GH-16676 GLM: Remove offset effects

[maurever]

Issue: #16676

[Copilot]

Pull request overview

Adds a new experimental GLM option remove_offset_effects to keep offsets during training but remove their effect during scoring/prediction and model metrics, aligning with the “restricted vs unrestricted” model pattern already used for control_variables.

Changes:

• Introduces remove_offset_effects parameter in GLM (backend + REST schema) and exposes it in R/Python clients.
• Updates GLM scoring/metrics/scoring-history flow to compute both restricted (offset removed) and unrestricted metrics, and enables make_unrestricted_glm_model for this use case.
• Adds docs + new tests/examples across Java/R/Python to exercise the feature.

Reviewed changes

Copilot reviewed 15 out of 16 changed files in this pull request and generated 20 comments.

Show a summary per file

File	Description
h2o-algos/src/main/java/hex/glm/GLM.java	Implements restricted/unrestricted scoring-history + metrics computation when remove_offset_effects is enabled.
h2o-algos/src/main/java/hex/glm/GLMModel.java	Adds new parameter + basic validation for remove_offset_effects.
h2o-algos/src/main/java/hex/glm/GLMScore.java	Skips adding offset into the linear predictor when restricted scoring is enabled.
h2o-algos/src/main/java/hex/glm/GLMUtils.java	Renames/extends scoring history combiner for “restricted” use.
h2o-algos/src/main/java/hex/schemas/GLMV3.java	Exposes remove_offset_effects via REST schema.
h2o-algos/src/main/java/hex/api/MakeGLMModelHandler.java	Allows creating unrestricted model when remove_offset_effects was used; resets the flag on the derived model.
h2o-algos/src/test/java/hex/glm/GLMControlVariablesTest.java	Adds backend tests for remove_offset_effects behavior and its interaction with control_variables.
h2o-r/h2o-package/R/glm.R	Adds R API parameter + expands make_unrestricted_glm_model guard.
h2o-bindings/bin/custom/R/gen_glm.py	Updates generated R binding template for make_unrestricted_glm_model guard.
h2o-r/tests/testdir_algos/glm/runit_GLM_remove_offset_effects_explain.R	Adds an R explain/learning-curve smoke test with remove_offset_effects.
h2o-py/h2o/estimators/glm.py	Adds Python API parameter + getter/setter.
h2o-py/tests/testdir_algos/glm/pyunit_remove_offset_effects.py	Adds Python test comparing behavior with/without remove_offset_effects.
h2o-py/tests/testdir_algos/glm/pyunit_remove_offset_glm.py	Adds Python test scaffold around offset scoring behavior.
h2o-docs/src/product/data-science/algo-params/remove_offset_effects.rst	Documents the new parameter and provides examples.
h2o-docs/src/product/data-science/algo-params/control_variables.rst	Links control_variables docs to remove_offset_effects docs.
h2o-core/src/main/java/hex/ModelMetricsBinomial.java	Minor signature cleanup (parameter rename).

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[Copilot]

Pull request overview

Copilot reviewed 15 out of 16 changed files in this pull request and generated 3 comments.

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[tomasfryda]

@maurever Correct me if I am wrong but it looks to me that there is no way of having both control variables(CV) and remove offset and getting:

• model that uses CV and offset
• model that doesn't use CV but does use offset
• model that uses CV and doesn't use offset
• model that doesn't use CV and doesn't use offset

(No way other than training multiple models)

[Copilot]

Pull request overview

Copilot reviewed 15 out of 16 changed files in this pull request and generated 4 comments.

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[Copilot]

Pull request overview

Copilot reviewed 18 out of 20 changed files in this pull request and generated 13 comments.

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[Copilot]

Pull request overview

Copilot reviewed 18 out of 20 changed files in this pull request and generated 4 comments.

Comments suppressed due to low confidence (1)

h2o-algos/src/test/java/hex/glm/GLMControlVariablesTest.java:433

• train is created with a key and then referenced via params._train = train._key, but it is no longer put into the DKV. This will likely cause the builder to fail with a missing-frame error (or a different exception than expected), making the test unreliable. Add DKV.put(train); here (as done in the surrounding tests) before training the model.

            Vec cat1 = Vec.makeVec(new long[]{1,1,1,0,0},new String[]{"black","red"},Vec.newKey());
            Vec cat2 = Vec.makeVec(new long[]{1,1,1,0,0},new String[]{"a","b"},Vec.newKey());
            Vec res = Vec.makeVec(new double[]{1,1,2,0,0},cat1.group().addVec());
            train = new Frame(Key.<Frame>make("train"),new String[]{"x1", "x2", "y"},new Vec[]{cat1, cat2,res});

            GLMModel.GLMParameters params = new GLMModel.GLMParameters();
            params._train = train._key;
            params._alpha = new double[]{0};
            params._response_column = "y";
            params._intercept = false;
            params._control_variables = new String[]{"x1"};
            params._distribution = DistributionFamily.multinomial;
            glm = new GLM(params).trainModel().get();

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[sonarqubecloud]

Quality Gate failed

Failed conditions
16.4% Duplication on New Code (required ≤ 3%)

See analysis details on SonarQube Cloud

[tomasfryda]

LGTM, thank you @maurever !