Tools for doing hyperparameter search with Scikit-Learn and Dask
Project description
Tools for performing hyperparameter search with Scikit-Learn and Dask.
This library provides implementations of Scikit-Learn’s GridSearchCV and RandomizedSearchCV. They implement many (but not all) of the same parameters, and should be a drop-in replacement for the subset that they do implement. For certain problems, these implementations can be more efficient than those in Scikit-Learn, as they can avoid expensive repeated computations.
For more information, check out the documentation.
Install
Dask-searchcv is available via pip:
$ pip install dask-searchcv
Example
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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dask-searchcv-0.0.2.tar.gz
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