Metadata-Version: 1.0
Name: dask-searchcv
Version: 0.2.0
Summary: Tools for doing hyperparameter search with Scikit-Learn and Dask
Home-page: http://github.com/dask/dask-searchcv
Author: Jim Crist
Author-email: jcrist@continuum.io
License: BSD
Description-Content-Type: UNKNOWN
Description: dask-searchcv
        =============
        
        |Travis Status| |Doc Status| |Conda Badge| |PyPI Badge|
        
        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)
        
        
        .. |Travis Status| image:: https://travis-ci.org/dask/dask-searchcv.svg?branch=master
           :target: https://travis-ci.org/dask/dask-searchcv
        .. |Doc Status| image:: http://readthedocs.org/projects/dask-searchcv/badge/?version=latest
           :target: http://dask-searchcv.readthedocs.io/en/latest/index.html
           :alt: Documentation Status
        .. |PyPI Badge| image:: https://img.shields.io/pypi/v/dask-searchcv.svg
           :target: https://pypi.python.org/pypi/dask-searchcv
        .. |Conda Badge| image:: https://anaconda.org/conda-forge/dask-searchcv/badges/version.svg
           :target: https://anaconda.org/conda-forge/dask-searchcv
        
Platform: UNKNOWN
