Rankfm

Changed
* needed to instruct Python to compile the created `.c` file instead of the `.pyx` file as the latter doesn't get added to the `sdist`
* build tested and working now on both OSX and Linux

no changes, just syncing things up.

Added
* Cython back-end for `_fit()`, `_predict()`, `_recommend()` - the Cython `_fit()` function is 5X-10X faster than the original Numba version, and `predict()`/`recommend()` are about the same speed.
Changed
* split `regularization` into two parameters: `alpha` to control the L2 regularization for user/item indicators and `beta` to control the regularization for user-features/item-features. In testing user-features/item-features tended to have exploding gradients/overwhelm utility scores unless more strongly regularized, especially with fairly dense side features. Typically `beta` should be set fairly high (e.g. 0.1) to avoid numerical instability.

Changed
* pull the string `loss` param out of the private Numba internals and into the public `fit()` function
* change `_init_interactions` to extend rather than replace the `user_items` dictionary item sets
* added conditional logic to skip expensive user-feature/item-feature dot products if user and/or item features were not provided in the call to `fit()`. This reduces training time by over 50% if just using the base interaction matrix (no additional user/item features).
Fixed
* bug where `similar_users()`, `similar_items()` were performing validation checks on the original ID versus the zero-based index (wrong) instead of original values (correct) - this was causes a bunch of bogus assertion errors saying that the item_id wasn't in the training set

Added
* WARP loss - while slower to train this yields slightly better performance on dense interaction data and much better performance on highly sparse interaction data relative to BPR
* new hyperparameters `loss` and `max_samples`
* re-wrote the numba `_fit()` function to elegantly (IMHO) handle both BPR and WARP loss

Added
* added support for sample weights - you can now pass importance weights in addition to interactions
* automatically determine the input data class (np.ndarray vs. pd.dataframe/pd.series)
* assert/ensure that all model weights are finite after each training epoch to fail fast for exploding weights
Fixed
* bug where pd.dataframe interactions with columns not named `[user_id, item_id]` were not getting loaded/indexed correctly - fixed by using the input class determination utility created
Changed
* more efficient loops for updating item feature and user/item feature factor weights - this cuts training time by around 30% with no auxiliary features, and by 50%+ in the presence of auxiliary features