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diff --git a/content/blog/csca5632-final/index.md b/content/blog/csca5632-final/index.md new file mode 100644 index 0000000..e2b1b66 --- /dev/null +++ b/content/blog/csca5632-final/index.md @@ -0,0 +1,115 @@ ++++ +title = "🌸 Clustering Iris Species with K-Means and Hierarchical Methods" +description = "A hands-on comparison of K-Means and Agglomerative Clustering on the Iris dataset, with insights into performance, parameter tuning, and practical trade-offs." +date = 2025-11-01 +[taxonomies] +tags = ["machine_learning"] +[extra] +styles = ["notebooks.css", ] ++++ + +## Why Clustering? + +Clustering is a foundational technique in unsupervised learning, used to +uncover patterns in data without predefined labels. This project explores how +two popular clustering algorithms—**K-Means** and **Agglomerative Hierarchical +Clustering**—perform on the well-known **Iris flower dataset**, aiming to group +samples by species based solely on their morphological features. + +*** + +## About the Dataset + +The Iris dataset contains **150 samples** from three species: *setosa*, +*versicolor*, and *virginica*. Each sample includes four features: + +* Sepal length +* Sepal width +* Petal length +* Petal width + +While *setosa* is linearly separable, *versicolor* and *virginica* overlap +significantly, making this dataset ideal for testing clustering algorithms. + +*** + +## What Was Explored + +The analysis focused on: + +* Comparing **K-Means** and **Agglomerative Clustering** +* Evaluating performance using **accuracy**, **silhouette score**, and +**Adjusted Rand Index (ARI)** +* Testing different **linkage methods** and **distance metrics** +* Visualizing clusters and errors using **PCA** + +*** + +## Key Experiments & Findings + +### Dimensionality Reduction with PCA + +* **95.8%** of the variance was captured using just **2 principal components**, confirming +strong correlations among features—especially between petal length and width. + +### Optimal Cluster Count + +* Using metrics like **inertia**, **silhouette score**, and **accuracy**, the optimal +number of clusters was found to be **3**, matching the true number of species. + +### Parameter Tuning for Agglomerative Clustering + +* Tried combinations of: + * Linkage: `ward`, `complete`, `average`, `single` + * Metrics: `euclidean`, `manhattan`, `cosine` +* **Best result**: `average linkage` with `manhattan distance` achieved **88.7% +accuracy**, outperforming default settings. + +### Performance Comparison + +| Algorithm | Accuracy | Silhouette Score | ARI | +| ----------------------- | :------: | :--------------: | :--: | +| K-Means | 83.3% | 0.46 | 0.62 | +| Agglomerative (default) | 82.7% | 0.45 | 0.61 | +| Agglomerative (best) | 88.7% | 0.45 | 0.72 | + +*** + +## Error Analysis + +* **Setosa** was classified almost perfectly across all methods. +* Most errors occurred between **versicolor** and **virginica**, confirming +their overlapping nature. +* Agglomerative Clustering showed **bias** depending on parameters—sometimes +misclassifying one species more than the other. + +*** + +## Final Thoughts + +While Agglomerative Clustering achieved the highest accuracy with tuned +parameters, its **sensitivity to configuration** and **instability** in cluster +composition make it less reliable for real-world applications without labeled +data. + +**K-Means**, despite slightly lower accuracy, offered **more balanced results** +and **greater stability**, making it a safer choice for practical clustering +tasks. + +*** + +## Future Work + +* Extend analysis to other clustering algorithms like DBSCAN or Spectral Clustering +* Apply to more complex datasets +* Explore automated parameter tuning techniques + +*** + +The full notebook with code and visualizations is embedded below 👇 + +<!-- markdownlint-disable MD033 --> +<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe> + +You can also view the notebook in [a separate page](notebook.html), or check it +on [GitHub](https://github.com/Farzat07/Unsupervised-Learning-Final-Project-Iris-Species-Clustering-Analysis). diff --git a/content/blog/csca5632-final/notebook.html b/content/blog/csca5632-final/notebook.html new file mode 100644 index 0000000..67bd813 --- /dev/null +++ b/content/blog/csca5632-final/notebook.html @@ -0,0 +1,9074 @@ +<!DOCTYPE html> + +<html lang="en"> +<head><meta charset="utf-8"/> +<meta content="width=device-width, initial-scale=1.0" name="viewport"/> +<title>cours2FinalSubmission</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script> +<style type="text/css"> + pre { line-height: 125%; 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+|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Collapse { + display: flex; + flex-direction: column; + align-items: stretch; +} + +.jp-Collapse-header { + padding: 1px 12px; + background-color: var(--jp-layout-color1); + border-bottom: solid var(--jp-border-width) var(--jp-border-color2); + color: var(--jp-ui-font-color1); + cursor: pointer; + display: flex; + align-items: center; + font-size: var(--jp-ui-font-size0); + font-weight: 600; + text-transform: uppercase; + user-select: none; +} + +.jp-Collapser-icon { + height: 16px; +} + +.jp-Collapse-header-collapsed .jp-Collapser-icon { + transform: rotate(-90deg); + margin: auto 0; +} + +.jp-Collapser-title { + line-height: 25px; +} + +.jp-Collapse-contents { + padding: 0 12px; + background-color: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + overflow: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */ + +/** + * (DEPRECATED) Support for consuming icons as CSS background images + */ + +/* Icons urls */ + +:root { + --jp-icon-add-above: 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+ --jp-icon-add-below: 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+ --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=); + --jp-icon-bell: url(data:image/svg+xml;base64,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); + --jp-icon-bug-dot: 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+ --jp-icon-bug: url(data:image/svg+xml;base64,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); + --jp-icon-build: 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+ --jp-icon-notebook: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtbm90ZWJvb2staWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiNFRjZDMDAiPgogICAgPHBhdGggZD0iTTE4LjcgMy4zdjE1LjRIMy4zVjMuM2gxNS40bTEuNS0xLjVIMS44djE4LjNoMTguM2wuMS0xOC4zeiIvPgogICAgPHBhdGggZD0iTTE2LjUgMTYuNWwtNS40LTQuMy01LjYgNC4zdi0xMWgxMXoiLz4KICA8L2c+Cjwvc3ZnPgo=); + --jp-icon-numbering: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjIiIGhlaWdodD0iMjIiIHZpZXdCb3g9IjAgMCAyOCAyOCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CgkJPHBhdGggZD0iTTQgMTlINlYxOS41SDVWMjAuNUg2VjIxSDRWMjJIN1YxOEg0VjE5Wk01IDEwSDZWNkg0VjdINVYxMFpNNCAxM0g1LjhMNCAxNS4xVjE2SDdWMTVINS4yTDcgMTIuOVYxMkg0VjEzWk05IDdWOUgyM1Y3SDlaTTkgMjFIMjNWMTlIOVYyMVpNOSAxNUgyM1YxM0g5VjE1WiIvPgoJPC9nPgo8L3N2Zz4K); + --jp-icon-offline-bolt: 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+ --jp-icon-paste: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE5IDJoLTQuMThDMTQuNC44NCAxMy4zIDAgMTIgMGMtMS4zIDAtMi40Ljg0LTIuODIgMkg1Yy0xLjEgMC0yIC45LTIgMnYxNmMwIDEuMS45IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjRjMC0xLjEtLjktMi0yLTJ6bS03IDBjLjU1IDAgMSAuNDUgMSAxcy0uNDUgMS0xIDEtMS0uNDUtMS0xIC40NS0xIDEtMXptNyAxOEg1VjRoMnYzaDEwVjRoMnYxNnoiLz4KICAgIDwvZz4KPC9zdmc+Cg==); + --jp-icon-pdf: 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+ --jp-icon-python: 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+ --jp-icon-r-kernel: url(data:image/svg+xml;base64,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); + --jp-icon-react: 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url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=); + --jp-icon-regex: url(data:image/svg+xml;base64,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); + --jp-icon-run: 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+ --jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==); + --jp-icon-toc: url(data:image/svg+xml;base64,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); + --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K); + --jp-icon-trusted: url(data:image/svg+xml;base64,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); + --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-users: url(data:image/svg+xml;base64,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); + --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==); + --jp-icon-word: url(data:image/svg+xml;base64,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); + --jp-icon-yaml: url(data:image/svg+xml;base64,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); +} + +/* Icon CSS class declarations */ + +.jp-AddAboveIcon { + background-image: var(--jp-icon-add-above); +} + +.jp-AddBelowIcon { + background-image: var(--jp-icon-add-below); +} + +.jp-AddIcon { + background-image: var(--jp-icon-add); +} + +.jp-BellIcon { + background-image: var(--jp-icon-bell); +} + +.jp-BugDotIcon { + background-image: var(--jp-icon-bug-dot); +} + +.jp-BugIcon { + background-image: var(--jp-icon-bug); +} + +.jp-BuildIcon { + background-image: var(--jp-icon-build); +} + +.jp-CaretDownEmptyIcon { + background-image: var(--jp-icon-caret-down-empty); +} + +.jp-CaretDownEmptyThinIcon { + background-image: var(--jp-icon-caret-down-empty-thin); +} + +.jp-CaretDownIcon { + background-image: var(--jp-icon-caret-down); +} + +.jp-CaretLeftIcon { + background-image: var(--jp-icon-caret-left); +} + +.jp-CaretRightIcon { + background-image: var(--jp-icon-caret-right); +} + +.jp-CaretUpEmptyThinIcon { + background-image: var(--jp-icon-caret-up-empty-thin); +} + +.jp-CaretUpIcon { + background-image: var(--jp-icon-caret-up); +} + +.jp-CaseSensitiveIcon { + background-image: var(--jp-icon-case-sensitive); +} + +.jp-CheckIcon { + background-image: var(--jp-icon-check); +} + +.jp-CircleEmptyIcon { + background-image: var(--jp-icon-circle-empty); +} + +.jp-CircleIcon { + background-image: var(--jp-icon-circle); +} + +.jp-ClearIcon { + background-image: var(--jp-icon-clear); +} + +.jp-CloseIcon { + background-image: var(--jp-icon-close); +} + +.jp-CodeCheckIcon { + background-image: var(--jp-icon-code-check); +} + +.jp-CodeIcon { + background-image: var(--jp-icon-code); +} + +.jp-CollapseAllIcon { + background-image: var(--jp-icon-collapse-all); +} + +.jp-ConsoleIcon { + background-image: var(--jp-icon-console); +} + +.jp-CopyIcon { + background-image: var(--jp-icon-copy); +} + +.jp-CopyrightIcon { + background-image: var(--jp-icon-copyright); +} + +.jp-CutIcon { + background-image: var(--jp-icon-cut); +} + +.jp-DeleteIcon { + background-image: var(--jp-icon-delete); +} + +.jp-DownloadIcon { + background-image: var(--jp-icon-download); +} + +.jp-DuplicateIcon { + background-image: var(--jp-icon-duplicate); +} + +.jp-EditIcon { + background-image: var(--jp-icon-edit); +} + +.jp-EllipsesIcon { + background-image: var(--jp-icon-ellipses); +} + +.jp-ErrorIcon { + background-image: var(--jp-icon-error); +} + +.jp-ExpandAllIcon { + background-image: var(--jp-icon-expand-all); +} + +.jp-ExtensionIcon { + background-image: var(--jp-icon-extension); +} + +.jp-FastForwardIcon { + background-image: var(--jp-icon-fast-forward); +} + +.jp-FileIcon { + background-image: var(--jp-icon-file); +} + +.jp-FileUploadIcon { + background-image: var(--jp-icon-file-upload); +} + +.jp-FilterDotIcon { + background-image: var(--jp-icon-filter-dot); +} + +.jp-FilterIcon { + background-image: var(--jp-icon-filter); +} + +.jp-FilterListIcon { + background-image: var(--jp-icon-filter-list); +} + +.jp-FolderFavoriteIcon { + background-image: var(--jp-icon-folder-favorite); +} + +.jp-FolderIcon { + background-image: var(--jp-icon-folder); +} + +.jp-HomeIcon { + background-image: var(--jp-icon-home); +} + +.jp-Html5Icon { + background-image: var(--jp-icon-html5); +} + +.jp-ImageIcon { + background-image: var(--jp-icon-image); +} + +.jp-InfoIcon { + background-image: var(--jp-icon-info); +} + +.jp-InspectorIcon { + background-image: var(--jp-icon-inspector); +} + +.jp-JsonIcon { + background-image: var(--jp-icon-json); +} + +.jp-JuliaIcon { + background-image: var(--jp-icon-julia); +} + +.jp-JupyterFaviconIcon { + background-image: var(--jp-icon-jupyter-favicon); +} + +.jp-JupyterIcon { + background-image: var(--jp-icon-jupyter); +} + +.jp-JupyterlabWordmarkIcon { + background-image: var(--jp-icon-jupyterlab-wordmark); +} + +.jp-KernelIcon { + background-image: var(--jp-icon-kernel); +} + +.jp-KeyboardIcon { + background-image: var(--jp-icon-keyboard); +} + +.jp-LaunchIcon { + background-image: var(--jp-icon-launch); +} + +.jp-LauncherIcon { + background-image: var(--jp-icon-launcher); +} + +.jp-LineFormIcon { + background-image: var(--jp-icon-line-form); +} + +.jp-LinkIcon { + background-image: var(--jp-icon-link); +} + +.jp-ListIcon { + background-image: var(--jp-icon-list); +} + +.jp-MarkdownIcon { + background-image: var(--jp-icon-markdown); +} + +.jp-MoveDownIcon { + background-image: var(--jp-icon-move-down); +} + +.jp-MoveUpIcon { + background-image: var(--jp-icon-move-up); +} + +.jp-NewFolderIcon { + background-image: var(--jp-icon-new-folder); +} + +.jp-NotTrustedIcon { + background-image: var(--jp-icon-not-trusted); +} + +.jp-NotebookIcon { + background-image: var(--jp-icon-notebook); +} + +.jp-NumberingIcon { + background-image: var(--jp-icon-numbering); +} + +.jp-OfflineBoltIcon { + background-image: var(--jp-icon-offline-bolt); +} + +.jp-PaletteIcon { + background-image: var(--jp-icon-palette); +} + +.jp-PasteIcon { + background-image: var(--jp-icon-paste); +} + +.jp-PdfIcon { + background-image: var(--jp-icon-pdf); +} + +.jp-PythonIcon { + background-image: var(--jp-icon-python); +} + +.jp-RKernelIcon { + background-image: var(--jp-icon-r-kernel); +} + +.jp-ReactIcon { + background-image: var(--jp-icon-react); +} + +.jp-RedoIcon { + background-image: var(--jp-icon-redo); +} + +.jp-RefreshIcon { + background-image: var(--jp-icon-refresh); +} + +.jp-RegexIcon { + background-image: var(--jp-icon-regex); +} + +.jp-RunIcon { + background-image: var(--jp-icon-run); +} + +.jp-RunningIcon { + background-image: var(--jp-icon-running); +} + +.jp-SaveIcon { + background-image: var(--jp-icon-save); +} + +.jp-SearchIcon { + background-image: var(--jp-icon-search); +} + +.jp-SettingsIcon { + background-image: var(--jp-icon-settings); +} + +.jp-ShareIcon { + background-image: var(--jp-icon-share); +} + +.jp-SpreadsheetIcon { + background-image: var(--jp-icon-spreadsheet); +} + +.jp-StopIcon { + background-image: var(--jp-icon-stop); +} + +.jp-TabIcon { + background-image: var(--jp-icon-tab); +} + +.jp-TableRowsIcon { + background-image: var(--jp-icon-table-rows); +} + +.jp-TagIcon { + background-image: var(--jp-icon-tag); +} + +.jp-TerminalIcon { + background-image: var(--jp-icon-terminal); +} + +.jp-TextEditorIcon { + background-image: var(--jp-icon-text-editor); +} + +.jp-TocIcon { + background-image: var(--jp-icon-toc); +} + +.jp-TreeViewIcon { + background-image: var(--jp-icon-tree-view); +} + +.jp-TrustedIcon { + background-image: var(--jp-icon-trusted); +} + +.jp-UndoIcon { + background-image: var(--jp-icon-undo); +} + +.jp-UserIcon { + background-image: var(--jp-icon-user); +} + +.jp-UsersIcon { + background-image: var(--jp-icon-users); +} + +.jp-VegaIcon { + background-image: var(--jp-icon-vega); +} + +.jp-WordIcon { + background-image: var(--jp-icon-word); +} + +.jp-YamlIcon { + background-image: var(--jp-icon-yaml); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * (DEPRECATED) Support for consuming icons as CSS background images + */ + +.jp-Icon, +.jp-MaterialIcon { + background-position: center; + background-repeat: no-repeat; + background-size: 16px; + min-width: 16px; + min-height: 16px; +} + +.jp-Icon-cover { + background-position: center; + background-repeat: no-repeat; + background-size: cover; +} + +/** + * (DEPRECATED) Support for specific CSS icon sizes + */ + +.jp-Icon-16 { + background-size: 16px; + min-width: 16px; + min-height: 16px; +} + +.jp-Icon-18 { + background-size: 18px; + min-width: 18px; + min-height: 18px; +} + +.jp-Icon-20 { + background-size: 20px; + min-width: 20px; + min-height: 20px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.lm-TabBar .lm-TabBar-addButton { + align-items: center; + display: flex; + padding: 4px; + padding-bottom: 5px; + margin-right: 1px; + background-color: var(--jp-layout-color2); +} + +.lm-TabBar .lm-TabBar-addButton:hover { + background-color: var(--jp-layout-color1); +} + +.lm-DockPanel-tabBar .lm-TabBar-tab { + width: var(--jp-private-horizontal-tab-width); +} + +.lm-DockPanel-tabBar .lm-TabBar-content { + flex: unset; +} + +.lm-DockPanel-tabBar[data-orientation='horizontal'] { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * Support for icons as inline SVG HTMLElements + */ + +/* recolor the primary elements of an icon */ +.jp-icon0[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon1[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon2[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon3[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon4[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon0[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon1[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon2[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon3[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon4[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/* recolor the accent elements of an icon */ +.jp-icon-accent0[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-accent1[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-accent2[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-accent3[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-accent4[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-accent0[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-accent1[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-accent2[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-accent3[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-accent4[stroke] { + stroke: var(--jp-layout-color4); +} + +/* set the color of an icon to transparent */ +.jp-icon-none[fill] { + fill: none; +} + +.jp-icon-none[stroke] { + stroke: none; +} + +/* brand icon colors. Same for light and dark */ +.jp-icon-brand0[fill] { + fill: var(--jp-brand-color0); +} + +.jp-icon-brand1[fill] { + fill: var(--jp-brand-color1); +} + +.jp-icon-brand2[fill] { + fill: var(--jp-brand-color2); +} + +.jp-icon-brand3[fill] { + fill: var(--jp-brand-color3); +} + +.jp-icon-brand4[fill] { + fill: var(--jp-brand-color4); +} + +.jp-icon-brand0[stroke] { + stroke: var(--jp-brand-color0); +} + +.jp-icon-brand1[stroke] { + stroke: var(--jp-brand-color1); +} + +.jp-icon-brand2[stroke] { + stroke: var(--jp-brand-color2); +} + +.jp-icon-brand3[stroke] { + stroke: var(--jp-brand-color3); +} + +.jp-icon-brand4[stroke] { + stroke: var(--jp-brand-color4); +} + +/* warn icon colors. Same for light and dark */ +.jp-icon-warn0[fill] { + fill: var(--jp-warn-color0); +} + +.jp-icon-warn1[fill] { + fill: var(--jp-warn-color1); +} + +.jp-icon-warn2[fill] { + fill: var(--jp-warn-color2); +} + +.jp-icon-warn3[fill] { + fill: var(--jp-warn-color3); +} + +.jp-icon-warn0[stroke] { + stroke: var(--jp-warn-color0); +} + +.jp-icon-warn1[stroke] { + stroke: var(--jp-warn-color1); +} + +.jp-icon-warn2[stroke] { + stroke: var(--jp-warn-color2); +} + +.jp-icon-warn3[stroke] { + stroke: var(--jp-warn-color3); +} + +/* icon colors that contrast well with each other and most backgrounds */ +.jp-icon-contrast0[fill] { + fill: var(--jp-icon-contrast-color0); +} + +.jp-icon-contrast1[fill] { + fill: var(--jp-icon-contrast-color1); +} + +.jp-icon-contrast2[fill] { + fill: var(--jp-icon-contrast-color2); +} + +.jp-icon-contrast3[fill] { + fill: var(--jp-icon-contrast-color3); +} + +.jp-icon-contrast0[stroke] { + stroke: var(--jp-icon-contrast-color0); +} + +.jp-icon-contrast1[stroke] { + stroke: var(--jp-icon-contrast-color1); +} + +.jp-icon-contrast2[stroke] { + stroke: var(--jp-icon-contrast-color2); +} + +.jp-icon-contrast3[stroke] { + stroke: var(--jp-icon-contrast-color3); +} + +.jp-icon-dot[fill] { + fill: var(--jp-warn-color0); +} + +.jp-jupyter-icon-color[fill] { + fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0)); +} + +.jp-notebook-icon-color[fill] { + fill: var(--jp-notebook-icon-color, var(--jp-warn-color0)); +} + +.jp-json-icon-color[fill] { + fill: var(--jp-json-icon-color, var(--jp-warn-color1)); +} + +.jp-console-icon-color[fill] { + fill: var(--jp-console-icon-color, white); +} + +.jp-console-icon-background-color[fill] { + fill: var(--jp-console-icon-background-color, var(--jp-brand-color1)); +} + +.jp-terminal-icon-color[fill] { + fill: var(--jp-terminal-icon-color, var(--jp-layout-color2)); +} + +.jp-terminal-icon-background-color[fill] { + fill: var( + --jp-terminal-icon-background-color, + var(--jp-inverse-layout-color2) + ); +} + +.jp-text-editor-icon-color[fill] { + fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3)); +} + +.jp-inspector-icon-color[fill] { + fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3)); +} + +/* CSS for icons in selected filebrowser listing items */ +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] { + fill: #fff; +} + +.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] { + fill: var(--jp-brand-color1); +} + +/* stylelint-disable selector-max-class, selector-max-compound-selectors */ + +/** +* TODO: come up with non css-hack solution for showing the busy icon on top +* of the close icon +* CSS for complex behavior of close icon of tabs in the main area tabbar +*/ +.lm-DockPanel-tabBar + .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty + > .lm-TabBar-tabCloseIcon + > :not(:hover) + > .jp-icon3[fill] { + fill: none; +} + +.lm-DockPanel-tabBar + .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty + > .lm-TabBar-tabCloseIcon + > :not(:hover) + > .jp-icon-busy[fill] { + fill: var(--jp-inverse-layout-color3); +} + +/* stylelint-enable selector-max-class, selector-max-compound-selectors */ + +/* CSS for icons in status bar */ +#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] { + fill: #fff; +} + +#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] { + fill: var(--jp-brand-color1); +} + +/* special handling for splash icon CSS. While the theme CSS reloads during + splash, the splash icon can loose theming. To prevent that, we set a + default for its color variable */ +:root { + --jp-warn-color0: var(--md-orange-700); +} + +/* not sure what to do with this one, used in filebrowser listing */ +.jp-DragIcon { + margin-right: 4px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/** + * Support for alt colors for icons as inline SVG HTMLElements + */ + +/* alt recolor the primary elements of an icon */ +.jp-icon-alt .jp-icon0[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-alt .jp-icon1[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-alt .jp-icon2[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-alt .jp-icon3[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-alt .jp-icon4[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-alt .jp-icon0[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-alt .jp-icon1[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-alt .jp-icon2[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-alt .jp-icon3[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-alt .jp-icon4[stroke] { + stroke: var(--jp-layout-color4); +} + +/* alt recolor the accent elements of an icon */ +.jp-icon-alt .jp-icon-accent0[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-alt .jp-icon-accent1[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-alt .jp-icon-accent2[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-alt .jp-icon-accent3[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-alt .jp-icon-accent4[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-alt .jp-icon-accent0[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-alt .jp-icon-accent1[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-alt .jp-icon-accent2[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-alt .jp-icon-accent3[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-alt .jp-icon-accent4[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content { + display: none !important; +} + +/** + * Support for hover colors for icons as inline SVG HTMLElements + */ + +/** + * regular colors + */ + +/* recolor the primary elements of an icon */ +.jp-icon-hover :hover .jp-icon0-hover[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover :hover .jp-icon1-hover[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover :hover .jp-icon2-hover[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover :hover .jp-icon3-hover[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover :hover .jp-icon4-hover[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-hover :hover .jp-icon0-hover[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover :hover .jp-icon1-hover[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover :hover .jp-icon2-hover[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover :hover .jp-icon3-hover[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover :hover .jp-icon4-hover[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/* recolor the accent elements of an icon */ +.jp-icon-hover :hover .jp-icon-accent0-hover[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-hover :hover .jp-icon-accent1-hover[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-hover :hover .jp-icon-accent2-hover[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-hover :hover .jp-icon-accent3-hover[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-hover :hover .jp-icon-accent4-hover[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] { + stroke: var(--jp-layout-color4); +} + +/* set the color of an icon to transparent */ +.jp-icon-hover :hover .jp-icon-none-hover[fill] { + fill: none; +} + +.jp-icon-hover :hover .jp-icon-none-hover[stroke] { + stroke: none; +} + +/** + * inverse colors + */ + +/* inverse recolor the primary elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] { + fill: var(--jp-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] { + fill: var(--jp-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] { + fill: var(--jp-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] { + fill: var(--jp-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] { + fill: var(--jp-layout-color4); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] { + stroke: var(--jp-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] { + stroke: var(--jp-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] { + stroke: var(--jp-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] { + stroke: var(--jp-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] { + stroke: var(--jp-layout-color4); +} + +/* inverse recolor the accent elements of an icon */ +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] { + fill: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] { + fill: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] { + fill: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] { + fill: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] { + fill: var(--jp-inverse-layout-color4); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] { + stroke: var(--jp-inverse-layout-color0); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] { + stroke: var(--jp-inverse-layout-color1); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] { + stroke: var(--jp-inverse-layout-color2); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] { + stroke: var(--jp-inverse-layout-color3); +} + +.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] { + stroke: var(--jp-inverse-layout-color4); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-IFrame { + width: 100%; + height: 100%; +} + +.jp-IFrame > iframe { + border: none; +} + +/* +When drag events occur, `lm-mod-override-cursor` is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-IFrame { + position: relative; +} + +body.lm-mod-override-cursor .jp-IFrame::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-HoverBox { + position: fixed; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-FormGroup-content fieldset { + border: none; + padding: 0; + min-width: 0; + width: 100%; +} + +/* stylelint-disable selector-max-type */ + +.jp-FormGroup-content fieldset .jp-inputFieldWrapper input, +.jp-FormGroup-content fieldset .jp-inputFieldWrapper select, +.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea { + font-size: var(--jp-content-font-size2); + border-color: var(--jp-input-border-color); + border-style: solid; + border-radius: var(--jp-border-radius); + border-width: 1px; + padding: 6px 8px; + background: none; + color: var(--jp-ui-font-color0); + height: inherit; +} + +.jp-FormGroup-content fieldset input[type='checkbox'] { + position: relative; + top: 2px; + margin-left: 0; +} + +.jp-FormGroup-content button.jp-mod-styled { + cursor: pointer; +} + +.jp-FormGroup-content .checkbox label { + cursor: pointer; + font-size: var(--jp-content-font-size1); +} + +.jp-FormGroup-content .jp-root > fieldset > legend { + display: none; +} + +.jp-FormGroup-content .jp-root > fieldset > p { + display: none; +} + +/** copy of `input.jp-mod-styled:focus` style */ +.jp-FormGroup-content fieldset input:focus, +.jp-FormGroup-content fieldset select:focus { + -moz-outline-radius: unset; + outline: var(--jp-border-width) solid var(--md-blue-500); + outline-offset: -1px; + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-FormGroup-content fieldset input:hover:not(:focus), +.jp-FormGroup-content fieldset select:hover:not(:focus) { + background-color: var(--jp-border-color2); +} + +/* stylelint-enable selector-max-type */ + +.jp-FormGroup-content .checkbox .field-description { + /* Disable default description field for checkbox: + because other widgets do not have description fields, + we add descriptions to each widget on the field level. + */ + display: none; +} + +.jp-FormGroup-content #root__description { + display: none; +} + +.jp-FormGroup-content .jp-modifiedIndicator { + width: 5px; + background-color: var(--jp-brand-color2); + margin-top: 0; + margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1); + flex-shrink: 0; +} + +.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator { + background-color: var(--jp-error-color0); + margin-right: 0.5em; +} + +/* RJSF ARRAY style */ + +.jp-arrayFieldWrapper legend { + font-size: var(--jp-content-font-size2); + color: var(--jp-ui-font-color0); + flex-basis: 100%; + padding: 4px 0; + font-weight: var(--jp-content-heading-font-weight); + border-bottom: 1px solid var(--jp-border-color2); +} + +.jp-arrayFieldWrapper .field-description { + padding: 4px 0; + white-space: pre-wrap; +} + +.jp-arrayFieldWrapper .array-item { + width: 100%; + border: 1px solid var(--jp-border-color2); + border-radius: 4px; + margin: 4px; +} + +.jp-ArrayOperations { + display: flex; + margin-left: 8px; +} + +.jp-ArrayOperationsButton { + margin: 2px; +} + +.jp-ArrayOperationsButton .jp-icon3[fill] { + fill: var(--jp-ui-font-color0); +} + +button.jp-ArrayOperationsButton.jp-mod-styled:disabled { + cursor: not-allowed; + opacity: 0.5; +} + +/* RJSF form validation error */ + +.jp-FormGroup-content .validationErrors { + color: var(--jp-error-color0); +} + +/* Hide panel level error as duplicated the field level error */ +.jp-FormGroup-content .panel.errors { + display: none; +} + +/* RJSF normal content (settings-editor) */ + +.jp-FormGroup-contentNormal { + display: flex; + align-items: center; + flex-wrap: wrap; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-contentItem { + margin-left: 7px; + color: var(--jp-ui-font-color0); +} + +.jp-FormGroup-contentNormal .jp-FormGroup-description { + flex-basis: 100%; + padding: 4px 7px; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-default { + flex-basis: 100%; + padding: 4px 7px; +} + +.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel { + font-size: var(--jp-content-font-size1); + font-weight: normal; + min-width: 120px; +} + +.jp-FormGroup-contentNormal fieldset:not(:first-child) { + margin-left: 7px; +} + +.jp-FormGroup-contentNormal .field-array-of-string .array-item { + /* Display `jp-ArrayOperations` buttons side-by-side with content except + for small screens where flex-wrap will place them one below the other. + */ + display: flex; + align-items: center; + flex-wrap: wrap; +} + +.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group { + padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent); + margin-top: 2px; +} + +/* RJSF compact content (metadata-form) */ + +.jp-FormGroup-content.jp-FormGroup-contentCompact { + width: 100%; +} + +.jp-FormGroup-contentCompact .form-group { + display: flex; + padding: 0.5em 0.2em 0.5em 0; +} + +.jp-FormGroup-contentCompact + .jp-FormGroup-compactTitle + .jp-FormGroup-description { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color2); +} + +.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel { + padding-bottom: 0.3em; +} + +.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control { + width: 100%; + box-sizing: border-box; +} + +.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle { + padding-bottom: 7px; +} + +.jp-FormGroup-contentCompact + .jp-objectFieldWrapper + .jp-objectFieldWrapper + .form-group { + padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent); + margin-top: 2px; +} + +.jp-FormGroup-contentCompact ul.error-detail { + margin-block-start: 0.5em; + margin-block-end: 0.5em; + padding-inline-start: 1em; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-SidePanel { + display: flex; + flex-direction: column; + min-width: var(--jp-sidebar-min-width); + overflow-y: auto; + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + font-size: var(--jp-ui-font-size1); +} + +.jp-SidePanel-header { + flex: 0 0 auto; + display: flex; + border-bottom: var(--jp-border-width) solid var(--jp-border-color2); + font-size: var(--jp-ui-font-size0); + font-weight: 600; + letter-spacing: 1px; + margin: 0; + padding: 2px; + text-transform: uppercase; +} + +.jp-SidePanel-toolbar { + flex: 0 0 auto; +} + +.jp-SidePanel-content { + flex: 1 1 auto; +} + +.jp-SidePanel-toolbar, +.jp-AccordionPanel-toolbar { + height: var(--jp-private-toolbar-height); +} + +.jp-SidePanel-toolbar.jp-Toolbar-micro { + display: none; +} + +.lm-AccordionPanel .jp-AccordionPanel-title { + box-sizing: border-box; + line-height: 25px; + margin: 0; + display: flex; + align-items: center; + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + font-size: var(--jp-ui-font-size0); +} + +.jp-AccordionPanel-title { + cursor: pointer; + user-select: none; + -moz-user-select: none; + -webkit-user-select: none; + text-transform: uppercase; +} + +.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title { + /* Title is rotated for horizontal accordion panel using CSS */ + display: block; + transform-origin: top left; + transform: rotate(-90deg) translate(-100%); +} + +.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel { + user-select: none; + text-overflow: ellipsis; + white-space: nowrap; + overflow: hidden; +} + +.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser { + transform: rotate(-90deg); + margin: auto 0; + height: 16px; +} + +.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser { + transform: rotate(0deg); +} + +.lm-AccordionPanel .jp-AccordionPanel-toolbar { + background: none; + box-shadow: none; + border: none; + margin-left: auto; +} + +.lm-AccordionPanel .lm-SplitPanel-handle:hover { + background: var(--jp-layout-color3); +} + +.jp-text-truncated { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Spinner { + position: absolute; + display: flex; + justify-content: center; + align-items: center; + z-index: 10; + left: 0; + top: 0; + width: 100%; + height: 100%; + background: var(--jp-layout-color0); + outline: none; +} + +.jp-SpinnerContent { + font-size: 10px; + margin: 50px auto; + text-indent: -9999em; + width: 3em; + height: 3em; + border-radius: 50%; + background: var(--jp-brand-color3); + background: linear-gradient( + to right, + #f37626 10%, + rgba(255, 255, 255, 0) 42% + ); + position: relative; + animation: load3 1s infinite linear, fadeIn 1s; +} + +.jp-SpinnerContent::before { + width: 50%; + height: 50%; + background: #f37626; + border-radius: 100% 0 0; + position: absolute; + top: 0; + left: 0; + content: ''; +} + +.jp-SpinnerContent::after { + background: var(--jp-layout-color0); + width: 75%; + height: 75%; + border-radius: 50%; + content: ''; + margin: auto; + position: absolute; + top: 0; + left: 0; + bottom: 0; + right: 0; +} + +@keyframes fadeIn { + 0% { + opacity: 0; + } + + 100% { + opacity: 1; + } +} + +@keyframes load3 { + 0% { + transform: rotate(0deg); + } + + 100% { + transform: rotate(360deg); + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +button.jp-mod-styled { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + border: none; + box-sizing: border-box; + text-align: center; + line-height: 32px; + height: 32px; + padding: 0 12px; + letter-spacing: 0.8px; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +input.jp-mod-styled { + background: var(--jp-input-background); + height: 28px; + box-sizing: border-box; + border: var(--jp-border-width) solid var(--jp-border-color1); + padding-left: 7px; + padding-right: 7px; + font-size: var(--jp-ui-font-size2); + color: var(--jp-ui-font-color0); + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +input[type='checkbox'].jp-mod-styled { + appearance: checkbox; + -webkit-appearance: checkbox; + -moz-appearance: checkbox; + height: auto; +} + +input.jp-mod-styled:focus { + border: var(--jp-border-width) solid var(--md-blue-500); + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-select-wrapper { + display: flex; + position: relative; + flex-direction: column; + padding: 1px; + background-color: var(--jp-layout-color1); + box-sizing: border-box; + margin-bottom: 12px; +} + +.jp-select-wrapper:not(.multiple) { + height: 28px; +} + +.jp-select-wrapper.jp-mod-focused select.jp-mod-styled { + border: var(--jp-border-width) solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); + background-color: var(--jp-input-active-background); +} + +select.jp-mod-styled:hover { + cursor: pointer; + color: var(--jp-ui-font-color0); + background-color: var(--jp-input-hover-background); + box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5); +} + +select.jp-mod-styled { + flex: 1 1 auto; + width: 100%; + font-size: var(--jp-ui-font-size2); + background: var(--jp-input-background); + color: var(--jp-ui-font-color0); + padding: 0 25px 0 8px; + border: var(--jp-border-width) solid var(--jp-input-border-color); + border-radius: 0; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; +} + +select.jp-mod-styled:not([multiple]) { + height: 32px; +} + +select.jp-mod-styled[multiple] { + max-height: 200px; + overflow-y: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-switch { + display: flex; + align-items: center; + padding-left: 4px; + padding-right: 4px; + font-size: var(--jp-ui-font-size1); + background-color: transparent; + color: var(--jp-ui-font-color1); + border: none; + height: 20px; +} + +.jp-switch:hover { + background-color: var(--jp-layout-color2); +} + +.jp-switch-label { + margin-right: 5px; + font-family: var(--jp-ui-font-family); +} + +.jp-switch-track { + cursor: pointer; + background-color: var(--jp-switch-color, var(--jp-border-color1)); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 34px; + height: 16px; + width: 35px; + position: relative; +} + +.jp-switch-track::before { + content: ''; + position: absolute; + height: 10px; + width: 10px; + margin: 3px; + left: 0; + background-color: var(--jp-ui-inverse-font-color1); + -webkit-transition: 0.4s; + transition: 0.4s; + border-radius: 50%; +} + +.jp-switch[aria-checked='true'] .jp-switch-track { + background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0)); +} + +.jp-switch[aria-checked='true'] .jp-switch-track::before { + /* track width (35) - margins (3 + 3) - thumb width (10) */ + left: 19px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-toolbar-height: calc( + 28px + var(--jp-border-width) + ); /* leave 28px for content */ +} + +.jp-Toolbar { + color: var(--jp-ui-font-color1); + flex: 0 0 auto; + display: flex; + flex-direction: row; + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + background: var(--jp-toolbar-background); + min-height: var(--jp-toolbar-micro-height); + padding: 2px; + z-index: 8; + overflow-x: hidden; +} + +/* Toolbar items */ + +.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer { + flex-grow: 1; + flex-shrink: 1; +} + +.jp-Toolbar-item.jp-Toolbar-kernelStatus { + display: inline-block; + width: 32px; + background-repeat: no-repeat; + background-position: center; + background-size: 16px; +} + +.jp-Toolbar > .jp-Toolbar-item { + flex: 0 0 auto; + display: flex; + padding-left: 1px; + padding-right: 1px; + font-size: var(--jp-ui-font-size1); + line-height: var(--jp-private-toolbar-height); + height: 100%; +} + +/* Toolbar buttons */ + +/* This is the div we use to wrap the react component into a Widget */ +div.jp-ToolbarButton { + color: transparent; + border: none; + box-sizing: border-box; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; + padding: 0; + margin: 0; +} + +button.jp-ToolbarButtonComponent { + background: var(--jp-layout-color1); + border: none; + box-sizing: border-box; + outline: none; + appearance: none; + -webkit-appearance: none; + -moz-appearance: none; + padding: 0 6px; + margin: 0; + height: 24px; + border-radius: var(--jp-border-radius); + display: flex; + align-items: center; + text-align: center; + font-size: 14px; + min-width: unset; + min-height: unset; +} + +button.jp-ToolbarButtonComponent:disabled { + opacity: 0.4; +} + +button.jp-ToolbarButtonComponent > span { + padding: 0; + flex: 0 0 auto; +} + +button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label { + font-size: var(--jp-ui-font-size1); + line-height: 100%; + padding-left: 2px; + color: var(--jp-ui-font-color1); + font-family: var(--jp-ui-font-family); +} + +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar.jp-Toolbar-micro { + padding: 0; + min-height: 0; +} + +#jp-main-dock-panel[data-mode='single-document'] + .jp-MainAreaWidget + > .jp-Toolbar { + border: none; + box-shadow: none; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-WindowedPanel-outer { + position: relative; + overflow-y: auto; +} + +.jp-WindowedPanel-inner { + position: relative; +} + +.jp-WindowedPanel-window { + position: absolute; + left: 0; + right: 0; + overflow: visible; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* Sibling imports */ + +body { + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); +} + +/* Disable native link decoration styles everywhere outside of dialog boxes */ +a { + text-decoration: unset; + color: unset; +} + +a:hover { + text-decoration: unset; + color: unset; +} + +/* Accessibility for links inside dialog box text */ +.jp-Dialog-content a { + text-decoration: revert; + color: var(--jp-content-link-color); +} + +.jp-Dialog-content a:hover { + text-decoration: revert; +} + +/* Styles for ui-components */ +.jp-Button { + color: var(--jp-ui-font-color2); + border-radius: var(--jp-border-radius); + padding: 0 12px; + font-size: var(--jp-ui-font-size1); + + /* Copy from blueprint 3 */ + display: inline-flex; + flex-direction: row; + border: none; + cursor: pointer; + align-items: center; + justify-content: center; + text-align: left; + vertical-align: middle; + min-height: 30px; + min-width: 30px; +} + +.jp-Button:disabled { + cursor: not-allowed; +} + +.jp-Button:empty { + padding: 0 !important; +} + +.jp-Button.jp-mod-small { + min-height: 24px; + min-width: 24px; + font-size: 12px; + padding: 0 7px; +} + +/* Use our own theme for hover styles */ +.jp-Button.jp-mod-minimal:hover { + background-color: var(--jp-layout-color2); +} + +.jp-Button.jp-mod-minimal { + background: none; +} + +.jp-InputGroup { + display: block; + position: relative; +} + +.jp-InputGroup input { + box-sizing: border-box; + border: none; + border-radius: 0; + background-color: transparent; + color: var(--jp-ui-font-color0); + box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color); + padding-bottom: 0; + padding-top: 0; + padding-left: 10px; + padding-right: 28px; + position: relative; + width: 100%; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; + font-size: 14px; + font-weight: 400; + height: 30px; + line-height: 30px; + outline: none; + vertical-align: middle; +} + +.jp-InputGroup input:focus { + box-shadow: inset 0 0 0 var(--jp-border-width) + var(--jp-input-active-box-shadow-color), + inset 0 0 0 3px var(--jp-input-active-box-shadow-color); +} + +.jp-InputGroup input:disabled { + cursor: not-allowed; + resize: block; + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color2); +} + +.jp-InputGroup input:disabled ~ span { + cursor: not-allowed; + color: var(--jp-ui-font-color2); +} + +.jp-InputGroup input::placeholder, +input::placeholder { + color: var(--jp-ui-font-color2); +} + +.jp-InputGroupAction { + position: absolute; + bottom: 1px; + right: 0; + padding: 6px; +} + +.jp-HTMLSelect.jp-DefaultStyle select { + background-color: initial; + border: none; + border-radius: 0; + box-shadow: none; + color: var(--jp-ui-font-color0); + display: block; + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + height: 24px; + line-height: 14px; + padding: 0 25px 0 10px; + text-align: left; + -moz-appearance: none; + -webkit-appearance: none; +} + +.jp-HTMLSelect.jp-DefaultStyle select:disabled { + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color2); + cursor: not-allowed; + resize: block; +} + +.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span { + cursor: not-allowed; +} + +/* Use our own theme for hover and option styles */ +/* stylelint-disable-next-line selector-max-type */ +.jp-HTMLSelect.jp-DefaultStyle select:hover, +.jp-HTMLSelect.jp-DefaultStyle select > option { + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color0); +} + +select { + box-sizing: border-box; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-StatusBar-Widget { + display: flex; + align-items: center; + background: var(--jp-layout-color2); + min-height: var(--jp-statusbar-height); + justify-content: space-between; + padding: 0 10px; +} + +.jp-StatusBar-Left { + display: flex; + align-items: center; + flex-direction: row; +} + +.jp-StatusBar-Middle { + display: flex; + align-items: center; +} + +.jp-StatusBar-Right { + display: flex; + align-items: center; + flex-direction: row-reverse; +} + +.jp-StatusBar-Item { + max-height: var(--jp-statusbar-height); + margin: 0 2px; + height: var(--jp-statusbar-height); + white-space: nowrap; + text-overflow: ellipsis; + color: var(--jp-ui-font-color1); + padding: 0 6px; +} + +.jp-mod-highlighted:hover { + background-color: var(--jp-layout-color3); +} + +.jp-mod-clicked { + background-color: var(--jp-brand-color1); +} + +.jp-mod-clicked:hover { + background-color: var(--jp-brand-color0); +} + +.jp-mod-clicked .jp-StatusBar-TextItem { + color: var(--jp-ui-inverse-font-color1); +} + +.jp-StatusBar-HoverItem { + box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)'; +} + +.jp-StatusBar-TextItem { + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + line-height: 24px; + color: var(--jp-ui-font-color1); +} + +.jp-StatusBar-GroupItem { + display: flex; + align-items: center; + flex-direction: row; +} + +.jp-Statusbar-ProgressCircle svg { + display: block; + margin: 0 auto; + width: 16px; + height: 24px; + align-self: normal; +} + +.jp-Statusbar-ProgressCircle path { + fill: var(--jp-inverse-layout-color3); +} + +.jp-Statusbar-ProgressBar-progress-bar { + height: 10px; + width: 100px; + border: solid 0.25px var(--jp-brand-color2); + border-radius: 3px; + overflow: hidden; + align-self: center; +} + +.jp-Statusbar-ProgressBar-progress-bar > div { + background-color: var(--jp-brand-color2); + background-image: linear-gradient( + -45deg, + rgba(255, 255, 255, 0.2) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.2) 50%, + rgba(255, 255, 255, 0.2) 75%, + transparent 75%, + transparent + ); + background-size: 40px 40px; + float: left; + width: 0%; + height: 100%; + font-size: 12px; + line-height: 14px; + color: #fff; + text-align: center; + animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite; +} + +.jp-Statusbar-ProgressBar-progress-bar p { + color: var(--jp-ui-font-color1); + font-family: var(--jp-ui-font-family); + font-size: var(--jp-ui-font-size1); + line-height: 10px; + width: 100px; +} + +@keyframes jp-Statusbar-ExecutionTime-progress-bar { + 0% { + background-position: 0 0; + } + + 100% { + background-position: 40px 40px; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-commandpalette-search-height: 28px; +} + +/*----------------------------------------------------------------------------- +| Overall styles +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette { + padding-bottom: 0; + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); +} + +/*----------------------------------------------------------------------------- +| Modal variant +|----------------------------------------------------------------------------*/ + +.jp-ModalCommandPalette { + position: absolute; + z-index: 10000; + top: 38px; + left: 30%; + margin: 0; + padding: 4px; + width: 40%; + box-shadow: var(--jp-elevation-z4); + border-radius: 4px; + background: var(--jp-layout-color0); +} + +.jp-ModalCommandPalette .lm-CommandPalette { + max-height: 40vh; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header { + display: none; +} + +.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item { + margin-left: 4px; + margin-right: 4px; +} + +.jp-ModalCommandPalette + .lm-CommandPalette + .lm-CommandPalette-item.lm-mod-disabled { + display: none; +} + +/*----------------------------------------------------------------------------- +| Search +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette-search { + padding: 4px; + background-color: var(--jp-layout-color1); + z-index: 2; +} + +.lm-CommandPalette-wrapper { + overflow: overlay; + padding: 0 9px; + background-color: var(--jp-input-active-background); + height: 30px; + box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color); +} + +.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper { + box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color), + inset 0 0 0 3px var(--jp-input-active-box-shadow-color); +} + +.jp-SearchIconGroup { + color: white; + background-color: var(--jp-brand-color1); + position: absolute; + top: 4px; + right: 4px; + padding: 5px 5px 1px; +} + +.jp-SearchIconGroup svg { + height: 20px; + width: 20px; +} + +.jp-SearchIconGroup .jp-icon3[fill] { + fill: var(--jp-layout-color0); +} + +.lm-CommandPalette-input { + background: transparent; + width: calc(100% - 18px); + float: left; + border: none; + outline: none; + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + line-height: var(--jp-private-commandpalette-search-height); +} + +.lm-CommandPalette-input::-webkit-input-placeholder, +.lm-CommandPalette-input::-moz-placeholder, +.lm-CommandPalette-input:-ms-input-placeholder { + color: var(--jp-ui-font-color2); + font-size: var(--jp-ui-font-size1); +} + +/*----------------------------------------------------------------------------- +| Results +|----------------------------------------------------------------------------*/ + +.lm-CommandPalette-header:first-child { + margin-top: 0; +} + +.lm-CommandPalette-header { + border-bottom: solid var(--jp-border-width) var(--jp-border-color2); + color: var(--jp-ui-font-color1); + cursor: pointer; + display: flex; + font-size: var(--jp-ui-font-size0); + font-weight: 600; + letter-spacing: 1px; + margin-top: 8px; + padding: 8px 0 8px 12px; + text-transform: uppercase; +} + +.lm-CommandPalette-header.lm-mod-active { + background: var(--jp-layout-color2); +} + +.lm-CommandPalette-header > mark { + background-color: transparent; + font-weight: bold; + color: var(--jp-ui-font-color1); +} + +.lm-CommandPalette-item { + padding: 4px 12px 4px 4px; + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + font-weight: 400; + display: flex; +} + +.lm-CommandPalette-item.lm-mod-disabled { + color: var(--jp-ui-font-color2); +} + +.lm-CommandPalette-item.lm-mod-active { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark { + color: var(--jp-ui-inverse-font-color0); +} + +.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] { + fill: var(--jp-layout-color0); +} + +.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) { + background: var(--jp-layout-color2); +} + +.lm-CommandPalette-itemContent { + overflow: hidden; +} + +.lm-CommandPalette-itemLabel > mark { + color: var(--jp-ui-font-color0); + background-color: transparent; + font-weight: bold; +} + +.lm-CommandPalette-item.lm-mod-disabled mark { + color: var(--jp-ui-font-color2); +} + +.lm-CommandPalette-item .lm-CommandPalette-itemIcon { + margin: 0 4px 0 0; + position: relative; + width: 16px; + top: 2px; + flex: 0 0 auto; +} + +.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon { + opacity: 0.6; +} + +.lm-CommandPalette-item .lm-CommandPalette-itemShortcut { + flex: 0 0 auto; +} + +.lm-CommandPalette-itemCaption { + display: none; +} + +.lm-CommandPalette-content { + background-color: var(--jp-layout-color1); +} + +.lm-CommandPalette-content:empty::after { + content: 'No results'; + margin: auto; + margin-top: 20px; + width: 100px; + display: block; + font-size: var(--jp-ui-font-size2); + font-family: var(--jp-ui-font-family); + font-weight: lighter; +} + +.lm-CommandPalette-emptyMessage { + text-align: center; + margin-top: 24px; + line-height: 1.32; + padding: 0 8px; + color: var(--jp-content-font-color3); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Dialog { + position: absolute; + z-index: 10000; + display: flex; + flex-direction: column; + align-items: center; + justify-content: center; + top: 0; + left: 0; + margin: 0; + padding: 0; + width: 100%; + height: 100%; + background: var(--jp-dialog-background); +} + +.jp-Dialog-content { + display: flex; + flex-direction: column; + margin-left: auto; + margin-right: auto; + background: var(--jp-layout-color1); + padding: 24px 24px 12px; + min-width: 300px; + min-height: 150px; + max-width: 1000px; + max-height: 500px; + box-sizing: border-box; + box-shadow: var(--jp-elevation-z20); + word-wrap: break-word; + border-radius: var(--jp-border-radius); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color1); + resize: both; +} + +.jp-Dialog-content.jp-Dialog-content-small { + max-width: 500px; +} + +.jp-Dialog-button { + overflow: visible; +} + +button.jp-Dialog-button:focus { + outline: 1px solid var(--jp-brand-color1); + outline-offset: 4px; + -moz-outline-radius: 0; +} + +button.jp-Dialog-button:focus::-moz-focus-inner { + border: 0; +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus, +button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus { + outline-offset: 4px; + -moz-outline-radius: 0; +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus { + outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1)); +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus { + outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1)); +} + +button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus { + outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600)); +} + +button.jp-Dialog-close-button { + padding: 0; + height: 100%; + min-width: unset; + min-height: unset; +} + +.jp-Dialog-header { + display: flex; + justify-content: space-between; + flex: 0 0 auto; + padding-bottom: 12px; + font-size: var(--jp-ui-font-size3); + font-weight: 400; + color: var(--jp-ui-font-color1); +} + +.jp-Dialog-body { + display: flex; + flex-direction: column; + flex: 1 1 auto; + font-size: var(--jp-ui-font-size1); + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + overflow: auto; +} + +.jp-Dialog-footer { + display: flex; + flex-direction: row; + justify-content: flex-end; + align-items: center; + flex: 0 0 auto; + margin-left: -12px; + margin-right: -12px; + padding: 12px; +} + +.jp-Dialog-checkbox { + padding-right: 5px; +} + +.jp-Dialog-checkbox > input:focus-visible { + outline: 1px solid var(--jp-input-active-border-color); + outline-offset: 1px; +} + +.jp-Dialog-spacer { + flex: 1 1 auto; +} + +.jp-Dialog-title { + overflow: hidden; + white-space: nowrap; + text-overflow: ellipsis; +} + +.jp-Dialog-body > .jp-select-wrapper { + width: 100%; +} + +.jp-Dialog-body > button { + padding: 0 16px; +} + +.jp-Dialog-body > label { + line-height: 1.4; + color: var(--jp-ui-font-color0); +} + +.jp-Dialog-button.jp-mod-styled:not(:last-child) { + margin-right: 12px; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-Input-Boolean-Dialog { + flex-direction: row-reverse; + align-items: end; + width: 100%; +} + +.jp-Input-Boolean-Dialog > label { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-MainAreaWidget > :focus { + outline: none; +} + +.jp-MainAreaWidget .jp-MainAreaWidget-error { + padding: 6px; +} + +.jp-MainAreaWidget .jp-MainAreaWidget-error > pre { + width: auto; + padding: 10px; + background: var(--jp-error-color3); + border: var(--jp-border-width) solid var(--jp-error-color1); + border-radius: var(--jp-border-radius); + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + white-space: pre-wrap; + word-wrap: break-word; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/** + * google-material-color v1.2.6 + * https://github.com/danlevan/google-material-color + */ +:root { + --md-red-50: #ffebee; + --md-red-100: #ffcdd2; + --md-red-200: #ef9a9a; + --md-red-300: #e57373; + --md-red-400: #ef5350; + --md-red-500: #f44336; + --md-red-600: #e53935; + --md-red-700: #d32f2f; + --md-red-800: #c62828; + --md-red-900: #b71c1c; + --md-red-A100: #ff8a80; + --md-red-A200: #ff5252; + --md-red-A400: #ff1744; + --md-red-A700: #d50000; + --md-pink-50: #fce4ec; + --md-pink-100: #f8bbd0; + --md-pink-200: #f48fb1; + --md-pink-300: #f06292; + --md-pink-400: #ec407a; + --md-pink-500: #e91e63; + --md-pink-600: #d81b60; + --md-pink-700: #c2185b; + --md-pink-800: #ad1457; + --md-pink-900: #880e4f; + --md-pink-A100: #ff80ab; + --md-pink-A200: #ff4081; + --md-pink-A400: #f50057; + --md-pink-A700: #c51162; + --md-purple-50: #f3e5f5; + --md-purple-100: #e1bee7; + --md-purple-200: #ce93d8; + --md-purple-300: #ba68c8; + --md-purple-400: #ab47bc; + --md-purple-500: #9c27b0; + --md-purple-600: #8e24aa; + --md-purple-700: #7b1fa2; + --md-purple-800: #6a1b9a; + --md-purple-900: #4a148c; + --md-purple-A100: #ea80fc; + --md-purple-A200: #e040fb; + --md-purple-A400: #d500f9; + --md-purple-A700: #a0f; + --md-deep-purple-50: #ede7f6; + --md-deep-purple-100: #d1c4e9; + --md-deep-purple-200: #b39ddb; + --md-deep-purple-300: #9575cd; + --md-deep-purple-400: #7e57c2; + --md-deep-purple-500: #673ab7; + --md-deep-purple-600: #5e35b1; + --md-deep-purple-700: #512da8; + --md-deep-purple-800: #4527a0; + --md-deep-purple-900: #311b92; + --md-deep-purple-A100: #b388ff; + --md-deep-purple-A200: #7c4dff; + --md-deep-purple-A400: #651fff; + --md-deep-purple-A700: #6200ea; + --md-indigo-50: #e8eaf6; + --md-indigo-100: #c5cae9; + --md-indigo-200: #9fa8da; + --md-indigo-300: #7986cb; + --md-indigo-400: #5c6bc0; + --md-indigo-500: #3f51b5; + --md-indigo-600: #3949ab; + --md-indigo-700: #303f9f; + --md-indigo-800: #283593; + --md-indigo-900: #1a237e; + --md-indigo-A100: #8c9eff; + --md-indigo-A200: #536dfe; + --md-indigo-A400: #3d5afe; + --md-indigo-A700: #304ffe; + --md-blue-50: #e3f2fd; + --md-blue-100: #bbdefb; + --md-blue-200: #90caf9; + --md-blue-300: #64b5f6; + --md-blue-400: #42a5f5; + --md-blue-500: #2196f3; + --md-blue-600: #1e88e5; + --md-blue-700: #1976d2; + --md-blue-800: #1565c0; + --md-blue-900: #0d47a1; + --md-blue-A100: #82b1ff; + --md-blue-A200: #448aff; + --md-blue-A400: #2979ff; + --md-blue-A700: #2962ff; + --md-light-blue-50: #e1f5fe; + --md-light-blue-100: #b3e5fc; + --md-light-blue-200: #81d4fa; + --md-light-blue-300: #4fc3f7; + --md-light-blue-400: #29b6f6; + --md-light-blue-500: #03a9f4; + --md-light-blue-600: #039be5; + --md-light-blue-700: #0288d1; + --md-light-blue-800: #0277bd; + --md-light-blue-900: #01579b; + --md-light-blue-A100: #80d8ff; + --md-light-blue-A200: #40c4ff; + --md-light-blue-A400: #00b0ff; + --md-light-blue-A700: #0091ea; + --md-cyan-50: #e0f7fa; + --md-cyan-100: #b2ebf2; + --md-cyan-200: #80deea; + --md-cyan-300: #4dd0e1; + --md-cyan-400: #26c6da; + --md-cyan-500: #00bcd4; + --md-cyan-600: #00acc1; + --md-cyan-700: #0097a7; + --md-cyan-800: #00838f; + --md-cyan-900: #006064; + --md-cyan-A100: #84ffff; + --md-cyan-A200: #18ffff; + --md-cyan-A400: #00e5ff; + --md-cyan-A700: #00b8d4; + --md-teal-50: #e0f2f1; + --md-teal-100: #b2dfdb; + --md-teal-200: #80cbc4; + --md-teal-300: #4db6ac; + --md-teal-400: #26a69a; + --md-teal-500: #009688; + --md-teal-600: #00897b; + --md-teal-700: #00796b; + --md-teal-800: #00695c; + --md-teal-900: #004d40; + --md-teal-A100: #a7ffeb; + --md-teal-A200: #64ffda; + --md-teal-A400: #1de9b6; + --md-teal-A700: #00bfa5; + --md-green-50: #e8f5e9; + --md-green-100: #c8e6c9; + --md-green-200: #a5d6a7; + --md-green-300: #81c784; + --md-green-400: #66bb6a; + --md-green-500: #4caf50; + --md-green-600: #43a047; + --md-green-700: #388e3c; + --md-green-800: #2e7d32; + --md-green-900: #1b5e20; + --md-green-A100: #b9f6ca; + --md-green-A200: #69f0ae; + --md-green-A400: #00e676; + --md-green-A700: #00c853; + --md-light-green-50: #f1f8e9; + --md-light-green-100: #dcedc8; + --md-light-green-200: #c5e1a5; + --md-light-green-300: #aed581; + --md-light-green-400: #9ccc65; + --md-light-green-500: #8bc34a; + --md-light-green-600: #7cb342; + --md-light-green-700: #689f38; + --md-light-green-800: #558b2f; + --md-light-green-900: #33691e; + --md-light-green-A100: #ccff90; + --md-light-green-A200: #b2ff59; + --md-light-green-A400: #76ff03; + --md-light-green-A700: #64dd17; + --md-lime-50: #f9fbe7; + --md-lime-100: #f0f4c3; + --md-lime-200: #e6ee9c; + --md-lime-300: #dce775; + --md-lime-400: #d4e157; + --md-lime-500: #cddc39; + --md-lime-600: #c0ca33; + --md-lime-700: #afb42b; + --md-lime-800: #9e9d24; + --md-lime-900: #827717; + --md-lime-A100: #f4ff81; + --md-lime-A200: #eeff41; + --md-lime-A400: #c6ff00; + --md-lime-A700: #aeea00; + --md-yellow-50: #fffde7; + --md-yellow-100: #fff9c4; + --md-yellow-200: #fff59d; + --md-yellow-300: #fff176; + --md-yellow-400: #ffee58; + --md-yellow-500: #ffeb3b; + --md-yellow-600: #fdd835; + --md-yellow-700: #fbc02d; + --md-yellow-800: #f9a825; + --md-yellow-900: #f57f17; + --md-yellow-A100: #ffff8d; + --md-yellow-A200: #ff0; + --md-yellow-A400: #ffea00; + --md-yellow-A700: #ffd600; + --md-amber-50: #fff8e1; + --md-amber-100: #ffecb3; + --md-amber-200: #ffe082; + --md-amber-300: #ffd54f; + --md-amber-400: #ffca28; + --md-amber-500: #ffc107; + --md-amber-600: #ffb300; + --md-amber-700: #ffa000; + --md-amber-800: #ff8f00; + --md-amber-900: #ff6f00; + --md-amber-A100: #ffe57f; + --md-amber-A200: #ffd740; + --md-amber-A400: #ffc400; + --md-amber-A700: #ffab00; + --md-orange-50: #fff3e0; + --md-orange-100: #ffe0b2; + --md-orange-200: #ffcc80; + --md-orange-300: #ffb74d; + --md-orange-400: #ffa726; + --md-orange-500: #ff9800; + --md-orange-600: #fb8c00; + --md-orange-700: #f57c00; + --md-orange-800: #ef6c00; + --md-orange-900: #e65100; + --md-orange-A100: #ffd180; + --md-orange-A200: #ffab40; + --md-orange-A400: #ff9100; + --md-orange-A700: #ff6d00; + --md-deep-orange-50: #fbe9e7; + --md-deep-orange-100: #ffccbc; + --md-deep-orange-200: #ffab91; + --md-deep-orange-300: #ff8a65; + --md-deep-orange-400: #ff7043; + --md-deep-orange-500: #ff5722; + --md-deep-orange-600: #f4511e; + --md-deep-orange-700: #e64a19; + --md-deep-orange-800: #d84315; + --md-deep-orange-900: #bf360c; + --md-deep-orange-A100: #ff9e80; + --md-deep-orange-A200: #ff6e40; + --md-deep-orange-A400: #ff3d00; + --md-deep-orange-A700: #dd2c00; + --md-brown-50: #efebe9; + --md-brown-100: #d7ccc8; + --md-brown-200: #bcaaa4; + --md-brown-300: #a1887f; + --md-brown-400: #8d6e63; + --md-brown-500: #795548; + --md-brown-600: #6d4c41; + --md-brown-700: #5d4037; + --md-brown-800: #4e342e; + --md-brown-900: #3e2723; + --md-grey-50: #fafafa; + --md-grey-100: #f5f5f5; + --md-grey-200: #eee; + --md-grey-300: #e0e0e0; + --md-grey-400: #bdbdbd; + --md-grey-500: #9e9e9e; + --md-grey-600: #757575; + --md-grey-700: #616161; + --md-grey-800: #424242; + --md-grey-900: #212121; + --md-blue-grey-50: #eceff1; + --md-blue-grey-100: #cfd8dc; + --md-blue-grey-200: #b0bec5; + --md-blue-grey-300: #90a4ae; + --md-blue-grey-400: #78909c; + --md-blue-grey-500: #607d8b; + --md-blue-grey-600: #546e7a; + --md-blue-grey-700: #455a64; + --md-blue-grey-800: #37474f; + --md-blue-grey-900: #263238; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2017, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| RenderedText +|----------------------------------------------------------------------------*/ + +:root { + /* This is the padding value to fill the gaps between lines containing spans with background color. */ + --jp-private-code-span-padding: calc( + (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2 + ); +} + +.jp-RenderedText { + text-align: left; + padding-left: var(--jp-code-padding); + line-height: var(--jp-code-line-height); + font-family: var(--jp-code-font-family); +} + +.jp-RenderedText pre, +.jp-RenderedJavaScript pre, +.jp-RenderedHTMLCommon pre { + color: var(--jp-content-font-color1); + font-size: var(--jp-code-font-size); + border: none; + margin: 0; + padding: 0; +} + +.jp-RenderedText pre a:link { + text-decoration: none; + color: var(--jp-content-link-color); +} + +.jp-RenderedText pre a:hover { + text-decoration: underline; + color: var(--jp-content-link-color); +} + +.jp-RenderedText pre a:visited { + text-decoration: none; + color: var(--jp-content-link-color); +} + +/* console foregrounds and backgrounds */ +.jp-RenderedText pre .ansi-black-fg { + color: #3e424d; +} + +.jp-RenderedText pre .ansi-red-fg { + color: #e75c58; +} + +.jp-RenderedText pre .ansi-green-fg { + color: #00a250; +} + +.jp-RenderedText pre .ansi-yellow-fg { + color: #ddb62b; +} + +.jp-RenderedText pre .ansi-blue-fg { + color: #208ffb; +} + +.jp-RenderedText pre .ansi-magenta-fg { + color: #d160c4; +} + +.jp-RenderedText pre .ansi-cyan-fg { + color: #60c6c8; +} + +.jp-RenderedText pre .ansi-white-fg { + color: #c5c1b4; +} + +.jp-RenderedText pre .ansi-black-bg { + background-color: #3e424d; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-red-bg { + background-color: #e75c58; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-green-bg { + background-color: #00a250; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-yellow-bg { + background-color: #ddb62b; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-blue-bg { + background-color: #208ffb; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-magenta-bg { + background-color: #d160c4; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-cyan-bg { + background-color: #60c6c8; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-white-bg { + background-color: #c5c1b4; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-black-intense-fg { + color: #282c36; +} + +.jp-RenderedText pre .ansi-red-intense-fg { + color: #b22b31; +} + +.jp-RenderedText pre .ansi-green-intense-fg { + color: #007427; +} + +.jp-RenderedText pre .ansi-yellow-intense-fg { + color: #b27d12; +} + +.jp-RenderedText pre .ansi-blue-intense-fg { + color: #0065ca; +} + +.jp-RenderedText pre .ansi-magenta-intense-fg { + color: #a03196; +} + +.jp-RenderedText pre .ansi-cyan-intense-fg { + color: #258f8f; +} + +.jp-RenderedText pre .ansi-white-intense-fg { + color: #a1a6b2; +} + +.jp-RenderedText pre .ansi-black-intense-bg { + background-color: #282c36; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-red-intense-bg { + background-color: #b22b31; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-green-intense-bg { + background-color: #007427; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-yellow-intense-bg { + background-color: #b27d12; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-blue-intense-bg { + background-color: #0065ca; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-magenta-intense-bg { + background-color: #a03196; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-cyan-intense-bg { + background-color: #258f8f; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-white-intense-bg { + background-color: #a1a6b2; + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-default-inverse-fg { + color: var(--jp-ui-inverse-font-color0); +} + +.jp-RenderedText pre .ansi-default-inverse-bg { + background-color: var(--jp-inverse-layout-color0); + padding: var(--jp-private-code-span-padding) 0; +} + +.jp-RenderedText pre .ansi-bold { + font-weight: bold; +} + +.jp-RenderedText pre .ansi-underline { + text-decoration: underline; +} + +.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] { + background: var(--jp-rendermime-error-background); + padding-top: var(--jp-code-padding); +} + +/*----------------------------------------------------------------------------- +| RenderedLatex +|----------------------------------------------------------------------------*/ + +.jp-RenderedLatex { + color: var(--jp-content-font-color1); + font-size: var(--jp-content-font-size1); + line-height: var(--jp-content-line-height); +} + +/* Left-justify outputs.*/ +.jp-OutputArea-output.jp-RenderedLatex { + padding: var(--jp-code-padding); + text-align: left; +} + +/*----------------------------------------------------------------------------- +| RenderedHTML +|----------------------------------------------------------------------------*/ + +.jp-RenderedHTMLCommon { + color: var(--jp-content-font-color1); + font-family: var(--jp-content-font-family); + font-size: var(--jp-content-font-size1); + line-height: var(--jp-content-line-height); + + /* Give a bit more R padding on Markdown text to keep line lengths reasonable */ + padding-right: 20px; +} + +.jp-RenderedHTMLCommon em { + font-style: italic; +} + +.jp-RenderedHTMLCommon strong { + font-weight: bold; +} + +.jp-RenderedHTMLCommon u { + text-decoration: underline; +} + +.jp-RenderedHTMLCommon a:link { + text-decoration: none; + color: var(--jp-content-link-color); +} + +.jp-RenderedHTMLCommon a:hover { + text-decoration: underline; + color: var(--jp-content-link-color); +} + +.jp-RenderedHTMLCommon a:visited { + text-decoration: none; + color: var(--jp-content-link-color); +} + +/* Headings */ + +.jp-RenderedHTMLCommon h1, +.jp-RenderedHTMLCommon h2, +.jp-RenderedHTMLCommon h3, +.jp-RenderedHTMLCommon h4, +.jp-RenderedHTMLCommon h5, +.jp-RenderedHTMLCommon h6 { + line-height: var(--jp-content-heading-line-height); + font-weight: var(--jp-content-heading-font-weight); + font-style: normal; + margin: var(--jp-content-heading-margin-top) 0 + var(--jp-content-heading-margin-bottom) 0; +} + +.jp-RenderedHTMLCommon h1:first-child, +.jp-RenderedHTMLCommon h2:first-child, +.jp-RenderedHTMLCommon h3:first-child, +.jp-RenderedHTMLCommon h4:first-child, +.jp-RenderedHTMLCommon h5:first-child, +.jp-RenderedHTMLCommon h6:first-child { + margin-top: calc(0.5 * var(--jp-content-heading-margin-top)); +} + +.jp-RenderedHTMLCommon h1:last-child, +.jp-RenderedHTMLCommon h2:last-child, +.jp-RenderedHTMLCommon h3:last-child, +.jp-RenderedHTMLCommon h4:last-child, +.jp-RenderedHTMLCommon h5:last-child, +.jp-RenderedHTMLCommon h6:last-child { + margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom)); +} + +.jp-RenderedHTMLCommon h1 { + font-size: var(--jp-content-font-size5); +} + +.jp-RenderedHTMLCommon h2 { + font-size: var(--jp-content-font-size4); +} + +.jp-RenderedHTMLCommon h3 { + font-size: var(--jp-content-font-size3); +} + +.jp-RenderedHTMLCommon h4 { + font-size: var(--jp-content-font-size2); +} + +.jp-RenderedHTMLCommon h5 { + font-size: var(--jp-content-font-size1); +} + +.jp-RenderedHTMLCommon h6 { + font-size: var(--jp-content-font-size0); +} + +/* Lists */ + +/* stylelint-disable selector-max-type, selector-max-compound-selectors */ + +.jp-RenderedHTMLCommon ul:not(.list-inline), +.jp-RenderedHTMLCommon ol:not(.list-inline) { + padding-left: 2em; +} + +.jp-RenderedHTMLCommon ul { + list-style: disc; +} + +.jp-RenderedHTMLCommon ul ul { + list-style: square; +} + +.jp-RenderedHTMLCommon ul ul ul { + list-style: circle; +} + +.jp-RenderedHTMLCommon ol { + list-style: decimal; +} + +.jp-RenderedHTMLCommon ol ol { + list-style: upper-alpha; +} + +.jp-RenderedHTMLCommon ol ol ol { + list-style: lower-alpha; +} + +.jp-RenderedHTMLCommon ol ol ol ol { + list-style: lower-roman; +} + +.jp-RenderedHTMLCommon ol ol ol ol ol { + list-style: decimal; +} + +.jp-RenderedHTMLCommon ol, +.jp-RenderedHTMLCommon ul { + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon ul ul, +.jp-RenderedHTMLCommon ul ol, +.jp-RenderedHTMLCommon ol ul, +.jp-RenderedHTMLCommon ol ol { + margin-bottom: 0; +} + +/* stylelint-enable selector-max-type, selector-max-compound-selectors */ + +.jp-RenderedHTMLCommon hr { + color: var(--jp-border-color2); + background-color: var(--jp-border-color1); + margin-top: 1em; + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon > pre { + margin: 1.5em 2em; +} + +.jp-RenderedHTMLCommon pre, +.jp-RenderedHTMLCommon code { + border: 0; + background-color: var(--jp-layout-color0); + color: var(--jp-content-font-color1); + font-family: var(--jp-code-font-family); + font-size: inherit; + line-height: var(--jp-code-line-height); + padding: 0; + white-space: pre-wrap; +} + +.jp-RenderedHTMLCommon :not(pre) > code { + background-color: var(--jp-layout-color2); + padding: 1px 5px; +} + +/* Tables */ + +.jp-RenderedHTMLCommon table { + border-collapse: collapse; + border-spacing: 0; + border: none; + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + table-layout: fixed; + margin-left: auto; + margin-bottom: 1em; + margin-right: auto; +} + +.jp-RenderedHTMLCommon thead { + border-bottom: var(--jp-border-width) solid var(--jp-border-color1); + vertical-align: bottom; +} + +.jp-RenderedHTMLCommon td, +.jp-RenderedHTMLCommon th, +.jp-RenderedHTMLCommon tr { + vertical-align: middle; + padding: 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} + +.jp-RenderedMarkdown.jp-RenderedHTMLCommon td, +.jp-RenderedMarkdown.jp-RenderedHTMLCommon th { + max-width: none; +} + +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th, +:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr { + text-align: right; +} + +.jp-RenderedHTMLCommon th { + font-weight: bold; +} + +.jp-RenderedHTMLCommon tbody tr:nth-child(odd) { + background: var(--jp-layout-color0); +} + +.jp-RenderedHTMLCommon tbody tr:nth-child(even) { + background: var(--jp-rendermime-table-row-background); +} + +.jp-RenderedHTMLCommon tbody tr:hover { + background: var(--jp-rendermime-table-row-hover-background); +} + +.jp-RenderedHTMLCommon p { + text-align: left; + margin: 0; + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon img { + -moz-force-broken-image-icon: 1; +} + +/* Restrict to direct children as other images could be nested in other content. */ +.jp-RenderedHTMLCommon > img { + display: block; + margin-left: 0; + margin-right: 0; + margin-bottom: 1em; +} + +/* Change color behind transparent images if they need it... */ +[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background { + background-color: var(--jp-inverse-layout-color1); +} + +[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background { + background-color: var(--jp-inverse-layout-color1); +} + +.jp-RenderedHTMLCommon img, +.jp-RenderedImage img, +.jp-RenderedHTMLCommon svg, +.jp-RenderedSVG svg { + max-width: 100%; + height: auto; +} + +.jp-RenderedHTMLCommon img.jp-mod-unconfined, +.jp-RenderedImage img.jp-mod-unconfined, +.jp-RenderedHTMLCommon svg.jp-mod-unconfined, +.jp-RenderedSVG svg.jp-mod-unconfined { + max-width: none; +} + +.jp-RenderedHTMLCommon .alert { + padding: var(--jp-notebook-padding); + border: var(--jp-border-width) solid transparent; + border-radius: var(--jp-border-radius); + margin-bottom: 1em; +} + +.jp-RenderedHTMLCommon .alert-info { + color: var(--jp-info-color0); + background-color: var(--jp-info-color3); + border-color: var(--jp-info-color2); +} + +.jp-RenderedHTMLCommon .alert-info hr { + border-color: var(--jp-info-color3); +} + +.jp-RenderedHTMLCommon .alert-info > p:last-child, +.jp-RenderedHTMLCommon .alert-info > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-warning { + color: var(--jp-warn-color0); + background-color: var(--jp-warn-color3); + border-color: var(--jp-warn-color2); +} + +.jp-RenderedHTMLCommon .alert-warning hr { + border-color: var(--jp-warn-color3); +} + +.jp-RenderedHTMLCommon .alert-warning > p:last-child, +.jp-RenderedHTMLCommon .alert-warning > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-success { + color: var(--jp-success-color0); + background-color: var(--jp-success-color3); + border-color: var(--jp-success-color2); +} + +.jp-RenderedHTMLCommon .alert-success hr { + border-color: var(--jp-success-color3); +} + +.jp-RenderedHTMLCommon .alert-success > p:last-child, +.jp-RenderedHTMLCommon .alert-success > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon .alert-danger { + color: var(--jp-error-color0); + background-color: var(--jp-error-color3); + border-color: var(--jp-error-color2); +} + +.jp-RenderedHTMLCommon .alert-danger hr { + border-color: var(--jp-error-color3); +} + +.jp-RenderedHTMLCommon .alert-danger > p:last-child, +.jp-RenderedHTMLCommon .alert-danger > ul:last-child { + margin-bottom: 0; +} + +.jp-RenderedHTMLCommon blockquote { + margin: 1em 2em; + padding: 0 1em; + border-left: 5px solid var(--jp-border-color2); +} + +a.jp-InternalAnchorLink { + visibility: hidden; + margin-left: 8px; + color: var(--md-blue-800); +} + +h1:hover .jp-InternalAnchorLink, +h2:hover .jp-InternalAnchorLink, +h3:hover .jp-InternalAnchorLink, +h4:hover .jp-InternalAnchorLink, +h5:hover .jp-InternalAnchorLink, +h6:hover .jp-InternalAnchorLink { + visibility: visible; +} + +.jp-RenderedHTMLCommon kbd { + background-color: var(--jp-rendermime-table-row-background); + border: 1px solid var(--jp-border-color0); + border-bottom-color: var(--jp-border-color2); + border-radius: 3px; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); + display: inline-block; + font-size: var(--jp-ui-font-size0); + line-height: 1em; + padding: 0.2em 0.5em; +} + +/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0. + * At the bottom of cells this is a bit too much as there is also spacing + * between cells. Going all the way to 0 gets too tight between markdown and + * code cells. + */ +.jp-RenderedHTMLCommon > *:last-child { + margin-bottom: 0.5em; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Copyright (c) 2014-2017, PhosphorJS Contributors +| +| Distributed under the terms of the BSD 3-Clause License. +| +| The full license is in the file LICENSE, distributed with this software. +|----------------------------------------------------------------------------*/ + +.lm-cursor-backdrop { + position: fixed; + width: 200px; + height: 200px; + margin-top: -100px; + margin-left: -100px; + will-change: transform; + z-index: 100; +} + +.lm-mod-drag-image { + will-change: transform; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-lineFormSearch { + padding: 4px 12px; + background-color: var(--jp-layout-color2); + box-shadow: var(--jp-toolbar-box-shadow); + z-index: 2; + font-size: var(--jp-ui-font-size1); +} + +.jp-lineFormCaption { + font-size: var(--jp-ui-font-size0); + line-height: var(--jp-ui-font-size1); + margin-top: 4px; + color: var(--jp-ui-font-color0); +} + +.jp-baseLineForm { + border: none; + border-radius: 0; + position: absolute; + background-size: 16px; + background-repeat: no-repeat; + background-position: center; + outline: none; +} + +.jp-lineFormButtonContainer { + top: 4px; + right: 8px; + height: 24px; + padding: 0 12px; + width: 12px; +} + +.jp-lineFormButtonIcon { + top: 0; + right: 0; + background-color: var(--jp-brand-color1); + height: 100%; + width: 100%; + box-sizing: border-box; + padding: 4px 6px; +} + +.jp-lineFormButton { + top: 0; + right: 0; + background-color: transparent; + height: 100%; + width: 100%; + box-sizing: border-box; +} + +.jp-lineFormWrapper { + overflow: hidden; + padding: 0 8px; + border: 1px solid var(--jp-border-color0); + background-color: var(--jp-input-active-background); + height: 22px; +} + +.jp-lineFormWrapperFocusWithin { + border: var(--jp-border-width) solid var(--md-blue-500); + box-shadow: inset 0 0 4px var(--md-blue-300); +} + +.jp-lineFormInput { + background: transparent; + width: 200px; + height: 100%; + border: none; + outline: none; + color: var(--jp-ui-font-color0); + line-height: 28px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) 2014-2016, Jupyter Development Team. +| +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-JSONEditor { + display: flex; + flex-direction: column; + width: 100%; +} + +.jp-JSONEditor-host { + flex: 1 1 auto; + border: var(--jp-border-width) solid var(--jp-input-border-color); + border-radius: 0; + background: var(--jp-layout-color0); + min-height: 50px; + padding: 1px; +} + +.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host { + border-color: red; + outline-color: red; +} + +.jp-JSONEditor-header { + display: flex; + flex: 1 0 auto; + padding: 0 0 0 12px; +} + +.jp-JSONEditor-header label { + flex: 0 0 auto; +} + +.jp-JSONEditor-commitButton { + height: 16px; + width: 16px; + background-size: 18px; + background-repeat: no-repeat; + background-position: center; +} + +.jp-JSONEditor-host.jp-mod-focused { + background-color: var(--jp-input-active-background); + border: 1px solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); +} + +.jp-Editor.jp-mod-dropTarget { + border: var(--jp-border-width) solid var(--jp-input-active-border-color); + box-shadow: var(--jp-input-box-shadow); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ +.jp-DocumentSearch-input { + border: none; + outline: none; + color: var(--jp-ui-font-color0); + font-size: var(--jp-ui-font-size1); + background-color: var(--jp-layout-color0); + font-family: var(--jp-ui-font-family); + padding: 2px 1px; + resize: none; +} + +.jp-DocumentSearch-overlay { + position: absolute; + background-color: var(--jp-toolbar-background); + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color); + top: 0; + right: 0; + z-index: 7; + min-width: 405px; + padding: 2px; + font-size: var(--jp-ui-font-size1); + + --jp-private-document-search-button-height: 20px; +} + +.jp-DocumentSearch-overlay button { + background-color: var(--jp-toolbar-background); + outline: 0; +} + +.jp-DocumentSearch-overlay button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-overlay button:active { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-overlay-row { + display: flex; + align-items: center; + margin-bottom: 2px; +} + +.jp-DocumentSearch-button-content { + display: inline-block; + cursor: pointer; + box-sizing: border-box; + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-button-content svg { + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-input-wrapper { + border: var(--jp-border-width) solid var(--jp-border-color0); + display: flex; + background-color: var(--jp-layout-color0); + margin: 2px; +} + +.jp-DocumentSearch-input-wrapper:focus-within { + border-color: var(--jp-cell-editor-active-border-color); +} + +.jp-DocumentSearch-toggle-wrapper, +.jp-DocumentSearch-button-wrapper { + all: initial; + overflow: hidden; + display: inline-block; + border: none; + box-sizing: border-box; +} + +.jp-DocumentSearch-toggle-wrapper { + width: 14px; + height: 14px; +} + +.jp-DocumentSearch-button-wrapper { + width: var(--jp-private-document-search-button-height); + height: var(--jp-private-document-search-button-height); +} + +.jp-DocumentSearch-toggle-wrapper:focus, +.jp-DocumentSearch-button-wrapper:focus { + outline: var(--jp-border-width) solid + var(--jp-cell-editor-active-border-color); + outline-offset: -1px; +} + +.jp-DocumentSearch-toggle-wrapper, +.jp-DocumentSearch-button-wrapper, +.jp-DocumentSearch-button-content:focus { + outline: none; +} + +.jp-DocumentSearch-toggle-placeholder { + width: 5px; +} + +.jp-DocumentSearch-input-button::before { + display: block; + padding-top: 100%; +} + +.jp-DocumentSearch-input-button-off { + opacity: var(--jp-search-toggle-off-opacity); +} + +.jp-DocumentSearch-input-button-off:hover { + opacity: var(--jp-search-toggle-hover-opacity); +} + +.jp-DocumentSearch-input-button-on { + opacity: var(--jp-search-toggle-on-opacity); +} + +.jp-DocumentSearch-index-counter { + padding-left: 10px; + padding-right: 10px; + user-select: none; + min-width: 35px; + display: inline-block; +} + +.jp-DocumentSearch-up-down-wrapper { + display: inline-block; + padding-right: 2px; + margin-left: auto; + white-space: nowrap; +} + +.jp-DocumentSearch-spacer { + margin-left: auto; +} + +.jp-DocumentSearch-up-down-wrapper button { + outline: 0; + border: none; + width: var(--jp-private-document-search-button-height); + height: var(--jp-private-document-search-button-height); + vertical-align: middle; + margin: 1px 5px 2px; +} + +.jp-DocumentSearch-up-down-button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-up-down-button:active { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-filter-button { + border-radius: var(--jp-border-radius); +} + +.jp-DocumentSearch-filter-button:hover { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-filter-button-enabled { + background-color: var(--jp-layout-color2); +} + +.jp-DocumentSearch-filter-button-enabled:hover { + background-color: var(--jp-layout-color3); +} + +.jp-DocumentSearch-search-options { + padding: 0 8px; + margin-left: 3px; + width: 100%; + display: grid; + justify-content: start; + grid-template-columns: 1fr 1fr; + align-items: center; + justify-items: stretch; +} + +.jp-DocumentSearch-search-filter-disabled { + color: var(--jp-ui-font-color2); +} + +.jp-DocumentSearch-search-filter { + display: flex; + align-items: center; + user-select: none; +} + +.jp-DocumentSearch-regex-error { + color: var(--jp-error-color0); +} + +.jp-DocumentSearch-replace-button-wrapper { + overflow: hidden; + display: inline-block; + box-sizing: border-box; + border: var(--jp-border-width) solid var(--jp-border-color0); + margin: auto 2px; + padding: 1px 4px; + height: calc(var(--jp-private-document-search-button-height) + 2px); +} + +.jp-DocumentSearch-replace-button-wrapper:focus { + border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color); +} + +.jp-DocumentSearch-replace-button { + display: inline-block; + text-align: center; + cursor: pointer; + box-sizing: border-box; + color: var(--jp-ui-font-color1); + + /* height - 2 * (padding of wrapper) */ + line-height: calc(var(--jp-private-document-search-button-height) - 2px); + width: 100%; + height: 100%; +} + +.jp-DocumentSearch-replace-button:focus { + outline: none; +} + +.jp-DocumentSearch-replace-wrapper-class { + margin-left: 14px; + display: flex; +} + +.jp-DocumentSearch-replace-toggle { + border: none; + background-color: var(--jp-toolbar-background); + border-radius: var(--jp-border-radius); +} + +.jp-DocumentSearch-replace-toggle:hover { + background-color: var(--jp-layout-color2); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.cm-editor { + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + font-family: var(--jp-code-font-family); + border: 0; + border-radius: 0; + height: auto; + + /* Changed to auto to autogrow */ +} + +.cm-editor pre { + padding: 0 var(--jp-code-padding); +} + +.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog { + background-color: var(--jp-layout-color0); + color: var(--jp-content-font-color1); +} + +.jp-CodeMirrorEditor { + cursor: text; +} + +/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */ +@media screen and (min-width: 2138px) and (max-width: 4319px) { + .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor { + border-left: var(--jp-code-cursor-width1) solid + var(--jp-editor-cursor-color); + } +} + +/* When zoomed out less than 33% */ +@media screen and (min-width: 4320px) { + .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor { + border-left: var(--jp-code-cursor-width2) solid + var(--jp-editor-cursor-color); + } +} + +.cm-editor.jp-mod-readOnly .cm-cursor { + display: none; +} + +.jp-CollaboratorCursor { + border-left: 5px solid transparent; + border-right: 5px solid transparent; + border-top: none; + border-bottom: 3px solid; + background-clip: content-box; + margin-left: -5px; + margin-right: -5px; +} + +.cm-searching, +.cm-searching span { + /* `.cm-searching span`: we need to override syntax highlighting */ + background-color: var(--jp-search-unselected-match-background-color); + color: var(--jp-search-unselected-match-color); +} + +.cm-searching::selection, +.cm-searching span::selection { + background-color: var(--jp-search-unselected-match-background-color); + color: var(--jp-search-unselected-match-color); +} + +.jp-current-match > .cm-searching, +.jp-current-match > .cm-searching span, +.cm-searching > .jp-current-match, +.cm-searching > .jp-current-match span { + background-color: var(--jp-search-selected-match-background-color); + color: var(--jp-search-selected-match-color); +} + +.jp-current-match > .cm-searching::selection, +.cm-searching > .jp-current-match::selection, +.jp-current-match > .cm-searching span::selection { + background-color: var(--jp-search-selected-match-background-color); + color: var(--jp-search-selected-match-color); +} + +.cm-trailingspace { + background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=); + background-position: center left; + background-repeat: repeat-x; +} + +.jp-CollaboratorCursor-hover { + position: absolute; + z-index: 1; + transform: translateX(-50%); + color: white; + border-radius: 3px; + padding-left: 4px; + padding-right: 4px; + padding-top: 1px; + padding-bottom: 1px; + text-align: center; + font-size: var(--jp-ui-font-size1); + white-space: nowrap; +} + +.jp-CodeMirror-ruler { + border-left: 1px dashed var(--jp-border-color2); +} + +/* Styles for shared cursors (remote cursor locations and selected ranges) */ +.jp-CodeMirrorEditor .cm-ySelectionCaret { + position: relative; + border-left: 1px solid black; + margin-left: -1px; + margin-right: -1px; + box-sizing: border-box; +} + +.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo { + white-space: nowrap; + position: absolute; + top: -1.15em; + padding-bottom: 0.05em; + left: -1px; + font-size: 0.95em; + font-family: var(--jp-ui-font-family); + font-weight: bold; + line-height: normal; + user-select: none; + color: white; + padding-left: 2px; + padding-right: 2px; + z-index: 101; + transition: opacity 0.3s ease-in-out; +} + +.jp-CodeMirrorEditor .cm-ySelectionInfo { + transition-delay: 0.7s; + opacity: 0; +} + +.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo { + opacity: 1; + transition-delay: 0s; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-MimeDocument { + outline: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-filebrowser-button-height: 28px; + --jp-private-filebrowser-button-width: 48px; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-FileBrowser .jp-SidePanel-content { + display: flex; + flex-direction: column; +} + +.jp-FileBrowser-toolbar.jp-Toolbar { + flex-wrap: wrap; + row-gap: 12px; + border-bottom: none; + height: auto; + margin: 8px 12px 0; + box-shadow: none; + padding: 0; + justify-content: flex-start; +} + +.jp-FileBrowser-Panel { + flex: 1 1 auto; + display: flex; + flex-direction: column; +} + +.jp-BreadCrumbs { + flex: 0 0 auto; + margin: 8px 12px; +} + +.jp-BreadCrumbs-item { + margin: 0 2px; + padding: 0 2px; + border-radius: var(--jp-border-radius); + cursor: pointer; +} + +.jp-BreadCrumbs-item:hover { + background-color: var(--jp-layout-color2); +} + +.jp-BreadCrumbs-item:first-child { + margin-left: 0; +} + +.jp-BreadCrumbs-item.jp-mod-dropTarget { + background-color: var(--jp-brand-color2); + opacity: 0.7; +} + +/*----------------------------------------------------------------------------- +| Buttons +|----------------------------------------------------------------------------*/ + +.jp-FileBrowser-toolbar > .jp-Toolbar-item { + flex: 0 0 auto; + padding-left: 0; + padding-right: 2px; + align-items: center; + height: unset; +} + +.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent { + width: 40px; +} + +/*----------------------------------------------------------------------------- +| Other styles +|----------------------------------------------------------------------------*/ + +.jp-FileDialog.jp-mod-conflict input { + color: var(--jp-error-color1); +} + +.jp-FileDialog .jp-new-name-title { + margin-top: 12px; +} + +.jp-LastModified-hidden { + display: none; +} + +.jp-FileSize-hidden { + display: none; +} + +.jp-FileBrowser .lm-AccordionPanel > h3:first-child { + display: none; +} + +/*----------------------------------------------------------------------------- +| DirListing +|----------------------------------------------------------------------------*/ + +.jp-DirListing { + flex: 1 1 auto; + display: flex; + flex-direction: column; + outline: 0; +} + +.jp-DirListing-header { + flex: 0 0 auto; + display: flex; + flex-direction: row; + align-items: center; + overflow: hidden; + border-top: var(--jp-border-width) solid var(--jp-border-color2); + border-bottom: var(--jp-border-width) solid var(--jp-border-color1); + box-shadow: var(--jp-toolbar-box-shadow); + z-index: 2; +} + +.jp-DirListing-headerItem { + padding: 4px 12px 2px; + font-weight: 500; +} + +.jp-DirListing-headerItem:hover { + background: var(--jp-layout-color2); +} + +.jp-DirListing-headerItem.jp-id-name { + flex: 1 0 84px; +} + +.jp-DirListing-headerItem.jp-id-modified { + flex: 0 0 112px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; +} + +.jp-DirListing-headerItem.jp-id-filesize { + flex: 0 0 75px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; +} + +.jp-id-narrow { + display: none; + flex: 0 0 5px; + padding: 4px; + border-left: var(--jp-border-width) solid var(--jp-border-color2); + text-align: right; + color: var(--jp-border-color2); +} + +.jp-DirListing-narrow .jp-id-narrow { + display: block; +} + +.jp-DirListing-narrow .jp-id-modified, +.jp-DirListing-narrow .jp-DirListing-itemModified { + display: none; +} + +.jp-DirListing-headerItem.jp-mod-selected { + font-weight: 600; +} + +/* increase specificity to override bundled default */ +.jp-DirListing-content { + flex: 1 1 auto; + margin: 0; + padding: 0; + list-style-type: none; + overflow: auto; + background-color: var(--jp-layout-color1); +} + +.jp-DirListing-content mark { + color: var(--jp-ui-font-color0); + background-color: transparent; + font-weight: bold; +} + +.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark { + color: var(--jp-ui-inverse-font-color0); +} + +/* Style the directory listing content when a user drops a file to upload */ +.jp-DirListing.jp-mod-native-drop .jp-DirListing-content { + outline: 5px dashed rgba(128, 128, 128, 0.5); + outline-offset: -10px; + cursor: copy; +} + +.jp-DirListing-item { + display: flex; + flex-direction: row; + align-items: center; + padding: 4px 12px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-DirListing-checkboxWrapper { + /* Increases hit area of checkbox. */ + padding: 4px; +} + +.jp-DirListing-header + .jp-DirListing-checkboxWrapper + + .jp-DirListing-headerItem { + padding-left: 4px; +} + +.jp-DirListing-content .jp-DirListing-checkboxWrapper { + position: relative; + left: -4px; + margin: -4px 0 -4px -8px; +} + +.jp-DirListing-checkboxWrapper.jp-mod-visible { + visibility: visible; +} + +/* For devices that support hovering, hide checkboxes until hovered, selected... +*/ +@media (hover: hover) { + .jp-DirListing-checkboxWrapper { + visibility: hidden; + } + + .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper, + .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper { + visibility: visible; + } +} + +.jp-DirListing-item[data-is-dot] { + opacity: 75%; +} + +.jp-DirListing-item.jp-mod-selected { + color: var(--jp-ui-inverse-font-color1); + background: var(--jp-brand-color1); +} + +.jp-DirListing-item.jp-mod-dropTarget { + background: var(--jp-brand-color3); +} + +.jp-DirListing-item:hover:not(.jp-mod-selected) { + background: var(--jp-layout-color2); +} + +.jp-DirListing-itemIcon { + flex: 0 0 20px; + margin-right: 4px; +} + +.jp-DirListing-itemText { + flex: 1 0 64px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + user-select: none; +} + +.jp-DirListing-itemText:focus { + outline-width: 2px; + outline-color: var(--jp-inverse-layout-color1); + outline-style: solid; + outline-offset: 1px; +} + +.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus { + outline-color: var(--jp-layout-color1); +} + +.jp-DirListing-itemModified { + flex: 0 0 125px; + text-align: right; +} + +.jp-DirListing-itemFileSize { + flex: 0 0 90px; + text-align: right; +} + +.jp-DirListing-editor { + flex: 1 0 64px; + outline: none; + border: none; + color: var(--jp-ui-font-color1); + background-color: var(--jp-layout-color1); +} + +.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before { + color: var(--jp-success-color1); + content: '\25CF'; + font-size: 8px; + position: absolute; + left: -8px; +} + +.jp-DirListing-item.jp-mod-running.jp-mod-selected + .jp-DirListing-itemIcon::before { + color: var(--jp-ui-inverse-font-color1); +} + +.jp-DirListing-item.lm-mod-drag-image, +.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image { + font-size: var(--jp-ui-font-size1); + padding-left: 4px; + margin-left: 4px; + width: 160px; + background-color: var(--jp-ui-inverse-font-color2); + box-shadow: var(--jp-elevation-z2); + border-radius: 0; + color: var(--jp-ui-font-color1); + transform: translateX(-40%) translateY(-58%); +} + +.jp-Document { + min-width: 120px; + min-height: 120px; + outline: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Main OutputArea +| OutputArea has a list of Outputs +|----------------------------------------------------------------------------*/ + +.jp-OutputArea { + overflow-y: auto; +} + +.jp-OutputArea-child { + display: table; + table-layout: fixed; + width: 100%; + overflow: hidden; +} + +.jp-OutputPrompt { + width: var(--jp-cell-prompt-width); + color: var(--jp-cell-outprompt-font-color); + font-family: var(--jp-cell-prompt-font-family); + padding: var(--jp-code-padding); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; + opacity: var(--jp-cell-prompt-opacity); + + /* Right align prompt text, don't wrap to handle large prompt numbers */ + text-align: right; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + + /* Disable text selection */ + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-OutputArea-prompt { + display: table-cell; + vertical-align: top; +} + +.jp-OutputArea-output { + display: table-cell; + width: 100%; + height: auto; + overflow: auto; + user-select: text; + -moz-user-select: text; + -webkit-user-select: text; + -ms-user-select: text; +} + +.jp-OutputArea .jp-RenderedText { + padding-left: 1ch; +} + +/** + * Prompt overlay. + */ + +.jp-OutputArea-promptOverlay { + position: absolute; + top: 0; + width: var(--jp-cell-prompt-width); + height: 100%; + opacity: 0.5; +} + +.jp-OutputArea-promptOverlay:hover { + background: var(--jp-layout-color2); + box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0); + cursor: zoom-out; +} + +.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover { + cursor: zoom-in; +} + +/** + * Isolated output. + */ +.jp-OutputArea-output.jp-mod-isolated { + width: 100%; + display: block; +} + +/* +When drag events occur, `lm-mod-override-cursor` is added to the body. +Because iframes steal all cursor events, the following two rules are necessary +to suppress pointer events while resize drags are occurring. There may be a +better solution to this problem. +*/ +body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated { + position: relative; +} + +body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + background: transparent; +} + +/* pre */ + +.jp-OutputArea-output pre { + border: none; + margin: 0; + padding: 0; + overflow-x: auto; + overflow-y: auto; + word-break: break-all; + word-wrap: break-word; + white-space: pre-wrap; +} + +/* tables */ + +.jp-OutputArea-output.jp-RenderedHTMLCommon table { + margin-left: 0; + margin-right: 0; +} + +/* description lists */ + +.jp-OutputArea-output dl, +.jp-OutputArea-output dt, +.jp-OutputArea-output dd { + display: block; +} + +.jp-OutputArea-output dl { + width: 100%; + overflow: hidden; + padding: 0; + margin: 0; +} + +.jp-OutputArea-output dt { + font-weight: bold; + float: left; + width: 20%; + padding: 0; + margin: 0; +} + +.jp-OutputArea-output dd { + float: left; + width: 80%; + padding: 0; + margin: 0; +} + +.jp-TrimmedOutputs pre { + background: var(--jp-layout-color3); + font-size: calc(var(--jp-code-font-size) * 1.4); + text-align: center; + text-transform: uppercase; +} + +/* Hide the gutter in case of + * - nested output areas (e.g. in the case of output widgets) + * - mirrored output areas + */ +.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt { + display: none; +} + +/* Hide empty lines in the output area, for instance due to cleared widgets */ +.jp-OutputArea-prompt:empty { + padding: 0; + border: 0; +} + +/*----------------------------------------------------------------------------- +| executeResult is added to any Output-result for the display of the object +| returned by a cell +|----------------------------------------------------------------------------*/ + +.jp-OutputArea-output.jp-OutputArea-executeResult { + margin-left: 0; + width: 100%; +} + +/* Text output with the Out[] prompt needs a top padding to match the + * alignment of the Out[] prompt itself. + */ +.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output { + padding-top: var(--jp-code-padding); + border-top: var(--jp-border-width) solid transparent; +} + +/*----------------------------------------------------------------------------- +| The Stdin output +|----------------------------------------------------------------------------*/ + +.jp-Stdin-prompt { + color: var(--jp-content-font-color0); + padding-right: var(--jp-code-padding); + vertical-align: baseline; + flex: 0 0 auto; +} + +.jp-Stdin-input { + font-family: var(--jp-code-font-family); + font-size: inherit; + color: inherit; + background-color: inherit; + width: 42%; + min-width: 200px; + + /* make sure input baseline aligns with prompt */ + vertical-align: baseline; + + /* padding + margin = 0.5em between prompt and cursor */ + padding: 0 0.25em; + margin: 0 0.25em; + flex: 0 0 70%; +} + +.jp-Stdin-input::placeholder { + opacity: 0; +} + +.jp-Stdin-input:focus { + box-shadow: none; +} + +.jp-Stdin-input:focus::placeholder { + opacity: 1; +} + +/*----------------------------------------------------------------------------- +| Output Area View +|----------------------------------------------------------------------------*/ + +.jp-LinkedOutputView .jp-OutputArea { + height: 100%; + display: block; +} + +.jp-LinkedOutputView .jp-OutputArea-output:only-child { + height: 100%; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +@media print { + .jp-OutputArea-child { + break-inside: avoid-page; + } +} + +/*----------------------------------------------------------------------------- +| Mobile +|----------------------------------------------------------------------------*/ +@media only screen and (max-width: 760px) { + .jp-OutputPrompt { + display: table-row; + text-align: left; + } + + .jp-OutputArea-child .jp-OutputArea-output { + display: table-row; + margin-left: var(--jp-notebook-padding); + } +} + +/* Trimmed outputs warning */ +.jp-TrimmedOutputs > a { + margin: 10px; + text-decoration: none; + cursor: pointer; +} + +.jp-TrimmedOutputs > a:hover { + text-decoration: none; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Table of Contents +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-toc-active-width: 4px; +} + +.jp-TableOfContents { + display: flex; + flex-direction: column; + background: var(--jp-layout-color1); + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + height: 100%; +} + +.jp-TableOfContents-placeholder { + text-align: center; +} + +.jp-TableOfContents-placeholderContent { + color: var(--jp-content-font-color2); + padding: 8px; +} + +.jp-TableOfContents-placeholderContent > h3 { + margin-bottom: var(--jp-content-heading-margin-bottom); +} + +.jp-TableOfContents .jp-SidePanel-content { + overflow-y: auto; +} + +.jp-TableOfContents-tree { + margin: 4px; +} + +.jp-TableOfContents ol { + list-style-type: none; +} + +/* stylelint-disable-next-line selector-max-type */ +.jp-TableOfContents li > ol { + /* Align left border with triangle icon center */ + padding-left: 11px; +} + +.jp-TableOfContents-content { + /* left margin for the active heading indicator */ + margin: 0 0 0 var(--jp-private-toc-active-width); + padding: 0; + background-color: var(--jp-layout-color1); +} + +.jp-tocItem { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.jp-tocItem-heading { + display: flex; + cursor: pointer; +} + +.jp-tocItem-heading:hover { + background-color: var(--jp-layout-color2); +} + +.jp-tocItem-content { + display: block; + padding: 4px 0; + white-space: nowrap; + text-overflow: ellipsis; + overflow-x: hidden; +} + +.jp-tocItem-collapser { + height: 20px; + margin: 2px 2px 0; + padding: 0; + background: none; + border: none; + cursor: pointer; +} + +.jp-tocItem-collapser:hover { + background-color: var(--jp-layout-color3); +} + +/* Active heading indicator */ + +.jp-tocItem-heading::before { + content: ' '; + background: transparent; + width: var(--jp-private-toc-active-width); + height: 24px; + position: absolute; + left: 0; + border-radius: var(--jp-border-radius); +} + +.jp-tocItem-heading.jp-tocItem-active::before { + background-color: var(--jp-brand-color1); +} + +.jp-tocItem-heading:hover.jp-tocItem-active::before { + background: var(--jp-brand-color0); + opacity: 1; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +.jp-Collapser { + flex: 0 0 var(--jp-cell-collapser-width); + padding: 0; + margin: 0; + border: none; + outline: none; + background: transparent; + border-radius: var(--jp-border-radius); + opacity: 1; +} + +.jp-Collapser-child { + display: block; + width: 100%; + box-sizing: border-box; + + /* height: 100% doesn't work because the height of its parent is computed from content */ + position: absolute; + top: 0; + bottom: 0; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +/* +Hiding collapsers in print mode. + +Note: input and output wrappers have "display: block" propery in print mode. +*/ + +@media print { + .jp-Collapser { + display: none; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Header/Footer +|----------------------------------------------------------------------------*/ + +/* Hidden by zero height by default */ +.jp-CellHeader, +.jp-CellFooter { + height: 0; + width: 100%; + padding: 0; + margin: 0; + border: none; + outline: none; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Input +|----------------------------------------------------------------------------*/ + +/* All input areas */ +.jp-InputArea { + display: table; + table-layout: fixed; + width: 100%; + overflow: hidden; +} + +.jp-InputArea-editor { + display: table-cell; + overflow: hidden; + vertical-align: top; + + /* This is the non-active, default styling */ + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + border-radius: 0; + background: var(--jp-cell-editor-background); +} + +.jp-InputPrompt { + display: table-cell; + vertical-align: top; + width: var(--jp-cell-prompt-width); + color: var(--jp-cell-inprompt-font-color); + font-family: var(--jp-cell-prompt-font-family); + padding: var(--jp-code-padding); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + opacity: var(--jp-cell-prompt-opacity); + line-height: var(--jp-code-line-height); + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; + + /* Right align prompt text, don't wrap to handle large prompt numbers */ + text-align: right; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + + /* Disable text selection */ + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +/*----------------------------------------------------------------------------- +| Mobile +|----------------------------------------------------------------------------*/ +@media only screen and (max-width: 760px) { + .jp-InputArea-editor { + display: table-row; + margin-left: var(--jp-notebook-padding); + } + + .jp-InputPrompt { + display: table-row; + text-align: left; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Placeholder +|----------------------------------------------------------------------------*/ + +.jp-Placeholder { + display: table; + table-layout: fixed; + width: 100%; +} + +.jp-Placeholder-prompt { + display: table-cell; + box-sizing: border-box; +} + +.jp-Placeholder-content { + display: table-cell; + padding: 4px 6px; + border: 1px solid transparent; + border-radius: 0; + background: none; + box-sizing: border-box; + cursor: pointer; +} + +.jp-Placeholder-contentContainer { + display: flex; +} + +.jp-Placeholder-content:hover, +.jp-InputPlaceholder > .jp-Placeholder-content:hover { + border-color: var(--jp-layout-color3); +} + +.jp-Placeholder-content .jp-MoreHorizIcon { + width: 32px; + height: 16px; + border: 1px solid transparent; + border-radius: var(--jp-border-radius); +} + +.jp-Placeholder-content .jp-MoreHorizIcon:hover { + border: 1px solid var(--jp-border-color1); + box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25); + background-color: var(--jp-layout-color0); +} + +.jp-PlaceholderText { + white-space: nowrap; + overflow-x: hidden; + color: var(--jp-inverse-layout-color3); + font-family: var(--jp-code-font-family); +} + +.jp-InputPlaceholder > .jp-Placeholder-content { + border-color: var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background); +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Private CSS variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-private-cell-scrolling-output-offset: 5px; +} + +/*----------------------------------------------------------------------------- +| Cell +|----------------------------------------------------------------------------*/ + +.jp-Cell { + padding: var(--jp-cell-padding); + margin: 0; + border: none; + outline: none; + background: transparent; +} + +/*----------------------------------------------------------------------------- +| Common input/output +|----------------------------------------------------------------------------*/ + +.jp-Cell-inputWrapper, +.jp-Cell-outputWrapper { + display: flex; + flex-direction: row; + padding: 0; + margin: 0; + + /* Added to reveal the box-shadow on the input and output collapsers. */ + overflow: visible; +} + +/* Only input/output areas inside cells */ +.jp-Cell-inputArea, +.jp-Cell-outputArea { + flex: 1 1 auto; +} + +/*----------------------------------------------------------------------------- +| Collapser +|----------------------------------------------------------------------------*/ + +/* Make the output collapser disappear when there is not output, but do so + * in a manner that leaves it in the layout and preserves its width. + */ +.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser { + border: none !important; + background: transparent !important; +} + +.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser { + min-height: var(--jp-cell-collapser-min-height); +} + +/*----------------------------------------------------------------------------- +| Output +|----------------------------------------------------------------------------*/ + +/* Put a space between input and output when there IS output */ +.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper { + margin-top: 5px; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea { + overflow-y: auto; + max-height: 24em; + margin-left: var(--jp-private-cell-scrolling-output-offset); + resize: vertical; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] { + max-height: unset; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after { + content: ' '; + box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%); + width: 100%; + height: 100%; + position: sticky; + bottom: 0; + top: 0; + margin-top: -50%; + float: left; + display: block; + pointer-events: none; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child { + padding-top: 6px; +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt { + width: calc( + var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset) + ); +} + +.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay { + left: calc(-1 * var(--jp-private-cell-scrolling-output-offset)); +} + +/*----------------------------------------------------------------------------- +| CodeCell +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| MarkdownCell +|----------------------------------------------------------------------------*/ + +.jp-MarkdownOutput { + display: table-cell; + width: 100%; + margin-top: 0; + margin-bottom: 0; + padding-left: var(--jp-code-padding); +} + +.jp-MarkdownOutput.jp-RenderedHTMLCommon { + overflow: auto; +} + +/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */ +.jp-collapseHeadingButton { + display: flex; + min-height: var(--jp-cell-collapser-min-height); + font-size: var(--jp-code-font-size); + position: absolute; + background-color: transparent; + background-size: 25px; + background-repeat: no-repeat; + background-position-x: center; + background-position-y: top; + background-image: var(--jp-icon-caret-down); + right: 0; + top: 0; + bottom: 0; +} + +.jp-collapseHeadingButton.jp-mod-collapsed { + background-image: var(--jp-icon-caret-right); +} + +/* + set the container font size to match that of content + so that the nested collapse buttons have the right size +*/ +.jp-MarkdownCell .jp-InputPrompt { + font-size: var(--jp-content-font-size1); +} + +/* + Align collapseHeadingButton with cell top header + The font sizes are identical to the ones in packages/rendermime/style/base.css +*/ +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] { + font-size: var(--jp-content-font-size5); + background-position-y: calc(0.3 * var(--jp-content-font-size5)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] { + font-size: var(--jp-content-font-size4); + background-position-y: calc(0.3 * var(--jp-content-font-size4)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] { + font-size: var(--jp-content-font-size3); + background-position-y: calc(0.3 * var(--jp-content-font-size3)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] { + font-size: var(--jp-content-font-size2); + background-position-y: calc(0.3 * var(--jp-content-font-size2)); +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] { + font-size: var(--jp-content-font-size1); + background-position-y: top; +} + +.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] { + font-size: var(--jp-content-font-size0); + background-position-y: top; +} + +/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */ +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton { + display: none; +} + +.jp-Notebook.jp-mod-showHiddenCellsButton + :is(.jp-MarkdownCell:hover, .jp-mod-active) + .jp-collapseHeadingButton { + display: flex; +} + +/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which +is a consequence of the showHiddenCellsButton option in Notebook Settings)*/ +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton { + margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding)); + margin-top: var(--jp-code-padding); + border: 1px solid var(--jp-border-color2); + background-color: var(--jp-border-color3) !important; + color: var(--jp-content-font-color0) !important; + display: flex; +} + +.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover { + background-color: var(--jp-border-color2) !important; +} + +.jp-showHiddenCellsButton { + display: none; +} + +/*----------------------------------------------------------------------------- +| Printing +|----------------------------------------------------------------------------*/ + +/* +Using block instead of flex to allow the use of the break-inside CSS property for +cell outputs. +*/ + +@media print { + .jp-Cell-inputWrapper, + .jp-Cell-outputWrapper { + display: block; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-notebook-toolbar-padding: 2px 5px 2px 2px; +} + +/*----------------------------------------------------------------------------- + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-NotebookPanel-toolbar { + padding: var(--jp-notebook-toolbar-padding); + + /* disable paint containment from lumino 2.0 default strict CSS containment */ + contain: style size !important; +} + +.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused { + border: none; + box-shadow: none; +} + +.jp-Notebook-toolbarCellTypeDropdown select { + height: 24px; + font-size: var(--jp-ui-font-size1); + line-height: 14px; + border-radius: 0; + display: block; +} + +.jp-Notebook-toolbarCellTypeDropdown span { + top: 5px !important; +} + +.jp-Toolbar-responsive-popup { + position: absolute; + height: fit-content; + display: flex; + flex-direction: row; + flex-wrap: wrap; + justify-content: flex-end; + border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color); + box-shadow: var(--jp-toolbar-box-shadow); + background: var(--jp-toolbar-background); + min-height: var(--jp-toolbar-micro-height); + padding: var(--jp-notebook-toolbar-padding); + z-index: 1; + right: 0; + top: 0; +} + +.jp-Toolbar > .jp-Toolbar-responsive-opener { + margin-left: auto; +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Variables +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- + +/*----------------------------------------------------------------------------- +| Styles +|----------------------------------------------------------------------------*/ + +.jp-Notebook-ExecutionIndicator { + position: relative; + display: inline-block; + height: 100%; + z-index: 9997; +} + +.jp-Notebook-ExecutionIndicator-tooltip { + visibility: hidden; + height: auto; + width: max-content; + width: -moz-max-content; + background-color: var(--jp-layout-color2); + color: var(--jp-ui-font-color1); + text-align: justify; + border-radius: 6px; + padding: 0 5px; + position: fixed; + display: table; +} + +.jp-Notebook-ExecutionIndicator-tooltip.up { + transform: translateX(-50%) translateY(-100%) translateY(-32px); +} + +.jp-Notebook-ExecutionIndicator-tooltip.down { + transform: translateX(calc(-100% + 16px)) translateY(5px); +} + +.jp-Notebook-ExecutionIndicator-tooltip.hidden { + display: none; +} + +.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip { + visibility: visible; +} + +.jp-Notebook-ExecutionIndicator span { + font-size: var(--jp-ui-font-size1); + font-family: var(--jp-ui-font-family); + color: var(--jp-ui-font-color1); + line-height: 24px; + display: block; +} + +.jp-Notebook-ExecutionIndicator-progress-bar { + display: flex; + justify-content: center; + height: 100%; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +/* + * Execution indicator + */ +.jp-tocItem-content::after { + content: ''; + + /* Must be identical to form a circle */ + width: 12px; + height: 12px; + background: none; + border: none; + position: absolute; + right: 0; +} + +.jp-tocItem-content[data-running='0']::after { + border-radius: 50%; + border: var(--jp-border-width) solid var(--jp-inverse-layout-color3); + background: none; +} + +.jp-tocItem-content[data-running='1']::after { + border-radius: 50%; + border: var(--jp-border-width) solid var(--jp-inverse-layout-color3); + background-color: var(--jp-inverse-layout-color3); +} + +.jp-tocItem-content[data-running='0'], +.jp-tocItem-content[data-running='1'] { + margin-right: 12px; +} + +/* + * Copyright (c) Jupyter Development Team. + * Distributed under the terms of the Modified BSD License. + */ + +.jp-Notebook-footer { + height: 27px; + margin-left: calc( + var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) + + var(--jp-cell-padding) + ); + width: calc( + 100% - + ( + var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) + + var(--jp-cell-padding) + var(--jp-cell-padding) + ) + ); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + color: var(--jp-ui-font-color3); + margin-top: 6px; + background: none; + cursor: pointer; +} + +.jp-Notebook-footer:focus { + border-color: var(--jp-cell-editor-active-border-color); +} + +/* For devices that support hovering, hide footer until hover */ +@media (hover: hover) { + .jp-Notebook-footer { + opacity: 0; + } + + .jp-Notebook-footer:focus, + .jp-Notebook-footer:hover { + opacity: 1; + } +} + +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| Imports +|----------------------------------------------------------------------------*/ + +/*----------------------------------------------------------------------------- +| CSS variables +|----------------------------------------------------------------------------*/ + +:root { + --jp-side-by-side-output-size: 1fr; + --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size); + --jp-private-notebook-dragImage-width: 304px; + --jp-private-notebook-dragImage-height: 36px; + --jp-private-notebook-selected-color: var(--md-blue-400); + --jp-private-notebook-active-color: var(--md-green-400); +} + +/*----------------------------------------------------------------------------- +| Notebook +|----------------------------------------------------------------------------*/ + +/* stylelint-disable selector-max-class */ + +.jp-NotebookPanel { + display: block; + height: 100%; +} + +.jp-NotebookPanel.jp-Document { + min-width: 240px; + min-height: 120px; +} + +.jp-Notebook { + padding: var(--jp-notebook-padding); + outline: none; + overflow: auto; + background: var(--jp-layout-color0); +} + +.jp-Notebook.jp-mod-scrollPastEnd::after { + display: block; + content: ''; + min-height: var(--jp-notebook-scroll-padding); +} + +.jp-MainAreaWidget-ContainStrict .jp-Notebook * { + contain: strict; +} + +.jp-Notebook .jp-Cell { + overflow: visible; +} + +.jp-Notebook .jp-Cell .jp-InputPrompt { + cursor: move; +} + +/*----------------------------------------------------------------------------- +| Notebook state related styling +| +| The notebook and cells each have states, here are the possibilities: +| +| - Notebook +| - Command +| - Edit +| - Cell +| - None +| - Active (only one can be active) +| - Selected (the cells actions are applied to) +| - Multiselected (when multiple selected, the cursor) +| - No outputs +|----------------------------------------------------------------------------*/ + +/* Command or edit modes */ + +.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt { + opacity: var(--jp-cell-prompt-not-active-opacity); + color: var(--jp-cell-prompt-not-active-font-color); +} + +.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt { + opacity: var(--jp-cell-prompt-not-active-opacity); + color: var(--jp-cell-prompt-not-active-font-color); +} + +/* cell is active */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser { + background: var(--jp-brand-color1); +} + +/* cell is dirty */ +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt { + color: var(--jp-warn-color1); +} + +.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before { + color: var(--jp-warn-color1); + content: '•'; +} + +.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser { + background: var(--jp-warn-color1); +} + +/* collapser is hovered */ +.jp-Notebook .jp-Cell .jp-Collapser:hover { + box-shadow: var(--jp-elevation-z2); + background: var(--jp-brand-color1); + opacity: var(--jp-cell-collapser-not-active-hover-opacity); +} + +/* cell is active and collapser is hovered */ +.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover { + background: var(--jp-brand-color0); + opacity: 1; +} + +/* Command mode */ + +.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected { + background: var(--jp-notebook-multiselected-color); +} + +.jp-Notebook.jp-mod-commandMode + .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) { + background: transparent; +} + +/* Edit mode */ + +.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor { + border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color); + box-shadow: var(--jp-input-box-shadow); + background-color: var(--jp-cell-editor-active-background); +} + +/*----------------------------------------------------------------------------- +| Notebook drag and drop +|----------------------------------------------------------------------------*/ + +.jp-Notebook-cell.jp-mod-dropSource { + opacity: 0.5; +} + +.jp-Notebook-cell.jp-mod-dropTarget, +.jp-Notebook.jp-mod-commandMode + .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget { + border-top-color: var(--jp-private-notebook-selected-color); + border-top-style: solid; + border-top-width: 2px; +} + +.jp-dragImage { + display: block; + flex-direction: row; + width: var(--jp-private-notebook-dragImage-width); + height: var(--jp-private-notebook-dragImage-height); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background); + overflow: visible; +} + +.jp-dragImage-singlePrompt { + box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12); +} + +.jp-dragImage .jp-dragImage-content { + flex: 1 1 auto; + z-index: 2; + font-size: var(--jp-code-font-size); + font-family: var(--jp-code-font-family); + line-height: var(--jp-code-line-height); + padding: var(--jp-code-padding); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + background: var(--jp-cell-editor-background-color); + color: var(--jp-content-font-color3); + text-align: left; + margin: 4px 4px 4px 0; +} + +.jp-dragImage .jp-dragImage-prompt { + flex: 0 0 auto; + min-width: 36px; + color: var(--jp-cell-inprompt-font-color); + padding: var(--jp-code-padding); + padding-left: 12px; + font-family: var(--jp-cell-prompt-font-family); + letter-spacing: var(--jp-cell-prompt-letter-spacing); + line-height: 1.9; + font-size: var(--jp-code-font-size); + border: var(--jp-border-width) solid transparent; +} + +.jp-dragImage-multipleBack { + z-index: -1; + position: absolute; + height: 32px; + width: 300px; + top: 8px; + left: 8px; + background: var(--jp-layout-color2); + border: var(--jp-border-width) solid var(--jp-input-border-color); + box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12); +} + +/*----------------------------------------------------------------------------- +| Cell toolbar +|----------------------------------------------------------------------------*/ + +.jp-NotebookTools { + display: block; + min-width: var(--jp-sidebar-min-width); + color: var(--jp-ui-font-color1); + background: var(--jp-layout-color1); + + /* This is needed so that all font sizing of children done in ems is + * relative to this base size */ + font-size: var(--jp-ui-font-size1); + overflow: auto; +} + +.jp-ActiveCellTool { + padding: 12px 0; + display: flex; +} + +.jp-ActiveCellTool-Content { + flex: 1 1 auto; +} + +.jp-ActiveCellTool .jp-ActiveCellTool-CellContent { + background: var(--jp-cell-editor-background); + border: var(--jp-border-width) solid var(--jp-cell-editor-border-color); + border-radius: 0; + min-height: 29px; +} + +.jp-ActiveCellTool .jp-InputPrompt { + min-width: calc(var(--jp-cell-prompt-width) * 0.75); +} + +.jp-ActiveCellTool-CellContent > pre { + padding: 5px 4px; + margin: 0; + white-space: normal; +} + +.jp-MetadataEditorTool { + flex-direction: column; + padding: 12px 0; +} + +.jp-RankedPanel > :not(:first-child) { + margin-top: 12px; +} + +.jp-KeySelector select.jp-mod-styled { + font-size: var(--jp-ui-font-size1); + color: var(--jp-ui-font-color0); + border: var(--jp-border-width) solid var(--jp-border-color1); +} + +.jp-KeySelector label, +.jp-MetadataEditorTool label, +.jp-NumberSetter label { + line-height: 1.4; +} + +.jp-NotebookTools .jp-select-wrapper { + margin-top: 4px; + margin-bottom: 0; +} + +.jp-NumberSetter input { + width: 100%; + margin-top: 4px; +} + +.jp-NotebookTools .jp-Collapse { + margin-top: 16px; +} + +/*----------------------------------------------------------------------------- +| Presentation Mode (.jp-mod-presentationMode) +|----------------------------------------------------------------------------*/ + +.jp-mod-presentationMode .jp-Notebook { + --jp-content-font-size1: var(--jp-content-presentation-font-size1); + --jp-code-font-size: var(--jp-code-presentation-font-size); +} + +.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt, +.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt { + flex: 0 0 110px; +} + +/*----------------------------------------------------------------------------- +| Side-by-side Mode (.jp-mod-sideBySide) +|----------------------------------------------------------------------------*/ +.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell { + margin-top: 3em; + margin-bottom: 3em; + margin-left: 5%; + margin-right: 5%; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell { + display: grid; + grid-template-columns: minmax(0, 1fr) min-content minmax( + 0, + var(--jp-side-by-side-output-size) + ); + grid-template-rows: auto minmax(0, 1fr) auto; + grid-template-areas: + 'header header header' + 'input handle output' + 'footer footer footer'; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell { + grid-template-columns: minmax(0, 1fr) min-content minmax( + 0, + var(--jp-side-by-side-resized-cell) + ); +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader { + grid-area: header; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper { + grid-area: input; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper { + /* overwrite the default margin (no vertical separation needed in side by side move */ + margin-top: 0; + grid-area: output; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter { + grid-area: footer; +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle { + grid-area: handle; + user-select: none; + display: block; + height: 100%; + cursor: ew-resize; + padding: 0 var(--jp-cell-padding); +} + +.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after { + content: ''; + display: block; + background: var(--jp-border-color2); + height: 100%; + width: 5px; +} + +.jp-mod-sideBySide.jp-Notebook + .jp-CodeCell.jp-mod-resizedCell + .jp-CellResizeHandle::after { + background: var(--jp-border-color0); +} + +.jp-CellResizeHandle { + display: none; +} + +/*----------------------------------------------------------------------------- +| Placeholder +|----------------------------------------------------------------------------*/ + +.jp-Cell-Placeholder { + padding-left: 55px; +} + +.jp-Cell-Placeholder-wrapper { + background: #fff; + border: 1px solid; + border-color: #e5e6e9 #dfe0e4 #d0d1d5; + border-radius: 4px; + -webkit-border-radius: 4px; + margin: 10px 15px; +} + +.jp-Cell-Placeholder-wrapper-inner { + padding: 15px; + position: relative; +} + +.jp-Cell-Placeholder-wrapper-body { + background-repeat: repeat; + background-size: 50% auto; +} + +.jp-Cell-Placeholder-wrapper-body div { + background: #f6f7f8; + background-image: -webkit-linear-gradient( + left, + #f6f7f8 0%, + #edeef1 20%, + #f6f7f8 40%, + #f6f7f8 100% + ); + background-repeat: no-repeat; + background-size: 800px 104px; + height: 104px; + position: absolute; + right: 15px; + left: 15px; + top: 15px; +} + +div.jp-Cell-Placeholder-h1 { + top: 20px; + height: 20px; + left: 15px; + width: 150px; +} + +div.jp-Cell-Placeholder-h2 { + left: 15px; + top: 50px; + height: 10px; + width: 100px; +} + +div.jp-Cell-Placeholder-content-1, +div.jp-Cell-Placeholder-content-2, +div.jp-Cell-Placeholder-content-3 { + left: 15px; + right: 15px; + height: 10px; +} + +div.jp-Cell-Placeholder-content-1 { + top: 100px; +} + +div.jp-Cell-Placeholder-content-2 { + top: 120px; +} + +div.jp-Cell-Placeholder-content-3 { + top: 140px; +} + +</style> +<style type="text/css"> +/*----------------------------------------------------------------------------- +| Copyright (c) Jupyter Development Team. +| Distributed under the terms of the Modified BSD License. +|----------------------------------------------------------------------------*/ + +/* +The following CSS variables define the main, public API for styling JupyterLab. +These variables should be used by all plugins wherever possible. In other +words, plugins should not define custom colors, sizes, etc unless absolutely +necessary. This enables users to change the visual theme of JupyterLab +by changing these variables. + +Many variables appear in an ordered sequence (0,1,2,3). These sequences +are designed to work well together, so for example, `--jp-border-color1` should +be used with `--jp-layout-color1`. The numbers have the following meanings: + +* 0: super-primary, reserved for special emphasis +* 1: primary, most important under normal situations +* 2: secondary, next most important under normal situations +* 3: tertiary, next most important under normal situations + +Throughout JupyterLab, we are mostly following principles from Google's +Material Design when selecting colors. We are not, however, following +all of MD as it is not optimized for dense, information rich UIs. +*/ + +:root { + /* Elevation + * + * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: + * + * https://github.com/material-components/material-components-web + * https://material-components-web.appspot.com/elevation.html + */ + + --jp-shadow-base-lightness: 0; + --jp-shadow-umbra-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.2 + ); + --jp-shadow-penumbra-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.14 + ); + --jp-shadow-ambient-color: rgba( + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + var(--jp-shadow-base-lightness), + 0.12 + ); + --jp-elevation-z0: none; + --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color), + 0 1px 1px 0 var(--jp-shadow-penumbra-color), + 0 1px 3px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color), + 0 2px 2px 0 var(--jp-shadow-penumbra-color), + 0 1px 5px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color), + 0 4px 5px 0 var(--jp-shadow-penumbra-color), + 0 1px 10px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color), + 0 6px 10px 0 var(--jp-shadow-penumbra-color), + 0 1px 18px 0 var(--jp-shadow-ambient-color); + --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color), + 0 8px 10px 1px var(--jp-shadow-penumbra-color), + 0 3px 14px 2px var(--jp-shadow-ambient-color); + --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color), + 0 12px 17px 2px var(--jp-shadow-penumbra-color), + 0 5px 22px 4px var(--jp-shadow-ambient-color); + --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color), + 0 16px 24px 2px var(--jp-shadow-penumbra-color), + 0 6px 30px 5px var(--jp-shadow-ambient-color); + --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color), + 0 20px 31px 3px var(--jp-shadow-penumbra-color), + 0 8px 38px 7px var(--jp-shadow-ambient-color); + --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color), + 0 24px 38px 3px var(--jp-shadow-penumbra-color), + 0 9px 46px 8px var(--jp-shadow-ambient-color); + + /* Borders + * + * The following variables, specify the visual styling of borders in JupyterLab. + */ + + --jp-border-width: 1px; + --jp-border-color0: var(--md-grey-400); + --jp-border-color1: var(--md-grey-400); + --jp-border-color2: var(--md-grey-300); + --jp-border-color3: var(--md-grey-200); + --jp-inverse-border-color: var(--md-grey-600); + --jp-border-radius: 2px; + + /* UI Fonts + * + * The UI font CSS variables are used for the typography all of the JupyterLab + * user interface elements that are not directly user generated content. + * + * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 + * is applied to a parent element. When children elements, such as headings, are sized + * in em all things will be computed relative to that body size. + */ + + --jp-ui-font-scale-factor: 1.2; + --jp-ui-font-size0: 0.83333em; + --jp-ui-font-size1: 13px; /* Base font size */ + --jp-ui-font-size2: 1.2em; + --jp-ui-font-size3: 1.44em; + --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI', + helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', + 'Segoe UI Symbol'; + + /* + * Use these font colors against the corresponding main layout colors. + * In a light theme, these go from dark to light. + */ + + /* Defaults use Material Design specification */ + --jp-ui-font-color0: rgba(0, 0, 0, 1); + --jp-ui-font-color1: rgba(0, 0, 0, 0.87); + --jp-ui-font-color2: rgba(0, 0, 0, 0.54); + --jp-ui-font-color3: rgba(0, 0, 0, 0.38); + + /* + * Use these against the brand/accent/warn/error colors. + * These will typically go from light to darker, in both a dark and light theme. + */ + + --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); + --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); + --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); + --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); + + /* Content Fonts + * + * Content font variables are used for typography of user generated content. + * + * The font sizing here is done assuming that the body font size of --jp-content-font-size1 + * is applied to a parent element. When children elements, such as headings, are sized + * in em all things will be computed relative to that body size. + */ + + --jp-content-line-height: 1.6; + --jp-content-font-scale-factor: 1.2; + --jp-content-font-size0: 0.83333em; + --jp-content-font-size1: 14px; /* Base font size */ + --jp-content-font-size2: 1.2em; + --jp-content-font-size3: 1.44em; + --jp-content-font-size4: 1.728em; + --jp-content-font-size5: 2.0736em; + + /* This gives a magnification of about 125% in presentation mode over normal. */ + --jp-content-presentation-font-size1: 17px; + --jp-content-heading-line-height: 1; + --jp-content-heading-margin-top: 1.2em; + --jp-content-heading-margin-bottom: 0.8em; + --jp-content-heading-font-weight: 500; + + /* Defaults use Material Design specification */ + --jp-content-font-color0: rgba(0, 0, 0, 1); + --jp-content-font-color1: rgba(0, 0, 0, 0.87); + --jp-content-font-color2: rgba(0, 0, 0, 0.54); + --jp-content-font-color3: rgba(0, 0, 0, 0.38); + --jp-content-link-color: var(--md-blue-900); + --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont, + 'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji', + 'Segoe UI Emoji', 'Segoe UI Symbol'; + + /* + * Code Fonts + * + * Code font variables are used for typography of code and other monospaces content. + */ + + --jp-code-font-size: 13px; + --jp-code-line-height: 1.3077; /* 17px for 13px base */ + --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ + --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace; + --jp-code-font-family: var(--jp-code-font-family-default); + + /* This gives a magnification of about 125% in presentation mode over normal. */ + --jp-code-presentation-font-size: 16px; + + /* may need to tweak cursor width if you change font size */ + --jp-code-cursor-width0: 1.4px; + --jp-code-cursor-width1: 2px; + --jp-code-cursor-width2: 4px; + + /* Layout + * + * The following are the main layout colors use in JupyterLab. In a light + * theme these would go from light to dark. + */ + + --jp-layout-color0: white; + --jp-layout-color1: white; + --jp-layout-color2: var(--md-grey-200); + --jp-layout-color3: var(--md-grey-400); + --jp-layout-color4: var(--md-grey-600); + + /* Inverse Layout + * + * The following are the inverse layout colors use in JupyterLab. In a light + * theme these would go from dark to light. + */ + + --jp-inverse-layout-color0: #111; + --jp-inverse-layout-color1: var(--md-grey-900); + --jp-inverse-layout-color2: var(--md-grey-800); + --jp-inverse-layout-color3: var(--md-grey-700); + --jp-inverse-layout-color4: var(--md-grey-600); + + /* Brand/accent */ + + --jp-brand-color0: var(--md-blue-900); + --jp-brand-color1: var(--md-blue-700); + --jp-brand-color2: var(--md-blue-300); + --jp-brand-color3: var(--md-blue-100); + --jp-brand-color4: var(--md-blue-50); + --jp-accent-color0: var(--md-green-900); + --jp-accent-color1: var(--md-green-700); + --jp-accent-color2: var(--md-green-300); + --jp-accent-color3: var(--md-green-100); + + /* State colors (warn, error, success, info) */ + + --jp-warn-color0: var(--md-orange-900); + --jp-warn-color1: var(--md-orange-700); + --jp-warn-color2: var(--md-orange-300); + --jp-warn-color3: var(--md-orange-100); + --jp-error-color0: var(--md-red-900); + --jp-error-color1: var(--md-red-700); + --jp-error-color2: var(--md-red-300); + --jp-error-color3: var(--md-red-100); + --jp-success-color0: var(--md-green-900); + --jp-success-color1: var(--md-green-700); + --jp-success-color2: var(--md-green-300); + --jp-success-color3: var(--md-green-100); + --jp-info-color0: var(--md-cyan-900); + --jp-info-color1: var(--md-cyan-700); + --jp-info-color2: var(--md-cyan-300); + --jp-info-color3: var(--md-cyan-100); + + /* Cell specific styles */ + + --jp-cell-padding: 5px; + --jp-cell-collapser-width: 8px; + --jp-cell-collapser-min-height: 20px; + --jp-cell-collapser-not-active-hover-opacity: 0.6; + --jp-cell-editor-background: var(--md-grey-100); + --jp-cell-editor-border-color: var(--md-grey-300); + --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); + --jp-cell-editor-active-background: var(--jp-layout-color0); + --jp-cell-editor-active-border-color: var(--jp-brand-color1); + --jp-cell-prompt-width: 64px; + --jp-cell-prompt-font-family: var(--jp-code-font-family-default); + --jp-cell-prompt-letter-spacing: 0; + --jp-cell-prompt-opacity: 1; + --jp-cell-prompt-not-active-opacity: 0.5; + --jp-cell-prompt-not-active-font-color: var(--md-grey-700); + + /* A custom blend of MD grey and blue 600 + * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ + --jp-cell-inprompt-font-color: #307fc1; + + /* A custom blend of MD grey and orange 600 + * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ + --jp-cell-outprompt-font-color: #bf5b3d; + + /* Notebook specific styles */ + + --jp-notebook-padding: 10px; + --jp-notebook-select-background: var(--jp-layout-color1); + --jp-notebook-multiselected-color: var(--md-blue-50); + + /* The scroll padding is calculated to fill enough space at the bottom of the + notebook to show one single-line cell (with appropriate padding) at the top + when the notebook is scrolled all the way to the bottom. We also subtract one + pixel so that no scrollbar appears if we have just one single-line cell in the + notebook. This padding is to enable a 'scroll past end' feature in a notebook. + */ + --jp-notebook-scroll-padding: calc( + 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - + var(--jp-code-padding) - var(--jp-cell-padding) - 1px + ); + + /* Rendermime styles */ + + --jp-rendermime-error-background: #fdd; + --jp-rendermime-table-row-background: var(--md-grey-100); + --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); + + /* Dialog specific styles */ + + --jp-dialog-background: rgba(0, 0, 0, 0.25); + + /* Console specific styles */ + + --jp-console-padding: 10px; + + /* Toolbar specific styles */ + + --jp-toolbar-border-color: var(--jp-border-color1); + --jp-toolbar-micro-height: 8px; + --jp-toolbar-background: var(--jp-layout-color1); + --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24); + --jp-toolbar-header-margin: 4px 4px 0 4px; + --jp-toolbar-active-background: var(--md-grey-300); + + /* Statusbar specific styles */ + + --jp-statusbar-height: 24px; + + /* Input field styles */ + + --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); + --jp-input-active-background: var(--jp-layout-color1); + --jp-input-hover-background: var(--jp-layout-color1); + --jp-input-background: var(--md-grey-100); + --jp-input-border-color: var(--jp-inverse-border-color); + --jp-input-active-border-color: var(--jp-brand-color1); + --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); + + /* General editor styles */ + + --jp-editor-selected-background: #d9d9d9; + --jp-editor-selected-focused-background: #d7d4f0; + --jp-editor-cursor-color: var(--jp-ui-font-color0); + + /* Code mirror specific styles */ + + --jp-mirror-editor-keyword-color: #008000; + --jp-mirror-editor-atom-color: #88f; + --jp-mirror-editor-number-color: #080; + --jp-mirror-editor-def-color: #00f; + --jp-mirror-editor-variable-color: var(--md-grey-900); + --jp-mirror-editor-variable-2-color: rgb(0, 54, 109); + --jp-mirror-editor-variable-3-color: #085; + --jp-mirror-editor-punctuation-color: #05a; + --jp-mirror-editor-property-color: #05a; + --jp-mirror-editor-operator-color: #a2f; + --jp-mirror-editor-comment-color: #408080; + --jp-mirror-editor-string-color: #ba2121; + --jp-mirror-editor-string-2-color: #708; + --jp-mirror-editor-meta-color: #a2f; + --jp-mirror-editor-qualifier-color: #555; + --jp-mirror-editor-builtin-color: #008000; + --jp-mirror-editor-bracket-color: #997; + --jp-mirror-editor-tag-color: #170; + --jp-mirror-editor-attribute-color: #00c; + --jp-mirror-editor-header-color: blue; + --jp-mirror-editor-quote-color: #090; + --jp-mirror-editor-link-color: #00c; + --jp-mirror-editor-error-color: #f00; + --jp-mirror-editor-hr-color: #999; + + /* + RTC user specific colors. + These colors are used for the cursor, username in the editor, + and the icon of the user. + */ + + --jp-collaborator-color1: #ffad8e; + --jp-collaborator-color2: #dac83d; + --jp-collaborator-color3: #72dd76; + --jp-collaborator-color4: #00e4d0; + --jp-collaborator-color5: #45d4ff; + --jp-collaborator-color6: #e2b1ff; + --jp-collaborator-color7: #ff9de6; + + /* Vega extension styles */ + + --jp-vega-background: white; + + /* Sidebar-related styles */ + + --jp-sidebar-min-width: 250px; + + /* Search-related styles */ + + --jp-search-toggle-off-opacity: 0.5; + --jp-search-toggle-hover-opacity: 0.8; + --jp-search-toggle-on-opacity: 1; + --jp-search-selected-match-background-color: rgb(245, 200, 0); + --jp-search-selected-match-color: black; + --jp-search-unselected-match-background-color: var( + --jp-inverse-layout-color0 + ); + --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); + + /* Icon colors that work well with light or dark backgrounds */ + --jp-icon-contrast-color0: var(--md-purple-600); + --jp-icon-contrast-color1: var(--md-green-600); + --jp-icon-contrast-color2: var(--md-pink-600); + --jp-icon-contrast-color3: var(--md-blue-600); + + /* Button colors */ + --jp-accept-color-normal: var(--md-blue-700); + --jp-accept-color-hover: var(--md-blue-800); + --jp-accept-color-active: var(--md-blue-900); + --jp-warn-color-normal: var(--md-red-700); + --jp-warn-color-hover: var(--md-red-800); + --jp-warn-color-active: var(--md-red-900); + --jp-reject-color-normal: var(--md-grey-600); + --jp-reject-color-hover: var(--md-grey-700); + --jp-reject-color-active: var(--md-grey-800); + + /* File or activity icons and switch semantic variables */ + --jp-jupyter-icon-color: #f37626; + --jp-notebook-icon-color: #f37626; + --jp-json-icon-color: var(--md-orange-700); + --jp-console-icon-background-color: var(--md-blue-700); + --jp-console-icon-color: white; + --jp-terminal-icon-background-color: var(--md-grey-800); + --jp-terminal-icon-color: var(--md-grey-200); + --jp-text-editor-icon-color: var(--md-grey-700); + --jp-inspector-icon-color: var(--md-grey-700); + --jp-switch-color: var(--md-grey-400); + --jp-switch-true-position-color: var(--md-orange-900); +} +</style> +<style type="text/css"> +/* Force rendering true colors when outputing to pdf */ +* { + -webkit-print-color-adjust: exact; +} + +/* Misc */ +a.anchor-link { + display: none; +} + +/* Input area styling */ +.jp-InputArea { + overflow: hidden; +} + +.jp-InputArea-editor { + overflow: hidden; +} + +.cm-editor.cm-s-jupyter .highlight pre { +/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */ + padding: var(--jp-code-padding) 4px; + margin: 0; + + font-family: inherit; + font-size: inherit; + line-height: inherit; + color: inherit; + +} + +.jp-OutputArea-output pre { + line-height: inherit; + font-family: inherit; +} + +.jp-RenderedText pre { + color: var(--jp-content-font-color1); + font-size: var(--jp-code-font-size); +} + +/* Hiding the collapser by default */ +.jp-Collapser { + display: none; +} + +@page { + margin: 0.5in; /* Margin for each printed piece of paper */ +} + +@media print { + .jp-Cell-inputWrapper, + .jp-Cell-outputWrapper { + display: block; + } +} +</style> +<!-- Load mathjax --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS_CHTML-full,Safe"> </script> +<!-- MathJax configuration --> +<script type="text/x-mathjax-config"> + init_mathjax = function() { + if (window.MathJax) { + // MathJax loaded + MathJax.Hub.Config({ + TeX: { + equationNumbers: { + autoNumber: "AMS", + useLabelIds: true + } + }, + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + displayMath: [ ['$$','$$'], ["\\[","\\]"] ], + processEscapes: true, + processEnvironments: true + }, + displayAlign: 'center', + messageStyle: 'none', + CommonHTML: { + linebreaks: { + automatic: true + } + } + }); + + MathJax.Hub.Queue(["Typeset", MathJax.Hub]); + } + } + init_mathjax(); + </script> +<!-- End of mathjax configuration --><script type="module"> + document.addEventListener("DOMContentLoaded", async () => { + const diagrams = document.querySelectorAll(".jp-Mermaid > pre.mermaid"); + // do not load mermaidjs if not needed + if (!diagrams.length) { + return; + } + const mermaid = (await import("https://cdnjs.cloudflare.com/ajax/libs/mermaid/10.7.0/mermaid.esm.min.mjs")).default; + const parser = new DOMParser(); + + mermaid.initialize({ + maxTextSize: 100000, + maxEdges: 100000, + startOnLoad: false, + fontFamily: window + .getComputedStyle(document.body) + .getPropertyValue("--jp-ui-font-family"), + theme: document.querySelector("body[data-jp-theme-light='true']") + ? "default" + : "dark", + }); + + let _nextMermaidId = 0; + + function makeMermaidImage(svg) { + const img = document.createElement("img"); + const doc = parser.parseFromString(svg, "image/svg+xml"); + const svgEl = doc.querySelector("svg"); + const { maxWidth } = svgEl?.style || {}; + const firstTitle = doc.querySelector("title"); + const firstDesc = doc.querySelector("desc"); + + img.setAttribute("src", `data:image/svg+xml,${encodeURIComponent(svg)}`); + if (maxWidth) { + img.width = parseInt(maxWidth); + } + if (firstTitle) { + img.setAttribute("alt", firstTitle.textContent); + } + if (firstDesc) { + const caption = document.createElement("figcaption"); + caption.className = "sr-only"; + caption.textContent = firstDesc.textContent; + return [img, caption]; + } + return [img]; + } + + async function makeMermaidError(text) { + let errorMessage = ""; + try { + await mermaid.parse(text); + } catch (err) { + errorMessage = `${err}`; + } + + const result = document.createElement("details"); 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+ if (!rest.endsWith('/')) { + rest = `${rest} /`; + } + return `<${tag} ${rest}>`; + } + + void Promise.all([...diagrams].map(renderOneMarmaid)); + }); +</script> +<style> + .jp-Mermaid:not(.jp-RenderedMermaid) { + display: none; + } + + .jp-RenderedMermaid { + overflow: auto; + display: flex; + } + + .jp-RenderedMermaid.jp-mod-warning { + width: auto; + padding: 0.5em; + margin-top: 0.5em; + border: var(--jp-border-width) solid var(--jp-warn-color2); + border-radius: var(--jp-border-radius); + color: var(--jp-ui-font-color1); + font-size: var(--jp-ui-font-size1); + white-space: pre-wrap; + word-wrap: break-word; + } + + .jp-RenderedMermaid figure { + margin: 0; + overflow: auto; + max-width: 100%; + } + + .jp-RenderedMermaid img { + max-width: 100%; + } + + .jp-RenderedMermaid-Details > pre { + margin-top: 1em; + } + + .jp-RenderedMermaid-Summary { + color: var(--jp-warn-color2); + } + + .jp-RenderedMermaid:not(.jp-mod-warning) pre { + display: none; + } + + .jp-RenderedMermaid-Summary > pre { + display: inline-block; + white-space: normal; + } +</style> +<!-- End of mermaid configuration --></head> +<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light"> +<main> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=25c16740"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h1 id="Unsupervised-Learning-Final-Project:-Iris-Species-Clustering-Analysis">Unsupervised Learning Final Project: Iris Species Clustering Analysis<a class="anchor-link" href="#Unsupervised-Learning-Final-Project:-Iris-Species-Clustering-Analysis">¶</a></h1><h2 id="Comparative-Study-of-K-Means-vs-Agglomerative-Hierarchical-Clustering">Comparative Study of K-Means vs Agglomerative Hierarchical Clustering<a class="anchor-link" href="#Comparative-Study-of-K-Means-vs-Agglomerative-Hierarchical-Clustering">¶</a></h2><p>A copy of this notebook can be found on <a href="git@github.com:Farzat07/Unsupervised-Learning-Final-Project-Iris-Species-Clustering-Analysis.git">GitHub</a></p> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=08f46d2d"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Problem-Description">Problem Description<a class="anchor-link" href="#Problem-Description">¶</a></h2><h3 id="Background">Background<a class="anchor-link" href="#Background">¶</a></h3><p>The Iris flower dataset is a classic multivariate dataset introduced by Sir Ronald Fisher in 1936. It consists of 150 samples from three species of Iris flowers (Iris setosa, Iris versicolor, and Iris virginica) with four features measured from each sample: sepal length, sepal width, petal length, and petal width.</p> +<p>This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. While one class is linearly separable from the other 2, the latter are NOT linearly separable from each other, making the problem interesting.</p> +<h3 id="Objectives">Objectives<a class="anchor-link" href="#Objectives">¶</a></h3><p>This project aims to:</p> +<ul> +<li>Apply and compare two fundamental clustering algorithms: K-Means and Agglomerative Hierarchical Clustering</li> +<li>Evaluate clustering performance using both internal (silhouette score) and external (accuracy, ARI) metrics</li> +<li>Determine optimal number of clusters using multiple validation techniques</li> +<li>Compare different linkage methods and metric parameters for Agglomerative Clustering</li> +</ul> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=0653efb8"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Import-Necessary-Libraries">Import Necessary Libraries<a class="anchor-link" href="#Import-Necessary-Libraries">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6500b472"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">permutations</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">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</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">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <span class="n">KMeans</span><span class="p">,</span> <span class="n">AgglomerativeClustering</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">StandardScaler</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">PCA</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">silhouette_score</span><span class="p">,</span> <span class="n">adjusted_rand_score</span><span class="p">,</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">confusion_matrix</span> +<span class="kn">import</span><span class="w"> </span><span class="nn">scipy.cluster.hierarchy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sch</span> +<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">stats</span> + +<span class="o">%</span><span class="k">matplotlib</span> inline +</pre></div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3b028dd1"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Load-and-Explore-the-Dataset">Load and Explore the Dataset<a class="anchor-link" href="#Load-and-Explore-the-Dataset">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=9fcf1e5d"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Load the Iris dataset</span> +<span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span> +<span class="n">X</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">data</span> +<span class="n">target</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span> +<span class="n">species_names</span> <span class="o">=</span> <span class="n">iris</span><span class="o">.</span><span class="n">target_names</span> + +<span class="c1"># Create a DataFrame for better visualization</span> +<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span> +<span class="n">df</span><span class="p">[</span><span class="s1">'species'</span><span class="p">]</span> <span class="o">=</span> <span class="n">species_names</span><span class="p">[</span><span class="n">target</span><span class="p">]</span> +<span class="n">df</span><span class="p">[</span><span class="s1">'true_labels'</span><span class="p">]</span> <span class="o">=</span> <span class="n">target</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"Dataset Overview:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Features:"</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Species:"</span><span class="p">,</span> <span class="n">species_names</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Dataframe structure:"</span><span class="p">)</span> +<span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">()</span> +<span class="n">df</span><span class="o">.</span><span class="n">species</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span> +<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Dataset Overview: +Features: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'] +Species: ['setosa', 'versicolor', 'virginica'] + +Dataframe structure: +<class 'pandas.core.frame.DataFrame'> +RangeIndex: 150 entries, 0 to 149 +Data columns (total 6 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 sepal length (cm) 150 non-null float64 + 1 sepal width (cm) 150 non-null float64 + 2 petal length (cm) 150 non-null float64 + 3 petal width (cm) 150 non-null float64 + 4 species 150 non-null object + 5 true_labels 150 non-null int64 +dtypes: float64(4), int64(1), object(1) +memory usage: 7.2+ KB +</pre> +</div> +</div> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div> +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0"> +<div> +<style scoped=""> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> +<thead> +<tr style="text-align: right;"> +<th></th> +<th>sepal length (cm)</th> +<th>sepal width (cm)</th> +<th>petal length (cm)</th> +<th>petal width (cm)</th> +<th>species</th> +<th>true_labels</th> +</tr> +</thead> +<tbody> +<tr> +<th>0</th> +<td>5.1</td> +<td>3.5</td> +<td>1.4</td> +<td>0.2</td> +<td>setosa</td> +<td>0</td> +</tr> +<tr> +<th>1</th> +<td>4.9</td> +<td>3.0</td> +<td>1.4</td> +<td>0.2</td> +<td>setosa</td> +<td>0</td> +</tr> +<tr> +<th>2</th> +<td>4.7</td> +<td>3.2</td> +<td>1.3</td> +<td>0.2</td> +<td>setosa</td> +<td>0</td> +</tr> +<tr> +<th>3</th> +<td>4.6</td> +<td>3.1</td> +<td>1.5</td> +<td>0.2</td> +<td>setosa</td> +<td>0</td> +</tr> +<tr> +<th>4</th> +<td>5.0</td> +<td>3.6</td> +<td>1.4</td> +<td>0.2</td> +<td>setosa</td> +<td>0</td> +</tr> +</tbody> +</table> +</div> +</div> +</div> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4602986e"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="EDA-(Exploratory-Data-Analysis)">EDA (Exploratory Data Analysis)<a class="anchor-link" href="#EDA-(Exploratory-Data-Analysis)">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=24f44dd6"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Basic statistics</span> +<span class="n">df</span><span class="p">[</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child jp-OutputArea-executeResult"> +<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div> +<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0"> +<div> +<style scoped=""> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> +<thead> +<tr style="text-align: right;"> +<th></th> +<th>sepal length (cm)</th> +<th>sepal width (cm)</th> +<th>petal length (cm)</th> +<th>petal width (cm)</th> +</tr> +</thead> +<tbody> +<tr> +<th>count</th> +<td>150.00</td> +<td>150.00</td> +<td>150.00</td> +<td>150.00</td> +</tr> +<tr> +<th>mean</th> +<td>5.84</td> +<td>3.06</td> +<td>3.76</td> +<td>1.20</td> +</tr> +<tr> +<th>std</th> +<td>0.83</td> +<td>0.44</td> +<td>1.77</td> +<td>0.76</td> +</tr> +<tr> +<th>min</th> +<td>4.30</td> +<td>2.00</td> +<td>1.00</td> +<td>0.10</td> +</tr> +<tr> +<th>25%</th> +<td>5.10</td> +<td>2.80</td> +<td>1.60</td> +<td>0.30</td> +</tr> +<tr> +<th>50%</th> +<td>5.80</td> +<td>3.00</td> +<td>4.35</td> +<td>1.30</td> +</tr> +<tr> +<th>75%</th> +<td>6.40</td> +<td>3.30</td> +<td>5.10</td> +<td>1.80</td> +</tr> +<tr> +<th>max</th> +<td>7.90</td> +<td>4.40</td> +<td>6.90</td> +<td>2.50</td> +</tr> +</tbody> +</table> +</div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ff94232b"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Check for missing values</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"MISSING VALUES:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">30</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>MISSING VALUES: +============================== +sepal length (cm) 0 +sepal width (cm) 0 +petal length (cm) 0 +petal width (cm) 0 +species 0 +true_labels 0 +dtype: int64 +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3998453c"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Statistical-Analysis-by-Species">Statistical Analysis by Species<a class="anchor-link" href="#Statistical-Analysis-by-Species">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=4af6c182"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Statistics by species</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"STATISTICS BY SPECIES:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">50</span><span class="p">)</span> +<span class="n">species_stats</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'species'</span><span class="p">)[</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'std'</span><span class="p">,</span> <span class="s1">'min'</span><span class="p">,</span> <span class="s1">'max'</span><span class="p">])</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">species_stats</span><span class="p">)</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>STATISTICS BY SPECIES: +================================================== + sepal length (cm) sepal width (cm) \ + mean std min max mean std min max +species +setosa 5.01 0.35 4.3 5.8 3.43 0.38 2.3 4.4 +versicolor 5.94 0.52 4.9 7.0 2.77 0.31 2.0 3.4 +virginica 6.59 0.64 4.9 7.9 2.97 0.32 2.2 3.8 + + petal length (cm) petal width (cm) + mean std min max mean std min max +species +setosa 1.46 0.17 1.0 1.9 0.25 0.11 0.1 0.6 +versicolor 4.26 0.47 3.0 5.1 1.33 0.20 1.0 1.8 +virginica 5.55 0.55 4.5 6.9 2.03 0.27 1.4 2.5 +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=24e38cd1"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Visual-EDA">Visual EDA<a class="anchor-link" href="#Visual-EDA">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8d60fe92"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Pairplot to see relationships between features</span> +<span class="n">sns</span><span class="o">.</span><span class="n">pairplot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'species'</span><span class="p">,</span> <span class="n">diag_kind</span><span class="o">=</span><span class="s1">'hist'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Pairplot of Iris Features by Species'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">1.02</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=62129f7c"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Correlation matrix</span> +<span class="n">correlation_matrix</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span> +<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">correlation_matrix</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'coolwarm'</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'.2f'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Correlation Matrix of Iris Features'</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=49bd7eb6-c2fb-4e09-98d8-43d25ffb04a8"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">¶</a></h3><p>It seems that petal length and with are very highly correlated, meaning that they could be treated as one dimention.</p> +<p>Sepal width seems to be much less correlated with the petal dimention. It is likely that after the petal dimention, sepal width would be the second decisive factor in the classification.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=7e7de513"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Boxplots for each feature by species</span> +<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> +<span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> + +<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">):</span> + <span class="n">sns</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'species'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">feature</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span> + <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span> + <span class="n">ax</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> + +<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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> +</div> +<div 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+</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=62bf6bb1"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Distribution plots for each feature</span> +<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> +<span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span> + +<span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">feature</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">feature_names</span><span class="p">):</span> + <span class="k">for</span> <span class="n">species</span> <span class="ow">in</span> <span class="n">species_names</span><span class="p">:</span> + <span class="n">species_data</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'species'</span><span class="p">]</span> <span class="o">==</span> <span class="n">species</span><span class="p">][</span><span class="n">feature</span><span class="p">]</span> + <span class="n">ax</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">species_data</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">species</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Distribution of </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">feature</span><span class="p">)</span> + <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span> + <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Density"</span><span class="p">)</span> + <span class="n">ax</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">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4a3f6e48"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Data-Preprocessing">Data Preprocessing<a class="anchor-link" href="#Data-Preprocessing">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=2de6b0c3"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Scale the numerical features</span> +<span class="c1"># This is necessary as the above EDA confirms that the features are well varied in their means and standard deviations</span> +<span class="c1"># Scaling the data should make the models less biased</span> +<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span> +<span class="n">X_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> +<span class="c1"># There is no need to perform other transformations, such as log transformation, as the data seems to almost follow a normal distribution</span> +<span class="c1"># The number of outliers is also negligible</span> +</pre></div> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=21df2f07"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="PCA-for-Dimensionality-Reduction-and-Visualization">PCA for Dimensionality Reduction and Visualization<a class="anchor-link" href="#PCA-for-Dimensionality-Reduction-and-Visualization">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=7280b101"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Perform PCA for visualization</span> +<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> +<span class="n">X_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"PCA RESULTS:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">50</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Explained variance ratio:"</span><span class="p">,</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Total explained variance: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="nb">sum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>PCA RESULTS: +================================================== +Explained variance ratio: [0.72962445 0.22850762] +Total explained variance: 0.958 +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2a8605cc-6a7e-4612-a94e-fe4303b4243b"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">¶</a></h3><p>Note that only two dimentions were enough to describe 95.8% of the variance, suggesting that the observations made about the correlation above were correct.</p> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=81002915"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Visualize PCA results</span> +<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">target</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'PCA: Actual Species'</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">handles</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">species_names</span><span class="o">.</span><span class="n">tolist</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=2e79a17f-f12a-4f0c-b77d-b35a03766fb1"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">¶</a></h3><p>Based on this visualisation, it is expected that the model will classify setosa with almost 100% accuracy, while struggling with the versicolor/virginica pair.</p> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ae05d031"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Determine-Optimal-Number-of-Clusters">Determine Optimal Number of Clusters<a class="anchor-link" href="#Determine-Optimal-Number-of-Clusters">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=489eaee6"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Function to find best label permutation for accuracy</span> +<span class="k">def</span><span class="w"> </span><span class="nf">label_permute_compare</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span> +<span class="w"> </span><span class="sd">"""</span> +<span class="sd"> y_true: true labels</span> +<span class="sd"> y_pred: predicted cluster labels</span> +<span class="sd"> Returns:</span> +<span class="sd"> - best_perm: best permutation of labels found</span> +<span class="sd"> - best_acc: best accuracy achieved</span> +<span class="sd"> - best_y_pred_mapped: predicted labels mapped using best permutation</span> +<span class="sd"> """</span> + <span class="n">unique_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_pred</span><span class="p">)</span> + <span class="n">best_acc</span> <span class="o">=</span> <span class="mf">0.</span> + + <span class="c1"># Generate all possible permutations of the unique labels</span> + <span class="k">for</span> <span class="n">perm</span> <span class="ow">in</span> <span class="n">permutations</span><span class="p">(</span><span class="n">unique_labels</span><span class="p">):</span> + <span class="c1"># Create mapping from original cluster labels to permuted labels</span> + <span class="n">y_pred_mapped</span> <span class="o">=</span> <span class="p">[</span><span class="n">perm</span><span class="p">[</span><span class="n">label</span><span class="p">]</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">y_pred</span><span class="p">]</span> + + <span class="n">accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred_mapped</span><span class="p">)</span> + <span class="k">if</span> <span class="n">accuracy</span> <span class="o">></span> <span class="n">best_acc</span><span class="p">:</span> + <span class="n">best_acc</span> <span class="o">=</span> <span class="n">accuracy</span> + <span class="n">best_perm</span> <span class="o">=</span> <span class="n">perm</span> + <span class="n">best_y_pred_mapped</span> <span class="o">=</span> <span class="n">y_pred_mapped</span> + + <span class="k">return</span> <span class="n">best_perm</span><span class="p">,</span> <span class="n">best_acc</span><span class="p">,</span> <span class="n">best_y_pred_mapped</span> +</pre></div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=f9b3624e"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Determine optimal number of clusters using multiple metrics</span> +<span class="n">inertia</span> <span class="o">=</span> <span class="p">[]</span> +<span class="n">silhouette_scores_kmeans</span> <span class="o">=</span> <span class="p">[]</span> +<span class="n">silhouette_scores_agg</span> <span class="o">=</span> <span class="p">[]</span> +<span class="n">accuracy_scores_kmeans</span> <span class="o">=</span> <span class="p">[]</span> +<span class="n">accuracy_scores_agg</span> <span class="o">=</span> <span class="p">[]</span> +<span class="n">k_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span> + +<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">k_range</span><span class="p">:</span> + <span class="c1"># K-Means</span> + <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> + <span class="n">kmeans_labels</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + <span class="n">inertia</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span> + <span class="n">silhouette_scores_kmeans</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">kmeans_labels</span><span class="p">))</span> + <span class="n">accuracy_scores_kmeans</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">kmeans_labels</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> + + <span class="c1"># Agglomerative Clustering (default parameters)</span> + <span class="n">agg</span> <span class="o">=</span> <span class="n">AgglomerativeClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">)</span> + <span class="n">agg_labels</span> <span class="o">=</span> <span class="n">agg</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + <span class="n">silhouette_scores_agg</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">agg_labels</span><span class="p">))</span> + <span class="n">accuracy_scores_agg</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span> +</pre></div> +</div> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=84bc8262"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Plot comparison of methods</span> +<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span> + +<span class="c1"># Elbow Method</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_range</span><span class="p">,</span> <span class="n">inertia</span><span class="p">,</span> <span class="s1">'bo-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'K-Means'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Number of clusters'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Inertia'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Elbow Method (K-Means Only)'</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="c1"># Silhouette Scores Comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_range</span><span class="p">,</span> <span class="n">silhouette_scores_kmeans</span><span class="p">,</span> <span class="s1">'ro-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'K-Means'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_range</span><span class="p">,</span> <span class="n">silhouette_scores_agg</span><span class="p">,</span> <span class="s1">'go-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Agglomerative'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Number of clusters'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Silhouette Score'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Silhouette Scores Comparison'</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="c1"># Accuracy Scores Comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_range</span><span class="p">,</span> <span class="n">accuracy_scores_kmeans</span><span class="p">,</span> <span class="s1">'ro-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'K-Means'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">k_range</span><span class="p">,</span> <span class="n">accuracy_scores_agg</span><span class="p">,</span> <span class="s1">'go-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Agglomerative'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Number of clusters'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Accuracy Score'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Accuracy Scores Comparison'</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="c1"># Dendrogram for Agglomerative Clustering</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> +<span class="n">dendrogram</span> <span class="o">=</span> <span class="n">sch</span><span class="o">.</span><span class="n">dendrogram</span><span class="p">(</span><span class="n">sch</span><span class="o">.</span><span class="n">linkage</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'ward'</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Dendrogram for Agglomerative Clustering</span><span class="se">\n</span><span class="s1">(ward linkage)'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Samples'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Euclidean Distance'</span><span class="p">)</span> + +<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=65953621"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Compare-Different-Linkage-Methods-and-Metric-Parameters">Compare Different Linkage Methods and Metric Parameters<a class="anchor-link" href="#Compare-Different-Linkage-Methods-and-Metric-Parameters">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8f52240f"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Define different linkage methods to test</span> +<span class="n">linkage_methods</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'ward'</span><span class="p">,</span> <span class="s1">'complete'</span><span class="p">,</span> <span class="s1">'average'</span><span class="p">,</span> <span class="s1">'single'</span><span class="p">)</span> + +<span class="c1"># Test different parameter combinations</span> +<span class="n">agg_results</span> <span class="o">=</span> <span class="p">{</span> + <span class="s1">'linkage'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'metric'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'silhouette_score'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'accuracy'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'ari'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'labels'</span><span class="p">:</span> <span class="p">[],</span> + <span class="s1">'mapped_labels'</span><span class="p">:</span> <span class="p">[]</span> +<span class="p">}</span> + +<span class="n">optimal_clusters</span> <span class="o">=</span> <span class="mi">3</span> + +<span class="k">for</span> <span class="n">linkage</span> <span class="ow">in</span> <span class="n">linkage_methods</span><span class="p">:</span> + <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'euclidean'</span><span class="p">,</span> <span class="s1">'manhattan'</span><span class="p">,</span> <span class="s1">'cosine'</span><span class="p">):</span> + <span class="c1"># Skip incompatible combinations</span> + <span class="k">if</span> <span class="n">linkage</span> <span class="o">==</span> <span class="s1">'ward'</span> <span class="ow">and</span> <span class="n">metric</span> <span class="o">!=</span> <span class="s1">'euclidean'</span><span class="p">:</span> + <span class="k">continue</span> + + <span class="n">agg</span> <span class="o">=</span> <span class="n">AgglomerativeClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">optimal_clusters</span><span class="p">,</span> <span class="n">linkage</span><span class="o">=</span><span class="n">linkage</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="n">metric</span><span class="p">)</span> + <span class="n">agg_labels</span> <span class="o">=</span> <span class="n">agg</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + + <span class="c1"># Calculate metrics</span> + <span class="n">silhouette</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">agg_labels</span><span class="p">)</span> + <span class="n">_</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">mapped_labels</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels</span><span class="p">)</span> + <span class="n">ari</span> <span class="o">=</span> <span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels</span><span class="p">)</span> + + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'linkage'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">linkage</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'metric'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">metric</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'silhouette_score'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">silhouette</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">accuracy</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'ari'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ari</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'labels'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">agg_labels</span><span class="p">)</span> + <span class="n">agg_results</span><span class="p">[</span><span class="s1">'mapped_labels'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mapped_labels</span><span class="p">)</span> + +<span class="c1"># Convert results to DataFrame, sorted by accuracy (descending)</span> +<span class="n">agg_comparison_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">agg_results</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'accuracy'</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"AGGLOMERATIVE CLUSTERING PARAMETER COMPARISON:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">agg_comparison_df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,:</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>AGGLOMERATIVE CLUSTERING PARAMETER COMPARISON: +============================================================ + linkage metric silhouette_score accuracy ari +5 average manhattan 0.4530 0.8867 0.7184 +3 complete cosine 0.4466 0.8400 0.6335 +0 ward euclidean 0.4467 0.8267 0.6153 +2 complete manhattan 0.4350 0.8200 0.6146 +6 average cosine 0.4302 0.8200 0.6097 +1 complete euclidean 0.4496 0.7867 0.5726 +4 average euclidean 0.4803 0.6867 0.5621 +8 single manhattan 0.4949 0.6800 0.5638 +9 single cosine 0.0956 0.6667 0.5414 +7 single euclidean 0.5046 0.6600 0.5584 +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a202fbfb"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Visualize-Performance-of-Different-Parameter-Combinations">Visualize Performance of Different Parameter Combinations<a class="anchor-link" href="#Visualize-Performance-of-Different-Parameter-Combinations">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=45a5a6ae"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Plot comparison of different parameter combinations</span> +<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span> + +<span class="c1"># Accuracy comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="k">for</span> <span class="n">linkage</span> <span class="ow">in</span> <span class="n">linkage_methods</span><span class="p">:</span> + <span class="n">linkage_data</span> <span class="o">=</span> <span class="n">agg_comparison_df</span><span class="p">[</span><span class="n">agg_comparison_df</span><span class="p">[</span><span class="s1">'linkage'</span><span class="p">]</span> <span class="o">==</span> <span class="n">linkage</span><span class="p">]</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">linkage_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">linkage_data</span><span class="p">[</span><span class="s1">'metric'</span><span class="p">],</span> <span class="n">linkage_data</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">],</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">linkage</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Metric'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Accuracy'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Accuracy by Linkage Method and Metric'</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">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> + +<span class="c1"># Silhouette score comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> +<span class="k">for</span> <span class="n">linkage</span> <span class="ow">in</span> <span class="n">linkage_methods</span><span class="p">:</span> + <span class="n">linkage_data</span> <span class="o">=</span> <span class="n">agg_comparison_df</span><span class="p">[</span><span class="n">agg_comparison_df</span><span class="p">[</span><span class="s1">'linkage'</span><span class="p">]</span> <span class="o">==</span> <span class="n">linkage</span><span class="p">]</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">linkage_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">linkage_data</span><span class="p">[</span><span class="s1">'metric'</span><span class="p">],</span> <span class="n">linkage_data</span><span class="p">[</span><span class="s1">'silhouette_score'</span><span class="p">],</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">linkage</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Metric'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Silhouette Score'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Silhouette Score by Linkage Method and Metric'</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">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> + +<span class="c1"># ARI comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> +<span class="k">for</span> <span class="n">linkage</span> <span class="ow">in</span> <span class="n">linkage_methods</span><span class="p">:</span> + <span class="n">linkage_data</span> <span class="o">=</span> <span class="n">agg_comparison_df</span><span class="p">[</span><span class="n">agg_comparison_df</span><span class="p">[</span><span class="s1">'linkage'</span><span class="p">]</span> <span class="o">==</span> <span class="n">linkage</span><span class="p">]</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">linkage_data</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">linkage_data</span><span class="p">[</span><span class="s1">'metric'</span><span class="p">],</span> <span class="n">linkage_data</span><span class="p">[</span><span class="s1">'ari'</span><span class="p">],</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">linkage</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Metric'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Adjusted Rand Index'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'ARI by Linkage Method and Metric'</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">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> + +<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this 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+</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4913fd5a"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Dendrograms-for-Comparison">Dendrograms for Comparison<a class="anchor-link" href="#Dendrograms-for-Comparison">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=67e758d4"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create side-by-side dendrograms for comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span> + +<span class="c1"># First dendrogram (ward linkage with Euclidean distance)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">dendrogram1</span> <span class="o">=</span> <span class="n">sch</span><span class="o">.</span><span class="n">dendrogram</span><span class="p">(</span><span class="n">sch</span><span class="o">.</span><span class="n">linkage</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'ward'</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Dendrogram: Ward Linkage with Euclidean Metric'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Samples'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Euclidean Distance'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Cut line for 3 clusters'</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="c1"># Second dendrogram (best parameters)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> +<span class="n">dendrogram2</span> <span class="o">=</span> <span class="n">sch</span><span class="o">.</span><span class="n">dendrogram</span><span class="p">(</span><span class="n">sch</span><span class="o">.</span><span class="n">linkage</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"average"</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s2">"cityblock"</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Dendrogram: Average Linkage with Manhattan Metric'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Samples'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Manhattan Distance'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mf">3.7</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Cut line for 3 clusters'</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">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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"/> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4aa3b45b"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Apply-Both-Clustering-Algorithms-with-Best-Parameters">Apply Both Clustering Algorithms with Best Parameters<a class="anchor-link" href="#Apply-Both-Clustering-Algorithms-with-Best-Parameters">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=367e5fcb"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Apply both clustering algorithms with optimal clusters (3)</span> +<span class="n">optimal_clusters</span> <span class="o">=</span> <span class="mi">3</span> + +<span class="c1"># K-Means</span> +<span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">optimal_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> +<span class="n">kmeans_labels</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + +<span class="c1"># Agglomerative Clustering with best parameters</span> +<span class="n">best_linkage</span> <span class="o">=</span> <span class="s2">"average"</span> +<span class="n">best_metric</span> <span class="o">=</span> <span class="s2">"manhattan"</span> +<span class="n">agg_best</span> <span class="o">=</span> <span class="n">AgglomerativeClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">optimal_clusters</span><span class="p">,</span> + <span class="n">linkage</span><span class="o">=</span><span class="n">best_linkage</span><span class="p">,</span> + <span class="n">metric</span><span class="o">=</span><span class="n">best_metric</span><span class="p">)</span> +<span class="n">agg_labels_best</span> <span class="o">=</span> <span class="n">agg_best</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + +<span class="c1"># Also keep default agglomerative for comparison</span> +<span class="n">agg_default</span> <span class="o">=</span> <span class="n">AgglomerativeClustering</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">optimal_clusters</span><span class="p">)</span> +<span class="n">agg_labels_default</span> <span class="o">=</span> <span class="n">agg_default</span><span class="o">.</span><span class="n">fit_predict</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"Cluster distributions:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"K-Means:"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">kmeans_labels</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Agglomerative (default):"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">agg_labels_default</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Agglomerative (best):"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">agg_labels_best</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Cluster distributions: +K-Means: [53 50 47] +Agglomerative (default): [71 49 30] +Agglomerative (best): [50 35 65] +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=ee087586"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Map-Clusters-to-True-Labels-for-Accuracy-Calculation">Map Clusters to True Labels for Accuracy Calculation<a class="anchor-link" href="#Map-Clusters-to-True-Labels-for-Accuracy-Calculation">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d3b6c8fe"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Find best label mappings using permutation method</span> +<span class="n">_</span><span class="p">,</span> <span class="n">kmeans_accuracy</span><span class="p">,</span> <span class="n">kmeans_mapped</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">kmeans_labels</span><span class="p">)</span> +<span class="n">_</span><span class="p">,</span> <span class="n">agg_accuracy_default</span><span class="p">,</span> <span class="n">agg_mapped_default</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels_default</span><span class="p">)</span> +<span class="n">_</span><span class="p">,</span> <span class="n">agg_accuracy_best</span><span class="p">,</span> <span class="n">agg_mapped_best</span> <span class="o">=</span> <span class="n">label_permute_compare</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels_best</span><span class="p">)</span> + +<span class="c1"># Add mapped labels to dataframe</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"Accuracy scores:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"K-Means = </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">kmeans_accuracy</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Agglomerative (default) = </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_default</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"Agglomerative (best) = </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_best</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>Accuracy scores: +K-Means = 83.3% +Agglomerative (default) = 82.7% +Agglomerative (best) = 88.7% +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e483ddee"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Calculate-Performance-Metrics">Calculate Performance Metrics<a class="anchor-link" href="#Calculate-Performance-Metrics">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=35f8321f"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Calculate performance metrics for all methods</span> +<span class="n">kmeans_silhouette</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">kmeans_labels</span><span class="p">)</span> +<span class="n">agg_silhouette_default</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">agg_labels_default</span><span class="p">)</span> +<span class="n">agg_silhouette_best</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">agg_labels_best</span><span class="p">)</span> + +<span class="n">kmeans_ari</span> <span class="o">=</span> <span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">kmeans_labels</span><span class="p">)</span> +<span class="n">agg_ari_default</span> <span class="o">=</span> <span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels_default</span><span class="p">)</span> +<span class="n">agg_ari_best</span> <span class="o">=</span> <span class="n">adjusted_rand_score</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_labels_best</span><span class="p">)</span> + +<span class="c1"># Create confusion matrices</span> +<span class="n">kmeans_cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">kmeans_mapped</span><span class="p">)</span> +<span class="n">agg_cm_default</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_mapped_default</span><span class="p">)</span> +<span class="n">agg_cm_best</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">agg_mapped_best</span><span class="p">)</span> + +<span class="c1"># Create comparison table</span> +<span class="n">comparison_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span> + <span class="s1">'Algorithm'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'K-Means'</span><span class="p">,</span> <span class="s1">'Agglomerative (default)'</span><span class="p">,</span> <span class="s1">'Agglomerative (best)'</span><span class="p">],</span> + <span class="s1">'Accuracy'</span><span class="p">:</span> <span class="p">[</span><span class="n">kmeans_accuracy</span><span class="p">,</span> <span class="n">agg_accuracy_default</span><span class="p">,</span> <span class="n">agg_accuracy_best</span><span class="p">],</span> + <span class="s1">'Silhouette Score'</span><span class="p">:</span> <span class="p">[</span><span class="n">kmeans_silhouette</span><span class="p">,</span> <span class="n">agg_silhouette_default</span><span class="p">,</span> <span class="n">agg_silhouette_best</span><span class="p">],</span> + <span class="s1">'Adjusted Rand Index'</span><span class="p">:</span> <span class="p">[</span><span class="n">kmeans_ari</span><span class="p">,</span> <span class="n">agg_ari_default</span><span class="p">,</span> <span class="n">agg_ari_best</span><span class="p">],</span> +<span class="p">})</span> + +<span class="nb">print</span><span class="p">(</span><span class="s2">"PERFORMANCE COMPARISON:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="n">comparison_df</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>PERFORMANCE COMPARISON: +============================================================ + Algorithm Accuracy Silhouette Score Adjusted Rand Index +0 K-Means 0.8333 0.4599 0.6201 +1 Agglomerative (default) 0.8267 0.4467 0.6153 +2 Agglomerative (best) 0.8867 0.4530 0.7184 +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=40a07c6f"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Visualize-Confusion-Matrices">Visualize Confusion Matrices<a class="anchor-link" href="#Visualize-Confusion-Matrices">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=faa58c91"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Plot confusion matrices</span> +<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span> + +<span class="c1"># K-Means Confusion Matrix</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">kmeans_cm</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'d'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">,</span> + <span class="n">xticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'K-Means Confusion Matrix</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">kmeans_accuracy</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted Label'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Label'</span><span class="p">)</span> + +<span class="c1"># Agglomerative Confusion Matrix (default)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> +<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">agg_cm_default</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'d'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Greens'</span><span class="p">,</span> + <span class="n">xticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Agglomerative Confusion Matrix (default)</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_default</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted Label'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Label'</span><span class="p">)</span> + +<span class="c1"># Agglomerative Confusion Matrix (best)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> +<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">agg_cm_best</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'d'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Oranges'</span><span class="p">,</span> + <span class="n">xticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">species_names</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Agglomerative Confusion Matrix (best)</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_best</span><span class="p">))</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Predicted Label'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'True Label'</span><span class="p">)</span> + +<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0"> +<img alt="No description has been provided for this image" class="" 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+</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=47fc86d9"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Visualize-the-Results">Visualize the Results<a class="anchor-link" href="#Visualize-the-Results">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=d9b35880"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create comparison visualization</span> +<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">25</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span> + +<span class="c1"># Actual species</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> +<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">target</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Actual Species'</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">handles</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">species_names</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> + +<span class="c1"># K-Means clusters (mapped)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> +<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">kmeans_mapped</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'K-Means Clusters (Mapped)</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">kmeans_accuracy</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">handles</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">species_names</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> + +<span class="c1"># Agglomerative clusters default (mapped)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> +<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">agg_mapped_default</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Agglomerative Clusters (Default)</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_default</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">handles</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">species_names</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> + +<span class="c1"># Agglomerative clusters best (mapped)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> +<span class="n">scatter</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">agg_mapped_best</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'viridis'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Agglomerative Clusters (best)</span><span class="se">\n</span><span class="s1">Accuracy: </span><span class="si">%.1f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">agg_accuracy_best</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">handles</span><span class="o">=</span><span class="n">scatter</span><span class="o">.</span><span class="n">legend_elements</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">labels</span><span class="o">=</span><span class="n">species_names</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> + +<span class="c1"># Error comparison</span> +<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> +<span class="n">kmeans_errors</span> <span class="o">=</span> <span class="p">(</span><span class="n">target</span> <span class="o">!=</span> <span class="n">kmeans_mapped</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span> +<span class="n">agg_default_errors</span> <span class="o">=</span> <span class="p">(</span><span class="n">target</span> <span class="o">!=</span> <span class="n">agg_mapped_default</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span> +<span class="n">agg_best_errors</span> <span class="o">=</span> <span class="p">(</span><span class="n">target</span> <span class="o">!=</span> <span class="n">agg_mapped_best</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</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_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">X_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">kmeans_errors</span> <span class="o">+</span> <span class="n">agg_default_errors</span> <span class="o">+</span> <span class="n">agg_best_errors</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Reds'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'First Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Second Principal Component'</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Classification Errors</span><span class="se">\n</span><span class="s1">(Red intensity = more methods failed)'</span><span class="p">)</span> + +<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</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> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser 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+</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=cdfe73f4"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h3 id="Detailed-Error-Analysis">Detailed Error Analysis<a class="anchor-link" href="#Detailed-Error-Analysis">¶</a></h3> +</div> +</div> +</div> +</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=6562656d"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"> +<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div> +<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline"> +<div class="cm-editor cm-s-jupyter"> +<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Detailed error analysis</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"DETAILED ERROR ANALYSIS:"</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"="</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span> + +<span class="k">for</span> <span class="n">method</span><span class="p">,</span> <span class="n">mapped_labels</span><span class="p">,</span> <span class="n">accuracy</span> <span class="ow">in</span> <span class="p">(</span> + <span class="p">(</span><span class="s1">'K-Means'</span><span class="p">,</span> <span class="n">kmeans_mapped</span><span class="p">,</span> <span class="n">kmeans_accuracy</span><span class="p">),</span> + <span class="p">(</span><span class="s1">'Agglomerative (default)'</span><span class="p">,</span> <span class="n">agg_mapped_default</span><span class="p">,</span> <span class="n">agg_accuracy_default</span><span class="p">),</span> + <span class="p">(</span><span class="s1">'Agglomerative (best)'</span><span class="p">,</span> <span class="n">agg_mapped_best</span><span class="p">,</span> <span class="n">agg_accuracy_best</span><span class="p">),</span> +<span class="p">):</span> + <span class="n">errors</span> <span class="o">=</span> <span class="n">target</span> <span class="o">!=</span> <span class="n">mapped_labels</span> + <span class="n">error_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">errors</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> + + <span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%s</span><span class="s2">:"</span> <span class="o">%</span> <span class="n">method</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">" Accuracy: </span><span class="si">%.1f%%</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">" Total errors: </span><span class="si">%2d</span><span class="s2">/</span><span class="si">%3d</span><span class="s2"> errors (</span><span class="si">%.1f%%</span><span class="s2">)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">errors</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">errors</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span> + + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">error_indices</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> + <span class="n">error_samples</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">error_indices</span><span class="p">]</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">" Error breakdown by species:"</span><span class="p">)</span> + <span class="k">for</span> <span class="n">species</span> <span class="ow">in</span> <span class="n">species_names</span><span class="p">:</span> + <span class="n">species_errors</span> <span class="o">=</span> <span class="n">error_samples</span><span class="p">[</span><span class="n">error_samples</span><span class="p">[</span><span class="s1">'species'</span><span class="p">]</span> <span class="o">==</span> <span class="n">species</span><span class="p">]</span> + <span class="n">total_species</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">target</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">species_names</span> <span class="o">==</span> <span class="n">species</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">species_errors</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">" </span><span class="si">%s</span><span class="s2">: </span><span class="si">%2d</span><span class="s2">/</span><span class="si">%2d</span><span class="s2"> errors (</span><span class="si">%4.1f%%</span><span class="s2">)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">species</span><span class="o">.</span><span class="n">ljust</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">species_errors</span><span class="p">),</span> <span class="n">total_species</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">species_errors</span><span class="p">)</span><span class="o">/</span><span class="n">total_species</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span> + <span class="nb">print</span><span class="p">()</span> +</pre></div> +</div> +</div> +</div> +</div> +<div class="jp-Cell-outputWrapper"> +<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser"> +</div> +<div class="jp-OutputArea jp-Cell-outputArea"> +<div class="jp-OutputArea-child"> +<div class="jp-OutputPrompt jp-OutputArea-prompt"></div> +<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0"> +<pre>DETAILED ERROR ANALYSIS: +============================================================ +K-Means: + Accuracy: 83.3% + Total errors: 25/150 errors (16.7%) + Error breakdown by species: + versicolor: 11/50 errors (22.0%) + virginica : 14/50 errors (28.0%) + +Agglomerative (default): + Accuracy: 82.7% + Total errors: 26/150 errors (17.3%) + Error breakdown by species: + setosa : 1/50 errors ( 2.0%) + versicolor: 23/50 errors (46.0%) + virginica : 2/50 errors ( 4.0%) + +Agglomerative (best): + Accuracy: 88.7% + Total errors: 17/150 errors (11.3%) + Error breakdown by species: + versicolor: 1/50 errors ( 2.0%) + virginica : 16/50 errors (32.0%) + +</pre> +</div> +</div> +</div> +</div> +</div> +<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=844c0784"> +<div class="jp-Cell-inputWrapper" tabindex="0"> +<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser"> +</div> +<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt"> +</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown"> +<h2 id="Discussion-and-Interpretation">Discussion and Interpretation<a class="anchor-link" href="#Discussion-and-Interpretation">¶</a></h2><p>Based on the comprehensive analysis with different linkage methods and metric parameters:</p> +<h3 id="Species-Specific-Insights:">Species-Specific Insights:<a class="anchor-link" href="#Species-Specific-Insights:">¶</a></h3><p>Iris setosa was classified with near-perfect accuracy across all methods due to its distinct morphological features. The main challenge remains distinguishing between versicolor and virginica species, which are not linearly separable.</p> +<h3 id="Key-Findings:">Key Findings:<a class="anchor-link" href="#Key-Findings:">¶</a></h3><ol> +<li><strong>Parameter Sensitivity</strong>: Agglomerative Clustering performance varies significantly with different parameter combinations</li> +<li><strong>Best Parameters</strong>: The combination of Average linkage with Manhattan metric achieved the highest accuracy of 88.7%</li> +<li><strong>Performance Improvement</strong>: Optimized parameters improved Agglomerative Clustering accuracy from 82.7% to 88.7%</li> +<li><strong>Confusion Matrix Balance</strong>: K-Means had a much more balanced confusion matrix, with the errors among versicolor and virginica split almost equally, whereas both Agglomerative Clustering methods had a bias towards one of them over the other</li> +</ol> +<h3 id="Practical-Implications:">Practical Implications:<a class="anchor-link" href="#Practical-Implications:">¶</a></h3><ul> +<li>Parameter tuning is crucial for Agglomerative Clustering performance</li> +<li>Different linkage methods capture different cluster structures</li> +<li>The optimal parameter combination may vary across different datasets</li> +</ul> +<p>This poses a problem when using Agglomerative Clustering for such types of tasks, as in cases where the labels are not readily available, it would be difficult to check which parameters perform best for the current task.</p> +<p>Furthermore, the fact that even with parameter tuning the confusion matrix of Agglomerative Clustering was still biased (but to the opposite group) suggests an inherent instability in the method. Given a few different datapoints, the balance in the middle group in the hierarchy could tip again, completely changing the composition of the clusters and potentially harming accuracy alot.</p> +<p>Given this analysis, I would suggest that practical applications on tasks very similar to this one to use K-Means Clustering instead of Agglomerative Clustering, even though the above accuracy values might suggest otherwise.</p> +<h3 id="Limitations-and-Future-Work:">Limitations and Future Work:<a class="anchor-link" href="#Limitations-and-Future-Work:">¶</a></h3><p><strong>Current Limitations:</strong></p> +<ul> +<li>Limited to Iris dataset characteristics</li> +<li>Did not perform deep parameter optimization on K-Means Clustering</li> +</ul> +<p><strong>Future Directions:</strong></p> +<ul> +<li>Extend analysis to other clustering variants</li> +<li>Incorporate additional validation metrics</li> +<li>Apply to larger, more complex botanical datasets</li> +</ul> +</div> +</div> +</div> +</div> +</main> +</body> +</html> |