Lightfm Versions Save

Fixed

• Re-Cythonized cython files to fix compilation errors with newer compilers.
• Fixed np.object usage in tests.

Addded

• Set the LIGHTFM_NO_CFLAGS environment variable when building LightFM to prevent it from setting -ffast-math or -march=native compiler flags.

Changed

• predict now returns float32 predictions.

Added

• Added a check that there is no overlap between test and train in predict_ranks (thanks to @artdgn).
• Added dataset builder functionality.

Fixed

• Fixed error message when item features have the wrong dimensions.
• Predict now checks for overflow in inputs to predict.
• WARP fitting is now numerically stable when there are very few items to draw negative samples from (< max_sampled).

Added

• added additional input checks for non-normal inputs (NaNs, infinites) for features
• added additional input checks for non-normal inputs (NaNs, infinites) for interactions
• cross validation module with dataset splitting utilities

Changed

• LightFM model now raises a ValueError (instead of assertion) when the number of supplied features exceeds the number of estimated feature embeddings.
• Warn and delete downloaded file when Movielens download is corrputed. This happens in the wild cofuses users terribly.

Added

• added get_{user/item}_representations functions to facilitate extracting the latent representations out of the model.

Fixed

• recall_at_k and precision_at_k now work correctly at k=1 (thanks to Zank Bennett).
• Moved Movielens data to data release to prevent grouplens server flakiness from affecting users.
• Fix segfault when trying to predict from a model that has not been fitted.

Changed

• Ranks are now computed pessimistically: when two items are tied, the positive item is assumed to have higher rank. This will lead to zero precision scores for models that predict all zeros, for example.
• The model will raise a ValueError if, during fitting, any of the parameters become non-finite (NaN or +/- infinity).
• Added mid-epoch regularization when a lot of regularization is used. This reduces the likelihood of numerical instability at high regularization rates.