Surprise v1.0.5 Release Notes
Release Date: 2018-01-09 // over 6 years ago-
Date: 09/01/18
✨ Enhancements
- Cross-validation tools have been entirely reworked. We can now rely on powerful and flexible cross-validation iterators, inspired by scikit-learn's API.
- the evaluate() method has been replaced by cross-validate which is parallel and can return measures on trainset as well as computation times.
- 👷 GridSearch is now parallel, using joblib.
- GridSearch now allows to refit an algorithm on the whole dataset.
- 0️⃣ default data directory can now be custom with env variable SURPRISE_DATA_FOLDER
- the fit() (and train()) methods now return self, which allows one-liners like algo.fit(trainset).test(testset)
- Algorithms using a random initialization (e.g. SVD, NMF, CoClustering) now have a random_state parameter for seeding the RNG.
- The getting started guide has been rewritten
API Changes
- 🗄 The train() method is now deprecated and replaced by the fit() method (same signature). Calls to train() should still work as before.
- 🗄 Using data.split() or accessing the data.folds() generator is deprecated and replaced by the use of the more powefull CV iterators.
- evaluate() is deprecated and replaced by model_selection.cross_validate(), which is parallel.
- 🗄 GridSearch is deprecated and replaced by model_selection.GridSearchCV()