Progetto - Riccardo Romagnoli.html

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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://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
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/**
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 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
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 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
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@-webkit-keyframes fadeIn {
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}
.tooltiptext {
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}
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#find-and-replace #replace-preview pre {
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.terminal-app {
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}
.terminal-app #header {
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  background: black;
  padding: 0.4em;
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}
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<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="Progetto-Data-Intensive">Progetto Data Intensive<a class="anchor-link" href="#Progetto-Data-Intensive">&#182;</a></h1><p><strong>Programmazione di Applicazioni Data Intensive</strong><br>
Laurea in Ingegneria e Scienze Informatiche<br>
DISI - Università di Bologna, Cesena</p>
<p>Riccardo Romagnoli <code>832198</code><br>
<code>riccardo.romagnoli3@studio.unibo.it</code></p>

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</div>
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<h2 id="Introduzione">Introduzione<a class="anchor-link" href="#Introduzione">&#182;</a></h2><p>In questo progetto analizzeremo un caso di studio che mi riguarda personalmente nella mia attività di venditore terzo in Amazon.</p>
<p>L'obbiettivo consiste nel ricercare un modello predittivo di vendite di un determinato prodotto. Questa informazione verrà poi utilizzata per stimare la durata residua dell'inventario attualmente disponibile in magazzino, quindi sapere in quanti giorni il prodotto andrà <strong>out of stock</strong> così da organizzare eventuali rifornimenti.</p>
<p>Amazon fornisce ai venditori dei dati separati per <strong>marketplace</strong> (Amazon IT, Amazon ES ...), per questo progetto condidereremo solo i dati relativi al marcato Spagnolo poichè unirli non porterebbe un aumento di valore consistente. Inoltre l'unione delle due sorgenti dati potrebbe far perdere caratteristiche differenti per il paese di provenienza.</p>

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<h2 id="1---Panoramica">1 - Panoramica<a class="anchor-link" href="#1---Panoramica">&#182;</a></h2><p>Scaricato il documento dal sito riservato dei venditori amazon ci troviamo con <em>BusinessReport-ES.csv</em> da cui potremo iniziare ad eseguire operazioni di pulizia di istanze e drop di colonne non rilevanti.</p>

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<h3 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h3><ul>
<li>Importare i package necessari per il progetto in questione</li>
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<div class=" highlight hl-ipython3"><pre><span></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">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">sklearn</span>
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<ul>
<li>In Jupyter, configurare l'output di matplotlib integrato nel notebook</li>
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<li>Disabilito i warning</li>
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<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">class</span> <span class="nc">bcolors</span><span class="p">:</span>
    <span class="n">HEADER</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[95m&#39;</span>
    <span class="n">OKBLUE</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[94m&#39;</span>
    <span class="n">OKGREEN</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[92m&#39;</span>
    <span class="n">WARNING</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[93m&#39;</span>
    <span class="n">FAIL</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[91m&#39;</span>
    <span class="n">ENDC</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[0m&#39;</span>
    <span class="n">BOLD</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[1m&#39;</span>
    <span class="n">UNDERLINE</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\033</span><span class="s1">[4m&#39;</span>
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<p>Lettura dei due file utilizzando <code>read_csv</code> impostando come indice la data e come etichette la prima riga (default).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;BusinessReport-ES.csv&quot;</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="s2">&quot;Data&quot;</span><span class="p">,</span> <span class="n">parse_dates</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Data&quot;</span><span class="p">],</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">tail</span><span class="p">()</span>
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      <th></th>
      <th>Vendite prodotti ordinati</th>
      <th>Vendite di prodotti ordinati - B2B</th>
      <th>Totale prodotti ordinati</th>
      <th>Tot. articoli ordinati - B2B</th>
      <th>Prezzo medio di vendita</th>
      <th>Prezzo medio di vendita - B2B</th>
      <th>Visualizzazioni pagina</th>
      <th>Sessioni</th>
      <th>Percentuale Buy Box</th>
      <th>Conteggio medio delle offerte</th>
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      <th>2020-05-23</th>
      <td>143,32 €</td>
      <td>0,00 €</td>
      <td>7</td>
      <td>0</td>
      <td>21,24 €</td>
      <td>21,24 €</td>
      <td>148</td>
      <td>85</td>
      <td>80%</td>
      <td>6</td>
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      <th>2020-05-24</th>
      <td>210,04 €</td>
      <td>0,00 €</td>
      <td>10</td>
      <td>0</td>
      <td>21,24 €</td>
      <td>21,24 €</td>
      <td>142</td>
      <td>85</td>
      <td>100%</td>
      <td>6</td>
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      <th>2020-05-25</th>
      <td>84,05 €</td>
      <td>0,00 €</td>
      <td>4</td>
      <td>0</td>
      <td>21,24 €</td>
      <td>21,24 €</td>
      <td>86</td>
      <td>44</td>
      <td>89%</td>
      <td>6</td>
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      <th>2020-05-26</th>
      <td>89,47 €</td>
      <td>0,00 €</td>
      <td>4</td>
      <td>0</td>
      <td>21,24 €</td>
      <td>21,24 €</td>
      <td>91</td>
      <td>34</td>
      <td>100%</td>
      <td>5</td>
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      <th>2020-05-27</th>
      <td>93,87 €</td>
      <td>0,00 €</td>
      <td>4</td>
      <td>0</td>
      <td>21,24 €</td>
      <td>21,24 €</td>
      <td>87</td>
      <td>45</td>
      <td>99%</td>
      <td>6</td>
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<h3 id="Dati-Disponibili">Dati Disponibili<a class="anchor-link" href="#Dati-Disponibili">&#182;</a></h3><ul>
<li><code>Vendite prodotti ordinati</code>: Tot fatturato dalle vendite</li>
<li><code>Vendite di prodotti ordinati - B2B</code>: Tot fatturato dalle vendite B2B</li>
<li><code>Totale prodotti ordinati</code>: Totale dei prodotti ordinati </li>
<li><code>Tot. articoli ordinati - B2B</code>: Totale dei prodotti ordinati B2B</li>
<li><code>Prezzo medio di vendita</code>: Media dei prezzi dei prodotti venduti</li>
<li><code>Prezzo medio di vendita - B2B</code>: Media dei prezzi dei prodotti venduti B2B</li>
<li><code>Visualizzazioni pagina</code>: Comparizioni nei risultati di ricerca</li>
<li><code>Sessioni</code>: Per sessione s'intende la visita da parte di un utente alle pagine Amazon del venditore. Qualsiasi attività effettuata entro un periodo di tempo di 24 ore è considerata come una unica sessione.</li>
<li><code>Percentuale Buy Box</code>: Si tratta della percentuale di visualizzazioni di pagine in cui compare la Buy Box (link "Aggiungi al carrello") e che consente ai clienti di aggiungere il prodotto al carrello.</li>
<li><code>Conteggio medio delle offerte</code>: Si tratta di una metrica di conversione percentuale indicante la quantità di unità acquistate relativamente al numero di persone che hanno visualizzato i prodotti. (Impreciso, spesso a 0 quando invece vengono generate vendite)</li>
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<p>Da una prima analisi possiamo subito decidere di unificare i dati relativi al B2B poichè le restanti feature sono influenzate anche da visitatori <em>Business</em>. Quindi non eseguire questa operazione ci farebbe mantenere, per esempio, delle sessioni e visualizzazioni che si sono convertite in vendite di cui però non teniamo traccia.</p>
<p>Possiamo eliminare le colonne che fanno riferimento al fatturato giornaliero poichè non rilevante, come anche il prezzo medio delle vendite B2B poichè duplicato in <code>Prezzo medio di vendita</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;Vendite prodotti ordinati&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Vendite</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,[</span><span class="s2">&quot;Vendite prodotti ordinati&quot;</span><span class="p">,</span>
                                                      <span class="s2">&quot;Vendite di prodotti ordinati - B2B&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="n">Vendite</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span>
    <span class="s2">&quot;Vendite prodotti ordinati&quot;</span><span class="p">,</span> 
    <span class="s2">&quot;Vendite di prodotti ordinati - B2B&quot;</span><span class="p">,</span> 
    <span class="s2">&quot;Tot. articoli ordinati - B2B&quot;</span><span class="p">,</span> 
    <span class="s2">&quot;Prezzo medio di vendita - B2B&quot;</span><span class="p">,</span>
    <span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
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<p>Ora, analizzando il tipo dei dati delle colonne dei nostri DF constatiamo che è errato a causa del simbolo dell'euro, delle percentuali e le virgole. Proseguiamo trasformando questi dati in tipo float dopo aver correttamente sostituito i caratteri problematici.</p>

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<pre>Totale prodotti ordinati          int64
Prezzo medio di vendita          object
Visualizzazioni pagina            int64
Sessioni                          int64
Percentuale Buy Box              object
Conteggio medio delle offerte     int64
dtype: object</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;Prezzo medio di vendita&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;Prezzo medio di vendita&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;\€&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="p">:</span> <span class="s1">&#39;.&#39;</span><span class="p">},</span> <span class="n">regex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;Percentuale Buy Box&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;Percentuale Buy Box&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;\%&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="s1">&#39;,&#39;</span><span class="p">:</span> <span class="s1">&#39;.&#39;</span><span class="p">},</span> <span class="n">regex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
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<pre>Totale prodotti ordinati           int64
Prezzo medio di vendita          float64
Visualizzazioni pagina             int64
Sessioni                           int64
Percentuale Buy Box              float64
Conteggio medio delle offerte      int64
dtype: object</pre>
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<span class="n">Vendite</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;ordini&#39;</span><span class="p">,</span><span class="s1">&#39;prezzo_medio&#39;</span><span class="p">,</span><span class="s1">&#39;visualizzazioni&#39;</span><span class="p">,</span><span class="s1">&#39;sessioni&#39;</span><span class="p">,</span><span class="s1">&#39;per_buybox&#39;</span><span class="p">,</span><span class="s1">&#39;offerte_attive&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">));</span>
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<p>A questo punto è necessario specificare più nel dettaglio di che tipo di prodotto stiamo parlando e a cosa si riferisce ogni riga. Il propdotto in questione ha la caratteristica di essere suddiviso in taglie (S M L XL ...) e quindi una taglia potrebbe essere <em>out-of-stock</em>, non generando vendite e allo stesso tempo generando volume di dati raccolti (<strong>ES: visualizzo la pagina ma non posso comprare perchè la taglia non è disponibile</strong>). In ogni instanza abbiamo i dati totali generati da tutte le taglie.</p>
<p>Quindi il Prossimo filtro applicato rimuoverà le righe con offerte attive &lt; 3 e vendite &lt; 2. Questo perchè questi giorni molto probabilmente sono influenzati dalla disponibilità in magazzino del prodotto. Un'altra accortezza di correzzione dei dati sta nel correggere il valore offerte_attive = 0 quando in realta sono presenti delle vendite per quei giorni. (Purtroppo questo errore è attribuibile a calcoli errati di Amazon).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Rimozione Istanze &quot;Critiche&quot;</span>
<span class="n">to_remove</span> <span class="o">=</span> <span class="p">((</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;offerte_attive&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">3</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">))</span>
<span class="n">Vendite</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">Vendite</span><span class="p">[</span><span class="n">to_remove</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1">#Assegnamento valore medio di offerte medie attive quando è queste sono 0 e in realtà ci sono vendite registrate</span>
<span class="n">Vendite</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;offerte_attive&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">),</span>
            <span class="s2">&quot;offerte_attive&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;offerte_attive&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
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<h3 id="A---Il-problema">A - Il problema<a class="anchor-link" href="#A---Il-problema">&#182;</a></h3><p>Finita la fase di preparazione dei dati possiamo proseguire analizzando più nel dettaglio il nostro dataset facendo tutte le considerazioni del caso.</p>
<p>Come anticipato, il seguente progetto si pone come obbiettivo finale la stima dei giorni di inventario rimanendi sulla base di una quantità attuale di prodotti in magazzino. Questo è possibile predicendo le vendite giornaliere (variabile discreta) dei giorni a seguire stimando le possibili visualizzazioni che l'annuncio puo avere sulla base del trend settimanale e stagionale.</p>
<p>Le feature principali, e quindi le possibili variabili predittive, sono:</p>
<ul>
<li><code>Prezzo medio di vendita</code>: Media dei prezzi dei prodotti venduti</li>
<li><code>Visualizzazioni pagina</code>: Comparizioni nei risultati di ricerca</li>
<li><code>Sessioni</code>: Per sessione s'intende la visita da parte di un utente alle pagine Amazon del venditore. Qualsiasi attività effettuata entro un periodo di tempo di 24 ore è considerata come una unica sessione.</li>
<li><code>Percentuale Buy Box</code>: Si tratta della percentuale di visualizzazioni di pagine in cui compare la Buy Box (link "Aggiungi al carrello") e che consente ai clienti di aggiungere il prodotto al carrello.</li>
<li><code>Conteggio medio delle offerte</code>: Il numero medio di offerte in vendita, nell'intervallo tempo. Il calcolo si basa sul numero totale di offerte e il numero totale di giorni nell'intervallo di tempo (1 giorno). </li>
</ul>
<p>Per quanto riguarda il numeo di istanza, attualmente si hanno dati rilevanti per 1080 giorni, quasi tre anni solare, che dovranno poi essere suddivisi in set di Training, Validation e Test. Questi spaziano da maggio 2017 a maggio 2020</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">Vendite</span><span class="p">)</span>
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<pre>1080</pre>
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<h3 id="B---Analisi-esplorativa">B - Analisi esplorativa<a class="anchor-link" href="#B---Analisi-esplorativa">&#182;</a></h3><p>Iniziamo ottenendo le statistiche generali sulle variabili sopra commentate</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ordini</th>
      <th>prezzo_medio</th>
      <th>visualizzazioni</th>
      <th>sessioni</th>
      <th>per_buybox</th>
      <th>offerte_attive</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>1080.000000</td>
      <td>1080.000000</td>
      <td>1080.000000</td>
      <td>1080.000000</td>
      <td>1080.000000</td>
      <td>1080.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>5.210185</td>
      <td>21.797769</td>
      <td>94.873148</td>
      <td>58.720370</td>
      <td>72.525000</td>
      <td>5.794756</td>
    </tr>
    <tr>
      <th>std</th>
      <td>3.608415</td>
      <td>3.674146</td>
      <td>58.868459</td>
      <td>36.294077</td>
      <td>20.380153</td>
      <td>0.937160</td>
    </tr>
    <tr>
      <th>min</th>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>7.000000</td>
      <td>6.000000</td>
      <td>8.000000</td>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>3.000000</td>
      <td>21.240000</td>
      <td>53.750000</td>
      <td>34.000000</td>
      <td>57.000000</td>
      <td>6.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>4.000000</td>
      <td>21.240000</td>
      <td>83.000000</td>
      <td>52.500000</td>
      <td>72.000000</td>
      <td>6.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>7.000000</td>
      <td>23.240000</td>
      <td>121.000000</td>
      <td>73.000000</td>
      <td>91.000000</td>
      <td>6.000000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>26.000000</td>
      <td>31.990000</td>
      <td>309.000000</td>
      <td>201.000000</td>
      <td>100.000000</td>
      <td>7.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

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<p>Con le giuste supposizioni possiamo identificale la feature <code>visualizzazioni</code> inclusa nelle <code>sessioni</code> poichè quest'ultima ne è un sotto insieme. Intuitamente, la sola comparsa nei risultati di ricerca non apporta un valore aggiuntivo della gia presente <code>sessione</code> che identifica la visita della pagina del prodotto, da dove si può finalizzare l'acquisto.</p>
<p>Nei dati sopra descritti si evinche che gli ordini non assumono un range di valori molto sparsi, consideranzo il primo quartile di 3 ordini e il terzo quartile di 7 ordini.</p>

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<div class="prompt input_prompt">In&nbsp;[16]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span> <span class="c1">#change your figure size</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;sessioni&quot;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span> <span class="mi">50</span><span class="p">);</span>
</pre></div>

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<p>Aggiungiamo la colonna giorno della settimana per ordinare i grafici box</p>

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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s2">&quot;giorno&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Vendite</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">dayofweek</span>
<span class="n">columns_my_order</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Monday&#39;</span><span class="p">,</span> <span class="s1">&#39;Tusday&#39;</span><span class="p">,</span> <span class="s1">&#39;Wednesday&#39;</span><span class="p">,</span> <span class="s1">&#39;Thursday&#39;</span><span class="p">,</span> <span class="s2">&quot;Friday&quot;</span><span class="p">,</span> <span class="s2">&quot;Saturday&quot;</span><span class="p">,</span> <span class="s2">&quot;Sunday&quot;</span><span class="p">]</span>

<span class="n">Vendite</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s2">&quot;ordini&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;giorno&quot;</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">8</span><span class="p">));</span>
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<p>Osservando il grafico si evince che nonostanze durante i giorni intermedi della settimana si verifichino più valori outlier, i weekend sono i giorni che registrano più vendite.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">column</span><span class="o">=</span><span class="s2">&quot;sessioni&quot;</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="s2">&quot;giorno&quot;</span><span class="p">,</span> <span class="n">showmeans</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">));</span>
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jGtqrtnKRy+urX2xm7zZ6rqzO7+M5Pc3G2/MclDlj38wUk+vTblbn7T09OZmprK/Px8Dh48mPn5+UxNTWV6errv0gAAgIE77ghiLQ0VziZZbK29bNldb0nyjCQv6r6/edn211TVy7K0SM0jkvzFWha9mR1eiGbv3r1ZXFzM2NhYZmZmLFADAAD07kSmmP5Akqcn+cuquqbb9twsBcPXV9VUkr9N8q+SpLX24ap6fZKPZGkF1F+wgumRJicnMzk5afoCAACwoRw3ILbW9mXl8wqTZM8xHjOTxLKcAAAAm8gJnYMIAABb3dzcXHbu3Jk9e/Zk586dmZub67skWHcndZkLAADYiubm5jI9PZ3Z2dkcOnQo27Zty9TUVJJYK4JBMYIIAHAXjCoNw8zMTGZnZzMxMZHt27dnYmIis7OzmZlx1hTDYgQRAOAYjCoNx+LiYnbv3n3Ett27d2dxcbGniqAfRhABAI7BqNJwjI2NZd++fUds27dvX8bGxnqqCPohIAIAHINRpeGYnp7O1NRU5ufnc/DgwczPz2dqairT09N9lwbryhRTAIBjODyqNDExcec2o0pb0+Epw3v37s3i4mLGxsYyMzNjKjGDYwQRAOAYjCoNy+TkZK699tpcddVVufbaa4VDBskIIgDAMUxOTubP/uzP8vjHPz633357TjvttDzrWc8SHIAtS0AEADiGubm5XHnllfnjP/7jI1Yx/f7v/34hEdiSTDEFADgGq5gOi2teghFEAIBjsorpcLjmJSwxgggAcAyujTccRothiYAIAHAMVjEdDqPFsERA7IH57QCwOUxOTmZmZiZ79+7NYx/72Ozdu9e18bYoo8WwxDmI68z8dgDYXCYnJzM5OZmFhYWMj4/3XQ4jcni0+PDfaIdHi00xZWgExHW2fH774V80s7Oz2bt3r4AIANCTw3+H7d27N4uLixkbGzNazCAJiOvM/HYAgI3JaDE4B3Hdmd8OW5fziwE2N+/jYARx3ZnfDluT84sBNjfv47DECOI6sxoabE2unwWwuXkfhyVGEHtgfjtsPc4vBtjcvI/DEiOIAGvA+cUAm9vY2Fie8pSn5J73vGcmJiZyz3veM095ylO8jzM4AiLAGjh8fvH8/HwOHjx45/nF09PTfZcGwAk466yzcsUVV+S8887LH/3RH+W8887LFVdckbPOOqvv0mBdmWIKsAZcPwtgc3vXu96Vc889N+9+97vzO7/zOxkbG8u5556byy+/vO/SYF0JiABrxPnFAJvX7bffnksuuSTf9E3fdOf7+Fe+8pW8+tWv7rs0WFemmAKsEdfPAti8TjvttFx88cVHbLv44otz2mmn9VQR9MMIIsAacP0sgM3tWc96Vi688MIkyTnnnJOXvexlufDCC3PBBRf0XBmsLwERYA0sv37W4alJs7Oz2bt3r4AIsAm84hWvSJI897nPze23357TTjstF1xwwZ3bYShMMQVYA66fBbD5veIVr8hXv/rVzM/P56tf/apwyCAJiABrwHUQAYCtQEAEWAOugwgAbAXOQQRYA66DOCxzc3OZmZm5s6+np6f1NQBbgoAIsEZcB3EYrFgLwFZmiikAnITlK9Zu3749ExMTmZ2dzczMTN+lAcCqCYgAcBKsWAvAViYgAsBJsGItAFuZgAgAJ8GKtQBsZRapAYCTYMVaALYyAREATpIVawHYqkwxBQAAIMkJBMSqurSqbq6qa5dte11VXdN93VBV13Tbz66q25bdd/EoiwcAAGDtnMgI4u8ledzyDa21n26tPbK19sgkb0jyxmV3f+zwfa21C9auVAAAGJ25ubns3Lkze/bsyc6dOzM3N9d3SbDujnsOYmvt3VV19kr3VVUleUqSf7G2ZQEAwPqZm5vL9PR0Zmdnc+jQoWzbti1TU1NJYhEqBmW15yD+8ySfaa39zbJtD6+qD1TVu6rqn6/y+AAAMHIzMzOZnZ3NxMREtm/fnomJiczOzmZmZqbv0mBdrXYV08kky8feb0ry0Nba56rq0UmuqKrvaq198egHVtX5Sc5Pkh07dmRhYWGVpWw++/fvH+TzHiJ9PSz6ezj09bDo761tcXExhw4dysLCwp19fejQoSwuLur3AdDHX3fKAbGqtif5qSSPPryttXZ7ktu721dX1ceSfEeS9x39+NbaJUkuSZJdu3a1IS4Tbnn04dDXw6K/h0NfD4v+3trGxsaysLCQK6644s5rnD7pSU/K2NiYft/q3n6lPl5mNSOIP5zkr1prNx7eUFUPSHJra+1QVX1bkkckuX6VNQIAwEhNTEzkxS9+cV784hfnnHPOyUc+8pFceOGFueACay4yLMcNiFU1l2Q8yRlVdWOS57fWZpM8NUdOL02SH0zya1V1MMmhJBe01m5d25IBAGBtzc/P58ILL8yll1565wjihRdemCuuuKLv0mBdncgqpisu29Rae+YK296QpcteAADAprG4uJgPfOAD+fVf//U7pxMfOHAgv/Ebv9F3abCuVruKKafANXYAADaWsbGx7Nu374ht+/bty9jYWE8VQT9Wu4opJ8k1dgAANp7p6elMTU3d+Tfa/Px8pqamXOaCwREQ19nya+wcnr4wOzubvXv3CogAAD05/HfY3r177zwHcWZmxt9nDI6AuM4WFxeze/fuI7bt3r07i4uLPVUEAECyFBInJydd0oRBcw7iOjO/HQAA2KgExHV2eH77/Px8Dh48eOf89unp6b5LAwAABs4U03VmfjsAALBRCYg9ML8dAADYiEwxBQAAIImACAAAQEdABAAAIIlzEAEA2IKqqtf2W2u9tg+nyggiAABbTmvtlL8eduFbV/V44ZDNTEAEAAAgiYAIAABAR0AEAAAgiYAIAABAR0AEAAAgiYAIAABAR0AEAAAgiYAIIzU3N5edO3dmz5492blzZ+bm5vouCQAAjml73wXAVjU3N5fp6enMzs7m0KFD2bZtW6amppIkk5OTPVcHAADfyAgijMjMzExmZ2czMTGR7du3Z2JiIrOzs5mZmem7NAAAWJGACCOyuLiY3bt3H7Ft9+7dWVxc7KkiAAC4awIijMjY2Fj27dt3xLZ9+/ZlbGysp4oAAOCuCYgwItPT05mamsr8/HwOHjyY+fn5TE1NZXp6uu/SAABgRRapgRE5vBDN3r17s7i4mLGxsczMzFigBgCADUtAhBGanJzM5ORkFhYWMj4+3nc5AABwl0wxBYCT5BqnAGxVRhAB4CS4xikAW5mACAAnYWZmJk972tOOOL/4aU97mnOMAdgSBEQAOAkf+chH8uUvfzmXXnrpnSOI5513Xj7xiU/0XRoArJpzEAHgJNzjHvfI3r17MzExke3bt2diYiJ79+7NPe5xj75LA4BVM4IIACfha1/7Wl75ylfmUY96VA4dOpT5+fm88pWvzNe+9rW+SwOAVRMQAeAknHPOOXnSk570DecgXnHFFX2XBgCrJiACwEmYnp5ecRXTmZmZvksDgFUTEAHgJBxeqXT5CKIVTLe2ubm5zMzM3Nnf09PT+hvYsgREADhJk5OTmZyczMLCQsbHx/suhxFy3UtgaKxiCgBwDDMzM5mdnT1i1drZ2VlTioEtS0AEADiGxcXF7N69+4htu3fvzuLiYk8VAYyWgAgAJ2lubi47d+7Mnj17snPnzszNzfVdEiMyNjaWffv2HbFt3759GRsb66kigNFyDiIAnATnpA3L9PR0pqam7uzv+fl5q9YCW9pxRxCr6tKqurmqrl227QVV9amquqb7+rFl9z2nqq6rqo9W1WNHVTjARmNUaRickzYsk5OTmZmZyd69e/PYxz42e/futWotsKWdyAji7yV5ZZLfP2r7y1trv7l8Q1Wdk+SpSb4ryYOS/GlVfUdr7dAa1AqwYRlVGg7npA2PVWuBITnuCGJr7d1Jbj3B4z0xyWtba7e31j6e5Lokj1lFfQCbglGl4XBOGgBb2WrOQXx2Vf1skvcl+eXW2ueTnJXkPcv2ubHb9g2q6vwk5yfJjh07srCwsIpSNqf9+/cP8nkPkb7e+hYXF3Po0KEsLCzc2d+HDh3K4uKivt9invzkJ+fcc8/Nr/zKr+ThD394Xv7yl+clL3lJpqam9PUW5718WPT1sOjvrzvVgPjbSV6YpHXfX5rkvCS1wr5tpQO01i5JckmS7Nq1qw1xyoapKsOhr7e+sbGxbNu2LePj43f29/z8fMbGxvT9FjM+Pp5zzjknMzMzWVxczNjYWF760peaSjwA3ssH5O1X6ush0d9HOKXLXLTWPtNaO9RauyPJq/L1aaQ3JnnIsl0fnOTTqysRYOM7vNLh/Px8Dh48eOdKh9PT032XxghMTk7m2muvzVVXXZVrr71WOARgyzilEcSqOrO1dlP345OTHF7h9C1JXlNVL8vSIjWPSPIXq64SYIM7HBD27t1756iSlQ4BgM3muAGxquaSjCc5o6puTPL8JONV9cgsTR+9IcnPJ0lr7cNV9fokH0lyMMkvWMEUGAorHQIAm91xA2JrbaWPv2fvYv+ZJJbtAwAA2GRO6RxEAAAAth4BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEQAAgCQCIgAAAB0BEUZobm4uO3fuzJ49e7Jz587Mzc31XRIAJ8l7OTAk2/suALaqubm5TE9PZ3Z2NocOHcq2bdsyNTWVJJmcnOy5OgBOhPdyYGiMIMKIzMzMZHZ2NhMTE9m+fXsmJiYyOzubmZmZvksD4AR5LweGRkCEEVlcXMzu3buP2LZ79+4sLi72VBEAJ8t7OTA0AiKMyNjYWPbt23fEtn379mVsbKynigA4Wd7LgaEREGFEpqenMzU1lfn5+Rw8eDDz8/OZmprK9PR036UBcIK8lwNDc9xFaqrq0iQ/nuTm1trObttLkvxEkq8l+ViSn2ut/X1VnZ1kMclHu4e/p7V2wQjqhg3v8OIFe/fuzeLiYsbGxjIzM2NRA4BNxHs5MDQnMoL4e0ked9S2dybZ2Vr7x0n+Oslzlt33sdbaI7sv4ZBBm5yczLXXXpurrroq1157rT8oADYh7+XAkBw3ILbW3p3k1qO2vaO1drD78T1JHjyC2gAAAFhHa3EdxPOSvG7Zzw+vqg8k+WKS57XW/s9KD6qq85OcnyQ7duzIwsLCGpSyuezfv3+Qz3uI9PWw6O/h0NfDor+HRV8Pi/7+ulUFxKqaTnIwyau7TTcleWhr7XNV9egkV1TVd7XWvnj0Y1trlyS5JEl27drVxsfHV1PKprSwsJAhPu8h0tfDor+HQ18Pi/4ekLdfqa+HRH8f4ZRXMa2qZ2Rp8ZpzW2stSVprt7fWPtfdvjpLC9h8x1oUCgAAwGidUkCsqscluTDJT7bWvrJs+wOqalt3+9uSPCLJ9WtRKAAAAKN1Ipe5mEsynuSMqroxyfOztGrpaUneWVXJ1y9n8YNJfq2qDiY5lOSC1tqtKx4YAACADeW4AbG1ttJazrPH2PcNSd6w2qIAAABYf6d8DiIAAABbi4AIAABAEgERAACAjoAIAABAEgERAACAjoAIAABAEgERAACAjoAIAABAEgERAACAzva+C9jsqqq3tltrvbUNAABsPUYQV6m1dspfD7vwrat6PAAAwFoSEAEAAEgiIAIAANAREAEAAEgiIMJIzc3NZefOndmzZ0927tyZubm5vksCAIBjsoopjMjc3Fymp6czOzubQ4cOZdu2bZmamkqSTE5O9lwdAAB8IyOIMCIzMzOZnZ3NxMREtm/fnomJiczOzmZmZqbv0gAAYEUCIozI4uJidu/efcS23bt3Z3FxsaeKAADgrgmIMCJjY2PZt2/fEdv27duXsbGxnioCAIC7JiDCiExPT2dqairz8/M5ePBg5ufnMzU1lenp6b5LAwCAFVmkBkbk8EI0e/fuzeLiYsbGxjIzM2OBGgAANiwBEUZocnIykyWNHwcAABX9SURBVJOTWVhYyPj4eN/lcIKqqtf2W2u9tg8ADJcppjBCroO4ObXWVvX1sAvfuqrHAwD0xQgijIjrIMLmYMQYAL7OCCKMiOsgwubQ52ixcAjARiMgwoi4DiIAAJuNgAgj4jqIAABsNs5BhBE5fB3Ew+cgHr4OoimmAP1wvinA8QmIMCKugwiwsaw2oJ190ZW54UVPWKNqADYmARFGyHUQAQDYTJyDCAAAQBIjiHDCnLsCAMBWZwQRTpBrpQEAsNUZQQQAAHr1Pb/6jnzhtgO9tX/2RVeue5v3vdfd88Hn/+i6t3s8AiIAANCrL9x2oLdVgvtaTLCPUHoiTDEFAAAgiYAIAABAR0AEAAAgiYAIAABAR0AEAAAgiYAIAABA57gBsaouraqbq+raZdvuX1XvrKq/6b5/y7L7nlNV11XVR6vqsaMqHAAAgLV1IiOIv5fkcUdtuyjJVa21RyS5qvs5VXVOkqcm+a7uMf9PVW1bs2oBAAAYmeMGxNbau5PcetTmJya5rLt9WZInLdv+2tba7a21jye5Lslj1qhWAAAARuhUz0Hc0Vq7KUm67w/stp+V5JPL9rux2wYAAMAGt32Nj1crbGsr7lh1fpLzk2THjh1ZWFhY41I2h6E+7yHS18Oiv4dDXw+L/h4Ofb3++nrN9+/f31vbG/Hf2akGxM9U1ZmttZuq6swkN3fbb0zykGX7PTjJp1c6QGvtkiSXJMmuXbva+Pj4KZayib39ygzyeQ+Rvh4W/T0c+npY9Pdw6Ov11+NrvrCw0E/bG/Tf2alOMX1Lkmd0t5+R5M3Ltj+1qk6rqocneUSSv1hdiQAAAKyH444gVtVckvEkZ1TVjUmen+RFSV5fVVNJ/jbJv0qS1tqHq+r1ST6S5GCSX2itHRpR7QAAAKyh4wbE1trkMe7ac4z9Z5LMrKYoAAAA1t+pTjEFAABgixEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASJJs77sAAAA42vf86jvyhdsO9Nb+2Rdd2Uu7973X3fPB5/9oL21DIiACALABfeG2A7nhRU/ope2FhYWMj4/30nZfwRQOM8UUAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAne2n+sCq+s4kr1u26duS/Jck90vyrCS3dNuf21p72ylXCAAAwLo45YDYWvtokkcmSVVtS/KpJG9K8nNJXt5a+801qRAAAIB1sVZTTPck+Vhr7RNrdDwAAICRuuUrt+S3/u638tnbPtt3KRvGKY8gHuWpSeaW/fzsqvrZJO9L8suttc8f/YCqOj/J+UmyY8eOLCwsrFEpm8tQn/cQ6eth0d/Doa+HRX+vr75e7/379/fa10P9d9bH837d516X62+/Pv/5bf85P/2tP73u7W/Evl51QKyqeyT5ySTP6Tb9dpIXJmnd95cmOe/ox7XWLklySZLs2rWrjY+Pr7aUzeftV2aQz3uI9PWw6O/h0NfDor/XV4+v98LCQn99PdR/Zz0871u+ckve+8b3pqXlvbe9Ny/83hfmjHudsX4FbNC+Xosppo9P8v7W2meSpLX2mdbaodbaHUleleQxa9AGAADAmrn4QxfnjnZHkuSOdkcu/uDFPVe0MaxFQJzMsumlVXXmsvuenOTaNWgDAABgTdzylVvy5uvenAN3HEiSHLjjQK647grnImaVAbGqvinJjyR547LN/62q/rKqPpRkIsm/X00bAAAAa2n56OFhRhGXrOocxNbaV5J861Hbnr6qigAAoCeHV7XcedvO9T0fjXX1wZs/eOfo4WEH7jiQa26+pqeKNo61WsUUAAA2vYs/dHGuv/36XPzBi/O8f/a8vsthRC7/ycvvvN3rokQb0FpdBxEAADa1w+eltTTnozFYAiIAAMSqlpAIiAAAYFVL6Az+HMTv+dV35Au3HTj+jiNy9kVX9tLufe9193zw+T/aS9sAa8n7OLAW7mpVS+ciMiSDD4hfuO1AbnjRE3ppu88TYvv6gwZgrXkfB9aCVS1hyeADIgCweRgxZlSsaglLBEQAYNMwYgwwWhapAQAAIImACAAAQMcUU2BLcp4SAMDJExCBLcl5SgAAJ88UUwAAAJIIiAAAAHQERAAAAJIIiAAAAHQsUsOg9LmypVUtAQBWdp+xi/Ldl13UXwGXrX+T9xlLkn4W1LsrAiKD0tfKlla1BAA4ti8tvmhwq49v1L/RTDEFAAAgiYAIAABAR0AEAAAgiYAIAABAR0AEAAAgiYAIAABAx2UuAADYcIZ4Xbxk414bj+EQEAEA2HCGeF28ZONeG4/hMMUUAACAJAIiAAAAHQERAACAJAIiAAAAHQERAACAJAIiAAAAHQGxJ7d85Zb81t/9Vj5722f7LgUAACCJgNibiz90ca6//fpc/MGL+y4FADgOH+wCQyEg9uCWr9ySN1/35rS0XHHdFX7ZAMAG54NdYCgExB5c/KGLc0e7I0lyR7vDLxsA2MB8sAsMiYC4zg7/kjlwx4EkyYE7DvhlA7AJmXI4HD7YBYZEQFxny3/JHOaXDcDmY8rhMPhgFxgaAXGdffDmD975S+awA3ccyDU3X9NTRYyaUQbYekw5HA4f7AJDs73vAobm8p+8/M7bCwsLGR8f768Y1sXyUYbn/bPn9V0OI3b4A4Gdt+3MGfc6o+9yGJGVphz6/70+7jN2Ub77sot6reHAHQfyuo++Lq/76OvWrc37jCXJE9atPWC4BEQYoaNHGS74nguEhi3OBwJb37GmHPr/vT6+tPii3PCifoJSnx/snn3Rlb20CwzPqqaYVtUNVfWXVXVNVb2v23b/qnpnVf1N9/1b1qZU2HwsbDAsph0OgymHAGxla3EO4kRr7ZGttV3dzxcluaq19ogkV3U/w+BY2GB4fCAwDM4lB2ArG8UU0ycmGe9uX5ZkIcmFI2gHNrS7GmUw9XDrMe1wOJxLDsBWttoRxJbkHVV1dVWd323b0Vq7KUm67w9cZRuwKRllGBbTDgGArWC1I4g/0Fr7dFU9MMk7q+qvTvSBXaA8P0l27NiRhYWFVZZyanpfDe2yfpq9z1iysHDvfhrv2Xr9W3v2Nz87+eal2/v378/pp5++7jX01d5GsZ7P+/9++v+u+IHAvuv3ZeGr61dHMrz+9j4+PH39G9+/f3+v/7+G9n870ddDM8T+3pB93Vpbk68kL0jyH5N8NMmZ3bYzk3z0eI999KMf3frysAvf2lvb8/PzvbXd5/PuU1/PW1+vP/+3h0NfD4v+Hg59PSxD7O8+n3OS97VjZLNTHkGsqnsnuVtr7Uvd7R9N8mtJ3pLkGUle1H1/86nHV1hbvY409DjKMMRrZw15VGmI/Q0ArI3VTDHdkeRNVXX4OK9prb29qt6b5PVVNZXkb5P8q9WXCWujr+tnuXbW+nOtNACAk3fKAbG1dn2S71lh++eS7FlNUQAAAKy/tbgOIgAAAFuAgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAEASAREAAICOgAgAAECSZHvfBQAAwErOvujK/hp/ez9t3/ded++lXThMQAQAYMO54UVP6K3tsy+6stf2h2poHwhs1A8DBEQAAKBXPhDYOJyDCAAAQBIBEQAAgI6ACAAAQBIBEQAAgI5FagCATWVoKx0mG3e1Q2DrERABgE3DSocAoyUgMji9ffLsU2cAADY4AZFB6euTX586AwCwGVikBgAAgCQCIgAAAB0BEQAAgCQCIgAAAB2L1MT1lAA2O+/jALA2Bh8QXU8JYHPzPg4Aa8cUUwAAAJIIiAAAAHQGP8UU2LqclwYAcHIERGBLcl4aAMDJM8UUAACAJAIiAAAAnVMOiFX1kKqar6rFqvpwVf1it/0FVfWpqrqm+/qxtSsXAACAUVnNOYgHk/xya+39VXWfJFdX1Tu7+17eWvvN1ZcHAADAejnlgNhauynJTd3tL1XVYpKz1qowAAAA1teanINYVWcneVSSP+82PbuqPlRVl1bVt6xFGwAAAIzWqi9zUVWnJ3lDkl9qrX2xqn47yQuTtO77S5Oct8Ljzk9yfpLs2LEjCwsLqy1lUxrq8x4ifT0s+ns49PWw6O/h0NfDor+/blUBsarunqVw+OrW2huTpLX2mWX3vyrJW1d6bGvtkiSXJMmuXbva+Pj4akrZnN5+ZQb5vIdIXw+L/h4OfT0s+ns49PWw6O8jrGYV00oym2SxtfayZdvPXLbbk5Nce+rlAQAAsF5WM4L4A0menuQvq+qabttzk0xW1SOzNMX0hiQ/v6oKAQAAWBerWcV0X5Ja4a63nXo5AAAA9GVNVjEFAABg8xMQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0NnedwEAG01Vrf4YLz71x7bWVt0+AMCpMIIIcJTW2qq+5ufnV/V4AIC+GEEEAAbB7ACA4zOCCAAMgtkBAMdnBHGVVvtppE8iAQDWXp9/oyX+TmPzMoK4Sj6JBADYePr8G83faWxmAiIAAABJBEQAAAA6AiIAAABJBEQAAAA6VjGFE2Q1NACAjcc1TteWEUQ4QVZDAwDYeFzjdG0ZQQRg0MwOAICvM4IIwKCZHQAAXycgAgAAkERABAAAoCMgAgAAkERABAAAoCMgAgAAkERABAAAoCMgAgAAkGSEAbGqHldVH62q66rqolG1AwAAwNoYSUCsqm1J/meSxyc5J8lkVZ0zirYAAABYG6MaQXxMkutaa9e31r6W5LVJnjiitgAAAFgD1Vpb+4NW/cskj2ut/evu56cn+d7W2rOX7XN+kvOTZMeOHY9+7Wtfu+Z1bHT79+/P6aef3ncZrAN9PSz6ezj09bDo7+HQ18MyxP6emJi4urW2a6X7to+ozVph2xFJtLV2SZJLkmTXrl1tfHx8RKVsXAsLCxni8x4ifT0s+ns49PWw6O/h0NfDor+PNKoppjcmeciynx+c5NMjagsAAIA1MKqA+N4kj6iqh1fVPZI8NclbRtQWAAAAa2AkU0xbawer6tlJ/iTJtiSXttY+PIq2AAAAWBujOgcxrbW3JXnbqI4PAADA2hrVFFMAAAA2GQERAACAJCO6DuJJF1F1S5JP9F1HD85I8tm+i2Bd6Oth0d/Doa+HRX8Ph74eliH298Naaw9Y6Y4NERCHqqred6wLVLK16Oth0d/Doa+HRX8Ph74eFv19JFNMAQAASCIgAgAA0BEQ+3VJ3wWwbvT1sOjv4dDXw6K/h0NfD4v+XsY5iAAAACQxgggAAEBHQOxBVT2uqj5aVddV1UV918PoVNWlVXVzVV3bdy2MVlU9pKrmq2qxqj5cVb/Yd02MTlXds6r+oqo+2PX3r/ZdE6NVVduq6gNV9da+a2G0quqGqvrLqrqmqt7Xdz2MTlXdr6our6q/6n5/f1/fNW0Eppius6raluSvk/xIkhuTvDfJZGvtI70WxkhU1Q8m2Z/k91trO/uuh9GpqjOTnNlae39V3SfJ1Ume5P/21lRVleTerbX9VXX3JPuS/GJr7T09l8aIVNV/SLIryTe31n6873oYnaq6Icmu1trQros3OFV1WZL/01r73aq6R5Jvaq39fd919c0I4vp7TJLrWmvXt9a+luS1SZ7Yc02MSGvt3Ulu7bsORq+1dlNr7f3d7S8lWUxyVr9VMSptyf7ux7t3Xz5x3aKq6sFJnpDkd/uuBVgbVfXNSX4wyWyStNa+JhwuERDX31lJPrns5xvjj0jYUqrq7CSPSvLn/VbCKHVTDq9JcnOSd7bW9PfW9VtJ/lOSO/ouhHXRkryjqq6uqvP7LoaR+bYktyT5X9308d+tqnv3XdRGICCuv1phm0+dYYuoqtOTvCHJL7XWvth3PYxOa+1Qa+2RSR6c5DFVZRr5FlRVP57k5tba1X3Xwrr5gdbaP0ny+CS/0J0uwtazPck/SfLbrbVHJflyEmuDREDsw41JHrLs5wcn+XRPtQBrqDsX7Q1JXt1ae2Pf9bA+uilJC0ke13MpjMYPJPnJ7ry01yb5F1X1h/2WxCi11j7dfb85yZuydHoQW8+NSW5cNvvj8iwFxsETENffe5M8oqoe3p0M+9Qkb+m5JmCVukVLZpMsttZe1nc9jFZVPaCq7tfdvleSH07yV/1WxSi01p7TWntwa+3sLP3O/t+ttZ/puSxGpKru3S00lm664Y8msRL5FtRa+7skn6yq7+w27UliYbksDa2yjlprB6vq2Un+JMm2JJe21j7cc1mMSFXNJRlPckZV3Zjk+a212X6rYkR+IMnTk/xld15akjy3tfa2HmtidM5Mclm3MvXdkry+tebyB7D57UjypqXP/LI9yWtaa2/vtyRGaG+SV3eDNtcn+bme69kQXOYCAACAJKaYAgAA0BEQAQAASCIgAgAA0BEQAQAASCIgAgAA0BEQAWAFVfVrVfXDfdcBAOvJZS4AYA1U1bbW/v/27p01qjCIw/jzh4BBAoJ+gRSCIhhQUgYRLMXGCxYiYiPYpxIU8ykUC020UBHESykISgohmmBsgo3a23iPRRiLfYtlIUjMxoj7/JrDnBn2nVMOczhbK5vdhyRJ6+EGUZI08JJcTLKU5EmS20kmk0wnOd7yh5IsJHmT5HqSLe3++ySXkswCJ1o8lWS+1e5udduTPEiymORFkrFNfFxJklblgChJGmhJxoFjwD7gKDDekx8GpoGTVbUXGALOd5UsV9VEVd1p8ceq2g9cASbbvSlgoarGgAvAzQ16HEmS1sUBUZI06CaAh1X1o6q+AI978ruAd1X1tsUzwIGu/N2e+vvt+goY7TrjFkBVPQV2JNnWn/YlSeofB0RJ0qDLOvPfeuKf7bpCZ9u42m/4EQBJ0j/HAVGSNOhmgSNJhpOMAId78kvAaJKdLT4NPFvjGc+BUwBJDtJ5DfXzn7csSdLGGPp9iSRJ/6+qmkvyCHgNfABeAp+68stJzgL3kgwBc8DVNR5zGbiRZBH4DpzpR++SJPWbf3MhSRp4SUaq6muSrXS2feeqan6z+5Ik6W9zgyhJElxLsgcYBmYcDiVJg8oNoiRJkiQJ8CM1kiRJkqTGAVGSJEmSBDggSpIkSZIaB0RJkiRJEuCAKEmSJElqHBAlSZIkSQD8ArZ5cJaB350gAAAAAElFTkSuQmCC
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<p>Come poteva essere prevedibile, anche i dati relativi alle sessioni sono più marcati durante i weekend.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;sessioni&quot;</span><span class="p">,</span><span class="s2">&quot;ordini&quot;</span><span class="p">);</span>
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<li>Il grafico evidenzia che tendenzialmente gli ordini (come prevedibile) sono tanto più alti quanto lo sono le sessioni.</li>
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<div class="prompt input_prompt">In&nbsp;[20]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;per_buybox&quot;</span><span class="p">,</span><span class="s2">&quot;ordini&quot;</span><span class="p">);</span>
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<div class="prompt input_prompt">In&nbsp;[21]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Vendite</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;prezzo_medio&quot;</span><span class="p">,</span><span class="s2">&quot;ordini&quot;</span><span class="p">);</span>
<span class="n">Vendite</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;visualizzazioni&quot;</span><span class="p">,</span><span class="s2">&quot;ordini&quot;</span><span class="p">);</span>
<span class="n">Vendite</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="s2">&quot;offerte_attive&quot;</span><span class="p">,</span><span class="s2">&quot;ordini&quot;</span><span class="p">);</span>
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pSdZ6rb9zr38v6+ims1itgTYo7pHv8HAfwpgG+htNq/GsBdqvq5eM0rkfY9fpukVRtU9aSbNPeXqp7WpF4d/x4A21T1jPV8IYCHGrXV066Ov1GOIoqbangFowCVgTkmdcbV76gCgGodr3r7Fff1EId9afuwS5s9YdRrb706fkH5vXYL1jFiEbVcr1HyP5MUvGHt22WBUpbBzqygoFqRbleB0Drj6ndUaX1rHa96+xX39RCHfSbz3UjSLKn1Ik57TVf8fwXgepRuvQiUtnq+pKr/EIkVIaR9xR+1XK9R8j+/du67cTvecceDRu2bSEkBoDMnAATTc/51xtXvauutdlzCxqvefsV9PcRhX2cuA0AxPXfOvyQpYU2zpNaLqOytS85pZeH8EwAnAYyiFMDVEKffDEQt12uU/K+aFLx+7ZtISQEgKxlkM+VfEt11xtXvqNL6+o1L2HjV26+4r4c47MtmpCJFeJIS1jRLar2I217TrR6o6uMAHo+k1RYjarleo+R/1aTg9WvfREoKoJQuWssdv7vOuPodVVpfv3EJG696+xX39RCHfYWiAq7btSQpYU2zpNaLuO01lXOSAKKW6zVK/meagjeofWcd9u3jOrNSkW53146t2LUjuM64+h1VWl+/cQkbr3r7Fff1EId9u3Zswa4dW1MjYU2zpNaLuO012uNPmrTv8dtQ1UNVD1U9VPVESVyqHjp+QghpUeqVc9bS4BdRuiH7MVW9xDq2DMC/ANgE4AUA16nqaFw2tAJJ3kyiFFRzGoDg4jWLfVexJoE3tXx7MLlJRz3fVvz6V883A7e9tdxwxM8+AKHzETUm8xvVN8a4AriSWuVH8Q0wLttjc/wAvgTgDpRSOtvcDOAnqnqriNxsPb8pRhuamnp0vFHosj/yjScwWyh9I8xlgE9fN1ChTf/wPbtD0+nWEhOQz2QwOTtXEQtgoo03ac+vf9Vqz51tTc0VoKrozucqUmO7XzOp12mfAMhkxPrR1Hs+osZkfqOKAzG5jmrtQxLa/SjiOhLX8ddcucgmAPc5VvzPALhSVY+KyGoA/6aqF4TV045bPfXoeKPQZb/h1p+Wae6Bkhb/5ze/aX4l+4Zbf1Km0y6VKU+nW40tYTEBJtp4kxgEv/51ZAGRTGCsQTX2BhFWr5d9bpzzETUm8xtVHIjJdVRrH5LQ7oe1a2JXKnT8EbJSVY8CgPX/PL+CInKDiAyJyNDIyEjDDEwL9eh4o9BluzX3QEmL79Smu3XaQGU63WpsCYsJMNHGm8Qg+PVPEB5rUI29QYTV62WfG+d8RI3J/EYVB2JyHdVCUtr9sHZN7Irb9tTKOVX1TlUdVNXBFStWJG1Ow6lHxxuFLtveUnBS0HJtekErV6TudLrV2BIWE2CijTeJQfDrn6JYcbxavb8pYfV62efGOR9RYzK/UcWBmFxHtZCUdj+sXRO7Wk3HP2xt8cD6f6zB7TcN9eh4o9Bl79qxBfnsuVVnLgPs2rG1TJu+a8fW0HS61djiLuuOBTDRxpvEIPj17/ZrB0JjDYLszVvxC16psd2vhdXrtk+Asm8B7vmIGpP5jSoOxOQ6qrUPSWj3w9o1saupdfwee/y7AJxw/Li7TFU/GlZPO+7x21DVQ1UPVT3196FdVT0N1/GLyNcAXAlgOYBhAJ8A8B0A9wDYAOBFANeq6smwutrZ8VeDVxCV0/nYxxd2ZHHk9CQAwZolXfPHnOcE1R92EQaV88rLbr/h1yzprrBl9MwMdh88hU19C3B2toDDo5OYnitge/+K+Zzwzv4dOT2Jsck5TEzNYmquiO39y8tyx+8bHseD+0YwW1Dks4Lt/Stw6uwMHnjuOK44fzkGN/dV2D+0/wR++KthvGz5Qrzl4lUAUOHMnWMbNI7VO0O34082Z30jgxTjDIhrtkCuWmEAV4vjlRpZMjIvKdSiYtpydrZE0MY+Zp9Tj7QsqNzHv/Mk7n74xfmyl/f34aHnT8ApXnHaMlNQBF2dO7dtwKUbl+Gme/fM98+v3Cff9cqK9r24YOVCHDg5OW//hmXdeHb4TFmZXAbozufm5abZjGBqtoisAAXFfOqKelIzu+WcGQFUz2W/iVLyaEojU4/Hmea62dIz1wMdfwtTj6zQi1qlZUHlRs/M4M2feSAS+5x0ZIGZQni5L+y8FO+/+7HI2w+i1tTM5nLO+iWPpjQy9TiAuuWQjepH2kmLnJPEQD2yQi9qlZYFldt98FRk9jkRw0v4/l8Nx9J+ELWmZjaWc0YgeTQlanlhUH1RyCFrabedoONvAeqRFXpRq7QsqNzA+qWR2edEYdbvt1y0Mpb2g6g1NbOxnDMCyaMpUcsLg+qLQg5ZS7vtBB1/C+CUfjlTIzslhZ2WNNApEbSxj9nn1CotCyrXv7IHO7dtKCt/eX9fmYzPbUvYmnfntg24/dqBsv75lXvTRasq2vfigpULy+y/YOXCijK2LNOWm9pjbptgz0OtqZm95JwZKb/XaVSSR1OilhcG1ReFHLJR/WhWuMffQlDVQ1VP3FDV01zwx11CCGkzGp6WmZhhcvMK82Ck8tWgafBQtTfQcLflXJXnc9nQQKagPoX1w892e6U9O1fACyfOondBHqNnZ41tAs4FR61Z0oVfvzSO4xPTWN/bjdGzs+hdkMevXxrH2Zk5rOtdgLW9C7BmSdf8Nyd7Re73TcBrbE2+ZUVBVDdYqdVek5V7owPTTKg16DAt9gdBx58gXnpid1pgZ2rfoPSt7rS2733tetwzdCg0vbGJDe7yzrYE5XdWzWUAEfFNTzxbLOK6wXXztrlT+Qb1w892W8Nv6+jdZKWU0jgoZXKhqDD4PdUXAZDLlsbYTsGcy2bKYiqcY2vb7Kf5j4oo0nPXY6+JHj8s/XcS1JpKvFniArjVkxBeeuLOXAaAVqSodeKdYrgyrW0QQXrpzpwAEM/UxACqbsvUnvtu3I7f+9z/Da07yPa04zW2NnFoyaNIz+03xib1mKQnDkv/nQT1phJPU1wAdfwpw0tPnM2IZ4paJ54phkPO8avD0wbxT01cS1um9uw+eMqo7iDb047X2NrEoSWvV7MeNMYm9Zjo8cPSfydBtTEXzRgXwK2ehPDSE5e028ErXs8Uwx5pbU3rqLBBi4CKb/lq2zK1Z2D9UqO6g2xPO15jaxOHlrxezXpQfIhJPSZ6/LD030lQbcxFM8YFNNeSqYXw0hPv2rEFu3ZsLTvmTO3rn2K4Mq2t8zy/9MbeNmz1TU3s1Zbbjdlt+aUn9utT/8qe0H742W5r+P2k/FkJtimfFRgEygYiODfGdlvumArn2No2e2n+o6JezbrXGFdjr4kePyz9dxJUG3PRjHEB3ONPGKp6qOqhqoeqnrigjp8QQtoM6vhTiDuKNQq8VmdA+OrTa0XstK93YUdZuYd+cwLHJ6ZwyZolyOeyZSvup46MoTOXwdrebs9oXK+V76MvnMQP9x7FxPQcfvfClejsyKEzl0FPVx6Lu/NlK2p7Vb+pbwGeOzaBxw6cRKGoyGQEG3oXYKZQxIKOHN568aqy6F6TG4rsPTKGsckZq83Kcs7V6YJ8Bi+cOFs2f+fqmJ23u55VdlIryHpvcBOF7UmPQStDx58Q7tzwds74evDKyV9QhUgph6Vbh+3U69vac1vnPrixFw/uOzFfdzYjWJAvadTd+fwzAhQVgTp6d556u23bVidDB05X1JGRcznpfdLuV7Dr/mfnc/a74wPcuezdMQRe5dyacyde7QC16dKT1oX7tW9iV1S2Jz0GrQ63ehJg3/C4Z276H3/oippX/tXm5A/SlMeNSbxClOQzgHtYnLnsg2Ih7HIAQnPke7VTqsNcl560Ltyv/ftu3I533PFgoF1R2Z70GLQS1PGnCL/c9PXkrK9W1x6kKY8bk3iFSBEPrbgjl31QfIJdzixHvvfr1ejSk9aF+7W/++CpULuisj3pMWgHuNWTAH656evJWV9tTv4gTXncmMQrRIrHt1pnLvugWAhnufAc+d6vV6NLT1oX7tf+wPqloXZFZXvSY9AOcMWfAF656Xdu21DXD7x+OfltXb1bh+3W67t17pf395XVn82c06i7sRfCQTr68rbPxSvYtoaRkdJ+eUDafU92btuAT103UBEf4Mxl7xWf4C7npTkPaweoXpeetC7cr/3+lT2hdkVle9Jj0A5wjz9BqOqhqidsHqnqoaqnHqjjj5FablACnHPGziAg+8dGr4CWICftFcj0yP6TODExjb5FHXjrxasdNz4557ge2X8SZ2fmsL53AdbMByVNYWxyFuNTszg0ehYA8IrVS7BqcSce2X8SAPC6zcvmA6NGz8zgwX0j6MxlLbvOYkFHDq/bvAwvjU3h+MQMLlmzGGdnizgyehZTc8X557aDdLYLKMan5nDyzAw29i3AhasWl314OMfM6Xx7F3Zg75HTGJucs0Zcsbi7wzWu5c7f68Yw7pu6LO7O+QbF2U5+fGoW03OF+Q/CsKA5v2vD9IYuhJhAHX9MmMrOnOVs6WRWBNMOaWAuA7zvtRvwtUcPVqSpdUov3WmW7RTDdupcd6pkANh1/3PY3t+HR/af9JQj1kJWSu3Uk87YlHxWMBuQehk4Jyt1k8sA73vdBnz1kRfLJJ2v27ysTLK6vb8PQwdGAVRKTN0poqfmCpgrqOeuvlcaaJO0vvb82X11pnOmlJFECVf8dWAqO6tWaummIysQQcPkjyR6qknra3ouIWFQzhkDprKzelMIizRY/kgip5q0vqbnElIr3OqpA1PZWbVSSzeqikIj5Y8kcqpJ62t6LiG1wmVkHZjKztzlbOlkp0samMuUZIHuNLW3X7u1LF2zO82ynWLYrs9P8Xh5f5+vHLEWsoK60xmbYtsdZL6fLfa4uiWdbsnq5f19vhJTd4rofFZ8x9krDbRJWl97/uy+2umcKWUkUcM9/gigqoeqHqp6SBqhnJMQQtqMVMk5ReQFAOMACgDmvAxLK36rbvdj54rdr5zfTUaquTHF0P4T+OGvhnFeTycuWNWDi9csAVAKsDpwYgKduSx+MzKB4bEpbFm3FP3n9WB8ahbPDo9j/8gE5orAxWsWIze/zZDB8YlpLOzI4fiZaXRkM1i2sAO9CzowPVfExr6F2PbyPuwfmcAPfzUMAXD01CQ68llcdckq5HNZPPjcCJ4ZHsMbNvehsyOL2YIin83gkjWLy4K97FX14dFJPDs8jtlCEb9zwXmYnC3i+MQU1vcuwK9fGseZmTn0LujAzFwRyxZ2oKcrP/9tRAFctHoxVi3uwlNHxjBXKCLnaMsZ7GWvxP1uDBMW4OVm3/D4/Dedtb3dZefZczg2OYfxqVnr20tp7KoNiCIkahJZ8VuOf1BVj5uUT8uK30uL353Plenqp+YKKBR1Xk8uAHLZ0msV+vvBdfO6cFvrrUBZ6t+gtL5/9E8Pl+nQgVJqhfCcMslh6/CD9Phxkc8KshmpGPfrBtdVaPzdaZvduNNqO89zz6ETAfDZ9wwYpzkmpB5StdXTjI6/Xi2+CX6pkr3S+g7tP4Ed/+vh2Gxpd5xpm934pdUG7JiL4HTXHVng+39xRWiaY0LqJW06fgVwv4g8JiI3eBUQkRtEZEhEhkZGRhpsXiX1avFNyErGUynildb3geeMPjNJjTjTNrsJSp8t4q/2mS8DszTHhMRFUo7/MlV9NYC3A/iAiFzhLqCqd6rqoKoOrlixovEWuqhXi29CQYuean2vtL5XnL88VlvaHWc6ZjdB6bNVvdM4lJWBWZpjQuIiEcevqkes/8cAfBvAa5Owoxr8tPhuXX0+K2V6csG51/z09/ZzO1WyW8fvldZ3cHNfhQ4dQGI3VzHF7lqE4QTG5C1dvHvcvTT+zrTNbrzSatvnlWIu/NM3C4Dbrx0wSnNMSFw0fI9fRBYCyKjquPX4RwA+qao/8DsnDXv8NlT1UNVjQ1UPSTup+XFXRF6G0iofKMlJv6qqfxd0TpocPyGENAup0fGr6vMAtjayzThWVe5VOeCMxD0Xgbq4uwOzcwU8vP8kFnZksXRBHtv7S79Z/HDvSzg0ehbZjODqgbUY3NxX8Y3iKw+9gAefG8H6ZQuwsW8hjk/MYPmiDsxaNxu5cFUPHn9xFMNjU7h04zK8ZtMyHDk9hSOjZ3Fw9CwmZwo4dXYGI3gXmw8AABDmSURBVOPTWN7TiTVLu7HUWj135jLYNzKBQ6OT6MplMD1bwPKeTqxe2o2LVi/BXKGA7z1xFEUtYvWSLixb2IlCUfHU4TEs6MjiNZuXAQCeeWkM+WwGx8am8cxL4+jMZ/GmV5yH7ecvx5ol3ThyehLPvDSOZ14aQ6GoWNiZx+DGpTg1OYfZQhEzcwVs7FuEC1f1eEbmlqKFdT762D2n9tg753do/wn8YO8wVi3uxG+tWlzxzamWG4rYz4+PT+GJQ6dxxfnLMbi5r6o6CUkDLR+5G4dW+ru7D5fptG2tfjYjdck9L1i5EAdOTs7n3A9QBLYsdi56P3Zu24BLNy7zvTfBbddswT1DByviG5zxECbXhLuMfc+D2bliWfzB5f19+Mqfvp6afJJKUrPVUwu1On7TfPnV1vmGW38aqNMm8dKRBWYKfq9lMFPwnpvOnOBf//Plofr5amM2vrDzUnzga7+kJp+kjrTp+BuCab78autMu3Km1ZHAy9Z/IZMVM/18tTEb9/9qmJp80lS0tOM3zZdfbZ1pTonQDiiCVuL+H8oFNdPPVxuz8ZaLVlKTT5qKlnb8pvnyq63TrdO2tfpeedyr4YKVC8ty7rcjYfcL2LltA26/dsD33gS3X7vFM77Bjocw0c97XTe29t9t3uX9fXjTRauoySdNRUvv8dtQ1UNVD1U9pB1pyx93ndRysxT3DVD2j4zjB3tfwpol3Xjf6zY6bmwyhiOjZ3HCCtJZtbgTTx0Zw+mzM5iYKeCtF63E5hWLcGh00rpBygkcn5jBgRNn8cLxM1iQz+C8Jd144wUrcHpqDvuOjWNkfAaLu7OYKwAnz8xgfGoWXR1ZdOWyWLO0C70LOgCUVqc5AX6+/ySWdOZw8uwMiqpYs7Qbb79kNZb3dLkCpuwPJeDo6Un8+qUxLOnKY83SboyencGRU5OYnC1i+8v7cHRsCs8NTwBQrOtdgPNX9uDM9ByGx6exsbcbj+w/idGzM7jygvOwefnC+Q+3sclZzBQUVw+sme+313jW6iBrcdqEtCNt7fhNpXZe5RTATffuwcxcEe6t/cv7+/DQ8yeMZJcZKaVTCJIqxkmYTDJOejpzFeNZq+yxFikmpZWkXWlbx28q6fQq55cmmdSG13hWI3s0mcs4JLyENCttKecEzCWdXuWykqF0M0K8xrMa2aPJXMYh4SWk1Ujk1ouNxFTS6VWuoEVA6fijwms8q5E9msxlHBJeQlqNll/xm0o6vcrZaZK78hl4Lfwv7+8zll1mJFyqGCdJtu01nrXIHk3mMg4JLyGtRsvv8dtQ1UNVDyHtRtv+uEsIIe1KatIypwG/wBx7RfzsS2MYHp8GikUMHTyFpZ15HDk9CQVw5QXnYUFHFo+9MIqpuQImpmYxMT2HBR05vP2Vq/GOLWtwZqaA2bkCnjoyhuWLOrBqcRce2X8CgGDt0i48dWQcOQHmFBAt4mfPjmBxdx5/OLDWscou7YmPjE9j7dIF6MpncHx8Gvl8FhNTc5iZLUAh2LpuCbJZweHRSYyemcFsQfG6ly3DpuULcfLMNPoWdmJRVx6Lu3OeNxgJWhlz1UxIa9J2K/6KdLuD63DP0CFoUTEdkc49K0BCkvlA8lnBp67dOq9pD9K7UwtPSPPDrR5Un263FenMZfDzm98IAL5696DXuPInpHngVg/OabynArM7tjbZjMxr2t1j4dS7+71Gx09I89NWjr/adLutSKGo85r2IL07tfCEtC4tr+N3EpRutzNCnXuCkvlA8lnBrh0lTXuQ3p1aeEJam7ba47ehqsd/LExfI4Skn7bc43fnbN97ZAxjkzNY3J33dIK9C0tBUc+PjOO8nk48/PxxHDpxFocAdOWzyAhwz6MvogjFK1YtwfrebgwdmMKJs9OAAJv6FpTli8/nsvMfJssWduLQ6Bk8e2wOA+uX4MJVi3FmpoB1vd342O9fUmazned/zZKussArQHHxmiUA4PnB5ffcWfcTB09V1Ld1/dL515zn2Kt/k7HlBwQhzUPLrvidcsSpuQLmClp2N1Zb2uhMEzwxPRdwx1YzVi/uwM1XXYSb7t0DAIEKIvuOXbZU8ru7D+Mj33iiLH1yLoOytM92eueuXLZMjuqWp7plmPZ4uG3KZwXvfc163PNY5Tl+uMdWVdGdz1H2SUjKaCs5p6lsszOXAaCYnot2DLIAClWU78pncN+N2/F7n3sw8hTQdt3vuONBYxlrkHQzbGwp+yQkPbRVWmav1Lx+ZCX6Iaj2YySfyWD3wVOxpIC26zYdD/scvzTGYWPLFMiEpJ+WdPzVyDYLGr28s1r3PVssYmD9UhTct/iKALvuamSsQdLNsLGl7JOQ9NOSjt8tR8xnpcIZ29LGXTu2zpeLYr29enEHPvOeAXTlM/N7+H7YZW67Zgv6V/Zg144tFemT3Wmf7fTObjmq33O7bns83Dbls+J5jt9WjdfY5jKg7JOQJqIl9/htTFU9znKjZ2bw4L4RzBYUDz9/HHsOngZwTtVzenJ2XtWzdmkXhg6M4viZKfQt7MSHf/cCXP3q9WV12gqaw6NncWj0DBSCi1YvLlP1uJU3cap6nDbZ9QWdYzq2VPUQkj7a6sddQgghKdPxi8jbAHwWJQHMP6nqrY1sf9/wOHYfPIWB9UvLdPf2SvviNYsDg5mAc98exqfmcNK6Acu2l/dVfZMXE7se+s1xHJ+Ywfb+5ehd2FH23C5XTV/DYOAWIa1Nwx2/iGQB/A8AvwvgEIBHReR7qvqrRrT/8e88ibsffnH++c5tG3DpxmVl+vlcBvj0dQOeKYq9YgJsBMBn3zPgqWMPS3PsZ9eH/mU3gn7z3bltAz75rlca99WvrKmdhJDmp+FbPSKyDcAtqvpW6/lfA4Cq/ne/c6La6tk3PI43f+aBiuMdWcGMK4F+Z07w85vfBKAyRXEQHVngob9+c8XeelCaYz+77LQOYfz4Q1dUrOb96vQqa2onIaS5SJOOfy2Ag47nh6xjZYjIDSIyJCJDIyMjkTS8++Ap47JZKenRq4kJAABBpY7dqw6n3t3PLjWUGXmd71dn0BiE2UkIaQ2ScPxe7qxiXauqd6rqoKoOrlixIpKGB9YvNS5b0JIevdpUzopKHbtXHU69u59dYvhlzOt8vzqDxiDMTkJIa5CE4z8EYL3j+ToARxrRcP/KHuzctqHs2M5tG3D7tVvL9PO5DLBrx1bPFMVeMQE2AuD2awcqtkXC0hz72fXpdw8gLJh357YNnls3fnUG/cDLdMyEtAdJ7PHnADwL4E0ADgN4FMD7VHWv3zlRyzmp6gmGqh5CWoNU6fhF5CoA/4CSnPOLqvp3QeWp4yeEkOpJlY5fVb8P4PtJtE0IIe1OS+bqIYQQ4g8dPyGEtBl0/IQQ0mbQ8RNCSJvRFNk5RWQEwIGAIssBHG+QOXHTKn1hP9JHq/SF/TBno6pWRMA2heMPQ0SGvCRLzUir9IX9SB+t0hf2o3641UMIIW0GHT8hhLQZreL470zagAhplb6wH+mjVfrCftRJS+zxE0IIMadVVvyEEEIMoeMnhJA2o+kdv4i8TUSeEZF9InJz0vZUg4i8ICJPishuERmyji0TkR+JyHPW/96k7XQjIl8UkWMi8pTjmK/dIvLX1vw8IyJvTcZqb3z6couIHLbmZbeVTdZ+LZV9EZH1IvIzEXlaRPaKyAet4001LwH9aMY56RKRX4jIE1Zf/tY6nvycqGrT/qGU1vk3AF4GoAPAEwAuStquKux/AcBy17HbANxsPb4ZwN8nbaeH3VcAeDWAp8LsBnCRNS+dADZb85VNug8hfbkFwEc8yqa2LwBWA3i19bgHpXteXNRs8xLQj2acEwGwy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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Correlazione di Pearson</span>
<span class="n">corr</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">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">],</span> 
             <span class="n">index</span><span class="o">=</span><span class="n">Vendite</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">corr</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;ordini&quot;</span><span class="p">,</span> <span class="s2">&quot;giorno&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">corr</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
    <span class="n">corr</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">r</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span><span class="p">(</span><span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">],</span> <span class="n">Vendite</span><span class="p">[</span><span class="n">r</span><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="n">corr</span>
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      <th>ordini</th>
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      <th>prezzo_medio</th>
      <td>0.407855</td>
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      <th>visualizzazioni</th>
      <td>0.747777</td>
    </tr>
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      <th>sessioni</th>
      <td>0.748732</td>
    </tr>
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      <th>per_buybox</th>
      <td>0.258814</td>
    </tr>
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      <th>offerte_attive</th>
      <td>0.0474824</td>
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<li>Il _coefficiente di correlazione di Pearson è un indice usato per valutare numericamente il grado di correlazione tra due variabili X e Y</li>
<li>Il suo valore è compreso tra -1 e 1<ul>
<li>valori vicini a 1 indicano correlazione diretta (Y cresce al crescere di X)</li>
<li>valori vicini a -1 indicano correlazione inversa (Y descresce al crescere di X)</li>
<li>valori vicini a 0 indicano assenza di correlazione</li>
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</li>
<li>Date due variabili casuali $X$ e $Y$, il coefficiente è il rapporto tra la loro covarianza $\sigma_{XY}$ e il prodotto delle deviazioni standard $\sigma_X$ e $\sigma_Y$
$$ \rho(X,Y) = \frac{\sigma_{XY}}{\sigma_X\sigma_Y} $$</li>
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<p>Usiamo la funzione di numpy per più immediatezza e pulizia di calcolo e scopriamo che come i grafici suggerivano le visualizzazioni e le sessioni sono i dati più correlati con gli ordini. Il numero di offerte attive ha una correlazione di quasi 0, cioè assente. Possiamo identificare una certa correlazione con il prezzo_medio e la % di buy box</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">features</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sessioni&quot;</span><span class="p">:</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;sessioni&quot;</span><span class="p">],</span> 
            <span class="s2">&quot;visualizzazioni&quot;</span><span class="p">:</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;visualizzazioni&quot;</span><span class="p">],</span> 
            <span class="s2">&quot;prezzo_medio&quot;</span><span class="p">:</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;prezzo_medio&quot;</span><span class="p">],</span> 
            <span class="s2">&quot;per_buybox&quot;</span><span class="p">:</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;per_buybox&quot;</span><span class="p">],</span> 
            <span class="s2">&quot;offerte_attive&quot;</span><span class="p">:</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;offerte_attive&quot;</span><span class="p">],</span> <span class="p">}</span>
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<h2 id="2---Elaborazione-Features">2 - Elaborazione Features<a class="anchor-link" href="#2---Elaborazione-Features">&#182;</a></h2><p>Procediamo con l'importazione di moduli sklearn, definizione di funzioni utili in questa sezione e le successive e la separazione del set in traning e validation.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>

<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Ridge</span>                  <span class="c1">#Regolarizzazione L2</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">ElasticNet</span>

<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">PolynomialFeatures</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>

<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">r2_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">mean_squared_error</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">norm</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">relative_error</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">((</span><span class="n">y_true</span> <span class="o">-</span> <span class="n">y_pred</span><span class="p">)</span> <span class="o">/</span> <span class="n">y_true</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">print_eval</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
    <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;   Mean squared error: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;       Relative error: </span><span class="si">{:.5%}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">relative_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;R-squared coefficient: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plot_model_on_data</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">xlim</span><span class="p">,</span> <span class="n">ylim</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(),</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">()</span>
        <span class="n">line_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">xlim</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xlim</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">100</span><span class="p">)</span>
        <span class="n">line_y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">line_x</span><span class="p">[:,</span> <span class="kc">None</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">line_x</span><span class="p">,</span> <span class="n">line_y</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xlim</span><span class="p">);</span> <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Sessioni&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;Ordini&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">split_before</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">date</span><span class="p">):</span>
    <span class="n">is_train</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">index</span> <span class="o">&lt;</span> <span class="n">date</span>
    <span class="n">X_train</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">is_train</span><span class="p">]</span>
    <span class="n">y_train</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">is_train</span><span class="p">]</span>
    <span class="n">X_val</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="n">is_train</span><span class="p">]</span>
    <span class="n">y_val</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="o">~</span><span class="n">is_train</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a function called lasso,</span>
<span class="k">def</span> <span class="nf">lasso</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">alphas</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Takes in a list of alphas. Outputs a dataframe containing the coefficients of lasso regressions from each alpha.</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="c1"># Create an empty data frame</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">()</span>
    
    <span class="c1"># Create a column of feature names</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Feature Name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">columns</span>
    
    <span class="c1"># For each alpha value in the list of alpha values,</span>
    <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">alphas</span><span class="p">:</span>
        <span class="c1"># Create a lasso regression with that alpha value,</span>
        <span class="n">lasso</span> <span class="o">=</span> <span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
        
        <span class="c1"># Fit the lasso regression</span>
        <span class="n">lasso</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
        
        <span class="c1"># Create a column name for that alpha value</span>
        <span class="n">column_name</span> <span class="o">=</span> <span class="s1">&#39;Alpha </span><span class="si">%f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">alpha</span>

        <span class="c1"># Create a column of coefficient values</span>
        <span class="n">df</span><span class="p">[</span><span class="n">column_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">lasso</span><span class="o">.</span><span class="n">coef_</span>
        
    <span class="c1"># Return the datafram    </span>
    <span class="k">return</span> <span class="n">df</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">diff_interval</span><span class="p">(</span><span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">N1</span><span class="p">,</span> <span class="n">N2</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
    <span class="n">d</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">a1</span> <span class="o">-</span> <span class="n">a2</span><span class="p">)</span>
    <span class="n">sd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">((</span><span class="n">a1</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">a1</span><span class="p">)</span> <span class="o">/</span> <span class="n">N1</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">a2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">a2</span><span class="p">)</span> <span class="o">/</span> <span class="n">N2</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">d</span> <span class="o">-</span> <span class="n">Z</span> <span class="o">*</span> <span class="n">sd</span><span class="p">,</span> <span class="n">d</span> <span class="o">+</span> <span class="n">Z</span> <span class="o">*</span> <span class="n">sd</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Se l&#39;intervallo ottenuto non include lo zero (l&#39;estremo inferiore è positivo), </span>
<span class="c1"># abbiamo la certezza al 95% (o altro livello di confidenza) che il modello con </span>
<span class="c1"># accuratezza stimata maggiore sia effettivamente migliore</span>

<span class="k">def</span> <span class="nf">model_diff_interval</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">X1</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">y1</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">P</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">same</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="k">if</span><span class="p">(</span><span class="n">same</span><span class="p">):</span>
        <span class="n">X1</span> <span class="o">=</span> <span class="n">X</span>
        <span class="n">y1</span> <span class="o">=</span> <span class="n">y</span>
    <span class="n">a1</span> <span class="o">=</span> <span class="n">m1</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    <span class="n">a2</span> <span class="o">=</span> <span class="n">m2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X1</span><span class="p">,</span> <span class="n">y1</span><span class="p">)</span>
    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">P</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">diff_interval</span><span class="p">(</span><span class="n">a1</span><span class="p">,</span> <span class="n">a2</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">conf_interval</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">Z</span><span class="o">=</span><span class="mf">1.96</span><span class="p">):</span>
    <span class="n">c</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">N</span> <span class="o">*</span> <span class="n">a</span> <span class="o">+</span> <span class="n">Z</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="n">Z</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
    <span class="n">d</span> <span class="o">=</span> <span class="n">Z</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">Z</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">N</span><span class="o">*</span><span class="n">a</span> <span class="o">-</span> <span class="mi">4</span><span class="o">*</span><span class="n">N</span><span class="o">*</span><span class="n">a</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">N</span> <span class="o">+</span> <span class="n">Z</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">c</span> <span class="o">-</span> <span class="n">d</span><span class="p">,</span> <span class="n">c</span> <span class="o">+</span> <span class="n">d</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">model_conf_interval</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">):</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">level</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">conf_interval</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">Z</span><span class="p">)</span>
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<h3 id="Divisione-tra-training-e-validation-set">Divisione tra training e validation set<a class="anchor-link" href="#Divisione-tra-training-e-validation-set">&#182;</a></h3><p>Per verificare se un modello generalizza correttamente i dati su cui è addestrato, è importante <strong>validarlo su dati diversi</strong>.
Il metodo <em>hold-out</em> prevede di dividere i dati a disposizione in</p>
<ul>
<li>un <em>training set</em> su cui addestrare il modello</li>
<li>un <em>validation set</em> su cui calcolare le metriche di accuratezza del modello addestrato
La proporzione tra training e validation set è arbitraria, possono essere ad es. 50-50 e 66-33, noi faremo riferimento a quest'ultimo esempio di suddivizione</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span>
<span class="n">X</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">features</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_val</span> <span class="o">=</span> <span class="n">split_before</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s2">&quot;2019-05-27&quot;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;train set: </span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;val set: </span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">X_val</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<pre>train set: 	 0.6601851851851852
val set: 	 0.3398148148148148
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<div class="prompt input_prompt">In&nbsp;[37]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_train</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">40</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">4</span><span class="p">));</span>
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<h3 id="Confronto-con-un-modello-casuale">Confronto con un modello casuale<a class="anchor-link" href="#Confronto-con-un-modello-casuale">&#182;</a></h3><ul>
<li>Per avere una valutazione più completa dei modelli che andremo ad ottenere, possiamo metterli a confronto con quello che accadrebbe prendendo decisioni casuali</li>
<li>Consideriamo un modello che predica dei valori $\hat{\Delta}_d$ casuali, ma con la stessa distribuzione dei valori $\Delta_d$ reali</li>
<li>Disegnando un istogramma, vediamo che $\Delta_d$ ha una distribuzione quasi normale, riusciamo a replicare una distribuzione simile invertendo i valori negativi e facendo dei tentativi di seed.</li>
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<div class="prompt input_prompt">In&nbsp;[38]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">5555415</span><span class="p">)</span>
<span class="n">random_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span>
    <span class="n">y_train</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span>     <span class="c1"># centro (media)</span>
    <span class="n">y_train</span><span class="o">.</span><span class="n">std</span><span class="p">(),</span>    <span class="c1"># scala (dev. standard)</span>
    <span class="nb">len</span><span class="p">(</span><span class="n">y_val</span><span class="p">)</span>          <span class="c1"># numero di campioni</span>
<span class="p">)</span>
<span class="n">random_preds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="o">-</span><span class="n">i</span> <span class="k">if</span><span class="p">(</span><span class="n">i</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span> <span class="k">else</span> <span class="n">i</span><span class="p">,</span> <span class="n">random_preds</span><span class="p">)))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">random_preds</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">bins</span><span class="o">=</span><span class="mi">40</span><span class="p">);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">test_random</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">P</span><span class="o">=</span><span class="mf">0.99</span><span class="p">):</span>
    <span class="n">preds</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">a1</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    <span class="n">a2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">random_preds</span><span class="p">)</span>
    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">Z</span> <span class="o">=</span> <span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="n">P</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MODEL    Mean squared error: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MODEL        Relative error: </span><span class="si">{:.5%}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">relative_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MODEL R-squared coefficient: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">r2_score</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RANDOM   Mean squared error: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">random_preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RANDOM       Relative error: </span><span class="si">{:.5%}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">relative_error</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">random_preds</span><span class="p">)))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RANDOM R-squared coefficient: </span><span class="si">{:.5}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">a2</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
    <span class="c1"># Non possiamo utilizzare l&#39;intervallo di confidenza</span>
    <span class="c1">#print(&quot;CONFIDENZA MODELLO RANDOM 99%: &quot;, diff_interval(a1, a2, N, N, Z))</span>
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<p>Nella definizione della funzione, che verrà utilizzata per confrontare i modelli con una modello di generazione random, non è stato possibile utilizzare l'intervallo di confidenza con la metrica r^2 delle predizini random. Questo perchè il calcolo del parametro di confidenza si basa sulla risoluzione di una radice quadrata che con un parametro di accuratezza r^2 arbitrariamente negativo (modello random) diventa di risoluzione impossibile (entra nello spazio dei numeri immaginari).</p>
<p>Per questo motivo faremo riferimento alle sole metriche MSE, RE e R^2.</p>
<p>Utilizzeremo invece il confronto degli intervalli di confidenza al momento della selezione dei modelli migliori.</p>

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<h3 id="Lasso">Lasso<a class="anchor-link" href="#Lasso">&#182;</a></h3><p>Di seguito notiamo la rilevanza di ogni feature con la regressione lasso. Osserviamo come aumentando il valore dei <code>alpha</code> le feature che vengono escluse prima sono <code>offerte_attive</code> e <code>prezzo_medio</code> nonostante entrambe mostrassero una correlazione di Pearson maggiore di 0.4. La <code>per_buybox</code> invece, che mostrava una correlazione minore, sembra avere una modesta rilevanza.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lasso</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.001</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</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 {
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Feature Name</th>
      <th>Alpha 0.001000</th>
      <th>Alpha 0.334222</th>
      <th>Alpha 0.667444</th>
      <th>Alpha 1.000667</th>
      <th>Alpha 1.333889</th>
      <th>Alpha 1.667111</th>
      <th>Alpha 2.000333</th>
      <th>Alpha 2.333556</th>
      <th>Alpha 2.666778</th>
      <th>Alpha 3.000000</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>sessioni</td>
      <td>0.051118</td>
      <td>0.049280</td>
      <td>0.048544</td>
      <td>0.047803</td>
      <td>0.047070</td>
      <td>0.046343</td>
      <td>0.045208</td>
      <td>0.044025</td>
      <td>0.042838</td>
      <td>0.041655</td>
    </tr>
    <tr>
      <th>1</th>
      <td>visualizzazioni</td>
      <td>0.011726</td>
      <td>0.013173</td>
      <td>0.014179</td>
      <td>0.015187</td>
      <td>0.016190</td>
      <td>0.017191</td>
      <td>0.017889</td>
      <td>0.018548</td>
      <td>0.019209</td>
      <td>0.019868</td>
    </tr>
    <tr>
      <th>2</th>
      <td>prezzo_medio</td>
      <td>0.137693</td>
      <td>0.112930</td>
      <td>0.085462</td>
      <td>0.057994</td>
      <td>0.030525</td>
      <td>0.003054</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>3</th>
      <td>per_buybox</td>
      <td>0.028447</td>
      <td>0.026441</td>
      <td>0.025612</td>
      <td>0.024783</td>
      <td>0.023956</td>
      <td>0.023130</td>
      <td>0.022094</td>
      <td>0.021031</td>
      <td>0.019968</td>
      <td>0.018905</td>
    </tr>
    <tr>
      <th>4</th>
      <td>offerte_attive</td>
      <td>-0.157141</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
      <td>-0.000000</td>
    </tr>
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</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_ses</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span>
<span class="n">X_ses</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">features</span><span class="p">[</span><span class="s2">&quot;sessioni&quot;</span><span class="p">])</span>
<span class="n">X_ses_train</span><span class="p">,</span> <span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_train</span><span class="p">,</span> <span class="n">y_ses_val</span> <span class="o">=</span> <span class="n">split_before</span><span class="p">(</span><span class="n">X_ses</span><span class="p">,</span> <span class="n">y_ses</span><span class="p">,</span> <span class="s2">&quot;2019-05-27&quot;</span><span class="p">)</span>

<span class="n">y_some</span> <span class="o">=</span> <span class="n">Vendite</span><span class="p">[</span><span class="s2">&quot;ordini&quot;</span><span class="p">]</span>
<span class="n">X_some</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">features</span><span class="p">)</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s2">&quot;sessioni&quot;</span><span class="p">,</span> <span class="s2">&quot;visualizzazioni&quot;</span><span class="p">,</span> <span class="s2">&quot;per_buybox&quot;</span><span class="p">]]</span>
<span class="n">X_some_train</span><span class="p">,</span> <span class="n">X_some_val</span><span class="p">,</span> <span class="n">y_some_train</span><span class="p">,</span> <span class="n">y_some_val</span> <span class="o">=</span> <span class="n">split_before</span><span class="p">(</span><span class="n">X_some</span><span class="p">,</span> <span class="n">y_some</span><span class="p">,</span> <span class="s2">&quot;2019-05-27&quot;</span><span class="p">)</span>
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<h3 id="Standardizzazione">Standardizzazione<a class="anchor-link" href="#Standardizzazione">&#182;</a></h3>
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<p>Durante il preprocessing risulta utile utilizzare lo <code>StandardScalare</code> per mantenere la stessa distribuzione dei dati ma con media 0 e deviazione standard 1. Nell'esempio successivo viene mostrato come la standardizzazione dei dati è essenziale quando vendono utilizzate delle feature polinominali di grado elevato. Per un primo momento utilizziamo la sola feature <code>sessioni</code></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lnrstd1</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span>   <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="c1">#(&quot;scale&quot;,  StandardScaler()),   # &lt;--</span>
    <span class="p">(</span><span class="s2">&quot;linreg&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>
<span class="n">lnrstd1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_ses_train</span><span class="p">,</span> <span class="n">y_ses_train</span><span class="p">)</span>
<span class="n">print_eval</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">lnrstd1</span><span class="p">)</span>
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<pre>   Mean squared error: 23.016
       Relative error: 60.52542%
R-squared coefficient: -0.5323
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lnrstd2</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span>   <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span>  <span class="n">StandardScaler</span><span class="p">()),</span>   <span class="c1"># &lt;--</span>
    <span class="p">(</span><span class="s2">&quot;linreg&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>
<span class="n">lnrstd2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_ses_train</span><span class="p">,</span> <span class="n">y_ses_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">lnrstd2</span><span class="p">)</span>
<span class="n">plot_model_on_data</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">lnrstd2</span><span class="p">)</span>
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<pre>MODEL    Mean squared error: 18.537
MODEL        Relative error: 34.82648%
MODEL R-squared coefficient: -0.2341

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<p>In questo ultimo modello, l'over-fitting agli estremi e a 3/4 del grafico puo essere risolto con una regressione Ridge. Proseguiamo confrontando modelli generati con e senza feature risultate poco rilevanti dalla regressione lasso.</p>

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<div class="prompt input_prompt">In&nbsp;[44]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">some1</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span>   <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span>  <span class="n">StandardScaler</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;linreg&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>
<span class="n">some1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_some_train</span><span class="p">,</span> <span class="n">y_some_train</span><span class="p">)</span>
<span class="n">print_eval</span><span class="p">(</span><span class="n">X_some_val</span><span class="p">,</span> <span class="n">y_some_val</span><span class="p">,</span> <span class="n">some1</span><span class="p">)</span>
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<pre>   Mean squared error: 3.156
       Relative error: 25.47884%
R-squared coefficient: 0.78988
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">all1</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span>   <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span>  <span class="n">StandardScaler</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;linreg&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>
<span class="n">all1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">print_eval</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">all1</span><span class="p">)</span>
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<pre>   Mean squared error: 3.1198
       Relative error: 24.44845%
R-squared coefficient: 0.7923
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">only1</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span>   <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">degree</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span>  <span class="n">StandardScaler</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;linreg&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>
<span class="n">only1</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_ses_train</span><span class="p">,</span> <span class="n">y_ses_train</span><span class="p">)</span>
<span class="n">print_eval</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">only1</span><span class="p">)</span>
<span class="n">plot_model_on_data</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">only1</span><span class="p">)</span>
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<pre>   Mean squared error: 5.4413
       Relative error: 34.18079%
R-squared coefficient: 0.63774
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<div class="prompt input_prompt">In&nbsp;[47]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_diff_interval</span><span class="p">(</span>
    <span class="n">all1</span><span class="p">,</span> 
    <span class="n">some1</span><span class="p">,</span> 
    <span class="n">X_val</span><span class="p">,</span> 
    <span class="n">y_val</span><span class="p">,</span> 
    <span class="n">X1</span><span class="o">=</span><span class="n">X_some_val</span><span class="p">,</span> 
    <span class="n">y1</span><span class="o">=</span> <span class="n">y_val</span><span class="p">,</span> 
    <span class="n">same</span><span class="o">=</span><span class="kc">False</span> 
<span class="p">)</span>
</pre></div>

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    <div class="prompt output_prompt">Out[47]:</div>


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<pre>(-0.05640414680292054, 0.06123482818592156)</pre>
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<div class="prompt input_prompt">In&nbsp;[48]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model_diff_interval</span><span class="p">(</span>
    <span class="n">all1</span><span class="p">,</span> 
    <span class="n">only1</span><span class="p">,</span> 
    <span class="n">X_val</span><span class="p">,</span> 
    <span class="n">y_val</span><span class="p">,</span> 
    <span class="n">X1</span><span class="o">=</span><span class="n">X_ses_val</span><span class="p">,</span> 
    <span class="n">y1</span><span class="o">=</span> <span class="n">y_ses_val</span><span class="p">,</span> 
    <span class="n">same</span><span class="o">=</span><span class="kc">False</span> 
<span class="p">)</span>
</pre></div>

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    <div class="prompt output_prompt">Out[48]:</div>


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<pre>(0.09021170331991177, 0.2189086628868914)</pre>
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<p>La comparazione dei primi due modelli <code>all1</code> <code>some1</code>, con esclusione delle 2 feature che la reg. lasso ha mostrato poco rilevanti, evidenzia che i rispettivi intervalli di confidenza non sono sufficientemente diversi per definirne uno migliore dell'altro.</p>
<p>Nell'ultimo esempio inceve, comparando <code>only1</code> con <code>all1</code> la metrica R^2 di quest'ultimo puo essere considerata migliore nei rispettivi intervalli di confidenza. Anche considerando le altre due metrice, <code>all1</code> produce risultati migliori. Questo sta a significare che prendere in considerazione altre feature oltre alla sola feature <code>sessioni</code>, che apparentemente è quella con più correlazione alla X, migliora il modello. Nel prossimo capitolo genereremo modelli più sofisticati che potenzialmente mostreranno in modo più marcato le differenze tra essi.</p>

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<h2 id="3---Generazione-Modelli">3 - Generazione Modelli<a class="anchor-link" href="#3---Generazione-Modelli">&#182;</a></h2><p>In questo capitolo:</p>
<ul>
<li>Creazione di modelli di apprendimento con tutti gli algoritmi visti a lezione</li>
<li>Uso di random search, grid search, cross fold e nested cross fold</li>
<li>Primi confronti tra modelli, calcolo metriche</li>
</ul>
<p>Dalle analisi precedenti, partiamo dal presupposto di utilizzare l'intero set di features.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">TimeSeriesSplit</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">KFold</span>

<span class="kn">from</span> <span class="nn">sklearn.kernel_ridge</span> <span class="kn">import</span> <span class="n">KernelRidge</span>


<span class="n">kf</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">43</span><span class="p">)</span>
<span class="n">tss</span> <span class="o">=</span> <span class="n">TimeSeriesSplit</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
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<p>Definiamo una funzione che ci calcoli la nested-cross-val</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">outer_cv</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">inner_cv</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">nested_cv</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
    <span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">bestscore</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
    <span class="k">for</span> <span class="n">train_indices</span><span class="p">,</span> <span class="n">val_indices</span> <span class="ow">in</span> <span class="n">outer_cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
        <span class="n">gs</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">inner_cv</span><span class="p">);</span>
        <span class="n">gs</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_indices</span><span class="p">],</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">train_indices</span><span class="p">]);</span>
        <span class="n">score</span> <span class="o">=</span> <span class="n">gs</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">val_indices</span><span class="p">],</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">val_indices</span><span class="p">]);</span>
        <span class="k">if</span><span class="p">(</span><span class="n">score</span> <span class="o">&gt;</span> <span class="n">bestscore</span><span class="p">):</span>
            <span class="n">bestscore</span> <span class="o">=</span> <span class="n">score</span>
            <span class="n">bestmodel</span> <span class="o">=</span> <span class="n">gs</span>
        <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="n">score</span> <span class="p">:</span> <span class="n">gs</span><span class="o">.</span><span class="n">best_params_</span><span class="p">})</span>
    <span class="k">return</span> <span class="n">results</span><span class="p">,</span> <span class="n">bestmodel</span>
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<h3 id="Validazione-k-fold-per-serie-temporali">Validazione k-fold per serie temporali<a class="anchor-link" href="#Validazione-k-fold-per-serie-temporali">&#182;</a></h3><p>Nella cross validation a <em>k</em> fold ordinaria, i dati vengono divisi in <em>k</em> fold mescolati casualmente, nel caso come questo di una serie temporale, vogliamo però mantenere l'ordine temporale in training e validation set. Utilizzeremo <code>TimeSeriesSplit(k)</code>. Faremo comunque dei tentadivi con dei fold classici.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tss</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">start</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FOLD </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">i</span><span class="p">))</span>
    <span class="n">train_dates</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">train</span><span class="p">]</span>
    <span class="n">val_dates</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">val</span><span class="p">]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training set da </span><span class="si">{}</span><span class="s2"> a </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">train_dates</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">train_dates</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Validation set da </span><span class="si">{}</span><span class="s2"> a </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">val_dates</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">val_dates</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span>
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<pre>FOLD 1
Training set da 2017-01-07 00:00:00 a 2018-12-02 00:00:00
Validation set da 2018-01-03 00:00:00 a 2018-12-11 00:00:00
FOLD 2
Training set da 2017-01-07 00:00:00 a 2018-12-11 00:00:00
Validation set da 2018-05-12 00:00:00 a 2019-12-08 00:00:00
FOLD 3
Training set da 2017-01-07 00:00:00 a 2019-12-08 00:00:00
Validation set da 2019-01-09 00:00:00 a 2020-12-05 00:00:00
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<p>Definiamo 4 modelli diversi in cui usiamo ElasticNet, LinearRegression, Kernel rbf, Kernel poly. Per ognuno di essi definiamo una serie di possibili scelte di iperparametri da utilizzare nelle successive GridSearch.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model0</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span> <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
    <span class="p">(</span><span class="s2">&quot;regr&quot;</span><span class="p">,</span> <span class="n">ElasticNet</span><span class="p">())</span>
<span class="p">])</span>

<span class="n">grid0</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;scale&quot;</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">StandardScaler</span><span class="p">()],</span>
    <span class="s2">&quot;regr__alpha&quot;</span><span class="p">:</span>    <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">],</span>
    <span class="s2">&quot;regr__l1_ratio&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]</span>
<span class="p">}</span>

<span class="c1">##############################################</span>

<span class="n">model1</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;poly&quot;</span><span class="p">,</span> <span class="n">PolynomialFeatures</span><span class="p">(</span><span class="n">include_bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
    <span class="p">(</span><span class="s2">&quot;regr&quot;</span><span class="p">,</span> <span class="n">LinearRegression</span><span class="p">())</span>
<span class="p">])</span>

<span class="n">grid1</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;scale&quot;</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">StandardScaler</span><span class="p">()],</span>
    <span class="s2">&quot;poly__degree&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="p">}</span>

<span class="c1">##############################################</span>

<span class="n">model2</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
    <span class="p">(</span><span class="s2">&quot;regr&quot;</span><span class="p">,</span> <span class="n">KernelRidge</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;rbf&quot;</span><span class="p">))</span>
<span class="p">])</span>

<span class="n">grid2</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;scale&quot;</span><span class="p">:</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">StandardScaler</span><span class="p">()],</span>
    <span class="s2">&quot;regr__gamma&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span>
    <span class="s2">&quot;regr__alpha&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
<span class="p">}</span>

<span class="c1">#############################################</span>

<span class="n">model3</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">([</span>
    <span class="p">(</span><span class="s2">&quot;scale&quot;</span><span class="p">,</span>  <span class="n">StandardScaler</span><span class="p">()),</span>
    <span class="p">(</span><span class="s2">&quot;regr&quot;</span><span class="p">,</span> <span class="n">KernelRidge</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;poly&quot;</span><span class="p">))</span>
<span class="p">])</span>
<span class="n">grid3</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">&quot;regr__degree&quot;</span><span class="p">:</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
    <span class="s2">&quot;regr__alpha&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="p">}</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="s2">## MODEL 0 TSS ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs00</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model0</span><span class="p">,</span> <span class="n">grid0</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">tss</span><span class="p">)</span>
<span class="n">gs00</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs00</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 0 KF ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs01</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model0</span><span class="p">,</span> <span class="n">grid0</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs01</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs01</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 0 NESTED ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1">#Utilizziamo il training set in modo tale da poter utilizzare il val set per la comparazione con il modello random</span>
<span class="n">nested_res02</span><span class="p">,</span> <span class="n">nested_best_model02</span> <span class="o">=</span> <span class="n">nested_cv</span><span class="p">(</span><span class="n">model0</span><span class="p">,</span> <span class="n">grid0</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">nested_best_model02</span><span class="p">)</span>
<span class="n">nested_res02</span>
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<pre><span class="ansi-blue-intense-fg ansi-bold ansi-underline">## MODEL 0 TSS ## </span>
MODEL    Mean squared error: 4.614
MODEL        Relative error: 36.48983%
MODEL R-squared coefficient: 0.69281

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 0 KF ## </span>
MODEL    Mean squared error: 3.3592
MODEL        Relative error: 26.18399%
MODEL R-squared coefficient: 0.77635

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 0 NESTED ## </span>
MODEL    Mean squared error: 3.1721
MODEL        Relative error: 24.66715%
MODEL R-squared coefficient: 0.78881

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<pre>[{0.5265263200051856: {&#39;regr__alpha&#39;: 1,
   &#39;regr__l1_ratio&#39;: 0.3,
   &#39;scale&#39;: None}},
 {0.6124370663902201: {&#39;regr__alpha&#39;: 1,
   &#39;regr__l1_ratio&#39;: 0.3,
   &#39;scale&#39;: None}},
 {0.5607956224699234: {&#39;regr__alpha&#39;: 1,
   &#39;regr__l1_ratio&#39;: 0.1,
   &#39;scale&#39;: None}}]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="s2">## MODEL 1 TSS ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs10</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model1</span><span class="p">,</span> <span class="n">grid1</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">tss</span><span class="p">)</span>
<span class="n">gs10</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs10</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 1 KF ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs11</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model1</span><span class="p">,</span> <span class="n">grid1</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs11</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs11</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 1 NESTED ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1">#Utilizziamo il training set in modo tale da poter utilizzare il val set per la comparazione con il modello random</span>
<span class="n">nested_res12</span><span class="p">,</span> <span class="n">nested_best_model12</span> <span class="o">=</span> <span class="n">nested_cv</span><span class="p">(</span><span class="n">model1</span><span class="p">,</span> <span class="n">grid1</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">nested_best_model12</span><span class="p">)</span>
<span class="n">nested_res12</span>
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<pre><span class="ansi-blue-intense-fg ansi-bold ansi-underline">## MODEL 1 TSS ## </span>
MODEL    Mean squared error: 4.6365
MODEL        Relative error: 33.22213%
MODEL R-squared coefficient: 0.69132

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 1 KF ## </span>
MODEL    Mean squared error: 3.9415
MODEL        Relative error: 18.56537%
MODEL R-squared coefficient: 0.73759

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 1 NESTED ## </span>
MODEL    Mean squared error: 2.9336
MODEL        Relative error: 22.73171%
MODEL R-squared coefficient: 0.80469

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<pre>[{0.5167538524492781: {&#39;poly__degree&#39;: 2, &#39;scale&#39;: None}},
 {0.6149391034485121: {&#39;poly__degree&#39;: 2, &#39;scale&#39;: None}},
 {0.5657776590856112: {&#39;poly__degree&#39;: 2,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}}]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="s2">## MODEL 2 TSS ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs20</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span> <span class="n">grid2</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">tss</span><span class="p">)</span>
<span class="n">gs20</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs20</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 2 KF ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs21</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span> <span class="n">grid2</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs21</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs21</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 2 NESTED ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1">#Utilizziamo il training set in modo tale da poter utilizzare il val set per la comparazione con il modello random</span>
<span class="n">nested_res22</span><span class="p">,</span> <span class="n">nested_best_model22</span> <span class="o">=</span> <span class="n">nested_cv</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span> <span class="n">grid2</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">nested_best_model22</span><span class="p">)</span>
<span class="n">nested_res22</span>
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<pre><span class="ansi-blue-intense-fg ansi-bold ansi-underline">## MODEL 2 TSS ## </span>
MODEL    Mean squared error: 4.1238
MODEL        Relative error: 29.98936%
MODEL R-squared coefficient: 0.72545

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 2 KF ## </span>
MODEL    Mean squared error: 2.5451
MODEL        Relative error: 16.53727%
MODEL R-squared coefficient: 0.83055

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 2 NESTED ## </span>
MODEL    Mean squared error: 2.4789
MODEL        Relative error: 16.86304%
MODEL R-squared coefficient: 0.83496

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<pre>[{0.5921196454675517: {&#39;regr__alpha&#39;: 0.001,
   &#39;regr__gamma&#39;: 0.01,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}},
 {0.6746662133086062: {&#39;regr__alpha&#39;: 0.001,
   &#39;regr__gamma&#39;: 0.01,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}},
 {0.6185225781260214: {&#39;regr__alpha&#39;: 0.1,
   &#39;regr__gamma&#39;: 0.1,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}}]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="s2">## MODEL 3 TSS ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs30</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model3</span><span class="p">,</span> <span class="n">grid3</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">tss</span><span class="p">)</span>
<span class="n">gs30</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs30</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 3 KF ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs31</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model3</span><span class="p">,</span> <span class="n">grid3</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs31</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">gs31</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 3 NESTED ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="c1">#Utilizziamo il training set in modo tale da poter utilizzare il val set per la comparazione con il modello random</span>
<span class="n">nested_res32</span><span class="p">,</span> <span class="n">nested_best_model32</span> <span class="o">=</span> <span class="n">nested_cv</span><span class="p">(</span><span class="n">model3</span><span class="p">,</span> <span class="n">grid3</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">,</span> <span class="n">nested_best_model32</span><span class="p">)</span>
<span class="n">nested_res32</span>
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<pre><span class="ansi-blue-intense-fg ansi-bold ansi-underline">## MODEL 3 TSS ## </span>
MODEL    Mean squared error: 3.2034
MODEL        Relative error: 23.61932%
MODEL R-squared coefficient: 0.78673

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 3 KF ## </span>
MODEL    Mean squared error: 2.6118
MODEL        Relative error: 18.75663%
MODEL R-squared coefficient: 0.82612

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 3 NESTED ## </span>
MODEL    Mean squared error: 2.4838
MODEL        Relative error: 18.11004%
MODEL R-squared coefficient: 0.83464

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<pre>[{0.5877462466463899: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}},
 {0.6672317022822323: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}},
 {0.6072553098478701: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}}]</pre>
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<li>Referenziamo ogni mdello generato con una numerazione da 00 a 32 dove la prima cifra è il numero di modello e la seconda la gs differente.</li>
</ul>
<p>Notiamo come i risultati ottenuti utilizzando KF sono sensibilimente migliori di quelli con TSS.</p>
<p>Ora proviamo a creare un'ultimo modello, a partire dall'apparente migliore tra il precedenti, utilizzando un set univariato o miltivariato con meno features:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 2 SES ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs_ses</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span> <span class="n">grid2</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs_ses</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_ses_train</span><span class="p">,</span> <span class="n">y_ses_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_ses_val</span><span class="p">,</span> <span class="n">y_ses_val</span><span class="p">,</span> <span class="n">gs_ses</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">UNDERLINE</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">BOLD</span><span class="si">}{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">OKBLUE</span><span class="si">}</span><span class="se">\n</span><span class="s2">## MODEL 2 SOME ## </span><span class="si">{</span><span class="n">bcolors</span><span class="o">.</span><span class="n">ENDC</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">gs_some</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">model2</span><span class="p">,</span> <span class="n">grid2</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">kf</span><span class="p">)</span>
<span class="n">gs_some</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_some_train</span><span class="p">,</span> <span class="n">y_some_train</span><span class="p">)</span>
<span class="n">test_random</span><span class="p">(</span><span class="n">X_some_val</span><span class="p">,</span> <span class="n">y_some_val</span><span class="p">,</span> <span class="n">gs_some</span><span class="p">)</span>
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<pre><span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 2 SES ## </span>
MODEL    Mean squared error: 5.6369
MODEL        Relative error: 34.33898%
MODEL R-squared coefficient: 0.62471

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

<span class="ansi-blue-intense-fg ansi-bold ansi-underline">
## MODEL 2 SOME ## </span>
MODEL    Mean squared error: 3.0603
MODEL        Relative error: 24.88030%
MODEL R-squared coefficient: 0.79626

RANDOM   Mean squared error: 24.401
RANDOM       Relative error: 85.31813%
RANDOM R-squared coefficient: -0.62453

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<p>Notiamo come in entrambi i casi (0.62, 0.79) il R-squared coefficient (come anche le altre metriche) è minore del modello che utilizza tutte le feature (0.83).</p>

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<h2 id="4---Comparazione-Modelli">4 - Comparazione Modelli<a class="anchor-link" href="#4---Comparazione-Modelli">&#182;</a></h2><p>In questa sezione andremo a selezionare un set di modelli sulla base delle metriche già calcolate, poi faremo delle comparazioni di confidenza in modo da farne le dovute considerazioni.</p>
<p>I 3 modelli che generano metriche migliori sono i seguenti: <code>nested_best_model12</code> <code>nested_best_model22</code> <code>nested_best_model32</code>, analizziamone gli iperparametri che ne hanno portato valore:</p>

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<pre>[{0.5167538524492781: {&#39;poly__degree&#39;: 2, &#39;scale&#39;: None}},
 {0.6149391034485121: {&#39;poly__degree&#39;: 2, &#39;scale&#39;: None}},
 {0.5657776590856112: {&#39;poly__degree&#39;: 2,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}}]</pre>
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<pre>[{0.5921196454675517: {&#39;regr__alpha&#39;: 0.001,
   &#39;regr__gamma&#39;: 0.01,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}},
 {0.6746662133086062: {&#39;regr__alpha&#39;: 0.001,
   &#39;regr__gamma&#39;: 0.01,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}},
 {0.6185225781260214: {&#39;regr__alpha&#39;: 0.1,
   &#39;regr__gamma&#39;: 0.1,
   &#39;scale&#39;: StandardScaler(copy=True, with_mean=True, with_std=True)}}]</pre>
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<pre>[{0.5877462466463899: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}},
 {0.6672317022822323: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}},
 {0.6072553098478701: {&#39;regr__alpha&#39;: 7.196856730011514, &#39;regr__degree&#39;: 3}}]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">model_conf_interval</span><span class="p">(</span><span class="n">nested_best_model12</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_conf_interval</span><span class="p">(</span><span class="n">nested_best_model22</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_conf_interval</span><span class="p">(</span><span class="n">nested_best_model32</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span>
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<pre>(0.7610609545375031, 0.8420051570683784)
(0.7935498625067254, 0.8694311771348024)
(0.7932017568682754, 0.8691413607153999)
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<span class="nb">print</span><span class="p">(</span><span class="n">model_diff_interval</span><span class="p">(</span><span class="n">nested_best_model22</span><span class="p">,</span> <span class="n">nested_best_model32</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">model_diff_interval</span><span class="p">(</span><span class="n">nested_best_model32</span><span class="p">,</span> <span class="n">nested_best_model12</span><span class="p">,</span> <span class="n">X_val</span><span class="p">,</span> <span class="n">y_val</span><span class="p">))</span>
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<pre>(-0.025293934944689997, 0.08583600393072831)
(-0.05340897701949944, 0.05405357632889431)
(-0.025636557879438075, 0.08553402755608153)
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<p>Analizzando accuratamente tutti gli intervalli di confidenza e tutte le comparazioni tra modelli possiamo notare che <code>nested_best_mdel12</code> è quello che si distacca di più. Infatti la comparazione tra <code>nested_best_model22</code> e <code>nested_best_model32</code> lascia intendere che ci sia veramente poca differenza tra i due (in termini di confidenza), nonostante i modelli su cui si basano sono completamente diversi (<code>rbf</code>, <code>poly</code>). Di seguito ripropongo le relative metriche:</p>

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<pre>   Mean squared error: 2.4789
       Relative error: 16.86304%
R-squared coefficient: 0.83496
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<pre>   Mean squared error: 2.4838
       Relative error: 18.11004%
R-squared coefficient: 0.83464
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<h2 id="5---Modello-finale">5 - Modello finale<a class="anchor-link" href="#5---Modello-finale">&#182;</a></h2><p>In questo ultimo punto vedremo più nel dettaglio i parametri appresi dal modello da noi selezionato come migliore.</p>
<p>Dalla scelta finale posta qui sopra decidiamo di selezionare come modello finale <code>nested_best_model22</code> poichè presenta metriche leggermente migliori rispetto a <code>nested_best_model32</code>.</p>

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<pre>   Mean squared error: 2.4789
       Relative error: 16.86304%
R-squared coefficient: 0.83496
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<p>Poichè stiamo parlando di un modello KernelRidge, non abbiamo coefficienti facilmente interpretabili come in altri modelli di regressione classici. Dall'attributo <code>dual_coef_</code> di un oggetto KernelRidge è possibile ottenere coefficienti che fanno riferimento  allo spazio kernel.</p>

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<h2 id="Conclusioni">Conclusioni<a class="anchor-link" href="#Conclusioni">&#182;</a></h2>
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<p>In conclusione posso ritenermi soddisfatto dei risultati ottenuti, infatti, le predizioni ottenute hanno sempre avuto parametri di gran lunga migliori rispetto a dei valori casuali imputabili a predizioni di un modello random. Nonostante ciò sono convinto ci siano margini di miglioramento. Il prossimo step per rendere il modello utilzzabile è quello dello sviluppo della applicazione che calcoli l'inventario residuo a partire da una certa data e un certo numeoro di unità presenti al momento. Dalla data si possono ricavare informazioni in merito alle possibili <code>sessioni</code> e <code>visualizzazioni</code> nei giorni a seguire sulla base dei trend giornalieri/settimanli/mensili. La <code>per_buybox</code> giornaliera è disponibile al venditore e può essere presa in considerazione anche per le date successive. Il nuemro di <code>offerte_attive</code> è conosciuto al venditore sulla base degli attuali prodotti out-of-stock, come anche il <code>prezzo_medio</code> valore che imposta lui direttamente.</p>

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