Changelog

v0.1.5 (2018-05-20)

Other

  • Migration to PyTorch v0.4.0.

v0.1.4 (2018-02-18)

Fixed

  • Bugs due to use of int32s instead of int64s on Windows (thanks to Roman Yurchak).

Other

  • Added Appveyor for Windows CI (thanks to Roman Yurchak).

v0.1.3 (2017-12-14)

Added

  • Goodbooks dataset.
  • Mixture-of-tastes representations.

Changed

  • Raise ValueError if loss becomes NaN or 0.
  • Updated to work with PyTorch 0.3.0.

v0.1.2 (2017-09-10)

Added

  • spotlight.layers.BloomEmbedding: bloom embedding layers that reduce the number of parameters required by hashing embedding indices into some fixed smaller dimensionality, following Serrà, Joan, and Alexandros Karatzoglou. “Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks.”
  • sequence_mrr_score now accepts an option that excludes previously seen items from scoring.

Changed

  • optimizer arguments is now optimizer_func. It accepts a function that takes a single argument (list of model parameters) and return a PyTorch optimizer (thanks to Ethan Rosenthal).
  • fit calls will resume from previous model state when called repeatedly (Ethan Rosenthal).
  • Updated to work with PyTorch v0.2.0.

Fixed

  • Factorization predict APIs now work as advertised in the documentation.