Comments (3)
This seems to be a bug - else how come the chi2_val is so huge?
It may also be useful to plot theory as ratio to data, to better identify this kind of discrepancies. But I don't see how one can get such a poor chi2 since the data in the validation subset agrees rather well with the theory
from nnusf.
This is admittedly an odd issue. For a different replica, the results are given below. Notice how the
Dataset | Epoch | REP ID | ||||||
---|---|---|---|---|---|---|---|---|
NUTEV_F2 | 5559 | 1 | 58 | 0.659 | 20 | 11.721 | 78 | 1.893 |
In the same way as before, the 20 data-points forming the validation set are shown in the table below:
- The issue does not appear to be in the computation of the
$\chi^2_{\rm vl}$ (nor in how the results are presented in the report) since: (a) this artifact does not concern the other datasets, and (b) some replicas are better with a somehow reasonable values of$\chi^2_{\rm vl}$ . The problem is that most (over$90$ %) of the replicas for these datasets are bad. - Based on the two results above, it indeed seems that even a small discrepancy between data & predictions can lead to a very large value of
$\chi^2_{\rm vl}$ . The question is: how is this possible?
from nnusf.
So the problem was that some datapoints (in the above example, only a single point) were artificially large because the shifts were so large due to a bug that is fixed in #45. Now the results are reasonable (and converge faster):
Dataset | Epoch | REP ID | ||||||
---|---|---|---|---|---|---|---|---|
NUTEV_F2 | 567 | 35 | 58 | 1.171 | 20 | 2.7866 | 78 | 2.501 |
from nnusf.
Related Issues (20)
- Collect various analyses plots HOT 9
- Some improvements we may want to make to the code HOT 1
- Fix coefficients for some cross sections HOT 6
- Add A=1 boundary conditions HOT 3
- Add option for log frequency of chi2 history to runcard
- Matching for high-Q2 and A=1 does not properly work
- Add the possibility to include a Docker container
- Quote experimental chi2 of real data in report summary
- Fix github workflows
- Improve predictions in the small-x extrapolation region HOT 1
- Add theory covmat to Yadism data
- Address potential overfitting wrt matching data HOT 8
- Implement pdf+th error for yadism grids HOT 8
- Re-compute Yadism theory with FFNS5 @ NLO
- Add module that corrects isoscalarity for a given PDF set HOT 1
- Tests to be carried out for the paper once final fit is ready
- Make the Code public
- PyPI package is broken
- Computation of GLS sum rules broke due to changes in EKO HOT 2
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 nnusf.