Add ability to specify algo parameters (e.g. monotonicity constraints) in H2O AutoML

[exalate-issue-sync]

h1. Initial demand

Expose monotonicity constraints to AutoML.

We can follow the existing convention for GBM and XGBoost, which is to use an argument called monotone_constraints. [https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/data-science/algo-params/monotone_constraints.rst|https://github.com/h2oai/h2o-3/blob/master/h2o-docs/src/product/data-science/algo-params/monotone_constraints.rst|smart-link]

[http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm.html?highlight=monotone|http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/gbm.html?highlight=monotone]
[http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html?highlight=monotone|http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html?highlight=monotone]

h1. Extended Proposal

To avoid multiplication of algo-specific parameters, and to improve customizability of AutoML internals (at users own risks), I suggest to expose all algo-specific constraints through a single {{algo_parameters}} param.

For example, in Python:

{code:python}algo_parameters=dict(
monotone_constraints=dict(AGE=1)
){code}

will apply the given {{monotone_constraints}} to all models and grids that support this parameter.

Whereas:

{code:python}algo_parameters=dict(
XGBoost__monotone_constraints=dict(AGE=1)
){code}

will apply it only to all {{XGBoost}} models (more precisely, to all models created by the {{XGBoostStepsProvider}}, itself registered with name=XGBoost).

This can be used in combination to constraint multiple algorithms:

{code}algo_parameters=dict(
monotone_constraints=dict(AGE=1),
ntrees=100,
XGBoost__ntrees=200,
GBM__grids__ntrees=[50, 100, 200],
){code}

will apply given parameters as follow:

• monotone_constraints to all models and grids supporting it (GBM + XGBoost)
• ntrees=100 to all models and grids supporting it (DRF, GBM, XGBoost), overriding the value defined on backend.
• ntrees=200 to all XGBoost models and grids, overriding the value defined on backend and ignoring the previous ntrees=100.
• ntrees=[50, 100, 200] as an hyperparameter for all GBM grids, ignoring the default ntrees param or hyperparam for those grids. The array syntax is interpreted as an hyperparameter only if set explicitely for grids.

[exalate-issue-sync]

Sebastien Poirier commented: [~accountid:557058:afd6e9a4-1891-4845-98ea-b5d34a2bc42c] didn’t we have a ticket about customizing algo parameters?

Just wondering if we could not provide a dictionary for those params instead of exposing them one by one.
Something like algo_parameters and for each algo in AutoML, if it supports a key, we set it… could it be risky?
for example in Py:

{code:python}aml = H2OAutoML(..., algo_parameters=dict(monotone_constraint={})){code}

A safer alternative approach would be to offer the possibility to expose each algo individually:

{code:python}aml = H2OAutoML(..., algo_parameters=dict(
XGBoost=dict(
monotone_constraints={},
ntrees=50,
),
GBM=dict(
monotone_constraints={},
)
)){code}

or we could combine both…
This would directly bring a lot of flexibility for users who want to tune AutoML instead of exposing params one after another.
Any thoughts?

[h2o-ops]

JIRA Issue Migration Info

Jira Issue: PUBDEV-6737
Assignee: Sebastien Poirier
Reporter: Nidhi Mehta
State: Resolved
Fix Version: 3.28.0.1
Attachments: N/A
Development PRs: Available

Linked PRs from JIRA

#4020
#4071
#4005