Improve integration of H2O Py client with Sklearn

[exalate-issue-sync]

Current H2O-3 python client supports Sklearn {{fit}}/{{transform}}/{{predict}} syntax for all simple algorithms, but has several drawbacks:

• estimators can't be used with {{numpy}} arrays or {{pandas}} frames.
• they always return {{H2OFrames}}.
• for the reasons above, they can't be combined in a {{Pipeline}} using standard {{sklearn}} transformers (e.g. {{sklearn.preprocessing}} module).
• they don't provide simple params discovery (no params auto-completion, get_params returns only the params that have previously been set...).
• AutoML is currently not usable in sklearn context.

The objective of this task is to provide a new h2o.sklearn module that will expose wrappers of existing H2O estimators (including AutoML) and transformers, so that they will provide all the functionalities expected in {{sklearn}} context with no risk of backwards incompatible changes on the existing.

[exalate-issue-sync]

Sebastien Poirier commented: Currently merged to {{master}}, if we want this as part of {{3.26.0.4}}, it would need to be backported to {{rel-yau}}: do we? [~accountid:557058:04659f86-fbfe-4d01-90c9-146c34df6ee6] , [~accountid:557058:afd6e9a4-1891-4845-98ea-b5d34a2bc42c]

[exalate-issue-sync]

Sebastien Poirier commented: As {{3.28.0.1}} is approaching, no point to push this to {{rel-yau}}, so resolving to for next major.

[h2o-ops]

JIRA Issue Migration Info

Jira Issue: PUBDEV-6715
Assignee: Sebastien Poirier
Reporter: Sebastien Poirier
State: Resolved
Fix Version: 3.28.0.1
Attachments: N/A
Development PRs: Available

Linked PRs from JIRA

#3744