Comments (3)
I guess the surrogate is fine with out of domain values but i agree that an out of domain check would be helpful. I ended up creating my own configs with a validate()
and manually specifying the bounds to counter this.
from jahs_bench_201.
This is a very valid point, thank you for bringing it up! The way the number of epochs is currently being handled is definitely sub-optimal and will be revised in the upcoming updates.
Regarding the domain checks, this is something that requires some discussion.
- For the surrogate, there is technically no reason for forbidding out-of-domain values as long as a user is aware that our underlying model, XGBoost, is not very suitable for extrapolation. My personal preference would be to only raise a warning when out-of-domain values are encountered. Adding the option to enforce domain bounds and/or raise an error instead would then be a nice-to-have for users who want that.
- For the tabular dataset, domain checks actually make no sense, since the dataset can only be queried on the specific values that were originally sampled in any case. The thought behind the current design was that exposing the
ConfigSpace
implementation of the search space is enough to provide the requisite information to any algorithms that need the domain bounds, but maybe this can be improved.
What is your opinion?
from jahs_bench_201.
Hi, thanks for the response:)
For the tabular, I don't think we need the check, but for the surrogate model, it would be grateful to have warning at least because XGBoost gives, by nature of the decision tree, the same prediction on each parameter which is rounded to the original range.
from jahs_bench_201.
Related Issues (14)
- Incompatible checksums error HOT 8
- Provide a fixed tag version once stable HOT 2
- Add a `py.typed` to export type information
- Export the ability to create the `joint_config_space` as a function HOT 1
- Issue with config and trajectory HOT 1
- XGBoost and MacOS incompatibility HOT 3
- pip install error HOT 1
- Termination in colorectal_histology due to memory overflow HOT 3
- Bug in argument check (metrics) HOT 1
- Wrong path when loading tabular data HOT 2
- Moving away from pickles
- Issue with evaluating multiobjective benchmarks HOT 4
- Data download
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 jahs_bench_201.