Comments (5)
In case anyone's curious why a feature would be infinite: a few features assume that the data are log-scale like magnitudes, and in converting large values back to linear scale we can over- or under-flow. So I think these features are behaving as expected, but just don't make sense for every application.
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Another issue: sometimes NaN or infinite values appear during the computation of a feature and this currently breaks the whole pipeline. Need to handle such errors more gracefully (how?).
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What is the current status here, Brett?
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Seems like this should be easy to fix, just need to decide what to do exactly:
- Notify user as soon as invalid feature values are detected? i.e., mid- or post-featurization
- Throw a (human-readable) exception informing the user which features are invalid?
- ...?
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How about both? An error message that pops up, and then a little report when you click on the failed result?
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Related Issues (20)
- possible overflow? (cesium lomb_scargle model)
- New version of dask broke this library HOT 1
- Citation question -- I am citing your library in my publication HOT 1
- pinned scikit-learn HOT 4
- Error: Microsoft Visual C++ 14.0 is required. HOT 7
- provide RectangleGeometry extrudedHeight gradient ramp Color Material bottom-up (floor-sky) HOT 2
- Cadence/Error features HOT 2
- Move tests to GitHub Actions HOT 1
- No screenshot, no use. HOT 4
- How to use dask? HOT 6
- Without any processing, it should be a straight line rendered according to the value of absolute position X. now it is < minvalue, and the boundary is not clear.
- Use another augmented assignment statement HOT 1
- installation issue on macos HOT 3
- Is cesium compatible with the current sklearn version (scikit-learn 1.1) ? HOT 9
- Migrate away from numpy.distutils HOT 1
- Python 3.10 support? HOT 4
- Add lazy loading for submodules
- test_roundtrip_featureset fails on fresh install HOT 2
- Test suite fails with pandas 2.0 (`cesium/tests/test_featurize.py::test_impute`)
- Interactive examples for cesium
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