LinearRegression doesn't return lazy object

[AlexeyPechnikov]

LinearRegression requires even longer time than sklearn version and it doesn't return a lazy object:

%%time

from sklearn.pipeline import make_pipeline
from dask_ml.linear_model import LinearRegression

size = 1e6

X = dask.array.arange(2*size).reshape(-1,2)
y = dask.array.arange(size).reshape(-1,1)
reg = LinearRegression()
reg.fit(X, y)

It looks as a Dask-incompatible function.

[TomAugspurger]

No comment on the performance, but all .fit methods in dask-ml are eager.

On Tue, Nov 1, 2022 at 2:26 PM Alexey Pechnikov @.***> wrote:

LinearRegression requires even longer time than sklearn version and it doesn't return a lazy object:

%%time

from sklearn.pipeline import make_pipeline from dask_ml.linear_model import LinearRegression

size = 1e6

X = dask.array.arange(2*size).reshape(-1,2) y = dask.array.arange(size).reshape(-1,1) reg = LinearRegression() reg.fit(X, y)

[image: image] https://user-images.githubusercontent.com/7342379/199320461-14182b29-9156-4875-b678-e3cdec294976.png

It looks as a Dask-incompatible function.

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[AlexeyPechnikov]

@TomAugspurger Do you mean dask-ml has no any advantages and it’s slower vs sklearn? Obviously, we can’t select and process just a subset of data later when dask-ml methods are not lazy.