GH-16033: Add GBLinear #16034
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GH-16033: Add GBLinear #16034
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tomasfryda
commented
Jan 25, 2024
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| aml_with_gblinear = H2OAutoML(max_models=MAX_MODELS, seed=1, modeling_plan=[ | ||
| dict(name="XGBoost", steps=[ | ||
| dict(id="def_2", group=1, weight=10), | ||
| dict(id="def_1", group=2, weight=10), | ||
| dict(id="def_3", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=90), | ||
| dict(id="grid_gblinear", group=4, weight=90), # << XGBoost GBLinear booster grid | ||
| dict(id="lr_search", group=7, weight=30), | ||
| ]), dict(name="GLM", steps=[ | ||
| dict(id="def_1", group=1, weight=10), | ||
| ]), dict(name="DRF", steps=[ | ||
| dict(id="def_1", group=2, weight=10), | ||
| dict(id="XRT", group=3, weight=10), | ||
| ]), dict(name="GBM", steps=[ | ||
| dict(id="def_5", group=1, weight=10), | ||
| dict(id="def_2", group=2, weight=10), | ||
| dict(id="def_3", group=2, weight=10), | ||
| dict(id="def_4", group=2, weight=10), | ||
| dict(id="def_1", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=60), | ||
| dict(id="lr_annealing", group=7, weight=10), | ||
| ]), dict(name="DeepLearning", steps=[ | ||
| dict(id="def_1", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=30), | ||
| dict(id="grid_2", group=5, weight=30), | ||
| dict(id="grid_3", group=5, weight=30), | ||
| ]), dict(name="completion", steps=[ | ||
| dict(id="resume_best_grids", group=6, weight=60), | ||
| ]), dict(name="StackedEnsemble", steps=[ | ||
| dict(id="monotonic", group=9, weight=10), | ||
| dict(id="best_of_family_xglm", group=10, weight=10), | ||
| dict(id="all_xglm", group=10, weight=10), | ||
| ])]) | ||
| aml_with_gblinear.train(y=ds.target, training_frame=ds.train) |
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Example of how to run normal automl with gblinear grid.
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is this what we want to give to the customer when they want to try gblinear?
I mean if it's one single customer, I think it's fine, but it fixes AutoML behavior once and for all…
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AFAIK it's just one customer and from a limited benchmark gblinear doesn't seem to bring much (except for higher training time). But it might be worth experimenting with once we have more gblinear parameters exposed.
wendycwong
reviewed
Jan 26, 2024
| int ncols = aml().getTrainingFrame().numCols() - (aml().getBuildSpec().getNonPredictors().length + | ||
| (aml().getBuildSpec().input_spec.ignored_columns != null ? aml().getBuildSpec().input_spec.ignored_columns.length : 0)); | ||
|
|
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| searchParams.put("_top_k", IntStream.range(0, ncols-1).boxed().toArray(Integer[]::new)); |
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Nice to add this in now. You can uncomment it once Adam exposed those parameters.
wendycwong
approved these changes
Jan 26, 2024
sebhrusen
approved these changes
Jan 29, 2024
Comment on lines
+82
to
+115
| aml_with_gblinear = H2OAutoML(max_models=MAX_MODELS, seed=1, modeling_plan=[ | ||
| dict(name="XGBoost", steps=[ | ||
| dict(id="def_2", group=1, weight=10), | ||
| dict(id="def_1", group=2, weight=10), | ||
| dict(id="def_3", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=90), | ||
| dict(id="grid_gblinear", group=4, weight=90), # << XGBoost GBLinear booster grid | ||
| dict(id="lr_search", group=7, weight=30), | ||
| ]), dict(name="GLM", steps=[ | ||
| dict(id="def_1", group=1, weight=10), | ||
| ]), dict(name="DRF", steps=[ | ||
| dict(id="def_1", group=2, weight=10), | ||
| dict(id="XRT", group=3, weight=10), | ||
| ]), dict(name="GBM", steps=[ | ||
| dict(id="def_5", group=1, weight=10), | ||
| dict(id="def_2", group=2, weight=10), | ||
| dict(id="def_3", group=2, weight=10), | ||
| dict(id="def_4", group=2, weight=10), | ||
| dict(id="def_1", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=60), | ||
| dict(id="lr_annealing", group=7, weight=10), | ||
| ]), dict(name="DeepLearning", steps=[ | ||
| dict(id="def_1", group=3, weight=10), | ||
| dict(id="grid_1", group=4, weight=30), | ||
| dict(id="grid_2", group=5, weight=30), | ||
| dict(id="grid_3", group=5, weight=30), | ||
| ]), dict(name="completion", steps=[ | ||
| dict(id="resume_best_grids", group=6, weight=60), | ||
| ]), dict(name="StackedEnsemble", steps=[ | ||
| dict(id="monotonic", group=9, weight=10), | ||
| dict(id="best_of_family_xglm", group=10, weight=10), | ||
| dict(id="all_xglm", group=10, weight=10), | ||
| ])]) | ||
| aml_with_gblinear.train(y=ds.target, training_frame=ds.train) |
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is this what we want to give to the customer when they want to try gblinear?
I mean if it's one single customer, I think it's fine, but it fixes AutoML behavior once and for all…
tomasfryda
deleted the
tomf_GH-8381_AutoML_experiment_XGB_with_different_booster_gblinear_now_safe_to_use
branch
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#16033
Initial step of adding
gblinearto automl. Now only as an optional step.