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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.9.0] - 11.12.2024

Added

  • from_config, get_config and get_params methods to all models except neural-net-based (#170)
  • fit_partial implementation for ImplicitALSWrapperModel and LightFMWrapperModel (#203, #210, #223)
  • save and load methods to all of the models (#206)
  • Model configs example (#207,#219)
  • use_gpu argument to ImplicitRanker.rank method (#201)
  • keep_extra_cols argument to Dataset.construct and Interactions.from_raw methods. include_extra_cols argument to Dataset.get_raw_interactions and Interactions.to_external methods (#208)
  • dtype adjustment to recommend, recommend_to_items methods of ModelBase (#211)
  • load_model function (#213)
  • model_from_config function (#214)
  • get_cat_features method to SparseFeatures (#221)
  • LightFM Python 3.12+ support (#224)

Fixed

  • Implicit ALS matrix zero assignment size (#228)

Removed

  • Python 3.8 support (#222)

[0.8.0] - 28.08.2024

Added

  • Debias mechanism for classification, ranking and auc 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)

[0.7.0] - 29.07.2024

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 PureSVD model (#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] - 13.05.2024

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] - 22.03.2024

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] - 01.12.2023

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] - 31.10.2023

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] - 27.10.2023

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 from main dependencies 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)