cannot describe your changes in a sentence, you are not committing often enough. Meaningful commit messages identifying the corresponding issue in the issue tracker (below). See here.
A log of the commits, named Netflix.log.

Basic implementation where we predict movie average (around 3.7) every time. Also with working RMSE implementation.

A GitHub issue tracker with an issue for each of the requirements in this table and at least 10 more issues, one for each bug or feature, both open and closed with a good description and a label.

HTML documentation in the file Netflix.html, that documents the interfaces to your functions.
Inline comments if you need to explain the why of a particular implementation.
A consistent coding convention with good variable names, good indentation, blank lines, and blank spaces.

A standard-compliant Python (3.2.3) program, including a function to compute RMSE, in the file Netflix.py, with assertions that check pre-conditions, post-conditions, argument validity, and return-value validity, that runs as fast as possible and uses as little memory as possible.

Basic RMSE implementation

At least 1000 lines of acceptance tests, in the files RunNetflix.in and RunNetflix.out, that tests corner cases and failure cases.
A clone of the public test repo with a copy of your acceptance tests in the files -RunNetflix.in and -RunNetflix.out.

Create basic implementation of netflix predictor and just guess average rating for any movie/customer.

We should lookup the actual ratings and calculate the RMSE between our prediction and the actual ratings.

We are currently taking the RMSE of each individual movie, and then RMSE the results of those. This gives the incorrect result. We need to run RMSE once for all the predictions

To be able to compute rmse we need probe results cache data. In addition to this cache we will have to load other caches. We need a cache loader function which can load any of the cache files and build a dictionary from them.