Dask-SearchCV is now included in Dask-ML <http://dask-ml.readthedocs.io/>
__.
Further development to Dask-SearchCV is occuring in the
Dask-ML repository <https://github.com/dask/dask-ml>
__. Please post issues
and make pull requests there.
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Tools for performing hyperparameter search with
Scikit-Learn <http://scikit-learn.org>
and Dask <http://dask.pydata.org>
.
Drop-in replacement for Scikit-Learn's GridSearchCV
and
RandomizedSearchCV
.
Hyperparameter optimization can be done in parallel using threads, processes, or distributed across a cluster.
Works well with Dask collections. Dask arrays, dataframes, and delayed can be
passed to fit
.
Candidate estimators with identical parameters and inputs will only be fit
once. For composite-estimators such as Pipeline
this can be significantly
more efficient as it can avoid expensive repeated computations.
For more information, check out the documentation <http://dask-searchcv.readthedocs.io>
__.
Dask-searchcv is available via conda
or pip
:
::
$ conda install dask-searchcv -c conda-forge
$ pip install dask-searchcv
.. code-block:: python
from sklearn.datasets import load_digits
from sklearn.svm import SVC
import dask_searchcv as dcv
import numpy as np
digits = load_digits()
param_space = {'C': np.logspace(-4, 4, 9),
'gamma': np.logspace(-4, 4, 9),
'class_weight': [None, 'balanced']}
model = SVC(kernel='rbf')
search = dcv.GridSearchCV(model, param_space, cv=3)
search.fit(digits.data, digits.target)
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