dask-searchcv

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>__.

Highlights

• 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>__.

Install

Dask-searchcv is available via conda or pip:

::

Install with conda

$ conda install dask-searchcv -c conda-forge

Install with pip

$ pip install dask-searchcv

Example

.. 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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