2013/6/9 Joel Nothman <jnoth...@student.usyd.edu.au>:
> Again, it's probably over the top, but I think it's a useful interface
> (prototyped at
> https://github.com/jnothman/scikit-learn/tree/search_results):
>
>>>> from __future__ import print_function
>>>> from sklearn.grid_search import GridSearchCV
>>>> from sklearn.datasets import load_iris
>>>> from sklearn.svm import SVC
>>>> iris = load_iris()
>>>> grid = {'C': [0.01, 0.1, 1], 'degree': [1, 2, 3]}
>>>> search = GridSearchCV(SVC(kernel='poly'),
>>>> param_grid=grid).fit(iris.data, iris.target)
>>>> res = search.results_
>>>> res.best().mean_test_score
> 0.97333333333333338
>>>> res
> <9 candidates. Best results:
>   <0.973 for {'C': 0.10000000000000001, 'degree': 3}>,
>   <0.967 for {'C': 1.0, 'degree': 3}>,
>   <0.967 for {'C': 1.0, 'degree': 2}>, ...>
>>>> for tup in res.zipped('parameters', 'mean_test_score',
>>>> 'std_test_score'):
> ...     print(*tup)
> ...
> {'C': 0.01, 'degree': 1} 0.673333333333 0.033993463424
> {'C': 0.01, 'degree': 2} 0.926666666667 0.00942809041582
> {'C': 0.01, 'degree': 3} 0.966666666667 0.0188561808316
> {'C': 0.10000000000000001, 'degree': 1} 0.94 0.0163299316186
> {'C': 0.10000000000000001, 'degree': 2} 0.966666666667 0.0188561808316
> {'C': 0.10000000000000001, 'degree': 3} 0.973333333333 0.00942809041582
> {'C': 1.0, 'degree': 1} 0.966666666667 0.0249443825785
> {'C': 1.0, 'degree': 2} 0.966666666667 0.00942809041582
> {'C': 1.0, 'degree': 3} 0.966666666667 0.0188561808316

I very much like that but I still think that we should keep the raw
evaluation log to make it easier to implement future extensions.

--
Olivier
http://twitter.com/ogrisel - http://github.com/ogrisel

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