Comments (3)
Hey @maypink, thank you for the kind feedback. Remember to ⭐️ the library if you like it.
Based on the information you provided, one option might be to use the predict_expectation() method to store the predicted expectations for each arm at different points in time.
Alternatively, if you don't know exactly what information you need, you could serialize the MAB object by using the Python pickle library and save this for later use.
Hope this helps!
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Thank you for reply!
I forgot to clarify that I want to save Contextual bandits, so it's improtant to save the whole object as it is. [predict_expectation()](https://fidelity.github.io/mabwiser/api.html#mabwiser.mab.MAB.predict_expectations)
is not enough for me in that case. Also, saving with json is much faster and compact than with pickle, so is it possible that save
and load
methods will be implemented in mabwiser?
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Hey @maypink
We currently don't have plans to implement dedicated save
and load
methods. As mentioned in the previous comment using pickle (or similar) to serialize the MAB object itself would be our suggested approach as this ensures all the variables corresponding to the bandit state is captured.
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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
- Consistently get the actual expected value for each arm HOT 3
- Parallel fit/predict for contextual policies HOT 1
- There's no good way of getting the rewards of arms, period. HOT 1
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