Skip to content

sample_weight in LinearSVR .fit #6862

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
kingjr opened this issue Jun 6, 2016 · 4 comments
Closed

sample_weight in LinearSVR .fit #6862

kingjr opened this issue Jun 6, 2016 · 4 comments

Comments

@kingjr
Copy link
Contributor

kingjr commented Jun 6, 2016

Is there any reason why there's no sample_weight param for LinearSVR.fit ?

@TomDLT
Copy link
Member

TomDLT commented Jun 6, 2016

_fit_liblinear started handling sample_weight only recently in #5274, and this pull-request only updated LogisticRegression for merging simplicity.
Adding them to LinearSVC should not be too difficult. See also #2784.

@imaculate
Copy link
Contributor

@TomDLT @kingjr I would like to work on this.

@jnothman
Copy link
Member

Thanks @imaculate. Would you consider doing the same for LinearSVC?

@imaculate
Copy link
Contributor

sure , Ill look into it.

olologin pushed a commit to olologin/scikit-learn that referenced this issue Aug 24, 2016
… and documentation. Fixes scikit-learn#6862 (scikit-learn#6907)

* Make KernelCenterer a _pairwise operation

Replicate solution to scikit-learn@9a52077 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.

Happy to add tests, just tell me what should be covered.

* Adding test for PR scikit-learn#6900

* Simplifying imports and test

* updating changelog links on homepage (scikit-learn#6901)

* first commit

* changed binary average back to macro

* changed binomialNB to multinomialNB

* emphasis on "higher return values are better..." (scikit-learn#6909)

* fix typo in comment of hierarchical clustering (scikit-learn#6912)

* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (scikit-learn#6846)

* Fix sklearn.base.clone for all scipy.sparse formats (scikit-learn#6910)

* DOC If git is not installed, need to catch OSError

Fixes scikit-learn#6860

* DOC add what's new for clone fix

* fix a typo in ridge.py (scikit-learn#6917)

* pep8

* TST: Speed up: cv=2

This is a smoke test. Hence there is no point having cv=4

* Added support for sample_weight in linearSVR, including tests and documentation

* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance

* Fixed pep8 violations and sampleweight format

* rebased with upstream
TomDLT pushed a commit to TomDLT/scikit-learn that referenced this issue Oct 3, 2016
… and documentation. Fixes scikit-learn#6862 (scikit-learn#6907)

* Make KernelCenterer a _pairwise operation

Replicate solution to scikit-learn@9a52077 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.

Happy to add tests, just tell me what should be covered.

* Adding test for PR scikit-learn#6900

* Simplifying imports and test

* updating changelog links on homepage (scikit-learn#6901)

* first commit

* changed binary average back to macro

* changed binomialNB to multinomialNB

* emphasis on "higher return values are better..." (scikit-learn#6909)

* fix typo in comment of hierarchical clustering (scikit-learn#6912)

* [MRG] Allows KMeans/MiniBatchKMeans to use float32 internally by using cython fused types (scikit-learn#6846)

* Fix sklearn.base.clone for all scipy.sparse formats (scikit-learn#6910)

* DOC If git is not installed, need to catch OSError

Fixes scikit-learn#6860

* DOC add what's new for clone fix

* fix a typo in ridge.py (scikit-learn#6917)

* pep8

* TST: Speed up: cv=2

This is a smoke test. Hence there is no point having cv=4

* Added support for sample_weight in linearSVR, including tests and documentation

* Changed assert to assert_allclose and assert_almost_equal, reduced the test tolerance

* Fixed pep8 violations and sampleweight format

* rebased with upstream
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

4 participants