Comments (4)
Thank you for sharing positive feedback -- we are glad to hear that the library is helpful on your cases!
The behavior changes depending on the Learning Policy (and its combination with Nhood policy as in your case). This behavior is not always "pick the max". That strategy would be %100 exploitation. Some learning policies allow exploration where the bandit literature comes into the picture.
In its simplest case; a random learning policy will return 5 random predictions, pure %100 exploration
In the case you shared, Thompson Sampling is the culprit behind the exploration that tries to give arms, that is not the best, a chance to be selected. As a result, you can see some arms, that are not deemed for the highest expectation, are selected. Btw, this seemingly random behavior should be "deterministic" when using the same seed.
If you change the example, to use Epsilon Greedy (instead of TS) and then set the epsilon parameter to zero, the results should give you the arm with max expectation at all times. Hope this helps!
from mabwiser.
Hello @skadio
Thanks for your reply and clarification of the model behavior!
Upon your suggestion, I ran the experiment with Epsilon Greedy having 0 epsilon, and have gotten my maximum from each arm. I will gladly close the issue, but before I did it, maybe you could give me some hints on where I can get a bit more background on the listed Learning Policies and Neighbor policies particularly since not all of them are intuitive for me (i.e. Radius policy, etc)? What would be also interesting is to have a look at the "context" features after the training meaning, which of the features has contributed the most to the result evaluation? I didn't find any info or methods on that matter in the repo...
from mabwiser.
Thanks for your reply and clarification of the model behavior!
Upon your suggestion, I ran the experiment with Epsilon Greedy having 0 epsilon, and have gotten my maximum from each arm.
Glad to hear that it worked as expected.
could give me some hints on where I can get a bit more background on the listed Learning Policies and Neighbor policies particularly since not all of them are intuitive for me (i.e. Radius policy, etc)?
The best place to start would be our paper to go into the background and inner workings of these policies:
https://www.worldscientific.com/doi/abs/10.1142/S0218213021500214
See also the references in the README.
What would be also interesting is to have a look at the "context" features after the training meaning, which of the features has contributed the most to the result evaluation? I didn't find any info or methods on that matter in the repo...
Feature selection/importance is an orthogonal topic to Bandits. You can check out the Selective library as a start
https://github.com/fidelity/selective
from mabwiser.
Wow, big thanks for the provided links I will go through them carefully!
Once again, thank you for your comprehensive responses.
from mabwiser.
Related Issues (20)
- Using categorical variables in the context HOT 2
- init order HOT 2
- 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
- Parallel fit/predict for contextual policies HOT 1
- There's no good way of getting the rewards of arms, period. HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from mabwiser.