DOC Notebook style and enhanced descriptions in SVM kernels example #26805

StefanieSenger · 2023-07-08T11:10:31Z

What does this implement/fix? Explain your changes.

This PR suggests to introduce notebook style formatting and enhanced descriptions in the SVM kernels example,

Any other comments?

A few issues:

The math formulas break when also applying a line break. I couldn't find how to fix that.
I'd like to include the sigmoid kernel later, but need to do some reading on it first.

Looking forward to your comments. :)

github-actions · 2023-07-08T11:12:15Z

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adrinjalali

I think you can fix the math with this patch:

diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py
index 3c4772fa3..e475d6936 100644
--- a/examples/svm/plot_svm_kernels.py
+++ b/examples/svm/plot_svm_kernels.py
@@ -210,8 +210,8 @@ plot_training_data_with_decision_boundary("linear")
 # The polynomial kernel changes the notion of similarity. The kernel function
 # is defined as:
 #
-# .. math:: K(\mathbf{x}_1, \mathbf{x}_2) = (\gamma \cdot \
-# .. math:: \mathbf{x}_1^\top\mathbf{x}_2 + r)^d
+# .. math::
+#   K(\mathbf{x}_1, \mathbf{x}_2) = (\gamma \cdot \mathbf{x}_1^\top\mathbf{x}_2 + r)^d
 #
 # where :math:`{d}` is the degree (`degree`) of the polynomial, :math:`{\gamma}`
 # (`gamma`) controls the influence of each individual training sample on the
@@ -238,8 +238,9 @@ plot_training_data_with_decision_boundary("poly")
 # similarity between two data points in infinite dimensions and then approaches
 # classification by majority vote. The kernel function is defined as:
 #
-# .. math:: K(\mathbf{x}_1, \mathbf{x}_2) = \exp\left(-\gamma \cdot \
-# .. math:: {\|\mathbf{x}_1 - \mathbf{x}_2\|^2}\right)
+# .. math::
+#   K(\mathbf{x}_1, \mathbf{x}_2) = \exp\left(-\gamma \cdot \
+#       {\|\mathbf{x}_1 - \mathbf{x}_2\|^2}\right)
 #
 # where :math:`{\gamma}` (`gamma`) controls the influence of each individual
 # training sample on the decision boundary.
@@ -258,8 +259,9 @@ plot_training_data_with_decision_boundary("rbf")
 # **************
 # The sigmoid kernel function is defined as:
 #
-# .. math:: K(\mathbf{x}_1, \mathbf{x}_2) = \tanh(\gamma \cdot \\
-# .. math:: \mathbf{x}_1^\top\mathbf{x}_2 + r)
+# .. math::
+#   K(\mathbf{x}_1, \mathbf{x}_2) = \tanh(\gamma \cdot \
+#       \mathbf{x}_1^\top\mathbf{x}_2 + r)
 #
 plot_training_data_with_decision_boundary("sigmoid")

examples/svm/plot_svm_kernels.py

adrinjalali

@glemaitre do you wanna have a look?

adrinjalali · 2023-07-11T08:32:43Z

@StefanieSenger could you please also add a link to this example in the SVM api docs?

StefanieSenger · 2023-07-11T08:57:48Z

@StefanieSenger could you please also add a link to this example in the SVM api docs?

@adrinjalali Sure. Where exactly do you think of putting it? It is shown automatically at the bottom of sklearn.svm.SVC. Or do you mean the user guide?

adrinjalali · 2023-07-11T09:12:59Z

The issue with the ones automatically shown on the bottom of API docs is that it's really not clear what example is relevant for what. Adding the link explicitly in the API doc would make these much more discoverable. I'd add it to the docstring of kernel parameter, or under the Examples section.

glemaitre · 2023-07-11T09:41:02Z

@glemaitre do you wanna have a look?

Yep I will have a look now.

glemaitre

I propose some simplification regarding the plotting part.
I will check the narrative in early afternoon.

examples/svm/plot_svm_kernels.py

glemaitre

I am fine with the rest of the example.

examples/svm/plot_svm_kernels.py

StefanieSenger · 2023-07-15T03:23:15Z

Thanks for your review @glemaitre. I have substituted the old matplotlib plotting by our DecisionBoundaryDisplay and made some other small adjustments that you suggested. :)

glemaitre

Just a couple of matplotlib simplifications.

Otherwise LGTM.

sklearn/svm/_classes.py

examples/svm/plot_svm_kernels.py

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>

StefanieSenger · 2023-08-19T06:07:38Z

Thank you, @ArturoAmorQ for your comments. You are right with your procaution regarding the margins being at a distance from the boundary equal to the C param on the linear kernel. I was assuming something empirically, that might as well have been coincidence. I have deleted that information to be sure.

I have modified the supervised_learning.rst file, that refers to the plots, and added the sigmoid plot there, too.

Unfortunately I have pushed some wrong files along with the changes (was late yesterday). I will take care of this, but I am not sure if I have time for this before Wednesday. I have turned this PR into a draft in the meantime.

StefanieSenger · 2023-08-23T08:55:37Z

I've now deleted the accidentally pushed files and I hope I did it in the cleanest possible way. Sorry for adding confusion.

ArturoAmorQ

Just some final nitpicks :)

examples/svm/plot_svm_kernels.py

sklearn/svm/_classes.py

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>

…it-learn into examples_svm_kernels

examples/svm/plot_svm_kernels.py

Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com>

ArturoAmorQ

LGTM, thanks for the PR @StefanieSenger! 🎉

On a side note, this is going to be my very first merge on scikit-learn :)

StefanieSenger · 2023-08-24T13:21:22Z

On a side note, this is going to be my very first merge on scikit-learn :)

Yeahhhh, let's celebrate, @ArturoAmorQ 🎉🎉🎉

jjerphan · 2023-08-24T13:23:33Z

Thank you, @StefanieSenger!

And congratulations, @ArturoAmorQ! 🎉

With great power comes great responsibility. 🙂

…cikit-learn#26805)

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DOC Notebook style and enhanced descriptions in SVM kernels example

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DOC Add link to plot_classifier_chain_yeast example in ClassifierChain #26937

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Add links to examples from the docstrings and user guides #26927

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DOC Notebook style and enhanced descriptions in SVM kernels example #26805

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DOC Notebook style and enhanced descriptions in SVM kernels example #26805

DOC Notebook style and enhanced descriptions in SVM kernels example #26805

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What does this implement/fix? Explain your changes.

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