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Apr 30, 2024
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DOC [PST] FIX/RFC machine learning map #28630

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de2e2b8
DOC [PST] fix/rfc machine learning map
Charlie-XIAO Mar 14, 2024
ecac146
add pan limit
Charlie-XIAO Mar 15, 2024
1fcb42e
Merge branch 'new_web_theme' into pst-estimator-map
adrinjalali Apr 23, 2024
d9c2d05
add license of svg-pan-zoom
Charlie-XIAO Apr 30, 2024
8a4dd0d
Merge branch 'pst-estimator-map' of https://github.com/Charlie-XIAO/s…
Charlie-XIAO Apr 30, 2024
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1 change: 1 addition & 0 deletions .pre-commit-config.yaml
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Expand Up @@ -32,4 +32,5 @@ repos:
hooks:
- id: prettier
files: ^doc/scss/|^doc/js/scripts/
exclude: ^doc/js/scripts/vendor/
types_or: ["scss", "javascript"]
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The scikit-learn machine learning cheat sheet was originally created by Andreas Mueller:
https://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html

The current version of the chart is located at `doc/images/ml_map.svg` in SVG+XML
format, created using [draw.io](https://draw.io/). To edit the chart, open the file in
draw.io, make changes, and export as SVG with the same filename. Export configurations
are:

- Zoom: 100%
- Border width: 15
- Size: Diagram
- Transparent Background: False
- Appearance: Light

Each node in the chart that contains an estimator should have a link, where the root
directory is at `../../`. Note that after exporting the SVG, the links may be prefixed
with e.g. `https://app.diagrams.net/`. Remember to check and remove them.
101 changes: 62 additions & 39 deletions doc/tutorial/machine_learning_map/index.rst
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:html_theme.sidebar_secondary.remove:

.. _ml_map:

Choosing the right estimator
=======================================================
============================

Often the hardest part of solving a machine learning problem can be finding the right
estimator for the job. Different estimators are better suited for different types of
data and different problems.

The flowchart below is designed to give users a bit of a rough guide on how to approach
problems with regard to which estimators to try on your data. Click on any estimator in
the chart below to see its documentation. Use scroll wheel to zoom in and out, and click
and drag to pan around. You can also download the chart:
:download:`ml_map.svg <../../images/ml_map.svg>`.

.. raw:: html

<style>
#sk-ml-map {
height: 80vh;
margin: 1.5rem 0;
}

#sk-ml-map svg {
height: 100%;
width: 100%;
border: 2px solid var(--pst-color-border);
border-radius: 0.5rem;
}

Often the hardest part of solving a machine learning problem can
be finding the right estimator for the job.
html[data-theme="dark"] #sk-ml-map svg {
filter: invert(90%) hue-rotate(180deg);
}
</style>

Different estimators are better suited for different types of data
and different problems.
<script src="../../_static/scripts/vendor/svg-pan-zoom.min.js"></script>
<script>
document.addEventListener("DOMContentLoaded", function () {
const beforePan = function (oldPan, newPan) {
const gutterWidth = 100, gutterHeight = 100;
const sizes = this.getSizes();

The flowchart below is designed to give users a bit of
a rough guide on how to approach problems with regard to
which estimators to try on your data.
// Compute pan limits
const leftLimit = -((sizes.viewBox.x + sizes.viewBox.width) * sizes.realZoom) + gutterWidth;
const rightLimit = sizes.width - gutterWidth - (sizes.viewBox.x * sizes.realZoom);
const topLimit = -((sizes.viewBox.y + sizes.viewBox.height) * sizes.realZoom) + gutterHeight;
const bottomLimit = sizes.height - gutterHeight - (sizes.viewBox.y * sizes.realZoom);

Click on any estimator in the chart below to see its documentation.
return {
x: Math.max(leftLimit, Math.min(rightLimit, newPan.x)),
y: Math.max(topLimit, Math.min(bottomLimit, newPan.y))
};
};

// Limit the pan
svgPanZoom("#sk-ml-map svg", {
zoomEnabled: true,
controlIconsEnabled: true,
fit: 1,
center: 1,
beforePan: beforePan,
});
});
</script>

<div id="sk-ml-map">

.. raw:: html
:file: ../../images/ml_map.svg

.. raw:: html

<img src="../../_static/ml_map.png" class="map" alt="Move mouse over image" usemap="#imgmap">
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