mabwiser 2.7.3

minor:

• Changed return type of functions annotated with NoReturn to None by @LiHRaM

mabwiser 2.7.2

• Fixed default KMeans n_init parameters instead of using 'auto' used in scikit-learn>=1.4

mabwiser 2.7.1

• Implemented LearningPolicyType and NeighborhoodPolicyType to simplify input for MAB.
• Updated tests to accommodate LearningPolicyType and NeighborhoodPolicyType.

mabwiser 2.7.0

• 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

mabwiser 2.6.0

• Implement tracking of warm started and partial fitted arms
• Implement tracking of cold arms
• Limit warm start to learning policies

mabwiser 2.5.0

• Update warm start logic to only warm start an arm once
• Implement fix for fitting scalers in Linear policies when variance is too small

mabwiser 2.4.1

• Bug fixes in examples
• Validate tree parameters of TreeBandit to be compatible with sklearn.tree.DecisionTreeRegressor

mabwiser 2.4.0

• 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.

mabwiser 2.3.0

• New Algorithm: LinGreedy as a learning policy.

mabwiser 2.2.0

• Modified predict_expectations, such that predict can use predict_expectations directly in all non-contextual learning policies.