main.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>main</title>
<style type=text/css>
div.input_area{
font-size: 8.5pt;
}</style>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<script>
(function() {
  function addWidgetsRenderer() {
    var mimeElement = document.querySelector('script[type="application/vnd.jupyter.widget-view+json"]');
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    document.body.appendChild(scriptElement);
  }

  document.addEventListener('DOMContentLoaded', addWidgetsRenderer);
}());
</script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
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<style type="text/css">
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.highlight  { background: #f8f8f8; }
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.highlight .o { color: #666666 } /* Operator */
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.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
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.highlight .na { color: #7D9029 } /* Name.Attribute */
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.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
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.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
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.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
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.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
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.highlight .vi { color: #19177C } /* Name.Variable.Instance */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
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            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
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<h1 id="Sentiment-Analysis-in-French">Sentiment Analysis in French<a class="anchor-link" href="#Sentiment-Analysis-in-French">&#182;</a></h1>
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<h2 id="0.-Introduction">0. Introduction<a class="anchor-link" href="#0.-Introduction">&#182;</a></h2>
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We will be performing sentiment analysis on Tweeter data from the open dataset by Charles MALAFOSSE which can be found on the following <a href="https://github.com/charlesmalafosse/open-dataset-for-sentiment-analysis?files=1">GitHub page</a>. Sentiment is there categorized as either POSITIVE, NEGATIVE, or NEUTRAL.<br />
As we're focusing on NLP Sentiment Classification in French language, we will only consider the 250,000 tweets in French language from that dataset.
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<pre style='background-color: rgba(0, 255, 255, 0.01);'><code style='background-color: rgba(0, 255, 255, 0.001); word-break: keep-all;'>As we will explain in the upcoming sections, we will be training a dual bidirectional-GRU NLP model (@see chapter <a href="#gru"><b>&sect;4.</b></a>) using fastText French word embeddings (@see chapter <a href="#embeddings"><b>&sect;3.</b></a>) and transfer learning (@see chapter <a href="#transfer"><b>&sect;2.</b></a>). It will be first done using review data that we have scrapped from the Web (@see chapter <a href="#scraping"><b>&sect;2.1.</b></a>), and then again on the French Tweets dataset (@see chapters <a href="#dataset"><b>&sect;1.</b></a> and <a href="#warming"><b>&sect;5.2.a</b></a>).<br />
This will translate in 3 steps as described in the below figure : i) pre-training, ii) warming-up and iii) fine-tuning.
<img src="images/training_graph.png" width="100%" />
</code></pre>
One important note : when training our model following the above-depicted 3-steps, the only difference beween the step 1 on one hand (pre-training) and steps 2 &amp; 3 on the other (warming-up &amp; fine tuning) consists in the model output layer.<br />
It is important to note that the entire data pipeline otherwise remains identical. Neither the pre-processing nor the text tokenization, nor the dimensions of the model input layer (max number of tokens in the imput text) are modified.
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<h1 id="Table-of-contents">Table of contents<a class="anchor-link" href="#Table-of-contents">&#182;</a></h1><ol>
<li><a href="#dataset">The Dataset</a><br />
 1.1. <a href="#preprocessing">Pre-processing</a><br />
 1.2. <a href="#specificity">Domain-specificity</a><br />
 1.3. <a href="#imbalance">High Imbalance Characteristic</a><br />
<br /></li>
<li><a href="#transfer">Transfer Learning</a><br />
 2.1. <a href="#scraping">Web Scraping</a><br />
 2.2. <a href="#tokenizer">Custom Text Tokenizer</a><br />
<br /></li>
<li><a href="#embeddings">Word Embeddings</a><br />
 3.1. <a href="#fasttext">fastText</a><br />
 3.2. <a href="#ukn">The &lt;UKN&gt; Token</a><br />
<br /></li>
<li><a href="#gru">Dual bi-GRU NLP Model</a><br />
 4.1. <a href="#architecture">Architecture</a><br />
 4.2. <a href="#optimization">Hyperparameters optimization</a><br />
<br /></li>
<li><a href="#training">NLP Model Training</a><br />
 5.1. <a href="#pretraining">Pre-training</a><br />
 5.2. <a href="#warming">Warming-Up</a><br />
 5.3. <a href="#tuning">Fine-tuning</a><br />
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<li><a href="#conclusion">Conclusion</a></li>
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<h2>1. The Dataset <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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<span class="kn">import</span> <span class="nn">base64</span><span class="o">,</span> <span class="nn">cv2</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">suppress</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1">#prevent numpy exponential </span>
                                   <span class="c1">#notation on print</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">&#39;font.size&#39;</span><span class="p">:</span> <span class="mi">12</span><span class="p">})</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="o">%</span><span class="k">reload_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> \
    <span class="n">TWEET_MAX_CHAR_COUNT</span><span class="p">,</span> <span class="n">html_escape</span>
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First, let us download French Tweets dataset, which consists of 3 zip files :
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">download_tweet_fr</span> <span class="p">;</span> <span class="n">download_tweet_fr</span><span class="p">()</span>
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<pre>     100%|██████████████████████████████████████████████████| 2320/2320 [01:48&lt;00:00, 21.37it/s]
     100%|██████████████████████████████████████████████████| 3221/3221 [01:52&lt;00:00, 28.58it/s]
     100%|██████████████████████████████████████████████████| 3328/3328 [00:06&lt;00:00, 543.04it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">load_tweet_fr</span> <span class="p">;</span> <span class="n">tweets</span> <span class="o">=</span> <span class="n">load_tweet_fr</span><span class="p">()</span>
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<pre>Loaded the dataset in 2.6395 seconds
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<p><a name="preprocessing"></a></p>
<h3>1.1. Pre-processing <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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As far as data cleaning goes, we ensure that :
    <ul style="margin: 0 5px 0 50px; /* top right bottom left*/">
        <li>no '@' mention,</li>
        <li>no URL link,</li>
        <li>no html tag,</li>
        <li>no special character (such as '&oelig;', which we replace with 'oe') and</li>
        <li>no duplicate posts</li>
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remain in the body of the French Tweets we feed our model. This information does indeed not add value to build a Sentiment Analysis model.
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<br />
In addition to that, we also deal with hashtags (#) in a manner such that we get the better out of them. Below we can see 2 examples of how we handle them&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">hashtag_converter_tweet_fr</span>
<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.display_helper</span> <span class="kn">import</span> <span class="n">dataframe_pretty_print_center</span>

<span class="n">dummy_tweet_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;tweet_text&#39;</span><span class="p">:</span>
                               <span class="p">[</span><span class="n">html_escape</span><span class="p">(</span><span class="s1">&#39;Allez #tousAvecLesBleus, gÃ©nial!&#39;</span><span class="p">),</span> <span class="n">html_escape</span><span class="p">(</span><span class="s2">&quot;#AllezLePSG&quot;</span><span class="p">)]})</span>
<span class="n">result_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span>
    <span class="p">[</span><span class="n">dummy_tweet_df</span><span class="p">,</span> <span class="n">hashtag_converter_tweet_fr</span><span class="p">(</span><span class="n">dummy_tweet_df</span><span class="p">[</span> <span class="s1">&#39;tweet_text&#39;</span> <span class="p">])],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span> <span class="p">)</span>
<span class="n">result_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="s1">&#39;converted&#39;</span> <span class="p">]</span>
<span class="n">dataframe_pretty_print_center</span><span class="p">(</span><span class="n">result_df</span><span class="p">)</span> <span class="p">;</span> <span class="k">del</span> <span class="n">result_df</span>
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                                <td id="T_27345b2c_f042_11ea_9a5b_309c23ff89f2row0_col0" class="data row0 col0" >Allez #tousAvecLesBleus, g&eacute;nial!</td>
                        <td id="T_27345b2c_f042_11ea_9a5b_309c23ff89f2row0_col1" class="data row0 col1" >Allez tous Avec Les Bleus, g&eacute;nial!</td>
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                        <td id="T_27345b2c_f042_11ea_9a5b_309c23ff89f2row1_col1" class="data row1 col1" >Allez Le PSG</td>
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Lets apply this data pre-processing to the whole dataset, then :
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">clean_tweet_fr</span> <span class="p">;</span> <span class="n">tweets</span> <span class="o">=</span> <span class="n">clean_tweet_fr</span><span class="p">(</span><span class="n">tweets</span><span class="p">)</span>
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The dataset is made up of 219,599  records at this stage.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tweets_count</span> <span class="o">=</span> <span class="n">tweets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">tweets</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span> <span class="n">tweets</span><span class="p">[</span><span class="n">tweets</span><span class="p">[</span> <span class="s1">&#39;tweet_text&#39;</span> <span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
    <span class="k">lambda</span> <span class="n">txt</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">txt</span><span class="p">)))</span> <span class="o">&gt;</span> <span class="n">TWEET_MAX_CHAR_COUNT</span><span class="p">]</span><span class="o">.</span><span class="n">index</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">tweets</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</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">dropped_tweets_count</span> <span class="o">=</span> <span class="n">tweets_count</span> <span class="o">-</span> <span class="n">tweets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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After the pre-processing of the French Twitter dataset, 287 entries unfortunately ended up being more than 280 characters long, mostly as a result of hashtags words splitting. In an effort to keeping things simple, we dropped those tweets (which barely accounted for 0.13% of the entire dataset).
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<u>REMARK&nbsp;</u>: any real-life entry that would exceed this max length would be truncated (as part of the <em><b><small>my_NLP_RNN_fr_lib.fastText.sentences_to_indices</small></b></em> input procedure).
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<h3>1.2. Domain-specificity <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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By design, the French Tweets dataset we're using is <u>specific to soccer</u>. To that extent, it can be considered to be <span style="background-color: #e0e0e0">&nbsp;domain-specific&nbsp;</span>.
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Below are displayed 3 random tweets from that dataset :
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">display</span>
<span class="k">def</span> <span class="nf">printmd</span><span class="p">(</span><span class="n">string</span><span class="p">):</span>
    <span class="c1"># @see &#39;https://stackoverflow.com/questions/23271575#46934204&#39;</span>
    <span class="c1"># (printing-bold-colored-etc-text-in-ipython-qtconsole)</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">string</span><span class="p">))</span>

<span class="n">dummy</span> <span class="o">=</span> <span class="p">[</span><span class="n">printmd</span><span class="p">(</span><span class="s1">&#39;&lt;div style=&quot;margin-left:45px;text-align: justify;&quot;&gt;&#39;</span> <span class="o">+</span>
                 <span class="s1">&#39;&lt;span style=&quot;color:darkred;padding-left:45px&quot;&gt;&#39;</span> <span class="o">+</span>
                 <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">html_escape</span><span class="p">(</span><span class="n">tweet</span><span class="p">[</span><span class="s1">&#39;tweet_text&#39;</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;&lt;/span&gt;&lt;/div&gt;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="p">)</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tweet</span> <span class="ow">in</span>
         <span class="n">tweets</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tweets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">tweets_sample_size</span><span class="p">)]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()]</span>
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<div style="margin-left:45px;text-align: justify;"><span style="color:darkred;padding-left:45px"> - ?? Allez les bleus?? Nice Magnifique France Nice 06 Nice Moments Nissa La Bella frenchriviera I Lovenice Coted Azur France Metropole NCA</span></div>
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<div style="margin-left:45px;text-align: justify;"><span style="color:darkred;padding-left:45px"> - On regrette que tu n'aies pas &eacute;t&eacute; l&agrave; pour ce match, quoique, c'est pas tout &agrave; fait s&ucirc;r ?. Rendez -vous au Parc ?</span></div>
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<div style="margin-left:45px;text-align: justify;"><span style="color:darkred;padding-left:45px"> - Encore 8 ?? 5 ?? jours ou je vais mentionner ? pour qu'il m'envoie son maillot ! ?? Mendy Manchester City</span></div>
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<p><a name="imbalance"></a></p>
<h3>1.3. High Imbalance Characteristic <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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<p>highly inbalanced =&gt; downsampling =&gt; small dataset =&gt; need for transfer learning (from "generic" domain [resto+movies+appliances] to domain-specific [soccer])</p>

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Lets have a look at how tweets are distributed among the three classes that are "NEGATIVE", "NEUTRAL" and "POSITIVE"&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.display_helper</span> <span class="kn">import</span> <span class="n">two_columns_display__classes_distribution</span>

<span class="n">two_columns_display__classes_distribution</span><span class="p">(</span> <span class="n">tweets</span><span class="p">[[</span><span class="s1">&#39;sentiment&#39;</span><span class="p">]]</span> <span class="p">)</span>
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<!DOCTYPE html>
<html>
<head>
<style>

* {
    box-sizing: border-box;
}

.left_column {
    float: left;
    padding: 10px;
    #padding-left: 80px;
}

.right_column {
    float:right;
    text-align; right;
    padding: 10px;
    #padding-left: 80px;
    #background: yellow;
}

/* Clearfix (clear floats) */
.row::after {
    content: "";
    clear: both;
    display: table;
}

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<body>

<h2></h2>

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      <center>
          <style  type="text/css" >
    #T_d961e058_f045_11ea_8719_309c23ff89f2 th {
          text-align: center;
    }    #T_d961e058_f045_11ea_8719_309c23ff89f2row0_col0 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row0_col1 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row0_col2 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row1_col0 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row1_col1 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row1_col2 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row2_col0 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row2_col1 {
            width:  20em;
            text-align:  center;
        }    #T_d961e058_f045_11ea_8719_309c23ff89f2row2_col2 {
            width:  20em;
            text-align:  center;
        }</style><table id="T_d961e058_f045_11ea_8719_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >sentiment</th>        <th class="col_heading level0 col1" >count</th>        <th class="col_heading level0 col2" >percentage</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row0_col1" class="data row0 col1" >6,069</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row0_col2" class="data row0 col2" >2.76%</td>
            </tr>
            <tr>
                                <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row1_col1" class="data row1 col1" >182,049</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row1_col2" class="data row1 col2" >82.90%</td>
            </tr>
            <tr>
                                <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row2_col0" class="data row2 col0" >POSITIVE</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row2_col1" class="data row2 col1" >31,481</td>
                        <td id="T_d961e058_f045_11ea_8719_309c23ff89f2row2_col2" class="data row2 col2" >14.34%</td>
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As can be observed above, the "NEUTRAL" sentiment is over-represented in the French Tweets dataset with a staggering 45.60%. If a model predicts every tweet to be "NEUTRAL", then that model will have a 45.60% accuracy. This gives high of an incentive for any model trained on such a dataset to be biased towards tagging a tweet as "NEUTRAL".<br />
To avoid that, we will use <span style="background-color: #e0e0e0">&nbsp;downsampling&nbsp;</span> when training our NLP model, i.e. we will not use all the "NEUTRAL" tweets but randomly pick some of them only. That way, we will train our NLP model on a dataset that is more evenly balanced between the three classes ; even if not perfectly.<br />
One of the adverse direct consequences of downsampling however is that our training dataset will shrink from 219,599 records down to around 50,000.
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However still of non-negligible size, this smaller dataset will not allow for the highest predictive power of an NLP model. To circumvent that bereavement, we will resort to <span style="background-color: #e0e0e0">&nbsp;transfer learning&nbsp;</span>. We will first train our model on a different dataset, larger and more generic in nature, and then fine-tune such a model using our French tweet dataset. That way, our model will have gained insight into how to classify french-language text in general and will use that knowledge when starting to learn how to do so on our more specific content.
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<h2>2. Transfer Learning <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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<h3>2.1. Web Scraping <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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In order to train a model with the highest predictive power, we turn towards transfer learning from a domain as generic as possible.<br />
For that purpose, we scrapped the web using the <span style="background-color: #e0e0e0">&nbsp;BeautifullSoup&nbsp;</span> python library (<a href="https://pypi.org/project/beautifulsoup4/">https://pypi.org/project/beautifulsoup4/</a>), which basically provides convenience methods to scan thru web pages via an HTML/XML parser.
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We collected review data from the web on the three below broad product families :
    <ol style="list-style-type: square; margin: 0 5px 0 50px; /* top right bottom left*/">
        <li>bars & restaurants (across french cities)
            <ol style="list-style-type: disc;">
                <li>casual dining</li>
                <li>family style</li>
                <li>cafeteria</li>
                <li>brasserie and bistro</li>
                <li>coffee house</li>
                <li>wine bar</li>
                <li>etc.</li>
            </ol>
        </li>
        <li>movies (french and international)
            <ol style="list-style-type: disc;">
                <li>action</li>
                <li>anime</li>
                <li>adventure</li>
                <li>...</li>
                <li>historical film</li>
                <li>psychological movie</li>
                <li>romantic movie.</li>
                <li>...</li>
            </ol>
        </li>
        <li>appliances &amp; electronics (french market)
            <ol style="list-style-type: disc;">
                <li>large appliances (washing machine, fridge, oven, etc)</li>
                <li>kitchen and cooking</li>
                <li>video-game console</li>
                <li>tablet, smartphone, connected object</li>
                <li>computers</li>
                <li>video &amp; stereo</li>
            </ol>
        </li>
    </ol>
Lets load and apply the same pre-processing as before to it&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">load_review_fr</span>

<span class="n">reviews</span> <span class="o">=</span> <span class="n">load_review_fr</span><span class="p">()</span>
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<pre>Loaded the dataset in 1.7199 seconds
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reviews</span> <span class="o">=</span> <span class="n">clean_tweet_fr</span><span class="p">(</span><span class="n">reviews</span><span class="p">,</span> <span class="n">col_name</span> <span class="o">=</span> <span class="s1">&#39;comment&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.display_helper</span> <span class="kn">import</span> <span class="n">two_columns_display</span>

<span class="n">df</span> <span class="o">=</span> <span class="n">reviews</span><span class="p">[[</span> <span class="s1">&#39;type&#39;</span><span class="p">,</span> <span class="s1">&#39;rating&#39;</span> <span class="p">]]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;rating&#39;</span><span class="p">:</span><span class="s1">&#39;count&#39;</span><span class="p">})</span>
<span class="n">df</span><span class="p">[</span> <span class="s1">&#39;percentage&#39;</span> <span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span> <span class="n">reviews</span> <span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)[</span><span class="s1">&#39;count&#39;</span><span class="p">]</span>
<span class="n">df_html</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">hide_index</span><span class="p">()</span> \
    <span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="o">**</span><span class="p">{</span><span class="s1">&#39;width&#39;</span><span class="p">:</span><span class="s1">&#39;20em&#39;</span><span class="p">,</span> <span class="s1">&#39;text-align&#39;</span><span class="p">:</span><span class="s1">&#39;center&#39;</span><span class="p">})</span> \
    <span class="o">.</span><span class="n">format</span><span class="p">({</span><span class="s2">&quot;count&quot;</span><span class="p">:</span> <span class="s2">&quot;</span><span class="si">{:20,.0f}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;percentage&quot;</span><span class="p">:</span> <span class="s2">&quot;</span><span class="si">{0:.2f}</span><span class="s2">%&quot;</span><span class="p">})</span> \
    <span class="o">.</span><span class="n">set_table_styles</span><span class="p">([</span><span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s2">&quot;th&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;text-align&#39;</span><span class="p">,</span> <span class="s1">&#39;center&#39;</span><span class="p">)])])</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
<span class="n">two_columns_display</span><span class="p">(</span><span class="n">df_html</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
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        }</style><table id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >type</th>        <th class="col_heading level0 col1" >count</th>        <th class="col_heading level0 col2" >percentage</th>    </tr></thead><tbody>
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                                <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row0_col0" class="data row0 col0" >appliances_n_electronics</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row0_col1" class="data row0 col1" >              37,096</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row0_col2" class="data row0 col2" >21.30%</td>
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                                <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row1_col0" class="data row1 col0" >bars_n_restaurants</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row1_col1" class="data row1 col1" >              48,664</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row1_col2" class="data row1 col2" >27.94%</td>
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                                <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row2_col0" class="data row2 col0" >movies</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row2_col1" class="data row2 col1" >              88,418</td>
                        <td id="T_7222f7e8_ee92_11ea_913a_309c23ff89f2row2_col2" class="data row2 col2" >50.76%</td>
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Contrarily to the French Tweets dataset which has 3 classes (NEGATIVE/NEUTRAL/POSITIVE), the French Reviews dataset has 5 classes. The commented reviews are indeed here accompanied by a score between 1 and 5&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">FuncFormatter</span>

<span class="n">hist1</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span>
    <span class="n">reviews</span><span class="p">[(</span><span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;bars_n_restaurants&#39;</span><span class="p">)][</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span>
    <span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="nb">range</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">hist2</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span>
    <span class="n">reviews</span><span class="p">[(</span><span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;movies&#39;</span><span class="p">)][</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span>
    <span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="nb">range</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">hist3</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span>
    <span class="n">reviews</span><span class="p">[(</span><span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;appliances_n_electronics&#39;</span><span class="p">)][</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span>
    <span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="nb">range</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>

<span class="n">normalized_hist</span> <span class="o">=</span> \
    <span class="p">(</span><span class="n">hist1</span><span class="o">+</span><span class="n">hist2</span><span class="o">+</span><span class="n">hist3</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">hist1</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">+</span><span class="n">hist2</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">+</span><span class="n">hist3</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>

<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span> <span class="p">;</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">width</span> <span class="o">=</span> <span class="o">.</span><span class="mi">7</span> <span class="p">;</span> <span class="n">center</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">bar1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist1</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">)</span>
<span class="n">bar2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist2</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">hist1</span><span class="p">)</span>
<span class="n">bar3</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">center</span><span class="p">,</span> <span class="n">hist3</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">width</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">hist1</span><span class="o">+</span><span class="n">hist2</span><span class="p">)</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span>
    <span class="n">FuncFormatter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">y</span><span class="p">,</span> <span class="n">p</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="mi">1000</span><span class="p">))</span> <span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span> <span class="s2">&quot;reviews $\it{(thousands)}$&quot;</span> <span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span> <span class="s2">&quot;rating&quot;</span> <span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">bar1</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">bar2</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">bar3</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
           <span class="p">,</span> <span class="p">(</span><span class="s1">&#39;bars_n_restaurants&#39;</span><span class="p">,</span> <span class="s1">&#39;movies&#39;</span><span class="p">,</span> <span class="s1">&#39;appliances_n_electronics&#39;</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="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">center</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">hist1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="n">hist2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="n">hist3</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span> <span class="s2">&quot;</span><span class="si">{0:.0%}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> <span class="n">normalized_hist</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">),</span> <span class="p">(</span><span class="n">x</span><span class="o">-.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="o">+</span><span class="mi">1000</span><span class="p">)</span> <span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mf">1.05</span><span class="p">])</span> <span class="p">;</span> <span class="c1">#plt.show()</span>
<span class="n">fig_url</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="s1">&#39;tmp&#39;</span><span class="p">,</span> <span class="s2">&quot;reviews_dataset.jpg&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fig_url</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span> <span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>

<span class="c1"># use base64 encoding to circumvent image (external file) caching (webbrowser)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fig_url</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">image_file</span><span class="p">:</span>
    <span class="n">encoded_string</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64encode</span><span class="p">(</span><span class="n">image_file</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="n">encoded_string</span> <span class="o">=</span> <span class="s2">&quot;data:image/jpeg;charset=utf-8;base64, &quot;</span> <span class="o">+</span> <span class="n">encoded_string</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">fig_url</span><span class="p">)</span>

<span class="n">two_columns_display</span><span class="p">(</span>
    <span class="s1">&#39;&lt;div style=&quot;text-align: center;&quot;&gt;&lt;img style=&quot;display: inline-block;&quot; src=&quot;&#39;</span> <span class="o">+</span>
    <span class="n">encoded_string</span> <span class="o">+</span> <span class="s1">&#39;&quot; /&gt;&lt;/div&gt;&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
</pre></div>

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We will treat this Sentiment Classification problem as a regression problem (we treat the "rating" variable as a continuous variable). That way, our model will be highly penalized if it classifies a "5 stars" product review as a "1 star" product review (even more so than if the matter was dealt with as a pure classification problem) since the objective function will bring to minimizing the distance between the prediction and the real rating (as opposed to simply categorizing a rating in the proper class). 
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Note that, however imbalanced to a certain extend, the French Reviews is way less so than the French Tweets dataset.
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<h3>2.2. Custom Text Tokenizer <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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We will be using a custom tokenizer for our datasets. It uses a <span style="background-color: #e0e0e0">&nbsp;regex pattern&nbsp;</span> which has a few specificities to better take advantage of the raw text comments we are encountering in real life.<br />
Most of those specificities have to do with quantities, units, and whether or not the two are space-separated. We deam those specificities important since many times in the French Reviews dataset, prices, volumes, weights and distances do count in the satisfaction one has with regards to a product that is being rated and commented. We want to optimize the chances for our model to grab those relations not less than any other.
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Lets apply this tokenizer to our French Tweets dataset and look at the tokens distribution accros our records&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tokenizer_utils</span> <span class="kn">import</span> <span class="n">tokenize_tweet_fr</span>

<span class="n">tokenized_tweets</span> <span class="o">=</span> <span class="n">tokenize_tweet_fr</span><span class="p">(</span><span class="n">tweets</span><span class="p">)</span>
<span class="c1">#del tweets ; silent = gc.collect()</span>
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<div class="prompt input_prompt">In&nbsp;[15]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.tweet_utils</span> <span class="kn">import</span> <span class="n">plt_hist_bin_width</span>

<span class="n">tweet_length_data</span> <span class="o">=</span> <span class="n">tokenized_tweets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> <span class="k">lambda</span> <span class="n">words_list</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span> <span class="n">words_list</span> <span class="p">)</span> <span class="p">)</span>
<span class="n">binwidth</span> <span class="o">=</span> <span class="mi">50</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt_hist_bin_width</span><span class="p">(</span> <span class="n">data</span> <span class="o">=</span> <span class="n">tweet_length_data</span><span class="p">,</span> <span class="n">binwidth</span> <span class="o">=</span> <span class="n">binwidth</span>
                         <span class="p">,</span> <span class="n">ylog</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">rotate_text</span> <span class="o">=</span> <span class="kc">False</span> <span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;tweets count</span><span class="se">\n</span><span class="s1">($\it{log scale}$)&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;words count&#39;</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="n">max_length</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">tweet_length_data</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">tweet_length_data</span><span class="p">)</span> <span class="o">+</span> <span class="n">binwidth</span><span class="p">,</span> <span class="n">binwidth</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">tweets_count</span> <span class="o">=</span> <span class="n">tweet_length_data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span>
    <span class="k">lambda</span> <span class="n">tweet_length</span><span class="p">:</span> <span class="p">(</span><span class="n">tweet_length</span> <span class="o">&lt;=</span> <span class="n">max_length</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">tweet_length</span> <span class="o">&gt;</span> <span class="n">max_length</span><span class="o">-</span><span class="n">binwidth</span><span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">del</span> <span class="n">tokenized_tweets</span><span class="p">,</span> <span class="n">tweet_length_data</span> <span class="p">;</span> <span class="n">dummy</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
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NLP models allow for a fixed number of tokens per input text. This maximum number of words (of tokens) is to be determined from the start.
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As can be observed on the above chart, with a character limit of 280 characters for each tweet (imposed by the Twiter platform), 219886 still manage to count between 150 and 200 words. To remain on the safe side and not constrain ourselves too much with regards to maximum allowable length of input text, we then chose to take a limit of no less than 280 tokens max for the input layer to our sentiment analysis NLP model.
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Lets then filter out comments that are more than 280 tokens long in our pre-training French Reviews dataset&nbsp;:
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<div class="prompt input_prompt">In&nbsp;[16]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tokenized_reviews</span> <span class="o">=</span> <span class="n">tokenize_tweet_fr</span><span class="p">(</span><span class="n">reviews</span><span class="p">,</span> <span class="n">col_name</span><span class="o">=</span><span class="s1">&#39;comment&#39;</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reviews_length_data</span> <span class="o">=</span> \
    <span class="n">tokenized_reviews</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> <span class="k">lambda</span> <span class="n">sentence_tokens_list</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span> <span class="n">sentence_tokens_list</span> <span class="p">)</span> <span class="p">)</span>
<span class="n">reviews_count</span> <span class="o">=</span> <span class="n">tokenized_reviews</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">reviews</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
    <span class="n">reviews_length_data</span><span class="p">[</span>
        <span class="p">(</span><span class="n">reviews_length_data</span> <span class="o">&gt;</span> <span class="n">TWEET_MAX_CHAR_COUNT</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">reviews_length_data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
    <span class="p">]</span><span class="o">.</span><span class="n">index</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">reviews</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</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">dropped_reviews_count</span> <span class="o">=</span> <span class="n">reviews_count</span> <span class="o">-</span> <span class="n">tokenized_reviews</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">del</span> <span class="n">tokenized_reviews</span><span class="p">,</span> <span class="n">reviews_length_data</span> <span class="p">;</span> <span class="n">dummy</span> <span class="o">=</span> <span class="n">gc</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
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0 entries had to be dropped. They accounted for 0.00% of the dataset.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># data sanity check ; ensure no &quot;rating&quot; is malformed =&gt;</span>
<span class="k">assert</span> <span class="p">(</span><span class="ow">not</span> <span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">and</span>
        <span class="n">np</span><span class="o">.</span><span class="n">issubdtype</span><span class="p">(</span><span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">number</span><span class="p">))</span> \
    <span class="p">,</span> <span class="s1">&#39;we have a &quot;rating is malformed&quot; issue with the training data !&#39;</span>
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<h2>3. Word Embeddings <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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<h3>3.1. fastText <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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We're using word embeddings from fastText. They have been generated by training fastText NLP models on text gethered from <a href="https://commoncrawl.org/">Common Crawl</a> and <a href="https://www.wikipedia.org/">Wikipedia</a>. For more on this, please visit <a href="https://fasttext.cc/docs/en/crawl-vectors.html">https://fasttext.cc/docs/en/crawl-vectors.html</a>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.fastText</span> <span class="kn">import</span> <span class="n">download_fastText_fr_300_vec</span> <span class="p">;</span> <span class="n">download_fastText_fr_300_vec</span><span class="p">()</span>
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<pre>100%|██████████████████████████████████████████████████████| 314395/314395 [11:17&lt;00:00, 464.09it/s]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.fastText</span> <span class="kn">import</span> <span class="n">read_fastText_vecs</span>

<span class="n">word_to_index</span><span class="p">,</span> <span class="n">index_to_word</span><span class="p">,</span> <span class="n">word_to_vec_map</span> <span class="o">=</span> \
    <span class="n">read_fastText_vecs</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="s1">&#39;data&#39;</span><span class="p">,</span> <span class="s1">&#39;fastText_french&#39;</span><span class="p">,</span> <span class="s1">&#39;cc.fr.300.vec&#39;</span><span class="p">)</span> <span class="p">)</span>
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<pre>Loaded the Vocabulary in 138.4681 seconds [1,234,042 word embeddings of 300 features]
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Our vocabulary is comprised of 1,234,043 words and the shape of a word vector is "(300,)", meaning each token is codified in <u>a 300 dimensions space</u>.
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<p><a name="ukn"></a></p>
<h3>3.2. The &lt;UKN&gt; Token <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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Below, we calculate the dimensions for the vector associated to the "&lt;UKN&gt;" token. This "&lt;UKN&gt;" token will serve as a placeholder for any word encountered that does not belong to our vocabulary. For instance, when a word is mis-spelled, it probably doesn't belong to our vocabulary. We then associate it to the "&lt;UKN&gt;" ("unknown") token prior to feeding it to our model.<br />
For our "&lt;UKN&gt;" token vector, we take the average of all word embeddings, and we add that vector to our vocab in the last position.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">avg_vec</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">word_to_vec_map</span><span class="p">[</span> <span class="s1">&#39;bonjour&#39;</span> <span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="p">)</span>

<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">word_to_vec_map</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="p">:</span>
    <span class="n">avg_vec</span> <span class="o">+=</span> <span class="n">v</span>

<span class="n">avg_vec</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span> <span class="n">word_to_vec_map</span> <span class="p">)</span>

<span class="n">w</span> <span class="o">=</span> <span class="s2">&quot;&lt;UNK&gt;&quot;</span>
<span class="n">word_to_vec_map</span><span class="p">[</span> <span class="n">w</span> <span class="p">]</span> <span class="o">=</span> <span class="n">avg_vec</span> <span class="p">;</span> <span class="k">del</span> <span class="n">avg_vec</span>
<span class="n">word_to_index</span><span class="p">[</span> <span class="n">w</span> <span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span> <span class="n">word_to_index</span> <span class="p">)</span>
<span class="n">index_to_word</span><span class="p">[</span> <span class="nb">len</span><span class="p">(</span> <span class="n">index_to_word</span> <span class="p">)</span> <span class="p">]</span> <span class="o">=</span> <span class="n">w</span>
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Lets visualize how the <small><b><em>sentences_to_indices</em></b></small> procedure of the <small><b><em>my_NLP_RNN_fr_lib.fastText</em></b></small> module <a href='.\my_NLP_RNN_fr_lib\fastText.py' target='_blank'><img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a> works with an example of 4 dummy input texts where we arbitrarily fix a maximum length to 7 tokens per text for illustration&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.fastText</span> <span class="kn">import</span> <span class="n">sentences_to_indices</span>

<span class="n">X1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="s2">&quot;trop drole mdr&quot;</span><span class="p">,</span> <span class="s2">&quot;on joue au foot ?&quot;</span><span class="p">,</span> <span class="s2">&quot;le repas est pret&quot;</span>
               <span class="p">,</span> <span class="s2">&quot;ceci est un mot inconnu : klugkjygjlyffl&quot;</span>
               <span class="p">,</span> <span class="s2">&quot;cette phrase est bien plus longue que la limite autorisee&quot;</span><span class="p">])</span>
<span class="n">X1_indices</span> <span class="o">=</span> <span class="n">sentences_to_indices</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span><span class="n">word_to_index</span><span class="p">,</span> <span class="n">max_len</span> <span class="o">=</span> <span class="n">max_len</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s2">&quot;INPUT SENTENCE&quot;</span><span class="p">:</span><span class="n">X1</span><span class="p">}))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">chr</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;text_vectors =</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">X1_indices</span><span class="p">)</span>
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<pre>                                      INPUT SENTENCE
0                                     trop drole mdr
1                                  on joue au foot ?
2                                  le repas est pret
3           ceci est un mot inconnu : klugkjygjlyffl
4  cette phrase est bien plus longue que la limit...

text_vectors =
 [[1184231.  925728. 1040209.       0.       0.       0.       0.]
 [1069401. 1011262.  847095.  964350.   61594.       0.       0.]
 [1022097. 1123299.  950048. 1098621.       0.       0.       0.]
 [ 878293.  950048. 1188986. 1050709.  998286.   61589. 1234043.]
 [ 879671. 1086497.  950048.  858923. 1091407. 1028989. 1110576.]]
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As illustrated above, each input sentence is "padded" so that each sequence is of the same length. As for the last sentence, which is longuer than the limit we used for this example, it has been truncated.
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<h2>4. Dual bi-GRU NLP Model <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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Compared to <u>L</u>ong <u>S</u>hort <u>T</u>erm <u>M</u>emory networks, the <u>G</u>ated <u>R</u>ecurrent <u>U</u>nit model is simpler. It consists of only the <u>update gate</u> and the <u>reset gate</u>. GRU counts one less gate than LSTM, so there are fewer parameters and faster convergence time during training.
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Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been widely used in the field of text sentiment analysis and have achieved good results. However, although CNN can extract local information between consecutive words of a sentence, it ignores the contextual semantic information between words. Bidirectional GRU can make up for the shortcomings that CNN can't extract contextual semantic information of long text, but it can't extract the local features of the text as well as CNN. Therefore, we consider a multi-channel model that combines the power of the two.
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<h3>4.1. Architecture <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.python.util</span> <span class="kn">import</span> <span class="n">deprecation</span>
<span class="n">deprecation</span><span class="o">.</span><span class="n">_PRINT_DEPRECATION_WARNINGS</span> <span class="o">=</span> <span class="kc">False</span>
<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="kn">import</span> <span class="n">plot_model</span>
<span class="kn">import</span> <span class="nn">keras.backend</span> <span class="k">as</span> <span class="nn">K</span>

<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.architecture</span> <span class="kn">import</span> <span class="n">build_model</span>
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<pre>Using TensorFlow backend.
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<pre>Built the model in 12.7192 seconds
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<span class="n">architecture_plot</span> <span class="o">=</span> <span class="n">plot_model</span><span class="p">(</span>
    <span class="n">model</span>
    <span class="p">,</span> <span class="n">bgcolor</span> <span class="o">=</span> <span class="s1">&#39;#123456&#39;</span><span class="p">,</span> <span class="n">forecolor</span> <span class="o">=</span> <span class="s1">&#39;#a18a12&#39;</span>
    <span class="p">,</span> <span class="n">framecolor</span> <span class="o">=</span> <span class="s1">&#39;#e6c619&#39;</span><span class="p">,</span> <span class="n">watermark_text</span> <span class="o">=</span> <span class="s2">&quot;NLP / sentiment of French text&quot;</span>
    <span class="p">,</span> <span class="n">fillcolors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;InputLayer&#39;</span><span class="p">:</span> <span class="s1">&#39;#654987&#39;</span><span class="p">,</span> <span class="s1">&#39;Embedding&#39;</span><span class="p">:</span> <span class="s1">&#39;#995511&#39;</span>
                    <span class="p">,</span> <span class="s1">&#39;Bidirectional(GRU)&#39;</span><span class="p">:</span> <span class="s1">&#39;#0e0e0e&#39;</span><span class="p">,</span> <span class="s1">&#39;Conv1D&#39;</span><span class="p">:</span> <span class="s1">&#39;#444444&#39;</span><span class="p">,</span> <span class="s1">&#39;GlobalMaxPooling1D&#39;</span><span class="p">:</span> <span class="s1">&#39;#263340&#39;</span>
                    <span class="p">,</span> <span class="s1">&#39;Concatenate&#39;</span><span class="p">:</span> <span class="s1">&#39;#654321&#39;</span><span class="p">,</span> <span class="s1">&#39;Dense&#39;</span><span class="p">:</span> <span class="s1">&#39;#556699&#39;</span><span class="p">}</span>
    <span class="p">,</span> <span class="n">fontcolors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Embedding&#39;</span><span class="p">:</span> <span class="s1">&#39;#ccae0c&#39;</span><span class="p">,</span> <span class="s1">&#39;Bidirectional(GRU)&#39;</span><span class="p">:</span> <span class="s1">&#39;#ffffff&#39;</span>
                    <span class="p">,</span> <span class="s1">&#39;Concatenate&#39;</span><span class="p">:</span> <span class="s1">&#39;#000000&#39;</span><span class="p">,</span> <span class="s1">&#39;Dense&#39;</span><span class="p">:</span> <span class="s1">&#39;#333333&#39;</span><span class="p">}</span>
<span class="p">)</span>
<span class="n">silent</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</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="s1">&#39;tmp&#39;</span><span class="p">,</span> <span class="s1">&#39;architecture.png&#39;</span><span class="p">),</span> <span class="n">architecture_plot</span><span class="p">)</span>
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For each input (in <span style="background-color:#987db9; color: #e6c619;">&nbsp;<b>purple</b>&nbsp;</span> on the left figure), the sequence of words is branched-out into two bi-directionnal GRU cells (in <span style="background-color:#0e0e0e; color: #ffffff;">&nbsp;<b>black</b>&nbsp;</span> on the left figure). Each generated signal is then fed to two distinct 1D convolutional units for feature extraction (in <span style="background-color:#999999; color: #e6c619;">&nbsp;<b>grey</b>&nbsp;</span> on the left figure). At this stage, we have four distinct signals, which we combine for treatment in a 3-steps densely-connected network (in <span style="background-color:#a4b4e2; color: #777777;">&nbsp;<b>light-blue</b>&nbsp;</span> on the left figure). The remaining of the model is mostly there for regulation (to avoid over-fitting).
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These different units all have hyperparameters that need tuning for our NLP model to deliver the best predictions it can. For a complete view of which hyperparameter is concerned for each of the units constitutive of our NLP model, please refere to the <small><b><em>build_model</em></b></small> method of the <small><b><em>my_NLP_RNN_fr_lib.model.architecture</em></b></small> module <a href='.\my_NLP_RNN_fr_lib\model\architecture.py' target='_blank'><img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a>.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">K</span><span class="o">.</span><span class="n">clear_session</span><span class="p">()</span>
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<p><a name="optimization"></a></p>
<h3>4.2. Hyperparameters optimization <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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To optimize the set of hyperparameters values of our NLP model, we first relate to random search and, in a second time, we add <span style="background-color: #e0e0e0">&nbsp;XGBoost&nbsp;</span> + <span style="background-color: #e0e0e0">&nbsp;Scikit-Learn&nbsp;</span> to the mix to get to an even better performing model instance.
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Complete details on how it has all been done can be found in <a href="https://github.com/aurelienmorgan/french_text_sentiment/blob/master/model/optimization/README.md" target="_blank">this autonomous step-by-step Notebook with full running code <img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a>.
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In short, the whole procedure has taken place <u>on a small subset of the French Reviews training data</u>&nbsp;:
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    <li>First, we trained a bunch of model instances, each using a different set of randomly-generated hyperparameter values.</li>
    <li>Second, with this information, we trained an XGBoost regressor to predict NLP model performance based on the set of hyperparameters values</li>
    <li>Third, we feed the XGBoost regressor with tens millions of sets of randomly-generated hyperparameters values and select the ones predicted to bring the best performance for our NLP model architecture.</li>
</ul>
The first step gave us one NLP model instance which we call the "<b>best random search</b>" model instance. The third step gave us one NLP model instance which we call the "<b>best XGBoost-identified</b>" model instance.
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We then fitted few most promissing sets of hyperparameters values on the entire training dataset, and this finally gave us one NLP model instance which we call the "<b>best refitted</b>" model instance.
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For comparison purposes, to highlight the gain in predictive power made at each step of the optimization procedure, we plot the training history (loss and performance metric over training epoch) for the 3 above-mentioned NLP model instances&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.training</span> <span class="kn">import</span> <span class="n">train_models</span>
<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.plot</span> <span class="kn">import</span> <span class="n">plot_trained_models_history</span>

<span class="n">csv_full_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="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;trained_instances.csv&#39;</span><span class="p">)</span>

<span class="n">trained_models_df</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">csv_full_path</span><span class="p">)</span>

<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">512</span> <span class="p">;</span> <span class="n">epochs</span> <span class="o">=</span> <span class="mi">120</span>

<span class="n">best_random_20th_idx</span> <span class="o">=</span> \
    <span class="n">trained_models_df</span><span class="p">[</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;batch_size&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;epochs&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">epochs</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;is_random_set&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;training records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">7352</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;validation records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1298</span><span class="p">)][</span><span class="s2">&quot;val_rmse__last_10_avg&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()</span>
<span class="n">best_xgboost_20th_idx</span> <span class="o">=</span> \
    <span class="n">trained_models_df</span><span class="p">[</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;batch_size&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;epochs&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">epochs</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;is_random_set&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;training records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">7352</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;validation records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1298</span><span class="p">)][</span><span class="s2">&quot;val_rmse__last_10_avg&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()</span>
<span class="n">best_xgboost_idx</span> <span class="o">=</span> \
    <span class="n">trained_models_df</span><span class="p">[</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;batch_size&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">batch_size</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span> <span class="s2">&quot;epochs&quot;</span> <span class="p">]</span> <span class="o">==</span> <span class="n">epochs</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;is_random_set&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;training records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">147061</span><span class="p">)</span> <span class="o">&amp;</span>
        <span class="p">(</span><span class="n">trained_models_df</span><span class="p">[</span><span class="s1">&#39;validation records&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">25952</span><span class="p">)][</span><span class="s2">&quot;val_rmse__last_10_avg&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()</span>

<span class="n">plot_trained_models_history</span><span class="p">(</span>
    <span class="n">trained_models_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span>
        <span class="n">best_random_20th_idx</span><span class="p">,</span> <span class="n">best_xgboost_20th_idx</span><span class="p">,</span> <span class="n">best_xgboost_idx</span><span class="p">]]</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="p">,</span> <span class="n">validation_only</span> <span class="o">=</span> <span class="kc">True</span>
    <span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">r</span><span class="s1">&#39; / $\bf</span><span class="si">{best}</span><span class="s1">$ $\bf</span><span class="si">{random}</span><span class="s1">$ $\bf</span><span class="si">{search}</span><span class="s1">$&#39;</span>
                <span class="p">,</span> <span class="sa">r</span><span class="s1">&#39; / $\bf</span><span class="si">{best}</span><span class="s1">$ $\bf{XGBoost-identified}$&#39;</span>
                <span class="p">,</span> <span class="sa">r</span><span class="s1">&#39; / $\bf</span><span class="si">{best}</span><span class="s1">$ $\bf</span><span class="si">{refitted}</span><span class="s1">$&#39;</span><span class="p">]</span>
<span class="p">)</span>
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These performances have respectively been obtained when using the below-listed sets of hyperparameters values&nbsp;:
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<div class="prompt input_prompt">In&nbsp;[28]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.display_helper</span> <span class="kn">import</span> <span class="n">format_vertical_headers</span>

<span class="n">best_models_df</span> <span class="o">=</span> \
    <span class="n">trained_models_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
        <span class="p">[</span><span class="n">best_random_20th_idx</span><span class="p">,</span> <span class="n">best_xgboost_20th_idx</span><span class="p">,</span> <span class="n">best_xgboost_idx</span><span class="p">]</span>
        <span class="p">,</span> <span class="n">trained_models_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span>
            <span class="s1">&#39;training records&#39;</span><span class="p">,</span> <span class="s1">&#39;validation records&#39;</span><span class="p">,</span> <span class="s1">&#39;spatial_dropout_prop&#39;</span><span class="p">,</span> <span class="s1">&#39;recurr_units&#39;</span><span class="p">,</span>
            <span class="s1">&#39;recurrent_regularizer_l1_factor&#39;</span><span class="p">,</span> <span class="s1">&#39;recurrent_regularizer_l2_factor&#39;</span><span class="p">,</span>
            <span class="s1">&#39;recurrent_dropout_prop&#39;</span><span class="p">,</span> <span class="s1">&#39;conv_units&#39;</span><span class="p">,</span> <span class="s1">&#39;kernel_size_1&#39;</span><span class="p">,</span>
            <span class="s1">&#39;kernel_size_2&#39;</span><span class="p">,</span> <span class="s1">&#39;dense_units_1&#39;</span><span class="p">,</span> <span class="s1">&#39;dense_units_2&#39;</span><span class="p">,</span> <span class="s1">&#39;dropout_prop&#39;</span><span class="p">,</span> <span class="s1">&#39;lr&#39;</span><span class="p">,</span>
            <span class="s1">&#39;val_rmse__last_10_avg&#39;</span>
        <span class="p">])]</span>
<span class="n">best_models_df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;best random search&#39;</span><span class="p">,</span> <span class="s1">&#39;best XGBoost-identified&#39;</span><span class="p">,</span> <span class="s1">&#39;best refitted&#39;</span><span class="p">]</span>
<span class="n">best_models_df</span> <span class="o">=</span> \
    <span class="n">best_models_df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;index&#39;</span><span class="p">:</span> <span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;val_rmse__last_10_avg&#39;</span><span class="p">:</span><span class="s1">&#39;valiadation RMSE&#39;</span><span class="p">})</span>
<span class="n">format_vertical_headers</span><span class="p">(</span>
    <span class="n">best_models_df</span>
    <span class="p">,</span> <span class="n">greyed_out_colnames</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;training records&#39;</span><span class="p">,</span> <span class="s1">&#39;validation records&#39;</span><span class="p">,</span> <span class="s1">&#39;valiadation RMSE&#39;</span><span class="p">]</span>
<span class="p">)</span>
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    <tr style="text-align: right;">
      <th class="rotate4853958390"><div>model</div></th>
      <th class="rotate4853958390"><div><p style="color: darkgray;">training records</p></div></th>
      <th class="rotate4853958390"><div><p style="color: darkgray;">validation records</p></div></th>
      <th class="rotate4853958390"><div>spatial_dropout_prop</div></th>
      <th class="rotate4853958390"><div>recurr_units</div></th>
      <th class="rotate4853958390"><div>recurrent_regularizer_l1_factor</div></th>
      <th class="rotate4853958390"><div>recurrent_regularizer_l2_factor</div></th>
      <th class="rotate4853958390"><div>recurrent_dropout_prop</div></th>
      <th class="rotate4853958390"><div>conv_units</div></th>
      <th class="rotate4853958390"><div>kernel_size_1</div></th>
      <th class="rotate4853958390"><div>kernel_size_2</div></th>
      <th class="rotate4853958390"><div>dense_units_1</div></th>
      <th class="rotate4853958390"><div>dense_units_2</div></th>
      <th class="rotate4853958390"><div>dropout_prop</div></th>
      <th class="rotate4853958390"><div>lr</div></th>
      <th class="rotate4853958390"><div><p style="color: darkgray;">valiadation RMSE</p></div></th>
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      <td>best random search</td>
      <td>7352</td>
      <td>1298</td>
      <td>0.65</td>
      <td>115</td>
      <td>0.04136</td>
      <td>0.00843</td>
      <td>0.00356</td>
      <td>25</td>
      <td>3</td>
      <td>2</td>
      <td>49</td>
      <td>42</td>
      <td>0.22</td>
      <td>0.0348</td>
      <td>0.650973</td>
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      <td>best XGBoost-identified</td>
      <td>7352</td>
      <td>1298</td>
      <td>0.62</td>
      <td>68</td>
      <td>0.03626</td>
      <td>0.00635</td>
      <td>0.00616</td>
      <td>28</td>
      <td>2</td>
      <td>3</td>
      <td>46</td>
      <td>32</td>
      <td>0.04</td>
      <td>0.0362</td>
      <td>0.621018</td>
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      <td>best refitted</td>
      <td>147061</td>
      <td>25952</td>
      <td>0.62</td>
      <td>68</td>
      <td>0.03626</td>
      <td>0.00635</td>
      <td>0.00616</td>
      <td>28</td>
      <td>2</td>
      <td>3</td>
      <td>46</td>
      <td>32</td>
      <td>0.04</td>
      <td>0.0362</td>
      <td>0.549117</td>
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However tedious, the process followed to get to the optimized set of hyperparameters values for our NLP model architecture represents a dramatic enhancement in performance compared to the best we could come up with using pure random search.
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<p><a name="training"></a></p>
<h2>5. NLP Model Training <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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As explained in the summary figure seen in <a href="#page-top"><b>&sect;0.</b></a>, we follow a 3-Steps process to train our Fench Sentiment NLP model&nbsp;:
    <ul style="margin: 0 5px 0 50px; /* top right bottom left*/">
        <li>Step 1&nbsp;: pre-training</li>
        <li>Step 2&nbsp;: warming-up</li>
        <li>Step 3&nbsp;: fine-tuning</li>
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<p><a name="pretraining"></a></p>
<h3>5.1. Pre-training <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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For the pre-training of our French Sentiment NLP model, we work with the French Reviews dataset (that we web-scrapped, as explained <a href="#scraping"><b>&sect;2.1.</b></a>). We detail here what we basically did to train the "<b>best-refitted</b>" model instance mentioned in <a href="#optimization"><b>&sect;4.2.</b></a> above.
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Below, we start by splitting the French Reviews dataset into 'training' and 'validation' subsets&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reviews_X</span> <span class="o">=</span> \
    <span class="n">sentences_to_indices</span><span class="p">(</span><span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;comment&#39;</span><span class="p">],</span> <span class="n">word_to_index</span><span class="p">,</span> <span class="n">max_len</span> <span class="o">=</span> <span class="n">TWEET_MAX_CHAR_COUNT</span><span class="p">)</span>
<span class="n">reviews_Y</span> <span class="o">=</span> \
    <span class="n">reviews</span><span class="p">[</span><span class="s1">&#39;rating&#39;</span><span class="p">]</span>
<span class="c1">#del reviews ; silent = gc.collect()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>

<span class="n">test_prop</span> <span class="o">=</span> <span class="o">.</span><span class="mi">15</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> \
    <span class="n">train_test_split</span><span class="p">(</span><span class="n">reviews_X</span><span class="p">,</span> <span class="n">reviews_Y</span><span class="p">,</span>
                     <span class="n">test_size</span><span class="o">=</span><span class="n">test_prop</span><span class="p">)</span>
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Our training sample is made up of 147,061 records and we have 25,952 additional ones for validation purposes. We use the <small><b><em>keras.optimizers.RMSprop</em></b></small> optimizer., We employ learning rate decay. Our loss function is the MeanSquarredError function. And our performance metric is the RootMeanSquaredError metric.
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It has all been wrapped-up within the <b>add_trained_model</b> convenience method that is part of the <small><b><em>my_NLP_RNN_fr_lib.model.training</em></b></small> module <a href='.\my_NLP_RNN_fr_lib\model\training.py' target='_blank'><img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a>.
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Information pertaining to each of the NLP model trained instances are stored on-the-fly in <a href="./model/trained_instances.csv" target="_blank">a dedicated global csv file</a>.
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To save on space, saved NLP model instances are not made available here but running the below cell would retrain the best performing one of them and save it locally&nbsp;:
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<code>from my_NLP_RNN_fr_lib.model.training import add_trained_model

csv_full_path = os.path.join('model', 'trained_instances.csv')
save_root = os.path.join('model', 'trained_instances')

trained_model_timestamp = add_trained_model(
    (X_train, X_valid, y_train, y_valid)
    , word_to_vec_map, word_to_index
    , **trained_models_df.loc[best_xgboost_idx, trained_models_df.columns.isin([
        'spatial_dropout_prop', 'recurr_units',
        'recurrent_regularizer_l1_factor', 'recurrent_regularizer_l2_factor',
        'recurrent_dropout_prop', 'conv_units', 'kernel_size_1',
        'kernel_size_2', 'dense_units_1', 'dense_units_2', 'dropout_prop', 'lr',
       'lr_decay_rate', 'lr_decay_step', 'batch_size', 'epochs'
    ])]
    , local_path = save_root, csv_full_path = csv_full_path
)
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Any trained instance of our NLP model can then be reloaded as shown below&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">load_model</span>
<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.training</span> <span class="kn">import</span> <span class="n">root_mean_squared_error</span>

<span class="k">if</span> <span class="s1">&#39;trained_model_timestamp&#39;</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="p">:</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">trained_model_timestamp</span> <span class="p">:</span>
        <span class="n">trained_model_timestamp</span> <span class="o">=</span> <span class="n">trained_models_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">best_xgboost_idx</span><span class="p">,</span> <span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>
<span class="k">else</span> <span class="p">:</span>
    <span class="n">trained_model_timestamp</span> <span class="o">=</span> <span class="n">trained_models_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">best_xgboost_idx</span><span class="p">,</span> <span class="s1">&#39;timestamp&#39;</span><span class="p">]</span>

<span class="n">custom_objects</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;root_mean_squared_error&#39;</span><span class="p">:</span> <span class="n">root_mean_squared_error</span><span class="p">}</span>
<span class="n">trained_model</span> <span class="o">=</span> <span class="n">load_model</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="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;trained_instances&#39;</span><span class="p">,</span> <span class="n">trained_model_timestamp</span>
                 <span class="p">,</span> <span class="s1">&#39;model_&#39;</span> <span class="o">+</span> <span class="n">trained_model_timestamp</span> <span class="o">+</span> <span class="s1">&#39;_last.h5&#39;</span><span class="p">)</span>
    <span class="p">,</span> <span class="n">custom_objects</span> <span class="o">=</span> <span class="n">custom_objects</span>
<span class="p">)</span>
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Now that we have an optimum trained instance of our NLP model, we can start looking into some French Reviews ratings predictions. Lets randomly pick 10 from our training dataset&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>

<span class="n">rows</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">reviews</span><span class="o">.</span><span class="n">index</span><span class="p">),</span> <span class="n">reviews_sample_size</span><span class="p">)</span>

<span class="n">predicted_ratings</span> <span class="o">=</span> <span class="n">trained_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reviews_X</span><span class="p">[</span><span class="n">rows</span><span class="p">])</span>
<span class="c1">#[(predicted_rating[0], int(round(predicted_rating[0]))) for predicted_rating in predicted_ratings]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># table</span>
<span class="n">sampled_results</span> <span class="o">=</span> \
    <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">reviews</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">rows</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="s1">&#39;comment&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
              <span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="mi">2</span><span class="p">:</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pred</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> <span class="k">for</span> <span class="n">pred</span> <span class="ow">in</span> <span class="n">predicted_ratings</span><span class="p">]}))</span>
             <span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">sampled_results</span> <span class="o">=</span> \
    <span class="n">sampled_results</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">html_escape</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
                          <span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">result_type</span><span class="o">=</span><span class="s1">&#39;expand&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;rating&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;review&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="s1">&#39;predicted rating (rounded)&#39;</span><span class="p">})</span>

<span class="c1"># plot</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span> <span class="p">;</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span>
<span class="n">x_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">reviews_sample_size</span><span class="p">)</span>
<span class="n">bar_width</span> <span class="o">=</span> <span class="mf">0.35</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_index</span><span class="p">,</span> <span class="n">reviews_Y</span><span class="p">[</span><span class="n">rows</span><span class="p">]</span>
       <span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;actual (training dataset sample)&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">4</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_index</span> <span class="o">+</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">predicted_ratings</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">reviews_sample_size</span><span class="p">,))</span>
       <span class="p">,</span> <span class="n">bar_width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;predicted&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">4</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;French Reviews ratings&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="c1">#plt.show()</span>

<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</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="s1">&#39;tmp&#39;</span><span class="p">))</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="s1">&#39;tmp&#39;</span><span class="p">)</span>
<span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;reviews_predictions&quot;</span>
<span class="n">fig_url</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="s1">&#39;tmp&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">+</span><span class="s2">&quot;.jpg&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">fig_url</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span> <span class="p">;</span> <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>

<span class="c1"># use base64 encoding to circumvent image (external file) caching (webbrowser)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fig_url</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">image_file</span><span class="p">:</span>
    <span class="n">encoded_string</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">b64encode</span><span class="p">(</span><span class="n">image_file</span><span class="o">.</span><span class="n">read</span><span class="p">())</span>
<span class="n">encoded_string</span> <span class="o">=</span> <span class="s2">&quot;data:image/jpeg;charset=utf-8;base64, &quot;</span> <span class="o">+</span> <span class="n">encoded_string</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">fig_url</span><span class="p">)</span>

<span class="n">two_columns_display</span><span class="p">(</span><span class="n">sampled_results</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">hide_index</span><span class="p">()</span><span class="o">.</span><span class="n">set_table_styles</span><span class="p">(</span>
            <span class="p">[</span><span class="nb">dict</span><span class="p">(</span><span class="n">selector</span><span class="o">=</span><span class="s2">&quot;th&quot;</span><span class="p">,</span> <span class="n">props</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;text-align&#39;</span><span class="p">,</span><span class="s1">&#39;center&#39;</span><span class="p">)])]</span>
        <span class="p">)</span><span class="o">.</span><span class="n">render</span><span class="p">(),</span> <span class="s1">&#39;&lt;img src=&quot;&#39;</span> <span class="o">+</span> <span class="n">encoded_string</span> <span class="o">+</span> <span class="s1">&#39;&quot; /&gt;&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">55</span><span class="p">)</span>
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    }</style><table id="T_03493992_e9ed_11ea_8459_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >rating</th>        <th class="col_heading level0 col1" >review</th>        <th class="col_heading level0 col2" >predicted rating (rounded)</th>    </tr></thead><tbody>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row0_col0" class="data row0 col0" >3</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row0_col1" class="data row0 col1" >Un restaurant de sp&eacute;cialit&eacute;s s&eacute;n&eacute;galaise sans pr&eacute;tention mais c'est bon et correct au niveau tarif.</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row0_col2" class="data row0 col2" >2</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row1_col0" class="data row1 col0" >5</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row1_col1" class="data row1 col1" >Tout &eacute;tait excellent! Les d&eacute;cors, les acteurs, l'histoire. En bref, c'est un tr&egrave;s bon film. Ce qui ne m'a pas plu: Rien du tout.</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row1_col2" class="data row1 col2" >4</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row2_col0" class="data row2 col0" >2</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row2_col1" class="data row2 col1" >l'innocence, la magie, la nostalgie, la fausse naivet&eacute; Ce qui ne m'a pas plu: un peu cucul par moment et d&eacute;mod&eacute;</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row2_col2" class="data row2 col2" >4</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row3_col0" class="data row3 col0" >3</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row3_col1" class="data row3 col1" >la transposition &agrave; l'&eacute;cran d'un fait divers qui tend &agrave; se banaliser Ce qui ne m'a pas plu: quelques longueurs et certains roles qui ne sonnent pas justes</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row3_col2" class="data row3 col2" >3</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row4_col0" class="data row4 col0" >4</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row4_col1" class="data row4 col1" >tr&egrave;s romantique</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row4_col2" class="data row4 col2" >3</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row5_col0" class="data row5 col0" >4</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row5_col1" class="data row5 col1" >Joli cadre moderne avec une terrasse dans le m&ecirc;me ton et une qualit&eacute; de galette surprenante.</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row5_col2" class="data row5 col2" >3</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row6_col0" class="data row6 col0" >5</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row6_col1" class="data row6 col1" >Couteau au top-Coupe bien, se nettoie bien, tr&egrave;s jolie sur le plan de travail</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row6_col2" class="data row6 col2" >3</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row7_col0" class="data row7 col0" >4</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row7_col1" class="data row7 col1" >Pas d&eacute;&ccedil;ue, le livre est bien respect&eacute;. je doutais du choix des acteur et au final ils sont parfaits. j'ai pass&eacute; un agr&eacute;able moment.</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row7_col2" class="data row7 col2" >4</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row8_col0" class="data row8 col0" >4</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row8_col1" class="data row8 col1" >j'ai tout appr&eacute;ci&eacute; dans ce film, la mise en sc&egrave;ne, lesmagnifiques paysages d' Egypte tout comme le Jardin des Plantes, ainsi que le jeux des acteurs souvent difficiles &agrave; reconnaitre (Mr Almaric). Avec une speciale dedicace &agrave; Louise Bourgoin qui excelle d'intelligence dans son role et qui illumine la pellicule. Merci Mr Besson ! Ce qui ne m'a pas plu: Tout m'a plu!</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row8_col2" class="data row8 col2" >4</td>
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                                <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row9_col0" class="data row9 col0" >5</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row9_col1" class="data row9 col1" >Tr&egrave;s beau film et admirablement interpr&eacute;t&eacute;, mais surtout n'oubliez pas votre mouchoir</td>
                        <td id="T_03493992_e9ed_11ea_8459_309c23ff89f2row9_col2" class="data row9 col2" >4</td>
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That's all for the pre-training of our NLP model. We're now in a position to transfer that knowledge our model has acquired from the rather generic domain that our web-scrapped French Reviews dataset consitutes and use it as a learning starting point for our (smaller ; soccer-specific) target French Tweets dataset.
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<h3>5.2. Warming-Up <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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<h4>5.2.a. Training / Validation / Holdout data subsetting <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h4>
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In the pre-training phase (<a href="#pretraining"><b>&sect;5.1.</b></a> above), transfer Learning took place on the French Reviews dataset. The warming-up phase here, on the other hand, takes place on our target French Tweets dataset. As seen in <a href="#imbalance"><b>&sect;1.3.</b></a> , this dataset is made up of three sentiment classes which are "NEGATIVE", "NEUTRAL" and "POSITIVE".
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tweets_X</span> <span class="o">=</span> \
    <span class="n">sentences_to_indices</span><span class="p">(</span><span class="n">tweets</span><span class="p">[</span><span class="s1">&#39;tweet_text&#39;</span><span class="p">],</span> <span class="n">word_to_index</span><span class="p">,</span> <span class="n">max_len</span> <span class="o">=</span> <span class="n">TWEET_MAX_CHAR_COUNT</span><span class="p">)</span>
<span class="n">tweets_Y</span> <span class="o">=</span> \
    <span class="n">tweets</span><span class="p">[</span><span class="s1">&#39;sentiment&#39;</span><span class="p">]</span>
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If we recall from <a href="#imbalance"><b>&sect;1.3.</b></a> above, the French Tweets dataset being highly imbalanced, it needs <b>downsampling</b> to address the fact that the "NEUTRAL" sentiment class is way over-represented. But prior to doing that, in order to evaluate fairly the performance of our model, we need splitting our real-life dataset into <u>training</u> / <u>validation</u> / <u>holdout</u> subsets.
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This operation has to take place now, since we indeed want to respect the elementary rules of model training which are&nbsp;:
    <ul style="list-style-type: disc; margin: 0px 200px 0px 50px; /* top right bottom left*/">
        <li>we measure the final performance of a model on&nbsp;:
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                <li>data it had never been presented before (the holdout subset),</li>
                <li>data drawn from the real life distribution it will have to predict from when deployed into production (no alteration of the resampling kind),</li>
            </ol>
        <li>the validation and holdout datasets MUST be drawn from identical distributions,</li>
        <li>it is acceptable to <b>train</b> a model on manipulated data (using techinques such as oversampling/undersampling or data augmentation)</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>

<span class="n">test_prop</span> <span class="o">=</span> <span class="o">.</span><span class="mi">15</span> <span class="p">;</span> <span class="n">holdout_size</span> <span class="o">=</span> <span class="mi">3000</span>
<span class="n">tweets_X_train</span><span class="p">,</span> <span class="n">tweets_X_valid</span><span class="p">,</span> <span class="n">tweets_y_train</span><span class="p">,</span> <span class="n">tweets_y_valid</span> <span class="o">=</span> \
    <span class="n">train_test_split</span><span class="p">(</span><span class="n">tweets_X</span><span class="p">,</span> <span class="n">tweets_Y</span><span class="p">,</span>
                     <span class="n">test_size</span><span class="o">=</span><span class="n">test_prop</span><span class="p">)</span>
<span class="n">tweets_X_valid</span><span class="p">,</span> <span class="n">tweets_X_holdout</span><span class="p">,</span> <span class="n">tweets_y_valid</span><span class="p">,</span> <span class="n">tweets_y_holdout</span> <span class="o">=</span> \
    <span class="n">train_test_split</span><span class="p">(</span><span class="n">tweets_X_valid</span><span class="p">,</span> <span class="n">tweets_y_valid</span><span class="p">,</span>
                     <span class="n">test_size</span><span class="o">=</span><span class="n">holdout_size</span><span class="p">)</span>
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We thus take 85% of our French Tweets and will downsample these 186,659 records and will use the result of that for training. Save the 3,000 records which constitute our holdout data, the remaing 15% are to be used for validation (making it 29,940 validation records).
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It is on the training dataset (and on the training dataset only) that we operate resampling. We employ <b>downsampling</b> on the French Tweets dataset to address the fact that the "NEUTRAL" sentiment class is way over-represented there. This results in the below classes distribution for our resized training dataset&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">imblearn.under_sampling</span> <span class="kn">import</span> <span class="n">RandomUnderSampler</span>

<span class="n">rus</span> <span class="o">=</span> <span class="n">RandomUnderSampler</span><span class="p">(</span> <span class="n">sampling_strategy</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;NEUTRAL&#39;</span><span class="p">:</span><span class="n">tweets_y_train</span><span class="p">[</span><span class="n">tweets_y_train</span> <span class="o">==</span> <span class="s1">&#39;POSITIVE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()})</span>
<span class="n">tweets_X_train_resampled</span><span class="p">,</span> <span class="n">tweets_y_train_resampled</span> <span class="o">=</span> <span class="n">rus</span><span class="o">.</span><span class="n">fit_sample</span><span class="p">(</span><span class="n">tweets_X_train</span><span class="p">,</span> <span class="n">tweets_y_train</span> <span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">two_columns_display__classes_distribution</span><span class="p">(</span> <span class="n">tweets_y_train_resampled</span> <span class="p">)</span>
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        }</style><table id="T_fb49386e_f045_11ea_b73b_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >sentiment</th>        <th class="col_heading level0 col1" >count</th>        <th class="col_heading level0 col2" >percentage</th>    </tr></thead><tbody>
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                                <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row0_col1" class="data row0 col1" >5,164</td>
                        <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row0_col2" class="data row0 col2" >8.80%</td>
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                                <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
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                        <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row1_col2" class="data row1 col2" >45.60%</td>
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                        <td id="T_fb49386e_f045_11ea_b73b_309c23ff89f2row2_col2" class="data row2 col2" >45.60%</td>
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Oo18LV/SXCX/ACTOE/wL85H8wcY/8lTjP8b/ACiFFFFfRHzQUUUUAFfsJ/wb8f8AJmnib/sp17/6btOr8e6/YT/g34/5M08Tf9lOvf8A03adXw/iJ/yTMv8AHD82fe+Gv/JUx/wVPyifdFFFFfgZ/Q54z/wUb/5R6fHj/sjPij/003NezV4z/wAFG/8AlHp8eP8AsjPij/003NezUAFfGH7S/wAb/wBoL9kv9obQ7nQ/2ltb+JR1VPEXiPxd8J7vwzpMFl4e8I2em31xHfxXFrapeW7Jdx2NmklzPOty88gWNSrNF9n186/Db/gnja/DT44+NfjVafta/FDWIviLr82peM/CniCy8M3dhqcLRtFFpzTtowv1sYIm8uGBLtRGueSXkLgHn/7J3xo/aq8O/GL4LeH/AI/fHk+PbP48fCDUvFV3Yv4dsLGLwtq9mulTm309rSGOSSweLUpY8XTTzBraJvOPmMotftT/ABZ/an8afH/4r/Df9n74+N8PbX4LfBfS/F8SReHdPvk8SavqEusmG2vjeQyslhHHo4DC2aCd2umImXywD1/wx/4JueFPgtp91c+APj748v8AWdJ+HF94K+Fl/wCMb23vovAGl3AiIhsUt4baWYB7ayJkuZpblls4k89QKd8S/wDgmp8PvifpmmW9/wDH74o6TfH4a2ngLxvrGjeIoDeeONCgD7bfVJ7q3nkaQtPdP9qt2gugbyfEw3mgDwXx7+2n+1b8cPAHxQ/aQ+Bvxc/4QHS/g58CPD/jux8GnQbC8t/E+pXujz61Pa6lNdQPPHaC3SC2T7I9vKrvNI0j4WMWP2gP2z/2nrzQf2gP2rfhT8XpvDXhb9nLS9Evbf4d/wBgWFxb+K1fQbDxBqC3888D3SF7XUI7WD7LLb+XJCzv52dg95+Nn/BNH4H/ABn1u/uYPG/jLwhoviPwhY+FPHnhHwbqNrbaZ4t0OzMot7C9WW2lljRY7ieEyWklvK8MpiaRkCqs3xm/4Jw/Bv40/EXWPGep+PfGWi6P4uTS0+IngTw/f2kWjeL104j7KL1JLaSdAI1SF/ss1v50MaRS+YigUAexfEv4l+HPhP4Xfxh4p03xBdWiTJEYfDPhPUdautzHAIttPgnnK+rBNq9yK84/4JwOJP8Agnj8BZFBAb4MeFiAykH/AJBNr1B5Fez14z/wTk/5R6fAf/sjPhf/ANNNtQB7NRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfmT/wcZf80c/7mH/3GV+ZNfpt/wAHGX/NHP8AuYf/AHGV+ZNf0PwN/wAkth/+3v8A0uR/NnH/APyVmI/7c/8ASIhRRRX1p8cFFFFABX7Cf8G/H/Jmnib/ALKde/8Apu06vx7r9hP+Dfj/AJM08Tf9lOvf/Tdp1fD+In/JMy/xw/Nn3vhr/wAlTH/BU/KJ90UUUV+Bn9DhRRRQAUUUUAfj5/wcD/8AJ5fhn/smNl/6cdRr4Wr7p/4OB/8Ak8vwz/2TGy/9OOo18LV/SXCX/JM4T/AvzkfzBxj/AMlTjP8AG/yiFFFFfRHzQUUUUAFfsJ/wb8f8maeJv+ynXv8A6btOr8e6/YT/AIN+P+TNPE3/AGU69/8ATdp1fD+In/JMy/xw/Nn3vhr/AMlTH/BU/KJ90UUUV+Bn9DnjP/BRv/lHp8eP+yM+KP8A003NezV4z/wUb/5R6fHj/sjPij/003NezUAFFFch4e/aD+Ani74ia18IfCfxv8Iap4s8N2/n+IvC+neJbWfUdKi3bfMubZJDLAu4gbnUDJxQB19FedfBP9sH9kn9pbU7zRf2cv2pPhz8QLzTrZbjULTwT43sNVltYWbaski2srlFJ4DMACeK1vid+0H8BPgnqWi6N8Zvjf4Q8I3niS8+yeHbTxP4ltbCXVbjKjybZZ5FM75ZRtQE/MOORQB19Fch4/8A2gvgJ8KPFug+Afil8bvCHhrXfFNx5HhjRdf8S2tnd6vLuC+XawzSK9w25lG2MMckDvR4v/aC+Anw++Imh/CHx98bvCGh+LPE3/It+F9Y8S2ttqOrfMV/0a2kkEs/zAj5FbkYoA6+vGf+Ccn/ACj0+A//AGRnwv8A+mm2r2avGf8AgnJ/yj0+A/8A2Rnwv/6abagD2aiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/Mn/AIOMv+aOf9zD/wC4yvzJr9Nv+DjL/mjn/cw/+4yvzJr+h+Bv+SWw/wD29/6XI/mzj/8A5KzEf9uf+kRCiiivrT44KKKKACv2E/4N+P8AkzTxN/2U69/9N2nV+PdfsJ/wb8f8maeJv+ynXv8A6btOr4fxE/5JmX+OH5s+98Nf+Spj/gqflE+6KKKK/Az+hwooooAKKKKAPx8/4OB/+Ty/DP8A2TGy/wDTjqNfC1fdP/BwP/yeX4Z/7JjZf+nHUa+Fq/pLhL/kmcJ/gX5yP5g4x/5KnGf43+UQooor6I+aCiiigAr9hP8Ag34/5M08Tf8AZTr3/wBN2nV+PdfsJ/wb8f8AJmnib/sp17/6btOr4fxE/wCSZl/jh+bPvfDX/kqY/wCCp+UT7oooor8DP6HPGf8Ago3/AMo9Pjx/2RnxR/6abmvZq8Z/4KN/8o9Pjx/2RnxR/wCmm5r2agAr4T179pn9inwt/wAFjtS8Ct+0H8N9K1eX4FahpWt6Xa+JLFL06u2rx3EltJCr+Y155KtKYiplKKWwRzX3ZRQB8Ef8E/PjR4C+CXjrRv2YP2fv221+OnwI+H3wHkuNT8VNDpV3/wAINNpT2FrY2DX+j20MU32iza8c28yyXSf2dvZyJMVzH7Vfxw/ZjPxg+MPxW+PeoWl14T+NH7Img2fwTfWtLljfxKkkmvTXukWEE8ayvfzG90iQ2gT7Q4eD5Mwnb+iXiDw/oPi3Qb7wr4q0Sz1PS9Ts5bTUtN1C2Sa3u7eRCkkUsbgrIjKSrKwIIJBBBqzBBBawJbW0KRxxoFjjjUBVUDAAA6ADtQB+UHxqutH+E3wu+Pvwh/bR2TfFL4kfspeEdA+GthqS+dqHijVIdHvrd9N0onLXF5HrUjTtHES8bXMM7ALhxe/a3vtO8KaF+1p8BfjJf2r/ABx+KWkeEY/glZXCBtQ8Q3MWg6fa2LaYMbp/smvxahcyeVn7P5hmfYrbj+qlFAHP/EvTfipqvhd7T4O+MvD+g60ZkKah4m8Mz6taiMH51NvBe2bliOjecAO6tXnH/BOAOP8Agnj8BRKwLf8ACmPC24quAT/ZNr0GTivZ68Z/4Jyf8o9PgP8A9kZ8L/8ApptqAPZqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD8yf+DjL/mjn/cw/wDuMr8ya/Tb/g4y/wCaOf8Acw/+4yvzJr+h+Bv+SWw//b3/AKXI/mzj/wD5KzEf9uf+kRCiiivrT44KKKKACv2E/wCDfj/kzTxN/wBlOvf/AE3adX491+wn/Bvx/wAmaeJv+ynXv/pu06vh/ET/AJJmX+OH5s+98Nf+Spj/AIKn5RPuiiiivwM/ocKKKKACiiigD8fP+Dgf/k8vwz/2TGy/9OOo18LV90/8HA//ACeX4Z/7JjZf+nHUa+Fq/pLhL/kmcJ/gX5yP5g4x/wCSpxn+N/lEKKKK+iPmgooooAK/YT/g34/5M08Tf9lOvf8A03adX491+wn/AAb8f8maeJv+ynXv/pu06vh/ET/kmZf44fmz73w1/wCSpj/gqflE+6KKKK/Az+hzxn/go3/yj0+PH/ZGfFH/AKabmvZq8Z/4KN/8o9Pjx/2RnxR/6abmvZqACiivn+1/bP8AGvhz9rDxj8APjR8FdO8MeGvDvw6u/GeleNLfxib6W/022u1t5GmsltEFqcEyACaU4XBANAH0BRXzX+zB+3l4++M/xC8HeC/jF+zivgGD4peALnxn8L7uPxcuqS3+nW72fnW+oRC2hFhfLHqFpL5Eb3MZV5AJi0TCpv2nf25fiJ8IPiZ4m+GvwQ/ZxT4gz/Dz4eQeNviPJP4vXSmsNMnlvI7a3slNtOL2/lGnXzrBI1tGFhXdOpkUUAfR1FfJnxe/4Kf3vhxPEvjT4D/AMePvA3w7+G+l+OviT4kbxUNOuLPSb6Ca7iTTrQ2sv9oXaWVu908MklsoR4lWR3fYJ/jh/wAFMdQ+GXibx3r3gX4FQ+KPhr8IbbSJ/ix43Pi0Wdzp0d9bQ3pawsfssgvxb2Fxb3c3mT22I5gI/NcFaAPqyvGf+Ccn/KPT4D/9kZ8L/wDpptq9mrxn/gnJ/wAo9PgP/wBkZ8L/APpptqAPZqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD8yf+DjL/AJo5/wBzD/7jK/Mmv02/4OMv+aOf9zD/AO4yvzJr+h+Bv+SWw/8A29/6XI/mzj//AJKzEf8Abn/pEQooor60+OCiiigAr9hP+Dfj/kzTxN/2U69/9N2nV+PdfsJ/wb8f8maeJv8Asp17/wCm7Tq+H8RP+SZl/jh+bPvfDX/kqY/4Kn5RPuiiiivwM/ocKKKKACiiigD8fP8Ag4H/AOTy/DP/AGTGy/8ATjqNfC1fdP8AwcD/APJ5fhn/ALJjZf8Apx1Gvhav6S4S/wCSZwn+BfnI/mDjH/kqcZ/jf5RCiiivoj5oKKKKACv2E/4N+P8AkzTxN/2U69/9N2nV+PdfsJ/wb8f8maeJv+ynXv8A6btOr4fxE/5JmX+OH5s+98Nf+Spj/gqflE+6KKKK/Az+hzxn/go3/wAo9Pjx/wBkZ8Uf+mm5r2avGf8Ago3/AMo9Pjx/2RnxR/6abmvZqACvkr4nfs1ftefFb9t/WviJ4j+GvwyX4Wa98Lb/AOHl/dQ/E7URrh066uBO98tmdE8gTDmMW/2orzu87jbX1rRQB8b/AAg/ZZ/bX8Bah4Q+JHxeh8Ca7qXwH+DGr+Ffhlp3hDVbl5fGOpXEVii398t5FbR6czJpkMQtkmmQNdzM1yFVQJ/ir+yv+3VrHiDXvil8L9Y+F6eKfi98ENH8FfFVdXvdQtLPQdVshqBGr6XHHDcteIDq14v2OaSDcIbcm4B35+waKAPiH4l/8E4f2hPCPh7x18FP2VfFPg1PBHxZ+EOi+AfFGpeL9Qu4tS8NLYafNpR1KyhgtpYtQkksJY1FvLJbKk1ur+Y6uyCf47f8E6vj14hPxe+BvwX1vwZD8LPj/YaPZ+Ob7XtTu49Y8OxW2l2ujXosreK2khvzcabY2yJ5k9t5Modz5wIQfa9FAHP/ABL+E3wr+NHhd/A/xi+Gnh/xZoskyTPo/ibRoL+1aRDlHMU6MhZT0OMjtXnH/BOCNIv+CePwFiiQKq/BjwsFVRgAf2Ta8CvZ68Z/4Jyf8o9PgP8A9kZ8L/8ApptqAPZqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD8yf+DjL/mjn/cw/wDuMr8ya/Tb/g4y/wCaOf8Acw/+4yvzJr+h+Bv+SWw//b3/AKXI/mzj/wD5KzEf9uf+kRCiiivrT44KKKKACv2E/wCDfj/kzTxN/wBlOvf/AE3adX491+wn/Bvx/wAmaeJv+ynXv/pu06vh/ET/AJJmX+OH5s+98Nf+Spj/AIKn5RPuiiiivwM/ocKKKKACiiigD8fP+Dgf/k8vwz/2TGy/9OOo18LV90/8HA//ACeX4Z/7JjZf+nHUa+Fq/pLhL/kmcJ/gX5yP5g4x/wCSpxn+N/lEKKKK+iPmgooooAK/YT/g34/5M08Tf9lOvf8A03adX491+wn/AAb8f8maeJv+ynXv/pu06vh/ET/kmZf44fmz73w1/wCSpj/gqflE+6KKKK/Az+hzxn/go3/yj0+PH/ZGfFH/AKabmvZq8Z/4KN/8o9Pjx/2RnxR/6abmvZqACiiigAor4Z8AeGvjR+yr+2Va+Nf2pdO8Va5pPxP+M+uaZ8PvFOmftE+I76z0yO9S9uNOsb3wvOYtNhhFtC8SSQ/aDHJ5bFI8eYnW/tLaJ4m/aG/bh1T9nXWvjt428C+GfCfwJTxTpU3gjxfd6JJLrF5qV3a/bZ5bWRGuY7OOyjIt5S9uxuyZYpPlwAfXNFfmR4B+MXxy/bm+F118SPij8e/GngDU/B/7Ffgj4iaKPBvia50WFvEms2us3F1q1zFbsiX0EbaXbolrcLLbANMGjJcEP+IPxp+N/wC0T8KPjX+0v4g+OXjHwDr3wc/Zr8M+MvBOj+GfE11punWms3OhXes3F7f2kTrFqUTzRJamC7WWJY7WQIiu7PQB+mleM/8ABOT/AJR6fAf/ALIz4X/9NNtXY6h8UPGOm/B/SPiPpXwV8Q+KNU1CwtJ5/C/hu40+G7iaWNWf5tRu7WHahOCDKG9Aa43/AIJwMX/4J4/AV2jKE/BjwsSrYyP+JTa8HHFAHs9FFFABRXzJ/wAFGf8Agoz/AMMAf8Id/wAWc/4S3/hLf7Q/5mH7B9l+y/Zv+nebzN32j/Zxs754+ZP+IjL/AKs6/wDMhf8A3vr6LA8KZ/mWFjicNR5oSvZ80Vs7PRtPdHzWYcX8PZXi5YbE1uWcbXXLJ7q61Sa2Z+m1FfmT/wARGX/VnX/mQv8A730f8RGX/VnX/mQv/vfXX/qNxT/0D/8Ak0P/AJI4/wDX/hP/AKCP/JJ//In6bUV+ZP8AxEZf9Wdf+ZC/+99H/ERl/wBWdf8AmQv/AL30f6jcU/8AQP8A+TQ/+SD/AF/4T/6CP/JJ/wDyJ+m1FfmT/wARGX/VnX/mQv8A7319N/8ABOb/AIKM/wDDf/8AwmP/ABZz/hEv+ES/s/8A5mH7f9q+1faf+neHy9v2f/azv7Y55Mdwpn+W4WWJxNHlhG13zRe7stE292dmX8X8PZpi44bDVuacr2XLJbK71aS2R9N0UUV86fShRRRQAUUUUAfmT/wcZf8ANHP+5h/9xlfmTX6bf8HGX/NHP+5h/wDcZX5k1/Q/A3/JLYf/ALe/9LkfzZx//wAlZiP+3P8A0iIUUUV9afHBRRRQAV+wn/Bvx/yZp4m/7Kde/wDpu06vx7r9hP8Ag34/5M08Tf8AZTr3/wBN2nV8P4if8kzL/HD82fe+Gv8AyVMf8FT8on3RRRRX4Gf0OFFFFABRRRQB+Pn/AAcD/wDJ5fhn/smNl/6cdRr4Wr7p/wCDgf8A5PL8M/8AZMbL/wBOOo18LV/SXCX/ACTOE/wL85H8wcY/8lTjP8b/ACiFFFFfRHzQUUUUAFfsJ/wb8f8AJmnib/sp17/6btOr8e6/YT/g34/5M08Tf9lOvf8A03adXw/iJ/yTMv8AHD82fe+Gv/JUx/wVPyifdFFFFfgZ/Q54z/wUb/5R6fHj/sjPij/003NezV4z/wAFG/8AlHp8eP8AsjPij/003NezUAFFFFAHjtn+wh+zpp3xPb4z2umeJ5/E0V5f3ui32s+PtX1W30G8vElSe602xv7qey0+YrNKoaCBMLIyAbWKnDvf+CY37Gur/B7wF8ENa+HOoy6P8OPBy+FfDs9h4q1HTLuTR/KhilsbqfT54HvLeYQRNNbzFoZWUFozXv1FAHj/AMaf2B/2TP2gp9Km+J3wmEy6PoX9hwW+ja5faVBc6RuVv7Lu4rGeFL6wyoP2O4WWDr+7+Y5f8aP2Ef2VP2g/Gdn49+K/wtN/qFrp1vp1wlprt/Y2uqWMEzTwWWoWtrPHBqVtHK7ulvdpNEjSOQo3tn12igArxn/gnJ/yj0+A/wD2Rnwv/wCmm2r2avGf+Ccn/KPT4D/9kZ8L/wDpptqAPZqKKKAPzJ/4OMv+aOf9zD/7jK/Mmv02/wCDjL/mjn/cw/8AuMr8ya/ofgb/AJJbD/8Ab3/pcj+bOP8A/krMR/25/wCkRCiiivrT44KKKKACv02/4Nzf+ax/9y9/7k6/Mmv02/4Nzf8Amsf/AHL3/uTr5Ljn/klsR/27/wClxPseAP8AkrMP/wBv/wDpEj9NqKKK/ng/pMKKKKACiiigD8yf+DjL/mjn/cw/+4yvzJr9Nv8Ag4y/5o5/3MP/ALjK/Mmv6H4G/wCSWw//AG9/6XI/mzj/AP5KzEf9uf8ApEQooor60+OCiiigAr9hP+Dfj/kzTxN/2U69/wDTdp1fj3X7Cf8ABvx/yZp4m/7Kde/+m7Tq+H8RP+SZl/jh+bPvfDX/AJKmP+Cp+UT7oooor8DP6HCiiigAooooA/Hz/g4H/wCTy/DP/ZMbL/046jXwtX3T/wAHA/8AyeX4Z/7JjZf+nHUa+Fq/pLhL/kmcJ/gX5yP5g4x/5KnGf43+UQooor6I+aCiiigAr9hP+Dfj/kzTxN/2U69/9N2nV+PdfsJ/wb8f8maeJv8Asp17/wCm7Tq+H8RP+SZl/jh+bPvfDX/kqY/4Kn5RPuiiiivwM/oc8Z/4KN/8o9Pjx/2RnxR/6abmvZq8Z/4KN/8AKPT48f8AZGfFH/ppua9moAKKKKACiiigAryf9oL9t/8AZi/Zb1+08M/G74jT6Xe3OlyapNFZeHtQ1Eadp0cgjfUL57OCVdPs1c7TdXJihBDDf8rY9Yr4i/a6Tx18Hf2lPjd4xuP2f/G/jyw+Mn7Pmj+FvBB8IeE7rVk/tiwm8QCXS7t4EdNPimGr2si3NyYrbiffICgBAPtq3uILqBLq1mSSKRA8ckbAqykZBBHUEd68c/4Jyf8AKPT4D/8AZGfC/wD6abatD4afAv4ieH/2SfAXwJl+NOveFvEHhrwfo+mal4m8LQ6fPcyT2tpFDLtGo2l1CVdkJJMW7HQrXJ/DH9hn4hfB/wCG3h74SeAf+ChfxptNC8LaHaaRotrJpXgyVobS2hSGFC7+HizkIijcxJOMkk0AfQVFeM/8Mv8Axu/6SN/Gb/wR+Cv/AJnaP+GX/jd/0kb+M3/gj8Ff/M7QB8X/APBxl/zRz/uYf/cZX5k1+3P7Rv8AwSY8Pftaf2N/w0F+2r8ZvEH/AAj/ANo/sj914WtPI8/yvN/49tCj3bvJj+9nG3jGTnzH/iHR/ZP/AOjj/jN/4MtB/wDlRX6pw3xxlOT5NSwlaE3KN72Ubayb6yXfsfkfE/AWcZ1nlXGUJwUZ8tk3K+kUukWt13PyUor9a/8AiHR/ZP8A+jj/AIzf+DLQf/lRR/xDo/sn/wDRx/xm/wDBloP/AMqK93/iJeRf8+6n3R/+SPA/4hbn/wDz9pffL/5A/JSiv09/Z/8A+DfL4NeLvgP4J8V/Gj42fGHR/GOp+EdNu/FmkQXuhxpY6nJaxvdQKraU5UJMzqAWYgLjcetdd/xDo/sn/wDRx/xm/wDBloP/AMqKP+Il5F/z7qfdH/5IP+IW5/8A8/aX3y/+QPyUr9Nv+Dc3/msf/cvf+5Ou1/4h0f2T/wDo4/4zf+DLQf8A5UV6d+zn/wAEmPD37Jf9s/8ADPv7avxm8P8A/CQfZ/7X/deFrvz/ACPN8r/j50KTbt86T7uM7uc4GPC4k44ynOMmq4SjCalK1rqNtJJ9JPt2Pf4Y4CzjJc8pYyvODjDmuk5X1i11ilu+59Z0V4z/AMMv/G7/AKSN/Gb/AMEfgr/5naP+GX/jd/0kb+M3/gj8Ff8AzO1+Vn64ezUV4z/wy/8AG7/pI38Zv/BH4K/+Z2j/AIZf+N3/AEkb+M3/AII/BX/zO0AezUV4z/wy/wDG7/pI38Zv/BH4K/8Amdo/4Zf+N3/SRv4zf+CPwV/8ztAHxf8A8HGX/NHP+5h/9xlfmTX7c/tG/wDBJjw9+1p/Y3/DQX7avxm8Qf8ACP8A2j+yP3Xha08jz/K83/j20KPdu8mP72cbeMZOfMf+IdH9k/8A6OP+M3/gy0H/AOVFfqnDfHGU5Pk1LCVoTco3vZRtrJvrJd+x+R8T8BZxnWeVcZQnBRny2Tcr6RS6Ra3Xc/JSiv1r/wCIdH9k/wD6OP8AjN/4MtB/+VFcj+0B/wAG+Xwa8I/Afxt4r+C/xs+MOseMdM8I6ld+E9InvdDkS+1OO1ke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The resulting dataset is still too widely biased against NEGATIVE tweets, though. If we were to train a model on it, it would neglect to learn to detect NEGATIVE instances altogether. To adress that, we now choose to use <b>upsampling</b>. There are indeed too few NEGATIVE records for us to use downsampling again. The resulting training dataset would become way too small for any learning to happen otherwise.
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<div class="prompt input_prompt">In&nbsp;[38]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">imblearn.over_sampling</span> <span class="kn">import</span> <span class="n">RandomOverSampler</span>

<span class="n">ros</span> <span class="o">=</span> <span class="n">RandomOverSampler</span><span class="p">(</span> <span class="s1">&#39;not majority&#39;</span> <span class="p">)</span>
<span class="n">tweets_X_train_resampled</span><span class="p">,</span> <span class="n">tweets_y_train_resampled</span> <span class="o">=</span> <span class="n">ros</span><span class="o">.</span><span class="n">fit_sample</span><span class="p">(</span><span class="n">tweets_X_train_resampled</span><span class="p">,</span> <span class="n">tweets_y_train_resampled</span><span class="p">)</span>
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Following the principles listed above, we end up with the below 3 data subsets&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[4m&#39;</span> <span class="o">+</span> <span class="s1">&#39;Training&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39; dataset&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span><span class="p">)</span>
<span class="n">two_columns_display__classes_distribution</span><span class="p">(</span> <span class="n">tweets_y_train_resampled</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[4m&#39;</span> <span class="o">+</span> <span class="s1">&#39;Validation&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39; dataset&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span><span class="p">)</span>
<span class="n">two_columns_display__classes_distribution</span><span class="p">(</span> <span class="n">tweets_y_valid</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[4m&#39;</span> <span class="o">+</span> <span class="s1">&#39;Holdout&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span> <span class="o">+</span> <span class="s1">&#39; dataset&#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span><span class="p">)</span>
<span class="n">two_columns_display__classes_distribution</span><span class="p">(</span> <span class="n">tweets_y_holdout</span> <span class="p">)</span>
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<pre><span class="ansi-bold ansi-underline">Training</span><span class="ansi-bold"> dataset</span>
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        }    #T_0b7949ae_f046_11ea_a721_309c23ff89f2row2_col1 {
            width:  20em;
            text-align:  center;
        }    #T_0b7949ae_f046_11ea_a721_309c23ff89f2row2_col2 {
            width:  20em;
            text-align:  center;
        }</style><table id="T_0b7949ae_f046_11ea_a721_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >sentiment</th>        <th class="col_heading level0 col1" >count</th>        <th class="col_heading level0 col2" >percentage</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row0_col1" class="data row0 col1" >26,758</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row0_col2" class="data row0 col2" >33.33%</td>
            </tr>
            <tr>
                                <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row1_col1" class="data row1 col1" >26,758</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row1_col2" class="data row1 col2" >33.33%</td>
            </tr>
            <tr>
                                <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row2_col0" class="data row2 col0" >POSITIVE</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row2_col1" class="data row2 col1" >26,758</td>
                        <td id="T_0b7949ae_f046_11ea_a721_309c23ff89f2row2_col2" class="data row2 col2" >33.33%</td>
            </tr>
    </tbody></table>
      </center>
  </div>
  <div class="right_column" style="width: 50.00%;">
      <center>
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<pre><span class="ansi-bold ansi-underline">Validation</span><span class="ansi-bold"> dataset</span>
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                                <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row0_col1" class="data row0 col1" >843</td>
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                                <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
                        <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row1_col1" class="data row1 col1" >24,821</td>
                        <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row1_col2" class="data row1 col2" >82.90%</td>
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                                <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row2_col0" class="data row2 col0" >POSITIVE</td>
                        <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row2_col1" class="data row2 col1" >4,276</td>
                        <td id="T_0b88cf76_f046_11ea_a922_309c23ff89f2row2_col2" class="data row2 col2" >14.28%</td>
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<pre><span class="ansi-bold ansi-underline">Holdout</span><span class="ansi-bold"> dataset</span>
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        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row0_col1 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row0_col2 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col0 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col1 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col2 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col0 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col1 {
            width:  20em;
            text-align:  center;
        }    #T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col2 {
            width:  20em;
            text-align:  center;
        }</style><table id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >sentiment</th>        <th class="col_heading level0 col1" >count</th>        <th class="col_heading level0 col2" >percentage</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row0_col1" class="data row0 col1" >62</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row0_col2" class="data row0 col2" >2.07%</td>
            </tr>
            <tr>
                                <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col1" class="data row1 col1" >2,491</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row1_col2" class="data row1 col2" >83.03%</td>
            </tr>
            <tr>
                                <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col0" class="data row2 col0" >POSITIVE</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col1" class="data row2 col1" >447</td>
                        <td id="T_0b98ac5c_f046_11ea_8de0_309c23ff89f2row2_col2" class="data row2 col2" >14.90%</td>
            </tr>
    </tbody></table>
      </center>
  </div>
  <div class="right_column" style="width: 50.00%;">
      <center>
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<h4>5.2.b. model training <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h4>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tweets_y_train_resampled_1_hot</span> <span class="o">=</span> \
    <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span> <span class="n">tweets_y_train_resampled</span> <span class="p">)</span> <span class="c1">#One-Hot encoded</span>
<span class="n">tweets_y_valid_1_hot</span> <span class="o">=</span> \
    <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span> <span class="n">tweets_y_valid</span> <span class="p">)</span> <span class="c1">#One-Hot encoded</span>
<span class="n">tweets_y_holdout_1_hot</span> <span class="o">=</span> \
    <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span> <span class="n">tweets_y_holdout</span> <span class="p">)</span> <span class="c1">#One-Hot encoded</span>
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During the pre-training phase, we had 5 (ordered) classes. Thus, as introduced in <a href="#scraping"><b>&sect;2.1.</b></a>, we legitimately handeled the Sentiment Analysis problem as a regression problem. It translated in having <u>a relu-activated 1 Node Dense output layer</u>.
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For the remaining of the training process of our NLP model, the target dataset is made-up of 3 classes where we can't really consider any hierarchy. We then now use <u>a softmax-activated 3 Nodes Dense output layer</u>&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span>

<span class="n">o</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span> <span class="o">=</span> <span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;tweets_sentiment&quot;</span><span class="p">)(</span><span class="n">trained_model</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">output</span><span class="p">)</span> <span class="c1"># ignore original last layer</span>

<span class="n">tweets_model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span> <span class="n">inputs</span> <span class="o">=</span> <span class="n">trained_model</span><span class="o">.</span><span class="n">input</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">o</span><span class="p">])</span>
<span class="c1">#tweets_model.summary()</span>
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Remember that we are using the architecture depicted in <a href="#architecture"><b>&sect;4.1.</b></a> above. With our optimum set of hyperparameter values, for each input sentence (of maximum length 280), the inner tensors of our NLP model are of the shapes listed in the below table. Note that, for the warming-up of our model with its new output, we freeze all layers except the (new) very last one. This represents the "<b>Step 2</b>" in the summary figure seen in <a href="#page-top"><b>&sect;0.</b></a> &nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">tweets_model</span><span class="o">.</span><span class="n">layers</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="p">:</span>
    <span class="n">layer</span><span class="o">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="kc">False</span>

<span class="n">layers_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="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">tweets_model</span><span class="o">.</span><span class="n">layers</span> <span class="p">:</span>
    <span class="n">layers_df</span> <span class="o">=</span> <span class="n">layers_df</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][:</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">input_shape</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;None, &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
                                   <span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">output_shape</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;None, &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">),</span> <span class="n">layer</span><span class="o">.</span><span class="n">trainable</span><span class="p">]]</span>
                                 <span class="p">,</span> <span class="n">ignore_index</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">False</span>
    <span class="p">)</span>
<span class="n">layers_df</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">,</span> <span class="s1">&#39;input_shape&#39;</span><span class="p">,</span> <span class="s1">&#39;output_shape&#39;</span><span class="p">,</span> <span class="s1">&#39;trainable&#39;</span><span class="p">]</span>
<span class="n">dataframe_pretty_print_center</span><span class="p">(</span><span class="n">layers_df</span><span class="p">)</span>
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        }</style><table id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >class</th>        <th class="col_heading level0 col1" >input_shape</th>        <th class="col_heading level0 col2" >output_shape</th>        <th class="col_heading level0 col3" >trainable</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row0_col0" class="data row0 col0" >InputLayer</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row0_col1" class="data row0 col1" >(280)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row0_col2" class="data row0 col2" >(280)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row0_col3" class="data row0 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row1_col0" class="data row1 col0" >Embedding</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row1_col1" class="data row1 col1" >(280)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row1_col2" class="data row1 col2" >(280, 300)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row1_col3" class="data row1 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row2_col0" class="data row2 col0" >SpatialDropout1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row2_col1" class="data row2 col1" >(280, 300)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row2_col2" class="data row2 col2" >(280, 300)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row2_col3" class="data row2 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row3_col0" class="data row3 col0" >Bidirectional</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row3_col1" class="data row3 col1" >(280, 300)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row3_col2" class="data row3 col2" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row3_col3" class="data row3 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row4_col0" class="data row4 col0" >Bidirectional</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row4_col1" class="data row4 col1" >(280, 300)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row4_col2" class="data row4 col2" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row4_col3" class="data row4 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row5_col0" class="data row5 col0" >Conv1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row5_col1" class="data row5 col1" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row5_col2" class="data row5 col2" >(279, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row5_col3" class="data row5 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row6_col0" class="data row6 col0" >Conv1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row6_col1" class="data row6 col1" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row6_col2" class="data row6 col2" >(278, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row6_col3" class="data row6 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row7_col0" class="data row7 col0" >Conv1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row7_col1" class="data row7 col1" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row7_col2" class="data row7 col2" >(279, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row7_col3" class="data row7 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row8_col0" class="data row8 col0" >Conv1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row8_col1" class="data row8 col1" >(280, 136)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row8_col2" class="data row8 col2" >(278, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row8_col3" class="data row8 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row9_col0" class="data row9 col0" >GlobalMaxPooling1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row9_col1" class="data row9 col1" >(279, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row9_col2" class="data row9 col2" >(28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row9_col3" class="data row9 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row10_col0" class="data row10 col0" >GlobalMaxPooling1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row10_col1" class="data row10 col1" >(278, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row10_col2" class="data row10 col2" >(28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row10_col3" class="data row10 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row11_col0" class="data row11 col0" >GlobalMaxPooling1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row11_col1" class="data row11 col1" >(279, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row11_col2" class="data row11 col2" >(28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row11_col3" class="data row11 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row12_col0" class="data row12 col0" >GlobalMaxPooling1D</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row12_col1" class="data row12 col1" >(278, 28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row12_col2" class="data row12 col2" >(28)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row12_col3" class="data row12 col3" >False</td>
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                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row13_col0" class="data row13 col0" >Concatenate</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row13_col1" class="data row13 col1" >[(28), (28), (28), (28)]</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row13_col2" class="data row13 col2" >(112)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row13_col3" class="data row13 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row14_col0" class="data row14 col0" >BatchNormalization</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row14_col1" class="data row14 col1" >(112)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row14_col2" class="data row14 col2" >(112)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row14_col3" class="data row14 col3" >False</td>
            </tr>
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                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row15_col0" class="data row15 col0" >Dense</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row15_col1" class="data row15 col1" >(112)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row15_col2" class="data row15 col2" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row15_col3" class="data row15 col3" >False</td>
            </tr>
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                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row16_col0" class="data row16 col0" >Dropout</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row16_col1" class="data row16 col1" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row16_col2" class="data row16 col2" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row16_col3" class="data row16 col3" >False</td>
            </tr>
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                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row17_col0" class="data row17 col0" >BatchNormalization</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row17_col1" class="data row17 col1" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row17_col2" class="data row17 col2" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row17_col3" class="data row17 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row18_col0" class="data row18 col0" >Dense</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row18_col1" class="data row18 col1" >(46)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row18_col2" class="data row18 col2" >(32)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row18_col3" class="data row18 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row19_col0" class="data row19 col0" >Dropout</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row19_col1" class="data row19 col1" >(32)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row19_col2" class="data row19 col2" >(32)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row19_col3" class="data row19 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row20_col0" class="data row20 col0" >Dense</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row20_col1" class="data row20 col1" >(32)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row20_col2" class="data row20 col2" >(3)</td>
                        <td id="T_e94b35a4_eeb8_11ea_9f2b_309c23ff89f2row20_col3" class="data row20 col3" >True</td>
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We perform our model warming-up using the same setup as the one we'll use next, during the fine-tuning of our model. That is that we use 'categorical_crossentropy' as our loss function, <small><b><em>keras.optimizers.Adam</em></b></small> as our optimizer, we do measure not only 'accuracy' (not that meaningful with imbalanced datasets) but also 'precision' and 'recall', both globally and per class&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm.keras</span> <span class="kn">import</span> <span class="n">TqdmCallback</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="kn">import</span> <span class="n">Adam</span>
<span class="kn">from</span> <span class="nn">keras.metrics</span> <span class="kn">import</span> <span class="n">Precision</span><span class="p">,</span> <span class="n">Recall</span><span class="p">,</span> <span class="n">CategoricalCrossentropy</span><span class="p">,</span> <span class="n">CategoricalAccuracy</span>
<span class="c1"># f1_score = 2*(precision*recall)/(precision+recall+K.epsilon())</span>

<span class="n">warming_up_lr</span><span class="o">=</span><span class="mf">0.001</span> <span class="c1"># default: 0.01</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span> <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">warming_up_lr</span> <span class="p">)</span>
<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">CategoricalCrossentropy</span><span class="p">(),</span> <span class="n">CategoricalAccuracy</span><span class="p">()</span>
           <span class="p">,</span> <span class="n">Precision</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;precision&#39;</span><span class="p">),</span> <span class="n">Recall</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;recall&#39;</span><span class="p">)</span>
           <span class="p">,</span> <span class="n">Precision</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;precision_NEG&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">Recall</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;recall_NEG&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
           <span class="p">,</span> <span class="n">Precision</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;precision_NEUT&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">Recall</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;recall_NEUT&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
           <span class="p">,</span> <span class="n">Precision</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;precision_POS&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">Recall</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;recall_POS&#39;</span><span class="p">,</span> <span class="n">class_id</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
          <span class="p">]</span>

<span class="n">tweets_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">warming_up_history</span> <span class="o">=</span> \
    <span class="n">tweets_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">tweets_X_train_resampled</span><span class="p">,</span> <span class="n">tweets_y_train_resampled_1_hot</span>
                     <span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">15</span>
                     <span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">tweets_X_valid</span><span class="p">,</span> <span class="n">tweets_y_valid_1_hot</span><span class="p">)</span>
                     <span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">0</span>
                     <span class="p">,</span> <span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">TqdmCallback</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)]</span>
                    <span class="p">)</span>
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<p><img align="left" alt="fine-tuning widget" src="./images/widget.png?uncache=464453868" /></p>
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<li style="margin: 0px 10px 0px 190px;">15/15 [16:55&lt;00:00, 67.70s/epoch, val_loss=1.54, val_categorical_crossentropy=1.13, val_categorical_accuracy=0.165, val_precision=0, val_recall=0, val_precision_NEG=0, val_recall_NEG=0, val_precision_NEUT=0, val_recall_NEUT=0, val_precision_POS=0, val_recall_POS=0, loss=1.51, categorical_crossentropy=1.09, categorical_accuracy=0.368, precision=0.485, recall=0.00517, precision_NEG=0, recall_NEG=0, precision_NEUT=0.487, recall_NEUT=0.0153, precision_POS=0.583, recall_POS=0.000263]</p>
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The whole idea behind warming-up consists in bootstrapping the transfer learning. We do that by assigning weights to the new output layer that are more optimum than the initial random values. By freezing all the preceeding layers, the optimizer focuses and these last weights alone, which gives them a huge advantage when later actually operating the model fine-tuning in the upcoming section.
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<p><a name="tuning"></a></p>
<h3>5.3. Fine-Tuning <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h3>
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Now that our model has learned from a generic large corpus of french texts and that our new output layer has been warmed-up on the target French Tweets dataset, the actual fine-tuning of our French Sentiment NLP model can start. Lets first "un-freeze" the model's inner layer&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">tweets_model</span><span class="o">.</span><span class="n">layers</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> <span class="p">:</span>
    <span class="n">layer</span><span class="o">.</span><span class="n">trainable</span> <span class="o">=</span> <span class="kc">True</span>

<span class="n">layers_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="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">tweets_model</span><span class="o">.</span><span class="n">layers</span> <span class="p">:</span>
    <span class="n">layers_df</span> <span class="o">=</span> <span class="n">layers_df</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">][:</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">input_shape</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;None, &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
                                   <span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">output_shape</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;None, &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">),</span> <span class="n">layer</span><span class="o">.</span><span class="n">trainable</span><span class="p">]]</span>
                                 <span class="p">,</span> <span class="n">ignore_index</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">verify_integrity</span><span class="o">=</span><span class="kc">False</span>
    <span class="p">)</span>
<span class="n">layers_df</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;class&#39;</span><span class="p">,</span> <span class="s1">&#39;input_shape&#39;</span><span class="p">,</span> <span class="s1">&#39;output_shape&#39;</span><span class="p">,</span> <span class="s1">&#39;trainable&#39;</span><span class="p">]</span>
<span class="n">dataframe_pretty_print_center</span><span class="p">(</span><span class="n">layers_df</span><span class="p">)</span>
</pre></div>

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</div>
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        }</style><table id="T_31e13f48_eebb_11ea_8396_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" >class</th>        <th class="col_heading level0 col1" >input_shape</th>        <th class="col_heading level0 col2" >output_shape</th>        <th class="col_heading level0 col3" >trainable</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row0_col0" class="data row0 col0" >InputLayer</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row0_col1" class="data row0 col1" >(280)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row0_col2" class="data row0 col2" >(280)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row0_col3" class="data row0 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row1_col0" class="data row1 col0" >Embedding</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row1_col1" class="data row1 col1" >(280)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row1_col2" class="data row1 col2" >(280, 300)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row1_col3" class="data row1 col3" >False</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row2_col0" class="data row2 col0" >SpatialDropout1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row2_col1" class="data row2 col1" >(280, 300)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row2_col2" class="data row2 col2" >(280, 300)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row2_col3" class="data row2 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row3_col0" class="data row3 col0" >Bidirectional</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row3_col1" class="data row3 col1" >(280, 300)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row3_col2" class="data row3 col2" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row3_col3" class="data row3 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row4_col0" class="data row4 col0" >Bidirectional</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row4_col1" class="data row4 col1" >(280, 300)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row4_col2" class="data row4 col2" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row4_col3" class="data row4 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row5_col0" class="data row5 col0" >Conv1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row5_col1" class="data row5 col1" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row5_col2" class="data row5 col2" >(279, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row5_col3" class="data row5 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row6_col0" class="data row6 col0" >Conv1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row6_col1" class="data row6 col1" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row6_col2" class="data row6 col2" >(278, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row6_col3" class="data row6 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row7_col0" class="data row7 col0" >Conv1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row7_col1" class="data row7 col1" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row7_col2" class="data row7 col2" >(279, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row7_col3" class="data row7 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row8_col0" class="data row8 col0" >Conv1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row8_col1" class="data row8 col1" >(280, 136)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row8_col2" class="data row8 col2" >(278, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row8_col3" class="data row8 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row9_col0" class="data row9 col0" >GlobalMaxPooling1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row9_col1" class="data row9 col1" >(279, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row9_col2" class="data row9 col2" >(28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row9_col3" class="data row9 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row10_col0" class="data row10 col0" >GlobalMaxPooling1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row10_col1" class="data row10 col1" >(278, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row10_col2" class="data row10 col2" >(28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row10_col3" class="data row10 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row11_col0" class="data row11 col0" >GlobalMaxPooling1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row11_col1" class="data row11 col1" >(279, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row11_col2" class="data row11 col2" >(28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row11_col3" class="data row11 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row12_col0" class="data row12 col0" >GlobalMaxPooling1D</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row12_col1" class="data row12 col1" >(278, 28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row12_col2" class="data row12 col2" >(28)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row12_col3" class="data row12 col3" >True</td>
            </tr>
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                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row13_col0" class="data row13 col0" >Concatenate</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row13_col1" class="data row13 col1" >[(28), (28), (28), (28)]</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row13_col2" class="data row13 col2" >(112)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row13_col3" class="data row13 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row14_col0" class="data row14 col0" >BatchNormalization</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row14_col1" class="data row14 col1" >(112)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row14_col2" class="data row14 col2" >(112)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row14_col3" class="data row14 col3" >True</td>
            </tr>
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                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row15_col0" class="data row15 col0" >Dense</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row15_col1" class="data row15 col1" >(112)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row15_col2" class="data row15 col2" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row15_col3" class="data row15 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row16_col0" class="data row16 col0" >Dropout</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row16_col1" class="data row16 col1" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row16_col2" class="data row16 col2" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row16_col3" class="data row16 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row17_col0" class="data row17 col0" >BatchNormalization</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row17_col1" class="data row17 col1" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row17_col2" class="data row17 col2" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row17_col3" class="data row17 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row18_col0" class="data row18 col0" >Dense</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row18_col1" class="data row18 col1" >(46)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row18_col2" class="data row18 col2" >(32)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row18_col3" class="data row18 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row19_col0" class="data row19 col0" >Dropout</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row19_col1" class="data row19 col1" >(32)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row19_col2" class="data row19 col2" >(32)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row19_col3" class="data row19 col3" >True</td>
            </tr>
            <tr>
                                <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row20_col0" class="data row20 col0" >Dense</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row20_col1" class="data row20 col1" >(32)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row20_col2" class="data row20 col2" >(3)</td>
                        <td id="T_31e13f48_eebb_11ea_8396_309c23ff89f2row20_col3" class="data row20 col3" >True</td>
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We're now ready for "<b>Step 3</b>" of the summary figure seen in <a href="#page-top"><b>&sect;0.</b></a>, i.e. the actual fine-tuning of our French NLP model&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fine_tuning_lr</span> <span class="o">=</span> <span class="mf">0.001</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span> <span class="n">learning_rate</span> <span class="o">=</span> <span class="n">fine_tuning_lr</span> <span class="p">)</span>
<span class="n">tweets_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="kn">import</span> <span class="n">LearningRateScheduler</span>
<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.callbacks</span> <span class="kn">import</span> <span class="n">LearningRatePrinter</span><span class="p">,</span> <span class="n">lr_sheduler_func</span>

<span class="n">my_learning_rate_printer</span> <span class="o">=</span> <span class="n">LearningRatePrinter</span><span class="p">()</span>

<span class="n">learning_rates_decays</span> <span class="o">=</span> <span class="p">{</span><span class="mi">30</span><span class="p">:</span> <span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">40</span><span class="p">:</span> <span class="o">.</span><span class="mi">1</span><span class="p">}</span>
<span class="n">my_lr_scheduler</span> <span class="o">=</span> <span class="n">LearningRateScheduler</span><span class="p">(</span>
    <span class="n">lr_sheduler_func</span><span class="p">(</span><span class="n">learning_rates_decays</span> <span class="o">=</span> <span class="n">learning_rates_decays</span>
                     <span class="p">,</span> <span class="n">learning_rate_printer</span> <span class="o">=</span> <span class="n">my_learning_rate_printer</span><span class="p">))</span>

<span class="n">verbose</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">my_lr_scheduler</span><span class="p">,</span> <span class="n">my_learning_rate_printer</span><span class="p">,</span> <span class="n">TqdmCallback</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)]</span>

<span class="n">fine_tuning_history</span> <span class="o">=</span> \
    <span class="n">tweets_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">tweets_X_train_resampled</span><span class="p">,</span> <span class="n">tweets_y_train_resampled_1_hot</span>
                     <span class="p">,</span> <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">45</span>
                     <span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">tweets_X_valid</span><span class="p">,</span> <span class="n">tweets_y_valid_1_hot</span><span class="p">)</span>
                     <span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">callbacks</span> <span class="o">=</span> <span class="n">callbacks</span><span class="p">)</span>
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<p><img align="left" alt="fine-tuning widget" src="./images/widget.png?uncache=464453868" /></p>
<p><ul style="line-height:26px; list-style-type:none"></p>
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<p style="font-family: Helvetica; font-size: 10pt">45/45 [2:03:59&lt;00:00, 165.33s/epoch, val_loss=0.869, val_categorical_crossentropy=0.867, val_categorical_accuracy=0.63, val_precision=0.734, val_recall=0.508, val_precision_NEG=0.0952, val_recall_NEG=0.339, val_precision_NEUT=0.942, val_recall_NEUT=0.549, val_precision_POS=0.426, val_recall_POS=0.318, loss=0.942, categorical_crossentropy=0.94, categorical_accuracy=0.547, precision=0.632, recall=0.375, precision_NEG=0.575, recall_NEG=0.374, precision_NEUT=0.694, recall_NEUT=0.479, precision_POS=0.619, recall_POS=0.272, lr=1e-5]</p>
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epoch 0 ; lr: 0.001000<br />
epoch 30 ; lr: 0.000100<br />
epoch 40 ; lr: 0.000010</p>
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Lets visualize the history of our 2-phases training. In the below figure, the grayed-out area represents the warming-up. The remaining covers the fine-tuning phase&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">warming_up_history_df</span> <span class="o">=</span> <span class="n">warming_up_history</span><span class="o">.</span><span class="n">history</span>
<span class="n">fine_tuning_history_df</span> <span class="o">=</span> <span class="n">fine_tuning_history</span><span class="o">.</span><span class="n">history</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.model.plot</span> <span class="kn">import</span> <span class="n">warming_up_fine_tuning_history</span>

<span class="n">fig1</span><span class="p">,</span> <span class="n">fig2</span> <span class="o">=</span> <span class="n">warming_up_fine_tuning_history</span><span class="p">(</span>
    <span class="n">warming_up_history_df</span><span class="p">,</span> <span class="n">warming_up_lr</span><span class="p">,</span> <span class="n">fine_tuning_history_df</span>
    <span class="p">,</span> <span class="n">fig1_size</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">fig2_size</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">18</span><span class="p">)</span>
<span class="p">)</span><span class="c1"># ; plt.show()</span>

<span class="n">fig_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="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;images&#39;</span><span class="p">)</span>
<span class="n">fig1</span><span class="o">.</span><span class="n">savefig</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">fig_path</span><span class="p">,</span> <span class="s1">&#39;tweets_model_history.png&#39;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig1</span><span class="p">)</span>
<span class="n">fig2</span><span class="o">.</span><span class="n">savefig</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">fig_path</span><span class="p">,</span> <span class="s1">&#39;tweets_model_classes_history.png&#39;</span><span class="p">),</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig2</span><span class="p">)</span>
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<img alt='French Tweets model history' style='border-right: 5px solid transparent;' width='446px' align='left' src='./images/tweets_model_history.png?uncache=6315568' />
Thanks to the knowledge it had previously acquired during the Step 1 (pre-training), the French Tweets model is consistently better performing on the validation dataset than on the training dataset accross all the measured performance metrics. It is excellent at generalizing.<br />
The only exception to that is if we look at 'class' level, for the 2 non-dominant classes, on the 'precision' front. But 'recall' is much stronger. It can be better observed on <a target='_blank' href='./images/tweets_model_classes_history.png'>that other arragement of the data <img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a>.
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Now that the training of our French Sentiment NLP model has been completed, it is time to independently measure how good it is actuially performing and there's no better way to do that than to predict some French Tweets sentiment from a real-life data distribution that had never been presented to that model before ; the holdout dataset&nbsp;:
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">predictions_1_hot</span> <span class="o">=</span> <span class="n">tweets_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tweets_X_holdout</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>

<span class="n">cm_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">confusion_matrix</span><span class="p">(</span><span class="n">tweets_y_holdout_1_hot</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                     <span class="p">,</span> <span class="n">predictions_1_hot</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
<span class="p">)</span>
<span class="n">cm_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">cm_df</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">tweets_y_holdout_1_hot</span><span class="o">.</span><span class="n">columns</span>
<span class="n">cm_html</span> <span class="o">=</span> <span class="n">cm_df</span><span class="o">.</span><span class="n">to_html</span><span class="p">(</span><span class="n">formatters</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;NEUTRAL&#39;</span><span class="p">:</span> <span class="s1">&#39;</span><span class="si">{:,}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">})</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.display_helper</span> <span class="kn">import</span> <span class="n">classification_report_pretty_html</span>

<span class="n">report_dict</span> <span class="o">=</span> \
    <span class="n">classification_report</span><span class="p">(</span><span class="n">tweets_y_holdout_1_hot</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                          <span class="p">,</span> <span class="n">predictions_1_hot</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                          <span class="p">,</span> <span class="n">target_names</span> <span class="o">=</span> <span class="n">tweets_y_holdout_1_hot</span><span class="o">.</span><span class="n">columns</span>
                          <span class="p">,</span> <span class="n">zero_division</span>  <span class="o">=</span> <span class="mi">1</span>
                          <span class="p">,</span> <span class="n">output_dict</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>

<span class="n">report_html</span> <span class="o">=</span> <span class="n">classification_report_pretty_html</span><span class="p">(</span><span class="n">report_dict</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">&#39;Performance of the final model on the holdout dataset :&#39;</span><span class="p">)</span>
<span class="n">two_columns_display</span><span class="p">(</span><span class="s1">&#39;&lt;br /&gt;&lt;br /&gt;&lt;div&gt;Confusion Matrix&lt;/div&gt;&#39;</span> <span class="o">+</span> <span class="n">cm_html</span>
                    <span class="p">,</span> <span class="s1">&#39;&lt;div&gt;Classification Report&lt;/div&gt;&#39;</span> <span class="o">+</span> <span class="n">report_html</span><span class="p">,</span> <span class="n">left_ratio</span>  <span class="o">=</span> <span class="o">.</span><span class="mi">4</span><span class="p">)</span>
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<pre>Performance of the final model on the holdout dataset :
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<h2></h2>

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      <center>
          <br /><br /><div>Confusion Matrix</div><table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>NEGATIVE</th>
      <th>NEUTRAL</th>
      <th>POSITIVE</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>NEGATIVE</th>
      <td>61</td>
      <td>18</td>
      <td>10</td>
    </tr>
    <tr>
      <th>NEUTRAL</th>
      <td>500</td>
      <td>1,705</td>
      <td>282</td>
    </tr>
    <tr>
      <th>POSITIVE</th>
      <td>115</td>
      <td>124</td>
      <td>185</td>
    </tr>
  </tbody>
</table>
      </center>
  </div>
  <div class="right_column" style="width: 60.00%;">
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          <div>Classification Report</div><style  type="text/css" >
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            border:  0px;
        }</style><table id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2" ><thead>    <tr>        <th class="col_heading level0 col0" ></th>        <th class="col_heading level0 col1" >precision</th>        <th class="col_heading level0 col2" >recall</th>        <th class="col_heading level0 col3" >f1-score</th>        <th class="col_heading level0 col4" >support</th>    </tr></thead><tbody>
                <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row0_col0" class="data row0 col0" >NEGATIVE</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row0_col1" class="data row0 col1" >0.09</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row0_col2" class="data row0 col2" >0.69</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row0_col3" class="data row0 col3" >0.16</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row0_col4" class="data row0 col4" >89</td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row1_col0" class="data row1 col0" >NEUTRAL</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row1_col1" class="data row1 col1" >0.92</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row1_col2" class="data row1 col2" >0.69</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row1_col3" class="data row1 col3" >0.79</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row1_col4" class="data row1 col4" >2,487</td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row2_col0" class="data row2 col0" >POSITIVE</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row2_col1" class="data row2 col1" >0.39</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row2_col2" class="data row2 col2" >0.44</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row2_col3" class="data row2 col3" >0.41</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row2_col4" class="data row2 col4" >424</td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row3_col0" class="data row3 col0" >&nbsp;</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row3_col1" class="data row3 col1" ></td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row3_col2" class="data row3 col2" ></td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row3_col3" class="data row3 col3" ></td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row3_col4" class="data row3 col4" ></td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row4_col0" class="data row4 col0" >accuracy</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row4_col1" class="data row4 col1" ></td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row4_col2" class="data row4 col2" ></td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row4_col3" class="data row4 col3" >0.65</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row4_col4" class="data row4 col4" >3,000</td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row5_col0" class="data row5 col0" >macro avg</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row5_col1" class="data row5 col1" >0.47</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row5_col2" class="data row5 col2" >0.60</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row5_col3" class="data row5 col3" >0.45</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row5_col4" class="data row5 col4" >3,000</td>
            </tr>
            <tr>
                                <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row6_col0" class="data row6 col0" >weighted avg</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row6_col1" class="data row6 col1" >0.82</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row6_col2" class="data row6 col2" >0.65</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row6_col3" class="data row6 col3" >0.72</td>
                        <td id="T_54e28f76_f060_11ea_8fb0_309c23ff89f2row6_col4" class="data row6 col4" >3,000</td>
            </tr>
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<div style='text-align: justify; margin-left: 5px; margin-right: 5px;'>
As the Confusion Matrix and the Classification Report show it above &nbsp;:
    <ul style="list-style-type: disc; margin: 0px 150px 0px 50px; /* top right bottom left*/">
        <li>the overall Precision of our model stands at the more than honorable level of 82%. Despite all of our efforts, it does however still vary a lot accross classes. For example, <b>when our model predicts a French Tweet to be 'NEGATIVE', it is correct only 9% of the time</b>. This is undesirable and shall increase when more training data becomes available.</li>
        <li>if the overall Recall of our model is a little less impressive than its Precision with an however still respectable 65%, it is more even accross classes than the latter. <b><span style="background-color: #00FFFF;">&nbsp;Regarding NEGATIVE French Tweets for instance, our NLP model succesfully identifies them 69% of the time. This is an impressive performance considering such an imbalanced real-life data distribution (and the tiny total occurences of NEGATIVE records in the entire population)&nbsp;</span></b>.</li>
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<p>backup</p>

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<div class="prompt input_prompt">In&nbsp;[52]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">dill</span> <span class="k">as</span> <span class="nn">pickle</span>
<span class="n">root</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">realpath</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="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;tweets_model&#39;</span><span class="p">))</span>
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<div class="prompt input_prompt">In&nbsp;[53]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s2">&quot;model.json&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span> <span class="p">:</span>
    <span class="n">json_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">tweets_model</span><span class="o">.</span><span class="n">to_json</span><span class="p">())</span>
<span class="n">tweets_model</span><span class="o">.</span><span class="n">save_weights</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">root</span><span class="p">,</span> <span class="s1">&#39;model_weights.h5&#39;</span><span class="p">))</span>

<span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s1">&#39;warming_up_history_df.pickle&#39;</span><span class="p">),</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file_pi</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">warming_up_history</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">file_pi</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s1">&#39;fine_tuning_history_df.pickle&#39;</span><span class="p">),</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file_pi</span><span class="p">:</span>
    <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">fine_tuning_history</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">file_pi</span><span class="p">)</span>
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<p>reload</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">model_from_json</span>

<span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s2">&quot;model.json&quot;</span><span class="p">),</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">json_file</span> <span class="p">:</span>
    <span class="n">loaded_model_json</span> <span class="o">=</span> <span class="n">json_file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="n">tweets_model</span> <span class="o">=</span> <span class="n">model_from_json</span><span class="p">(</span><span class="n">loaded_model_json</span><span class="p">)</span>
<span class="n">tweets_model</span><span class="o">.</span><span class="n">load_weights</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">root</span><span class="p">,</span> <span class="s1">&#39;model_weights.h5&#39;</span><span class="p">))</span>

<span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s1">&#39;warming_up_history_df.pickle&#39;</span><span class="p">),</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file_pi</span><span class="p">:</span>
    <span class="n">warming_up_history_df</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_pi</span><span class="p">)</span>

<span class="k">with</span> <span class="nb">open</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">root</span><span class="p">,</span> <span class="s1">&#39;fine_tuning_history_df.pickle&#39;</span><span class="p">),</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file_pi</span><span class="p">:</span>
    <span class="n">fine_tuning_history_df</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_pi</span><span class="p">)</span>
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<p><a name="conclusion"></a></p>
<h2>6. Conclusion <small><em><a href="#page-top">(go back to top &uarr;)]</a></em></small></h2>
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NLP models on languages others than English are seldom. And it's mostly because labelled data in those languages is hard to come by.
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The project of the herein notebook being Sentiment Classification on French Texts, we've covered many aspects of techniques that can be handy when working on real-world data which can often be imperfect and/or costly to acquire&nbsp;:
    <ul style="list-style-type: disc; margin: 0px 150px 0px 50px; /* top right bottom left*/">
        <li>we've seen the power of <u>transfer learning</u> from web-scrapped, rather generic, French Reviews to domain-specific French Tweets (specific to soccer in our use case).</li>
        <li>we've also used <u>XGBoost</u> as a supercharger to our hyperparameters optimization (in <a href="https://github.com/aurelienmorgan/french_text_sentiment/blob/master/model/optimization/README.md" target="_blank">this separate dedicated notebook <img href='.' src='./images/target_blank.png' style='vertical-align: baseline; display: inline;' /></a>)</li>
        <li>we've also covered means at our disposal when dealing with <u>highly imbalanced datasets</u></li>
    </ul>
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The above-depicted technical "tricks" in our toolset allowed to train a dual bi-GRU model architecture in an optimum fashion for such a model to tackle efficiently some everyday-life problems.
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    Ours here is <em>domain-specific to soccer</em>, but any public or private organization could "tune" our model, for instance on a <u>brand-specific dataset</u> (which it would posess internally), by simply following <a href="#warming"><b>&sect;5.2.</b></a> &amp; <a href="#tuning"><b>&sect;5.3.</b></a>, which is really quick.


Such an NLP model would for instance be a well suited technical solution for anyone willing to implement an automatic Twitter monitoring system for a brand operating on French-speaking markets. This would allow to identify any potential "bad buzz" on the social media platform around the brand as-soon-as any ever occurs.
On the other hand, being notified on POSITIVE French Tweets would for instance allow to implement a targetted promotional campaign involving enthousiastic/supportive customers and thus build a resilient community around the brand.
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<h1 id="EXTRA">EXTRA<a class="anchor-link" href="#EXTRA">&#182;</a></h1>
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<p><b>Export Notebook to HTML (with Markdown extension cells evaluated)</b></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_NLP_RNN_fr_lib.jupyter_markdown_extension</span> <span class="kn">import</span> <span class="n">md_extension_to_html</span>

<span class="n">md_extension_to_html</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">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">realpath</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">),</span> <span class="s1">&#39;main.ipynb&#39;</span><span class="p">))</span>
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