From e280526abf47a35ca053fadf89e48bc6bd01edbe Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Mon, 13 May 2024 15:54:53 +0000 Subject: [PATCH] =?UTF-8?q?=E2=AC=86=EF=B8=8F=20Update=20Tutorials=20from?= =?UTF-8?q?=20DataScienceTutorials.jl?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- head.js | 437 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 437 insertions(+) create mode 100644 head.js diff --git a/head.js b/head.js new file mode 100644 index 0000000..8d6df4d --- /dev/null +++ b/head.js @@ -0,0 +1,437 @@ +const navItems = [ + { name: "Home", href: "/", sections: [], sectionItemWidth: "", id: "home" }, + { + name: "Data Basics", + id: "data", + href: "/info/data", + sections: [ + { + name: "Choosing a model", + href: "/getting-started/choosing-a-model/", + tags: ["Data Processing"], + }, + { + name: "Fit, predict, transform", + href: "/getting-started/fit-and-predict/", + tags: ["Data Processing"], + }, + { + name: "Model tuning", + href: "/getting-started/model-tuning/", + tags: ["Data Processing"], + }, + { + name: "Ensembles", + href: "/getting-started/ensembles/", + tags: ["Data Processing"], + }, + { + name: "Ensembles (2)", + href: "/getting-started/ensembles-2/", + tags: ["Data Processing"], + }, + { + name: "Composing models", + href: "/getting-started/composing-models/", + tags: ["Data Processing", "Missing Value Imputation"], + }, + ], + sectionItemWidth: "short-item", + }, + { + name: "Getting Started", + id: "getting-started", + href: "/info/getting-started", + sections: [ + { + name: "Choosing a model", + href: "/getting-started/choosing-a-model/", + tags: ["Classification", "Regression"], + }, + { + name: "Fit, predict, transform", + href: "/getting-started/fit-and-predict/", + tags: ["Classification", "Encoders"], + }, + { + name: "Model tuning", + href: "/getting-started/model-tuning/", + tags: ["Classification", "Hyperparameter Tuning"], + }, + { + name: "Ensembles", + href: "/getting-started/ensembles/", + tags: ["Regression", "Ensemble Models", "Hyperparameter Tuning"], + }, + { + name: "Ensembles (2)", + href: "/getting-started/ensembles-2/", + tags: ["Regression", "Ensemble Models", "Hyperparameter Tuning"], + }, + { + name: "Composing models", + href: "/getting-started/composing-models/", + tags: ["Regression", "Encoders", "Pipelines"], + }, + ], + sectionItemWidth: "medium-item", + }, + { + name: "Intro to Stats Learning", + id: "stats-learning", + href: "/info/isl", + sections: [ + { + name: "Basic Operations", + href: "/isl/lab-2/", + tags: ["Data Processing"], + }, + { name: "Linear Regression", href: "/isl/lab-3/", tags: ["Regression"] }, + { + name: "Logistic Regression & Friends", + href: "/isl/lab-4/", + tags: ["Classification", "Bayesian Models", "Distribution Fitter"], + }, + { + name: "Cross Validation", + href: "/isl/lab-5/", + tags: ["Regression", "Feature Selection", "Hyperparameter Tuning"], + }, + { + name: "Ridge & Lasso Regression", + href: "/isl/lab-6b/", + tags: ["Regression", "Encoders", "Hyperparameter Tuning"], + }, + { + name: "Tree-based Models", + href: "/isl/lab-8/", + tags: ["Iterative Models", "Classification", "Regression"], + }, + { + name: "Support Vector Machine", + href: "/isl/lab-9/", + tags: ["Classification", "Hyperparameter Tuning"], + }, + { + name: "PCA & Clustering", + href: "/isl/lab-10/", + tags: ["Dimensionality Reduction", "Clustering", "Pipelines"], + }, + ], + sectionItemWidth: "long-item", + }, + + { + name: "End to End", + id: "end-to-end", + href: "/info/end-to-end", + sections: [ + { + name: "Telco Churn", + href: "/end-to-end/telco/", + tags: [ + "Classification", + "Data Processing", + "Pipelines", + "Feature Selection", + "Iterative Models", + "Hyperparameter Optimization", + ], + }, + { + name: "AMES", + href: "/end-to-end/AMES/", + tags: ["Regression", "Learning Networks", "Hyperparameter Tuning"], + }, + { + name: "Wine", + href: "/end-to-end/wine/", + tags: [ + "Encoders", + "Classification", + "Pipelines", + "Dimensionality Reduction", + ], + }, + { + name: "Crabs (XGB)", + href: "/end-to-end/crabs-xgb/", + tags: ["Classification", "Iterative Models", "Hyperparameter Tuning"], + }, + { + name: "Horse", + href: "/end-to-end/horse/", + tags: [ + "Missing Value Imputation", + "Classification", + "Pipelines", + "Iterative Models", + "Hyperparameter Tuning", + ], + }, + { + name: "King County Houses", + href: "/end-to-end/HouseKingCounty/", + tags: ["Regression", "Iterative Models"], + }, + { + name: "Airfoil", + href: "/end-to-end/airfoil", + tags: ["Encoders", "Regression", "Hyperparameter Tuning"], + }, + { + name: "Boston (lgbm)", + href: "/end-to-end/boston-lgbm", + tags: ["Regression", "Hyperparameter Tuning", "Iterative Models"], + }, + { + name: "Using GLM.jl", + href: "/end-to-end/glm/", + tags: ["Pipelines", "Encoders", "Classification"], + }, + { + name: "Power Generation", + href: "/end-to-end/powergen/", + tags: ["Data Processing", "Regression"], + }, + { + name: "Boston (Flux)", + href: "/end-to-end/boston-flux", + tags: [ + "Neural Networks", + "Hyperparameter Tuning", + "Regression", + "Iterative Models", + ], + }, + { + name: "Breast Cancer", + href: "/end-to-end/breastcancer", + tags: [ + "Encoders", + "Classification", + "Iterative Models", + "Distribution Fitter", + "Bayesian Models", + "Neural Networks", + ], + }, + { + name: "Credit Fraud", + href: "/end-to-end/creditfraud", + tags: [ + "Classification", + "Class Imbalance", + "Encoders", + "Pipelines", + "Neural Networks", + ], + }, + ], + sectionItemWidth: "long-item", + }, + { + name: "Advanced", + id: "advanced", + href: "#!", + sections: [ + { + name: "Ensembles (3)", + href: "/advanced/ensembles-3", + tags: ["Regression", "Learning Networks"], + }, + { + name: "Stacking", + href: "/advanced/stacking/", + tags: ["Ensemble Models", "Learning Networks", "Hyperparamter Tuning"], + }, + ], + sectionItemWidth: "medium-item", + }, +]; + +// first get info on whether hosted or not +const origin = window.location.origin; +const hosted = origin.includes("github.io"); + +const navList = document.querySelector(".nav-list"); + +// for each object above we will call this function +function createNavItem(item) { + // a nav item is an li wrapping an anchor + const li = document.createElement("li"); + const link = document.createElement("a"); + // set style, link and text content of anchor + link.textContent = item.name; + link.classList.add("main-nav-item"); + link.href = hosted + ? origin + "/DataScienceTutorials.jl" + item.href + : item.href; + link.id = item.id; + + li.appendChild(link); + + // create a dropdown it item.sections is not empty + if (item.sections.length > 0) { + const dropdown = createDropDown(item.sections, item.sectionItemWidth); + li.appendChild(dropdown); + } + + return li; +} + +function createDropDown(sections, sectionWidth) { + // a dropdown is a ul wrapping a sequence of li wrapping an anchor for each item + const dropdown = document.createElement("ul"); + dropdown.classList.add("nav-dropdown"); + // for each section make an li wrapping an anchor + sections.forEach((section) => { + const subItem = document.createElement("li"); + subItem.classList.add(sectionWidth); + const subLink = document.createElement("a"); + subLink.textContent = section.name; + subLink.href = hosted + ? origin + "/DataScienceTutorials.jl" + section.href + : section.href; + subItem.appendChild(subLink); + dropdown.appendChild(subItem); + }); + + return dropdown; +} + +navList.innerHTML = ""; // Clear existing content + +navItems.forEach((item) => { + navList.appendChild(createNavItem(item)); +}); + +// add a final li as searchform +let formAction = hosted + ? origin + "/DataScienceTutorials.jl" + "/search/index.html" + : "/search/index.html"; +let searchForm = ` +
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    • `; + +navList.innerHTML += searchForm; + +// For the mobile navigation bar: +function createListItem(item) { + const href = hosted + ? origin + "/DataScienceTutorials.jl" + item.href + : item.href; + return ` +
  • + •${item.name} +
    • + `; +} + +// Function to create dropdown content +function createDropdownContent(category) { + const items = category.sections.map(createListItem).join(""); + return ` +
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  • + Home +
    • + `; + + // Then comes the rest of the categories and the dropdowns + navItems.forEach((item) => { + if (item.sections.length > 0) { + navHTML += createDropdownContent(item); + } + }); + + navList.innerHTML = navHTML; +} + +// Call the function to generate the sidebar +generateSidebar(navItems); + +// Flatten the nav items so we can easily iterate through them +function flattenNavItems(items) { + return items.reduce((acc, item) => { + const mainHrefs = [ + "/info/data", + "/info/end-to-end", + "/info/getting-started", + "/info/isl", + "#!", + ]; + if (!mainHrefs.includes(item.href)) { + acc.push(item); + } + if (item.sections) { + acc.push(...flattenNavItems(item.sections)); + } + return acc; + }, []); +} + +const flatItems = flattenNavItems(navItems); + +// loop and roate based on clicks +function getPreviousAndNextTutorials(currentHref) { + let currentIndex = flatItems.findIndex( + (item) => currentHref.includes(item.href) && item.href != "/" + ); + console.log(currentIndex); + currentIndex = currentIndex === -1 ? 0 : currentIndex; + const totalItems = flatItems.length; + + // Calculate previous and next indices considering rotation + const previousIndex = (currentIndex - 1 + totalItems) % totalItems; + const nextIndex = (currentIndex + 1) % totalItems; + return { + previousTutorial: flatItems[previousIndex], + nextTutorial: flatItems[nextIndex], + currentIndex, + }; +} + +// Update buttons based on current href +function updateNavigationButtons(currentHref) { + const { previousTutorial, nextTutorial, currentIndex } = + getPreviousAndNextTutorials(currentHref); + const prevHref = hosted + ? origin + "/DataScienceTutorials.jl" + previousTutorial.href + : previousTutorial.href; + const nextHref = hosted + ? origin + "/DataScienceTutorials.jl" + nextTutorial.href + : nextTutorial.href; + document.getElementById("prev-tutorial").setAttribute("href", prevHref); + document.getElementById("next-tutorial").setAttribute("href", nextHref); + document.getElementById("prev-label").innerHTML = previousTutorial.name; + document.getElementById("next-label").innerHTML = nextTutorial.name; + // if we are at home disable previous button + document.getElementById("prev-tutorial").style.display = + currentIndex === 0 ? "none" : "block"; + // if we are at last item disable next button + document.getElementById("next-tutorial").style.display = + currentIndex === flatItems.length - 1 ? "none" : "block"; +} + +const currentHref = window.location.href; +updateNavigationButtons(currentHref);