Comments (3)
Hi @alansaid,
thanks for this tool, it could be very useful as a guide for newcomers so they can implement tests of the missing pieces and understand the framework at the same time.
Right now, it looks like the most covered module is the evaluation one, that is good. However, it would not be too difficult to add tests in the split and core modules.
So, definitively this is a good idea and we should aim at raising those numbers in every release.
Cheers,
Alex
from rival.
Hello @alansaid ,
Do you refer to the coverage metric? http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.8494&rep=rep1&type=pdf
Seems that this is still a broad concept, there no clear definition for coverage.
Do you have your own implementation? If so, what is the formula being used.
Thanks,
André
from rival.
Hi @afcarvalho1991 ,
The numbers I refer to are not related to recommender systems, they are related to how much of the code in RiVal is covered/checked by unit tests.
Currently we have very few of those (~17%), meaning that there is a lot of functionality that is implemented but not tested.
/A
from rival.
Related Issues (20)
- Check evaluation metrics are suitable for unary/binary data
- Check splitters are suitable for unary/binary data
- Check parsers are suitable for unary/binary data
- Not generalized DataModel for RandomSplitter class
- Not generalized DataModel for Temporal Splitter class
- Not generalized DataModel for SplitterRunner class
- Using Examples with a dataset like ML-10M HOT 11
- Update license snippet in gh-pages HOT 1
- Issue with Precision and Recall HOT 3
- Create jar file of 0.3-SNAPSHOT to be used outside eclipse HOT 2
- DataModel does not support duplicate ratings in dataset
- Implement RecSys Challenge 2016 metric
- [Question]: Split dataset in training, validation and test HOT 12
- Missing documentation for unsupported combination in RandomSplitter HOT 3
- Add custom behaviour to DataModelUtils.saveModel HOT 3
- [Question] Maven install command HOT 5
- Bug in CrossValidatedMahoutKNNRecommenderEvaluator
- Type mismatch: cannot convert from CSVParser to Iterable<CSVRecord> HOT 11
- Precision values are not consistent with those from other ranking metrics
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