[BUG] 'normalize' in LinearRegression deprecated in scikit-learn 1.0

[mmccarty]

Describe the bug
PR scikit-learn/scikit-learn#17743 released in sklearn v1.0 makes an API change that is not reflected in cuml.

See release notes for detail.

Steps/Code to reproduce bug
Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression(fit_intercept=True,
                            normalize=True,
                            n_jobs=-1).fit(X, y)

Results in a warning...

/home/mmccarty/miniconda3/envs/rapids-22.06/lib/python3.9/site-packages/sklearn/linear_model/_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.
If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:

from sklearn.pipeline import make_pipeline

model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())

If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:

kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}
model.fit(X, y, **kwargs)


  warnings.warn(

However, cuml.linear_model.LinearRegression doesn't issue a warning.

Expected behavior
cuml estimator match the behavior of sklearn.

[github-actions]

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[jcrist]

#7415 deprecated the normalize option in all linear models, we'll rip it on in 26.02. Closing.