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| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
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}
.lm-close-icon:after {
content: 'X';
display: block;
width: 15px;
height: 15px;
text-align: center;
color: #000;
font-weight: normal;
font-size: 12px;
cursor: pointer;
}
/*
* 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-DockPanel {
z-index: 0;
}
.lm-DockPanel-widget {
z-index: 0;
}
.lm-DockPanel-tabBar {
z-index: 1;
}
.lm-DockPanel-handle {
z-index: 2;
}
.lm-DockPanel-handle.lm-mod-hidden {
display: none !important;
}
.lm-DockPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
.lm-DockPanel-handle[data-orientation='horizontal'] {
cursor: ew-resize;
}
.lm-DockPanel-handle[data-orientation='vertical'] {
cursor: ns-resize;
}
.lm-DockPanel-handle[data-orientation='horizontal']:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
.lm-DockPanel-handle[data-orientation='vertical']:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
.lm-DockPanel-overlay {
z-index: 3;
box-sizing: border-box;
pointer-events: none;
}
.lm-DockPanel-overlay.lm-mod-hidden {
display: none !important;
}
/*
* 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-Menu {
z-index: 10000;
position: absolute;
white-space: nowrap;
overflow-x: hidden;
overflow-y: auto;
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.lm-Menu-content {
margin: 0;
padding: 0;
display: table;
list-style-type: none;
}
.lm-Menu-item {
display: table-row;
}
.lm-Menu-item.lm-mod-hidden,
.lm-Menu-item.lm-mod-collapsed {
display: none !important;
}
.lm-Menu-itemIcon,
.lm-Menu-itemSubmenuIcon {
display: table-cell;
text-align: center;
}
.lm-Menu-itemLabel {
display: table-cell;
text-align: left;
}
.lm-Menu-itemShortcut {
display: table-cell;
text-align: right;
}
/*
* 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-MenuBar {
outline: none;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.lm-MenuBar-content {
margin: 0;
padding: 0;
display: flex;
flex-direction: row;
list-style-type: none;
}
.lm-MenuBar-item {
box-sizing: border-box;
}
.lm-MenuBar-itemIcon,
.lm-MenuBar-itemLabel {
display: inline-block;
}
/*
* 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-ScrollBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.lm-ScrollBar[data-orientation='horizontal'] {
flex-direction: row;
}
.lm-ScrollBar[data-orientation='vertical'] {
flex-direction: column;
}
.lm-ScrollBar-button {
box-sizing: border-box;
flex: 0 0 auto;
}
.lm-ScrollBar-track {
box-sizing: border-box;
position: relative;
overflow: hidden;
flex: 1 1 auto;
}
.lm-ScrollBar-thumb {
box-sizing: border-box;
position: absolute;
}
/*
* 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-SplitPanel-child {
z-index: 0;
}
.lm-SplitPanel-handle {
z-index: 1;
}
.lm-SplitPanel-handle.lm-mod-hidden {
display: none !important;
}
.lm-SplitPanel-handle:after {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
content: '';
}
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
cursor: ew-resize;
}
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
cursor: ns-resize;
}
.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
left: 50%;
min-width: 8px;
transform: translateX(-50%);
}
.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
top: 50%;
min-height: 8px;
transform: translateY(-50%);
}
/*
* 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-TabBar {
display: flex;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.lm-TabBar[data-orientation='horizontal'] {
flex-direction: row;
align-items: flex-end;
}
.lm-TabBar[data-orientation='vertical'] {
flex-direction: column;
align-items: flex-end;
}
.lm-TabBar-content {
margin: 0;
padding: 0;
display: flex;
flex: 1 1 auto;
list-style-type: none;
}
.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
flex-direction: row;
}
.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
flex-direction: column;
}
.lm-TabBar-tab {
display: flex;
flex-direction: row;
box-sizing: border-box;
overflow: hidden;
touch-action: none; /* Disable native Drag/Drop */
}
.lm-TabBar-tabIcon,
.lm-TabBar-tabCloseIcon {
flex: 0 0 auto;
}
.lm-TabBar-tabLabel {
flex: 1 1 auto;
overflow: hidden;
white-space: nowrap;
}
.lm-TabBar-tabInput {
user-select: all;
width: 100%;
box-sizing: border-box;
}
.lm-TabBar-tab.lm-mod-hidden {
display: none !important;
}
.lm-TabBar-addButton.lm-mod-hidden {
display: none !important;
}
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
position: relative;
}
.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
left: 0;
transition: left 150ms ease;
}
.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
top: 0;
transition: top 150ms ease;
}
.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
transition: none;
}
.lm-TabBar-tabLabel .lm-TabBar-tabInput {
user-select: all;
width: 100%;
box-sizing: border-box;
background: inherit;
}
/*
* 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-TabPanel-tabBar {
z-index: 1;
}
.lm-TabPanel-stackedPanel {
z-index: 0;
}
/*
* 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.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapse {
display: flex;
flex-direction: column;
align-items: stretch;
}
.jp-Collapse-header {
padding: 1px 12px;
background-color: var(--jp-layout-color1);
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
align-items: center;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
text-transform: uppercase;
user-select: none;
}
.jp-Collapser-icon {
height: 16px;
}
.jp-Collapse-header-collapsed .jp-Collapser-icon {
transform: rotate(-90deg);
margin: auto 0;
}
.jp-Collapser-title {
line-height: 25px;
}
.jp-Collapse-contents {
padding: 0 12px;
background-color: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
/* Icons urls */
:root {
--jp-icon-add-above: 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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);
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--jp-icon-text-editor: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8cGF0aCBjbGFzcz0ianAtdGV4dC1lZGl0b3ItaWNvbi1jb2xvciBqcC1pY29uLXNlbGVjdGFibGUiIGZpbGw9IiM2MTYxNjEiIGQ9Ik0xNSAxNUgzdjJoMTJ2LTJ6bTAtOEgzdjJoMTJWN3pNMyAxM2gxOHYtMkgzdjJ6bTAgOGgxOHYtMkgzdjJ6TTMgM3YyaDE4VjNIM3oiLz4KPC9zdmc+Cg==);
--jp-icon-toc: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIyNCIgaGVpZ2h0PSIyNCIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjNjE2MTYxIj4KICAgIDxwYXRoIGQ9Ik03LDVIMjFWN0g3VjVNNywxM1YxMUgyMVYxM0g3TTQsNC41QTEuNSwxLjUgMCAwLDEgNS41LDZBMS41LDEuNSAwIDAsMSA0LDcuNUExLjUsMS41IDAgMCwxIDIuNSw2QTEuNSwxLjUgMCAwLDEgNCw0LjVNNCwxMC41QTEuNSwxLjUgMCAwLDEgNS41LDEyQTEuNSwxLjUgMCAwLDEgNCwxMy41QTEuNSwxLjUgMCAwLDEgMi41LDEyQTEuNSwxLjUgMCAwLDEgNCwxMC41TTcsMTlWMTdIMjFWMTlIN000LDE2LjVBMS41LDEuNSAwIDAsMSA1LjUsMThBMS41LDEuNSAwIDAsMSA0LDE5LjVBMS41LDEuNSAwIDAsMSAyLjUsMThBMS41LDEuNSAwIDAsMSA0LDE2LjVaIiAvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
--jp-icon-trusted: url(data:image/svg+xml;base64,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);
--jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-users: url(data:image/svg+xml;base64,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);
--jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
--jp-icon-word: url(data:image/svg+xml;base64,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);
--jp-icon-yaml: url(data:image/svg+xml;base64,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);
}
/* Icon CSS class declarations */
.jp-AddAboveIcon {
background-image: var(--jp-icon-add-above);
}
.jp-AddBelowIcon {
background-image: var(--jp-icon-add-below);
}
.jp-AddIcon {
background-image: var(--jp-icon-add);
}
.jp-BellIcon {
background-image: var(--jp-icon-bell);
}
.jp-BugDotIcon {
background-image: var(--jp-icon-bug-dot);
}
.jp-BugIcon {
background-image: var(--jp-icon-bug);
}
.jp-BuildIcon {
background-image: var(--jp-icon-build);
}
.jp-CaretDownEmptyIcon {
background-image: var(--jp-icon-caret-down-empty);
}
.jp-CaretDownEmptyThinIcon {
background-image: var(--jp-icon-caret-down-empty-thin);
}
.jp-CaretDownIcon {
background-image: var(--jp-icon-caret-down);
}
.jp-CaretLeftIcon {
background-image: var(--jp-icon-caret-left);
}
.jp-CaretRightIcon {
background-image: var(--jp-icon-caret-right);
}
.jp-CaretUpEmptyThinIcon {
background-image: var(--jp-icon-caret-up-empty-thin);
}
.jp-CaretUpIcon {
background-image: var(--jp-icon-caret-up);
}
.jp-CaseSensitiveIcon {
background-image: var(--jp-icon-case-sensitive);
}
.jp-CheckIcon {
background-image: var(--jp-icon-check);
}
.jp-CircleEmptyIcon {
background-image: var(--jp-icon-circle-empty);
}
.jp-CircleIcon {
background-image: var(--jp-icon-circle);
}
.jp-ClearIcon {
background-image: var(--jp-icon-clear);
}
.jp-CloseIcon {
background-image: var(--jp-icon-close);
}
.jp-CodeCheckIcon {
background-image: var(--jp-icon-code-check);
}
.jp-CodeIcon {
background-image: var(--jp-icon-code);
}
.jp-CollapseAllIcon {
background-image: var(--jp-icon-collapse-all);
}
.jp-ConsoleIcon {
background-image: var(--jp-icon-console);
}
.jp-CopyIcon {
background-image: var(--jp-icon-copy);
}
.jp-CopyrightIcon {
background-image: var(--jp-icon-copyright);
}
.jp-CutIcon {
background-image: var(--jp-icon-cut);
}
.jp-DeleteIcon {
background-image: var(--jp-icon-delete);
}
.jp-DownloadIcon {
background-image: var(--jp-icon-download);
}
.jp-DuplicateIcon {
background-image: var(--jp-icon-duplicate);
}
.jp-EditIcon {
background-image: var(--jp-icon-edit);
}
.jp-EllipsesIcon {
background-image: var(--jp-icon-ellipses);
}
.jp-ErrorIcon {
background-image: var(--jp-icon-error);
}
.jp-ExpandAllIcon {
background-image: var(--jp-icon-expand-all);
}
.jp-ExtensionIcon {
background-image: var(--jp-icon-extension);
}
.jp-FastForwardIcon {
background-image: var(--jp-icon-fast-forward);
}
.jp-FileIcon {
background-image: var(--jp-icon-file);
}
.jp-FileUploadIcon {
background-image: var(--jp-icon-file-upload);
}
.jp-FilterDotIcon {
background-image: var(--jp-icon-filter-dot);
}
.jp-FilterIcon {
background-image: var(--jp-icon-filter);
}
.jp-FilterListIcon {
background-image: var(--jp-icon-filter-list);
}
.jp-FolderFavoriteIcon {
background-image: var(--jp-icon-folder-favorite);
}
.jp-FolderIcon {
background-image: var(--jp-icon-folder);
}
.jp-HomeIcon {
background-image: var(--jp-icon-home);
}
.jp-Html5Icon {
background-image: var(--jp-icon-html5);
}
.jp-ImageIcon {
background-image: var(--jp-icon-image);
}
.jp-InfoIcon {
background-image: var(--jp-icon-info);
}
.jp-InspectorIcon {
background-image: var(--jp-icon-inspector);
}
.jp-JsonIcon {
background-image: var(--jp-icon-json);
}
.jp-JuliaIcon {
background-image: var(--jp-icon-julia);
}
.jp-JupyterFaviconIcon {
background-image: var(--jp-icon-jupyter-favicon);
}
.jp-JupyterIcon {
background-image: var(--jp-icon-jupyter);
}
.jp-JupyterlabWordmarkIcon {
background-image: var(--jp-icon-jupyterlab-wordmark);
}
.jp-KernelIcon {
background-image: var(--jp-icon-kernel);
}
.jp-KeyboardIcon {
background-image: var(--jp-icon-keyboard);
}
.jp-LaunchIcon {
background-image: var(--jp-icon-launch);
}
.jp-LauncherIcon {
background-image: var(--jp-icon-launcher);
}
.jp-LineFormIcon {
background-image: var(--jp-icon-line-form);
}
.jp-LinkIcon {
background-image: var(--jp-icon-link);
}
.jp-ListIcon {
background-image: var(--jp-icon-list);
}
.jp-MarkdownIcon {
background-image: var(--jp-icon-markdown);
}
.jp-MoveDownIcon {
background-image: var(--jp-icon-move-down);
}
.jp-MoveUpIcon {
background-image: var(--jp-icon-move-up);
}
.jp-NewFolderIcon {
background-image: var(--jp-icon-new-folder);
}
.jp-NotTrustedIcon {
background-image: var(--jp-icon-not-trusted);
}
.jp-NotebookIcon {
background-image: var(--jp-icon-notebook);
}
.jp-NumberingIcon {
background-image: var(--jp-icon-numbering);
}
.jp-OfflineBoltIcon {
background-image: var(--jp-icon-offline-bolt);
}
.jp-PaletteIcon {
background-image: var(--jp-icon-palette);
}
.jp-PasteIcon {
background-image: var(--jp-icon-paste);
}
.jp-PdfIcon {
background-image: var(--jp-icon-pdf);
}
.jp-PythonIcon {
background-image: var(--jp-icon-python);
}
.jp-RKernelIcon {
background-image: var(--jp-icon-r-kernel);
}
.jp-ReactIcon {
background-image: var(--jp-icon-react);
}
.jp-RedoIcon {
background-image: var(--jp-icon-redo);
}
.jp-RefreshIcon {
background-image: var(--jp-icon-refresh);
}
.jp-RegexIcon {
background-image: var(--jp-icon-regex);
}
.jp-RunIcon {
background-image: var(--jp-icon-run);
}
.jp-RunningIcon {
background-image: var(--jp-icon-running);
}
.jp-SaveIcon {
background-image: var(--jp-icon-save);
}
.jp-SearchIcon {
background-image: var(--jp-icon-search);
}
.jp-SettingsIcon {
background-image: var(--jp-icon-settings);
}
.jp-ShareIcon {
background-image: var(--jp-icon-share);
}
.jp-SpreadsheetIcon {
background-image: var(--jp-icon-spreadsheet);
}
.jp-StopIcon {
background-image: var(--jp-icon-stop);
}
.jp-TabIcon {
background-image: var(--jp-icon-tab);
}
.jp-TableRowsIcon {
background-image: var(--jp-icon-table-rows);
}
.jp-TagIcon {
background-image: var(--jp-icon-tag);
}
.jp-TerminalIcon {
background-image: var(--jp-icon-terminal);
}
.jp-TextEditorIcon {
background-image: var(--jp-icon-text-editor);
}
.jp-TocIcon {
background-image: var(--jp-icon-toc);
}
.jp-TreeViewIcon {
background-image: var(--jp-icon-tree-view);
}
.jp-TrustedIcon {
background-image: var(--jp-icon-trusted);
}
.jp-UndoIcon {
background-image: var(--jp-icon-undo);
}
.jp-UserIcon {
background-image: var(--jp-icon-user);
}
.jp-UsersIcon {
background-image: var(--jp-icon-users);
}
.jp-VegaIcon {
background-image: var(--jp-icon-vega);
}
.jp-WordIcon {
background-image: var(--jp-icon-word);
}
.jp-YamlIcon {
background-image: var(--jp-icon-yaml);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* (DEPRECATED) Support for consuming icons as CSS background images
*/
.jp-Icon,
.jp-MaterialIcon {
background-position: center;
background-repeat: no-repeat;
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-cover {
background-position: center;
background-repeat: no-repeat;
background-size: cover;
}
/**
* (DEPRECATED) Support for specific CSS icon sizes
*/
.jp-Icon-16 {
background-size: 16px;
min-width: 16px;
min-height: 16px;
}
.jp-Icon-18 {
background-size: 18px;
min-width: 18px;
min-height: 18px;
}
.jp-Icon-20 {
background-size: 20px;
min-width: 20px;
min-height: 20px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.lm-TabBar .lm-TabBar-addButton {
align-items: center;
display: flex;
padding: 4px;
padding-bottom: 5px;
margin-right: 1px;
background-color: var(--jp-layout-color2);
}
.lm-TabBar .lm-TabBar-addButton:hover {
background-color: var(--jp-layout-color1);
}
.lm-DockPanel-tabBar .lm-TabBar-tab {
width: var(--jp-private-horizontal-tab-width);
}
.lm-DockPanel-tabBar .lm-TabBar-content {
flex: unset;
}
.lm-DockPanel-tabBar[data-orientation='horizontal'] {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for icons as inline SVG HTMLElements
*/
/* recolor the primary elements of an icon */
.jp-icon0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-accent1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-accent2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-accent3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-accent4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-accent0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-accent1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-accent2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-accent3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-accent4[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-none[fill] {
fill: none;
}
.jp-icon-none[stroke] {
stroke: none;
}
/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
fill: var(--jp-brand-color0);
}
.jp-icon-brand1[fill] {
fill: var(--jp-brand-color1);
}
.jp-icon-brand2[fill] {
fill: var(--jp-brand-color2);
}
.jp-icon-brand3[fill] {
fill: var(--jp-brand-color3);
}
.jp-icon-brand4[fill] {
fill: var(--jp-brand-color4);
}
.jp-icon-brand0[stroke] {
stroke: var(--jp-brand-color0);
}
.jp-icon-brand1[stroke] {
stroke: var(--jp-brand-color1);
}
.jp-icon-brand2[stroke] {
stroke: var(--jp-brand-color2);
}
.jp-icon-brand3[stroke] {
stroke: var(--jp-brand-color3);
}
.jp-icon-brand4[stroke] {
stroke: var(--jp-brand-color4);
}
/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
fill: var(--jp-warn-color0);
}
.jp-icon-warn1[fill] {
fill: var(--jp-warn-color1);
}
.jp-icon-warn2[fill] {
fill: var(--jp-warn-color2);
}
.jp-icon-warn3[fill] {
fill: var(--jp-warn-color3);
}
.jp-icon-warn0[stroke] {
stroke: var(--jp-warn-color0);
}
.jp-icon-warn1[stroke] {
stroke: var(--jp-warn-color1);
}
.jp-icon-warn2[stroke] {
stroke: var(--jp-warn-color2);
}
.jp-icon-warn3[stroke] {
stroke: var(--jp-warn-color3);
}
/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
fill: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[fill] {
fill: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[fill] {
fill: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[fill] {
fill: var(--jp-icon-contrast-color3);
}
.jp-icon-contrast0[stroke] {
stroke: var(--jp-icon-contrast-color0);
}
.jp-icon-contrast1[stroke] {
stroke: var(--jp-icon-contrast-color1);
}
.jp-icon-contrast2[stroke] {
stroke: var(--jp-icon-contrast-color2);
}
.jp-icon-contrast3[stroke] {
stroke: var(--jp-icon-contrast-color3);
}
.jp-icon-dot[fill] {
fill: var(--jp-warn-color0);
}
.jp-jupyter-icon-color[fill] {
fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}
.jp-notebook-icon-color[fill] {
fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}
.jp-json-icon-color[fill] {
fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}
.jp-console-icon-color[fill] {
fill: var(--jp-console-icon-color, white);
}
.jp-console-icon-background-color[fill] {
fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}
.jp-terminal-icon-color[fill] {
fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}
.jp-terminal-icon-background-color[fill] {
fill: var(
--jp-terminal-icon-background-color,
var(--jp-inverse-layout-color2)
);
}
.jp-text-editor-icon-color[fill] {
fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}
.jp-inspector-icon-color[fill] {
fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}
/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* stylelint-disable selector-max-class, selector-max-compound-selectors */
/**
* TODO: come up with non css-hack solution for showing the busy icon on top
* of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon3[fill] {
fill: none;
}
.lm-DockPanel-tabBar
.lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
> .lm-TabBar-tabCloseIcon
> :not(:hover)
> .jp-icon-busy[fill] {
fill: var(--jp-inverse-layout-color3);
}
/* stylelint-enable selector-max-class, selector-max-compound-selectors */
/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
fill: #fff;
}
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
fill: var(--jp-brand-color1);
}
/* special handling for splash icon CSS. While the theme CSS reloads during
splash, the splash icon can loose theming. To prevent that, we set a
default for its color variable */
:root {
--jp-warn-color0: var(--md-orange-700);
}
/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
margin-right: 4px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/**
* Support for alt colors for icons as inline SVG HTMLElements
*/
/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-alt .jp-icon0[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-alt .jp-icon1[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-alt .jp-icon2[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-alt .jp-icon3[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-alt .jp-icon4[stroke] {
stroke: var(--jp-layout-color4);
}
/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-alt .jp-icon-accent0[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-alt .jp-icon-accent1[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-alt .jp-icon-accent2[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-alt .jp-icon-accent3[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-alt .jp-icon-accent4[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
display: none !important;
}
/**
* Support for hover colors for icons as inline SVG HTMLElements
*/
/**
* regular colors
*/
/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
fill: none;
}
.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
stroke: none;
}
/**
* inverse colors
*/
/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
fill: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
fill: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
fill: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
fill: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
fill: var(--jp-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
stroke: var(--jp-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
stroke: var(--jp-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
stroke: var(--jp-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
stroke: var(--jp-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
stroke: var(--jp-layout-color4);
}
/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
fill: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
fill: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
fill: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
fill: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
fill: var(--jp-inverse-layout-color4);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
stroke: var(--jp-inverse-layout-color0);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
stroke: var(--jp-inverse-layout-color1);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
stroke: var(--jp-inverse-layout-color2);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
stroke: var(--jp-inverse-layout-color3);
}
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
stroke: var(--jp-inverse-layout-color4);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-IFrame {
width: 100%;
height: 100%;
}
.jp-IFrame > iframe {
border: none;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
position: relative;
}
body.lm-mod-override-cursor .jp-IFrame::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-HoverBox {
position: fixed;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FormGroup-content fieldset {
border: none;
padding: 0;
min-width: 0;
width: 100%;
}
/* stylelint-disable selector-max-type */
.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
font-size: var(--jp-content-font-size2);
border-color: var(--jp-input-border-color);
border-style: solid;
border-radius: var(--jp-border-radius);
border-width: 1px;
padding: 6px 8px;
background: none;
color: var(--jp-ui-font-color0);
height: inherit;
}
.jp-FormGroup-content fieldset input[type='checkbox'] {
position: relative;
top: 2px;
margin-left: 0;
}
.jp-FormGroup-content button.jp-mod-styled {
cursor: pointer;
}
.jp-FormGroup-content .checkbox label {
cursor: pointer;
font-size: var(--jp-content-font-size1);
}
.jp-FormGroup-content .jp-root > fieldset > legend {
display: none;
}
.jp-FormGroup-content .jp-root > fieldset > p {
display: none;
}
/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
-moz-outline-radius: unset;
outline: var(--jp-border-width) solid var(--md-blue-500);
outline-offset: -1px;
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
background-color: var(--jp-border-color2);
}
/* stylelint-enable selector-max-type */
.jp-FormGroup-content .checkbox .field-description {
/* Disable default description field for checkbox:
because other widgets do not have description fields,
we add descriptions to each widget on the field level.
*/
display: none;
}
.jp-FormGroup-content #root__description {
display: none;
}
.jp-FormGroup-content .jp-modifiedIndicator {
width: 5px;
background-color: var(--jp-brand-color2);
margin-top: 0;
margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
flex-shrink: 0;
}
.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
background-color: var(--jp-error-color0);
margin-right: 0.5em;
}
/* RJSF ARRAY style */
.jp-arrayFieldWrapper legend {
font-size: var(--jp-content-font-size2);
color: var(--jp-ui-font-color0);
flex-basis: 100%;
padding: 4px 0;
font-weight: var(--jp-content-heading-font-weight);
border-bottom: 1px solid var(--jp-border-color2);
}
.jp-arrayFieldWrapper .field-description {
padding: 4px 0;
white-space: pre-wrap;
}
.jp-arrayFieldWrapper .array-item {
width: 100%;
border: 1px solid var(--jp-border-color2);
border-radius: 4px;
margin: 4px;
}
.jp-ArrayOperations {
display: flex;
margin-left: 8px;
}
.jp-ArrayOperationsButton {
margin: 2px;
}
.jp-ArrayOperationsButton .jp-icon3[fill] {
fill: var(--jp-ui-font-color0);
}
button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
cursor: not-allowed;
opacity: 0.5;
}
/* RJSF form validation error */
.jp-FormGroup-content .validationErrors {
color: var(--jp-error-color0);
}
/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
display: none;
}
/* RJSF normal content (settings-editor) */
.jp-FormGroup-contentNormal {
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
margin-left: 7px;
color: var(--jp-ui-font-color0);
}
.jp-FormGroup-contentNormal .jp-FormGroup-description {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-default {
flex-basis: 100%;
padding: 4px 7px;
}
.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
font-size: var(--jp-content-font-size1);
font-weight: normal;
min-width: 120px;
}
.jp-FormGroup-contentNormal fieldset:not(:first-child) {
margin-left: 7px;
}
.jp-FormGroup-contentNormal .field-array-of-string .array-item {
/* Display `jp-ArrayOperations` buttons side-by-side with content except
for small screens where flex-wrap will place them one below the other.
*/
display: flex;
align-items: center;
flex-wrap: wrap;
}
.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
/* RJSF compact content (metadata-form) */
.jp-FormGroup-content.jp-FormGroup-contentCompact {
width: 100%;
}
.jp-FormGroup-contentCompact .form-group {
display: flex;
padding: 0.5em 0.2em 0.5em 0;
}
.jp-FormGroup-contentCompact
.jp-FormGroup-compactTitle
.jp-FormGroup-description {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color2);
}
.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
padding-bottom: 0.3em;
}
.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
width: 100%;
box-sizing: border-box;
}
.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
padding-bottom: 7px;
}
.jp-FormGroup-contentCompact
.jp-objectFieldWrapper
.jp-objectFieldWrapper
.form-group {
padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
margin-top: 2px;
}
.jp-FormGroup-contentCompact ul.error-detail {
margin-block-start: 0.5em;
margin-block-end: 0.5em;
padding-inline-start: 1em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-SidePanel {
display: flex;
flex-direction: column;
min-width: var(--jp-sidebar-min-width);
overflow-y: auto;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
font-size: var(--jp-ui-font-size1);
}
.jp-SidePanel-header {
flex: 0 0 auto;
display: flex;
border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin: 0;
padding: 2px;
text-transform: uppercase;
}
.jp-SidePanel-toolbar {
flex: 0 0 auto;
}
.jp-SidePanel-content {
flex: 1 1 auto;
}
.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
height: var(--jp-private-toolbar-height);
}
.jp-SidePanel-toolbar.jp-Toolbar-micro {
display: none;
}
.lm-AccordionPanel .jp-AccordionPanel-title {
box-sizing: border-box;
line-height: 25px;
margin: 0;
display: flex;
align-items: center;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
font-size: var(--jp-ui-font-size0);
}
.jp-AccordionPanel-title {
cursor: pointer;
user-select: none;
-moz-user-select: none;
-webkit-user-select: none;
text-transform: uppercase;
}
.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
/* Title is rotated for horizontal accordion panel using CSS */
display: block;
transform-origin: top left;
transform: rotate(-90deg) translate(-100%);
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
user-select: none;
text-overflow: ellipsis;
white-space: nowrap;
overflow: hidden;
}
.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
transform: rotate(-90deg);
margin: auto 0;
height: 16px;
}
.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
transform: rotate(0deg);
}
.lm-AccordionPanel .jp-AccordionPanel-toolbar {
background: none;
box-shadow: none;
border: none;
margin-left: auto;
}
.lm-AccordionPanel .lm-SplitPanel-handle:hover {
background: var(--jp-layout-color3);
}
.jp-text-truncated {
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Spinner {
position: absolute;
display: flex;
justify-content: center;
align-items: center;
z-index: 10;
left: 0;
top: 0;
width: 100%;
height: 100%;
background: var(--jp-layout-color0);
outline: none;
}
.jp-SpinnerContent {
font-size: 10px;
margin: 50px auto;
text-indent: -9999em;
width: 3em;
height: 3em;
border-radius: 50%;
background: var(--jp-brand-color3);
background: linear-gradient(
to right,
#f37626 10%,
rgba(255, 255, 255, 0) 42%
);
position: relative;
animation: load3 1s infinite linear, fadeIn 1s;
}
.jp-SpinnerContent::before {
width: 50%;
height: 50%;
background: #f37626;
border-radius: 100% 0 0;
position: absolute;
top: 0;
left: 0;
content: '';
}
.jp-SpinnerContent::after {
background: var(--jp-layout-color0);
width: 75%;
height: 75%;
border-radius: 50%;
content: '';
margin: auto;
position: absolute;
top: 0;
left: 0;
bottom: 0;
right: 0;
}
@keyframes fadeIn {
0% {
opacity: 0;
}
100% {
opacity: 1;
}
}
@keyframes load3 {
0% {
transform: rotate(0deg);
}
100% {
transform: rotate(360deg);
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
button.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: none;
box-sizing: border-box;
text-align: center;
line-height: 32px;
height: 32px;
padding: 0 12px;
letter-spacing: 0.8px;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input.jp-mod-styled {
background: var(--jp-input-background);
height: 28px;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color1);
padding-left: 7px;
padding-right: 7px;
font-size: var(--jp-ui-font-size2);
color: var(--jp-ui-font-color0);
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
input[type='checkbox'].jp-mod-styled {
appearance: checkbox;
-webkit-appearance: checkbox;
-moz-appearance: checkbox;
height: auto;
}
input.jp-mod-styled:focus {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-select-wrapper {
display: flex;
position: relative;
flex-direction: column;
padding: 1px;
background-color: var(--jp-layout-color1);
box-sizing: border-box;
margin-bottom: 12px;
}
.jp-select-wrapper:not(.multiple) {
height: 28px;
}
.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-input-active-background);
}
select.jp-mod-styled:hover {
cursor: pointer;
color: var(--jp-ui-font-color0);
background-color: var(--jp-input-hover-background);
box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}
select.jp-mod-styled {
flex: 1 1 auto;
width: 100%;
font-size: var(--jp-ui-font-size2);
background: var(--jp-input-background);
color: var(--jp-ui-font-color0);
padding: 0 25px 0 8px;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
}
select.jp-mod-styled:not([multiple]) {
height: 32px;
}
select.jp-mod-styled[multiple] {
max-height: 200px;
overflow-y: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-switch {
display: flex;
align-items: center;
padding-left: 4px;
padding-right: 4px;
font-size: var(--jp-ui-font-size1);
background-color: transparent;
color: var(--jp-ui-font-color1);
border: none;
height: 20px;
}
.jp-switch:hover {
background-color: var(--jp-layout-color2);
}
.jp-switch-label {
margin-right: 5px;
font-family: var(--jp-ui-font-family);
}
.jp-switch-track {
cursor: pointer;
background-color: var(--jp-switch-color, var(--jp-border-color1));
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 34px;
height: 16px;
width: 35px;
position: relative;
}
.jp-switch-track::before {
content: '';
position: absolute;
height: 10px;
width: 10px;
margin: 3px;
left: 0;
background-color: var(--jp-ui-inverse-font-color1);
-webkit-transition: 0.4s;
transition: 0.4s;
border-radius: 50%;
}
.jp-switch[aria-checked='true'] .jp-switch-track {
background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}
.jp-switch[aria-checked='true'] .jp-switch-track::before {
/* track width (35) - margins (3 + 3) - thumb width (10) */
left: 19px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
:root {
--jp-private-toolbar-height: calc(
28px + var(--jp-border-width)
); /* leave 28px for content */
}
.jp-Toolbar {
color: var(--jp-ui-font-color1);
flex: 0 0 auto;
display: flex;
flex-direction: row;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: 2px;
z-index: 8;
overflow-x: hidden;
}
/* Toolbar items */
.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
flex-grow: 1;
flex-shrink: 1;
}
.jp-Toolbar-item.jp-Toolbar-kernelStatus {
display: inline-block;
width: 32px;
background-repeat: no-repeat;
background-position: center;
background-size: 16px;
}
.jp-Toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
display: flex;
padding-left: 1px;
padding-right: 1px;
font-size: var(--jp-ui-font-size1);
line-height: var(--jp-private-toolbar-height);
height: 100%;
}
/* Toolbar buttons */
/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
color: transparent;
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0;
margin: 0;
}
button.jp-ToolbarButtonComponent {
background: var(--jp-layout-color1);
border: none;
box-sizing: border-box;
outline: none;
appearance: none;
-webkit-appearance: none;
-moz-appearance: none;
padding: 0 6px;
margin: 0;
height: 24px;
border-radius: var(--jp-border-radius);
display: flex;
align-items: center;
text-align: center;
font-size: 14px;
min-width: unset;
min-height: unset;
}
button.jp-ToolbarButtonComponent:disabled {
opacity: 0.4;
}
button.jp-ToolbarButtonComponent > span {
padding: 0;
flex: 0 0 auto;
}
button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
font-size: var(--jp-ui-font-size1);
line-height: 100%;
padding-left: 2px;
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar.jp-Toolbar-micro {
padding: 0;
min-height: 0;
}
#jp-main-dock-panel[data-mode='single-document']
.jp-MainAreaWidget
> .jp-Toolbar {
border: none;
box-shadow: none;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-WindowedPanel-outer {
position: relative;
overflow-y: auto;
}
.jp-WindowedPanel-inner {
position: relative;
}
.jp-WindowedPanel-window {
position: absolute;
left: 0;
right: 0;
overflow: visible;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/* Sibling imports */
body {
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
}
/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
text-decoration: unset;
color: unset;
}
a:hover {
text-decoration: unset;
color: unset;
}
/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
text-decoration: revert;
color: var(--jp-content-link-color);
}
.jp-Dialog-content a:hover {
text-decoration: revert;
}
/* Styles for ui-components */
.jp-Button {
color: var(--jp-ui-font-color2);
border-radius: var(--jp-border-radius);
padding: 0 12px;
font-size: var(--jp-ui-font-size1);
/* Copy from blueprint 3 */
display: inline-flex;
flex-direction: row;
border: none;
cursor: pointer;
align-items: center;
justify-content: center;
text-align: left;
vertical-align: middle;
min-height: 30px;
min-width: 30px;
}
.jp-Button:disabled {
cursor: not-allowed;
}
.jp-Button:empty {
padding: 0 !important;
}
.jp-Button.jp-mod-small {
min-height: 24px;
min-width: 24px;
font-size: 12px;
padding: 0 7px;
}
/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
background-color: var(--jp-layout-color2);
}
.jp-Button.jp-mod-minimal {
background: none;
}
.jp-InputGroup {
display: block;
position: relative;
}
.jp-InputGroup input {
box-sizing: border-box;
border: none;
border-radius: 0;
background-color: transparent;
color: var(--jp-ui-font-color0);
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
padding-bottom: 0;
padding-top: 0;
padding-left: 10px;
padding-right: 28px;
position: relative;
width: 100%;
-webkit-appearance: none;
-moz-appearance: none;
appearance: none;
font-size: 14px;
font-weight: 400;
height: 30px;
line-height: 30px;
outline: none;
vertical-align: middle;
}
.jp-InputGroup input:focus {
box-shadow: inset 0 0 0 var(--jp-border-width)
var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-InputGroup input:disabled {
cursor: not-allowed;
resize: block;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input:disabled ~ span {
cursor: not-allowed;
color: var(--jp-ui-font-color2);
}
.jp-InputGroup input::placeholder,
input::placeholder {
color: var(--jp-ui-font-color2);
}
.jp-InputGroupAction {
position: absolute;
bottom: 1px;
right: 0;
padding: 6px;
}
.jp-HTMLSelect.jp-DefaultStyle select {
background-color: initial;
border: none;
border-radius: 0;
box-shadow: none;
color: var(--jp-ui-font-color0);
display: block;
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
height: 24px;
line-height: 14px;
padding: 0 25px 0 10px;
text-align: left;
-moz-appearance: none;
-webkit-appearance: none;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color2);
cursor: not-allowed;
resize: block;
}
.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
cursor: not-allowed;
}
/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color0);
}
select {
box-sizing: border-box;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-StatusBar-Widget {
display: flex;
align-items: center;
background: var(--jp-layout-color2);
min-height: var(--jp-statusbar-height);
justify-content: space-between;
padding: 0 10px;
}
.jp-StatusBar-Left {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-StatusBar-Middle {
display: flex;
align-items: center;
}
.jp-StatusBar-Right {
display: flex;
align-items: center;
flex-direction: row-reverse;
}
.jp-StatusBar-Item {
max-height: var(--jp-statusbar-height);
margin: 0 2px;
height: var(--jp-statusbar-height);
white-space: nowrap;
text-overflow: ellipsis;
color: var(--jp-ui-font-color1);
padding: 0 6px;
}
.jp-mod-highlighted:hover {
background-color: var(--jp-layout-color3);
}
.jp-mod-clicked {
background-color: var(--jp-brand-color1);
}
.jp-mod-clicked:hover {
background-color: var(--jp-brand-color0);
}
.jp-mod-clicked .jp-StatusBar-TextItem {
color: var(--jp-ui-inverse-font-color1);
}
.jp-StatusBar-HoverItem {
box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}
.jp-StatusBar-TextItem {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
line-height: 24px;
color: var(--jp-ui-font-color1);
}
.jp-StatusBar-GroupItem {
display: flex;
align-items: center;
flex-direction: row;
}
.jp-Statusbar-ProgressCircle svg {
display: block;
margin: 0 auto;
width: 16px;
height: 24px;
align-self: normal;
}
.jp-Statusbar-ProgressCircle path {
fill: var(--jp-inverse-layout-color3);
}
.jp-Statusbar-ProgressBar-progress-bar {
height: 10px;
width: 100px;
border: solid 0.25px var(--jp-brand-color2);
border-radius: 3px;
overflow: hidden;
align-self: center;
}
.jp-Statusbar-ProgressBar-progress-bar > div {
background-color: var(--jp-brand-color2);
background-image: linear-gradient(
-45deg,
rgba(255, 255, 255, 0.2) 25%,
transparent 25%,
transparent 50%,
rgba(255, 255, 255, 0.2) 50%,
rgba(255, 255, 255, 0.2) 75%,
transparent 75%,
transparent
);
background-size: 40px 40px;
float: left;
width: 0%;
height: 100%;
font-size: 12px;
line-height: 14px;
color: #fff;
text-align: center;
animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}
.jp-Statusbar-ProgressBar-progress-bar p {
color: var(--jp-ui-font-color1);
font-family: var(--jp-ui-font-family);
font-size: var(--jp-ui-font-size1);
line-height: 10px;
width: 100px;
}
@keyframes jp-Statusbar-ExecutionTime-progress-bar {
0% {
background-position: 0 0;
}
100% {
background-position: 40px 40px;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-commandpalette-search-height: 28px;
}
/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/
.lm-CommandPalette {
padding-bottom: 0;
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/
.jp-ModalCommandPalette {
position: absolute;
z-index: 10000;
top: 38px;
left: 30%;
margin: 0;
padding: 4px;
width: 40%;
box-shadow: var(--jp-elevation-z4);
border-radius: 4px;
background: var(--jp-layout-color0);
}
.jp-ModalCommandPalette .lm-CommandPalette {
max-height: 40vh;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
display: none;
}
.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
margin-left: 4px;
margin-right: 4px;
}
.jp-ModalCommandPalette
.lm-CommandPalette
.lm-CommandPalette-item.lm-mod-disabled {
display: none;
}
/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/
.lm-CommandPalette-search {
padding: 4px;
background-color: var(--jp-layout-color1);
z-index: 2;
}
.lm-CommandPalette-wrapper {
overflow: overlay;
padding: 0 9px;
background-color: var(--jp-input-active-background);
height: 30px;
box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}
.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}
.jp-SearchIconGroup {
color: white;
background-color: var(--jp-brand-color1);
position: absolute;
top: 4px;
right: 4px;
padding: 5px 5px 1px;
}
.jp-SearchIconGroup svg {
height: 20px;
width: 20px;
}
.jp-SearchIconGroup .jp-icon3[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-input {
background: transparent;
width: calc(100% - 18px);
float: left;
border: none;
outline: none;
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
line-height: var(--jp-private-commandpalette-search-height);
}
.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
color: var(--jp-ui-font-color2);
font-size: var(--jp-ui-font-size1);
}
/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/
.lm-CommandPalette-header:first-child {
margin-top: 0;
}
.lm-CommandPalette-header {
border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
color: var(--jp-ui-font-color1);
cursor: pointer;
display: flex;
font-size: var(--jp-ui-font-size0);
font-weight: 600;
letter-spacing: 1px;
margin-top: 8px;
padding: 8px 0 8px 12px;
text-transform: uppercase;
}
.lm-CommandPalette-header.lm-mod-active {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-header > mark {
background-color: transparent;
font-weight: bold;
color: var(--jp-ui-font-color1);
}
.lm-CommandPalette-item {
padding: 4px 12px 4px 4px;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
font-weight: 400;
display: flex;
}
.lm-CommandPalette-item.lm-mod-disabled {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item.lm-mod-active {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-inverse-font-color0);
}
.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
fill: var(--jp-layout-color0);
}
.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
background: var(--jp-layout-color2);
}
.lm-CommandPalette-itemContent {
overflow: hidden;
}
.lm-CommandPalette-itemLabel > mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.lm-CommandPalette-item.lm-mod-disabled mark {
color: var(--jp-ui-font-color2);
}
.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
margin: 0 4px 0 0;
position: relative;
width: 16px;
top: 2px;
flex: 0 0 auto;
}
.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
opacity: 0.6;
}
.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
flex: 0 0 auto;
}
.lm-CommandPalette-itemCaption {
display: none;
}
.lm-CommandPalette-content {
background-color: var(--jp-layout-color1);
}
.lm-CommandPalette-content:empty::after {
content: 'No results';
margin: auto;
margin-top: 20px;
width: 100px;
display: block;
font-size: var(--jp-ui-font-size2);
font-family: var(--jp-ui-font-family);
font-weight: lighter;
}
.lm-CommandPalette-emptyMessage {
text-align: center;
margin-top: 24px;
line-height: 1.32;
padding: 0 8px;
color: var(--jp-content-font-color3);
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Dialog {
position: absolute;
z-index: 10000;
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
top: 0;
left: 0;
margin: 0;
padding: 0;
width: 100%;
height: 100%;
background: var(--jp-dialog-background);
}
.jp-Dialog-content {
display: flex;
flex-direction: column;
margin-left: auto;
margin-right: auto;
background: var(--jp-layout-color1);
padding: 24px 24px 12px;
min-width: 300px;
min-height: 150px;
max-width: 1000px;
max-height: 500px;
box-sizing: border-box;
box-shadow: var(--jp-elevation-z20);
word-wrap: break-word;
border-radius: var(--jp-border-radius);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color1);
resize: both;
}
.jp-Dialog-content.jp-Dialog-content-small {
max-width: 500px;
}
.jp-Dialog-button {
overflow: visible;
}
button.jp-Dialog-button:focus {
outline: 1px solid var(--jp-brand-color1);
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button:focus::-moz-focus-inner {
border: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline-offset: 4px;
-moz-outline-radius: 0;
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}
button.jp-Dialog-close-button {
padding: 0;
height: 100%;
min-width: unset;
min-height: unset;
}
.jp-Dialog-header {
display: flex;
justify-content: space-between;
flex: 0 0 auto;
padding-bottom: 12px;
font-size: var(--jp-ui-font-size3);
font-weight: 400;
color: var(--jp-ui-font-color1);
}
.jp-Dialog-body {
display: flex;
flex-direction: column;
flex: 1 1 auto;
font-size: var(--jp-ui-font-size1);
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
overflow: auto;
}
.jp-Dialog-footer {
display: flex;
flex-direction: row;
justify-content: flex-end;
align-items: center;
flex: 0 0 auto;
margin-left: -12px;
margin-right: -12px;
padding: 12px;
}
.jp-Dialog-checkbox {
padding-right: 5px;
}
.jp-Dialog-checkbox > input:focus-visible {
outline: 1px solid var(--jp-input-active-border-color);
outline-offset: 1px;
}
.jp-Dialog-spacer {
flex: 1 1 auto;
}
.jp-Dialog-title {
overflow: hidden;
white-space: nowrap;
text-overflow: ellipsis;
}
.jp-Dialog-body > .jp-select-wrapper {
width: 100%;
}
.jp-Dialog-body > button {
padding: 0 16px;
}
.jp-Dialog-body > label {
line-height: 1.4;
color: var(--jp-ui-font-color0);
}
.jp-Dialog-button.jp-mod-styled:not(:last-child) {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Input-Boolean-Dialog {
flex-direction: row-reverse;
align-items: end;
width: 100%;
}
.jp-Input-Boolean-Dialog > label {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MainAreaWidget > :focus {
outline: none;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error {
padding: 6px;
}
.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
width: auto;
padding: 10px;
background: var(--jp-error-color3);
border: var(--jp-border-width) solid var(--jp-error-color1);
border-radius: var(--jp-border-radius);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
white-space: pre-wrap;
word-wrap: break-word;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/**
* google-material-color v1.2.6
* https://github.com/danlevan/google-material-color
*/
:root {
--md-red-50: #ffebee;
--md-red-100: #ffcdd2;
--md-red-200: #ef9a9a;
--md-red-300: #e57373;
--md-red-400: #ef5350;
--md-red-500: #f44336;
--md-red-600: #e53935;
--md-red-700: #d32f2f;
--md-red-800: #c62828;
--md-red-900: #b71c1c;
--md-red-A100: #ff8a80;
--md-red-A200: #ff5252;
--md-red-A400: #ff1744;
--md-red-A700: #d50000;
--md-pink-50: #fce4ec;
--md-pink-100: #f8bbd0;
--md-pink-200: #f48fb1;
--md-pink-300: #f06292;
--md-pink-400: #ec407a;
--md-pink-500: #e91e63;
--md-pink-600: #d81b60;
--md-pink-700: #c2185b;
--md-pink-800: #ad1457;
--md-pink-900: #880e4f;
--md-pink-A100: #ff80ab;
--md-pink-A200: #ff4081;
--md-pink-A400: #f50057;
--md-pink-A700: #c51162;
--md-purple-50: #f3e5f5;
--md-purple-100: #e1bee7;
--md-purple-200: #ce93d8;
--md-purple-300: #ba68c8;
--md-purple-400: #ab47bc;
--md-purple-500: #9c27b0;
--md-purple-600: #8e24aa;
--md-purple-700: #7b1fa2;
--md-purple-800: #6a1b9a;
--md-purple-900: #4a148c;
--md-purple-A100: #ea80fc;
--md-purple-A200: #e040fb;
--md-purple-A400: #d500f9;
--md-purple-A700: #a0f;
--md-deep-purple-50: #ede7f6;
--md-deep-purple-100: #d1c4e9;
--md-deep-purple-200: #b39ddb;
--md-deep-purple-300: #9575cd;
--md-deep-purple-400: #7e57c2;
--md-deep-purple-500: #673ab7;
--md-deep-purple-600: #5e35b1;
--md-deep-purple-700: #512da8;
--md-deep-purple-800: #4527a0;
--md-deep-purple-900: #311b92;
--md-deep-purple-A100: #b388ff;
--md-deep-purple-A200: #7c4dff;
--md-deep-purple-A400: #651fff;
--md-deep-purple-A700: #6200ea;
--md-indigo-50: #e8eaf6;
--md-indigo-100: #c5cae9;
--md-indigo-200: #9fa8da;
--md-indigo-300: #7986cb;
--md-indigo-400: #5c6bc0;
--md-indigo-500: #3f51b5;
--md-indigo-600: #3949ab;
--md-indigo-700: #303f9f;
--md-indigo-800: #283593;
--md-indigo-900: #1a237e;
--md-indigo-A100: #8c9eff;
--md-indigo-A200: #536dfe;
--md-indigo-A400: #3d5afe;
--md-indigo-A700: #304ffe;
--md-blue-50: #e3f2fd;
--md-blue-100: #bbdefb;
--md-blue-200: #90caf9;
--md-blue-300: #64b5f6;
--md-blue-400: #42a5f5;
--md-blue-500: #2196f3;
--md-blue-600: #1e88e5;
--md-blue-700: #1976d2;
--md-blue-800: #1565c0;
--md-blue-900: #0d47a1;
--md-blue-A100: #82b1ff;
--md-blue-A200: #448aff;
--md-blue-A400: #2979ff;
--md-blue-A700: #2962ff;
--md-light-blue-50: #e1f5fe;
--md-light-blue-100: #b3e5fc;
--md-light-blue-200: #81d4fa;
--md-light-blue-300: #4fc3f7;
--md-light-blue-400: #29b6f6;
--md-light-blue-500: #03a9f4;
--md-light-blue-600: #039be5;
--md-light-blue-700: #0288d1;
--md-light-blue-800: #0277bd;
--md-light-blue-900: #01579b;
--md-light-blue-A100: #80d8ff;
--md-light-blue-A200: #40c4ff;
--md-light-blue-A400: #00b0ff;
--md-light-blue-A700: #0091ea;
--md-cyan-50: #e0f7fa;
--md-cyan-100: #b2ebf2;
--md-cyan-200: #80deea;
--md-cyan-300: #4dd0e1;
--md-cyan-400: #26c6da;
--md-cyan-500: #00bcd4;
--md-cyan-600: #00acc1;
--md-cyan-700: #0097a7;
--md-cyan-800: #00838f;
--md-cyan-900: #006064;
--md-cyan-A100: #84ffff;
--md-cyan-A200: #18ffff;
--md-cyan-A400: #00e5ff;
--md-cyan-A700: #00b8d4;
--md-teal-50: #e0f2f1;
--md-teal-100: #b2dfdb;
--md-teal-200: #80cbc4;
--md-teal-300: #4db6ac;
--md-teal-400: #26a69a;
--md-teal-500: #009688;
--md-teal-600: #00897b;
--md-teal-700: #00796b;
--md-teal-800: #00695c;
--md-teal-900: #004d40;
--md-teal-A100: #a7ffeb;
--md-teal-A200: #64ffda;
--md-teal-A400: #1de9b6;
--md-teal-A700: #00bfa5;
--md-green-50: #e8f5e9;
--md-green-100: #c8e6c9;
--md-green-200: #a5d6a7;
--md-green-300: #81c784;
--md-green-400: #66bb6a;
--md-green-500: #4caf50;
--md-green-600: #43a047;
--md-green-700: #388e3c;
--md-green-800: #2e7d32;
--md-green-900: #1b5e20;
--md-green-A100: #b9f6ca;
--md-green-A200: #69f0ae;
--md-green-A400: #00e676;
--md-green-A700: #00c853;
--md-light-green-50: #f1f8e9;
--md-light-green-100: #dcedc8;
--md-light-green-200: #c5e1a5;
--md-light-green-300: #aed581;
--md-light-green-400: #9ccc65;
--md-light-green-500: #8bc34a;
--md-light-green-600: #7cb342;
--md-light-green-700: #689f38;
--md-light-green-800: #558b2f;
--md-light-green-900: #33691e;
--md-light-green-A100: #ccff90;
--md-light-green-A200: #b2ff59;
--md-light-green-A400: #76ff03;
--md-light-green-A700: #64dd17;
--md-lime-50: #f9fbe7;
--md-lime-100: #f0f4c3;
--md-lime-200: #e6ee9c;
--md-lime-300: #dce775;
--md-lime-400: #d4e157;
--md-lime-500: #cddc39;
--md-lime-600: #c0ca33;
--md-lime-700: #afb42b;
--md-lime-800: #9e9d24;
--md-lime-900: #827717;
--md-lime-A100: #f4ff81;
--md-lime-A200: #eeff41;
--md-lime-A400: #c6ff00;
--md-lime-A700: #aeea00;
--md-yellow-50: #fffde7;
--md-yellow-100: #fff9c4;
--md-yellow-200: #fff59d;
--md-yellow-300: #fff176;
--md-yellow-400: #ffee58;
--md-yellow-500: #ffeb3b;
--md-yellow-600: #fdd835;
--md-yellow-700: #fbc02d;
--md-yellow-800: #f9a825;
--md-yellow-900: #f57f17;
--md-yellow-A100: #ffff8d;
--md-yellow-A200: #ff0;
--md-yellow-A400: #ffea00;
--md-yellow-A700: #ffd600;
--md-amber-50: #fff8e1;
--md-amber-100: #ffecb3;
--md-amber-200: #ffe082;
--md-amber-300: #ffd54f;
--md-amber-400: #ffca28;
--md-amber-500: #ffc107;
--md-amber-600: #ffb300;
--md-amber-700: #ffa000;
--md-amber-800: #ff8f00;
--md-amber-900: #ff6f00;
--md-amber-A100: #ffe57f;
--md-amber-A200: #ffd740;
--md-amber-A400: #ffc400;
--md-amber-A700: #ffab00;
--md-orange-50: #fff3e0;
--md-orange-100: #ffe0b2;
--md-orange-200: #ffcc80;
--md-orange-300: #ffb74d;
--md-orange-400: #ffa726;
--md-orange-500: #ff9800;
--md-orange-600: #fb8c00;
--md-orange-700: #f57c00;
--md-orange-800: #ef6c00;
--md-orange-900: #e65100;
--md-orange-A100: #ffd180;
--md-orange-A200: #ffab40;
--md-orange-A400: #ff9100;
--md-orange-A700: #ff6d00;
--md-deep-orange-50: #fbe9e7;
--md-deep-orange-100: #ffccbc;
--md-deep-orange-200: #ffab91;
--md-deep-orange-300: #ff8a65;
--md-deep-orange-400: #ff7043;
--md-deep-orange-500: #ff5722;
--md-deep-orange-600: #f4511e;
--md-deep-orange-700: #e64a19;
--md-deep-orange-800: #d84315;
--md-deep-orange-900: #bf360c;
--md-deep-orange-A100: #ff9e80;
--md-deep-orange-A200: #ff6e40;
--md-deep-orange-A400: #ff3d00;
--md-deep-orange-A700: #dd2c00;
--md-brown-50: #efebe9;
--md-brown-100: #d7ccc8;
--md-brown-200: #bcaaa4;
--md-brown-300: #a1887f;
--md-brown-400: #8d6e63;
--md-brown-500: #795548;
--md-brown-600: #6d4c41;
--md-brown-700: #5d4037;
--md-brown-800: #4e342e;
--md-brown-900: #3e2723;
--md-grey-50: #fafafa;
--md-grey-100: #f5f5f5;
--md-grey-200: #eee;
--md-grey-300: #e0e0e0;
--md-grey-400: #bdbdbd;
--md-grey-500: #9e9e9e;
--md-grey-600: #757575;
--md-grey-700: #616161;
--md-grey-800: #424242;
--md-grey-900: #212121;
--md-blue-grey-50: #eceff1;
--md-blue-grey-100: #cfd8dc;
--md-blue-grey-200: #b0bec5;
--md-blue-grey-300: #90a4ae;
--md-blue-grey-400: #78909c;
--md-blue-grey-500: #607d8b;
--md-blue-grey-600: #546e7a;
--md-blue-grey-700: #455a64;
--md-blue-grey-800: #37474f;
--md-blue-grey-900: #263238;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/
:root {
/* This is the padding value to fill the gaps between lines containing spans with background color. */
--jp-private-code-span-padding: calc(
(var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
);
}
.jp-RenderedText {
text-align: left;
padding-left: var(--jp-code-padding);
line-height: var(--jp-code-line-height);
font-family: var(--jp-code-font-family);
}
.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
border: none;
margin: 0;
padding: 0;
}
.jp-RenderedText pre a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedText pre a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
color: #3e424d;
}
.jp-RenderedText pre .ansi-red-fg {
color: #e75c58;
}
.jp-RenderedText pre .ansi-green-fg {
color: #00a250;
}
.jp-RenderedText pre .ansi-yellow-fg {
color: #ddb62b;
}
.jp-RenderedText pre .ansi-blue-fg {
color: #208ffb;
}
.jp-RenderedText pre .ansi-magenta-fg {
color: #d160c4;
}
.jp-RenderedText pre .ansi-cyan-fg {
color: #60c6c8;
}
.jp-RenderedText pre .ansi-white-fg {
color: #c5c1b4;
}
.jp-RenderedText pre .ansi-black-bg {
background-color: #3e424d;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-bg {
background-color: #e75c58;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-bg {
background-color: #00a250;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-bg {
background-color: #ddb62b;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-bg {
background-color: #208ffb;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-bg {
background-color: #d160c4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-bg {
background-color: #60c6c8;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-bg {
background-color: #c5c1b4;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-black-intense-fg {
color: #282c36;
}
.jp-RenderedText pre .ansi-red-intense-fg {
color: #b22b31;
}
.jp-RenderedText pre .ansi-green-intense-fg {
color: #007427;
}
.jp-RenderedText pre .ansi-yellow-intense-fg {
color: #b27d12;
}
.jp-RenderedText pre .ansi-blue-intense-fg {
color: #0065ca;
}
.jp-RenderedText pre .ansi-magenta-intense-fg {
color: #a03196;
}
.jp-RenderedText pre .ansi-cyan-intense-fg {
color: #258f8f;
}
.jp-RenderedText pre .ansi-white-intense-fg {
color: #a1a6b2;
}
.jp-RenderedText pre .ansi-black-intense-bg {
background-color: #282c36;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-red-intense-bg {
background-color: #b22b31;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-green-intense-bg {
background-color: #007427;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-yellow-intense-bg {
background-color: #b27d12;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-blue-intense-bg {
background-color: #0065ca;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-magenta-intense-bg {
background-color: #a03196;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-cyan-intense-bg {
background-color: #258f8f;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-white-intense-bg {
background-color: #a1a6b2;
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-default-inverse-fg {
color: var(--jp-ui-inverse-font-color0);
}
.jp-RenderedText pre .ansi-default-inverse-bg {
background-color: var(--jp-inverse-layout-color0);
padding: var(--jp-private-code-span-padding) 0;
}
.jp-RenderedText pre .ansi-bold {
font-weight: bold;
}
.jp-RenderedText pre .ansi-underline {
text-decoration: underline;
}
.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
background: var(--jp-rendermime-error-background);
padding-top: var(--jp-code-padding);
}
/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/
.jp-RenderedLatex {
color: var(--jp-content-font-color1);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
}
/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
padding: var(--jp-code-padding);
text-align: left;
}
/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/
.jp-RenderedHTMLCommon {
color: var(--jp-content-font-color1);
font-family: var(--jp-content-font-family);
font-size: var(--jp-content-font-size1);
line-height: var(--jp-content-line-height);
/* Give a bit more R padding on Markdown text to keep line lengths reasonable */
padding-right: 20px;
}
.jp-RenderedHTMLCommon em {
font-style: italic;
}
.jp-RenderedHTMLCommon strong {
font-weight: bold;
}
.jp-RenderedHTMLCommon u {
text-decoration: underline;
}
.jp-RenderedHTMLCommon a:link {
text-decoration: none;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:hover {
text-decoration: underline;
color: var(--jp-content-link-color);
}
.jp-RenderedHTMLCommon a:visited {
text-decoration: none;
color: var(--jp-content-link-color);
}
/* Headings */
.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
line-height: var(--jp-content-heading-line-height);
font-weight: var(--jp-content-heading-font-weight);
font-style: normal;
margin: var(--jp-content-heading-margin-top) 0
var(--jp-content-heading-margin-bottom) 0;
}
.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}
.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}
.jp-RenderedHTMLCommon h1 {
font-size: var(--jp-content-font-size5);
}
.jp-RenderedHTMLCommon h2 {
font-size: var(--jp-content-font-size4);
}
.jp-RenderedHTMLCommon h3 {
font-size: var(--jp-content-font-size3);
}
.jp-RenderedHTMLCommon h4 {
font-size: var(--jp-content-font-size2);
}
.jp-RenderedHTMLCommon h5 {
font-size: var(--jp-content-font-size1);
}
.jp-RenderedHTMLCommon h6 {
font-size: var(--jp-content-font-size0);
}
/* Lists */
/* stylelint-disable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
padding-left: 2em;
}
.jp-RenderedHTMLCommon ul {
list-style: disc;
}
.jp-RenderedHTMLCommon ul ul {
list-style: square;
}
.jp-RenderedHTMLCommon ul ul ul {
list-style: circle;
}
.jp-RenderedHTMLCommon ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol ol {
list-style: upper-alpha;
}
.jp-RenderedHTMLCommon ol ol ol {
list-style: lower-alpha;
}
.jp-RenderedHTMLCommon ol ol ol ol {
list-style: lower-roman;
}
.jp-RenderedHTMLCommon ol ol ol ol ol {
list-style: decimal;
}
.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
margin-bottom: 0;
}
/* stylelint-enable selector-max-type, selector-max-compound-selectors */
.jp-RenderedHTMLCommon hr {
color: var(--jp-border-color2);
background-color: var(--jp-border-color1);
margin-top: 1em;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon > pre {
margin: 1.5em 2em;
}
.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
border: 0;
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
font-family: var(--jp-code-font-family);
font-size: inherit;
line-height: var(--jp-code-line-height);
padding: 0;
white-space: pre-wrap;
}
.jp-RenderedHTMLCommon :not(pre) > code {
background-color: var(--jp-layout-color2);
padding: 1px 5px;
}
/* Tables */
.jp-RenderedHTMLCommon table {
border-collapse: collapse;
border-spacing: 0;
border: none;
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
table-layout: fixed;
margin-left: auto;
margin-bottom: 1em;
margin-right: auto;
}
.jp-RenderedHTMLCommon thead {
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
vertical-align: bottom;
}
.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
vertical-align: middle;
padding: 0.5em;
line-height: normal;
white-space: normal;
max-width: none;
border: none;
}
.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
max-width: none;
}
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
text-align: right;
}
.jp-RenderedHTMLCommon th {
font-weight: bold;
}
.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
background: var(--jp-layout-color0);
}
.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
background: var(--jp-rendermime-table-row-background);
}
.jp-RenderedHTMLCommon tbody tr:hover {
background: var(--jp-rendermime-table-row-hover-background);
}
.jp-RenderedHTMLCommon p {
text-align: left;
margin: 0;
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon img {
-moz-force-broken-image-icon: 1;
}
/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
display: block;
margin-left: 0;
margin-right: 0;
margin-bottom: 1em;
}
/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
background-color: var(--jp-inverse-layout-color1);
}
[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
background-color: var(--jp-inverse-layout-color1);
}
.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
max-width: 100%;
height: auto;
}
.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
max-width: none;
}
.jp-RenderedHTMLCommon .alert {
padding: var(--jp-notebook-padding);
border: var(--jp-border-width) solid transparent;
border-radius: var(--jp-border-radius);
margin-bottom: 1em;
}
.jp-RenderedHTMLCommon .alert-info {
color: var(--jp-info-color0);
background-color: var(--jp-info-color3);
border-color: var(--jp-info-color2);
}
.jp-RenderedHTMLCommon .alert-info hr {
border-color: var(--jp-info-color3);
}
.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-warning {
color: var(--jp-warn-color0);
background-color: var(--jp-warn-color3);
border-color: var(--jp-warn-color2);
}
.jp-RenderedHTMLCommon .alert-warning hr {
border-color: var(--jp-warn-color3);
}
.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-success {
color: var(--jp-success-color0);
background-color: var(--jp-success-color3);
border-color: var(--jp-success-color2);
}
.jp-RenderedHTMLCommon .alert-success hr {
border-color: var(--jp-success-color3);
}
.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon .alert-danger {
color: var(--jp-error-color0);
background-color: var(--jp-error-color3);
border-color: var(--jp-error-color2);
}
.jp-RenderedHTMLCommon .alert-danger hr {
border-color: var(--jp-error-color3);
}
.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
margin-bottom: 0;
}
.jp-RenderedHTMLCommon blockquote {
margin: 1em 2em;
padding: 0 1em;
border-left: 5px solid var(--jp-border-color2);
}
a.jp-InternalAnchorLink {
visibility: hidden;
margin-left: 8px;
color: var(--md-blue-800);
}
h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
visibility: visible;
}
.jp-RenderedHTMLCommon kbd {
background-color: var(--jp-rendermime-table-row-background);
border: 1px solid var(--jp-border-color0);
border-bottom-color: var(--jp-border-color2);
border-radius: 3px;
box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
display: inline-block;
font-size: var(--jp-ui-font-size0);
line-height: 1em;
padding: 0.2em 0.5em;
}
/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
* At the bottom of cells this is a bit too much as there is also spacing
* between cells. Going all the way to 0 gets too tight between markdown and
* code cells.
*/
.jp-RenderedHTMLCommon > *:last-child {
margin-bottom: 0.5em;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/
.lm-cursor-backdrop {
position: fixed;
width: 200px;
height: 200px;
margin-top: -100px;
margin-left: -100px;
will-change: transform;
z-index: 100;
}
.lm-mod-drag-image {
will-change: transform;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-lineFormSearch {
padding: 4px 12px;
background-color: var(--jp-layout-color2);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
font-size: var(--jp-ui-font-size1);
}
.jp-lineFormCaption {
font-size: var(--jp-ui-font-size0);
line-height: var(--jp-ui-font-size1);
margin-top: 4px;
color: var(--jp-ui-font-color0);
}
.jp-baseLineForm {
border: none;
border-radius: 0;
position: absolute;
background-size: 16px;
background-repeat: no-repeat;
background-position: center;
outline: none;
}
.jp-lineFormButtonContainer {
top: 4px;
right: 8px;
height: 24px;
padding: 0 12px;
width: 12px;
}
.jp-lineFormButtonIcon {
top: 0;
right: 0;
background-color: var(--jp-brand-color1);
height: 100%;
width: 100%;
box-sizing: border-box;
padding: 4px 6px;
}
.jp-lineFormButton {
top: 0;
right: 0;
background-color: transparent;
height: 100%;
width: 100%;
box-sizing: border-box;
}
.jp-lineFormWrapper {
overflow: hidden;
padding: 0 8px;
border: 1px solid var(--jp-border-color0);
background-color: var(--jp-input-active-background);
height: 22px;
}
.jp-lineFormWrapperFocusWithin {
border: var(--jp-border-width) solid var(--md-blue-500);
box-shadow: inset 0 0 4px var(--md-blue-300);
}
.jp-lineFormInput {
background: transparent;
width: 200px;
height: 100%;
border: none;
outline: none;
color: var(--jp-ui-font-color0);
line-height: 28px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-JSONEditor {
display: flex;
flex-direction: column;
width: 100%;
}
.jp-JSONEditor-host {
flex: 1 1 auto;
border: var(--jp-border-width) solid var(--jp-input-border-color);
border-radius: 0;
background: var(--jp-layout-color0);
min-height: 50px;
padding: 1px;
}
.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
border-color: red;
outline-color: red;
}
.jp-JSONEditor-header {
display: flex;
flex: 1 0 auto;
padding: 0 0 0 12px;
}
.jp-JSONEditor-header label {
flex: 0 0 auto;
}
.jp-JSONEditor-commitButton {
height: 16px;
width: 16px;
background-size: 18px;
background-repeat: no-repeat;
background-position: center;
}
.jp-JSONEditor-host.jp-mod-focused {
background-color: var(--jp-input-active-background);
border: 1px solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
.jp-Editor.jp-mod-dropTarget {
border: var(--jp-border-width) solid var(--jp-input-active-border-color);
box-shadow: var(--jp-input-box-shadow);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
border: none;
outline: none;
color: var(--jp-ui-font-color0);
font-size: var(--jp-ui-font-size1);
background-color: var(--jp-layout-color0);
font-family: var(--jp-ui-font-family);
padding: 2px 1px;
resize: none;
}
.jp-DocumentSearch-overlay {
position: absolute;
background-color: var(--jp-toolbar-background);
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
top: 0;
right: 0;
z-index: 7;
min-width: 405px;
padding: 2px;
font-size: var(--jp-ui-font-size1);
--jp-private-document-search-button-height: 20px;
}
.jp-DocumentSearch-overlay button {
background-color: var(--jp-toolbar-background);
outline: 0;
}
.jp-DocumentSearch-overlay button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-overlay button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-overlay-row {
display: flex;
align-items: center;
margin-bottom: 2px;
}
.jp-DocumentSearch-button-content {
display: inline-block;
cursor: pointer;
box-sizing: border-box;
width: 100%;
height: 100%;
}
.jp-DocumentSearch-button-content svg {
width: 100%;
height: 100%;
}
.jp-DocumentSearch-input-wrapper {
border: var(--jp-border-width) solid var(--jp-border-color0);
display: flex;
background-color: var(--jp-layout-color0);
margin: 2px;
}
.jp-DocumentSearch-input-wrapper:focus-within {
border-color: var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
all: initial;
overflow: hidden;
display: inline-block;
border: none;
box-sizing: border-box;
}
.jp-DocumentSearch-toggle-wrapper {
width: 14px;
height: 14px;
}
.jp-DocumentSearch-button-wrapper {
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
}
.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
outline: var(--jp-border-width) solid
var(--jp-cell-editor-active-border-color);
outline-offset: -1px;
}
.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
outline: none;
}
.jp-DocumentSearch-toggle-placeholder {
width: 5px;
}
.jp-DocumentSearch-input-button::before {
display: block;
padding-top: 100%;
}
.jp-DocumentSearch-input-button-off {
opacity: var(--jp-search-toggle-off-opacity);
}
.jp-DocumentSearch-input-button-off:hover {
opacity: var(--jp-search-toggle-hover-opacity);
}
.jp-DocumentSearch-input-button-on {
opacity: var(--jp-search-toggle-on-opacity);
}
.jp-DocumentSearch-index-counter {
padding-left: 10px;
padding-right: 10px;
user-select: none;
min-width: 35px;
display: inline-block;
}
.jp-DocumentSearch-up-down-wrapper {
display: inline-block;
padding-right: 2px;
margin-left: auto;
white-space: nowrap;
}
.jp-DocumentSearch-spacer {
margin-left: auto;
}
.jp-DocumentSearch-up-down-wrapper button {
outline: 0;
border: none;
width: var(--jp-private-document-search-button-height);
height: var(--jp-private-document-search-button-height);
vertical-align: middle;
margin: 1px 5px 2px;
}
.jp-DocumentSearch-up-down-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-up-down-button:active {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-filter-button {
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-filter-button:hover {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled {
background-color: var(--jp-layout-color2);
}
.jp-DocumentSearch-filter-button-enabled:hover {
background-color: var(--jp-layout-color3);
}
.jp-DocumentSearch-search-options {
padding: 0 8px;
margin-left: 3px;
width: 100%;
display: grid;
justify-content: start;
grid-template-columns: 1fr 1fr;
align-items: center;
justify-items: stretch;
}
.jp-DocumentSearch-search-filter-disabled {
color: var(--jp-ui-font-color2);
}
.jp-DocumentSearch-search-filter {
display: flex;
align-items: center;
user-select: none;
}
.jp-DocumentSearch-regex-error {
color: var(--jp-error-color0);
}
.jp-DocumentSearch-replace-button-wrapper {
overflow: hidden;
display: inline-block;
box-sizing: border-box;
border: var(--jp-border-width) solid var(--jp-border-color0);
margin: auto 2px;
padding: 1px 4px;
height: calc(var(--jp-private-document-search-button-height) + 2px);
}
.jp-DocumentSearch-replace-button-wrapper:focus {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}
.jp-DocumentSearch-replace-button {
display: inline-block;
text-align: center;
cursor: pointer;
box-sizing: border-box;
color: var(--jp-ui-font-color1);
/* height - 2 * (padding of wrapper) */
line-height: calc(var(--jp-private-document-search-button-height) - 2px);
width: 100%;
height: 100%;
}
.jp-DocumentSearch-replace-button:focus {
outline: none;
}
.jp-DocumentSearch-replace-wrapper-class {
margin-left: 14px;
display: flex;
}
.jp-DocumentSearch-replace-toggle {
border: none;
background-color: var(--jp-toolbar-background);
border-radius: var(--jp-border-radius);
}
.jp-DocumentSearch-replace-toggle:hover {
background-color: var(--jp-layout-color2);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.cm-editor {
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
border: 0;
border-radius: 0;
height: auto;
/* Changed to auto to autogrow */
}
.cm-editor pre {
padding: 0 var(--jp-code-padding);
}
.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
background-color: var(--jp-layout-color0);
color: var(--jp-content-font-color1);
}
.jp-CodeMirrorEditor {
cursor: text;
}
/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width1) solid
var(--jp-editor-cursor-color);
}
}
/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
.jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
border-left: var(--jp-code-cursor-width2) solid
var(--jp-editor-cursor-color);
}
}
.cm-editor.jp-mod-readOnly .cm-cursor {
display: none;
}
.jp-CollaboratorCursor {
border-left: 5px solid transparent;
border-right: 5px solid transparent;
border-top: none;
border-bottom: 3px solid;
background-clip: content-box;
margin-left: -5px;
margin-right: -5px;
}
.cm-searching,
.cm-searching span {
/* `.cm-searching span`: we need to override syntax highlighting */
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.cm-searching::selection,
.cm-searching span::selection {
background-color: var(--jp-search-unselected-match-background-color);
color: var(--jp-search-unselected-match-color);
}
.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
background-color: var(--jp-search-selected-match-background-color);
color: var(--jp-search-selected-match-color);
}
.cm-trailingspace {
background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
background-position: center left;
background-repeat: repeat-x;
}
.jp-CollaboratorCursor-hover {
position: absolute;
z-index: 1;
transform: translateX(-50%);
color: white;
border-radius: 3px;
padding-left: 4px;
padding-right: 4px;
padding-top: 1px;
padding-bottom: 1px;
text-align: center;
font-size: var(--jp-ui-font-size1);
white-space: nowrap;
}
.jp-CodeMirror-ruler {
border-left: 1px dashed var(--jp-border-color2);
}
/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
position: relative;
border-left: 1px solid black;
margin-left: -1px;
margin-right: -1px;
box-sizing: border-box;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
white-space: nowrap;
position: absolute;
top: -1.15em;
padding-bottom: 0.05em;
left: -1px;
font-size: 0.95em;
font-family: var(--jp-ui-font-family);
font-weight: bold;
line-height: normal;
user-select: none;
color: white;
padding-left: 2px;
padding-right: 2px;
z-index: 101;
transition: opacity 0.3s ease-in-out;
}
.jp-CodeMirrorEditor .cm-ySelectionInfo {
transition-delay: 0.7s;
opacity: 0;
}
.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
opacity: 1;
transition-delay: 0s;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-MimeDocument {
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-filebrowser-button-height: 28px;
--jp-private-filebrowser-button-width: 48px;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-FileBrowser .jp-SidePanel-content {
display: flex;
flex-direction: column;
}
.jp-FileBrowser-toolbar.jp-Toolbar {
flex-wrap: wrap;
row-gap: 12px;
border-bottom: none;
height: auto;
margin: 8px 12px 0;
box-shadow: none;
padding: 0;
justify-content: flex-start;
}
.jp-FileBrowser-Panel {
flex: 1 1 auto;
display: flex;
flex-direction: column;
}
.jp-BreadCrumbs {
flex: 0 0 auto;
margin: 8px 12px;
}
.jp-BreadCrumbs-item {
margin: 0 2px;
padding: 0 2px;
border-radius: var(--jp-border-radius);
cursor: pointer;
}
.jp-BreadCrumbs-item:hover {
background-color: var(--jp-layout-color2);
}
.jp-BreadCrumbs-item:first-child {
margin-left: 0;
}
.jp-BreadCrumbs-item.jp-mod-dropTarget {
background-color: var(--jp-brand-color2);
opacity: 0.7;
}
/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/
.jp-FileBrowser-toolbar > .jp-Toolbar-item {
flex: 0 0 auto;
padding-left: 0;
padding-right: 2px;
align-items: center;
height: unset;
}
.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
width: 40px;
}
/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/
.jp-FileDialog.jp-mod-conflict input {
color: var(--jp-error-color1);
}
.jp-FileDialog .jp-new-name-title {
margin-top: 12px;
}
.jp-LastModified-hidden {
display: none;
}
.jp-FileSize-hidden {
display: none;
}
.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
display: none;
}
/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/
.jp-DirListing {
flex: 1 1 auto;
display: flex;
flex-direction: column;
outline: 0;
}
.jp-DirListing-header {
flex: 0 0 auto;
display: flex;
flex-direction: row;
align-items: center;
overflow: hidden;
border-top: var(--jp-border-width) solid var(--jp-border-color2);
border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
box-shadow: var(--jp-toolbar-box-shadow);
z-index: 2;
}
.jp-DirListing-headerItem {
padding: 4px 12px 2px;
font-weight: 500;
}
.jp-DirListing-headerItem:hover {
background: var(--jp-layout-color2);
}
.jp-DirListing-headerItem.jp-id-name {
flex: 1 0 84px;
}
.jp-DirListing-headerItem.jp-id-modified {
flex: 0 0 112px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-DirListing-headerItem.jp-id-filesize {
flex: 0 0 75px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
}
.jp-id-narrow {
display: none;
flex: 0 0 5px;
padding: 4px;
border-left: var(--jp-border-width) solid var(--jp-border-color2);
text-align: right;
color: var(--jp-border-color2);
}
.jp-DirListing-narrow .jp-id-narrow {
display: block;
}
.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
display: none;
}
.jp-DirListing-headerItem.jp-mod-selected {
font-weight: 600;
}
/* increase specificity to override bundled default */
.jp-DirListing-content {
flex: 1 1 auto;
margin: 0;
padding: 0;
list-style-type: none;
overflow: auto;
background-color: var(--jp-layout-color1);
}
.jp-DirListing-content mark {
color: var(--jp-ui-font-color0);
background-color: transparent;
font-weight: bold;
}
.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
color: var(--jp-ui-inverse-font-color0);
}
/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
outline: 5px dashed rgba(128, 128, 128, 0.5);
outline-offset: -10px;
cursor: copy;
}
.jp-DirListing-item {
display: flex;
flex-direction: row;
align-items: center;
padding: 4px 12px;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-DirListing-checkboxWrapper {
/* Increases hit area of checkbox. */
padding: 4px;
}
.jp-DirListing-header
.jp-DirListing-checkboxWrapper
+ .jp-DirListing-headerItem {
padding-left: 4px;
}
.jp-DirListing-content .jp-DirListing-checkboxWrapper {
position: relative;
left: -4px;
margin: -4px 0 -4px -8px;
}
.jp-DirListing-checkboxWrapper.jp-mod-visible {
visibility: visible;
}
/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
.jp-DirListing-checkboxWrapper {
visibility: hidden;
}
.jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
.jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
visibility: visible;
}
}
.jp-DirListing-item[data-is-dot] {
opacity: 75%;
}
.jp-DirListing-item.jp-mod-selected {
color: var(--jp-ui-inverse-font-color1);
background: var(--jp-brand-color1);
}
.jp-DirListing-item.jp-mod-dropTarget {
background: var(--jp-brand-color3);
}
.jp-DirListing-item:hover:not(.jp-mod-selected) {
background: var(--jp-layout-color2);
}
.jp-DirListing-itemIcon {
flex: 0 0 20px;
margin-right: 4px;
}
.jp-DirListing-itemText {
flex: 1 0 64px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
user-select: none;
}
.jp-DirListing-itemText:focus {
outline-width: 2px;
outline-color: var(--jp-inverse-layout-color1);
outline-style: solid;
outline-offset: 1px;
}
.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
outline-color: var(--jp-layout-color1);
}
.jp-DirListing-itemModified {
flex: 0 0 125px;
text-align: right;
}
.jp-DirListing-itemFileSize {
flex: 0 0 90px;
text-align: right;
}
.jp-DirListing-editor {
flex: 1 0 64px;
outline: none;
border: none;
color: var(--jp-ui-font-color1);
background-color: var(--jp-layout-color1);
}
.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
color: var(--jp-success-color1);
content: '\25CF';
font-size: 8px;
position: absolute;
left: -8px;
}
.jp-DirListing-item.jp-mod-running.jp-mod-selected
.jp-DirListing-itemIcon::before {
color: var(--jp-ui-inverse-font-color1);
}
.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
font-size: var(--jp-ui-font-size1);
padding-left: 4px;
margin-left: 4px;
width: 160px;
background-color: var(--jp-ui-inverse-font-color2);
box-shadow: var(--jp-elevation-z2);
border-radius: 0;
color: var(--jp-ui-font-color1);
transform: translateX(-40%) translateY(-58%);
}
.jp-Document {
min-width: 120px;
min-height: 120px;
outline: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/
.jp-OutputArea {
overflow-y: auto;
}
.jp-OutputArea-child {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-OutputPrompt {
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-outprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
opacity: var(--jp-cell-prompt-opacity);
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-OutputArea-prompt {
display: table-cell;
vertical-align: top;
}
.jp-OutputArea-output {
display: table-cell;
width: 100%;
height: auto;
overflow: auto;
user-select: text;
-moz-user-select: text;
-webkit-user-select: text;
-ms-user-select: text;
}
.jp-OutputArea .jp-RenderedText {
padding-left: 1ch;
}
/**
* Prompt overlay.
*/
.jp-OutputArea-promptOverlay {
position: absolute;
top: 0;
width: var(--jp-cell-prompt-width);
height: 100%;
opacity: 0.5;
}
.jp-OutputArea-promptOverlay:hover {
background: var(--jp-layout-color2);
box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
cursor: zoom-out;
}
.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
cursor: zoom-in;
}
/**
* Isolated output.
*/
.jp-OutputArea-output.jp-mod-isolated {
width: 100%;
display: block;
}
/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
position: relative;
}
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: transparent;
}
/* pre */
.jp-OutputArea-output pre {
border: none;
margin: 0;
padding: 0;
overflow-x: auto;
overflow-y: auto;
word-break: break-all;
word-wrap: break-word;
white-space: pre-wrap;
}
/* tables */
.jp-OutputArea-output.jp-RenderedHTMLCommon table {
margin-left: 0;
margin-right: 0;
}
/* description lists */
.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
display: block;
}
.jp-OutputArea-output dl {
width: 100%;
overflow: hidden;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dt {
font-weight: bold;
float: left;
width: 20%;
padding: 0;
margin: 0;
}
.jp-OutputArea-output dd {
float: left;
width: 80%;
padding: 0;
margin: 0;
}
.jp-TrimmedOutputs pre {
background: var(--jp-layout-color3);
font-size: calc(var(--jp-code-font-size) * 1.4);
text-align: center;
text-transform: uppercase;
}
/* Hide the gutter in case of
* - nested output areas (e.g. in the case of output widgets)
* - mirrored output areas
*/
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
display: none;
}
/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
padding: 0;
border: 0;
}
/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/
.jp-OutputArea-output.jp-OutputArea-executeResult {
margin-left: 0;
width: 100%;
}
/* Text output with the Out[] prompt needs a top padding to match the
* alignment of the Out[] prompt itself.
*/
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
padding-top: var(--jp-code-padding);
border-top: var(--jp-border-width) solid transparent;
}
/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/
.jp-Stdin-prompt {
color: var(--jp-content-font-color0);
padding-right: var(--jp-code-padding);
vertical-align: baseline;
flex: 0 0 auto;
}
.jp-Stdin-input {
font-family: var(--jp-code-font-family);
font-size: inherit;
color: inherit;
background-color: inherit;
width: 42%;
min-width: 200px;
/* make sure input baseline aligns with prompt */
vertical-align: baseline;
/* padding + margin = 0.5em between prompt and cursor */
padding: 0 0.25em;
margin: 0 0.25em;
flex: 0 0 70%;
}
.jp-Stdin-input::placeholder {
opacity: 0;
}
.jp-Stdin-input:focus {
box-shadow: none;
}
.jp-Stdin-input:focus::placeholder {
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/
.jp-LinkedOutputView .jp-OutputArea {
height: 100%;
display: block;
}
.jp-LinkedOutputView .jp-OutputArea-output:only-child {
height: 100%;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
@media print {
.jp-OutputArea-child {
break-inside: avoid-page;
}
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-OutputPrompt {
display: table-row;
text-align: left;
}
.jp-OutputArea-child .jp-OutputArea-output {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
}
/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
margin: 10px;
text-decoration: none;
cursor: pointer;
}
.jp-TrimmedOutputs > a:hover {
text-decoration: none;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/
:root {
--jp-private-toc-active-width: 4px;
}
.jp-TableOfContents {
display: flex;
flex-direction: column;
background: var(--jp-layout-color1);
color: var(--jp-ui-font-color1);
font-size: var(--jp-ui-font-size1);
height: 100%;
}
.jp-TableOfContents-placeholder {
text-align: center;
}
.jp-TableOfContents-placeholderContent {
color: var(--jp-content-font-color2);
padding: 8px;
}
.jp-TableOfContents-placeholderContent > h3 {
margin-bottom: var(--jp-content-heading-margin-bottom);
}
.jp-TableOfContents .jp-SidePanel-content {
overflow-y: auto;
}
.jp-TableOfContents-tree {
margin: 4px;
}
.jp-TableOfContents ol {
list-style-type: none;
}
/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
/* Align left border with triangle icon center */
padding-left: 11px;
}
.jp-TableOfContents-content {
/* left margin for the active heading indicator */
margin: 0 0 0 var(--jp-private-toc-active-width);
padding: 0;
background-color: var(--jp-layout-color1);
}
.jp-tocItem {
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.jp-tocItem-heading {
display: flex;
cursor: pointer;
}
.jp-tocItem-heading:hover {
background-color: var(--jp-layout-color2);
}
.jp-tocItem-content {
display: block;
padding: 4px 0;
white-space: nowrap;
text-overflow: ellipsis;
overflow-x: hidden;
}
.jp-tocItem-collapser {
height: 20px;
margin: 2px 2px 0;
padding: 0;
background: none;
border: none;
cursor: pointer;
}
.jp-tocItem-collapser:hover {
background-color: var(--jp-layout-color3);
}
/* Active heading indicator */
.jp-tocItem-heading::before {
content: ' ';
background: transparent;
width: var(--jp-private-toc-active-width);
height: 24px;
position: absolute;
left: 0;
border-radius: var(--jp-border-radius);
}
.jp-tocItem-heading.jp-tocItem-active::before {
background-color: var(--jp-brand-color1);
}
.jp-tocItem-heading:hover.jp-tocItem-active::before {
background: var(--jp-brand-color0);
opacity: 1;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-Collapser {
flex: 0 0 var(--jp-cell-collapser-width);
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
border-radius: var(--jp-border-radius);
opacity: 1;
}
.jp-Collapser-child {
display: block;
width: 100%;
box-sizing: border-box;
/* height: 100% doesn't work because the height of its parent is computed from content */
position: absolute;
top: 0;
bottom: 0;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Hiding collapsers in print mode.
Note: input and output wrappers have "display: block" propery in print mode.
*/
@media print {
.jp-Collapser {
display: none;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/
/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
height: 0;
width: 100%;
padding: 0;
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/
/* All input areas */
.jp-InputArea {
display: table;
table-layout: fixed;
width: 100%;
overflow: hidden;
}
.jp-InputArea-editor {
display: table-cell;
overflow: hidden;
vertical-align: top;
/* This is the non-active, default styling */
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
background: var(--jp-cell-editor-background);
}
.jp-InputPrompt {
display: table-cell;
vertical-align: top;
width: var(--jp-cell-prompt-width);
color: var(--jp-cell-inprompt-font-color);
font-family: var(--jp-cell-prompt-font-family);
padding: var(--jp-code-padding);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
opacity: var(--jp-cell-prompt-opacity);
line-height: var(--jp-code-line-height);
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
/* Right align prompt text, don't wrap to handle large prompt numbers */
text-align: right;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
/* Disable text selection */
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
.jp-InputArea-editor {
display: table-row;
margin-left: var(--jp-notebook-padding);
}
.jp-InputPrompt {
display: table-row;
text-align: left;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Placeholder {
display: table;
table-layout: fixed;
width: 100%;
}
.jp-Placeholder-prompt {
display: table-cell;
box-sizing: border-box;
}
.jp-Placeholder-content {
display: table-cell;
padding: 4px 6px;
border: 1px solid transparent;
border-radius: 0;
background: none;
box-sizing: border-box;
cursor: pointer;
}
.jp-Placeholder-contentContainer {
display: flex;
}
.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
border-color: var(--jp-layout-color3);
}
.jp-Placeholder-content .jp-MoreHorizIcon {
width: 32px;
height: 16px;
border: 1px solid transparent;
border-radius: var(--jp-border-radius);
}
.jp-Placeholder-content .jp-MoreHorizIcon:hover {
border: 1px solid var(--jp-border-color1);
box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
background-color: var(--jp-layout-color0);
}
.jp-PlaceholderText {
white-space: nowrap;
overflow-x: hidden;
color: var(--jp-inverse-layout-color3);
font-family: var(--jp-code-font-family);
}
.jp-InputPlaceholder > .jp-Placeholder-content {
border-color: var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-private-cell-scrolling-output-offset: 5px;
}
/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/
.jp-Cell {
padding: var(--jp-cell-padding);
margin: 0;
border: none;
outline: none;
background: transparent;
}
/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: flex;
flex-direction: row;
padding: 0;
margin: 0;
/* Added to reveal the box-shadow on the input and output collapsers. */
overflow: visible;
}
/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
flex: 1 1 auto;
}
/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/
/* Make the output collapser disappear when there is not output, but do so
* in a manner that leaves it in the layout and preserves its width.
*/
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
border: none !important;
background: transparent !important;
}
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
min-height: var(--jp-cell-collapser-min-height);
}
/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/
/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
margin-top: 5px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
overflow-y: auto;
max-height: 24em;
margin-left: var(--jp-private-cell-scrolling-output-offset);
resize: vertical;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
max-height: unset;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
content: ' ';
box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
width: 100%;
height: 100%;
position: sticky;
bottom: 0;
top: 0;
margin-top: -50%;
float: left;
display: block;
pointer-events: none;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
padding-top: 6px;
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
width: calc(
var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
);
}
.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}
/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/
.jp-MarkdownOutput {
display: table-cell;
width: 100%;
margin-top: 0;
margin-bottom: 0;
padding-left: var(--jp-code-padding);
}
.jp-MarkdownOutput.jp-RenderedHTMLCommon {
overflow: auto;
}
/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
display: flex;
min-height: var(--jp-cell-collapser-min-height);
font-size: var(--jp-code-font-size);
position: absolute;
background-color: transparent;
background-size: 25px;
background-repeat: no-repeat;
background-position-x: center;
background-position-y: top;
background-image: var(--jp-icon-caret-down);
right: 0;
top: 0;
bottom: 0;
}
.jp-collapseHeadingButton.jp-mod-collapsed {
background-image: var(--jp-icon-caret-right);
}
/*
set the container font size to match that of content
so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
font-size: var(--jp-content-font-size1);
}
/*
Align collapseHeadingButton with cell top header
The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
font-size: var(--jp-content-font-size5);
background-position-y: calc(0.3 * var(--jp-content-font-size5));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
font-size: var(--jp-content-font-size4);
background-position-y: calc(0.3 * var(--jp-content-font-size4));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
font-size: var(--jp-content-font-size3);
background-position-y: calc(0.3 * var(--jp-content-font-size3));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
font-size: var(--jp-content-font-size2);
background-position-y: calc(0.3 * var(--jp-content-font-size2));
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
font-size: var(--jp-content-font-size1);
background-position-y: top;
}
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
font-size: var(--jp-content-font-size0);
background-position-y: top;
}
/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
display: none;
}
.jp-Notebook.jp-mod-showHiddenCellsButton
:is(.jp-MarkdownCell:hover, .jp-mod-active)
.jp-collapseHeadingButton {
display: flex;
}
/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
margin-top: var(--jp-code-padding);
border: 1px solid var(--jp-border-color2);
background-color: var(--jp-border-color3) !important;
color: var(--jp-content-font-color0) !important;
display: flex;
}
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
background-color: var(--jp-border-color2) !important;
}
.jp-showHiddenCellsButton {
display: none;
}
/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/
/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
:root {
--jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-NotebookPanel-toolbar {
padding: var(--jp-notebook-toolbar-padding);
/* disable paint containment from lumino 2.0 default strict CSS containment */
contain: style size !important;
}
.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
border: none;
box-shadow: none;
}
.jp-Notebook-toolbarCellTypeDropdown select {
height: 24px;
font-size: var(--jp-ui-font-size1);
line-height: 14px;
border-radius: 0;
display: block;
}
.jp-Notebook-toolbarCellTypeDropdown span {
top: 5px !important;
}
.jp-Toolbar-responsive-popup {
position: absolute;
height: fit-content;
display: flex;
flex-direction: row;
flex-wrap: wrap;
justify-content: flex-end;
border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
box-shadow: var(--jp-toolbar-box-shadow);
background: var(--jp-toolbar-background);
min-height: var(--jp-toolbar-micro-height);
padding: var(--jp-notebook-toolbar-padding);
z-index: 1;
right: 0;
top: 0;
}
.jp-Toolbar > .jp-Toolbar-responsive-opener {
margin-left: auto;
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/
.jp-Notebook-ExecutionIndicator {
position: relative;
display: inline-block;
height: 100%;
z-index: 9997;
}
.jp-Notebook-ExecutionIndicator-tooltip {
visibility: hidden;
height: auto;
width: max-content;
width: -moz-max-content;
background-color: var(--jp-layout-color2);
color: var(--jp-ui-font-color1);
text-align: justify;
border-radius: 6px;
padding: 0 5px;
position: fixed;
display: table;
}
.jp-Notebook-ExecutionIndicator-tooltip.up {
transform: translateX(-50%) translateY(-100%) translateY(-32px);
}
.jp-Notebook-ExecutionIndicator-tooltip.down {
transform: translateX(calc(-100% + 16px)) translateY(5px);
}
.jp-Notebook-ExecutionIndicator-tooltip.hidden {
display: none;
}
.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
visibility: visible;
}
.jp-Notebook-ExecutionIndicator span {
font-size: var(--jp-ui-font-size1);
font-family: var(--jp-ui-font-family);
color: var(--jp-ui-font-color1);
line-height: 24px;
display: block;
}
.jp-Notebook-ExecutionIndicator-progress-bar {
display: flex;
justify-content: center;
height: 100%;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
/*
* Execution indicator
*/
.jp-tocItem-content::after {
content: '';
/* Must be identical to form a circle */
width: 12px;
height: 12px;
background: none;
border: none;
position: absolute;
right: 0;
}
.jp-tocItem-content[data-running='0']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background: none;
}
.jp-tocItem-content[data-running='1']::after {
border-radius: 50%;
border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
background-color: var(--jp-inverse-layout-color3);
}
.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
margin-right: 12px;
}
/*
* Copyright (c) Jupyter Development Team.
* Distributed under the terms of the Modified BSD License.
*/
.jp-Notebook-footer {
height: 27px;
margin-left: calc(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding)
);
width: calc(
100% -
(
var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
var(--jp-cell-padding) + var(--jp-cell-padding)
)
);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
color: var(--jp-ui-font-color3);
margin-top: 6px;
background: none;
cursor: pointer;
}
.jp-Notebook-footer:focus {
border-color: var(--jp-cell-editor-active-border-color);
}
/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
.jp-Notebook-footer {
opacity: 0;
}
.jp-Notebook-footer:focus,
.jp-Notebook-footer:hover {
opacity: 1;
}
}
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/
/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/
:root {
--jp-side-by-side-output-size: 1fr;
--jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
--jp-private-notebook-dragImage-width: 304px;
--jp-private-notebook-dragImage-height: 36px;
--jp-private-notebook-selected-color: var(--md-blue-400);
--jp-private-notebook-active-color: var(--md-green-400);
}
/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/
/* stylelint-disable selector-max-class */
.jp-NotebookPanel {
display: block;
height: 100%;
}
.jp-NotebookPanel.jp-Document {
min-width: 240px;
min-height: 120px;
}
.jp-Notebook {
padding: var(--jp-notebook-padding);
outline: none;
overflow: auto;
background: var(--jp-layout-color0);
}
.jp-Notebook.jp-mod-scrollPastEnd::after {
display: block;
content: '';
min-height: var(--jp-notebook-scroll-padding);
}
.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
contain: strict;
}
.jp-Notebook .jp-Cell {
overflow: visible;
}
.jp-Notebook .jp-Cell .jp-InputPrompt {
cursor: move;
}
/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
| - Command
| - Edit
| - Cell
| - None
| - Active (only one can be active)
| - Selected (the cells actions are applied to)
| - Multiselected (when multiple selected, the cursor)
| - No outputs
|----------------------------------------------------------------------------*/
/* Command or edit modes */
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
opacity: var(--jp-cell-prompt-not-active-opacity);
color: var(--jp-cell-prompt-not-active-font-color);
}
/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
background: var(--jp-brand-color1);
}
/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
color: var(--jp-warn-color1);
}
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
color: var(--jp-warn-color1);
content: '•';
}
.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
background: var(--jp-warn-color1);
}
/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
box-shadow: var(--jp-elevation-z2);
background: var(--jp-brand-color1);
opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}
/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
background: var(--jp-brand-color0);
opacity: 1;
}
/* Command mode */
.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
background: var(--jp-notebook-multiselected-color);
}
.jp-Notebook.jp-mod-commandMode
.jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
background: transparent;
}
/* Edit mode */
.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
box-shadow: var(--jp-input-box-shadow);
background-color: var(--jp-cell-editor-active-background);
}
/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/
.jp-Notebook-cell.jp-mod-dropSource {
opacity: 0.5;
}
.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
.jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
border-top-color: var(--jp-private-notebook-selected-color);
border-top-style: solid;
border-top-width: 2px;
}
.jp-dragImage {
display: block;
flex-direction: row;
width: var(--jp-private-notebook-dragImage-width);
height: var(--jp-private-notebook-dragImage-height);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background);
overflow: visible;
}
.jp-dragImage-singlePrompt {
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
.jp-dragImage .jp-dragImage-content {
flex: 1 1 auto;
z-index: 2;
font-size: var(--jp-code-font-size);
font-family: var(--jp-code-font-family);
line-height: var(--jp-code-line-height);
padding: var(--jp-code-padding);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
background: var(--jp-cell-editor-background-color);
color: var(--jp-content-font-color3);
text-align: left;
margin: 4px 4px 4px 0;
}
.jp-dragImage .jp-dragImage-prompt {
flex: 0 0 auto;
min-width: 36px;
color: var(--jp-cell-inprompt-font-color);
padding: var(--jp-code-padding);
padding-left: 12px;
font-family: var(--jp-cell-prompt-font-family);
letter-spacing: var(--jp-cell-prompt-letter-spacing);
line-height: 1.9;
font-size: var(--jp-code-font-size);
border: var(--jp-border-width) solid transparent;
}
.jp-dragImage-multipleBack {
z-index: -1;
position: absolute;
height: 32px;
width: 300px;
top: 8px;
left: 8px;
background: var(--jp-layout-color2);
border: var(--jp-border-width) solid var(--jp-input-border-color);
box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}
/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/
.jp-NotebookTools {
display: block;
min-width: var(--jp-sidebar-min-width);
color: var(--jp-ui-font-color1);
background: var(--jp-layout-color1);
/* This is needed so that all font sizing of children done in ems is
* relative to this base size */
font-size: var(--jp-ui-font-size1);
overflow: auto;
}
.jp-ActiveCellTool {
padding: 12px 0;
display: flex;
}
.jp-ActiveCellTool-Content {
flex: 1 1 auto;
}
.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
background: var(--jp-cell-editor-background);
border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
border-radius: 0;
min-height: 29px;
}
.jp-ActiveCellTool .jp-InputPrompt {
min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}
.jp-ActiveCellTool-CellContent > pre {
padding: 5px 4px;
margin: 0;
white-space: normal;
}
.jp-MetadataEditorTool {
flex-direction: column;
padding: 12px 0;
}
.jp-RankedPanel > :not(:first-child) {
margin-top: 12px;
}
.jp-KeySelector select.jp-mod-styled {
font-size: var(--jp-ui-font-size1);
color: var(--jp-ui-font-color0);
border: var(--jp-border-width) solid var(--jp-border-color1);
}
.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
line-height: 1.4;
}
.jp-NotebookTools .jp-select-wrapper {
margin-top: 4px;
margin-bottom: 0;
}
.jp-NumberSetter input {
width: 100%;
margin-top: 4px;
}
.jp-NotebookTools .jp-Collapse {
margin-top: 16px;
}
/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/
.jp-mod-presentationMode .jp-Notebook {
--jp-content-font-size1: var(--jp-content-presentation-font-size1);
--jp-code-font-size: var(--jp-code-presentation-font-size);
}
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
flex: 0 0 110px;
}
/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
margin-top: 3em;
margin-bottom: 3em;
margin-left: 5%;
margin-right: 5%;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
display: grid;
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-output-size)
);
grid-template-rows: auto minmax(0, 1fr) auto;
grid-template-areas:
'header header header'
'input handle output'
'footer footer footer';
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
grid-template-columns: minmax(0, 1fr) min-content minmax(
0,
var(--jp-side-by-side-resized-cell)
);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
grid-area: header;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
grid-area: input;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
/* overwrite the default margin (no vertical separation needed in side by side move */
margin-top: 0;
grid-area: output;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
grid-area: footer;
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
grid-area: handle;
user-select: none;
display: block;
height: 100%;
cursor: ew-resize;
padding: 0 var(--jp-cell-padding);
}
.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
content: '';
display: block;
background: var(--jp-border-color2);
height: 100%;
width: 5px;
}
.jp-mod-sideBySide.jp-Notebook
.jp-CodeCell.jp-mod-resizedCell
.jp-CellResizeHandle::after {
background: var(--jp-border-color0);
}
.jp-CellResizeHandle {
display: none;
}
/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/
.jp-Cell-Placeholder {
padding-left: 55px;
}
.jp-Cell-Placeholder-wrapper {
background: #fff;
border: 1px solid;
border-color: #e5e6e9 #dfe0e4 #d0d1d5;
border-radius: 4px;
-webkit-border-radius: 4px;
margin: 10px 15px;
}
.jp-Cell-Placeholder-wrapper-inner {
padding: 15px;
position: relative;
}
.jp-Cell-Placeholder-wrapper-body {
background-repeat: repeat;
background-size: 50% auto;
}
.jp-Cell-Placeholder-wrapper-body div {
background: #f6f7f8;
background-image: -webkit-linear-gradient(
left,
#f6f7f8 0%,
#edeef1 20%,
#f6f7f8 40%,
#f6f7f8 100%
);
background-repeat: no-repeat;
background-size: 800px 104px;
height: 104px;
position: absolute;
right: 15px;
left: 15px;
top: 15px;
}
div.jp-Cell-Placeholder-h1 {
top: 20px;
height: 20px;
left: 15px;
width: 150px;
}
div.jp-Cell-Placeholder-h2 {
left: 15px;
top: 50px;
height: 10px;
width: 100px;
}
div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
left: 15px;
right: 15px;
height: 10px;
}
div.jp-Cell-Placeholder-content-1 {
top: 100px;
}
div.jp-Cell-Placeholder-content-2 {
top: 120px;
}
div.jp-Cell-Placeholder-content-3 {
top: 140px;
}
</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.
Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:
* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations
Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/
:root {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
--jp-shadow-base-lightness: 0;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
0 1px 1px 0 var(--jp-shadow-penumbra-color),
0 1px 3px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
0 2px 2px 0 var(--jp-shadow-penumbra-color),
0 1px 5px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
0 4px 5px 0 var(--jp-shadow-penumbra-color),
0 1px 10px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
0 6px 10px 0 var(--jp-shadow-penumbra-color),
0 1px 18px 0 var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
0 8px 10px 1px var(--jp-shadow-penumbra-color),
0 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
0 12px 17px 2px var(--jp-shadow-penumbra-color),
0 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
0 16px 24px 2px var(--jp-shadow-penumbra-color),
0 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
0 20px 31px 3px var(--jp-shadow-penumbra-color),
0 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
0 24px 38px 3px var(--jp-shadow-penumbra-color),
0 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-inverse-border-color: var(--md-grey-600);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
'Segoe UI Symbol';
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-900);
--jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
'Segoe UI Emoji', 'Segoe UI Symbol';
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-900);
--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-900);
--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-inverse-border-color);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #a2f;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #a2f;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/*
RTC user specific colors.
These colors are used for the cursor, username in the editor,
and the icon of the user.
*/
--jp-collaborator-color1: #ffad8e;
--jp-collaborator-color2: #dac83d;
--jp-collaborator-color3: #72dd76;
--jp-collaborator-color4: #00e4d0;
--jp-collaborator-color5: #45d4ff;
--jp-collaborator-color6: #e2b1ff;
--jp-collaborator-color7: #ff9de6;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
/* Button colors */
--jp-accept-color-normal: var(--md-blue-700);
--jp-accept-color-hover: var(--md-blue-800);
--jp-accept-color-active: var(--md-blue-900);
--jp-warn-color-normal: var(--md-red-700);
--jp-warn-color-hover: var(--md-red-800);
--jp-warn-color-active: var(--md-red-900);
--jp-reject-color-normal: var(--md-grey-600);
--jp-reject-color-hover: var(--md-grey-700);
--jp-reject-color-active: var(--md-grey-800);
/* File or activity icons and switch semantic variables */
--jp-jupyter-icon-color: #f37626;
--jp-notebook-icon-color: #f37626;
--jp-json-icon-color: var(--md-orange-700);
--jp-console-icon-background-color: var(--md-blue-700);
--jp-console-icon-color: white;
--jp-terminal-icon-background-color: var(--md-grey-800);
--jp-terminal-icon-color: var(--md-grey-200);
--jp-text-editor-icon-color: var(--md-grey-700);
--jp-inspector-icon-color: var(--md-grey-700);
--jp-switch-color: var(--md-grey-400);
--jp-switch-true-position-color: var(--md-orange-900);
}
</style>
<style type="text/css">
/* Force rendering true colors when outputing to pdf */
* {
-webkit-print-color-adjust: exact;
}
/* Misc */
a.anchor-link {
display: none;
}
/* Input area styling */
.jp-InputArea {
overflow: hidden;
}
.jp-InputArea-editor {
overflow: hidden;
}
.cm-editor.cm-s-jupyter .highlight pre {
/* weird, but --jp-code-padding defined to be 5px but 4px horizontal padding is hardcoded for pre.cm-line */
padding: var(--jp-code-padding) 4px;
margin: 0;
font-family: inherit;
font-size: inherit;
line-height: inherit;
color: inherit;
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.jp-OutputArea-output pre {
line-height: inherit;
font-family: inherit;
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.jp-RenderedText pre {
color: var(--jp-content-font-color1);
font-size: var(--jp-code-font-size);
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/* Hiding the collapser by default */
.jp-Collapser {
display: none;
}
@page {
margin: 0.5in; /* Margin for each printed piece of paper */
}
@media print {
.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
display: block;
}
}
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.jp-RenderedMermaid.jp-mod-warning {
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padding: 0.5em;
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<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
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<h1 id="Twitter-Disaster-Classification-Analysis">Twitter Disaster Classification Analysis<a class="anchor-link" href="#Twitter-Disaster-Classification-Analysis">¶</a></h1><h2 id="Binary-Classification-of-Disaster-Related-Tweets">Binary Classification of Disaster-Related Tweets<a class="anchor-link" href="#Binary-Classification-of-Disaster-Related-Tweets">¶</a></h2>
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<p><strong>Objective:</strong>
Develop a machine learning model to classify tweets as either disaster-related or non-disaster content. This can be tricky, as some words normally disaster-related can be used figuratively in otherwise unrelated sentences.</p>
<p>This problem has important applications, as disaster-relief organisations can use the analysed tweets for early-detection of disasters. News agencies could also get an early lead as well as an insight to the reactions of the people.</p>
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<p>This analysis utilizes data from the <a href="https://www.kaggle.com/competitions/nlp-getting-started">NLP with Disaster Tweets competition</a>.</p>
<p><strong>Dataset Characteristics:</strong></p>
<ul>
<li><strong>id</strong>: Unique tweet identifier</li>
<li><strong>text</strong>: Tweet content</li>
<li><strong>location</strong>: Geographic origin (optional)</li>
<li><strong>keyword</strong>: Significant term from tweet (optional)</li>
<li><strong>target</strong>: Binary classification label (train set only)</li>
</ul>
<p><strong>Composition:</strong></p>
<ul>
<li>Training set contains 7613 samples with balanced classes</li>
<li>Test set includes 3263 samples for prediction</li>
<li>Combination of text and metadata features available</li>
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<p><strong>Import required libraries</strong></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">os</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">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">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.compose</span><span class="w"> </span><span class="kn">import</span> <span class="n">ColumnTransformer</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">RandomForestClassifier</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">f1_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">ConfusionMatrixDisplay</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="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">OrdinalEncoder</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">re</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tensorflow</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">tf</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras</span><span class="w"> </span><span class="kn">import</span> <span class="n">Sequential</span><span class="p">,</span> <span class="n">layers</span><span class="p">,</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Model</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.models</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_model</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.callbacks</span><span class="w"> </span><span class="kn">import</span> <span class="n">EarlyStopping</span><span class="p">,</span> <span class="n">ModelCheckpoint</span><span class="p">,</span> <span class="n">CSVLogger</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.optimizers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Nadam</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.preprocessing.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">Tokenizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.preprocessing.sequence</span><span class="w"> </span><span class="kn">import</span> <span class="n">pad_sequences</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorflow.keras.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">custom_object_scope</span>
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<pre>2025-09-17 10:47:19.818964: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
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<p><strong>Attack strategy:</strong></p>
<ol>
<li>Perform comprehensive data exploration</li>
<li>Implement text preprocessing pipeline</li>
<li>Utilize pretrained word embeddings</li>
<li>Develop and compare multiple model architectures</li>
<li>Tune the hyperparameters of both RNN models</li>
<li>Evaluate performance using F1-score and generate predictions</li>
</ol>
<p><strong>Modeling Strategy:</strong></p>
<ul>
<li>Baseline model using metadata features</li>
<li>RNN architectures with LSTM/GRU cells</li>
<li>Model selection based on F1-score optimization</li>
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<h2 id="Initial-Data-Inspection">Initial Data Inspection<a class="anchor-link" href="#Initial-Data-Inspection">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">if</span> <span class="s1">'KAGGLE_URL_BASE'</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
<span class="n">df_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"/kaggle/input/nlp-getting-started/train.csv"</span><span class="p">)</span>
<span class="n">df_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"/kaggle/input/nlp-getting-started/test.csv"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">DATA_DIR</span> <span class="o">=</span> <span class="s2">"data"</span>
<span class="n">df_train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">"train.csv"</span><span class="p">))</span>
<span class="n">df_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">"test.csv"</span><span class="p">))</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Training dataset overview:'</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">info</span><span class="p">())</span>
<span class="n">df_train</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Training dataset overview:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7613 entries, 0 to 7612
Data columns (total 5 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 7613 non-null int64
1 keyword 7552 non-null object
2 location 5080 non-null object
3 text 7613 non-null object
4 target 7613 non-null int64
dtypes: int64(2), object(3)
memory usage: 297.5+ KB
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<pre>None</pre>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>keyword</th>
<th>location</th>
<th>text</th>
<th>target</th>
</tr>
</thead>
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<tr>
<th>0</th>
<td>1</td>
<td>NaN</td>
<td>NaN</td>
<td>Our Deeds are the Reason of this #earthquake M...</td>
<td>1</td>
</tr>
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<th>1</th>
<td>4</td>
<td>NaN</td>
<td>NaN</td>
<td>Forest fire near La Ronge Sask. Canada</td>
<td>1</td>
</tr>
<tr>
<th>2</th>
<td>5</td>
<td>NaN</td>
<td>NaN</td>
<td>All residents asked to 'shelter in place' are ...</td>
<td>1</td>
</tr>
<tr>
<th>3</th>
<td>6</td>
<td>NaN</td>
<td>NaN</td>
<td>13,000 people receive #wildfires evacuation or...</td>
<td>1</td>
</tr>
<tr>
<th>4</th>
<td>7</td>
<td>NaN</td>
<td>NaN</td>
<td>Just got sent this photo from Ruby #Alaska as ...</td>
<td>1</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Test dataset overview:'</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_test</span><span class="o">.</span><span class="n">info</span><span class="p">())</span>
<span class="n">df_test</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>Test dataset overview:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3263 entries, 0 to 3262
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 3263 non-null int64
1 keyword 3237 non-null object
2 location 2158 non-null object
3 text 3263 non-null object
dtypes: int64(1), object(3)
memory usage: 102.1+ KB
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<pre>None</pre>
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text-align: right;
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</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>keyword</th>
<th>location</th>
<th>text</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>0</td>
<td>NaN</td>
<td>NaN</td>
<td>Just happened a terrible car crash</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>NaN</td>
<td>NaN</td>
<td>Heard about #earthquake is different cities, s...</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>NaN</td>
<td>NaN</td>
<td>there is a forest fire at spot pond, geese are...</td>
</tr>
<tr>
<th>3</th>
<td>9</td>
<td>NaN</td>
<td>NaN</td>
<td>Apocalypse lighting. #Spokane #wildfires</td>
</tr>
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<th>4</th>
<td>11</td>
<td>NaN</td>
<td>NaN</td>
<td>Typhoon Soudelor kills 28 in China and Taiwan</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">'Sample tweet instances:</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_train</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">replace</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="s1">'(id=</span><span class="si">%s</span><span class="s1">, label=</span><span class="si">%s</span><span class="s1">), text: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span><span class="s1">'id'</span><span class="p">],</span> <span class="n">df_train</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span><span class="s1">'target'</span><span class="p">],</span> <span class="n">df_train</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">row</span><span class="p">,</span><span class="s1">'text'</span><span class="p">]))</span>
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<pre>Sample tweet instances:
(id=10605, label=0), text: Have you ever seen the President
who killed your wounded child?
Or the man that crashed your sister's plane
claimin' he was sent of God?
(id=7603, label=0), text: @J3Lyon I'm going to put the FFVII ones out at the weekend so I think Pandemonium! (Don't forget the exclamation mark) would be midweek.
(id=6844, label=0), text: matako_milk: Breaking news! Unconfirmed! I just heard a loud bang nearby. in what appears to be a blast of wind from my neighbour's ass.
(id=680, label=1), text: Suspect in latest US theatre attack had psychological issues http://t.co/OnPnBx0ZEx http://t.co/uM5IcN5Et2
(id=7301, label=1), text: Salem 2 nuclear reactor shut down over electrical circuit failure on pump: The Salem 2 nuclear reactor had bee... http://t.co/5hkGXzJLmX
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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="c1"># Class distribution</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Class distribution:'</span><span class="p">)</span>
<span class="n">df_train</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">to_string</span><span class="p">(</span><span class="n">header</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
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<pre>Class distribution:
0 4342
1 3271
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"/>
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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"># Text length (training)</span>
<span class="n">df_train</span><span class="p">[</span><span class="s1">'text_length'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="s1">'text'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">displot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df_train</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'text_length'</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'target'</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">'Text length by label (training dataset)'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average training text length: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">df_train</span><span class="p">[</span><span class="s1">'text_length'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t\t</span><span class="s1">when label=1: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">df_train</span><span class="p">[</span><span class="n">df_train</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">][</span><span class="s1">'text_length'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t\t</span><span class="s1">when label=0: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">df_train</span><span class="p">[</span><span class="n">df_train</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">][</span><span class="s1">'text_length'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
</pre></div>
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<pre>Average training text length: 101
when label=1: 108
when label=0: 95
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"/>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Text length (test)</span>
<span class="n">df_test</span><span class="p">[</span><span class="s1">'text_length'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_test</span><span class="p">[</span><span class="s1">'text'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">displot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df_test</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'text_length'</span><span class="p">,</span> <span class="n">kde</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">'Text length (test dataset)'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Average training text length: </span><span class="si">%d</span><span class="s1">'</span> <span class="o">%</span> <span class="n">df_test</span><span class="p">[</span><span class="s1">'text_length'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
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<pre>Average training text length: 102
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ZMBP/74YwwfPhzdunXD+PHj0aFDB5w9exbr16+HXq/HihUrGvV5rFmzBoMGDbJfpiAXJt3EcqKmu9otRosXLxbJycnC09NT+Pr6ih49eog///nP4ty5c6Kurk7069dPREZGipKSEof3vfPOOwKA+OKLL+yvff/99yIpKUmo1err3o51+S1YNuvXrxepqanCz89PeHh4iPj4eDFu3Dixc+dO+z5paWnC29v7imPOnj37ijYWFBSIRx55RPj6+go/Pz8xbtw48dtvvwkA4vPPP7fvV1dXJ6ZMmSJCQkKEQqGwH8d2C9Zrr712xflwlVuLLvfNN98IhUIhcnJyHF4/fPiwuOWWW4Snp6cA4HA7Vl5enkhPTxdRUVFCo9GIsLAwcccdd4jFixfb9/n73/8ubrnlFhEUFCR0Op2Ij48X06dPF6WlpQ7nefHFF0WHDh2EUqls1O1YH3zwgYiLixM6nU707dtXbNy4Udx6660Ot2BZrVbx8ssvi5iYGKHT6USfPn3EypUrRVpamoiJiXE43ubNm0VycrLQarUOn9mZM2fEvffeK/z9/YWfn5+4//777bdA2fYxmUxi+vTpolevXsLX11d4e3uLXr16iQ8++OCKunfv3i1Gjhxp/zxiYmLEAw88INatW9eoz6OkpERotVrx8ccfX/PzIdegEKKR0zWJSFa+++473Hvvvdi0aVOzZ0I3hcViQVJSEh544IEGLy+QPLz99ttYsGABjh8/ft15GiR/DGkiF1BdXe3wA9disWDYsGHYuXMncnNz2+yH8RdffIFJkyYhJyen0TONqe2YzWbEx8fjL3/5C5566impyyEnYEgTuYDHHnsM1dXVSElJgclkwjfffIPNmzfj5ZdfxsyZM6Uuj4haCUOayAUsX74cb7zxBo4dO4aamhp06tQJkyZNwuTJk6UujYhaEUOaiIhIpnifNBERkUwxpImIiGSKIY361YLKysoa/fAAIiKitsCQRv1qQH5+figvL5e6FCIiIjuGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikilJQ3rOnDlQKBQOXwkJCfbtNTU1SE9PR1BQEHx8fDBq1Cjk5eU5HCMnJwcjRoyAl5cXQkNDMX36dNTV1bV1U4iIiJxOLXUB3bp1w9q1a+3fq9UXS3r22Wfxv//9D1999RX8/PwwefJkjBw5Er/99hsAwGKxYMSIEQgLC8PmzZtx/vx5/PGPf4RGo8HLL7/c5m0hIiJyJoUQQkh18jlz5uC7777Dnj17rthWWlqKkJAQLF++HPfddx8A4PDhw0hMTMSWLVswcOBArFq1CnfddRfOnTsHg8EAAPjwww8xY8YMFBQUQKvVNqqOsrIy+Pn5obS0FHq93mntIyKi5snJyYHRaHTKsYKDgxEdHe2UY7U1yXvSR48eRUREBDw8PJCSkoL58+cjOjoaGRkZMJvNGDJkiH3fhIQEREdH20N6y5Yt6NGjhz2gASA1NRWTJk3CwYMH0adPnwbPaTKZYDKZ7N+XlZW1XgOJiKhJcnJykJCYiOqqKqccz9PLC4czM10yqCUN6QEDBmDp0qXo2rUrzp8/j7lz5+Lmm2/GgQMHkJubC61WC39/f4f3GAwG5ObmAgByc3MdAtq23bbtaubPn4+5c+c6tzFEROQURqMR1VVVGD3jNRii41t0rLyc41j26nQYjUaGdFMNHz7c/ueePXtiwIABiImJwZdffglPT89WO+/MmTMxbdo0+/dlZWWIiopqtfMREVHTGaLjEdm5m9RlSEpWt2D5+/ujS5cuOHbsGMLCwlBbW4uSkhKHffLy8hAWFgYACAsLu2K2t+172z4N0el00Ov1Dl9ERERyI6uQrqiowPHjxxEeHo7k5GRoNBqsW7fOvj0rKws5OTlISUkBAKSkpGD//v3Iz8+377NmzRro9XokJSW1ef1ERETOJOlw95/+9CfcfffdiImJwblz5zB79myoVCo8/PDD8PPzw4QJEzBt2jQEBgZCr9djypQpSElJwcCBAwEAw4YNQ1JSEsaOHYsFCxYgNzcXzz//PNLT06HT6aRsGhERUYtJGtJnzpzBww8/jMLCQoSEhOCmm27C1q1bERISAgB46623oFQqMWrUKJhMJqSmpuKDDz6wv1+lUmHlypWYNGkSUlJS4O3tjbS0NMybN0+qJhERETmNpCH9+eefX3O7h4cHFi5ciIULF151n5iYGPzwww/OLo2IiEhysromTURERBdJvpgJERG5D2esFJaZmemkalwfQ5qIiJzC2SuFVVRUOOU4rowhTURETuGslcIyt2/Aqk/fQU1NjROrc00MaSIicqqWrhSWl3PcidW4Nk4cIyIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMsWQJiIikimGNBERkUwxpImIiGSKIU1ERCRTDGkiIiKZYkgTERHJFEOaiIhIphjSREREMqWWugAiImrfrELgaF4FThgrUFJlRii8pC5JNhjSREQkqQ1ZBdh3ttT+vRFR8IjtLV1BMsLhbiIiksypwkp7QCdHByA2yAtWKBE6ahYqrOxHMqSJiEgSpjoL1mbmAwB6Rvrhps7BGNEzHP6ogEKtxWmzj8QVSo8hTUREkth7phQVpjr4eWowKD4YAKBWKhGLAgBAgcUTlaY6KUuUHEOaiIjanBACh86VAQD6xwVCq74YRz6oQc2ZgxBQYN+Z0qsdol1gSBMRUZs7U1yN0moztColOodeOaxdnrECALD/bCnqLNa2Lk82GNJERNTmDl7oRXcJ84FGdWUUVWVthlZhQbXZgpyiqrYuTzYY0kRE1KZqzBYcK6gAAHSP8Gt4J2FFsKoGAHCqkCFNRETUJo4XVMBiFQjy0SLUV3fV/QKUJgDAKfakiYiI2oatZxwf7AOFQnHV/fxVJigVQGm1GaXV5rYqT1YY0kRE1GasVmG/xhwbfO3lP9UKgTA/DwD1i560RwxpIiJqM7llNTDVWaFTK2HQe1x3/5hAbwBot5PHGNJERNRmbEPdMYFeUF5jqNsmOqi+t326qBoWq2jV2uSIIU1ERG3m5IVh65gg70btH+qrg06tRK3FCmOFqTVLkyWGNBERtYmq2jrkl9cHbUxQ4x5HqVQo7MPi+WUMaSIiolZxtrgaABDko4W3rvFPuDLo62/TyiuvaZW65IwhTUREbeJcaX3IRvh5Nul9ob7sSRMREbWq86X1PekI/+vP6r5U6IWedGGlqd2t482QJiKiVme2WFFw4Xp0U3vSvjo1PDUqWAVgrKhtjfJkiyFNREStLq+sBlYB+OjU8PVo/PVoAFAoFPbedF5Z+7ouzZAmIqJWd66kPlzD/TyuuRTo1RguXJdub5PHGNJERNTqztmvRzdtqNvGNsO7vU0eY0gTEVGrEkLgvH1md9MmjdmEXrhXuqiytl1NHmNIExFRqyquMqO2zgq1UoFgn6s/mvJavLUqeKiVEBeO117IJqRfeeUVKBQKTJ061f5aTU0N0tPTERQUBB8fH4waNQp5eXkO78vJycGIESPg5eWF0NBQTJ8+HXV1dW1cPRERXY1tsleIrw5KZdOvRwP1k8cCvbUA6nvT7YUsQnrHjh34+9//jp49ezq8/uyzz2LFihX46quvsGHDBpw7dw4jR460b7dYLBgxYgRqa2uxefNmfPrpp1i6dClmzZrV1k0gIqKrsC0F2pinXl0LQ1oCFRUVGD16ND766CMEBATYXy8tLcUnn3yCN998E4MHD0ZycjKWLFmCzZs3Y+vWrQCAn376CYcOHcK//vUv9O7dG8OHD8eLL76IhQsXora2/fwlEhHJma0nHerbvKFuG1tIF1a2n8ljkod0eno6RowYgSFDhji8npGRAbPZ7PB6QkICoqOjsWXLFgDAli1b0KNHDxgMBvs+qampKCsrw8GDB696TpPJhLKyMocvIiJyPqsQ9kVMnNWTLq5sP9ekm3ZHuZN9/vnn2LVrF3bs2HHFttzcXGi1Wvj7+zu8bjAYkJuba9/n0oC2bbdtu5r58+dj7ty5LayeiIiup7iyFnVWAY1KAX8vTYuOZQvpkupaWKwCqmZe33YlkvWkT58+jWeeeQbLli2Dh0fLfrtqqpkzZ6K0tNT+dfr06TY9PxFRe2G7Hh3iq4OyGYuYXMpHp4ZWpYRVACVV7eOSpmQhnZGRgfz8fNxwww1Qq9VQq9XYsGED3n33XajVahgMBtTW1qKkpMThfXl5eQgLCwMAhIWFXTHb2/a9bZ+G6HQ66PV6hy8iInI+2/Vo24phLaFQKBDgXd8bL2JIt6477rgD+/fvx549e+xfffv2xejRo+1/1mg0WLdunf09WVlZyMnJQUpKCgAgJSUF+/fvR35+vn2fNWvWQK/XIykpqc3bREREjmw9adva2y3V3mZ4S3ZN2tfXF927d3d4zdvbG0FBQfbXJ0yYgGnTpiEwMBB6vR5TpkxBSkoKBg4cCAAYNmwYkpKSMHbsWCxYsAC5ubl4/vnnkZ6eDp3OOf8giIioeS6dNBbqhJ40AAR56wCUM6Tl4K233oJSqcSoUaNgMpmQmpqKDz74wL5dpVJh5cqVmDRpElJSUuDt7Y20tDTMmzdPwqqJiAgASqvNqLMKqJUtnzRmYx/uZki3vV9++cXhew8PDyxcuBALFy686ntiYmLwww8/tHJlRETUVIUXnv0c6K1t8aQxm0CvC7dhVZkhhGjWE7VcieT3SRMRkXsqrKgf6g7y0TrtmL4eGigUgMUqUGmyOO24csWQJiKiVlF4YUg62Nt5c4RUSgX0HvVD3qXV7r+oCUOaiIhahW2425k9aQDw86wP6ZJq978uzZAmIiKnq7NaUXwhRIOc2JMGLoY0e9JERETNUFxphhCATq2Et07l1GP720K6HTxXmiFNREROZ3tSVZCP1ukzsP28bMPdDGkiIqImM1a0zlA3wOFuIiKiFrHdfhXs5EljwMWQNtVZUWN279uwGNJEROR0ttuvWqMnrVEp4a2tv87t7kPeDGkiInIqsxUor6kD4Pzbr2xs16XdffIYQ5qIiJyqzFw/UcxHp4aHxrkzu23ay3VphjQRETmVLaSDvFunFw0A/p71x3b3BU0Y0kRE5FSltRdCupWGuoFLetIc7iYiImo8e0/ax/mTxmxsIV124dq3u2JIExGRU5W2wXC3r0f9k5YrTXWwWEWrnUdqDGkiInIapZc/aq31IR3YiiHtpVVBpVRAAKgwuW9vmiFNREROow2JAVC/vrZG1XoRo1Ao7L3pMjee4c2QJiIip9FcCOnWnDRmY3uudLkbX5dmSBMRkdNogm0h3XqTxmzsPeka9qSJiIiuyzbcHdyK16Nt2JMmIiJqJKsQ7Ek7GUOaiIicoqDSAqXWE0oI+F+4j7k1sSdNRETUSDll9WHpqxFQKhWtfj5fz/qedHmNGUK4573SDGkiInKKUyX1Ia3XtE1g+mjVUCgAqwAqTe75XGmGNBEROcXpsvprw37atglppVIBH517X5dmSBMRkVPklLZtTxpw/+vSDGkiImoxs8WKs+VShDR70kRERNeUbaxEnRWwmqrgpWq78/qyJ01ERHRtWbnlAIBa4ykoWn9it51thjd70kRERFdhC2lzwak2Pa/vhYlj7vokLIY0ERG1WFaeLaRPtul5bbO7KzjcTURE1LAjeReHu9uS7Zq0qc4Ks8XapuduCwxpIiJqkaraOuQUVQFo++FurVoJ7YXnVrtjb5ohTURELXI0rwJCAP4eSliry9r8/D4XbsMqd8Pr0gxpIiJqEduksWi9WpLz+7rxdWmGNBERtYht0liUnzQhbetJu+MMb4Y0ERG1iG3SWIxf6z+esiG2Gd7lbnivNEOaiIha5LBtuJs9aadjSBMRUbMVVdaioNwEAIiS+po0Q5qIiOgi21B3VKAnPDXSRMrF4W6GNBERkZ1tZndXg69kNdiGu91xQROGNBERNZttZnfXMOlCWqdWue2CJgxpIiJqtiMXetJdJOxJA5cMebvZdWmGNBERNYsQQhY9acB9Z3gzpImIqFnOl9agvKYOaqUCHYN9JK3FXZ+GxZAmIqJmsfWiO4Z4Q6uWNk4urt/tXguaMKSJiKhZsmRyPRq42JOuNFkkrsS5GNJERNQstkljCRJfjwYAb50KAFDJa9JEREQXh7tl0ZPW2nrSDGkiImrn6ixWHM2vACD9zG4A8L4w3F1Va4HVKiSuxnkY0kRE1GSniqpQW2eFp0aFqAAvqcuBp1YFhQIQAKrM7nNdmiFNRERNdnEREx8olQqJqwGUCgW83XDImyFNRERNlimjmd027jh5jCFNRERNlnm+DACQGK6XuJKLbD1pd1p1jCFNRERNdjhXhiFtu1e6ltekiYionSqvMeN0UTUAIDFcPsPdFxc0YU+aiIjaqcMXrkeH+3nA30srcTUX8Zo0ERG1e3K8Hg3gktndHO4mIqJ26mJIy2eoG7h4TZoTx4iIqN3KPG9bs1tmPekLw93VZgssbrLqGEOaiIgazWIV9qdfyW2421Ojgm1dlapa9+hNM6SJiKjRThVWotpsgYdGibhgb6nLcaBQKC7ehuUm16UZ0kRE1Gi2oe6uBl+oZLAc6OXcbUEThjQRETWabRETuV2PtnG327AY0kRE1GhyndltY1/QhNekiYiovbENd8tt0piNu92GxZAmIqJGKa0y42xJ/XKgCTIPaU4cIyKidsV2PbqDvyf8PDUSV9Mwby2vSRMRUTsk9+vRgPs9ZIMhTUREjSL369HAxeHumjor6ixWiatpOUlDetGiRejZsyf0ej30ej1SUlKwatUq+/aamhqkp6cjKCgIPj4+GDVqFPLy8hyOkZOTgxEjRsDLywuhoaGYPn066urc4zcoIiI5yZThM6Qvp1Mr7fdvu8NzpSUN6cjISLzyyivIyMjAzp07MXjwYPzhD3/AwYMHAQDPPvssVqxYga+++gobNmzAuXPnMHLkSPv7LRYLRowYgdraWmzevBmffvopli5dilmzZknVJCIit3TpcqAJYfId7lYoFG415K2W8uR33323w/cvvfQSFi1ahK1btyIyMhKffPIJli9fjsGDBwMAlixZgsTERGzduhUDBw7ETz/9hEOHDmHt2rUwGAzo3bs3XnzxRcyYMQNz5syBViuf55wSEbmybGMlTHVWeGpUiAmS13Kgl/PWqlBabUalqQ6eUhfTQrK5Jm2xWPD555+jsrISKSkpyMjIgNlsxpAhQ+z7JCQkIDo6Glu2bAEAbNmyBT169IDBYLDvk5qairKyMntvvCEmkwllZWUOX0REdHUHz5UCABLC5bkc6KXc6V5pyUN6//798PHxgU6nw5NPPolvv/0WSUlJyM3NhVarhb+/v8P+BoMBubm5AIDc3FyHgLZtt227mvnz58PPz8/+FRUV5dxGERG5mf1n6kO6Rwc/iSu5Pvu90rwm3XJdu3bFnj17sG3bNkyaNAlpaWk4dOhQq55z5syZKC0ttX+dPn26Vc9HROTq9p+tD+nuLhHS7nOvtKTXpAFAq9WiU6dOAIDk5GTs2LED77zzDh588EHU1taipKTEoTedl5eHsLAwAEBYWBi2b9/ucDzb7G/bPg3R6XTQ6XRObgkRkXuyWgUOnqu/LNgzUv4h7XPpk7BcfGqS5D3py1mtVphMJiQnJ0Oj0WDdunX2bVlZWcjJyUFKSgoAICUlBfv370d+fr59nzVr1kCv1yMpKanNaycickfZhZWoMNXBQ6NEpxAfqcu5Lm/O7naOmTNnYvjw4YiOjkZ5eTmWL1+OX375BT/++CP8/PwwYcIETJs2DYGBgdDr9ZgyZQpSUlIwcOBAAMCwYcOQlJSEsWPHYsGCBcjNzcXzzz+P9PR09pSJiJzkwIWh7sRwPdQq2fXtruBO63dLGtL5+fn44x//iPPnz8PPzw89e/bEjz/+iKFDhwIA3nrrLSiVSowaNQomkwmpqan44IMP7O9XqVRYuXIlJk2ahJSUFHh7eyMtLQ3z5s2TqklERG5nnwtNGgMuXpOutVhR5+KLjkka0p988sk1t3t4eGDhwoVYuHDhVfeJiYnBDz/84OzSiIjoAtukMVcJaa1KCY1KAbNFoNrFO9PyH7cgIiLJWK0Chy5MGuvhApPGgPpVx7wvTB6rscj7nu7rYUgTEdFVnTC61qQxG/uDNhjSRETkrnbnFAOoH+p2hUljNrbr0hzuJiIit7XndAkAoE90gLSFNBGHu4mIyO3tzikBAPSO8pe0jqa6ONwtcSEt1KyQ7tixIwoLC694vaSkBB07dmxxUUREJL2q2jpk5dU/nrJPtL+0xTSRt7Z+uLtd9qRPnjwJi+XKX09MJhPOnj3b4qKIiEh6+8+UwmIVMOh1CPdzrYc+ernJxLEm3Sf93//+1/5n26pgNhaLBevWrUNsbKzTiiMiIunYr0dHudb1aODSnrTEhbRQk0L6nnvuAVB/D1paWprDNo1Gg9jYWLzxxhtOK46IiKRjC+neLjbUDVy8Jm0WCijUrvuUjSaFtNVav75aXFwcduzYgeDg4FYpioiIpGebNNbHxSaNAYBOrYRKqYDFKqDydr2RAJtmLQuanZ3t7DqIiEhGzpZUI7esBiqlwmVWGrtU/apjKpTV1EHlEyh1Oc3W7LW7161bh3Xr1iE/P9/ew7b5xz/+0eLCiIhIOjuyiwAA3SP08NJK+piHZvPWqdtnSM+dOxfz5s1D3759ER4eDoXCtWfPERGRo+0n60O6X6zrBpzXhclj7W64+8MPP8TSpUsxduxYZ9dDREQyYOtJ94tz3ZC2TR5z5Z50s+6Trq2txY033ujsWoiISAaKK2txNL8CANA3xnV7obalQVU+rtuGZoX0Y489huXLlzu7FiIikoGdp+ofqhEf4o0gH53E1TSf7SEbKm/X7Uk3a7i7pqYGixcvxtq1a9GzZ09oNBqH7W+++aZTiiMiora348L16P4uPNQNXNqTdt12NCuk9+3bh969ewMADhw44LCNk8iIiFzb9mzXnzQGXHJNur1NHFu/fr2z6yAiIhkorzFj/9lSAG7Qk7YPd/ujziokrqZ5+KhKIiKy255dBItVICbIC5EBXlKX0yKeGhUUqA/nkhrrdfaWp2b1pG+//fZrDmv//PPPzS6IiIjaVk5ODoxGIwDg291lAIAufsCuXbuadJzMzEyn19YSCoUCHiqg2gIUu+iTNpoV0rbr0TZmsxl79uzBgQMHrnjwBhERyVdOTg4SEhNRXVUFAAgf/x60oXFY/tYsfHx4U7OOWVFR4cwSW8RDJVBtUaC4uh31pN96660GX58zZ46s/nKIiOjajEYjqquqMHrGa/DrEI//na1/YtT4p6ZBp5rWpGNlbt+AVZ++g5qamtYotVk86i9Lt6+e9NWMGTMG/fv3x+uvv+7MwxIRUSszRMej0jcaOJuLEB8d4hM6N/kYeTnHW6GylvFQ1V+TdtWetFMnjm3ZsgUeHh7OPCQREbWR08X1Q95RgZ4SV+I8HhdSrrg9TRwbOXKkw/dCCJw/fx47d+7ECy+84JTCiIio7QgBnCq8ENIuPqv7UvaedHsa7vbzc3y2qFKpRNeuXTFv3jwMGzbMKYUREVHbKTMrUGGqg1qpQGSAG/WkXXy4u1khvWTJEmfXQUREEjpfXX9bbWSAJ9Qq91lCo11PHMvIyLDfF9etWzf06dPHKUUREVHbyq2pD+a4YG+JK3EuW0+6tMYKq1VAqXStpaubFdL5+fl46KGH8Msvv8Df3x8AUFJSgttvvx2ff/45QkJCnFkjERG1IqWHLwpN9eEV63YhDQhhhQVKFFXVItjFnurVrDGNKVOmoLy8HAcPHkRRURGKiopw4MABlJWV4emnn3Z2jURE1Io84m4AoECQtxZ6D81193clSgVgrapfRS2/zCRxNU3XrJ706tWrsXbtWiQmJtpfS0pKwsKFCzlxjIjIxXh16gfA/Ya6bSyVxVB5+yO/vAZJ0EtdTpM0qydttVqveIY0AGg0GlitrjmDjoioPaq1CHjG9wcAdAxx05CuqH/0Zn656/WkmxXSgwcPxjPPPINz587ZXzt79iyeffZZ3HHHHU4rjoiIWteeXBOUOi94qgTC9O65GJWlsj6kC9pLSL///vsoKytDbGws4uPjER8fj7i4OJSVleG9995zdo1ERNRKtpypX2e7g5f1mk83dGWWimIAQH6ZfNYUb6xmXZOOiorCrl27sHbtWhw+fBgAkJiYiCFDhji1OCIiaj2mOgt2nLsY0u6q3Qx3//zzz0hKSkJZWRkUCgWGDh2KKVOmYMqUKejXrx+6deuGX3/9tbVqJSIiJ9p01Igqs0BdeSGCtELqclqNpfJCT9rdQ/rtt9/G448/Dr3+ytlxfn5+mDhxIt58802nFUdERK1nxd76eUVVWb/BTUe6AVzak3a94e4mhfTevXtx5513XnX7sGHDkJGR0eKiiIiodZXXmLH6YC4AoPLQL9IW08rsIV1mghCuNWLQpJDOy8tr8NYrG7VajYKCghYXRURErWvV/lzUmK3o4KtC7fkjUpfTqmzD3aY6K8pq6iSupmmaFNIdOnTAgQMHrrp93759CA8Pb3FRRETUuv6TcQYAcHus+zyW8mpEXS28NPXj+QUuNuTdpJD+3e9+hxdeeAE1NVc2srq6GrNnz8Zdd93ltOKIiMj5ThVWYvvJIigUwC0x7vNYymsJ8KiPO1dbGrRJt2A9//zz+Oabb9ClSxdMnjwZXbt2BQAcPnwYCxcuhMViwXPPPdcqhRIRkXN8ufM0AOCmTsEI9lJJXE3bCPBU4Wy5xeVmeDcppA0GAzZv3oxJkyZh5syZ9gvwCoUCqampWLhwIQwGQ6sUSkRELVdjtuDz7fUh/XD/aMB8XuKK2oa9J+1iw91NXswkJiYGP/zwA4qLi3Hs2DEIIdC5c2cEBAS0Rn1EROREP+w/j8LKWoT7eWBYkgH79raXkK4fMXDr4e5LBQQEoF+/fs6shYiIWtmnm08CAEYPiIZa1ayVoV1SgKetJ+1aId1+/oaIiNq53TnF2HumFFqVEg/1j5a6nDZl70m72HA3Q5qIqJ1Y9MtxAMBdvcIR7KOTuJq2xZ40ERHJVlZuOX46lAeFAph0a7zU5bQ5W0+6wMWuSTOkiYjagQ9+OQYAuLNbGDobfCWupu3ZZneXm+pQVes6q44xpImI3Fy2sdL+MI302ztJXI00vDQKeGhcb0EThjQRkZt7/ccsWAVwR0Iounfwk7ocSSgUCoT6egBwrevSDGkiIje2K6cY/9t/HkoF8Oc7E6QuR1KhvvWT5VxphjdDmojITQkhMP+HTADAfcmR6BrW/q5FXypUfyGkOdxNRERS++/ec9hxshgeGiWeHdpF6nIkx+FuIiKShdJqM15cWd+Lfuq2Tgj3ax9Pu7qWEA53ExGRHLz+YxaMFSZ0DPHGxFs7Sl2OLNiuSRewJ01ERFLZfNyIf207BQD42z3doVO3j8dRXk+o/sJwN69JExGRFEqrzfjTl3shBPBQvyjcGB8sdUmyYdBzuJuIiCQihMCs7w/gXGkNYoK88MJdSVKXJCu2iWPFVWbU1lklrqZxGNJERG5i2bYcfL/nHJQK4M0HesNb1+ynEbulAC8NNCoFAKCgwjWGvPk3SESykZOTA6PR2OLjBAcHIzq6fT2KcXdOMeauOAigftGS5JgAiSuSH4VCgRAfHc6V1iC/rAYd/OU/450hTUSykJOTg4TERFRXVbX4WJ5eXjicmdlugvp8aTWe/FcGzBaB1G4GTLyFs7mvJkTvUR/SLjLDmyFNRLJgNBpRXVWF0TNegyG6+Y9SzMs5jmWvTofRaGwXIV1eY8b4JTuQV2ZC51AfvH5/LygUCqnLkq2LS4MypImImswQHY/Izt2kLsMl1JgtmPSvXTicW45gHx2WjO8HXw+N1GXJmv1e6TLXmOHNiWNERC7IbLFiyr93Y9MxI7y0KnyS1heRAV5SlyV7rrY0KEOaiMjFmOosePrfu7HmUB50aiU+TuuLXlH+UpflEuwP2XCRkOZwNxGRC6mqrcPEzzLw61EjtColFo25gQuWNIGrPa6SIU1E5CJKq8wYv3Q7duWUwEurwuKxfXFTZwZ0U9iHu11kaVCGNBGRC8gtrcG4JdtxOLccfp4aLB3fD32ieS90U9mGu40VJlisAiqlvGfCS3pNev78+ejXrx98fX0RGhqKe+65B1lZWQ771NTUID09HUFBQfDx8cGoUaOQl5fnsE9OTg5GjBgBLy8vhIaGYvr06airq2vLphARtZqMU8W4+/1NOJxbjlBfHb6cmMKAbqYgby0UCsAqgMJK+femJQ3pDRs2ID09HVu3bsWaNWtgNpsxbNgwVFZW2vd59tlnsWLFCnz11VfYsGEDzp07h5EjR9q3WywWjBgxArW1tdi8eTM+/fRTLF26FLNmzZKiSURETvXFjhw8vHgrCspN6GrwxdeTbkTXMF+py3JZapUSQd4Xrku7wJC3pMPdq1evdvh+6dKlCA0NRUZGBm655RaUlpbik08+wfLlyzF48GAAwJIlS5CYmIitW7di4MCB+Omnn3Do0CGsXbsWBoMBvXv3xosvvogZM2Zgzpw50Gq1UjSNiFpZpakOxgoTiqvMqKqtg9kioFQANaVKeHe/A4eNtUiorYOX1jWv6pnqLHjpf5n455b6R07e2S0MbzzQi+txO0Gorw7GCpNLPFdaVn/bpaWlAIDAwEAAQEZGBsxmM4YMGWLfJyEhAdHR0diyZQsGDhyILVu2oEePHjAYDPZ9UlNTMWnSJBw8eBB9+vS54jwmkwkm08W/nLKystZqEhE5UWGFCYdzy5FtrERhZe1V9lIjeMSz+OvPhXjhl5/QLUKPwQmhGNEjHJ0NrtEDPZJXjmc+34PM8/U/m6YN7YLJt3eCUubXT11FqF6HQ+ddY4a3bELaarVi6tSpGDRoELp37w4AyM3NhVarhb+/v8O+BoMBubm59n0uDWjbdtu2hsyfPx9z5851cguIqDUIIXCysAo7TxXhXInjD9VALy0CvDXw1qmhUSkhhEBRYSEyD+5Dh8RkFFVbse9MKfadKcXba4+ic6gPRvQMx4P9ohDuJ7+HKwgh8M8tp/DyD5kw1VkR6K3Fa/f1xB2Jhuu/mRrNfhsWh7sbLz09HQcOHMCmTZta/VwzZ87EtGnT7N+XlZUhKiqq1c9LRE1zvrQaG48YkXthCUelAogN8kYXgy+ig7zgqVFd8Z4zyMeGL17AqowMhHVMxG/HjFh1IBe/Hi3A0fwKvL32KN77+RiGJRnwx5RYDOwYKIu1rk8VVuKF7w9i45ECAMCtXULw2v097bcMkfO40qpjsgjpyZMnY+XKldi4cSMiIyPtr4eFhaG2thYlJSUOvem8vDyEhYXZ99m+fbvD8Wyzv237XE6n00Gn0zm5FUTkLKY6CzYdNeLAufrhXrVSgZ6RfugTFQAfj8b/2Irw98T9faNwf98olFabseZQHr7aeRrbsouw6kAuVh3IRVeDL8YPisU9fTrAo4HQb201ZgsW/XIcizYcR22dFVq1En8dnoC0G2Nl8cuDO7q46hiHu69JCIEpU6bg22+/xS+//IK4uDiH7cnJydBoNFi3bh1GjRoFAMjKykJOTg5SUlIAACkpKXjppZeQn5+P0NBQAMCaNWug1+uRlJTUtg0iohYrqFHgx605qDDV30aZGO6LG+OD4dPCCVN+nhrclxyJ+5IjcTi3DJ9tOYVvdp1FVl45/vLNfrz2YxZGD4zB2IExCPFt/V/irVaBnw7l4uUfDiOnqP7xnDd3Dsbc33dDxxCfVj9/e+ZKT8KSNKTT09OxfPlyfP/99/D19bVfQ/bz84Onpyf8/PwwYcIETJs2DYGBgdDr9ZgyZQpSUlIwcOBAAMCwYcOQlJSEsWPHYsGCBcjNzcXzzz+P9PR09paJXIgQAvr+9+LXfDUE6uDnqcHQRAM6BDTv2nFmZuY1t4+KAe4MD8ba7Cr872gVjJW1eHfdUXyw/ihujvbE8E5eiA/QoLa21mk/S4KDg9EhMgo/7D+P938+hqy8cgBAmN4DL9yVhN/1CGt07zknJwdGo7HFNV3vc3JHIS606pikIb1o0SIAwG233ebw+pIlSzBu3DgAwFtvvQWlUolRo0bBZDIhNTUVH3zwgX1flUqFlStXYtKkSUhJSYG3tzfS0tIwb968tmoGUbvmjLCoMlsxf91pBNw+AQJAQpgvBieEQqNq+lIOZUX113THjBnT+DcplPDqeiP0fe8BOiRg/clqrD9ZjdqCk6jYvw6VB9fDWlXS5FoupdR5w7/3MHS75ynklNSHg69OjXGDYjHx1vgmjRTk5OQgITER1VVVLarpUhUVFU47ltwZLgx3F5SbIISQ9WUFyYe7r8fDwwMLFy7EwoULr7pPTEwMfvjhB2eWRkSN4IywUPuHI/S+2dAERULUmdFNX4shSZ2a/YOzuqL+OvaIic+ha8/kJr+/0GTG8XIlzlYpoQ2JReDgCQi8/VGEeAiEeVoR4iHgrxG4XnlCAJUWoKBGifPVSuRWAwJK5JSY4KNV4K7O3hjR2Rve2kocObivSTVmZmaiuqoKo2e8BkN0fJPb6HCs7Ruw6tN3UFMj/+uzzmKbOFZrsaKoshZBPvIddZXFxDEick1Go7FFYVFsUuC3AjVMVgXUlmqcXv4cbnvyT07p2QRFxCCyc7cmvy8SQC8AJrMFazb+hkPnSqGLSECBSYECU33PXqNSIMBLiwAvLby0KmjVSigUQJ1FoNpsQWm1GUWVtaiqtTgcuzY/GxX7fkLO/rU4WFuNV1vYRu9AQ7PaeKm8nOMtrML1aNVKBPtoYayoxfnSGoY0Ebk3Q3R8k8PiVGElft1/HmarQIivDnHlR3D8/JFWqrDpdBoVwlGCnz+bjvtnfwKPqG44VVSF8yU1qLVYkV9uuu7EI6UCMOg9EBPoBVP2Tny35On6Hv6j41tUW3vs/TpbmJ8HjBW1yCurQfcOflKXc1UMaSJqc5nny7A2Mw9WAUQHemFEj3Ds37BH6rKuylNpQe/oAPSJDoBVCJRU1feUS6vNqK61oNZihYCAWqGEp1YFHw81Ar20CPLR2q+rZ2TXB3pze/iXao+9X2cL03viwNkynC+V9y86DGkialP7z5Ti56x8AEDXMF8MTTTI/nGBl1IqFAj01iLQm88FcGVhfvVD3HllDGkiIgDA3tMl+OXCilq9I/1xS5dgWc+sJfdlWxaWPWkiIgC7corx69H6W7VuiPbHTZ0Y0CQdg75+hncuQ5qI2rudp4rw27FCAEC/2ACkdAxiQJOkwv0uhDSHu4moPduVU2wP6AFxgRgQJ48HWlD7FubnGj3ppi/nQ0TUSHvPlNiHuAfEBWIge9AkE2EXhrsrTHUorzFLXM3VMaSJqFUcOFeKX7LqJ4n1jQnAgLhAiSsiushbp4bvhSeqyXmGN0OaiJwu83wZ1mXW32bVJ8ofN8azB03yY+tNy3mGN0OaiJzqSF451hyqf6Z7zw5+uLkzZ3GTPLnCdWmGNBE5zbH8Cqw+mAsBoFuEHrd1DWFAk2yFM6SJqL3INlZi1YHzEKL+UZN3JIQyoEnWbMPdcr4Ni7dgEVGL5dUosGX/eVgF0CXUB0MTDQxokr2wC6uOybknzZAmohbRRXbDlgI1LEIgPsQbw7qFQelCa3FT+2Vbv5sTx4jILR0prEXofbNhEQrEBnlhePdwl3pYBrVvYfr6njRvwSIit3PgbCle3FgEpc4LITorRvRgQJNrsU0cK6yshanOInE1DWNIE1GTHckrx9hPtqHSLFBz5iBuDKmDWsUfJ+Ra/L000Krr/93ml5kkrqZh/F9FRE1yoqACj3y0DcVVZnQK1CD/qzlQ8ycJuSCFQmHvTcv1ujT/axFRo50uqsLoj7fBWGFCQpgvXrg5EKK2WuqyiJrNIPPbsBjSRNQo50ur8cjHW3G+tAadQn3wr8cGwFfHHyHk2i4uaCLPXzb5P4yIrqug3ITRH23D6aJqxAR5YdljAxDso5O6LKIWC+NwNxG5sqLKWoz5eBtOGCvRwd8Tyx4bYB8iJHJ1tlXH5HobFkOaiK6qtNqMP/5jG7LyyhHqq8OyxwYgMsBL6rKInIYTx4jIJVWY6jBuyXYcOFuGIG8tlj8+ALHB3lKXReRUtlGhPIY0EbmK6loLJizdgd05JfDz1OCzCQPQKdRX6rKInC78wvrdeeUmWKxC4mquxJAmIgemOgue+GwntmUXwVenxj8f7Y+kCL3UZRG1ihBfHVRKBSxWAWOF/BY0YUgTkZ3ZYkX6st349agRnhoVlozvh15R/lKXRdRqVEoFQi7cqSDHp2ExpIkIAFBnsWLqF3uwNjMPWrUSn6T1Rd/YQKnLImp1cr4NiyFNRLBaBf789T78b995aFQK/H1sMm7sFCx1WURtQs63YTGkido5IQSe//4Avtl1FiqlAu89fANu7xoqdVlEbYY9aSKSJSEE5q08hOXbcqBQAG8+0At3dg+TuiyiNmW7V5o9aSKSldd/ysKS304CAF4d1RN/6N1B2oKIJGDrSZ8rkd/63QxponbqvXVHsXD9cQDAi3/ohgf6RklcEZE0Ivzr75U+J8OHbDCkidqhjzaewBtrjgAAnvtdIsamxEpbEJGEOlwI6dzSGtktaMKQJmpnPttyEi/9kAkAmDa0Cx6/paPEFRFJK/TCgiZmi0BBubwWNGFIE7UjX+48jRe+PwgAeOq2eEwZ3Eniioikp1Yp7bdhnZXZdWmGNFE78f2es5jx9T4AwPhBsZie2hUKhULiqojkwTbkLbeQVktdABG1vtUHcjHty70QAni4fzTG9fTB7t27W3zczMxMJ1RHJL0OAZ7ASfnN8GZIE7m59Vn5mPLvXbBYBUb26YCJyXokJiWhuqrKaeeoqKhw2rGIpBDhf2G4u5ghTURtZPMxI578LANmi8CIHuFYcF9P7Nu7B9VVVRg94zUYouNbdPzM7Ruw6tN3UFMjv0UgiJqig78XAPakiaiN7DxZhAmf7oSpzoohiaF4+6HeUKsuTkMxRMcjsnO3Fp0jL+d4S8skkgV7T1pmIc2JY0RuaO/pEoxbsgPVZgtu7hyM9x+5ARoV/7sTXU1kgDwnjvF/LZGb2X+mFGM/2YYKUx36xwVi8di+8NCopC6LSNZsq46V19ShrMYscTUXMaSJ3Mj+M6UY/fFWlNXUITkmAP8Y1w+eWgY00fV4adUI8NIAkNd1aYY0kZu4PKCXju8HHx2nnRA1ln0Nb4Y0ETnTgbOlGPPJNoeA9vXQSF0WkUuxL2gio9uwGNJELu7A2VKM/ngbSqvNuCHanwFN1EwR9lXH5HNLIUOayIXtO1PiENCfPtqfAU3UTLYZ3meKnbfQT0vxghWRi8jJyYHRaLR/fyDfhPmbilFdJ9A1SINpyTocPbT/usfhUp5EDbOF9GkZDXczpIlcQE5ODhISE+1LeXrG90fIPX+BQq1Fzam9WPPW3/BTbdN+sHApTyJHkQH1q46dKWJPmoiawGg02pfyNAV1xs5CFQQUCPe0YsBNiVDdvKzRx+JSnkQNiw6qD+nCylpUmurgLYO7I6SvgIgarcS/Mw4U1v+3TQjzxdBEA5TKpj1ukkt5EjVM76GBn6cGpdVmnCmuRtcwX6lL4sQxIldQZxUITE3HgZL6gO4d6Y9hSU0PaCK6tqjAC9elZTLkzZAmkrnyGjNe/rUIvr2HAxC4tUsIbu0aAoWCAU3kbFEXrkuflskMbw53E8lYtrESEz/biSN5tbDW1mBQhAq9o/ylLovIbUUFXgjpInnM8GZPmkimfjyYi9+/twlH8irg76FE3vIZiPASUpdF5NaiLtyGlcPhbiJqSJ3FildWHcbEzzJQbqpDv9gAvD40GLV5nPBF1NoiL/Sk5bKgCUOaSEZOF1XhkY+34cMN9YE84aY4LH98IAI9+SQrorYQbR/uroIQ0o9c8Zo0kQwIIfCfjDOYu+IQKkx18Naq8Op9PXFXzwipSyNqV2wP2aistaC4yoxAb62k9TCkiSRmrDDhuW/348eDeQCAvjEBePOB3vaFFYio7XhoVDDodcgrM+F0URVDmqi9sloFvth5Gq+sOozSajM0KgWeHdoFE2+Jh4r3PxNJJirAqz6ki6vQS+K7KRjSRBI4nFuGv36zH7tySgAASeF6LLivJ7p38JO2MCJCVKAXdp4qlsUMb4Y0URsqqqzF22uPYNm2HFisAt5aFaYN64q0lBioVZzHSSQHl04ekxpDmqgNmOosWPrbSby//hjKa+oAAKndDJjz+24I9/OUuDoiulRscH1IZxsrJa6EIU10hcuf29wSVdU12JprxZeHKpBfaQEAxPmrMa6XHj0MSpw/nonzjTgOnwFN1HZigrwBAKcK2ZMmkpXLn9vcbAolvBJuhv+gh6EJigQA1JUXomTjP3Hq4Hr8IqzNOiyfAU3U+uIuhPT50hpU11rgqZVunQKGNNElLn1usyE6vsnvFwI4V63AoVIVysz115hVVjOSAhXoGOkLdbd0AOlNPi6fAU3Udvy9NNB7qFFWU4ecoipJH1nJkCZqgCE6HpGduzV6fyEEsgsrsfVEEQrKTQAAFSwwblyGu1KHom/yLS2qh8+AJmo7CoUCccHe2HumFNnGSklDWtLppBs3bsTdd9+NiIgIKBQKfPfddw7bhRCYNWsWwsPD4enpiSFDhuDo0aMO+xQVFWH06NHQ6/Xw9/fHhAkTOCRIbUYIgZyiKny58wxW7D2PgnITtCol+scGoh+OoWzLl1ArpF9akIia5uJ1aWknj0ka0pWVlejVqxcWLlzY4PYFCxbg3XffxYcffoht27bB29sbqampDkN+o0ePxsGDB7FmzRqsXLkSGzduxBNPPNFWTaB27GxxNb7edRbf7j6L3LIaqJUKJMcEYNygWKTEB0GN5l13JiLpxQbXh/RJiUNa0uHu4cOHY/jw4Q1uE0Lg7bffxvPPP48//OEPAIB//vOfMBgM+O677/DQQw8hMzMTq1evxo4dO9C3b18AwHvvvYff/e53eP311xERwXWPyflyy2qw9XghTl24h1KlUKBHpB/6xgTAW8crSETuIPbCsrwnjdLO8JbtT5Ts7Gzk5uZiyJAh9tf8/PwwYMAAbNmyBQ899BC2bNkCf39/e0ADwJAhQ6BUKrFt2zbce++9DR7bZDLBZDLZvy8rK2u9hpDbKCg3YeuJQpy4cO+kUgEkRejRPzYQvh4aiasjImdiT/o6cnNzAQAGg8HhdYPBYN+Wm5uL0NBQh+1qtRqBgYH2fRoyf/58zJ0718kVk7sqrzFjy4lCZJ4vBwAoACSE+2JAXBD8PBnORO4o9pLbsGrMFnhopLkNS7Yh3ZpmzpyJadOm2b8vKytDVFSUhBWRHNXWWZFxqhi7copRZ62f/NU51AcDOwZJ/mQcImpdAZfchnWqULrbsGQb0mFhYQCAvLw8hIeH21/Py8tD79697fvk5+c7vK+urg5FRUX29zdEp9NBp9M5v2hyDwolsiuUWLXlJKpq61cJi/DzwM2dQxDm5yFxcUTUFhQKBWKDvbHvTClOFkp3G5ZsV/SPi4tDWFgY1q1bZ3+trKwM27ZtQ0pKCgAgJSUFJSUlyMjIsO/z888/w2q1YsCAAW1eM7m+zIJahI97B7uK1KiqtcDPU4MRPcJxX3IkA5qonbENeZ8okO66tKQ96YqKChw7dsz+fXZ2Nvbs2YPAwEBER0dj6tSp+Nvf/obOnTsjLi4OL7zwAiIiInDPPfcAABITE3HnnXfi8ccfx4cffgiz2YzJkyfjoYce4sxuahJjhQnzfziMr3cVQhsaB41SICU+BD0j/flsZ6J2Kj7EBwBwvEC6tTckDemdO3fi9ttvt39vu06clpaGpUuX4s9//jMqKyvxxBNPoKSkBDfddBNWr14ND4+LPZply5Zh8uTJuOOOO6BUKjFq1Ci8++67bd4Wck0Wq8Dybafw2o9ZKLvwdKryvT/i4d/djvjoAImrIyIpdQqtD+lj+e00pG+77TYIcfXVmBQKBebNm4d58+ZddZ/AwEAsX768NcojN7f/TCn++u1+7D9bCgDoFqHHmEQNHnn1Pejuvv067yYid2cL6eMFFRBCQKFo+1E12U4cI2otNWYL3lp7BB9tPAGrAHw91PjTsK4YMzAGe/fslro8IpKJ2GAvKBVAeU0dCspNCNW3/bwUhjS1KztOFmHGf/bZFyS5u1cEZt2VhBBfzvYnIkc6tQrRgV44WViFY/kVDGmi1lJpqsOC1Yfxz62nIAQQ6qvDS/f2wNAkw/XfTETtVqdQH5wsrMLxggrc2Cm4zc/PkCa3kZOTA6PReMXre3JNWLSzFAVV9fc83xHniXG99PCuOYtdu8467JuZmdkmtRKRa4gP8cHazHzJJo8xpMkt5OTkICExEdVVFxfDV+i8ETh4Anx6DgMA1JXmoXD1e/jHyT34x3WOx8edEhEAxNtmeEt0GxZDmtyC0WhEdVUVRs94DYboeJyrUmB3sRo1FgUAgXgfK7pHBkDdY9Y1j5O5fQNWffqOw+NQiaj9ss/wzpdmQROGNLkV/w7x2G8KxBFj/W+9/l4aDEk0oIO/Z6Pen5dzvDXLIyIXY1vQJLesBuU15jZ/4h1DmtyCEAJeibfgp/Ma1ForoFAAN0QHYGBcINQq2a5+S0Qy5+epQYivDgXlJhwvqETvKP82PT9/epHLO19ajfm/FSPk939GrVWBYB8tHuwbhZs6BTOgiajFOl8Y8j6SV97m52ZPmlyW1Srw7x05eOWHwyg31UFYzOgWqMDgGzpxvW0icpquYb7YfLwQWbltH9LsZpBLyjZW4uGPtuK5bw+g3FSHzoEanF86FYl+VgY0ETlVYpgeAHA4t6zNz82eNLmUOosVH2/KxltrjsBUZ4WnRoU/pXZFT48i9J9xSuryiMgNJYTXP0s683x5m6/hzZAml3HwXClmfL0PB87W/zZ7U6dgzB/ZA1GBXti1q1ji6ojIXXUO9YVSARRV1qKgwoRQ37ZbHpQhTbJXXmPGu+uOYslvJ1FnFdB7qPHCXUm4LzlSkqfSEFH74qlVITbYGycKKnH4fDlDmgionxj27e6zeGX1YRSUmwAAw7uHYe4furXpfxIiosQwPU4UVCIrtxy3dAlps/MypEmWDpwtxez/HkTGqfph7Lhgb8y6Owm3dw2VuDIiao+6hvnif/vPI7ONJ48xpElWiitr8dpPWfj39hwIAXhpVZgyuDMevSkWOrVK6vKIqJ1KCKufPHb4fNvehsWQJlmwWAWWb8/BGz9loaTKDAD4fa8I/PV3iQjz49A2EUkrMbz+Nqxj+RUwW6zQtNFCSQxparKrPRKyOYKDg5Fv9cGs7w/i0Pn6YaSEMF/M+X03DOwY5JRzEBG1VAd/T/jo1Kgw1SHbWIkuBt82OS9DmpqkoUdCNpfKOwDBQx6HR8ItAAC9hxr/N6wrRg+I5nKeRCQrSqUCPSP9UFptRoWprs3Oy5CmJrn8kZDNYRXAsXIlDpUoYIEKCgjcEeeF0T184edRhH17i5p8zMzMzGbVQkTUWMseG9Dmt30ypKlZDNHxiOzcrcnvO1VYiQ1HClB84bqz6VwWitZ8iE9yj+ITJ9RVUSHNg9mJyP1JsS4DQ5raRFm1GRuPFuB4Qf2D0z01KkSaT2PtZ3/CiIl/RdeeyS06fub2DVj16TuoqalxRrlERLLAkKZWVWexYuepYuw8VQyLVUChAHp18MfAjoE4sPEAAIGgiJhm9covlZdz3DkFExHJCEOaWoUQAieMldh4pABlNfWTLCL9PXFr1xAE++gkro6IyDUwpMnpiqtqsSGrAKeK6meA++jUuLlzMDqH+nCtbSKiJmBIk9PUWazYcbIYGaeKYRECKoUCfaL90S82EFo1b6kiImoqhjQ5RbaxEr9k5duHtmMCvXBr1xAEeGklroyIyHUxpKlFymrM2Hjk4qxtH50at3QJRqcQDm0TEbUUQ5qaRQhgz+kSbD5uhNkioFQAfaIC0D+OQ9tERM7CkKYmUwd2wIZ8NQpNBQCACD8PDE4IRRBnbRMRORVDmhqtzmLFN5kViBj/HgpNSmhUCgzqFIyeHfw4tE1E1AoY0tQoWbnl+NNXe7H/bDkUai0MHlb87oaO0HtqpC6NiMht8eIhXZPVKvDJpmzc/f4m7D9bCm+NAsb/vYlBIXUMaCKiVsaQpqvKLa3BH/+xHS+uPITaOisGJ4TinTtDUHngZ3B0m4io9XG4mxr0w/7zmPnNfpRWm+GhUeKFu5LwSP9o7N69W+rSiIjaDYY0Oag01WH2fw/iPxlnAAA9Ovjh7Yd6Iz7ER+LKiIjaH4Y02R3OLcNTy3bhREElFArgqdvi8cwdXXjfMxGRRBjSBAD4audpvPD9AdSYrQjTe+Cdh3pjQMcgqcsiImrXGNLtRE5ODoxG4xWvm+oEFu8qxfqT1QCA3gYtnhmgh6bkFHbtOnXF/pmZma1eKxER1WNItwM5OTlISExEdVWVw+vqwEiE3PMXaENiIawWlG5aju+3fInvIa57zIqKitYql4iILmBItwNGoxHVVVUYPeM1GKLjAQA5lUrsKlLBIhTQKQX6h1oROuZBYMyD1zxW5vYNWPXpO6ipqWmL0omI2jWGdDtiiI5HWMdEbDhagAOFZQCASH9P3Nk9DN66xv1TyMs53polEhHRJRjS7UiFGfhy5xkUVJgAAP1jAzGgYyCUXJmEiEiWGNLthFfXQViXq0GdMMFTo0JqNwNigrylLouIqE04a9JrcHAwoqOjnXKsxmBIuzlTnQUf7ypFyD0zUSeAcD8PDO8eBl8PrrtNRO6vrKj+kbpjxoxxyvE8vbxwODOzzYKaIe3GThdVYfLyXdh7pn5WdxdfC4bdEAmVksPbRNQ+VFfUz78ZMfE5dO2Z3KJj5eUcx7JXp8NoNDKkqWV+OpiLP321F2U1dfDRKnBi+RyMmjaTAU1E7VJQRAwiO3eTuowm43qPbsZsseJvKw/hic8yUFZTh95R/nh9aDCqj++QujQiImoi9qTdyNmSakxevgu7c0oAABNuisOMOxNwYN8eSesiIqLmYUi7iZ8P52Hal3tRUmWGr4car9/fC6ndwqQui4iIWoAh7eJqzBa8suowlm4+CQDoGemHhY/cgKhAL2kLIyKiFmNIu7DM82V45vPdOJJXv472uBtjMfN3CdCpVRJXRkREzsCQdkFWq8A/fsvGgtVZqLVYEeyjw2v398TtXUOlLo2IiJyIIe1icktr8Kev9mLTsfrHTg5JDMUro3oi2EcncWVERORsDGkZu/QZ0EII/JxdjaV7y1BpFtCqgPG99RjWUYmcIweRc43j8BnQRESuiSEtU5c+A1rtZ0DgnVPgGdsbAGA6fwRnV76BvxadxV+bcEw+A5qIyLUwpGXKaDSiuroGt834BDmKUFiEAiqFQJKfBZ2iYqEc8F6jj8VnQBMRuSaGtExll5gRNuY1ZMMAiPrnPt+RGAp/L22Tj8VnQBMRuSaGtMyUVNXijZ+OYNk2I3QRXaFWCNza1YBuEXoo+NxnIqJ2hSEtExarwOc7cvD6j1korjIDACozN+L+Owaicwc/iasjIiIpMKQlJoTAz4fz8cqqwziaXz+xq4vBB6MTtRj36gJ4pn4jcYVERCQVhrSEducUY/6qw9ieXQQA8PPU4Jk7OmNsSgz2790jbXFERCQ5hrQEMk4V4911R7HhSAEAQKtWYvygWDx1ayf4eWkkro6IiOSCIe1kly5AcikhBA4W1OI/mRXYl1cLAFAqgNtiPPFQd18Ee1Xj+OH99v25AAkRETGknejSBUjslGp4J9wE3373QBfWCQAgLHWoOLAOZVu/wpKSXCy5xjG5AAkRUfvFkHYio9GI6qoqjJ7xGnwj4nGyUonsChVqLPW3TikVAjHeVnTVW+Eddztw9+1XPRYXICEiIoa0E5ktAl5dbsRRj67IO6e0v+6tVaFnlD96RPjBU9u4x0hyARIiImJIO9EbW4oRcu9fkXeh8xsZ4IluEXp0DvWFSsmFSIiIqGkY0k40MNIDm4+cR7cIPwzs1rFZS3gSERHZKK+/i2tYuHAhYmNj4eHhgQEDBmD79u1tXsNN0Z44+8E4dPe3MKCJiKjF3CKkv/jiC0ybNg2zZ8/Grl270KtXL6SmpiI/P79N61ArFYCwtuk5iYjIfblFSL/55pt4/PHHMX78eCQlJeHDDz+El5cX/vGPf0hdGhERUbO5fEjX1tYiIyMDQ4YMsb+mVCoxZMgQbNmyRcLKiIiIWsblJ44ZjUZYLBYYDAaH1w0GAw4fPtzge0wmE0wmk/370tJSAEBZWVmLarEtPHLm6EGYqquus/e12W7Byj15BMe9vXisNjqWHGvisVy/Jh7L9WsCgIIz2QDqf9a3NC9sfH19r/0YYuHizp49KwCIzZs3O7w+ffp00b9//wbfM3v2bAGAX/ziF7/4xS9Jv0pLS6+ZcS7fkw4ODoZKpUJeXp7D63l5eQgLC2vwPTNnzsS0adPs31utVhQVFSEoKMjhN5qysjJERUXh9OnT0Ov1rdOANuJObQHcqz3u1BaA7ZEzd2oL4B7t8fX1veZ2lw9prVaL5ORkrFu3Dvfccw+A+tBdt24dJk+e3OB7dDoddDqdw2v+/v5XPYder3fZfwCXc6e2AO7VHndqC8D2yJk7tQVwv/ZcyuVDGgCmTZuGtLQ09O3bF/3798fbb7+NyspKjB8/XurSiIiIms0tQvrBBx9EQUEBZs2ahdzcXPTu3RurV6++YjIZERGRK3GLkAaAyZMnX3V4u7l0Oh1mz559xdC4K3KntgDu1R53agvA9siZO7UFcL/2NEQhhBBSF0FERERXcvnFTIiIiNwVQ5qIiEimGNJEREQyxZAmIiKSKYb0Vcjh+dRNNX/+fPTr1w++vr4IDQ3FPffcg6ysLId9ampqkJ6ejqCgIPj4+GDUqFFXrNYmV6+88goUCgWmTp1qf83V2nP27FmMGTMGQUFB8PT0RI8ePbBz5077diEEZs2ahfDwcHh6emLIkCE4evSohBU3zGKx4IUXXkBcXBw8PT0RHx+PF198EZfOQ5VzWzZu3Ii7774bERERUCgU+O677xy2N6b2oqIijB49Gnq9Hv7+/pgwYYJ9/f62dq32mM1mzJgxAz169IC3tzciIiLwxz/+EefOnXM4hlzac72/m0s9+eSTUCgUePvttx1el0tbnIEh3QC5PJ+6qTZs2ID09HRs3boVa9asgdlsxrBhw1BZWWnf59lnn8WKFSvw1VdfYcOGDTh37hxGjhwpYdWNs2PHDvz9739Hz549HV53pfYUFxdj0KBB0Gg0WLVqFQ4dOoQ33ngDAQEB9n0WLFiAd999Fx9++CG2bdsGb29vpKamoqamRsLKr/Tqq69i0aJFeP/995GZmYlXX30VCxYswHvvvWffR85tqaysRK9evbBw4cIGtzem9tGjR+PgwYNYs2YNVq5ciY0bN+KJJ55oqyY4uFZ7qqqqsGvXLrzwwgvYtWsXvvnmG2RlZeH3v/+9w35yac/1/m5svv32W2zduhURERFXbJNLW5zCCc+4cDv9+/cX6enp9u8tFouIiIgQ8+fPl7CqpsvPzxcAxIYNG4QQQpSUlAiNRiO++uor+z6ZmZkCgNiyZYtUZV5XeXm56Ny5s1izZo249dZbxTPPPCOEcL32zJgxQ9x0001X3W61WkVYWJh47bXX7K+VlJQInU4n/v3vf7dFiY02YsQI8eijjzq8NnLkSDF69GghhGu1BYD49ttv7d83pvZDhw4JAGLHjh32fVatWiUUCoU4e/Zsm9XekMvb05Dt27cLAOLUqVNCCPm252ptOXPmjOjQoYM4cOCAiImJEW+99ZZ9m1zb0lzsSV/GnZ5PbXsEZ2BgIAAgIyMDZrPZoW0JCQmIjo6WddvS09MxYsQIh7oB12vPf//7X/Tt2xf3338/QkND0adPH3z00Uf27dnZ2cjNzXVoj5+fHwYMGCC79tx4441Yt24djhw5AgDYu3cvNm3ahOHDhwNwrbZcrjG1b9myBf7+/ujbt699nyFDhkCpVGLbtm1tXnNTlZaWQqFQ2J9Z4ErtsVqtGDt2LKZPn45u3bpdsd2V2tIYbrPimLM05/nUcmS1WjF16lQMGjQI3bt3BwDk5uZCq9Ve8TARg8GA3NxcCaq8vs8//xy7du3Cjh07rtjmau05ceIEFi1ahGnTpuGvf/0rduzYgaeffhparRZpaWn2mhv6tye39vzlL39BWVkZEhISoFKpYLFY8NJLL2H06NEA4FJtuVxjas/NzUVoaKjDdrVajcDAQNm3r6amBjNmzMDDDz9sfyiFK7Xn1VdfhVqtxtNPP93gdldqS2MwpN1Ueno6Dhw4gE2bNkldSrOdPn0azzzzDNasWQMPDw+py2kxq9WKvn374uWXXwYA9OnTBwcOHMCHH36ItLQ0iatrmi+//BLLli3D8uXL0a1bN+zZswdTp05FRESEy7WlPTGbzXjggQcghMCiRYukLqfJMjIy8M4772DXrl0OjxV2Zxzuvkxznk8tN5MnT8bKlSuxfv16REZG2l8PCwtDbW0tSkpKHPaXa9syMjKQn5+PG264AWq1Gmq1Ghs2bMC7774LtVoNg8HgUu0JDw9HUlKSw2uJiYnIyckBAHvNrvBvb/r06fjLX/6Chx56CD169MDYsWPx7LPPYv78+QBcqy2Xa0ztYWFhV0wkraurQ1FRkWzbZwvoU6dOYc2aNQ6PdnSV9vz666/Iz89HdHS0/WfCqVOn8H//93+IjY0F4DptaSyG9GUufT61je351CkpKRJWdn1CCEyePBnffvstfv75Z8TFxTlsT05OhkajcWhbVlYWcnJyZNm2O+64A/v378eePXvsX3379sXo0aPtf3al9gwaNOiKW+KOHDmCmJgYAEBcXBzCwsIc2lNWVoZt27bJrj1VVVVQKh1/fKhUKlitVgCu1ZbLNab2lJQUlJSUICMjw77Pzz//DKvVigEDBrR5zddjC+ijR49i7dq1CAoKctjuKu0ZO3Ys9u3b5/AzISIiAtOnT8ePP/4IwHXa0mhSz1yTo88//1zodDqxdOlScejQIfHEE08If39/kZubK3Vp1zRp0iTh5+cnfvnlF3H+/Hn7V1VVlX2fJ598UkRHR4uff/5Z7Ny5U6SkpIiUlBQJq26aS2d3C+Fa7dm+fbtQq9XipZdeEkePHhXLli0TXl5e4l//+pd9n1deeUX4+/uL77//Xuzbt0/84Q9/EHFxcaK6ulrCyq+UlpYmOnToIFauXCmys7PFN998I4KDg8Wf//xn+z5ybkt5ebnYvXu32L17twAg3nzzTbF79277bOfG1H7nnXeKPn36iG3btolNmzaJzp07i4cfflh27amtrRW///3vRWRkpNizZ4/DzwaTySS79lzv7+Zyl8/uFkI+bXEGhvRVvPfeeyI6OlpotVrRv39/sXXrVqlLui4ADX4tWbLEvk91dbV46qmnREBAgPDy8hL33nuvOH/+vHRFN9HlIe1q7VmxYoXo3r270Ol0IiEhQSxevNhhu9VqFS+88IIwGAxCp9OJO+64Q2RlZUlU7dWVlZWJZ555RkRHRwsPDw/RsWNH8dxzzzn80JdzW9avX9/g/5W0tDQhRONqLywsFA8//LDw8fERer1ejB8/XpSXl0vQmmu3Jzs7+6o/G9avXy+79lzv7+ZyDYW0XNriDHxUJRERkUzxmjQREZFMMaSJiIhkiiFNREQkUwxpIiIimWJIExERyRRDmoiISKYY0kRERDLFkCaiRvvll1+gUCiuWC9dKrfddhumTp0qdRlErYYhTeQiWiOQXCXk5PbLAVFbYUgTERHJFEOayAWMGzcOGzZswDvvvAOFQgGFQoGTJ0/iwIEDGD58OHx8fGAwGDB27FgYjUYA9b1PrVaLX3/91X6cBQsWIDQ0FHl5eVc9ZlNt2rQJN998Mzw9PREVFYWnn34alZWV9u2xsbF4+eWX8eijj8LX1xfR0dFYvHixwzE2b96M3r17w8PDA3379sV3330HhUKBPXv24OTJk7j99tsBAAEBAVAoFBg3bpz9vVarFX/+858RGBiIsLAwzJkzp8ltIJItqRcPJ6LrKykpESkpKeLxxx+3P8HIaDSKkJAQMXPmTJGZmSl27dolhg4dKm6//Xb7+6ZPny5iYmJESUmJ2LVrl9BqteL777+/6jHr6uquWYft4QfFxcVCCCGOHTsmvL29xVtvvSWOHDkifvvtN9GnTx8xbtw4+3tiYmJEYGCgWLhwoTh69KiYP3++UCqV4vDhw0IIIUpLS0VgYKAYM2aMOHjwoPjhhx9Ely5dBACxe/duUVdXJ77++msBQGRlZYnz58+LkpISIUT9A1f0er2YM2eOOHLkiPj000+FQqEQP/30kzM/fiLJMKSJXMTlTwB78cUXxbBhwxz2OX36tD3MhBDCZDKJ3r17iwceeEAkJSWJxx9//JrHvJ7LQ3rChAniiSeecNjn119/FUql0v5Yx5iYGDFmzBj7dqvVKkJDQ8WiRYuEEEIsWrRIBAUFOTwG8qOPPrKHdEPnvbT+m266yeG1fv36iRkzZjS6TURyppawE09ELbB3716sX78ePj4+V2w7fvw4unTpAq1Wi2XLlqFnz56IiYnBW2+95fQa9u3bh2XLltlfE0LAarUiOzsbiYmJAICePXvatysUCoSFhSE/Px8AkJWVhZ49e8LDw8O+T//+/Rtdw6XHBoDw8HD7sYlcHUOayEVVVFTg7rvvxquvvnrFtvDwcPufN2/eDAAoKipCUVERvL29nVrDxIkT8fTTT1+xLTo62v5njUbjsE2hUMBqtTqlhtY8NpHUGNJELkKr1cJisdi/v+GGG/D1118jNjYWanXD/5WPHz+OZ599Fh999BG++OILpKWlYe3atVAqlQ0es6luuOEGHDp0CJ06dWr2Mbp27Yp//etfMJlM0Ol0AIAdO3Y47KPVagGgRbUSuSLO7iZyEbGxsdi2bRtOnjwJo9GI9PR0FBUV4eGHH8aOHTtw/Phx/Pjjjxg/fjwsFgssFgvGjBmD1NRUjB8/HkuWLMG+ffvwxhtvXPWYTe2BzpgxA5s3b8bkyZOxZ88eHD16FN9//z0mT57c6GM88sgjsFqteOKJJ5CZmYkff/wRr7/+OoD6XjEAxMTEQKFQYOXKlSgoKEBFRUWT6iRyVQxpIhfxpz/9CSqVCklJSQgJCUFtbS1+++03WCwWDBs2DD169MDUqVPh7+8PpVKJl156CadOncLf//53APVD4IsXL8bzzz+PvXv3NnjMnJycJtXUs2dPbNiwAUeOHMHNN9+MPn36YNasWYiIiGj0MfR6PVasWIE9e/agd+/eeO655zBr1iwAsF+n7tChA+bOnYu//OUvMBgMTfolgMiVKYQQQuoiiIgutWzZMowfPx6lpaXw9PSUuhwiyfCaNBFJ7p///Cc6duyIDh06YO/evZgxYwYeeOABBjS1exzuJiK7J598Ej4+Pg1+Pfnkk6123tzcXIwZMwaJiYl49tlncf/991+xKhlRe8ThbiKyy8/PR1lZWYPb9Ho9QkND27giovaNIU1ERCRTHO4mIiKSKYY0ERGRTDGkiYiIZIohTUREJFMMaSIiIpliSBMREckUQ5qIiEimGNJEREQy9f/YWAv1xXOEJwAAAABJR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<h3 id="Observations">Observations<a class="anchor-link" href="#Observations">¶</a></h3><p>It seems that negatives are slightly more that the positives in this dataset, but not enough to be a source of concern. As for the text length, it seems that the training and test datasets are close, having a similar pattern of a strong spike near Twitter's word limit. It seems though that there is a difference depending on the label: positives are more skewed towards the word limit.</p>
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<h2 id="Data-cleaning">Data cleaning<a class="anchor-link" href="#Data-cleaning">¶</a></h2><p>As seen above, the columns <code>location</code> and <code>keyword</code> are sometimes blank. To fix this, the empty cells will be filled with the value <code>unkown</code>. The text also has many characters/objects which need to be cleaned/generalized, such as URLs, mentions, and emojis. This is required to use GloVe later on.</p>
<p>Later, the text will need to be tokenized for processing be the ML algorithms. The GloVe models will be used to vectorize the tweets based on their trained models.</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Fill empty cells</span>
<span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">:</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'location'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">'unknown'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'keyword'</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="s1">'unknown'</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<pre>/tmp/ipykernel_141633/2307373696.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
df['location'].fillna('unknown', inplace=True)
/tmp/ipykernel_141633/2307373696.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
df['keyword'].fillna('unknown', inplace=True)
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<h3 id="Text-Cleaning-Process">Text Cleaning Process<a class="anchor-link" href="#Text-Cleaning-Process">¶</a></h3>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">clean_text</span><span class="p">(</span><span class="n">text</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Apply regex transformations to normalize tweet text following GloVe preprocessing"""</span>
<span class="k">def</span><span class="w"> </span><span class="nf">process_hashtag</span><span class="p">(</span><span class="n">hashtag</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Process hashtag content according to GloVe specifications"""</span>
<span class="n">hashtag_body</span> <span class="o">=</span> <span class="n">hashtag</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">hashtag_body</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="o">==</span> <span class="n">hashtag_body</span><span class="p">:</span>
<span class="k">return</span> <span class="s2">"<HASHTAG> "</span> <span class="o">+</span> <span class="n">hashtag_body</span> <span class="o">+</span> <span class="s2">" <ALLCAPS>"</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># Split on uppercase letters and numbers followed by uppercase</span>
<span class="n">split_pattern</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">'(?=[A-Z])|(?<=[0-9])(?=[A-Z])|(?<=[A-Z])(?=[0-9])'</span>
<span class="n">parts</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_pattern</span><span class="p">,</span> <span class="n">hashtag_body</span><span class="p">)</span>
<span class="n">parts</span> <span class="o">=</span> <span class="p">[</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">parts</span> <span class="k">if</span> <span class="n">p</span><span class="p">]</span> <span class="c1"># Remove empty strings</span>
<span class="k">return</span> <span class="s2">"<HASHTAG> "</span> <span class="o">+</span> <span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">parts</span><span class="p">)</span>
<span class="c1"># Different regex parts for smiley faces</span>
<span class="n">eyes</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"[8:=;]"</span>
<span class="n">nose</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">"['`\-]?"</span>
<span class="n">transformations</span> <span class="o">=</span> <span class="p">[</span>
<span class="c1"># URLs first</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"https?://\S+\b|www\.(\w+\.)+\S*"</span><span class="p">,</span> <span class="s2">"<URL>"</span><span class="p">),</span>
<span class="c1"># Force splitting words appended with slashes</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"/"</span><span class="p">,</span> <span class="s2">" / "</span><span class="p">),</span>
<span class="c1"># User mentions</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"@\w+"</span><span class="p">,</span> <span class="s2">"<USER>"</span><span class="p">),</span>
<span class="c1"># Smileys and emoticons</span>
<span class="p">(</span><span class="sa">rf</span><span class="s2">"</span><span class="si">{</span><span class="n">eyes</span><span class="si">}{</span><span class="n">nose</span><span class="si">}</span><span class="s2">[)d]+|[)d]+</span><span class="si">{</span><span class="n">nose</span><span class="si">}{</span><span class="n">eyes</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"<SMILE>"</span><span class="p">,</span> <span class="n">re</span><span class="o">.</span><span class="n">IGNORECASE</span><span class="p">),</span>
<span class="p">(</span><span class="sa">rf</span><span class="s2">"</span><span class="si">{</span><span class="n">eyes</span><span class="si">}{</span><span class="n">nose</span><span class="si">}</span><span class="s2">p+"</span><span class="p">,</span> <span class="s2">"<LOLFACE>"</span><span class="p">,</span> <span class="n">re</span><span class="o">.</span><span class="n">IGNORECASE</span><span class="p">),</span>
<span class="p">(</span><span class="sa">rf</span><span class="s2">"</span><span class="si">{</span><span class="n">eyes</span><span class="si">}{</span><span class="n">nose</span><span class="si">}</span><span class="s2">\(+|\)+</span><span class="si">{</span><span class="n">nose</span><span class="si">}{</span><span class="n">eyes</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"<SADFACE>"</span><span class="p">),</span>
<span class="p">(</span><span class="sa">rf</span><span class="s2">"</span><span class="si">{</span><span class="n">eyes</span><span class="si">}{</span><span class="n">nose</span><span class="si">}</span><span class="s2">[\/|l*]"</span><span class="p">,</span> <span class="s2">"<NEUTRALFACE>"</span><span class="p">),</span>
<span class="c1"># Hearts</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"<3"</span><span class="p">,</span> <span class="s2">"<HEART>"</span><span class="p">),</span>
<span class="c1"># Numbers</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"[-+]?[.\d]*[\d]+[:,.\d]*"</span><span class="p">,</span> <span class="s2">"<NUMBER>"</span><span class="p">),</span>
<span class="c1"># Hashtags</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"#(\S+)"</span><span class="p">,</span> <span class="n">process_hashtag</span><span class="p">),</span>
<span class="c1"># Punctuation repetitions</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"([!?.]){2,}"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"\1 <REPEAT>"</span><span class="p">),</span>
<span class="c1"># Elongated words</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"\b(\S*?)(.)\2{2,}\b"</span><span class="p">,</span> <span class="sa">r</span><span class="s2">"\1\2 <ELONG>"</span><span class="p">),</span>
<span class="c1"># All caps words (but not tags that already start with <)</span>
<span class="p">(</span><span class="sa">r</span><span class="s2">"(?<!\<)\b([A-Z0-9()<>'`\-]{2,})\b"</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">m</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" <ALLCAPS>"</span><span class="p">)</span>
<span class="p">]</span>
<span class="k">for</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="n">transformations</span><span class="p">:</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
<span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span><span class="p">,</span> <span class="n">flags</span> <span class="o">=</span> <span class="n">transformation</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span><span class="p">,</span> <span class="n">text</span><span class="p">,</span> <span class="n">flags</span><span class="o">=</span><span class="n">flags</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span> <span class="o">=</span> <span class="n">transformation</span>
<span class="n">text</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">replacement</span><span class="p">,</span> <span class="n">text</span><span class="p">)</span>
<span class="k">return</span> <span class="n">text</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_train</span><span class="p">[</span><span class="s2">"clean_text"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="s2">"text"</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">clean_text</span><span class="p">)</span>
<span class="n">df_test</span><span class="p">[</span><span class="s2">"clean_text"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_test</span><span class="p">[</span><span class="s2">"text"</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">clean_text</span><span class="p">)</span>
<span class="c1"># Show sample of processed text</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Sample processed tweets:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_train</span><span class="p">),</span> <span class="mi">3</span><span class="p">,</span> <span class="n">replace</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">"Original:"</span><span class="p">,</span> <span class="n">df_train</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="s1">'text'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Cleaned :"</span><span class="p">,</span> <span class="n">df_train</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="s1">'clean_text'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"-"</span> <span class="o">*</span> <span class="mi">80</span><span class="p">)</span>
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<pre>Sample processed tweets:
Original: Coastal German Shepherd Rescue OC shared a link: 'Ecstatic Rescued Racco... http://t.co/t8Q6DzVgwX #animalrescue
Cleaned : Coastal German Shepherd Rescue oc <ALLCAPS> shared a link: 'Ecstatic Rescued Racco. <REPEAT> <URL> <HASHTAG> animalrescue
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Original: Long Road To Ruin - Foo Fighters
Cleaned : Long Road To Ruin - Foo Fighters
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Original: @NBCNews Yea bombing #pearlharbor not so good of an idea!
Cleaned : <USER> Yea bombing <HASHTAG> pearlharbor not so good of an idea!
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<h2 id="Word-Tokenization">Word Tokenization<a class="anchor-link" href="#Word-Tokenization">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">Tokenizer</span><span class="p">(</span><span class="n">num_words</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">filters</span><span class="o">=</span><span class="s1">'</span><span class="se">\t\n</span><span class="s1">'</span><span class="p">,</span> <span class="n">lower</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s1">' '</span><span class="p">)</span>
<span class="n">training_texts</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="s1">'clean_text'</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
<span class="n">training_texts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">'<blank>'</span><span class="p">)</span>
<span class="n">tokenizer</span><span class="o">.</span><span class="n">fit_on_texts</span><span class="p">(</span><span class="n">training_texts</span><span class="p">)</span>
<span class="n">idx_word</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">index_word</span>
<span class="n">word_idx</span> <span class="o">=</span> <span class="p">{</span><span class="n">word</span><span class="p">:</span><span class="n">idx</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">idx_word</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="n">num_words</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx_word</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">df</span> <span class="ow">in</span> <span class="n">df_train</span><span class="p">,</span> <span class="n">df_test</span><span class="p">:</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'sequences'</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">texts_to_sequences</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'clean_text'</span><span class="p">])</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'seq_len'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'sequences'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">len</span><span class="p">)</span>
<span class="n">blank_id</span> <span class="o">=</span> <span class="n">word_idx</span><span class="p">[</span><span class="s1">'<blank>'</span><span class="p">]</span>
<span class="n">num_words</span> <span class="o">+=</span> <span class="mi">1</span>
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<h2 id="Load-GloVe-Embeddings-from-Text-File">Load GloVe Embeddings from Text File<a class="anchor-link" href="#Load-GloVe-Embeddings-from-Text-File">¶</a></h2><p>GloVe (Global Vectors for Word Representation) serves as the semantic foundation for this NLP project.</p>
<p>The use of GloVe is an example of Transfer learning which we learned about in the last module.</p>
<p>GloVe provides 100-dimensional dense vector representations pre-trained on 27 billion Twitter tokens, capturing linguistic patterns and word relationships specific to social media language. This brings external linguistic knowledge into the model, reducing reliance on our limited training data.</p>
<p>As the limited data in our dataset would add little benefit to the model, the implementation is simplified by applying it as a fixed feature extractor (the pretrained weights are loaded and kept static during training).</p>
<p>The cleaning pipeline applied above aligns with GloVe's training conventions.</p>
<p>This has the benefit of better performace with limited data and faster convergence of the model.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">load_glove_embeddings</span><span class="p">(</span><span class="n">glove_file_path</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Load GloVe embeddings from text file format"""</span>
<span class="n">embeddings</span> <span class="o">=</span> <span class="p">{}</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Loading GloVe embeddings from </span><span class="si">%s</span><span class="s2">..."</span> <span class="o">%</span> <span class="n">glove_file_path</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">glove_file_path</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">"Processing GloVe vectors"</span><span class="p">):</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">values</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># Skip empty lines</span>
<span class="k">continue</span>
<span class="n">word</span> <span class="o">=</span> <span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">vector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">'float32'</span><span class="p">)</span>
<span class="n">embeddings</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="n">vector</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Loaded </span><span class="si">%d</span><span class="s2"> word vectors"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">embeddings</span><span class="p">))</span>
<span class="k">return</span> <span class="n">embeddings</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Load GloVe embeddings</span>
<span class="k">if</span> <span class="s1">'KAGGLE_URL_BASE'</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
<span class="n">glove_file_path</span> <span class="o">=</span> <span class="s2">"/kaggle/input/glovetwitter27b100dtxt/glove.twitter.27B.100d.txt"</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">glove_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">DATA_DIR</span><span class="p">,</span> <span class="s2">"glove.twitter.27B.100d.txt"</span><span class="p">)</span>
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">load_glove_embeddings</span><span class="p">(</span><span class="n">glove_file_path</span><span class="p">)</span>
<span class="n">embed_dim</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">embeddings</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Embedding dimension:"</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">)</span>
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<pre>Loading GloVe embeddings from data/glove.twitter.27B.100d.txt...
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<pre>Processing GloVe vectors: 1193514it [00:22, 53595.23it/s]</pre>
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<pre>Loaded 1193514 word vectors
Embedding dimension: 100
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create embedding matrix with proper handling of special tokens</span>
<span class="n">embedding_matrix</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">num_words</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">))</span>
<span class="k">for</span> <span class="n">word</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">word_idx</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="c1"># GloVe embeddings use lowercase versions of special tokens</span>
<span class="n">glove_word</span> <span class="o">=</span> <span class="n">word</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="k">if</span> <span class="n">glove_word</span> <span class="ow">in</span> <span class="n">embeddings</span><span class="p">:</span>
<span class="n">embedding_matrix</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">glove_word</span><span class="p">]</span>
<span class="k">elif</span> <span class="n">word</span> <span class="o">==</span> <span class="s1">'<blank>'</span><span class="p">:</span>
<span class="c1"># Use a zero vector for padding</span>
<span class="n">embedding_matrix</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</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">embed_dim</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="c1"># For out-of-vocabulary words, use a random initialization</span>
<span class="n">embedding_matrix</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</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="mf">0.1</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">)</span>
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<h2 id="Data-Partitioning">Data Partitioning<a class="anchor-link" href="#Data-Partitioning">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df_train_sub</span><span class="p">,</span> <span class="n">df_cv_sub</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">.2</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">df_train</span><span class="p">[</span><span class="s1">'target'</span><span class="p">])</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">df_train_sub</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span>
<span class="n">y_cv</span> <span class="o">=</span> <span class="n">df_cv_sub</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span>
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<h1 id="Model-Development-Optimization">Model Development Optimization<a class="anchor-link" href="#Model-Development-Optimization">¶</a></h1>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># First write some functions to handle metrics</span>
<span class="k">def</span><span class="w"> </span><span class="nf">calc_model_metrics</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="k">return</span> <span class="p">{</span>
<span class="s1">'accuracy'</span><span class="p">:</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">),</span>
<span class="s1">'f1'</span><span class="p">:</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">),</span>
<span class="s1">'precision'</span><span class="p">:</span> <span class="n">precision_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">),</span>
<span class="s1">'recall'</span><span class="p">:</span> <span class="n">recall_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">),</span>
<span class="s1">'cm'</span><span class="p">:</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">),</span>
<span class="p">}</span>
<span class="k">def</span><span class="w"> </span><span class="nf">display_model_metrics</span><span class="p">(</span><span class="n">model_name</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">precision</span><span class="p">,</span> <span class="n">recall</span><span class="p">,</span> <span class="n">cm</span><span class="p">,</span> <span class="n">show_cm</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Model:'</span><span class="p">,</span> <span class="n">model_name</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">' F1-score: </span><span class="si">%.3f</span><span class="s1"> Accuracy: </span><span class="si">%.3f</span><span class="s1"> Precision: </span><span class="si">%.3f</span><span class="s1"> Recall: </span><span class="si">%.3f</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">f1</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">precision</span><span class="p">,</span> <span class="n">recall</span><span class="p">))</span>
<span class="k">if</span> <span class="n">show_cm</span><span class="p">:</span>
<span class="n">ConfusionMatrixDisplay</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span><span class="o">.</span><span class="n">plot</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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<h2 id="Baseline-Model-with-Metadata-Features">Baseline Model with Metadata Features<a class="anchor-link" href="#Baseline-Model-with-Metadata-Features">¶</a></h2><p>A Random Forest model is used as a baseline to compare RNNs to. It was chosen as it is fast to train, can be trained well on the metadata, and is suitable for classification jobs.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">cols_to_encode</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'keyword'</span><span class="p">,</span><span class="s1">'location'</span><span class="p">]</span>
<span class="n">ct</span> <span class="o">=</span> <span class="n">ColumnTransformer</span><span class="p">(</span>
<span class="p">[(</span><span class="s2">"ordinal_encoder"</span><span class="p">,</span> <span class="n">OrdinalEncoder</span><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s1">'use_encoded_value'</span><span class="p">,</span> <span class="n">unknown_value</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span> <span class="n">cols_to_encode</span><span class="p">)]</span>
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df_train</span><span class="p">[</span><span class="n">cols_to_encode</span><span class="p">])</span>
<span class="n">X_train_rf</span> <span class="o">=</span> <span class="n">ct</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_train_sub</span><span class="p">[</span><span class="n">cols_to_encode</span><span class="p">])</span>
<span class="n">X_cv_rf</span> <span class="o">=</span> <span class="n">ct</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_cv_sub</span><span class="p">[</span><span class="n">cols_to_encode</span><span class="p">])</span>
<span class="n">X_test_rf</span> <span class="o">=</span> <span class="n">ct</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_test</span><span class="p">[</span><span class="n">cols_to_encode</span><span class="p">])</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">rf_classifier</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_rf</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_pred_cv</span> <span class="o">=</span> <span class="n">rf_classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_cv_rf</span><span class="p">)</span>
<span class="n">rf_metrics</span> <span class="o">=</span> <span class="n">calc_model_metrics</span><span class="p">(</span><span class="n">y_cv</span><span class="p">,</span> <span class="n">y_pred_cv</span><span class="p">)</span>
<span class="n">display_model_metrics</span><span class="p">(</span><span class="s1">'Random Forest Classifier'</span><span class="p">,</span> <span class="o">**</span><span class="n">rf_metrics</span><span class="p">)</span>
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<pre>Model: Random Forest Classifier
F1-score: 0.617 Accuracy: 0.678 Precision: 0.630 Recall: 0.606
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"/>
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<h2 id="RNN-Models-with-Text-Features">RNN Models with Text Features<a class="anchor-link" href="#RNN-Models-with-Text-Features">¶</a></h2>
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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"># Prepare the inputs for RNN handling by padding</span>
<span class="n">max_words</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="s1">'seq_len'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="n">X_train_rnn</span> <span class="o">=</span> <span class="n">pad_sequences</span><span class="p">(</span>
<span class="n">df_train_sub</span><span class="p">[</span><span class="s1">'sequences'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
<span class="n">maxlen</span><span class="o">=</span><span class="n">max_words</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="s1">'pre'</span><span class="p">,</span>
<span class="n">value</span><span class="o">=</span><span class="n">blank_id</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span>
<span class="p">)</span>
<span class="n">X_cv_rnn</span> <span class="o">=</span> <span class="n">pad_sequences</span><span class="p">(</span>
<span class="n">df_cv_sub</span><span class="p">[</span><span class="s1">'sequences'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
<span class="n">maxlen</span><span class="o">=</span><span class="n">max_words</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="s1">'pre'</span><span class="p">,</span>
<span class="n">value</span><span class="o">=</span><span class="n">blank_id</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span>
<span class="p">)</span>
<span class="n">X_test_rnn</span> <span class="o">=</span> <span class="n">pad_sequences</span><span class="p">(</span>
<span class="n">df_test</span><span class="p">[</span><span class="s1">'sequences'</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span>
<span class="n">maxlen</span><span class="o">=</span><span class="n">max_words</span><span class="p">,</span>
<span class="n">padding</span><span class="o">=</span><span class="s1">'pre'</span><span class="p">,</span>
<span class="n">value</span><span class="o">=</span><span class="n">blank_id</span><span class="p">,</span>
<span class="n">dtype</span><span class="o">=</span><span class="s1">'int32'</span>
<span class="p">)</span>
<span class="n">y_train_rnn</span> <span class="o">=</span> <span class="n">y_train</span><span class="o">.</span><span class="n">values</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">y_cv_rnn</span> <span class="o">=</span> <span class="n">y_cv</span><span class="o">.</span><span class="n">values</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>
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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="c1"># A function to create RNN models</span>
<span class="k">def</span><span class="w"> </span><span class="nf">create_rnn_model</span><span class="p">(</span><span class="n">rnn_layer</span><span class="o">=</span><span class="n">layers</span><span class="o">.</span><span class="n">GRU</span><span class="p">,</span> <span class="n">extra_layers</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">units</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">.3</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Create enhanced RNN architecture with GloVe embeddings"""</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span>
<span class="n">input_dim</span><span class="o">=</span><span class="n">num_words</span><span class="p">,</span>
<span class="n">output_dim</span><span class="o">=</span><span class="n">embed_dim</span><span class="p">,</span>
<span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="n">embedding_matrix</span><span class="p">],</span>
<span class="n">trainable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">mask_zero</span><span class="o">=</span><span class="kc">True</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">extra_layers</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">rnn_layer</span><span class="p">(</span><span class="n">units</span><span class="p">,</span> <span class="n">return_sequences</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">,</span> <span class="n">recurrent_dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">rnn_layer</span><span class="p">(</span><span class="n">units</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">,</span> <span class="n">recurrent_dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">.4</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'sigmoid'</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
<span class="n">optimizer</span><span class="o">=</span><span class="n">Nadam</span><span class="p">(</span><span class="n">learning_rate</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">'binary_crossentropy'</span><span class="p">,</span>
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">]</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">model</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Create the RNN models along with the appropriate meta-data</span>
<span class="n">rnn_models</span> <span class="o">=</span> <span class="p">[{</span>
<span class="s2">"params"</span><span class="p">:</span> <span class="p">{</span>
<span class="s2">"rnn_layer"</span><span class="p">:</span> <span class="n">layer_class</span><span class="p">,</span>
<span class="s2">"extra_layers"</span><span class="p">:</span> <span class="n">layers</span><span class="p">,</span>
<span class="s2">"units"</span><span class="p">:</span> <span class="n">units</span><span class="p">,</span>
<span class="s2">"dropout"</span><span class="p">:</span> <span class="n">dropout</span><span class="p">,</span>
<span class="p">}}</span> <span class="k">for</span> <span class="n">layer_class</span> <span class="ow">in</span> <span class="p">(</span><span class="n">layers</span><span class="o">.</span><span class="n">LSTM</span><span class="p">,</span> <span class="n">layers</span><span class="o">.</span><span class="n">GRU</span><span class="p">)</span> <span class="k">for</span> <span class="n">layers</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="k">for</span> <span class="n">units</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">48</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span> <span class="k">for</span> <span class="n">dropout</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">.2</span><span class="p">,</span> <span class="mf">.4</span><span class="p">)]</span>
<span class="c1"># Create directories for checkpoints and histories</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s2">"checkpoints"</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="s2">"histories"</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">rnn_models</span><span class="p">:</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">model</span><span class="p">[</span><span class="s2">"params"</span><span class="p">]</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"instance"</span><span class="p">]</span> <span class="o">=</span> <span class="n">create_rnn_model</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"</span><span class="si">%s</span><span class="s2">, layers=</span><span class="si">%d</span><span class="s2">, units=</span><span class="si">%d</span><span class="s2">, dropout=</span><span class="si">%.1f</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">params</span><span class="p">[</span><span class="s2">"rnn_layer"</span><span class="p">]</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="s2">"extra_layers"</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="s2">"units"</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="s2">"dropout"</span><span class="p">])</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"checkpoint"</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"checkpoints"</span><span class="p">,</span> <span class="s2">"</span><span class="si">%s</span><span class="s2">.keras"</span> <span class="o">%</span> <span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">])</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"history_file"</span><span class="p">]</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"histories"</span><span class="p">,</span> <span class="s2">"</span><span class="si">%s</span><span class="s2">.csv"</span> <span class="o">%</span> <span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">])</span>
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<pre>WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1758095267.102520 141633 gpu_device.cc:2020] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4300 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1660 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5
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<h3 id="Train-the-RNN-models">Train the RNN models<a class="anchor-link" href="#Train-the-RNN-models">¶</a></h3>
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</div># This cell can be left without running if the checkpoint files are available
class F1ScoreCallback(tf.keras.callbacks.Callback):
def __init__(self, validation_data, patience=5):
super().__init__()
self.X_val, self.y_val = validation_data
self.patience = patience
self.best_f1 = 0
self.best_weights = None
self.wait = 0
def on_epoch_end(self, epoch, logs=None):
y_pred = self.model.predict(self.X_val, verbose=0)
y_pred_binary = np.round(y_pred).flatten()
f1 = f1_score(self.y_val.flatten(), y_pred_binary)
logs['val_f1'] = f1 # This adds val_f1 to the logs
print(" - val_f1: %.4f" % f1, end='')
# Early stopping logic based on F1
if f1 > self.best_f1:
self.best_f1 = f1
self.best_weights = self.model.get_weights()
self.wait = 0
else:
self.wait += 1
if self.wait >= self.patience:
self.model.stop_training = True
self.model.set_weights(self.best_weights)
for i, model in enumerate(rnn_models):
f1_callback = F1ScoreCallback(validation_data=(X_cv_rnn, y_cv_rnn), patience=10)
model_checkpoint = ModelCheckpoint(model["checkpoint"], save_best_only=True, monitor='val_f1', mode='max')
hist_logger = CSVLogger(model["history_file"])
print("Training model number", i + 1, "of", len(rnn_models))
print(model["name"])
model["instance"].fit(
X_train_rnn, y_train_rnn,
batch_size=32, epochs=50,
validation_data=(X_cv_rnn, y_cv_rnn),
callbacks=[f1_callback, model_checkpoint, hist_logger],
verbose=1
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Load the models from the checkpoint files</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">rnn_models</span><span class="p">:</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"instance"</span><span class="p">]</span> <span class="o">=</span> <span class="n">load_model</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">"checkpoint"</span><span class="p">])</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"history"</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">"history_file"</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s1">','</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">'python'</span><span class="p">)</span>
<span class="n">y_pred_cv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">"instance"</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_cv_rnn</span><span class="p">))</span>
<span class="n">model</span><span class="p">[</span><span class="s2">"metrics"</span><span class="p">]</span> <span class="o">=</span> <span class="n">calc_model_metrics</span><span class="p">(</span><span class="n">y_cv</span><span class="p">,</span> <span class="n">y_pred_cv</span><span class="p">)</span>
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<pre>2025-09-17 10:47:49.923844: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:473] Loaded cuDNN version 91200
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<pre><span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 6ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">3s</span> 36ms/step
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<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 5ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 34ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 38ms/step
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<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">3s</span> 57ms/step
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<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">3s</span> 62ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 67ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 10ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 80ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 84ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 10ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 83ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 80ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 5ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 34ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 41ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">0s</span> 5ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">2s</span> 41ms/step
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<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">4s</span> 63ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 10ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 81ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 82ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">1s</span> 10ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 84ms/step
<span class="ansi-bold">48/48</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">5s</span> 82ms/step
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<h2 id="Display-the-results">Display the results<a class="anchor-link" href="#Display-the-results">¶</a></h2>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Add this function after your metric display functions</span>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_training_history</span><span class="p">(</span><span class="n">history</span><span class="p">,</span> <span class="n">model_name</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Plot training history with loss, accuracy, and F1-score"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s1">'Training History: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</span><span class="p">)</span>
<span class="c1"># Loss</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'loss'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Training Loss'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_loss'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Validation Loss'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Loss'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="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'</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="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="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">.3</span><span class="p">)</span>
<span class="c1"># Accuracy</span>
<span class="n">axes</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="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Training Accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_accuracy'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Validation Accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Accuracy'</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="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">0</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">.3</span><span class="p">)</span>
<span class="c1"># F1-score (validation only)</span>
<span class="k">if</span> <span class="s1">'val_f1'</span> <span class="ow">in</span> <span class="n">history</span><span class="o">.</span><span class="n">columns</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_f1'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Validation F1-score'</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">axes</span><span class="p">[</span><span class="mi">1</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="s1">'Validation F1-score'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'F1-score'</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="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">1</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">.3</span><span class="p">)</span>
<span class="c1"># Mark best F1 epoch</span>
<span class="n">best_f1_epoch</span> <span class="o">=</span> <span class="n">history</span><span class="p">[</span><span class="s1">'val_f1'</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()</span>
<span class="n">best_f1_value</span> <span class="o">=</span> <span class="n">history</span><span class="p">[</span><span class="s1">'val_f1'</span><span class="p">]</span><span class="o">.</span><span class="n">max</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">best_f1_epoch</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">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">.7</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">best_f1_epoch</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">best_f1_value</span> <span class="o">-</span> <span class="mf">0.05</span><span class="p">,</span>
<span class="sa">f</span><span class="s1">'Best: </span><span class="si">{</span><span class="n">best_f1_value</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</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="c1"># Learning rate (if available)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span> <span class="c1"># Remove empty subplot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># Add this function to compare multiple models</span>
<span class="k">def</span><span class="w"> </span><span class="nf">compare_training_histories</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">""</span><span class="p">):</span>
<span class="w"> </span><span class="sd">"""Compare training histories of multiple models"""</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">12</span><span class="p">))</span>
<span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Model Comparison: %s'</span> <span class="o">%</span> <span class="n">title</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">16</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">Set3</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="nb">len</span><span class="p">(</span><span class="n">models</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">model_name</span><span class="p">,</span> <span class="n">history</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">):</span>
<span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="c1"># Loss</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_loss'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
<span class="c1"># Accuracy</span>
<span class="n">axes</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="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">history</span><span class="p">[</span><span class="s1">'val_accuracy'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
<span class="c1"># F1-score</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</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">history</span><span class="p">[</span><span class="s1">'val_f1'</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">.8</span><span class="p">)</span>
<span class="c1"># Format loss subplot</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Validation Loss'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="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'</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="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="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">.3</span><span class="p">)</span>
<span class="c1"># Format accuracy subplot</span>
<span class="n">axes</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="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Validation Accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Accuracy'</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="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">0</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">.3</span><span class="p">)</span>
<span class="c1"># Format F1 subplot</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</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="s1">'Validation F1-score'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Epoch'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'F1-score'</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="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">1</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">.3</span><span class="p">)</span>
<span class="c1"># Remove empty subplot</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Display the LSTM models in descending order based on their F1 scores</span>
<span class="c1"># Also show the confusion matrix of the top model</span>
<span class="n">lstm_models</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">rnn_models</span> <span class="k">if</span> <span class="n">model</span><span class="p">[</span><span class="s2">"params"</span><span class="p">][</span><span class="s2">"rnn_layer"</span><span class="p">]</span> <span class="o">==</span> <span class="n">layers</span><span class="o">.</span><span class="n">LSTM</span><span class="p">]</span>
<span class="n">lstm_models</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">model</span><span class="p">:</span> <span class="n">model</span><span class="p">[</span><span class="s2">"metrics"</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lstm_models</span><span class="p">):</span>
<span class="n">display_model_metrics</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">],</span> <span class="o">**</span><span class="n">model</span><span class="p">[</span><span class="s2">"metrics"</span><span class="p">],</span> <span class="n">show_cm</span><span class="o">=</span><span class="n">i</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span>
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<pre>Model: LSTM, layers=1, units=48, dropout=0.0
F1-score: 0.829 Accuracy: 0.856 Precision: 0.848 Recall: 0.810
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"/>
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<div class="jp-OutputArea-child">
<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
<pre>Model: LSTM, layers=3, units=48, dropout=0.4
F1-score: 0.815 Accuracy: 0.844 Precision: 0.834 Recall: 0.797
Model: LSTM, layers=1, units=48, dropout=0.2
F1-score: 0.810 Accuracy: 0.834 Precision: 0.797 Recall: 0.823
Model: LSTM, layers=2, units=48, dropout=0.4
F1-score: 0.808 Accuracy: 0.840 Precision: 0.835 Recall: 0.783
Model: LSTM, layers=2, units=48, dropout=0.0
F1-score: 0.808 Accuracy: 0.838 Precision: 0.827 Recall: 0.789
Model: LSTM, layers=1, units=64, dropout=0.4
F1-score: 0.797 Accuracy: 0.831 Precision: 0.824 Recall: 0.772
Model: LSTM, layers=2, units=48, dropout=0.2
F1-score: 0.791 Accuracy: 0.825 Precision: 0.810 Recall: 0.774
Model: LSTM, layers=3, units=48, dropout=0.0
F1-score: 0.791 Accuracy: 0.820 Precision: 0.790 Recall: 0.792
Model: LSTM, layers=3, units=48, dropout=0.2
F1-score: 0.790 Accuracy: 0.829 Precision: 0.836 Recall: 0.749
Model: LSTM, layers=3, units=64, dropout=0.4
F1-score: 0.789 Accuracy: 0.825 Precision: 0.820 Recall: 0.760
Model: LSTM, layers=1, units=64, dropout=0.0
F1-score: 0.783 Accuracy: 0.822 Precision: 0.823 Recall: 0.746
Model: LSTM, layers=1, units=48, dropout=0.4
F1-score: 0.779 Accuracy: 0.814 Precision: 0.797 Recall: 0.761
Model: LSTM, layers=3, units=64, dropout=0.0
F1-score: 0.777 Accuracy: 0.812 Precision: 0.791 Recall: 0.763
Model: LSTM, layers=1, units=64, dropout=0.2
F1-score: 0.776 Accuracy: 0.810 Precision: 0.783 Recall: 0.769
Model: LSTM, layers=2, units=64, dropout=0.0
F1-score: 0.776 Accuracy: 0.820 Precision: 0.835 Recall: 0.725
Model: LSTM, layers=2, units=64, dropout=0.4
F1-score: 0.771 Accuracy: 0.794 Precision: 0.738 Recall: 0.807
Model: LSTM, layers=2, units=64, dropout=0.2
F1-score: 0.767 Accuracy: 0.797 Precision: 0.756 Recall: 0.780
Model: LSTM, layers=3, units=64, dropout=0.2
F1-score: 0.759 Accuracy: 0.798 Precision: 0.777 Recall: 0.742
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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="c1"># Display the GRU models in descending order based on their F1 scores</span>
<span class="c1"># Also show the confusion matrix of the top model</span>
<span class="n">gru_models</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">rnn_models</span> <span class="k">if</span> <span class="n">model</span><span class="p">[</span><span class="s2">"params"</span><span class="p">][</span><span class="s2">"rnn_layer"</span><span class="p">]</span> <span class="o">==</span> <span class="n">layers</span><span class="o">.</span><span class="n">GRU</span><span class="p">]</span>
<span class="n">gru_models</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">model</span><span class="p">:</span> <span class="n">model</span><span class="p">[</span><span class="s2">"metrics"</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gru_models</span><span class="p">):</span>
<span class="n">display_model_metrics</span><span class="p">(</span><span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">],</span> <span class="o">**</span><span class="n">model</span><span class="p">[</span><span class="s2">"metrics"</span><span class="p">],</span> <span class="n">show_cm</span><span class="o">=</span><span class="n">i</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span>
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<pre>Model: GRU, layers=3, units=64, dropout=0.2
F1-score: 0.857 Accuracy: 0.882 Precision: 0.892 Recall: 0.824
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<pre>Model: GRU, layers=1, units=48, dropout=0.0
F1-score: 0.821 Accuracy: 0.850 Precision: 0.844 Recall: 0.800
Model: GRU, layers=2, units=64, dropout=0.0
F1-score: 0.818 Accuracy: 0.846 Precision: 0.832 Recall: 0.804
Model: GRU, layers=1, units=64, dropout=0.2
F1-score: 0.813 Accuracy: 0.848 Precision: 0.859 Recall: 0.772
Model: GRU, layers=1, units=64, dropout=0.0
F1-score: 0.811 Accuracy: 0.840 Precision: 0.827 Recall: 0.795
Model: GRU, layers=3, units=48, dropout=0.2
F1-score: 0.807 Accuracy: 0.840 Precision: 0.839 Recall: 0.778
Model: GRU, layers=1, units=48, dropout=0.2
F1-score: 0.804 Accuracy: 0.834 Precision: 0.814 Recall: 0.795
Model: GRU, layers=2, units=48, dropout=0.2
F1-score: 0.792 Accuracy: 0.821 Precision: 0.788 Recall: 0.797
Model: GRU, layers=3, units=64, dropout=0.0
F1-score: 0.787 Accuracy: 0.823 Precision: 0.812 Recall: 0.765
Model: GRU, layers=3, units=48, dropout=0.4
F1-score: 0.786 Accuracy: 0.823 Precision: 0.815 Recall: 0.760
Model: GRU, layers=1, units=64, dropout=0.4
F1-score: 0.779 Accuracy: 0.814 Precision: 0.793 Recall: 0.766
Model: GRU, layers=2, units=48, dropout=0.0
F1-score: 0.777 Accuracy: 0.812 Precision: 0.789 Recall: 0.766
Model: GRU, layers=3, units=64, dropout=0.4
F1-score: 0.774 Accuracy: 0.800 Precision: 0.754 Recall: 0.795
Model: GRU, layers=3, units=48, dropout=0.0
F1-score: 0.773 Accuracy: 0.810 Precision: 0.792 Recall: 0.755
Model: GRU, layers=2, units=48, dropout=0.4
F1-score: 0.768 Accuracy: 0.798 Precision: 0.757 Recall: 0.780
Model: GRU, layers=1, units=48, dropout=0.4
F1-score: 0.766 Accuracy: 0.806 Precision: 0.792 Recall: 0.742
Model: GRU, layers=2, units=64, dropout=0.4
F1-score: 0.761 Accuracy: 0.791 Precision: 0.749 Recall: 0.772
Model: GRU, layers=2, units=64, dropout=0.2
F1-score: 0.759 Accuracy: 0.792 Precision: 0.756 Recall: 0.761
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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="c1"># Plot training history for top LSTM model</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Training History for Top LSTM Model:"</span><span class="p">)</span>
<span class="n">plot_training_history</span><span class="p">(</span><span class="n">lstm_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"history"</span><span class="p">],</span> <span class="n">lstm_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"name"</span><span class="p">])</span>
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<pre>Training History for Top LSTM Model:
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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"># Plot training history for top GRU model</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Training History for Top GRU Model:"</span><span class="p">)</span>
<span class="n">plot_training_history</span><span class="p">(</span><span class="n">gru_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"history"</span><span class="p">],</span> <span class="n">gru_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"name"</span><span class="p">])</span>
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<pre>Training History for Top GRU Model:
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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="c1"># Compare top models from each category</span>
<span class="n">top_models</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s1">'Top LSTM'</span><span class="p">,</span> <span class="n">lstm_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"history"</span><span class="p">]),</span>
<span class="p">(</span><span class="s1">'Top GRU'</span><span class="p">,</span> <span class="n">gru_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"history"</span><span class="p">])</span>
<span class="p">]</span>
<span class="n">compare_training_histories</span><span class="p">(</span><span class="n">top_models</span><span class="p">,</span> <span class="s2">"Best LSTM vs Best GRU"</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [31]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Compare different architectures within LSTM</span>
<span class="n">lstm_comparison</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lstm_models</span><span class="p">[:</span><span class="mi">3</span><span class="p">]):</span> <span class="c1"># Top 3 LSTM models</span>
<span class="n">lstm_comparison</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">"#</span><span class="si">%d</span><span class="s2"> </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]),</span> <span class="n">model</span><span class="p">[</span><span class="s2">"history"</span><span class="p">]))</span>
<span class="n">compare_training_histories</span><span class="p">(</span><span class="n">lstm_comparison</span><span class="p">,</span> <span class="s2">"Top 3 LSTM Architectures"</span><span class="p">)</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Compare different architectures within GRU</span>
<span class="n">gru_comparison</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">gru_models</span><span class="p">[:</span><span class="mi">3</span><span class="p">]):</span> <span class="c1"># Top 3 GRU models</span>
<span class="n">gru_comparison</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="s2">"#</span><span class="si">%d</span><span class="s2"> </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="p">[</span><span class="s2">"name"</span><span class="p">]),</span> <span class="n">model</span><span class="p">[</span><span class="s2">"history"</span><span class="p">]))</span>
<span class="n">compare_training_histories</span><span class="p">(</span><span class="n">gru_comparison</span><span class="p">,</span> <span class="s2">"Top 3 GRU Architectures"</span><span class="p">)</span>
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<h2 id="Model-Comparison-and-Selection-based-on-F1-score">Model Comparison and Selection based on F1-score<a class="anchor-link" href="#Model-Comparison-and-Selection-based-on-F1-score">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Store all model results for comparison</span>
<span class="n">model_results</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'Random Forest'</span><span class="p">:</span> <span class="n">rf_metrics</span><span class="p">,</span>
<span class="s1">'LSTM'</span><span class="p">:</span> <span class="n">lstm_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"metrics"</span><span class="p">],</span>
<span class="s1">'GRU'</span><span class="p">:</span> <span class="n">gru_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"metrics"</span><span class="p">],</span>
<span class="p">}</span>
<span class="c1"># Create comparison DataFrame</span>
<span class="n">comparison_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">model_results</span><span class="p">)[:</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
<span class="n">comparison_df</span> <span class="o">=</span> <span class="n">comparison_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'f1'</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">"Model Performance Comparison (Sorted by F1-score):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">comparison_df</span><span class="p">)</span>
<span class="c1"># Select best model based on F1-score</span>
<span class="n">best_model_name</span> <span class="o">=</span> <span class="n">comparison_df</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">best_model_metrics</span> <span class="o">=</span> <span class="n">comparison_df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Best Model:"</span><span class="p">,</span> <span class="n">best_model_name</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Best F1-score: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">best_model_metrics</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">])</span>
<span class="c1"># Visual comparison</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="c1"># F1-score comparison</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">comparison_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">comparison_df</span><span class="p">[</span><span class="s1">'f1'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'F1-score Comparison'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'F1-score'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="c1"># Accuracy comparison</span>
<span class="n">axes</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="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">comparison_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">comparison_df</span><span class="p">[</span><span class="s1">'accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Accuracy Comparison'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Accuracy'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="c1"># Precision comparison</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">comparison_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">comparison_df</span><span class="p">[</span><span class="s1">'precision'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Precision Comparison'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Precision'</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="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="c1"># Recall comparison</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">comparison_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">comparison_df</span><span class="p">[</span><span class="s1">'recall'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Recall Comparison'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Recall'</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="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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<pre>Model Performance Comparison (Sorted by F1-score):
accuracy f1 precision recall
GRU 0.881812 0.856916 0.892384 0.824159
LSTM 0.856205 0.828772 0.848 0.810398
Random Forest 0.67761 0.617303 0.629571 0.605505
Best Model: GRU
Best F1-score: 0.857
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"/>
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<h1 id="Results-Submission-with-Best-Model">Results Submission with Best Model<a class="anchor-link" href="#Results-Submission-with-Best-Model">¶</a></h1>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># Generate predictions using the best model based on F1-score</span>
<span class="k">if</span> <span class="n">best_model_name</span> <span class="o">==</span> <span class="s1">'Random Forest'</span><span class="p">:</span>
<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">rf_classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_rf</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">best_model_name</span> <span class="o">==</span> <span class="s1">'LSTM'</span><span class="p">:</span>
<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">lstm_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"instance"</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_rnn</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">flatten</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">best_model_name</span> <span class="o">==</span> <span class="s1">'GRU'</span><span class="p">:</span>
<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">gru_models</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"instance"</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_rnn</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">flatten</span><span class="p">()</span>
<span class="c1"># Create submission file</span>
<span class="n">submission</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">'id'</span><span class="p">:</span> <span class="n">df_test</span><span class="p">[</span><span class="s1">'id'</span><span class="p">],</span>
<span class="s1">'target'</span><span class="p">:</span> <span class="n">best_predictions</span>
<span class="p">})</span>
<span class="n">submission</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s1">'submission.csv'</span><span class="p">,</span> <span class="n">index</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="s1">'Submission file created successfully using </span><span class="si">%s</span><span class="s1"> (F1-score: </span><span class="si">%.3f</span><span class="s1">)'</span> <span class="o">%</span> <span class="p">(</span><span class="n">best_model_name</span><span class="p">,</span> <span class="n">best_model_metrics</span><span class="p">[</span><span class="s2">"f1"</span><span class="p">]))</span>
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<pre><span class="ansi-bold">102/102</span> <span class="ansi-green-fg">━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-bold">7s</span> 66ms/step
Submission file created successfully using GRU (F1-score: 0.857)
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<h2 id="Detailed-Model-Analysis">Detailed Model Analysis<a class="anchor-link" href="#Detailed-Model-Analysis">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [35]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Detailed Model Performance Analysis:"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">metrics</span> <span class="ow">in</span> <span class="n">model_results</span><span class="o">.</span><span class="n">items</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="si">%s</span><span class="s2">:"</span> <span class="o">%</span> <span class="n">model_name</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" F1-score: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">metrics</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" Accuracy: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">metrics</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" Precision: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">metrics</span><span class="p">[</span><span class="s1">'precision'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">" Recall: </span><span class="si">%.3f</span><span class="s2">"</span> <span class="o">%</span> <span class="n">metrics</span><span class="p">[</span><span class="s1">'recall'</span><span class="p">])</span>
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<pre>Detailed Model Performance Analysis:
Random Forest:
F1-score: 0.617
Accuracy: 0.678
Precision: 0.630
Recall: 0.606
LSTM:
F1-score: 0.829
Accuracy: 0.856
Precision: 0.848
Recall: 0.810
GRU:
F1-score: 0.857
Accuracy: 0.882
Precision: 0.892
Recall: 0.824
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<h2 id="Discussion">Discussion<a class="anchor-link" href="#Discussion">¶</a></h2><p>It seems that for this data, LSTM and GRU achieved almost the same score, with GRU having an edge. Inspecting the training histories, it seems the fluctuations are large enough such that randomness plays a huge factor. Nevertheless, it seems that a clear pattern can be seen in that increasing the dropout is significantly reducing the overfitting of the data. This can be seen in all the metrics, especially in the validation loss and validation accuracy.</p>
<p>To reduce the effect of overfitting, The F1ScoreCallback class stops the training early when it detects that the validation f1 score has been falling. This is not ideal however, as the top f1 score recorded might have been due to luck rather than good fitting. By looking at the training histories, it can be seen that models without dropout are stopped rather quickly, and the higher the dropout the higher the chance of the training continuing till the end.</p>
<p>When it comes to the number of layers, it also seems that they are performing better, especially when combined with a high dropout. The differences are not significant enough though to rule out it being to randomness.</p>
<p>It must be noted that there is a huge cost associated with the dropout, which is the training time. When certain conditions are met, TensorFlow can optimize the use of the GPU to a great extent. These conditions include no dropout in the RNN layer, meaning that using dropout prevents these optimizations. Quoting the <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU#used-in-the-notebooks">TensorFlow documentation</a>:</p>
<blockquote>
<p>Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or backend-native) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the cuDNN kernel (see below for details), the layer will use a fast cuDNN implementation when using the TensorFlow backend.</p>
<p>The requirements to use the cuDNN implementation are:</p>
<ol>
<li>activation == tanh</li>
<li>recurrent_activation == sigmoid</li>
<li>dropout == 0 and recurrent_dropout == 0</li>
<li>unroll is False</li>
<li>use_bias is True</li>
<li>reset_after is True</li>
<li>Inputs, if use masking, are strictly right-padded.</li>
<li>Eager execution is enabled in the outermost context.</li>
</ol>
</blockquote>
<p>This is clearly visible during training, as models with dropout take more than 10 times the training time.</p>
<p>To summarize, there was a big problem with overfitting when training the RNN models. To solve that two solutions were used: Adding dropout to the RNN layers themselves, and using early-stopping. The solutions did help remedy the situation, but both are less than ideal.</p>
<p>The results didn't seem to be consistent with the choices of parameters, suggesting that randomness is playing a bigger factor.</p>
<p>Future steps:</p>
<ul>
<li>Try to use other methods to reduce overfitting.</li>
<li>Check if even larger models can produce better results.</li>
<li><strong>When building the model, try to use the fact that positives have a higher average text length.</strong></li>
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<h2 id="References">References<a class="anchor-link" href="#References">¶</a></h2><ol>
<li><a href="https://www.kaggle.com/code/mikeliux/disaster-tweet-classificator-based-on-lstm-or-gru">Disaster tweet classificator based on LSTM or GRU</a> by <a href="https://www.kaggle.com/mikeliux">Miguel D B</a></li>
<li><a href="https://nlp.stanford.edu/projects/glove/">GloVe: Global Vectors for Word Representation</a></li>
<li><a href="https://www.kaggle.com/datasets/bertcarremans/glovetwitter27b100dtxt">glove.twitter.27B.100d.txt</a>, a kaggle dataset copied from the above project.</li>
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