Release v0.2.0 Alpha Release · fabioiuri/DRecPy

Improvements

Added list-sampler with negative_ratio support, sequence sorting and group records by multiple columns
Added support for early stopping rules, with the first built-in rule being the MaxValidationRule
Slightly improve MemoryInteractionDataset performance by removing unneeded type casts and simplifying record conversion
Improve recommender training workflow, by abstracting gradient computation, weight updates and improve separation of concerns through specific functions that should focus on each subtask (new methods: _sample_batch(), _predict_batch(), _compute_batch_loss(), _compute_reg_loss())
Improve recommender logging: optional console (defaults to on) and file (defaults to off) logger, with error, warning and info logs
Allow passing custom parameters to InteractionDataset instances, when instantiating them through the integrated datasets submodule
Automatically compute regularization for recommenders that use tf.keras.Model or tf.keras.Layer trainable variables with any regularization parameter set
Change how metrics are passed to evaluation procedures, to improve readability and extensibility. Now instead of receiving tuples with functions and extra params, it receives callable instances of those metrics:
- From: metrics={'HR': (hit_ratio, {}), 'NDCG': (ndcg, {})}, 'FScore': (f_score, {'beta': 1})
- To: metrics=[HitRatio(), NDCG(), FScore(beta=1)]

Fix bug on integrated datasets get_test_dataset that could end up generating test datasets (via leave_k_out) instead of using the offered test dataset
Fix temporary DatabaseInteractionDataset files not being deleted at times