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TODO.md

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TODO

  • Put some PredictionImpossible messages in every algo
  • Profile code (mostly cython) to see what could be optimized

Maybe, Maybe not

  • allow a back up algorithm when prediction is impossible. Right now it's just the mean rating that is predicted. Maybe user would want to choose it.

Done:

  • allow a 'clip' option to the predict method? Also, describe r_min and r_max
  • configure entrypoints to use surprise directly from command line
  • Allow a 'biased' option in the SVD algo. If true, use baselines, if False, don't. It should be pretty easy to do.
  • create option in main to clean the .recsys directory. Actually, the main module should be entirely reviewed.
  • when dumping, we should dump all the algorithm parameter. Use dict ?
  • do something about the generators Python 2 vs 3 (range, dict.items(), etc...)
  • should a Prediction output the raw id or the inner id? Right now it's the inner id. Maybe sort this out when working on the comparison tools.
  • allow the perf dict returned by evaluate to accept keys with lower/upper case for retarded users such as me.
  • Add a 'min_support' parameter to sim_options? Add a min_k to knns?
  • Do something about the user_based stuff. It should be better. Check knns BTW.
  • Do something about unknown users and unknown items, i.e. users or items that have no rating in the trainset. Right now, the predict method checks if the name starts with 'unknown' but this is shiiite because it's dependent on the construct_trainset method, which is sometimes never called (so the raw2inner stuff will come in play somehow). Plus, It should be up to the algorithms to choose whether it can (or can't) make a prediction even if user or item is unknown.
  • remove kwargs : done where useless.
  • say something quick about baseline computation (when not matrix facto)
  • Matrix facto algo
  • allow the 'estimate' method to return some details about prediction (such as the number of neighbors for a KNN)
  • allow to train on a SINGLE file without test set, and let user query for some predictions
  • write tuto for using only predict() (and not test)
  • maybe clean a little all the dataset machinery? Plus, are the raw2inner_id_users and raw2inner_id_items worth keeping? May be for analysing tools, I don't know right now. EDIT: yes, we need to keep them, simply because the similarity computation can only work with integer as indexes (numpy arrays).
  • sort out this warning issue coming from cython
  • say something about the sim > 0 in knns algos
  • get less restrictive requirements.txt
  • write the custom algorithm tutorial
  • improve test coverage
  • add the cool stickers on the readme just like scikit learn
  • set up travis
  • keep on testing
  • keep on documenting and commenting code
  • extensively test the reader class, + check that the doc is OK for reader
  • set up a nice API (looks ok now)
  • handle algo-specific or similarity-specific parameters (such as 'k' for knn, regularization parameters, shrinkage paramaters, etc.) in an appropriate manner, rather than pass them all to constructors... UPDATE: ok so using kwargs like matplotlib.pyplot might be enough. should we create a 'Similarity' class?
  • clean the main and all the dataset handling stuff (still needs to be polished)
  • rewrite this TODO in english
  • create a proper project structure
  • from camelCase to snake_case