NicolasHug
Cosine full vector implementation
Implemented cosine for full-vector (as was requested - adjusted cosine PR will be made separately, after this one). Tests and docs added.
Could you please provide a few benchmark (RMSE, MAE, maybe recall / precision as in FAQ, computation
time...)
No, sorry, I don't have time for this measurement. And I added full cosine only to cover all possible options and to prepare common grounds for adjusted cosine (basically, I just add features necessary to pass the "recommender systems" specialization on Coursera, as I have seen complaints there about a lack of Python libs supproting this course).
Regarding the reformatting in the test examples - with previous formatting they were awfully confusing, being improperly aligned (probably, I broke it with my previous PR). This certainly should be fixed.
Other issues fixed
No, sorry, I don't have time for this measurement.
I'm sorry but I can't accept a new algorithm / similarity measure without even having a vague idea of its performance.
I'm sorry but I can't accept a new algorithm / similarity measure without even having a vague idea of its performance.
For similarity metrics currently you haven't made such measurements yourself, at least I have found only measurements on a single default sim metric. So, probably you would allow to skip it for a new similarity metrics as well?
Otherwise, I kindly ask you to provide detailed requirements on what you are expecting of me.
(it makes sense to add this to "Contributors" instruction as well. In order for possible contributors to understand all your requirements in advance)
Regarding your desire to have computation time reported - I have a different machine than yours and this value will be misleading, in comparison to other algorithms. So, I offer to skip this requirement as well.
For similarity metrics currently you haven't made such measurements yourself
Ho trust me, I have.
So, probably you would allow to skip it for a new similarity metrics as well?
No, please don't ask me this, really.
Otherwise, I kindly ask you to provide detailed requirements on what you are expecting of me.
I'm not asking for much. A few CV procedures on ml-100k and ml-1m, comparing
performances between only_common_ratings=True and only_common_ratings=False
would be a good start.
(it makes sense to add this to "Contributors" instruction as well. In order for possible contributors to understand all your requirements in advance)
Good idea
Regarding your desire to have computation time reported - I have a different machine than yours and this value will be misleading, in comparison to other algorithms.
Absolutely. Benchmarking is about comparing, see my point above.
Could you give a link on an existing similarity metrics comparison, as an example of what you are asking for? Or just inform when you will update contributors guideline on this? Probably, you would agree to do this comparison after we add adjusted cosine metrics as well? Because it seems a waste of time to repeat this work twice.
I don't have any link. I'm just asking for what I've already described in my previous post.