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CHANGELOG.txt
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CHANGELOG.txt
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=====================
MABWiser CHANGELOG
=====================
February, 17, 2024 2.7.3
-------------------------------------------------------------------------------
minor:
- Changed return type of functions annotated with NoReturn to None by @LiHRaM
February, 05, 2024 2.7.2
-------------------------------------------------------------------------------
minor:
- Fixed default KMeans n_init parameters instead of using 'auto' used in scikit-learn>=1.4
August, 02, 2023 2.7.1
-------------------------------------------------------------------------------
minor:
- Implemented LearningPolicyType and NeighborhoodPolicyType to simplify input for MAB.
- Updated tests to accommodate LearningPolicyType and NeighborhoodPolicyType.
February, 07, 2023 2.7.0
-------------------------------------------------------------------------------
major:
- Implement vectorized functions for Linear policies to speed-up prediction for multiple inputs.
- Update tests for LinTS and LinGreedy Policies as a result of using global random seed in vectorized function
January, 24, 2023 2.6.0
-------------------------------------------------------------------------------
major:
- Implement tracking of warm started and partial fitted arms
- Implement tracking of cold arms
- Limit warm start to learning policies
January, 19, 2023 2.5.0
-------------------------------------------------------------------------------
major:
- Update warm start logic to only warm start an arm once
minor:
- Implement fix for fitting scalers in Linear policies when variance is too small
March, 28, 2022 2.4.1
-------------------------------------------------------------------------------
minor:
- Bug fixes in examples
- Validate tree parameters of TreeBandit to be compatible with sklearn.tree.DecisionTreeRegressor
March, 17, 2022 2.4.0
-------------------------------------------------------------------------------
major:
- Implement vectorized functions for non-contextual policies to speed-up prediction for multiple decisions.
- Change MAB predict and predict_expectations to allow empty contexts to be specified for non-contextual policies.
- Update scaler use in Linear policies so that standard scaler can be fit directly instead of pre-trained scalers.
- Change scaler argument from pre-trained `arm_to_scaler` input to a boolean scale flag.
March, 8, 2022 2.3.0
-------------------------------------------------------------------------------
major:
- New Algorithm: LinGreedy as a learning policy.
- Thanks to Yevgeny Popkov for suggesting this algorithm.
-------------------------------------------------------------------------------
March, 2, 2022 2.2.0
-------------------------------------------------------------------------------
major:
- Modified `predict_expectations`, such that `predict` can use `predict_expectations` directly in all non-contextual learning policies
-------------------------------------------------------------------------------
February, 25, 2022 2.1.0
-------------------------------------------------------------------------------
major:
- Added warm_start method to MAB, that allows untrained (cold) arms to be warm started based on features of each arm.
- Added remove_arm method to MAB to allow arms to be removed from bandit.
-------------------------------------------------------------------------------
February, 14, 2022 2.0.0
-------------------------------------------------------------------------------
major:
- Breaking: Updated NumPy RNG backend to utilize the new Generator class. This is a breaking change for algorithms with random components.
- Updated NumPy version dependency to >=1.17.0 to reflect the utilization of the new Generator class.
minor:
- Added multivariate sampling to base RNG class
- Updated multivariate sampling logic in LinTS to utilize updated NumPy RNG backend
-------------------------------------------------------------------------------
August, 01, 2021 1.12.0
-------------------------------------------------------------------------------
major:
- New Algorithm: TreeBandit as a decision-tree based neighborhood policy.
- Many thanks to @irmakbky for her contribution!
- See also Elmachtoub, A. N., et. al. "A practical method for solving contextual bandit problems using decision trees". UAI 2017
-------------------------------------------------------------------------------
April, 15, 2021 1.11.2
-------------------------------------------------------------------------------
minor:
- Updated dtype from np.int to int in base_mab.py line 294 to resolve deprecation warning from numpy>=1.20.0
- Updated sklearn version dependency to >=0.24.0 because of changes in the KMeans implementation
-------------------------------------------------------------------------------
September, 14, 2020 1.11.1
-------------------------------------------------------------------------------
minor:
- Internal variable rename for LSHNearest
- Clarifications to LSHNearest docstring
-------------------------------------------------------------------------------
September, 8, 2020 1.11.0
-------------------------------------------------------------------------------
major:
- New Algorithm: Locality Sensitive Hashing Approximate Nearest Neighbors
- This neighborhood policy uses locality-sensitive hashing with random projection via the SimHash algorithm to create approximate neighborhoods.
- More information: https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Random_projection
minor:
- Refactor of _Neighbors and _Radius classes to support reusability of empty neighborhood functionality
- Switch to centralized version incrementing and use of the opensource@fmr.com email
-------------------------------------------------------------------------------
August, 12, 2020 1.10.1
-------------------------------------------------------------------------------
major:
- Initial release on PyPi.org
-------------------------------------------------------------------------------
June, 22, 2020 1.10.0
-------------------------------------------------------------------------------
- Introduction of base random number generator class to abstract away RNG provider
- Currently, numpy random serves as the rng provider
-------------------------------------------------------------------------------
May, 27, 2020 1.9.1
-------------------------------------------------------------------------------
minor:
- Performance improvement to LinTS algorithm by pre-computing cholesky decomposition in fit.
-------------------------------------------------------------------------------
May, 1, 2020 1.9.0
-------------------------------------------------------------------------------
major:
- New Algorithm: Randomized Popularity (Popularity)
- This bandit uses a probabilistically weighted selection based on mean arm rewards
- Currently, it assumes that the rewards are non-negative
-------------------------------------------------------------------------------
April, 8, 2020 1.8.1
-------------------------------------------------------------------------------
minor:
- Correction for how Softmax normalizes exponents to prevent overflow.
- Removal of Simulator matplotlib backend specification to eliminate Mac OS error.
-------------------------------------------------------------------------------
January, 21, 2020 1.8.0
-------------------------------------------------------------------------------
major:
- New Algorithm: Linear Thompson Sampling (LinTS)
- LinTS implementation uses deterministic sampling of a multivariate normal distribution of the regression coefficients.
- Adapted from Agrawal, Shipra and Navin Goyal. "Thompson Sampling for Contextual Bandits with Linear Payoffs." ICML 2013.
- The implementation is based on an initial design by Anshuman Pradhan (anshuman.pradhan@fmr.com) and Pramod R (pramod.r@fmr.com)
minor:
- Scikit-learn version dependency on 0.22.0 or greater which provides a bug fix for sklearn.cluster.KMeans
-------------------------------------------------------------------------------
December, 17, 2019 1.7.1
-------------------------------------------------------------------------------
minor:
- Bug fix for partial fitting in UCB1 reported in #10 by @harisankarh
-------------------------------------------------------------------------------
November, 27, 2019 1.7.0
-------------------------------------------------------------------------------
major:
- The learning_policy and neighborhood_policy attributes have been changed to decorated properties.
- MAB objects can now be serialized with the pickle module.
-------------------------------------------------------------------------------
November, 14, 2019 1.6.3
-------------------------------------------------------------------------------
minor:
- Documentation now available at https://fidelity.github.io/mabwiser/
-------------------------------------------------------------------------------
October, 21, 2019 1.6.2
-------------------------------------------------------------------------------
minor:
- Changed backend parameter to optional type
-------------------------------------------------------------------------------
August, 27, 2019 1.6.1
-------------------------------------------------------------------------------
minor:
- Configurable parallel backend
-------------------------------------------------------------------------------
Aug, 13, 2019 1.6.0
-------------------------------------------------------------------------------
major:
- Configurable empty neighborhood operation for Radius policy
- Empty neighborhood operation changed to use numpy.random.choice instead of numpy.random.randint. Observed predictions for empty neighborhoods may differ from versions 1.5 and prior.
-------------------------------------------------------------------------------
August, 12, 2019 1.5.10
-------------------------------------------------------------------------------
minor:
- Bug fix for offline expectation tracking in simulator
-------------------------------------------------------------------------------
July, 1, 2019 1.5.9
-------------------------------------------------------------------------------
minor:
- Bug fix for expectation tracking in simulator
- Additional unit tests for simulator expectations tracking
-------------------------------------------------------------------------------
June, 25, 2019 1.5.8
-------------------------------------------------------------------------------
minor:
- Bug fix for chunking in simulator
- Parallelization backend change
-------------------------------------------------------------------------------
June, 17, 2019 1.5.7
-------------------------------------------------------------------------------
minor:
- Bug fix for edge-case handling in simulator
-------------------------------------------------------------------------------
June, 13, 2019 1.5.6
-------------------------------------------------------------------------------
major:
- Initial release on GitHub.com
-------------------------------------------------------------------------------
June, 11, 2019 1.5.6
-------------------------------------------------------------------------------
major:
- license set to Apache 2.0
minor:
- bug fix in Simulator n_jobs handling
-------------------------------------------------------------------------------
May, 16, 2019 1.5.5
-------------------------------------------------------------------------------
minor:
- bug fix in Simulator distance calculation
-------------------------------------------------------------------------------
May, 14, 2019 1.5.4
-------------------------------------------------------------------------------
minor:
- bug fix in Simulator for bandit_to_arm_to_stats_neighborhoods and bandit_to_expectations
-------------------------------------------------------------------------------
April, 30, 2019 1.5.3
-------------------------------------------------------------------------------
minor:
- bug fix in ucb
-------------------------------------------------------------------------------
April, 24, 2019 1.5.2
-------------------------------------------------------------------------------
minor:
- Simulator performs parallel distance calculations
- Bug fix with plotting functionality
- Chunk size in Simulator dependent on number of jobs
-------------------------------------------------------------------------------
April, 19, 2019 1.5.1
-------------------------------------------------------------------------------
minor:
- Simulator save results with a prefix
-------------------------------------------------------------------------------
April, 17, 2019 1.5.0
-------------------------------------------------------------------------------
major:
- New Feature: Simulation utility
- Utility supports both online and offline simulations with historic data sets
- Nearest neighbors simulations share distance calculations and track size, descriptive statistics
minor:
- Parallelization bug fix
-------------------------------------------------------------------------------
April, 2, 2019 1.4.1
-------------------------------------------------------------------------------
minor:
- Additional unit tests to improve code coverage
- Unit test data changes to remove warnings
- Logic fix for Softmax edge case
- Improvements to MAB handling of series data
- LinUCB with scaler array type conversion
-------------------------------------------------------------------------------
March, 12, 2019 1.4.0
-------------------------------------------------------------------------------
major:
- New Feature: Built-in parallelization for both training and testing components
- BaseMAB automates the parallelization of each policy
- Policies to be added in the future can take advantage of built-in parallization as well
- Allow initialization with standard scaler for _Linear class constructor
minor:
- binarization bug fix
- parallelization tests
-------------------------------------------------------------------------------
February, 12, 2019 1.3.0
-------------------------------------------------------------------------------
major:
- simulator script is added for experimentation
- rollback neighborhood calculation to iterative
- no more scikit-learn dependency
minor:
- _convert_array() and _convert_matrix() for efficient numpy transformations
-------------------------------------------------------------------------------
February, 07, 2019 1.2.0
-------------------------------------------------------------------------------
major:
- speed-up in neighborhood calculation.
- This change requires scikit-learn>=0.20.0
- Big thanks to Nicholas Cilfone for this pull request!
minor:
- the change in neighborhood calculation allows combining _predict_operation for radius and knearest
-------------------------------------------------------------------------------
December, 26, 2018 1.1.0
-------------------------------------------------------------------------------
major:
- speed-ups in greedy, ucb, softmax, thompson, and contextual mab
- speed-up numpy _convert_context with row major C arrays
minor:
- numpy _convert function for decision & rewards
- api docstring _convert_context return type fixed
- typo fix in website rst contributing and installation
- replace k in dict.keys() with faster version k in dict
- remove arm_expectation = 0 from uptake_arms in softmax/thompson/ucb
-------------------------------------------------------------------------------
December, 17, 2018 1.0.0
-------------------------------------------------------------------------------
- Initial public release.
-------------------------------------------------------------------------------
July, 7, 2018 0.1.0
-------------------------------------------------------------------------------
- Development starts. Unstable.