PUBDEV-7241 Monotone Quantile Constraints #4719
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h2o-docs/src/product/data-science/algo-params/monotone_constraints.rst
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| _vals[_vals_dim*b + 0] += weight; | ||
| _vals[_vals_dim*b + 1] += wy; | ||
| _vals[_vals_dim*b + 2] += wyy; | ||
| int binDimStart = _vals_dim*b; |
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imho this reads worse than before, maybe if you made binDimStart final it would look better
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So I made the binDimStart final. Maybe this reads worse, however, it is better from a code perspective. But I can change it back if you want @michalkurka.
| // left tree prediction quantile | ||
| if (bin <= b) { | ||
| if (bin == 1) { | ||
| ratio = 100 / wlo[b]; |
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Where is the 100 number coming from?
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Good question! I calculated the percentile instead of quantile here. My mistake. I fixed it and run tests and demo and, it looks good. The calculation of the quantile is similar, so the performance should be the same.
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First read - LGTM, since you are touching performance optimized code in DTree - it would be a good idea to re-run benchmarks on this branch before merging this. |
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PUBDEV-7241 * implement histogram approximation quantile monotone constraints * reset quantiles correctly with respect constraints in subtrees * add quatnile to MC documentation, add demo * add Java, Python and R tests (cherry picked from commit e1ab951)
JIRA: https://0xdata.atlassian.net/browse/PUBDEV-7241