Fixing parameter description (for assume_centered)#13456
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agramfort merged 3 commits intoscikit-learn:masterfrom Apr 17, 2019
Merged
Fixing parameter description (for assume_centered)#13456agramfort merged 3 commits intoscikit-learn:masterfrom
agramfort merged 3 commits intoscikit-learn:masterfrom
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Fixing parameter description (for assume_centered)
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They don't look like errors to me though. |
Member
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What if we used "will be" and "will not be" rather than "is" and "is not"
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+1 |
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👍 Updated according to your suggestion |
qinhanmin2014
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Mar 18, 2019
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please update empirical_covariance_.py accordingly
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I've changed my mind here. I don't think "will be centered" and "will not be centered" is appropriate, because sometimes (e.g., ShrunkCovariance), we simply use a different method to estimate the covariance when the data is assumed to be centered. Maybe use something like "the input data is assumed to be centered"? |
jnothman
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Mar 18, 2019
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There are several other instances of the same parameter description, and we should change all together:
$ git grep -p -A2 assume_centered.:
sklearn/covariance/elliptic_envelope.py=class EllipticEnvelope(MinCovDet, OutlierMixin):
--
sklearn/covariance/elliptic_envelope.py: assume_centered : boolean, optional (default=False)
sklearn/covariance/elliptic_envelope.py- If True, the support of robust location and covariance estimates
sklearn/covariance/elliptic_envelope.py- is computed, and a covariance estimate is recomputed from it,
--
sklearn/covariance/empirical_covariance_.py=def empirical_covariance(X, assume_centered=False):
--
sklearn/covariance/empirical_covariance_.py: assume_centered : boolean
sklearn/covariance/empirical_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/empirical_covariance_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/empirical_covariance_.py=class EmpiricalCovariance(BaseEstimator):
--
sklearn/covariance/empirical_covariance_.py: assume_centered : bool
sklearn/covariance/empirical_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/empirical_covariance_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/graph_lasso_.py=class GraphicalLasso(EmpiricalCovariance):
--
sklearn/covariance/graph_lasso_.py: assume_centered : boolean, default False
sklearn/covariance/graph_lasso_.py- If True, data are not centered before computation.
sklearn/covariance/graph_lasso_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/graph_lasso_.py=class GraphicalLassoCV(GraphicalLasso):
--
sklearn/covariance/graph_lasso_.py: assume_centered : boolean
sklearn/covariance/graph_lasso_.py- If True, data are not centered before computation.
sklearn/covariance/graph_lasso_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/graph_lasso_.py=class GraphLasso(GraphicalLasso):
--
sklearn/covariance/graph_lasso_.py: assume_centered : boolean, default False
sklearn/covariance/graph_lasso_.py- If True, data are not centered before computation.
sklearn/covariance/graph_lasso_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/graph_lasso_.py=class GraphLassoCV(GraphicalLassoCV):
--
sklearn/covariance/graph_lasso_.py: assume_centered : Boolean
sklearn/covariance/graph_lasso_.py- If True, data are not centered before computation.
sklearn/covariance/graph_lasso_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/robust_covariance.py=class MinCovDet(EmpiricalCovariance):
--
sklearn/covariance/robust_covariance.py: assume_centered : bool
sklearn/covariance/robust_covariance.py- If True, the support of the robust location and the covariance
sklearn/covariance/robust_covariance.py- estimates is computed, and a covariance estimate is recomputed from
--
sklearn/covariance/shrunk_covariance_.py=class ShrunkCovariance(EmpiricalCovariance):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : boolean, default False
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/shrunk_covariance_.py=def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : bool
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful to work with data whose mean is significantly equal to
--
sklearn/covariance/shrunk_covariance_.py=def ledoit_wolf(X, assume_centered=False, block_size=1000):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : boolean, default=False
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful to work with data whose mean is significantly equal to
--
sklearn/covariance/shrunk_covariance_.py=class LedoitWolf(EmpiricalCovariance):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : bool, default=False
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful when working with data whose mean is almost, but not exactly
--
sklearn/covariance/shrunk_covariance_.py=def oas(X, assume_centered=False):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : boolean
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful to work with data whose mean is significantly equal to
--
sklearn/covariance/shrunk_covariance_.py=class OAS(EmpiricalCovariance):
--
sklearn/covariance/shrunk_covariance_.py: assume_centered : bool, default=False
sklearn/covariance/shrunk_covariance_.py- If True, data are not centered before computation.
sklearn/covariance/shrunk_covariance_.py- Useful when working with data whose mean is almost, but not exactly
Member
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I'd not yet seen your comments, @qinhanmin2014 , sorry for any duplication. |
Contributor
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If you let me know the comment text we want to go with, I'll make the changes in all files @jnothman pointed out above. I feel that something like |
Member
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I'm okay with that. I still think there is a little ambiguity but it
appears hard to avoid.
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Contributor
Author
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How about |
jeremiedbb
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Apr 25, 2019
* Fixing parameter description (for assume_centered) Fixing parameter description (for assume_centered) * Update shrunk_covariance_.py * Update empirical_covariance_.py
xhluca
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Apr 28, 2019
* Fixing parameter description (for assume_centered) Fixing parameter description (for assume_centered) * Update shrunk_covariance_.py * Update empirical_covariance_.py
xhluca
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Apr 28, 2019
…arn#13456)" This reverts commit bbe482c.
xhluca
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Apr 28, 2019
…arn#13456)" This reverts commit bbe482c.
koenvandevelde
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Jul 12, 2019
* Fixing parameter description (for assume_centered) Fixing parameter description (for assume_centered) * Update shrunk_covariance_.py * Update empirical_covariance_.py
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Fixing parameter description (for the parameter assume_centered) and some minor grammatical errors.