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Changelog

0.7.0 - 📚🆕 Documentation and IF overhaul, new methods and bug fixes 💥🐞

This is our first β release! We have worked hard to deliver improvements across the board, with a focus on documentation and usability. We have also reworked the internals of the influence module, improved parallelism and handling of randomness.

Added

  • Implemented solving the Hessian equation via spectral low-rank approximation PR #365
  • Enabled parallel computation for Leave-One-Out values PR #406
  • Added more abbreviations to documentation PR #415
  • Added seed to functions from pydvl.utils.numeric, pydvl.value.shapley and pydvl.value.semivalues. Introduced new type Seed and conversion function ensure_seed_sequence. PR #396

Changed

  • Replaced sphinx with mkdocs for documentation. Major overhaul of documentation PR #352
  • Made ray an optional dependency, relying on joblib as default parallel backend PR #408
  • Decoupled ray.init from ParallelConfig PR #373
  • Breaking Changes
  • Signature change: return information about Hessian inversion from compute_influence_factors PR #375
  • Major changes to IF interface and functionality. Foundation for a framework abstraction for IF computation. PR #278 PR #394
  • Renamed semivalues to compute_generic_semivalues PR #413
  • New joblib backend as default instead of ray. Simplify MapReduceJob. PR #355
  • Bump torch dependency for influence package to 2.0 PR #365

Fixed

  • Fixes to parallel computation of generic semi-values: properly handle all samplers and stopping criteria, irrespective of parallel backend. PR #372
  • Optimises memory usage in IF calculation PR #375
  • Fix adding valuation results with overlapping indices and different lengths PR #370
  • Fixed bugs in conjugate gradient and linear_solve PR #358
  • Fix installation of dev requirements for Python3.10 PR #382
  • Improvements to IF documentation PR #371

0.6.1 - 🏗 Bug fixes and small improvements

  • Fix parsing keyword arguments of compute_semivalues dispatch function PR #333
  • Create new RayExecutor class based on the concurrent.futures API, use the new class to fix an issue with Truncated Monte Carlo Shapley (TMCS) starting too many processes and dying, plus other small changes PR #329
  • Fix creation of GroupedDataset objects using the from_arrays and from_sklearn class methods PR #324
  • Fix release job not triggering on CI when a new tag is pushed PR #331
  • Added alias ApproShapley from Castro et al. 2009 for permutation Shapley PR #332

0.6.0 - 🆕 New algorithms, cleanup and bug fixes 🏗

  • Fixes in ValuationResult: bugs around data names, semantics of empty(), new method zeros() and normalised random values PR #327
  • New method: Implements generalised semi-values for data valuation, including Data Banzhaf and Beta Shapley, with configurable sampling strategies PR #319
  • Adds kwargs parameter to from_array and from_sklearn Dataset and GroupedDataset class methods PR #316
  • PEP-561 conformance: added py.typed PR #307
  • Removed default non-negativity constraint on least core subsidy and added instead a non_negative_subsidy boolean flag. Renamed options to solver_options and pass it as dict. Change default least-core solver to SCS with 10000 max_iters. PR #304
  • Cleanup: removed unnecessary decorator @unpackable PR #233
  • Stopping criteria: fixed problem with StandardError and enable proper composition of index convergence statuses. Fixed a bug with n_jobs in truncated_montecarlo_shapley. PR #300 and PR #305
  • Shuffling code around to allow for simpler user imports, some cleanup and documentation fixes. PR #284
  • Bug fix: Warn instead of raising an error when n_iterations is less than the size of the dataset in Monte Carlo Least Core PR #281

0.5.0 - 💥 Fixes, nicer interfaces and... more breaking changes 😒

  • Fixed parallel and antithetic Owen sampling for Shapley values. Simplified and extended tests. PR #267
  • Added Scorer class for a cleaner interface. Fixed minor bugs around Group-Testing Shapley, added more tests and switched to cvxpy for the solver. PR #264
  • Generalised stopping criteria for valuation algorithms. Improved classes ValuationResult and Status with more operations. Some minor issues fixed. PR #252
  • Fixed a bug whereby compute_shapley_values would only spawn one process when using n_jobs=-1 and Monte Carlo methods. PR #270
  • Bugfix in RayParallelBackend: wrong semantics for kwargs. PR #268
  • Splitting of problem preparation and solution in Least-Core computation. Umbrella function for LC methods. PR #257
  • Operations on ValuationResult and Status and some cleanup PR #248
  • Bug fix and minor improvements: Fixes bug in TMCS with remote Ray cluster, raises an error for dummy sequential parallel backend with TMCS, clones model inside Utility before fitting by default, with flag clone_before_fit to disable it, catches all warnings in Utility when show_warnings is False. Adds Miner and Gloves toy games utilities PR #247

0.4.0 - 🏭💥 New algorithms and more breaking changes

  • GH action to mark issues as stale PR #201
  • Disabled caching of Utility values as well as repeated evaluations by default PR #211
  • Test and officially support Python version 3.9 and 3.10 PR #208
  • Breaking change: Introduces a class ValuationResult to gather and inspect results from all valuation algorithms PR #214
  • Fixes bug in Influence calculation with multidimensional input and adds new example notebook PR #195
  • Breaking change: Passes the input to MapReduceJob at initialization, removes chunkify_inputs argument from MapReduceJob, removes n_runs argument from MapReduceJob, calls the parallel backend's put() method for each generated chunk in _chunkify(), renames ParallelConfig's num_workers attribute to n_local_workers, fixes a bug in MapReduceJob's chunkification when n_runs >= n_jobs, and defines a sequential parallel backend to run all jobs in the current thread PR #232
  • New method: Implements exact and monte carlo Least Core for data valuation, adds from_arrays() class method to the Dataset and GroupedDataset classes, adds extra_values argument to ValuationResult, adds compute_removal_score() and compute_random_removal_score() helper functions PR #237
  • New method: Group Testing Shapley for valuation, from Jia et al. 2019 PR #240
  • Fixes bug in ray initialization in RayParallelBackend class PR #239
  • Implements "Egalitarian Least Core", adds cvxpy as a dependency and uses it instead of scipy as optimizer PR #243

0.3.0 - 💥 Breaking changes

  • Simplified and fixed powerset sampling and testing PR #181
  • Simplified and fixed publishing to PyPI from CI PR #183
  • Fixed bug in release script and updated contributing docs. PR #184
  • Added Pull Request template PR #185
  • Modified Pull Request template to automatically link PR to issue PR ##186
  • First implementation of Owen Sampling, squashed scores, better testing PR #194
  • Improved documentation on caching, Shapley, caveats of values, bibtex PR #194
  • Breaking change: Rearranging of modules to accommodate for new methods PR #194

0.2.0 - 📚 Better docs

Mostly API documentation and notebooks, plus some bugfixes.

Added

In PR #161: - Support for $$ math in sphinx docs. - Usage of sphinx extension for external links (introducing new directives like :gh:, :issue: and :tfl: to construct standardised links to external resources). - Only update auto-generated documentation files if there are changes. Some minor additions to update_docs.py. - Parallelization of exact combinatorial Shapley. - Integrated KNN shapley into the main interface compute_shapley_values.

Changed

In PR #161: - Improved main docs and Shapley notebooks. Added or fixed many docstrings, readme and documentation for contributors. Typos, grammar and style in code, documentation and notebooks. - Internal renaming and rearranging in the parallelization and caching modules.

Fixed

  • Bug in random matrix generation PR #161.
  • Bugs in MapReduceJob's _chunkify and _backpressure methods PR #176.

0.1.0 - 🎉 first release

This is very first release of pyDVL.

It contains:

  • Data Valuation Methods:

  • Leave-One-Out

  • Influence Functions
  • Shapley:
    • Exact Permutation and Combinatorial
    • Montecarlo Permutation and Combinatorial
    • Truncated Montecarlo Permutation
  • Caching of results with Memcached
  • Parallelization of computations with Ray
  • Documentation
  • Notebooks containing examples of different use cases
Last update: 2023-09-02
Created: 2021-04-01