student_project.html

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<title>student_project</title>

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}
.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:https://fontawesome.io - @fontawesome
 *  License - http:https://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:https://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:https://www.xcolors.net/dl/baskerville-ivorylight and
   http:https://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 .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 */
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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">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            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>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Overview">Overview<a class="anchor-link" href="#Overview">&#182;</a></h1>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li>Project Instructions &amp; Prerequisites</li>
<li>Learning Objectives</li>
<li>Data Preparation</li>
<li>Create Categorical Features with TF Feature Columns</li>
<li>Create Continuous/Numerical Features with TF Feature Columns</li>
<li>Build Deep Learning Regression Model with Sequential API and TF Probability Layers</li>
<li>Evaluating Potential Model Biases with Aequitas Toolkit</li>
</ol>

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<h1 id="1.-Project-Instructions-&amp;-Prerequisites">1. Project Instructions &amp; Prerequisites<a class="anchor-link" href="#1.-Project-Instructions-&amp;-Prerequisites">&#182;</a></h1>
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<h2 id="Project-Instructions">Project Instructions<a class="anchor-link" href="#Project-Instructions">&#182;</a></h2>
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<p><strong>Context</strong>: EHR data is becoming a key source of real-world evidence (RWE) for the pharmaceutical industry and regulators to <a href="https://www.fda.gov/news-events/speeches-fda-officials/breaking-down-barriers-between-clinical-trials-and-clinical-care-incorporating-real-world-evidence">make decisions on clinical trials</a>. You are a data scientist for an exciting unicorn healthcare startup that has created a groundbreaking diabetes drug that is ready for clinical trial testing. It is a very unique and sensitive drug that requires administering the drug over at least 5-7 days of time in the hospital with frequent monitoring/testing and patient medication adherence training with a mobile application. You have been provided a patient dataset from a client partner and are tasked with building a predictive model that can identify which type of patients the company should focus their efforts testing this drug on. Target patients are people that are likely to be in the hospital for this duration of time and will not incur significant additional costs for administering this drug to the patient and monitoring.</p>
<p>In order to achieve your goal you must build a regression model that can predict the estimated hospitalization time for a patient and use this to select/filter patients for your study.</p>

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<p><strong>Expected Hospitalization Time Regression Model:</strong> Utilizing a synthetic dataset(denormalized at the line level augmentation) built off of the UCI Diabetes readmission dataset, students will build a regression model that predicts the expected days of hospitalization time and then convert this to a binary prediction of whether to include or exclude that patient from the clinical trial.</p>
<p>This project will demonstrate the importance of building the right data representation at the encounter level, with appropriate filtering and preprocessing/feature engineering of key medical code sets. This project will also require students to analyze and interpret their model for biases across key demographic groups.</p>
<p>Please see the project rubric online for more details on the areas your project will be evaluated.</p>

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<h3 id="Dataset">Dataset<a class="anchor-link" href="#Dataset">&#182;</a></h3>
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<p>Due to healthcare PHI regulations (HIPAA, HITECH), there are limited number of publicly available datasets and some datasets require training and approval. So, for the purpose of this exercise, we are using a dataset from UC Irvine(<a href="https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008">https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008</a>) that has been modified for this course. Please note that it is limited in its representation of some key features such as diagnosis codes which are usually an unordered list in 835s/837s (the HL7 standard interchange formats used for claims and remits).</p>

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<p><strong>Data Schema</strong>
The dataset reference information can be <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/</a>
. There are two CSVs that provide more details on the fields and some of the mapped values.</p>

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<h2 id="Project-Submission">Project Submission<a class="anchor-link" href="#Project-Submission">&#182;</a></h2>
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<p>When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "student_project_submission.ipynb" and save another copy as an HTML file by clicking "File" -&gt; "Download as.."-&gt;"html". Include the "utils.py" and "student_utils.py" files in your submission. The student_utils.py should be where you put most of your code that you write and the summary and text explanations should be written inline in the notebook. Once you download these files, compress them into one zip file for submission.</p>

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<h2 id="Prerequisites">Prerequisites<a class="anchor-link" href="#Prerequisites">&#182;</a></h2>
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<ul>
<li>Intermediate level knowledge of Python</li>
<li>Basic knowledge of probability and statistics</li>
<li>Basic knowledge of machine learning concepts</li>
<li>Installation of Tensorflow 2.0 and other dependencies(conda environment.yml or virtualenv requirements.txt file provided)</li>
</ul>

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<h2 id="Environment-Setup">Environment Setup<a class="anchor-link" href="#Environment-Setup">&#182;</a></h2>
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<p>For step by step instructions on creating your environment, please go to <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/README.md">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/README.md</a>.</p>

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<h1 id="2.--Learning-Objectives">2.  Learning Objectives<a class="anchor-link" href="#2.--Learning-Objectives">&#182;</a></h1>
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<p>By the end of the project, you will be able to</p>
<ul>
<li>Use the Tensorflow Dataset API to scalably extract, transform, and load datasets and build datasets aggregated at the line, encounter, and patient data levels(longitudinal)</li>
<li>Analyze EHR datasets to check for common issues (data leakage, statistical properties, missing values, high cardinality) by performing exploratory data analysis.</li>
<li>Create categorical features from Key Industry Code Sets (ICD, CPT, NDC) and reduce dimensionality for high cardinality features by using embeddings </li>
<li>Create derived features(bucketing, cross-features, embeddings) utilizing Tensorflow feature columns on both continuous and categorical input features</li>
<li>SWBAT use the Tensorflow Probability library to train a model that provides uncertainty range predictions that allow for risk adjustment/prioritization and triaging of predictions</li>
<li>Analyze and determine biases for a model for key demographic groups by evaluating performance metrics across groups by using the Aequitas framework </li>
</ul>

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<h1 id="3.-Data-Preparation">3. Data Preparation<a class="anchor-link" href="#3.-Data-Preparation">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from __future__ import absolute_import, division, print_function, unicode_literals</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">layers</span>
<span class="kn">import</span> <span class="nn">tensorflow_probability</span> <span class="k">as</span> <span class="nn">tfp</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</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">aequitas</span> <span class="k">as</span> <span class="nn">ae</span>
<span class="c1"># Put all of the helper functions in utils</span>
<span class="kn">from</span> <span class="nn">utils</span> <span class="kn">import</span> <span class="n">build_vocab_files</span><span class="p">,</span> <span class="n">show_group_stats_viz</span><span class="p">,</span> <span class="n">aggregate_dataset</span><span class="p">,</span> <span class="n">preprocess_df</span><span class="p">,</span> <span class="n">df_to_dataset</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span>
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">&#39;display.max_columns&#39;</span><span class="p">,</span> <span class="mi">500</span><span class="p">)</span>
<span class="c1"># this allows you to make changes and save in student_utils.py and the file is reloaded every time you run a code block</span>
<span class="o">%</span><span class="k">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span>
<span class="o">%</span><span class="k">matplotlib</span> inline 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#OPEN ISSUE ON MAC OSX for TF model training</span>
<span class="c1">#import os</span>
<span class="c1">#os.environ[&#39;KMP_DUPLICATE_LIB_OK&#39;]=&#39;True&#39;</span>
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<h2 id="Dataset-Loading-and-Schema-Review">Dataset Loading and Schema Review<a class="anchor-link" href="#Dataset-Loading-and-Schema-Review">&#182;</a></h2>
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<p>Load the dataset and view a sample of the dataset along with reviewing the schema reference files to gain a deeper understanding of the dataset. The dataset is located at the following path <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/starter_code/data/final_project_dataset.csv">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/starter_code/data/final_project_dataset.csv</a>. Also, review the information found in the data schema <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/</a></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dataset_path</span> <span class="o">=</span> <span class="s2">&quot;./data/final_project_dataset.csv&quot;</span>
<span class="n">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">dataset_path</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;?&quot;</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>ndc_code</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2278392</td>
      <td>8222157</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[0-10)</td>
      <td>NaN</td>
      <td>6</td>
      <td>25</td>
      <td>1</td>
      <td>1</td>
      <td>NaN</td>
      <td>Pediatrics-Endocrinology</td>
      <td>250.83</td>
      <td>?|?</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>41</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>NaN</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149190</td>
      <td>55629189</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[10-20)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>276</td>
      <td>250.01|255</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>59</td>
      <td>9</td>
      <td>18</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&gt;30</td>
    </tr>
    <tr>
      <th>2</th>
      <td>64410</td>
      <td>86047875</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[20-30)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>648</td>
      <td>250|V27</td>
      <td>2</td>
      <td>1</td>
      <td>0</td>
      <td>11</td>
      <td>6</td>
      <td>13</td>
      <td>5</td>
      <td>0378-1110</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>3</th>
      <td>500364</td>
      <td>82442376</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[30-40)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>8</td>
      <td>250.43|403</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>44</td>
      <td>7</td>
      <td>16</td>
      <td>1</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
    <tr>
      <th>4</th>
      <td>16680</td>
      <td>42519267</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>197</td>
      <td>157|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<table border="1" class="dataframe">
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      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
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      <th>num_procedures</th>
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      <th>count</th>
      <td>1.434240e+05</td>
      <td>1.434240e+05</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
      <td>143424.000000</td>
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      <th>mean</th>
      <td>1.674290e+08</td>
      <td>5.493608e+07</td>
      <td>2.027694</td>
      <td>3.655316</td>
      <td>5.701096</td>
      <td>4.490190</td>
      <td>0.362429</td>
      <td>0.600855</td>
      <td>0.195086</td>
      <td>43.255745</td>
      <td>7.424434</td>
      <td>16.776035</td>
      <td>1.349021</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.019046e+08</td>
      <td>3.857840e+07</td>
      <td>1.427585</td>
      <td>5.219279</td>
      <td>4.064532</td>
      <td>2.999667</td>
      <td>1.249295</td>
      <td>1.207934</td>
      <td>0.920410</td>
      <td>19.657319</td>
      <td>1.924872</td>
      <td>8.397130</td>
      <td>1.719104</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.252200e+04</td>
      <td>1.350000e+02</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>8.829596e+07</td>
      <td>2.357219e+07</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>32.000000</td>
      <td>6.000000</td>
      <td>11.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>1.547637e+08</td>
      <td>4.630783e+07</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>7.000000</td>
      <td>4.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>44.000000</td>
      <td>8.000000</td>
      <td>15.000000</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>2.320897e+08</td>
      <td>8.823627e+07</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>7.000000</td>
      <td>6.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>57.000000</td>
      <td>9.000000</td>
      <td>21.000000</td>
      <td>2.000000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>4.438672e+08</td>
      <td>1.895026e+08</td>
      <td>8.000000</td>
      <td>28.000000</td>
      <td>25.000000</td>
      <td>14.000000</td>
      <td>42.000000</td>
      <td>21.000000</td>
      <td>76.000000</td>
      <td>132.000000</td>
      <td>16.000000</td>
      <td>81.000000</td>
      <td>6.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Unique patient ids&quot;</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">())</span>
<span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Unique encounter ids&quot;</span><span class="p">,</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">())</span>
<span class="nb">print</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>

<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">():</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Dataset could be at line level&quot;</span><span class="p">)</span>
<span class="k">else</span> <span class="p">:</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Data set is not at the line level&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">==</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">():</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Dataset could be at encounter level&quot;</span><span class="p">)</span>
<span class="k">else</span> <span class="p">:</span>
    <span class="nb">print</span> <span class="p">(</span><span class="s2">&quot;Data set is not at the encounter level&quot;</span><span class="p">)</span>
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<pre>Unique patient ids 71518
Unique encounter ids 101766
143424
Dataset could be at line level
Data set is not at the encounter level
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">key_fields</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span>
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<h2 id="Determine-Level-of-Dataset-(Line-or-Encounter)">Determine Level of Dataset (Line or Encounter)<a class="anchor-link" href="#Determine-Level-of-Dataset-(Line-or-Encounter)">&#182;</a></h2>
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<p><strong>Question 1</strong>: Based on analysis of the data, what level is this dataset? Is it at the line or encounter level? Are there any key fields besides the encounter_id and patient_nbr fields that we should use to aggregate on? Knowing this information will help inform us what level of aggregation is necessary for future steps and is a step that is often overlooked.</p>

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<p><strong>Student Response:</strong> We check for the total number of entries in the dataset and for the number of entries with unique patient ids. In this cases, since the total number of entries is more than the number of entries with uniue patient ids, it tells us that there are more than one entires for patient ids, and hence the dataset is at the line level.</p>
<p>Apart from encounter id and patient nbr fields, the ndc code is a column, the data can be aggregated on.</p>

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<h2 id="Analyze-Dataset">Analyze Dataset<a class="anchor-link" href="#Analyze-Dataset">&#182;</a></h2>
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<p><strong>Question 2</strong>: Utilizing the library of your choice (recommend Pandas and Seaborn or matplotlib though), perform exploratory data analysis on the dataset. In particular be sure to address the following questions:</p>

<pre><code>- a. Field(s) with high amount of missing/zero values
- b. Based off the frequency histogram for each numerical field, which numerical field(s) has/have a Gaussian(normal) distribution shape?
- c. Which field(s) have high cardinality and why (HINT: ndc_code is one feature)
- d. Please describe the demographic distributions in the dataset for the age and gender fields.</code></pre>

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<p><strong>OPTIONAL</strong>: Use the Tensorflow Data Validation and Analysis library to complete.</p>
<ul>
<li>The Tensorflow Data Validation and Analysis library(<a href="https://www.tensorflow.org/tfx/data_validation/get_started">https://www.tensorflow.org/tfx/data_validation/get_started</a>) is a useful tool for analyzing and summarizing dataset statistics. It is especially useful because it can scale to large datasets that do not fit into memory. </li>
<li>Note that there are some bugs that are still being resolved with Chrome v80 and we have moved away from using this for the project. </li>
</ul>

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<p><strong>Student Response</strong>: EDA on the dataset :</p>
<ol>
<li><p>Fields with high amount of mising /zero values :  The weight column has the maximum number of missing values, followed by the medical speciality and payer code columns</p>
</li>
<li><p>Numerical field(s) having a Gaussian distribution : Only 2 fields have a Gaussian distribution - the "num_lab_procedures" and "num_medications"</p>
</li>
<li><p>Fields with high cardinality : The fields with high cardinality are - Primary Diagnosis code and the NDC code.</p>
</li>
</ol>
<ul>
<li>NDC code is a column for the medicines and a generic drug name is seen to hv different proprietary names from different manufacturers. </li>
<li>The Primary Diagnosis Code contains values from the ICD9 - CM code set, which is a wide range of values for different disease conditions.</li>
</ul>
<ol>
<li>Demographic distribution in the dataset : There is relatively more information about female patients in the dataset. Most of the patients are in the 70-80 age range, followed by 60-70 age range. Most patients - 75% are of the "Caucasian" race. These indicate that its not a balanced dataset and has demographic bias</li>
</ol>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#high amount of missing /zero values</span>
<span class="n">ndf</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="s2">&quot;cols&quot;</span><span class="p">:</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> 
                   <span class="s2">&quot;null values&quot;</span> <span class="p">:</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span>
                     <span class="s2">&quot;null values %&quot;</span> <span class="p">:</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">),</span>
                   <span class="s2">&quot;zero values&quot;</span> <span class="p">:</span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">(),</span>
                    <span class="s2">&quot;zero values %&quot;</span> <span class="p">:</span><span class="n">df</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)}</span>
                <span class="p">)</span>
<span class="n">ndf</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>cols</th>
      <th>null values</th>
      <th>null values %</th>
      <th>zero values</th>
      <th>zero values %</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>encounter_id</th>
      <td>encounter_id</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>patient_nbr</th>
      <td>patient_nbr</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>race</th>
      <td>race</td>
      <td>3309</td>
      <td>0.023071</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>gender</th>
      <td>gender</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>age</th>
      <td>age</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>weight</th>
      <td>weight</td>
      <td>139122</td>
      <td>0.970005</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>admission_type_id</th>
      <td>admission_type_id</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>discharge_disposition_id</th>
      <td>discharge_disposition_id</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>admission_source_id</th>
      <td>admission_source_id</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>time_in_hospital</th>
      <td>time_in_hospital</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>payer_code</th>
      <td>payer_code</td>
      <td>54190</td>
      <td>0.377831</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>medical_specialty</th>
      <td>medical_specialty</td>
      <td>69463</td>
      <td>0.484319</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>primary_diagnosis_code</th>
      <td>primary_diagnosis_code</td>
      <td>33</td>
      <td>0.000230</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>other_diagnosis_codes</th>
      <td>other_diagnosis_codes</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>number_outpatient</th>
      <td>number_outpatient</td>
      <td>0</td>
      <td>0.000000</td>
      <td>120027</td>
      <td>0.836868</td>
    </tr>
    <tr>
      <th>number_inpatient</th>
      <td>number_inpatient</td>
      <td>0</td>
      <td>0.000000</td>
      <td>96698</td>
      <td>0.674211</td>
    </tr>
    <tr>
      <th>number_emergency</th>
      <td>number_emergency</td>
      <td>0</td>
      <td>0.000000</td>
      <td>127444</td>
      <td>0.888582</td>
    </tr>
    <tr>
      <th>num_lab_procedures</th>
      <td>num_lab_procedures</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>number_diagnoses</th>
      <td>number_diagnoses</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>num_medications</th>
      <td>num_medications</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>num_procedures</th>
      <td>num_procedures</td>
      <td>0</td>
      <td>0.000000</td>
      <td>65788</td>
      <td>0.458696</td>
    </tr>
    <tr>
      <th>ndc_code</th>
      <td>ndc_code</td>
      <td>23462</td>
      <td>0.163585</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>max_glu_serum</th>
      <td>max_glu_serum</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>A1Cresult</th>
      <td>A1Cresult</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>change</th>
      <td>change</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>readmitted</th>
      <td>readmitted</td>
      <td>0</td>
      <td>0.000000</td>
      <td>0</td>
      <td>0.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span>
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<pre>Index([&#39;encounter_id&#39;, &#39;patient_nbr&#39;, &#39;race&#39;, &#39;gender&#39;, &#39;age&#39;, &#39;weight&#39;,
       &#39;admission_type_id&#39;, &#39;discharge_disposition_id&#39;, &#39;admission_source_id&#39;,
       &#39;time_in_hospital&#39;, &#39;payer_code&#39;, &#39;medical_specialty&#39;,
       &#39;primary_diagnosis_code&#39;, &#39;other_diagnosis_codes&#39;, &#39;number_outpatient&#39;,
       &#39;number_inpatient&#39;, &#39;number_emergency&#39;, &#39;num_lab_procedures&#39;,
       &#39;number_diagnoses&#39;, &#39;num_medications&#39;, &#39;num_procedures&#39;, &#39;ndc_code&#39;,
       &#39;max_glu_serum&#39;, &#39;A1Cresult&#39;, &#39;change&#39;, &#39;readmitted&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#distribution shape of numerical fields</span>

<span class="n">numer_cols</span><span class="o">=</span> <span class="p">[</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">,</span><span class="s1">&#39;number_outpatient&#39;</span><span class="p">,</span> <span class="s1">&#39;number_inpatient&#39;</span><span class="p">,</span> <span class="s1">&#39;number_emergency&#39;</span><span class="p">,</span> <span class="s1">&#39;num_lab_procedures&#39;</span><span class="p">,</span>\
       <span class="s1">&#39;number_diagnoses&#39;</span><span class="p">,</span> <span class="s1">&#39;num_medications&#39;</span><span class="p">,</span> <span class="s1">&#39;num_procedures&#39;</span><span class="p">]</span>

<span class="nb">print</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">numer_cols</span><span class="p">))</span>
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<pre>8
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">16</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">numer_cols</span><span class="p">):</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">distplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">),</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="o">//</span><span class="mi">2</span><span class="p">][</span><span class="n">i</span><span class="o">%</span><span class="k">2</span>])
</pre></div>

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<div class="prompt input_prompt">In&nbsp;[11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#high cardinality</span>
<span class="n">cate_columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span><span class="s1">&#39;admission_type_id&#39;</span><span class="p">,</span> <span class="s1">&#39;discharge_disposition_id&#39;</span><span class="p">,</span> <span class="s1">&#39;admission_source_id&#39;</span><span class="p">,</span><span class="s1">&#39;payer_code&#39;</span><span class="p">,</span> <span class="s1">&#39;medical_specialty&#39;</span><span class="p">,</span>
       <span class="s1">&#39;primary_diagnosis_code&#39;</span><span class="p">,</span> <span class="s1">&#39;ndc_code&#39;</span><span class="p">,</span><span class="s1">&#39;max_glu_serum&#39;</span><span class="p">,</span> <span class="s1">&#39;A1Cresult&#39;</span><span class="p">,</span> <span class="s1">&#39;change&#39;</span><span class="p">,</span> <span class="s1">&#39;readmitted&#39;</span><span class="p">]</span>
<span class="n">cdf</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="s2">&quot;columns&quot;</span><span class="p">:</span><span class="n">cate_columns</span><span class="p">,</span>
                   <span class="s2">&quot;cardinality&quot;</span><span class="p">:[</span><span class="n">df</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">cate_columns</span><span class="p">]})</span>
<span class="n">cdf</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>columns</th>
      <th>cardinality</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>race</td>
      <td>5</td>
    </tr>
    <tr>
      <th>1</th>
      <td>gender</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2</th>
      <td>admission_type_id</td>
      <td>8</td>
    </tr>
    <tr>
      <th>3</th>
      <td>discharge_disposition_id</td>
      <td>26</td>
    </tr>
    <tr>
      <th>4</th>
      <td>admission_source_id</td>
      <td>17</td>
    </tr>
    <tr>
      <th>5</th>
      <td>payer_code</td>
      <td>17</td>
    </tr>
    <tr>
      <th>6</th>
      <td>medical_specialty</td>
      <td>72</td>
    </tr>
    <tr>
      <th>7</th>
      <td>primary_diagnosis_code</td>
      <td>716</td>
    </tr>
    <tr>
      <th>8</th>
      <td>ndc_code</td>
      <td>251</td>
    </tr>
    <tr>
      <th>9</th>
      <td>max_glu_serum</td>
      <td>4</td>
    </tr>
    <tr>
      <th>10</th>
      <td>A1Cresult</td>
      <td>4</td>
    </tr>
    <tr>
      <th>11</th>
      <td>change</td>
      <td>2</td>
    </tr>
    <tr>
      <th>12</th>
      <td>readmitted</td>
      <td>3</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#demographic dustributions - age</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">&quot;figure.figsize&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;age&#39;</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#demographic dustributions - gender</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">],</span>  <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">],</span> <span class="n">hue</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f4ac7187bd0&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>Female             76185
Male               67234
Unknown/Invalid        5
Name: gender, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#demographic dustributions - race</span>
<span class="c1">#import matplotlib as mpl</span>
<span class="c1">#mpl.rcParams.update(mpl.rcParamsDefault)</span>
<span class="n">fig</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">],</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">],</span><span class="n">hue</span><span class="o">=</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;gender&#39;</span><span class="p">],</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f4ac2ee5d50&gt;</pre>
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<pre>Caucasian          0.750837
AfricanAmerican    0.184258
Hispanic           0.020485
Other              0.015158
Asian              0.006191
Name: race, dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;ndc_code&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>68071-1701    20770
47918-902     20379
47918-898      6568
0173-0861      4060
50090-0353     4040
              ...  
33342-177         1
0228-2752         1
64764-302         1
0781-5635         1
0093-7457         1
Name: ndc_code, Length: 251, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">######NOTE: The visualization will only display in Chrome browser. ########</span>
<span class="kn">import</span> <span class="nn">tensorflow_data_validation</span> <span class="k">as</span> <span class="nn">tfdv</span>
<span class="c1">#full_data_stats = tfdv.generate_statistics_from_csv(data_location=&#39;./data/final_project_dataset.csv&#39;) </span>
<span class="c1">#tfdv.visualize_statistics(full_data_stats)</span>
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<pre>Error importing tfx_bsl_extension.arrow.array_util. Some tfx_bsl functionalities are not availableError importing tfx_bsl_extension.arrow.table_util. Some tfx_bsl functionalities are not availableError importing tfx_bsl_extension.arrow.table_util. Some tfx_bsl functionalities are not available</pre>
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<h2 id="Reduce-Dimensionality-of-the-NDC-Code-Feature">Reduce Dimensionality of the NDC Code Feature<a class="anchor-link" href="#Reduce-Dimensionality-of-the-NDC-Code-Feature">&#182;</a></h2>
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<p><strong>Question 3</strong>: NDC codes are a common format to represent the wide variety of drugs that are prescribed for patient care in the United States. The challenge is that there are many codes that map to the same or similar drug. You are provided with the ndc drug lookup file <a href="https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/ndc_lookup_table.csv">https://github.com/udacity/nd320-c1-emr-data-starter/blob/master/project/data_schema_references/ndc_lookup_table.csv</a> derived from the National Drug Codes List site(<a href="https://ndclist.com/">https://ndclist.com/</a>). Please use this file to come up with a way to reduce the dimensionality of this field and create a new field in the dataset called "generic_drug_name" in the output dataframe.</p>

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<span class="n">ndc_code_path</span> <span class="o">=</span> <span class="s2">&quot;./medication_lookup_tables/final_ndc_lookup_table&quot;</span>
<span class="n">ndc_code_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">ndc_code_path</span><span class="p">)</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>NDC_Code</th>
      <th>Proprietary Name</th>
      <th>Non-proprietary Name</th>
      <th>Dosage Form</th>
      <th>Route Name</th>
      <th>Company Name</th>
      <th>Product Type</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>0087-6060</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0087-6063</td>
      <td>Glucophage XR</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Extended Release</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0087-6064</td>
      <td>Glucophage XR</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Extended Release</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0087-6070</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0087-6071</td>
      <td>Glucophage</td>
      <td>Metformin Hydrochloride</td>
      <td>Tablet, Film Coated</td>
      <td>Oral</td>
      <td>Bristol-myers Squibb Company</td>
      <td>Human Prescription Drug</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">reduce_dimension_ndc</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduce_dim_df</span> <span class="o">=</span> <span class="n">reduce_dimension_ndc</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">ndc_code_df</span><span class="p">)</span>
<span class="n">reduce_dim_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>ndc_code</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
      <th>generic_drug_name</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>2278392</td>
      <td>8222157</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[0-10)</td>
      <td>NaN</td>
      <td>6</td>
      <td>25</td>
      <td>1</td>
      <td>1</td>
      <td>NaN</td>
      <td>Pediatrics-Endocrinology</td>
      <td>250.83</td>
      <td>?|?</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>41</td>
      <td>1</td>
      <td>1</td>
      <td>0</td>
      <td>NaN</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149190</td>
      <td>55629189</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[10-20)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>3</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>276</td>
      <td>250.01|255</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>59</td>
      <td>9</td>
      <td>18</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&gt;30</td>
      <td>Human Insulin</td>
    </tr>
    <tr>
      <th>2</th>
      <td>64410</td>
      <td>86047875</td>
      <td>AfricanAmerican</td>
      <td>Female</td>
      <td>[20-30)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>648</td>
      <td>250|V27</td>
      <td>2</td>
      <td>1</td>
      <td>0</td>
      <td>11</td>
      <td>6</td>
      <td>13</td>
      <td>5</td>
      <td>0378-1110</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
      <td>Glipizide</td>
    </tr>
    <tr>
      <th>3</th>
      <td>500364</td>
      <td>82442376</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[30-40)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>8</td>
      <td>250.43|403</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>44</td>
      <td>7</td>
      <td>16</td>
      <td>1</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
      <td>Human Insulin</td>
    </tr>
    <tr>
      <th>4</th>
      <td>16680</td>
      <td>42519267</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[40-50)</td>
      <td>NaN</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>1</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>197</td>
      <td>157|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>51</td>
      <td>5</td>
      <td>8</td>
      <td>0</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>NO</td>
      <td>Glipizide</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Number of unique values should be less for the new output field</span>
<span class="k">assert</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;ndc_code&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">reduce_dim_df</span><span class="p">[</span><span class="s1">&#39;generic_drug_name&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
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<h2 id="Select-First-Encounter-for-each-Patient">Select First Encounter for each Patient<a class="anchor-link" href="#Select-First-Encounter-for-each-Patient">&#182;</a></h2>
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<p><strong>Question 4</strong>: In order to simplify the aggregation of data for the model, we will only select the first encounter for each patient in the dataset. This is to reduce the risk of data leakage of future patient encounters and to reduce complexity of the data transformation and modeling steps. We will assume that sorting in numerical order on the encounter_id provides the time horizon for determining which encounters come before and after another.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">select_first_encounter</span>
<span class="n">first_encounter_df</span> <span class="o">=</span> <span class="n">select_first_encounter</span><span class="p">(</span><span class="n">reduce_dim_df</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># unique patients in transformed dataset</span>
<span class="n">unique_patients</span> <span class="o">=</span> <span class="n">first_encounter_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of unique patients:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unique_patients</span><span class="p">))</span>

<span class="c1"># unique encounters in transformed dataset</span>
<span class="n">unique_encounters</span> <span class="o">=</span> <span class="n">first_encounter_df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of unique encounters:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">unique_encounters</span><span class="p">))</span>

<span class="n">original_unique_patient_number</span> <span class="o">=</span> <span class="n">reduce_dim_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="c1"># number of unique patients should be equal to the number of unique encounters and patients in the final dataset</span>
<span class="k">assert</span> <span class="n">original_unique_patient_number</span> <span class="o">==</span> <span class="n">unique_patients</span>
<span class="k">assert</span> <span class="n">original_unique_patient_number</span> <span class="o">==</span> <span class="n">unique_encounters</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Tests passed!!&quot;</span><span class="p">)</span>
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<pre>Number of unique patients:71518
Number of unique encounters:71518
Tests passed!!
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<h2 id="Aggregate-Dataset-to-Right-Level-for-Modeling">Aggregate Dataset to Right Level for Modeling<a class="anchor-link" href="#Aggregate-Dataset-to-Right-Level-for-Modeling">&#182;</a></h2>
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<p>In order to provide a broad scope of the steps and to prevent students from getting stuck with data transformations, we have selected the aggregation columns and provided a function to build the dataset at the appropriate level. The 'aggregate_dataset" function that you can find in the 'utils.py' file can take the preceding dataframe with the 'generic_drug_name' field and transform the data appropriately for the project.</p>
<p>To make it simpler for students, we are creating dummy columns for each unique generic drug name and adding those are input features to the model. There are other options for data representation but this is out of scope for the time constraints of the course.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">exclusion_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;generic_drug_name&#39;</span><span class="p">]</span>
<span class="n">grouping_field_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">first_encounter_df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">exclusion_list</span><span class="p">]</span>
<span class="n">agg_drug_df</span><span class="p">,</span> <span class="n">ndc_col_list</span> <span class="o">=</span> <span class="n">aggregate_dataset</span><span class="p">(</span><span class="n">first_encounter_df</span><span class="p">,</span> <span class="n">grouping_field_list</span><span class="p">,</span> <span class="s1">&#39;generic_drug_name&#39;</span><span class="p">)</span>
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<pre>/home/workspace/starter_code/utils.py:10: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  array_field].apply(lambda x: x[array_field].values.tolist()).reset_index().rename(columns={
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">agg_drug_df</span><span class="p">)</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;encounter_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
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<h2 id="Prepare-Fields-and-Cast-Dataset">Prepare Fields and Cast Dataset<a class="anchor-link" href="#Prepare-Fields-and-Cast-Dataset">&#182;</a></h2>
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<h3 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h3>
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<p><strong>Question 5</strong>: After you have aggregated the dataset to the right level, we can do feature selection (we will include the ndc_col_list, dummy column features too). In the block below, please select the categorical and numerical features that you will use for the model, so that we can create a dataset subset.</p>
<p>For the payer_code and weight fields, please provide whether you think we should include/exclude the field in our model and give a justification/rationale for this based off of the statistics of the data. Feel free to use visualizations or summary statistics to support your choice.</p>

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<p><strong>Student response:</strong>  From the "number of missing values" analysis above, I will choose to ignore the weight field, since it can be seen in the original dataset that the column had a huge numner of missing values i.e 97% missing values.
However, the payer_code and medical speciality fields have 37.7% and 48.4%  missing values respectively which is less than 50%. Therefore I choose to keep them in the model so we dont lose the information contained in these 2 fields.</p>
<p>I have dropped number_emergency, number_inpatient, number_outpatient too since they have a very high percentage of zero values, more than 67%.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">agg_drug_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>encounter_id</th>
      <th>patient_nbr</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>weight</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>time_in_hospital</th>
      <th>payer_code</th>
      <th>medical_specialty</th>
      <th>primary_diagnosis_code</th>
      <th>other_diagnosis_codes</th>
      <th>number_outpatient</th>
      <th>number_inpatient</th>
      <th>number_emergency</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
      <th>ndc_code</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>readmitted</th>
      <th>generic_drug_name_array</th>
      <th>Acarbose</th>
      <th>Glimepiride</th>
      <th>Glipizide</th>
      <th>Glyburide</th>
      <th>Human_Insulin</th>
      <th>Insulin_Human</th>
      <th>Metformin_Hcl</th>
      <th>Metformin_Hydrochloride</th>
      <th>Nateglinide</th>
      <th>Pioglitazone</th>
      <th>Repaglinide</th>
      <th>Rosiglitazone_Maleate</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>88792836</td>
      <td>100654011</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>[75-100)</td>
      <td>3</td>
      <td>3</td>
      <td>1</td>
      <td>10</td>
      <td>UN</td>
      <td>Surgery-General</td>
      <td>715</td>
      <td>402|428</td>
      <td>1</td>
      <td>3</td>
      <td>1</td>
      <td>65</td>
      <td>9</td>
      <td>28</td>
      <td>1</td>
      <td>0049-4110</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>&lt;30</td>
      <td>[Glipizide]</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>88986678</td>
      <td>58682736</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[80-90)</td>
      <td>[50-75)</td>
      <td>1</td>
      <td>11</td>
      <td>5</td>
      <td>6</td>
      <td>UN</td>
      <td>Family/GeneralPractice</td>
      <td>250.2</td>
      <td>780|403</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>73</td>
      <td>9</td>
      <td>16</td>
      <td>0</td>
      <td>68071-1701</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>NO</td>
      <td>[Human Insulin]</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>89032962</td>
      <td>69250302</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>[100-125)</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>2</td>
      <td>CP</td>
      <td>Cardiology</td>
      <td>414</td>
      <td>411|401</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>58</td>
      <td>9</td>
      <td>12</td>
      <td>3</td>
      <td>0781-5422</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
      <td>[Pioglitazone]</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>89277516</td>
      <td>30950811</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[50-60)</td>
      <td>[100-125)</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>BC</td>
      <td>Surgery-General</td>
      <td>196</td>
      <td>199|250</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>5</td>
      <td>7</td>
      <td>11</td>
      <td>4</td>
      <td>0093-8342</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&gt;30</td>
      <td>[Glyburide]</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>89307582</td>
      <td>58763808</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>[75-100)</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>10</td>
      <td>MC</td>
      <td>InternalMedicine</td>
      <td>250.4</td>
      <td>403|276</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>63</td>
      <td>9</td>
      <td>20</td>
      <td>0</td>
      <td>0591-0460</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>&lt;30</td>
      <td>[Glipizide]</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
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<pre>[&#39;Acarbose&#39;,
 &#39;Glimepiride&#39;,
 &#39;Glipizide&#39;,
 &#39;Glyburide&#39;,
 &#39;Human_Insulin&#39;,
 &#39;Insulin_Human&#39;,
 &#39;Metformin_Hcl&#39;,
 &#39;Metformin_Hydrochloride&#39;,
 &#39;Nateglinide&#39;,
 &#39;Pioglitazone&#39;,
 &#39;Repaglinide&#39;,
 &#39;Rosiglitazone_Maleate&#39;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd">Please update the list to include the features you think are appropriate for the model </span>
<span class="sd">and the field that we will be using to train the model. There are three required demographic features for the model </span>
<span class="sd">and I have inserted a list with them already in the categorical list. </span>
<span class="sd">These will be required for later steps when analyzing data splits and model biases.</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="n">required_demo_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">]</span>
<span class="n">student_categorical_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;admission_type_id&#39;</span><span class="p">,</span> <span class="s1">&#39;discharge_disposition_id&#39;</span><span class="p">,</span> <span class="s1">&#39;admission_source_id&#39;</span><span class="p">,</span> <span class="s2">&quot;payer_code&quot;</span><span class="p">,</span> 
                                <span class="s2">&quot;primary_diagnosis_code&quot;</span><span class="p">,</span><span class="s1">&#39;medical_specialty&#39;</span><span class="p">,</span><span class="s1">&#39;max_glu_serum&#39;</span><span class="p">,</span>
                                <span class="s1">&#39;A1Cresult&#39;</span><span class="p">,</span> <span class="s1">&#39;change&#39;</span><span class="p">]</span><span class="o">+</span> <span class="n">required_demo_col_list</span> <span class="o">+</span> <span class="n">ndc_col_list</span>
<span class="n">student_numerical_col_list</span> <span class="o">=</span> <span class="p">[</span> <span class="s1">&#39;num_lab_procedures&#39;</span><span class="p">,</span> <span class="s1">&#39;number_diagnoses&#39;</span><span class="p">,</span><span class="s1">&#39;num_medications&#39;</span><span class="p">,</span> <span class="s1">&#39;num_procedures&#39;</span><span class="p">]</span>

<span class="n">PREDICTOR_FIELD</span> <span class="o">=</span> <span class="s1">&#39;time_in_hospital&#39;</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">select_model_features</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">categorical_col_list</span><span class="p">,</span> <span class="n">numerical_col_list</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">grouping_key</span><span class="o">=</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">):</span>
    <span class="n">selected_col_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">grouping_key</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">PREDICTOR_FIELD</span><span class="p">]</span> <span class="o">+</span> <span class="n">categorical_col_list</span> <span class="o">+</span> <span class="n">numerical_col_list</span>   
    <span class="k">return</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="n">selected_col_list</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">selected_features_df</span> <span class="o">=</span> <span class="n">select_model_features</span><span class="p">(</span><span class="n">agg_drug_df</span><span class="p">,</span> <span class="n">student_categorical_col_list</span><span class="p">,</span> <span class="n">student_numerical_col_list</span><span class="p">,</span>
                                            <span class="n">PREDICTOR_FIELD</span><span class="p">)</span>
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<h3 id="Preprocess-Dataset---Casting-and-Imputing">Preprocess Dataset - Casting and Imputing<a class="anchor-link" href="#Preprocess-Dataset---Casting-and-Imputing">&#182;</a></h3>
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<p>We will cast and impute the dataset before splitting so that we do not have to repeat these steps across the splits in the next step. For imputing, there can be deeper analysis into which features to impute and how to impute but for the sake of time, we are taking a general strategy of imputing zero for only numerical features.</p>
<p>OPTIONAL: What are some potential issues with this approach? Can you recommend a better way and also implement it?</p>
<p><strong>Student Answer</strong> - For numerical columns, imputing zero is a simple strategy but when replacing missing values with 0 we are altering the distribution of the data.
Instead if we replace the missing values by the mean of the values in the dataset or by the median of the values, we are reducing the deviation from the actual data. I have taken the mean of the values.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">processed_df</span> <span class="o">=</span> <span class="n">preprocess_df</span><span class="p">(</span><span class="n">selected_features_df</span><span class="p">,</span> <span class="n">student_categorical_col_list</span><span class="p">,</span> 
        <span class="n">student_numerical_col_list</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">categorical_impute_value</span><span class="o">=</span><span class="s1">&#39;nan&#39;</span><span class="p">,</span> <span class="n">numerical_impute_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>/home/workspace/starter_code/utils.py:29: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[predictor] = df[predictor].astype(float)
/home/workspace/starter_code/utils.py:31: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[c] = cast_df(df, c, d_type=str)
/home/workspace/starter_code/utils.py:32: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[c] = impute_df(df, c, categorical_impute_value)
/home/workspace/starter_code/utils.py:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[numerical_column] = cast_df(df, numerical_column, d_type=float)
/home/workspace/starter_code/utils.py:36: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[numerical_column] = impute_df(df, numerical_column, numerical_impute_value)
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<pre>591</pre>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>patient_nbr</th>
      <th>time_in_hospital</th>
      <th>admission_type_id</th>
      <th>discharge_disposition_id</th>
      <th>admission_source_id</th>
      <th>payer_code</th>
      <th>primary_diagnosis_code</th>
      <th>medical_specialty</th>
      <th>max_glu_serum</th>
      <th>A1Cresult</th>
      <th>change</th>
      <th>race</th>
      <th>gender</th>
      <th>age</th>
      <th>Acarbose</th>
      <th>Glimepiride</th>
      <th>Glipizide</th>
      <th>Glyburide</th>
      <th>Human_Insulin</th>
      <th>Insulin_Human</th>
      <th>Metformin_Hcl</th>
      <th>Metformin_Hydrochloride</th>
      <th>Nateglinide</th>
      <th>Pioglitazone</th>
      <th>Repaglinide</th>
      <th>Rosiglitazone_Maleate</th>
      <th>num_lab_procedures</th>
      <th>number_diagnoses</th>
      <th>num_medications</th>
      <th>num_procedures</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>100654011</td>
      <td>10.0</td>
      <td>3</td>
      <td>3</td>
      <td>1</td>
      <td>UN</td>
      <td>715</td>
      <td>Surgery-General</td>
      <td>None</td>
      <td>None</td>
      <td>Ch</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>65.0</td>
      <td>9.0</td>
      <td>28.0</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>58682736</td>
      <td>6.0</td>
      <td>1</td>
      <td>11</td>
      <td>5</td>
      <td>UN</td>
      <td>250.2</td>
      <td>Family/GeneralPractice</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[80-90)</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>73.0</td>
      <td>9.0</td>
      <td>16.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>69250302</td>
      <td>2.0</td>
      <td>1</td>
      <td>1</td>
      <td>7</td>
      <td>CP</td>
      <td>414</td>
      <td>Cardiology</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[60-70)</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>58.0</td>
      <td>9.0</td>
      <td>12.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>30950811</td>
      <td>2.0</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>BC</td>
      <td>196</td>
      <td>Surgery-General</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>Caucasian</td>
      <td>Male</td>
      <td>[50-60)</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>5.0</td>
      <td>7.0</td>
      <td>11.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>58763808</td>
      <td>10.0</td>
      <td>1</td>
      <td>3</td>
      <td>7</td>
      <td>MC</td>
      <td>250.4</td>
      <td>InternalMedicine</td>
      <td>None</td>
      <td>None</td>
      <td>No</td>
      <td>Caucasian</td>
      <td>Female</td>
      <td>[70-80)</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>63.0</td>
      <td>9.0</td>
      <td>20.0</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
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<h2 id="Split-Dataset-into-Train,-Validation,-and-Test-Partitions">Split Dataset into Train, Validation, and Test Partitions<a class="anchor-link" href="#Split-Dataset-into-Train,-Validation,-and-Test-Partitions">&#182;</a></h2>
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<p><strong>Question 6</strong>: In order to prepare the data for being trained and evaluated by a deep learning model, we will split the dataset into three partitions, with the validation partition used for optimizing the model hyperparameters during training. One of the key parts is that we need to be sure that the data does not accidently leak across partitions.</p>
<p>Please complete the function below to split the input dataset into three partitions(train, validation, test) with the following requirements.</p>
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<li>Approximately 60%/20%/20%  train/validation/test split</li>
<li>Randomly sample different patients into each data partition</li>
<li><strong>IMPORTANT</strong> Make sure that a patient's data is not in more than one partition, so that we can avoid possible data leakage.</li>
<li>Make sure that the total number of unique patients across the splits is equal to the total number of unique patients in the original dataset</li>
<li>Total number of rows in original dataset = sum of rows across all three dataset partitions</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">patient_dataset_splitter</span>
<span class="n">d_train</span><span class="p">,</span> <span class="n">d_val</span><span class="p">,</span> <span class="n">d_test</span> <span class="o">=</span> <span class="n">patient_dataset_splitter</span><span class="p">(</span><span class="n">processed_df</span><span class="p">,</span> <span class="s1">&#39;patient_nbr&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_train</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_val</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">d_test</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">processed_df</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test passed for number of total rows equal!&quot;</span><span class="p">)</span>
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<pre>Test passed for number of total rows equal!
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">assert</span> <span class="p">(</span><span class="n">d_train</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">+</span> <span class="n">d_val</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">+</span> <span class="n">d_test</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">())</span> <span class="o">==</span> <span class="n">agg_drug_df</span><span class="p">[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test passed for number of unique patients being equal!&quot;</span><span class="p">)</span>
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<pre>Test passed for number of unique patients being equal!
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<h2 id="Demographic-Representation-Analysis-of-Split">Demographic Representation Analysis of Split<a class="anchor-link" href="#Demographic-Representation-Analysis-of-Split">&#182;</a></h2>
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<p>After the split, we should check to see the distribution of key features/groups and make sure that there is representative samples across the partitions. The show_group_stats_viz function in the utils.py file can be used to group and visualize different groups and dataframe partitions.</p>

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<h3 id="Label-Distribution-Across-Partitions">Label Distribution Across Partitions<a class="anchor-link" href="#Label-Distribution-Across-Partitions">&#182;</a></h3>
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<p>Below you can see the distributution of the label across your splits. Are the histogram distribution shapes similar across partitions?</p>

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<pre>time_in_hospital
1.0      44
2.0      95
3.0     126
4.0      99
5.0      57
6.0      47
7.0      37
8.0      29
9.0      18
10.0     10
11.0      8
12.0     10
13.0      9
14.0      2
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">)</span>
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<pre>time_in_hospital
1.0     28
2.0     49
3.0     77
4.0     66
5.0     32
6.0     24
7.0     19
8.0     22
9.0     13
10.0     6
11.0     6
12.0     6
13.0     6
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
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<img src="data:image/png;base64,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<div class="prompt input_prompt">In&nbsp;[40]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">)</span>
</pre></div>

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<div class="output_subarea output_stream output_stdout output_text">
<pre>time_in_hospital
1.0     10
2.0     22
3.0     27
4.0     16
5.0      5
6.0     13
7.0     11
8.0      2
9.0      5
10.0     2
11.0     1
12.0     2
13.0     2
14.0     1
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
</pre>
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pIkacmMKslLkqRl5pnMeHcqg5aFXXL7Ds+zfvuupsOQnhGnQ5Y0LqM6k//ZZ9I5yQVJHuh5fSHJW/r6JMn1SWaT7E1y8YhilSTpjDDstLYvB34J+LruNgGqql5E58Odz2SnVfUQ8NLud68ADgMf7uu2CdjQfV0K3NB9lyRJQxj2cv27gZ+i87z88RHH8G3A/66qz/TVbwFurs4yefckWZ1kjWvKS5I0nGGT/HxVfXRMMVwO3DKgfgo41FOe69aZ5CVJGsKwSf7jSd4BfIgvn9b2/sXsPMlzgDcAbx3UPKDuaY/qJdkGbANY8fwXLiYcSZJaZdgkf+JeeO+i9AW8epH73wTcX1WfG9A2B6zrKa8FjvR3qqqdwE6A567Z4PP6kiR1DZXkq+pVY9r/VgZfqofOXPnXJLmVzh8Z896PlyRpeKdbhe6Hqur3k/z0oPaqeudCd5xkJfAa4Md66q7qfu8OYDewGZilswrelQvdlyRJZ6LTncmf1X0/e9Q7rqpjwFf31e3o+VzA1c/kOy+aWsWME4tIkgScfhW63+6+//Kp+iV5a1W9fZSBSZKkxRnVjHffN6LvkSRJIzKqJL8s5q6XJElPcalZSZJayjN5SZJaalRJ/r+P6HskSdKIDJXkk3xjko8l2d8tvyTJL5xor6pfHVeAkiRpYYY9k/8dOvPLfwmgqvbSWVhGkiQtU8Mm+ZVV9ed9dU+MOhhJkjQ6wy5Q8/kkX093FH2S72UZLvm67/A867fvajoMaWIcdIZIqdWGPZO/Gvht4JuSHAbeAvz4YnacZHWSDyT5dJIDSV7W154k1yeZTbI3ycWL2Z8kSWeaYVehexj49iRnAf+oqh4bwb5/C/ijqvre7rryK/vaNwEbuq9LgRt4aslbSZJ0GkMl+SSrgSuA9cCzks5j8VX15oXsNMnzgVcAP9z9nseBx/u6bQFu7i5Uc0/3zH+Ny81KkjScYe/J7wbuAfYBT45gvy8C/gZ4T5JvBvYA11bV3/f0mQIO9ZTnunUmeUmShjBskn9eVQ1cU34R+70Y+MmqujfJbwHbgV/s6TNoFr2nTZ+bZBuwDWDF8184whAlSZpsww68+70kP5pkTZKvOvFaxH7ngLmqurdb/gCdpN/fZ11PeS1wpP+LqmpnVU1X1fSKlasWEZIkSe0ybJJ/HHgH8Ak6l9b3ADML3WlV/TVwKMkF3apvAz7V1+124IruKPvLgHnvx0uSNLxhL9f/NPANVfX5Ee77J4H3dUfWPwxcmeQqgKraQWccwGZgFjgGXDnCfUuS1HrDJvkH6STakamqB4DpvuodPe1F5/l8SZK0AMMm+ePAA0k+DvzDicqFPkI3LhdNrWLGGbwkSQKGT/J/0H1JkqQJMeyMdzeNOxBJkjRap0zySW6rqu9Pso+nP6NeVfXN4wtNkiQtxunO5K/tvh8A/n1PfYBfH0tEkiRpJE6Z5HueS/+GqvpMb1uSbxpbVJIkadFOd7n+x4GfAF6UZG9P09nA/xpnYJIkaXFOd7n+/cBHgbfTmVv+hMeq6tGxRSVJkhbtdJfr54F5YOvShCNJkkZl2LnrJUnShBl2MpyJsO/wPOu372o6jIl00JkCJal1GjuTT3Iwyb4kDyR52op23dXnrk8ym2Rvkv6laCVJ0ik0fSb/qlOsbLcJ2NB9XQrc0H2XJElDWM735LcAN1fHPcDqJGuaDkqSpEnRZJIv4M4ke5JsG9A+BRzqKc91675Mkm1JZpLMHD82P6ZQJUmaPE1ern95VR1Jci5wV5JPV9XdPe0ZsE3//PlU1U5gJ8Bz12x4WrskSWeqxs7kq+pI9/0o8GHgkr4uc8C6nvJa4MjSRCdJ0uRrJMknOSvJ2Sc+A68F9vd1ux24ojvK/jJgvmcufUmSdBpNXa7/GuDDSU7E8P6q+qMkVwFU1Q5gN7AZmAWOAVc2FKskSRMpVe25jT09PV0zM0975F6SpNZKsqeqpge1LedH6CRJ0iKY5CVJaimTvCRJLWWSlySppUzykiS1lElekqSWMslLktRSJnlJklrKJC9JUks1uQrdyO07PM/67buaDkPSEjh43euaDkFa9ho9k0+yIslfJPnDAW1Jcn2S2SR7k1zcRIySJE2qpi/XXwscOEnbJmBD97UNuGGpgpIkqQ0aS/JJ1gKvA373JF22ADdXxz3A6iRrlixASZImXJNn8r8J/Azw5Enap4BDPeW5bp0kSRpCI0k+yeuBo1W151TdBtQ9bV3cJNuSzCSZOX5sfmQxSpI06Zo6k3858IYkB4FbgVcn+f2+PnPAup7yWuBI/xdV1c6qmq6q6RUrV40rXkmSJk4jSb6q3lpVa6tqPXA58D+r6of6ut0OXNEdZX8ZMF9Vjyx1rJIkTapl9Zx8kqsAqmoHsBvYDMwCx4ArGwxNkqSJ03iSr6o/Bv64+3lHT30BVzcTlSRJk6/xJD9KF02tYsZZsCRJApqfDEeSJI2JSV6SpJYyyUuS1FImeUmSWsokL0lSS5nkJUlqKZO8JEktZZKXJKmlWjUZzr7D86zfvqvpMCRJGujgEk/Y1tRSs89L8udJPpnkwSS/PKBPklyfZDbJ3iQXNxGrJEmTqqkz+X8AXl1VX0zybODPkny0qu7p6bMJ2NB9XQrc0H2XJElDaGqp2aqqL3aLz+6+qq/bFuDmbt97gNVJ1ixlnJIkTbLGBt4lWZHkAeAocFdV3dvXZQo41FOe69ZJkqQhNJbkq+p4Vb0UWAtckuTFfV0yaLP+iiTbkswkmTl+bH4coUqSNJEaf4Suqv6OznryG/ua5oB1PeW1wJEB2++squmqml6xctXY4pQkadI0Nbr+hUlWdz9/BfDtwKf7ut0OXNEdZX8ZMF9VjyxxqJIkTaymRtevAW5KsoLOHxq3VdUfJrkKoKp2ALuBzcAscAy4sqFYJUmaSI0k+araC3zLgPodPZ8LuHop45IkqU1aNePdRVOrmFni2YQkSVquGh94J0mSxsMkL0lSS5nkJUlqKZO8JEktZZKXJKmlTPKSJLWUSV6SpJYyyUuS1FImeUmSWqpVM97tOzzP+u27mg5DklrtoDOLToymVqFbl+TjSQ4keTDJtQP6JMn1SWaT7E1ycROxSpI0qZo6k38C+LdVdX+Ss4E9Se6qqk/19NkEbOi+LgVu6L5LkqQhNHImX1WPVNX93c+PAQeAqb5uW4Cbq+MeYHWSNUscqiRJE6vxgXdJ1tNZdvbevqYp4FBPeY6n/yEgSZJOotEkn+QrgQ8Cb6mqL/Q3D9ikBnzHtiQzSWaOH5sfR5iSJE2kxpJ8kmfTSfDvq6oPDegyB6zrKa8FjvR3qqqdVTVdVdMrVq4aT7CSJE2gpkbXB3g3cKCq3nmSbrcDV3RH2V8GzFfVI0sWpCRJE66p0fUvB/4NsC/JA926nwO+FqCqdgC7gc3ALHAMuLKBOCVJmliNJPmq+jMG33Pv7VPA1UsTkSRJ7dOqGe8umlrFjDMxSZIELINH6CRJ0niY5CVJaimTvCRJLWWSlySppUzykiS1lElekqSWMslLktRSJnlJklqqVZPh7Ds8z/rtu5oOQ2eYg07AJGmZanIVuhuTHE2y/yTtSXJ9ktkke5NcvNQxSpI0yZq8XP9eYOMp2jcBG7qvbcANSxCTJEmt0ViSr6q7gUdP0WULcHN13AOsTrJmaaKTJGnyLeeBd1PAoZ7yXLdOkiQNYTkn+UFL0dbTOiXbkswkmTl+bH4JwpIkaTIs5yQ/B6zrKa8FjvR3qqqdVTVdVdMrVq5asuAkSVrulnOSvx24ojvK/jJgvqoeaTooSZImRWPPySe5BXglcE6SOeBtwLMBqmoHsBvYDMwCx4Arm4lUkqTJ1FiSr6qtp2kv4OolCkeSpNZp1Yx3F02tYsbZxyRJApb3PXlJkrQIJnlJklrKJC9JUkuZ5CVJaql0BrG3Q5LHgIeajmNCnQN8vukgJpTHbuE8dgvjcVu4Nh67r6uqFw5qaNXoeuChqppuOohJlGTGY7cwHruF89gtjMdt4c60Y+flekmSWsokL0lSS7Utye9sOoAJ5rFbOI/dwnnsFsbjtnBn1LFr1cA7SZL0lLadyUuSpK7WJPkkG5M8lGQ2yfam45kkSQ4m2ZfkgSQzTceznCW5McnRJPt76r4qyV1J/qr7/oImY1yOTnLcfinJ4e7v7oEkm5uMcblKsi7Jx5McSPJgkmu79f7uTuMUx+6M+e214nJ9khXAXwKvAeaA+4CtVfWpRgObEEkOAtNV1bZnR0cuySuALwI3V9WLu3W/DjxaVdd1/8B8QVX9bJNxLjcnOW6/BHyxqn6jydiWuyRrgDVVdX+Ss4E9wHcCP4y/u1M6xbH7fs6Q315bzuQvAWar6uGqehy4FdjScExqoaq6G3i0r3oLcFP38010/hNRj5McNw2hqh6pqvu7nx8DDgBT+Ls7rVMcuzNGW5L8FHCopzzHGfYPuUgF3JlkT5JtTQczgb6mqh6Bzn8qwLkNxzNJrkmyt3s538vNp5FkPfAtwL34u3tG+o4dnCG/vbYk+Qyom/z7EEvn5VV1MbAJuLp7aVUatxuArwdeCjwC/Odmw1neknwl8EHgLVX1habjmSQDjt0Z89trS5KfA9b1lNcCRxqKZeJU1ZHu+1Hgw3Ruf2h4n+ve+ztxD/Bow/FMhKr6XFUdr6ongd/B391JJXk2nST1vqr6ULfa390QBh27M+m315Ykfx+wIcn5SZ4DXA7c3nBMEyHJWd0BKSQ5C3gtsP/UW6nP7cAbu5/fCHykwVgmxokE1fVd+LsbKEmAdwMHquqdPU3+7k7jZMfuTPrttWJ0PUD3EYjfBFYAN1bVrzQc0kRI8iI6Z+/QWbDo/R67k0tyC/BKOitZfQ54G/AHwG3A1wKfBb6vqhxk1uMkx+2VdC6XFnAQ+LET95j1lCTfCvwpsA94slv9c3TuLfu7O4VTHLutnCG/vdYkeUmS9OXacrlekiT1MclLktRSJnlJklrKJC9JUkuZ5CVJaimTvCRJLWWSlySppUzykiS11P8DbvM6XEzDej0AAAAASUVORK5CYII=
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<h2 id="Demographic-Group-Analysis">Demographic Group Analysis<a class="anchor-link" href="#Demographic-Group-Analysis">&#182;</a></h2>
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<p>We should check that our partitions/splits of the dataset are similar in terms of their demographic profiles. Below you can see how we might visualize and analyze the full dataset vs. the partitions.</p>

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<div class="prompt input_prompt">In&nbsp;[41]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Full dataset before splitting</span>
<span class="n">patient_demo_features</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;patient_nbr&#39;</span><span class="p">]</span>
<span class="n">patient_group_analysis_df</span> <span class="o">=</span> <span class="n">processed_df</span><span class="p">[</span><span class="n">patient_demo_features</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">1</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="n">show_group_stats_viz</span><span class="p">(</span><span class="n">patient_group_analysis_df</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">)</span>
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<pre>gender
Female    299
Male      292
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
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Jn5yuol1U07nmlnZuahM/NVZ65XL6je296avHR72EurN+1mwgvCQWtxU/WSmXnQzDy+urJ6xw7m25kz/+HavLi9fadO2NrMzFQ/Wd251vqn++468fvOQWtj36mZuXxmvnq7/pDq+dWvt4P95sT+QqS11udn5uXVze2diXDDWut9Ox5rlx5dvXHv722nqp9aa/38zLyzev3MfG/136s/t8MZj83MvLa6qrpsZj5U/XB1XedYi7XW+2bm9dX7q89XL1tr3buTwY/BAWtz1cw8pb1DmHdX31cnb22q51R/sbpje3+46m9m36mD1+Y77TtdUd04M5e09z/rr19rvXlmfqVj3m/8REMAoDrZbx8AAPuIAgCgEgUAwEYUAACVKAAANqIAAKhEAQCwEQUAQFX/D54635A0jZcjAAAAAElFTkSuQmCC
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training partition</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">)</span>
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<pre>gender
Female    181
Male      173
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Test partition</span>
<span class="n">show_group_stats_viz</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">)</span>
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<pre>gender
Female    66
Male      53
dtype: int64
AxesSubplot(0.125,0.125;0.775x0.755)
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<h2 id="Convert-Dataset-Splits-to-TF-Dataset">Convert Dataset Splits to TF Dataset<a class="anchor-link" href="#Convert-Dataset-Splits-to-TF-Dataset">&#182;</a></h2>
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<p>We have provided you the function to convert the Pandas dataframe to TF tensors using the TF Dataset API. 
Please note that this is not a scalable method and for larger datasets, the 'make_csv_dataset' method is recommended -<a href="https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset">https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Convert dataset from Pandas dataframes to TF dataset </span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">128</span>
<span class="n">diabetes_train_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
<span class="n">diabetes_val_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_val</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
<span class="n">diabetes_test_ds</span> <span class="o">=</span> <span class="n">df_to_dataset</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">PREDICTOR_FIELD</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># We use this sample of the dataset to show transformations later</span>
<span class="n">diabetes_batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">diabetes_train_ds</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">demo</span><span class="p">(</span><span class="n">feature_column</span><span class="p">,</span> <span class="n">example_batch</span><span class="p">):</span>
    <span class="n">feature_layer</span> <span class="o">=</span> <span class="n">layers</span><span class="o">.</span><span class="n">DenseFeatures</span><span class="p">(</span><span class="n">feature_column</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">(</span><span class="n">example_batch</span><span class="p">))</span>
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<h1 id="4.-Create-Categorical-Features-with-TF-Feature-Columns">4. Create Categorical Features with TF Feature Columns<a class="anchor-link" href="#4.-Create-Categorical-Features-with-TF-Feature-Columns">&#182;</a></h1>
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<h2 id="Build-Vocabulary-for-Categorical-Features">Build Vocabulary for Categorical Features<a class="anchor-link" href="#Build-Vocabulary-for-Categorical-Features">&#182;</a></h2>
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<p>Before we can create the TF categorical features, we must first create the vocab files with the unique values for a given field that are from the <strong>training</strong> dataset. Below we have provided a function that you can use that only requires providing the pandas train dataset partition and the list of the categorical columns in a list format. The output variable 'vocab_file_list' will be a list of the file paths that can be used in the next step for creating the categorical features.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vocab_file_list</span> <span class="o">=</span> <span class="n">build_vocab_files</span><span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">student_categorical_col_list</span><span class="p">)</span>
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<p><strong>Question 7</strong>: Using the vocab file list from above that was derived fromt the features you selected earlier, please create categorical features with the Tensorflow Feature Column API, <a href="https://www.tensorflow.org/api_docs/python/tf/feature_column">https://www.tensorflow.org/api_docs/python/tf/feature_column</a>. Below is a function to help guide you.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">create_tf_categorical_feature_cols</span>
<span class="n">tf_cat_col_list</span> <span class="o">=</span> <span class="n">create_tf_categorical_feature_cols</span><span class="p">(</span><span class="n">student_categorical_col_list</span><span class="p">)</span>
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<pre>INFO:tensorflow:vocabulary_size = 5 in admission_type_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/admission_type_id_vocab.txt.
INFO:tensorflow:vocabulary_size = 11 in discharge_disposition_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/discharge_disposition_id_vocab.txt.
INFO:tensorflow:vocabulary_size = 7 in admission_source_id is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/admission_source_id_vocab.txt.
INFO:tensorflow:vocabulary_size = 11 in payer_code is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/payer_code_vocab.txt.
INFO:tensorflow:vocabulary_size = 121 in primary_diagnosis_code is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/primary_diagnosis_code_vocab.txt.
INFO:tensorflow:vocabulary_size = 8 in medical_specialty is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/medical_specialty_vocab.txt.
INFO:tensorflow:vocabulary_size = 2 in max_glu_serum is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/max_glu_serum_vocab.txt.
INFO:tensorflow:vocabulary_size = 5 in A1Cresult is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/A1Cresult_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in change is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/change_vocab.txt.
INFO:tensorflow:vocabulary_size = 5 in race is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/race_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in gender is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/gender_vocab.txt.
INFO:tensorflow:vocabulary_size = 10 in age is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/age_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Acarbose is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Acarbose_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Glimepiride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glimepiride_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Glipizide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glipizide_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Glyburide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Glyburide_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Human_Insulin is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Human_Insulin_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Insulin_Human is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Insulin_Human_vocab.txt.
INFO:tensorflow:vocabulary_size = 2 in Metformin_Hcl is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Metformin_Hcl_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Metformin_Hydrochloride is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Metformin_Hydrochloride_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Nateglinide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Nateglinide_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Pioglitazone is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Pioglitazone_vocab.txt.
INFO:tensorflow:vocabulary_size = 2 in Repaglinide is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Repaglinide_vocab.txt.
INFO:tensorflow:vocabulary_size = 3 in Rosiglitazone_Maleate is inferred from the number of elements in the vocabulary_file ./diabetes_vocab/Rosiglitazone_Maleate_vocab.txt.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_cat_var1</span> <span class="o">=</span> <span class="n">tf_cat_col_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Example categorical field:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_cat_var1</span><span class="p">))</span>
<span class="n">demo</span><span class="p">(</span><span class="n">test_cat_var1</span><span class="p">,</span> <span class="n">diabetes_batch</span><span class="p">)</span>
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<pre>Example categorical field:
EmbeddingColumn(categorical_column=VocabularyFileCategoricalColumn(key=&#39;admission_type_id&#39;, vocabulary_file=&#39;./diabetes_vocab/admission_type_id_vocab.txt&#39;, vocabulary_size=5, num_oov_buckets=1, dtype=tf.string, default_value=-1), dimension=20, combiner=&#39;mean&#39;, initializer=&lt;tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7f4ac49b4310&gt;, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True)
WARNING:tensorflow:Layer dense_features is casting an input tensor from dtype float64 to the layer&#39;s dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it&#39;s dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx(&#39;float64&#39;)`. To change just this layer, pass dtype=&#39;float64&#39; to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

tf.Tensor(
[[-0.00240225 -0.09136553 -0.22726083 ...  0.3998544  -0.01167798
  -0.14428079]
 [ 0.25804165  0.10038965 -0.0324292  ...  0.03540505 -0.03175997
   0.14706144]
 [-0.00240225 -0.09136553 -0.22726083 ...  0.3998544  -0.01167798
  -0.14428079]
 ...
 [-0.00240225 -0.09136553 -0.22726083 ...  0.3998544  -0.01167798
  -0.14428079]
 [-0.00240225 -0.09136553 -0.22726083 ...  0.3998544  -0.01167798
  -0.14428079]
 [-0.11572375  0.00937233 -0.17077903 ... -0.42090985 -0.2711191
   0.10327604]], shape=(128, 20), dtype=float32)
</pre>
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<h1 id="5.-Create-Numerical-Features-with-TF-Feature-Columns">5. Create Numerical Features with TF Feature Columns<a class="anchor-link" href="#5.-Create-Numerical-Features-with-TF-Feature-Columns">&#182;</a></h1>
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<p><strong>Question 8</strong>: Using the TF Feature Column API(<a href="https://www.tensorflow.org/api_docs/python/tf/feature_column/">https://www.tensorflow.org/api_docs/python/tf/feature_column/</a>), please create normalized Tensorflow numeric features for the model. Try to use the z-score normalizer function below to help as well as the 'calculate_stats_from_train_data' function.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">create_tf_numeric_feature</span>
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<p>For simplicity the create_tf_numerical_feature_cols function below uses the same normalizer function across all features(z-score normalization) but if you have time feel free to analyze and adapt the normalizer based off the statistical distributions. You may find this as a good resource in determining which transformation fits best for the data <a href="https://developers.google.com/machine-learning/data-prep/transform/normalization">https://developers.google.com/machine-learning/data-prep/transform/normalization</a>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">calculate_stats_from_train_data</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">):</span>
    <span class="n">mean</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()[</span><span class="s1">&#39;mean&#39;</span><span class="p">]</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()[</span><span class="s1">&#39;std&#39;</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span>

<span class="k">def</span> <span class="nf">create_tf_numerical_feature_cols</span><span class="p">(</span><span class="n">numerical_col_list</span><span class="p">,</span> <span class="n">train_df</span><span class="p">):</span>
    <span class="n">tf_numeric_col_list</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">numerical_col_list</span><span class="p">:</span>
        <span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="o">=</span> <span class="n">calculate_stats_from_train_data</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
        <span class="n">tf_numeric_feature</span> <span class="o">=</span> <span class="n">create_tf_numeric_feature</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
        <span class="n">tf_numeric_col_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tf_numeric_feature</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">tf_numeric_col_list</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tf_cont_col_list</span> <span class="o">=</span> <span class="n">create_tf_numerical_feature_cols</span><span class="p">(</span><span class="n">student_numerical_col_list</span><span class="p">,</span> <span class="n">d_train</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_cont_var1</span> <span class="o">=</span> <span class="n">tf_cont_col_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Example continuous field:</span><span class="se">\n</span><span class="si">{}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_cont_var1</span><span class="p">))</span>
<span class="n">demo</span><span class="p">(</span><span class="n">test_cont_var1</span><span class="p">,</span> <span class="n">diabetes_batch</span><span class="p">)</span>
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<pre>Example continuous field:
NumericColumn(key=&#39;num_lab_procedures&#39;, shape=(1,), default_value=(0,), dtype=tf.float64, normalizer_fn=functools.partial(&lt;function normalize_numeric_with_zscore at 0x7f4ac9664320&gt;, mean=52.44632768361582, std=20.023462174843544))

WARNING:tensorflow:Layer dense_features_1 is casting an input tensor from dtype float64 to the layer&#39;s dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it&#39;s dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx(&#39;float64&#39;)`. To change just this layer, pass dtype=&#39;float64&#39; to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

tf.Tensor(
[[-0.4218215 ]
 [-0.47176293]
 [-0.82135284]
 [ 0.12753403]
 [ 0.92659664]
 [-0.4218215 ]
 [-0.5217043 ]
 [-2.419478  ]
 [-1.3707083 ]
 [-1.1210012 ]
 [ 2.0253077 ]
 [-0.5217043 ]
 [ 0.8267138 ]
 [-0.6715286 ]
 [-1.7202983 ]
 [-0.17211445]
 [-0.3219387 ]
 [-0.97117704]
 [ 0.6768896 ]
 [-0.3718801 ]
 [ 0.07759261]
 [-0.22205587]
 [-0.3718801 ]
 [-0.82135284]
 [ 0.8766552 ]
 [ 0.7767724 ]
 [-0.9212356 ]
 [-0.17211445]
 [-0.07223163]
 [-2.0199468 ]
 [-0.02229021]
 [-1.8701224 ]
 [ 0.8766552 ]
 [-0.17211445]
 [-1.1709427 ]
 [ 0.97653806]
 [ 0.0276512 ]
 [ 1.0264795 ]
 [ 0.8267138 ]
 [ 0.8766552 ]
 [ 0.7767724 ]
 [-1.7202983 ]
 [ 0.47712392]
 [-2.5693023 ]
 [ 1.2761866 ]
 [-0.57164574]
 [-1.9200639 ]
 [ 0.52706534]
 [-0.47176293]
 [-1.9200639 ]
 [-0.4218215 ]
 [ 1.6757178 ]
 [ 0.4271825 ]
 [-0.6715286 ]
 [ 0.92659664]
 [-0.72147   ]
 [ 0.07759261]
 [-0.07223163]
 [ 0.97653806]
 [ 1.3261279 ]
 [ 0.22741686]
 [-2.5693023 ]
 [ 0.97653806]
 [-0.82135284]
 [-0.3219387 ]
 [ 1.3261279 ]
 [ 0.27735826]
 [ 0.07759261]
 [-0.4218215 ]
 [ 0.07759261]
 [ 0.8766552 ]
 [ 0.4271825 ]
 [ 0.57700676]
 [ 0.27735826]
 [-2.419478  ]
 [-0.7714114 ]
 [-1.0211185 ]
 [ 0.6768896 ]
 [-0.12217304]
 [ 0.52706534]
 [ 1.1263623 ]
 [ 1.0264795 ]
 [ 1.3760693 ]
 [-2.5193608 ]
 [ 0.72683096]
 [ 0.17747544]
 [ 0.4271825 ]
 [-0.3219387 ]
 [ 0.6768896 ]
 [ 0.12753403]
 [ 1.0764209 ]
 [-0.17211445]
 [-0.02229021]
 [ 0.0276512 ]
 [ 0.97653806]
 [-1.07106   ]
 [-1.1210012 ]
 [ 0.17747544]
 [ 1.3760693 ]
 [ 0.8766552 ]
 [ 1.0764209 ]
 [ 1.575835  ]
 [-1.1709427 ]
 [ 0.7767724 ]
 [-0.02229021]
 [ 0.07759261]
 [-1.2208841 ]
 [ 0.47712392]
 [ 0.8267138 ]
 [ 0.47712392]
 [-0.47176293]
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 [ 0.4271825 ]
 [-0.3718801 ]
 [ 1.7256593 ]
 [-2.419478  ]
 [ 1.3760693 ]
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 [ 0.12753403]
 [-0.6715286 ]
 [-0.07223163]
 [ 0.6269482 ]
 [-0.5217043 ]
 [-2.5693023 ]
 [ 0.12753403]
 [-1.6204154 ]
 [-0.72147   ]
 [ 0.07759261]], shape=(128, 1), dtype=float32)
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<h1 id="6.-Build-Deep-Learning-Regression-Model-with-Sequential-API-and-TF-Probability-Layers">6. Build Deep Learning Regression Model with Sequential API and TF Probability Layers<a class="anchor-link" href="#6.-Build-Deep-Learning-Regression-Model-with-Sequential-API-and-TF-Probability-Layers">&#182;</a></h1>
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<h2 id="Use-DenseFeatures-to-combine-features-for-model">Use DenseFeatures to combine features for model<a class="anchor-link" href="#Use-DenseFeatures-to-combine-features-for-model">&#182;</a></h2>
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<p>Now that we have prepared categorical and numerical features using Tensorflow's Feature Column API, we can combine them into a dense vector representation for the model. Below we will create this new input layer, which we will call 'claim_feature_layer'.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">claim_feature_columns</span> <span class="o">=</span> <span class="n">tf_cat_col_list</span> <span class="o">+</span> <span class="n">tf_cont_col_list</span>
<span class="n">claim_feature_layer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DenseFeatures</span><span class="p">(</span><span class="n">claim_feature_columns</span><span class="p">)</span>
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<h2 id="Build-Sequential-API-Model-from-DenseFeatures-and-TF-Probability-Layers">Build Sequential API Model from DenseFeatures and TF Probability Layers<a class="anchor-link" href="#Build-Sequential-API-Model-from-DenseFeatures-and-TF-Probability-Layers">&#182;</a></h2>
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<p>Below we have provided some boilerplate code for building a model that connects the Sequential API, DenseFeatures, and Tensorflow Probability layers into a deep learning model. There are many opportunities to further optimize and explore different architectures through benchmarking and testing approaches in various research papers, loss and evaluation metrics, learning curves, hyperparameter tuning, TF probability layers, etc. Feel free to modify and explore as you wish.</p>

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<p><strong>OPTIONAL</strong>: Come up with a more optimal neural network architecture and hyperparameters. Share the process in discovering the architecture and hyperparameters.</p>
<p>I experimented with the following:</p>
<ol>
<li>Adding Dense layers</li>
<li>Different optimiser functions like Adam, SGD and RMSprop</li>
<li>Different learning rates in the range 0.01 to 0.00001</li>
</ol>
<p>The following is the model that performed well.
SGD worked best and lowering the learnig rate increased the loss</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">build_sequential_model</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
        <span class="n">feature_layer</span><span class="p">,</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">150</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span><span class="o">=</span><span class="mf">0.5</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">),</span>
        <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">rate</span><span class="o">=</span><span class="mf">0.4</span><span class="p">),</span>
        <span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DenseVariational</span><span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">posterior_mean_field</span><span class="p">,</span> <span class="n">prior_trainable</span><span class="p">),</span>
        <span class="n">tfp</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">DistributionLambda</span><span class="p">(</span>
            <span class="k">lambda</span> <span class="n">t</span><span class="p">:</span><span class="n">tfp</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">1</span><span class="p">],</span>
                                             <span class="n">scale</span><span class="o">=</span><span class="mf">1e-3</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">softplus</span><span class="p">(</span><span class="mf">0.01</span> <span class="o">*</span> <span class="n">t</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">:])</span>
                                             <span class="p">)</span>
        <span class="p">),</span>
    <span class="p">])</span>
    <span class="k">return</span> <span class="n">model</span>

<span class="k">def</span> <span class="nf">build_diabetes_model</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">val_ds</span><span class="p">,</span>  <span class="n">feature_layer</span><span class="p">,</span>  <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">loss_metric</span><span class="o">=</span><span class="s1">&#39;mse&#39;</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">build_sequential_model</span><span class="p">(</span><span class="n">feature_layer</span><span class="p">)</span>
    <span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="mf">0.01</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="n">loss_metric</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="n">loss_metric</span><span class="p">])</span>
    
    <span class="n">early_stop</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">EarlyStopping</span><span class="p">(</span><span class="n">monitor</span><span class="o">=</span><span class="n">loss_metric</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span><span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">)</span>     
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="n">val_ds</span><span class="p">,</span>
                        <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">early_stop</span><span class="p">],</span>
                        <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">history</span> 
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<div class="prompt input_prompt">In&nbsp;[55]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">diabetes_model</span><span class="p">,</span> <span class="n">history</span> <span class="o">=</span> <span class="n">build_diabetes_model</span><span class="p">(</span><span class="n">diabetes_train_ds</span><span class="p">,</span> <span class="n">diabetes_val_ds</span><span class="p">,</span>  <span class="n">claim_feature_layer</span><span class="p">,</span>  <span class="n">epochs</span><span class="o">=</span><span class="mi">300</span><span class="p">)</span>
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<pre>Train for 3 steps, validate for 1 steps
Epoch 1/300
3/3 [==============================] - 9s 3s/step - loss: 68.6762 - mse: 68.8264 - val_loss: 24.8420 - val_mse: 23.7053
Epoch 2/300
3/3 [==============================] - 0s 45ms/step - loss: 80.6968 - mse: 83.8870 - val_loss: 18.4493 - val_mse: 18.2142
Epoch 3/300
3/3 [==============================] - 0s 45ms/step - loss: 31.9018 - mse: 31.8294 - val_loss: 41.8892 - val_mse: 39.8374
Epoch 4/300
3/3 [==============================] - 0s 46ms/step - loss: 21.5350 - mse: 20.0434 - val_loss: 45.4515 - val_mse: 45.4941
Epoch 5/300
3/3 [==============================] - 0s 47ms/step - loss: 22.0287 - mse: 21.9190 - val_loss: 13.5390 - val_mse: 10.3176
Epoch 6/300
3/3 [==============================] - 0s 47ms/step - loss: 28.1970 - mse: 26.8434 - val_loss: 83.5024 - val_mse: 81.9898
Epoch 7/300
3/3 [==============================] - 0s 45ms/step - loss: 20.5371 - mse: 19.6893 - val_loss: 41.6243 - val_mse: 40.3912
Epoch 8/300
3/3 [==============================] - 0s 47ms/step - loss: 30.3328 - mse: 31.1181 - val_loss: 21.3469 - val_mse: 20.7454
Epoch 9/300
3/3 [==============================] - 0s 47ms/step - loss: 43.4769 - mse: 42.8409 - val_loss: 12.9946 - val_mse: 10.9520
Epoch 10/300
3/3 [==============================] - 0s 47ms/step - loss: 20.5289 - mse: 20.2139 - val_loss: 24.6981 - val_mse: 22.6539
Epoch 11/300
3/3 [==============================] - 0s 45ms/step - loss: 34.3087 - mse: 33.4828 - val_loss: 50.3960 - val_mse: 49.3778
Epoch 12/300
3/3 [==============================] - 0s 44ms/step - loss: 22.7384 - mse: 20.1585 - val_loss: 17.5699 - val_mse: 15.8596
Epoch 13/300
3/3 [==============================] - 0s 45ms/step - loss: 36.7814 - mse: 37.0825 - val_loss: 16.6869 - val_mse: 14.0207
Epoch 14/300
3/3 [==============================] - 0s 44ms/step - loss: 33.8159 - mse: 33.8520 - val_loss: 25.3502 - val_mse: 21.5640
Epoch 15/300
3/3 [==============================] - 0s 45ms/step - loss: 24.1843 - mse: 22.4976 - val_loss: 26.8819 - val_mse: 27.2451
Epoch 16/300
3/3 [==============================] - 0s 44ms/step - loss: 26.2675 - mse: 24.3656 - val_loss: 13.5803 - val_mse: 11.4742
Epoch 17/300
3/3 [==============================] - 0s 44ms/step - loss: 20.9561 - mse: 20.3331 - val_loss: 10.2794 - val_mse: 10.7316
Epoch 18/300
3/3 [==============================] - 0s 44ms/step - loss: 27.8800 - mse: 26.3952 - val_loss: 8.4508 - val_mse: 6.9164
Epoch 19/300
3/3 [==============================] - 0s 44ms/step - loss: 13.9197 - mse: 13.4011 - val_loss: 9.4333 - val_mse: 8.4300
Epoch 20/300
3/3 [==============================] - 0s 44ms/step - loss: 27.0883 - mse: 25.1400 - val_loss: 14.2189 - val_mse: 8.5068
Epoch 21/300
3/3 [==============================] - 0s 44ms/step - loss: 12.2859 - mse: 11.3145 - val_loss: 11.7653 - val_mse: 9.7804
Epoch 22/300
3/3 [==============================] - 0s 44ms/step - loss: 22.9797 - mse: 19.2862 - val_loss: 27.9522 - val_mse: 26.3522
Epoch 23/300
3/3 [==============================] - 0s 45ms/step - loss: 32.5463 - mse: 33.2043 - val_loss: 13.0543 - val_mse: 12.4837
Epoch 24/300
3/3 [==============================] - 0s 46ms/step - loss: 24.3503 - mse: 23.0916 - val_loss: 14.8729 - val_mse: 12.0183
Epoch 25/300
3/3 [==============================] - 0s 44ms/step - loss: 14.4350 - mse: 13.7515 - val_loss: 8.1737 - val_mse: 10.7722
Epoch 26/300
3/3 [==============================] - 0s 43ms/step - loss: 17.2634 - mse: 17.8937 - val_loss: 14.0798 - val_mse: 16.0750
Epoch 27/300
3/3 [==============================] - 0s 43ms/step - loss: 18.3100 - mse: 14.8737 - val_loss: 10.7698 - val_mse: 7.4606
Epoch 28/300
3/3 [==============================] - 0s 43ms/step - loss: 16.2127 - mse: 12.9915 - val_loss: 8.8699 - val_mse: 6.9673
Epoch 29/300
3/3 [==============================] - 0s 45ms/step - loss: 14.3435 - mse: 11.5755 - val_loss: 8.2353 - val_mse: 7.2827
Epoch 30/300
3/3 [==============================] - 0s 45ms/step - loss: 14.0942 - mse: 12.2929 - val_loss: 13.2974 - val_mse: 13.0408
Epoch 31/300
3/3 [==============================] - 0s 48ms/step - loss: 12.5406 - mse: 12.2132 - val_loss: 21.5855 - val_mse: 16.9206
Epoch 32/300
3/3 [==============================] - 0s 46ms/step - loss: 23.9478 - mse: 21.2903 - val_loss: 7.7706 - val_mse: 6.4078
Epoch 33/300
3/3 [==============================] - 0s 44ms/step - loss: 13.4535 - mse: 10.6431 - val_loss: 8.8806 - val_mse: 7.8982
Epoch 34/300
3/3 [==============================] - 0s 44ms/step - loss: 28.9627 - mse: 26.6125 - val_loss: 11.8354 - val_mse: 7.7839
Epoch 35/300
3/3 [==============================] - 0s 43ms/step - loss: 19.4386 - mse: 18.6097 - val_loss: 7.9503 - val_mse: 6.8004
Epoch 36/300
3/3 [==============================] - 0s 44ms/step - loss: 15.5780 - mse: 14.2051 - val_loss: 15.6553 - val_mse: 13.1451
Epoch 37/300
3/3 [==============================] - 0s 45ms/step - loss: 21.0669 - mse: 21.0641 - val_loss: 10.6048 - val_mse: 6.9818
Epoch 38/300
3/3 [==============================] - 0s 44ms/step - loss: 13.1156 - mse: 12.6888 - val_loss: 13.4225 - val_mse: 8.2043
Epoch 39/300
3/3 [==============================] - 0s 44ms/step - loss: 12.9106 - mse: 11.4221 - val_loss: 17.2639 - val_mse: 17.4902
Epoch 40/300
3/3 [==============================] - 0s 43ms/step - loss: 13.2130 - mse: 10.7888 - val_loss: 7.9544 - val_mse: 7.4903
Epoch 41/300
3/3 [==============================] - 0s 44ms/step - loss: 14.7448 - mse: 12.6001 - val_loss: 8.7074 - val_mse: 5.6292
Epoch 42/300
3/3 [==============================] - 0s 44ms/step - loss: 16.3332 - mse: 16.2158 - val_loss: 7.2723 - val_mse: 5.9494
Epoch 43/300
3/3 [==============================] - 0s 44ms/step - loss: 12.4667 - mse: 11.0376 - val_loss: 11.5288 - val_mse: 10.9474
Epoch 44/300
3/3 [==============================] - 0s 45ms/step - loss: 10.6904 - mse: 8.3483 - val_loss: 7.5134 - val_mse: 6.5807
Epoch 45/300
3/3 [==============================] - 0s 44ms/step - loss: 11.4202 - mse: 10.8797 - val_loss: 9.8328 - val_mse: 5.9800
Epoch 46/300
3/3 [==============================] - 0s 44ms/step - loss: 13.8211 - mse: 11.4021 - val_loss: 12.4025 - val_mse: 9.7788
Epoch 47/300
3/3 [==============================] - 0s 44ms/step - loss: 10.4107 - mse: 9.9028 - val_loss: 8.6156 - val_mse: 8.4147
Epoch 48/300
3/3 [==============================] - 0s 43ms/step - loss: 21.2187 - mse: 19.1530 - val_loss: 20.3911 - val_mse: 15.4054
Epoch 49/300
3/3 [==============================] - 0s 44ms/step - loss: 11.8306 - mse: 9.0626 - val_loss: 6.6320 - val_mse: 5.9998
Epoch 50/300
3/3 [==============================] - 0s 43ms/step - loss: 13.5458 - mse: 10.9065 - val_loss: 24.1644 - val_mse: 24.0046
Epoch 51/300
3/3 [==============================] - 0s 44ms/step - loss: 10.9455 - mse: 8.8228 - val_loss: 20.1005 - val_mse: 16.5829
Epoch 52/300
3/3 [==============================] - 0s 44ms/step - loss: 11.1764 - mse: 10.1141 - val_loss: 17.7574 - val_mse: 15.7979
Epoch 53/300
3/3 [==============================] - 0s 45ms/step - loss: 10.9745 - mse: 8.5651 - val_loss: 14.9232 - val_mse: 15.8114
Epoch 54/300
3/3 [==============================] - 0s 44ms/step - loss: 15.1060 - mse: 15.1288 - val_loss: 9.4680 - val_mse: 6.3095
Epoch 55/300
3/3 [==============================] - 0s 43ms/step - loss: 14.8501 - mse: 14.7555 - val_loss: 10.9537 - val_mse: 8.7632
Epoch 56/300
3/3 [==============================] - 0s 45ms/step - loss: 13.1873 - mse: 11.9292 - val_loss: 10.9989 - val_mse: 8.0740
Epoch 57/300
3/3 [==============================] - 0s 47ms/step - loss: 13.9473 - mse: 13.6395 - val_loss: 10.3452 - val_mse: 10.1494
Epoch 58/300
3/3 [==============================] - 0s 45ms/step - loss: 13.4892 - mse: 12.0124 - val_loss: 17.0781 - val_mse: 16.8622
Epoch 59/300
3/3 [==============================] - 0s 44ms/step - loss: 13.9296 - mse: 11.6594 - val_loss: 11.7695 - val_mse: 10.8446
Epoch 60/300
3/3 [==============================] - 0s 45ms/step - loss: 15.6996 - mse: 14.2314 - val_loss: 11.4824 - val_mse: 13.3423
Epoch 61/300
3/3 [==============================] - 0s 43ms/step - loss: 12.1541 - mse: 8.9192 - val_loss: 7.9011 - val_mse: 7.2195
Epoch 62/300
3/3 [==============================] - 0s 45ms/step - loss: 14.7916 - mse: 10.2024 - val_loss: 11.3785 - val_mse: 10.8002
Epoch 63/300
3/3 [==============================] - 0s 43ms/step - loss: 15.0116 - mse: 12.0741 - val_loss: 14.3508 - val_mse: 11.3093
Epoch 64/300
3/3 [==============================] - 0s 44ms/step - loss: 11.6331 - mse: 11.3840 - val_loss: 10.5677 - val_mse: 11.0326
Epoch 65/300
3/3 [==============================] - 0s 44ms/step - loss: 13.9537 - mse: 12.3485 - val_loss: 6.7793 - val_mse: 5.7101
Epoch 66/300
3/3 [==============================] - 0s 44ms/step - loss: 17.0631 - mse: 16.2551 - val_loss: 8.0005 - val_mse: 5.8646
Epoch 67/300
3/3 [==============================] - 0s 44ms/step - loss: 13.6401 - mse: 10.5772 - val_loss: 7.6065 - val_mse: 7.0458
Epoch 68/300
3/3 [==============================] - 0s 43ms/step - loss: 9.7457 - mse: 8.1401 - val_loss: 14.7564 - val_mse: 9.6380
Epoch 69/300
3/3 [==============================] - 0s 44ms/step - loss: 12.1055 - mse: 10.8126 - val_loss: 11.3219 - val_mse: 6.9149
Epoch 70/300
3/3 [==============================] - 0s 43ms/step - loss: 11.1132 - mse: 10.0528 - val_loss: 14.8218 - val_mse: 17.3235
Epoch 71/300
3/3 [==============================] - 0s 44ms/step - loss: 12.4254 - mse: 11.9956 - val_loss: 7.0677 - val_mse: 7.3528
Epoch 72/300
3/3 [==============================] - 0s 45ms/step - loss: 11.4808 - mse: 11.2408 - val_loss: 22.5974 - val_mse: 20.4353
Epoch 73/300
3/3 [==============================] - 0s 44ms/step - loss: 11.4188 - mse: 10.0937 - val_loss: 11.3526 - val_mse: 6.2757
Epoch 74/300
3/3 [==============================] - 0s 45ms/step - loss: 14.1750 - mse: 11.5264 - val_loss: 9.3848 - val_mse: 8.2384
Epoch 75/300
3/3 [==============================] - 0s 44ms/step - loss: 17.0657 - mse: 15.9952 - val_loss: 13.6536 - val_mse: 10.4037
Epoch 76/300
3/3 [==============================] - 0s 44ms/step - loss: 12.6343 - mse: 10.3406 - val_loss: 7.8323 - val_mse: 10.4115
Epoch 77/300
3/3 [==============================] - 0s 43ms/step - loss: 10.1690 - mse: 8.6538 - val_loss: 9.6570 - val_mse: 7.9229
Epoch 78/300
3/3 [==============================] - 0s 44ms/step - loss: 11.4593 - mse: 11.2700 - val_loss: 9.4376 - val_mse: 9.0100
Epoch 79/300
3/3 [==============================] - 0s 44ms/step - loss: 9.8414 - mse: 7.5996 - val_loss: 6.9530 - val_mse: 6.7033
Epoch 80/300
3/3 [==============================] - 0s 44ms/step - loss: 13.1440 - mse: 12.4964 - val_loss: 7.0105 - val_mse: 5.0969
Epoch 81/300
3/3 [==============================] - 0s 44ms/step - loss: 10.5907 - mse: 9.5502 - val_loss: 10.6972 - val_mse: 9.6563
Epoch 82/300
3/3 [==============================] - 0s 45ms/step - loss: 12.7182 - mse: 10.2345 - val_loss: 9.5200 - val_mse: 7.3473
Epoch 83/300
3/3 [==============================] - 0s 44ms/step - loss: 9.2298 - mse: 8.7585 - val_loss: 8.9513 - val_mse: 5.9979
Epoch 84/300
3/3 [==============================] - 0s 44ms/step - loss: 10.9973 - mse: 10.0313 - val_loss: 14.8016 - val_mse: 13.4184
Epoch 85/300
3/3 [==============================] - 0s 45ms/step - loss: 11.7192 - mse: 11.5559 - val_loss: 8.7624 - val_mse: 6.9752
Epoch 86/300
3/3 [==============================] - 0s 44ms/step - loss: 9.3318 - mse: 8.2769 - val_loss: 11.0617 - val_mse: 8.6754
Epoch 87/300
3/3 [==============================] - 0s 44ms/step - loss: 13.8574 - mse: 11.9467 - val_loss: 12.3672 - val_mse: 7.4311
Epoch 88/300
3/3 [==============================] - 0s 45ms/step - loss: 9.6728 - mse: 10.8611 - val_loss: 9.5698 - val_mse: 7.0856
Epoch 89/300
3/3 [==============================] - 0s 44ms/step - loss: 9.6165 - mse: 7.6533 - val_loss: 9.6280 - val_mse: 7.1715
Epoch 90/300
3/3 [==============================] - 0s 44ms/step - loss: 9.9612 - mse: 8.5793 - val_loss: 11.1828 - val_mse: 5.8240
Epoch 91/300
3/3 [==============================] - 0s 44ms/step - loss: 9.2219 - mse: 8.4607 - val_loss: 8.4404 - val_mse: 6.9766
Epoch 92/300
3/3 [==============================] - 0s 44ms/step - loss: 8.5569 - mse: 7.3477 - val_loss: 7.5021 - val_mse: 6.2943
Epoch 93/300
3/3 [==============================] - 0s 44ms/step - loss: 8.9218 - mse: 6.6656 - val_loss: 6.6052 - val_mse: 5.6431
Epoch 94/300
3/3 [==============================] - 0s 44ms/step - loss: 8.4448 - mse: 8.0091 - val_loss: 10.2173 - val_mse: 10.5256
Epoch 95/300
3/3 [==============================] - 0s 43ms/step - loss: 11.2406 - mse: 8.2322 - val_loss: 7.4575 - val_mse: 7.9485
Epoch 96/300
3/3 [==============================] - 0s 44ms/step - loss: 6.2846 - mse: 6.6405 - val_loss: 7.8626 - val_mse: 6.2317
Epoch 97/300
3/3 [==============================] - 0s 46ms/step - loss: 10.8712 - mse: 9.1088 - val_loss: 8.2777 - val_mse: 6.4760
Epoch 98/300
3/3 [==============================] - 0s 46ms/step - loss: 13.2288 - mse: 13.2674 - val_loss: 7.3775 - val_mse: 7.0676
Epoch 99/300
3/3 [==============================] - 0s 45ms/step - loss: 8.5144 - mse: 7.9756 - val_loss: 11.5065 - val_mse: 12.2950
Epoch 100/300
3/3 [==============================] - 0s 44ms/step - loss: 9.6979 - mse: 8.3610 - val_loss: 6.8529 - val_mse: 5.7531
Epoch 101/300
3/3 [==============================] - 0s 45ms/step - loss: 7.6813 - mse: 7.3580 - val_loss: 8.3946 - val_mse: 6.8208
Epoch 102/300
3/3 [==============================] - 0s 44ms/step - loss: 10.5342 - mse: 8.8650 - val_loss: 10.3232 - val_mse: 9.6016
Epoch 103/300
3/3 [==============================] - 0s 45ms/step - loss: 7.8208 - mse: 6.6152 - val_loss: 9.3680 - val_mse: 8.5013
Epoch 104/300
3/3 [==============================] - 0s 45ms/step - loss: 12.1554 - mse: 10.6425 - val_loss: 8.1774 - val_mse: 7.5735
Epoch 105/300
3/3 [==============================] - 0s 46ms/step - loss: 13.0067 - mse: 10.6832 - val_loss: 6.0354 - val_mse: 7.4317
Epoch 106/300
3/3 [==============================] - 0s 45ms/step - loss: 11.8654 - mse: 9.4857 - val_loss: 14.6926 - val_mse: 12.2897
Epoch 107/300
3/3 [==============================] - 0s 45ms/step - loss: 9.9963 - mse: 9.6890 - val_loss: 11.0177 - val_mse: 11.7157
Epoch 108/300
3/3 [==============================] - 0s 45ms/step - loss: 7.6924 - mse: 7.7983 - val_loss: 6.2023 - val_mse: 5.8817
Epoch 109/300
3/3 [==============================] - 0s 43ms/step - loss: 10.5602 - mse: 8.6208 - val_loss: 7.7328 - val_mse: 6.2512
Epoch 110/300
3/3 [==============================] - 0s 45ms/step - loss: 10.2176 - mse: 8.4424 - val_loss: 11.6061 - val_mse: 6.5917
Epoch 111/300
3/3 [==============================] - 0s 44ms/step - loss: 13.0906 - mse: 11.3694 - val_loss: 5.4926 - val_mse: 6.6873
Epoch 112/300
3/3 [==============================] - 0s 45ms/step - loss: 14.3366 - mse: 12.5049 - val_loss: 8.9219 - val_mse: 9.2760
Epoch 113/300
3/3 [==============================] - 0s 44ms/step - loss: 8.4488 - mse: 7.7024 - val_loss: 11.5158 - val_mse: 6.8129
Epoch 114/300
3/3 [==============================] - 0s 44ms/step - loss: 9.1909 - mse: 7.0642 - val_loss: 6.4599 - val_mse: 6.9354
Epoch 115/300
3/3 [==============================] - 0s 44ms/step - loss: 9.5909 - mse: 8.3925 - val_loss: 6.8097 - val_mse: 6.3015
Epoch 116/300
3/3 [==============================] - 0s 43ms/step - loss: 11.3882 - mse: 9.4958 - val_loss: 8.8069 - val_mse: 7.1360
Epoch 117/300
3/3 [==============================] - 0s 44ms/step - loss: 10.8072 - mse: 8.1761 - val_loss: 9.3013 - val_mse: 6.2920
Epoch 118/300
3/3 [==============================] - 0s 44ms/step - loss: 10.3706 - mse: 6.9105 - val_loss: 9.6658 - val_mse: 6.8365
Epoch 119/300
3/3 [==============================] - 0s 45ms/step - loss: 10.0069 - mse: 7.0648 - val_loss: 8.1098 - val_mse: 5.8778
Epoch 120/300
3/3 [==============================] - 0s 47ms/step - loss: 6.2465 - mse: 6.3681 - val_loss: 6.9195 - val_mse: 5.6509
Epoch 121/300
3/3 [==============================] - 0s 45ms/step - loss: 10.3465 - mse: 8.1315 - val_loss: 6.8719 - val_mse: 5.5068
Epoch 122/300
3/3 [==============================] - 0s 44ms/step - loss: 8.6886 - mse: 6.5035 - val_loss: 6.6396 - val_mse: 5.5990
Epoch 123/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0703 - mse: 7.5411 - val_loss: 5.8001 - val_mse: 5.0775
Epoch 124/300
3/3 [==============================] - 0s 43ms/step - loss: 7.9207 - mse: 5.8946 - val_loss: 9.7173 - val_mse: 5.8897
Epoch 125/300
3/3 [==============================] - 0s 44ms/step - loss: 7.4093 - mse: 6.2528 - val_loss: 8.4463 - val_mse: 5.0283
Epoch 126/300
3/3 [==============================] - 0s 45ms/step - loss: 6.7400 - mse: 6.4524 - val_loss: 8.3462 - val_mse: 6.2905
Epoch 127/300
3/3 [==============================] - 0s 45ms/step - loss: 9.4278 - mse: 7.8997 - val_loss: 9.3881 - val_mse: 6.8834
Epoch 128/300
3/3 [==============================] - 0s 46ms/step - loss: 10.0688 - mse: 8.6842 - val_loss: 13.8487 - val_mse: 9.0079
Epoch 129/300
3/3 [==============================] - 0s 45ms/step - loss: 7.9490 - mse: 6.5344 - val_loss: 6.9134 - val_mse: 5.7199
Epoch 130/300
3/3 [==============================] - 0s 45ms/step - loss: 9.1266 - mse: 7.0813 - val_loss: 8.3406 - val_mse: 5.7871
Epoch 131/300
3/3 [==============================] - 0s 46ms/step - loss: 6.2618 - mse: 6.1851 - val_loss: 6.6997 - val_mse: 5.1365
Epoch 132/300
3/3 [==============================] - 0s 45ms/step - loss: 7.1863 - mse: 5.5511 - val_loss: 10.8046 - val_mse: 7.4129
Epoch 133/300
3/3 [==============================] - 0s 44ms/step - loss: 5.9289 - mse: 6.4188 - val_loss: 7.9831 - val_mse: 4.9581
Epoch 134/300
3/3 [==============================] - 0s 44ms/step - loss: 8.6302 - mse: 6.4578 - val_loss: 4.9374 - val_mse: 4.8414
Epoch 135/300
3/3 [==============================] - 0s 44ms/step - loss: 10.0051 - mse: 8.7929 - val_loss: 3.8703 - val_mse: 6.9260
Epoch 136/300
3/3 [==============================] - 0s 44ms/step - loss: 9.4737 - mse: 7.4411 - val_loss: 8.9385 - val_mse: 7.3542
Epoch 137/300
3/3 [==============================] - 0s 45ms/step - loss: 9.9983 - mse: 7.9183 - val_loss: 8.7123 - val_mse: 6.7671
Epoch 138/300
3/3 [==============================] - 0s 44ms/step - loss: 10.2142 - mse: 8.3816 - val_loss: 9.3696 - val_mse: 6.5314
Epoch 139/300
3/3 [==============================] - 0s 46ms/step - loss: 12.3846 - mse: 11.2205 - val_loss: 9.0257 - val_mse: 5.5031
Epoch 140/300
3/3 [==============================] - 0s 45ms/step - loss: 10.5083 - mse: 9.4762 - val_loss: 10.0578 - val_mse: 6.6781
Epoch 141/300
3/3 [==============================] - 0s 45ms/step - loss: 10.4294 - mse: 8.3638 - val_loss: 12.0797 - val_mse: 9.8782
Epoch 142/300
3/3 [==============================] - 0s 47ms/step - loss: 9.0532 - mse: 7.1868 - val_loss: 9.1627 - val_mse: 7.2805
Epoch 143/300
3/3 [==============================] - 0s 45ms/step - loss: 7.5464 - mse: 5.5053 - val_loss: 9.9790 - val_mse: 12.3376
Epoch 144/300
3/3 [==============================] - 0s 44ms/step - loss: 9.8774 - mse: 8.2059 - val_loss: 10.9057 - val_mse: 7.4378
Epoch 145/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2585 - mse: 6.3204 - val_loss: 6.8919 - val_mse: 6.3470
Epoch 146/300
3/3 [==============================] - 0s 45ms/step - loss: 6.0760 - mse: 5.6268 - val_loss: 8.1648 - val_mse: 4.8651
Epoch 147/300
3/3 [==============================] - 0s 43ms/step - loss: 9.9092 - mse: 8.6319 - val_loss: 7.6243 - val_mse: 5.2659
Epoch 148/300
3/3 [==============================] - 0s 44ms/step - loss: 6.8016 - mse: 5.8718 - val_loss: 9.5470 - val_mse: 6.7530
Epoch 149/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2098 - mse: 7.5689 - val_loss: 13.3993 - val_mse: 12.2844
Epoch 150/300
3/3 [==============================] - 0s 45ms/step - loss: 6.8906 - mse: 5.7553 - val_loss: 10.8565 - val_mse: 6.8526
Epoch 151/300
3/3 [==============================] - 0s 44ms/step - loss: 8.6399 - mse: 6.2134 - val_loss: 8.1000 - val_mse: 5.7015
Epoch 152/300
3/3 [==============================] - 0s 43ms/step - loss: 12.6191 - mse: 10.5346 - val_loss: 6.9996 - val_mse: 7.4393
Epoch 153/300
3/3 [==============================] - 0s 48ms/step - loss: 6.1562 - mse: 5.5444 - val_loss: 5.1659 - val_mse: 5.8531
Epoch 154/300
3/3 [==============================] - 0s 45ms/step - loss: 11.0782 - mse: 8.1436 - val_loss: 4.0587 - val_mse: 4.9495
Epoch 155/300
3/3 [==============================] - 0s 45ms/step - loss: 8.2200 - mse: 7.9203 - val_loss: 10.5934 - val_mse: 10.4495
Epoch 156/300
3/3 [==============================] - 0s 44ms/step - loss: 10.4927 - mse: 8.2555 - val_loss: 7.2088 - val_mse: 5.8155
Epoch 157/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2814 - mse: 6.5788 - val_loss: 7.3864 - val_mse: 7.7755
Epoch 158/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0048 - mse: 5.6751 - val_loss: 9.9228 - val_mse: 8.1440
Epoch 159/300
3/3 [==============================] - 0s 45ms/step - loss: 10.0682 - mse: 7.9764 - val_loss: 8.1195 - val_mse: 6.0858
Epoch 160/300
3/3 [==============================] - 0s 44ms/step - loss: 9.1848 - mse: 7.3869 - val_loss: 7.5799 - val_mse: 6.1457
Epoch 161/300
3/3 [==============================] - 0s 45ms/step - loss: 6.6445 - mse: 7.2008 - val_loss: 9.7729 - val_mse: 8.0564
Epoch 162/300
3/3 [==============================] - 0s 45ms/step - loss: 9.1101 - mse: 6.6718 - val_loss: 8.0365 - val_mse: 5.4746
Epoch 163/300
3/3 [==============================] - 0s 46ms/step - loss: 7.0688 - mse: 5.7396 - val_loss: 4.7514 - val_mse: 5.7785
Epoch 164/300
3/3 [==============================] - 0s 47ms/step - loss: 6.7645 - mse: 5.5613 - val_loss: 7.2271 - val_mse: 7.3493
Epoch 165/300
3/3 [==============================] - 0s 46ms/step - loss: 8.8699 - mse: 7.9002 - val_loss: 7.8487 - val_mse: 6.4552
Epoch 166/300
3/3 [==============================] - 0s 45ms/step - loss: 6.9719 - mse: 6.2397 - val_loss: 10.8721 - val_mse: 7.9490
Epoch 167/300
3/3 [==============================] - 0s 44ms/step - loss: 7.8670 - mse: 5.4702 - val_loss: 4.8089 - val_mse: 5.4513
Epoch 168/300
3/3 [==============================] - 0s 45ms/step - loss: 5.9464 - mse: 5.7732 - val_loss: 16.8411 - val_mse: 15.9849
Epoch 169/300
3/3 [==============================] - 0s 45ms/step - loss: 9.0106 - mse: 6.8988 - val_loss: 7.1715 - val_mse: 6.2347
Epoch 170/300
3/3 [==============================] - 0s 44ms/step - loss: 8.0237 - mse: 6.8820 - val_loss: 8.0009 - val_mse: 5.4011
Epoch 171/300
3/3 [==============================] - 0s 44ms/step - loss: 7.6358 - mse: 6.7918 - val_loss: 8.9992 - val_mse: 5.6139
Epoch 172/300
3/3 [==============================] - 0s 44ms/step - loss: 6.9986 - mse: 5.2923 - val_loss: 8.2736 - val_mse: 5.7244
Epoch 173/300
3/3 [==============================] - 0s 45ms/step - loss: 7.2464 - mse: 6.2226 - val_loss: 10.0835 - val_mse: 10.3562
Epoch 174/300
3/3 [==============================] - 0s 43ms/step - loss: 5.8423 - mse: 4.6236 - val_loss: 9.8523 - val_mse: 6.1853
Epoch 175/300
3/3 [==============================] - 0s 45ms/step - loss: 5.8564 - mse: 5.9673 - val_loss: 6.2600 - val_mse: 5.5970
Epoch 176/300
3/3 [==============================] - 0s 45ms/step - loss: 8.7068 - mse: 6.7376 - val_loss: 8.8908 - val_mse: 10.1443
Epoch 177/300
3/3 [==============================] - 0s 46ms/step - loss: 8.9351 - mse: 6.7578 - val_loss: 8.6872 - val_mse: 5.7627
Epoch 178/300
3/3 [==============================] - 0s 44ms/step - loss: 7.5391 - mse: 6.1260 - val_loss: 8.6138 - val_mse: 7.7351
Epoch 179/300
3/3 [==============================] - 0s 43ms/step - loss: 7.5676 - mse: 5.7270 - val_loss: 7.6311 - val_mse: 7.6112
Epoch 180/300
3/3 [==============================] - 0s 44ms/step - loss: 8.1384 - mse: 6.6742 - val_loss: 6.4173 - val_mse: 5.6830
Epoch 181/300
3/3 [==============================] - 0s 44ms/step - loss: 8.3639 - mse: 6.4438 - val_loss: 6.4187 - val_mse: 6.0698
Epoch 182/300
3/3 [==============================] - 0s 44ms/step - loss: 9.0000 - mse: 5.5367 - val_loss: 5.5802 - val_mse: 5.3569
Epoch 183/300
3/3 [==============================] - 0s 45ms/step - loss: 7.7943 - mse: 6.3924 - val_loss: 8.1881 - val_mse: 5.5600
Epoch 184/300
3/3 [==============================] - 0s 44ms/step - loss: 8.5230 - mse: 7.2533 - val_loss: 10.1819 - val_mse: 5.8172
Epoch 185/300
3/3 [==============================] - 0s 45ms/step - loss: 6.1682 - mse: 5.3886 - val_loss: 7.0393 - val_mse: 5.6598
Epoch 186/300
3/3 [==============================] - 0s 43ms/step - loss: 5.5038 - mse: 4.8003 - val_loss: 8.1424 - val_mse: 5.8617
Epoch 187/300
3/3 [==============================] - 0s 44ms/step - loss: 6.8924 - mse: 4.9926 - val_loss: 6.1773 - val_mse: 5.4535
Epoch 188/300
3/3 [==============================] - 0s 43ms/step - loss: 5.7844 - mse: 5.8553 - val_loss: 6.9794 - val_mse: 6.0967
Epoch 189/300
3/3 [==============================] - 0s 43ms/step - loss: 8.4184 - mse: 5.9138 - val_loss: 11.1996 - val_mse: 8.5887
Epoch 190/300
3/3 [==============================] - 0s 45ms/step - loss: 8.3782 - mse: 6.2777 - val_loss: 8.2051 - val_mse: 5.7505
Epoch 191/300
3/3 [==============================] - 0s 45ms/step - loss: 6.9730 - mse: 5.3415 - val_loss: 7.6747 - val_mse: 5.8146
Epoch 192/300
3/3 [==============================] - 0s 43ms/step - loss: 5.4331 - mse: 4.6495 - val_loss: 7.3346 - val_mse: 5.5229
Epoch 193/300
3/3 [==============================] - 0s 43ms/step - loss: 6.1764 - mse: 5.4124 - val_loss: 6.1583 - val_mse: 6.7997
Epoch 194/300
3/3 [==============================] - 0s 44ms/step - loss: 6.4689 - mse: 5.1413 - val_loss: 7.0569 - val_mse: 6.5652
Epoch 195/300
3/3 [==============================] - 0s 45ms/step - loss: 7.6491 - mse: 6.1833 - val_loss: 10.2489 - val_mse: 8.9037
Epoch 196/300
3/3 [==============================] - 0s 44ms/step - loss: 6.9639 - mse: 5.2045 - val_loss: 6.7522 - val_mse: 6.7586
Epoch 197/300
3/3 [==============================] - 0s 45ms/step - loss: 5.7464 - mse: 5.2326 - val_loss: 7.5073 - val_mse: 6.0190
Epoch 198/300
3/3 [==============================] - 0s 45ms/step - loss: 7.7886 - mse: 6.2256 - val_loss: 6.2416 - val_mse: 6.6837
Epoch 199/300
3/3 [==============================] - 0s 44ms/step - loss: 6.1589 - mse: 7.0683 - val_loss: 8.6124 - val_mse: 6.8589
Epoch 200/300
3/3 [==============================] - 0s 45ms/step - loss: 7.5621 - mse: 4.9492 - val_loss: 9.5867 - val_mse: 9.4944
Epoch 201/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0834 - mse: 5.3657 - val_loss: 9.9228 - val_mse: 8.8712
Epoch 202/300
3/3 [==============================] - 0s 44ms/step - loss: 7.9819 - mse: 5.8730 - val_loss: 6.9369 - val_mse: 5.4035
Epoch 203/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2018 - mse: 6.6509 - val_loss: 6.6017 - val_mse: 6.4409
Epoch 204/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0626 - mse: 5.8446 - val_loss: 7.6195 - val_mse: 5.8398
Epoch 205/300
3/3 [==============================] - 0s 44ms/step - loss: 7.4841 - mse: 5.9803 - val_loss: 5.4312 - val_mse: 5.9275
Epoch 206/300
3/3 [==============================] - 0s 45ms/step - loss: 10.4583 - mse: 7.9323 - val_loss: 3.3501 - val_mse: 5.7663
Epoch 207/300
3/3 [==============================] - 0s 45ms/step - loss: 7.6865 - mse: 6.7674 - val_loss: 10.0742 - val_mse: 9.1204
Epoch 208/300
3/3 [==============================] - 0s 44ms/step - loss: 4.9209 - mse: 4.2392 - val_loss: 6.5576 - val_mse: 5.7992
Epoch 209/300
3/3 [==============================] - 0s 44ms/step - loss: 7.2346 - mse: 5.6696 - val_loss: 6.3462 - val_mse: 5.6804
Epoch 210/300
3/3 [==============================] - 0s 44ms/step - loss: 7.9593 - mse: 6.9663 - val_loss: 8.1228 - val_mse: 6.8989
Epoch 211/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2790 - mse: 6.1696 - val_loss: 7.5715 - val_mse: 5.2189
Epoch 212/300
3/3 [==============================] - 0s 44ms/step - loss: 9.8760 - mse: 7.5468 - val_loss: 11.1424 - val_mse: 7.9058
Epoch 213/300
3/3 [==============================] - 0s 44ms/step - loss: 6.5000 - mse: 6.2887 - val_loss: 6.8403 - val_mse: 5.5015
Epoch 214/300
3/3 [==============================] - 0s 44ms/step - loss: 7.4208 - mse: 6.2013 - val_loss: 7.4014 - val_mse: 6.2264
Epoch 215/300
3/3 [==============================] - 0s 43ms/step - loss: 10.8831 - mse: 9.7888 - val_loss: 5.2128 - val_mse: 5.4366
Epoch 216/300
3/3 [==============================] - 0s 43ms/step - loss: 6.4472 - mse: 5.2531 - val_loss: 10.3587 - val_mse: 5.3475
Epoch 217/300
3/3 [==============================] - 0s 45ms/step - loss: 5.5915 - mse: 4.7526 - val_loss: 9.0630 - val_mse: 6.2578
Epoch 218/300
3/3 [==============================] - 0s 45ms/step - loss: 6.9784 - mse: 5.3738 - val_loss: 6.8700 - val_mse: 6.1320
Epoch 219/300
3/3 [==============================] - 0s 46ms/step - loss: 6.4888 - mse: 5.8919 - val_loss: 7.0403 - val_mse: 6.7036
Epoch 220/300
3/3 [==============================] - 0s 45ms/step - loss: 8.0198 - mse: 6.0281 - val_loss: 9.1551 - val_mse: 8.1851
Epoch 221/300
3/3 [==============================] - 0s 46ms/step - loss: 7.2909 - mse: 5.1607 - val_loss: 9.9547 - val_mse: 8.0840
Epoch 222/300
3/3 [==============================] - 0s 44ms/step - loss: 7.1801 - mse: 5.5609 - val_loss: 10.8702 - val_mse: 5.2251
Epoch 223/300
3/3 [==============================] - 0s 44ms/step - loss: 6.7604 - mse: 5.2044 - val_loss: 9.4794 - val_mse: 8.4065
Epoch 224/300
3/3 [==============================] - 0s 44ms/step - loss: 8.3857 - mse: 6.3107 - val_loss: 8.3080 - val_mse: 7.0704
Epoch 225/300
3/3 [==============================] - 0s 44ms/step - loss: 7.8317 - mse: 4.4223 - val_loss: 9.0380 - val_mse: 5.5653
Epoch 226/300
3/3 [==============================] - 0s 44ms/step - loss: 4.4017 - mse: 4.3834 - val_loss: 10.9552 - val_mse: 8.1195
Epoch 227/300
3/3 [==============================] - 0s 45ms/step - loss: 6.4105 - mse: 5.7148 - val_loss: 9.5750 - val_mse: 6.0690
Epoch 228/300
3/3 [==============================] - 0s 45ms/step - loss: 7.3873 - mse: 5.2755 - val_loss: 2.7578 - val_mse: 5.4740
Epoch 229/300
3/3 [==============================] - 0s 46ms/step - loss: 8.2679 - mse: 5.6667 - val_loss: 8.5237 - val_mse: 6.6218
Epoch 230/300
3/3 [==============================] - 0s 45ms/step - loss: 6.4582 - mse: 5.0134 - val_loss: 8.6942 - val_mse: 6.4959
Epoch 231/300
3/3 [==============================] - 0s 47ms/step - loss: 7.6259 - mse: 5.1605 - val_loss: 9.8088 - val_mse: 6.8792
Epoch 232/300
3/3 [==============================] - 0s 46ms/step - loss: 6.4649 - mse: 5.4101 - val_loss: 6.1127 - val_mse: 5.5908
Epoch 233/300
3/3 [==============================] - 0s 45ms/step - loss: 8.6300 - mse: 6.7713 - val_loss: 8.8780 - val_mse: 5.7439
Epoch 234/300
3/3 [==============================] - 0s 44ms/step - loss: 5.0307 - mse: 4.3643 - val_loss: 7.8695 - val_mse: 5.0391
Epoch 235/300
3/3 [==============================] - 0s 45ms/step - loss: 6.1283 - mse: 5.2728 - val_loss: 11.5862 - val_mse: 9.4715
Epoch 236/300
3/3 [==============================] - 0s 45ms/step - loss: 6.4127 - mse: 4.9264 - val_loss: 11.1432 - val_mse: 6.5559
Epoch 237/300
3/3 [==============================] - 0s 44ms/step - loss: 7.6183 - mse: 4.6827 - val_loss: 7.8968 - val_mse: 6.1322
Epoch 238/300
3/3 [==============================] - 0s 44ms/step - loss: 8.7014 - mse: 7.3674 - val_loss: 7.5932 - val_mse: 5.8326
Epoch 239/300
3/3 [==============================] - 0s 45ms/step - loss: 5.3651 - mse: 4.3362 - val_loss: 9.4369 - val_mse: 5.4515
Epoch 240/300
3/3 [==============================] - 0s 45ms/step - loss: 6.7646 - mse: 7.6652 - val_loss: 7.5766 - val_mse: 6.3867
Epoch 241/300
3/3 [==============================] - 0s 44ms/step - loss: 5.4430 - mse: 4.8100 - val_loss: 9.0149 - val_mse: 6.8995
Epoch 242/300
3/3 [==============================] - 0s 44ms/step - loss: 9.4065 - mse: 7.4135 - val_loss: 5.2571 - val_mse: 5.9473
Epoch 243/300
3/3 [==============================] - 0s 44ms/step - loss: 5.6483 - mse: 5.1142 - val_loss: 7.8800 - val_mse: 6.4278
Epoch 244/300
3/3 [==============================] - 0s 44ms/step - loss: 7.3029 - mse: 5.8437 - val_loss: 9.3372 - val_mse: 7.3537
Epoch 245/300
3/3 [==============================] - 0s 43ms/step - loss: 6.6154 - mse: 6.6490 - val_loss: 7.8267 - val_mse: 5.5152
Epoch 246/300
3/3 [==============================] - 0s 44ms/step - loss: 6.9162 - mse: 5.5001 - val_loss: 7.8506 - val_mse: 6.2732
Epoch 247/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0173 - mse: 4.8027 - val_loss: 7.1987 - val_mse: 6.7349
Epoch 248/300
3/3 [==============================] - 0s 44ms/step - loss: 7.1918 - mse: 5.4253 - val_loss: 5.9742 - val_mse: 6.1502
Epoch 249/300
3/3 [==============================] - 0s 44ms/step - loss: 6.3811 - mse: 5.1945 - val_loss: 5.3732 - val_mse: 6.5010
Epoch 250/300
3/3 [==============================] - 0s 44ms/step - loss: 6.9804 - mse: 4.7105 - val_loss: 6.6630 - val_mse: 5.2108
Epoch 251/300
3/3 [==============================] - 0s 45ms/step - loss: 5.3647 - mse: 3.9284 - val_loss: 11.4776 - val_mse: 10.3957
Epoch 252/300
3/3 [==============================] - 0s 44ms/step - loss: 5.5128 - mse: 4.3479 - val_loss: 9.2219 - val_mse: 6.0720
Epoch 253/300
3/3 [==============================] - 0s 43ms/step - loss: 6.2716 - mse: 4.3066 - val_loss: 6.7136 - val_mse: 6.9974
Epoch 254/300
3/3 [==============================] - 0s 44ms/step - loss: 6.6396 - mse: 5.1201 - val_loss: 9.3901 - val_mse: 7.7935
Epoch 255/300
3/3 [==============================] - 0s 43ms/step - loss: 6.2389 - mse: 4.4081 - val_loss: 3.9633 - val_mse: 5.7151
Epoch 256/300
3/3 [==============================] - 0s 46ms/step - loss: 7.6160 - mse: 4.9935 - val_loss: 5.4060 - val_mse: 5.5353
Epoch 257/300
3/3 [==============================] - 0s 45ms/step - loss: 7.1666 - mse: 5.8896 - val_loss: 8.8370 - val_mse: 8.8603
Epoch 258/300
3/3 [==============================] - 0s 44ms/step - loss: 4.8747 - mse: 4.2160 - val_loss: 5.4874 - val_mse: 5.9085
Epoch 259/300
3/3 [==============================] - 0s 44ms/step - loss: 8.8761 - mse: 4.7088 - val_loss: 6.8010 - val_mse: 5.1024
Epoch 260/300
3/3 [==============================] - 0s 44ms/step - loss: 6.3907 - mse: 6.7239 - val_loss: 10.4829 - val_mse: 9.2789
Epoch 261/300
3/3 [==============================] - 0s 44ms/step - loss: 9.2290 - mse: 7.1669 - val_loss: 6.5525 - val_mse: 5.9125
Epoch 262/300
3/3 [==============================] - 0s 45ms/step - loss: 6.8964 - mse: 5.4356 - val_loss: 10.0883 - val_mse: 10.0637
Epoch 263/300
3/3 [==============================] - 0s 45ms/step - loss: 5.4516 - mse: 4.5693 - val_loss: 10.9009 - val_mse: 10.6375
Epoch 264/300
3/3 [==============================] - 0s 44ms/step - loss: 5.9714 - mse: 4.0735 - val_loss: 7.4307 - val_mse: 5.9122
Epoch 265/300
3/3 [==============================] - 0s 44ms/step - loss: 8.2866 - mse: 5.4247 - val_loss: 7.6515 - val_mse: 5.8604
Epoch 266/300
3/3 [==============================] - 0s 44ms/step - loss: 5.6039 - mse: 4.9992 - val_loss: 8.6352 - val_mse: 7.0418
Epoch 267/300
3/3 [==============================] - 0s 44ms/step - loss: 6.6504 - mse: 5.4866 - val_loss: 9.0290 - val_mse: 5.5999
Epoch 268/300
3/3 [==============================] - 0s 44ms/step - loss: 6.3714 - mse: 4.8944 - val_loss: 5.7569 - val_mse: 6.0731
Epoch 269/300
3/3 [==============================] - 0s 44ms/step - loss: 7.8756 - mse: 7.0095 - val_loss: 7.0316 - val_mse: 7.4811
Epoch 270/300
3/3 [==============================] - 0s 44ms/step - loss: 6.4462 - mse: 4.6361 - val_loss: 5.4172 - val_mse: 5.9341
Epoch 271/300
3/3 [==============================] - 0s 45ms/step - loss: 5.3403 - mse: 5.5278 - val_loss: 6.7280 - val_mse: 6.7500
Epoch 272/300
3/3 [==============================] - 0s 45ms/step - loss: 5.4250 - mse: 4.9312 - val_loss: 7.6949 - val_mse: 7.1194
Epoch 273/300
3/3 [==============================] - 0s 46ms/step - loss: 7.7697 - mse: 6.2556 - val_loss: 8.9082 - val_mse: 6.8458
Epoch 274/300
3/3 [==============================] - 0s 44ms/step - loss: 7.8407 - mse: 5.8864 - val_loss: 7.2670 - val_mse: 7.7324
Epoch 275/300
3/3 [==============================] - 0s 44ms/step - loss: 6.4543 - mse: 4.6888 - val_loss: 7.7503 - val_mse: 7.3948
Epoch 276/300
3/3 [==============================] - 0s 44ms/step - loss: 6.3592 - mse: 4.9592 - val_loss: 6.6631 - val_mse: 6.9616
Epoch 277/300
3/3 [==============================] - 0s 43ms/step - loss: 6.9076 - mse: 3.6575 - val_loss: 8.3690 - val_mse: 6.3099
Epoch 278/300
3/3 [==============================] - 0s 45ms/step - loss: 7.1762 - mse: 5.5194 - val_loss: 10.1655 - val_mse: 9.7272
Epoch 279/300
3/3 [==============================] - 0s 45ms/step - loss: 6.5579 - mse: 4.6815 - val_loss: 10.9944 - val_mse: 10.2779
Epoch 280/300
3/3 [==============================] - 0s 44ms/step - loss: 6.8675 - mse: 5.1235 - val_loss: 7.8391 - val_mse: 5.5155
Epoch 281/300
3/3 [==============================] - 0s 45ms/step - loss: 5.6008 - mse: 4.2133 - val_loss: 7.4592 - val_mse: 5.7172
Epoch 282/300
3/3 [==============================] - 0s 43ms/step - loss: 7.0121 - mse: 6.4801 - val_loss: 5.7799 - val_mse: 5.5503
Epoch 283/300
3/3 [==============================] - 0s 44ms/step - loss: 6.2696 - mse: 6.0222 - val_loss: 6.9589 - val_mse: 5.8148
Epoch 284/300
3/3 [==============================] - 0s 44ms/step - loss: 7.2758 - mse: 4.9323 - val_loss: 8.5781 - val_mse: 6.6789
Epoch 285/300
3/3 [==============================] - 0s 45ms/step - loss: 4.6629 - mse: 3.8689 - val_loss: 4.9039 - val_mse: 5.7599
Epoch 286/300
3/3 [==============================] - 0s 44ms/step - loss: 3.7045 - mse: 4.4385 - val_loss: 5.2464 - val_mse: 5.2712
Epoch 287/300
3/3 [==============================] - 0s 44ms/step - loss: 7.0170 - mse: 5.3845 - val_loss: 5.2347 - val_mse: 5.5407
Epoch 288/300
3/3 [==============================] - 0s 44ms/step - loss: 6.3011 - mse: 4.9675 - val_loss: 7.3776 - val_mse: 5.5302
Epoch 289/300
3/3 [==============================] - 0s 43ms/step - loss: 6.8421 - mse: 5.4106 - val_loss: 6.4975 - val_mse: 5.5355
Epoch 290/300
3/3 [==============================] - 0s 43ms/step - loss: 6.9492 - mse: 4.9129 - val_loss: 7.2967 - val_mse: 5.5823
Epoch 291/300
3/3 [==============================] - 0s 43ms/step - loss: 7.4627 - mse: 5.4017 - val_loss: 6.2400 - val_mse: 6.2815
Epoch 292/300
3/3 [==============================] - 0s 44ms/step - loss: 7.7122 - mse: 5.3792 - val_loss: 5.3097 - val_mse: 6.5199
Epoch 293/300
3/3 [==============================] - 0s 42ms/step - loss: 5.5038 - mse: 5.9789 - val_loss: 4.8891 - val_mse: 6.0262
Epoch 294/300
3/3 [==============================] - 0s 44ms/step - loss: 6.5333 - mse: 4.9910 - val_loss: 8.1370 - val_mse: 10.6754
Epoch 295/300
3/3 [==============================] - 0s 45ms/step - loss: 5.9787 - mse: 4.8107 - val_loss: 7.3364 - val_mse: 6.2864
Epoch 296/300
3/3 [==============================] - 0s 44ms/step - loss: 6.2983 - mse: 6.4698 - val_loss: 5.2896 - val_mse: 5.2875
Epoch 297/300
3/3 [==============================] - 0s 44ms/step - loss: 7.6357 - mse: 5.6223 - val_loss: 7.4022 - val_mse: 5.6202
Epoch 298/300
3/3 [==============================] - 0s 44ms/step - loss: 4.9388 - mse: 4.3029 - val_loss: 7.6674 - val_mse: 5.9445
Epoch 299/300
3/3 [==============================] - 0s 44ms/step - loss: 6.7860 - mse: 4.1036 - val_loss: 6.3046 - val_mse: 6.0615
Epoch 300/300
3/3 [==============================] - 0s 43ms/step - loss: 9.6550 - mse: 8.6097 - val_loss: 10.5453 - val_mse: 7.9580
</pre>
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<div class="prompt input_prompt">In&nbsp;[56]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">vloss</span><span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">c_ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">c_ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">loss</span><span class="p">)),</span> <span class="n">loss</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;Training loss&quot;</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="n">c_ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">vloss</span><span class="p">)),</span> <span class="n">vloss</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;Val loss loss&quot;</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
<span class="n">c_ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">c_ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Epoch&#39;</span><span class="p">)</span>
<span class="n">c_ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39; Loss&#39;</span><span class="p">)</span>
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<pre>Text(0, 0.5, &#39; Loss&#39;)</pre>
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<h2 id="Show-Model-Uncertainty-Range-with-TF-Probability">Show Model Uncertainty Range with TF Probability<a class="anchor-link" href="#Show-Model-Uncertainty-Range-with-TF-Probability">&#182;</a></h2>
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<p><strong>Question 9</strong>: Now that we have trained a model with TF Probability layers, we can extract the mean and standard deviation for each prediction. Please fill in the answer for the m and s variables below. The code for getting the predictions is provided for you below.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feature_list</span> <span class="o">=</span> <span class="n">student_categorical_col_list</span> <span class="o">+</span> <span class="n">student_numerical_col_list</span>
<span class="n">diabetes_x_tst</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">d_test</span><span class="p">[</span><span class="n">feature_list</span><span class="p">])</span>
<span class="n">diabetes_yhat</span> <span class="o">=</span> <span class="n">diabetes_model</span><span class="p">(</span><span class="n">diabetes_x_tst</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">diabetes_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">diabetes_test_ds</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">student_utils</span> <span class="kn">import</span> <span class="n">get_mean_std_from_preds</span>
<span class="n">m</span><span class="p">,</span> <span class="n">s</span> <span class="o">=</span> <span class="n">get_mean_std_from_preds</span><span class="p">(</span><span class="n">diabetes_yhat</span><span class="p">)</span>
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<h2 id="Show-Prediction-Output">Show Prediction Output<a class="anchor-link" href="#Show-Prediction-Output">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prob_outputs</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;pred&quot;</span><span class="p">:</span> <span class="n">preds</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
    <span class="s2">&quot;actual_value&quot;</span><span class="p">:</span> <span class="n">d_test</span><span class="p">[</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
    <span class="s2">&quot;pred_mean&quot;</span><span class="p">:</span> <span class="n">m</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
    <span class="s2">&quot;pred_std&quot;</span><span class="p">:</span> <span class="n">s</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="p">}</span>
<span class="n">prob_output_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">prob_outputs</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prob_output_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
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    <tr style="text-align: right;">
      <th></th>
      <th>pred</th>
      <th>actual_value</th>
      <th>pred_mean</th>
      <th>pred_std</th>
    </tr>
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    <tr>
      <th>0</th>
      <td>3.656905</td>
      <td>2.0</td>
      <td>1.794353</td>
      <td>0.701268</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3.550768</td>
      <td>2.0</td>
      <td>1.794353</td>
      <td>0.701268</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2.140946</td>
      <td>9.0</td>
      <td>5.169634</td>
      <td>0.699321</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2.492111</td>
      <td>4.0</td>
      <td>1.820046</td>
      <td>0.701209</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.365967</td>
      <td>13.0</td>
      <td>6.559491</td>
      <td>0.700102</td>
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<h2 id="Convert-Regression-Output-to-Classification-Output-for-Patient-Selection">Convert Regression Output to Classification Output for Patient Selection<a class="anchor-link" href="#Convert-Regression-Output-to-Classification-Output-for-Patient-Selection">&#182;</a></h2>
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<p><strong>Question 10</strong>: Given the output predictions, convert it to a binary label for whether the patient meets the time criteria or does not (HINT: use the mean prediction numpy array). The expected output is a numpy array with a 1 or 0 based off if the prediction meets or doesnt meet the criteria.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">student_utils</span> <span class="kn">import</span> <span class="n">get_student_binary_prediction</span>
<span class="n">student_binary_prediction</span> <span class="o">=</span> <span class="n">get_student_binary_prediction</span><span class="p">(</span><span class="n">prob_output_df</span><span class="p">,</span> <span class="s1">&#39;pred_mean&#39;</span><span class="p">)</span>
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<h3 id="Add-Binary-Prediction-to-Test-Dataframe">Add Binary Prediction to Test Dataframe<a class="anchor-link" href="#Add-Binary-Prediction-to-Test-Dataframe">&#182;</a></h3>
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<p>Using the student_binary_prediction output that is a numpy array with binary labels, we can use this to add to a dataframe to better visualize and also to prepare the data for the Aequitas toolkit. The Aequitas toolkit requires that the predictions be mapped to a binary label for the predictions (called 'score' field) and the actual value (called 'label_value').</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">add_pred_to_test</span><span class="p">(</span><span class="n">test_df</span><span class="p">,</span> <span class="n">pred_np</span><span class="p">,</span> <span class="n">demo_col_list</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">demo_col_list</span><span class="p">:</span>
        <span class="n">test_df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
    <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pred_np</span>
    <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;time_in_hospital&#39;</span><span class="p">]</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="mi">1</span> <span class="k">if</span> <span class="n">x</span> <span class="o">&gt;=</span><span class="mi">5</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">test_df</span>

<span class="n">pred_test_df</span> <span class="o">=</span> <span class="n">add_pred_to_test</span><span class="p">(</span><span class="n">d_test</span><span class="p">,</span> <span class="n">student_binary_prediction</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pred_test_df</span><span class="p">[[</span><span class="s1">&#39;patient_nbr&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;time_in_hospital&#39;</span><span class="p">,</span> <span class="s1">&#39;score&#39;</span><span class="p">,</span> <span class="s1">&#39;label_value&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th></th>
      <th>patient_nbr</th>
      <th>gender</th>
      <th>race</th>
      <th>time_in_hospital</th>
      <th>score</th>
      <th>label_value</th>
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      <th>0</th>
      <td>114691221</td>
      <td>Female</td>
      <td>Caucasian</td>
      <td>2.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2944836</td>
      <td>Female</td>
      <td>Caucasian</td>
      <td>2.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>62785548</td>
      <td>Male</td>
      <td>Caucasian</td>
      <td>9.0</td>
      <td>1</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>111481452</td>
      <td>Female</td>
      <td>Caucasian</td>
      <td>4.0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>11282175</td>
      <td>Female</td>
      <td>Caucasian</td>
      <td>13.0</td>
      <td>1</td>
      <td>1</td>
    </tr>
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<h2 id="Model-Evaluation-Metrics">Model Evaluation Metrics<a class="anchor-link" href="#Model-Evaluation-Metrics">&#182;</a></h2>
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<p><strong>Question 11</strong>: Now it is time to use the newly created binary labels in the 'pred_test_df' dataframe to evaluate the model with some common classification metrics. Please create a report summary of the performance of the model and be sure to give the ROC AUC, F1 score(weighted), class precision and recall scores.</p>

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<p>For the report please be sure to include the following three parts:</p>
<ul>
<li><p>With a non-technical audience in mind, explain the precision-recall tradeoff in regard to how you have optimized your model.</p>
</li>
<li><p>What are some areas of improvement for future iterations?</p>
</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># AUC, F1, precision and recall</span>
<span class="c1"># Summary</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="p">,</span> <span class="n">roc_curve</span> <span class="p">,</span><span class="n">f1_score</span> <span class="p">,</span> <span class="n">recall_score</span> <span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span><span class="n">auc</span> <span class="p">,</span> <span class="n">roc_auc_score</span>

<span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
<span class="nb">print</span> <span class="p">(</span><span class="n">tn</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">tp</span><span class="p">)</span>
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<pre>65 10 12 32
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#fpr, tpr, thresholds = roc_curve(pred_test_df[&#39;label_value&#39;], pred_tesZt_df[&#39;score&#39;])</span>
<span class="n">fpr</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">thresholds</span> <span class="o">=</span> <span class="n">roc_curve</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">prob_output_df</span><span class="p">[</span><span class="s1">&#39;pred_mean&#39;</span><span class="p">])</span>
<span class="n">aucs</span><span class="o">=</span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</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;fpr&quot;</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;tpr&quot;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">)</span>
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<pre>[&lt;matplotlib.lines.Line2D at 0x7f4aa4b64450&gt;]</pre>
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SJJUieqcia+Btidmc9m5jBwN7B+kn5fBb4HvFphLZIkdZwqQ3wJsKdue7DW9q6IWAL8ErCxwjokSepIVYZ4TNKWDdu/D9ySmaPve6CIDRGxPSK27927d8YKlCSpZL0VHnsQWFa3vRR4qaHPAHB3RAAsAq6LiJHM/EF9p8zcBGwCGBgYaPxFQJKkrlRliG8DVkXESuBF4HrgC/UdMnPlsY8j4i7gzxsDXJIkTa6yEM/MkYi4mfGrznuAOzNzV0TcVNvveXBJkk5ClTNxMnMLsKWhbdLwzsx/XWUtkiR1Gu/YJklSoQxxSZIKZYhLklQoQ1ySpEIZ4pIkFcoQlySpUIa4JEmFMsQlSSqUIS5JUqEMcUmSCmWIS5JUKENckqRCGeKSJBXKEJckqVCGuCRJhTLEJUkqlCEuSVKhDHFJkgpliEuSVChDXJKkQhnikiQVyhCXJKlQhrgkSYUyxCVJKpQhLklSoQxxSZIKZYhLklQoQ1ySpEIZ4pIkFcoQlySpUL3tLqDd3j4ywvDI2IS2kbFsUzWSJDWvq0P87155i7V/8ACjk4T2wHlntKEiSZKa19UhvvetI4yOJTf+/ApWnPWhCfuuuWhxm6qSJKk5XR3ix6y95Fyu+OhZ7S5DkqQT4oVtkiQVyhCXJKlQhrgkSYUyxCVJKpQhLklSoQxxSZIKVWmIR8TaiHg6InZHxK2T7P/liNhRez0YEZdXWY8kSZ2kshCPiB7gdmAdsBq4ISJWN3R7DvhkZl4G3AZsqqoeSZI6TZUz8TXA7sx8NjOHgbuB9fUdMvPBzHy9tvkQsLTCeiRJ6ihVhvgSYE/d9mCtbSpfAn5UYT2SJHWUKm+7GpO0Tfp4sIj4FOMhfvUU+zcAGwCWL18+U/VJklS0Kmfig8Cyuu2lwEuNnSLiMuAOYH1mvjbZgTJzU2YOZObA4sU+mESSJKg2xLcBqyJiZUT0A9cDm+s7RMRy4PvAr2TmMxXWIklSx6lsOT0zRyLiZuBeoAe4MzN3RcRNtf0bgd8AzgK+HREAI5k5UFVNkiR1kkofRZqZW4AtDW0b6z7+VeBXq6xBkqRO5R3bJEkqlCEuSVKhDHFJkgpliEuSVChDXJKkQhnikiQVyhCXJKlQhrgkSYUyxCVJKpQhLklSoQxxSZIKZYhLklQoQ1ySpEIZ4pIkFcoQlySpUIa4JEmFMsQlSSqUIS5JUqEMcUmSCmWIS5JUKENckqRCGeKSJBXKEJckqVCGuCRJhTLEJUkqlCEuSVKhDHFJkgpliEuSVChDXJKkQhnikiQVyhCXJKlQhrgkSYUyxCVJKpQhLklSoQxxSZIKZYhLklQoQ1ySpEIZ4pIkFcoQlySpUIa4JEmFMsQlSSpUpSEeEWsj4umI2B0Rt06yPyLim7X9OyLiY1XWI0lSJ6ksxCOiB7gdWAesBm6IiNUN3dYBq2qvDcAfVlWPJEmdpsqZ+Bpgd2Y+m5nDwN3A+oY+64Hv5LiHgNMj4sMV1iRJUseoMsSXAHvqtgdrbSfah4jYEBHbI2L73r17Z7xQSZJKVGWIxyRt+QH6kJmbMnMgMwcWL148I8UBXLbsdP78q1dzyZKFM3ZMSZJapbfCYw8Cy+q2lwIvfYA+lTl1bi+XGuCSpEJVORPfBqyKiJUR0Q9cD2xu6LMZ+GLtKvUrgQOZ+XKFNUmS1DEqm4ln5khE3AzcC/QAd2bmroi4qbZ/I7AFuA7YDRwCbqyqHkmSOk2Vy+lk5hbGg7q+bWPdxwl8pcoaJEnqVN6xTZKkQhnikiQVyhCXJKlQhrgkSYUyxCVJKpQhLklSoQxxSZIKFeNv1S5HROwF/n4GD7kI2DeDx+tWjuPJcwxPnmN48hzDk1fFGJ6Xmcc9PKS4EJ9pEbE9MwfaXUfpHMeT5xiePMfw5DmGJ6+VY+hyuiRJhTLEJUkqlCEOm9pdQIdwHE+eY3jyHMOT5xievJaNYdefE5ckqVTOxCVJKlTXhHhErI2IpyNid0TcOsn+iIhv1vbviIiPtaPO2ayJMfzl2tjtiIgHI+LydtQ5m003hnX9PhERoxHx+VbWV4pmxjEiromIRyNiV0Tc3+oaZ7smvp8XRsSfRcRjtTG8sR11zlYRcWdEvBoRO6fY35pMycyOfwE9wP8DPgr0A48Bqxv6XAf8CAjgSuD/tLvu2fRqcgyvAs6ofbzOMTzxMazrdx+wBfh8u+ueba8mvxZPB54Alte2z2533bPp1eQY/hfg67WPFwP7gf521z5bXsA/Bj4G7Jxif0sypVtm4muA3Zn5bGYOA3cD6xv6rAe+k+MeAk6PiA+3utBZbNoxzMwHM/P12uZDwNIW1zjbNfN1CPBV4HvAq60sriDNjOMXgO9n5gsAmelYTtTMGCawICICOJXxEB9pbZmzV2b+lPExmUpLMqVbQnwJsKdue7DWdqJ9utmJjs+XGP8tVO+ZdgwjYgnwS8DGFtZVmma+Fi8EzoiIv46IRyLiiy2rrgzNjOG3gJ8DXgIeB349M8daU15HaEmm9M70AWepmKSt8bL8Zvp0s6bHJyI+xXiIX11pReVpZgx/H7glM0fHJ0CaRDPj2At8HPgMMB/4WUQ8lJnPVF1cIZoZw2uBR4FPA+cDP46IBzLzzaqL6xAtyZRuCfFBYFnd9lLGf7s80T7drKnxiYjLgDuAdZn5WotqK0UzYzgA3F0L8EXAdRExkpk/aE2JRWj2+3lfZh4EDkbET4HLAUN8XDNjeCPwtRw/wbs7Ip4DLgYebk2JxWtJpnTLcvo2YFVErIyIfuB6YHNDn83AF2tXFF4JHMjMl1td6Cw27RhGxHLg+8CvOOOZ1LRjmJkrM3NFZq4A/hT4sgF+nGa+n38I/KOI6I2IU4ArgCdbXOds1swYvsD4SgYRcQ5wEfBsS6ssW0sypStm4pk5EhE3A/cyflXmnZm5KyJuqu3fyPiVwNcBu4FDjP8Wqpomx/A3gLOAb9dmkiPpgxTe1eQYahrNjGNmPhkRW4EdwBhwR2ZO+lagbtTk1+JtwF0R8TjjS8O3ZKZPN6uJiD8BrgEWRcQg8JtAH7Q2U7xjmyRJheqW5XRJkjqOIS5JUqEMcUmSCmWIS5JUKENckqRCGeKSjhMR/y4inoyIP253LZKm5lvMJB0nIp5i/K57zzXRtyczR1tQlqQGzsQlTRARGxl/ROXmiDgQEf8jIu6LiL+LiH9b63NNRPxVRHyX8YdjSGoDZ+KSjhMRzzN+H/ebGX+q2pXAh4C/ZfwWphcCfwFc2sxsXVI1nIlLms4PM/Od2i03/4rxZ1EDPGyAS+1liEuaTuNy3bHtg60uRNJEhrik6ayPiHkRcRbjD3zY1uZ6JNUY4pKm8zDj578fAm7LzBl/JrKkD8YL2yRNKSJ+C3g7M3+33bVIOp4zcUmSCuVMXJKkQjkTlySpUIa4JEmFMsQlSSqUIS5JUqEMcUmSCmWIS5JUqP8PcNSfjMSiN+kAAAAASUVORK5CYII=
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">auc</span><span class="p">(</span><span class="n">fpr</span><span class="p">,</span><span class="n">tpr</span><span class="p">)</span>
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<pre>0.9025757575757576</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">],</span><span class="n">average</span><span class="o">=</span><span class="s1">&#39;weighted&#39;</span><span class="p">)</span> <span class="c1"># for the decided threshold value of &gt;=5</span>
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<pre>0.796969696969697</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">f1_sc</span><span class="o">=</span><span class="n">f1_score</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">])</span>
<span class="n">f1_sc</span>
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<pre>0.7441860465116279</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">recall_sc</span><span class="o">=</span><span class="n">recall_score</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">])</span>
<span class="n">recall_sc</span>
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<pre>0.7272727272727273</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">prec_sc</span><span class="o">=</span><span class="n">precision_score</span><span class="p">(</span><span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;label_value&#39;</span><span class="p">],</span> <span class="n">pred_test_df</span><span class="p">[</span><span class="s1">&#39;score&#39;</span><span class="p">])</span>
<span class="n">prec_sc</span>
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<pre>0.7619047619047619</pre>
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<p><strong>Student Answer:</strong></p>
<ul>
<li>As can be seen from the numbers above, the model has been trained for a relatively better precision than recall. </li>
<li>Precision indicates how many patients of the ones targeted by the algorithm do spend time in hospital for at-least 5 days. A high value for precision is thus very beneficial in this scenario as it would be highly unwanted if the new diabetes drug was administered to a patient who didn’t need hospitalisation of atleast 5 days. </li>
<li>At the same time, we wouldn’t want to miss the patents who would satisfy the hospital stay criteria. A high recall rate would help with this. </li>
</ul>
<p>Areas of improvement –</p>
<ol>
<li>Increase precision further (through more exhaustive training) while at the same time having a good recall </li>
<li>Further analysis of data, for a better performing model.</li>
</ol>
<p>There is also a report.pdf in the repository for Model performance and bias.</p>

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<h1 id="7.-Evaluating-Potential-Model-Biases-with-Aequitas-Toolkit">7. Evaluating Potential Model Biases with Aequitas Toolkit<a class="anchor-link" href="#7.-Evaluating-Potential-Model-Biases-with-Aequitas-Toolkit">&#182;</a></h1>
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<h2 id="Prepare-Data-For-Aequitas-Bias-Toolkit">Prepare Data For Aequitas Bias Toolkit<a class="anchor-link" href="#Prepare-Data-For-Aequitas-Bias-Toolkit">&#182;</a></h2>
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<p>Using the gender and race fields, we will prepare the data for the Aequitas Toolkit.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Aequitas</span>
<span class="kn">from</span> <span class="nn">aequitas.preprocessing</span> <span class="kn">import</span> <span class="n">preprocess_input_df</span>
<span class="kn">from</span> <span class="nn">aequitas.group</span> <span class="kn">import</span> <span class="n">Group</span>
<span class="kn">from</span> <span class="nn">aequitas.plotting</span> <span class="kn">import</span> <span class="n">Plot</span>
<span class="kn">from</span> <span class="nn">aequitas.bias</span> <span class="kn">import</span> <span class="n">Bias</span>
<span class="kn">from</span> <span class="nn">aequitas.fairness</span> <span class="kn">import</span> <span class="n">Fairness</span>

<span class="n">ae_subset_df</span> <span class="o">=</span> <span class="n">pred_test_df</span><span class="p">[[</span><span class="s1">&#39;race&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">,</span> <span class="s1">&#39;score&#39;</span><span class="p">,</span> <span class="s1">&#39;label_value&#39;</span><span class="p">]]</span>
<span class="n">ae_df</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">preprocess_input_df</span><span class="p">(</span><span class="n">ae_subset_df</span><span class="p">)</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">Group</span><span class="p">()</span>
<span class="n">xtab</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">get_crosstabs</span><span class="p">(</span><span class="n">ae_df</span><span class="p">)</span>
<span class="n">absolute_metrics</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">list_absolute_metrics</span><span class="p">(</span><span class="n">xtab</span><span class="p">)</span>
<span class="n">clean_xtab</span> <span class="o">=</span> <span class="n">xtab</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">aqp</span> <span class="o">=</span> <span class="n">Plot</span><span class="p">()</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">Bias</span><span class="p">()</span>
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<pre>/opt/conda/lib/python3.7/site-packages/aequitas/group.py:143: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[&#39;score&#39;] = df[&#39;score&#39;].astype(float)
/opt/conda/lib/python3.7/site-packages/aequitas/group.py:30: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  divide = lambda x, y: x / y if y != 0 else pd.np.nan
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<pre>model_id, score_thresholds 1 {&#39;rank_abs&#39;: [42]}
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<h2 id="Reference-Group-Selection">Reference Group Selection<a class="anchor-link" href="#Reference-Group-Selection">&#182;</a></h2>
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<p>Below we have chosen the reference group for our analysis but feel free to select another one.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># test reference group with Caucasian Male</span>
<span class="n">bdf</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">get_disparity_predefined_groups</span><span class="p">(</span><span class="n">clean_xtab</span><span class="p">,</span> 
                    <span class="n">original_df</span><span class="o">=</span><span class="n">ae_df</span><span class="p">,</span> 
                    <span class="n">ref_groups_dict</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;race&#39;</span><span class="p">:</span><span class="s1">&#39;Caucasian&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">:</span><span class="s1">&#39;Male&#39;</span>
                                     <span class="p">},</span> 
                    <span class="n">alpha</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> 
                    <span class="n">check_significance</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>


<span class="n">f</span> <span class="o">=</span> <span class="n">Fairness</span><span class="p">()</span>
<span class="n">fdf</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">get_group_value_fairness</span><span class="p">(</span><span class="n">bdf</span><span class="p">)</span>
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<pre>get_disparity_predefined_group()
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<pre>/opt/conda/lib/python3.7/site-packages/aequitas/bias.py:368: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  df = df.replace(pd.np.inf, fill_divbyzero)
/opt/conda/lib/python3.7/site-packages/aequitas/fairness.py:32: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  self.fair_eval = lambda tau: lambda x: pd.np.nan if pd.np.isnan(x) else \
/opt/conda/lib/python3.7/site-packages/aequitas/fairness.py:45: FutureWarning: The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead
  self.high_level_pair_eval = lambda col1, col2: lambda x: pd.np.nan if (pd.np.isnan(x[col1]) and pd.np.isnan(x[col2])) \
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<h2 id="Race-and-Gender-Bias-Analysis-for-Patient-Selection">Race and Gender Bias Analysis for Patient Selection<a class="anchor-link" href="#Race-and-Gender-Bias-Analysis-for-Patient-Selection">&#182;</a></h2>
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<p><strong>Question 12</strong>: For the gender and race fields, please plot two metrics that are important for patient selection below and state whether there is a significant bias in your model across any of the groups along with justification for your statement.</p>

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<p><strong>Student Answer:</strong> The FPR, FNR  disparity plots for Race and Gender show that there is significant bias in the model for both.</p>
<ol>
<li><p>For race with Caucasian as reference, there is a bias towards the other races - African American and the "other" category which are more likely to incorrectly selected for the trial. However, looking at the FNR rates, there seems to be not much difference between Caucasians and African Americans for being incorrectly missed out (not selected) for the trial.</p>
</li>
<li><p>For Gender with Male as references, there is a bias towards Female. The false positive rates have a huge bias towards females i.e.females are more likely to be incorrectly identified for the trial of the medicine. The False negative rate shows a bias towards females too i.e. females are twice as more likely to be incorrectly missed out of the trial.</p>
</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Plot two metrics</span>

<span class="c1"># Is there significant bias in your model for either race or gender?</span>
<span class="n">fpr_disparity</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_disparity</span><span class="p">(</span><span class="n">bdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fpr_disparity&#39;</span><span class="p">,</span> <span class="n">attribute_name</span><span class="o">=</span><span class="s1">&#39;race&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fnr_disparity_race</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_disparity</span><span class="p">(</span><span class="n">bdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fnr_disparity&#39;</span><span class="p">,</span> <span class="n">attribute_name</span><span class="o">=</span><span class="s1">&#39;race&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fpr_disparity</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_disparity</span><span class="p">(</span><span class="n">bdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fpr_disparity&#39;</span><span class="p">,</span> <span class="n">attribute_name</span><span class="o">=</span><span class="s1">&#39;gender&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fnr_disparity_gender</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_disparity</span><span class="p">(</span><span class="n">bdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fnr_disparity&#39;</span><span class="p">,</span> <span class="n">attribute_name</span><span class="o">=</span><span class="s1">&#39;gender&#39;</span><span class="p">)</span>
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<h2 id="Fairness-Analysis-Example---Relative-to-a-Reference-Group">Fairness Analysis Example - Relative to a Reference Group<a class="anchor-link" href="#Fairness-Analysis-Example---Relative-to-a-Reference-Group">&#182;</a></h2>
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<p><strong>Question 13</strong>: Earlier we defined our reference group and then calculated disparity metrics relative to this grouping. Please provide a visualization of the fairness evaluation for this reference group and analyze whether there is disparity.</p>

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<div class="prompt input_prompt">In&nbsp;[77]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Reference group fairness plot</span>
<span class="n">fpr_fairness</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_fairness_group</span><span class="p">(</span><span class="n">fdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fpr&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<p><strong>Student Respone:</strong> The Red bars show the unfair evaluation - towards Females w.r.t males and 
    towards the African Americans and other races as compared to Caucasians</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fnr_fairness</span> <span class="o">=</span> <span class="n">aqp</span><span class="o">.</span><span class="n">plot_fairness_group</span><span class="p">(</span><span class="n">fdf</span><span class="p">,</span> <span class="n">group_metric</span><span class="o">=</span><span class="s1">&#39;fnr&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<p><strong>Student Respone:</strong> The Red bars show the unfair evaluation - towards Females w.r.t males.</p>

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