[MRG] addressing issue #6887, adding callable support for covariance comp. #7078
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…iance computation
sklearn/covariance/graph_lasso_.py
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| @@ -300,6 +300,12 @@ class GraphLasso(EmpiricalCovariance): | |||
| If verbose is True, the objective function and dual gap are | |||
| plotted at each iteration. | |||
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| cov : can be 'empirical' or a callable. If 'empirical' is given, | |||
| empirical covariance is computed. If a callable is given it is used | |||
| to compute covariance matrix; it should take two input values, | |||
| assert_array_almost_equal(covs['cd'], covs['lars'], decimal=4) | ||
| assert_array_almost_equal(icovs['cd'], icovs['lars'], decimal=4) | ||
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| # Smoke test the estimator |
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It's actually more than a smoke tests, you're checking the result, right?
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Yes, I compare the results of the object when passed with a callable to the one I compute manually and then call the graph_lasso with computed values.
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So I would remove that comment ;)
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lgtm apart from comments |
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Nowadays we can pass a precomputed covariance matrix that could fulfill this needs. |
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Reference Issue
#6887
What does this implement/fix? Explain your changes.
Adds support for a callable computing covariance matrix.
Any other comments?