https://github.com/fabianp/hoag

Hyperparameter optimization with approximate gradient
https://github.com/fabianp/hoag

Last synced: 26 days ago
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Hyperparameter optimization with approximate gradient

• Host: GitHub
• URL: https://github.com/fabianp/hoag
• Owner: fabianp
• Created: 2016-02-07T19:07:16.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-03-23T18:07:00.000Z (over 3 years ago)
• Last Synced: 2024-10-04T23:09:04.197Z (about 1 month ago)
• Language: Python
• Size: 1010 KB
• Stars: 65
• Watchers: 9
• Forks: 24
• Open Issues: 1
• Metadata Files:
  • Readme: README.rst

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README

        
.. image:: https://travis-ci.org/fabianp/hoag.svg?branch=master

    :target: https://travis-ci.org/fabianp/hoag

HOAG

====

Hyperparameter optimization with approximate gradient

.. image:: https://raw.githubusercontent.com/fabianp/hoag/master/doc/comparison_ho_real_sim.png

   :scale: 50 %

Depends

-------

  * scikit-learn 0.16

Usage

-----

This package exports a LogisticRegressionCV class which automatically estimates the L2 regularization of logistic regression. As other scikit-learn objects, it has a .fit and .predict method. However, unlike scikit-learn objects, the .fit method takes 4 arguments consisting of the train set and the test set. For example:

    >>> from hoag import LogisticRegressionCV

    >>> clf = LogisticRegressionCV()

    >>> clf.fit(X_train, y_train, X_test, y_test)

where X_train, y_train, X_test, y_test are numpy arrays representing the train and test set, respectively.

For full usage example check out `this ipython notebook `_.

.. image:: https://raw.githubusercontent.com/fabianp/hoag/master/doc/hoag_screenshot.png

   :target: https://github.com/fabianp/hoag/blob/master/doc/example_usage.ipynb

Usage tips

----------

Standardize features of the input data such that each feature has unit variance. This makes the Hessian better conditioned. This can be done using e.g. scikit-learn's StandardScaler.

Citing

------

If you use this, please cite it as

.. code-block::

  @inproceedings{PedregosaHyperparameter16,

    author    = {Fabian Pedregosa},

    title     = {Hyperparameter optimization with approximate gradient},

    booktitle = {Proceedings of the 33nd International Conference on Machine Learning,

                 {ICML}},

    year      = {2016},

    url       = {http://jmlr.org/proceedings/papers/v48/pedregosa16.html},

  }