GH-8487: implement HGLM gaussian [nocheck] #16403
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wendycwong
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maurever and
tomasfryda
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maurever
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Oct 23, 2024
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Hi @wendycwong. Thanks for this big contribution.
I reviewed 80/110 files. I will continue tomorrow. It would be nice if, in this PR, you keep only HGLM-related changes. For example, implementation of HGLM can be one PR, and removing old code from GLM can be another PR. Also implementation of Python and R API can be separate PR.
It would make the review process much easier. Also, there would be less space for bugs.
h2o-py/tests/testdir_algos/glm/pyunit_GH_6722_separate_linear_beta_gaussian.py
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h2o-py/tests/testdir_algos/glm/pyunit_link_functions_gaussian_glm.py
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maurever
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Oct 24, 2024
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@wendycwong, I finished my review. I found just minor bugs. I tried to check all the math, and everything looks good. Tests passed. Have you tried your test run on multinode? Just to be sure. Thanks for this huge contribution! |
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Hi @wendycwong. Thanks for incorporating the suggestions. There are still two HGLM tests failing. So, after all the tests pass, I can approve the PR. |
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GH-8487: crafting HGLM parameters. GH-8487: implement EM algo. GH-8487: forming the fixed matrices and vectors. GH-8487: add test to make sure correct initialization of fixed, random coefficients, sigma values and T matrix. GH-8487: Finished implementing EM to estimate fixed coefficients, random coefficients, tmat and tauEVar GH-8487: finished implementing prediction but still need to figure out the model metrics calculation. GH-8487: Adding support for models without random intercept. GH-8487: adding normalization and denormalization of coefficients for fixed and random. GH-8487: Completed prediction implementation and added tests to make sure prediction is correct when standardize=true/false, random_intercept = true/false. GH-8487: fixing model metric classes. GH-8487: add python and R tests. GH-8487: adding hooks to generate synthetic data. GH-8487: added scoring history, model summary, coefficient tables. GH-8487: added modelmetrics for validation frame. GH-8487: From experiment to find best tauEVar calculation process. The one in equation 10 is best. GH-8487: add capability in Python client to extract scoring history, model summary, model metrics, model coefficients (fixed and random), icc, T matrix, residual variance. GH-8487: done checking scoring history, model summary and model metrics. GH-8487: added R client test for utility functions. GH-8487: use lambda_ instead lf Lambda in pyunit_benign_glm.py GH-8487: remove standardize from HGLM as the convention does not do standardization. Co-authored-by: Veronika Maurerová <maurever@users.noreply.github.com> Move test to check init values are set correctly to Python from Java. I was not able to find a good combination of initial betas/ubetas and t matrix to make it work.
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krasinski
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Oct 29, 2024
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This PR fixes this issue: #8487
I have separated HGLM from GLM as its own toolbox. The only family that is supported now is Gaussian. I still need to do the following:
HGLM_H2O_Implementation.pdf