fm_amazon_recommender.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 4.2.0 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=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#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;
}
.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;
}
@-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 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  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 {
  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:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.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 {
  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: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-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.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";
}
/*!
*
* 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;
}
/**
 * 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 {
  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;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#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#login_widget {
  float: right;
}
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;
}
@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;
}
/*!
*
* 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 {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
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 {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.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: baseline;
  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;
}
.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_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;
}
.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;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.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.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.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.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.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;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.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;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-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.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.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.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.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);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* 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;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  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 {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@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. */
  padding: 0.4em;
}
.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. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

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;
}
/* 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: 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 {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.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;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.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;
}
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.unrendered .text_cell_render {
  display: none;
}
.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;
}
/* 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;
}
.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 {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#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;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -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.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.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;
}
.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;
}
#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;
}
#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;
}
#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.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.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.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.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.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.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
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<h1 id="Building-a-Recommender-System-with-Amazon-SageMaker-Factorization-Machines-and-BlazingText">Building a Recommender System with Amazon SageMaker Factorization Machines and BlazingText<a class="anchor-link" href="#Building-a-Recommender-System-with-Amazon-SageMaker-Factorization-Machines-and-BlazingText">&#182;</a></h1><hr>
<hr>
<h2 id="Background">Background<a class="anchor-link" href="#Background">&#182;</a></h2><ul>
<li>Recommender systems were a catalyst for ML's popularity (Amazon, Netflix Prize)</li>
<li>User item matrix factorization is a core methodology</li>
<li>Factorization machines combine linear prediction with a factorized representation of pairwise feature interaction</li>
</ul>
<p>$$\hat{r} = w_0 + \sum_{i} {w_i x_i} + \sum_{i} {\sum_{j &gt; i} {\langle v_i, v_j \rangle x_i x_j}}$$</p>
<ul>
<li>SageMaker has a highly scalable factorization machines algorithm built-in</li>
</ul>
<hr>
<h2 id="Setup">Setup<a class="anchor-link" href="#Setup">&#182;</a></h2><ul>
<li>S3 bucket</li>
<li>IAM role for SageMaker and data permissions</li>
<li>Import necessary libraries</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sagemaker</span>

<span class="n">sess</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>
<span class="n">bucket</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">default_bucket</span><span class="p">()</span>
<span class="n">base</span> <span class="o">=</span> <span class="s1">&#39;DEMO-loft-recommender&#39;</span>
<span class="n">prefix</span> <span class="o">=</span> <span class="s1">&#39;sagemaker/&#39;</span> <span class="o">+</span> <span class="n">base</span>

<span class="n">role</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">get_execution_role</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">sagemaker</span>
<span class="kn">import</span> <span class="nn">os</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">boto3</span>
<span class="kn">import</span> <span class="nn">json</span>
<span class="kn">import</span> <span class="nn">io</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">sagemaker.amazon.common</span> <span class="k">as</span> <span class="nn">smac</span>
<span class="kn">from</span> <span class="nn">sagemaker.predictor</span> <span class="k">import</span> <span class="n">json_deserializer</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="k">import</span> <span class="n">csr_matrix</span>
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<hr>
<h2 id="Data">Data<a class="anchor-link" href="#Data">&#182;</a></h2><p><a href="https://s3.amazonaws.com/amazon-reviews-pds/readme.html">Amazon Reviews AWS Public Dataset</a></p>
<ul>
<li>1 to 5 star ratings</li>
<li>2M+ Amazon customers</li>
<li>160K+ digital videos </li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>mkdir /tmp/recsys/
<span class="o">!</span>aws s3 cp s3://amazon-reviews-pds/tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz /tmp/recsys/
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<pre>download: s3://amazon-reviews-pds/tsv/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz to ../../../../../tmp/recsys/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;/tmp/recsys/amazon_reviews_us_Digital_Video_Download_v1_00.tsv.gz&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">error_bad_lines</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>b&#39;Skipping line 92523: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 343254: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 524626: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 623024: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 977412: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 1496867: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 1711638: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 1787213: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 2395306: expected 15 fields, saw 22\n&#39;
b&#39;Skipping line 2527690: expected 15 fields, saw 22\n&#39;
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<table border="1" class="dataframe">
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      <th></th>
      <th>marketplace</th>
      <th>customer_id</th>
      <th>review_id</th>
      <th>product_id</th>
      <th>product_parent</th>
      <th>product_title</th>
      <th>product_category</th>
      <th>star_rating</th>
      <th>helpful_votes</th>
      <th>total_votes</th>
      <th>vine</th>
      <th>verified_purchase</th>
      <th>review_headline</th>
      <th>review_body</th>
      <th>review_date</th>
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      <th>0</th>
      <td>US</td>
      <td>12190288</td>
      <td>R3FU16928EP5TC</td>
      <td>B00AYB1482</td>
      <td>668895143</td>
      <td>Enlightened: Season 1</td>
      <td>Digital_Video_Download</td>
      <td>5</td>
      <td>0</td>
      <td>0</td>
      <td>N</td>
      <td>Y</td>
      <td>I loved it and I wish there was a season 3</td>
      <td>I loved it and I wish there was a season 3... ...</td>
      <td>2015-08-31</td>
    </tr>
    <tr>
      <th>1</th>
      <td>US</td>
      <td>30549954</td>
      <td>R1IZHHS1MH3AQ4</td>
      <td>B00KQD28OM</td>
      <td>246219280</td>
      <td>Vicious</td>
      <td>Digital_Video_Download</td>
      <td>5</td>
      <td>0</td>
      <td>0</td>
      <td>N</td>
      <td>Y</td>
      <td>As always it seems that the best shows come fr...</td>
      <td>As always it seems that the best shows come fr...</td>
      <td>2015-08-31</td>
    </tr>
    <tr>
      <th>2</th>
      <td>US</td>
      <td>52895410</td>
      <td>R52R85WC6TIAH</td>
      <td>B01489L5LQ</td>
      <td>534732318</td>
      <td>After Words</td>
      <td>Digital_Video_Download</td>
      <td>4</td>
      <td>17</td>
      <td>18</td>
      <td>N</td>
      <td>Y</td>
      <td>Charming movie</td>
      <td>This movie isn't perfect, but it gets a lot of...</td>
      <td>2015-08-31</td>
    </tr>
    <tr>
      <th>3</th>
      <td>US</td>
      <td>27072354</td>
      <td>R7HOOYTVIB0DS</td>
      <td>B008LOVIIK</td>
      <td>239012694</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>Digital_Video_Download</td>
      <td>5</td>
      <td>0</td>
      <td>0</td>
      <td>N</td>
      <td>Y</td>
      <td>Five Stars</td>
      <td>excellant this is what tv should be</td>
      <td>2015-08-31</td>
    </tr>
    <tr>
      <th>4</th>
      <td>US</td>
      <td>26939022</td>
      <td>R1XQ2N5CDOZGNX</td>
      <td>B0094LZMT0</td>
      <td>535858974</td>
      <td>On The Waterfront</td>
      <td>Digital_Video_Download</td>
      <td>5</td>
      <td>0</td>
      <td>0</td>
      <td>N</td>
      <td>Y</td>
      <td>Brilliant film from beginning to end</td>
      <td>Brilliant film from beginning to end. All of t...</td>
      <td>2015-08-31</td>
    </tr>
  </tbody>
</table>
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<p>Dataset columns:</p>
<ul>
<li><code>marketplace</code>: 2-letter country code (in this case all "US").</li>
<li><code>customer_id</code>: Random identifier that can be used to aggregate reviews written by a single author.</li>
<li><code>review_id</code>: A unique ID for the review.</li>
<li><code>product_id</code>: The Amazon Standard Identification Number (ASIN).  <code>http://www.amazon.com/dp/&lt;ASIN&gt;</code> links to the product's detail page.</li>
<li><code>product_parent</code>: The parent of that ASIN.  Multiple ASINs (color or format variations of the same product) can roll up into a single parent parent.</li>
<li><code>product_title</code>: Title description of the product.</li>
<li><code>product_category</code>: Broad product category that can be used to group reviews (in this case digital videos).</li>
<li><code>star_rating</code>: The review's rating (1 to 5 stars).</li>
<li><code>helpful_votes</code>: Number of helpful votes for the review.</li>
<li><code>total_votes</code>: Number of total votes the review received.</li>
<li><code>vine</code>: Was the review written as part of the <a href="https://www.amazon.com/gp/vine/help">Vine</a> program?</li>
<li><code>verified_purchase</code>: Was the review from a verified purchase?</li>
<li><code>review_headline</code>: The title of the review itself.</li>
<li><code>review_body</code>: The text of the review.</li>
<li><code>review_date</code>: The date the review was written.</li>
</ul>
<p>Drop some fields that won't be used</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">,</span> <span class="s1">&#39;product_id&#39;</span><span class="p">,</span> <span class="s1">&#39;product_title&#39;</span><span class="p">,</span> <span class="s1">&#39;star_rating&#39;</span><span class="p">,</span> <span class="s1">&#39;review_date&#39;</span><span class="p">]]</span>
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<p>Most users don't rate most movies - Check our long tail</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">products</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;product_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>

<span class="n">quantiles</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">,</span> <span class="mf">0.03</span><span class="p">,</span> <span class="mf">0.04</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.96</span><span class="p">,</span> <span class="mf">0.97</span><span class="p">,</span> <span class="mf">0.98</span><span class="p">,</span> <span class="mf">0.99</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;customers</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">customers</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">quantiles</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;products</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">products</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">quantiles</span><span class="p">))</span>
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<pre>customers
 0.00       1.0
0.01       1.0
0.02       1.0
0.03       1.0
0.04       1.0
0.05       1.0
0.10       1.0
0.25       1.0
0.50       1.0
0.75       2.0
0.90       4.0
0.95       5.0
0.96       6.0
0.97       7.0
0.98       9.0
0.99      13.0
1.00    2704.0
Name: customer_id, dtype: float64
products
 0.00        1.00
0.01        1.00
0.02        1.00
0.03        1.00
0.04        1.00
0.05        1.00
0.10        1.00
0.25        1.00
0.50        3.00
0.75        9.00
0.90       31.00
0.95       73.00
0.96       95.00
0.97      130.00
0.98      199.00
0.99      386.67
1.00    32790.00
Name: product_id, dtype: float64
</pre>
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<p>Filter out customers who haven't rated many movies</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers</span> <span class="o">=</span> <span class="n">customers</span><span class="p">[</span><span class="n">customers</span> <span class="o">&gt;=</span> <span class="mi">5</span><span class="p">]</span>
<span class="n">products</span> <span class="o">=</span> <span class="n">products</span><span class="p">[</span><span class="n">products</span> <span class="o">&gt;=</span> <span class="mi">10</span><span class="p">]</span>

<span class="n">reduced_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;customer_id&#39;</span><span class="p">:</span> <span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="p">}))</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;product_id&#39;</span><span class="p">:</span> <span class="n">products</span><span class="o">.</span><span class="n">index</span><span class="p">}))</span>
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<p>Create a sequential index for customers and movies</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">products</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;product_id&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customer_index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;customer_id&#39;</span><span class="p">:</span> <span class="n">customers</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="s1">&#39;user&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">customers</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])})</span>
<span class="n">product_index</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;product_id&#39;</span><span class="p">:</span> <span class="n">products</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> 
                              <span class="s1">&#39;item&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">products</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">customer_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]})</span>

<span class="n">reduced_df</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">customer_index</span><span class="p">)</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">product_index</span><span class="p">)</span>
<span class="n">reduced_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>customer_id</th>
      <th>product_id</th>
      <th>product_title</th>
      <th>star_rating</th>
      <th>review_date</th>
      <th>user</th>
      <th>item</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>27072354</td>
      <td>B008LOVIIK</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>5</td>
      <td>2015-08-31</td>
      <td>10463</td>
      <td>140450</td>
    </tr>
    <tr>
      <th>1</th>
      <td>16030865</td>
      <td>B008LOVIIK</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>5</td>
      <td>2014-06-20</td>
      <td>489</td>
      <td>140450</td>
    </tr>
    <tr>
      <th>2</th>
      <td>44025160</td>
      <td>B008LOVIIK</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>5</td>
      <td>2014-05-27</td>
      <td>32100</td>
      <td>140450</td>
    </tr>
    <tr>
      <th>3</th>
      <td>18602179</td>
      <td>B008LOVIIK</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>5</td>
      <td>2014-12-23</td>
      <td>2237</td>
      <td>140450</td>
    </tr>
    <tr>
      <th>4</th>
      <td>14424972</td>
      <td>B008LOVIIK</td>
      <td>Masterpiece: Inspector Lewis Season 5</td>
      <td>5</td>
      <td>2015-08-31</td>
      <td>32340</td>
      <td>140450</td>
    </tr>
  </tbody>
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<p>Count days since first review (included as a feature to capture trend)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;review_date&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;review_date&#39;</span><span class="p">])</span>
<span class="n">customer_first_date</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;customer_id&#39;</span><span class="p">)[</span><span class="s1">&#39;review_date&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">customer_first_date</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">,</span> <span class="s1">&#39;first_review_date&#39;</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">reduced_df</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">customer_first_date</span><span class="p">)</span>
<span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;days_since_first&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;review_date&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;first_review_date&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">dt</span><span class="o">.</span><span class="n">days</span>
<span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;days_since_first&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="p">[</span><span class="s1">&#39;days_since_first&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<p>Split into train and test datasets</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_df</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;customer_id&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">last</span><span class="p">()</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>

<span class="n">train_df</span> <span class="o">=</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">test_df</span><span class="p">[[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">,</span> <span class="s1">&#39;product_id&#39;</span><span class="p">]],</span> 
                            <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;customer_id&#39;</span><span class="p">,</span> <span class="s1">&#39;product_id&#39;</span><span class="p">],</span> 
                            <span class="n">how</span><span class="o">=</span><span class="s1">&#39;outer&#39;</span><span class="p">,</span> 
                            <span class="n">indicator</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">train_df</span> <span class="o">=</span> <span class="n">train_df</span><span class="p">[(</span><span class="n">train_df</span><span class="p">[</span><span class="s1">&#39;_merge&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;left_only&#39;</span><span class="p">)]</span>
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<ul>
<li>Factorization machines expects data to look something like:<ul>
<li>Target variable is that user's rating for a movie  </li>
<li>One-hot encoding for users ($N$ features)</li>
<li>One-hot encoding for movies ($M$ features)</li>
</ul>
</li>
</ul>
<table>
<thead><tr>
<th>Rating</th>
<th>User1</th>
<th>User2</th>
<th>...</th>
<th>UserN</th>
<th>Movie1</th>
<th>Movie2</th>
<th>Movie3</th>
<th>...</th>
<th>MovieM</th>
<th>Feature1</th>
<th>Feature2</th>
<th>...</th>
</tr>
</thead>
<tbody>
<tr>
<td>4</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>20</td>
<td>2.2</td>
<td>...</td>
</tr>
<tr>
<td>5</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>17</td>
<td>9.1</td>
<td>...</td>
</tr>
<tr>
<td>3</td>
<td>0</td>
<td>1</td>
<td>...</td>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>...</td>
<td>0</td>
<td>3</td>
<td>11.0</td>
<td>...</td>
</tr>
<tr>
<td>4</td>
<td>0</td>
<td>1</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>1</td>
<td>...</td>
<td>0</td>
<td>15</td>
<td>6.4</td>
<td>...</td>
</tr>
</tbody>
</table>
<ul>
<li>Wouldn't want to hold this full matrix in memory<ul>
<li>Create a sparse matrix</li>
</ul>
</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">to_csr_matrix</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">num_users</span><span class="p">,</span> <span class="n">num_items</span><span class="p">):</span>
    <span class="n">feature_dim</span> <span class="o">=</span> <span class="n">num_users</span> <span class="o">+</span> <span class="n">num_items</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                           <span class="n">df</span><span class="p">[</span><span class="s1">&#39;days_since_first&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">])</span>
    <span class="n">row</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)</span>
    <span class="n">col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;user&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                          <span class="n">df</span><span class="p">[</span><span class="s1">&#39;item&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                          <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">feature_dim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])])</span>
    <span class="k">return</span> <span class="n">csr_matrix</span><span class="p">((</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">)),</span> 
                      <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">feature_dim</span><span class="p">),</span> 
                      <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">train_csr</span> <span class="o">=</span> <span class="n">to_csr_matrix</span><span class="p">(</span><span class="n">train_df</span><span class="p">,</span> <span class="n">customer_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">product_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">test_csr</span> <span class="o">=</span> <span class="n">to_csr_matrix</span><span class="p">(</span><span class="n">test_df</span><span class="p">,</span> <span class="n">customer_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">product_index</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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<p>Convert to sparse recordIO-wrapped protobuf that SageMaker factorization machines expects</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">to_s3_protobuf</span><span class="p">(</span><span class="n">csr</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="n">splits</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
    <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array_split</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">csr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">splits</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)):</span>
        <span class="n">index</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="n">buf</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span>
        <span class="n">smac</span><span class="o">.</span><span class="n">write_spmatrix_to_sparse_tensor</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">csr</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="p">],</span> <span class="n">label</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
        <span class="n">buf</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
        <span class="n">boto3</span><span class="o">.</span><span class="n">client</span><span class="p">(</span><span class="s1">&#39;s3&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">upload_fileobj</span><span class="p">(</span><span class="n">buf</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/data-</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">,</span> <span class="n">channel</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">to_s3_protobuf</span><span class="p">(</span><span class="n">train_csr</span><span class="p">,</span> <span class="n">train_df</span><span class="p">[</span><span class="s1">&#39;star_rating&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">)</span>
<span class="n">to_s3_protobuf</span><span class="p">(</span><span class="n">test_csr</span><span class="p">,</span> <span class="n">test_df</span><span class="p">[</span><span class="s1">&#39;star_rating&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">channel</span><span class="o">=</span><span class="s1">&#39;test&#39;</span><span class="p">,</span> <span class="n">splits</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<h2 id="Train">Train<a class="anchor-link" href="#Train">&#182;</a></h2><ul>
<li>Create a <a href="https://github.com/aws/sagemaker-python-sdk">SageMaker Python SDK</a> estimator to run a training jobs and specify:<ul>
<li>Algorithm container image</li>
<li>IAM role</li>
<li>Hardware setup</li>
<li>S3 output location</li>
<li>Algorithm hyperparameters<ul>
<li><code>feature_dim</code>: $N + M + 1$ (additional feature is <code>days_since_first</code> to capture trend)</li>
<li><code>num_factors</code>: reduced dimension of factorized interactions</li>
<li><code>epochs</code>: number of full passes through the dataset</li>
</ul>
</li>
</ul>
</li>
<li><code>.fit()</code> points to training and test data in S3 and begins the training job</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fm</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">Estimator</span><span class="p">(</span>
    <span class="n">sagemaker</span><span class="o">.</span><span class="n">amazon</span><span class="o">.</span><span class="n">amazon_estimator</span><span class="o">.</span><span class="n">get_image_uri</span><span class="p">(</span><span class="n">boto3</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span><span class="o">.</span><span class="n">region_name</span><span class="p">,</span> <span class="s1">&#39;factorization-machines&#39;</span><span class="p">,</span> <span class="s1">&#39;latest&#39;</span><span class="p">),</span>
    <span class="n">role</span><span class="p">,</span> 
    <span class="n">train_instance_count</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> 
    <span class="n">train_instance_type</span><span class="o">=</span><span class="s1">&#39;ml.c5.2xlarge&#39;</span><span class="p">,</span>
    <span class="n">output_path</span><span class="o">=</span><span class="s1">&#39;s3://</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/output&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">),</span>
    <span class="n">base_job_name</span><span class="o">=</span><span class="n">base</span><span class="p">,</span>
    <span class="n">sagemaker_session</span><span class="o">=</span><span class="n">sess</span><span class="p">)</span>

<span class="n">fm</span><span class="o">.</span><span class="n">set_hyperparameters</span><span class="p">(</span>
    <span class="n">feature_dim</span><span class="o">=</span><span class="n">customer_index</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">product_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
    <span class="n">predictor_type</span><span class="o">=</span><span class="s1">&#39;regressor&#39;</span><span class="p">,</span>
    <span class="n">mini_batch_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
    <span class="n">num_factors</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
    <span class="n">epochs</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>

<span class="n">fm</span><span class="o">.</span><span class="n">fit</span><span class="p">({</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">s3_input</span><span class="p">(</span><span class="s1">&#39;s3://</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/train/&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">),</span> <span class="n">distribution</span><span class="o">=</span><span class="s1">&#39;ShardedByS3Key&#39;</span><span class="p">),</span> 
        <span class="s1">&#39;test&#39;</span><span class="p">:</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">s3_input</span><span class="p">(</span><span class="s1">&#39;s3://</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/test/&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">),</span> <span class="n">distribution</span><span class="o">=</span><span class="s1">&#39;FullyReplicated&#39;</span><span class="p">)})</span>
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<pre>INFO:sagemaker:Creating training-job with name: DEMO-loft-recommender-2018-09-06-03-06-45-001
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<pre>..................
<span class="ansi-red-fg">Docker entrypoint called with argument(s): train</span>
<span class="ansi-green-fg">Docker entrypoint called with argument(s): train</span>
<span class="ansi-red-fg">[09/06/2018 03:09:40 INFO 140469731592000] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-conf.json: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: 1, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:40 INFO 140469731592000] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;num_factors&#39;: u&#39;256&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:40 INFO 140469731592000] Final configuration: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;num_factors&#39;: u&#39;256&#39;, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:40 WARNING 140469731592000] Loggers have already been setup.</span>
<span class="ansi-green-fg">[09/06/2018 03:09:41 INFO 139652459214656] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-conf.json: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: 1, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:41 INFO 139652459214656] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;num_factors&#39;: u&#39;256&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:41 INFO 139652459214656] Final configuration: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;num_factors&#39;: u&#39;256&#39;, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:41 WARNING 139652459214656] Loggers have already been setup.</span>
<span class="ansi-green-fg">[09/06/2018 03:09:43 INFO 139652459214656] Launching parameter server for role server</span>
<span class="ansi-green-fg">[09/06/2018 03:09:43 INFO 139652459214656] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/32e9876c-a3d7-4ad3-a4d1-c60c9e2dc9b0&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:43 INFO 139652459214656] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/32e9876c-a3d7-4ad3-a4d1-c60c9e2dc9b0&#39;, &#39;DMLC_ROLE&#39;: &#39;server&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:43 INFO 139652459214656] Environment: {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/32e9876c-a3d7-4ad3-a4d1-c60c9e2dc9b0&#39;, &#39;DMLC_ROLE&#39;: &#39;worker&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-red-fg">Docker entrypoint called with argument(s): train</span>
<span class="ansi-red-fg">[09/06/2018 03:09:43 INFO 140614843680576] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-conf.json: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: 1, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:43 INFO 140614843680576] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;num_factors&#39;: u&#39;256&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:43 INFO 140614843680576] Final configuration: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;num_factors&#39;: u&#39;256&#39;, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:43 WARNING 140614843680576] Loggers have already been setup.</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Launching parameter server for role scheduler</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/94d30e38-3526-4880-b4af-fc3376fa325e&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/94d30e38-3526-4880-b4af-fc3376fa325e&#39;, &#39;DMLC_ROLE&#39;: &#39;scheduler&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Launching parameter server for role server</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/94d30e38-3526-4880-b4af-fc3376fa325e&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/94d30e38-3526-4880-b4af-fc3376fa325e&#39;, &#39;DMLC_ROLE&#39;: &#39;server&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Environment: {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/94d30e38-3526-4880-b4af-fc3376fa325e&#39;, &#39;DMLC_ROLE&#39;: &#39;worker&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Using default worker.</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] nvidia-smi took: 0.0251448154449 secs to identify 0 gpus</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Number of GPUs being used: 0</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] [Sparse network] Building a sparse network.</span>
<span class="ansi-red-fg">[09/06/2018 03:09:44 INFO 140614843680576] Create Store: dist_async</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:43 INFO 139652459214656] Using default worker.</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:44 INFO 139652459214656] nvidia-smi took: 0.0251498222351 secs to identify 0 gpus</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:44 INFO 139652459214656] Number of GPUs being used: 0</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:44 INFO 139652459214656] [Sparse network] Building a sparse network.</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:44 INFO 139652459214656] Create Store: dist_async</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] Launching parameter server for role server</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/ed900454-ab6d-4686-ab36-c88d25247f0b&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/ed900454-ab6d-4686-ab36-c88d25247f0b&#39;, &#39;DMLC_ROLE&#39;: &#39;server&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] Environment: {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/ed900454-ab6d-4686-ab36-c88d25247f0b&#39;, &#39;DMLC_ROLE&#39;: &#39;worker&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] Using default worker.</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] nvidia-smi took: 0.0251350402832 secs to identify 0 gpus</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] Number of GPUs being used: 0</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] [Sparse network] Building a sparse network.</span>
<span class="ansi-green-fg">[09/06/2018 03:09:44 INFO 140469731592000] Create Store: dist_async</span>
<span class="ansi-yellow-fg">Docker entrypoint called with argument(s): train</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-conf.json: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: 1, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;num_factors&#39;: u&#39;256&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Final configuration: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;num_factors&#39;: u&#39;256&#39;, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 WARNING 140205845833536] Loggers have already been setup.</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Launching parameter server for role server</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/f0db6c98-e12f-44de-aa4e-6cf57f5855ad&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/f0db6c98-e12f-44de-aa4e-6cf57f5855ad&#39;, &#39;DMLC_ROLE&#39;: &#39;server&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Environment: {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/f0db6c98-e12f-44de-aa4e-6cf57f5855ad&#39;, &#39;DMLC_ROLE&#39;: &#39;worker&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Using default worker.</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] nvidia-smi took: 0.0251340866089 secs to identify 0 gpus</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Number of GPUs being used: 0</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] [Sparse network] Building a sparse network.</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:47 INFO 140205845833536] Create Store: dist_async</span>
<span class="ansi-magenta-fg">Docker entrypoint called with argument(s): train</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/resources/default-conf.json: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: 1, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;num_factors&#39;: u&#39;256&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Final configuration: {u&#39;factors_lr&#39;: u&#39;0.0001&#39;, u&#39;linear_init_sigma&#39;: u&#39;0.01&#39;, u&#39;epochs&#39;: u&#39;3&#39;, u&#39;feature_dim&#39;: u&#39;178730&#39;, u&#39;num_factors&#39;: u&#39;256&#39;, u&#39;_wd&#39;: u&#39;1.0&#39;, u&#39;_num_kv_servers&#39;: u&#39;auto&#39;, u&#39;use_bias&#39;: u&#39;true&#39;, u&#39;factors_init_sigma&#39;: u&#39;0.001&#39;, u&#39;_log_level&#39;: u&#39;info&#39;, u&#39;bias_init_method&#39;: u&#39;normal&#39;, u&#39;linear_init_method&#39;: u&#39;normal&#39;, u&#39;linear_lr&#39;: u&#39;0.001&#39;, u&#39;factors_init_method&#39;: u&#39;normal&#39;, u&#39;_tuning_objective_metric&#39;: u&#39;&#39;, u&#39;bias_wd&#39;: u&#39;0.01&#39;, u&#39;use_linear&#39;: u&#39;true&#39;, u&#39;bias_lr&#39;: u&#39;0.1&#39;, u&#39;mini_batch_size&#39;: u&#39;1000&#39;, u&#39;_use_full_symbolic&#39;: u&#39;true&#39;, u&#39;batch_metrics_publish_interval&#39;: u&#39;500&#39;, u&#39;predictor_type&#39;: u&#39;regressor&#39;, u&#39;bias_init_sigma&#39;: u&#39;0.01&#39;, u&#39;_num_gpus&#39;: u&#39;auto&#39;, u&#39;_data_format&#39;: u&#39;record&#39;, u&#39;factors_wd&#39;: u&#39;0.00001&#39;, u&#39;linear_wd&#39;: u&#39;0.001&#39;, u&#39;_kvstore&#39;: u&#39;auto&#39;, u&#39;_learning_rate&#39;: u&#39;1.0&#39;, u&#39;_optimizer&#39;: u&#39;adam&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 WARNING 140143585908544] Loggers have already been setup.</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Launching parameter server for role server</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/b1ffc328-0c8c-4afd-840f-5573fd405e77&#39;, &#39;PWD&#39;: &#39;/&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] envs={&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/b1ffc328-0c8c-4afd-840f-5573fd405e77&#39;, &#39;DMLC_ROLE&#39;: &#39;server&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Environment: {&#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION_VERSION&#39;: &#39;2&#39;, &#39;DMLC_PS_ROOT_PORT&#39;: &#39;9000&#39;, &#39;DMLC_NUM_WORKER&#39;: &#39;5&#39;, &#39;SAGEMAKER_HTTP_PORT&#39;: &#39;8080&#39;, &#39;HOME&#39;: &#39;/root&#39;, &#39;PYTHONUNBUFFERED&#39;: &#39;TRUE&#39;, &#39;CANONICAL_ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;LD_LIBRARY_PATH&#39;: &#39;/usr/local/nvidia/lib64:/opt/amazon/lib&#39;, &#39;LANG&#39;: &#39;en_US.utf8&#39;, &#39;DMLC_INTERFACE&#39;: &#39;ethwe&#39;, &#39;SHLVL&#39;: &#39;1&#39;, &#39;DMLC_PS_ROOT_URI&#39;: &#39;10.40.0.3&#39;, &#39;AWS_REGION&#39;: &#39;us-west-2&#39;, &#39;NVIDIA_VISIBLE_DEVICES&#39;: &#39;all&#39;, &#39;TRAINING_JOB_NAME&#39;: &#39;DEMO-loft-recommender-2018-09-06-03-06-45-001&#39;, &#39;PATH&#39;: &#39;/opt/amazon/bin:/usr/local/nvidia/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/amazon/bin:/opt/amazon/bin&#39;, &#39;PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION&#39;: &#39;cpp&#39;, &#39;ENVROOT&#39;: &#39;/opt/amazon&#39;, &#39;SAGEMAKER_DATA_PATH&#39;: &#39;/opt/ml&#39;, &#39;NVIDIA_DRIVER_CAPABILITIES&#39;: &#39;compute,utility&#39;, &#39;NVIDIA_REQUIRE_CUDA&#39;: &#39;cuda&gt;=9.0&#39;, &#39;OMP_NUM_THREADS&#39;: &#39;4&#39;, &#39;HOSTNAME&#39;: &#39;aws&#39;, &#39;AWS_CONTAINER_CREDENTIALS_RELATIVE_URI&#39;: &#39;/v2/credentials/b1ffc328-0c8c-4afd-840f-5573fd405e77&#39;, &#39;DMLC_ROLE&#39;: &#39;worker&#39;, &#39;PWD&#39;: &#39;/&#39;, &#39;DMLC_NUM_SERVER&#39;: &#39;5&#39;}</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Using default worker.</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] nvidia-smi took: 0.0251400470734 secs to identify 0 gpus</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Number of GPUs being used: 0</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] [Sparse network] Building a sparse network.</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:53 INFO 140143585908544] Create Store: dist_async</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;initialize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 422.9099750518799, &#34;sum&#34;: 422.9099750518799, &#34;min&#34;: 422.9099750518799}}, &#34;EndTime&#34;: 1536203394.261082, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203393.803728}
</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203394.261255, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Meta&#34;: &#34;init_train_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.261224}
</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;initialize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10031.01396560669, &#34;sum&#34;: 10031.01396560669, &#34;min&#34;: 10031.01396560669}}, &#34;EndTime&#34;: 1536203394.261606, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203384.197279}
</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203394.26177, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Meta&#34;: &#34;init_train_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.261741}
</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;initialize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10243.221044540405, &#34;sum&#34;: 10243.221044540405, &#34;min&#34;: 10243.221044540405}}, &#34;EndTime&#34;: 1536203394.263399, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203383.983532}
</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203394.263571, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Meta&#34;: &#34;init_train_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.26354}
</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;initialize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10055.996179580688, &#34;sum&#34;: 10055.996179580688, &#34;min&#34;: 10055.996179580688}}, &#34;EndTime&#34;: 1536203394.262936, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203384.179576}
</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203394.263111, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Meta&#34;: &#34;init_train_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.263077}
</span>
<span class="ansi-green-fg">[03:09:54] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;initialize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 6341.556072235107, &#34;sum&#34;: 6341.556072235107, &#34;min&#34;: 6341.556072235107}}, &#34;EndTime&#34;: 1536203394.263398, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203387.887629}
</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 0, &#34;sum&#34;: 0.0, &#34;min&#34;: 0}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1000, &#34;sum&#34;: 1000.0, &#34;min&#34;: 1000}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203394.263568, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Meta&#34;: &#34;init_train_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.263537}
</span>
<span class="ansi-yellow-fg">[03:09:54] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-magenta-fg">[03:09:54] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-magenta-fg">[03:09:55] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-magenta-fg">[09/06/2018 03:09:55 INFO 140143585908544] #quality_metric: host=algo-5, epoch=0, batch=0 train rmse &lt;loss&gt;=5.73586578033</span>
<span class="ansi-red-fg">[03:09:54] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-red-fg">[03:09:55] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-red-fg">[09/06/2018 03:09:55 INFO 140614843680576] #quality_metric: host=algo-1, epoch=0, batch=0 train rmse &lt;loss&gt;=6.84754175781</span>
<span class="ansi-blue-fg">[03:09:54] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-blue-fg">[03:09:55] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-blue-fg">[09/06/2018 03:09:55 INFO 139652459214656] #quality_metric: host=algo-4, epoch=0, batch=0 train rmse &lt;loss&gt;=6.97112655664</span>
<span class="ansi-green-fg">[03:09:55] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-green-fg">[09/06/2018 03:09:55 INFO 140469731592000] #quality_metric: host=algo-2, epoch=0, batch=0 train rmse &lt;loss&gt;=6.91789798277</span>
<span class="ansi-yellow-fg">[03:09:55] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.1.x.200686.0/RHEL5_64/generic-flavor/src/src/kvstore/./kvstore_local.h:280: Warning: non-default weights detected during kvstore pull. This call has been ignored. Please make sure to use row_sparse_pull with row_ids.</span>
<span class="ansi-yellow-fg">[09/06/2018 03:09:55 INFO 140205845833536] #quality_metric: host=algo-3, epoch=0, batch=0 train rmse &lt;loss&gt;=6.81204241583</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:04 INFO 140143585908544] #quality_metric: host=algo-5, epoch=0, train rmse &lt;loss&gt;=2.01874752175</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;epochs&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}, &#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10096.678972244263, &#34;sum&#34;: 10096.678972244263, &#34;min&#34;: 10096.678972244263}}, &#34;EndTime&#34;: 1536203404.35815, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.261171}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:04 INFO 140143585908544] #progress_metric: host=algo-5, completed 33 % of epochs</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 207, &#34;sum&#34;: 207.0, &#34;min&#34;: 207}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206721, &#34;sum&#34;: 206721.0, &#34;min&#34;: 206721}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2, &#34;sum&#34;: 2.0, &#34;min&#34;: 2}}, &#34;EndTime&#34;: 1536203404.358321, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 0}, &#34;StartTime&#34;: 1536203394.261445}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:04 INFO 140143585908544] #throughput_metric: host=algo-5, train throughput=20374.5626753 records/second</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:04 INFO 140143585908544] #quality_metric: host=algo-5, epoch=1, batch=0 train rmse &lt;loss&gt;=1.31124642516</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:04 INFO 140205845833536] #quality_metric: host=algo-3, epoch=0, train rmse &lt;loss&gt;=1.9596719347</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;epochs&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}, &#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10139.554977416992, &#34;sum&#34;: 10139.554977416992, &#34;min&#34;: 10139.554977416992}}, &#34;EndTime&#34;: 1536203404.403334, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.263488}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:04 INFO 140205845833536] #progress_metric: host=algo-3, completed 33 % of epochs</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 207, &#34;sum&#34;: 207.0, &#34;min&#34;: 207}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206721, &#34;sum&#34;: 206721.0, &#34;min&#34;: 206721}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2, &#34;sum&#34;: 2.0, &#34;min&#34;: 2}}, &#34;EndTime&#34;: 1536203404.403535, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 0}, &#34;StartTime&#34;: 1536203394.263749}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:04 INFO 140205845833536] #throughput_metric: host=algo-3, train throughput=20288.3354209 records/second</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:04 INFO 140205845833536] #quality_metric: host=algo-3, epoch=1, batch=0 train rmse &lt;loss&gt;=1.10112369363</span>
<span class="ansi-green-fg">[09/06/2018 03:10:04 INFO 140469731592000] #quality_metric: host=algo-2, epoch=0, train rmse &lt;loss&gt;=2.03279279171</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;epochs&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}, &#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 9988.441944122314, &#34;sum&#34;: 9988.441944122314, &#34;min&#34;: 9988.441944122314}}, &#34;EndTime&#34;: 1536203404.251817, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.263023}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:04 INFO 140469731592000] #progress_metric: host=algo-2, completed 33 % of epochs</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 207, &#34;sum&#34;: 207.0, &#34;min&#34;: 207}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206721, &#34;sum&#34;: 206721.0, &#34;min&#34;: 206721}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2, &#34;sum&#34;: 2.0, &#34;min&#34;: 2}}, &#34;EndTime&#34;: 1536203404.252063, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 0}, &#34;StartTime&#34;: 1536203394.263343}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:04 INFO 140469731592000] #throughput_metric: host=algo-2, train throughput=20595.0632551 records/second</span>
<span class="ansi-green-fg">[09/06/2018 03:10:04 INFO 140469731592000] #quality_metric: host=algo-2, epoch=1, batch=0 train rmse &lt;loss&gt;=1.29660934779</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:04 INFO 139652459214656] #quality_metric: host=algo-4, epoch=0, train rmse &lt;loss&gt;=1.94780342155</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;epochs&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}, &#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10218.36805343628, &#34;sum&#34;: 10218.36805343628, &#34;min&#34;: 10218.36805343628}}, &#34;EndTime&#34;: 1536203404.482155, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.263491}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:04 INFO 139652459214656] #progress_metric: host=algo-4, completed 33 % of epochs</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 207, &#34;sum&#34;: 207.0, &#34;min&#34;: 207}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206721, &#34;sum&#34;: 206721.0, &#34;min&#34;: 206721}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2, &#34;sum&#34;: 2.0, &#34;min&#34;: 2}}, &#34;EndTime&#34;: 1536203404.482743, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 0}, &#34;StartTime&#34;: 1536203394.263753}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:04 INFO 139652459214656] #throughput_metric: host=algo-4, train throughput=20130.8065329 records/second</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:04 INFO 139652459214656] #quality_metric: host=algo-4, epoch=1, batch=0 train rmse &lt;loss&gt;=1.26854370244</span>
<span class="ansi-red-fg">[09/06/2018 03:10:04 INFO 140614843680576] #quality_metric: host=algo-1, epoch=0, train rmse &lt;loss&gt;=2.05622698443</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;epochs&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}, &#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 10734.019994735718, &#34;sum&#34;: 10734.019994735718, &#34;min&#34;: 10734.019994735718}}, &#34;EndTime&#34;: 1536203404.995999, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203394.261693}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:04 INFO 140614843680576] #progress_metric: host=algo-1, completed 33 % of epochs</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 207, &#34;sum&#34;: 207.0, &#34;min&#34;: 207}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206722, &#34;sum&#34;: 206722.0, &#34;min&#34;: 206722}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2, &#34;sum&#34;: 2.0, &#34;min&#34;: 2}}, &#34;EndTime&#34;: 1536203404.996201, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 0}, &#34;StartTime&#34;: 1536203394.261951}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:04 INFO 140614843680576] #throughput_metric: host=algo-1, train throughput=19164.8268901 records/second</span>
<span class="ansi-red-fg">[09/06/2018 03:10:05 INFO 140614843680576] #quality_metric: host=algo-1, epoch=1, batch=0 train rmse &lt;loss&gt;=1.03547359522</span>
<span class="ansi-green-fg">[09/06/2018 03:10:13 INFO 140469731592000] #quality_metric: host=algo-2, epoch=1, train rmse &lt;loss&gt;=1.73037953711</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8827.538013458252, &#34;sum&#34;: 8827.538013458252, &#34;min&#34;: 8827.538013458252}}, &#34;EndTime&#34;: 1536203413.081044, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203404.251913}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:13 INFO 140469731592000] #progress_metric: host=algo-2, completed 66 % of epochs</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 413, &#34;sum&#34;: 413.0, &#34;min&#34;: 413}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 412442, &#34;sum&#34;: 412442.0, &#34;min&#34;: 412442}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}}, &#34;EndTime&#34;: 1536203413.081761, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 1}, &#34;StartTime&#34;: 1536203404.253466}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:13 INFO 140469731592000] #throughput_metric: host=algo-2, train throughput=23301.7297914 records/second</span>
<span class="ansi-green-fg">[09/06/2018 03:10:13 INFO 140469731592000] #quality_metric: host=algo-2, epoch=2, batch=0 train rmse &lt;loss&gt;=3.26323596823</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:13 INFO 140205845833536] #quality_metric: host=algo-3, epoch=1, train rmse &lt;loss&gt;=1.68890370331</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 9015.956163406372, &#34;sum&#34;: 9015.956163406372, &#34;min&#34;: 9015.956163406372}}, &#34;EndTime&#34;: 1536203413.424342, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203404.403407}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:13 INFO 140205845833536] #progress_metric: host=algo-3, completed 66 % of epochs</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 413, &#34;sum&#34;: 413.0, &#34;min&#34;: 413}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 412442, &#34;sum&#34;: 412442.0, &#34;min&#34;: 412442}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}}, &#34;EndTime&#34;: 1536203413.42453, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 1}, &#34;StartTime&#34;: 1536203404.408353}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:13 INFO 140205845833536] #throughput_metric: host=algo-3, train throughput=22816.5855574 records/second</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:13 INFO 140205845833536] #quality_metric: host=algo-3, epoch=2, batch=0 train rmse &lt;loss&gt;=1.15834944533</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:13 INFO 140143585908544] #quality_metric: host=algo-5, epoch=1, train rmse &lt;loss&gt;=1.75336836794</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8973.249912261963, &#34;sum&#34;: 8973.249912261963, &#34;min&#34;: 8973.249912261963}}, &#34;EndTime&#34;: 1536203413.336056, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203404.358216}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:13 INFO 140143585908544] #progress_metric: host=algo-5, completed 66 % of epochs</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 413, &#34;sum&#34;: 413.0, &#34;min&#34;: 413}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 412442, &#34;sum&#34;: 412442.0, &#34;min&#34;: 412442}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}}, &#34;EndTime&#34;: 1536203413.336308, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 1}, &#34;StartTime&#34;: 1536203404.362771}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:13 INFO 140143585908544] #throughput_metric: host=algo-5, train throughput=22924.908383 records/second</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:13 INFO 140143585908544] #quality_metric: host=algo-5, epoch=2, batch=0 train rmse &lt;loss&gt;=1.61698224845</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:13 INFO 139652459214656] #quality_metric: host=algo-4, epoch=1, train rmse &lt;loss&gt;=1.69000297607</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 9233.940839767456, &#34;sum&#34;: 9233.940839767456, &#34;min&#34;: 9233.940839767456}}, &#34;EndTime&#34;: 1536203413.718568, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203404.482306}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:13 INFO 139652459214656] #progress_metric: host=algo-4, completed 66 % of epochs</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 413, &#34;sum&#34;: 413.0, &#34;min&#34;: 413}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 412442, &#34;sum&#34;: 412442.0, &#34;min&#34;: 412442}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}}, &#34;EndTime&#34;: 1536203413.718773, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 1}, &#34;StartTime&#34;: 1536203404.484597}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:13 INFO 139652459214656] #throughput_metric: host=algo-4, train throughput=22277.6548736 records/second</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:13 INFO 139652459214656] #quality_metric: host=algo-4, epoch=2, batch=0 train rmse &lt;loss&gt;=1.37703914847</span>
<span class="ansi-red-fg">[09/06/2018 03:10:13 INFO 140614843680576] #quality_metric: host=algo-1, epoch=1, train rmse &lt;loss&gt;=1.85939993751</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8969.397068023682, &#34;sum&#34;: 8969.397068023682, &#34;min&#34;: 8969.397068023682}}, &#34;EndTime&#34;: 1536203413.967022, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203404.996064}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:13 INFO 140614843680576] #progress_metric: host=algo-1, completed 66 % of epochs</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 413, &#34;sum&#34;: 413.0, &#34;min&#34;: 413}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 412444, &#34;sum&#34;: 412444.0, &#34;min&#34;: 412444}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 3, &#34;sum&#34;: 3.0, &#34;min&#34;: 3}}, &#34;EndTime&#34;: 1536203413.967345, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 1}, &#34;StartTime&#34;: 1536203404.997593}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:13 INFO 140614843680576] #throughput_metric: host=algo-1, train throughput=22934.4505257 records/second</span>
<span class="ansi-red-fg">[09/06/2018 03:10:13 INFO 140614843680576] #quality_metric: host=algo-1, epoch=2, batch=0 train rmse &lt;loss&gt;=1.09116664782</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:22 INFO 139652459214656] #quality_metric: host=algo-4, epoch=2, train rmse &lt;loss&gt;=1.65408300432</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:22 INFO 139652459214656] #quality_metric: host=algo-4, train rmse &lt;loss&gt;=1.65408300432</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8967.994928359985, &#34;sum&#34;: 8967.994928359985, &#34;min&#34;: 8967.994928359985}}, &#34;EndTime&#34;: 1536203422.687395, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203413.718639}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:22 INFO 139652459214656] #progress_metric: host=algo-4, completed 100 % of epochs</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 619, &#34;sum&#34;: 619.0, &#34;min&#34;: 619}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 618163, &#34;sum&#34;: 618163.0, &#34;min&#34;: 618163}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4, &#34;sum&#34;: 4.0, &#34;min&#34;: 4}}, &#34;EndTime&#34;: 1536203422.687654, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 2}, &#34;StartTime&#34;: 1536203413.719374}
</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:22 INFO 139652459214656] #throughput_metric: host=algo-4, train throughput=22938.3178817 records/second</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:22 INFO 139652459214656] Pulling entire model from kvstore to finalize</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;finalize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 49.432992935180664, &#34;sum&#34;: 49.432992935180664, &#34;min&#34;: 49.432992935180664}}, &#34;EndTime&#34;: 1536203422.834681, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.687491}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:21 INFO 140469731592000] #quality_metric: host=algo-2, epoch=2, train rmse &lt;loss&gt;=1.7019501785</span>
<span class="ansi-green-fg">[09/06/2018 03:10:21 INFO 140469731592000] #quality_metric: host=algo-2, train rmse &lt;loss&gt;=1.7019501785</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8735.249996185303, &#34;sum&#34;: 8735.249996185303, &#34;min&#34;: 8735.249996185303}}, &#34;EndTime&#34;: 1536203421.818773, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203413.081216}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:21 INFO 140469731592000] #progress_metric: host=algo-2, completed 100 % of epochs</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 619, &#34;sum&#34;: 619.0, &#34;min&#34;: 619}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 618163, &#34;sum&#34;: 618163.0, &#34;min&#34;: 618163}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4, &#34;sum&#34;: 4.0, &#34;min&#34;: 4}}, &#34;EndTime&#34;: 1536203421.819044, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 2}, &#34;StartTime&#34;: 1536203413.083491}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:21 INFO 140469731592000] #throughput_metric: host=algo-2, train throughput=23549.1182672 records/second</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:21 INFO 140205845833536] #quality_metric: host=algo-3, epoch=2, train rmse &lt;loss&gt;=1.66767966538</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:21 INFO 140205845833536] #quality_metric: host=algo-3, train rmse &lt;loss&gt;=1.66767966538</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8279.520034790039, &#34;sum&#34;: 8279.520034790039, &#34;min&#34;: 8279.520034790039}}, &#34;EndTime&#34;: 1536203421.705264, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203413.424405}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:21 INFO 140205845833536] #progress_metric: host=algo-3, completed 100 % of epochs</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 619, &#34;sum&#34;: 619.0, &#34;min&#34;: 619}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 618163, &#34;sum&#34;: 618163.0, &#34;min&#34;: 618163}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4, &#34;sum&#34;: 4.0, &#34;min&#34;: 4}}, &#34;EndTime&#34;: 1536203421.705549, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 2}, &#34;StartTime&#34;: 1536203413.425702}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:21 INFO 140205845833536] #throughput_metric: host=algo-3, train throughput=24845.12013 records/second</span>
<span class="ansi-red-fg">[09/06/2018 03:10:22 INFO 140614843680576] #quality_metric: host=algo-1, epoch=2, train rmse &lt;loss&gt;=1.75117898222</span>
<span class="ansi-red-fg">[09/06/2018 03:10:22 INFO 140614843680576] #quality_metric: host=algo-1, train rmse &lt;loss&gt;=1.75117898222</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 8536.774158477783, &#34;sum&#34;: 8536.774158477783, &#34;min&#34;: 8536.774158477783}}, &#34;EndTime&#34;: 1536203422.505712, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203413.967086}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:22 INFO 140614843680576] #progress_metric: host=algo-1, completed 100 % of epochs</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 619, &#34;sum&#34;: 619.0, &#34;min&#34;: 619}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 618166, &#34;sum&#34;: 618166.0, &#34;min&#34;: 618166}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205722, &#34;sum&#34;: 205722.0, &#34;min&#34;: 205722}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4, &#34;sum&#34;: 4.0, &#34;min&#34;: 4}}, &#34;EndTime&#34;: 1536203422.505981, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 2}, &#34;StartTime&#34;: 1536203413.968908}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:22 INFO 140614843680576] #throughput_metric: host=algo-1, train throughput=24096.7356073 records/second</span>
<span class="ansi-red-fg">[09/06/2018 03:10:22 INFO 140614843680576] Pulling entire model from kvstore to finalize</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;finalize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 51.20587348937988, &#34;sum&#34;: 51.20587348937988, &#34;min&#34;: 51.20587348937988}}, &#34;EndTime&#34;: 1536203422.834276, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.505802}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:22 INFO 140469731592000] Pulling entire model from kvstore to finalize</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;finalize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 75.96707344055176, &#34;sum&#34;: 75.96707344055176, &#34;min&#34;: 75.96707344055176}}, &#34;EndTime&#34;: 1536203422.860633, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203421.818854}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:22 INFO 140143585908544] #quality_metric: host=algo-5, epoch=2, train rmse &lt;loss&gt;=1.6516564586</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:22 INFO 140143585908544] #quality_metric: host=algo-5, train rmse &lt;loss&gt;=1.6516564586</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;update.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 9442.929029464722, &#34;sum&#34;: 9442.929029464722, &#34;min&#34;: 9442.929029464722}}, &#34;EndTime&#34;: 1536203422.781084, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203413.336125}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:22 INFO 140143585908544] #progress_metric: host=algo-5, completed 100 % of epochs</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 206, &#34;sum&#34;: 206.0, &#34;min&#34;: 206}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 619, &#34;sum&#34;: 619.0, &#34;min&#34;: 619}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 618163, &#34;sum&#34;: 618163.0, &#34;min&#34;: 618163}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 205721, &#34;sum&#34;: 205721.0, &#34;min&#34;: 205721}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4, &#34;sum&#34;: 4.0, &#34;min&#34;: 4}}, &#34;EndTime&#34;: 1536203422.781382, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Meta&#34;: &#34;training_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;, &#34;epoch&#34;: 2}, &#34;StartTime&#34;: 1536203413.338127}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:22 INFO 140143585908544] #throughput_metric: host=algo-5, train throughput=21784.2033292 records/second</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:22 INFO 140143585908544] Pulling entire model from kvstore to finalize</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;finalize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 44.184207916259766, &#34;sum&#34;: 44.184207916259766, &#34;min&#34;: 44.184207916259766}}, &#34;EndTime&#34;: 1536203422.826369, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.781191}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:22 INFO 140205845833536] Pulling entire model from kvstore to finalize</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;finalize.time&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 44.88492012023926, &#34;sum&#34;: 44.88492012023926, &#34;min&#34;: 44.88492012023926}}, &#34;EndTime&#34;: 1536203422.830007, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203421.705348}
</span>
<span class="ansi-red-fg">[09/06/2018 03:10:23 INFO 140614843680576] Saved checkpoint to &#34;/tmp/tmpB7K8Es/state-0001.params&#34;</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203433.699183, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Meta&#34;: &#34;test_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.830789}
</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:33 INFO 140205845833536] #test_score (algo-3) : (&#39;rmse&#39;, 1.2691968631702171)</span>
<span class="ansi-yellow-fg">[09/06/2018 03:10:33 INFO 140205845833536] #quality_metric: host=algo-3, test rmse &lt;loss&gt;=1.26919686317</span>
<span class="ansi-yellow-fg">#metrics {&#34;Metrics&#34;: {&#34;totaltime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 45861.32097244263, &#34;sum&#34;: 45861.32097244263, &#34;min&#34;: 45861.32097244263}, &#34;setuptime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 43.402910232543945, &#34;sum&#34;: 43.402910232543945, &#34;min&#34;: 43.402910232543945}}, &#34;EndTime&#34;: 1536203433.700971, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-3&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.830208}
</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203433.821953, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Meta&#34;: &#34;test_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.863894}
</span>
<span class="ansi-green-fg">[09/06/2018 03:10:33 INFO 140469731592000] #test_score (algo-2) : (&#39;rmse&#39;, 1.2691968631702171)</span>
<span class="ansi-green-fg">[09/06/2018 03:10:33 INFO 140469731592000] #quality_metric: host=algo-2, test rmse &lt;loss&gt;=1.26919686317</span>
<span class="ansi-green-fg">#metrics {&#34;Metrics&#34;: {&#34;totaltime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 53689.56899642944, &#34;sum&#34;: 53689.56899642944, &#34;min&#34;: 53689.56899642944}, &#34;setuptime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 4041.07403755188, &#34;sum&#34;: 4041.07403755188, &#34;min&#34;: 4041.07403755188}}, &#34;EndTime&#34;: 1536203433.823838, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-2&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.863603}
</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203433.761361, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Meta&#34;: &#34;test_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.826945}
</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:33 INFO 140143585908544] #test_score (algo-5) : (&#39;rmse&#39;, 1.2691968631702171)</span>
<span class="ansi-magenta-fg">[09/06/2018 03:10:33 INFO 140143585908544] #quality_metric: host=algo-5, test rmse &lt;loss&gt;=1.26919686317</span>
<span class="ansi-magenta-fg">#metrics {&#34;Metrics&#34;: {&#34;totaltime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 40005.529165267944, &#34;sum&#34;: 40005.529165267944, &#34;min&#34;: 40005.529165267944}, &#34;setuptime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 41.24021530151367, &#34;sum&#34;: 41.24021530151367, &#34;min&#34;: 41.24021530151367}}, &#34;EndTime&#34;: 1536203433.763073, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-5&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.826573}
</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203433.788557, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Meta&#34;: &#34;test_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.835493}
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<span class="ansi-blue-fg">[09/06/2018 03:10:33 INFO 139652459214656] #test_score (algo-4) : (&#39;rmse&#39;, 1.2691968631702171)</span>
<span class="ansi-blue-fg">[09/06/2018 03:10:33 INFO 139652459214656] #quality_metric: host=algo-4, test rmse &lt;loss&gt;=1.26919686317</span>
<span class="ansi-blue-fg">#metrics {&#34;Metrics&#34;: {&#34;totaltime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 52384.59515571594, &#34;sum&#34;: 52384.59515571594, &#34;min&#34;: 52384.59515571594}, &#34;setuptime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 2540.0149822235107, &#34;sum&#34;: 2540.0149822235107, &#34;min&#34;: 2540.0149822235107}}, &#34;EndTime&#34;: 1536203433.790367, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-4&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.835109}
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<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;Max Batches Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Batches Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Number of Records Since Last Reset&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Total Batches Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 141, &#34;sum&#34;: 141.0, &#34;min&#34;: 141}, &#34;Total Records Seen&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Max Records Seen Between Resets&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 140344, &#34;sum&#34;: 140344.0, &#34;min&#34;: 140344}, &#34;Reset Count&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 1, &#34;sum&#34;: 1.0, &#34;min&#34;: 1}}, &#34;EndTime&#34;: 1536203434.595411, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Meta&#34;: &#34;test_data_iter&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203424.262494}
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<span class="ansi-red-fg">[09/06/2018 03:10:34 INFO 140614843680576] #test_score (algo-1) : (&#39;rmse&#39;, 1.2691968631702171)</span>
<span class="ansi-red-fg">[09/06/2018 03:10:34 INFO 140614843680576] #quality_metric: host=algo-1, test rmse &lt;loss&gt;=1.26919686317</span>
<span class="ansi-red-fg">#metrics {&#34;Metrics&#34;: {&#34;totaltime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 50944.952964782715, &#34;sum&#34;: 50944.952964782715, &#34;min&#34;: 50944.952964782715}, &#34;setuptime&#34;: {&#34;count&#34;: 1, &#34;max&#34;: 538.6178493499756, &#34;sum&#34;: 538.6178493499756, &#34;min&#34;: 538.6178493499756}}, &#34;EndTime&#34;: 1536203434.597227, &#34;Dimensions&#34;: {&#34;Host&#34;: &#34;algo-1&#34;, &#34;Operation&#34;: &#34;training&#34;, &#34;Algorithm&#34;: &#34;factorization-machines&#34;}, &#34;StartTime&#34;: 1536203422.834438}
</span>

Billable seconds: 792
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<h2 id="Host">Host<a class="anchor-link" href="#Host">&#182;</a></h2><p>Deploy trained model to a real-time production endpoint</p>

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<pre>INFO:sagemaker:Creating model with name: factorization-machines-2018-09-06-04-21-51-332
INFO:sagemaker:Creating endpoint with name DEMO-loft-recommender-2018-09-06-03-06-45-001
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<pre>----------------------------------------------------------------------------!</pre>
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<p>Setup predictor to serialize in-memory data for invocation requests</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">fm_serializer</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="n">feature_dim</span> <span class="o">=</span> <span class="n">customer_index</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">product_index</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
    <span class="n">js</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;instances&#39;</span><span class="p">:</span> <span class="p">[]}</span>
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
        <span class="n">js</span><span class="p">[</span><span class="s1">&#39;instances&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">&#39;data&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;features&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;values&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;days_since_first&#39;</span><span class="p">]],</span>
                                                      <span class="s1">&#39;keys&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;user&#39;</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;item&#39;</span><span class="p">],</span> <span class="n">feature_dim</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span>
                                                      <span class="s1">&#39;shape&#39;</span><span class="p">:</span> <span class="p">[</span><span class="n">feature_dim</span><span class="p">]}}})</span>
    <span class="k">return</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">js</span><span class="p">)</span>
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<span class="n">fm_predictor</span><span class="o">.</span><span class="n">serializer</span> <span class="o">=</span> <span class="n">fm_serializer</span>
<span class="n">fm_predictor</span><span class="o">.</span><span class="n">deserializer</span> <span class="o">=</span> <span class="n">json_deserializer</span>
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<p>Real-time prediction for a single user item</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
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    .dataframe tbody tr th {
        vertical-align: top;
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      <th></th>
      <th>customer_id</th>
      <th>product_id</th>
      <th>product_title</th>
      <th>star_rating</th>
      <th>review_date</th>
      <th>user</th>
      <th>item</th>
      <th>first_review_date</th>
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      <th>0</th>
      <td>10378</td>
      <td>B002PZDM9Y</td>
      <td>Ghosts of Girlfriends Past</td>
      <td>4</td>
      <td>2013-01-14</td>
      <td>8644</td>
      <td>173526</td>
      <td>2012-12-28</td>
      <td>17.0</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fm_predictor</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test_df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
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<pre>{&#39;predictions&#39;: [{&#39;score&#39;: 3.817375659942627}]}</pre>
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<p>Clean-up the endpoint</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fm_predictor</span><span class="o">.</span><span class="n">delete_endpoint</span><span class="p">()</span>
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<pre>INFO:sagemaker:Deleting endpoint with name: DEMO-loft-recommender-2018-09-06-03-06-45-001
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<h1 id="Extra-credit">Extra credit<a class="anchor-link" href="#Extra-credit">&#182;</a></h1><ul>
<li>What happens when a new movie is added?<ul>
<li>No feature to set to "1" in the dataset</li>
<li>No previous ratings to find similar items</li>
<li>Cold start problem is hard with factorization machines</li>
</ul>
</li>
<li>Word2vec<ul>
<li>Word embeddings for natural language processing (similar words get similar vectors)</li>
<li>Use concatenated product titles as words customer review history as sentences</li>
<li>SageMaker BlazingText is an extremely fast implementation that can work with subwords</li>
</ul>
</li>
</ul>

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<h2 id="Data">Data<a class="anchor-link" href="#Data">&#182;</a></h2><p>Concatenate product titles to treat each one as a single word</p>

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<p>Write customer purchase histories</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">first</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;customer_purchases.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">customer</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">reduced_df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;customer_id&#39;</span><span class="p">,</span> <span class="s1">&#39;review_date&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;customer_id&#39;</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">first</span><span class="p">:</span>
            <span class="n">first</span> <span class="o">=</span> <span class="kc">False</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;product_title&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()))</span>
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<p>Write to S3 so SageMaker training can use it</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">upload_data</span><span class="p">(</span><span class="s1">&#39;customer_purchases.txt&#39;</span><span class="p">,</span> <span class="n">bucket</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">/word2vec/train&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
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<h2 id="Train">Train<a class="anchor-link" href="#Train">&#182;</a></h2><p>Create a SageMaker estimator:</p>
<ul>
<li>Specify training job arguments</li>
<li>Set hyperparameters<ul>
<li>Remove titles that occur less than 5 times</li>
<li>Embed in a 100-dimensional subspace</li>
<li>Use subwords to capture similarity in titles</li>
</ul>
</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bt</span> <span class="o">=</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">Estimator</span><span class="p">(</span>
    <span class="n">sagemaker</span><span class="o">.</span><span class="n">amazon</span><span class="o">.</span><span class="n">amazon_estimator</span><span class="o">.</span><span class="n">get_image_uri</span><span class="p">(</span><span class="n">boto3</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span><span class="o">.</span><span class="n">region_name</span><span class="p">,</span> <span class="s1">&#39;blazingtext&#39;</span><span class="p">,</span> <span class="s1">&#39;latest&#39;</span><span class="p">),</span>
    <span class="n">role</span><span class="p">,</span> 
    <span class="n">train_instance_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> 
    <span class="n">train_instance_type</span><span class="o">=</span><span class="s1">&#39;ml.p3.2xlarge&#39;</span><span class="p">,</span>
    <span class="n">train_volume_size</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
    <span class="n">output_path</span><span class="o">=</span><span class="s1">&#39;s3://</span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">/output&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">bucket</span><span class="p">,</span> <span class="n">prefix</span><span class="p">),</span>
    <span class="n">sagemaker_session</span><span class="o">=</span><span class="n">sess</span><span class="p">)</span>

<span class="n">bt</span><span class="o">.</span><span class="n">set_hyperparameters</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="s2">&quot;skipgram&quot;</span><span class="p">,</span>
    <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
    <span class="n">min_count</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
    <span class="n">sampling_threshold</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">,</span>
    <span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
    <span class="n">window_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
    <span class="n">vector_dim</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
    <span class="n">negative_samples</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
    <span class="n">min_char</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
    <span class="n">max_char</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
    <span class="n">evaluation</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
    <span class="n">subwords</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">bt</span><span class="o">.</span><span class="n">fit</span><span class="p">({</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="n">sagemaker</span><span class="o">.</span><span class="n">s3_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">distribution</span><span class="o">=</span><span class="s1">&#39;FullyReplicated&#39;</span><span class="p">,</span> <span class="n">content_type</span><span class="o">=</span><span class="s1">&#39;text/plain&#39;</span><span class="p">)})</span>
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<pre>INFO:sagemaker:Creating training-job with name: blazingtext-2018-09-06-05-01-57-653
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<pre>.................
<span class="ansi-red-fg">Arguments: train</span>
<span class="ansi-red-fg">[09/06/2018 05:04:28 WARNING 140640081336128] Loggers have already been setup.</span>
<span class="ansi-red-fg">[09/06/2018 05:04:28 WARNING 140640081336128] Loggers have already been setup.</span>
<span class="ansi-red-fg">[09/06/2018 05:04:28 INFO 140640081336128] nvidia-smi took: 0.0252358913422 secs to identify 1 gpus</span>
<span class="ansi-red-fg">[09/06/2018 05:04:28 INFO 140640081336128] Running BlazingText on singe GPU using skipgram</span>
<span class="ansi-red-fg">[09/06/2018 05:04:28 INFO 140640081336128] Processing /opt/ml/input/data/train/customer_purchases.txt . File size: 23 MB</span>
<span class="ansi-red-fg">Read 1M words</span>
<span class="ansi-red-fg">Number of words:  17990</span>
<span class="ansi-red-fg">Initialized GPU 0 successfully! Now starting training....</span>
<span class="ansi-red-fg">##### Alpha: 0.0486  Progress: 2.76%  Million Words/sec: 0.60 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0456  Progress: 8.88%  Million Words/sec: 1.16 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0426  Progress: 14.84%  Million Words/sec: 1.39 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0395  Progress: 20.99%  Million Words/sec: 1.52 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0365  Progress: 27.06%  Million Words/sec: 1.61 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0334  Progress: 33.12%  Million Words/sec: 1.67 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0304  Progress: 39.29%  Million Words/sec: 1.71 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0274  Progress: 45.20%  Million Words/sec: 1.74 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0243  Progress: 51.45%  Million Words/sec: 1.77 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0213  Progress: 57.47%  Million Words/sec: 1.79 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0183  Progress: 63.49%  Million Words/sec: 1.80 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0152  Progress: 69.61%  Million Words/sec: 1.82 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0122  Progress: 75.59%  Million Words/sec: 1.83 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0091  Progress: 81.84%  Million Words/sec: 1.84 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0061  Progress: 87.83%  Million Words/sec: 1.85 #####</span>
<span class="ansi-red-fg">##### Alpha: 0.0031  Progress: 93.82%  Million Words/sec: 1.86 #####</span>
<span class="ansi-red-fg">##### Alpha: -0.0001  Progress: 100.11%  Million Words/sec: 1.87 #####</span>
<span class="ansi-red-fg">Exiting thread 5</span>
<span class="ansi-red-fg">Exiting thread 7</span>
<span class="ansi-red-fg">Exiting thread 3</span>
<span class="ansi-red-fg">Exiting thread 1</span>
<span class="ansi-red-fg">Exiting thread 6</span>
<span class="ansi-red-fg">Exiting thread 0</span>
<span class="ansi-red-fg">Exiting thread 2</span>
<span class="ansi-red-fg">Exiting thread 4</span>
<span class="ansi-red-fg">##### Alpha: 0.0000  Progress: 100.00%  Million Words/sec: 1.85 #####</span>
<span class="ansi-red-fg">Training finished.</span>
<span class="ansi-red-fg">Average throughput in Million words/sec: 1.85</span>
<span class="ansi-red-fg">Total training time in seconds: 7.07</span>

Billable seconds: 178
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<hr>
<h2 id="Model">Model<a class="anchor-link" href="#Model">&#182;</a></h2><ul>
<li>Bring in and extract the model from S3</li>
<li>Take a look at the embeddings</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>aws s3 cp <span class="nv">$bt</span>.model_data ./
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<pre>download: s3://sagemaker-us-west-2-345362745630/sagemaker/DEMO-loft-recommender/output/blazingtext-2018-09-06-05-01-57-653/output/model.tar.gz to ./model.tar.gz
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>tar -xvzf model.tar.gz
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<pre>vectors.bin
vectors.txt
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vectors</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="s1">&#39;vectors.txt&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="n">skiprows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
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<p>Do the embeddings appear to have meaning</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vectors</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
      <th>6</th>
      <th>7</th>
      <th>8</th>
      <th>9</th>
      <th>...</th>
      <th>92</th>
      <th>93</th>
      <th>94</th>
      <th>95</th>
      <th>96</th>
      <th>97</th>
      <th>98</th>
      <th>99</th>
      <th>100</th>
      <th>101</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>4420</th>
      <td>power-rangers-samurai</td>
      <td>-1.17660</td>
      <td>-0.699140</td>
      <td>0.525420</td>
      <td>1.015600</td>
      <td>1.075700</td>
      <td>0.391160</td>
      <td>0.492290</td>
      <td>-0.187150</td>
      <td>0.018988</td>
      <td>...</td>
      <td>0.827820</td>
      <td>0.454780</td>
      <td>-0.609520</td>
      <td>1.106500</td>
      <td>-0.090755</td>
      <td>-0.145180</td>
      <td>0.509000</td>
      <td>-0.827530</td>
      <td>-0.286520</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6037</th>
      <td>veggietales:-the-league-of-incredible-vegetables</td>
      <td>-1.08300</td>
      <td>-0.166570</td>
      <td>-0.043076</td>
      <td>0.120760</td>
      <td>0.494850</td>
      <td>0.121810</td>
      <td>0.145250</td>
      <td>0.197490</td>
      <td>-0.150900</td>
      <td>...</td>
      <td>0.120950</td>
      <td>-0.156490</td>
      <td>-0.513660</td>
      <td>0.476470</td>
      <td>-0.394770</td>
      <td>0.030671</td>
      <td>-0.185030</td>
      <td>-0.439740</td>
      <td>-0.136350</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5128</th>
      <td>veggietales:-the-little-drummer-boy</td>
      <td>-1.07930</td>
      <td>-0.115730</td>
      <td>-0.104510</td>
      <td>0.243980</td>
      <td>0.436550</td>
      <td>0.182310</td>
      <td>0.587360</td>
      <td>0.088761</td>
      <td>0.083688</td>
      <td>...</td>
      <td>0.358750</td>
      <td>0.127220</td>
      <td>-0.785760</td>
      <td>0.459790</td>
      <td>-0.532260</td>
      <td>0.281530</td>
      <td>-0.052781</td>
      <td>-0.443760</td>
      <td>-0.345460</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>16906</th>
      <td>veggietales:-noah's-ark</td>
      <td>-0.97747</td>
      <td>-0.438660</td>
      <td>0.299660</td>
      <td>0.373710</td>
      <td>1.034100</td>
      <td>0.310420</td>
      <td>0.328480</td>
      <td>-0.073739</td>
      <td>-0.058263</td>
      <td>...</td>
      <td>0.263930</td>
      <td>0.123490</td>
      <td>-0.719770</td>
      <td>0.378060</td>
      <td>-0.575200</td>
      <td>-0.194840</td>
      <td>-0.214720</td>
      <td>-0.516370</td>
      <td>-0.729870</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6439</th>
      <td>veggietales:-pistachio</td>
      <td>-0.96863</td>
      <td>-0.414440</td>
      <td>0.386700</td>
      <td>0.454040</td>
      <td>1.187200</td>
      <td>0.282820</td>
      <td>0.056857</td>
      <td>-0.120010</td>
      <td>-0.049071</td>
      <td>...</td>
      <td>0.145430</td>
      <td>0.025577</td>
      <td>-0.756080</td>
      <td>0.291300</td>
      <td>-0.344690</td>
      <td>-0.424310</td>
      <td>-0.166130</td>
      <td>-0.407070</td>
      <td>-0.706660</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4433</th>
      <td>veggietales:-lord-of-the-beans</td>
      <td>-0.95589</td>
      <td>-0.566900</td>
      <td>0.347620</td>
      <td>0.290560</td>
      <td>1.010800</td>
      <td>-0.011312</td>
      <td>0.555930</td>
      <td>0.118840</td>
      <td>-0.095057</td>
      <td>...</td>
      <td>0.370440</td>
      <td>-0.051236</td>
      <td>-0.696990</td>
      <td>0.175330</td>
      <td>-0.262680</td>
      <td>-0.422730</td>
      <td>-0.397280</td>
      <td>-0.880970</td>
      <td>-0.452250</td>
      <td>NaN</td>
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    <tr>
      <th>14000</th>
      <td>veggietales:-the-penniless-princess</td>
      <td>-0.94220</td>
      <td>0.000207</td>
      <td>-0.090729</td>
      <td>0.136700</td>
      <td>0.568470</td>
      <td>0.281730</td>
      <td>0.568290</td>
      <td>0.153600</td>
      <td>-0.157820</td>
      <td>...</td>
      <td>0.238290</td>
      <td>0.000217</td>
      <td>-0.513490</td>
      <td>0.360060</td>
      <td>-0.605800</td>
      <td>0.093079</td>
      <td>-0.145910</td>
      <td>-0.521600</td>
      <td>-0.308590</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>13967</th>
      <td>eating-out</td>
      <td>-0.92264</td>
      <td>0.934240</td>
      <td>1.412800</td>
      <td>-1.552400</td>
      <td>0.283310</td>
      <td>0.080636</td>
      <td>0.821970</td>
      <td>0.202890</td>
      <td>0.086593</td>
      <td>...</td>
      <td>0.108980</td>
      <td>-0.958870</td>
      <td>-0.703420</td>
      <td>-0.128580</td>
      <td>-0.551090</td>
      <td>0.393800</td>
      <td>0.144210</td>
      <td>0.510460</td>
      <td>1.014400</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>8111</th>
      <td>power-rangers-megaforce</td>
      <td>-0.91289</td>
      <td>-0.295450</td>
      <td>0.598850</td>
      <td>0.720740</td>
      <td>0.836380</td>
      <td>0.309410</td>
      <td>0.507790</td>
      <td>-0.027275</td>
      <td>0.080910</td>
      <td>...</td>
      <td>0.601200</td>
      <td>0.653300</td>
      <td>-0.580790</td>
      <td>1.060800</td>
      <td>-0.438910</td>
      <td>0.014386</td>
      <td>0.242970</td>
      <td>-0.940890</td>
      <td>-0.438580</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5076</th>
      <td>the-way-home</td>
      <td>-0.89391</td>
      <td>-0.196400</td>
      <td>0.348180</td>
      <td>0.303340</td>
      <td>0.506050</td>
      <td>-0.074683</td>
      <td>0.327070</td>
      <td>0.316650</td>
      <td>-0.400960</td>
      <td>...</td>
      <td>0.304900</td>
      <td>-0.101050</td>
      <td>-0.296790</td>
      <td>0.162250</td>
      <td>-0.012050</td>
      <td>0.751900</td>
      <td>-0.742550</td>
      <td>-0.512260</td>
      <td>0.016969</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3897</th>
      <td>sesame-street-season-35</td>
      <td>-0.88502</td>
      <td>-0.219990</td>
      <td>-0.281630</td>
      <td>-0.483520</td>
      <td>0.546620</td>
      <td>0.252460</td>
      <td>0.760950</td>
      <td>-0.095503</td>
      <td>0.156470</td>
      <td>...</td>
      <td>0.014536</td>
      <td>-0.108800</td>
      <td>-0.139800</td>
      <td>0.588240</td>
      <td>-0.197090</td>
      <td>-0.328500</td>
      <td>0.148140</td>
      <td>-1.140100</td>
      <td>-0.150270</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>15488</th>
      <td>the-card</td>
      <td>-0.88246</td>
      <td>0.078963</td>
      <td>0.105400</td>
      <td>-0.526750</td>
      <td>0.101900</td>
      <td>-0.010461</td>
      <td>0.691530</td>
      <td>-0.241600</td>
      <td>0.436130</td>
      <td>...</td>
      <td>0.253190</td>
      <td>0.203560</td>
      <td>-0.047833</td>
      <td>0.413470</td>
      <td>-0.467720</td>
      <td>0.115120</td>
      <td>-0.522150</td>
      <td>0.287180</td>
      <td>-0.039871</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>16421</th>
      <td>veggietales:-beauty-and-the-beet</td>
      <td>-0.88111</td>
      <td>-0.381660</td>
      <td>0.031004</td>
      <td>0.256840</td>
      <td>0.650560</td>
      <td>0.110580</td>
      <td>0.669710</td>
      <td>0.117910</td>
      <td>-0.035510</td>
      <td>...</td>
      <td>0.215200</td>
      <td>0.210610</td>
      <td>-0.711560</td>
      <td>0.275520</td>
      <td>-0.366410</td>
      <td>-0.031804</td>
      <td>-0.236420</td>
      <td>-0.541870</td>
      <td>-0.361120</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7124</th>
      <td>birds-and-the-bees</td>
      <td>-0.87139</td>
      <td>0.164250</td>
      <td>-0.085787</td>
      <td>-0.372180</td>
      <td>0.058610</td>
      <td>0.120460</td>
      <td>0.741400</td>
      <td>0.104750</td>
      <td>0.100090</td>
      <td>...</td>
      <td>0.074134</td>
      <td>-0.120110</td>
      <td>-0.189280</td>
      <td>0.095495</td>
      <td>-0.479270</td>
      <td>0.330610</td>
      <td>-0.354610</td>
      <td>0.068556</td>
      <td>-0.038825</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>9235</th>
      <td>caribbean</td>
      <td>-0.87060</td>
      <td>0.154610</td>
      <td>0.050924</td>
      <td>-0.524520</td>
      <td>0.336050</td>
      <td>-0.113300</td>
      <td>0.201680</td>
      <td>-0.130130</td>
      <td>0.641150</td>
      <td>...</td>
      <td>0.493940</td>
      <td>0.292780</td>
      <td>-0.249770</td>
      <td>0.156050</td>
      <td>-0.086783</td>
      <td>-0.182110</td>
      <td>-0.605540</td>
      <td>0.130580</td>
      <td>-0.189070</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2891</th>
      <td>amazing-grace</td>
      <td>-0.86734</td>
      <td>-0.110530</td>
      <td>-0.273430</td>
      <td>-0.580290</td>
      <td>0.087003</td>
      <td>-0.548700</td>
      <td>0.516570</td>
      <td>0.339920</td>
      <td>-0.079972</td>
      <td>...</td>
      <td>0.389130</td>
      <td>-0.657910</td>
      <td>-0.243480</td>
      <td>0.151790</td>
      <td>0.180570</td>
      <td>0.358030</td>
      <td>-0.400400</td>
      <td>-0.422400</td>
      <td>0.149250</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2098</th>
      <td>the-lord-of-the-rings:-the-return-of-the-king</td>
      <td>-0.85443</td>
      <td>-0.348730</td>
      <td>0.361560</td>
      <td>0.217030</td>
      <td>-0.055996</td>
      <td>-0.154310</td>
      <td>1.188400</td>
      <td>0.149540</td>
      <td>0.178410</td>
      <td>...</td>
      <td>0.642280</td>
      <td>-0.595750</td>
      <td>-0.428820</td>
      <td>-0.251830</td>
      <td>0.144930</td>
      <td>0.214230</td>
      <td>0.102220</td>
      <td>-0.392100</td>
      <td>0.169060</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2082</th>
      <td>lord-of-the-rings:-the-two-towers</td>
      <td>-0.85258</td>
      <td>-0.487980</td>
      <td>0.436010</td>
      <td>0.231410</td>
      <td>-0.041891</td>
      <td>-0.012596</td>
      <td>1.380400</td>
      <td>0.348920</td>
      <td>0.409460</td>
      <td>...</td>
      <td>0.558130</td>
      <td>-0.539110</td>
      <td>-0.555700</td>
      <td>-0.057410</td>
      <td>-0.058037</td>
      <td>0.561960</td>
      <td>0.246580</td>
      <td>-0.485600</td>
      <td>0.282160</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4120</th>
      <td>peter-and-the-wolf</td>
      <td>-0.84862</td>
      <td>-0.159190</td>
      <td>0.132590</td>
      <td>-0.495130</td>
      <td>-0.335140</td>
      <td>0.828620</td>
      <td>0.912740</td>
      <td>0.355140</td>
      <td>-0.213400</td>
      <td>...</td>
      <td>-0.037646</td>
      <td>-0.243750</td>
      <td>-0.481170</td>
      <td>-0.551000</td>
      <td>0.101580</td>
      <td>-0.163150</td>
      <td>-0.155420</td>
      <td>0.623580</td>
      <td>-0.363610</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7076</th>
      <td>sesame-street-season-36</td>
      <td>-0.84814</td>
      <td>-0.215060</td>
      <td>-0.266620</td>
      <td>-0.440480</td>
      <td>0.524050</td>
      <td>0.290710</td>
      <td>0.717140</td>
      <td>-0.152080</td>
      <td>0.178020</td>
      <td>...</td>
      <td>0.070418</td>
      <td>-0.027920</td>
      <td>-0.126650</td>
      <td>0.592120</td>
      <td>-0.186700</td>
      <td>-0.244920</td>
      <td>0.129840</td>
      <td>-1.197600</td>
      <td>-0.135910</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6265</th>
      <td>sesame-street-season-39</td>
      <td>-0.84263</td>
      <td>-0.250640</td>
      <td>-0.249100</td>
      <td>-0.365780</td>
      <td>0.461740</td>
      <td>0.285010</td>
      <td>0.691340</td>
      <td>-0.171190</td>
      <td>0.262820</td>
      <td>...</td>
      <td>0.074466</td>
      <td>0.058500</td>
      <td>-0.122400</td>
      <td>0.580660</td>
      <td>-0.155150</td>
      <td>-0.229490</td>
      <td>0.087454</td>
      <td>-1.273800</td>
      <td>-0.082589</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>9962</th>
      <td>assassin</td>
      <td>-0.84157</td>
      <td>0.646020</td>
      <td>0.377690</td>
      <td>0.193220</td>
      <td>0.672170</td>
      <td>0.046196</td>
      <td>-0.518090</td>
      <td>0.032216</td>
      <td>0.331010</td>
      <td>...</td>
      <td>0.506510</td>
      <td>-0.360180</td>
      <td>0.024444</td>
      <td>0.144680</td>
      <td>-0.245090</td>
      <td>0.021795</td>
      <td>-0.144850</td>
      <td>-0.073948</td>
      <td>0.006807</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3507</th>
      <td>veggietales:-princess-and-the-pop-star</td>
      <td>-0.84002</td>
      <td>-0.331920</td>
      <td>-0.099171</td>
      <td>0.112300</td>
      <td>0.604290</td>
      <td>0.153530</td>
      <td>0.615530</td>
      <td>0.155050</td>
      <td>-0.017573</td>
      <td>...</td>
      <td>0.095184</td>
      <td>-0.154610</td>
      <td>-0.680760</td>
      <td>0.222400</td>
      <td>-0.562440</td>
      <td>0.004180</td>
      <td>-0.109540</td>
      <td>-0.438840</td>
      <td>-0.269010</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>14049</th>
      <td>sesame-street-season-38</td>
      <td>-0.83719</td>
      <td>-0.184770</td>
      <td>-0.261420</td>
      <td>-0.426630</td>
      <td>0.552760</td>
      <td>0.271390</td>
      <td>0.730570</td>
      <td>-0.118140</td>
      <td>0.127310</td>
      <td>...</td>
      <td>0.046979</td>
      <td>-0.061584</td>
      <td>-0.189610</td>
      <td>0.602660</td>
      <td>-0.190970</td>
      <td>-0.245790</td>
      <td>0.124040</td>
      <td>-1.157900</td>
      <td>-0.106920</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7194</th>
      <td>power-rangers-wild-force-season-1</td>
      <td>-0.83318</td>
      <td>-0.046232</td>
      <td>0.408110</td>
      <td>0.552580</td>
      <td>0.648150</td>
      <td>0.289000</td>
      <td>0.662210</td>
      <td>-0.071248</td>
      <td>-0.062563</td>
      <td>...</td>
      <td>0.404210</td>
      <td>0.313390</td>
      <td>-0.390730</td>
      <td>1.043900</td>
      <td>-0.420650</td>
      <td>0.053204</td>
      <td>0.253820</td>
      <td>-0.862560</td>
      <td>-0.214910</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1148</th>
      <td>shaun-the-sheep-season-3</td>
      <td>-0.82954</td>
      <td>-0.555070</td>
      <td>0.842390</td>
      <td>-0.794490</td>
      <td>0.377230</td>
      <td>0.193440</td>
      <td>0.462450</td>
      <td>0.414240</td>
      <td>0.055584</td>
      <td>...</td>
      <td>0.086914</td>
      <td>-0.579000</td>
      <td>0.093780</td>
      <td>-0.493320</td>
      <td>-0.129850</td>
      <td>0.118090</td>
      <td>0.145760</td>
      <td>-0.365100</td>
      <td>-0.001799</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5757</th>
      <td>veggietales:-minnesota-cuke-and-the-search-for...</td>
      <td>-0.81926</td>
      <td>-0.195400</td>
      <td>0.063299</td>
      <td>0.451140</td>
      <td>0.763380</td>
      <td>0.391600</td>
      <td>0.561470</td>
      <td>0.021702</td>
      <td>-0.013875</td>
      <td>...</td>
      <td>0.348000</td>
      <td>0.092133</td>
      <td>-0.582050</td>
      <td>0.270040</td>
      <td>-0.212050</td>
      <td>0.095492</td>
      <td>-0.164660</td>
      <td>-0.420110</td>
      <td>-0.433860</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5248</th>
      <td>slipper-and-the-rose</td>
      <td>-0.81419</td>
      <td>0.141780</td>
      <td>0.133780</td>
      <td>-0.152390</td>
      <td>-0.264890</td>
      <td>-0.052448</td>
      <td>0.999830</td>
      <td>0.383860</td>
      <td>0.572150</td>
      <td>...</td>
      <td>-0.136250</td>
      <td>-0.045857</td>
      <td>-0.044832</td>
      <td>-0.135790</td>
      <td>-0.217280</td>
      <td>0.110260</td>
      <td>-0.487710</td>
      <td>-0.099188</td>
      <td>-0.033350</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>9054</th>
      <td>power-rangers-in-space-season-1</td>
      <td>-0.81298</td>
      <td>0.128160</td>
      <td>0.512650</td>
      <td>0.490430</td>
      <td>0.638940</td>
      <td>0.136920</td>
      <td>0.503940</td>
      <td>-0.007238</td>
      <td>0.094603</td>
      <td>...</td>
      <td>0.407430</td>
      <td>0.490120</td>
      <td>-0.340550</td>
      <td>1.016600</td>
      <td>-0.436520</td>
      <td>0.241630</td>
      <td>0.154440</td>
      <td>-0.730910</td>
      <td>-0.213830</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7655</th>
      <td>turbo:-a-power-rangers-movie</td>
      <td>-0.80537</td>
      <td>-0.171100</td>
      <td>0.403500</td>
      <td>0.594150</td>
      <td>0.493660</td>
      <td>0.025501</td>
      <td>0.273760</td>
      <td>-0.050237</td>
      <td>-0.118030</td>
      <td>...</td>
      <td>0.535560</td>
      <td>0.647310</td>
      <td>-0.400820</td>
      <td>0.621020</td>
      <td>-0.180180</td>
      <td>-0.454390</td>
      <td>0.357280</td>
      <td>-0.622080</td>
      <td>-0.224660</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>1877</th>
      <td>frasier-season-3</td>
      <td>1.04310</td>
      <td>-0.396420</td>
      <td>0.560220</td>
      <td>-0.933930</td>
      <td>0.320050</td>
      <td>0.387680</td>
      <td>0.560900</td>
      <td>-0.534900</td>
      <td>0.339390</td>
      <td>...</td>
      <td>-0.352780</td>
      <td>-0.643600</td>
      <td>0.995900</td>
      <td>0.226190</td>
      <td>-0.076486</td>
      <td>0.349980</td>
      <td>-0.613990</td>
      <td>-1.105700</td>
      <td>-0.178110</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4141</th>
      <td>black-sails,-season-2</td>
      <td>1.05510</td>
      <td>0.043450</td>
      <td>-0.045196</td>
      <td>-0.266090</td>
      <td>0.302760</td>
      <td>-0.283030</td>
      <td>-0.346400</td>
      <td>0.573300</td>
      <td>-0.095515</td>
      <td>...</td>
      <td>-0.523580</td>
      <td>0.129030</td>
      <td>-0.129810</td>
      <td>-0.049712</td>
      <td>-0.133870</td>
      <td>-0.404530</td>
      <td>-0.192270</td>
      <td>-0.583370</td>
      <td>0.589330</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3188</th>
      <td>helix-season-2</td>
      <td>1.05630</td>
      <td>0.352460</td>
      <td>0.289600</td>
      <td>0.015159</td>
      <td>0.273130</td>
      <td>-0.441910</td>
      <td>-0.201640</td>
      <td>0.267590</td>
      <td>0.000577</td>
      <td>...</td>
      <td>-0.272040</td>
      <td>0.129600</td>
      <td>0.019078</td>
      <td>0.152110</td>
      <td>-0.254900</td>
      <td>0.036755</td>
      <td>-0.236070</td>
      <td>-1.025400</td>
      <td>0.315250</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1833</th>
      <td>the-originals:-season-2</td>
      <td>1.06140</td>
      <td>-0.163690</td>
      <td>0.071307</td>
      <td>0.464440</td>
      <td>0.444130</td>
      <td>-0.331400</td>
      <td>0.396650</td>
      <td>0.363740</td>
      <td>0.197200</td>
      <td>...</td>
      <td>-0.244220</td>
      <td>0.542500</td>
      <td>-0.393280</td>
      <td>-0.625660</td>
      <td>-0.678170</td>
      <td>0.585030</td>
      <td>-0.193310</td>
      <td>-0.877360</td>
      <td>0.202940</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1345</th>
      <td>the-shield,-season-7</td>
      <td>1.06440</td>
      <td>-0.556130</td>
      <td>-0.186070</td>
      <td>-1.096500</td>
      <td>1.299700</td>
      <td>-1.278000</td>
      <td>-0.498520</td>
      <td>0.626560</td>
      <td>-0.229650</td>
      <td>...</td>
      <td>-0.656620</td>
      <td>-0.684470</td>
      <td>0.194220</td>
      <td>0.140260</td>
      <td>-0.513090</td>
      <td>0.164550</td>
      <td>0.303880</td>
      <td>-0.481850</td>
      <td>-0.121610</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7279</th>
      <td>mad-men-season-2</td>
      <td>1.06680</td>
      <td>0.305670</td>
      <td>0.058945</td>
      <td>-0.180810</td>
      <td>0.724820</td>
      <td>-0.079940</td>
      <td>0.234390</td>
      <td>0.358160</td>
      <td>-0.214250</td>
      <td>...</td>
      <td>-0.554560</td>
      <td>-0.322810</td>
      <td>-0.067942</td>
      <td>-0.132320</td>
      <td>-0.222270</td>
      <td>-0.118700</td>
      <td>0.584420</td>
      <td>0.068441</td>
      <td>0.106260</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2120</th>
      <td>the-shield-season-6</td>
      <td>1.06790</td>
      <td>-0.256320</td>
      <td>-0.247810</td>
      <td>-0.790860</td>
      <td>1.330400</td>
      <td>-0.850520</td>
      <td>-0.285630</td>
      <td>0.598800</td>
      <td>-0.145440</td>
      <td>...</td>
      <td>-0.932380</td>
      <td>-0.698760</td>
      <td>-0.102930</td>
      <td>0.304870</td>
      <td>-0.693040</td>
      <td>0.314210</td>
      <td>0.397510</td>
      <td>-0.780550</td>
      <td>-0.520230</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3605</th>
      <td>frasier-season-10</td>
      <td>1.06830</td>
      <td>-0.598380</td>
      <td>0.468250</td>
      <td>-0.424390</td>
      <td>0.119570</td>
      <td>0.324090</td>
      <td>0.398930</td>
      <td>-0.381280</td>
      <td>0.302820</td>
      <td>...</td>
      <td>-0.442980</td>
      <td>-0.653500</td>
      <td>1.050300</td>
      <td>0.271250</td>
      <td>-0.060496</td>
      <td>0.455890</td>
      <td>-0.642440</td>
      <td>-1.291100</td>
      <td>-0.289040</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1304</th>
      <td>empire-season-1</td>
      <td>1.08090</td>
      <td>-0.492280</td>
      <td>0.515010</td>
      <td>0.437590</td>
      <td>0.318140</td>
      <td>-0.044655</td>
      <td>-0.156000</td>
      <td>-0.759330</td>
      <td>0.173080</td>
      <td>...</td>
      <td>-0.359290</td>
      <td>0.145790</td>
      <td>-0.156930</td>
      <td>0.219600</td>
      <td>-0.099819</td>
      <td>0.123090</td>
      <td>0.093327</td>
      <td>-0.590270</td>
      <td>-0.193220</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>679</th>
      <td>the-strain-season-1</td>
      <td>1.10320</td>
      <td>0.335240</td>
      <td>0.749780</td>
      <td>0.150310</td>
      <td>0.383930</td>
      <td>-0.403170</td>
      <td>-0.062950</td>
      <td>0.078467</td>
      <td>-0.419900</td>
      <td>...</td>
      <td>-0.186830</td>
      <td>0.256370</td>
      <td>-0.040260</td>
      <td>-0.056374</td>
      <td>-0.383610</td>
      <td>0.289110</td>
      <td>0.041503</td>
      <td>-0.359390</td>
      <td>-0.424820</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1275</th>
      <td>how-to-get-away-with-murder-season-1</td>
      <td>1.10410</td>
      <td>-0.348430</td>
      <td>0.095577</td>
      <td>0.471970</td>
      <td>-0.027239</td>
      <td>-0.519820</td>
      <td>0.316200</td>
      <td>0.547270</td>
      <td>0.393310</td>
      <td>...</td>
      <td>-0.538100</td>
      <td>0.073240</td>
      <td>0.276830</td>
      <td>0.114360</td>
      <td>-0.337980</td>
      <td>0.496880</td>
      <td>-0.131820</td>
      <td>-0.321420</td>
      <td>-0.247130</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>10607</th>
      <td>sirens-season-2</td>
      <td>1.11270</td>
      <td>0.519850</td>
      <td>0.240020</td>
      <td>-0.042184</td>
      <td>0.292620</td>
      <td>-0.263150</td>
      <td>-0.490510</td>
      <td>0.170790</td>
      <td>0.215220</td>
      <td>...</td>
      <td>-0.805660</td>
      <td>0.048994</td>
      <td>0.388870</td>
      <td>0.385500</td>
      <td>-0.181320</td>
      <td>0.277300</td>
      <td>0.144420</td>
      <td>-0.844570</td>
      <td>0.294010</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1080</th>
      <td>the-bridge-season-1</td>
      <td>1.11610</td>
      <td>0.244710</td>
      <td>0.348420</td>
      <td>0.525740</td>
      <td>0.675410</td>
      <td>-0.361740</td>
      <td>-0.535710</td>
      <td>0.371260</td>
      <td>-0.126180</td>
      <td>...</td>
      <td>-0.103790</td>
      <td>-0.470160</td>
      <td>-0.175760</td>
      <td>-0.286140</td>
      <td>-0.204160</td>
      <td>-0.170770</td>
      <td>-0.153120</td>
      <td>-0.388980</td>
      <td>0.079515</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>17674</th>
      <td>the-big-bang</td>
      <td>1.11730</td>
      <td>0.328150</td>
      <td>0.812600</td>
      <td>-1.032200</td>
      <td>-0.603760</td>
      <td>-0.483720</td>
      <td>0.682790</td>
      <td>0.862410</td>
      <td>0.127240</td>
      <td>...</td>
      <td>0.993220</td>
      <td>0.170700</td>
      <td>0.165950</td>
      <td>0.849770</td>
      <td>0.747830</td>
      <td>0.249870</td>
      <td>0.160900</td>
      <td>0.256960</td>
      <td>0.524930</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>746</th>
      <td>frasier-season-1</td>
      <td>1.11870</td>
      <td>-0.408110</td>
      <td>0.774850</td>
      <td>-0.753440</td>
      <td>0.231050</td>
      <td>0.336990</td>
      <td>0.621880</td>
      <td>-0.470980</td>
      <td>0.035662</td>
      <td>...</td>
      <td>-0.169230</td>
      <td>-0.841260</td>
      <td>0.808460</td>
      <td>0.379450</td>
      <td>-0.047711</td>
      <td>0.142340</td>
      <td>-0.566070</td>
      <td>-1.140000</td>
      <td>-0.189690</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2622</th>
      <td>frasier-season-5</td>
      <td>1.13680</td>
      <td>-0.384980</td>
      <td>0.573370</td>
      <td>-0.715690</td>
      <td>0.283950</td>
      <td>0.443310</td>
      <td>0.596600</td>
      <td>-0.564410</td>
      <td>0.437080</td>
      <td>...</td>
      <td>-0.285680</td>
      <td>-0.686030</td>
      <td>0.960280</td>
      <td>0.452680</td>
      <td>-0.238930</td>
      <td>0.309490</td>
      <td>-0.861450</td>
      <td>-1.170800</td>
      <td>-0.153740</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>665</th>
      <td>mad-men-season-6</td>
      <td>1.16000</td>
      <td>0.348120</td>
      <td>-0.004853</td>
      <td>-0.004538</td>
      <td>0.893810</td>
      <td>0.077908</td>
      <td>0.199040</td>
      <td>0.155870</td>
      <td>0.090219</td>
      <td>...</td>
      <td>-0.520250</td>
      <td>-0.367470</td>
      <td>0.043946</td>
      <td>-0.177450</td>
      <td>-0.158540</td>
      <td>0.150810</td>
      <td>0.363450</td>
      <td>0.004869</td>
      <td>0.063695</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2025</th>
      <td>frasier-season-4</td>
      <td>1.17340</td>
      <td>-0.381780</td>
      <td>0.510490</td>
      <td>-0.808570</td>
      <td>0.309560</td>
      <td>0.378990</td>
      <td>0.591160</td>
      <td>-0.552570</td>
      <td>0.365460</td>
      <td>...</td>
      <td>-0.381500</td>
      <td>-0.765490</td>
      <td>0.862760</td>
      <td>0.351060</td>
      <td>-0.117920</td>
      <td>0.195260</td>
      <td>-0.840870</td>
      <td>-1.101300</td>
      <td>-0.201270</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>275</th>
      <td>mad-men-season-7</td>
      <td>1.18070</td>
      <td>0.260480</td>
      <td>-0.057148</td>
      <td>0.056015</td>
      <td>0.615470</td>
      <td>-0.019646</td>
      <td>0.029417</td>
      <td>0.108120</td>
      <td>0.128560</td>
      <td>...</td>
      <td>-0.512410</td>
      <td>-0.118800</td>
      <td>0.316680</td>
      <td>-0.051425</td>
      <td>-0.305860</td>
      <td>0.174300</td>
      <td>0.290520</td>
      <td>-0.151770</td>
      <td>0.118270</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1581</th>
      <td>frasier-season-2</td>
      <td>1.18370</td>
      <td>-0.370030</td>
      <td>0.639910</td>
      <td>-0.881460</td>
      <td>0.259990</td>
      <td>0.275830</td>
      <td>0.592910</td>
      <td>-0.414870</td>
      <td>0.235070</td>
      <td>...</td>
      <td>-0.265880</td>
      <td>-0.764090</td>
      <td>0.841140</td>
      <td>0.320230</td>
      <td>-0.124890</td>
      <td>0.207020</td>
      <td>-0.601830</td>
      <td>-1.162600</td>
      <td>-0.118420</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3419</th>
      <td>frasier-season-9</td>
      <td>1.18460</td>
      <td>-0.238770</td>
      <td>0.610620</td>
      <td>-0.698500</td>
      <td>0.067376</td>
      <td>0.211930</td>
      <td>0.552060</td>
      <td>-0.458110</td>
      <td>0.483450</td>
      <td>...</td>
      <td>-0.391540</td>
      <td>-0.633620</td>
      <td>1.175200</td>
      <td>0.281530</td>
      <td>-0.121460</td>
      <td>0.163310</td>
      <td>-0.815290</td>
      <td>-1.267500</td>
      <td>-0.267970</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3665</th>
      <td>frasier-season-8</td>
      <td>1.19070</td>
      <td>-0.333370</td>
      <td>0.536230</td>
      <td>-0.709770</td>
      <td>0.264390</td>
      <td>0.443800</td>
      <td>0.455430</td>
      <td>-0.360070</td>
      <td>0.412200</td>
      <td>...</td>
      <td>-0.516250</td>
      <td>-0.806120</td>
      <td>0.959660</td>
      <td>0.368720</td>
      <td>-0.057676</td>
      <td>0.349010</td>
      <td>-0.670260</td>
      <td>-1.281000</td>
      <td>-0.201860</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3126</th>
      <td>the-strain-season-2</td>
      <td>1.19570</td>
      <td>0.354550</td>
      <td>0.666630</td>
      <td>0.058555</td>
      <td>0.401740</td>
      <td>-0.387230</td>
      <td>-0.203910</td>
      <td>0.093231</td>
      <td>-0.252470</td>
      <td>...</td>
      <td>-0.265980</td>
      <td>0.390690</td>
      <td>-0.000746</td>
      <td>-0.127000</td>
      <td>-0.543650</td>
      <td>0.348430</td>
      <td>0.126920</td>
      <td>-0.330930</td>
      <td>-0.407570</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2383</th>
      <td>frasier-season-11</td>
      <td>1.22670</td>
      <td>-0.495890</td>
      <td>0.367400</td>
      <td>-0.663610</td>
      <td>0.216260</td>
      <td>0.442480</td>
      <td>0.558600</td>
      <td>-0.423330</td>
      <td>0.530970</td>
      <td>...</td>
      <td>-0.340830</td>
      <td>-0.753360</td>
      <td>0.913560</td>
      <td>0.385200</td>
      <td>-0.006673</td>
      <td>0.308940</td>
      <td>-0.809450</td>
      <td>-1.238300</td>
      <td>-0.332510</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1412</th>
      <td>the-bridge-season-2</td>
      <td>1.23610</td>
      <td>0.281550</td>
      <td>0.189870</td>
      <td>0.496080</td>
      <td>0.680430</td>
      <td>-0.403250</td>
      <td>-0.607660</td>
      <td>0.381760</td>
      <td>0.089049</td>
      <td>...</td>
      <td>-0.191740</td>
      <td>-0.420960</td>
      <td>-0.192060</td>
      <td>-0.331080</td>
      <td>-0.270030</td>
      <td>-0.109220</td>
      <td>-0.189780</td>
      <td>-0.394180</td>
      <td>0.105520</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3125</th>
      <td>frasier-season-7</td>
      <td>1.24450</td>
      <td>-0.261080</td>
      <td>0.547090</td>
      <td>-0.853920</td>
      <td>0.220460</td>
      <td>0.401920</td>
      <td>0.506700</td>
      <td>-0.536930</td>
      <td>0.442830</td>
      <td>...</td>
      <td>-0.292570</td>
      <td>-0.720420</td>
      <td>1.119200</td>
      <td>0.474200</td>
      <td>-0.219780</td>
      <td>0.449900</td>
      <td>-0.758600</td>
      <td>-1.253800</td>
      <td>-0.117660</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2658</th>
      <td>frasier-season-6</td>
      <td>1.28540</td>
      <td>-0.350620</td>
      <td>0.574820</td>
      <td>-0.712160</td>
      <td>0.443650</td>
      <td>0.556790</td>
      <td>0.549720</td>
      <td>-0.590800</td>
      <td>0.579820</td>
      <td>...</td>
      <td>-0.287950</td>
      <td>-0.730420</td>
      <td>0.895090</td>
      <td>0.336530</td>
      <td>-0.192320</td>
      <td>0.532730</td>
      <td>-0.763640</td>
      <td>-1.236000</td>
      <td>-0.219210</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>16823</th>
      <td>the-big-bang-theory</td>
      <td>1.28880</td>
      <td>0.180490</td>
      <td>0.966800</td>
      <td>-1.012400</td>
      <td>-0.529330</td>
      <td>-0.545580</td>
      <td>0.848080</td>
      <td>0.993910</td>
      <td>0.180720</td>
      <td>...</td>
      <td>1.095600</td>
      <td>0.172960</td>
      <td>0.449590</td>
      <td>0.879260</td>
      <td>0.971180</td>
      <td>0.255210</td>
      <td>0.189390</td>
      <td>0.228240</td>
      <td>0.579220</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1828</th>
      <td>salem-season-1</td>
      <td>1.37050</td>
      <td>0.523520</td>
      <td>0.674210</td>
      <td>0.071277</td>
      <td>0.315940</td>
      <td>-0.072411</td>
      <td>0.077283</td>
      <td>0.233560</td>
      <td>-0.654320</td>
      <td>...</td>
      <td>-0.198820</td>
      <td>0.173000</td>
      <td>-0.034679</td>
      <td>-0.314090</td>
      <td>-0.884750</td>
      <td>0.477990</td>
      <td>-0.344150</td>
      <td>-0.653630</td>
      <td>-0.051349</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2852</th>
      <td>salem-season-2</td>
      <td>1.50740</td>
      <td>0.480290</td>
      <td>0.419450</td>
      <td>-0.008684</td>
      <td>0.298880</td>
      <td>-0.059552</td>
      <td>-0.022603</td>
      <td>0.272170</td>
      <td>-0.371360</td>
      <td>...</td>
      <td>-0.325630</td>
      <td>0.312640</td>
      <td>0.010613</td>
      <td>-0.443680</td>
      <td>-0.965590</td>
      <td>0.499110</td>
      <td>-0.337510</td>
      <td>-0.628300</td>
      <td>-0.014363</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>17989 rows × 102 columns</p>
</div>
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</div>

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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[52]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vectors</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
</pre></div>

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<div class="prompt output_prompt">Out[52]:</div>


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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
      <th>6</th>
      <th>7</th>
      <th>8</th>
      <th>9</th>
      <th>...</th>
      <th>92</th>
      <th>93</th>
      <th>94</th>
      <th>95</th>
      <th>96</th>
      <th>97</th>
      <th>98</th>
      <th>99</th>
      <th>100</th>
      <th>101</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>783</th>
      <td>fireman-sam</td>
      <td>-0.065211</td>
      <td>-1.16540</td>
      <td>0.765780</td>
      <td>0.009694</td>
      <td>0.498130</td>
      <td>-1.257700</td>
      <td>0.193570</td>
      <td>0.580870</td>
      <td>-0.443270</td>
      <td>...</td>
      <td>0.188170</td>
      <td>-0.021216</td>
      <td>-0.130590</td>
      <td>0.355340</td>
      <td>-0.279300</td>
      <td>-0.267970</td>
      <td>-0.309390</td>
      <td>-1.506500</td>
      <td>-0.207510</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>659</th>
      <td>the-good-lie</td>
      <td>-0.194220</td>
      <td>-1.14360</td>
      <td>-0.144010</td>
      <td>0.564970</td>
      <td>0.662890</td>
      <td>0.295820</td>
      <td>0.607590</td>
      <td>-0.436890</td>
      <td>0.010599</td>
      <td>...</td>
      <td>-0.273540</td>
      <td>-0.236420</td>
      <td>-0.089721</td>
      <td>-0.026527</td>
      <td>-0.119320</td>
      <td>0.277760</td>
      <td>-0.444240</td>
      <td>-0.416630</td>
      <td>-0.485280</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1668</th>
      <td>top-five</td>
      <td>0.564250</td>
      <td>-1.12170</td>
      <td>-0.112090</td>
      <td>0.429860</td>
      <td>0.343400</td>
      <td>0.397500</td>
      <td>0.006915</td>
      <td>-0.485270</td>
      <td>0.202700</td>
      <td>...</td>
      <td>-0.272430</td>
      <td>0.210700</td>
      <td>0.644530</td>
      <td>0.071297</td>
      <td>-0.052676</td>
      <td>0.078141</td>
      <td>0.126690</td>
      <td>-0.493890</td>
      <td>0.118670</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3250</th>
      <td>the-trip</td>
      <td>0.395470</td>
      <td>-0.94731</td>
      <td>0.218570</td>
      <td>-0.626080</td>
      <td>-0.046134</td>
      <td>-0.039983</td>
      <td>-0.063817</td>
      <td>-0.416240</td>
      <td>-0.095196</td>
      <td>...</td>
      <td>-0.043862</td>
      <td>-0.570410</td>
      <td>0.154960</td>
      <td>0.027053</td>
      <td>0.146170</td>
      <td>0.227450</td>
      <td>-0.282050</td>
      <td>0.048281</td>
      <td>0.096639</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2397</th>
      <td>paw-patrol-volume-1</td>
      <td>0.179300</td>
      <td>-0.93015</td>
      <td>0.132390</td>
      <td>0.582210</td>
      <td>0.305200</td>
      <td>-0.520490</td>
      <td>0.201580</td>
      <td>0.303510</td>
      <td>0.351700</td>
      <td>...</td>
      <td>0.547600</td>
      <td>0.580600</td>
      <td>-0.210720</td>
      <td>0.490350</td>
      <td>-0.821880</td>
      <td>-0.289790</td>
      <td>0.671920</td>
      <td>-1.030700</td>
      <td>-1.112000</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2823</th>
      <td>my-last-day-without-you</td>
      <td>0.282970</td>
      <td>-0.90408</td>
      <td>0.467450</td>
      <td>0.714670</td>
      <td>0.199470</td>
      <td>-0.196310</td>
      <td>0.395910</td>
      <td>-0.154010</td>
      <td>0.107080</td>
      <td>...</td>
      <td>0.083558</td>
      <td>-0.063314</td>
      <td>-0.163650</td>
      <td>0.307050</td>
      <td>-0.346980</td>
      <td>0.386370</td>
      <td>-0.260950</td>
      <td>-0.240330</td>
      <td>-0.390340</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>612</th>
      <td>the-wedding-ringer</td>
      <td>0.964940</td>
      <td>-0.89863</td>
      <td>-0.337240</td>
      <td>0.407620</td>
      <td>0.399790</td>
      <td>0.172760</td>
      <td>-0.019798</td>
      <td>-0.242450</td>
      <td>0.739720</td>
      <td>...</td>
      <td>-0.303340</td>
      <td>0.157620</td>
      <td>-0.182200</td>
      <td>0.004058</td>
      <td>-0.717310</td>
      <td>0.173960</td>
      <td>-0.377660</td>
      <td>-0.259680</td>
      <td>-0.304910</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>286</th>
      <td>boyhood</td>
      <td>0.374110</td>
      <td>-0.89633</td>
      <td>0.227400</td>
      <td>0.349200</td>
      <td>0.095933</td>
      <td>-0.023439</td>
      <td>0.206530</td>
      <td>-0.057537</td>
      <td>0.228650</td>
      <td>...</td>
      <td>-0.031209</td>
      <td>0.148180</td>
      <td>0.389250</td>
      <td>-0.099151</td>
      <td>-0.067750</td>
      <td>-0.284990</td>
      <td>-0.076587</td>
      <td>-0.291630</td>
      <td>0.181880</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1389</th>
      <td>dolphin-tale-(2011)</td>
      <td>0.219910</td>
      <td>-0.87744</td>
      <td>-0.522180</td>
      <td>0.466400</td>
      <td>0.295340</td>
      <td>0.048140</td>
      <td>0.253570</td>
      <td>0.060539</td>
      <td>-0.066945</td>
      <td>...</td>
      <td>0.119050</td>
      <td>-0.021572</td>
      <td>-0.460870</td>
      <td>-0.303320</td>
      <td>-0.301460</td>
      <td>0.350960</td>
      <td>-0.725200</td>
      <td>-0.096180</td>
      <td>0.473850</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3798</th>
      <td>paw-patrol-volume-2</td>
      <td>0.308960</td>
      <td>-0.86444</td>
      <td>-0.021352</td>
      <td>0.589000</td>
      <td>0.235590</td>
      <td>-0.345400</td>
      <td>0.029976</td>
      <td>0.175000</td>
      <td>0.190500</td>
      <td>...</td>
      <td>0.507860</td>
      <td>0.414020</td>
      <td>-0.211200</td>
      <td>0.449540</td>
      <td>-0.804580</td>
      <td>0.035774</td>
      <td>0.661440</td>
      <td>-1.111400</td>
      <td>-1.117800</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5390</th>
      <td>the-song</td>
      <td>0.211170</td>
      <td>-0.84435</td>
      <td>0.158890</td>
      <td>0.279930</td>
      <td>0.244880</td>
      <td>-0.018907</td>
      <td>0.465250</td>
      <td>0.083340</td>
      <td>0.133070</td>
      <td>...</td>
      <td>-0.256350</td>
      <td>0.623050</td>
      <td>-0.061084</td>
      <td>-0.260420</td>
      <td>-0.357970</td>
      <td>0.710260</td>
      <td>-0.417820</td>
      <td>-0.665340</td>
      <td>-0.885020</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1264</th>
      <td>when-the-game-stands-tall</td>
      <td>-0.094850</td>
      <td>-0.84414</td>
      <td>-0.155070</td>
      <td>0.852210</td>
      <td>0.432500</td>
      <td>0.402500</td>
      <td>0.207030</td>
      <td>-0.139790</td>
      <td>0.133050</td>
      <td>...</td>
      <td>-0.048479</td>
      <td>-0.066251</td>
      <td>0.168430</td>
      <td>0.281980</td>
      <td>0.035450</td>
      <td>0.294110</td>
      <td>-0.342780</td>
      <td>-1.057200</td>
      <td>0.311360</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1379</th>
      <td>the-cobbler</td>
      <td>0.656350</td>
      <td>-0.82573</td>
      <td>-0.047598</td>
      <td>-0.241920</td>
      <td>0.099408</td>
      <td>-0.177280</td>
      <td>-0.021729</td>
      <td>-0.087511</td>
      <td>0.209730</td>
      <td>...</td>
      <td>-0.162080</td>
      <td>0.208560</td>
      <td>0.253670</td>
      <td>0.308390</td>
      <td>-0.516570</td>
      <td>0.062497</td>
      <td>-0.511190</td>
      <td>-0.393580</td>
      <td>-0.138210</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>978</th>
      <td>the-west-wing:-the-complete-first-season</td>
      <td>0.263360</td>
      <td>-0.81296</td>
      <td>-0.543420</td>
      <td>-0.453530</td>
      <td>0.569160</td>
      <td>-0.758120</td>
      <td>0.526500</td>
      <td>0.275830</td>
      <td>-0.574270</td>
      <td>...</td>
      <td>-0.564800</td>
      <td>-0.134770</td>
      <td>-0.555060</td>
      <td>0.069877</td>
      <td>0.245000</td>
      <td>-0.360240</td>
      <td>0.679040</td>
      <td>-0.620120</td>
      <td>0.163140</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2535</th>
      <td>elsa-&amp;-fred</td>
      <td>0.230200</td>
      <td>-0.80252</td>
      <td>0.156540</td>
      <td>-0.068552</td>
      <td>-0.312710</td>
      <td>-0.019822</td>
      <td>-0.160600</td>
      <td>0.099906</td>
      <td>0.326740</td>
      <td>...</td>
      <td>0.192030</td>
      <td>-0.504080</td>
      <td>-0.017005</td>
      <td>-0.248570</td>
      <td>-0.405560</td>
      <td>0.259980</td>
      <td>-1.055900</td>
      <td>-0.334530</td>
      <td>0.167010</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>7752</th>
      <td>paw-patrol-volume-4</td>
      <td>0.246760</td>
      <td>-0.79574</td>
      <td>-0.008937</td>
      <td>0.690780</td>
      <td>0.202400</td>
      <td>-0.350720</td>
      <td>-0.010859</td>
      <td>0.189270</td>
      <td>0.155240</td>
      <td>...</td>
      <td>0.441500</td>
      <td>0.610830</td>
      <td>-0.121520</td>
      <td>0.534290</td>
      <td>-0.858430</td>
      <td>0.014306</td>
      <td>0.649400</td>
      <td>-1.228800</td>
      <td>-1.032700</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>639</th>
      <td>alexander-and-the-terrible,-horrible,-no-good,...</td>
      <td>-0.024547</td>
      <td>-0.79494</td>
      <td>-0.234430</td>
      <td>0.070366</td>
      <td>0.045514</td>
      <td>0.410390</td>
      <td>0.028310</td>
      <td>0.379300</td>
      <td>-0.193650</td>
      <td>...</td>
      <td>-0.528750</td>
      <td>0.016403</td>
      <td>-0.040875</td>
      <td>0.242170</td>
      <td>-0.355310</td>
      <td>0.085783</td>
      <td>-0.496830</td>
      <td>-0.213110</td>
      <td>0.295430</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2531</th>
      <td>the-mentalist:-season-7</td>
      <td>0.483450</td>
      <td>-0.79009</td>
      <td>0.158500</td>
      <td>0.555300</td>
      <td>0.060187</td>
      <td>-0.630050</td>
      <td>-0.530450</td>
      <td>0.348340</td>
      <td>0.675170</td>
      <td>...</td>
      <td>-0.003446</td>
      <td>-0.338000</td>
      <td>0.394990</td>
      <td>-0.326020</td>
      <td>-0.982550</td>
      <td>0.486880</td>
      <td>0.005474</td>
      <td>-0.846710</td>
      <td>-0.210940</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5696</th>
      <td>paw-patrol-volume-3</td>
      <td>0.414550</td>
      <td>-0.78905</td>
      <td>0.090881</td>
      <td>0.701370</td>
      <td>0.243380</td>
      <td>-0.398550</td>
      <td>0.139830</td>
      <td>0.160820</td>
      <td>0.263450</td>
      <td>...</td>
      <td>0.550980</td>
      <td>0.729060</td>
      <td>-0.008195</td>
      <td>0.624770</td>
      <td>-0.975490</td>
      <td>-0.027426</td>
      <td>0.748470</td>
      <td>-1.080300</td>
      <td>-0.958040</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1168</th>
      <td>no-good-deed</td>
      <td>0.385580</td>
      <td>-0.78349</td>
      <td>0.180010</td>
      <td>0.698010</td>
      <td>0.392610</td>
      <td>0.371720</td>
      <td>0.232580</td>
      <td>-0.304880</td>
      <td>0.251550</td>
      <td>...</td>
      <td>-0.828360</td>
      <td>0.159600</td>
      <td>0.485040</td>
      <td>-0.055291</td>
      <td>-0.011552</td>
      <td>-0.301150</td>
      <td>-0.143030</td>
      <td>-0.738170</td>
      <td>-0.504560</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1343</th>
      <td>hit-by-lightning</td>
      <td>-0.048999</td>
      <td>-0.77312</td>
      <td>0.171270</td>
      <td>0.664010</td>
      <td>0.102460</td>
      <td>-0.351720</td>
      <td>0.356080</td>
      <td>-0.130780</td>
      <td>0.002894</td>
      <td>...</td>
      <td>-0.103460</td>
      <td>0.236260</td>
      <td>-0.260960</td>
      <td>0.177080</td>
      <td>-0.492040</td>
      <td>0.203150</td>
      <td>-0.034395</td>
      <td>-0.011754</td>
      <td>0.397900</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3396</th>
      <td>caillou-season-5</td>
      <td>0.052588</td>
      <td>-0.76319</td>
      <td>0.262650</td>
      <td>0.425400</td>
      <td>0.617780</td>
      <td>0.243640</td>
      <td>0.445680</td>
      <td>-0.543740</td>
      <td>-0.119400</td>
      <td>...</td>
      <td>0.531950</td>
      <td>0.572800</td>
      <td>-0.008840</td>
      <td>0.290230</td>
      <td>-0.276790</td>
      <td>0.194990</td>
      <td>0.107430</td>
      <td>-1.226900</td>
      <td>-0.267840</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3186</th>
      <td>the-mentalist:-the-complete-first-season</td>
      <td>0.093489</td>
      <td>-0.75874</td>
      <td>0.317830</td>
      <td>0.212680</td>
      <td>0.279160</td>
      <td>-0.992410</td>
      <td>-0.209340</td>
      <td>0.543170</td>
      <td>-0.038357</td>
      <td>...</td>
      <td>-0.033507</td>
      <td>-0.717760</td>
      <td>-0.087830</td>
      <td>-0.058075</td>
      <td>-0.638410</td>
      <td>0.096761</td>
      <td>0.292700</td>
      <td>-0.433440</td>
      <td>-0.080414</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>843</th>
      <td>babel</td>
      <td>-0.087450</td>
      <td>-0.75396</td>
      <td>-0.115550</td>
      <td>0.218480</td>
      <td>0.108160</td>
      <td>-0.184840</td>
      <td>0.303460</td>
      <td>-0.497020</td>
      <td>-0.403970</td>
      <td>...</td>
      <td>0.508340</td>
      <td>-0.577150</td>
      <td>-0.174170</td>
      <td>0.169130</td>
      <td>-0.274600</td>
      <td>-0.183890</td>
      <td>0.011433</td>
      <td>0.449340</td>
      <td>0.125420</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1744</th>
      <td>_duplicate_dolphin-tale-2</td>
      <td>0.291590</td>
      <td>-0.75080</td>
      <td>-0.407590</td>
      <td>0.784900</td>
      <td>-0.009772</td>
      <td>0.295460</td>
      <td>0.665600</td>
      <td>-0.107620</td>
      <td>0.156170</td>
      <td>...</td>
      <td>0.132410</td>
      <td>0.188690</td>
      <td>0.293810</td>
      <td>0.129140</td>
      <td>-0.326130</td>
      <td>0.231810</td>
      <td>-0.509010</td>
      <td>-0.536360</td>
      <td>0.039927</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>262</th>
      <td>the-interview</td>
      <td>0.629790</td>
      <td>-0.74927</td>
      <td>0.227340</td>
      <td>0.053137</td>
      <td>0.282740</td>
      <td>0.095613</td>
      <td>0.108110</td>
      <td>-0.030107</td>
      <td>0.226380</td>
      <td>...</td>
      <td>0.100780</td>
      <td>0.245680</td>
      <td>0.019630</td>
      <td>-0.046007</td>
      <td>-0.082837</td>
      <td>0.076031</td>
      <td>-0.348210</td>
      <td>-0.251930</td>
      <td>-0.007174</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1970</th>
      <td>home-alone-2:-lost-in-new-york</td>
      <td>0.417010</td>
      <td>-0.74750</td>
      <td>-0.486820</td>
      <td>0.234270</td>
      <td>0.184910</td>
      <td>-0.045567</td>
      <td>0.599360</td>
      <td>0.561060</td>
      <td>-0.326180</td>
      <td>...</td>
      <td>-0.340780</td>
      <td>-0.329270</td>
      <td>0.014639</td>
      <td>0.747020</td>
      <td>0.033954</td>
      <td>0.054714</td>
      <td>-0.020008</td>
      <td>-0.416330</td>
      <td>-0.306900</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1713</th>
      <td>cars-2</td>
      <td>0.032644</td>
      <td>-0.74577</td>
      <td>-0.540640</td>
      <td>0.502590</td>
      <td>0.083715</td>
      <td>0.070453</td>
      <td>0.044255</td>
      <td>0.223010</td>
      <td>0.065257</td>
      <td>...</td>
      <td>0.571020</td>
      <td>0.408230</td>
      <td>-0.295560</td>
      <td>0.586080</td>
      <td>-0.093282</td>
      <td>0.082456</td>
      <td>0.006015</td>
      <td>-0.691760</td>
      <td>-0.363730</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>808</th>
      <td>belle</td>
      <td>0.555910</td>
      <td>-0.74554</td>
      <td>0.428810</td>
      <td>0.616810</td>
      <td>0.040039</td>
      <td>-0.142790</td>
      <td>0.354170</td>
      <td>0.378160</td>
      <td>0.241960</td>
      <td>...</td>
      <td>-0.152540</td>
      <td>0.031074</td>
      <td>-0.253730</td>
      <td>0.451880</td>
      <td>0.002740</td>
      <td>0.091997</td>
      <td>0.228680</td>
      <td>-0.245760</td>
      <td>-0.198930</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>673</th>
      <td>the-flash:-the-complete-first-season</td>
      <td>0.569160</td>
      <td>-0.74321</td>
      <td>0.601660</td>
      <td>0.426830</td>
      <td>0.179750</td>
      <td>-0.661510</td>
      <td>0.029662</td>
      <td>0.436200</td>
      <td>0.127790</td>
      <td>...</td>
      <td>-0.337730</td>
      <td>0.058965</td>
      <td>-0.226480</td>
      <td>0.402480</td>
      <td>-0.082651</td>
      <td>0.527510</td>
      <td>-0.158200</td>
      <td>-0.418500</td>
      <td>0.155210</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>7939</th>
      <td>13th-child:-jersey-devil</td>
      <td>0.089378</td>
      <td>1.17290</td>
      <td>0.197330</td>
      <td>-0.527900</td>
      <td>0.014659</td>
      <td>-0.167360</td>
      <td>0.463580</td>
      <td>-0.172140</td>
      <td>-0.107860</td>
      <td>...</td>
      <td>0.431770</td>
      <td>0.238280</td>
      <td>-0.124130</td>
      <td>0.551990</td>
      <td>-0.348540</td>
      <td>0.568790</td>
      <td>-0.266980</td>
      <td>-0.340600</td>
      <td>-0.347250</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>8842</th>
      <td>reverb</td>
      <td>0.049489</td>
      <td>1.17440</td>
      <td>0.534330</td>
      <td>-0.774670</td>
      <td>-0.132450</td>
      <td>-0.178890</td>
      <td>0.225380</td>
      <td>-0.286580</td>
      <td>0.180790</td>
      <td>...</td>
      <td>0.257080</td>
      <td>-0.030098</td>
      <td>0.140040</td>
      <td>0.194710</td>
      <td>-0.601420</td>
      <td>0.554400</td>
      <td>-0.340180</td>
      <td>-0.146340</td>
      <td>-0.504670</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6970</th>
      <td>dead-of-winter</td>
      <td>0.218620</td>
      <td>1.17480</td>
      <td>0.282170</td>
      <td>-0.370750</td>
      <td>0.164800</td>
      <td>-0.477340</td>
      <td>0.189990</td>
      <td>-0.160370</td>
      <td>-0.242140</td>
      <td>...</td>
      <td>0.262170</td>
      <td>-0.192220</td>
      <td>-0.055628</td>
      <td>0.275020</td>
      <td>-0.380750</td>
      <td>0.224990</td>
      <td>-0.139630</td>
      <td>-0.191860</td>
      <td>-0.237270</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>931</th>
      <td>stargate-atlantis-season-4</td>
      <td>-0.066462</td>
      <td>1.18070</td>
      <td>0.171370</td>
      <td>-0.174730</td>
      <td>-0.305300</td>
      <td>-1.443800</td>
      <td>0.163790</td>
      <td>-0.189930</td>
      <td>-0.028013</td>
      <td>...</td>
      <td>-0.191000</td>
      <td>-0.410690</td>
      <td>-0.308060</td>
      <td>-0.089868</td>
      <td>-0.062348</td>
      <td>0.128370</td>
      <td>-0.131320</td>
      <td>-1.066900</td>
      <td>0.096324</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>866</th>
      <td>stargate-atlantis-season-3</td>
      <td>-0.109370</td>
      <td>1.18130</td>
      <td>0.212990</td>
      <td>-0.295620</td>
      <td>-0.285170</td>
      <td>-1.400200</td>
      <td>0.170410</td>
      <td>-0.152000</td>
      <td>-0.129830</td>
      <td>...</td>
      <td>-0.174220</td>
      <td>-0.418620</td>
      <td>-0.257820</td>
      <td>-0.143090</td>
      <td>-0.048601</td>
      <td>0.169170</td>
      <td>0.045183</td>
      <td>-1.064700</td>
      <td>0.112380</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>403</th>
      <td>stargate-atlantis-season-1</td>
      <td>-0.127510</td>
      <td>1.18220</td>
      <td>0.337310</td>
      <td>-0.270440</td>
      <td>-0.313250</td>
      <td>-1.459900</td>
      <td>0.198770</td>
      <td>-0.115430</td>
      <td>-0.244060</td>
      <td>...</td>
      <td>-0.090794</td>
      <td>-0.563780</td>
      <td>-0.355610</td>
      <td>-0.059771</td>
      <td>-0.018426</td>
      <td>0.098614</td>
      <td>0.038480</td>
      <td>-1.061000</td>
      <td>0.107250</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>12841</th>
      <td>final-destination-2</td>
      <td>-0.006295</td>
      <td>1.18290</td>
      <td>0.324160</td>
      <td>-0.258300</td>
      <td>-0.169010</td>
      <td>-0.525460</td>
      <td>0.370990</td>
      <td>-0.035728</td>
      <td>-0.506080</td>
      <td>...</td>
      <td>0.047789</td>
      <td>0.519880</td>
      <td>0.051772</td>
      <td>0.115110</td>
      <td>-0.048783</td>
      <td>0.114700</td>
      <td>-0.670470</td>
      <td>0.205560</td>
      <td>-0.227400</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>695</th>
      <td>mama</td>
      <td>0.368230</td>
      <td>1.19600</td>
      <td>0.661430</td>
      <td>-0.443270</td>
      <td>-0.222770</td>
      <td>0.055746</td>
      <td>0.647060</td>
      <td>0.095074</td>
      <td>0.078067</td>
      <td>...</td>
      <td>-0.042080</td>
      <td>-0.036276</td>
      <td>0.216380</td>
      <td>-0.004706</td>
      <td>-0.005003</td>
      <td>0.129410</td>
      <td>-0.249580</td>
      <td>-0.372260</td>
      <td>-0.424140</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6957</th>
      <td>dead-end-(2004)</td>
      <td>-0.202210</td>
      <td>1.19880</td>
      <td>0.302280</td>
      <td>-0.509340</td>
      <td>-0.085534</td>
      <td>-0.009318</td>
      <td>0.021434</td>
      <td>-0.277510</td>
      <td>0.053929</td>
      <td>...</td>
      <td>0.419490</td>
      <td>0.277320</td>
      <td>0.153220</td>
      <td>0.006879</td>
      <td>-0.478440</td>
      <td>0.432260</td>
      <td>-0.096252</td>
      <td>-0.196950</td>
      <td>-0.366700</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>13557</th>
      <td>psych:9</td>
      <td>0.055883</td>
      <td>1.19910</td>
      <td>0.522630</td>
      <td>-0.494810</td>
      <td>-0.067306</td>
      <td>0.064232</td>
      <td>-0.302010</td>
      <td>-0.249610</td>
      <td>-0.102030</td>
      <td>...</td>
      <td>0.013552</td>
      <td>-0.071527</td>
      <td>0.374150</td>
      <td>-0.155150</td>
      <td>-0.515580</td>
      <td>0.684350</td>
      <td>-0.176440</td>
      <td>0.014840</td>
      <td>-0.233700</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>10422</th>
      <td>true-nature</td>
      <td>0.168890</td>
      <td>1.21320</td>
      <td>0.453260</td>
      <td>-0.613580</td>
      <td>0.015675</td>
      <td>-0.433680</td>
      <td>0.043464</td>
      <td>-0.172060</td>
      <td>-0.289650</td>
      <td>...</td>
      <td>0.318370</td>
      <td>0.301910</td>
      <td>0.050207</td>
      <td>-0.192330</td>
      <td>-0.200630</td>
      <td>0.163910</td>
      <td>-0.086003</td>
      <td>-0.228430</td>
      <td>-0.494420</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4045</th>
      <td>extinction</td>
      <td>0.619530</td>
      <td>1.21590</td>
      <td>0.449570</td>
      <td>-0.089764</td>
      <td>0.233980</td>
      <td>-0.159340</td>
      <td>-0.646600</td>
      <td>-0.298110</td>
      <td>-0.162720</td>
      <td>...</td>
      <td>-0.013113</td>
      <td>0.093537</td>
      <td>-0.018618</td>
      <td>0.359810</td>
      <td>-0.785720</td>
      <td>0.876990</td>
      <td>0.138130</td>
      <td>-0.404570</td>
      <td>-0.522410</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>12385</th>
      <td>dead-end</td>
      <td>-0.019834</td>
      <td>1.21740</td>
      <td>0.243790</td>
      <td>-0.636810</td>
      <td>0.157390</td>
      <td>0.013563</td>
      <td>-0.216830</td>
      <td>0.015347</td>
      <td>0.311150</td>
      <td>...</td>
      <td>0.063537</td>
      <td>0.183910</td>
      <td>0.345130</td>
      <td>0.163820</td>
      <td>-0.825180</td>
      <td>0.511390</td>
      <td>-0.383370</td>
      <td>-0.014155</td>
      <td>-0.268470</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>794</th>
      <td>stargate-atlantis-season-2</td>
      <td>-0.072080</td>
      <td>1.21890</td>
      <td>0.249670</td>
      <td>-0.280380</td>
      <td>-0.320990</td>
      <td>-1.484600</td>
      <td>0.187680</td>
      <td>-0.061629</td>
      <td>-0.143640</td>
      <td>...</td>
      <td>-0.136840</td>
      <td>-0.497550</td>
      <td>-0.352170</td>
      <td>-0.081429</td>
      <td>-0.063022</td>
      <td>0.125590</td>
      <td>0.059499</td>
      <td>-1.089700</td>
      <td>0.148900</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>11441</th>
      <td>haunted</td>
      <td>0.136810</td>
      <td>1.22360</td>
      <td>0.053060</td>
      <td>-0.799830</td>
      <td>-0.497310</td>
      <td>-0.056785</td>
      <td>0.156690</td>
      <td>-0.804100</td>
      <td>-0.334610</td>
      <td>...</td>
      <td>0.185380</td>
      <td>0.499530</td>
      <td>0.605270</td>
      <td>-0.027248</td>
      <td>-0.405440</td>
      <td>1.311800</td>
      <td>-0.681020</td>
      <td>-1.185800</td>
      <td>-0.870870</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4326</th>
      <td>chained</td>
      <td>0.385410</td>
      <td>1.22660</td>
      <td>0.277600</td>
      <td>-0.038307</td>
      <td>0.132500</td>
      <td>-0.274050</td>
      <td>0.071656</td>
      <td>-0.235590</td>
      <td>-0.263140</td>
      <td>...</td>
      <td>0.106220</td>
      <td>-0.202970</td>
      <td>-0.025170</td>
      <td>0.033313</td>
      <td>-0.329410</td>
      <td>0.555740</td>
      <td>-0.148110</td>
      <td>-0.075041</td>
      <td>0.157090</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6579</th>
      <td>leprechaun</td>
      <td>-0.182150</td>
      <td>1.22880</td>
      <td>0.749170</td>
      <td>-0.768230</td>
      <td>0.050800</td>
      <td>-0.238440</td>
      <td>0.329020</td>
      <td>-0.385830</td>
      <td>0.366890</td>
      <td>...</td>
      <td>0.063988</td>
      <td>0.877250</td>
      <td>-0.241400</td>
      <td>0.043899</td>
      <td>-0.964430</td>
      <td>-0.085409</td>
      <td>0.455030</td>
      <td>-0.299260</td>
      <td>-0.054529</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>9143</th>
      <td>13-eerie</td>
      <td>0.006099</td>
      <td>1.23450</td>
      <td>0.513370</td>
      <td>-0.324670</td>
      <td>0.054438</td>
      <td>-0.405560</td>
      <td>-0.166900</td>
      <td>-0.300500</td>
      <td>-0.359860</td>
      <td>...</td>
      <td>0.043977</td>
      <td>0.019456</td>
      <td>0.071177</td>
      <td>0.226630</td>
      <td>-0.490360</td>
      <td>0.615210</td>
      <td>-0.073266</td>
      <td>-0.115250</td>
      <td>-0.319120</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>10966</th>
      <td>severed</td>
      <td>-0.224920</td>
      <td>1.23570</td>
      <td>0.471150</td>
      <td>-0.258710</td>
      <td>0.451390</td>
      <td>-0.079904</td>
      <td>-0.198650</td>
      <td>-0.618220</td>
      <td>-0.359370</td>
      <td>...</td>
      <td>0.131150</td>
      <td>-0.034913</td>
      <td>-0.290040</td>
      <td>0.582270</td>
      <td>-0.338830</td>
      <td>0.116840</td>
      <td>0.501710</td>
      <td>-0.194920</td>
      <td>0.113210</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>12884</th>
      <td>cinematic-titanic:-the-alien-factor</td>
      <td>0.139310</td>
      <td>1.23970</td>
      <td>0.281740</td>
      <td>-0.713140</td>
      <td>-0.082003</td>
      <td>0.262900</td>
      <td>-0.100990</td>
      <td>-0.041955</td>
      <td>0.402410</td>
      <td>...</td>
      <td>0.210960</td>
      <td>0.165200</td>
      <td>0.375420</td>
      <td>0.387090</td>
      <td>-1.304700</td>
      <td>0.312220</td>
      <td>0.131750</td>
      <td>-0.435330</td>
      <td>-0.055895</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>12737</th>
      <td>undead</td>
      <td>0.231260</td>
      <td>1.24090</td>
      <td>0.778370</td>
      <td>-0.177770</td>
      <td>0.419660</td>
      <td>-0.318810</td>
      <td>-0.482170</td>
      <td>0.033001</td>
      <td>-0.416860</td>
      <td>...</td>
      <td>-0.027804</td>
      <td>-0.218790</td>
      <td>-0.513110</td>
      <td>0.293790</td>
      <td>-0.248470</td>
      <td>0.478890</td>
      <td>0.126250</td>
      <td>-0.191680</td>
      <td>-0.125980</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>10983</th>
      <td>hatchet</td>
      <td>0.236750</td>
      <td>1.24360</td>
      <td>0.460170</td>
      <td>-0.197440</td>
      <td>0.231400</td>
      <td>-0.099891</td>
      <td>-0.243340</td>
      <td>-0.462530</td>
      <td>0.069761</td>
      <td>...</td>
      <td>0.271200</td>
      <td>0.066977</td>
      <td>0.091286</td>
      <td>0.040995</td>
      <td>-0.397860</td>
      <td>0.438940</td>
      <td>0.043585</td>
      <td>-0.198900</td>
      <td>-0.213740</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>11383</th>
      <td>devil's-farm</td>
      <td>0.067862</td>
      <td>1.24620</td>
      <td>0.310620</td>
      <td>-0.481850</td>
      <td>0.085550</td>
      <td>-0.290260</td>
      <td>-0.093069</td>
      <td>-0.341310</td>
      <td>0.059577</td>
      <td>...</td>
      <td>0.027883</td>
      <td>0.391560</td>
      <td>0.083869</td>
      <td>-0.013463</td>
      <td>-0.389230</td>
      <td>0.856050</td>
      <td>-0.307820</td>
      <td>-0.190560</td>
      <td>-0.138820</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>5327</th>
      <td>dark-spirits</td>
      <td>0.124280</td>
      <td>1.24780</td>
      <td>0.324280</td>
      <td>-0.745630</td>
      <td>0.084091</td>
      <td>-0.194810</td>
      <td>-0.015945</td>
      <td>-0.005165</td>
      <td>0.013948</td>
      <td>...</td>
      <td>0.149490</td>
      <td>0.115640</td>
      <td>0.045926</td>
      <td>0.161420</td>
      <td>-0.365900</td>
      <td>0.191460</td>
      <td>-0.211030</td>
      <td>-0.036646</td>
      <td>-0.294280</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>8879</th>
      <td>wolf-creek</td>
      <td>0.281670</td>
      <td>1.25030</td>
      <td>0.452270</td>
      <td>0.028517</td>
      <td>-0.157680</td>
      <td>-0.524270</td>
      <td>-0.388850</td>
      <td>-0.471230</td>
      <td>-0.024692</td>
      <td>...</td>
      <td>0.162540</td>
      <td>0.404410</td>
      <td>0.115140</td>
      <td>-0.239320</td>
      <td>0.155410</td>
      <td>0.448560</td>
      <td>-0.253320</td>
      <td>-0.923760</td>
      <td>0.063915</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6660</th>
      <td>elevator</td>
      <td>-0.017612</td>
      <td>1.25090</td>
      <td>0.520950</td>
      <td>-0.365170</td>
      <td>-0.080856</td>
      <td>-0.065852</td>
      <td>0.233860</td>
      <td>-0.657960</td>
      <td>-0.251160</td>
      <td>...</td>
      <td>0.088470</td>
      <td>0.004439</td>
      <td>0.244970</td>
      <td>0.368070</td>
      <td>-0.306190</td>
      <td>0.347790</td>
      <td>-0.155970</td>
      <td>0.294020</td>
      <td>-0.161470</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>6463</th>
      <td>the-thing-on-the-doorstep</td>
      <td>0.272980</td>
      <td>1.25800</td>
      <td>0.534850</td>
      <td>-0.486840</td>
      <td>-0.228520</td>
      <td>-0.145290</td>
      <td>-0.232650</td>
      <td>-0.315670</td>
      <td>0.246890</td>
      <td>...</td>
      <td>0.298560</td>
      <td>0.626570</td>
      <td>0.288260</td>
      <td>0.465620</td>
      <td>-0.318430</td>
      <td>0.840710</td>
      <td>-0.512750</td>
      <td>-0.436560</td>
      <td>-0.313470</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2918</th>
      <td>stargate</td>
      <td>-0.528630</td>
      <td>1.27230</td>
      <td>0.162970</td>
      <td>-0.496660</td>
      <td>-0.520150</td>
      <td>-1.293500</td>
      <td>0.246060</td>
      <td>-0.089921</td>
      <td>0.025558</td>
      <td>...</td>
      <td>-0.030210</td>
      <td>-0.787810</td>
      <td>-0.876020</td>
      <td>0.091535</td>
      <td>0.105270</td>
      <td>-0.564210</td>
      <td>0.146400</td>
      <td>-0.441500</td>
      <td>-0.565180</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>12354</th>
      <td>outpost</td>
      <td>0.074617</td>
      <td>1.27770</td>
      <td>0.716200</td>
      <td>-0.056137</td>
      <td>0.752370</td>
      <td>-0.023956</td>
      <td>-0.554940</td>
      <td>-0.432140</td>
      <td>0.310640</td>
      <td>...</td>
      <td>0.378290</td>
      <td>0.044229</td>
      <td>-0.087531</td>
      <td>0.699730</td>
      <td>-0.191570</td>
      <td>0.062013</td>
      <td>-0.224580</td>
      <td>0.066765</td>
      <td>-0.239380</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>17486</th>
      <td>zombie</td>
      <td>-0.312910</td>
      <td>1.73130</td>
      <td>0.412750</td>
      <td>-0.076979</td>
      <td>-0.053845</td>
      <td>0.561210</td>
      <td>-0.003228</td>
      <td>-0.758140</td>
      <td>-0.811200</td>
      <td>...</td>
      <td>0.326560</td>
      <td>0.253650</td>
      <td>-1.389300</td>
      <td>0.236600</td>
      <td>-0.835770</td>
      <td>1.355800</td>
      <td>0.060673</td>
      <td>0.148530</td>
      <td>-0.177800</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>17989 rows × 102 columns</p>
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<p>Yes, but there's 100.  Let's reduce this further with t-SNE and map the top 100 titles.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">product_titles</span> <span class="o">=</span> <span class="n">vectors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">vectors</span> <span class="o">=</span> <span class="n">vectors</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">101</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="k">import</span> <span class="n">TSNE</span>

<span class="n">tsne</span> <span class="o">=</span> <span class="n">TSNE</span><span class="p">(</span><span class="n">perplexity</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">init</span><span class="o">=</span><span class="s1">&#39;pca&#39;</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">tsne</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">vectors</span><span class="o">.</span><span class="n">values</span><span class="p">[:</span><span class="mi">100</span><span class="p">,</span> <span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pylab</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="n">embeddings</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
    <span class="n">pylab</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">20</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span>
        <span class="n">pylab</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="n">pylab</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</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">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset points&#39;</span><span class="p">,</span>
                       <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;right&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
    <span class="n">pylab</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="n">plot</span><span class="p">(</span><span class="n">embeddings</span><span class="p">,</span> <span class="n">product_titles</span><span class="p">[:</span><span class="mi">100</span><span class="p">])</span>
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<h2 id="Host">Host<a class="anchor-link" href="#Host">&#182;</a></h2><p>Deploy our model to a real-time endpoint.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bt_endpoint</span> <span class="o">=</span> <span class="n">bt</span><span class="o">.</span><span class="n">deploy</span><span class="p">(</span><span class="n">initial_instance_count</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span><span class="n">instance_type</span> <span class="o">=</span> <span class="s1">&#39;ml.m4.xlarge&#39;</span><span class="p">)</span>
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<pre>INFO:sagemaker:Creating model with name: blazingtext-2018-09-06-05-23-46-507
INFO:sagemaker:Creating endpoint with name blazingtext-2018-09-06-05-01-57-653
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<pre>--------------------------------------------------------------!</pre>
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<p>Try generating predictions for a set of titles (some of which are real, some of which are made up).</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">words</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;sherlock-season-1&quot;</span><span class="p">,</span> 
         <span class="s2">&quot;sherlock-season-2&quot;</span><span class="p">,</span>
         <span class="s2">&quot;sherlock-season-5&quot;</span><span class="p">,</span>
         <span class="s1">&#39;arbitrary-sherlock-holmes-string&#39;</span><span class="p">,</span>
         <span class="s1">&#39;the-imitation-game&#39;</span><span class="p">,</span>
         <span class="s2">&quot;abcdefghijklmn&quot;</span><span class="p">,</span>
         <span class="s2">&quot;keeping-up-with-the-kardashians-season-1&quot;</span><span class="p">]</span>

<span class="n">payload</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;instances&quot;</span> <span class="p">:</span> <span class="n">words</span><span class="p">}</span>

<span class="n">response</span> <span class="o">=</span> <span class="n">bt_endpoint</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">payload</span><span class="p">))</span>

<span class="n">vecs_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">loads</span><span class="p">(</span><span class="n">response</span><span class="p">))</span>
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<p>Calculate correlation and distance.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vecs_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">vecs_df</span><span class="p">[</span><span class="s1">&#39;vector&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">index</span><span class="o">=</span><span class="n">vecs_df</span><span class="p">[</span><span class="s1">&#39;word&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">vecs_df</span> <span class="o">=</span> <span class="n">vecs_df</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
<span class="n">vecs_df</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th>word</th>
      <th>sherlock-season-1</th>
      <th>sherlock-season-2</th>
      <th>sherlock-season-5</th>
      <th>arbitrary-sherlock-holmes-string</th>
      <th>the-imitation-game</th>
      <th>abcdefghijklmn</th>
      <th>keeping-up-with-the-kardashians-season-1</th>
    </tr>
    <tr>
      <th>word</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
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    </tr>
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    <tr>
      <th>sherlock-season-1</th>
      <td>1.000000</td>
      <td>0.975613</td>
      <td>0.937613</td>
      <td>0.651827</td>
      <td>0.296442</td>
      <td>0.186695</td>
      <td>0.134656</td>
    </tr>
    <tr>
      <th>sherlock-season-2</th>
      <td>0.975613</td>
      <td>1.000000</td>
      <td>0.952221</td>
      <td>0.627469</td>
      <td>0.274637</td>
      <td>0.185511</td>
      <td>0.153809</td>
    </tr>
    <tr>
      <th>sherlock-season-5</th>
      <td>0.937613</td>
      <td>0.952221</td>
      <td>1.000000</td>
      <td>0.649770</td>
      <td>0.332390</td>
      <td>0.199241</td>
      <td>0.180276</td>
    </tr>
    <tr>
      <th>arbitrary-sherlock-holmes-string</th>
      <td>0.651827</td>
      <td>0.627469</td>
      <td>0.649770</td>
      <td>1.000000</td>
      <td>0.397691</td>
      <td>0.144972</td>
      <td>0.001460</td>
    </tr>
    <tr>
      <th>the-imitation-game</th>
      <td>0.296442</td>
      <td>0.274637</td>
      <td>0.332390</td>
      <td>0.397691</td>
      <td>1.000000</td>
      <td>0.342502</td>
      <td>0.179073</td>
    </tr>
    <tr>
      <th>abcdefghijklmn</th>
      <td>0.186695</td>
      <td>0.185511</td>
      <td>0.199241</td>
      <td>0.144972</td>
      <td>0.342502</td>
      <td>1.000000</td>
      <td>0.313100</td>
    </tr>
    <tr>
      <th>keeping-up-with-the-kardashians-season-1</th>
      <td>0.134656</td>
      <td>0.153809</td>
      <td>0.180276</td>
      <td>0.001460</td>
      <td>0.179073</td>
      <td>0.313100</td>
      <td>1.000000</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">vecs_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">column</span> <span class="o">+</span> <span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">vecs_df</span><span class="p">[</span><span class="n">column</span><span class="p">]</span> <span class="o">-</span> <span class="n">vecs_df</span><span class="p">[</span><span class="s1">&#39;sherlock-season-1&#39;</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
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<pre>sherlock-season-1: 0.0
sherlock-season-2: 0.6693818296600973
sherlock-season-5: 1.6195205850083456
arbitrary-sherlock-holmes-string: 7.67011724427113
the-imitation-game: 14.239632481211343
abcdefghijklmn: 12.930583173774579
keeping-up-with-the-kardashians-season-1: 22.867526431909475
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<p>Relative to 'sherlock-season-1':</p>
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<li>'sherlock-season-5' is made up, but relates well with 'sherlock-season-1' and 'sherlock-season-2'</li>
<li>'arbitrary-sherlock-holmes-string' is also made up and relates less well but still fairly strong</li>
<li>'the-imitation-game' is another popular Prime video title starring Benedict Cumberbatch and has a moderate relationship, but worse than the arbitrary Sherlock title</li>
<li>'abcdefghijklmn' is made up and relates even worse</li>
<li>'keeping-up-with-the-kardashians-season-1' somehow manages to relate even worse</li>
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<p>Clean-up the endpoint</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bt_endpoint</span><span class="o">.</span><span class="n">delete_endpoint</span><span class="p">()</span>
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<pre>INFO:sagemaker:Deleting endpoint with name: blazingtext-2018-09-06-05-01-57-653
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<hr>
<h1 id="Wrap-up">Wrap-up<a class="anchor-link" href="#Wrap-up">&#182;</a></h1><ul>
<li>Built a recommender system on a large dataset quickly and accurately</li>
<li>Add more features to extend</li>
<li>Compare to other methods</li>
<li>Ensemble two models</li>
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