Comments (1)
Hi @risufaj
Very keen observation. All of the contextual policies have implementations that benefit from multi-core processing. As such we do not also parallelize across arms (for fit) or contexts (for predict) as that could actually slow down performance.
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Related Issues (20)
- Using categorical variables in the context HOT 2
- init order HOT 2
- Predict and Predict_expectation difference in results HOT 4
- number of arms HOT 2
- Simulator usage - train and test split for target encoded features to avoid leakage HOT 1
- Thompson Sampling for Gaussian priors? HOT 6
- How to use Categorical variables as context? HOT 1
- Evluation erroring out HOT 5
- [Question] How to deal with cold start HOT 3
- `context` isn't passed to `_parallel_fit` in Thompson Sampling HOT 3
- Cascading feedback type HOT 3
- Is there a way to retrieve DecisionTree output? HOT 4
- [Question] A way to only predict arms from a given subset? HOT 2
- Need an LP and NP Type Definition
- interpreting `predict_expectations` HOT 1
- Make protocols out of LearningPolicyType and NeighborhoodPolicyType? HOT 3
- Save the state of Contextual MAB HOT 3
- Consistently get the actual expected value for each arm HOT 3
- There's no good way of getting the rewards of arms, period. HOT 1
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