lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
89a87f784349ae6aaa8240edb93e12f1dcc9d620
FYS-STK4155/doc/LectureNotes/_build/html/exercisesweek34.html
T
KarlHenrik 89a87f7843 fixed w38 4a error
2025-09-17 13:02:40 +02:00

673 lines
39 KiB
HTML
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Exercises week 34 &#8212; Applied Data Analysis and Machine Learning</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="_static/styles/bootstrap.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link href="_static/vendor/fontawesome/6.5.2/css/all.min.css?digest=dfe6caa3a7d634c4db9b" rel="stylesheet" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=03e43079" />
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=eba8b062" />
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="_static/mystnb.8ecb98da25f57f5357bf6f572d296f466b2cfe2517ffebfabe82451661e28f02.css?v=6644e6bb" />
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=95c83b7e" />
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b" />
<script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=dfe6caa3a7d634c4db9b"></script>
<script src="_static/documentation_options.js?v=9eb32ce0"></script>
<script src="_static/doctools.js?v=9a2dae69"></script>
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="_static/clipboard.min.js?v=a7894cd8"></script>
<script src="_static/copybutton.js?v=f281be69"></script>
<script src="_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
<script>let toggleHintShow = 'Click to show';</script>
<script>let toggleHintHide = 'Click to hide';</script>
<script>let toggleOpenOnPrint = 'true';</script>
<script src="_static/togglebutton.js?v=4a39c7ea"></script>
<script>var togglebuttonSelector = '.toggle, .admonition.dropdown';</script>
<script src="_static/design-tabs.js?v=f930bc37"></script>
<script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
<script async="async" src="_static/sphinx-thebe.js?v=c100c467"></script>
<script>var togglebuttonSelector = '.toggle, .admonition.dropdown';</script>
<script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
<script>DOCUMENTATION_OPTIONS.pagename = 'exercisesweek34';</script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Week 34: Introduction to the course, Logistics and Practicalities" href="week34.html" />
<link rel="prev" title="17. Recurrent neural networks: Overarching view" href="chapter13.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<input type="checkbox"
class="sidebar-toggle"
id="pst-primary-sidebar-checkbox"/>
<label class="overlay overlay-primary" for="pst-primary-sidebar-checkbox"></label>
<input type="checkbox"
class="sidebar-toggle"
id="pst-secondary-sidebar-checkbox"/>
<label class="overlay overlay-secondary" for="pst-secondary-sidebar-checkbox"></label>
<div class="search-button__wrapper">
<div class="search-button__overlay"></div>
<div class="search-button__search-container">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
id="search-input"
placeholder="Search this book..."
aria-label="Search this book..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form></div>
</div>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<div class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<a class="navbar-brand logo" href="intro.html">
<img src="_static/logo.png" class="logo__image only-light" alt="Applied Data Analysis and Machine Learning - Home"/>
<script>document.write(`<img src="_static/logo.png" class="logo__image only-dark" alt="Applied Data Analysis and Machine Learning - Home"/>`);</script>
</a></div>
<div class="sidebar-primary-item">
<script>
document.write(`
<button class="btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
`);
</script></div>
<div class="sidebar-primary-item"><nav class="bd-links bd-docs-nav" aria-label="Main">
<div class="bd-toc-item navbar-nav active">
<ul class="nav bd-sidenav bd-sidenav__home-link">
<li class="toctree-l1">
<a class="reference internal" href="intro.html">
Applied Data Analysis and Machine Learning
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">About the course</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="schedule.html">Course setting</a></li>
<li class="toctree-l1"><a class="reference internal" href="teachers.html">Teachers and Grading</a></li>
<li class="toctree-l1"><a class="reference internal" href="textbooks.html">Textbooks</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Review of Statistics with Resampling Techniques and Linear Algebra</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="statistics.html">1. Elements of Probability Theory and Statistical Data Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="linalg.html">2. Linear Algebra, Handling of Arrays and more Python Features</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">From Regression to Support Vector Machines</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="chapter1.html">3. Linear Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter2.html">4. Ridge and Lasso Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter3.html">5. Resampling Methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter4.html">6. Logistic Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapteroptimization.html">7. Optimization, the central part of any Machine Learning algortithm</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter5.html">8. Support Vector Machines, overarching aims</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Decision Trees, Ensemble Methods and Boosting</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="chapter6.html">9. Decision trees, overarching aims</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter7.html">10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Dimensionality Reduction</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="chapter8.html">11. Basic ideas of the Principal Component Analysis (PCA)</a></li>
<li class="toctree-l1"><a class="reference internal" href="clustering.html">12. Clustering and Unsupervised Learning</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning Methods</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="chapter9.html">13. Neural networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter10.html">14. Building a Feed Forward Neural Network</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter11.html">15. Solving Differential Equations with Deep Learning</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter12.html">16. Convolutional Neural Networks</a></li>
<li class="toctree-l1"><a class="reference internal" href="chapter13.html">17. Recurrent neural networks: Overarching view</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Weekly material, notes and exercises</span></p>
<ul class="current nav bd-sidenav">
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Exercises week 34</a></li>
<li class="toctree-l1"><a class="reference internal" href="week34.html">Week 34: Introduction to the course, Logistics and Practicalities</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek35.html">Exercises week 35</a></li>
<li class="toctree-l1"><a class="reference internal" href="week35.html">Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek36.html">Exercises week 36</a></li>
<li class="toctree-l1"><a class="reference internal" href="week36.html">Week 36: Linear Regression and Gradient descent</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek37.html">Exercises week 37</a></li>
<li class="toctree-l1"><a class="reference internal" href="week37.html">Week 37: Gradient descent methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek38.html">Exercises week 38</a></li>
<li class="toctree-l1"><a class="reference internal" href="week38.html">Week 38: Statistical analysis, bias-variance tradeoff and resampling methods</a></li>
<li class="toctree-l1"><a class="reference internal" href="exercisesweek39.html">Exercises week 39</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Projects</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="project1.html">Project 1 on Machine Learning, deadline October 6 (midnight), 2025</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
<div id="rtd-footer-container"></div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="sbt-scroll-pixel-helper"></div>
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item"><button class="sidebar-toggle primary-toggle btn btn-sm" title="Toggle primary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
<span class="fa-solid fa-bars"></span>
</button></div>
</div>
<div class="header-article-items__end">
<div class="header-article-item">
<div class="article-header-buttons">
<div class="dropdown dropdown-download-buttons">
<button class="btn dropdown-toggle" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download this page">
<i class="fas fa-download"></i>
</button>
<ul class="dropdown-menu">
<li><a href="_sources/exercisesweek34.ipynb" target="_blank"
class="btn btn-sm btn-download-source-button dropdown-item"
title="Download source file"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-file"></i>
</span>
<span class="btn__text-container">.ipynb</span>
</a>
</li>
<li>
<button onclick="window.print()"
class="btn btn-sm btn-download-pdf-button dropdown-item"
title="Print to PDF"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-file-pdf"></i>
</span>
<span class="btn__text-container">.pdf</span>
</button>
</li>
</ul>
</div>
<button onclick="toggleFullScreen()"
class="btn btn-sm btn-fullscreen-button"
title="Fullscreen mode"
data-bs-placement="bottom" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-expand"></i>
</span>
</button>
<script>
document.write(`
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto"></i>
</button>
`);
</script>
<script>
document.write(`
<button class="btn btn-sm pst-navbar-icon search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
`);
</script>
<button class="sidebar-toggle secondary-toggle btn btn-sm" title="Toggle secondary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
<span class="fa-solid fa-list"></span>
</button>
</div></div>
</div>
</div>
</div>
<div id="jb-print-docs-body" class="onlyprint">
<h1>Exercises week 34</h1>
<!-- Table of contents -->
<div id="print-main-content">
<div id="jb-print-toc">
<div>
<h2> Contents </h2>
</div>
<nav aria-label="Page">
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#coding-setup-and-linear-regression">Coding Setup and Linear Regression</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-goals">Learning goals</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#deliverables">Deliverables</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-github-setup">Exercise 1 - Github Setup</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-setting-up-a-github-repository">Exercise 2 - Setting up a Github repository</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-setting-up-a-python-virtual-environment">Exercise 3 - Setting up a Python virtual environment</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-fitting-an-ols-model-to-data">Exercise 3 - Fitting an OLS model to data</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-4-the-train-test-split">Exercise 4 - The train-test split</a></li>
</ul>
</nav>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section class="tex2jax_ignore mathjax_ignore" id="exercises-week-34">
<h1>Exercises week 34<a class="headerlink" href="#exercises-week-34" title="Link to this heading">#</a></h1>
<section id="coding-setup-and-linear-regression">
<h2>Coding Setup and Linear Regression<a class="headerlink" href="#coding-setup-and-linear-regression" title="Link to this heading">#</a></h2>
<p>Welcome to FYS-STK3155/4155!</p>
<p>In this first week will focus on getting you set up with the programs you are going to be using throughout this course. We expect that many of you will encounter some trouble with setting these programs up, as they can be extremely finnicky and prone to not working the same on all machines, so we strongly encourage you to not get discouraged, and to show up to the group-sessions where we can help you along. The group sessions are also the best place to find group partners for the projects and to be challenged on your understanding of the material, which are both essential to doing well in this course. We strongly encourage you to form groups of 2-3 participants.</p>
<p>If you are unable to complete this week’s exercises, don’t worry, this will likely be the most frustrating week for many of you. You have time to get back on track next week, especially if you come to the group-sessions! Note also that this week’s set of exercises does not count for the additional score. The deadline for the weekly exercises is set to Fridays, at midnight.</p>
<section id="learning-goals">
<h3>Learning goals<a class="headerlink" href="#learning-goals" title="Link to this heading">#</a></h3>
<p>After completing these exercises, you will know how to</p>
<ul class="simple">
<li><p>Create and use a Github repository</p></li>
<li><p>Set up and use a virtual environment in Python</p></li>
<li><p>Fit an OLS model to data using scikit-learn</p></li>
<li><p>Fit a model on training data and evaluate it on test data</p></li>
</ul>
</section>
<section id="deliverables">
<h3>Deliverables<a class="headerlink" href="#deliverables" title="Link to this heading">#</a></h3>
<p>Complete the following exercises while working in a jupyter notebook. Exercises 1,2 and 3 require no writing in the notebook. Then, in canvas, include</p>
<ul class="simple">
<li><p>The jupyter notebook with the exercises completed</p></li>
<li><p>An exported PDF of the notebook (<a class="reference external" href="https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_export-your-jupyter-notebook">https://code.visualstudio.com/docs/datascience/jupyter-notebooks#_export-your-jupyter-notebook</a>)</p></li>
<li><p>Optional: A link to your github repository, which must be set to public, include the notebook file, a README file, requirements file and gitignore file.</p></li>
</ul>
<p>We require you to deliver a jupyter notebook so that we can evaluate the results of your code without needing to download and run the code of every student, as well as to teach you to use this useful tool.</p>
</section>
</section>
<section id="exercise-1-github-setup">
<h2>Exercise 1 - Github Setup<a class="headerlink" href="#exercise-1-github-setup" title="Link to this heading">#</a></h2>
<p>In this course, we require you to pay extra mind to the reproducibility of your results and the shareability of your code. The first step toward these goals is using a version control system like git and online repository like Github.</p>
<p><strong>a)</strong> Download git if you don’t already have it on your machine, check with the terminal command ´git –version´ (<a class="reference external" href="https://git-scm.com/downloads">https://git-scm.com/downloads</a>).</p>
<p><strong>b)</strong> Create a Github account(<a class="github reference external" href="https://github.com/"></a>), or log in to github with your UiO account (<a class="reference external" href="https://github.uio.no/login">https://github.uio.no/login</a>).</p>
<p><strong>c)</strong> Learn the basics of opening the terminal and navigating folders on your operating system. Things to learn: Opening a terminal, opening a terminal in a specific folder, listing the contents of the current folder, navigating into a folder, navigating out of a folder.</p>
<p><strong>d)</strong> Download the Github CLI tool and run ´gh auth login´ in your terminal to authenticate your local machine for some of the later steps. (<a class="github reference external" href="https://github.com/cli/cli#installation">cli/cli</a>). You might need to change file permissions to make it work, ask us or ChatGPT for help with these issues.</p>
<p><strong>e)</strong> As an alternative to the above terminal based instructions, you could install GitHub Desktop (see <a class="reference external" href="https://desktop.github.com/download/">https://desktop.github.com/download/</a>) or if you prefer GitLab, GitLab desktop (see <a class="reference external" href="https://about.gitlab.com/install/">https://about.gitlab.com/install/</a>). This sets up all communications between your PC/Laptop and the repository. This allows you to combine exercises 1 and 2 in an easy way if you don’t want to use terminarl. Keep in mind that these GUIs (graphical user interfaces) are not text editors.</p>
</section>
<section id="exercise-2-setting-up-a-github-repository">
<h2>Exercise 2 - Setting up a Github repository<a class="headerlink" href="#exercise-2-setting-up-a-github-repository" title="Link to this heading">#</a></h2>
<p><strong>a)</strong> Create an empty repository for your coursework in this course in your browser at <a class="reference external" href="http://github.com">github.com</a> (or uio github).</p>
<p><strong>b)</strong> Open a terminal in the location you want to create your local folder for this repository, like your desktop.</p>
<p><strong>c)</strong> Clone the repository to your laptop using the terminal command ´gh repo clone username/repository-name´. This creates a folder with the same name as the repository. Moving it or renaming it might require some extra steps.</p>
<p><strong>d)</strong> Download this jupyter notebook. Add the notebook to the local folder.</p>
<p><strong>e)</strong> Run the ´git add .´ command command in a terminal opened in the local folder to stage the current changes in the folder to be commited to the version control history. Run ´git status´ to see the staged files.</p>
<p><strong>f)</strong> Run the ´git commit -m “Adding first weekly assignment file”´ command to commit the staged changes to the version control history. Run ´git status´ to see that no files are staged.</p>
<p><strong>g)</strong> Run the ´git push” command to upload the commited changes to the remote repository on Github.</p>
<p><strong>h)</strong> Add a file called README.txt to the repository at <a class="reference external" href="http://Github.com">Github.com</a>. Don’t do this in your local folder. Add a suitable title for your repository and some inforomation to the file.</p>
<p><strong>i)</strong> Run the ´git fetch origin´ command to fetch the latest remote changes to your repository.</p>
<p><strong>j)</strong> Run the ´git pull´ command to download and update files to match the remote changes.</p>
</section>
<section id="exercise-3-setting-up-a-python-virtual-environment">
<h2>Exercise 3 - Setting up a Python virtual environment<a class="headerlink" href="#exercise-3-setting-up-a-python-virtual-environment" title="Link to this heading">#</a></h2>
<p>Following the themes from the previous exercises, another way of improving the reproducibility of your results and shareability of your code is having a good handle on which python packages you are using.</p>
<p>There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don’t need to worry about messing up old projects. Also, it’s easy to just delete the folder and start over if anything goes wrong.</p>
<p>Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let for example VS Code (a popular cross-paltform package) handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.</p>
<p><strong>a)</strong> Open this notebook in VS Code (<a class="reference external" href="https://code.visualstudio.com/Download">https://code.visualstudio.com/Download</a>). Download the Python and Jupyter extensions.</p>
<p><strong>b)</strong> Press ´Cmd + Shift + P´, then search and run ´Python: Create Environment…´</p>
<p><strong>c)</strong> Select ´Venv´</p>
<p><strong>d)</strong> Choose the most up-to-date version of Python your have installed.</p>
<p><strong>e)</strong> Press ´Cmd + Shift + P´, then search and run ´Python: Select Interpreter´</p>
<p><strong>f)</strong> Selevet the (.venv) option you just created.</p>
<p><strong>g)</strong> Open a terminal in VS Code, the venv name should be visible at the beginning of the line. Run <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">list</span></code> to see that there are no packages install in the environment.</p>
<p><strong>h)</strong> In this terminal, run <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">matplotlib</span> <span class="pre">numpy</span> <span class="pre">scikit-learn</span></code>. This will install the listed packages.</p>
<p><strong>i)</strong> To make these installations reproducible, which is important for reproducing results and sharing your code, run ´pip freeze &gt; requirements.txt´ to create the file requirements.txt with all your dependencies.</p>
<p>Now, anyone who wants to recreate your package setup can download your requirements.txt file and run ´pip install -r requirements.txt´ to install the correct packages and versions. To keep the requirements.txt file up to date with your environment, you will need to re-run the freeze command whenever you install a new package.</p>
<p><strong>j)</strong> Create a .gitignore file at the root of your project folder, and add the line ´.venv´ to it. This way, you won’t try to upload a copy of all your python packages when you regularly push your changes to Github. Ignored files should not show up when you run ´git status´, and are not staged when running ´git add .´, try it!</p>
</section>
<section id="exercise-3-fitting-an-ols-model-to-data">
<h2>Exercise 3 - Fitting an OLS model to data<a class="headerlink" href="#exercise-3-fitting-an-ols-model-to-data" title="Link to this heading">#</a></h2>
<p>Great job on getting through all of that! Now it is time to do some actual machine learning!</p>
<p><strong>a)</strong> Complete the code below so that you fit a second order polynomial to the data. You will need to look up some scikit-learn documentation online (look at the imported functions for hints).</p>
<p><strong>b)</strong> Compute the mean square error for the line model and for the second degree polynomial model.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">PolynomialFeatures</span> <span class="c1"># use the fit_transform method of the created object!</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span>
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">+</span> <span class="mi">5</span> <span class="o">*</span> <span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">line_model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">line_predict</span> <span class="o">=</span> <span class="n">line_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="c1">#line_mse = ...</span>
<span class="c1">#poly_features = ...</span>
<span class="c1">#poly_model = LinearRegression().fit(..., y)</span>
<span class="c1">#poly_predict = ...</span>
<span class="c1">#poly_mse = ...</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;Data&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">line_predict</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;Line model&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<img alt="_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png" src="_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png" />
</div>
</div>
</section>
<section id="exercise-4-the-train-test-split">
<h2>Exercise 4 - The train-test split<a class="headerlink" href="#exercise-4-the-train-test-split" title="Link to this heading">#</a></h2>
<p>Hopefully your model fit the data quite well, but to know how well the model actually generalizes to unseen data, which is most often what we care about, we need to split our data into training and testing data.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
</pre></div>
</div>
</div>
</div>
<p><strong>a)</strong> Complete the code below so that the polynomial features and the targets y get split into training and test data.</p>
<p><strong>b)</strong> What is the shape of X_test?</p>
<p><strong>c)</strong> Fit your model to X_train</p>
<p><strong>d)</strong> Compute the MSE when your model predicts on the training data and on the testing data, using y_train and y_test as targets for the two cases.</p>
<p><strong>e)</strong> Why do we not fit the model to X_test?</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">polynomial_features</span> <span class="o">=</span> <span class="o">...</span>
<span class="c1">#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)</span>
</pre></div>
</div>
</div>
</div>
</section>
</section>
<script type="text/x-thebe-config">
{
requestKernel: true,
binderOptions: {
repo: "binder-examples/jupyter-stacks-datascience",
ref: "master",
},
codeMirrorConfig: {
theme: "abcdef",
mode: "python"
},
kernelOptions: {
name: "python3",
path: "./."
},
predefinedOutput: true
}
</script>
<script>kernelName = 'python3'</script>
</article>
<footer class="prev-next-footer d-print-none">
<div class="prev-next-area">
<a class="left-prev"
href="chapter13.html"
title="previous page">
<i class="fa-solid fa-angle-left"></i>
<div class="prev-next-info">
<p class="prev-next-subtitle">previous</p>
<p class="prev-next-title"><span class="section-number">17. </span>Recurrent neural networks: Overarching view</p>
</div>
</a>
<a class="right-next"
href="week34.html"
title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title">Week 34: Introduction to the course, Logistics and Practicalities</p>
</div>
<i class="fa-solid fa-angle-right"></i>
</a>
</div>
</footer>
</div>
<div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
<div class="sidebar-secondary-item">
<div class="page-toc tocsection onthispage">
<i class="fa-solid fa-list"></i> Contents
</div>
<nav class="bd-toc-nav page-toc">
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#coding-setup-and-linear-regression">Coding Setup and Linear Regression</a><ul class="nav section-nav flex-column">
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#learning-goals">Learning goals</a></li>
<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#deliverables">Deliverables</a></li>
</ul>
</li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-1-github-setup">Exercise 1 - Github Setup</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-2-setting-up-a-github-repository">Exercise 2 - Setting up a Github repository</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-setting-up-a-python-virtual-environment">Exercise 3 - Setting up a Python virtual environment</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-3-fitting-an-ols-model-to-data">Exercise 3 - Fitting an OLS model to data</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exercise-4-the-train-test-split">Exercise 4 - The train-test split</a></li>
</ul>
</nav></div>
</div></div>
</div>
<footer class="bd-footer-content">
<div class="bd-footer-content__inner container">
<div class="footer-item">
<p class="component-author">
By Morten Hjorth-Jensen
</p>
</div>
<div class="footer-item">
<p class="copyright">
© Copyright 2023.
<br/>
</p>
</div>
<div class="footer-item">
</div>
<div class="footer-item">
</div>
</div>
</footer>
</main>
</div>
</div>
<!-- Scripts loaded after <body> so the DOM is not blocked -->
<script src="_static/scripts/bootstrap.js?digest=dfe6caa3a7d634c4db9b"></script>
<script src="_static/scripts/pydata-sphinx-theme.js?digest=dfe6caa3a7d634c4db9b"></script>
<footer class="bd-footer">
</footer>
</body>
</html>
Reference in New Issue View Git Blame Copy Permalink