0.8.0

✨ Highlights ✨

Option for debiased calculation to all of the TruePositive-based metrics (both ranking & classification). See our Debiased metrics calculation user guide for full info. Pass debias_config during metric's initialization to enable this feature.

All updates

Added

• debias_config parameter for classification and ranking metrics.
• New parameter is_debiased to calc_from_confusion_df, calc_per_user_from_confusion_df methods of classification metrics, calc_from_fitted, calc_per_user_from_fitted methods of auc and rankning (MAP) metrics, calc_from_merged, calc_per_user_from_merged methods of ranking (NDCG, MRR) metrics. (#152)
• nbformat >= 4.2.0 dependency to [visuals] extra (#169)
• filter_interactions method of Dataset (#177)
• on_unsupported_targets parameter to recommend and recommend_to_items model methods (#177)
• Use nmslib-metabrainz for Python 3.11 and upper (#180)

Fixed

• display() method in MetricsApp (#169)
• IntraListDiversity metric computation in cross_validate (#177)
• Allow warp-kos loss for LightFMWrapperModel (#175)

Removed

• [Breaking] assume_external_ids parameter in recommend and recommend_to_items model methods (#177)

New contributors

@chezou made their first contribution in #180 and #175

0.7.0

✨ Highlights ✨

• Interactive MetricsApp widget is now here! Extremely useful for metrics trade-off analysis. Has options to color models on plot based on their hyper-params. Check screenshots in cross-validation example.
• A lot of new metrics: recommendations DQ, PartilAUC based ranking metrics, recommendations intersection between multiple models, r-precision. All computations are highly optimized and fully compatible with cross_validate
• Theory & Practice RecSys Baselines Extended Tutorial

All updates

Added

• Extended Theory&Practice RecSys baselines tutorial (#139)
• MetricsApp to create plotly scatterplot widgets for metric-to-metric trade-off analysis (#140, #154)
• Intersection metric (#148)
• PartialAUC and PAP metrics (#149)
• New params (tol, maxiter, random_state) to the PureSVDModel (#130)
• Recommendations data quality metrics: SufficientReco, UnrepeatedReco, CoveredUsers (#155)
• r_precision parameter to Precision metric (#155)

Fixed

• Used rectools-lightfm instead of pure lightfm that allowed to install it using poetry>=1.5.0 (#165)
• Added restriction to pytorch version for MacOSX + x86_64 that allows to install it on such platforms (#142)
• PopularInCategoryModel fitting for multiple times, cross_validate compatibility, behaviour with empty category interactions (#163)

0.6.0

✨ Highlights ✨

• Added support of recommendations for cold and warm users/items
• Added support for Python 3.11 and 3.12
• Stopped supporting Python 3.7 and old versions of some dependencies

All updates

Added

• Warm users/items support in Dataset (#77)
• Warm and cold users/items support in ModelBase and all possible models (#77, #120, #122)
• Warm and cold users/items support in cross_validate (#77)
• [Breaking] Default value for train dataset type and params for user and item dataset types in DSSMModel (#122)
• [Breaking] n_factors and deterministic params to DSSMModel (#122)
• Hit Rate metric (#124)
• Python 3.11 support (without nmslib) (#126)
• Python 3.12 support (without nmslib and lightfm) (#126)

Changed

• Changed the logic of choosing random sampler for RandomModel and increased the sampling speed (#120)
• [Breaking] Changed the logic of RandomModel: now the recommendations are different for repeated calls of recommend methods (#120)
• Torch datasets to support warm recommendations (#122)
• [Breaking] Replaced include_warm parameter in Dataset.get_user_item_matrix to pair include_warm_users and include_warm_items (#122)
• [Breaking] Renamed torch datasets and dataset_type to train_dataset_type param in DSSMModel (#122)
• [Breaking] Updated minimum versions of numpy, scipy, pandas, typeguard (#126)
• [Breaking] Set restriction scipy < 1.13 (#126)

Removed

• [Breaking] return_external_ids parameter in recommend and recommend_to_items model methods (#77)
• [Breaking] Python 3.7 support (#126)

0.5.0

✨ Highlights ✨

Visualization app is now here! See our extended example for cool interactive Jupyter widgets.
Also we introduced EASE model and a new popularity bias metric AvgRecPopularity.

All updates

Added

• VisualApp and ItemToItemVisualApp widgets for visual comparison of recommendations (#80, #82, #85, #115)
• Methods for conversion Interactions to raw form and for getting raw interactions from Dataset (#69)
• AvgRecPopularity (Average Recommendation Popularity) to metrics (#81)
• Added normalized parameter to AvgRecPopularity metric (#89)
• Added EASE model (#107)

Changed

• Loosened pandas, torch and torch-light versions for python >= 3.8 (#58)

Fixed

• Bug in Interactions.from_raw method (#58)
• Mistakes in formulas for Serendipity and MIUF in docstrings (#115)
• Examples reproducibility on Google Colab (#115)

0.4.2

Added

• Ability to pass internal ids to recommend and recommend_to_items methods and get internal ids back (#70)
• rectools.model_selection.cross_validate function (#71, #73)

Changed

• Loosened lightfm version, now it's possible to use 1.16 and 1.17 (#72)

Fixed

• Small bug in LastNSplitter with incorrect i_split in info (#70)

0.4.1

Summary

• Enhanced examples
• Optimised DSSM inference
• Updated high border of attrs version to 24.0.0

All updates

Added

• LightFM wrapper inference speed benchmark (#60)
• iALS with features quality benchmark (#60)

Changed

• Updated attrs version (#56)
• Optimized inference for vector models with EUCLIDEAN distance using implicit library topk method (#57)
• Changed features processing example (#60)

0.4.0

✨ Highlights ✨

We have much simplified RecTools installation with pip and poetry. If you faced problems before, we recommend to try version 0.4.0 and above

• [Breaking] Bumped implicit from 0.4.4 to ^0.7.1
• [Breaking] Moved nmslib and lightfm to extras. Renamed nn extra to torch
• [Breaking] Improved TimeRangeSplitter interface: no need for pre-computing fold borders any more
• New metrics:MRR, F1Beta, MCC
• New splitters for cross-validation:RandomSplitter, LastNSplitter
• Significantly optimized inference speed for latent factors models (iALS, LightFM, PureSVD)
• Python 3.10 support 🎉

All updates

Added

• MRR (Mean Reciprocal Rank) to metrics (#29)
• F1beta, MCC (Matthew correlation coefficient) to metrics (#32)
• Base Splitter class to construct data splitters (#31)
• RandomSplitter to model_selection (#31)
• LastNSplitter to model_selection (#33)
• Support for Python 3.10 (#47)

Changed

• Bumped implicit version to 0.7.1 (#45)
• Bumped lightfm version to 1.17 (#43)
• Bumped pylint version to 2.17.6 (#43)
• Moved nmslib to extras (#36)
• Moved lightfm to extras (#51)
• Renamed nn extra to torch (#51)
• Optimized inference for vector models with COSINE and DOT distances using implicit library topk method (#52)
• Changed initialization of TimeRangeSplitter (instead of date_range argument, use test_size and n_splits) (#53)
• Changed split infos key names in splitters (#53)

Fixed

• Bugs with new version of pytorch_lightning (#43)
• pylint config for new version (#43)
• Cyclic imports (#45)

Removed

• Markdown dependancy (#54)

Release v0.3.0

• Optimized IdMap. In the new version internal ids are not sorted.
• Significantly optimized TimeRangeSplit. Renamed to TimeRangeSplitter. Changed interface.
• Fixed bug in MAP metric calculation.

Public release

Merge pull request #14 from MobileTeleSystems/release/0.2.0

bumped version to 0.2.0

Alpha release

Merge pull request #7 from MobileTeleSystems/release/0.1.0

bumped version