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DS-for-LA/notebooks/html/06_modelling_101.html
Pawel a9620635d9 modelling 101
2026-04-14 17:43:23 -04:00

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/* Icons urls */
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--jp-icon-add-above: url(data:image/svg+xml;base64,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);
--jp-icon-add-below: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTQiIGhlaWdodD0iMTQiIHZpZXdCb3g9IjAgMCAxNCAxNCIgZmlsbD0ibm9uZSIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KPGcgY2xpcC1wYXRoPSJ1cmwoI2NsaXAwXzEzN18xOTQ5OCkiPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGQ9Ik05LjI1IDEwLjA2OTNMNy4zNzUgMTAuMDY5M0w3LjM3NSA4LjE5NDM0QzcuMzc1IDcuOTg4MDkgNy4yMDYyNSA3LjgxOTM0IDcgNy44MTkzNEM2Ljc5Mzc1IDcuODE5MzQgNi42MjUgNy45ODgwOSA2LjYyNSA4LjE5NDM0TDYuNjI1IDEwLjA2OTNMNC43NSAxMC4wNjkzQzQuNTQzNzUgMTAuMDY5MyA0LjM3NSAxMC4yMzgxIDQuMzc1IDEwLjQ0NDNDNC4zNzUgMTAuNjUwNiA0LjU0Mzc1IDEwLjgxOTMgNC43NSAxMC44MTkzTDYuNjI1IDEwLjgxOTNMNi42MjUgMTIuNjk0M0M2LjYyNSAxMi45MDA2IDYuNzkzNzUgMTMuMDY5MyA3IDEzLjA2OTNDNy4yMDYyNSAxMy4wNjkzIDcuMzc1IDEyLjkwMDYgNy4zNzUgMTIuNjk0M0w3LjM3NSAxMC44MTkzTDkuMjUgMTAuODE5M0M5LjQ1NjI1IDEwLjgxOTMgOS42MjUgMTAuNjUwNiA5LjYyNSAxMC40NDQzQzkuNjI1IDEwLjIzODEgOS40NTYyNSAxMC4wNjkzIDkuMjUgMTAuMDY5M1oiIGZpbGw9IiM2MTYxNjEiIHN0cm9rZT0iIzYxNjE2MSIgc3Ryb2tlLXdpZHRoPSIwLjciLz4KPC9nPgo8cGF0aCBjbGFzcz0ianAtaWNvbjMiIGZpbGwtcnVsZT0iZXZlbm9kZCIgY2xpcC1ydWxlPSJldmVub2RkIiBkPSJNMi41IDUuNUwyLjUgMy41TDExLjUgMy41TDExLjUgNS41TDIuNSA1LjVaTTIgN0MxLjQ0NzcyIDcgMSA2LjU1MjI4IDEgNkwxIDNDMSAyLjQ0NzcyIDEuNDQ3NzIgMiAyIDJMMTIgMkMxMi41NTIzIDIgMTMgMi40NDc3MiAxMyAzTDEzIDZDMTMgNi41NTIyOSAxMi41NTIzIDcgMTIgN0wyIDdaIiBmaWxsPSIjNjE2MTYxIi8+CjxkZWZzPgo8Y2xpcFBhdGggaWQ9ImNsaXAwXzEzN18xOTQ5OCI+CjxyZWN0IGNsYXNzPSJqcC1pY29uMyIgd2lkdGg9IjYiIGhlaWdodD0iNiIgZmlsbD0id2hpdGUiIHRyYW5zZm9ybT0ibWF0cml4KDEgMS43NDg0NmUtMDcgMS43NDg0NmUtMDcgLTEgNCAxMy40NDQzKSIvPgo8L2NsaXBQYXRoPgo8L2RlZnM+Cjwvc3ZnPgo=);
--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: url(data:image/svg+xml;base64,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);
--jp-icon-bug: url(data:image/svg+xml;base64,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);
--jp-icon-build: url(data:image/svg+xml;base64,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);
--jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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--jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
--jp-icon-regex: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KICA8ZyBjbGFzcz0ianAtaWNvbjIiIGZpbGw9IiM0MTQxNDEiPgogICAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiAgPC9nPgoKICA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiBmaWxsPSIjRkZGIj4KICAgIDxjaXJjbGUgY2xhc3M9InN0MiIgY3g9IjUuNSIgY3k9IjE0LjUiIHI9IjEuNSIvPgogICAgPHJlY3QgeD0iMTIiIHk9IjQiIGNsYXNzPSJzdDIiIHdpZHRoPSIxIiBoZWlnaHQ9IjgiLz4KICAgIDxyZWN0IHg9IjguNSIgeT0iNy41IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjg2NiAtMC41IDAuNSAwLjg2NiAtMi4zMjU1IDcuMzIxOSkiIGNsYXNzPSJzdDIiIHdpZHRoPSI4IiBoZWlnaHQ9IjEiLz4KICAgIDxyZWN0IHg9IjEyIiB5PSI0IiB0cmFuc2Zvcm09Im1hdHJpeCgwLjUgLTAuODY2IDAuODY2IDAuNSAtMC42Nzc5IDE0LjgyNTIpIiBjbGFzcz0ic3QyIiB3aWR0aD0iMSIgaGVpZ2h0PSI4Ii8+CiAgPC9nPgo8L3N2Zz4K);
--jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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--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,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KIDxnIGNsYXNzPSJqcC1pY29uMiIgZmlsbD0iIzQxNDE0MSI+CiAgPHJlY3QgeD0iMiIgeT0iMiIgd2lkdGg9IjE2IiBoZWlnaHQ9IjE2Ii8+CiA8L2c+CiA8ZyBjbGFzcz0ianAtaWNvbi1hY2NlbnQyIiB0cmFuc2Zvcm09InRyYW5zbGF0ZSguNDMgLjA0MDEpIiBmaWxsPSIjZmZmIj4KICA8cGF0aCBkPSJtNC4xNCA4Ljc2cTAuMDY4Mi0xLjg5IDIuNDItMS44OSAxLjE2IDAgMS42OCAwLjQyIDAuNTY3IDAuNDEgMC41NjcgMS4xNnYzLjQ3cTAgMC40NjIgMC41MTQgMC40NjIgMC4xMDMgMCAwLjItMC4wMjMxdjAuNzE0cS0wLjM5OSAwLjEwMy0wLjY1MSAwLjEwMy0wLjQ1MiAwLTAuNjkzLTAuMjItMC4yMzEtMC4yLTAuMjg0LTAuNjYyLTAuOTU2IDAuODcyLTIgMC44NzItMC45MDMgMC0xLjQ3LTAuNDcyLTAuNTI1LTAuNDcyLTAuNTI1LTEuMjYgMC0wLjI2MiAwLjA0NTItMC40NzIgMC4wNTY3LTAuMjIgMC4xMTYtMC4zNzggMC4wNjgyLTAuMTY4IDAuMjMxLTAuMzA0IDAuMTU4LTAuMTQ3IDAuMjYyLTAuMjQyIDAuMTE2LTAuMDkxNCAwLjM2OC0wLjE2OCAwLjI2Mi0wLjA5MTQgMC4zOTktMC4xMjYgMC4xMzYtMC4wNDUyIDAuNDcyLTAuMTAzIDAuMzM2LTAuMDU3OCAwLjUwNC0wLjA3OTggMC4xNTgtMC4wMjMxIDAuNTY3LTAuMDc5OCAwLjU1Ni0wLjA2ODIgMC43NzctMC4yMjEgMC4yMi0wLjE1MiAwLjIyLTAuNDQxdi0wLjI1MnEwLTAuNDMtMC4zNTctMC42NjItMC4zMzYtMC4yMzEtMC45NzYtMC4yMzEtMC42NjIgMC0wLjk5OCAwLjI2Mi0wLjMzNiAwLjI1Mi0wLjM5OSAwLjc5OHptMS44OSAzLjY4cTAuNzg4IDAgMS4yNi0wLjQxIDAuNTA0LTAuNDIgMC41MDQtMC45MDN2LTEuMDVxLTAuMjg0IDAuMTM2LTAuODYxIDAuMjMxLTAuNTY3IDAuMDkxNC0wLjk4NyAwLjE1OC0wLjQyIDAuMDY4Mi0wLjc2NiAwLjMyNi0wLjMzNiAwLjI1Mi0wLjMzNiAwLjcwNHQwLjMwNCAwLjcwNCAwLjg2MSAwLjI1MnoiIHN0cm9rZS13aWR0aD0iMS4wNSIvPgogIDxwYXRoIGQ9Im0xMCA0LjU2aDAuOTQ1djMuMTVxMC42NTEtMC45NzYgMS44OS0wLjk3NiAxLjE2IDAgMS44OSAwLjg0IDAuNjgyIDAuODQgMC42ODIgMi4zMSAwIDEuNDctMC43MDQgMi40Mi0wLjcwNCAwLjg4Mi0xLjg5IDAuODgyLTEuMjYgMC0xLjg5LTEuMDJ2MC43NjZoLTAuODV6bTIuNjIgMy4wNHEtMC43NDYgMC0xLjE2IDAuNjQtMC40NTIgMC42My0wLjQ1MiAxLjY4IDAgMS4wNSAwLjQ1MiAxLjY4dDEuMTYgMC42M3EwLjc3NyAwIDEuMjYtMC42MyAwLjQ5NC0wLjY0IDAuNDk0LTEuNjggMC0xLjA1LTAuNDcyLTEuNjgtMC40NjItMC42NC0xLjI2LTAuNjR6IiBzdHJva2Utd2lkdGg9IjEuMDUiLz4KICA8cGF0aCBkPSJtMi43MyAxNS44IDEzLjYgMC4wMDgxYzAuMDA2OSAwIDAtMi42IDAtMi42IDAtMC4wMDc4LTEuMTUgMC0xLjE1IDAtMC4wMDY5IDAtMC4wMDgzIDEuNS0wLjAwODMgMS41LTJlLTMgLTAuMDAxNC0xMS4zLTAuMDAxNC0xMS4zLTAuMDAxNGwtMC4wMDU5Mi0xLjVjMC0wLjAwNzgtMS4xNyAwLjAwMTMtMS4xNyAwLjAwMTN6IiBzdHJva2Utd2lkdGg9Ii45NzUiLz4KIDwvZz4KPC9zdmc+Cg==);
--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),
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<h1 id="Modelling-101:-Train/Test-Splits-&amp;-Beyond-Linear-Regression">Modelling 101: Train/Test Splits &amp; Beyond Linear Regression<a class="anchor-link" href="#Modelling-101:-Train/Test-Splits-&amp;-Beyond-Linear-Regression">¶</a></h1><h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">¶</a></h2><p>So far we have seen how linear regression (ordinary least squares) solves $\tilde{X}\tilde{\beta} = y$ by minimizing $\|y - \tilde{X}\tilde{\beta}\|_2^2$. This is a powerful tool, but real data often breaks the assumptions that make linear regression the best choice. We address several of the points made in <a href="03_what_goes_wrong.ipynb">notebook 03</a>.</p>
<blockquote>
<p><strong>Why linear regression might not cut it:</strong></p>
<ul>
<li><strong>Nonlinear relationships</strong> – The true dependency may be curved, periodic, or otherwise not linear.</li>
<li><strong>High dimensionality</strong> – When the number of features $p$ is close to or larger than the number of observations $n$, the matrix $\tilde{X}^T\tilde{X}$ becomes singular or nearly singular.</li>
<li><strong>Multicollinearity</strong> – Features are correlated, leading to large condition numbers and unstable coefficients.</li>
<li><strong>Overfitting</strong> – A complex model fits noise instead of signal, especially when $p$ is large.</li>
<li><strong>Outliers</strong> – The $L^2$ norm magnifies large errors, pulling the fit away from the bulk of the data.</li>
</ul>
</blockquote>
<p>In this notebook we will:</p>
<ul>
<li>Work with a real, moderately sized dataset.</li>
<li>Learn how to properly split data into training, validation, and test sets.</li>
<li>Apply linear and polynomial regression, then diagnose their limitations.</li>
<li>Introduce <strong>regularisation</strong> methods (Ridge and Lasso) from a linear algebra perspective.</li>
<li>Explore <strong>gradient descent</strong> as a numerical optimisation alternative to the normal equations.</li>
<li>Look at <strong>decision trees and random forests</strong> – nonlinear models that can capture complex interactions without feature engineering.</li>
<li>Cover <strong>logistic regression</strong> for classification.</li>
<li>Discuss <strong>feature scaling</strong>, <strong>cross‑validation</strong>, <strong>model interpretation</strong>, and <strong>hyperparameter tuning</strong>.</li>
</ul>
<p>The goal is to equip the linear algebraist with practical modelling tools while maintaining a geometric / algebraic intuition.</p>
<hr/>
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<h2 id="A-Real-Dataset:-California-Housing">A Real Dataset: California Housing<a class="anchor-link" href="#A-Real-Dataset:-California-Housing">¶</a></h2><p>A natural next step from our toy housing example is the <strong>California housing dataset</strong> from <code>sklearn.datasets</code>. It contains 20,640 observations of 8 features (median income, house age, average rooms, etc.) and the target is the median house value for blocks in California. This dataset is large enough to illustrate interesting effects but small enough to run quickly.</p>
<blockquote>
<p><strong>Linear algebra view</strong>: Each observation is a row vector $x_i \in \mathbb{R}^8$. The features form the columns of the design matrix $X \in \mathbb{R}^{20640 \times 8}$. We will add an intercept column $\mathbb{1}$ to obtain $\tilde{X} \in \mathbb{R}^{20640 \times 9}$.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">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">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_california_housing</span>
<span class="c1"># Load the data</span>
<span class="n">housing</span> <span class="o">=</span> <span class="n">fetch_california_housing</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">housing</span><span class="o">.</span><span class="n">data</span> <span class="c1"># shape (20640, 8)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">housing</span><span class="o">.</span><span class="n">target</span> <span class="c1"># shape (20640,)</span>
<span class="n">feature_names</span> <span class="o">=</span> <span class="n">housing</span><span class="o">.</span><span class="n">feature_names</span>
<span class="c1"># Convert to DataFrame for convenience</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">feature_names</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'MedHouseVal'</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Data shape: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Data shape: (20640, 9)
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<th></th>
<th>MedInc</th>
<th>HouseAge</th>
<th>AveRooms</th>
<th>AveBedrms</th>
<th>Population</th>
<th>AveOccup</th>
<th>Latitude</th>
<th>Longitude</th>
<th>MedHouseVal</th>
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<th>0</th>
<td>8.3252</td>
<td>41.0</td>
<td>6.984127</td>
<td>1.023810</td>
<td>322.0</td>
<td>2.555556</td>
<td>37.88</td>
<td>-122.23</td>
<td>4.526</td>
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<th>1</th>
<td>8.3014</td>
<td>21.0</td>
<td>6.238137</td>
<td>0.971880</td>
<td>2401.0</td>
<td>2.109842</td>
<td>37.86</td>
<td>-122.22</td>
<td>3.585</td>
</tr>
<tr>
<th>2</th>
<td>7.2574</td>
<td>52.0</td>
<td>8.288136</td>
<td>1.073446</td>
<td>496.0</td>
<td>2.802260</td>
<td>37.85</td>
<td>-122.24</td>
<td>3.521</td>
</tr>
<tr>
<th>3</th>
<td>5.6431</td>
<td>52.0</td>
<td>5.817352</td>
<td>1.073059</td>
<td>558.0</td>
<td>2.547945</td>
<td>37.85</td>
<td>-122.25</td>
<td>3.413</td>
</tr>
<tr>
<th>4</th>
<td>3.8462</td>
<td>52.0</td>
<td>6.281853</td>
<td>1.081081</td>
<td>565.0</td>
<td>2.181467</td>
<td>37.85</td>
<td>-122.25</td>
<td>3.422</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Basic statistics</span>
<span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<th></th>
<th>MedInc</th>
<th>HouseAge</th>
<th>AveRooms</th>
<th>AveBedrms</th>
<th>Population</th>
<th>AveOccup</th>
<th>Latitude</th>
<th>Longitude</th>
<th>MedHouseVal</th>
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<th>count</th>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
<td>20640.000000</td>
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<th>mean</th>
<td>3.870671</td>
<td>28.639486</td>
<td>5.429000</td>
<td>1.096675</td>
<td>1425.476744</td>
<td>3.070655</td>
<td>35.631861</td>
<td>-119.569704</td>
<td>2.068558</td>
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<th>std</th>
<td>1.899822</td>
<td>12.585558</td>
<td>2.474173</td>
<td>0.473911</td>
<td>1132.462122</td>
<td>10.386050</td>
<td>2.135952</td>
<td>2.003532</td>
<td>1.153956</td>
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<th>min</th>
<td>0.499900</td>
<td>1.000000</td>
<td>0.846154</td>
<td>0.333333</td>
<td>3.000000</td>
<td>0.692308</td>
<td>32.540000</td>
<td>-124.350000</td>
<td>0.149990</td>
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<th>25%</th>
<td>2.563400</td>
<td>18.000000</td>
<td>4.440716</td>
<td>1.006079</td>
<td>787.000000</td>
<td>2.429741</td>
<td>33.930000</td>
<td>-121.800000</td>
<td>1.196000</td>
</tr>
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<th>50%</th>
<td>3.534800</td>
<td>29.000000</td>
<td>5.229129</td>
<td>1.048780</td>
<td>1166.000000</td>
<td>2.818116</td>
<td>34.260000</td>
<td>-118.490000</td>
<td>1.797000</td>
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<th>75%</th>
<td>4.743250</td>
<td>37.000000</td>
<td>6.052381</td>
<td>1.099526</td>
<td>1725.000000</td>
<td>3.282261</td>
<td>37.710000</td>
<td>-118.010000</td>
<td>2.647250</td>
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<th>max</th>
<td>15.000100</td>
<td>52.000000</td>
<td>141.909091</td>
<td>34.066667</td>
<td>35682.000000</td>
<td>1243.333333</td>
<td>41.950000</td>
<td>-114.310000</td>
<td>5.000010</td>
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<p>Let's see the relationships between these features and the price.</p>
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<h2 id="Train-/-Test-Split-(and-Validation)">Train / Test Split (and Validation)<a class="anchor-link" href="#Train-/-Test-Split-(and-Validation)">¶</a></h2><p>When it comes to real world modelling, we must split our data into training and tests sets.</p>
<blockquote>
<p><strong>Why split?</strong> If we evaluate a model on the same data we used to train it, we get an overly optimistic estimate of performance. The model may have memorised the training set (overfitting). Splitting mimics a real‑world scenario: we test on unseen data.</p>
</blockquote>
<p>A common workflow:</p>
<ol>
<li><strong>Training set</strong> (e.g., 60‑80%): used to fit the model parameters.</li>
<li><strong>Validation set</strong> (e.g., 10‑20%): used to tune hyperparameters (e.g., degree of polynomial, regularisation strength).</li>
<li><strong>Test set</strong> (e.g., 10‑20%): used only once at the end to report final performance.</li>
</ol>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Illustrate the three-way split</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="c1"># Create rectangles for each split</span>
<span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</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">label</span><span class="o">=</span><span class="s1">'Training (60%)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</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">label</span><span class="o">=</span><span class="s1">'Validation (20%)'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</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">label</span><span class="o">=</span><span class="s1">'Test (20%)'</span><span class="p">)</span>
<span class="c1"># Add labels</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Train Model</span><span class="se">\n</span><span class="s1">Parameters'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">70</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Tune</span><span class="se">\n</span><span class="s1">Hyperparams'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mi">90</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Final</span><span class="se">\n</span><span class="s1">Evaluation'</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</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="s1">'Percentage of Data'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</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="s1">'Train/Validation/Test Split'</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">loc</span><span class="o">=</span><span class="s1">'upper center'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.15</span><span class="p">),</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">3</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">savefig</span><span class="p">(</span><span class="s1">'../images/train_validation_test_split.png'</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>
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"/>
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<p>Let us first fix a random state.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">RANDOM_STATE</span><span class="o">=</span><span class="mi">3</span>
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<p>Let's visualize this.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="c1"># Generate synthetic data to illustrate the concept</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="n">n</span><span class="p">)</span> <span class="c1"># synthetic, wider range</span>
<span class="c1"># True relationship</span>
<span class="n">a_true</span> <span class="o">=</span> <span class="mf">2.0</span>
<span class="n">c_true</span> <span class="o">=</span> <span class="mf">5.0</span>
<span class="n">noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">n</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">a_true</span> <span class="o">*</span> <span class="n">X</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">c_true</span> <span class="o">+</span> <span class="n">noise</span>
<span class="c1"># Perform train/test split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">3</span>
<span class="p">)</span>
<span class="c1"># Sort for plotting</span>
<span class="n">X_curve</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">X</span><span class="o">.</span> <span class="nb">max</span><span class="p">())</span>
<span class="n">y_true</span> <span class="o">=</span> <span class="n">a_true</span> <span class="o">*</span> <span class="n">X_curve</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">c_true</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Training data'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Test data'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X_curve</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'True relationship'</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">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'X'</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="s1">'y'</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="s1">'Train/Test Split'</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">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</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">savefig</span><span class="p">(</span><span class="s1">'../images/train_test_split_illustration.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total samples: </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Training samples: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span><span class="o">/</span><span class="n">n</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">%)"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test samples: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span><span class="o">/</span><span class="n">n</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s2">.0f</span><span class="si">}</span><span class="s2">%)"</span><span class="p">)</span>
</pre></div>
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<pre>Total samples: 50
Training samples: 35 (70%)
Test samples: 15 (30%)
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<p>Back to the housing data. We will use <code>sklearn.model_selection.train_test_split</code> to create two splits (train+validation vs. test), then further split the train+validation part if needed. For simplicity we will first do a single train/test split and use cross‑validation later.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="c1"># Separate features and target</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">feature_names</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'MedHouseVal'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="c1"># Split: 80% train, 20% test</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Training set size: </span><span class="si">{</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test set size: </span><span class="si">{</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Training set size: 16512
Test set size: 4128
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<p>With that, let's see the relationship between the features and the price.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Visualize relationships between features and target</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">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</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">flatten</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">feature_names</span><span class="p">,</span> <span class="n">axes</span><span class="p">)):</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</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">name</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="s1">'MedHouseVal'</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="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s1"> vs Price'</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">savefig</span><span class="p">(</span><span class="s1">'../images/california_housing_scatter.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<h2 id="Linear-Regression-in-Practice">Linear Regression in Practice<a class="anchor-link" href="#Linear-Regression-in-Practice">¶</a></h2><p>We can use <code>sklearn.linear_model.LinearRegression</code>, which internally solves the normal equations using either a direct solver or an SVD‑based approach (the <code>lstsq</code> method we saw earlier).</p>
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<p><strong>Linear algebra reminder</strong>: The least‑squares solution minimises $\|y - X\beta\|_2^2$. The closed form is $\beta = (X^T X)^{-1} X^T y$ when $X$ has full column rank.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">r2_score</span>
<span class="c1"># Fit linear regression</span>
<span class="n">lin_reg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">lin_reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Predict on train and test</span>
<span class="n">y_train_pred</span> <span class="o">=</span> <span class="n">lin_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">lin_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Evaluate</span>
<span class="n">train_mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">)</span>
<span class="n">test_mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
<span class="n">train_r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">)</span>
<span class="n">test_r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Train MSE: </span><span class="si">{</span><span class="n">train_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">, Train R²: </span><span class="si">{</span><span class="n">train_r2</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Test MSE: </span><span class="si">{</span><span class="n">test_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">, Test R²: </span><span class="si">{</span><span class="n">test_r2</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Train MSE: 0.5229, Train R²: 0.6079
Test MSE: 0.5381, Test R²: 0.5931
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<p>The test $R^2$ is respectable (~0.6), but perhaps we can do better with a more flexible model. However, simply adding polynomial features might lead to overfitting. Let's examine that.</p>
<h2 id="Polynomial-Regression-and-the-Danger-of-Overfitting">Polynomial Regression and the Danger of Overfitting<a class="anchor-link" href="#Polynomial-Regression-and-the-Danger-of-Overfitting">¶</a></h2><p>Polynomial regression creates new features by taking powers of the original features. For example, with one feature $x$, a degree‑2 model uses $[1, x, x^2]$. For multiple features, we can include interaction terms.</p>
<blockquote>
<p><strong>Linear algebra view</strong>: The Vandermonde matrix (for one feature) or its multivariate generalisation becomes the new design matrix. As degree increases, the condition number often explodes, leading to numerical instability and wild coefficients – a sign of overfitting.</p>
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<p>Let's illustrate underfitting and overfitting on synthetic data before moving to the housing dataset.</p>
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<h3 id="Illustration:-Underfitting-vs-Overfitting">Illustration: Underfitting vs Overfitting<a class="anchor-link" href="#Illustration:-Underfitting-vs-Overfitting">¶</a></h3><p>We generate data from a quadratic function with noise, then fit polynomials of different degrees.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Generate quadratic data (similar to notebook 02) – using distinct names</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">n_synth</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">x_synth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">n_synth</span><span class="p">)</span>
<span class="n">y_true_synth</span> <span class="o">=</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">x_synth</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mf">5.0</span>
<span class="n">noise_synth</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">n_synth</span><span class="p">)</span>
<span class="n">y_synth</span> <span class="o">=</span> <span class="n">y_true_synth</span> <span class="o">+</span> <span class="n">noise_synth</span>
<span class="c1"># Fit polynomials of degree 1 (underfit), 2 (good), 11 (overfit)</span>
<span class="n">degrees</span> <span class="o">=</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">11</span><span class="p">]</span>
<span class="n">x_plot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</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">1</span><span class="p">,</span> <span class="mi">3</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">4</span><span class="p">))</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">degrees</span><span class="p">):</span>
<span class="n">coeff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">polyfit</span><span class="p">(</span><span class="n">x_synth</span><span class="p">,</span> <span class="n">y_synth</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">poly1d</span><span class="p">(</span><span class="n">coeff</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x_synth</span><span class="p">,</span> <span class="n">y_synth</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">label</span><span class="o">=</span><span class="s1">'Data'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x_plot</span><span class="p">,</span> <span class="n">p</span><span class="p">(</span><span class="n">x_plot</span><span class="p">),</span> <span class="s1">'r-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Degree </span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Degree </span><span class="si">{</span><span class="n">d</span><span class="si">}</span><span class="s1"> fit'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'x'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'y'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</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">savefig</span><span class="p">(</span><span class="s1">'../images/underfitting_vs_overfitting.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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"/>
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<li><strong>Degree 1 (underfitting)</strong>: The linear model cannot capture the curvature, resulting in high bias.</li>
<li><strong>Degree 2 (good)</strong>: The quadratic model matches the true underlying structure.</li>
<li><strong>Degree 11 (overfitting)</strong>: The polynomial oscillates wildly to fit the noise, leading to poor generalisation.</li>
</ul>
<p>Now back to the housing dataset. Let's create polynomial features and see the effect on condition number and test error.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">PolynomialFeatures</span>
<span class="c1"># Create polynomial features of degree 2 (includes interactions)</span>
<span class="n">poly</span> <span class="o">=</span> <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">X_train_poly</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_poly</span> <span class="o">=</span> <span class="n">poly</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Original training features: </span><span class="si">{</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Polynomial training features: </span><span class="si">{</span><span class="n">X_train_poly</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Condition number of the augmented polynomial design matrix (with intercept added later)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">numpy.linalg</span><span class="w"> </span><span class="kn">import</span> <span class="n">cond</span>
<span class="n">X_train_poly_with_intercept</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_train_poly</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_train_poly</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Condition number of polynomial design matrix: </span><span class="si">{</span><span class="n">cond</span><span class="p">(</span><span class="n">X_train_poly_with_intercept</span><span class="p">)</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Original training features: 8
Polynomial training features: 44
Condition number of polynomial design matrix: 1.55e+11
Condition number of polynomial design matrix: 1.55e+11
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<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Fit linear regression on polynomial features</span>
<span class="n">poly_reg</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">poly_reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_train_pred_poly</span> <span class="o">=</span> <span class="n">poly_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_poly</span><span class="p">)</span>
<span class="n">y_test_pred_poly</span> <span class="o">=</span> <span class="n">poly_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly</span><span class="p">)</span>
<span class="n">train_mse_poly</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred_poly</span><span class="p">)</span>
<span class="n">test_mse_poly</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_poly</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Polynomial (deg=2) Train MSE: </span><span class="si">{</span><span class="n">train_mse_poly</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Polynomial (deg=2) Test MSE: </span><span class="si">{</span><span class="n">test_mse_poly</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Polynomial (deg=2) Train MSE: 0.4217
Polynomial (deg=2) Test MSE: 0.4669
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<p>The test error is <strong>worse</strong> than the linear model – this is a clear sign of overfitting. The model is too flexible and fits noise in the training data. We need <strong>regularisation</strong>.</p>
<h2 id="Ridge-Regression-($L%5E2$-Regularisation)">Ridge Regression ($L^2$ Regularisation)<a class="anchor-link" href="#Ridge-Regression-($L%5E2$-Regularisation)">¶</a></h2><p>Ridge regression adds a penalty on the squared $L^2$ norm of the coefficient vector:</p>
<p>$$
\min_{\beta} \|y - X\beta\|_2^2 + \lambda \|\beta\|_2^2
$$</p>
<p>where $\lambda \ge 0$ is the regularisation strength.</p>
<blockquote>
<p><strong>Linear algebra interpretation</strong>: The normal equations become $(X^T X + \lambda I)\beta = X^T y$. Adding $\lambda I$ to $X^T X$ increases all eigenvalues by $\lambda$, thereby improving the condition number and making the problem well‑posed even when $X^T X$ is singular. This is a form of <strong>Tikhonov regularisation</strong>.
This directly shifts the eigenvalues (and singular values) of $X^TX$.</p>
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<p>Ridge regression shrinks coefficients towards zero but rarely makes them exactly zero. It is especially useful when features are correlated (multicollinearity).</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">Ridge</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="c1"># We'll use the polynomial features because ridge can help with overfitting</span>
<span class="c1"># Choose lambda via cross-validation on the training set</span>
<span class="n">alphas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="n">cv_scores</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">:</span>
<span class="n">ridge</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
<span class="c1"># 5-fold cross-validation, negative MSE (scoring expects higher = better)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">ridge</span><span class="p">,</span> <span class="n">X_train_poly</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">)</span>
<span class="n">cv_scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">best_alpha</span> <span class="o">=</span> <span class="n">alphas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">cv_scores</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Best alpha from CV: </span><span class="si">{</span><span class="n">best_alpha</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Plot CV error vs alpha</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">8</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">alphas</span><span class="p">,</span> <span class="n">cv_scores</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">'alpha (λ)'</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">'Cross-validated MSE'</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">'Ridge Regularisation on Polynomial Features'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/ridge_regularization_polynomial_features_unscaled.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.61091e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.8355e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.12863e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.2106e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.54461e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.8756e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.92268e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.38803e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.47667e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.81721e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.42347e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.1031e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.92486e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.02729e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.38133e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.55789e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.47656e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.03616e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.16704e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.54886e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=9.90852e-21): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.24995e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.03379e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.05273e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.09661e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.47609e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.8532e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.51111e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.54201e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.59756e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.48243e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.18458e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.5036e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.55854e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.63843e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.58073e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.5141e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.57846e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.68043e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.81413e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.99842e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.97942e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.95471e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=9.14672e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=9.41068e-20): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.84197e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=6.10261e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.82904e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.8654e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.92511e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.2525e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.40749e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.23705e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.31565e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=4.36802e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.62957e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.85107e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.62987e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.72841e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.85718e-19): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.79172e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.9137e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.79522e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.81032e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.83658e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.74634e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.23675e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.75365e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.78706e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.837e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.84044e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=2.6169e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.83226e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.97521e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=8.10899e-18): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.62385e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=5.40767e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.62848e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.64981e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=1.67422e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.37154e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.37402e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.42091e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=3.46597e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.00456e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.00113e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.10119e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
/usr/lib64/python3.14/site-packages/scipy/_lib/_util.py:1233: LinAlgWarning: Ill-conditioned matrix (rcond=7.18479e-17): result may not be accurate.
return f(*arrays, *other_args, **kwargs)
</pre>
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<pre>Best alpha from CV: 1000.0000
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4NlZWWhV69esLCwwIcffgh/f3+oVCrcvHkTI0eOrPLwHwBo165duRdsJSUlVfheBQBfX1+giu+16vDgMVryGRQYGFhu/5L/jAmCgN27d2PBggUICwvDG2+8AQcHBzz77LNYuHAhbGxsjBIv1X0sXsmktWjRQndBU58+faDRaLBq1Sr88ssvlRZ3jo6OOH78eJn2By/YKvlH9sGLpcrrW3Kxzvbt28vdp74f1Pf/AxccHIwBAwZg5cqVeOGFFxAYGKhb9tVXX1V4lbyLiwvUajUEQdArdgsLCwAoM5bwYQULAFy4cAFnz57FunXrMH78eF17yXjK8uh7pXKvXr3Qq1cvaDQanDx5El999RVmzpwJFxcXPP300/jpp5+gUCjw559/6l4DAGzdulWv7ZfH3t4eMpkMt2/fLrOs5CKs6rhivOTYqmg/Mpms1HRvj0omk+neKxXF8yiveezYsXj77bfxzTffoGvXrkhMTMT06dOrFKujoyMKCwtx586dUgWsKIpITEyssGiqDnv27EFCQgL27dunO9sKwKhjNJ2cnCAIAg4ePKgr5O9X0laV91p5lEplmfc6Knm/P/h+LTkWfvnlF91Z34p4eXlh9erVAIB///0XmzZtQmhoKAoKCioc707EYQNUr4SFhcHe3h7vv/9+pV9P9enTB5mZmfj9999LtYeHh5d63qxZM7i6umLTpk2l2uPi4hAZGVmq7fHHH8e9e/eg0WgQEBBQ5tGsWTODX48gCPjmm28gl8vx7rvvAgB69OiBBg0a4NKlS+XuJyAgAObm5rCyskJAQAC2bt1a6gKnrKws3ZXYJVxcXGBhYYFz586Vav/tt9/0ihH3/QNbYsWKFQa/3orI5XJ06dJF97X06dOndfs2MzODXC7X9c3NzcX3339fZhtKpVKvM1NWVlbo0qULNm/eXKq/VqvFDz/8AA8Pj4deEKiPZs2aoVGjRggPDy81M0J2djZ+/fVX3QwENaVfv364dOmSLrclvvvuOwiC8NBp2iwsLHRDPBYvXoz27duXGVpjSCwovjDxfr/++iuys7N1y42hJo7nBz3++OMQRRHx8fHlvp/btGljcGyVnZX39vYu817fs2cPsrKy9Ip3wIABMDMzw7Vr1yr8DCqPv78/3n33XbRp06bMcUZ0P555pXrF3t4ec+fOxezZsxEeHl7hFd/PP/88Pv/8czz//PNYuHAhmjZtim3btmHHjh2l+slkMsyfPx9TpkzB6NGjMWnSJKSlpWH+/Plwc3MrNVbx6aefxoYNGzB48GC89tpr6Ny5MxQKBW7duoW9e/di+PDhZa661kfTpk0xefJkLF26FIcOHULPnj3x1VdfYfz48UhJScHo0aPh7OyMO3fu4OzZs7hz5w6WLVsGAFiwYAGGDBmCAQMG4LXXXoNGo8Enn3wCa2trpKSk6PZRMt5uzZo18PPzQ7t27XD8+PEyxXx5mjdvDj8/P8yZMweiKMLBwQF//PEHIiIiDH6t91u+fDn27NmDIUOGoHHjxsjLy8OaNWuA4vlvAWDIkCFYvHgxxo4di8mTJ+PevXv49NNPyz171aZNG/z000/YuHEjfH19YWFhoSsKHvTRRx8hODgYffr0wZtvvglzc3MsXboUFy5cwI8//lgtc1zKZDKEhYXh2WefxeOPP44pU6YgPz8fn3zyCdLS0vDxxx8/8j4M8frrr+O7777DkCFDsGDBAnh5eeGvv/7C0qVL8fLLL+tVsE+bNg1hYWE4deoUVq1aVeVYSr5xePvtt5GRkYEePXrg3LlzmDdvHjp06IBx48ZVedsP0717d9jb22Pq1KmYN28eFAoFNmzYgLNnzxptnz169MDkyZMxceJEnDx5Eo899hisrKxw+/ZtHDp0CG3atMHLL79s0Hut5Nj+4osvMH78eCgUCjRr1gw2NjYYN24c3nvvPbz//vvo3bs3Ll26hK+//rrMDRQq4u3tjQULFuCdd97B9evXddcbJCUl4fjx47CyssL8+fNx7tw5vPLKK3jyySfRtGlTmJubY8+ePTh37hzmzJlT7XkkEyL1FWNExlByFeyJEyfKLMvNzRUbN24sNm3aVCwsLBTFcmYbEEVRvHXrljhq1CjR2tpatLGxEUeNGiVGRkaWe9X9ypUrxSZNmojm5uaiv7+/uGbNGnH48OFihw4dSvVTq9Xip59+KrZr1060sLAQra2txebNm4tTpkwRr1y5UulrKrki+c6dO2WWJSUlidbW1mKfPn10bfv37xeHDBkiOjg4iAqFQmzUqJE4ZMgQ8eeffy617pYtW8Q2bdqI5ubmYuPGjcWPP/5YnDFjhmhvb1+qX3p6uvjiiy+KLi4uopWVlTh06FAxNjZWr9kGLl26JAYHB4s2Njaivb29+OSTT4pxcXFl1q3sNT545faRI0fEJ554QvTy8hKVSqXo6Ogo9u7du9SV4qIoimvWrBGbNWsmKpVK0dfXV/zoo4/E1atXl4kxNjZWDAkJEW1sbEQAupkiKppp4eDBg2Lfvn1FKysr0dLSUuzatav4xx9/lOpT0XFYcvX93r17y7zOB23dulXs0qWLaGFhIVpZWYn9+vUTDx8+XG5uHszbw2Z+KFEy28DD3LhxQxw7dqzo6OgoKhQKsVmzZuInn3xSajYEsZKr1UVRFIOCgkQHBwcxJyenzDJDXkdubq749ttvi15eXqJCoRDd3NzEl19+WUxNTS21rpeXlzhkyJAy+wIgTp8+vVRbyd/6k08+KRPT/SIjI8Vu3bqJKpVKbNiwofjiiy+Kp0+fLnOcGDrbQHnH/f3WrFkjdunSRXfM+fn5ic8//7x48uRJXR9932uiKIpz584V3d3dRZlMVup4zM/PF2fPni16enqKlpaWYu/evcWoqKgKZxso73NWLD52+/TpI9ra2opKpVL08vISR48eLe7atUsUiz+3JkyYIDZv3ly0srISra2txbZt24qff/657rOZqDyCeP/3UURULdLS0uDv748RI0Zg5cqVUodjELVarbs6e+fOnVKHQyYkOTkZXl5eePXVVxEWFiZ1OERUR3HYANEjSkxMxMKFC9GnTx84Ojrixo0b+Pzzz5GZmam76r02e+GFFxAcHAw3NzckJiZi+fLluHz5Mr744gupQyMTcevWLVy/fh2ffPIJZDJZnXhfEFHtxeKV6BEplUrExsZi2rRpSElJgUqlQteuXbF8+XK0atVK6vAeKjMzE2+++Sbu3LkDhUKBjh07Ytu2bbpxo0SPatWqVViwYAG8vb2xYcOGUnOuEhEZisMGiIiIiKjO4FRZRERERFRnsHglIiIiojqDxSsRERER1Rkmf8GWVqtFQkICbGxsqmXicCIiIiKqXqIoIjMzE+7u7qVu8FMeky9eExIS4OnpKXUYRERERPQQN2/ehIeHR6V9TL54tbGxAYqTYWtra/T9qdVq7Ny5EyEhIVAoFEbfHxVh3qXBvEuDeZcG8y4N5l0aNZ33jIwMeHp66uq2yph88VoyVMDW1rbGileVSgVbW1u+yWoQ8y4N5l0azLs0mHdpMO/SkCrv+gzx5AVbRERERFRnsHglIiIiojqDxSsRERER1RksXomIiIiozmDxSkRERER1BotXIiIiIqozWLwSERERUZ3B4pWIiIiI6gwWr0RERERUZ7B4JSIiIqI6w+RvD1vTnvr2OKIT5Pjw/D4o5DLI5QIUMhnkMgFymVDUJhOgkBc9N5PJYCYXYFb8u/y+381kgm6ZXCb7bx25rLitZDtFy8zlMliay2GpkENlbnbf7/Ki34ufK+T8PwsRERHVTSxeq1laTgGyCwVkZxVIHUqFFHIBFiVFrUIOS3MzWCpkUJmbPdBe9FApShe/KnN5cT8zqMzlaGijhKOVOcxYFBMREZGRsXitZque74idu/ehe49eEGRyqLVaaLQi1Jqin4VaEYUaERqtFmqNeF+bttTP+/sWav9rK9mOungb9/fJV2uRq9Ygp0CDvOKfuWoNcgs0yCkohFYsilGtEaHWFCIzr7DaXrdMABraKOFiawFnGwu42inhYmMBFzsLuNhawNXWAi62SthZKiAIQrXtl4iIiOoXFq/VzNNeBTcV0MLNBgqFQupwdERRRIFGi7wCLXLUhUWFbani9sGCt7BsIVy87P7fs/ILcS8rH1oRSMrIR1JGPoD0CuNQmsngUlzIFv0sKmydbZXFBW7Rw9JcXqP5ISIiorqBxWs9IQgClGZyKM3ksEP1FtUarYi7WflIyshDUkY+EjPykJyRh8T0PCRl5iMpPQ9JmXlIy1Ejv1CLuJQcxKXkVLpNWwuzosLW7r4zubqzuhbwtLeEo7WyWl8HERER1X4sXumRyWWC7oxpZfLUGiRn5CMps7iwzchDcma+7vekjDwkZuQhT61FRl4hMvKycCU5q8LtNXW2Rjc/R3T3c0QnTzsjvDIiIiKqbVi8Uo2xUMjR2FGFxo6qCvuIooiMvEIk33cWNynj/uI2v+isbkYeriQXFbffHbkBQQAaqeQ4J4tGj6YNEejtABuL2jNsg4iIiKoHi1eqVQRBgJ2lAnaWCjR1samwX2p2AY7F3EPktaLH1eQs3MoWsPrwDaw+fANymYC2Hnbo7ueI7n5O6ORlDwsFx9ESERHVdSxeqU6ytzLHwNZuGNjaDQAQn5KFFVv2IM+2MY7GpCIuJQdn4tJwJi4N3+y9BnO5DB0aN0B3Pyd0b+KIdh4NYG7Gqb2IiIjqGhavZBKcbZTo5CRi8OBWUCgUuJWagyPX7uFI8ZnZxIw8HItJwbGYFHy+C7BUyBHo44BuvkVjZls3soNcxim8iIiIajsWr2SSPOxVeDJAhScDPCGKImLuZuPI9aJC9si1e0jJLsCBf+/gwL93AAA2Fmbo4lNUyHbzc0QzFxvIWMwSERHVOixeyeQJggDfhtbwbWiNZ7t4QasV8W9yJiKv3sOR6/dw9Po9ZOYVYtflJOy6nAQAcLAyRzdfR91sBj5OVry5AhERUS3A4pXqHZlMQHNXWzR3tcWknj7QaEVcTEjXnZU9HpOClOwC/HX+Nv46fxsA4GKrRHc/Jwxr744g/4YsZImIiCTC4pXqvaKZCRqgrUcDTO3th4JCLc7dStMVs6fiUpGUkY8tZ+Kx5Uw8gpo1xPuPt4RvQ2upQyciIqp3WLwSPcDcTIYAbwcEeDtgRr+myFNrcPpGKiIuJ+GHozewL/oODl89gBd6+uLVvk1gpeTbiIiIqKZwriCih7BQyNG9iRPmDW2Fna/3Rp9mDaHWiFi+/xr6frYPv0XFQxRFqcMkIiKqF1i8EhnAx8kKayd2xurxAfByVCEpIx+v/RSFp1YcxaWEDKnDIyIiMnksXomqoF8LF+yY+RjeGtAMlgo5jsem4PGvDuL93y4gLadA6vCIiIhMlqTF67Jly9C2bVvY2trC1tYW3bp1w99//61bLooiQkND4e7uDktLSwQFBeHixYtShkykY6GQY3qfJtj9Rm8MaesGrQh8d+QG+ny6D+HH4qDRcigBERFRdZO0ePXw8MDHH3+MkydP4uTJk+jbty+GDx+uK1DDwsKwePFifP311zhx4gRcXV0RHByMzMxMKcMmKsW9gSW+GdsR4S91QTMXG6TmqPG/Lecx/JtDOHUjVerwiIiITIqkxevQoUMxePBg+Pv7w9/fHwsXLoS1tTWOHj0KURSxZMkSvPPOOxg5ciRat26N9evXIycnB+Hh4VKGTVSu7n5O+GtGT8wb2hI2Fma4EJ+BUcsiMWtTFJIz86QOj4iIyCTUmjl+NBoNfv75Z2RnZ6Nbt26IiYlBYmIiQkJCdH2USiV69+6NyMhITJkypdzt5OfnIz8/X/c8I6PoIhq1Wg21Wm3011Gyj5rYF/2nNuX9uc4eGNSyIT7bdRW/nI7H5tPx2HExEa/28cPzXRtDITedoea1Ke/1CfMuDeZdGsy7NGo674bsRxAlnuPn/Pnz6NatG/Ly8mBtbY3w8HAMHjwYkZGR6NGjB+Lj4+Hu7q7rP3nyZNy4cQM7duwod3uhoaGYP39+mfbw8HCoVCqjvhaiB93IAn6NkeNGVtEduVwsRYz01qJ5A46HJSIiKpGTk4OxY8ciPT0dtra2lfaVvHgtKChAXFwc0tLS8Ouvv2LVqlXYv38/0tLS0KNHDyQkJMDNzU3X/6WXXsLNmzexffv2crdX3plXT09P3L1796HJqA5qtRoREREIDg6GQqEw+v6oSG3Ou1YrYnNUAj7deQX3sotmIghu4Yz/DWoGD3tLqcN7JLU576aMeZcG8y4N5l0aNZ33jIwMODk56VW8Sj5swNzcHE2aNAEABAQE4MSJE/jiiy/w9ttvAwASExNLFa/JyclwcXGpcHtKpRJKpbJMu0KhqNGDvqb3R0Vqa96f6eKNwW0b4YtdV7D+SCwiLifjwJW7eDnID1N7+8FCIZc6xEdSW/Nu6ph3aTDv0mDepVFTeTdkH7Vu8J0oisjPz4ePjw9cXV0RERGhW1ZQUID9+/eje/fuksZIVBV2lgq8P7Ql/n6tF7r5OiK/UIslu66g/+L92H4hkXfpIiIi0oOkZ17/97//YdCgQfD09ERmZiZ++ukn7Nu3D9u3b4cgCJg5cyYWLVqEpk2bomnTpli0aBFUKhXGjh0rZdhEj8TfxQbhL3XBtvOJWPjXJdxKzcXUH06hV9OiW9A2cbaWOkQiIqJaS9LiNSkpCePGjcPt27dhZ2eHtm3bYvv27QgODgYAzJ49G7m5uZg2bRpSU1PRpUsX7Ny5EzY2NlKGTfTIBEHAkLZu6NO8IZbtu4YVB67j4JW7GLjkACb28MaMfk1hY8Gvx4iIiB4kafG6evXqSpcLgoDQ0FCEhobWWExENUllboY3QprhyU6eWPDnJey6nIRvD8Zga1QC5gxsjic6NIJMJkgdJhERUa1R68a8EtVHjR1VWDU+AOsmBsLHyQp3MvPxxs9nMXp5JC7Ep0sdHhERUa3B4pWoFglq5oztM3vh7YHNoTKX43RcGoZ+fQjrDsdIHRoREVGtwOKVqJZRmsnxcpAf9r4ZhGHt3CGKwPw/L2HnxUSpQyMiIpIci1eiWsrF1gJfPN0eY7s0higCr/0UhfO3OISAiIjqNxavRLWYIAiYP6wVejV1Qq5agxfWn0BCWq7UYREREUmGxStRLaeQy/DNsx3h72KN5Mx8TFp3Aln5hVKHRUREJAkWr0R1gK2FAmsmBMLJWol/EjPxavhpFGq0UodFRERU41i8EtURHvZF02lZKGTYG30HH/x5SeqQiIiIahyLV6I6pL1nA3w+pj0AYP2RG1jLKbSIiKieYfFKVMcMauOGOYOaAwA++PMSdl9OkjokIiKiGsPilagOmvKYL54K8IRWBF798QwuJnAKLSIiqh9YvBLVQYIg4MMnWqNHE0fkFGjwwrqTSEzPkzosIiIio2PxSlRHKeQyLH22E5o4WyMxIw8vrD+BbE6hRUREJo7FK1EdZmepwNoJgXC0MsfFhAy89tMZaLSi1GEREREZDYtXojrO00GFlc8HwNxMhl2Xk7Hwr8tSh0RERGQ0LF6JTEAnL3ssHtMOALDmcAy+PxIrdUhERERGweKVyEQ83tYdbw1oBgCY9/tF7I1OljokIiKiasfilciETAvyw+hOHtCKwCsbTuPy7QypQyIiIqpWLF6JTIggCFj0RBt083VEdoEGL6w7geQMTqFFRESmg8UrkYkxN5Nh+XOd4NvQCgnpeXhh/UnkFHAKLSIiMg0sXolMkJ2qaAotBytznI9Px8yfojiFFhERmQQWr0QmysvRCivHdYK5XIadl5Lw8d+cQouIiOo+Fq9EJizA2wGfPNkWAPDtwRhsOHZD6pCIiIgeCYtXIhM3vH0jvN7fHwDw/m8XceDfO1KHREREVGUsXonqgRn9mmBkh0bQaEVM33Aa0YmZUodERERUJSxeieoBQRDw0ag26OzjgMz8QkxadwJ3MvOlDouIiMhgLF6J6gmlmRwrnusEHycrxKfl4sXvTiK3QCN1WERERAZh8UpUj9hbmWPNhEA0UClw9mYaZm2KgpZTaBERUR3C4pWonvFxssKK5zpBIRfw94VEhO2IljokIiIivbF4JaqHuvg6Imx00RRay/dfw0/H46QOiYiISC8sXonqqSc6eGBGv6YAgHe3XsDhq3elDomIiOihWLwS1WOv92+K4e3dUagVMfWHU7iSxCm0iIiodmPxSlSPCYKA/xvVFgFe9sjMK8TEdSdwN4tTaBERUe0lafH60UcfITAwEDY2NnB2dsaIESMQHV364pEJEyZAEIRSj65du0oWM5GpsVDIsfL5AHg5qnArNRcvfXcSeWpOoUVERLWTpMXr/v37MX36dBw9ehQREREoLCxESEgIsrOzS/UbOHAgbt++rXts27ZNspiJTJFD8RRadpYKnIlLwxs/n+UUWkREVCuZSbnz7du3l3q+du1aODs749SpU3jsscd07UqlEq6urhJESFR/+DW0xvLnOmHc6mP469xteDuq8NaA5lKHRUREVIqkxeuD0tPTAQAODg6l2vft2wdnZ2c0aNAAvXv3xsKFC+Hs7FzuNvLz85Gf/9+YvYyMDACAWq2GWq02avwl+7n/J9UM5r16BDS2xYfDW2LOlov4Zu81dPW2R1dfhwr7M+/SYN6lwbxLg3mXRk3n3ZD9CKIo1orvBkVRxPDhw5GamoqDBw/q2jdu3Ahra2t4eXkhJiYG7733HgoLC3Hq1Ckolcoy2wkNDcX8+fPLtIeHh0OlUhn9dRCZgo3XZYhMksHLWsTrrTUQBKkjIiIiU5aTk4OxY8ciPT0dtra2lfatNcXr9OnT8ddff+HQoUPw8PCosN/t27fh5eWFn376CSNHjiyzvLwzr56enrh79+5Dk1Ed1Go1IiIiEBwcDIVCYfT9URHmvXrdzcpHv88PIadAg2+eaYeQli7l9mPepcG8S4N5lwbzLo2azntGRgacnJz0Kl5rxbCBV199Fb///jsOHDhQaeEKAG5ubvDy8sKVK1fKXa5UKss9I6tQKGr0oK/p/VER5r16uNkrMKmHD77eexWf776GAa3dYSav+PpO5l0azLs0mHdpMO/SqKm8G7IPSWcbEEURr7zyCjZv3ow9e/bAx8fnoevcu3cPN2/ehJubW43ESFRfTe7tiwYqBa4mZ2HzmXipwyEiIgKkLl6nT5+OH374AeHh4bCxsUFiYiISExORm5sLAMjKysKbb76JI0eOIDY2Fvv27cPQoUPh5OSEJ554QsrQiUyerYUC04L8AABLIv7l3K9ERFQrSFq8Llu2DOnp6QgKCoKbm5vusXHjRgCAXC7H+fPnMXz4cPj7+2P8+PHw9/fHkSNHYGNjI2XoRPXC89284WprgYT0PGw4Fid1OERERPoXr5s2bUJBQYHueWxsLDSa/87E5OTkICwszKCdi6JY7mPChAkAAEtLS+zYsQPJyckoKCjAjRs3sG7dOnh6ehq0HyKqGguFHDP7NwUAfLP3KjLzOFUNERFJS+/i9ZlnnkFaWpruedu2bXHjxg3d88zMTMydO7f6IyQiSY3u5AFfJyukZBdg1cEYqcMhIqJ6Tu/i9cEZtWrJDFtEZGRmchneCGkGAFh18DruZuU/dB0iIiJjkXTMKxHVDYNau6JNIztkF2jwzd6rUodDRET1GItXInoomUzA7IFFZ183HI3DrdQcqUMiIqJ6yqCbFOzYsQN2dnYAAK1Wi927d+PChQsAUGo8LBGZnp5NnNDdzxGR1+5hya4r+PTJdlKHRERE9ZBBxev48eNLPZ8yZUqp5wJvgE5ksgRBwFsDmuGJpZHYfPoWJj/mCx8HC6nDIiKiekbvYQNarfahj/unziIi09OhsT0GtHKBVgQ+3REtdThERFQPccwrERnkzZBmkAnAzktJOHOTw4WIiKhm6V28Xr16FadOnSrVtnv3bvTp0wedO3fGokWLjBEfEdUyTV1sMKqjBwDgs4gr4Kx5RERUk/QuXt966y1s3bpV9zwmJgZDhw6Fubk5unXrho8++ghLliwxVpxEVIvMDPaHuVyGYzGpiE7nWHciIqo5ehevJ0+exODBg3XPN2zYAH9/f+zYsQNffPEFlixZgnXr1hkrTiKqRRo1sMS4bl4AgD/iZNBqefqViIhqht7F6927d+Hh4aF7vnfvXgwdOlT3PCgoCLGxsdUfIRHVStOC/GBlLsetbAHbLyZJHQ4REdUTehevDg4OuH37NlA888DJkyfRpUsX3fKCggLeMpaoHnG0VuKFHt4AgM93X4Vao5U6JCIiqgf0Ll579+6NDz74ADdv3sSSJUug1WrRp08f3fJLly7B29vbWHESUS00sYcXrMxExN7LwS+nbkkdDhER1QN636Rg4cKFCA4Ohre3N2QyGb788ktYWVnpln///ffo27evseIkolrIWmmGEA8ttsTKsWTXv3iiQyNYKORSh0VERCZM7+LVx8cHly9fxqVLl9CwYUO4u7uXWj5//vxSY2KJqH7o6SLiWKoFEtLzsD4yFlN6+0kdEhERmTCDblKgUCjQrl27MoUrALRr1w6Ojo7VGRsR1QFmMmBG36KCdem+a0jPVUsdEhERmTC9z7wuWLBAr37vv//+o8RDRHXQiPbuWH34Bq4kZ2HlgWt4a0BzqUMiIiITpXfxGhoaCnd3dzg7O1c4q4AgCCxeieohuUzAmwOaYcr3p7DmUCzGd/eGs42F1GEREZEJ0rt4HThwIPbu3YuAgABMmjQJQ4YMgVzOCzOIqEhISxe092yAqJtp+HrPVSwY3lrqkIiIyATpPeZ127ZtuH79Orp06YK33noLHh4eePvttxEdHW3cCImoThAEAW8PLBouEH4sDnH3cqQOiYiITJBBF2y5ublh7ty5iI6OxsaNG5GcnIzAwED06NEDubm5xouSiOqEbn6O6NXUCYVaEYsj+B9bIiKqfgYVr/cLDAxEnz590KJFC5w5cwZqNa8wJiJgdvHFWr+dTcDl2xlSh0NERCbG4OL1yJEjeOmll+Dq6oqvvvoK48ePR0JCAmxtbY0TIRHVKW087DCkrRtEEfh0B8++EhFR9dK7eA0LC0OLFi0wfPhwWFtb49ChQzhx4gSmTZuGBg0aGDdKIqpT3gj2h1wmYPc/yTgRmyJ1OEREZEL0nm1gzpw5aNy4McaMGQNBELB27dpy+y1evLg64yOiOsi3oTXGBHjix+Nx+L+//8HPU7tBEASpwyIiIhOgd/H62GOPQRAEXLx4scI+/MeJiEq81q8pNp++hZM3UrE3Ohl9m7tIHRIREZkAvYvXffv2GTcSIjIprnYWmNDdGysOXEfY9mgE+TtDJuN/cImI6NFUebYBIqKHeTnIDzYWZvgnMRN/nEuQOhwiIjIBLF6JyGgaqMwxtbcfAOCznf+ioFArdUhERFTHsXglIqOa2MMbTtZKxKXkYOOJOKnDISKiOo7FKxEZlcrcDDP6NQEAfLH7KnIKCqUOiYiI6jAWr0RkdE8HNoangyXuZuVj7eFYqcMhIqI6TK/i9dy5c3o/DPHRRx8hMDAQNjY2cHZ2xogRIxAdXfqOPKIoIjQ0FO7u7rC0tERQUFCl03URUe1jbibDG8HNAADL919DWk6B1CEREVEdpddUWe3bt4cgCBBF8aFzuWo0Gr13vn//fkyfPh2BgYEoLCzEO++8g5CQEFy6dAlWVlZA8Z29Fi9ejHXr1sHf3x8ffvghgoODER0dDRsbG733RUTSGtbOHcv3X8M/iZlYtv8a5g5qIXVIRERUB+l15jUmJgbXr19HTEwMfv31V/j4+GDp0qU4c+YMzpw5g6VLl8LPzw+//vqrQTvfvn07JkyYgFatWqFdu3ZYu3Yt4uLicOrUKaD4rOuSJUvwzjvvYOTIkWjdujXWr1+PnJwchIeHV+0VE5EkZDIBswcWnX1ddzgWiel5UodERER1kF5nXr28vHS/P/nkk/jyyy8xePBgXVvbtm3h6emJ9957DyNGjKhyMOnp6QAABwcHoLhoTkxMREhIiK6PUqlE7969ERkZiSlTppTZRn5+PvLz83XPMzIyAABqtRpqtbrKsemrZB81sS/6D/MuDUPz3tPXHp0aN8CpuDR8HhGND4e3NHKEponHuzSYd2kw79Ko6bwbsh9BFEXRkI1bWlri9OnTaNGi9Fd+ly9fRseOHZGbm2vI5nREUcTw4cORmpqKgwcPAgAiIyPRo0cPxMfHw93dXdd38uTJuHHjBnbs2FFmO6GhoZg/f36Z9vDwcKhUqirFRkTV51oG8OVFM8ggYm57DZwtpY6IiIiklpOTg7FjxyI9PR22traV9tX79rAlWrRogQ8//BCrV6+GhYUFUHy288MPPyxT0BrilVdewblz53Do0KEyyx4cZ1vZ2Nu5c+di1qxZuucZGRnw9PRESEjIQ5NRHdRqNSIiIhAcHAyFQmH0/VER5l0aVc37ue9PY9+/dxFV6IElg9saNUZTxONdGsy7NJh3adR03ku+KdeHwcXr8uXLMXToUHh6eqJdu3YAgLNnz0IQBPz555+Gbg4A8Oqrr+L333/HgQMH4OHhoWt3dXUFACQmJsLNzU3XnpycDBcXl3K3pVQqoVQqy7QrFIoaPehren9UhHmXhqF5f3tQC+y/chB/XUjEy32aoHUjO6PGZ6p4vEuDeZcG8y6Nmsq7IfsweJ7Xzp07IyYmBgsXLkTbtm3Rpk0bLFq0CDExMejcubNB2xJFEa+88go2b96MPXv2wMfHp9RyHx8fuLq6IiIiQtdWUFCA/fv3o3v37oaGTkS1RAs3WwxvVzQUKGxH9EP7ExERlTD4zCsAqFQqTJ48+ZF3Pn36dISHh+O3336DjY0NEhMTAQB2dnawtLSEIAiYOXMmFi1ahKZNm6Jp06ZYtGgRVCoVxo4d+8j7JyLpzApuhj/P3caBf+8g8tpddPdzkjokIiKqA6p0h63vv/8ePXv2hLu7O27cuAEA+Pzzz/Hbb78ZtJ1ly5YhPT0dQUFBcHNz0z02btyo6zN79mzMnDkT06ZNQ0BAAOLj47Fz507O8UpUxzV2VOGZzo0BAGHbo2HgtaNERFRPGVy8Llu2DLNmzcKgQYOQmpqquymBvb09lixZYtC2RFEs9zFhwgRdH0EQEBoaitu3byMvLw/79+9H69atDQ2biGqhV/s2gaVCjqibaYi4lCR1OEREVAcYXLx+9dVX+Pbbb/HOO+/AzOy/UQcBAQE4f/58dcdHRCbM2dYCk3p6AwA+2RENjZZnX4mIqHIGF68xMTHo0KFDmXalUons7OzqiouI6onJj/nBzlKBK8lZ2HImXupwiIioljO4ePXx8UFUVFSZ9r///hstW/JuOURkGDtLBaYF+QEAPo/4F/mFGqlDIiKiWszg2QbeeustTJ8+HXl5eRBFEcePH8ePP/6Ijz76CKtWrTJOlERk0sZ398aawzGIT8vFhqNxmNTTR4+1iIioPjK4eJ04cSIKCwsxe/Zs3a28GjVqhC+++AJPP/20caIkIpNmoZDjtX7++N+W81i67yqe7doYSjO51GEREVEtVKWpsl566SXcuHEDycnJSExMxM2bN/HCCy9Uf3REVG88GeABNzsL3M0qwB9nb0sdDhER1VIGF699+/ZFWloaAMDJyQnOzs5A8T1p+/btW/0RElG9oJDL8Hy3opkH1hyK4byvRERULoOL13379qGgoKBMe15eHg4ePFhdcRFRPfRMZ09YKGS4dDsDx2JSpA6HiIhqIb3HvJ47d073+6VLl3S3cgUAjUaD7du3o1GjRtUfIRHVGw1U5hjV0QMbjsVh9aEYdPV1lDokIiKqZfQuXtu3bw9BECAIQrnDAywtLfHVV19Vd3xEVM9M7OGNDcfisOtyEm7cy4aXo5XUIRERUS2id/EaE1M0Bs3X1xfHjx9Hw4YNdcvMzc3h7OwMuZxXBxPRo2nibIPe/g2x/987WBcZi3lDW0kdEhER1SJ6F69eXl4AAK1Wa8x4iIgwqacP9v97Bz+fvIVZwf6wsVBIHRIREdUSBs/zWuLSpUuIi4src/HWsGHDqiMuIqrHHmvqhCbO1rianIVNJ2/hBd60gIiIihlcvF6/fh1PPPEEzp8/D0EQdNPZCIIAFF+8RUT0KARBwMQe3nhnywWsi4zBhO7ekMsEqcMiIqJawOCpsl577TX4+PggKSkJKpUKFy9exIEDBxAQEIB9+/YZJ0oiqndGdvCAnaUCN1NysetyktThEBFRLWFw8XrkyBEsWLAADRs2hEwmg0wmQ8+ePfHRRx9hxowZxomSiOodS3M5xnZpDBTftICIiAhVKV41Gg2sra2B4jtsJSQkAMUXdEVHR1d/hERUbz3fzQtmMgHHYlJwIT5d6nCIiKgWMLh4bd26te6GBV26dEFYWBgOHz6MBQsWwNfX1xgxElE95WZnicFt3AAAaw/HSh0OERHVAgYXr++++65uuqwPP/wQN27cQK9evbBt2zZ8+eWXxoiRiOqxScUzDfxxNgHJmXlSh0NERBIzeLaBAQMG6H739fXFpUuXkJKSAnt7e92MA0RE1aW9ZwN0bNwAp+PSsOFoHF4P9pc6JCIikpDBZ17L4+DgwMKViIym5OzrhmM3kKfmdHxERPWZXmdeR44cqfcGN2/e/CjxEBGVMbCVK9ztLJCQnoffzyZgTICn1CEREZFE9Drzamdnp3vY2tpi9+7dOHnypG75qVOnsHv3btjZ2RkzViKqp8zkMjzf3Rsonjar5OYoRERU/+h15nXt2rW6399++22MGTMGy5cvh1wuB4qnz5o2bRpsbW2NFykR1WtPB3rii11X8E9iJo5cv4fufk5Sh0RERBIweMzrmjVr8Oabb+oKVwCQy+WYNWsW1qxZU93xEREBABqozDGqUyMAwJpDnDaLiKi+Mrh4LSwsxOXLl8u0X758WTeFFhGRMUzoXnTh1u5/khB7N1vqcIiISAIGT5U1ceJETJo0CVevXkXXrl0BAEePHsXHH3+MiRMnGiNGIiIAQBNnawQ1a4h90XewLjIWocNaSR0SERHVMIOL108//RSurq74/PPPcfv2bQCAm5sbZs+ejTfeeMMYMRIR6Uzq4YN90Xfw88mbmBXiD1sLhdQhERFRDTJ42IBMJsPs2bMRHx+PtLQ0pKWlIT4+HrNnzy41DpaIyBh6NXVCE2drZBdosOnETanDISKiGvZINymwtbXlDANEVKMEQcCkHkVjX9dFxkKj5bRZRET1iV7DBjp27Ijdu3fD3t4eHTp0qPRuWqdPn67O+IiIyhjZsRE+2fEPbqXmIuJSEga2dpU6JCIiqiF6Fa/Dhw+HUqkEAIwYMcLYMRERVcpCIcfYLo3xzd5rWHM4hsUrEVE9olfxOm/evHJ/JyKSyriu3lix/zqOx6TgQnw6WjfiHf6IiOqDRxrz+qgOHDiAoUOHwt3dHYIgYOvWraWWT5gwAYIglHqUTM9FRPWbq50FhrR1AwCsORwjdThERFRD9Drzam9vX+k41/ulpKTovfPs7Gy0a9cOEydOxKhRo8rtM3DgwFK3pzU3N9d7+0Rk2ib28MFvUQn442wC5gxsDmdbC6lDIiIiI9OreF2yZIlRdj5o0CAMGjSo0j5KpRKurhzPRkRltfdsgE5e9jh1IxU/HL2BWSHNpA6JiIiMTK/idfz48caPpAL79u2Ds7MzGjRogN69e2PhwoVwdnausH9+fj7y8/N1zzMyMgAAarUaarXa6PGW7KMm9kX/Yd6lURvyPr6rJ07dSMX3R29gck8vKBWmP990bch7fcS8S4N5l0ZN592Q/QiiKFZ5ksTc3NwyO6vqvK+CIGDLli2lZjPYuHEjrK2t4eXlhZiYGLz33nsoLCzEqVOndLMfPCg0NBTz588v0x4eHg6VSlWl2Iio9tKIwILTcqQVCHjGT4Ouzpz3lYiorsnJycHYsWORnp7+0FrS4OI1Ozsbb7/9NjZt2oR79+6VWa7RaAyPuILi9UG3b9+Gl5cXfvrpJ4wcObLcPuWdefX09MTdu3dr5IYKarUaERERCA4OhkLB21bWFOZdGrUl798eikHYjito5mKNP6Z303uMfl1VW/Je3zDv0mDepVHTec/IyICTk5NexatewwbuN3v2bOzduxdLly7F888/j2+++Qbx8fFYsWIFPv7440eJ+6Hc3Nzg5eWFK1euVNhHqVSWe1ZWoVDU6EFf0/ujIsy7NKTO+7NdfPDVnuuITsrCybgMdG/iJFksNUnqvNdXzLs0mHdp1FTeDdmHwVNl/fHHH1i6dClGjx4NMzMz9OrVC++++y4WLVqEDRs2GLo5g9y7dw83b96Em5ubUfdDRHWLnUqB0Z08AE6bRURk8gwuXlNSUuDjU3RfcVtbW93UWD179sSBAwcM2lZWVhaioqIQFRUFAIiJiUFUVBTi4uKQlZWFN998E0eOHEFsbCz27duHoUOHwsnJCU888YShYRORiZvQwxsAsPufZMTczZY6HCIiMhKDi1dfX1/ExsYCAFq2bIlNmzYBxWdkGzRoYNC2Tp48iQ4dOqBDhw4AgFmzZqFDhw54//33IZfLcf78eQwfPhz+/v4YP348/P39ceTIEdjY2BgaNhGZOL+G1ujTrCFEEVgfGSt1OEREZCQGj3mdOHEizp49i969e2Pu3LkYMmQIvvrqKxQWFmLx4sUGbSsoKAiVXS+2Y8cOQ8MjonrshZ6+2Bt9B5tO3sTrwf6ws+T4OCIiU2Nw8fr666/rfu/Tpw/++ecfnDx5En5+fmjXrl11x0dEpLceTRzh72KNf5Oy8PPJm3ixl6/UIRERUTUzeNhAyZCBEo0bN8bIkSNZuBKR5ARBwKQeRWPy1x6ORaFGK3VIRERUzao05rVnz55YsWKF7mItIqLaYkSHRrBXKRCflotdl5OkDoeIiKqZwcXryZMn0a1bN3z44Ydwd3fH8OHD8fPPP5e6MQARkVQsFHI828ULALD6EKfNIiIyNQYXrx07dsQnn3yCuLg4/P3333B2dsaUKVPg7OyMSZMmGSdKIiIDjOvmBTOZgBOxqTh3K03qcIiIqBoZXLyWEAQBffr0wbfffotdu3bB19cX69evr97oiIiqwMXWAo+3LbqZydrDnDaLiMiUVLl4vXnzJsLCwtC+fXsEBgbCysoKX3/9dfVGR0RURZN6Fl249ee5BCRl5EkdDhERVRODi9eVK1eid+/e8PHxwfr16zFmzBhcu3YNhw4dwssvv2ycKImIDNTWowECvOyh1oj44egNqcMhIqJqYnDx+sEHH6Bz5844efIkLl68iP/973/w9vY2TnRERI+g5OzrhmNxyFNrpA6HiIiqgcE3KYiLi4MgCACAw4cPIyAgAEql0hixERE9kpCWLmjUwBLxabn4LSoeTwU2ljokIiJ6RAafeS0pXAFg0KBBiI+Pr+6YiIiqhZlchvHdi6bNWnMottLbURMRUd1Q5Qu2APAfAiKq9Z4KbAyVuRzRSZmIvHZP6nCIiOgRPVLxSkRU29lZKvBkJw8AwBretICIqM57pOJ1xYoVcHFxqb5oiIiMYEKPogu3dv+TjJi72VKHQ0REj+CRitexY8dCo9Fg69atuHz5cvVFRURUjXycrNCvuTMAYN1hnn0lIqrLDC5ex4wZo7sZQW5uLgICAjBmzBi0bdsWv/76qzFiJCJ6ZCXTZv186hbSc9VSh0NERFVkcPF64MAB9OrVCwCwZcsWiKKItLQ0fPnll/jwww+NESMR0SPr7ueIZi42yCnQYNOJm1KHQ0REVWRw8Zqeng4HBwcAwPbt2zFq1CioVCoMGTIEV65cMUaMRESPTBAETOpZdEOVdZGxKNRopQ6JiIiqwODi1dPTE0eOHEF2dja2b9+OkJAQAEBqaiosLCyMESMRUbUY3r4RHKzMEZ+Wi52XkqQOh4iIqsDg4nXmzJl49tln4eHhAXd3dwQFBQHFwwnatGljjBiJiKqFhUKOZ7sU3WWL02YREdVNBhev06ZNw5EjR7BmzRocOnQIMlnRJnx9fTnmlYhqvee6ekEhF3DyRirO3kyTOhwiIjJQlabKCggIwBNPPAFra2toNBpERUWhe/fu6NGjR/VHSERUjVxsLfB4W3cAwFpOm0VEVOdUadjA6tWrAQAajQa9e/dGx44d4enpiX379hkjRiKiajWp+KYFf567jaSMPKnDISIiAxhcvP7yyy9o164dAOCPP/5ATEwM/vnnH8ycORPvvPOOMWIkIqpWbTzsEOhtj0KtiO+P3JA6HCIiMoDBxevdu3fh6uoKANi2bRuefPJJ+Pv744UXXsD58+eNESMRUbUrOfu64dgN5Kk1UodDRER6Mrh4dXFxwaVLl6DRaLB9+3b0798fAJCTkwO5XG6MGImIql1IK1d42FsiNUeNrWfipQ6HiIj0ZHDxOnHiRIwZMwatW7eGIAgIDg4GABw7dgzNmzc3RoxERNVOLhMwoXvRTQvWHI6BKIpSh0RERHowuHgNDQ3FqlWrMHnyZBw+fBhKpRIAIJfLMWfOHGPESERkFGMCPWFlLse/SVk4dPWu1OEQEZEezKqy0ujRo8u0jR8/vjriISKqMbYWCjwZ4Il1kbH4avdV9GziBEEQpA6LiIgqUaV5Xvfv34+hQ4eiSZMmaNq0KYYNG4aDBw9Wf3REREY2pbcvzM1kOB6bgn3/3pE6HCIiegiDi9cffvgB/fv3h0qlwowZM/DKK6/A0tIS/fr1Q3h4uHGiJCIyEjc7S93Y17Dt0dBqOfaViKg2M7h4XbhwIcLCwrBx40bMmDEDr732GjZu3IiPP/4YH3zwgXGiJCIyopd7+8FGaYbLtzPwx7kEqcMhIqJKGFy8Xr9+HUOHDi3TPmzYMMTE8FaLRFT32FuZY/JjvgCAxRH/Qq3RSh0SERFVwODi1dPTE7t37y7Tvnv3bnh6ehq0rQMHDmDo0KFwd3eHIAjYunVrqeWiKCI0NBTu7u6wtLREUFAQLl68aGjIREQPNamnD5yszXHjXg42nrgpdThERFQBg4vXN954AzNmzMDLL7+M77//Hj/88AOmTp2K1157DW+++aZB28rOzka7du3w9ddfl7s8LCwMixcvxtdff40TJ07A1dUVwcHByMzMNDRsIqJKWSnN8GrfpgCAL3ZfQW4B77pFRFQbGTxV1ssvvwxXV1d89tln2LRpEwCgRYsW2LhxI4YPH27QtgYNGoRBgwaVu0wURSxZsgTvvPMORo4cCQBYv349XFxcEB4ejilTphgaOhFRpZ7p3BjfHryOW6m5WBsZg2lBTaQOiYiIHmBQ8VpYWIiFCxdi0qRJOHTokPGiAhATE4PExESEhITo2pRKJXr37o3IyMgKi9f8/Hzk5+frnmdkZAAA1Go11Gq1UWMu2c/9P6lmMO/SMLW8CwBe6+uHt369gOX7rmFMR3fYWSqkDqsMU8t7XcG8S4N5l0ZN592Q/QiigfdEtLa2xoULF+Dt7V2V2CoORBCwZcsWjBgxAgAQGRmJHj16ID4+Hu7u7rp+kydPxo0bN7Bjx45ytxMaGor58+eXaQ8PD4dKparWmInI9GhFIOysHLdzBfR312KoFy/eIiIytpycHIwdOxbp6emwtbWttK/Bwwb69++Pffv2YcKECY8So94evNuNKIqV3gFn7ty5mDVrlu55RkYGPD09ERIS8tBkVAe1Wo2IiAgEBwdDoah9Z2xMFfMuDVPNu6VfMqZuiMKhO2YIfbYnXGwtpA6pFFPNe23HvEuDeZdGTee95JtyfRhcvA4aNAhz587FhQsX0KlTJ1hZWZVaPmzYMEM3WS5XV1cAQGJiItzc3HTtycnJcHFxqXA9pVIJpVJZpl2hUNToQV/T+6MizLs0TC3vA1q7o2PjWJyOS8OyA7FY+EQbqUMql6nlva5g3qXBvEujpvJuyD6qdMEWACxevLjMMkEQoNFUzxW6Pj4+cHV1RUREBDp06AAAKCgowP79+/F///d/1bIPIqLyCIKAtwc2x1Mrj2LjiZt4qZcvvJ2s9FiTiIiMzeCpsrRabYUPQwvXrKwsREVFISoqCii+SCsqKgpxcXEQBAEzZ87EokWLsGXLFly4cAETJkyASqXC2LFjDQ2biMggXXwdEdSsIQq1IhZH/Ct1OEREVMzgM6/V6eTJk+jTp4/ueclY1fHjx2PdunWYPXs2cnNzMW3aNKSmpqJLly7YuXMnbGxsJIyaiOqLtwY0w77oO/j9bAKm9PZFK3c7qUMiIqr39D7zumfPHrRs2bLcAbXp6elo1aoVDhw4YNDOg4KCIIpimce6deuA4q/uQkNDcfv2beTl5WH//v1o3bq1QfsgIqqqVu52GNauaLaTT3ZESx0OEREZUrwuWbIEL730UrlX7NvZ2WHKlCn4/PPPqzs+IiJJzQr2h5lMwL7oOzh6/Z7U4RAR1Xt6F69nz57FwIEDK1weEhKCU6dOVVdcRES1greTFZ4K9AQAhG3/BwZOjU1ERNVM7+I1KSmp0mkMzMzMcOfOneqKi4io1pjRryksFDKcjkvD7svJUodDRFSv6V28NmrUCOfPn69w+blz50rNx0pEZCpcbC0wsYcPUDz2VaPl2VciIqnoXbwOHjwY77//PvLy8sosy83Nxbx58/D4449Xd3xERLXC1Mf8YGthhuikTPwWFS91OERE9Zbexeu7776LlJQU+Pv7IywsDL/99ht+//13/N///R+aNWuGlJQUvPPOO8aNlohIInYqBaYG+QEAFkf8i4JCrdQhERHVS3rP8+ri4oLIyEi8/PLLmDt3ru6iBUEQMGDAACxdurTS27YSEdV1E7v7YN3hWNxKzcWPx+Mwvru31CEREdU7Bt2kwMvLC9u2bUNqaiquXr0KURTRtGlT2NvbGy9CIqJawtJcjhn9muLdrRfw1Z4rGN3JA1ZKSe/1QkRU7xh8e1gAsLe3R2BgIDp37szClYjqlacCPeHlqMLdrAKsORQjdThERPVOlYpXIqL6SiGXYVawPwBg5YHrSM0ukDokIqJ6hcUrEZGBhrZ1Rws3W2TmF2LZ/mtSh0NEVK+weCUiMpBMJmD2wGYAgHWRsbidnit1SERE9QaLVyKiKgjyb4jO3g4oKNTii11XpA6HiKjeYPFKRFQFgvDf2ddNJ2/ianKW1CEREdULLF6JiKoowNsB/Vs4QysCiyOipQ6HiKheYPFKRPQI3hzQDIIAbDufiHO30qQOh4jI5LF4JSJ6BM1dbTGifSMAwCc7ePaViMjYWLwSET2i1/v7QyEXcPDKXRy+elfqcIiITBqLVyKiR9TYUYWxnRsDAMJ2REMURalDIiIyWSxeiYiqwSt9m0JlLsfZm2nYcTFJ6nCIiEwWi1ciomrQ0EaJF3r6AAA+3RmNQo1W6pCIiEwSi1ciomry0mO+aKBS4GpyFjafiZc6HCIik8TilYiomthaKDAtyA8AsCTiX+SpNVKHRERkcli8EhFVo+e7ecPV1gIJ6Xn44egNqcMhIjI5LF6JiKqRhUKOmf2bAgCW7ruGzDy11CEREZkUFq9ERNVsdCcP+DpZISW7AKsOxkgdDhGRSWHxSkRUzczkMrwR0gwAsOrgddzNypc6JCIik8HilYjICAa1dkWbRnbILtDgm71XpQ6HiMhksHglIjICmUzA7IFFZ183HI3DrdQcqUMiIjIJLF6JiIykZxMndPN1RIFGiyW7rkgdDhGRSWDxSkRkJILw39nXzadv4d+kTKlDIiKq81i8EhEZUYfG9hjQygVaEfh0R7TU4RAR1XksXomIjOzNkGaQCcDOS0k4HZcqdThERHVarS5eQ0NDIQhCqYerq6vUYRERGaSpiw1GdvQAAIRt/weiKEodEhFRnVWri1cAaNWqFW7fvq17nD9/XuqQiIgMNrN/U5jLZTh6PQUHr9yVOhwiojqr1hevZmZmcHV11T0aNmwodUhERAbzsFfhua5eAICwHf9Aq+XZVyKiqqj1xeuVK1fg7u4OHx8fPP3007h+/brUIRERVcn0Pn6wMpfjQnwGtl24LXU4RER1kpnUAVSmS5cu+O677+Dv74+kpCR8+OGH6N69Oy5evAhHR8dy18nPz0d+/n+3YszIyAAAqNVqqNVqo8dcso+a2Bf9h3mXBvNuGFulDJN6eOGrvdfx6Y5o9PV3hEJu+DkE5l0azLs0mHdp1HTeDdmPINahKweys7Ph5+eH2bNnY9asWeX2CQ0Nxfz588u0h4eHQ6VS1UCUREQVyysEFpyRI7tQwFO+GnR3qTMfwURERpOTk4OxY8ciPT0dtra2lfatU8UrAAQHB6NJkyZYtmxZucvLO/Pq6emJu3fvPjQZ1UGtViMiIgLBwcFQKBRG3x8VYd6lwbxXzdrIG1j0dzRcbJT4e0Z32FgYljvmXRrMuzSYd2nUdN4zMjLg5OSkV/Faq4cNPCg/Px+XL19Gr169KuyjVCqhVCrLtCsUiho96Gt6f1SEeZcG826Y57v7YP2ROMSn5WLC+tP4blJnNFCZG7wd5l0azLs0mHdp1FTeDdlHrb5g680338T+/fsRExODY8eOYfTo0cjIyMD48eOlDo2IqMosFHKsGNcJ9ioFzt1Kx9Mrj+JuVr4eaxIRUa0uXm/duoVnnnkGzZo1w8iRI2Fubo6jR4/Cy8tL6tCIiB5J60Z22DilG5yslfgnMRNPrTiCxPQ8qcMiIqr1avWwgZ9++knqEIiIjMbfxQabpnTFs6uO4dqdbIxZcQThL3WBhz0vLiUiqkitPvNKRGTqfBtaY9OUbvB0sERcSg7GLD+CmLvZUodFRFRrsXglIpKYp4MKP0/pDt+GVkhIz8OYFUdwJSlT6rCIiGolFq9ERLWAq50FNk7uhuauNriTmY+nVh7Fhfh0qcMiIqp1WLwSEdUSDW2U+PGlrmjTyA4p2QUY++1RnIlLlTosIqJahcUrEVEtYm9ljg0vdUEnL3tk5BXiuVXHcOz6PanDIiKqNVi8EhHVMrYWCnw3qTO6+Toiu0CD8WuP4+CVO1KHRURUK7B4JSKqhayUZlg7MRBBzRoiT63FC+tOYtelJKnDIiKSHItXIqJaquROXANauaBAo8XUH07hr3O3pQ6LiEhSLF6JiGoxpZkcX4/tiGHt3FGoFfHqj6exNSpB6rCIiCTD4pWIqJZTyGX4/Kn2GBPgAa0IzN58AZFJgtRhERFJgsUrEVEdIJcJ+HhkWzzfzQuiCGy8LsfayBtSh0VEVONYvBIR1REymYD5w1rhxZ7eAIBFf0fjm71XpQ6LiKhGsXglIqpDBEHA7JCmGOihBQB8siMan+2MhiiKUodGRFQjWLwSEdUxgiBgkKcWb4U0BQB8tecqFv51mQUsEdULLF6JiOqoyb18MH9YKwDAqkMxeHfrBWi1LGCJyLSxeCUiqsPGd/fGxyPbQBCADcfi8NYv56BhAUtEJozFKxFRHfd058b4fEx7yGUCfj19C6/9dAZqjVbqsIiIjILFKxGRCRjRoRG+fqYDFHIBf567jZd/OI38Qo3UYRERVTsWr0REJmJQGzesHBcAczMZdl1OwovrTyK3gAUsEZkWFq9ERCakT3NnrJ0QCEuFHAev3MWEtceRlV8odVhERNWGxSsRkYnp0cQJ373QGdZKMxyLScG41ceQnquWOiwiomrB4pWIyAQFejtgw4tdYGepwJm4NIz99ihSsgukDouI6JGxeCUiMlHtPBvgx5e6wtHKHBcTMvD0yiNIzsyTOiwiokfC4pWIyIS1dLfFxild4WKrxL9JWXhqxVHcTMmROiwioiozkzoAIiIyribONtg0pRvGfnsMMXez0StsL5o6WyPQxwGdvR0Q4G0PD3uV1GESEemFxSsRUT3g5WiFTVO74dXw0zgdl4YryVm4kpyF8GNxAAB3O4uiYra4oG3ibA1BEKQOmx5QUKhFTkEhcgo0yCkoRHa+Rvd7ToEGIgBRLHuHtfubRIhl28SSZfe3iWXaoOv3X6sgCLBQyKE0k8FCIYdFyc/72xQy3XMeV/SoWLwSEdUTjRpYYvO0HkjJLsCJ2BSciEnBidgUXEjIQEJ6Hn6LSsBvUQkAAHuVAgHeDgj0tkegtwNaN7KDQs6RZvpSa7TIydcgR11SYJYuOHMLNMguU4QWIrugeFl+YamiNDu/ELlqDdSaun/rX6WZ7L6i9r/C1sJMDqVCBqXZfW0KGRQyAbfiZIjZdx1WSgUsFDIoFXJYmZvBxqLkoYCtpRlsLRQskOsBFq9ERPWMg5U5BrRyxYBWrgCA7PxCnIlLw/HigvbMzVSk5qgRcSkJEZeSAACWCjk6NG6AQO+is7MdGjeAyrx+/BMiiiKyCzRISstBbCaw+59kZORpcS+7ACnZ+biXVVD8e9HjblY+8guNe3tehVyAytwMVuZyWJrLYaU0g4VCDnlx0XZ/7Vbyu4Cyy/7rU7zsgXVKt5XfR6MVkafWIq9Qg/z7f6o1RY9CLTTa/4ru/EIt8gu1yMgzZP5hGSLir+rVUyEXYGOh+K+wVSp0Ba6NhRlsLcxga1m67f7+LIBrv/rxyUNERBWyUpqhZ1Mn9GzqBBR/NX0hIV13ZvZEbCrSc9WIvHYPkdfuAQDMZAJaN7JDZx8HBHo7IMDLHvZW5hK/Ev2Ioois/EKkZBcXnVkFuJedr/s9JbsAd4sL05Ssot8LdMWoGXAhSu99mckEqIqLS5W5HCpzM91zS3M5rB5oK+pT1GallMNSUfRT18e8aD1zs7p1FrxQo0Ve4X0FbXFxm1+oRb5ag7zC/9ruX5an1iAnX43oq9fh2sgTBRoR+YVa5KqLzkZn5hU9MvLUyMovhCgCao2o+49EVZUUwLYPFLZFbf+d5bW1VJQqhkvabJRmkMlY/BoLi1ciIirF3EyGjo3t0bGxPab09oNWK+JKcpbuzOyJ2BTcTs9D1M00RN1Mw8oD1wEA/i7WCPQuKmYDfRzQqIGlUeIr+Uo+q6AQOflFX7Xrft7/FXy+Bhl5al2Rei8rX/d7QRXOjFooZLAUNGjkZAcnGyUcrMzhaGUOByslHK3M4WhtXtymhK2lGVTmZnWuyDQWM7kM1nIZrJWGlx1qtRrbNFcxeHArKBSKCvtptSKyC/4raDPz1LrC9v62B58bowAWBMBaWXmBW9JWqhgu/t3GQgE5i98KsXglIqJKyWQCmrnaoJmrDcZ19YIoiriVmlt8VjYFx2NScO1ONv5NysK/SVnYUHwRWKMGlkVjZn0c0MHTHnKZUDTOM79kvGdRgZldWQFa3J5TMkY0X4MCTfV8JW+pkBcVm9b3FaG6ArSkGP2vMFUIIrZt24bBg7tWWkSRNGSykuECVf/bVFYAZ9z/PLfoedFPdann+YVaiCJ024hPy61SLEXFb1GBa6U0g9JMBnMzGczlRWN+zeVFz0vGEJuX+im/r6/svr7yctYp7lvcZiYTav2QCRavRERkEEEQ4OmggqeDCiM7egAA7mXl40Rsqq6gvZiQgfi0XMRH5WJr8UVg1c3cTKb72r3kq3XdT3M5VEoz2CjNShWh/xWrSliayw3an1rNW+yauuoogPMLNRUUuIX3FboVFcFFF+YBQFZ+IbLyC5GQXrM3FhEEFBW9ZjJYyeQYPLhGd68XFq9ERPTIHK2VGNjaFQNbF10ElpVfiDNxqTgRk4LjxcWsuVwGlbJo3Ob9YzytzM3uay8qQMtb9l9hWjfHfVL9oDSTQ2kth5O1skrrFxRqi4c3FBW1mXmFyMovOqNbUHyxW0GhFgUaLfLVWhRoNMU/Sy/PL9SUWSe/UFOmX0GhFoX3XVAniv9dVGdWtZdgdCxeiYio2lkrzdCraUP0atpQ6lCI6hRzMxkcrZVwrGLxWxUarfhfcVtcuGbnFeDAgf01FoMh6sR/W5cuXQofHx9YWFigU6dOOHjwoNQhEREREZkEuUyApbkcDVTmcLa1gKeDCn4NreBinGsuH1mtL143btyImTNn4p133sGZM2fQq1cvDBo0CHFxcVKHRkREREQ1rNYXr4sXL8YLL7yAF198ES1atMCSJUvg6emJZcuWSR0aEREREdWwWj3mtaCgAKdOncKcOXNKtYeEhCAyMrLcdfLz85Gfn697npGRARRfJVoTV4qW7INXpdYs5l0azLs0mHdpMO/SYN6lUdN5N2Q/giiKtfZGyQkJCWjUqBEOHz6M7t2769oXLVqE9evXIzo6usw6oaGhmD9/fpn28PBwqFQqo8dMRERERIbJycnB2LFjkZ6eDltb20r71uozryUenCxXFMUKJ9CdO3cuZs2apXuekZEBT09PhISEPDQZ1UGtViMiIgLBwcGcxLoGMe/SYN6lwbxLg3mXBvMujZrOe8k35fqo1cWrk5MT5HI5EhMTS7UnJyfDxcWl3HWUSiWUyrLTSygUiho96Gt6f1SEeZcG8y4N5l0azLs0mHdp1FTeDdlHrb5gy9zcHJ06dUJERESp9oiIiFLDCIiIiIiofqjVZ14BYNasWRg3bhwCAgLQrVs3rFy5EnFxcZg6darUoRERERFRDav1xetTTz2Fe/fuYcGCBbh9+zZat26Nbdu2wcvLS+rQiIiIiKiG1friFQCmTZuGadOmSR0GEREREUmsThSvj6JkJjBDrmJ7FGq1Gjk5OcjIyODA8hrEvEuDeZcG8y4N5l0azLs0ajrvJXWaPjO4mnzxmpmZCQDw9PSUOhQiIiIiqkRmZibs7Owq7VOrb1JQHbRaLRISEmBjY1Pu3LCBgYE4ceJEueuWt+xhbSXzyt68ebNG5pWtKCZjb0Of/obmtrJlzLv+/Zn36t+GsfJuSDvzbngf5r1q22Deaz7v+vY15byLoojMzEy4u7tDJqt8MiyTP/Mqk8ng4eFR4XK5XF7hH6W8Zfq22dra1tibrLLXYKxt6NPf0NxWtox5178/81792zBW3g1pZ94N78O8V20bzHvN513fvqae94edcS1Rq+d5rQnTp083aJm+bTWpOvZv6Db06W9obitbxrzr3595r/5tGCvvhrQz74b3Yd6rtg3mvebzrm9fU8+7vkx+2EBNy8jIgJ2dnV735qXqw7xLg3mXBvMuDeZdGsy7NGpz3uv9mdfqplQqMW/evHJvUUvGw7xLg3mXBvMuDeZdGsy7NGpz3nnmlYiIiIjqDJ55JSIiIqI6g8UrEREREdUZLF6JiIiIqM5g8UpEREREdQaLVyIiIiKqM1i8SigzMxOBgYFo37492rRpg2+//VbqkOqFmzdvIigoCC1btkTbtm3x888/Sx1SvfDEE0/A3t4eo0ePljoUk/bnn3+iWbNmaNq0KVatWiV1OPUGj++ax89yadSG2oVTZUlIo9EgPz8fKpUKOTk5aN26NU6cOAFHR0epQzNpt2/fRlJSEtq3b4/k5GR07NgR0dHRsLKykjo0k7Z3715kZWVh/fr1+OWXX6QOxyQVFhaiZcuW2Lt3L2xtbdGxY0ccO3YMDg4OUodm8nh81zx+lkujNtQuPPMqIblcDpVKBQDIy8uDRqMB/y9hfG5ubmjfvj0AwNnZGQ4ODkhJSZE6LJPXp08f2NjYSB2GSTt+/DhatWqFRo0awcbGBoMHD8aOHTukDqte4PFd8/hZLo3aULuweK3EgQMHMHToULi7u0MQBGzdurVMn6VLl8LHxwcWFhbo1KkTDh48aNA+0tLS0K5dO3h4eGD27NlwcnKqxldQN9VE3kucPHkSWq0Wnp6e1RB53VWTOaeKPerfISEhAY0aNdI99/DwQHx8fI3FX1fx+JdGdeadn+X6q468S127sHitRHZ2Ntq1a4evv/663OUbN27EzJkz8c477+DMmTPo1asXBg0ahLi4OF2fTp06oXXr1mUeCQkJAIAGDRrg7NmziImJQXh4OJKSkmrs9dVWNZF3ALh37x6ef/55rFy5skZeV21WUzmnyj3q36G8sx+CIBg97rquOo5/Mlx15Z2f5YapjrxLXruIpBcA4pYtW0q1de7cWZw6dWqptubNm4tz5syp0j6mTp0qbtq06ZHiNDXGynteXp7Yq1cv8bvvvqu2WE2FMY/1vXv3iqNGjaqWOE1dVf4Ohw8fFkeMGKFbNmPGDHHDhg01FLFpeJTjn8d31VU17/wsfzTV8XkvRe3CM69VVFBQgFOnTiEkJKRUe0hICCIjI/XaRlJSEjIyMgAAGRkZOHDgAJo1a2aUeE1FdeRdFEVMmDABffv2xbhx44wUqemojpzTo9Pn79C5c2dcuHAB8fHxyMzMxLZt2zBgwACJIjYNPP6loU/e+Vle/fTJe22oXcxqdG8m5O7du9BoNHBxcSnV7uLigsTERL22cevWLbzwwgsQRRGiKOKVV15B27ZtjRSxaaiOvB8+fBgbN25E27ZtdWN9vv/+e7Rp08YoMdd11ZFzABgwYABOnz6N7OxseHh4YMuWLQgMDDRCxKZJn7+DmZkZPvvsM/Tp0wdarRazZ8/m7CWPSN/jn8d39dIn7/wsr3765L021C4sXh/Rg+PJRFHUe4xZp06dEBUVZaTITNuj5L1nz57QarVGisx0PUrOAfCq92rysL/DsGHDMGzYMAkiM20PyzuPb+OoLO/8LDeeyvJeG2oXDhuoIicnJ8jl8jJnnpKTk8v8j4WqD/Ne85jz2oF/B2kw79Jg3qVRV/LO4rWKzM3N0alTJ0RERJRqj4iIQPfu3SWLy9Qx7zWPOa8d+HeQBvMuDeZdGnUl7xw2UImsrCxcvXpV9zwmJgZRUVFwcHBA48aNMWvWLIwbNw4BAQHo1q0bVq5cibi4OEydOlXSuOs65r3mMee1A/8O0mDepcG8S8Mk8l6jcxvUMXv37hUBlHmMHz9e1+ebb74Rvby8RHNzc7Fjx47i/v37JY3ZFDDvNY85rx34d5AG8y4N5l0appB3QeT9SImIiIiojuCYVyIiIiKqM1i8EhEREVGdweKViIiIiOoMFq9EREREVGeweCUiIiKiOoPFKxERERHVGSxeiYiIiKjOYPFKRERERHUGi1ciIiOKjY2FIAiIiorSe51169ahQYMGRomnoKAATZo0weHDh3VtPj4+cHFxwXfffVfuOoGBgdi8ebNR4iEiMhSLVyKiemTlypXw8vJCjx49dG1Hjx7FuHHj8OqrryI3N7fMOu+99x7mzJkDrVZbw9ESEZXF4pWIqB756quv8OKLL5Zqc3FxwYIFC6DVavH777+XWWfIkCFIT0/Hjh07ajBSIqLysXglInoE27dvR8+ePdGgQQM4Ojri8ccfx7Vr1yrsv2/fPgiCgL/++gvt2rWDhYUFunTpgvPnz5fpu2PHDrRo0QLW1tYYOHAgbt++rVt24sQJBAcHw8nJCXZ2dujduzdOnz5daaynT5/G1atXMWTIkDLLVCoVWrVqhQ0bNpRZJpfLMXjwYPz44496ZISIyLhYvBIRPYLs7GzMmjULJ06cwO7duyGTyfDEE0889Cv2t956C59++ilOnDgBZ2dnDBs2DGq1Wrc8JycHn376Kb7//nscOHAAcXFxePPNN3XLMzMzMX78eBw8eBBHjx5F06ZNMXjwYGRmZla4zwMHDsDf3x+2trZlll2+fBnHjx/H9u3bce/evTLLO3fujIMHDxqQGSIi4zCTOgAiorps1KhRpZ6vXr0azs7OuHTpElq3bl3hevPmzUNwcDAAYP369fDw8MCWLVswZswYAIBarcby5cvh5+cHAHjllVewYMEC3fp9+/Yttb0VK1bA3t4e+/fvx+OPP17uPmNjY+Hu7l7usiVLlqBLly64fv06Nm7ciGnTppVa3qhRI8TFxUGr1UIm43kPIpIOP4GIiB7BtWvXMHbsWPj6+sLW1hY+Pj4AgLi4uErX69atm+53BwcHNGvWDJcvX9a1qVQqXeEKAG5ubkhOTtY9T05OxtSpU+Hv7w87OzvY2dkhKyur0v3m5ubCwsKiTHtKSgp++OEHvPXWW3j66afxww8/lOljaWkJrVaL/Pz8Sl8XEZGx8cwrEdEjGDp0KDw9PfHtt9/C3d0dWq0WrVu3RkFBgcHbEgRB97tCoSizTBRF3fMJEybgzp07WLJkCby8vKBUKtGtW7dK9+vk5FTu2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<p>You'll notice we are getting a bunch of errors about ill-conditioned matrices. This happens because the polynomial features are on wildly different scales. Let's standardize our features first.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">Ridge</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</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="c1"># Add scaler</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train_poly_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_train_poly</span><span class="p">)</span>
<span class="n">X_test_poly_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test_poly</span><span class="p">)</span>
<span class="c1"># We'll use the polynomial features because ridge can help with overfitting</span>
<span class="c1"># Choose lambda via cross-validation on the training set</span>
<span class="n">alphas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="n">cv_scores</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">:</span>
<span class="n">ridge</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">ridge</span><span class="p">,</span> <span class="n">X_train_poly_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">)</span>
<span class="n">cv_scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="n">best_alpha</span> <span class="o">=</span> <span class="n">alphas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">cv_scores</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Best alpha from CV: </span><span class="si">{</span><span class="n">best_alpha</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Plot CV error vs alpha</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">8</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">semilogx</span><span class="p">(</span><span class="n">alphas</span><span class="p">,</span> <span class="n">cv_scores</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">'alpha (λ)'</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">'Cross-validated MSE'</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">'Ridge Regularisation on Polynomial Features'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/ridge_regularization_polynomial_features_scaled.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Best alpha from CV: 233.5721
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Fit ridge with best alpha on SCALED polynomial features</span>
<span class="n">ridge_best</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">best_alpha</span><span class="p">)</span>
<span class="n">ridge_best</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_pred_ridge</span> <span class="o">=</span> <span class="n">ridge_best</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly_scaled</span><span class="p">)</span>
<span class="n">test_mse_ridge</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_ridge</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Ridge (poly deg=2) Test MSE: </span><span class="si">{</span><span class="n">test_mse_ridge</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Ridge improved over plain polynomial (MSE </span><span class="si">{</span><span class="n">test_mse_poly</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> → </span><span class="si">{</span><span class="n">test_mse_ridge</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
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<pre>Ridge (poly deg=2) Test MSE: 0.4791
Ridge improved over plain polynomial (MSE 0.4669 → 0.4791)
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<h3 id="Ridge-from-the-SVD-Perspective">Ridge from the SVD Perspective<a class="anchor-link" href="#Ridge-from-the-SVD-Perspective">¶</a></h3><p>The Ridge solution has a beautiful interpretation in terms of singular values. Recall from Notebook 2 that if $X = U\Sigma V^T$ is the SVD of the (centered) design matrix, then the OLS solution is</p>
<p>$$
\hat{\beta}_{OLS} = V\Sigma^{-1}U^T y = \sum_{i=1}^{p} \frac{1}{\sigma_i} (u_i^T y) v_i.
$$</p>
<p>When $\sigma_i$ is small, the coefficient $\frac{1}{\sigma_i}$ explodes — this is the condition number problem.</p>
<p>For Ridge regression, one can show that</p>
<p>$$
\hat{\beta}_{Ridge} = \sum_{i=1}^{p} \frac{\sigma_i}{\sigma_i^2 + \lambda} (u_i^T y) v_i.
$$</p>
<p>Notice what happens:</p>
<ul>
<li>When $\sigma_i \gg \sqrt{\lambda}$, the coefficient is approximately $\frac{1}{\sigma_i}$ (same as OLS).</li>
<li>When $\sigma_i \ll \sqrt{\lambda}$, the coefficient is approximately $\frac{\sigma_i}{\lambda}$ — <strong>shrunk towards zero</strong>.</li>
<li>The effective condition number becomes $\frac{\sigma_1^2 + \lambda}{\sigma_p^2 + \lambda}$, which is much better than $\frac{\sigma_1^2}{\sigma_p^2}$.</li>
</ul>
<p>This is why Ridge helps with multicollinearity: it dampens precisely those directions that were poorly determined.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Visualize how Ridge shrinks coefficients relative to singular values</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="c1"># Use scaled data for clean SVD interpretation</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train_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_train</span><span class="p">)</span>
<span class="c1"># Compute SVD of centered design matrix</span>
<span class="n">U</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">Vt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># For different lambda values, compute the "shrinkage factor" for each singular direction</span>
<span class="n">lambdas</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">]</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">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="k">for</span> <span class="n">lam</span> <span class="ow">in</span> <span class="n">lambdas</span><span class="p">:</span>
<span class="k">if</span> <span class="n">lam</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="c1"># OLS: no shrinkage</span>
<span class="n">shrinkage</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="s1">'OLS (λ=0)'</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Ridge shrinkage factor: sigma / (sigma^2 + lambda)</span>
<span class="n">shrinkage</span> <span class="o">=</span> <span class="n">s</span> <span class="o">/</span> <span class="p">(</span><span class="n">s</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">lam</span><span class="p">)</span>
<span class="c1"># Normalize so we can compare shapes</span>
<span class="n">shrinkage</span> <span class="o">=</span> <span class="n">shrinkage</span> <span class="o">/</span> <span class="n">shrinkage</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># normalize to first component</span>
<span class="n">label</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'Ridge (λ=</span><span class="si">{</span><span class="n">lam</span><span class="si">}</span><span class="s1">)'</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="n">shrinkage</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">label</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">8</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">'Singular value index (decreasing)'</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">'Shrinkage factor (normalized)'</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">'Ridge Shrinkage: How λ Dampens Small Singular Directions'</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">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</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="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">+</span><span class="mi">1</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">savefig</span><span class="p">(</span><span class="s1">'../images/ridge_svd_shrinkage.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Show condition number improvement</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Singular values:"</span><span class="p">,</span> <span class="n">s</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="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Condition number (OLS): </span><span class="si">{</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">lam</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">]:</span>
<span class="n">effective_cond</span> <span class="o">=</span> <span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">lam</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">lam</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Effective condition number (λ=</span><span class="si">{</span><span class="n">lam</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">effective_cond</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Singular values: [182.41 176.4 144.85 130.91 128.68 104.03 37.56 28.16]
Condition number (OLS): 6.48
Effective condition number (λ=0.1): 41.96
Effective condition number (λ=1): 41.91
Effective condition number (λ=10): 41.45
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<h2 id="Lasso-Regression-($L%5E1$-Regularisation)">Lasso Regression ($L^1$ Regularisation)<a class="anchor-link" href="#Lasso-Regression-($L%5E1$-Regularisation)">¶</a></h2><p>Lasso replaces the $L^2$ penalty with an $L^1$ penalty:</p>
<p>$$
\min_{\beta} \|y - X\beta\|_2^2 + \lambda \|\beta\|_1
$$</p>
<blockquote>
<p><strong>Geometric intuition</strong>: The $L^1$ ball is a diamond (in $\mathbb{R}^2$). The intersection of the quadratic loss contours with this diamond often occurs at a corner, forcing some coefficients to be <strong>exactly zero</strong>. Thus Lasso performs <strong>feature selection</strong>.</p>
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<p>Lasso is useful when we suspect that only a few features are truly relevant, especially in high‑dimensional settings. However, it does not have a closed‑form solution; it is typically solved via coordinate descent or other optimisation algorithms.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">Lasso</span>
<span class="c1"># Lasso also requires tuning of alpha</span>
<span class="n">lasso</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span> <span class="c1"># start with a small alpha</span>
<span class="n">lasso</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Count non-zero coefficients</span>
<span class="n">n_nonzero</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">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">1e-10</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of non-zero coefficients: </span><span class="si">{</span><span class="n">n_nonzero</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">lasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">y_test_pred_lasso</span> <span class="o">=</span> <span class="n">lasso</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly</span><span class="p">)</span>
<span class="n">test_mse_lasso</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_lasso</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Lasso (poly deg=2) Test MSE: </span><span class="si">{</span><span class="n">test_mse_lasso</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Cross-validation for Lasso alpha</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LassoCV</span>
<span class="n">lasso_cv</span> <span class="o">=</span> <span class="n">LassoCV</span><span class="p">(</span><span class="n">alphas</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">lasso_cv</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Best alpha from LassoCV: </span><span class="si">{</span><span class="n">lasso_cv</span><span class="o">.</span><span class="n">alpha_</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of non-zero coefficients (CV best): </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lasso_cv</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span><span class="w"> </span><span class="o">&gt;</span><span class="w"> </span><span class="mf">1e-10</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">y_test_pred_lasso_cv</span> <span class="o">=</span> <span class="n">lasso_cv</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly</span><span class="p">)</span>
<span class="n">test_mse_lasso_cv</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_lasso_cv</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"LassoCV Test MSE: </span><span class="si">{</span><span class="n">test_mse_lasso_cv</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:716: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.725e+03, tolerance: 2.202e+00
model = cd_fast.enet_coordinate_descent(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.501e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.286e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
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<pre>Number of non-zero coefficients: 33 out of 44
Lasso (poly deg=2) Test MSE: 0.4538
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<pre>/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.247e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.192e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.136e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.106e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.047e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.927e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.995e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.035e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.017e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.002e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.989e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.977e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.966e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.957e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.949e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.941e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.933e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.926e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.920e+03, tolerance: 1.774e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.902e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.045e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.033e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.987e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.939e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.892e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.049e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.055e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.031e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.009e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.990e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.975e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.964e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.953e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.944e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.936e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.929e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.921e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.913e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.906e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.900e+03, tolerance: 1.759e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.612e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.935e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.917e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.900e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.885e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.871e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.857e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.845e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.834e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.826e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.816e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.806e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.798e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.791e+03, tolerance: 1.753e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.694e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.248e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.219e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.166e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.117e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.075e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.039e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.133e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.112e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.090e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.071e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.056e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.044e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.033e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.022e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.012e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.002e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.991e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.981e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.972e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.965e+03, tolerance: 1.771e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.153e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.201e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.168e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.116e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.067e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.026e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.987e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.057e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.040e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.016e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.995e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.979e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.965e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.952e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.940e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.930e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.922e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.913e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.896e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:701: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.889e+03, tolerance: 1.752e+00
model = cd_fast.enet_coordinate_descent_gram(
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<pre>Best alpha from LassoCV: 0.0067
Number of non-zero coefficients (CV best): 34
LassoCV Test MSE: 0.4587
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<pre>/home/pks/.local/lib/python3.14/site-packages/sklearn/linear_model/_coordinate_descent.py:716: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.709e+03, tolerance: 2.202e+00
model = cd_fast.enet_coordinate_descent(
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<p>Again, there are unscaled polynomial features, so we get convergence warnings. LASSO is sensivitve to scalling becuase the penalty treats all coefficients equally. We also get a suggestion to increase the number of iterations.</p>
<p>Let's fix this.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">Lasso</span><span class="p">,</span> <span class="n">LassoCV</span>
<span class="c1"># Lasso with more iterations</span>
<span class="n">lasso</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100000</span><span class="p">,</span> <span class="n">tol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span>
<span class="n">lasso</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Count non-zero coefficients</span>
<span class="n">n_nonzero</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">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mf">1e-10</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of non-zero coefficients: </span><span class="si">{</span><span class="n">n_nonzero</span><span class="si">}</span><span class="s2"> out of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">lasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">y_test_pred_lasso</span> <span class="o">=</span> <span class="n">lasso</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly_scaled</span><span class="p">)</span>
<span class="n">test_mse_lasso</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_lasso</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Lasso Test MSE: </span><span class="si">{</span><span class="n">test_mse_lasso</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Cross-validation for Lasso alpha</span>
<span class="n">lasso_cv</span> <span class="o">=</span> <span class="n">LassoCV</span><span class="p">(</span>
<span class="n">alphas</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span>
<span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="n">max_iter</span><span class="o">=</span><span class="mi">100000</span><span class="p">,</span>
<span class="n">tol</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span>
<span class="p">)</span>
<span class="n">lasso_cv</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_poly_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Best alpha from LassoCV: </span><span class="si">{</span><span class="n">lasso_cv</span><span class="o">.</span><span class="n">alpha_</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Number of non-zero coefficients (CV best): </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lasso_cv</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span><span class="w"> </span><span class="o">&gt;</span><span class="w"> </span><span class="mf">1e-10</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">y_test_pred_lasso_cv</span> <span class="o">=</span> <span class="n">lasso_cv</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_poly_scaled</span><span class="p">)</span>
<span class="n">test_mse_lasso_cv</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_lasso_cv</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"LassoCV Test MSE: </span><span class="si">{</span><span class="n">test_mse_lasso_cv</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Number of non-zero coefficients: 15 out of 44
Lasso Test MSE: 0.5347
Best alpha from LassoCV: 0.0067
Number of non-zero coefficients (CV best): 16
LassoCV Test MSE: 0.5305
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<h3 id="Why-Lasso-Produces-Sparse-Solutions:-The-L%C2%B9-Geometry">Why Lasso Produces Sparse Solutions: The L¹ Geometry<a class="anchor-link" href="#Why-Lasso-Produces-Sparse-Solutions:-The-L%C2%B9-Geometry">¶</a></h3><p>Recall from Notebook 3 that the $L^1$ unit ball is a diamond (a rotated square in $\mathbb{R}^1$). This geometric fact is precisely why Lasso tends to produce coefficients that are <strong>exactly zero</strong>.</p>
<p>Consider the constrained form of the problem:</p>
<p>$$
\min_{\beta} \|y - X\beta\|_2^2 \quad \text{subject to} \quad \|\beta\|_1 \leq t.
$$</p>
<p>The constraint region is the $L^1$ ball — a diamond with corners on the axes. The contours of the loss function $\|y - X\beta\|_2^2$ are ellipses centered at the OLS solution.</p>
<p><strong>Key insight</strong>: When an elliptical contour expands and first touches the diamond, it often hits a <strong>corner</strong>. Corners lie on the axes, meaning some coefficients are exactly zero.</p>
<p>This is in contrast to Ridge, where the constraint region is a ball (circle in $\mathbb{R}^1$), and the first contact is typically at a smooth point — coefficients are shrunk but rarely zero.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Visualize L1 vs L2 constraint regions and why Lasso gives sparsity</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="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">1</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">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="c1"># L1 ball (diamond)</span>
<span class="n">theta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">r</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># L1 ball vertices</span>
<span class="n">l1_x</span> <span class="o">=</span> <span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">r</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">r</span><span class="p">]</span>
<span class="n">l1_y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">r</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">r</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="c1"># L2 ball (circle)</span>
<span class="n">l2_x</span> <span class="o">=</span> <span class="n">r</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="n">l2_y</span> <span class="o">=</span> <span class="n">r</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="c1"># Simulated loss contours (ellipses centered away from origin)</span>
<span class="c1"># The OLS solution is at some point (beta1_ols, beta2_ols)</span>
<span class="n">beta_ols</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">ball_type</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="p">,</span> <span class="p">[</span><span class="s1">'Lasso (L¹)'</span><span class="p">,</span> <span class="s1">'Ridge (L²)'</span><span class="p">])):</span>
<span class="c1"># Draw constraint region</span>
<span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Lasso - L1 ball</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">l1_x</span><span class="p">,</span> <span class="n">l1_y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'L¹ constraint region'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">l1_x</span><span class="p">,</span> <span class="n">l1_y</span><span class="p">,</span> <span class="s1">'b-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># Ridge - L2 ball</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="n">l2_x</span><span class="p">,</span> <span class="n">l2_y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'green'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'L² constraint region'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">l2_x</span><span class="p">,</span> <span class="n">l2_y</span><span class="p">,</span> <span class="s1">'g-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># Draw loss contours (ellipses)</span>
<span class="c1"># Simplified: concentric ellipses around OLS solution</span>
<span class="k">for</span> <span class="n">scale</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]:</span>
<span class="n">ellipse_x</span> <span class="o">=</span> <span class="n">beta_ols</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="mf">0.4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="n">ellipse_y</span> <span class="o">=</span> <span class="n">beta_ols</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">scale</span> <span class="o">*</span> <span class="mf">0.2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ellipse_x</span><span class="p">,</span> <span class="n">ellipse_y</span><span class="p">,</span> <span class="s1">'r--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Mark OLS solution</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">beta_ols</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'OLS solution'</span><span class="p">)</span>
<span class="c1"># Mark the "first contact" point (approximate)</span>
<span class="k">if</span> <span class="n">idx</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Lasso hits corner</span>
<span class="n">contact</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span> <span class="c1"># on the axis!</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">contact</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Lasso solution (sparse!)'</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="c1"># Ridge hits smooth part</span>
<span class="n">contact</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.85</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">])</span> <span class="c1"># not on axis</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">contact</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'*'</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Ridge solution'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">1.5</span><span class="p">,</span> <span class="mf">1.5</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="sa">r</span><span class="s1">'$\beta_1$'</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="sa">r</span><span class="s1">'$\beta_2$'</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="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">ball_type</span><span class="si">}</span><span class="s1"> Constraint'</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">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">'equal'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</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">savefig</span><span class="p">(</span><span class="s1">'../images/lasso_vs_ridge_geometry.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Key insight: Lasso's $L^1$ constraint has corners on the axes.
When the loss contour touches a corner, that coefficient becomes exactly zero.</p>
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<h2 id="Principal-Component-Regression-(PCR)">Principal Component Regression (PCR)<a class="anchor-link" href="#Principal-Component-Regression-(PCR)">¶</a></h2><p>Principal Component Regression combines the dimensionality reduction from Notebook 4 with linear regression. The idea is simple:</p>
<ol>
<li>Compute the principal components of $X$ (via SVD on centered data).</li>
<li>Keep only the top $k$ components (those with largest singular values).</li>
<li>Regress $y$ on these $k$ components.</li>
</ol>
<p><strong>Linear algebra perspective</strong>: We project $X$ onto its best rank-$k$ approximation (in Frobenius norm) and then solve a least-squares problem in the reduced space. This is different from Ridge:</p>
<ul>
<li>Ridge <strong>shrinks</strong> all directions but keeps them.</li>
<li>PCR <strong>discards</strong> the smallest singular directions entirely.</li>
</ul>
<p>PCR is particularly useful when:</p>
<ul>
<li>Features are highly correlated (multicollinearity).</li>
<li>You want interpretable, low-dimensional representations.</li>
<li>The signal lives in the top principal components while noise dominates the rest.</li>
</ul>
<p>The tradeoff: if the target $y$ is correlated with a small singular direction, PCR will discard useful information.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div>
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<div class="highlight hl-ipython3"><pre><span></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.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="c1"># Compare PCR with varying number of components</span>
<span class="n">n_components_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">X_train_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">pcr_scores</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">n_comp</span> <span class="ow">in</span> <span class="n">n_components_range</span><span class="p">:</span>
<span class="n">pcr</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_comp</span><span class="p">),</span>
<span class="n">LinearRegression</span><span class="p">()</span>
<span class="p">)</span>
<span class="c1"># Negative MSE (sklearn convention: higher is better)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">pcr</span><span class="p">,</span> <span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">)</span>
<span class="n">pcr_scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">-</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="c1"># Also compute variance explained</span>
<span class="n">pca_full</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="n">pca_full</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">)</span>
<span class="n">var_explained</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca_full</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="c1"># Plot</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax1</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="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_components_range</span><span class="p">,</span> <span class="n">pcr_scores</span><span class="p">,</span> <span class="s1">'b-o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'CV MSE'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Number of Principal Components'</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Cross-Validated MSE'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'b'</span><span class="p">)</span>
<span class="n">ax1</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">'y'</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">'b'</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">n_components_range</span><span class="p">,</span> <span class="n">var_explained</span><span class="p">,</span> <span class="s1">'r--s'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Variance Explained'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Cumulative Variance Explained'</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">ax2</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">'y'</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">'r'</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.05</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">'Principal Component Regression: Choosing k'</span><span class="p">)</span>
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'center right'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.85</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</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">savefig</span><span class="p">(</span><span class="s1">'../images/pcr_components_selection.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Best number of components</span>
<span class="n">best_n_comp</span> <span class="o">=</span> <span class="n">n_components_range</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">pcr_scores</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Best number of components: </span><span class="si">{</span><span class="n">best_n_comp</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Variance explained: </span><span class="si">{</span><span class="n">var_explained</span><span class="p">[</span><span class="n">best_n_comp</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.2%</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compare with OLS and Ridge</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Model Comparison (Test MSE):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" OLS (all features): </span><span class="si">{</span><span class="n">test_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" PCR (k=</span><span class="si">{</span><span class="n">best_n_comp</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">pcr_scores</span><span class="p">[</span><span class="n">best_n_comp</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" Ridge (λ=</span><span class="si">{</span><span class="n">best_alpha</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">): </span><span class="si">{</span><span class="n">test_mse_ridge</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
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"/>
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<pre>Best number of components: 8
Variance explained: 100.00%
Model Comparison (Test MSE):
OLS (all features): 0.5381
PCR (k=8): 0.5272
Ridge (λ=233.57): 0.4791
</pre>
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<h2 id="Gradient-Descent:-When-the-Normal-Equations-Are-Not-Enough">Gradient Descent: When the Normal Equations Are Not Enough<a class="anchor-link" href="#Gradient-Descent:-When-the-Normal-Equations-Are-Not-Enough">¶</a></h2><p>For very large datasets, computing $(X^T X)^{-1}$ or even forming $X^T X$ becomes prohibitive. <strong>Gradient descent</strong> is an iterative optimisation method that uses only first‑order derivatives.</p>
<h3 id="The-Linear-Algebra-of-Convergence">The Linear Algebra of Convergence<a class="anchor-link" href="#The-Linear-Algebra-of-Convergence">¶</a></h3><p>The loss function and its gradient:</p>
<p>$$
L(\beta) = \frac{1}{2n}\|y - X\beta\|_2^2, \qquad \nabla L(\beta) = -\frac{1}{n} X^T (y - X\beta).
$$</p>
<p>Starting from $\beta^{(0)}$, we update:</p>
<p>$$
\beta^{(t+1)} = \beta^{(t)} - \eta \nabla L(\beta^{(t)}).
$$</p>
<p><strong>Convergence depends on the eigenvalues of $X^T X$.</strong> Let $\lambda_{\max}$ and $\lambda_{\min}$ be the largest and smallest eigenvalues. Then:</p>
<ul>
<li>The learning rate must satisfy $\eta &lt; \frac{2}{\lambda_{\max}}$ for convergence.</li>
<li>The convergence rate is governed by the <strong>condition number</strong> $\kappa = \frac{\lambda_{\max}}{\lambda_{\min}}$.</li>
<li>When $\kappa$ is large, gradients point in "wrong" directions — the loss surface is a narrow valley.</li>
</ul>
<p>This is why feature scaling matters: it reduces $\kappa$, making the loss surface more spherical and convergence faster.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Demonstrate how condition number affects gradient descent convergence</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="k">def</span><span class="w"> </span><span class="nf">gradient_descent_linear</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Batch gradient descent for linear regression."""</span>
<span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="n">beta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_iter</span><span class="p">):</span>
<span class="n">residual</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
<span class="n">grad</span> <span class="o">=</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">residual</span>
<span class="n">beta</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">grad</span>
<span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">return</span> <span class="n">beta</span><span class="p">,</span> <span class="n">losses</span>
<span class="c1"># Use a subset for illustration</span>
<span class="n">X_subset</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">y_subset</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="c1"># Add intercept</span>
<span class="n">X_subset_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_subset</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_subset</span><span class="p">])</span>
<span class="c1"># Compute eigenvalues of X^T X</span>
<span class="n">eigenvalues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="n">X_subset_aug</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_subset_aug</span><span class="p">)</span>
<span class="n">lambda_max</span><span class="p">,</span> <span class="n">lambda_min</span> <span class="o">=</span> <span class="n">eigenvalues</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">eigenvalues</span><span class="p">[</span><span class="n">eigenvalues</span> <span class="o">&gt;</span> <span class="mf">1e-10</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">cond_num</span> <span class="o">=</span> <span class="n">lambda_max</span> <span class="o">/</span> <span class="n">lambda_min</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Eigenvalue range: [</span><span class="si">{</span><span class="n">lambda_min</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">lambda_max</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">]"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Condition number: </span><span class="si">{</span><span class="n">cond_num</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Max stable learning rate: </span><span class="si">{</span><span class="mi">2</span><span class="o">/</span><span class="n">lambda_max</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Try gradient descent with different learning rates on UNSCALED data</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">1</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">14</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="c1"># UNSCALED</span>
<span class="n">learning_rates</span> <span class="o">=</span> <span class="p">[</span><span class="mf">1e-10</span><span class="p">,</span> <span class="mf">1e-9</span><span class="p">,</span> <span class="mf">1e-8</span><span class="p">]</span>
<span class="k">for</span> <span class="n">lr</span> <span class="ow">in</span> <span class="n">learning_rates</span><span class="p">:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">losses</span> <span class="o">=</span> <span class="n">gradient_descent_linear</span><span class="p">(</span><span class="n">X_subset_aug</span><span class="p">,</span> <span class="n">y_subset</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'η = </span><span class="si">{</span><span class="n">lr</span><span class="si">:</span><span class="s1">.0e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Iteration'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Loss (MSE)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Unscaled Data (κ = </span><span class="si">{</span><span class="n">cond_num</span><span class="si">:</span><span class="s1">.1e</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">'log'</span><span class="p">)</span>
<span class="c1"># SCALED</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_subset_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_subset</span><span class="p">)</span>
<span class="n">X_subset_scaled_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_subset_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_subset_scaled</span><span class="p">])</span>
<span class="n">eigenvalues_scaled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigvalsh</span><span class="p">(</span><span class="n">X_subset_scaled_aug</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_subset_scaled_aug</span><span class="p">)</span>
<span class="n">lambda_max_s</span><span class="p">,</span> <span class="n">lambda_min_s</span> <span class="o">=</span> <span class="n">eigenvalues_scaled</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">eigenvalues_scaled</span><span class="p">[</span><span class="n">eigenvalues_scaled</span> <span class="o">&gt;</span> <span class="mf">1e-10</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
<span class="n">cond_num_scaled</span> <span class="o">=</span> <span class="n">lambda_max_s</span> <span class="o">/</span> <span class="n">lambda_min_s</span>
<span class="n">learning_rates_scaled</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">lr</span> <span class="ow">in</span> <span class="n">learning_rates_scaled</span><span class="p">:</span>
<span class="n">_</span><span class="p">,</span> <span class="n">losses</span> <span class="o">=</span> <span class="n">gradient_descent_linear</span><span class="p">(</span><span class="n">X_subset_scaled_aug</span><span class="p">,</span> <span class="n">y_subset</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">losses</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'η = </span><span class="si">{</span><span class="n">lr</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Iteration'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Loss (MSE)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Scaled Data (κ = </span><span class="si">{</span><span class="n">cond_num_scaled</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">'log'</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">savefig</span><span class="p">(</span><span class="s1">'../images/gd_condition_number_effect.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Scaling reduced condition number from </span><span class="si">{</span><span class="n">cond_num</span><span class="si">:</span><span class="s2">.1e</span><span class="si">}</span><span class="s2"> to </span><span class="si">{</span><span class="n">cond_num_scaled</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"This allows much larger learning rates and faster convergence."</span><span class="p">)</span>
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<pre>Eigenvalue range: [5.59e-02, 3.07e+09]
Condition number: 5.49e+10
Max stable learning rate: 6.52e-10
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Scaling reduced condition number from 5.5e+10 to 42.2
This allows much larger learning rates and faster convergence.
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<p>Let's apply gradient descent to our housing data.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Implement batch gradient descent for linear regression on a small subset for illustration</span>
<span class="k">def</span><span class="w"> </span><span class="nf">gradient_descent_linear</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="n">beta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_iter</span><span class="p">):</span>
<span class="n">grad</span> <span class="o">=</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span><span class="p">)</span>
<span class="n">beta</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">grad</span>
<span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">if</span> <span class="n">verbose</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">200</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="sa">f</span><span class="s2">"Iter </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: loss = </span><span class="si">{</span><span class="n">loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">beta</span><span class="p">,</span> <span class="n">losses</span>
<span class="c1"># Use a small subset for speed</span>
<span class="n">X_small</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">y_small</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="c1"># Add intercept column</span>
<span class="n">X_small_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_small</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_small</span><span class="p">])</span>
<span class="n">beta_gd</span><span class="p">,</span> <span class="n">losses</span> <span class="o">=</span> <span class="n">gradient_descent_linear</span><span class="p">(</span><span class="n">X_small_aug</span><span class="p">,</span> <span class="n">y_small</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">)</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">8</span><span class="p">,</span><span class="mi">4</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">losses</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">'Iteration'</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">'Loss'</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">'Gradient Descent Convergence'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/gradient_descent_convergence_unscaled'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Compare with closed-form solution on the same subset</span>
<span class="n">beta_closed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X_small_aug</span><span class="p">,</span> <span class="n">y_small</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</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="sa">f</span><span class="s2">"Difference between GD and closed-form: </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">beta_gd</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">beta_closed</span><span class="p">)</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
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<pre>/usr/lib64/python3.14/site-packages/numpy/linalg/_linalg.py:2792: RuntimeWarning: overflow encountered in dot
sqnorm = x.dot(x)
/tmp/ipykernel_66479/3132444904.py:7: RuntimeWarning: overflow encountered in matmul
grad = - (1/n) * X.T @ (y - X @ beta)
/tmp/ipykernel_66479/3132444904.py:8: RuntimeWarning: invalid value encountered in subtract
beta -= learning_rate * grad
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<pre>Difference between GD and closed-form: nan
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<p>Again, we have scaling issues causing some errors. Large values will dominate gradients giving rise to instability.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div>
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<div class="highlight hl-ipython3"><pre><span></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="c1"># 1. Prepare Data</span>
<span class="c1"># Use a small subset for speed</span>
<span class="n">X_small</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="c1"># Use .copy() to avoid SettingWithCopyWarning</span>
<span class="n">y_small</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># 2. SCALE THE FEATURES (Critical for Gradient Descent!)</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_small_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_small</span><span class="p">)</span>
<span class="c1"># Add intercept column AFTER scaling</span>
<span class="c1"># (We don't scale the intercept column, it stays as 1s)</span>
<span class="n">X_small_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_small_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_small_scaled</span><span class="p">])</span>
<span class="c1"># 3. Run Gradient Descent</span>
<span class="k">def</span><span class="w"> </span><span class="nf">gradient_descent_linear</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
<span class="n">beta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
<span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_iter</span><span class="p">):</span>
<span class="c1"># Predict</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
<span class="c1"># Residual</span>
<span class="n">residual</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">prediction</span>
<span class="c1"># Gradient</span>
<span class="n">grad</span> <span class="o">=</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">n</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">residual</span>
<span class="c1"># Update</span>
<span class="n">beta</span> <span class="o">-=</span> <span class="n">learning_rate</span> <span class="o">*</span> <span class="n">grad</span>
<span class="c1"># Calculate Loss (MSE)</span>
<span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">n</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
<span class="k">if</span> <span class="n">verbose</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">200</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="sa">f</span><span class="s2">"Iter </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">: loss = </span><span class="si">{</span><span class="n">loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">beta</span><span class="p">,</span> <span class="n">losses</span>
<span class="c1"># With scaled data, learning_rate=0.01 or even 0.1 is usually safe</span>
<span class="n">beta_gd</span><span class="p">,</span> <span class="n">losses</span> <span class="o">=</span> <span class="n">gradient_descent_linear</span><span class="p">(</span><span class="n">X_small_aug</span><span class="p">,</span> <span class="n">y_small</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># Plot convergence</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">8</span><span class="p">,</span><span class="mi">4</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">losses</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">'Iteration'</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">'Loss (MSE)'</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">'Gradient Descent Convergence (Scaled Data)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/gradient_descent_convergence_scaled'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Iter 0: loss = 2.758919
Iter 200: loss = 0.234124
Iter 400: loss = 0.230774
</pre>
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"/>
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<p>In practice, we use <strong>stochastic</strong> or <strong>mini‑batch</strong> gradient descent for large data. <code>sklearn</code>'s <code>SGDRegressor</code> implements these with various loss functions and penalties.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">SGDRegressor</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.pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="c1"># SGDRegressor is sensitive to feature scaling, so we use a pipeline</span>
<span class="c1"># penalty=None means no regularization (standard Linear Regression)</span>
<span class="n">sgd_reg</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="n">StandardScaler</span><span class="p">(),</span>
<span class="n">SGDRegressor</span><span class="p">(</span><span class="n">penalty</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="s1">'constant'</span><span class="p">,</span> <span class="n">eta0</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</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="p">)</span>
<span class="n">sgd_reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Note: SGDRegressor optimizes a different loss function formulation by default,</span>
<span class="c1"># so coefficients might differ slightly from closed-form, but the prediction quality is similar.</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Coefficients: </span><span class="si">{</span><span class="n">sgd_reg</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">'sgdregressor'</span><span class="p">]</span><span class="o">.</span><span class="n">coef_</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Coefficients: [ 3.97676073e+09 -1.14418633e+10 -1.78357850e+10 1.01065426e+11
-1.80378121e+10 -3.02815983e+09 -5.43520408e+10 -4.51215845e+10]
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<h2 id="Decision-Trees-and-Random-Forests">Decision Trees and Random Forests<a class="anchor-link" href="#Decision-Trees-and-Random-Forests">¶</a></h2><p>Linear models assume a linear relationship. Decision trees are <strong>non‑parametric</strong> models that partition the feature space into rectangular regions and assign a constant prediction (or a simple model) in each region. The prediction function is piecewise constant. The basis functions are indicator functions of the leaves. While not linear in the original features, the model is linear in the (high‑dimensional) leaf‑indicator basis.</p>
<p><strong>Random forests</strong> combine many decision trees, each trained on a bootstrapped sample and a random subset of features. They reduce variance (overfitting) and often outperform single trees.</p>
<p>When to use trees / forests:</p>
<ul>
<li>Nonlinear relationships with interactions.</li>
<li>When interpretability is desired (a single tree can be visualised).</li>
<li>When you have mixed categorical and continuous features.</li>
<li>As a strong baseline before trying deep learning.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.tree</span><span class="w"> </span><span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.ensemble</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomForestRegressor</span>
<span class="c1"># Single decision tree (max depth 10)</span>
<span class="n">tree</span> <span class="o">=</span> <span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">tree</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_pred_tree</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">test_mse_tree</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_tree</span><span class="p">)</span>
<span class="c1"># Random forest (100 trees)</span>
<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_pred_rf</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">test_mse_rf</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_rf</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Decision Tree Test MSE: </span><span class="si">{</span><span class="n">test_mse_tree</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Random Forest Test MSE: </span><span class="si">{</span><span class="n">test_mse_rf</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Compare with best linear model</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Ridge (poly) Test MSE: </span><span class="si">{</span><span class="n">test_mse_ridge</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"LassoCV Test MSE: </span><span class="si">{</span><span class="n">test_mse_lasso_cv</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Decision Tree Test MSE: 0.3961
Random Forest Test MSE: 0.2752
Ridge (poly) Test MSE: 0.4791
LassoCV Test MSE: 0.5305
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<p>Random forests often outperform linear models on complex real‑world data without requiring feature engineering or scaling.</p>
<h2 id="Logistic-Regression-for-Classification">Logistic Regression for Classification<a class="anchor-link" href="#Logistic-Regression-for-Classification">¶</a></h2><p>So far we have focused on regression (continuous targets). For binary classification (e.g., spam vs. not spam), <strong>logistic regression</strong> is a natural extension. It models the probability that an observation belongs to a class using the logistic (sigmoid) function:</p>
<p>$$
P(y=1 \mid x) = \frac{1}{1 + e^{-x^T\beta}}.
$$</p>
<p>The decision boundary is linear in the features: $x^T\beta = 0$. The model is fitted by <strong>maximum likelihood estimation</strong>, which is equivalent to minimising the <strong>log‑loss</strong> (cross‑entropy). There is no closed‑form solution; we typically use gradient descent or Newton's method.</p>
<p>We will illustrate logistic regression on a subset of the California housing data by creating a binary target (e.g., whether the median house value is above the median).</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</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">accuracy_score</span><span class="p">,</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">confusion_matrix</span>
<span class="c1"># Create binary target: 1 if house value &gt; median, else 0</span>
<span class="c1"># Use the ORIGINAL dataframe to avoid confusion with scaled/transformed versions</span>
<span class="n">y_binary</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'MedHouseVal'</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">df</span><span class="p">[</span><span class="s1">'MedHouseVal'</span><span class="p">]</span><span class="o">.</span><span class="n">median</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="o">.</span><span class="n">values</span>
<span class="n">X_original</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">feature_names</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1"># original features, not overwritten</span>
<span class="c1"># Split</span>
<span class="n">X_train_bin</span><span class="p">,</span> <span class="n">X_test_bin</span><span class="p">,</span> <span class="n">y_train_bin</span><span class="p">,</span> <span class="n">y_test_bin</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="n">X_original</span><span class="p">,</span> <span class="n">y_binary</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span>
<span class="p">)</span>
<span class="c1"># Scale features (important for logistic regression with regularization)</span>
<span class="n">scaler_bin</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train_bin_scaled</span> <span class="o">=</span> <span class="n">scaler_bin</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train_bin</span><span class="p">)</span>
<span class="n">X_test_bin_scaled</span> <span class="o">=</span> <span class="n">scaler_bin</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test_bin</span><span class="p">)</span>
<span class="c1"># Train logistic regression</span>
<span class="n">log_reg</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">log_reg</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_bin_scaled</span><span class="p">,</span> <span class="n">y_train_bin</span><span class="p">)</span>
<span class="c1"># Predict</span>
<span class="n">y_pred_bin</span> <span class="o">=</span> <span class="n">log_reg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_bin_scaled</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_test_bin</span><span class="p">,</span> <span class="n">y_pred_bin</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Logistic Regression Accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</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">Classification Report:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test_bin</span><span class="p">,</span> <span class="n">y_pred_bin</span><span class="p">))</span>
<span class="c1"># Coefficients (on scaled features)</span>
<span class="n">coef_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">'Feature'</span><span class="p">:</span> <span class="n">feature_names</span><span class="p">,</span>
<span class="s1">'Coefficient'</span><span class="p">:</span> <span class="n">log_reg</span><span class="o">.</span><span class="n">coef_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="p">})</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'Coefficient'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="nb">abs</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">"</span><span class="se">\n</span><span class="s2">Logistic Regression Coefficients (scaled features):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">coef_df</span><span class="p">)</span>
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<pre>Logistic Regression Accuracy: 0.8324
Classification Report:
precision recall f1-score support
0 0.83 0.84 0.83 2083
1 0.83 0.83 0.83 2045
accuracy 0.83 4128
macro avg 0.83 0.83 0.83 4128
weighted avg 0.83 0.83 0.83 4128
Logistic Regression Coefficients (scaled features):
Feature Coefficient
6 Latitude -3.532385
7 Longitude -3.328767
5 AveOccup -3.094635
0 MedInc 2.512414
3 AveBedrms 0.899888
2 AveRooms -0.786384
1 HouseAge 0.276838
4 Population 0.062450
precision recall f1-score support
0 0.83 0.84 0.83 2083
1 0.83 0.83 0.83 2045
accuracy 0.83 4128
macro avg 0.83 0.83 0.83 4128
weighted avg 0.83 0.83 0.83 4128
Logistic Regression Coefficients (scaled features):
Feature Coefficient
6 Latitude -3.532385
7 Longitude -3.328767
5 AveOccup -3.094635
0 MedInc 2.512414
3 AveBedrms 0.899888
2 AveRooms -0.786384
1 HouseAge 0.276838
4 Population 0.062450
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<h2 id="Cross%E2%80%91Validation:-A-Deeper-Look">Cross‑Validation: A Deeper Look<a class="anchor-link" href="#Cross%E2%80%91Validation:-A-Deeper-Look">¶</a></h2><p>Cross‑validation (CV) is a technique for assessing how well a model generalises to unseen data. Instead of a single train/validation split, we partition the training data into $k$ folds (typically 5 or 10). For each fold $i$, we train on the other $k-1$ folds and validate on fold $i$. The performance is averaged over the $k$ folds.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Illustrate 5-fold cross-validation</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">KFold</span>
<span class="n">n_points</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">X_cv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_points</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</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="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">3</span><span class="p">)</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">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kf</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X_cv</span><span class="p">)):</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="c1"># Plot all points</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_points</span><span class="p">):</span>
<span class="k">if</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">test_idx</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'s'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Test'</span> <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">test_idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">else</span> <span class="s1">''</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'blue'</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Train'</span> <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="n">train_idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">else</span> <span class="s1">''</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_points</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</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="sa">f</span><span class="s1">'Fold </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</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">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Sample Index'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'5-Fold Cross-Validation'</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">20</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">savefig</span><span class="p">(</span><span class="s1">'../images/cross_validation_illustration.png'</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>
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<p><strong>Why cross‑validate?</strong> It reduces the variance of the performance estimate and makes better use of limited data. It is also essential for hyperparameter tuning (as we did with Ridge and Lasso).</p>
</blockquote>
<p>We already used <code>cross_val_score</code> above. Here's an explicit example with a linear model on the housing data.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_score</span><span class="p">,</span> <span class="n">KFold</span>
<span class="c1"># 5-fold CV on linear regression</span>
<span class="n">lin_reg_cv</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">lin_reg_cv</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'r2'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"5-fold CV R² scores: </span><span class="si">{</span><span class="n">scores</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean R²: </span><span class="si">{</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> (+/- </span><span class="si">{</span><span class="n">scores</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="o">*</span><span class="mi">2</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">)"</span><span class="p">)</span>
<span class="c1"># We can also use a custom cross-validator</span>
<span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>
<span class="n">scores_shuffled</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">lin_reg_cv</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'r2'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Shuffled CV R² scores: </span><span class="si">{</span><span class="n">scores_shuffled</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Mean R² (shuffled): </span><span class="si">{</span><span class="n">scores_shuffled</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>5-fold CV R² scores: [0.60709214 0.59544452 0.58112984 0.63060861 0.61005689]
Mean R²: 0.6049 (+/- 0.0328)
Shuffled CV R² scores: [0.60563739 0.59602593 0.5917264 0.61941109 0.62268184]
Mean R² (shuffled): 0.6071
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<h2 id="Feature-Scaling">Feature Scaling<a class="anchor-link" href="#Feature-Scaling">¶</a></h2><p>Many machine learning algorithms are sensitive to the scale of features. For example:</p>
<ul>
<li>Gradient descent converges faster when features are on similar scales.</li>
<li>Regularisation (Ridge, Lasso) penalises coefficients equally; if features have different scales, the penalty is not meaningful.</li>
<li>Distance‑based methods (k‑nearest neighbours, SVM with RBF kernel) assume all features are comparable.</li>
</ul>
<blockquote>
<p><strong>Linear algebra view</strong>: Scaling corresponds to multiplying each column of $X$ by a positive scalar. This changes the condition number and the geometry of the optimisation landscape.</p>
</blockquote>
<p>Common scaling techniques:</p>
<ul>
<li><strong>Standardisation</strong> (Z‑score): $x' = \frac{x - \mu}{\sigma}$ (mean 0, variance 1).</li>
<li><strong>Min‑max scaling</strong>: $x' = \frac{x - \min}{\max - \min}$ (range [0,1]).</li>
</ul>
<p>We should always fit the scaler on the training set and then transform both train and test sets to avoid data leakage.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div>
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<div class="highlight hl-ipython3"><pre><span></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="c1"># Create scaler</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="c1"># Fit on training data only</span>
<span class="n">X_train_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_train</span><span class="p">)</span>
<span class="n">X_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># Compare condition number before and after scaling</span>
<span class="n">X_train_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_train</span><span class="p">])</span>
<span class="n">X_train_scaled_aug</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">X_train_scaled</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">X_train_scaled</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Condition number (original): </span><span class="si">{</span><span class="n">cond</span><span class="p">(</span><span class="n">X_train_aug</span><span class="p">)</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Condition number (scaled): </span><span class="si">{</span><span class="n">cond</span><span class="p">(</span><span class="n">X_train_scaled_aug</span><span class="p">)</span><span class="si">:</span><span class="s2">.2e</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="c1"># Fit linear regression on scaled data</span>
<span class="n">lin_reg_scaled</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">lin_reg_scaled</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_pred_scaled</span> <span class="o">=</span> <span class="n">lin_reg_scaled</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
<span class="n">test_mse_scaled</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_scaled</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Linear regression (scaled) Test MSE: </span><span class="si">{</span><span class="n">test_mse_scaled</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Linear regression (original) Test MSE: </span><span class="si">{</span><span class="n">test_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Condition number (original): 2.40e+05
Condition number (scaled): 6.48e+00
Linear regression (scaled) Test MSE: 0.5381
Linear regression (original) Test MSE: 0.5381
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<p>Scaling did not change the linear regression performance because OLS is scale‑invariant (the coefficients adjust accordingly). However, it improves numerical stability and is crucial for regularised models and gradient descent.</p>
<h2 id="Model-Interpretation">Model Interpretation<a class="anchor-link" href="#Model-Interpretation">¶</a></h2><p>Interpretability is important in many applications. Different models offer different levels of insight.</p>
<h3 id="Linear-Models-(Ridge,-Lasso)">Linear Models (Ridge, Lasso)<a class="anchor-link" href="#Linear-Models-(Ridge,-Lasso)">¶</a></h3><ul>
<li>Coefficients directly indicate the effect of each feature (assuming features are scaled).</li>
<li>Sign and magnitude tell us direction and importance.</li>
</ul>
<h3 id="Decision-Trees">Decision Trees<a class="anchor-link" href="#Decision-Trees">¶</a></h3><ul>
<li>We can visualise the tree structure.</li>
<li>Feature importance based on how much each feature reduces impurity (e.g., variance for regression, Gini for classification).</li>
</ul>
<h3 id="Random-Forests">Random Forests<a class="anchor-link" href="#Random-Forests">¶</a></h3><ul>
<li>Aggregate feature importance across all trees.</li>
<li>Can also use SHAP or LIME for local explanations.</li>
</ul>
<p>Let's examine coefficients from a scaled linear model and feature importance from a random forest.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Train Ridge on scaled data (with default alpha)</span>
<span class="n">ridge_scaled</span> <span class="o">=</span> <span class="n">Ridge</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
<span class="n">ridge_scaled</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># Display coefficients</span>
<span class="n">coef_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">'Feature'</span><span class="p">:</span> <span class="n">feature_names</span><span class="p">,</span>
<span class="s1">'Coefficient'</span><span class="p">:</span> <span class="n">ridge_scaled</span><span class="o">.</span><span class="n">coef_</span>
<span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Ridge coefficients (scaled features):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">coef_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'Coefficient'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="nb">abs</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="c1"># Random forest feature importance</span>
<span class="n">rf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span> <span class="c1"># already fitted earlier, but ensure</span>
<span class="n">importances</span> <span class="o">=</span> <span class="n">rf</span><span class="o">.</span><span class="n">feature_importances_</span>
<span class="n">importance_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">'Feature'</span><span class="p">:</span> <span class="n">feature_names</span><span class="p">,</span>
<span class="s1">'Importance'</span><span class="p">:</span> <span class="n">importances</span>
<span class="p">})</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'Importance'</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">"</span><span class="se">\n</span><span class="s2">Random Forest Feature Importances:"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">importance_df</span><span class="p">)</span>
<span class="c1"># Plot</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">8</span><span class="p">,</span><span class="mi">4</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">importance_df</span><span class="p">[</span><span class="s1">'Feature'</span><span class="p">],</span> <span class="n">importance_df</span><span class="p">[</span><span class="s1">'Importance'</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">'Importance'</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">'Random Forest Feature Importance'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s1">'../images/RF_feature_importance.png'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Ridge coefficients (scaled features):
Feature Coefficient
6 Latitude -0.896656
7 Longitude -0.870257
0 MedInc 0.848402
3 AveBedrms 0.332536
2 AveRooms -0.287161
1 HouseAge 0.125807
5 AveOccup -0.040522
4 Population -0.002190
Random Forest Feature Importances:
Feature Importance
0 MedInc 0.589486
5 AveOccup 0.137379
6 Latitude 0.078123
7 Longitude 0.077486
1 HouseAge 0.047525
2 AveRooms 0.034377
4 Population 0.018634
3 AveBedrms 0.016990
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"/>
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<h2 id="Hyperparameter-Tuning-with-Grid-Search">Hyperparameter Tuning with Grid Search<a class="anchor-link" href="#Hyperparameter-Tuning-with-Grid-Search">¶</a></h2><p>Most models have hyperparameters that are not learned from data (e.g., <code>alpha</code> in Ridge, <code>max_depth</code> in trees, <code>n_estimators</code> in random forests). Tuning them properly is essential for good performance. Choosing hyperparameters is like selecting the optimal basis or regularisation parameter – it changes the solution space.</p>
<p><strong>Grid search</strong> exhaustively tries a predefined set of hyperparameter combinations using cross‑validation. <code>sklearn.model_selection.GridSearchCV</code> does this efficiently.</p>
<p>Let's tune a random forest regressor on the housing data.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="c1"># Define parameter grid</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'n_estimators'</span><span class="p">:</span> <span class="p">[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">100</span><span class="p">],</span>
<span class="s1">'max_depth'</span><span class="p">:</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="kc">None</span><span class="p">],</span>
<span class="s1">'min_samples_split'</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
<span class="p">}</span>
<span class="c1"># Create random forest</span>
<span class="n">rf_tune</span> <span class="o">=</span> <span class="n">RandomForestRegressor</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_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># Grid search with 3-fold CV (use a subset of training data for speed)</span>
<span class="n">X_train_subset</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[:</span><span class="mi">5000</span><span class="p">]</span>
<span class="n">y_train_subset</span> <span class="o">=</span> <span class="n">y_train</span><span class="p">[:</span><span class="mi">5000</span><span class="p">]</span>
<span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">rf_tune</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s1">'neg_mean_squared_error'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_subset</span><span class="p">,</span> <span class="n">y_train_subset</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Best parameters:"</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Best CV MSE:"</span><span class="p">,</span> <span class="o">-</span><span class="n">grid_search</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
<span class="c1"># Evaluate on test set</span>
<span class="n">best_rf</span> <span class="o">=</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_estimator_</span>
<span class="n">y_test_pred_best_rf</span> <span class="o">=</span> <span class="n">best_rf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">test_mse_best_rf</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred_best_rf</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Tuned Random Forest Test MSE: </span><span class="si">{</span><span class="n">test_mse_best_rf</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Fitting 3 folds for each of 12 candidates, totalling 36 fits
Best parameters: {'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}
Best CV MSE: 0.3210370034255883
Tuned Random Forest Test MSE: 0.2928
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<h2 id="Summary-and-Additional-Considerations">Summary and Additional Considerations<a class="anchor-link" href="#Summary-and-Additional-Considerations">¶</a></h2><p>We have covered a progression of modelling techniques and essential practices:</p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Linearity</th>
<th>Regularisation</th>
<th>Feature Selection</th>
<th>Scalability</th>
</tr>
</thead>
<tbody>
<tr>
<td>Linear regression</td>
<td>Yes</td>
<td>No</td>
<td>No</td>
<td>Good (closed‑form)</td>
</tr>
<tr>
<td>Polynomial regression</td>
<td>In features</td>
<td>No</td>
<td>No</td>
<td>Poor (exploding dimension)</td>
</tr>
<tr>
<td>Ridge</td>
<td>Yes</td>
<td>$L^2$</td>
<td>No (shrinks only)</td>
<td>Good</td>
</tr>
<tr>
<td>Lasso</td>
<td>Yes</td>
<td>$L^1$</td>
<td>Yes</td>
<td>Good (via coordinate descent)</td>
</tr>
<tr>
<td>Logistic regression</td>
<td>Decision boundary linear</td>
<td>Optional</td>
<td>With L1/L2</td>
<td>Good</td>
</tr>
<tr>
<td>Gradient descent</td>
<td>Yes (or any differentiable)</td>
<td>Optional</td>
<td>Optional</td>
<td>Excellent (very large data)</td>
</tr>
<tr>
<td>Decision trees</td>
<td>No</td>
<td>No (but depth limits)</td>
<td>Implicitly</td>
<td>Moderate</td>
</tr>
<tr>
<td>Random forests</td>
<td>No</td>
<td>No (ensemble reduces variance)</td>
<td>Implicitly</td>
<td>Moderate (parallelisable)</td>
</tr>
</tbody>
</table>
<blockquote>
<p><strong>Bias–variance tradeoff</strong>: Simple models (linear) have high bias but low variance. Complex models (deep trees) have low bias but high variance. Regularisation and ensembles (random forests) try to balance this.</p>
</blockquote>
<p><strong>What else could be added?</strong></p>
<ul>
<li><strong>Support vector machines</strong> (SVM) – geometric margin classifiers.</li>
<li><strong>Neural networks</strong> – highly flexible nonlinear models.</li>
<li><strong>Time series models</strong> (ARIMA, etc.).</li>
<li><strong>Model selection criteria</strong> (AIC, BIC).</li>
</ul>
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