2018-09-13 20:12:21 +01:00
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{ stdenv
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, buildPythonPackage
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, fetchPypi
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2019-12-10 08:18:07 +00:00
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, pythonOlder
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2018-09-13 20:12:21 +01:00
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, dask
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, numpy, toolz # dask[array]
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, numba
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, pandas
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, scikitlearn
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, scipy
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, dask-glm
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, six
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, multipledispatch
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, packaging
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, pytest
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, xgboost
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, tensorflow
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, joblib
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, distributed
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}:
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buildPythonPackage rec {
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2020-06-06 07:47:03 +01:00
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version = "1.5.0";
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2018-09-13 20:12:21 +01:00
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pname = "dask-ml";
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2019-12-10 08:18:07 +00:00
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disabled = pythonOlder "3.6"; # >= 3.6
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2018-09-13 20:12:21 +01:00
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src = fetchPypi {
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inherit pname version;
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2020-06-06 07:47:03 +01:00
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sha256 = "8a9879b7d1642ed8cd48115d81f92a246eb7ffeadc42748053c5339a56f569b4";
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2018-09-13 20:12:21 +01:00
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};
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2019-12-10 08:18:07 +00:00
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propagatedBuildInputs = [
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dask
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dask-glm
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distributed
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multipledispatch
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numba
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numpy
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packaging
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pandas
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scikitlearn
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scipy
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six
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toolz
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];
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2018-09-13 20:12:21 +01:00
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2019-12-10 08:18:07 +00:00
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# has non-standard build from source, and pypi doesn't include tests
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2018-09-13 20:12:21 +01:00
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doCheck = false;
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2019-12-10 08:18:07 +00:00
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# in lieu of proper tests
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pythonImportsCheck = [
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"dask_ml"
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"dask_ml.naive_bayes"
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"dask_ml.wrappers"
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"dask_ml.utils"
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];
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2018-09-13 20:12:21 +01:00
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meta = with stdenv.lib; {
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2020-04-01 02:11:51 +01:00
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homepage = "https://github.com/dask/dask-ml";
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2018-09-13 20:12:21 +01:00
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description = "Scalable Machine Learn with Dask";
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license = licenses.bsd3;
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maintainers = [ maintainers.costrouc ];
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};
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}
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