MNMG Logistic Regression (dask-glm wrapper)

[daxiongshu]

In this PR, I'll wrap dask-glm models so that it accepts dask_cudf Dataframes and behaves like other cuml.dask models. dask-glm provides three estimators: LogisticRegression, LinearRegression and PoissonRegression. MNMG LogisticRegression is requested by @beckernick . @JohnZed for visibility. Thank you all.

[codecov-io]

Codecov Report

Merging #3512 (2fa2d54) into branch-0.19 (f5d86b9) will increase coverage by 7.96%.
The diff coverage is 0.00%.

@@               Coverage Diff               @@
##           branch-0.19    #3512      +/-   ##
===============================================
+ Coverage        72.91%   80.87%   +7.96%     
===============================================
  Files              214      229      +15     
  Lines            16856    17777     +921     
===============================================
+ Hits             12290    14378    +2088     
+ Misses            4566     3399    -1167     

Flag	Coverage Δ
dask	45.20% <0.00%> (?)
non-dask	73.16% <0.00%> (+0.25%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
...thon/cuml/dask/linear_model/logistic_regression.py	0.00% <0.00%> (ø)
python/cuml/common/numba_utils.py	0.00% <0.00%> (ø)
python/cuml/neighbors/__init__.py	100.00% <0.00%> (ø)
python/cuml/model_selection/__init__.py	100.00% <0.00%> (ø)
python/cuml/internals/global_settings.py	100.00% <0.00%> (ø)
python/cuml/neighbors/nearest_neighbors_mg.pyx	98.51% <0.00%> (ø)
python/cuml/decomposition/base_mg.pyx	100.00% <0.00%> (ø)
python/cuml/linear_model/base_mg.pyx	100.00% <0.00%> (ø)
python/cuml/cluster/dbscan_mg.pyx	100.00% <0.00%> (ø)
python/cuml/neighbors/kneighbors_classifier_mg.pyx	100.00% <0.00%> (ø)
... and 78 more

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[daxiongshu]

While I'm fixing the CI errors, I'd like to raise some issues with this PR. It lacks several functionalities of other cuml.dask.linear_models because it doesn't have an internal model.

1. No get_combined_model() and _set_internal_model
2. No mutli-gpu training and single-gpu inference.
3. No single-gpu training and multi-gpu inference.
4. No pickling of the model.

The functionalities 2) and 4) can be added quite easily but they will look different from other cuml.dask.linear_models.

Currently it subclassed the cuml.dask.common.base.BaseEstimator quite trivially. The only things it utilized from BaseEstimator is checking if client is valid.

This implementation also has some different hyperparameters from other cuml.dask.linear_models:

• No hyperparameter normalized. dask-glm always normalizes inputs internally.
• No hyperparameter delayed

So I have two questions:

• Are these missing functionalities required by the customers? @beckernick
• Is it necessary to subclass cuml.dask.common.base.BaseEstimator? Due to the differences, @JohnZed suggests that we put it into another namespace say cuml wrappers.

Thank you all. Please let me know.

[beckernick]

Does this implementation implicitly support a GPU based LogisticRegression.decision_function via Dask dispatching?

Due to the differences, @JohnZed suggests that we put it into another namespace say cuml wrappers.

This sounds like a great idea!

[dantegd]

@gpucibot merge