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Transfer large synthetic data approach from dask-glm #67

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PeterDSteinberg opened this issue Oct 25, 2017 · 1 comment
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Transfer large synthetic data approach from dask-glm #67

PeterDSteinberg opened this issue Oct 25, 2017 · 1 comment
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@PeterDSteinberg
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@PeterDSteinberg PeterDSteinberg commented Oct 25, 2017

See make_poisson and similar functions in dask-glm's datasets.py. Transfer that approach into dask-ml's datasets.py so larger synthetic data sets can be created. Currently the approach in dask-ml's datasets.py just wraps sklearn.datasets.

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@mrocklin mrocklin commented Oct 26, 2017

I also ran into this just last night

TomAugspurger added a commit to TomAugspurger/dask-ml that referenced this issue May 22, 2018
Change the methods in dask_ml.datasets to not overwhelm a single
machine when generating large random datasets.

Closes dask#67
TomAugspurger added a commit to TomAugspurger/dask-ml that referenced this issue May 24, 2018
Change the methods in dask_ml.datasets to not overwhelm a single
machine when generating large random datasets.

Closes dask#67
TomAugspurger added a commit that referenced this issue May 24, 2018
Change the methods in dask_ml.datasets to not overwhelm a single
machine when generating large random datasets.

Closes #67
TomAugspurger pushed a commit to TomAugspurger/dask-ml that referenced this issue Jun 28, 2018
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