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janvanrijn
commented
Apr 11, 2017
janvanrijn
commented
- Fixed issue 210 by making a copy of the model for every CV fold (this is important to prevent data leakage).
- Restructured the way an arff trace is generated (arff content and arff header are extracted separately, this was needed because of new assumptions and in order to keep the code clean after previous point)
- Apparently, I also added the 'upload incompatible run with error message' in this branch. Sorry, but it's it's just two additional lines anyway.
…e model is cloned from an untrained version
…the sklearn connector
…e model is cloned from an untrained version
…the sklearn connector
Collaborator
|
Unittests are broken. |
disallowed the run of 'illegal' combinations (e.g., regression on classification)
Codecov Report
@@ Coverage Diff @@
## develop #223 +/- ##
===========================================
- Coverage 89.15% 88.72% -0.43%
===========================================
Files 23 23
Lines 1807 1818 +11
===========================================
+ Hits 1611 1613 +2
- Misses 196 205 +9
Continue to review full report at Codecov.
|
mfeurer
requested changes
Apr 20, 2017
| return True | ||
| count = 0 | ||
| returnValue = None | ||
| if isinstance(model, sklearn.pipeline.Pipeline): |
Collaborator
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I do not see any usecase for this. Do you have one in mind?
| model_classes = model.classes_ | ||
| if get_traceble_model(model_fold): | ||
| arff_tracecontent.extend(_extract_arfftrace(model_fold, rep_no, fold_no)) | ||
| model_classes = model_fold.best_estimator_.classes_ |
Collaborator
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In the worst case, the model classes can be different for each fold. Do we cope for that?
mfeurer
approved these changes
Apr 20, 2017
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