RecSys-final.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>RecSys</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
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/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
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/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
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}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
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}
input[type="checkbox"],
input[type="radio"] {
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  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
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}
input[type="search"] {
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  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
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}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
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}
textarea {
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}
optgroup {
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}
table {
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  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
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  *:after {
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    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
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  a:visited {
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  .dropup > .btn > .caret {
    border-top-color: #000 !important;
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@font-face {
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  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
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}
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  top: 1px;
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  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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<p>2018 Edition</p>

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<h1 id="Building-a-music-recommender-system">Building a music recommender system<a class="anchor-link" href="#Building-a-music-recommender-system">&#182;</a></h1><p>As its name implies, a recommender system is a tool that helps predicting what a user may or may not like among a list of given items. In some sense, you can view this as an alternative to content search, as recommendation engines help users discover products or content that they may not come across otherwise. For example, Facebook suggests friends and pages to users. Youtube recommends videos which users may be interested in. Amazon suggests the products which users may need... Recommendation engines engage users to services, can be seen as a revenue optimization process, and in general help maintaining interest in a service.</p>
<p>In this notebook, we study how to build a simple recommender system: we focus on music recommendations, and we use a simple algorithm to predict which items users might like, that is called ALS, alternating least squares.</p>
<h2 id="Goals">Goals<a class="anchor-link" href="#Goals">&#182;</a></h2><p>In this lecture, we expect students to:</p>
<ul>
<li><p>Revisit (or learn) recommender algorithms</p>
</li>
<li><p>Understand the idea of Matrix Factorization and the ALS algorithm (serial and parallel versions)</p>
</li>
<li><p>Build a simple model for a real usecase: music recommender system</p>
</li>
<li><p>Understand how to validate the results</p>
</li>
</ul>
<h2 id="Steps">Steps<a class="anchor-link" href="#Steps">&#182;</a></h2><p>We assume students to work outside lab hours on the learning material. These are the steps by which we guide students, during labs, to build a good basis for the end-to-end development of a recommender system:</p>
<ul>
<li>Inspect the data using Spark SQL, and build some basic, but very valuable knowledge about the information we have at hand</li>
<li>Formally define what is a sensible algorithm to achieve our goal: given the "history" of user taste for music, recommend new music to discover. Essentialy, we want to build a statistical model of user preferences such that we can use it to "predict" which additional music the user could like</li>
<li>With our formal definition at hand, we will learn different ways to implement such an algorithm. Our goal here is to illustrate what are the difficulties to overcome when implementing a (parallel) algorithm</li>
<li>Finally, we will focus on an existing implementation, available in the Apache Spark MLLib, which we will use out of the box to build a reliable statistical model</li>
</ul>
<p>Now, you may think at this point we will be done!</p>
<p>Well, you'd better think twice: one important topic we will cover in all our Notebooks is <strong>how to validate the results we obtain</strong>, and <strong>how to choose good parameters to train models</strong> especially when using an "opaque" library for doing the job. As a consequence, we will focus on the statistical validation of our recommender system.</p>

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<h1 id="1.-Data">1. Data<a class="anchor-link" href="#1.-Data">&#182;</a></h1><p>Understanding data is one of the most important part when designing any machine learning algorithm. In this notebook, we will use a data set published by Audioscrobbler - a music recommendation system for last.fm. Audioscrobbler is also one of the first internet streaming radio sites, founded in 2002. It provided an open API for “scrobbling”, or recording listeners’ plays of artists’ songs. last.fm used this information to build a powerful music recommender engine.</p>
<h2 id="1.1.-Data-schema">1.1. Data schema<a class="anchor-link" href="#1.1.-Data-schema">&#182;</a></h2><p>Unlike a rating dataset which contains information about users' preference for products (one star, 3 stars, and so on), the datasets from Audioscrobbler only has information about events: specifically, it keeps track of how many times a user played songs of a given artist and the names of artists. That means it carries less information than a rating: in the literature, this is called explicit vs. implicit ratings.</p>
<h3 id="Reading-material">Reading material<a class="anchor-link" href="#Reading-material">&#182;</a></h3><ul>
<li><a href="http://people.csail.mit.edu/teevan/work/publications/papers/sigir-forum03.pdf">Implicit Feedback for Inferring User Preference: A Bibliography</a></li>
<li><a href="http://trec.nist.gov/pubs/trec10/papers/glasgow.pdf">Comparing explicit and implicit feedback techniques for web retrieval: TREC-10 interactive track report</a></li>
<li><a href="http://mlg.eng.cam.ac.uk/pub/pdf/WilGha08.pdf">Probabilistic Models for Data Combination in Recommender Systems</a></li>
</ul>
<p>The data we use in this Notebook is available in 3 files (these files are stored in our HDFS layer, in the directory  <code>/datasets/lastfm</code>):</p>
<ul>
<li><strong><code>user_artist_data.txt</code></strong>: It contains about 140,000+ unique users, and 1.6 million unique artists. About 24.2 million users’ plays of artists’ are recorded, along with their count. It has 3 columns separated by spaces: </li>
</ul>
<table>
<thead><tr>
<th>UserID</th>
<th>ArtistID</th>
<th>PlayCount</th>
</tr>
</thead>
<tbody>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</tbody>
</table>
<ul>
<li><strong><code>artist_data.txt</code></strong> : It prodives the names of each artist by their IDs. It has 2 columns separated by tab characters (<code>\t</code>).</li>
</ul>
<table>
<thead><tr>
<th>ArtistID</th>
<th>Name</th>
</tr>
</thead>
<tbody>
<tr>
<td>...</td>
<td>...</td>
</tr>
</tbody>
</table>
<ul>
<li><strong><code>artist_alias.txt</code></strong>: Note that when plays are scrobbled, the client application submits the name of the artist being played. This name could be misspelled or nonstandard. For example, "The Smiths", "Smiths, The", and "the smiths" may appear as distinct artist IDs in the data set, even though they are plainly the same. <code>artist_alias.txt</code> maps artist IDs that are known misspellings or variants to the canonical ID of that artist. The data in this file has 2 columns separated by tab characters (<code>\t</code>).</li>
</ul>
<table>
<thead><tr>
<th>MisspelledArtistID</th>
<th>StandardArtistID</th>
</tr>
</thead>
<tbody>
<tr>
<td>...</td>
<td>...</td>
</tr>
</tbody>
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<h2 id="1.2.-Understanding-data:-simple-descriptive-statistic">1.2. Understanding data: simple descriptive statistic<a class="anchor-link" href="#1.2.-Understanding-data:-simple-descriptive-statistic">&#182;</a></h2><p>In order to choose or design a suitable algorithm for achieving our goals, given the data we have, we should first understand data characteristics. To start, we import the necessary packages to work with regular expressions, Data Frames, and other nice features of our programming environment.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">pprint</span>
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="k">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="k">import</span> <span class="n">SQLContext</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.types</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="k">import</span> <span class="n">Row</span>
<span class="kn">from</span> <span class="nn">pyspark.sql.functions</span> <span class="k">import</span> <span class="o">*</span>

<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>

<span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">base</span> <span class="o">=</span> <span class="s2">&quot;/datasets/lastfm/&quot;</span>
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<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><h4 id="Question-1.0-(Non-grading)">Question 1.0 (Non-grading)<a class="anchor-link" href="#Question-1.0-(Non-grading)">&#182;</a></h4><div class="alert alert-info">
Using SPARK SQL, load data from `/datasets/lastfm/user_artist_data.txt` and show the first 20 entries (via function `show()`).
</div><p>For this Notebook, from a programming point of view, we are given the schema for the data we use, which is as follows:</p>

<pre><code>userID: long int
artistID: long int
playCount: int</code></pre>
<p>Each line of the dataset contains the above three fields, separated by a "white space".</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistDataSchema</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">([</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">,</span> <span class="n">IntegerType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">)])</span>

<span class="n">userArtistDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">read</span> \
    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;com.databricks.spark.csv&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">options</span><span class="p">(</span><span class="n">header</span><span class="o">=</span><span class="s1">&#39;false&#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="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;user_artist_data.txt&quot;</span><span class="p">,</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">userArtistDataSchema</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="c1"># we can cache an Dataframe to avoid computing it from the beginning everytime it is accessed.</span>
<span class="n">userArtistDF</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">userArtistDF</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+-------+--------+---------+
| userID|artistID|playCount|
+-------+--------+---------+
|1000002|       1|       55|
|1000002| 1000006|       33|
|1000002| 1000007|        8|
|1000002| 1000009|      144|
|1000002| 1000010|      314|
|1000002| 1000013|        8|
|1000002| 1000014|       42|
|1000002| 1000017|       69|
|1000002| 1000024|      329|
|1000002| 1000025|        1|
|1000002| 1000028|       17|
|1000002| 1000031|       47|
|1000002| 1000033|       15|
|1000002| 1000042|        1|
|1000002| 1000045|        1|
|1000002| 1000054|        2|
|1000002| 1000055|       25|
|1000002| 1000056|        4|
|1000002| 1000059|        2|
|1000002| 1000062|       71|
+-------+--------+---------+
only showing top 20 rows

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<h4 id="Question-1.1:">Question 1.1:<a class="anchor-link" href="#Question-1.1:">&#182;</a></h4><div class="alert alert-info">
How many distinct users do we have in our data? 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">uniqueUsers</span> <span class="o">=</span> <span class="n">userArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s1">&#39;userID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total n. of users: &quot;</span><span class="p">,</span> <span class="n">uniqueUsers</span><span class="p">)</span>
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<pre>Total n. of users:  148111
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<h4 id="Question-1.2">Question 1.2<a class="anchor-link" href="#Question-1.2">&#182;</a></h4><div class="alert alert-info">
How many distinct artists do we have in our data ?
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">uniqueArtists</span> <span class="o">=</span> <span class="n">userArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total n. of artists: &quot;</span><span class="p">,</span> <span class="n">uniqueArtists</span><span class="p">)</span>
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<pre>Total n. of artists:  1631028
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<h4 id="Question-1.3">Question 1.3<a class="anchor-link" href="#Question-1.3">&#182;</a></h4><div class="alert alert-info">
One limitation of Spark MLlib's ALS implementation - which we will use later - is that it requires IDs for users and items to be nonnegative 32-bit integers. This means that IDs larger than `Integer.MAX_VALUE`, or `2147483647`, can't be used. So we need to check whether this data set conforms to the strict requirements of our library.  

What are the maximum and minimum values of column `userID` ?  
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistDF</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="s1">&#39;userID&#39;</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="s1">&#39;userID&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+-----------+-----------+
|min(userID)|max(userID)|
+-----------+-----------+
|         90|    2443548|
+-----------+-----------+

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<h4 id="Question-1.4">Question 1.4<a class="anchor-link" href="#Question-1.4">&#182;</a></h4><div class="alert alert-info">
What is the maximum and minimum values of column `artistID` ?
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistDF</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+-------------+-------------+
|min(artistID)|max(artistID)|
+-------------+-------------+
|            1|     10794401|
+-------------+-------------+

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<p>We just discovered that we have a total of 148,111 users in our dataset. Similarly, we have a total of 1,631,028 artists in our dataset. The maximum values of <code>userID</code> and <code>artistID</code> are still smaller than the biggest number of integer type.  No additional transformation will be necessary to use these IDs.</p>
<p>One thing we can see here is that SPARK SQL provides very concise and powerful methods for data analytics (compared to using RDD and their low-level API). You can see more examples <a href="https://databricks.com/blog/2015/06/02/statistical-and-mathematical-functions-with-dataframes-in-spark.html">here</a>.</p>

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<h3 id="Descriptive-Statistics">Descriptive Statistics<a class="anchor-link" href="#Descriptive-Statistics">&#182;</a></h3><p>As suggested in the link, we try to run simple statistics for our dataFrame for what conccern the PlayCount column.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;PlayCount&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+-------+-----------------+
|summary|        PlayCount|
+-------+-----------------+
|  count|         24296858|
|   mean|15.29576248089362|
| stddev|153.9153244697963|
|    min|                1|
|    max|           439771|
+-------+-----------------+

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
From the statistics we can observe that it's possible to have some *Play Counts* which have value 1. This means that there will be possible users which have only one play for an artist. As well we can notice that the maximum value is a quite huge number (439771) considering that a single user has listened that amount of times a single artists. On average, the number of *Play Count* is close to 15, but on contrary the standard deviation is high.
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<p>Next, we might want to understand better user activity and artist popularity.</p>
<p>Here is a list of simple descriptive queries that helps us reaching these purposes:</p>
<ul>
<li>How many times each user has played a song? This is a good indicator of who are the most active users of our service. Note that a very active user with many play counts does not necessarily mean that the user is also "curious"! Indeed, she could have played the same song several times.</li>
<li>How many play counts for each artist? This is a good indicator of the artist popularity. Since we do not have time information associated to our data, we can only build a, e.g., top-10 ranking of the most popular artists in the dataset. Later in the notebook, we will learn that our dataset has a very "loose" definition about artists: very often artist IDs point to song titles as well. This means we have to be careful when establishing popular artists. Indeed, artists whose data is "well formed" will have the correct number of play counts associated to them. Instead, artists that appear mixed with song titles may see their play counts "diluted" across their songs.</li>
</ul>

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<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><h4 id="Question-2.1">Question 2.1<a class="anchor-link" href="#Question-2.1">&#182;</a></h4><div class="alert alert-info">
How many times each user has played a song? Show 5 samples of the result.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compute user activity</span>
<span class="c1"># We are interested in how many playcounts each user has scored.</span>
<span class="n">userActivity_DF</span> <span class="o">=</span> <span class="n">userArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s1">&#39;userID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s1">&#39;PlayCount&#39;</span><span class="p">)</span>
<span class="n">userActivity</span> <span class="o">=</span> <span class="n">userActivity_DF</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">userActivity</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;User: </span><span class="si">{}</span><span class="s1">, listened to songs </span><span class="si">{}</span><span class="s1"> times&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>

<span class="c1"># which are the users the most active?</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Top-20 active users&#39;</span><span class="p">)</span>
<span class="n">userActivity_DF</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;sum(PlayCount)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
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<pre>User: 2289066, listened to songs 51 times
User: 2289173, listened to songs 5938 times
User: 2290086, listened to songs 1080 times
User: 2291343, listened to songs 37 times
User: 2291463, listened to songs 8802 times

Top-20 active users
+-------+--------------+
| userID|sum(PlayCount)|
+-------+--------------+
|1059637|        674412|
|2064012|        548427|
|2069337|        393515|
|2023977|        285978|
|1046559|        183972|
|1052461|        175822|
|1070932|        168977|
|1031009|        167273|
|2020513|        165642|
|2062243|        151504|
|2069889|        143092|
|1001440|        136293|
|2014936|        135266|
|2017397|        134032|
|1024631|        122316|
|1007308|        111546|
|2023742|        102037|
|1058890|         98477|
|1021940|         97449|
|1059245|         96058|
+-------+--------------+
only showing top 20 rows

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<h4 id="Question-2.2">Question 2.2<a class="anchor-link" href="#Question-2.2">&#182;</a></h4><div class="alert alert-info">
Plot CDF (or ECDF) of the number of play counts per User ID.  

Explain and comment the figure you just created:   
<ul>
<li>for example, look at important percentiles (25%, median, 75%, tails such as >90%) and cross check with what you have found above to figure out if the result is plausible. </li>
<li>discuss about your users, with respect to the application domain we target in the notebook: you will notice that for some users, there is very little interaction with the system, which means that maybe reccommending something to them is going to be more difficult than for other users who interact more with the system. </li>
<li>look at outliers and reason about their impact on your reccommender algorithm</li>
</ul>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="n">title</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">111</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Play Counts&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;ECDF&quot;</span><span class="p">,</span> <span class="n">line_type</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span> <span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Plot the ECDF&quot;&quot;&quot;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">subplot</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span> <span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))),</span> <span class="p">[</span><span class="n">line</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">),</span> <span class="n">line_type</span> <span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">xlabel</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="n">ylabel</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span><span class="n">which</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span><span class="n">ls</span><span class="o">=</span><span class="s2">&quot;-&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># some statistics for userActivity_DF, about the play count field</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">userActivity_DF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;sum(PlayCount)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">withColumnRenamed</span><span class="p">(</span><span class="s2">&quot;sum(PlayCount)&quot;</span><span class="p">,</span> <span class="s2">&quot;Play Counts per user&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="o">.</span><span class="mi">25</span><span class="p">,</span> <span class="o">.</span><span class="mi">50</span><span class="p">,</span> <span class="o">.</span><span class="mi">75</span><span class="p">,</span> <span class="o">.</span><span class="mi">90</span><span class="p">,</span> <span class="o">.</span><span class="mi">99</span><span class="p">,</span> <span class="o">.</span><span class="mi">999</span><span class="p">])</span>
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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>Play Counts per user</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>148111.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>2509.192221</td>
    </tr>
    <tr>
      <th>std</th>
      <td>5448.314385</td>
    </tr>
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      <th>min</th>
      <td>1.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>204.000000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>892.000000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>2800.000000</td>
    </tr>
    <tr>
      <th>90%</th>
      <td>6484.000000</td>
    </tr>
    <tr>
      <th>99%</th>
      <td>21569.200000</td>
    </tr>
    <tr>
      <th>99.9%</th>
      <td>50380.220000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>674412.000000</td>
    </tr>
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<div class="cell border-box-sizing code_cell rendered">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pdf</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">data</span><span class="o">=</span><span class="n">userActivity</span><span class="p">)</span>
<span class="n">Y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">yvals</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
<span class="c1">#print(np.arange(len(Y)))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">18</span><span class="p">))</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 1: ECDF of number of play counts per User ID&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">321</span><span class="p">)</span>

<span class="c1"># plots considering the different percentiles </span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 2: ECDF of number of play counts per User ID - 0.25 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">322</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">])</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 3: ECDF of number of play counts per User ID - 0.5 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">323</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="mf">0.50</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1500</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 4: ECDF of number of play counts per User ID - 0.75 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">324</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">2000</span><span class="p">,</span> <span class="mi">4000</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 5: ECDF of number of play counts per User ID - 0.90 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">325</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="mf">0.90</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">6000</span><span class="p">,</span> <span class="mi">7000</span><span class="p">,</span> <span class="o">.</span><span class="mi">87</span><span class="p">,</span> <span class="o">.</span><span class="mi">92</span><span class="p">])</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 6: ECDF of number of play counts per User ID - 0.99 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">326</span><span class="p">,</span> <span class="n">line</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">10000</span><span class="p">,</span> <span class="mi">40000</span><span class="p">,</span> <span class="o">.</span><span class="mi">98</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h3 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h3><div class="alert alert-warning">
The *Total Plays* values range is from 1 to 674412 as shown using the *describe* method from Pandas Data Frames. The table gives us addiitonal information about the values considering the set percentiles.<br>
<br>
<b>Some important considerations</b>:
<br>
From the first graph we can notice something strange in the data distribution: in a more detailed analysis, zooming in we can see that:
<ul>
    <li>something more than the 80% of the users have a number of play counts that is lower than 4000 (0.59% wrt the maximum counter value)</li>
    <li>around the 99% of users have a number of play counts that is lower than 21569.2 (3.19% wrt the maximum counter value)</li>
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<h2>The problem</h2>

<ul>
    <li>There are outliers, and this is shown by the previous query about the <u>Top 20 active users</u>. Also, this can be observed from <i>Figure 1</i> and <i>Figure 6</i> where the tail of the distribution associated with high values refers only to a really small percentage of the population. Those outliers are not significative for our model because we want to create a raccomendation algorithm which is based on popular artists among the average. We want to avoid to classify an artist as a popular one only because a strict amount of users like him/her a lot.</li>
    <li>Also, there are users for which the total amount of plays (and so interaction with the system) is very low. This is a problem for reccomendation systems, because most for those users, may have only plays for few artists or many few plays for a lot of artist. In this scenario called also **cold start** it's difficult for the algorithm to suggested reccomendations for a *new user* due to the fact that it's impossible to compare this user which more active ones.</li>
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<h2>An explanation about outliers</h2>
The most curious users could actually be outliers. Their behaviour is very different from the others: in fact, while the mean value for the playCount field is 2509 we have to consider that it is affected by the *new users* (whose interaction with the system is of course very limited).<br>
Anyway, this difference may be due to the fact they were pioneers, registering longtime ago to a service that has recently become very famous, or maybe they are bots. Another possibility is an error inside the measured data or a tampering.
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<h4 id="Question-2.3">Question 2.3<a class="anchor-link" href="#Question-2.3">&#182;</a></h4><div class="alert alert-info">
How many play counts for each artist? Plot CDF or ECDF of the result.  

Similarly to the previous question, you need to comment and interpret your result: what is the figure telling you?
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compute artist popularity</span>
<span class="c1"># We are interested in how many playcounts per artist</span>
<span class="c1"># ATTENTION! Grouping by artistID may be problematic, as stated above.</span>
<span class="n">artistPopularity_DF</span> <span class="o">=</span> <span class="n">userArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s1">&#39;PlayCount&#39;</span><span class="p">)</span>
<span class="n">artistPopularity</span> <span class="o">=</span> <span class="n">artistPopularity_DF</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Let us see which are the most popular artists</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Top-20 popular artists&#39;</span><span class="p">)</span>
<span class="n">artistPopularity_DF</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;sum(PlayCount)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>Top-20 popular artists
+--------+--------------+
|artistID|sum(PlayCount)|
+--------+--------------+
|     979|       2502130|
| 1000113|       2259185|
|    4267|       1930592|
| 1000024|       1542806|
|    4468|       1425942|
|      82|       1399418|
|     831|       1361392|
| 1001779|       1328869|
| 1000130|       1234387|
|     976|       1203226|
| 1000139|       1148568|
|    4192|       1117143|
|    1274|       1088657|
|    1193|       1080412|
|    5705|       1046922|
|    1854|       1028627|
|    1205|       1015064|
| 1008419|       1010807|
|    1177|       1001228|
| 1001909|        979539|
+--------+--------------+
only showing top 20 rows

</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># some statistics about the &quot;PlayCount&quot; field</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">artistPopularity_DF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;sum(PlayCount)&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">withColumnRenamed</span><span class="p">(</span><span class="s2">&quot;sum(PlayCount)&quot;</span><span class="p">,</span> <span class="s2">&quot;Play Counts&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
<span class="n">df</span><span class="o">.</span><span class="n">describe</span><span class="p">(</span><span class="n">percentiles</span><span class="o">=</span><span class="p">[</span><span class="o">.</span><span class="mi">25</span><span class="p">,</span> <span class="o">.</span><span class="mi">50</span><span class="p">,</span> <span class="o">.</span><span class="mi">75</span><span class="p">,</span> <span class="o">.</span><span class="mi">90</span><span class="p">,</span> <span class="o">.</span><span class="mi">99</span><span class="p">,</span> <span class="o">.</span><span class="mi">999</span><span class="p">])</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
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    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Play Counts</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>1.631028e+06</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>2.278557e+02</td>
    </tr>
    <tr>
      <th>std</th>
      <td>7.988112e+03</td>
    </tr>
    <tr>
      <th>min</th>
      <td>1.000000e+00</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>1.000000e+00</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>3.000000e+00</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>1.100000e+01</td>
    </tr>
    <tr>
      <th>90%</th>
      <td>4.500000e+01</td>
    </tr>
    <tr>
      <th>99%</th>
      <td>1.402000e+03</td>
    </tr>
    <tr>
      <th>99.9%</th>
      <td>3.462489e+04</td>
    </tr>
    <tr>
      <th>max</th>
      <td>2.502130e+06</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we want now to visualize the data distribution, for the artists</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">artistPopularity</span><span class="p">)</span>
<span class="n">Y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">yvals</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
<span class="c1">#print(np.arange(len(Y)))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">18</span><span class="p">))</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 7: ECDF of number of play counts per Artist ID &quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">321</span><span class="p">)</span>

<span class="c1"># figure considering the different percentiles</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 8: ECDF of number of play counts per Artist ID - 0.25 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">322</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">8</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 9: ECDF of number of play counts per Artist ID - 0.95 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">323</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">95</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 10: ECDF of number of play counts per Artist ID - 0.99 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">324</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">99</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">700</span><span class="p">,</span> <span class="mi">3000</span><span class="p">,</span> <span class="o">.</span><span class="mi">97</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>


<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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9Rmnr2Am5qG3VSXNVaNT+h5kPKjNFZ3NLx/CCAzm4c1Lm/zfqsJnHsp27AfcGRj0EsJhB7Vat4Who+pNQ3DxlsPt2SWSyYN828VeEfEIyPitNrU+X5KXyiN++cL1CCw/j21zfqWUIK1Vn2LNO/XmyhJn20dz8Oa9+mCUZJkpwJnAadFub3goxExb5RlN+6HxjraD3hH0z5cwsg63Nb3/ERKALixBr/fpKlp/xiW0ez3KYHoTVFulx1X8/0xGD7G7h11KkkzRV/HOPWWpo9SWvHMpyQp/iMixnMR6x+bYpzh83AnY5yvNq2jVV9newErMnOoYdhkY5zRfg+b3ZNb+gAaTj6NNcYZorSkG45x9mr6fXwvI/ffaMfIIsq+bI4LxlMP9+XIfkrbxTg7RMSno9weeT/l4t3C2NJf1BeAV9RExasp+/HhFutbQvke3Ndi3IgYp8aD94xjezbv04bvWcvYtSZHP0m52+COiDg5InYZZdnNMc48Sv3vB7y0aR8+g5Hfh7HEON/PzOGL119h8jHOG4DHAFdF6XriheOcf1Q1HlxMixgnRj7c63ttFrGW0lfgsF2AtU3xdvNyj6LUzfGZec3w8JoYvafGiGdSLs7/3ijFbxfX70fZr7c17MtPUxJow5r3wxJKYrLZWL7bkzkHNa/r4w3ruZfSumw854EZy0SX+sVtwCMiYoeGYUsa3j9AaTINbL6StbhpGc0nyBWUZrWNQdOCLPeDt1SvAuxF6Vi1U34IPD9KH0BjFqXT7b2Bn21r2ia3Uk58jfalNK+drOGAqHE/TfZpPpv3c0TsRLlCdCtl//2kaf/tlJlvbpi37Y9iXcYjouHpLIy/HvZuusK0L1ta6zT6cC3LkzJzF0oyq3G+bwNPitIHxAspP8StrAD2bfNj17xf96U0o76DsX0/RjOiHuuVrg9m5qGUBye8kJIAbqfxu9pYRysoV74b9+EOWVoZtlx3oyh9gPw28KooT9u5ndKi7wVRn1jaZhmjHRfUq2/HUQKYbwNfHct84/C7lKuGV3doeZKmt17HOIdRbgO7sLYKuQA4D+jE03Z/CBxRz9djVltdPxX46TjXdyuwJEb2gdipGAfKP53dinEGKLeaDsc4NzTtv50zs7E10Gi/SXdTWu40xwXjqYfdmmLTdjHOOygdkR9ZY5zfGt4kgNpiaD2lL6xX0P5i3grK96BV68ERMU4t1+6U7Zls7LlVPWbmJzLzqZTbCh9DeUBDO80xznCLphWU1l6N+3DHzPz70dY9LCK2p7SgO7ohxvkz4MnR0Jdbi2VsK8a5NjNfTolxPgJ8vdZnp2Kc4yjx5/kt1t34cK9j28x/OSNbMD25DmspIp5CaQH7+sz80TbKloyMv5u1i+tXUFq7LmrYl7tk5uOblt1oBa37ORzLd3tb2zBWKyitcxvXtX1m/nwcy5ixTHSpL9SmnhcCH4jymOrfZOStdtdQst2/U68kvI9yD/9o/h342+HmmxGxOCKO28Y8JwLfaGoFNFmnUk5E34jSufpAROwepQPQrU56EbFLvfpyGuU++UvHub4zgcdExCsiYm6UxwAfSotm5OOV5ba8WyiJhzkR8Xom35ntCyLiGVGeTvnXwHmZuYJS3sdExKsjYl59/UZEPG6MZV0B/Bz4cJTO1Z9EucrVLsnUyiOBt9Z1v5RyD/xWDxCgNIFfS+ngc2+aAqYsTc2/TrkadX5m3txmfedT/iH6+4jYsZZ7uAn2fwJ/FqVD450ozZNPr62/JvL9aHQHDZ2kRsSzIuKJ9Z+t+ylB3WhPsfnjiNgnSget76XcxgClqfabIuLIKHasZdy5/aJGeHXdtkMo/6gdRglIVzL6bSojtqdRPb+8MiJ2zdL8/v6GbbsD2D1qJ//jFRF7RMRbgL+i3E4ytK15JM18fRDjXAA8M2oLrvqP4zOBX094o6rM/CFbnjb71Bp37BwRb6oxwghRWgcdTelb6nxa/6aO5jxK4uNd9bd5GaUuT5vUhmxxCaV10pyIOIZ6i94kPDUifq9ewHob5Z/pcynbfn+UB7JsX9f3hBjlycKNaquyr1KOgZ3rcfB2Sovy8fhgPSafSbmo9bUW0+xMaam2qv7O/1WLab5IaSW1Mbd0udBc5tsoD335f1E6uJ8XEcNJs68Ar4uIwyJiO0qMc15m3tiB2LM5xvmNGpfMoxxL6xg9xnlVRBxaE9UfAr5e6/9LwIsi4vm1XAuidPI/1qTvS+p6D2VLjPM4SvJ3tIuLbWOcun2viojFNQZZVQdvovQnNzTavKOJ8iCBV1Jawn0kM++ZyHIox8rbI2LvKLcnv4PS11ardT4B+B/gTzLzOy3GHx8RO9X/rZ5HudA82m3hLeP6emx+H/in+n/YQJQHTY32/f8Pyi3dT60x7kH1ezip7zbb2L9N/p3yYIDHw+YO9V+6jXlmDRNd6ievpDyB5x5KJ+anUwICMnM1paPG/2DL1Z2VrRez2ccpJ7vvR8QaSmBxZLuJozwp7g9ocdti/ce47dWGaviJLsOvi2rZH6ZcNb2KEgzeTzkJLqIEbMO+U8u5gtJX1T9TOmIfl/rD80LKD8c9wLuAFzY0i56sN1ISOfdQroRN9qrBVyhB072Uq7uvBKjJxudR7mm/ldLM9yOML4HzckpfEbcC3wL+KksfCmN1HuXe/7spfVIc3+aH/YOUDlRXU/q4+GaLab4APJH2VzqHA9cXUfowu5lyjL+sjv5cnfdsSme26yidYk70+9How8D7ojR9fiflSunXKcfqlZROjEcLnr9CCRCur6+/qeW6kHK8fJLyQITltHmyThsnAv8vy9MMN78oP+zNTfsbfZzSV819EfGJFuNfDdwY5RaMN1FvK83MqygJxeujff8wrayK8jSkSym3RL40Mz83xnklzQ49i3Gy9IP0AUrLjjWU/i7/Lku/mUTEMyNi7TbW966mGKcxpjiekrA6nfI7eBmlg+kfNkzzybruOygdSn8DOGa8FwQycz2lU/1jKb/N/w94TT1/d8KfUn6Hh7tLaPn0wHH4L8rv+PBDgn6vtpoe/r0/jPKbfjdl/4/nQsufUI6V6ymt/79CiRXG6vZarlspFwHf1KYeP0bpfPtuynH2Py2mOZXSYXvbGKd6NeXi2VWUls9vA6gtdf6SclzcRklkNfZpNJnYszkm2IVyIe4+yq1r91AeFNHOqZREzO2UPtbeWsu8gtK66b2UJNKKWsax/n99IvD5zLy5Kcb5JPDKaH8r22cp/Yy27BOQ0hfs5fU7/XHghCx9vj5IiWXPqfMe1WLeVn5Vl7Wc0i/Vn2Xm+7cxz2g+TXng0aWUc8V36zAA6vnlmfXjOyitWz/bcO5p/H/sTynnzFXAPwBvzPJU13ZGi+tfQ7kd+ArKsfF1RrktOzO/VpfxFcoDMb5N6dh+st/t5pi8rcz8FuV/o9NqTHsZ5dwotjyNQuo7EXE6cFVmtrpyJHVVRLyW8lSTZ3RoeftSArtH5egdu08rEXEjpZ5+uK1pJUmFMY56qbaE+1JmjuuW01GWtz0lcXV4Zl7biWX2g4gYpNTTf/S6LJqcTsf16n+26FLfqE2JH12bix5DuUoy2StpUs9F6Zfj7ZRHIs+YJJckaWyMcTTDvRm4YCYluSRNbxPp3V/qlkdRbvnandJk/82Z+cveFkmanCgdgN5BaR5/TI+LI0mTEhGfo9wef2dmPqHF+KDcLvMCSsfer83Mi6e2lH3JGEczUm3ZHZQ+pySpL3jroiRJksakdh69Fvhim0TXCyh9B72A0mfUxzOzbf+YkiRJneati5IkSRqTzDyb8vCQdo6jJMEyM88FFkZE2w59JUmSOs1ElyRJkjplb8rTx4atrMMkSZKmxKzoo2vRokW5//77d2XZDzzwADvuuGNXlq32rPfesN6nnnXeG9b71Ot2nV900UV3Z+birq1Aw6LFsK36yYiIk4CTABYsWPDUfffdt9vlmnWGhoYYGPCadqdZr91hvXaH9dod1mt3XHPNNR2L1WZFomv//ffnwgsv7MqyBwcHWbZsWVeWrfas996w3qeedd4b1vvU63adR8RNXVu4Gq0EljR83ge4tXmizDwZOBngkEMOyauvvnpqSjeLeB7rDuu1O6zX7rBeu8N67Y5OxmqmISVJktQpZwCvieIoYHVm3tbrQkmSpNljVrTokiRJ0uRFxH8Cy4BFEbES+CtgHkBm/jtwJuWJi8uBB4HX9aakkiRptuqrRFdEfA54IXBnm0dWB/BxSgD1IPDazLx4akspSZI0O2Xmy7cxPoE/nqLiSJIkbaXfbl08BThmlPHHAgfX10nAv01BmSRJkiRJkjQN9FWLrsw8OyL2H2WS44Av1quF50bEwojYs9d9P9y/bgOX33I/a9ZtYONQsmHTEBs3JRuHhtiwacvnDUN1+KahzY8fyqbnEGXDg4kaxzVONto8bd6STTONZdntytKscdmjTNZRt9zyMP+7+rIpWlvnjVaf/eyWWx/mR6umpt5zyo6m/nbrLQ/zw1WX9roYs471PvVuveVhVu16Cy95yt69LookSZKmsb5KdI3B3sCKhs8r67CtEl2Nj63eY489GBwc7HhhMpNTfv0Ab/z+99kwNL55G5+9HdF+XNvho8zTbrrm5bZdT5sRW80/xum6ITOJ26b3A7Smop46bcrrfTpWUodlJnHHzb0uxqxjvU+9zGTeeZexcPW1vS6KJEmSprHpluhq9W9vy2YfjY+tXrp0aXbj8Z+/WrGKn5x1Dr/zxD054Ygl7LbDfObNGWDunGDeQPnb+H7enAHmDgRzBoJolyXSmPhI196w3qeedd4b1vvUs84lSZLUCdMt0bUSWNLweR/g1h6VhevvXgvA25/3GB69eKdeFUOSJEmSJEn0X2f023IG8JoojgJW97J/rvUby/2K28+b06siSJIkSZIkqeqrFl0R8Z/AMmBRRKwE/gqYB5CZ/w6cCbwAWA48CLyuNyWVJEmSJElSv+mrRFdmvnwb4xP44ykqjiRJkiRJkqaR6XbroiRJkiRJktSSia5JyJbPe5QkSZIkSVIvmOjqgIhel0CSJEmSJEkmuiRJkiRJkjQjmOiSJEmSJEnSjGCiS5IkSZIkSTOCia5JsC96SZIkSZKkidu4aaijy5vb0aXNUoG90UuSJEmSpOnjzjXreGj9JoYShjLJhMxkKCFJhobK32wYP1THQ7JpCE674GbmDQyU8bRazvCwkfNvmSb58dV3dXS7THRJkiRJkiRNgczk/nUbtySCGhJLzQmlrAmhoZoQyjr/DXc/yFmX385ADI8vwxsTTZvnrwmrxvkz4crb7ufW1es6tl177rqAgQgiIILyHhqGBQN1OHX4wEBpOPSEvXfhpo6VxESXJEmSJEnSNp2z/G6+de16Lt5wzebWSFslmoZycwspaEhS1en+8/ybO1aeRTttx/w5QdRk0kDT3+FEU2OyKYCBAdh9p+2YN3eA1z1tfxbuMH9EMiqofzcP2zJfNCWwtps7h8P3XcjcOZPrGSve2oEKqUx0SZIkSZKkvnXtHWtY/dCG9rfYDSeWGm6Na5xuUybv/6/LmTtQkjOtWkENDQ23eBqZnNrcOgrYNFR76r7u2poIis1/tySVaqul2PK5cbpFO81nzkDwpqMfXZNHZd5oSEhttcza8qkxGbX3wu15yr679WiP9DcTXZOQ9kYvSZIkSVJL9z2wns///EYe3rBpc2unVv04Nd9W19iP0w13r+Xim1d1pDwL5g1w3JP33qpl0ubEUrT+3NiCadG6lZz44md3pDzqDhNdHRD2RS9JkiRJ6lPZ1MppRP9Nja2jGsb/+Oq7+OIvbqzzNyafckQrp+aWVJs7Mc8c0QfU9vPmbH1b3cCW2+KioTVT4zAot+j96XMO5sBFOza0fmpqURVNLaoY2Rpq7pwo80/yH/jBwdsmNb+6z0SXJEmSJEl94qH1m7jngYfbdkQ+1Dy86fa6oUz+7rtXcuFNDxBnfXfSdyItO2Tx5gQSbEkkDSeWRtyix/C4LUmrxTtvxzueewgDA7YQ0dQw0SVJkiRJUgfcvnrdqEmq4b6hRutn6nWfv6Bj5fmTZx0EDa2cNvcJ1dTh+ObkFLVFVe036uA9duaoA3fvWHmkqWCiS5IkSZI069266iEeXL+x5S11lgw7AAAgAElEQVR+jX9Lf+Q1MdXQ59T9D23gTV+6uCNl2Xvh9rztOQdv1RH5QNMteq1aWA13WH7fDZfykucd0pHySNOJia5JKKc0SZKk2SEijgE+DswB/iMz/75p/L7AF4CFdZp3Z+aZU15QSbPOg+s3smFTY19Rzf1HNdzaN7R1/1QX37yKd37tVx0py4uevBcvetKebZ+W19j/VKt+puZE8Lg9d2H+3IFJlWPw1snNL01XJro6wDuNJUnSTBcRc4BPAc8FVgIXRMQZmXlFw2TvA76amf8WEYcCZwL7T3lhJc0q/3XJLfzpaZd0ZFlvedZBPG7PXTb3L1WSUMO3/DU/qW84gbUlebXdvDk8ce9dmWN/VFLPmOiSJEnSWBwBLM/M6wEi4jTgOKAx0ZXALvX9rsCt21roDg/eAp//nZEDH/8SOOKNsP5B+PJLt57psFfAU14JD9wDX33N1uN/4/XwhN+H1Svhm3+09finvQUOORbuvha+87atx//WO+HRz4Lbfg3/856txz/7/bDvkXDzefCjD209/pgPw55Pgut+DGf/49bjX/QxWHQwXP09+Pkntx7/e5+GXfeBy74BF3xu6/F/8EXYcXf45Zfhkq9sPf6VXyt/z/8MXP7trce/7rvl7zmfgGvOGjlu3gJ41TfK+598FK7/ycjxO+wGL/tSef/DD8CKpr6EdtkLfv8z5f333g23Xzpy/O6Phhd/orw/461wz3Ujxz/qiXBsbSj4jTfC/U2H0JLfgOd8oLw//VXw4H0jxx94NBz9rvL+S78PG9aNHP+Y58PT31reNx93sM1j71ELDgeWeeyNduzN32Fcx96da9axy70PcP5Pt+fdO/wVJLzy4dM4fNOvATZ3pL46dubdc/+cTHjzxi/xxLx682Iflcm/zHsEdz7nX5k3Z4Ajr/kou6+5ZvP4CFiz435c+MQPMBDB0ks/wM4P3LRlPLB64WNZccT7Ofoxi4lvntR3x95EznuHrVoFNyws4z32yt8OnPcOu/QHW+oVZvx5b8p+czvIRJckSZLGYm9gRcPnlcCRTdN8APh+RPwJsCPwnFYLioiTgJMAnvCo7Vi1atWI8Xdeey23PjjIwKaHeVLTOIDbr7qK21cPMm/9/Ty+xfhbrriCu+7ene3W3cXjWoxfceml3HPb9mz/4EoOaTH+pl/9ivtWBDutuZ6DWoy//pcXc//1D7HL6is5sMX45RdeyNqd72W3e3/Ffi3GX33+eTy0wy3sfvelLGkx/spf/IKHFyxm8Z1XsHeL8Zefcw4b5u/Co267ike1GP/rn/6UtQ9t4JpbruWRLcZfMjgIwJKbr2P3pvGb5szn0jp+vxtvYLem8Rse2MTldfwBN9/MrqtHjn/4oTlcWccftHIlO60dOf7B9bdxTR3/mNtuY4cHR45fu3Ely+v4x91xB9s9PHL86ryZG+r4x991N/M2rBkx/r4bbuCmLOOfeO+9zNm0fsT4e667jhUbyvjDWtTNto69dbutY3DQY2/42HtgY/LQhpKJSuDfPv8jNsR2POOBq3jKQ/eyeUT984//VhIMz33gap70cBn/wIZk4xDMnzvEojnrCGDnjZvYruFRgQFsGhjikF02EcDiB5KdNtIwPthu/gC75QrYCLtuWsOCoZH/7G/auIZHPXg9AAs23M+8jQ+NGL/dulVw2xX85Lb+PPYmct7btGnT5vPrTDv2mv36pz9laM527DUF572dGuoVZv55b6p+czspcrLPGu2wbvT9sHTp0rzwwgs7XtYvn3cTf/Gtyzj/vc/mkbss6Pjy1d7g4CDLli3rdTFmHet96lnnvWG9T71u13lEXJSZS7u2glkgIl4KPD8z/7B+fjVwRGb+ScM0b6fEl/8UEb8JfBZ4QmYOtVvuIYcckldffXW70Zogz2PdMVPrNRs7XKfxiYBNw4ZGPh3whJPPZfmdazcvp7GT9Fadp7frTP3xCzfxhbc8v0dbP3PN1OO116zX7uhkrNZXLbqmW98PfZYjlCRJ6qaVwJKGz/uw9a2JbwCOAcjMX0TEAmARcOeUlFDSmGQmz//Y2Sy/c219guDEHXfYXnzsZYcRMfEWGYO1tYokdUJfJbroUt8PXWc/g5Ikaea7ADg4Ig4AbgFOAF7RNM3NwLOBUyLiccAC4K4pLaU0w2Um599wL6se2kDm8FMDt7S8yuEnDjY8aXDLkwhLi6xNQ8k1d6zlyAMewZEHPGLz0/4Gmp7+FzQ8JbDx6YDAwEDpfP23HrN4UkkuSeq0fkt0dazvB0mSJHVOZm6MiLcAZ1G6j/hcZl4eER8CLszMM4B3AJ+JiD+jXJx8bfZbPxnSNHfdXWt52cnndmRZxz91H166dMm2J5SkaaTfEl2tLgU0B0cvB05p6Pvh1IjYqu+Hxk5O99hjj640h73m5g0A/PznP2fhdgMdX77aW7t2rU2ce8B6n3rWeW9Y71PPOp8ear+oZzYNe3/D+yuAp091uaTp4qH1mzjr8ttZt2FTU19Ywy2ztvR/BQ2tsIZbbGVy+/2lk/X3v/BQjjpwdwYGaosrGlpcNfeTVVtfbe4XK2DewAC77Ti/V1UhSV3Tb4mujvX9kJknAydD6Yy+G53FrTz3JrjiMp72tKfxyJ3tjH4q2QFgb1jvU8867w3rfepZ55Jmg+9fcTtvO/2SSS9nu7kDHHXg7hy61y7bnliSZpl+S3RNq74fbIcvSZIkzQ7rNmzimvs2seD6exr6vWp+UmEyNMSIz419ZZ13w70AfOPNT2PvhdvXfq+2tMjaqi+s2NJHFmxpkTUQwcCA/WJJUit9leiarn0/hL3RS5IkSTPap368nH89bx2cN7n+sebNCQ5avBO77jCvQyWTJDXqq0QX2PeDJEmSpP6z9uGNzJ8Dp7z+yNqyavhJhUDT0wkHGp9a2NSH1sId5pnkkqQu6rtElyRJkiR10qm/uJFvXHzLlk7fKbcYbt3pe27p+J3GzuJh1YPrmRvwtEcv6um2SJJGZ6JLkiRJ0ox25qW3c/1dazl8v91a94VVW15FjHw6YTS20AIWPHTnNtYkSeo1E12T0duuwSRJkqRZp7HV1XDrrM2tsBqG09Aya8OmIR77qF045XVHTGrdg4ODndgESVIXmejqgLAvekmSJKkrXn7yuZx/472bbzucqKcftHvnCiVJ6lsmuiRJkiT1rStvv5/H77ULyx6zGKKh03dgYKCh0/dodUtinQY44gATXZI0G5jokiRJktTXnrJkIW9/3iG9LoYkaRow0SVJkiSp44aGko+cdRV33v/wiP6zsumphwlbnoa4ud+t4ffJ2nUbe70pkqRpxETXJNgVvSRJktTanWse5tM/uZ7ddpjHLtvP23y7YTTcYlj+Dt+GSMM0W8Yfvu9uPOuxj+zx1kiSpgsTXR1gX/SSJElSa+865rG8/Ih9e10MSdIsMdDrAkiSJEmSJEmdYIsuSZIkSSOsenA9Hz7zKh5Yv3FE31pDtQ8tKH8bh0MdP1T64Fq3Yain2yBJmp1MdEmSJEka4Zc3r+L0C1ew98Lt2X7+nNKHVu03KyIYiDafG6ZbMG+Aox+zmCMOeESvN0eSNIuY6JqEtDd6SZIkzWCfeuXhHLZkYa+LIUnSmNlHVwdE2B29JEmSJElSr5nokiRJkiRJ0ozgrYuSJEnSDHLD3Q9wymUP8993/YpMyMzSaTxs7kCebXQwf8/a9T3eCkmSJsZElyRJkjSDfO+y2xhcuZE919y9uWP4gdphfDR8btXB/OZpB4JnHryIAxbt2OvNkSRpXEx0TULaG70kSZL61I/fuYwF8+b0uhiSJE0p++jqALuilyRJkiRJ6j0TXZIkSRqTiDgmIq6OiOUR8e420/xBRFwREZdHxFemuoySJGl267tElwGUJElS/4mIOcCngGOBQ4GXR8ShTdMcDLwHeHpmPh5425QXVJIkzWp91UdXQwD1XGAlcEFEnJGZVzRM0xhA3RcRj+xNaSVJkmaVI4DlmXk9QEScBhwHXNEwzRuBT2XmfQCZeeeUl3Ka2zSU3Lrqoc1PQsyGv+WpiVuelFieqDj81MQyPjNZed9Dvd4MSZJ6pq8SXUyzAMqu6CVJ0iyyN7Ci4fNK4MimaR4DEBHnAHOAD2Tm/0xN8WaGv/nuFXz+nBsnvZx5AzBnwJ5kJUmzT78luqZlABXGEJIkaeZrFfE0X/ebCxwMLAP2AX4aEU/IzFUjFhRxEnASwOLFixkcHOx4YaerK65bxy7zg5cdMo+IICgVH1FfjZ9b/QUiggVDD3HOT8/u3YbMUGvXrvV47QLrtTus1+6wXvtfvyW6uhJA7bHHHl05EJffuAGAn/3sHHaab7ZrKnly6Q3rfepZ571hvU8963xaWAksafi8D3Bri2nOzcwNwA0RcTUlbrugcaLMPBk4GeCQQw7JZcuWdavM0843bvsld21czV+8ctmkljM4OIj12nnWa3dYr91hvXaH9dr/+i3R1ZUAaunSpV0JoG445wa46gqe8Yyns3CH+R1fvtrz5NIb1vvUs857w3qfetb5tHABcHBEHADcApwAvKJpmm8DLwdOiYhFlJb4109pKSVJ0qzWb09d3BxARcR8SgB1RtM03waeBWAAJUmSNDUycyPwFuAs4Ergq5l5eUR8KCJeXCc7C7gnIq4Afgz8eWbe05sSS5Kk2aivWnRl5saIGA6g5gCfGw6ggAsz84w67nk1gNpEDwOotDd6SZI0i2TmmcCZTcPe3/A+gbfXlyRJ0pTreKIrIubWK34TMh0DqGjZtZgkSVJ/mWycJkmS1O+6cevi+cNvIuJfu7B8SZIkTYxxmiRJmtG6cetiY/Omp3dh+ZIkSZoY47QuWv3gBm669wEyYSiToQQof4eGyt8kN49v9ff21Q/1ejMkSZrWupHosucqSZKk/mSc1kWvO+V8Lr551aSX8+QlCztQGkmSZqduJLoeGxG/plwxfHR9T/2cmfmkLqyzJ4wUJUnSNDNr4rReuH/dRpbutxt//KyDIGAggoEo/bkOBEQEUYeXv3UYw9OW4Ut226HXmyJJ0rTVjUTX47qwzP5mX/SSJGl6mH1x2hTbY5cFPOuxj+x1MSRJmrU6nujKzJsAImIhcHAdfE1mru70uiRJkjR2xmmSJGmm63iiKyLmAycDLwFuoLR32i8ivgW8KTPXd3qdkiRJ2jbjNEmSNNMNdGGZ7wPmAUsy8ymZeRiwLyWp9pddWJ8kSZLGxjhNkiTNaN1IdP0e8MbMXDM8oL7/P8DvdmF9PZNpd/SSJGlamTVxmiRJmp26kegayswHmwdm5lpm6IMKw87oJUnS9DDr4jRJkjS7dOOpixkRu9H6WYRDXVifJEmSxsY4TZIkzWjdSHTtClxE6wDKK4WSJEm9Y5wmSZJmtI4nujJz/04vU5IkSZNnnCZJkma6jvfRFRHPj4jjWwx/RUQ8t9PrkyRJ0tgYp0mSpJmuG7cufhB4UYvh/wt8C/hBF9bZU/ZFL0mSpolZF6dNRGYylDCUSda/UP4O5Zbx2TB+KGHTkHd/SpLUa91IdO2QmXc1D8zM2yNixy6sT5IkSWNjnNbC0FDy/I+dzfK71pKTzFU9eZ9dO1MoSZI0Id1IdC2IiLmZubFxYETMA7bvwvokSZI0NsZpLQxlcu2daznygEdw5IG7MxAQBAMBAwOl7f5ABBGUYbFl2EBA1L9EcPTBi3u4JZIkqRuJrm8Cn4mIt2TmAwD1CuEn6jhJkiT1hnHaKJ5x0CL+5NkH97oYkiRpEjreGT3wPuAO4KaIuCgiLgJuBO6q42aMyTZtlyRJmmKzJk6TJEmzU8dbdNWm8O+OiA8CB9XByzPzoU6vS5IkSWNnnCZJkma6jrfoioh3AdSA6bGZeelw8BQRf9fp9fWDCJ+7KEmS+t9sjNMkSdLs0o1bF09oeP+epnHHbGvmiDgmIq6OiOUR8e5Rpjs+IjIilk60oJIkSbPMpOI0SZKkfteNRFe0ed/q88iREXOATwHHAocCL4+IQ1tMtzPwVuC8yRVVkiRpVplwnAZekJQkSf2vG4mubPO+1edmR1D6ibg+M9cDpwHHtZjur4GPAusmXEpJkqTZZ8JxmhckJUnSdNCNRNeTI+L+iFgDPKm+H/78xG3MuzewouHzyjpss4h4CrAkM/+7o6WegNxm3k6SJKmvTCZO84KkJEnqe9146uKcSczeqsn85mxSRAwA/wK8dpsLijgJOAlgjz32YHBwcBLFau26GzYA8LOf/pQFc+2QfiqtXbu2K/tUo7Pep5513hvW+9SzzqfGJOO0Vhckj2ycoPGCZES8cxLrkiRJmpCOJ7omaSWwpOHzPsCtDZ93Bp4ADNYnHT4KOCMiXpyZFzYuKDNPBk4GWLp0aS5btqzjhb1m4Dq4+iqe+cxnsuN2/VaVM9vg4CDd2KcanfU+9azz3rDep551Pi105YLk4sWLe57k3DRUNuOGG29gcPCWnpalU0wed4f12h3Wa3dYr91hvfa/fsvOXAAcHBEHALdQngz0iuGRmbkaWDT8OSIGgXc2J7kkSZLUcV25IHnIIYd05YLkeGzcNATf/x4H7H8Ay5Yd3NOydIrJ4+6wXrvDeu0O67U7rNf+140+uiYsMzcCbwHOAq4EvpqZl0fEhyLixb0tnSRJ0qy2+YJkRMynXJA8Y3hkZq7OzEWZuX9m7g+cC2yV5JIkSeqmfmvRRWaeCZzZNOz9baZdNhVlaifti16SJM0SmbkxIoYvSM4BPjd8QRK4MDPPGH0JkiRJ3dd3ia7pKOyHXpIkzQLT6YKkJEmanfrq1kVJkiRJkiRpokx0SZIkSZIkaUYw0SVJkiRJkqQZwUTXJNgXvSRJkiRJUv8w0dUBgb3RS5IkSZIk9ZqJLkmSJEmSJM0Ic3tdAEmSJGky1qzbwEPrNzGUMJRJAkNDpZOJoUyGErLhbw4PH4Ik2bjJDikkSZopTHRJkiRp2rpl1UMs+4cfs6EDyart58/pQIkkSVIvmeiahPTinyRJUk/du3Y9GzYlrzpqXw7dc1cGAgYioP4dCIjhYQwPizoMIkpvq/PmDHDUgbv3dmMkSdKkmejqgLAvekmSpJ5a9phH8pxD9+h1MSRJUo/ZGb0kSZIkSZJmBBNdkiRJkiRJmhFMdEmSJEmSJGlGMNE1CeXh1JIkSZIkSeoHJrokSZIkSZI0I5jokiRJkiRJ0oxgokuSJEmSJEkzgokuSZIkSZIkzQgmuiYh7YtekiRJkiSpb/RdoisijomIqyNieUS8u8X4t0fEFRHx64j4UUTs14tyjixTr0sgSZIkSZKkvkp0RcQc4FPAscChwMsj4tCmyX4JLM3MJwFfBz46taWUJEmSJElSP+qrRBdwBLA8M6/PzPXAacBxjRNk5o8z88H68VxgnykuoyRJkiRJkvpQvyW69gZWNHxeWYe18wbge10tkSRJkiRJkqaFub0uQJNWvV217PI9Il4FLAWObjP+JOAkgD322IPBwcEOFXGL669fD8DZZ5/NvAE76ppKa9eu7co+1eis96lnnfeG9T71rHNJkiR1Qr8lulYCSxo+7wPc2jxRRDwH+Avg6Mx8uNWCMvNk4GSApUuX5rJlyzpe2MtzOVx7NUf/1tHMn9tvjeNmtsHBQbqxTzU6633qWee9Yb1PPet8eoiIY4CPA3OA/8jMv28a/3bgD4GNwF3A6zPzpikvqCRJmrX6LTtzAXBwRBwQEfOBE4AzGieIiKcAnwZenJl39qCMkiRJs44PDZIkSdNBXyW6MnMj8BbgLOBK4KuZeXlEfCgiXlwn+wdgJ+BrEXFJRJzRZnGSJEnqHB8aJEmS+l6/3bpIZp4JnNk07P0N758z5YWSJElSq4cGHTnK9G0fGtTYl+rixYsn1T/bjas3AXDpZZcy984rJ7ycmcZ+77rDeu0O67U7rNfusF77X98luqaTzJb95EuSJM1EHXtoUGNfqocccsik+lK9dOVq+MXPeOITnsiyQ/eY8HJmGvu96w7rtTus1+6wXrvDeu1/Jro6IHzgoiRJmvk69tAgSZKkbumrProkSZLUt3xokCRJ6nsmuiRJkrRNPjRIkiRNB966KEmSpDHxoUGSJKnf2aJrEuyLXpIkSZIkqX+Y6OoA+6KXJEmSJEnqPRNdkiRJkiRJmhFMdEmSJEmSJGlGMNElSZIkSZKkGcFE1yTYF70kSZIkSVL/MNHVARF2Ry9JkiRJktRrJrokSZIkSZI0I5jokiRJkiRJ0oxgokuSJEmSJEkzgomuSUh7o5ckSZIkSeobJro6wK7oJUmSJEmSes9ElyRJkiRJkmYEE12SJEmSJEmaEUx0SZIkSZIkaUYw0TUJib3RS5IkSZIk9Yu+S3RFxDERcXVELI+Id7cYv11EnF7HnxcR+099KZvL1OsSSJIkSZIkqa8SXRExB/gUcCxwKPDyiDi0abI3APdl5kHAvwAfmdpSSpIkSZIkqR/1VaILOAJYnpnXZ+Z64DTguKZpjgO+UN9/HXh2hG2qJEmSJEmSZru5vS5Ak72BFQ2fVwJHtpsmMzdGxGpgd+Duxoki4iTgJIA99tiDwcHBjhf2xhvXAzA4OIi5tqm1du3aruxTjc56n3rWeW9Y71PPOpckSVIn9Fuiq1W2qLnH97FMQ2aeDJwMsHTp0ly2bNmkC9fs0Keu4yk/Podly5aZ6Jpig4ODdGOfanTW+9SzznvDep961rkkSZI6od9uXVwJLGn4vA9wa7tpImIusCtw75SUrskjd17AfrvMMcklSZIkSZLUB/ot0XUBcHBEHBAR84ETgDOapjkDOLG+Px7438zcqkWXJEmSJEmSZpe+SnRl5kbgLcBZwJXAVzPz8oj4UES8uE72WWD3iFgOvB14d29KK0mSNLtExDERcXVELI+IrWKwiNguIk6v48+LiP2nvpSSJGk267c+usjMM4Ezm4a9v+H9OuClU10uSZKk2Swi5gCfAp5L6Urigog4IzOvaJjsDcB9mXlQRJwAfAR42dSXVpIkzVZ91aJLkiRJfesIYHlmXp+Z64HTgOOapjkO+EJ9/3Xg2WFnppIkaQqZ6JIkSdJY7A2saPi8sg5rOU3tkmI1sPuUlE6SJIk+vHWxGy666KK7I+KmLi1+EXB3l5at9qz33rDep5513hvW+9Trdp3v18VlzxatWmY1PxBoLNMQEScBJ9WPD0fEZZMsG8/9yGSXMON4HusO67U7rNfusF67w3rtjkM6taBZkejKzMXdWnZEXJiZS7u1fLVmvfeG9T71rPPesN6nnnU+LawEljR83ge4tc00KyNiLrArcG/zgjLzZOBkcN93i/XaHdZrd1iv3WG9dof12h0RcWGnluWti5IkSRqLC4CDI+KAiJgPnACc0TTNGcCJ9f3xwP9m5lYtuiRJkrplVrTokiRJ0uRk5saIeAtwFjAH+FxmXh4RHwIuzMwzgM8Cp0bEckpLrhN6V2JJkjQbmeiavJN7XYBZynrvDet96lnnvWG9Tz3rfBrIzDOBM5uGvb/h/TrgpeNcrPu+O6zX7rBeu8N67Q7rtTus1+7oWL2GrcklSZIkSZI0E9hHlyRJkiRJkmYEE12TEBHHRMTVEbE8It7d6/JMFxFxY0RcGhGXDD9ZISIeERE/iIhr69/d6vCIiE/UOv51RBzesJwT6/TXRsSJDcOfWpe/vM4bo61jpoqIz0XEnY2PbO9lPY+2jpmiTZ1/ICJuqcf7JRHxgoZx76n1cXVEPL9heMtzS+0A+rxat6fXzqCJiO3q5+V1/P7bWsdMEhFLIuLHEXFlRFweEX9ah3u8d8kode7xrnFpt/81OdEi1tL4tfldn1XxZDeMN17Sto03FtLYTCTe0bZFxIKIOD8iflXr9YN1eMvYb0Iy09cEXpROWK8DDgTmA78CDu11uabDC7gRWNQ07KPAu+v7dwMfqe9fAHwPCOAo4Lw6/BHA9fXvbvX9bnXc+cBv1nm+Bxw72jpm6gv4LeBw4LJ+qOd265hJrzZ1/gHgnS2mPbSeN7YDDqjnkzmjnVuArwIn1Pf/Dry5vv8/wL/X9ycAp4+2jl7XUxfqfU/g8Pp+Z+Cauu0e71Nf5x7vvsZzHBlLda9ub6Qp1vI1oXoccyzla9L12vL3w9eY63RcsZCvSderx+vk6jWAner7ecB5NV5uGftN5GWLrok7Aliemddn5nrgNOC4HpdpOjsO+EJ9/wXgJQ3Dv5jFucDCiNgTeD7wg8y8NzPvA34AHFPH7ZKZv8jyDfli07JarWNGysyzKU+8atTLem63jhmjTZ23cxxwWmY+nJk3AMsp55WW55bagui3ga/X+ZvrdrjOvw48u07fbh0zSmbelpkX1/drgCuBvfF475pR6rwdj3e1YiylvjbOWEpjNM54SWMwgVhIYzCBeEdjUOPjtfXjvPpK2sd+42aia+L2BlY0fF6JB/1YJfD9iLgoIk6qw/bIzNugnFCAR9bh7ep5tOErWwwfbR2zSS/reTZ/Z95Sb1/7XEOT8fHW+e7Aqszc2DR8xLLq+NV1+llX5/U2tqdQrgx5vE+BpjoHj3eNnfuse1rFWuoM48nuafX7oXEaYyykcRpjvKMxiog5EXEJcCfl4vJ1tI/9xs1E18RFi2E+wnJsnp6ZhwPHAn8cEb81yrTt6nm8wzW6qajn2bpv/g14NHAYcBvwT3V4J+vc7wMQETsB3wDelpn3jzZpi2Ee7xPQos493jUe7rPuGU+sJfWDdr8fGodxxEIah3HEOxqjzNyUmYcB+1BaeD+u1WQTXb6JrolbCSxp+LwPcGuPyjKtZOat9e+dwLcoB/Ydw7f11L931snb1fNow/dpMZxR1jGb9LKeZ+V3JjPvqCfyIeAzbLmVarx1fjfl9re5TcNHLKuO35VyS8CsqfOImEcJQL6cmd+sgz3eu6hVnXu8a5zcZ13SJtZSZxhPdsEovx8ao3HGQhqjccY7GqfMXA6WPqcAACAASURBVAUMUvroahf7jZuJrom7ADi4PhlgPqVD3DN6XKa+FxE7RsTOw++B5wGXUepu+AlnJwL/Vd+fAbwmiqOA1bXZ7VnA8yJit9pU9HnAWXXcmog4qvbZ8pqmZbVax2zSy3put44Zralfpt+lHO9Q6uOEKE+QOwA4mNLhectzS+0b6sfA8XX+5rodrvPjgf+t07dbx4xSj8HPAldm5j83jPJ475J2de7xrnEyluqCUWItdYbxZBeM8vuhMZhALKQxmEC8ozGIiMURsbC+3x54DqX/s3ax3/hlH/S6P11flKdqXUO5n/Qvel2e6fCiPFnpV/V1+XC9UfpX+RFwbf37iDo8gE/VOr4UWNqwrNdTOhteDryuYfhSysnmOuCTQIy2jpn6Av6T0pR2A+Wq+Rt6Wc+jrWOmvNrU+al1e39NCTb2bJj+L2p9XE19il8d3vLcUr8/59d98TVguzp8Qf28vI4/cFvrmEkv4BmUps2/Bi6prxd4vPekzj3efY33WDKW6nydtoy1fE2oLsccS/madL22/f3wNaY6HVcs5GvS9erxOrl6fRLwy1p/lwHvr8Nbxn4TeQ0H6pIkSZIkSdK05q2LkiRJkiRJmhFMdEmSJEmSJGlGMNElSZIkSZKkGcFElyRJkiRJkmYEE12SJEmSJEmaEUx0SZpyEbEpIi6JiMsi4msRsUMdvrZDy39URJwWEddFxBURcWZEPKYTy25Yx7KIeFonlylJktQPjNUkTWcmuiT1wkOZeVhmPgFYD7ypUwuOiAC+BQxm5qMz81DgvcAenVpHtQwweJIkSTORsZqkactEl6Re+ylwUOOAiNgpIn4UERdHxKURcVwd/tcR8acN0/1tRLy1aXnPAjZk5r8PD8jMSzLzp1H8Q706eWlEvKwuZ1lE/HfDcj8ZEa+t72+MiA82lOWxEbE/JeD7s3q185kR8dK63F9FxNmdrCBJkqQeMlaTNK3M7XUBJM1eETEXOBb4n6ZR64Dfzcz7I2IRcG5EnAF8Fvgm/5+9+46vrK7zP/763JqezGR6pXdwlCaKy4AFrKiIDVFs6K7l59pddxGxYFsr7loRUAFdVMQVxFUZQEB678PMML1kMunJrZ/fH9+TzM3NTSYZEjLJvJ+PRx6593zPOfe0e87nfs73+z3wHTOLAW8Gjiub9gjg7mE+8vXAMuA5wCzgzlEGOi3u/jwz+xfg4+7+HjP7AdDl7t+I1uVB4FR332BmTaOYp4iIiMgeTbGaiExFqtElIpOh2szuA+4C1hKColIGfNnMHgD+AiwE5rr7GmC7mT0XeBlwr7tvH8Pnnghc4e4Fd98C3AgcO4rpfhv9vxvYZ5hxbgEuMbP3AvExLJOIiIjInkaxmohMWarRJSKTodfdl41QfhYwGzja3XNmtgaoisp+ApwDzAMurjDtw8AbhpmvDTM8z+DEf1VZeSb6X2CY86a7v9/MjgdeCdxnZsvGGNiJiIiI7CkUq4nIlKUaXSKyJ2oEtkaB08nA0pKy3wGnEe7uXV9h2r8B6ehuHQBmdqyZnQTcBLzJzOJmNhv4J+AO4GngMDNLm1kj8OJRLGMnUF/yGfu7++3ufh7QAiwew/qKiIiITCWK1URkj6UaXSKyJ/ol8Aczuwu4D3isv8Dds2Z2A9Dm7oXyCd3dzex1wLfN7NOEPiTWAB8hBE8nAPcDDnzS3TcDmNmvgQeAJ4F7R7GMfwCuijpf/RChs9MDCXci/xp9hoiIiMh0pFhNRPZY5u6TvQwiIqMWdWx6D3Cmuz852csjIiIiIjspVhORyaamiyIyZZjZYcBK4K8KnERERET2LIrVRGRPoBpdIiIiIiIiIiIyLahGl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnSJiIiIiIiIiMi0oESXiIiIiIiIiIhMC0p0iYiIiIiIiIjItKBEl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnSJiIiIiIiIiMi0oESXiIiIiIiIiIhMC0p0iYiIiIiIiIjItKBEl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnSJiIiIiIiIiMi0oESXiIiIiIiIiIhMC0p0iYiIiIiIiIjItKBEl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnSJiIiIiIiIiMi0oESXiIiIiIiIiIhMC0p0iYiIiIiIiIjItKBEl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnSJiIiIiIiIiMi0oESXiIiIiIiIiIhMC0p0iYiIiIiIiIjItKBEl4iIiIiIiIiITAtKdImIiIiIiIiIyLSgRJeIiIiIiIiIiEwLSnRNUWa2xMy6zCw+2cuyNzGzuWZ2k5l1mtl/TvKyXGJmX5ykzzYz+5mZ7TCzO3Zj+hVm9p6JWDYZf9G5Zr/JXg4Zf2b2IjN7vOT9GjN7yWQuk8hUpLhsz2Rm/2xmW6J90zyJy7GPmbmZJSbp819oZk9G2+G1Y5x2uZmtn6hlk/FlZv9mZj+Z7OWQiWFmPzCz/4he67s5AiW69nDRj47e6MLU/7fA3de6e527FyZ7GQHM7D1mtjJavj+Z2YIxTLvCzPrK1vEPJeUNZvZtM1sbla2M3s+Kyvu3UaeZtZnZrWb2fjOLlczjEjPLln3Gm3ZjVc8FWoAGd//Ybkw/XZwIvBRY5O7HTfbCTKY9PTEQJSVXmdkjoxx/SBIyOtes2sV0u7zYliZnS4L+/u/jFjP7XzN76WiWc4TPSJvZxWbWYWabzeyjuxj3W2a2MUra/peZJUvKy89Njw83r6ki2uYH9L9395vd/eDJXCaRqWQqxGVmljKzq6JldTNbXlZuZvZVM9se/X3NzGyU815uZsWy9e8ysxNKxjnVdt4U3GZmN5rZa6Kyc8ysUDLdags3zg4qmb78+tBlZvfvxnZIAt8EXhbtm+1jncc0cgFwUbQdrp7shZks0fH398lejpFEy+hm9sZRjDsk9nL3L7v7Lm8mV4r3ysoHJWdLfkt1Rn8PmdmFZtY4mvUa4XNebGaPmVmPmd1gZktHGPcFZnZH9PkPmNmJJWVmZp+18Huxw8yuNLOGZ7Jsk63S8eru73f3L0zWMk0lSnRNDa+OLkz9fxsn8sNsjHebzOwk4MvA6cBMYDVwxRg/9oNl6/jqaN4p4K/A4cBpQAPwAmA7UJpgebW71wNLga8AnwJ+WvYZXyv7jF+NcRmJ5v+Iu/tuTLvHsrHfgV4KrHH37olYHhmbXXxn/wmYA+xnZseOMA+zkuTws6jJ3euA5wD/B/zOzM55BvM7HziQcIyeDHzSzE4bZtxPA8cARwAHAc8D/r1snNJz07OSEBrrOVhEnnV7dFwW+TvwNmBzhbJzgdcSzrtHAa8C3jeGeW8sW/86d78tWtY3AP8DXAYsAuYC5wGvLpn+tui83wi8BOgF7jazI8o+p6lk/s8Zw/L1mwtUAQ/vxrR7rN08HpYyzbbDVLaLffgOoDX6v7vzmChfi35vzQbeCTwfuMXMandnZhYqLfwW+A/Cb8i7gIq/z8xsJnAN8HWgCfga8AczmxGN8nbgbOCFwAKgGvje7izXWChm24O5u/724D9gDfCSCsP3ARxIRO/3BW4COoG/AN8HfhGVLQfWDzdfwg/Dq4BfAB3AewhJ0E8DTxGSSr8GZg6zjN8Avl/yfkG0bPuPch1XAO8Zpuw9wBagbizbiJAEKwJHRO8vAb44yuV5AXAn0B79f0HJPHJAFugaZr9cEm37P0b74vb+7VC+z8rXHTgHuAX4FtAGrIqW5RxgHbAVeEfZZ/2AkBzoBG4ElpaUHxKVtQKPA28sm/a/gWuB7mHWZQHhgtIKrATeGw1/N9AHFKLt8PkK0/avy/ei7fgY8OJh1nt/4G/RcdYC/JIQ3AJ8AvhN2by/B3x7mH23mHDB3BbN76JoeIyQwHg62o6XAY1j+H78OpqmkxAoHhOV/ZxwnPVG2+KThKD6F9HntxGOobkjHLufAR4BdgA/A6pKyl8F3BfN51bgqLJpPwU8AGQoOa7KPuPiaJv+tn97lO2HL0X7qjcarxDt366S7efAAdHrV0TL2wlsAD4O1EbTF6PpuoAFw3w/vjjc9yEa/nHCdz42mu9rhc/YQLh73//+C8CVw4x7F3Bmyfu3AutGc26qMK/zCefRX0Xb5h7gOWXfp98Qjs3VwIcrTFt6Do4D/0Y4B3cCdwOLR/ndHu4cdFO0zbujffQmyo5/Bh/7o74O6E9/e8sfUyAuK5vvemB52bBbgXNL3r8b+Mco13/IspeUGbAW+MQI058D/L3C8P8Frqq0LXexPGng28DG6O/b0bCDonOdR+e7v42wz94RLXcL8NmS8ksoiR2HOV9+gnAd7ibcYJ0LXFey32eUfda50XJuAj5WMq9h92/JtO+OlvOmYbbFewnxWishflsQDX+KwbFKepjjumI8UmG9+5ezMxr/dSX7ohU4smTcOdHnzh5hmR8tmdfzouGHEq7BbYS46zUl06yg5NpM2TEVbav3A09G6/J9wrF5KIPj17Zo/CFxzQjH7kixbWN0DGyK5vNFIF427beibVTxNwkhIVkEzgDylMSP/fuBEPttJiSUh8RehPNH/7mmYkxKiP2GxHu7OKddUr7cQH20vh8czfmjwmecC9xa8r4/njykwrivAh4uG/YE8O7o9VWUnHsIv6H6gJphPnsN4xiDM8xvkGj8dxGO8x3A9Qz+vTbW43VgPzD0uzlsrLk3/qlG1/RxOXAH0Ew4wZ09xulPJ5wgmgg/dj9MuNt3EuFL0//Fq8Siv9L3EGpJYGZvNbMHxrg8/V4C/Mndu8YykbvfQbgYvGgs00V3C/4IfJewLb8J/NHMmt39HMK26a8Z9pdhZvMW4PPADELA8aUxLMLxhJNmM2GfXgkcCxxAuDN7kZnVlYx/FuGH/CzCyfiX0XrUEn4IX04IMt4C/JeZHV4y7VujZasn3PktdwVhGy4A3gB82cxe7O4/JZyQb4u2w+dGWJdV0bJ9DvhttH3LGXBh9DmHEi4U50dlvwBOM7OmaL0ShB/nPx8yk1Ar7X8Jyax9gIWE7QchwDiHUMNnP6AOuGiY5a7kNdG8mgjB40UA7n42Iejsv7v/NULA3BitRzNhW/WOMO+zgFMJCb+DiGoUmdnzCEmq90Xz+SFwjZmlS6Z9C/BKQmIwXz5jM6sh7LtfRn9vjmpJljqbEGjUE7bRzeysxfTBCsv7U+B9Hu7oHUH48dANvJzBd/l3t4bDbwnH7JhrT0V39RYApU1c7ifUCK04CUPPXYvKquFfaGYtZnZLefOfCk4nBJ4zCd+9q80sGdWU+0O0LAuBFwMfMbNTy6YtPQd/lLB/X0GoyfouoGeU3+2K5yB3/6eo/Dk+ulqtY7kOiMhgkxmX7crhjHCejJoEvXU35nsw4dp31W5M+1vGGLNFPkuoVbKMUEPtOODf3f0Jdq5Tk7ufMsI8TiQs+4uB88zs0DF8/hmErhwOItRau45wk2IWIXn14bLxTybUOn4Z8Gnb2fXBaPbvSYQ46dSy4ZjZKYRY6o3AfEIsdCWAu+/P4FglM8y6VIxHKniKsK8aCdeaX5jZ/Gi+VxLi1X5vAf7i7tsqLPOZhO/G2wnXudcA26Mmp38A/ky4zn0I+KWZjSUueBUhfn4OYZuc6u6PMjh+bYrGHRLXjDDfkWLbSwnJqQOA5xL28XsqTDuH4X8bvB24y91/Q0iMnFVWPo8QYyyNxt1V7FUxJnX3z7LreG+X3L2TEJPszncXys5FUTz5FJXjtvKYrX/YEcOUGyH5euAInz8uMTghWVXxN4iFPvH+DXg9oSbczQxt+TSW47WiUcaaexUluqaGqy30PdVmZkPa1ZvZEsKX4zx3z7r73wk/xsfiNne/2t2L7t5L+GJ/1t3XRxeu84E3DFM981rgjWZ2lJlVE6qoO1AD4O6Xu/tRu/j875asY5uZ9bc9bibcKdgdGwkXg34fL5l/yzDTvBJ40t1/7u55d7+CcMfm1cOMX8lv3f2OKPHwS0LwNVqr3f1nHvr4+BXhwnSBu2fc/c+E2mQHlIz/R3e/KdpHnwVOMLPFhBPmmmheeXe/h5Dhf0PJtL9391uifd5XuhDRPE4EPuXufe5+H/ATxhaobyXUvMpFP6gfJ2zfQdx9pbv/X7SO2wjJxZOisk2EO+JnRqOfBrS4+90VPu84QnD4CXfvjpa7P4F3FvBNd18VJU0/Q0j6jLa68d/d/dpov/yccCEaTo5w3B7g7gV3v9vdO0YY/yJ3X+furYTA5y3R8PcCP3T326P5XEq4a/T8kmm/G007XCLt9dE0fyZcgBMM3QeXuPvD0XGSG2E5S9fvMDNrcPcd0bE1nvqDtEpJ0V3pTwK3lwxrJyTxKrkO+H9mNtvM5rHzB0lN9P9ThMToQuBHhCry+4/w+Xe7+1XRdvwm4U7q8wnn59nufkF0jl4F/Bh4c8m05efg9xB+rD3uwf0e+pcZzXf7mZyDSo3lOiCyN9nT47JdqWPoebLOLPTT5e5HufvlI0y/oCxma4uS8P2dve9O3FYeswG0lMz/48NMdxYhTtoaxRCfZ+xJxc+7e6+730/4kTiWZpLfc/ct7r6B8AP2dne/N9pHvyMkPMo/q9vdHyTUIOm/5o9m/54fTVvpmn8WcLG73xNN/xlCTLjPGNZluHhkEHf/H3ffGB2bvyLUROnvTuRS4K0lXSGcTYWbk5H3EG4e3xld51a6+9OE62Yd8JXo+/M3QgxTcXmG8RV3b3P3tcANjHwdHEtcUzG2NbO5hKTTR6J9tJVQe6v0Or/R3b8XXbuHi9veTkiSE/1/R1l5EfhcFDOPdBO1dN3GEpPujkrf3dEqPxfB8HHbrYRzz1uim4jvICSo+mO264D3WOhbrJEQw1FSXsl4xeAj/QZ5H3Chuz8axWVfBpbZ4L7IxnK8Dmc0seZeRYmuqeG17t4U/VV6UsoCoNXde0qGrRvjZ5SPv5TQV06bmbUR7ioUCNVdB3H3vxLuavyGkMleQ6j+O5anQHy4ZB2b3P0/ouHbCXemdsdCQvXgft8omf+sYaZZQFiHUk9H8xqt0v4wetj543s0tpS87gVw9/JhpfMb2G9RAqeVsA5LgeNLg1BCEDSv0rQV9B9TnSXDxrodNriHerQl0w95SIGZzYk6jNxgZh2EWlyl++dSdt4dfBvDB0yLgae9Qs0mhu7XpwlJnyHH8zDK92nVCD8ufk6olnylhU7Ov2YlHZxXULofSrfRUuBjZftwMYO34a6+5+8Afh0FVRnCHfPyoGms54ozCLWMnrbQwfAJu5pgjPqPsdbyAgtPEurvmPgHFabtr/lZ2vloA+F8VMmXgHsJtSFvBa4mBIVbAaIApzMKKC8lNDt4xQjLXvp9LLKzRuRSyn4YEu7uza00bWQx4a5mudF8t5/JOaj8s0Z1HRDZy+zRcdkodDH0PNlVds0eycaymK3JQ02M/s7edyduK4/ZAGaVzP8bw0xX6fo+6gciRcYzbhspZoORr/m72r+7itsGtkMUE25nbHHbcMs2iJm93czuK1nWI4jiNne/ndCM8yQzO4Rwc3a4JO9w17kFhG4EimXLM1Gx+FjimuFi26VAEthUsl1+SKi91W/Ec4CZvZDQ5Lm/NcLlwJFmVpr02OZlN6d3Yawx6e6o9N0FBp7a3f+3pMIo5eciGCZui272nU6o8b6FcPP7L+z8vXkxoabUCkJz1xui4SP9Hh2vGHyk3yBLge+UzKeVUNus9Hgej7htNLHmXkWJrulhEzDTQjOlfotLXndTks220MRrdtk8yoObdcDLy4KYquiO1RDu/n13P9Dd5xASXgngod1cn1J/AU61MXZyaKHT7YVUbpI3ko2EE0WpJYS29s9Uf8ftpftpXqURx2BgP1to0jiTsA7rgBvL9l+du/9zybQjBbQbCcdU6R2VsW6Hhf13h0umr9Sc7cJoWY5y9wZCMqt0uquBoyx0UvsqouaZFawDlgyTgCrfr0sI1cu3MLrvx0gGbcfoLt/n3f0wQv8AryLcoRtO6Xe1dButA75Utg9rPNQyrPjZpcxsEXAK8DYLTx/cTKj18wqLnlg6zDxG/KET3Xk9nRC8XU3oR2SX043B6wiJpiFPOPTwJKH+6vnvr1C+g3A+LL0b/xyG6YDXwx38D7r7Qnffj/Cj4G4f/qlpztBq86VKv48xQkfM/d/H1WX7st7dS5Nmlc7BlWqPjea7PV7GdB0QkQGTHpftwsOM8jw5Ro9Hy3nGbkz7OkKNqLGqdH0fr4cDDNpPPPOYDUa+5u9q/+4qbhvYDiU17MZyfAy3bAOiWig/Bj4INHtoTvUQg6+N/Tcozyb0uzZcYma469xGYLENfkBOaQz6TPbLkG04QlxTyXCx7TpCjZ/S5GyDu5c2wdtVnPQOwna8L4rZbo+Gl8aQY43ZRopJn3HcFv32eAnDfHd98AMr1lYYZdC5KDpu92f4uO1Gdz/W3WcSjq+DCU3E8VDD8HPuvo+7L4rmsYGRvwPjFYOP9BtkHaFpbOm8qt391hGWq9Jn7MpoYs29ihJd04CHar53AedbeKT0CQxuavcEoQbKK6Ms/r8T2iyP5AfAl/qrVUZNe06vNKKZVZnZERYsITTx+U70o/OZ+jnhi/sbMzvEzGJm1hzV7BjyxTWzBjN7FeFuyC88VA0fi2uBgyz0K5YwszcBhxGqTD8jHqrUbyAkHuJm9i4qX+DH4hVmdqKFfpe+QKgyv46wvAeZ2dlR9d6kmR1ro+x3IprHrYT+iarM7ChCJ6jDJZkqmQN8OPrsMwn9SlxbYbx6ok4WzWwhoWPX0mXpI/T3cTlwxzAXSggXuk3AV8ysNlruF0ZlVwD/amb7RhflLwO/iu687M73o9QWQvM2AMzsZDM7Mvrh0kGoITTS4+Y/YGaLLPTx8G/sfNrMj4H3m9nx0XerNlrG4ZrhlTs7WreDCVWglxH6H1jPyNX/B61Pqej8cpaZNXpontdRsm5bgGbbzcdMm9lcM/sgoXboZ8ru5I7FZcC/m9mM6G7yewkdd1b6zIVmtiDavs8nPPXnc1FZk5mdGh1HCTM7i/AEy+tH+Oyjzez1UaDzEULQ+w/CsdlhZp8ys+ro+3+EjfAUTEJT4S+Y2YHR8h1lZs08w+82I+zfCkZ9HRCRnSY7LovK02ZWFb1NReey/h/olwEf7T8HAh9jmPPkWEQ1XT4K/IeZvTOKyWJRnPKjCssYj67L3yN0qvz53fjYKwjn/NkWbuKcR6gZPh7uI8RZMy00b//IOMzzP8ysxkK/iu9k5zX/mZ5vLwfeaWbLLPQj9GVCTLhmDPMYLh4pVUv48b0tWs53srOPpH4/JyQu30Y41obzE0K3IkdH17kDovXvrxX2yegat5zw/emv6XQf8PpoOx5AiE9HawuhL85UtPwjxTWVVIxtPXS18WfgP0uO+/0tPJl+l6Lv6hsJfaYuK/n7EHCWDd+KYMTYaxcx6VjigfL5ps3saEJisL8j993xO+AIMzsj2gbnAQ+4+2PDfO5zo23fQHgY2np3vz4qmxltczOzwwhdSFywi3hyvGLwkX6D/AD4TPSdx8wao2NnNAYdr7uwO7HmtKZE1/RxFnACoUbCFwlf1AyAu7cD/0K4oGwgXDx21azwO4Sqxn82s07Cj7Xjhxm3inCB7SJ8yW4j/GAEILqA7OpO4UU2uHrr3dGyZwh3Ch4jdHbYEX3GLHbe6YDQd04nISn2WcLJ7Z27+MwhfGcfOB8jbMtPAq9y9+H69Bqr9xISOdsJHS2OJps/kssJP8xbgaOJOq300OTwZYR22RsJVWK/ytgSOG8hdKi4kXAh+py7/98Ypr+d0AFkC6GJ2Bui7Vvu88DzCG3y/0hoXlfuUuBIhm+2SFQL59WEavJrCcf4m6Lii6NpbyI8haSPEDzs7vej1IWEILu/D5F5hMRcB6HpwY2MHHRfTgiOVkV/X4yW6y7C8XIRIYhYSegsfrTeAfyXu28u/SNccMubL5b6DqFfkB1m9t0K5WcDayw0M30/UbPSKCi5AlgVbYvRNh1pM7Nu4EFC04Ez3f3iUU5byecITSGeJmz7r7v7nyD0m2ODq8/vT/gOdhOOsU976AsPQhOELxKC+RbC8fJadx9S06zE7wnH3A7Cdnp9dDe1/9hcRjj+WgjH20hJwW8S7ir/mXAs/RSoHofv9vnApdE+euMuxh3LdUBEBpvMuAxC7apeQu3266PX/TV+fkjotPhBQm2cP0bDADCzhy0k94ezoCxm6zKzM6J1u4pwHnwX4Ry1JVr/35dMf4KZdRHObSsITZWO3Y2bk0TzvovwIJ8HCU+8/eJuzKeSnxP67FpDOBfv6gEeo3Ej4Xr+V0KXGv3XnGd0vvXQjch/EFpVbCJc38baN0/FeKTscx4B/pMQ628hxGa3lI2znrAfnBFq6bn7/xDiw8sJTdWuJjxpMkvomP7lhOvlfwFvL0l+fIvQZ+0WwrV7LDdh/0ao6bPZdvbXWzGuGcZIse3bgRQ7n+J3FaNvxvtawnf0srKY7aeEpzCfVmmiUcReI8Wku4r3KvlkdHy2EpKYdxOeTt898mSVRZUAziBsyx2EY37guDWzH9jgrio+Sdj26wjb9nUlZbPY+TT56wh91g1JsJcZlxh8pN8g7v47Qpx2ZXSMPUQ4tkej0vG6q2UYS6w5rZmPujm+TCVm9ivgMR/+iXgiE8bMziE8+vnEcZrfEkKyc56Pfyeak8bM1hC203BP8JQpwszOJ3T2OlKALCJ7KcVlsicb73jEzC4m9OU23JMbp5zxjm1lcikGn/5Uo2uasNB0Zf+oquxphM76hjwJSGSqsdBHw0eBK6dTkktERKYvxWWyt7LwpMfXE2ojiYhMiglNdJnZXX5OnQAAIABJREFUxWa21cwqdkoetXn9rpmtNLMHzOx5JWXvMLMno793lAw/2swejKb5rpmN1DHw3mQeofp3F/Bd4J/d/d5JXSKRZ8hCp5QdwEuJ+k4SEZHxoThtQikuk72OmX2B0DTr6+6+erKXR0T2XhPadNHM/olwgb/M3cs7KsRCZ+IfIvTLcjyhA/PjLXQIdxdwDKF9993A0e6+w8zuAP4foe36tcB33f26CVsJERERkWlIcZqIiIhMRxNao8vdbyJ0Vjec0wnBlbv7P4AmM5sPnAr8n7u3Rk/u+z/gtKiswd1vi57schmh8z4RERERGQPFaSIiIjIdTXYfXQsJT03otz4aNtLw9RWGi4iIiMj4UpwmIiIiU05ikj+/Ur8NvhvDh87Y7FzgXIDq6uqjFy9evLvLOGGKxSKx2GTnGvdO2vaTS9t/8mjbTy5t/8nzxBNPtLj77Mlejilmr47TJovOE1Ob9t/Upv03tWn/TW3jGatNdqJrPVAa2SwCNkbDl5cNXxENX1Rh/CHc/UfAjwCOOeYYv+uuu8ZrmcfNihUrWL58+WQvxl5J235yaftPHm37yaXtP3nM7OnJXoYpaK+O0yaLzhNTm/bf1Kb9N7Vp/01t4xmrTXa68xrg7dFTfZ4PtLv7JuB64GVmNsPMZgAvA66PyjrN7PnRU3zeDvx+0pZeREREZPpSnCYiIiJTzoTW6DKzKwh3/GaZ2Xrgc0ASwN1/QHgazyuAlUAP8M6orDV6PO2d0awucPf+zlL/GbgEqAaui/5EREREZAwUp4mIiMh0NKGJLnd/yy7KHfjAMGUXAxdXGH4XMOQR2CIiIiIyeorTREREZDqa7KaLIiIiIiIiIiIi40KJLhERERERERERmRaU6BIRERERERERkWlBiS4REREREREREZkWlOgSEREREREREZFpQYkuERERERERERGZFpToEhERERERERGRaUGJLhERERERERERmRaU6BIRERERERERkWlBiS4REREREREREZkWlOgSEREREREREZFpYUITXWZ2mpk9bmYrzezTFcqXmtlfzewBM1thZoui4Seb2X0lf31m9tqo7BIzW11Stmwi10FERERERERERKaGxETN2MziwPeBlwLrgTvN7Bp3f6RktG8Al7n7pWZ2CnAhcLa73wAsi+YzE1gJ/Llkuk+4+1UTtewiIiIiIiIiIjL1TGSNruOAle6+yt2zwJXA6WXjHAb8NXp9Q4VygDcA17l7z4QtqYiIiIiIiIiITHkTmehaCKwreb8+GlbqfuCM6PXrgHozay4b583AFWXDvhQ1d/yWmaXHa4FFRERERERERGTqmrCmi4BVGOZl7z8OXGRm5wA3ARuA/MAMzOYDRwLXl0zzGWAzkAJ+BHwKuGDIh5udC5wLMHfuXFasWLGbqzFxurq69sjl2hto208ubf/Jo20/ubT9RUREREQm1kQmutYDi0veLwI2lo7g7huB1wOYWR1whru3l4zyRuB37p4rmWZT9DJjZj8jJMuGcPcfERJhHHPMMb58+fJntDITYcWKFeyJy7U30LafXNr+k0fbfnJp++8+dydfdPIFJ1cski84+UKR7myBTL5ANl8kky+yoztLMh4jVyiSLzq5QpHW7uxkL76IiIiIPEsmMtF1J3Cgme1LqKn1ZuCtpSOY2Syg1d2LhJpaF5fN4y3R8NJp5rv7JjMz4LXAQxO0/CIiInu9YtHJFor0Zgtk8kUy+fC/OxMqYPcnk/IFZ2tnhpgxKBmVKxRZ1dJNQ1UyGq9Iruh09eXZ0NZLQ1Vi0Dyebu0hFQ89KxSKTr5YJFdw2ntzIy2miIiIiAgwgYkud8+b2QcJzQ7jwMXu/rCZXQDc5e7XAMuBC83MCU0XP9A/vZntQ6gRdmPZrH9pZrMJTSPvA94/UesgIiKyJylECaJMoUAmV6SjLzdQkymbL7K5vY9kwgbKWrtzpOJGplBkY1sf6USMYtHJ5Is83drNjJrUwLRrtnczs/99oci2KGlVLO904BmoScVJxmMk40bMjEy+SH1Vgtn1aZKxGKlEjEPnN7CtM8Oh8+pJxI1EPEYyFv539uXYd1YdybiRiIYBJGLG7Po0qUSMokN9VYJUPBamj4XP2+er47ce04WZnQZ8hxCn/cTdv1JWvpRwE3I20Aq8zd3XR2VfBV4ZjfoFd/9VNPwS4CSgv4b+Oe5+3wSvioiIiMiAiazRhbtfC1xbNuy8ktdXAVcNM+0ahnZej7ufMr5LKSIisnv6azu19eQGajq19eTIF4pkCiGBtGFHL6lEjEy+yGOrslzf+gD1VUmy+ZCM2tLRR20qQSZKVFnUw2W2UGRNSze16cTA5+QKu5d1SiVCsqg7W2DRjGrSiRipRJwntnSyT3Mt9VUJjlk6gx09OZY215BOxIkZ5ApFFjRVk0rEyOaLNFYnaahKkk7G6MsVaa5NDUomJeIxUvEYDdUJkvHYQDIqFY9RnYqP45aXZ8rM4sD3gZcSupu408yucfdHSkb7BnCZu19qZqcAFwJnm9krgecBy4A0cKOZXefuHdF0n4hiPBEREZFn3YQmukRERCZLrlCkJ+q/KZMr0t6bI190MrkC27oy5AsheZTNF1m1rZt0MiRzsvkiT2zppKkmJKN6sgVWtXTTVJ0cqOnUky1gBr5beafwQOLG6uRAX1I1qThz6tM0Vidp7cmydGYNVak4yxY30daTY79ZtaQSocZTb7bA4pk1IXkV1YyaXRdqM8VixoyaFOlEjHQiJJeqk3HMKj0fRvZyxwEr3X0VgJldCZwOlCa6DgP+NXp9A3B1yfAb3T0P5M3sfuA04NfPxoKLiIiIjESJLhERmTS5QpGeTGGgNtSWjj7yRSebL7K9OySU+pvardneTVUiPpCcemRTB43VyajfqCKPbGynsTpFvhhqVe2O2lScdDJOPGb0ZPLsN7uOdCLG4hnVxGPGgqZQG6ork2dBYzX1Vcko+ZRnaXMt6WSMdCJO0Z3Z9elQcyoeozadoDaV4B+3/Z2XnLyceEyJJ5l0C+nPugbrgePLxrkfOIPQvPF1QL2ZNUfDP2dm3wRqgJMZnCD7kpmdB/wV+LS7ZyZmFURERESGUqJLRERGrVB0WruzdGXyZPNFujJ5Nrb1AoR+n7Z3h47Fo36eHtnYQV1VYqAfqQfXt9NYkxyobZXNF8e8DE01SVLxGMl4jLWtPRwyr57G6iQvPGAWhaIP1H7K5ovMqkvTVBtqOGVyBRbNCDWh0lHtqJm1qfA+Hqe+KkFsghNQ6bgpySV7ikoHYnkdxY8DF5nZOYS+VDcAeXf/s5kdC9wKbANuA/LRNJ8BNgMpwtOvPwVcMOTDzc4FzgWYO3cuK1aseIarM310dXVpe0xh2n9Tm/bf1Kb9J/2U6BIRmcb6cgW6M3ky+SK9uQIPtxSwJ7aRyRXY0NZLJl8cSEo9uqmTqmSMlVu7aOivKZUr8NjmTlLxGAV3CqPsmbwmFSediA10IH7EgkaqkjFO2L+ZTL7AQXPrScVjOFCbTjAnqv2UK3jUR1RIRFUn4zTVpAaSU+lETM3wRMbHesJDf/otAjaWjuDuG4HXA5hZHXCGu7dHZV8CvhSVXQ48GQ3fFE2eMbOfEZJlQ7j7jwiJMI455hhfvnz5uKzUdLBixQq0PaYu7b+pTftvatP+k35KdImI7GHyhVBTqq0nR3tvju5MfqA53+qWbnLF4kBfUg9uaI86Mg9N/+5c07rr5nt33TFkkFl4cl2u4By5sJEtHX2hKV59mqXNNcRjxn6z6kglQp9SC5uqaahOkk7EiJmxeGY1qXicdDJGU02SdEIdj4vs4e4EDjSzfQk1td4MvLV0BDObBbS6e5FQU+viaHgcaHL37WZ2FHAU8OeobL67b7KQkX4t8NCztUIiIiIioESXiMi4cg8dnGfyRdp7cmxq74ua7YWaUblCkSe3dFGXTpAtFOnO5LlvXdtAx+cbo/FHIxEzYjEjmy9y5MJG0okYz108g3jMOHBuHamoo/N5jdXMqAlPynv4kUd5+YlHk07ESSVCUmpGTYpEzFRTSmQv4u55M/sgcD0QBy5294fN7ALgLne/BlgOXGhmTmi6+IFo8iRwc3TO6ADeFnVMD/BLM5tNaBp5H/D+Z2udRERERECJLhGRQdydvlyRHT1ZOvpybI4ST6tbujGDlVu7iJmxsb2PvmyBjr4crd1Z8lHfVaPR339UqAUVo64qQVUyziHzGjh2nxg9uQJHLmwkETMWz6zBYGdfUok4s6On8+1OX08z2ldy9NKZY55ORKYfd78WuLZs2Hklr68CrqowXR/hyYuV5nnKOC+miIiITBHuTq7g9EVPPe/LhVYnHX05isXwO6t/WF+uwOaOPqqTcTK70W/vSJToEpFpqVh02ntztPXm2NrRR3c2z8a2Ptp7c3T2hQ7UHVjd0kUqHuOJLV1UJWO0dI0uWdVcm6I2naAmFefQ+Q04sN+sWvpyBRbPrBnoTyoeiw3UrkolYuw7q5aqpJr1iYiIiIjI+Ou/cd+bK9DVl6cvXxhoYdKXC/87+/J09uWJx4xMlHjKRF2jbO7oA0LV7Ew0XbakfHVLNzNrUwNPQu//y0Tv9wRKdInIHq9YdLZ1ZdjWmaGtJ8fa1h4K7jy5pZN4zHh0UwdVyTiPbeqkJhVnVUv3LufZ39fUIfMa2N6X5cQDZtGTK3DIvHqy+SL7z6nDgMUza6hJxWmuTTG7Pk1tauKfzCciIiIiItOPu9ObK9CbLdCbK9CXK9DWk6NQ9Oh9SCxtbOujOhkbSEBlohpS63b0kErEyeWL9OQKrN3eTSLqrqQnW2BbZ+YZLV/MIBEPrU/mNqSpSSVIxWOkk7GB/4ctaKAnW2DRjOqBm/n9f+nofW+uwKy6NLXpBOlEeFp6PGbMqElRlQytVKqSYXhtKkEqEaPmq+O0kVGiS0QmQaHobO3so7U7y7rWXjL5Ak9s7iRfdFZu7aI7m2dbZ4YtHRl6snlyhZGf9DevoYpMvsAh8xrI5AucefQiEnFjYVM1jdVJmmpSzGusoi6dYFZdaPaXSsSepbUVEREREZGpoFB0erJ5erMFOvryZPNFenN5erNFtnb2EY8ZPdkCPdkC61p7qEnF6ckW2N6dpaM3RzIeG0hK9eULrN7WTUN1kr5cmKY3V9it5UrEjKpknGTcaOvNceCcOqpTCeY1VtHRm+fwBQ3UpOJUJxN0Z/LsM6uW6mSMmlSCTKHIvIYq0okYVcnwZPR0MkYiZtSlk4Oebp6IT4/fSEp0ici46MsVWL+jhy0dGTa39/F0aw/5QpFHNnWQiMV4dFMH6WSMVdui2lZ//uuw82quTVFflWDZ4iZiBkcubKTocNC8elLxGEtm1tBYk6S5NkU6EVMn6iIiIiIiewl3J5Mv0tmXpzuTpyuTZ1tXhvu35ul+YBM92Twb2nqpTsZD7alcgfU7eknGjEy+yMa2XnpzBWJm9EXlWzp2vyZUfVXoziRXcBqqEsyoTVGVCC1CFjZV09aTY7/ZtSERlUrQk8mzpLmGqkScqlR84CnmM2tDbaeqZHwgIVWTipOKT58E1LNFiS4RGVauUGRLRx9Pb++hozfHI5s6ALh/fTvuzqpt3XRl8rT35oadR306QSZfZNmSJvpyBd587GI2bNzEScsOJJ2IsWhGDbPr08yqSzO7Pr1bHayLiIiIiMiepT8hFWpAhYRUR2+efNTMrrUnS2dfeGhvbzZPT7bA6pZu6tIJurN5Vm7toqEqOdDU7+nWHhIxI190CsVhWnzcc8+QQf01ljr78hw4p47adALDmN9YRX1VkupkaEbX2ZdnaXN/QipONl9kQVM11ak41ck4ibjRUJWkNh3eV6fipBPqe3dPNKGJLjM7DfgO4bHVP3H3r5SVLwUuBmYDrYTHU6+PygrAg9Goa939NdHwfYErgZnAPcDZ7j663qNFZMDm9j42tPWytrWbp7Z205Mt8OCGNuIx4751bSRjMToz+YrTpuIxatNx5jVWs/+cOuqrEhwwu45k3Dh8YSMNVQkWz6xhRk2KZIW7DytWtLL8RftN9CqKiIiIiMgY9Cen2ntzbOvM0JXJDySaNuzoJRE3ujN5Vrf0UJ2K0ZMpsHp7N3Ez+vIFVm7tGniKXm+ugI/cA8kgMYNELAYGS6J+clu6Mhwwp46qZJxj9plBR2+e/efUUpNKkIgZNekEc6Ob5asff4gXPf84aqJaUrXp8GRz3Ujf+0xYosvM4sD3gZcC64E7zewad3+kZLRvAJe5+6VmdgpwIXB2VNbr7ssqzPqrwLfc/Uoz+wHwbuC/J2o9RKaa9p4c69t6eGJLJ325Iveu3UG+6DyxpZPOvjyb2/uGfXzrnPo0mXyRF+w/i1yhyHMWNZGIGwfOqWdOQ5oFTdXMb6hSZ+wiIiIiInuAbL5IZ19uoP+n1u4shaLTF3Vs3psLCap0MkZPJs/q7T2ko87C23qybGrrI52M053Js7a1h5jBcJWlSpmBOyyaUU1dOkFnX56D59Wz76w6ujN59ptVG2pCpeL05YosbKqiNp2g6OE3R01US6oqGaehOklNKl7xBvlYrNjyKAfPq39G85DpYSJrdB0HrHT3VQBmdiVwOlCa6DoM+Nfo9Q3A1SPN0EJHPKcAb40GXQqcz64SXS1Pws9eGV4f/lo47r2Q7YFfnjl03GVvheeeBd3b4ddvH1p+7LvgiDOgfT389n1Dy1/wQTj45eEz//CRoeX/9HHY/2TY9ADL7v0srG4aXP7i82DJ8bD2dvjrBUOnP+1CmH8UPHUD3PSNoeWv/jbMOhAevw5uvWho+et/CI2L4KHfwJ0XDy1/42VQ2wz3/hLuu3xo+Vn/A6kauOPH8HCF3fXOP4b/t3wXnrh+cFmyCt72m/D6xq/BqhsHl9fMgDf9Irz+y/mw7s7B5Q0L4Iwfh9fXfRo2Pzi4vHl/eM13w+trPgzbnxpcPu9IeHmoVHjoI9+E1V8fXL74WHjJ+eH1r94GPTsGl+93Epz0yfD6F2dArm9w+UGnwgs/HF73H2+lxuHY6znodFo2rKL+ug/QkylQcKezNwcG3+x6GTdyDEt8A19O/pT50WT7Rv8vS5zJ2hnH8YKaVs7t/TExg7p0gkQsRnUqjr34PJL7nhAde+dDDNgYTbyOcOw1jc+xt+zebw499veSY4/fvBc6Ng4ufxaPvYrnnWfxvMefPjO0fC867y279/eDt/9edOxN9jVXRERkT+Du9GQLdPaFmlLdmXxITrnTncmzYUcvyXgs6meqj1yhSK5Q5IktXdSl43T25XlqWxc1qQS92QLZQuUb2MPp77dqv9m11KYS1KYTVKfiHDy3jhP2awZgSXMNhaKz3+xaErEYcxpCYqommaAmHae+KqEme7JHm8hE10LCz+N+64Hjy8a5HziD0LzxdUC9mTW7+3agyszuAvLAV9z9aqAZaHP3fMk8F1b6cDM7FzgX4Ij5adra2gDY+uSTbOxZQayQ4ahoWKnNjz3G5vYVJLMdHF6hfMMjj7CtpZl03zYOrVC+7sEH2b6pmuqe9Rxcofzp++9nxzqjrnMV+xYKA8vVb9W999CxqpeG9kfZr8L0K++6i676Vma03s/SCuWP33E7vTUbaG55kMUVyh+97TYyVbOZvfURFlYof/iWW8ilGpi36THmVSh/4OabKcbTLNjwJHMqlN+3YgUAi9c+RXNZeSGe4sGofOma1cwoK891F3g4Kt937Voa2weXZ3rjPBqVH7B+PXVdg8t7spt4Iio/aNMmanoGl3fl17Oyf/p8nra29kHl7b6W1VH54dtaSOY6B5XvWL2apz2UH9naSrwwuMXs9qeeYl0ulC+rsG1Gc+w9sfUGurraOGFLKwWHbAFyBSfv8LMn7+V/i1XMZzvfSu1ctpgBDvs2xmlJxDi+JsGcdiMdM5JxSBjEzHjf0jg7Zhao68wwY2XUp1YuhwM9GVj1wH10PJ15Vo69eRWO/b3l2Dt0yxbSmcHlz+axd3iFbf9snvcOqFC+N533Gsq2/9507E32NVdEROSZcHe6s6EWVFcmT1dfnk3tfRSKzuqWblKJkJzqyRZ4als39ekEPdk863b0UnQnkyuyoa13TJ/ZX7tq0YxqGqqSbOvMcODceg6d30BXJs/+s+uoS4caU0tm1gz0JRWLQXNtmupUnKpEnHQyRl06EZWpZYZMf+ZjaTQ7lhmbnQmc6u7vid6fDRzn7h8qGWcBcBGh0slNhKTX4e7ebmYL3H2jme0H/A14MdAB3ObuB0TTLwaudfcjR1qWY445xu+6667xX8lnaMWKFSxfvnyyF2OvNBnb3t3Z3p1lTUs3D25opydb4O9PtpCIGzc/2TLQsWK5WXUpErEYx+83EwOO27eZ2nScw+Y3sGhGuKBNNTr2J4+2/eTS9p88Zna3ux8z2cshQ+2pcdpk0XliatP+27MVik5XX56Ovhw7erJs787S2pWloy/H2tYeNqxfz4zZ83lyaye16cTA08LberJ0Zwu7nH88ZgNPycvmi+wzK3Rs3p3Nc8DsOuqrkuQKYXhVIkY8ZsxtqBro5Ly/CV9NKkFdOkEqoSftjYW+f1PbeMZqE1mjaz2wuOT9InY2hALA3TcCrwcwszrgDHdvLynD3VeZ2QrgucBvgCYzS0S1uobMU2QyFYvOwxs7eGpbF2u2d/PQhnZ6cwVuX9VaMYkFofrwC/ZvpiaV4MC5dew3q5aFM6o5eG49DdXJZ9xWXURERERkuuhv+tfem2NrZ4aO3hybO/rIF5yntnWRiBlPbOkklYjR3ptjdUs3vdkCXZn8qPqemrdjGzXpOJvb+zhgbj292TwnHzKbRCxGY3WSeY1VFN1ZPKOGqmScmbVJGqtTUXO+GKG3HRGZTBOZ6LoTODB6SuIG4M3s7FsLADObBbS6exH4DOEJjJjZDKDH3TPROC8EvububmY3AG8gPHnxHcDvJ3AdRIbo7Muxub2P21ZtZ0d3jttWtdDanWXl1q4hF8/+6sYnHTSbojsv2H8W9VUJDlvQwP6z62isTk7OSoiIiIiITKJi0enM5GnrydLRGzpCzxWKrG7pxgwe29RJPG48sbmTdDLGE5u7SCdjdPZVfip4qdn1aXqzBQ6aW8d+s+pIJ2M0VCVZ2lxDY3WSojtLm2tJxWPMb6qioSrJzNoU/7jlZtUIEpkGJizR5e55M/sgcD0QBy5294fN7ALgLne/BlgOXGhmTmi6+IFo8kOBH5pZkdAl9ldKntb4KeBKM/sicC/w04laB9l7uTvrd/Ty5NZObl/VSk+2wHUPbSIRi7G5o2/I+AubqjlyYSNHL51JfVWCFx4wi4UzqlnYVD0JSy8iIiIiMvGKRac7m6etJ0d7b7gZHGpXFenK5HlqWzfJuPHopk5qUnHW7+glky/Qmy3ssilgdTJOIm4sba7BMF52+FzcYZ9ZNeQLzj6zaknEjMUza6hLJ2iuS1FflaQ2FVetKpG93ETW6MLdrwWuLRt2Xsnrq4CrKkx3K1Cx363oKY7Hje+Syt6qsy/H+h293Lu2jXvW7mB7V4abn2yp2MxwYVN4dO7LDp/LwqZqDppbz/OWzKChOqGLqYiIiIhMae5RDavuHFs6++jLFejOFNjQ1ktPJs/WzgyrW7opFEMTwa2dmVHNNxEzlsysoTuT59D59fTliuw3u5bqZJx0Ms7CpirisRgLm6qZXZ+msTpJY3VS/VOJyG6b0ESXyJ6iL1egpSvDDY9t5entPfztwV4+8Lc/VbyTdPy+MzGDUw6Zw5z6Ko5Y2Mg+zTUk1FeWiIiIiEwhPdk827uytPWEfqx6snme2trF9u4snX15ntrWRUtXhr5ckfbe3C7n11CVIJWIc/C8Oo7dZya16TgHzqmn4M4+zaHPqkUzqmmIklXpxNR7aJKITH1KdMm0ks0X2dzex+2rt9PSleUP92/EgUc3dQwZ96hFjdSlE7zmOQuY21DF85bOoD6d0CN3RURERGSP1dmXY0NbL2u397ChrZfeXIHHN3dSdHhqaxcAT23rIpMv7nJeh8yrZ2Ztmjn1aQ5b0EDMYGFTDbXpOHMbqqivSlCdjNNcm6a+SnGyiEwNSnTJlNWbLfD4lk4e3tjOdQ9upjOT5/51bUPGa65N8eJD5nD4ggYOmd/AiQfO4u7b/s7JJ584CUstIiIiIjKYu9PWk+Pp1h427Ojl6dZuikXn0U2dtHZnKRSdVS3dtHRVbi6YjIcE1D7NtdSkE5x88Bxq0wkaq5McNLeOWNR8sC6dYF5jFTNrUkpaici0pUSXTAnZfJFbnmrh7jU7eGxzBzc+sY1cYWg/Wq88cj7NdSlesH8zz1syg9n16Yr9Z6lPLRERERF5NnRl8mxs62X9jh66MgUe3dTBxrZeig6PRa+H65g9lYiRjsdoqE6ybHEjEJJZ+8yqZb/ZtTTXplk4I/QjKyIigc6IssfZ1plhbWs3V929njUtPdyxppVCWefwz13SRMyM1z13IfvOqmXZ4iZq9IQVERGRUTOz04DvEJ6O/RN3/0pZ+VLgYmA20Aq8zd3XR2VfBV4ZjfoFd/9VNHxf4EpgJnAPcLa7Z5+F1RF51vVmC6xt7WF1SxfrWnvp7Mtx+yN9fOeRW4iZ8eCGdrIjNB9MxWMcPK+ewxc2sqCxigVN1SyZWcO+s2pZOKOa5to01Sn1cSUiMlZKdMmkcnfuX9/OE1s6+dWd6+jNFnikrD+t2fVpTj18LvMbq3n5EfOY21BFre5aiYiI7DYziwPfB14KrAfuNLNr3P2RktG+AVzm7pea2SnAhcDZZvZK4HnAMiAN3Ghm17l7B/BV4FvufqWZ/QB4N/Dfz96aiYyP7kyeta09rNrWzbodPXT05li5tYu1rT305go8vb2n4nRxg+a6XmpScU4+eDbVyThzGqrYd1Yt8xqqmF2fZn5jFTNrU7pBKyIyQZQtkGdNsehsaOufWsoFAAAgAElEQVTl1qdaeGpbN5feumZIJ5npRIyTDprN8oNn89wlM3jOokYFASIiIuPvOGClu68CMLMrgdOB0kTXYcC/Rq9vAK4uGX6ju+eBvJndD5xmZv8DnAK8NRrvUuB8lOiSPYy7Ry0Ieli5tYs120OtrKe39/D09pDIqqQunaCpJkl9VZJXHjWfBY1VLJpRw5LmGpbOrGFBUzX/uOVmli9f/uyukIiIDKJEl0yYfKHI/evbufnJbTy0oYO/PLplUHlzbYp9mtO8ZtkCjl46g0PnN9BYnZykpRUREdmrLATWlbxfDxxfNs79wBmE5o2vA+rNrDka/jkz+yZQA5xMSJA1A21RAqx/ngt3uSTbHoMvl4124EvhzEvC6+8sg+5tg8sPfy2c/v3w+usHQq6sds2ys+AVXwN3uHDR0M88/n3w4vOgrwO+eejQ8hd9FF70MejYBBcdM7T8xeeFebSshB+dNLT85V+D554FG++DS145tPz0i+Dw18GaW+DyNw4qOrGQh4W/CNvgievhqncNnf6sq2DpCfDQb+CaDw8tf+d1MP8ouOfn8KdPDy1/303QvD/847/hb18cWv6he6B+Ltz0dfj7t4eWf+xxSNfBX86HO348tPzfNoT/f/w43H/F4LJUHXz88fD66n+BR34/uLxuLnz4nvD612+HlX8dXD5zP3j/zeH1z18P624fXD7vKIrnXMu6HT00XPEq6nY8RtFDcsuBeziEs3o/AcC1qc/wKtsZn5rB/bXHccXSC1jQWMUHHzqT6mwrMQv9uxrAISMfewfMPgmWL5+Sxx4Qvnd787F31DfD69089njXdeH1T0+FLQ8NLl9yArztqvD6v0+EHasHl+/F5z1gfI49mLrH3jM8702LY28cKdEl46ZYdO5c08odq1v5y2NbhzwB8bh9ZlJXleCNxyziuUtmMLehapKWVEREZK9Xqbp0+VNePg5cZGb/v707j6+rOu/9//laljzKlkfZyDY22ASMAQMOYSo4kIBDBiCQBJow3B8NmUh6e5PeQkpoSkNpc5O26SVDnYQwNGEohIbemBhCrJokBmzwhA0G2RhbnkdZ8izp+f2xt8yxLFmyraOjc/R9v17n5X322nudtdfeOjw8Z++1bgZmA2uA+oh4VtJ7gT8Cm4A5QH0760w+XLoVuBVg/IhSVg+/9KDynfUVrK+sBGDsgPdR1G/3QeW1e4axMS0fN+RP6NFYf1D5jrqBbKqshAhObFY3wPZtvdhSWUmPhn2Ma6F866YebKuspKh+J2NbKN+8bj81lZUU79vOmBbKN62qY0dNJb32bGJUC+UbVmylblMlfXat47hm5fv272PLm+vYtaaSvjs3MLKF/dcsXcmet/fSv3Yb5S2UVy98k73LtjKgZifDWihf9cpr7C9ZTdm2eoa0UL7ypVdo6NmXwVuKGNRC+Yo/zCF6FDNkex/KWihfnp6bYTvLGNCsvLFHMW+n5eV7htO/WXl9z768k5aPrB9F32bl+4sHsHLWLLbtCYbtPoGSvn3ZuT/YXR/U7Ye33x7Ev319BgC3FJ3CCI04sG/PHrC15wjOLi/iuH492LDvQhq1g77FojidgXBwv9FcM7IGqGHLsAsoajiya29jyfFU5em1B7Cum197dXV1VFZWtnrtrUr3P67HifQZ3v+g8r3FQ6hOy0eVTKTX8PKDyndrBGvT8jF9J1NcfMJB5d35ew865tqrq+/Nq3l67bX1vdcdrr2OpIgW44+CMmXKlJg3b16um3GIysrKvL+1+dVV21iypobpL6xg9daD/yDOPWEww0t78z8uGMvYIf0Y1K8kR608VCH0fT5z/+eO+z633P+5I+mViGjhJ+ruSdJ5wDcj4vL0/R0AEXFvK9v3B96IiEN+qpX0C+DfgWdIEl8jIqK++We0pqvGabni7wloaAzW1exm1ZZdvL6+luptySOG62r2sL5mD3V761vcb/TgPpT1KWHs0H6cPKKUE4f1Z9zQfhxX1pvS3p3z1IDPX37z+ctvPn/5rSNjNd/RZUekamMtS9fV8r3fvknd3no27Nh7oOzkEaVMqhjIzeePZdSgPpT17TqJLTMzMzvIXGBCOkviGuA63h1bCwBJQ4GtEdEI3EEyA2PTQPZlEbFF0unA6cCzERGSZgHXksy8eBPQ7PkMs+Qxwm279vPWhloWVdewZvtu3tpYy8rNu1i/Y88hs21DMo5rWd9iJpT354Sh/Rk7pC8njxxAWd9iThjajyH9e+XgSMzMrCvKaqLraKetljSZZODSAUADcE/GtNUPABcDNWk1N0fEgmweR3dWs2s/i9fUcP8f3mZ9zZ6DZkQs7d2Ta88excfPqmDiyAFObJmZmeWJ9I6r24CZJHHa/RGxRNLdwLyIeBqYCtwrKUgeXfxSunsx8EI6WcwOkvit6RabvwIelfQtYD7w0846JutaGhqD6m27WLJ2B9XbdrF4zQ621O1l6bodbN+1/5Dtexf3YPSgvlw4fijlA3oxZnBfxg7tx/jh/Rk7pB+9i4tycBRmZpaPspboOpZpq4FdwI0R8Zak44BXJM2MiKZBn/4yIp7IVtu7s4igettufr14HS+u2ELlsoMHovvYGcdx8UnD+JMJQxnuMbbMzMzyVkTMAGY0W3dXxvITwCHxVkTsIZl5saU6V5DM6GjdRM2u/cxduZVFa2pYuraGLTv3sXTtjkNm1gbo2UOcWjGQyaOLOa1iICcM68eE4aWMH97fiSwzM+sw2byj66inrY6IN5s2iIi1kjaS3PV18Ojm1mHW1ezm4Tnv8OAfV7Jz37tTKl8wfggThpfyySmjGT+8PyU9e+SwlWZmZmbW2fbWN7B8406WbdjBglXbWbpuByu37GJT7d5Dti3rW8yZY8oY2r8XJ5WXclrFQMYP78/Igb3pWeQ40szMsi+bia6jnrY6IrY0bSDpHKAEWJ6x3z2S7gKeB26PiEP/K2uH1dAY/HH5Zn67dAOPzVvNnv3Jr26lvXtSUdaHb3zkFE49biCjB/fNcUvNzMzMrDM0NAZvrN/Ba2tqmLN8Cys276RqYx27Mn4EbTJ6cB8uOXk4E8r7M3HkAKaMHcxxA3uTPtJqZmaWM9lMdB31tNUHKpBGAg8DN6UDoUIyGOp6kuTXdJKxIO4+5MMzpq0uLy+nsoOnq+wITdPXdpb9jcG2PcEjb+xj2dYGdmVMWHPpmJ5MKCvi3OPSS2LzMpZvPji7WEg6u+/tYO7/3HHf55b738y6gohg6859LKzezmtrksTWiyu2sGPPobMZTqoYwKkjB3LSiFLeU17KqccN6FIzaZuZmTWXzURXNTA64/0oYG3mBhGxFvg4HJi2+pqIqEnfDwB+DdwZES9m7LMuXdwr6WckybJDRMR0kkQYU6ZMia44zWhnTX86+81NzF+1nX/+7YEnQunVswcXjh/MHVeczPFD+tG/V/eagNNTz+aW+z933Pe55f43s862t74hTWRt5aW3t/LOlp28s2XXIduNGNCbSRUDuWD8UM4aM4hTKwYwoHdxDlpsZmZ2bLKZ3TiWaatLgKdIBqr/j2b7jIyIdUrui74KeC2Lx5CXGhqDLXV7+cffLKNqYy0Lq2sOlH30jOP48Gkj+cApwz1OgpmZmVkB2bm3nldXbWP2m5t4Y30ti6prqNl98AyHpb16cvFJw5gwvD+TKgYyeXQZxw/p60cOzcysYGQt0XWM01Z/ErgIGJI+1ghwc0QsAH4uaRjJo5ELgM9n6xjyzcrNO/nj8i18/anFB62fduoIbvmTcZw1ZhBFPRzEmJmZmeWz+oZGlm2oZf6q7SxZu4OFq5MB4ps7fkhfLpyQ3KF1/olDOHlEqRNaZmZW8LL6vNoxTFv978C/t1LnJR3czLz3h6rN/OLlVfx60boD68YO6ctffPAkLj5pGGV9PY6CmZmZWT7as7+BuSu3sqi6hpfe3krVhlrW1uw5aJs+xUWcPmogY4f048LxQzln3GDfpWVmZt1W9xqYqUBEBO9s2cUDf1zJY3NXs3t/MhPOGaMGcvFJw7j14hPpV1Lk4MbMzMwsj2zcsYcX19Yz61evsWhNDW9v3sn2XQc/ejigd08+cEo5k0cP5KzjBzF5dBl9SxzSm5mZNfF/FfPInv0NzFyynp/+/m0WZYy79ZVLxnPuCUM4f/zQHLbOzMzMzNpr9dZdzFmxhaVrd/CHqs28tbEuo/QdAEYN6sP73zOc0yoGcvF7hjFuSD96eBgKMzOzw3KiKw/U7tnPfb+r4tG5qw8aUHT6DWdzysgBjB7cN4etMzMzM7PWRATLN+3k1Xe2MX/1Nl56eysrNu08ZLsTh/XjnHGDGbp/I3/20YsZ2NczHpqZmR0NJ7q6sLq99fyvxxbw7NINB9ZNqhjAT258L0P7l3jWRDMzM7MuZlPtXuau3MrMJet5fd0O3txQd1B5cZE4fdRAzhxdxsXvGcbZxw9mYJ93k1qVlZVOcpmZmR2Dwya6JPWMiPrOaowlavfs59ofzmHZhloA+pUU8bHJx3HXR06lT0lRjltnZmZmXYVjtdzaubeeP1RtZu7KrSysruHlt7cess15JwzhtHQc1dNGDWRAbyexzMzMsqmtO7peBs4CkPR/I+LL2W9S97SvvpHNdXu54l9fODDo6ND+Jdx43lhuuXAc/Xr55jszMzM7hGO1TlS3t57fvLae2W9u4vnXN7BzX8NB5eOH9+e0ioFcfuoIpowdxND+vXLUUjMzs+6rrexJ5miXF2SzId3V7n0NrNq6i6t/8Ad2pcHSyIG9ue2S8Vw5uYL+TnCZmZlZ6xyrZdHa7bt55rX1zFm+hd9XbWLP/sYDZWV9i7lwzCA+esZIpr5nOMNLe3nGazMzsy6grSxKdEoruqH9DY2s39nIRf9nFptq9wJQUdaHv/7wKVx80jDfwWVmZmbt4Vitg0QEb22sY+Zr63nhrc28smobDY3vdu/w0l5cOH4gH5o0kg+eWu5HEM3MzLqotrIpJ0taRPJr4YnpMun7iIjTs9q6AtTQGLyzZSdf+PdXWbZhNwBD+/finz55BlPGDqJviRNcZmZm1m6O1Y7Bxto9PPbyal54azMvrzx4fK2Ksj6ce8IQPnL6SC4YP5SSnp4EyMzMLB+0lVU5pVNa0Q1EBGtr9vBXTyzi91WbASgS/NsNU5gydhBlfUty3EIzMzPLQ47VjsC++kaeeW0dzy7dwHNLNrCv4d1HEU8eUcr5Jw7litNGcOaYQRT18GOIZmZm+eiwia6IeAdAUhkwIV39ZkTUZLthhWTn3nq+/Zs3eHDOOwfW/fSmKdStWsoHJpbnsGVmZmaWzxyrtW3Z+lr+c8Eafr1oHau27jqwvl9JEZedOpKPnnEcl5w8nOIi37FlZmZWCA6b6JJUAkwHrgLeJrkN/nhJTwGfj4h9bew/DfgeUAT8JCL+oVn58cD9wDBgK/CZiKhOy24C7kw3/VZEPJiuPxt4AOgDzAD+PCK67PgUTy9cy1cemX/g/Q8/fRaTKgYyenBfKje8nsOWmZmZWb47lljtGOO0bwMfBnoAz5HGY5IqgZHA7rSayyJiYwcdbrvsb2jk2SUbeGjOSl56++DHEU8eUcq1Z4/imrNGMaif76Y3MzMrRG09ungnUAyMjohaAEmlwPeBb6SvFkkqSrf7IFANzJX0dEQszdjsO8BDEfGgpEuAe4EbJA0G/gaYQjLI6ivpvtuAHwK3Ai+SJLqmAc8c2WFn34LV2/n8w6+wfsceAO6+8lQmjy7j9FFlOW6ZmZmZFZCjitWOMU47n2SGx6bxv34PXAxUpu8/HRHzOu4Q27a/oZEnX6nmsXmrmb9q+4H1Q/qVcOXkCj56xkgmjy7zrIhmZmbdQFuJro8D50TEgfu8I6JW0hdJEk2tJrqAc4CqiFgBIOlR4EogM4CaCPxFujwL+M90+XLguYjYmu77HDAt/ZVwQETMSdc/RPILZpdJdG3YsYev/cdCVmzayfode7jh3OM5+/hBXHVmRa6bZmZmZoXnaGO1Y4nTAugNlJDcQVYMbOiQozkC23bu45fz1/Af81bzxvraA+tHD+7DJ84ezZ++bwxD+/fq7GaZmZlZjrWV6GrMDJyaRESdpLYeF6wAVme8rwbe12ybhcA1JLfNXw2UShrSyr4V6au6hfU5t7+hkft+V8X81dt54a3NnDNuMNMmjeCvrziFHh7M1MzMzLLjaGO1o47TImKOpFnAOpJE130RkTkew88kNQBPkgw/0WFDTLy6ahtPvFLNs0vWs7nu3acyTxjWj2vOGsVN54+lfy/PYG1mZtadtRUJhKRBJEFMc40trMvU0j7NA52vAfdJuhmYDawB6g+zb3vqTD5cupXkEUfKy8uprKxso7lH781tDbyxtYFfvrWf/sVwwsAe3DBuD6Ule5k9u/VhKerq6rLaLmud+z633P+5477PLfe/ZcHRxmpHHadJGk8y2+OodLvnJF0UEbNJHltckz4++SRwA/DQIR9+BHFadW0jv357H3PWNhy0/tQhPbhoVDFTyotIxpGvZt6c6hbryCf+nshvPn/5zecvv/n8WZO2El0DgVc4ggRThmpgdMb7UcDagyqIWEtyyz2S+gPXRESNpGpgarN9K9M6RzVbf1CdGXVPJxmclSlTpsTUqVNb2uyYNDYGa7bv5uZvzwKgh+AXn7ug3eNwVVZWko12Wdvc97nl/s8d931uuf8tC442VjuWOO1W4MWIqEvLngHOBWZHxJp031pJvyB5RPKQRNfh4rSI4HdvbOThF9+hctmmg/a7cvJxfPmS8YwfXnqYQ8tv/p7Ibz5/+c3nL7/5/FmTwya6ImLsMdQ9F5ggaRzJL4DXAX+auYGkocDWiGgE7iCZ2QdgJvD36S+UAJcBd0TEVkm1ks4FXgJuBP7vMbTxqDU2Bu//biXvbEmeFrjzw6fwyfeOZkDv4lw0x8zMzLqhY4jVjiVOWwV8VtK9JAm2i4F/kdQTKIuIzZKKgY8Av21vg95Yv4PvPvsmzy19d7ivQX2LufSUcm4873hP6GNmZmbtcthEl6TLgdKIeKLZ+j8FNkXEc63tGxH1km4jSVoVAfdHxBJJdwPzIuJpkru27k3HkJgNfCndd6ukvyMJwgDubhqYHvgC8ADQh2QQ+k4fiP4HlVXMWb6Fd7bsYup7hvGR04/jitNG0LfEY0KYmZlZ5znaWO1Y4jTgCeASYDHJXWO/iYj/ktQPmJkmuYpIklw/busY1m7fzYlfn0FDY3IDWs8e4vpzxvCVSycwrNSDyZuZmdmRaSsz87fAR1tY/zvgKaDVRBdARMwAZjRbd1fG8hMkwVJL+97Pu78cZq6fB0xqo91ZsXrrLl5bU8N9v6uiX6+eXHTSML76wfdw2qiBuWiOmZmZ2VHHakcbp0VEA/C5FtbvBM5ub8ObbNm5j5GNwYdPG8kXpp7IpArHVWZmZnb02kp09Y2ITc1XRsT69Fe7buWLP3+VxWtqAPjS+8fzpfePz3GLzMzMrJvL+1jtpPJSFt17BZJnqTYzM7Nj11aiq7eknhFRn7kyvSW9T/aa1bVMn72cX7y0itXbdnPxScO45+pJVJR1m8M3MzOzrivvY7VePXs4yWVmZmYdpkcb5b8Efpz5i2C6/KO0rKBt3bmPGYvX8djc1dTtredT7x3NVy4dz6hBfR2QmZmZWVfQrWM1MzMzs+bauqPrTuBbwDuS3knXjQF+Cnwjmw3rCv7hmdd5fF41AB894zj+/urTctwiMzMzs4N061jNzMzMrLnDJrrS2+Bvl/S3QNOAVFURsTvrLcuh37+1mX/57Zu8uaGWUYP68NjnzqPcs/6YmZlZF9NdYzUzMzOz1hz20UVJ/xsgDZZOjojFTYGTpL/vhPZ1uk21e3lq/hrmr97OeScO4cuXjKeirA89i9p6ytPMzMysc3XHWM3MzMzscNrK3lyXsXxHs7JpHdyWnHvylWree89vefLVaob0K+HfbpjCp947JtfNMjMzM2tNt4rVzMzMzNrS1hhdamW5pfd5a299A5XLNvHb1zcA8E+fPIPTKgbmuFVmZmZmbeoWsZqZmZlZe7WV6IpWllt6n7dmLtnAVx6ZD8Cw0l5cObmCoh6ODc3MzKzL6xaxmpmZmVl7tZXoOkPSDpJfBPuky6Tve2e1ZZ1g5956ps9ewctvbwXgl188n4kjBzjJZWZmZvmioGM1MzMzsyPV1qyLRZ3VkFyYu3Ir33v+LQb2KebMMWVMHDmA3sUFfchmZmZWQAo9VjMzMzM7Um3d0XVMJE0DvgcUAT+JiH9oVj4GeBAoS7e5PSJmSPo08JcZm54OnBURCyRVAiOBpmmzL4uIjUfSrj37G/ibXy1h6brkR88H/79zmDy67IiPz8zMzMzMzMzMuo62Zl08apKKgO8DHwImAtdLmthsszuBxyPiTJJZg34AEBE/j4jJETEZuAFYGRELMvb7dFP5kSa5AN7aUMdj81azc189004dwYnD+h3FEZqZmZmZmZmZWVeSzTu6zgGqImIFgKRHgSuBpRnbBDAgXR4IrG2hnuuBRzqiQQ2NwV8/tfjAnVxf/9ApfGBieUdUbWZmZmZmZmZmOZa1O7qACmB1xvvqdF2mbwKfkVQNzAC+3EI9n+LQRNfPJC2Q9A1J7R45flPtXh6du5q6PfVccdoIzvDjimZmZmZmZmZmBSObd3S1lIBqPs319cADEfFdSecBD0uaFBGNAJLeB+yKiNcy9vl0RKyRVAo8SfJo40OHfLh0K3ArQHl5OX/+42dZXtMIwEXl+5laUcuSV+Yc4yEem7q6OiorK3Pahu7KfZ9b7v/ccd/nlvvfzMzMzCy7spnoqgZGZ7wfxaGPJt4CTAOIiDmSegNDgaZxt66j2d1cEbEm/bdW0i9IHpE8JNEVEdOB6QBnT5kSv1q+n+GlvbhgfBmfuXwS44f3P9bjO2aVlZVMnTo1183oltz3ueX+zx33fW65/83MzMzMsiubjy7OBSZIGiephCRp9XSzbVYBlwJIOgXoDWxK3/cAPgE82rSxpJ6ShqbLxcBHgNdow8YdewC44dzj+fmfndslklxmZmZmZmZmZtaxspboioh64DZgJvA6yeyKSyTdLelj6WZfBT4raSHJnVs3R0TT440XAdVNg9mnegEzJS0CFgBrgB+31ZaNtXsp7d2Tk0cOaGtTMzMzs25D0jRJyyRVSbq9hfLjJT0vaZGkSkmjMsq+LWmJpNcl/WvTuKmSzpa0OK3zwHozMzOzzpDNRxeJiBkkg8xnrrsrY3kpcEEr+1YC5zZbtxM4+0jbMaRfCYu/efmR7mZmZmZWsCQVAd8HPkgy5MRcSU+n8VmT7wAPRcSDki4B7gVukHQ+SQx3errd74GLgUrghyTjpL5IEgdOA57J/hGZmZmZZffRxS6jqId/SDQzMzNr5hygKiJWRMQ+kuEirmy2zUTg+XR5VkZ5kAw5UUJyx30xsEHSSGBARMxJ79J/CLgqu4dhZmZm9q5ukegaPqB3rptgZmZm1tVUAKsz3len6zItBK5Jl68GSiUNiYg5JImvdelrZkS8nu5f3UadZmZmZlmT1UcXuwrfz2VmZmZ2iJZCpGj2/mvAfZJuBmaTjI9aL2k8cArJrNoAz0m6CNjdjjqRdCvJ442Ul5dTWVl5NO0vSHV1de6PPObzl998/vKbz5816RaJLjMzMzM7RDUwOuP9KGBt5gYRsRb4OICk/sA1EVGTJqpejIi6tOwZkrFVH+bd5FeLdab1TgemA0yZMiWmTp3aQYeU/yorK3F/5C+fv/zm85fffP6sSbd4dNHMzMzMDjEXmCBpnKQS4Drg6cwNJA2V1BQv3gHcny6vAi6W1FNSMclA9K9HxDqgVtK56WyLNwK/6oyDMTMzMwMnuszMzMy6pYioB24DZgKvA49HxBJJd0v6WLrZVGCZpDeBcuCedP0TwHJgMck4Xgsj4r/Ssi8APwGq0m0846KZmZl1Gj+6aGZmZtZNRcQMYEazdXdlLD9BktRqvl8D8LlW6pwHTOrYlpqZmZm1j+/oMjMzMzMzMzOzguBEl5mZmZmZmZmZFQQnuszMzMzMzMzMrCA40WVmZmZmZmZmZgXBiS4zMzMzMzMzMysIWU10SZomaZmkKkm3t1A+RtIsSfMlLZJ0Rbp+rKTdkhakrx9l7HO2pMVpnf8qSdk8BjMzMzMzMzMzyw9ZS3RJKgK+D3wImAhcL2lis83uBB6PiDOB64AfZJQtj4jJ6evzGet/CNwKTEhf07J1DGZmZmZmZmZmlj+yeUfXOUBVRKyIiH3Ao8CVzbYJYEC6PBBYe7gKJY0EBkTEnIgI4CHgqo5ttpmZmZmZmZmZ5aOeWay7Alid8b4aeF+zbb4JPCvpy0A/4AMZZeMkzQd2AHdGxAtpndXN6qxo6cMl3Upy5xfl5eVUVlYe9YFkS11dXZdsV3fgvs8t93/uuO9zy/1vZmZmZpZd2Ux0tTR2VjR7fz3wQER8V9J5wMOSJgHrgDERsUXS2cB/Sjq1nXUmKyOmA9MBpkyZElOnTj3Kw8ieyspKumK7ugP3fW65/3PHfZ9b7n8zMzMzs+zKZqKrGhid8X4Uhz6aeAvpGFsRMUdSb2BoRGwE9qbrX5G0HDgprXNUG3WamZmZmZmZmVk3lM0xuuYCEySNk1RCMtj80822WQVcCiDpFKA3sEnSsHQweySdQDLo/IqIWAfUSjo3nW3xRuBXWTwGMzMzMzMzMzPLE1m7oysi6iXdBswEioD7I2KJpLuBeRHxNPBV4MeS/oLkEcSbIyIkXQTcLakeaAA+HxFb06q/ADwA9AGeSV9mZmZmZmZmZtbNZfPRRSJiBjCj2bq7MpaXAhe0sN+TwJOt1DkPmNSxLTUzMzMzMzMzs3yXzUcXzczMzMzMzMzMOo0TXWZmZmZmZmZmVhCc6DIzMzMzMzMzs4LgRJeZmZmZmZmZmRUEJ/0IaSYAABCbSURBVLrMzMzMuiFJ0yQtk1Ql6fYWyo+X9LykRZIqJY1K179f0oKM1x5JV6VlD0h6O6Nscmcfl5mZmXVvWZ110czMzMy6HklFwPeBDwLVwFxJT6czYjf5DvBQRDwo6RLgXuCGiJgFTE7rGQxUAc9m7PeXEfFEZxyHmZmZWXO+o8vMzMys+zkHqIqIFRGxD3gUuLLZNhOB59PlWS2UA1wLPBMRu7LWUjMzM7Mj4ESXmZmZWfdTAazOeF+drsu0ELgmXb4aKJU0pNk21wGPNFt3T/q44z9L6tVRDTYzMzNrDz+6aGZmZtb9qIV10ez914D7JN0MzAbWAPUHKpBGAqcBMzP2uQNYD5QA04G/Au4+5MOlW4FbAcrLy6msrDzKwyg8dXV17o885vOX33z+8pvPnzVxosvMzMys+6kGRme8HwWszdwgItYCHweQ1B+4JiJqMjb5JPBUROzP2GddurhX0s9IkmWHiIjpJIkwpkyZElOnTj2mgykklZWVuD/yl89ffvP5y28+f9bEjy6amZmZdT9zgQmSxkkqIXkE8enMDSQNldQUK94B3N+sjutp9thiepcXkgRcBbyWhbabmZmZtSqria52TFs9RtIsSfPTsRyuSNd/UNIrkhan/16SsU9lWmfTtNXDs3kMZmZmZoUmIuqB20geO3wdeDwilki6W9LH0s2mAsskvQmUA/c07S9pLMkdYf/drOqfS1oMLAaGAt/K4mGYmZmZHSJrjy62c9rqO0kCqx9KmgjMAMYCm4GPRsRaSZNIgrDMAVI/HRHzstV2MzMzs0IXETNIYq/MdXdlLD8BPNHKvis5dPB6IuKSQ7c2MzMz6zzZvKOrPdNWBzAgXR5IOjZERMxPx4UAWAL09qw9ZmZmZmZmZmZ2ONlMdLVn2upvAp+RVE3yi+KXW6jnGmB+ROzNWPez9LHFb6RjQJiZmZmZmZmZWTeXzVkX2zNt9fXAAxHxXUnnAQ9LmhQRjQCSTgX+EbgsY59PR8QaSaXAk8ANwEOHfHgeTFvt6U9zx32fW+7/3HHf55b738zMzMwsu7KZ6Gpz2mrgFmAaQETMkdSbZODSjZJGAU8BN0bE8qYdImJN+m+tpF+QPCJ5SKIrH6at9vSnueO+zy33f+6473PL/W9mZmZmll3ZfHSxzWmrgVXApQCSTgF6A5sklQG/Bu6IiD80bSypp6Sh6XIx8BE8bbWZmZmZmZmZmZHFRFc7p63+KvBZSQuBR4CbIyLS/cYD30jH4logaTjQC5gpaRGwAFgD/Dhbx2BmZmZmZmZmZvkjm48utmfa6qXABS3s9y3gW61Ue3ZHttHMzMzMzMzMzApDNh9dNDMzMzMzMzMz6zROdJmZmZmZmZmZWUFwosvMzMzMzMzMzAqCE11mZmZmZmZmZlYQnOgyMzMzMzMzM7OC4ESXmZmZmZmZmZkVBCe6zMzMzMzMzMysIDjRZWZmZmZmZmZmBcGJLjMzMzMzMzMzKwhOdJmZmZmZmZmZWUFwosvMzMzMzMzMzApCVhNdkqZJWiapStLtLZSPkTRL0nxJiyRdkVF2R7rfMkmXt7dOMzMzM2tbO+K04yU9n8ZolZJGpevfL2lBxmuPpKvSsnGSXpL0lqTHJJV09nGZmZlZ95a1RJekIuD7wIeAicD1kiY22+xO4PGIOBO4DvhBuu/E9P2pwDTgB5KK2lmnmZmZmR1GO2Oq7wAPRcTpwN3AvQARMSsiJkfEZOASYBfwbLrPPwL/HBETgG3ALVk/GDMzM7MM2byj6xygKiJWRMQ+4FHgymbbBDAgXR4IrE2XrwQejYi9EfE2UJXW1546zczMzOzw2hNTTQSeT5dntVAOcC3wTETskiSSxNcTadmDwFUd3nIzMzOzw8hmoqsCWJ3xvjpdl+mbwGckVQMzgC+3sW976jQzMzOzw2tPTLUQuCZdvhoolTSk2TbXAY+ky0OA7RFRf5g6zczMzLKqZxbrVgvrotn764EHIuK7ks4DHpY06TD7tpSYa15n8uHSrcCt6ds6Scva1+xONRTYnOtGdFPu+9xy/+eO+z633P+5855cN6CLaU+c9jXgPkk3A7OBNUBTEgtJI4HTgJlHUGfTvvkQp+WKvyfym89ffvP5y28+f/mtw2K1bCa6qoHRGe9H8e6jiU1uIRmDi4iYI6k3ycV5uH3bqpO0vunA9KNtfGeQNC8ipuS6Hd2R+z633P+5477PLfd/7kial+s2dDFtxmkRsRb4OICk/sA1EVGTsckngaciYn/6fjNQJqlneldXXsdpueLvifzm85fffP7ym89ffuvIWC2bjy7OBSaks++UkNza/nSzbVYBlwJIOgXoDWxKt7tOUi9J44AJwMvtrNPMzMzMDq/NmErSUElNseIdwP3N6riedx9bJCKCZCyva9NVNwG/ykLbzczMzFqVtURX+kvebSS3s79OMrviEkl3S/pYutlXgc9KWkgSKN0ciSXA48BS4DfAlyKiobU6s3UMZmZmZoWonXHaVGCZpDeBcuCepv0ljSW5I+y/m1X9V8D/klRFMmbXT7N4GGZmZmaHUPLjm+WCpFvTW/etk7nvc8v9nzvu+9xy/+eO+97yha/V/Obzl998/vKbz19+68jz50SXmZmZmZmZmZkVhGyO0WVmZmZmZmZmZtZpnOjqRJJWSlosaUHTjAKSBkt6TtJb6b+Dct3OQiDpfkkbJb2Wsa7FvlbiXyVVSVok6azctbwwtNL/35S0Jr3+F0i6IqPsjrT/l0m6PDetLgySRkuaJel1SUsk/Xm63td/lh2m733tdwJJvSW9LGlh2v9/m64fJ+ml9Np/LB14nXTCm8fS/n8pHXPKrMN1VEwi6aZ0+7ck3ZSLY+mOOiqmkTQtXVcl6fbOPo7uqiPjIv8Ndr6OjK38N9j5OjI2O+KYOSL86qQXsBIY2mzdt4Hb0+XbgX/MdTsL4QVcBJwFvNZWXwNXAM8AAs4FXsp1+/P91Ur/fxP4WgvbTgQWAr2AccByoCjXx5CvL2AkcFa6XAq8mfaxr//c9b2v/c7pfwH90+Vi4KX0mn4cuC5d/yPgC+nyF4EfpcvXAY/l+hj8KsxXR8QkwGBgRfrvoHR5UK6PrTu8OiKmSV/LgROAknSbibk+tu7w6qi4yH+DXe78+W8wD14dFZu1dl4P99m+oyv3rgQeTJcfBK7KYVsKRkTMBrY2W91aX18JPBSJF4EySSM7p6WFqZX+b82VwKMRsTci3gaqgHOy1rgCFxHrIuLVdLmWZDa1Cnz9Z91h+r41vvY7UHoN16Vvi9NXAJcAT6Trm1/7TX8TTwCXSlInNde6kQ6KSS4HnouIrRGxDXgOmJb91lsHxTTnAFURsSIi9gGPpttalnVgXOS/wRzowNjKf4M50IGx2RHHzE50da4AnpX0iqRb03XlEbEOkj9kYHjOWlf4WuvrCmB1xnbVHP4L1I7ebelt4Pfr3cd03f9Zkt7ueybJrye+/jtRs74HX/udQlKRpAXARpL/CVkObI+I+nSTzD4+0P9peQ0wpHNbbN3YkX4n+/ui6zmS73Wfvy7gGOMin8McO8bYyucvRzooNjvi8+dEV+e6ICLOAj4EfEnSRblukAHJLZXNeTrSjvdD4ERgMrAO+G663v2fBZL6A08C/zMidhxu0xbWuf+PQQt972u/k0REQ0RMBkaR/NJ3Skubpf+6/60rau269PXatRzp97rPX451QFzkc5hDHRBb+fzlSAfFZkd8/pzo6kQRsTb9dyPwFMmJ3tD0mFD678bctbDgtdbX1cDojO1GAWs7uW0FLyI2pF90jcCPefd2U/d/B5NUTBIM/Dwifpmu9vXfCVrqe1/7nS8itgOVJONAlEnqmRZl9vGB/k/LB9L+x5PMjtWRfif7+6ILOYrvdZ+/HOqguMjnMEc6KLby+cuxY4zNjvj8OdHVSST1k1TatAxcBrwGPA00zdpxE/Cr3LSwW2itr58GbkxnWTkXqGm6ldk6TrNxn64muf4h6f/r0lk2xgETgJc7u32FIn2O/afA6xHxTxlFvv6zrLW+97XfOSQNk1SWLvcBPkAylscs4Np0s+bXftPfxLXA7yId8dSsExzpd/JM4DJJg9JHdC5L11kOHMX3+lxgQjrTWAnJIMtPd2abu6sOjIv8N5gDHRhb+W8wBzowNjvimLnn4QqtQ5UDT6Xj3PYEfhERv5E0F3hc0i3AKuATOWxjwZD0CDAVGCqpGvgb4B9oua9nkMywUgXsAv5Hpze4wLTS/1MlTSa5zXQl8DmAiFgi6XFgKVAPfCkiGnLR7gJxAXADsDh9Hh7g6/j67wyt9f31vvY7xUjgQUlFJD/kPR4R/0/SUuBRSd8C5pMEzKT/PiypiuTXwuty0WgrfB0Rk0TEVkl/R/I/awB3R4TvQOwEHRXTSLqNJDFSBNwfEUs6+VC6qw6Ji/w3mDMdFlv5bzAnOiQ2O5qYWf7x0szMzMzMzMzMCoEfXTQzMzMzMzMzs4LgRJeZmZmZmZmZmRUEJ7rMzMzMzMzMzKwgONFlZmZmZmZmZmYFwYkuMzMzMzMzMzMrCE50mVmnk9QgaYGk1yT9h6S+6fq6Dqp/hKRHJS2XtFTSDEkndUTdGZ8xVdL5HVmnmZmZWVfgWM3M8pkTXWaWC7sjYnJETAL2AZ/vqIolCXgKqIyIEyNiIvB1oLyjPiM1FXDwZGZmZoXIsZqZ5S0nusws114AxmeukNRf0vOSXpW0WNKV6fq/k/TnGdvdI+krzep7P7A/In7UtCIiFkTEC0r8n/TXycWSPpXWM1XS/8uo9z5JN6fLKyX9bUZbTpY0liTg+4v0184/kfSJtN6FkmZ3ZAeZmZmZ5ZBjNTPLKz1z3QAz674k9QQ+BPymWdEe4OqI2CFpKPCipKeBnwK/BL4nqQdwHXBOs30nAa+08pEfByYDZwBDgbntDHQ2R8RZkr4IfC0i/kzSj4C6iPhOeiyLgcsjYo2ksnbUaWZmZtalOVYzs3zkO7rMLBf6SFoAzANWkQRFmQT8vaRFwG+BCqA8IlYCWySdCVwGzI+ILUfwuRcCj0REQ0RsAP4beG879vtl+u8rwNhWtvkD8ICkzwJFR9AmM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<h3 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h3><div class="alert alert-warning">
The *Total Plays* values range is from 1 to 1 to 2502130 as shown using the *describe* method from Pandas Data Frames. The table gives us additional information about the values, considering the set percentiles.<br><br>
From the <b>first graph</b> we can notice something similar (but more accentuated) to what we have considered before about the data distribution: in a more detailed analysis, zooming in we can see that:
<ul>
    <li>the 80% of the artists seem to be very unknown to the users, because they have a number of play counts lower than 15 (0.0006% wrt the maximum counter value)</li>
    <li>the 99% of the artists seem to be unknown too! Their play counters are lower than 1400 (0.056% wrt the maximum counter value)</li>
</ul>

<h3>The problem</h3>
What we can see from the graphs is actually something we are aware of: artistIDs are not consistent. We are plenty of aliases, that should be remapped in order to obtain a more correct data distribution. We will repeat the analysis later, after the remapping process, in order to understand if there are some outliers and how effective the aliases dataset we have is.
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<h4 id="Question-2.4">Question 2.4<a class="anchor-link" href="#Question-2.4">&#182;</a></h4><div class="alert alert-info">
Plot a bar chart to show top 5 artists In terms of absolute play counts.  

Comment the figure you just obtained: 
<ul>
<li>are these reasonable results?</li>
<li>is looking at top-5 artists enough to learn more about your data?</li>
<li>do you see anything strange in the data?</li>
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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># retrieve the most listened artists</span>
<span class="n">sortedArtist</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">artistPopularity</span><span class="p">,</span> <span class="n">key</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]</span>

<span class="n">artistID</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">sortedArtist</span><span class="p">]</span>

<span class="n">y_pos</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sortedArtist</span><span class="p">))</span>
<span class="n">frequency</span> <span class="o">=</span> <span class="p">[</span><span class="n">w</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">sortedArtist</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">frequency</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">artistID</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Play Count&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Artist&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Top-10 Artist ID per play counts&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h3 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h3><div class="alert alert-warning">
Altough these data may not be representative (because of the presence of artistID aliases), counter values are reasonable. There exist several single songs (eg: YouTube music videos) that have been listened to more than 2.5 million times.<br>
Anyway, looking at the top-5 artists is not enough to understand how data are distributed: in order to do that, the graphs we did before (cumulative density functions) are more useful. Also, the top-5 does not allow us to compute expectations (mean, variance... etc).
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<h2 id="Correlating-most-active-users-and-most-popular-artists">Correlating most active users and most popular artists<a class="anchor-link" href="#Correlating-most-active-users-and-most-popular-artists">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we are considering outliers those user which are considered to be strangely active in the system (up to 10e5) plays</span>
<span class="n">userOutliers_DF</span> <span class="o">=</span> <span class="n">userActivity_DF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">userActivity_DF</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">100000</span><span class="p">)</span>

<span class="c1"># we are considering outliers (or say very popular artists) those artists which total play counts in the system is up to 10e6</span>
<span class="n">artistOutliers_DF</span> <span class="o">=</span> <span class="n">artistPopularity_DF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">artistPopularity_DF</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1000000</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">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">df</span> <span class="o">=</span> <span class="p">(</span>  
    <span class="n">userArtistDF</span> 
    <span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">userOutliers_DF</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;userID&quot;</span><span class="p">])</span>
    <span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">artistOutliers_DF</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;artistID&quot;</span><span class="p">])</span>
    <span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="o">.</span><span class="n">toPandas</span><span class="p">()</span>
<span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">,</span> <span class="s2">&quot;artistID&quot;</span><span class="p">,</span> <span class="s2">&quot;playCount&quot;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">16</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Heatmap for showing the number of plays given ArtistID and UserID&quot;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=.</span><span class="mi">1</span><span class="p">)</span>
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<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fdd6294da90&gt;</pre>
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<h3 id="Comments-to-our-results">Comments to our results<a class="anchor-link" href="#Comments-to-our-results">&#182;</a></h3><div class="alert alert-warning">
We have now considered the *most active users* (as defined in this context) and the *most listened artists* (as well, as defined in this context): for plotting reasons, we can only show a very strict amount of the population. <br>
To see if there exists a correletion between the *most active users* and the *most popular artists* we crossed (or let's say: joint) the two statistics, focusing our attention to those users which are above a given threshold of plays and for which the played artists was one among the most popular (defined also with a threshold). 

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What came out is that only for a given user, whose id is "2064012", there is actually a strange value which is 4 times above the ones for the most active users. Curiosly, the ID is not the one of the user which has the highest number of plays in the system.
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Finally, we can observe from the heatmap that the *most active users* do not have all plays for a given artists above our set threshold, indeed only few present this characteristic of having an high number of plays for many *famous artists*. Clearly, for the seek of raccomendation, users which like most of the artist do not represent a good comaprative model for suggestion to new users.
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<p>All seems clear right now, but ... wait a second! What about the problems indicated above about artist "disambiguation"? Are these artist ID we are using referring to unique artists? How can we make sure that such "opaque" identifiers point to different bands? Let's try to use some additional dataset to answer this question:  <code>artist_data.txt</code> dataset. This time, the schema of the dataset consists in:</p>

<pre><code>artist ID: long int
name: string</code></pre>
<p>We will try to find whether a single artist has two different IDs.</p>

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<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><h4 id="Question-3.1">Question 3.1<a class="anchor-link" href="#Question-3.1">&#182;</a></h4><div class="alert alert-info">
Load the data from `/datasets/lastfm/artist_data.txt` and use the SparkSQL API to show 5 samples.  

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<div class="label label-success">HINT:</div> If you encounter some error when parsing lines in data because of invalid entries, parameter `mode='DROPMALFORMED'` will help you to eliminate these entries. The suggested syntax is: `<df>.options(header='false', delimiter='\t', mode='DROPMALFORMED')`.

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customSchemaArtist</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">([</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;name&quot;</span><span class="p">,</span> <span class="n">StringType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">)])</span>

<span class="n">artistDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">read</span> \
    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;com.databricks.spark.csv&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">options</span><span class="p">(</span><span class="n">header</span><span class="o">=</span><span class="s1">&#39;false&#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">mode</span><span class="o">=</span><span class="s1">&#39;DROPMALFORMED&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;artist_data.txt&quot;</span><span class="p">,</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">customSchemaArtist</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">cache</span><span class="p">()</span>
    
<span class="n">artistDF</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre>+--------+--------------------+
|artistID|                name|
+--------+--------------------+
| 1134999|        06Crazy Life|
| 6821360|        Pang Nakarin|
|10113088|Terfel, Bartoli- ...|
|10151459| The Flaming Sidebur|
| 6826647|   Bodenstandig 3000|
+--------+--------------------+
only showing top 5 rows

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<h4 id="Question-3.2">Question 3.2<a class="anchor-link" href="#Question-3.2">&#182;</a></h4><div class="alert alert-info">
Find 20 artists whose name contains `Aerosmith`. Take a look at artists that have ID equal to `1000010` and `2082323`. In your opinion, are they pointing to the same artist?  

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<div class="label label-success">HINT:</div> Function `locate(sub_string, string)` can be useful in this case.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get artists whose name contains &quot;Aerosmith&quot;</span>
<span class="n">artistDF</span><span class="p">[</span><span class="n">locate</span><span class="p">(</span><span class="s2">&quot;Aerosmith&quot;</span><span class="p">,</span> <span class="n">artistDF</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>

<span class="c1"># show two examples</span>
<span class="n">artistDF</span><span class="p">[</span><span class="n">artistDF</span><span class="o">.</span><span class="n">artistID</span><span class="o">==</span><span class="mi">1000010</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">artistDF</span><span class="p">[</span><span class="n">artistDF</span><span class="o">.</span><span class="n">artistID</span><span class="o">==</span><span class="mi">2082323</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+--------------------+
|artistID|                name|
+--------+--------------------+
|10586006|Dusty Springfield...|
| 6946007|    Aerosmith/RunDMC|
|10475683|Aerosmith: Just P...|
| 1083031|    Aerosmith/ G n R|
| 6872848|Britney, Nsync, N...|
|10586963|Green Day - Oasis...|
|10028830|The Aerosmith Ant...|
|10300357| Run-DMC + Aerosmith|
| 2027746|Aerosmith by Musi...|
| 1140418|[rap]Run DMC and ...|
|10237208| Aerosmith + Run DMC|
|10588537|Aerosmith, Kid Ro...|
| 9934757|Aerosmith - Big Ones|
|10437510|Green Day ft. Oas...|
| 6936680| RUN DNC &amp; Aerosmith|
|10479781|      Aerosmith Hits|
|10114147|Charlies Angels -...|
| 1262439|Kid Rock, Run DMC...|
| 7032554|Aerosmith &amp; Run-D...|
|10033592|          Aerosmith?|
+--------+--------------------+
only showing top 20 rows

+--------+---------+
|artistID|     name|
+--------+---------+
| 1000010|Aerosmith|
+--------+---------+

+--------+------------+
|artistID|        name|
+--------+------------+
| 2082323|01 Aerosmith|
+--------+------------+

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</div>cleaningVector = ["feat.", "unknown", "featuring", "feat"]
artistDF.filter(lambda s : any(x in s for x in cleaningVector))
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">artistDF</span><span class="p">[</span><span class="n">artistDF</span><span class="o">.</span><span class="n">artistID</span><span class="o">==</span><span class="mi">1000311</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+-------------+
|artistID|         name|
+--------+-------------+
| 1000311|Steve Winwood|
+--------+-------------+

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
It looks like they point to the same artist: what we can do is to browse the "artist_alias" dataset in order to confirm this idea.
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<p>To answer this question correctly, we need to use an additional dataset <code>artist_alias.txt</code> which contains the ids of mispelled artists and standard artists. The schema of the dataset consists in:</p>

<pre><code>mispelledID ID: long int
standard ID: long int</code></pre>

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<h4 id="Question-3.3">Question 3.3<a class="anchor-link" href="#Question-3.3">&#182;</a></h4><div class="alert alert-info">
Using SparkSQL API, load the dataset from `/datasets/lastfm/artist_alias.txt` then show 5 samples.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customSchemaArtistAlias</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">([</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;misspelledID&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span> <span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;standardID&quot;</span><span class="p">,</span> <span class="n">LongType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">)])</span>

<span class="n">artistAliasDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">read</span> \
    <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;com.databricks.spark.csv&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">options</span><span class="p">(</span><span class="n">header</span><span class="o">=</span><span class="s1">&#39;false&#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">mode</span><span class="o">=</span><span class="s1">&#39;DROPMALFORMED&#39;</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;artist_alias.txt&quot;</span><span class="p">,</span> <span class="n">schema</span> <span class="o">=</span> <span class="n">customSchemaArtistAlias</span><span class="p">)</span> \
    <span class="o">.</span><span class="n">cache</span><span class="p">()</span>

<span class="n">artistAliasDF</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre>+------------+----------+
|misspelledID|standardID|
+------------+----------+
|     1092764|   1000311|
|     1095122|   1000557|
|     6708070|   1007267|
|    10088054|   1042317|
|     1195917|   1042317|
+------------+----------+
only showing top 5 rows

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<h3 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h3><div class="alert alert-warning">
What if the aliases dataset presents some *errors* or *inconsistencies*?
<ul>
    <li><b>misspelledIDs</b> that do not exist in the original datasets are just ignored (so, they do not interfere)</li>
    <li><b>standardIDs</b> that do not exist in the original datasets make the misspelledIDs to be remapped anyway</li>
</ul>
<br>
Now: we are not able to state if a missing standardID is something wrong (invalid reference) or something that exist but has never been used in the original dataset. For example, it could be an artistID "artificially" created in order to make several aliases to be remapped in the same correct way.
<br><br>
The <u>conclusion</u> is: we will use the aliases dataset as a reference, assuming that it has been previously checked and provided by us without errors.
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<h4 id="Question-3.4">Question 3.4<a class="anchor-link" href="#Question-3.4">&#182;</a></h4><div class="alert alert-info">
Verify the answer of question 3.2 ("Are artists that have ID equal to `1000010` and `2082323` the same ?") by finding the standard ids corresponding to the mispelled ids `1000010` and `2082323` respectively.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">artistAliasDF</span><span class="p">[</span><span class="n">artistAliasDF</span><span class="o">.</span><span class="n">misspelledID</span><span class="o">==</span><span class="mi">1000010</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="n">artistAliasDF</span><span class="p">[</span><span class="n">artistAliasDF</span><span class="o">.</span><span class="n">misspelledID</span><span class="o">==</span><span class="mi">2082323</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>

<span class="c1"># 1000010 is a standard id, so it haven&#39;t been considered as mispelled id in the dataset</span>
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<pre>+------------+----------+
|misspelledID|standardID|
+------------+----------+
+------------+----------+

+------------+----------+
|misspelledID|standardID|
+------------+----------+
|     2082323|   1000010|
+------------+----------+

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<h3 id="Answer-3.4">Answer 3.4<a class="anchor-link" href="#Answer-3.4">&#182;</a></h3><div class="alert alert-warning">
From the result we can see that the two IDs belong to the same artist. As stated, the id "1000010" is already a standard ID and not a misspelled one (in fact, we do not have results in the first lookup).
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<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><p>The mispelled or nonstandard information about artist make our results in the previous queries a bit "sloppy". To overcome this problem, we can replace all mispelled artist ids by the corresponding standard ids and re-compute the basic descriptive statistics on the "amended" data.
First, we construct a "dictionary" that maps non-standard ids to a standard ones. Then this "dictionary" will be used to replace the mispelled artists.</p>
<h4 id="Question-4.1">Question 4.1<a class="anchor-link" href="#Question-4.1">&#182;</a></h4><div class="alert alert-info">
From data in the dataframe loaded from `/datasets/lastfm/artist_alias.txt`, construct a dictionary that maps each non-standard id to its standard id.  

<div class="label label-success">HINT:</div> Instead of using function `collect`, we can use `collectAsMap` to convert the collected data to a dictionary inline.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># because we are considering to map the mispelled to the standardID for the artist, we have only to convert the DataFrame in RDD and ask for a</span>
<span class="c1"># collectAsMap which return what we are looking for. The dictionary will be used then to clean the DataFrame with all standardIDs.</span>
<span class="n">artistAlias</span> <span class="o">=</span> <span class="n">artistAliasDF</span><span class="o">.</span><span class="n">rdd</span><span class="o">.</span><span class="n">collectAsMap</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">artistAlias</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    <span class="n">v</span> <span class="o">=</span> <span class="n">artistAlias</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
    <span class="n">old_v</span> <span class="o">=</span> <span class="n">v</span>

    <span class="k">while</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">artistAlias</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="c1"># the standard ID is not an entry in the dictionary anymore</span>
        <span class="n">v</span> <span class="o">=</span> <span class="n">artistAlias</span><span class="p">[</span><span class="n">v</span><span class="p">]</span>
        <span class="k">if</span><span class="p">(</span><span class="n">v</span> <span class="o">==</span> <span class="n">old_v</span><span class="p">):</span>
            <span class="c1"># avoid the infinite loop</span>
            <span class="k">break</span>
        <span class="n">old_v</span> <span class="o">=</span> <span class="n">v</span>
    <span class="c1"># remap the alias value</span>
    <span class="n">artistAlias</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
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<h4 id="Question-4.2">Question 4.2<a class="anchor-link" href="#Question-4.2">&#182;</a></h4><div class="alert alert-info">
Using the constructed dictionary in question 4.1, replace the non-standard artist ids in the dataframe that was loaded from `/datasets/lastfm/user_artist_data.txt` by the corresponding standard ids then show 5 samples.
</div><p><strong>NOTE 1</strong>: If an id doesn't exist in the dictionary as a mispelled id, it is really a standard id.</p>
<p>Using funtion <code>map</code> on Spark Dataframe will give us an RDD. We can convert this RDD back to Dataframe by using <code>sqlContext.createDataFrame(rdd_name, sql_schema)</code></p>
<p><strong>NOTE 2</strong>: be careful! you need to be able to verify that you indeed solved the problem of having bad artist IDs. In principle, for the new data to be correct, we should have duplicate pairs (user, artist), potentially with different play counts, right? In answering the question, please <strong>show</strong> that you indeed fixed the problem.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>

<span class="k">def</span> <span class="nf">replaceMispelledIDs</span><span class="p">(</span><span class="n">fields</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Convert the artist ID to the standardID. If the dictionary has not the key, the default value is the standardID&quot;&quot;&quot;</span>
    <span class="n">finalID</span> <span class="o">=</span> <span class="n">artistAlias</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">fields</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">fields</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">fields</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">finalID</span><span class="p">,</span> <span class="n">fields</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>

<span class="c1"># we want to remap all the aliases</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">newUserArtistDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
    <span class="n">userArtistDF</span><span class="o">.</span><span class="n">rdd</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">replaceMispelledIDs</span><span class="p">),</span> 
    <span class="n">userArtistDataSchema</span>
<span class="p">)</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>

<span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The script takes </span><span class="si">%f</span><span class="s1"> seconds&#39;</span> <span class="o">%</span><span class="p">(</span><span class="n">t1</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
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<pre>+-------+--------+---------+
| userID|artistID|playCount|
+-------+--------+---------+
|1000002|       1|       55|
|1000002| 1000006|       33|
|1000002| 1000007|        8|
|1000002| 1000009|      144|
|1000002| 1000010|      314|
+-------+--------+---------+
only showing top 5 rows

The script takes 0.729175 seconds
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<h3 id="Proof-of-&quot;bad-artist-IDs&quot;-solved">Proof of "bad artist IDs" solved<a class="anchor-link" href="#Proof-of-&quot;bad-artist-IDs&quot;-solved">&#182;</a></h3><div class="alert alert-warning">
To prove that the dictionary correctly solved the problem of misspelled IDs we decided to join the artistID we have mapped as *cleaned* with the dataFrame *artistAliasDF* (which contains the mapping between the mispelledID and standardID). 
<br><br>
What we obtain is a dataFrame with two columns: then, if the IDs are the same in both columns we esclude them (the IDs marked as "misspelled" was already the corrected one). <br>
<br>
Finally, if the final dataFrame is empty (after the filter) we indeed succeded in cleaning the data
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we use a join operation to asses if the misspelledID for artists have been cleaned correctly</span>
<span class="c1"># first we take only the cleaned IDs for artists removing the duplicates</span>
<span class="n">clenaedArtsitID</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span>

<span class="c1"># then we joind the two RDDs, we will observe that the result is not an empty RDD, but we have entries </span>
<span class="c1"># for which the misspelledID is the same of the standardID</span>
<span class="n">proofDF</span> <span class="o">=</span> <span class="n">artistAliasDF</span><span class="o">.</span><span class="n">withColumnRenamed</span><span class="p">(</span><span class="s2">&quot;misspelledID&quot;</span><span class="p">,</span> <span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">clenaedArtsitID</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span>
<span class="n">proofDF</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+----------+
|artistID|standardID|
+--------+----------+
| 1008798|   1008798|
| 1013851|   1013851|
| 1167860|   1167860|
|     754|       754|
| 1006322|   1006322|
| 1006347|   1006347|
|      15|        15|
| 1014609|   1014609|
| 1018110|   1018110|
| 1014175|   1014175|
| 1001864|   1001864|
+--------+----------+

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we prepare a DF containing the cleanedID and Alias for the artists</span>
<span class="n">artistAliasCleanedDF</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">artistDF</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span>
<span class="n">artistAliasCleanedDF</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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<pre>+--------+-----------------+
|artistID|             name|
+--------+-----------------+
|      26|    Barry Adamson|
|     964|        Jellyfish|
|    1677|         Neoangin|
|    1697|          Shantel|
|    1950|Lordz Of Brooklyn|
|    2040|           Hi-way|
|    2214|           Sun Ra|
|    2250|     Maru + Comix|
|    2509|             Simm|
|    2927|       Emergency!|
+--------+-----------------+
only showing top 10 rows

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># finally, we filter the occurencies where the misspelledID is also present as standardID. </span>
<span class="c1"># Counting the number of occurencies, if zero, the misspelled artist IDs have been cleaned</span>
<span class="n">proofDF</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">col</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span> <span class="o">!=</span> <span class="n">col</span><span class="p">(</span><span class="s2">&quot;standardID&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>0</pre>
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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
We have just demonstrated that there are no more known aliases to be remapped. Let us now repeat the data distribution analysis, to see what has actually changed
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># a more complete analysis, considering the new data distribution</span>

<span class="c1"># group by artist, in order to have aggregate counters</span>
<span class="n">newArtistPopularity_DF</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s1">&#39;PlayCount&#39;</span><span class="p">)</span>
<span class="n">newArtistPopularity</span> <span class="o">=</span> <span class="n">newArtistPopularity_DF</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>

<span class="c1"># retrieve the cdf</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">newArtistPopularity</span><span class="p">)</span>
<span class="n">Y</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">yvals</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span><span class="o">/</span><span class="nb">float</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">Y</span><span class="p">))</span>
<span class="c1">#print(np.arange(len(Y)))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">18</span><span class="p">))</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 7: ECDF of number of play counts per Artist ID &quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">321</span><span class="p">)</span>

<span class="c1"># figure considering the different percentiles</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 8: ECDF of number of play counts per Artist ID - 0.25 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">322</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">8</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 9: ECDF of number of play counts per Artist ID - 0.95 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">323</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">95</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">30</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>
<span class="n">plotECDF</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="n">yvals</span><span class="p">,</span> <span class="s2">&quot;Figure 10: ECDF of number of play counts per Artist ID - 0.99 percentile&quot;</span><span class="p">,</span> <span class="n">subplot</span><span class="o">=</span><span class="mi">324</span><span class="p">,</span> <span class="n">line</span><span class="o">=.</span><span class="mi">99</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">700</span><span class="p">,</span> <span class="mi">3000</span><span class="p">,</span> <span class="o">.</span><span class="mi">97</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="p">)</span>


<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
Although we were expecting to have a more uniform distribution of artists, this analysis clearly show that the situation is very similar to the previous one. Something has changed, but we still have lot of artists that look to be "not popular" at all.
<br><br>
Anyway: having a look to those artists, characterized by lower *PlayCount* values, we can actually confirm our idea. Most of them are not artists! They are song titles, featurings and missing or wrong values (like "unknown"). Later, we will try to clean the dataset from these values
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<h4 id="Question-4.3">Question 4.3<a class="anchor-link" href="#Question-4.3">&#182;</a></h4><div class="alert alert-info">
Spark actions are executed through a set of stages, separated by distributed "shuffle" operations. Spark can be instructed to **automatically and efficiently** broadcast common data needed by tasks within **each stage**. The data broadcasted this way is cached in **serialized form** and deserialized before running each task.   
<ul> </ul>
We can thus improve our answer to question 4.2: we can reduce the communication cost by shipping the "dictionary" in a more efficient way by using `broadcast variable`. Broadcast variables allow the programmer to keep a read-only variable cached on **each machine** rather than shipping a copy of it with tasks. They are cached in deserialized form. They can be used, for example, to give every node a copy of a large input dataset in an efficient manner.   
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The broadcast of variable `v` can be created by `bV = sc.broadcast(v)`. Then value of this broadcast variable can be access via `bV.value`  

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To question is then: using a broadcast variable, modify the script in question 4.2 to get better performance in terms of running time.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>

<span class="n">bArtistAlias</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">artistAlias</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">replaceMispelledIDs</span><span class="p">(</span><span class="n">fields</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Convert the artist ID to the standardID. If the dictionary has not the key, the default value is the standardID&quot;&quot;&quot;</span>
    <span class="n">finalID</span> <span class="o">=</span> <span class="n">bArtistAlias</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">fields</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">fields</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">fields</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">finalID</span><span class="p">,</span> <span class="n">fields</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>

<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">newUserArtistDF</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span>
    <span class="n">userArtistDF</span><span class="o">.</span><span class="n">rdd</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">replaceMispelledIDs</span><span class="p">),</span> 
    <span class="n">userArtistDataSchema</span>
<span class="p">)</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>

<span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The script takes </span><span class="si">%f</span><span class="s1"> seconds&#39;</span> <span class="o">%</span><span class="p">(</span><span class="n">t1</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
<span class="n">newUserArtistDF</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
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<pre>+-------+--------+---------+
| userID|artistID|playCount|
+-------+--------+---------+
|1000002|       1|       55|
|1000002| 1000006|       33|
|1000002| 1000007|        8|
|1000002| 1000009|      144|
|1000002| 1000010|      314|
+-------+--------+---------+
only showing top 5 rows

The script takes 0.017033 seconds
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<p>Although having some advantages, explicitly creating broadcast variables is only useful when tasks across multiple stages need the same data or when caching the data in deserialized form is important.</p>

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<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><p>Well, our data frame contains clean and "standard" data. We can use it to redo previous statistic queries.</p>
<h4 id="Question-5.1">Question 5.1<a class="anchor-link" href="#Question-5.1">&#182;</a></h4><div class="alert alert-info">
How many unique artists? Compare with the result when using old data.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">uniqueArtists</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total n. of artists: &quot;</span><span class="p">,</span> <span class="n">uniqueArtists</span><span class="p">)</span>
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<pre>Total n. of artists:  1568126
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<h3 id="Answer-5.1">Answer 5.1<a class="anchor-link" href="#Answer-5.1">&#182;</a></h3><div class="alert alert-warning">
The previous number is: 1631028
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<h4 id="Question-5.2">Question 5.2<a class="anchor-link" href="#Question-5.2">&#182;</a></h4><div class="alert alert-info">
Who are the top-10 artistis?
<ul>
  <li>In terms of absolute play counts</li>
  <li>In terms of "audience size", that is, how many users listened to one of their track at least once</li>
</ul>  

Plot the results, and explain the figures you obtain.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># calculate top-10 artists in term of play counts</span>
<span class="n">top10ArtistsPC</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;sum(playCount)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>

<span class="n">y_pos</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top10ArtistsPC</span><span class="p">))</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">top10ArtistsPC</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Play Count&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Artist&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Top-10 Artist ID per play counts&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[173]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># calculate the audience size</span>
<span class="n">top10ArtistsAS</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s1">&#39;artistID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="n">countDistinct</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;count(DISTINCT userID)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>

<span class="n">y_pos</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top10ArtistsAS</span><span class="p">))</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">top10ArtistsAS</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Users&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Artist&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Top-10 Artist ID per audience size&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<h4 id="Question-5.3">Question 5.3<a class="anchor-link" href="#Question-5.3">&#182;</a></h4><div class="alert alert-info">
Who are the top-10 users?
<ul>
  <li>In terms of absolute play counts</li>
  <li>In terms of "curiosity", that is, how many different artists they listened to</li>

</ul>  

Plot the results
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># calculate top 10 users interm of play counts</span>
<span class="n">top10UsersByPlayCount</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;sum(playCount)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>

<span class="n">y_pos</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top10UsersByPlayCount</span><span class="p">))</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">top10UsersByPlayCount</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Play Count&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;User&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Top-10 Users ID per play counts&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># calculate top 10 users interm of curiosity</span>
<span class="n">userByCuriosityDF</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s1">&#39;userID&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">(</span><span class="n">countDistinct</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">orderBy</span><span class="p">(</span><span class="s1">&#39;count(DISTINCT artistID)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">top10UsersByCuriosity</span> <span class="o">=</span> <span class="n">userByCuriosityDF</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="n">y_pos</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">top10UsersByCuriosity</span><span class="p">))</span>
<span class="n">pdf</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">data</span><span class="o">=</span><span class="n">top10UsersByCuriosity</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">plt</span><span class="o">.</span><span class="n">barh</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</span><span class="p">[</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">align</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">y_pos</span><span class="p">,</span> <span class="n">pdf</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">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Artist&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Users&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Top-10 User ID per curiosity&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Now we have some valuable information about the data. It's the time to study how to build a statistical models.</p>

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<p><hr></p>
<h2 id="Low-popularity-artists">Low popularity artists<a class="anchor-link" href="#Low-popularity-artists">&#182;</a></h2><p>Having a look to the dataset, we can notice that several artists are actually something else (for example: song titles, unknown values or featurings). Because most of this wrong values are "artists" with small associated playCount values, we think that we could be able to clean the dataset removing the artists whose total playCount is under a given value (for example, 10).</p>
<p>...But, unfortunately, there is a problem: those artists represent the majority! (more than 75%)</p>
<p>So, we need to think a strategy to implement. Our opinion, for the moment, is that artists whose songs have been listened to very few times are not relevant: although we may have "cold start" and an artist-discovery more focused on famous ones, the wrong/missing data we are going to delete will lead us to better results.</p>
<p>It is also true that:</p>
<ul>
<li>recommend most famous artists is something acceptable from the user-experience point of view: it works the majority of times</li>
<li>we cannot state a-priori if an artist listened only one time by an user has been appreciated or not. It could be that the user has listened to him/her once, has disliked the song and never listened to that artist again</li>
</ul>
<p>We will later check if, removing these low-popularity artists, we could be able to create better recommendation systems.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">playCountPerArtistDF</span> <span class="o">=</span> <span class="n">newUserArtistDF</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">)</span>
<span class="n">number_of_artists</span> <span class="o">=</span> <span class="n">playCountPerArtistDF</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">filterNonPopularArtists</span><span class="p">(</span><span class="n">numberOfPlays_threshold</span><span class="p">,</span> <span class="n">complement</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    This function removes the artists from the dataset whose songs have been listened to (in total) fewer times than what the threshold specifies.</span>
<span class="sd">    An extension of this function allows to retrieve the complement of the data set (that is, the PopularArtsits) define using the threshold. </span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">complement</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">playCountPerArtistDF</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="s2">&quot;sum(playCount) &gt; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">numberOfPlays_threshold</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">playCountPerArtistDF</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="s2">&quot;sum(playCount) &lt; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">numberOfPlays_threshold</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the complement of non-popular artists ratio</span>
<span class="n">filterNonPopularArtists</span><span class="p">(</span><span class="mi">11</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span><span class="o">/</span><span class="n">number_of_artists</span>
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<pre>0.23747581508118606</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">popularArtistsDF</span> <span class="o">=</span> <span class="n">filterNonPopularArtists</span><span class="p">(</span><span class="mi">11</span><span class="p">)</span>
<span class="c1"># show the association between the artistID and the name</span>
<span class="n">popularArtistsDF</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">artistDF</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;artistID&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+--------------+--------------------+
|artistID|sum(playCount)|                name|
+--------+--------------+--------------------+
|      26|          7895|       Barry Adamson|
|     964|          8482|           Jellyfish|
|    1677|          1372|            Neoangin|
|    1697|          4391|             Shantel|
|    1950|           879|   Lordz Of Brooklyn|
|    2214|         17567|              Sun Ra|
|    2509|            99|                Simm|
|    2927|            55|          Emergency!|
|    3091|            20|  PQM featuring Cica|
|    5385|           326|mathias schaffhauser|
|    5409|        526776|                 AFI|
|    5556|         39875|     Hundred Reasons|
| 1000311|          8178|       Steve Winwood|
| 1000313|        128236| Super Furry Animals|
| 1001409|           100|            Pnu Riff|
| 1001530|        199580|   The Starting Line|
| 1001731|           336|    Jay &amp; Silent Bob|
| 1001872|           467|             DJ Dawn|
| 1001925|           212|          J Lo &amp; Lox|
| 1002519|           436|Incubus &amp; DJ Greyboy|
+--------+--------------+--------------------+
only showing top 20 rows

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<h1 id="2.-Build-a-statistical-models-to-make-recommendations">2. Build a statistical models to make recommendations<a class="anchor-link" href="#2.-Build-a-statistical-models-to-make-recommendations">&#182;</a></h1><h2 id="2.1-Introduction-to-recommender-systems">2.1 Introduction to recommender systems<a class="anchor-link" href="#2.1-Introduction-to-recommender-systems">&#182;</a></h2><p>In a recommendation-system application there are two classes of entities, which we shall refer to as <code>users</code> and <code>items</code>. Users have preferences for certain items, and these preferences must be inferred from the data. The data itself is represented as a <code>preference matrix</code> $A$, giving for each user-item pair, a value that represents what is known about the degree of preference of that user for that item. The table below is an example for a <code>preference matrix</code> of 5 users and <code>k</code> items. The <code>preference matrix</code> is also known as <code>utility matrix</code>.</p>
<table>
<thead><tr>
<th></th>
<th>IT1</th>
<th>IT2</th>
<th>IT3</th>
<th>...</th>
<th>ITk</th>
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<td>U1</td>
<td>1</td>
<td></td>
<td>5</td>
<td>...</td>
<td>3</td>
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<td>U2</td>
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<td>2</td>
<td></td>
<td>...</td>
<td>2</td>
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<td>U3</td>
<td>5</td>
<td></td>
<td>3</td>
<td>...</td>
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<td>U4</td>
<td>3</td>
<td>3</td>
<td></td>
<td>...</td>
<td>4</td>
</tr>
<tr>
<td>U5</td>
<td></td>
<td>1</td>
<td></td>
<td>...</td>
<td>...</td>
</tr>
</tbody>
</table>
<p>The value of row i, column j expresses how much does user <code>i</code> like item <code>j</code>. The values are often the rating scores of users for items. An unknown value implies that we have no explicit information about the user's preference for the item. The goal of a recommendation system is to predict "the blanks" in the <code>preference matrix</code>. For example, assume that the rating score is from 1 (dislike) to 5 (love), would user <code>U5</code> like <code>IT3</code> ? We have two approaches:</p>
<ul>
<li><p>Designing our recommendation system to take into account properties of items such as brand, category, price... or even the similarity of their names. We can denote the similarity of items <code>IT2</code> and <code>IT3</code>, and then conclude that because user <code>U5</code> did not like <code>IT2</code>, they were unlikely to enjoy SW2 either.</p>
</li>
<li><p>We might observe that the people who rated both <code>IT2</code> and <code>IT3</code> tended to give them similar ratings. Thus, we could conclude that user <code>U5</code> would also give <code>IT3</code> a low rating, similar to <code>U5</code>'s rating of <code>IT2</code></p>
</li>
</ul>
<p>It is not necessary to predict every blank entry in a <code>utility matrix</code>. Rather, it is only necessary to discover some entries in each row that are likely to be high. In most applications, the recommendation system does not oﬀer users a ranking of all items, but rather suggests a few that the user should value highly. It may not even be necessary to ﬁnd all items with the highest expected ratings, but only to ﬁnd a large subset of those with the highest ratings.</p>
<h2 id="2.2-Families-of-recommender-systems">2.2 Families of recommender systems<a class="anchor-link" href="#2.2-Families-of-recommender-systems">&#182;</a></h2><p>In general, recommender systems can be categorized into two groups:</p>
<ul>
<li><p><strong>Content-Based</strong> systems focus on properties of items. Similarity of items is determined by measuring the similarity in their properties.</p>
</li>
<li><p><strong>Collaborative-Filtering</strong> systems focus on the relationship between users and items. Similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items.</p>
</li>
</ul>
<p>In the usecase of this notebook, artists take the role of <code>items</code>, and <code>users</code> keep the same role as <code>users</code>.
Since we have no information about <code>artists</code>, except their names, we cannot build a <code>content-based</code> recommender system.</p>
<p>Therefore, in the rest of this notebook, we only focus on <code>Collaborative-Filtering</code> algorithms.</p>
<h2 id="2.3-Collaborative-Filtering">2.3 Collaborative-Filtering<a class="anchor-link" href="#2.3-Collaborative-Filtering">&#182;</a></h2><p>In this section, we study a member of a broad class of algorithms called <code>latent-factor</code> models. They try to explain observed interactions between large numbers of users and products through a relatively small number of unobserved, underlying reasons. It is analogous to explaining why millions of people buy a particular few of thousands of possible albums by describing users and albums in terms of tastes for perhaps tens of genres, tastes which are <strong>not directly observable or given</strong> as data.</p>
<p>First, we formulate the learning problem as a matrix completion problem. Then, we will use a type of <code>matrix factorization</code> model to "fill in" the blanks.  We are given implicit ratings that users have given certain items (that is, the number of times they played a particular artist) and our goal is to predict their ratings for the rest of the items. Formally, if there are $n$ users and $m$ items, we are given an $n \times m$ matrix $R$ in which the generic entry $(u, i)$ represents the rating for item $i$ by user $u$. <strong>Matrix $R$ has many missing entries indicating unobserved ratings, and our task is to estimate these unobserved ratings</strong>.</p>
<p>A popular approach to the matrix completion problem is <strong>matrix factorization</strong>, where we want to "summarize" users and items with their <strong>latent factors</strong>.</p>

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<h3 id="2.3.1-Basic-idea-and-an-example-of-Matrix-Factorization">2.3.1 Basic idea and an example of Matrix Factorization<a class="anchor-link" href="#2.3.1-Basic-idea-and-an-example-of-Matrix-Factorization">&#182;</a></h3><p>For example, given a preference matrix 5x5 as below, we want to approximate this matrix into the product of two smaller matrixes $X$ and $Y$.</p>
<p>$$
M = 
\begin{bmatrix}
 5 &amp; 2 &amp; 4 &amp; 4 &amp; 3 \\
 3 &amp; 1 &amp; 2 &amp; 4 &amp; 1 \\
 2 &amp;  &amp; 3 &amp; 1 &amp; 4 \\
 2 &amp; 5 &amp; 4 &amp; 3 &amp; 5 \\
 4 &amp; 4 &amp; 5 &amp; 4 &amp;  \\
\end{bmatrix}
\approx M^\prime =
\begin{bmatrix}
 x_{11} &amp; x_{12} \\
 x_{21} &amp; x_{22} \\
 x_{31} &amp; x_{32} \\
 x_{41} &amp; x_{42} \\
 x_{51} &amp; x_{52} \\
\end{bmatrix}
\times
\begin{bmatrix}
 y_{11} &amp; y_{12} &amp; y_{13} &amp; y_{14} &amp; y_{15} \\
 y_{21} &amp; y_{22} &amp; y_{23} &amp; y_{24} &amp; y_{25} \\
\end{bmatrix}
$$</p>
<p>$M^\prime$ is an approximation that is as close to A as possible. To calculate how far from $M$ $M^\prime$ is, we often calculate the sum of squared distances of non-empty elements in $M$ and the corresponding elements in $M^\prime$.
In this way, for $M^\prime$, besides the approximated elements in $M$, we also have the non-observed elements. Therefore, to see how much does user <code>i</code> like item <code>j</code>, we simply pick up the value of $M^\prime_{i,j}$.</p>
<p>The challenge is how to calculate $X$ and $Y$. The bad news is that this can't be solved directly for both the best $X$ and best $Y$ at the same time. Fortunately, if $Y$ is known, we can calculate the best of $X$, and vice versa. It means from the initial values of $X$ and $Y$ in the beginning, we calculate the best $X$ according to $Y$, and then calculate the best $Y$ according to the new $X$. This process is repeated until the distance from $XY$ to $M$ is small. It's simple, right ?</p>
<p>Let's take an example. To compute the approximation for the above 5x5 matrix $M$, first, we initialize the value of $X$ and $Y$ as below.
<br></p>
<p>$$
M^\prime = X \times Y =
\begin{bmatrix}
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
\end{bmatrix}
\times
\begin{bmatrix}
 1 &amp; 1 &amp; 1 &amp; 1 &amp; 1 \
 1 &amp; 1 &amp; 1 &amp; 1 &amp; 1 \</p>
<h1 id="\end{bmatrix}">\end{bmatrix}<a class="anchor-link" href="#\end{bmatrix}">&#182;</a></h1><p>\begin{bmatrix}
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
\end{bmatrix}
$$</p>
<p>With the initial iteration, we calculate the the Root-Mean-Square Error from $XY$ to $M$.</p>
<p>Consider the ﬁrst rows of $M$ and $XY$ . We subtract the first row of $XY$ from the entries in the ﬁrst row of $M$, to get $3,0,2,2,1$. We square and sum these to get $18$.</p>
<p>In the second row, we do the same to get $1,−1,0,2,−1$, square and sum to get $7$.</p>
<p>In the third row, the second column is blank, so that entry is ignored when computing the RMSE. The diﬀerences are $0,1,−1,2$ and the sum of squares is $6$.</p>
<p>For the fourth row, the diﬀerences are $0,3,2,1,3$ and the sum of squares is $23$.</p>
<p>The ﬁfth row has a blank entry in the last column, so the diﬀerences are $2,2,3,2$ and the sum of squares is $21$.</p>
<p>When we sum the sums from each of the ﬁve rows, we get $18+7+6+23+21 = 75$. So, $RMSE=\sqrt{75/23}=1.806$ where $23$ is the number of non-empty values in $M$.</p>
<p>Next, with the given value of $Y$, we calculate $X$ by finding the best value for $X_{11}$.</p>
<p>$$
M^\prime = X \times Y =
\begin{bmatrix}
 x &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
\end{bmatrix}
\times
\begin{bmatrix}
 1 &amp; 1 &amp; 1 &amp; 1 &amp; 1 \
 1 &amp; 1 &amp; 1 &amp; 1 &amp; 1 \</p>
<h1 id="\end{bmatrix}">\end{bmatrix}<a class="anchor-link" href="#\end{bmatrix}">&#182;</a></h1><p>\begin{bmatrix}
 x+1 &amp; x+1 &amp; x+1 &amp; x+1 &amp; x+1 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
\end{bmatrix}
$$</p>
<p>Now, to minimize the $RMSE$  we minimize the difference of the first rows $(5−(x+1))^2 + (2−(x+1))^2 + (4−(x+1))^2 + (4−(x+1))^2 + (3−(x+1))^2$. By  taking the derivative and set that equal to 0, we pick $x=2.6$</p>
<p>Given the new value of $X$, we can calculate the best value for $Y$.</p>
<p>$$
M^\prime = X \times Y =
\begin{bmatrix}
 2.6 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
 1 &amp; 1 \\
\end{bmatrix}
\times
\begin{bmatrix}
 y &amp; 1 &amp; 1 &amp; 1 &amp; 1 \
 1 &amp; 1 &amp; 1 &amp; 1 &amp; 1 \</p>
<h1 id="\end{bmatrix}">\end{bmatrix}<a class="anchor-link" href="#\end{bmatrix}">&#182;</a></h1><p>\begin{bmatrix}
 3.6 &amp; 3.6 &amp; 3.6 &amp; 3.6 &amp; 3.6 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
 2 &amp; 2 &amp; 2 &amp; 2 &amp; 2 \\
\end{bmatrix}
$$</p>
<p>By doing the same process as before, we can pick value for $y=1.617$. After that, we can check if the $RMSE$ is not converged, we continue to update $X$ by $Y$ and vice versa. In this example, for simple, we only update one element of each matrix in each iteration. In practice, we can update a full row or full matrix at once.</p>

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<h3 id="2.3.2-Matrix-Factorization:-Objective-and-ALS-Algorithm-on-a-Single-Machine">2.3.2 Matrix Factorization: Objective and ALS Algorithm on a Single Machine<a class="anchor-link" href="#2.3.2-Matrix-Factorization:-Objective-and-ALS-Algorithm-on-a-Single-Machine">&#182;</a></h3><p>More formally, in general, we select $k$ latent features, and describe each user $u$ with a $k-$dimensional vector $x_u$, and each item $i$ with a $k-$dimensional vector $y_i$.</p>
<p>Then, to predict user $u$'s rating for item $i$, we do as follows: $ r_{ui} \approx x_{u}^{T}y_i$.</p>
<p>This can be put, more elegantly, in a matrix form. Let $x_1, \cdots x_n \in \mathbb{R}^k$ be the factors for the users, and $y_1, \cdots y_m \in \mathbb{R}^k$ the factors for the items. The $k \times n$ user matrix $X$ and the $k \times m$ item matrix $Y$ are then defined by:</p>
<p>$$
X = 
\begin{bmatrix}
 |   &amp;         &amp; |  \\
x_1  &amp;  \cdots &amp; x_n\\
 |   &amp;         &amp; |  \\
\end{bmatrix}
$$</p>
<p>$$
Y = 
\begin{bmatrix}
 |   &amp;         &amp; |  \\
y_1  &amp;  \cdots &amp; y_i\\
 |   &amp;         &amp; |  \\
\end{bmatrix}
$$</p>
<p>Our goal is to estimate the complete ratings matrix $R \approx X^{T} Y$. We can formulate this problem as an optimization problem in which we aim to minimize an objective function and find optimal $X$ and $Y$ . In particular, we aim to minimize the least squares error of the observed ratings (and regularize):</p>
<p>$$
\min_{X,Y} \sum_{r_{ui} \text{observed}}(r_{ui} - x_{u}^{T}y_i)^2 + \lambda \left( \sum_{u} \|x_u\|^2 + \sum_{i} \|y_i\|^2 \right) 
$$</p>
<p>Notice that this objective is non-convex (because of the $x_{u}^{T} y_i$ term); in fact it’s NP-hard to optimize. Gradient descent can be used as an approximate approach here, however it turns out to be slow and costs lots of iterations. Note however, that if we fix the set of variables $X$ and treat them as constants, then the objective is a convex function of $Y$ and vice versa. Our approach will therefore be to fix $Y$ and optimize $X$, then fix $X$ and optimize $Y$, and repeat until convergence. This approach is known as <strong>ALS (Alternating Least Squares)</strong>. For our objective function, the alternating least squares algorithm can be expressed with this simple pseudo-code:</p>

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<p><strong>Initialize</strong> $X$, $Y$</p>
<p><strong>while(convergence is not true) do</strong></p>
<p><strong>for</strong> $u = 1 \cdots n$ <strong>do</strong></p>
<p>$x_u = \left( \sum_{r_{ui} \in r_{u*}} y_i y_{i}^{T} + \lambda I_k \right)^{-1} \sum_{r_ui \in r_{u*}} r_{ui} y_i $</p>
<p><strong>end for</strong></p>
<p><strong>for</strong> $u = 1 \cdots n$ <strong>do</strong></p>
<p>$y_i = \left( \sum_{r_{ui} \in r_{*i}} x_u x_{u}^{T} + \lambda I_k \right)^{-1} \sum_{r_ui \in r_{*i}} r_{ui} x_u $</p>
<p><strong>end for</strong></p>
<p><strong>end while</strong></p>

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<p>For a single machine, we can analyze the computational cost of this algorithm. Updating each $x_u$ will cost $O(n_u k^2 + k^3)$, where $n_u$ is the number of items rated by user $u$, and similarly updating each $y_i$ will cost $O(n_i k^2 + k^3)$, where $n_i$ is the number of users that have rated item $i$.</p>
<p>Once we’ve computed the matrices $X$ and $Y$, there are several ways compute a prediction. The first is to do what was discussed before, which is to simply predict $ r_{ui} \approx x_{u}^{T}y_i$ for each user $u$ and item $i$. 
This approach will cost $O(nmk)$ if we’d like to estimate every user-item pair.</p>
<p>However, this approach is prohibitively expensive for most real-world datasets. A second (and more holistic) approach is to use the $x_u$ and $y_i$ as features in another learning algorithm, incorporating these features with others that are relevant to the prediction task.</p>

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<h3 id="2.3.3-Parallel-Altenating-Least-Squares">2.3.3 Parallel Altenating Least Squares<a class="anchor-link" href="#2.3.3-Parallel-Altenating-Least-Squares">&#182;</a></h3><p>There are several ways to distribute the computation of the ALS algorithm depending on how data is partitioned.</p>
<h4 id="Method-1:-using-joins">Method 1: using joins<a class="anchor-link" href="#Method-1:-using-joins">&#182;</a></h4><p>First we consider a fully distributed version, in the sense that all data (both input and output) is stored in a distributed file system. In practice, input data (ratings) and parameters ($X$ and $Y$) are stored in an a Spark RDD. Specifically, ratings -- that are always <strong>sparse</strong> -- are stored as RDD of triplets:</p>
<p>Ratings: RDD((u, i, $r_{ui}$), . . . )</p>
<p>Instead, we can use dense representation for factor matrices $X$ and $Y$, and these are stored as RDDs of vectors. More precisely, we can use the data types introduced in Spark MLLib to store such vectors and matrices:</p>
<p>X : RDD($x_1$,...,$x_n$)</p>
<p>Y : RDD($y_1$,...,$y_m$)</p>
<p>Now, recall the expression to compute $x_u$:</p>
<p>$x_u = \left( \sum_{r_ui \in r_{u*}} y_i y_{i}^{T} + \lambda I_k \right)^{-1} \sum_{r_ui \in r_{u*}} r_{ui} y_i $</p>
<p>Let's call the first summation <em>part A</em> and the second summation <em>part B</em>. To compute such parts, in parallel, we can proceed with the following high-level pseudocode:</p>
<ul>
<li>Join the Ratings RDD with the $Y$ matrix RDD using key $i$ (items)</li>
<li>Map to compute $y_i y_{i}^{T}$ and emit using key $u$ (user)</li>
<li>ReduceByKey $u$ (user) to compute $\sum_{r_ui \in r_{u*}} y_i y_{i}^{T}$</li>
<li>Invert</li>
<li>Another ReduceByKey $u$ (user) to compute $\sum_{r_ui \in r_{u*}} r_{ui} y_i$</li>
</ul>
<p>We can use the same template to copmute $y_i$.</p>
<p>This approach works fine, but note it requires computing $y_i y_{i}^{T}$ for each user that has rated item $i$.</p>
<h4 id="Method-2:-using-broadcast-variables-(advanced-topic)">Method 2: using broadcast variables (advanced topic)<a class="anchor-link" href="#Method-2:-using-broadcast-variables-(advanced-topic)">&#182;</a></h4><p>The next approach takes advantage of the fact that the $X$ and $Y$ factor matrices are often very small and can be stored locally on each machine.</p>
<ul>
<li>Partition the Ratings RDD <strong>by user</strong> to create $R_1$, and similarly partition the Ratings RDD <strong>by item</strong> to create $R_2$. This means there are two copies of the same Ratings RDD, albeit with different partitionings. In $R_1$, all ratings by the same user are on the same machine, and in $R_2$ all ratings for same item are on the same machine.</li>
<li>Broadcast the matrices $X$ and $Y$. Note that these matrices are not RDD of vectors: they are now "local" matrices.</li>
<li>Using $R_1$ and $Y$, we can use expression $x_u$ from above to compute the update of $x_u$ locally on each machine</li>
<li>Using $R_2$ and $X$, we can use expression $y_i$ from above to compute the update of $y_i$ locally on each machine</li>
</ul>
<p>A further optimization to this method is to group the $X$ and $Y$ factors matrices into blocks (user blocks and item blocks) and reduce the communication by only sending to each machine the block of users (or items) that are needed to compute the updates at that machine.</p>
<p>This method is called <strong>Block ALS</strong>. It is achieved by precomputing some information about the ratings matrix to determine the "out-links" of each user (which blocks of the items it will contribute to) and "in-link" information for each item (which of the factor vectors it receives from each user block it will depend on). For exmple, assume that machine 1 is responsible for users 1,2,...,37: these will be block 1 of users. The items rated by these users are block 1 of items. Only the factors of block 1 of users and block 1 of items will be broadcasted to machine 1.</p>
<h3 id="Further-readings">Further readings<a class="anchor-link" href="#Further-readings">&#182;</a></h3><p>Other methods for matrix factorization include:</p>
<ul>
<li>Low Rank Approximation and Regression in Input Sparsity Time, by Kenneth L. Clarkson, David P. Woodruff. <a href="http://arxiv.org/abs/1207.6365">http://arxiv.org/abs/1207.6365</a></li>
<li>Generalized Low Rank Models (GLRM), by Madeleine Udell, Corinne Horn, Reza Zadeh, Stephen Boyd. <a href="http://arxiv.org/abs/1410.0342">http://arxiv.org/abs/1410.0342</a></li>
<li>Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares, by Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh . Statistics Department and ICME, Stanford University, 2014. <a href="http://stanford.edu/~rezab/papers/fastals.pdf">http://stanford.edu/~rezab/papers/fastals.pdf</a></li>
</ul>

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<h1 id="3.-Usecase-:-Music-recommender-system">3. Usecase : Music recommender system<a class="anchor-link" href="#3.-Usecase-:-Music-recommender-system">&#182;</a></h1><p>In this usecase, we use the data of users and artists in the previous sections to build a statistical model to recommend artists for users.</p>
<h2 id="3.1-Requirements">3.1 Requirements<a class="anchor-link" href="#3.1-Requirements">&#182;</a></h2><p>According to the properties of data, we need to choose a recommender algorithm that is suitable for this implicit feedback data. It means that the algorithm should learn without access to user or artist attributes such as age, genre,.... Therefore, an algorithm of type <code>collaborative filtering</code> is the best choice.</p>
<p>Second, in the data, there are some users that have listened to only 1 artist. We need an algorithm that might provide decent recommendations to even these users. After all, at some point, every user starts out with just one play at some point!</p>
<p>Third, we need an algorithm that scales, both in its ability to build large models, and to create recommendations quickly. So, an algorithm which can run on a distributed system (SPARK, Hadoop...) is very suitable.</p>
<p>From these requirement, we can choose using ALS algorithm in SPARK's MLLIB.</p>
<p>Spark MLlib’s ALS implementation draws on ideas from <a href="http://yifanhu.net/PUB/cf.pdf">1</a> and <a href="http://link.springer.com/chapter/10.1007%2F978-3-540-68880-8_32">2</a>.</p>
<h2 id="3.2-Notes">3.2 Notes<a class="anchor-link" href="#3.2-Notes">&#182;</a></h2><p>Currently, MLLIB can only build models from an RDD. That means we have two ways to prepare data:</p>
<ul>
<li><p>Loading to into SPARK SQL DataFrame as before, and then access the corresponding RDD by calling <code>&lt;dataframe&gt;.rdd</code>. The invalid data is often sucessfully dropped by using mode <code>DROPMALFORMED</code>. However, this way might not work in all cases. Fortunately, we can use it with this usecase.</p>
</li>
<li><p>Loading data directly to RDD. However, we have to deal with the invalid data ourself. In the trade-off, this way is the most reliable, and can work in every case.</p>
</li>
</ul>
<p>In this notebook, we will use the second approach: it requires a bit more effort, but the reward is worth it!</p>
<h2 id="3.3-Cleanup-the-data">3.3 Cleanup the data<a class="anchor-link" href="#3.3-Cleanup-the-data">&#182;</a></h2><p>In section 1, we already replaced the ids of mispelled artists by the corresponding standard ids by using SPARK SQL API.
However, if the data has the invalid entries such that SPARK SQL API is stuck, the best way to work with it is using an RDD.</p>
<p>Just as a recall, we work with three datasets in <code>user_artist_data.txt</code>, <code>` and</code>artist_alias.txt`. The entries in these file can be empty or have only one field.</p>
<p>In details our goal now is:</p>
<ul>
<li>Read the input <code>user_artist_data.txt</code> and transforms its representation into an output dataset.</li>
<li>To produce an output "tuple" containing the original user identifier and play counts, but with the artist identifier replaced by its most common alias, as found in the <code>artist_alias.txt</code> dataset.</li>
<li>Since the <code>artist_alias.txt</code> file is small, we can use a technique called <strong>broadcast variables</strong> to make such transformation more efficient.</li>
</ul>

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<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><h4 id="Question-6.1">Question 6.1<a class="anchor-link" href="#Question-6.1">&#182;</a></h4><div class="alert alert-info">
Load data from `/datasets/lastfm/artist_alias.txt` and filter out the invalid entries to construct a dictionary to map from mispelled artists' ids to standard ids.
</div><p>NOTE: From now on, we will use the "standard" data to train our model.</p>
<p>HINT: If a line contains less than 2 fields or contains invalid numerical values, we can return a special tuple. After that, we can filter out these special tuples.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rawArtistAlias</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;artist_alias.txt&quot;</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">xtractFields</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
    <span class="c1"># Using white space or tab character as separetors,</span>
    <span class="c1"># split a line into list of strings </span>
    <span class="n">line</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;\s|</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
    <span class="c1"># if this line has at least 2 characters</span>
    <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">):</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="c1"># try to parse the first and the second components to integer type</span>
            <span class="k">return</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
            <span class="c1"># if parsing has any error, return a special tuple</span>
            <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># if this line has less than 2 characters, return a special tuple</span>
        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>

<span class="n">artistAlias</span> <span class="o">=</span> <span class="p">(</span>
                <span class="n">rawArtistAlias</span>
                    <span class="c1"># extract fields using function xtractFields</span>
                    <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">xtractFields</span><span class="p">)</span>
    
                    <span class="c1"># fileter out the special tuples</span>
                    <span class="c1"># assumption: the id of the user is non-negative, so a valid one may not be encountered</span>
                    <span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
    
                    <span class="c1"># collect result to the driver as a &quot;dictionary&quot;</span>
                    <span class="o">.</span><span class="n">collectAsMap</span><span class="p">()</span>
                <span class="p">)</span>

<span class="nb">list</span><span class="p">(</span><span class="n">artistAlias</span><span class="o">.</span><span class="n">values</span><span class="p">())[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>[1000311,
 1000557,
 1007267,
 1042317,
 1042317,
 1000557,
 1294511,
 1327092,
 1042317,
 1000557]</pre>
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<h4 id="Question-6.2">Question 6.2<a class="anchor-link" href="#Question-6.2">&#182;</a></h4><div class="alert alert-info">
Using the dictionary in question 6.1, prepare RDD `userArtistDataRDD` by replacing mispelled artists' ids to standard ids. Show 5 samples.
</div><p>HINT: Using broadcast variables can help us increase the effiency.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># note: artistAlias has been fixed with the right mapping between misspelledId and standardID</span>
<span class="n">bArtistAlias</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">artistAlias</span><span class="p">)</span>
<span class="n">rawUserArtistData</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;user_artist_data.txt&quot;</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">disambiguate</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
    <span class="p">[</span><span class="n">userID</span><span class="p">,</span> <span class="n">artistID</span><span class="p">,</span> <span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
    <span class="n">finalArtistID</span> <span class="o">=</span> <span class="n">bArtistAlias</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">artistID</span><span class="p">,</span> <span class="n">artistID</span><span class="p">)</span>
    <span class="c1"># we recontruct the triple</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">userID</span><span class="p">,</span> <span class="n">finalArtistID</span><span class="p">,</span> <span class="n">count</span><span class="p">)</span>


<span class="n">userArtistDataRDD</span> <span class="o">=</span> <span class="n">rawUserArtistData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">disambiguate</span><span class="p">)</span>
<span class="n">userArtistDataRDD</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre>[(&#39;1000002&#39;, &#39;1&#39;, &#39;55&#39;),
 (&#39;1000002&#39;, &#39;1000006&#39;, &#39;33&#39;),
 (&#39;1000002&#39;, &#39;1000007&#39;, &#39;8&#39;),
 (&#39;1000002&#39;, &#39;1000009&#39;, &#39;144&#39;),
 (&#39;1000002&#39;, &#39;1000010&#39;, &#39;314&#39;)]</pre>
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<h2 id="Unknown-artists">Unknown artists<a class="anchor-link" href="#Unknown-artists">&#182;</a></h2><p>We want now to remove fake artists: let us start from the "unknown" ones</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we need to work on the artist database (artist_id, artist_name)</span>
<span class="k">def</span> <span class="nf">xtractFields</span><span class="p">(</span><span class="n">s</span><span class="p">):</span>
    <span class="n">line</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;\s|</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">line</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">):</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="k">return</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">str</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()))</span>
        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
            <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span> 
        <span class="k">return</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>

<span class="n">rawArtistData</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="n">base</span> <span class="o">+</span> <span class="s2">&quot;artist_data.txt&quot;</span><span class="p">)</span>
<span class="n">artistByID</span> <span class="o">=</span> <span class="n">rawArtistData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">xtractFields</span><span class="p">)</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># now, let&#39;s see some of the unknown artists</span>
<span class="n">artistByID</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;unknown&#39;</span> <span class="ow">in</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="c1"># and filter them</span>
<span class="n">artistByID</span> <span class="o">=</span> <span class="n">artistByID</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;unknown&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
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<h2 id="Featuring-between-artists">Featuring between artists<a class="anchor-link" href="#Featuring-between-artists">&#182;</a></h2><p>We want now to consider featurings between two artists</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># several variants:</span>
<span class="n">feats</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">&#39;featurin&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featruing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featruring&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featurnig&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat/&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featt&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat.&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat &#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat: &#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat,&#39;</span><span class="p">,</span>
    <span class="s2">&quot;feat&#39;&quot;</span><span class="p">,</span>
    <span class="s1">&#39;feat[&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat(&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat-&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat:&#39;</span><span class="p">,</span>
    <span class="s1">&#39;(feat)&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat_&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feature&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat</span><span class="se">\x81</span><span class="s1">&#39;</span>
    <span class="s1">&#39;featl&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featering&#39;</span><span class="p">,</span>
    <span class="s1">&#39;featuing&#39;</span><span class="p">,</span>
    <span class="s1">&#39;feat．&#39;</span><span class="p">,</span>
    <span class="s1">&#39;*feat*&#39;</span>
<span class="p">]</span>

<span class="c1"># NOTE: we are still not considering &quot;feat&quot; at the end of the name and &quot;feat&quot; inserted </span>
<span class="c1"># between two artist names, without any spaces</span>
</pre></div>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># now, let&#39;s see some of the featuring artists</span>
<span class="n">artistByID</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">any</span><span class="p">([</span><span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">feats</span><span class="p">]))</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span>
<span class="c1"># and filter them</span>
<span class="c1">#artistByID = artistByID.filter(lambda x: &#39;unknown&#39; not in x[1])</span>

<span class="c1"># how many featuring artists?</span>
<span class="n">ratio</span> <span class="o">=</span> <span class="n">artistByID</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">any</span><span class="p">([</span><span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">feats</span><span class="p">]))</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> <span class="o">/</span> <span class="n">artistByID</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Featurings are the </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">__builtins__</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">ratio</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
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<pre>Featurings are the 3.42%
</pre>
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<h3 id="Comment:-our-problem">Comment: our problem<a class="anchor-link" href="#Comment:-our-problem">&#182;</a></h3><div class="alert alert-warning">
<b>We do not know how to manage featurings</b>: in fact, they represent a <u>potential source of information</u>. 
<br><br>
Theoretically speaking, it is possible to identify all the artists involved in a featuring and create duplicated version of the record (same userID and PlayCount value, one record per involved artistID).<br>
In practise, we are able to identify featuring partially (with great effort) and we are often not able to separate artists names: the reason is why there is not a common separator for the names. Several separators can be used, in several versions each: sometimes, they are missing completely (no blank spaces at all). 
<br><br>
Also: in order to map the names with the IDs, we would need to perform a join operation and several regex searches. This is something extremely expensive, and the effort is not remunerated because of the data conditions.<br>
Anyway, we won't delete all these records from the dataset: they could be used by the system to retrieve similar users and guide them to a given genre of artists and songs.
<br><br>
In <u>conclusion</u>: the problem is spotted, but not solved 
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<p><hr></p>
<h2 id="Experiment:-setting-up-the-utility-functions">Experiment: setting up the utility functions<a class="anchor-link" href="#Experiment:-setting-up-the-utility-functions">&#182;</a></h2><p>As anticipated before, we are trying to exclude those records which involves artistID whose total number of <em>Play Count</em> is below a given threshold.</p>
<p>From now on we have written some functions to help us in a further experiment. The idea is to check if eliminating those features, which represent artists whose total <em>Play Count</em> is below a given threshold, may improve the AUC for the model. We call these artist <em>fewListenedArtists</em>.
We start here defining the function used to remove the <em>fewListenedArtists</em>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">filterByArtistTotalPlays</span><span class="p">(</span><span class="n">RDD</span><span class="p">,</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">function</span><span class="o">=</span><span class="s2">&quot;upper_filter&quot;</span><span class="p">,</span> <span class="n">threshold_upper</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    The funciton is used to filter the RDD based on the total Play Counts of an artist. The function allows three filters:</span>
<span class="sd">    - upper_filter: which exclude all the artists with a total Play Count under the given threshold</span>
<span class="sd">    - lower_filter: which exclude all the artists with a total Play Count below the given threshold</span>
<span class="sd">    - middle_filter: which allow to select all the artists with a total Play Count in a given range (note: threshold_upper need to be specified)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="k">if</span> <span class="n">function</span> <span class="ow">is</span> <span class="s2">&quot;middle_filter&quot;</span> <span class="ow">and</span> <span class="n">threshold_upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">Exception</span>
    
    <span class="n">filters</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;upper_filter&quot;</span> <span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">,</span>
        <span class="s2">&quot;lower_filter&quot;</span> <span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">,</span> 
        <span class="s2">&quot;middle_filter&quot;</span> <span class="p">:</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">threshold</span> <span class="ow">and</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">threshold_upper</span>
    <span class="p">}</span>
    
    <span class="c1"># define the set of artistID which total listening by users in the system is above a given threshold </span>
    <span class="n">filteredIDsRDD</span> <span class="o">=</span> <span class="p">(</span>
        <span class="c1"># we start from the already available RDD with userID, artistID, plays</span>
        <span class="n">RDD</span>
        <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="p">))</span>
        <span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">+</span><span class="n">y</span><span class="p">)</span>
        <span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">filters</span><span class="p">[</span><span class="n">function</span><span class="p">])</span>
    <span class="p">)</span>

    <span class="c1"># construct a new RDD based on only those entries userID, artistID, plays which includes artsistIDs appearing in the previous RDD</span>
    <span class="n">filteredRDD</span> <span class="o">=</span> <span class="p">(</span>
        <span class="c1"># we start again from the already available RDD</span>
        <span class="n">RDD</span>
        <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">))</span>
        <span class="c1"># then we join the RDD containing only the IDs of artists which total play counts are above the threshold</span>
        <span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">filteredIDsRDD</span><span class="p">)</span>
        <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
    <span class="p">)</span> 

    <span class="k">return</span> <span class="n">filteredRDD</span>
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<p><hr></p>
<h2 id="Pre-training-data-processing">Pre-training data processing<a class="anchor-link" href="#Pre-training-data-processing">&#182;</a></h2><p>Before doing the training, we want to normalize the preference matrix. The idea is to subtract from each playCount value the user mean value for that field.<br>
The obtained result will be compared with the non-normalized one</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># casting the values of the triple as integers and float for the Play Counts</span>
<span class="n">userArtistDataRDD</span> <span class="o">=</span> <span class="n">userArtistDataRDD</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span> <span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#compute the total plays per user</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">userArtistDataRDD</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
                            
<span class="n">usersMeanRDD</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">mapValues</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
                            
<span class="n">userArtistDataRDD_norm</span> <span class="o">=</span> <span class="p">(</span> <span class="n">userArtistDataRDD</span>
    <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">x</span><span class="p">))</span>
    <span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">usersMeanRDD</span><span class="p">)</span>
    <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="o">-</span><span class="n">x</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="p">)</span>

<span class="c1"># manage the results with an array</span>
<span class="n">userArtistDataRDDs</span> <span class="o">=</span> <span class="p">[</span>
    <span class="p">{</span> <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">userArtistDataRDD</span><span class="p">,</span> <span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="s1">&#39;basic_model&#39;</span><span class="p">,</span> <span class="s1">&#39;ratings&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;validation&#39;</span><span class="p">:</span> <span class="kc">None</span> <span class="p">},</span>
    <span class="p">{</span> <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">userArtistDataRDD_norm</span><span class="p">,</span> <span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="s1">&#39;normalized_model&#39;</span><span class="p">,</span> <span class="s1">&#39;ratings&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;validation&#39;</span><span class="p">:</span> <span class="kc">None</span> <span class="p">}</span>
<span class="p">]</span>
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<h2 id="3.4-Training-our-statistical-model">3.4 Training our statistical model<a class="anchor-link" href="#3.4-Training-our-statistical-model">&#182;</a></h2><p>To train a model using ALS, we must use a preference matrix  as an input. MLLIB uses the class <code>Rating</code> to support the construction of a distributed preference matrix.</p>

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<h2>Please, note</h2><br>
Our strategy is now to follow the structure of the notebook, analysing the reccomandation system obtained from the <b>non-normalized dataset</b>, and then repeat the procedure with:
<ul>
    <li>the <b>normalized</b> version</li>
    <li>versions <b>without the least popular</b> artists</li>
    <li>a <b>multi-system voting</b> procedure (something similar to the boosting/bagging approaches)</li>
</ul>
<br>
In order to speed up later our analysis and computations, we have defined some *functions* which are useful for how we are going to work. The structure of the notebook <u>may look different</u> in some points, with respect to the originally proposed one. 
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<h3 id="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#182;</a></h3><h4 id="Question-7.1">Question 7.1<a class="anchor-link" href="#Question-7.1">&#182;</a></h4><div class="alert alert-info">
Given RDD `userArtistDataRDD` in question 6.2, construct a new RDD `trainingData` by tranforming each item of it into a `Rating` object.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pyspark.mllib.recommendation</span> <span class="k">import</span> <span class="n">ALS</span><span class="p">,</span> <span class="n">MatrixFactorizationModel</span><span class="p">,</span> <span class="n">Rating</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">ratingDefinitionRDD</span><span class="p">(</span><span class="n">myUserArtistDataRDD</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Collapse the duplicated entries considering the userID ad ArtistID as a single entity.</span>
<span class="sd">    Then, remap everything as an RDD of &quot;Ratings&quot; and cache it</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;* Map to Rating&#39;</span><span class="p">)</span>
    <span class="n">res</span> <span class="o">=</span> <span class="p">(</span>
        <span class="c1"># RDD composed of triples (userID, artistID, playCount)</span>
        <span class="n">myUserArtistDataRDD</span><span class="p">[</span><span class="s1">&#39;dataset&#39;</span><span class="p">]</span>

        <span class="c1"># apply a map to transform the RDD into a PairRDD (key, vale) where key -&gt; (userID, artistID) and value -&gt; playCount</span>
        <span class="c1"># note: the key is a coplex value, in this case a tuple composed of the two IDs</span>
        <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span> <span class="p">:</span> <span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>

        <span class="c1"># apply the reduceBykey to compute the aggragate of the plays for a given artist and user resulting in a single entry for each userID and artistID</span>
        <span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span><span class="o">+</span><span class="n">y</span><span class="p">)</span>

        <span class="c1"># transform each entry in a Rating object. To access the field due to the pairRDD: </span>
        <span class="c1"># x[0][0] -&gt; userID, x[0][1] -&gt; artistID, x[1] -&gt; playCount</span>
        <span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">Rating</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
        <span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
        <span class="o">.</span><span class="n">cache</span><span class="p">()</span>
    <span class="p">)</span>
    <span class="n">myUserArtistDataRDD</span><span class="p">[</span><span class="s1">&#39;ratings&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span>

    <span class="k">return</span> <span class="n">res</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">allData</span> <span class="o">=</span> <span class="n">ratingDefinitionRDD</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">allData</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>    
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">The script takes </span><span class="si">%f</span><span class="s1"> seconds&#39;</span> <span class="o">%</span><span class="p">(</span><span class="n">t1</span><span class="o">-</span><span class="n">t0</span><span class="p">))</span>
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<pre>* Map to Rating
[Rating(user=2037078, product=1007903, rating=8.0), Rating(user=2037081, product=6671142, rating=1.0), Rating(user=2037117, product=1073866, rating=1.0), Rating(user=2037192, product=1011961, rating=22.0), Rating(user=2037246, product=1003159, rating=3.0)]

The script takes 205.929726 seconds
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<b><u> From the scala doc:</u> </b><br>
<i>Train a matrix factorization model given an RDD of 'implicit preferences' of users for a subset of products. The ratings matrix is approximated as the product of two lower-rank matrices of a given rank (number of features). To solve for these features, ALS is run iteratively with a level of parallelism determined automatically based on the number of partitions in ratings.</i>
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<p><hr></p>
<h4 id="Question-7.2">Question 7.2<a class="anchor-link" href="#Question-7.2">&#182;</a></h4><div class="alert alert-info">
A model can be trained by using `ALS.trainImplicit(<training data>, <rank>)`, where:
<ul>
<li>`training data` is the input data you decide to feed to the ALS algorithm</li>
<li>`rank` is the number of laten features</li>
</ul>  


We can also use some additional parameters to adjust the quality of the model. Currently, let's set 
<ul>
<li>`rank=10`</li>
<li>`iterations=5`</li>
<li>`lambda_=0.01`</li>
<li>`alpha=1.0` </li>
</ul>
to build model.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">trainTheModel</span><span class="p">(</span><span class="n">myUserArtistDataRDD</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    This function return the artists suggestion model, trained on the dataset provided</span>
<span class="sd">    </span>
<span class="sd">    ratings - RDD of (userID, productID, rating) pairs</span>
<span class="sd">    rank - number of features to use</span>
<span class="sd">    iterations - number of iterations of ALS (recommended: 10-20)</span>
<span class="sd">    lambda - regularization factor (recommended: 0.01)</span>
<span class="sd">    alpha - confidence parameter</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">dataset</span> <span class="o">=</span> <span class="n">myUserArtistDataRDD</span><span class="p">[</span><span class="s1">&#39;dataset&#39;</span><span class="p">]</span>
    <span class="n">validation</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">training</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;* Training the model&#39;</span><span class="p">)</span>
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="c1"># ALS.trainImpliciuserArtistDataCleanedRDDt(RDD&lt;Rating&gt; ratings, int rank, int iterations, double lambda, double alpha)</span>
    <span class="k">if</span> <span class="n">split</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="n">iterations</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">(</span><span class="n">split</span><span class="p">)</span>    <span class="c1"># randomSplit(weights, seed=None)</span>
        <span class="n">training</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
        <span class="n">validation</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="n">iterations</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">)</span>
    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;finish training model in </span><span class="si">%f</span><span class="s2"> secs&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
    <span class="n">myUserArtistDataRDD</span><span class="p">[</span><span class="s1">&#39;validation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">validation</span>
    <span class="k">return</span> <span class="n">res</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">validation</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># building the model</span>
<span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">trainTheModel</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<pre>* Training the model
finish training model in 89.993638 secs
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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
The resulting model is a MatrixFactorizationModel which is composed of:
<ul>
    <li>rank: int -> Rank for the features in the model</li>
<li>userFeaturesRDD: RDD[(Int, Array[Double])] -> RDD of tuples representing the userID and the features computed for the user</li>
<li>productFeaturesRDD: RDD[(Int, Array[Double])] -> RDD of tuples representing the productID (in our case the artistID) and the features computed for the product (artist)</li>
</ul>
<br>
From the paper [Collaborative Filtering for Implicit Feedback Datasets] is clear the role of alfa: it is treated as a confidence level for the rating, in terms of postive ratings which give more confidence for ($p_{ui}$ = 1) with the repsect of having ratings ($p_{ui}$ < 1.0). 
<br><br>
The regularization factor must be determined using cross validation.
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<h4 id="Question-7.3">Question 7.3<a class="anchor-link" href="#Question-7.3">&#182;</a></h4><div class="alert alert-info">
The trained model can be saved into HDFS for later use. This can be done via `model.save(sc, <file_name>)`.
Let's use this function to store our model as name `lastfm_model.spark`.
</div><p>NOTE 1: since you may have noticed that building the model takes some time, it might come to your mind that this information could be stored, such that you can "interrupt" your laboratory session here, and restart next time by loading your model.</p>
<p>NOTE 2: funnily enough, it could take more time to save the model than to build it from scratch! So take a look at the execution time to save the model: this method actually stores the model as Parquet files, which are column-oriented and compressed.</p>
<p>NOTE 3: to check you have your file on HDFS, you are invited to open a terminal from the "Home" Jupyter dashboard, and type <code>hdfs dfs -ls</code> to check.</p>

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

<span class="k">def</span> <span class="nf">run_cmd</span><span class="p">(</span><span class="n">args_list</span><span class="p">,</span> <span class="n">lsShow</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    run linux commands</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="n">proc</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">Popen</span><span class="p">(</span><span class="n">args_list</span><span class="p">,</span> <span class="n">stdout</span><span class="o">=</span><span class="n">subprocess</span><span class="o">.</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">subprocess</span><span class="o">.</span><span class="n">PIPE</span><span class="p">)</span>
    <span class="n">s_output</span><span class="p">,</span> <span class="n">s_err</span> <span class="o">=</span> <span class="n">proc</span><span class="o">.</span><span class="n">communicate</span><span class="p">()</span>
    <span class="n">s_return</span> <span class="o">=</span>  <span class="n">proc</span><span class="o">.</span><span class="n">returncode</span>
    <span class="k">if</span> <span class="n">lsShow</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">s_output</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">s_return</span><span class="p">,</span> <span class="n">s_output</span><span class="p">,</span> <span class="n">s_err</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">saveModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">modelName</span><span class="p">,</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Save the trained model on disk, so that it can be reused in the future</span>
<span class="sd">    </span>
<span class="sd">    model - the model to save</span>
<span class="sd">    modalName - the name of the file on disk (extension will be .spark)</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;* Save the model on disk&#39;</span><span class="p">)</span>
    <span class="n">fullName</span> <span class="o">=</span> <span class="n">modelName</span> <span class="o">+</span> <span class="s1">&#39;.spark&#39;</span>
    <span class="c1"># ! hdfs dfs -rm -R -f -skipTrash lastfm_model.spark</span>
    <span class="n">run_cmd</span><span class="p">([</span><span class="s1">&#39;hdfs&#39;</span><span class="p">,</span> <span class="s1">&#39;dfs&#39;</span><span class="p">,</span> <span class="s1">&#39;-rm&#39;</span><span class="p">,</span> <span class="s1">&#39;-R&#39;</span><span class="p">,</span> <span class="s1">&#39;-f&#39;</span><span class="p">,</span> <span class="s1">&#39;-skipTrash&#39;</span><span class="p">,</span> <span class="n">fullName</span><span class="p">],</span> <span class="n">lsShow</span><span class="p">)</span>
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">fullName</span><span class="p">)</span>
    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;model saved on disk in </span><span class="si">%f</span><span class="s2"> secs&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
    <span class="c1"># ! hdfs dfs -ls</span>
    <span class="n">run_cmd</span><span class="p">([</span><span class="s1">&#39;hdfs&#39;</span><span class="p">,</span> <span class="s1">&#39;dfs&#39;</span><span class="p">,</span> <span class="s1">&#39;-ls&#39;</span><span class="p">],</span> <span class="n">lsShow</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># saving the model</span>
<span class="n">saveModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;filename&#39;</span><span class="p">],</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<pre>* Save the model on disk
model saved on disk in 2.493844 secs
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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
The model has been correctly saved in the HDFS.<br>
<u>From the documentation</u>: a human-readable (JSON) file for storing metadata is saved in the HDFS folder: *path/metadata/*
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<h4 id="Question-7.4">Question 7.4<a class="anchor-link" href="#Question-7.4">&#182;</a></h4><div class="alert alert-info">
A saved model can be load from file by using `MatrixFactorizationModel.load(sc, <file_name>)`. 

Let's load our model from file.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">loadModel</span><span class="p">(</span><span class="n">modelName</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Load a previously created model from the disk</span>
<span class="sd">    </span>
<span class="sd">    modelName - The name of the file on disk (it will have .spark extension)</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">fullName</span> <span class="o">=</span> <span class="n">modelName</span> <span class="o">+</span> <span class="s1">&#39;.spark&#39;</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;* Load the model from disk&#39;</span><span class="p">)</span>
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">MatrixFactorizationModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="n">fullName</span><span class="p">)</span>
    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;finish loading model in </span><span class="si">%f</span><span class="s2"> secs&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">model</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># loading the model</span>
<span class="n">loadModel</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;filename&#39;</span><span class="p">])</span>
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<pre>* Load the model from disk
finish loading model in 2.444047 secs
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<pre>&lt;pyspark.mllib.recommendation.MatrixFactorizationModel at 0x7fdd61873c88&gt;</pre>
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<h4 id="Question-7.5">Question 7.5<a class="anchor-link" href="#Question-7.5">&#182;</a></h4><div class="alert alert-info">
Print the first row of user features in our model.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># recalling that model is an object of MatrixFactorizationModel, the method userFeatures returns an RDD[(Int, Array[DOUBLE])]</span>
<span class="n">model</span><span class="o">.</span><span class="n">userFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">first</span><span class="p">()</span>
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<pre>(116,
 array(&#39;d&#39;, [0.015004981309175491, -0.03929213806986809, 0.00399028742685914, 0.059900421649217606, -0.034820374101400375, -0.017848314717411995, 0.02073773182928562, 0.013769001699984074, 0.03414800390601158, 0.04439891129732132]))</pre>
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<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#182;</a></h3><div class="alert alert-info">
Show the top-5 artist names recommendated for a given user, for example: `2093760` (please, try with different users!).
</div><p>HINT: The recommendations can be given by function <code>recommendProducts(userID, num_recommendations)</code>. These recommendations are only artist ids. You have to map them to artist names by using data in <code>artist_data.txt</code>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">topN_recommendations</span><span class="p">(</span><span class="n">myModel</span><span class="p">,</span> <span class="n">userID</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">fullNames</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">showRec</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Reccomend the top N artists for the given user</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="c1"># make N reccommendations to the user</span>
    <span class="k">if</span> <span class="n">showRec</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Recommendations for userID: </span><span class="si">{!s}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">userID</span><span class="p">))</span>
    <span class="n">recommendations</span> <span class="o">=</span> <span class="n">myModel</span><span class="o">.</span><span class="n">recommendProducts</span><span class="p">(</span><span class="n">userID</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
    <span class="c1"># construct set of recommendated artists (artistIDs)</span>
    <span class="n">recArtist</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">recommendations</span><span class="p">]</span> <span class="p">)</span>
    <span class="k">if</span><span class="p">(</span><span class="n">fullNames</span><span class="p">):</span>
        <span class="c1"># convert the artistID with the artist full name</span>
        <span class="k">def</span> <span class="nf">artistNames</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
            <span class="c1">#[artistID, name]</span>
            <span class="k">if</span> <span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="n">recArtist</span><span class="p">):</span>
                <span class="k">return</span> <span class="kc">True</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">return</span> <span class="kc">False</span>

        <span class="n">recArtist</span> <span class="o">=</span> <span class="n">artistByID</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">artistNames</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
    <span class="c1"># result</span>
    <span class="k">if</span> <span class="n">showRec</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
        <span class="n">pprint</span><span class="o">.</span><span class="n">pprint</span><span class="p">(</span><span class="n">recArtist</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">recArtist</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># top-5 reccomandations for the user: 2093760</span>
<span class="n">topN_recommendations</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="mi">2093760</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
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<pre>Recommendations for userID: 2093760
[&#39;50 Cent&#39;, &#39;Jay-Z&#39;, &#39;Ludacris&#39;, &#39;2Pac&#39;, &#39;The Game&#39;]

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<pre>[&#39;50 Cent&#39;, &#39;Jay-Z&#39;, &#39;Ludacris&#39;, &#39;2Pac&#39;, &#39;The Game&#39;]</pre>
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<h2 id="Mapping-different-user-with-the-reccomended-artists">Mapping different user with the reccomended artists<a class="anchor-link" href="#Mapping-different-user-with-the-reccomended-artists">&#182;</a></h2><p>As suggested, we are now going to try to reccomend artists to different users.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># extract some users from the allDataRDD in this case we avoid to generate invalid IDs. </span>
<span class="n">allUserIDs</span> <span class="o">=</span> <span class="n">allData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>

<span class="c1"># reccomandation variables: users and n</span>
<span class="n">users</span> <span class="o">=</span> <span class="n">allUserIDs</span><span class="p">[</span><span class="mi">10</span><span class="p">:</span><span class="mi">15</span><span class="p">]</span>
<span class="n">n</span> <span class="o">=</span> <span class="mi">5</span>

<span class="c1"># reccomandations contains the final result (array of array of reccomandations - one per user)</span>
<span class="n">recommendations</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">userId</span> <span class="ow">in</span> <span class="n">users</span><span class="p">:</span>
    <span class="n">recList</span> <span class="o">=</span> <span class="n">topN_recommendations</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">userId</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
    <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">recList</span><span class="p">)</span>
<span class="n">recommendations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
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<pre>Recommendations for userID: 2400812
[&#39;Radiohead&#39;,
 &#39;The Mars Volta&#39;,
 &#39;Muse&#39;,
 &#39;Nine Inch Nails&#39;,
 &#39;Queens of the Stone Age&#39;]

Recommendations for userID: 2400820
[&#39;The Postal Service&#39;,
 &#39;Death Cab for Cutie&#39;,
 &#39;Bright Eyes&#39;,
 &#39;Weezer&#39;,
 &#39;Jimmy Eat World&#39;]

Recommendations for userID: 2401104
[&#39;The Beatles&#39;, &#39;Radiohead&#39;, &#39;Coldplay&#39;, &#39;U2&#39;, &#39;Beck&#39;]

Recommendations for userID: 2401320
[&#39;Queen&#39;, &#39;The Beatles&#39;, &#39;Red Hot Chili Peppers&#39;, &#39;Coldplay&#39;, &#39;U2&#39;]

Recommendations for userID: 2401404
[&#39;The Beatles&#39;, &#39;Modest Mouse&#39;, &#39;Radiohead&#39;, &#39;Weezer&#39;, &#39;Beck&#39;]

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<h2 id="Validate-the-model">Validate the model<a class="anchor-link" href="#Validate-the-model">&#182;</a></h2><p>The technique we are going to use is the "Area under the ROC curve" originally presented in the next section</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">random</span> <span class="k">import</span> <span class="n">randint</span>

<span class="c1"># Depend on the number of item in userIDAndPosItemIDs,</span>
<span class="c1"># create a set of &quot;negative&quot; products for each user. These are randomly chosen</span>
<span class="c1"># from among all of the other items, excluding those that are &quot;positive&quot; for the user.</span>
<span class="c1"># NOTE 1: mapPartitions operates on many (user,positive-items) pairs at once</span>
<span class="c1"># NOTE 2: flatMap breaks the collections above down into one big set of tuples</span>
<span class="k">def</span> <span class="nf">xtractNegative</span><span class="p">(</span><span class="n">userIDAndPosItemIDs</span><span class="p">):</span>
    <span class="k">def</span> <span class="nf">pickEnoughNegatives</span><span class="p">(</span><span class="n">line</span><span class="p">):</span>
        <span class="n">userID</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">posItemIDSet</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
        <span class="c1">#posItemIDSet = line[1]</span>
        <span class="n">negative</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="n">allItemIDs</span> <span class="o">=</span> <span class="n">bAllItemIDs</span><span class="o">.</span><span class="n">value</span>
        <span class="c1"># Keep about as many negative examples per user as positive. Duplicates are OK.</span>
        <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">while</span> <span class="p">(</span><span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">allItemIDs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">negative</span><span class="p">)</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">posItemIDSet</span><span class="p">)):</span>
            <span class="n">itemID</span> <span class="o">=</span> <span class="n">allItemIDs</span><span class="p">[</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">allItemIDs</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)]</span>
            <span class="k">if</span> <span class="n">itemID</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">posItemIDSet</span><span class="p">:</span>
                <span class="n">negative</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itemID</span><span class="p">)</span>
            <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
        
        <span class="c1"># Result is a collection of (user,negative-item) tuples</span>
        <span class="k">return</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">itemID</span><span class="p">:</span> <span class="p">(</span><span class="n">userID</span><span class="p">,</span> <span class="n">itemID</span><span class="p">),</span> <span class="n">negative</span><span class="p">)</span>

    <span class="c1"># Init an RNG and the item IDs set once for partition</span>
    <span class="c1"># allItemIDs = bAllItemIDs.value</span>
    <span class="k">return</span> <span class="nb">map</span><span class="p">(</span><span class="n">pickEnoughNegatives</span><span class="p">,</span> <span class="n">userIDAndPosItemIDs</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">ratioOfCorrectRanks</span><span class="p">(</span><span class="n">positiveRatings</span><span class="p">,</span> <span class="n">negativeRatings</span><span class="p">):</span>
    
    <span class="c1"># find number elements in arr that has index &gt;= start and has value smaller than x</span>
    <span class="c1"># arr is a sorted array</span>
    <span class="k">def</span> <span class="nf">findNumElementsSmallerThan</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
        <span class="n">left</span> <span class="o">=</span> <span class="n">start</span>
        <span class="n">right</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">arr</span><span class="p">)</span> <span class="o">-</span><span class="mi">1</span>
        <span class="c1"># if x is bigger than the biggest element in arr</span>
        <span class="k">if</span> <span class="n">start</span> <span class="o">&gt;</span> <span class="n">right</span> <span class="ow">or</span> <span class="n">x</span> <span class="o">&gt;</span> <span class="n">arr</span><span class="p">[</span><span class="n">right</span><span class="p">]:</span>
            <span class="k">return</span> <span class="n">right</span> <span class="o">+</span> <span class="mi">1</span>
        <span class="n">mid</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
        <span class="k">while</span> <span class="n">left</span> <span class="o">&lt;=</span> <span class="n">right</span><span class="p">:</span>
            <span class="n">mid</span> <span class="o">=</span> <span class="p">(</span><span class="n">left</span> <span class="o">+</span> <span class="n">right</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
            <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">mid</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">x</span><span class="p">:</span>
                <span class="n">left</span> <span class="o">=</span> <span class="n">mid</span> <span class="o">+</span> <span class="mi">1</span>
            <span class="k">elif</span> <span class="n">arr</span><span class="p">[</span><span class="n">mid</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">x</span><span class="p">:</span>
                <span class="n">right</span> <span class="o">=</span> <span class="n">mid</span> <span class="o">-</span> <span class="mi">1</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">while</span> <span class="n">mid</span><span class="o">-</span><span class="mi">1</span> <span class="o">&gt;=</span> <span class="n">start</span> <span class="ow">and</span> <span class="n">arr</span><span class="p">[</span><span class="n">mid</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">x</span><span class="p">:</span>
                    <span class="n">mid</span> <span class="o">-=</span> <span class="mi">1</span>
                <span class="k">return</span> <span class="n">mid</span>
        <span class="k">return</span> <span class="n">mid</span> <span class="k">if</span> <span class="n">arr</span><span class="p">[</span><span class="n">mid</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">x</span> <span class="k">else</span> <span class="n">mid</span> <span class="o">+</span> <span class="mi">1</span>
    
    <span class="c1">## AUC may be viewed as the probability that a random positive item scores</span>
    <span class="c1">## higher than a random negative one. Here the proportion of all positive-negative</span>
    <span class="c1">## pairs that are correctly ranked is computed. The result is equal to the AUC metric.</span>
    <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1">## L</span>
    <span class="n">total</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1">## L</span>
    
    <span class="c1"># sorting positiveRatings array needs more cost</span>
    <span class="c1">#positiveRatings = np.array(map(lambda x: x.rating, positiveRatings))</span>

    <span class="n">negativeRatings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">.</span><span class="n">rating</span><span class="p">,</span> <span class="n">negativeRatings</span><span class="p">))</span>
    
    <span class="c1">#np.sort(positiveRatings)</span>
    <span class="n">negativeRatings</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span><span class="c1"># = np.sort(negativeRatings)</span>
    <span class="n">total</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">positiveRatings</span><span class="p">)</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">negativeRatings</span><span class="p">)</span>
    
    <span class="k">for</span> <span class="n">positive</span> <span class="ow">in</span> <span class="n">positiveRatings</span><span class="p">:</span>
        <span class="c1"># Count the correctly-ranked pairs</span>
        <span class="n">correct</span> <span class="o">+=</span> <span class="n">findNumElementsSmallerThan</span><span class="p">(</span><span class="n">negativeRatings</span><span class="p">,</span> <span class="n">positive</span><span class="o">.</span><span class="n">rating</span><span class="p">)</span>
        
    <span class="c1">## Return AUC: fraction of pairs ranked correctly</span>
    <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">correct</span><span class="p">)</span> <span class="o">/</span> <span class="n">total</span>

<span class="k">def</span> <span class="nf">calculateAUC</span><span class="p">(</span><span class="n">positiveData</span><span class="p">,</span> <span class="n">bAllItemIDs</span><span class="p">,</span> <span class="n">predictFunction</span><span class="p">):</span>
    <span class="c1"># Take held-out data as the &quot;positive&quot;, and map to tuples</span>
    <span class="n">positiveUserProducts</span> <span class="o">=</span> <span class="n">positiveData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
    <span class="c1"># Make predictions for each of them, including a numeric score, and gather by user</span>
    <span class="n">positivePredictions</span> <span class="o">=</span> <span class="n">predictFunction</span><span class="p">(</span><span class="n">positiveUserProducts</span><span class="p">)</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">r</span><span class="o">.</span><span class="n">user</span><span class="p">)</span>
    
    <span class="c1"># Create a set of &quot;negative&quot; products for each user. These are randomly chosen </span>
    <span class="c1"># from among all of the other items, excluding those that are &quot;positive&quot; for the user. </span>
    <span class="n">negativeUserProducts</span> <span class="o">=</span> <span class="n">positiveUserProducts</span><span class="o">.</span><span class="n">groupByKey</span><span class="p">()</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">xtractNegative</span><span class="p">)</span><span class="o">.</span><span class="n">flatMap</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span>
    <span class="c1"># Make predictions on the rest</span>
    <span class="n">negativePredictions</span> <span class="o">=</span> <span class="n">predictFunction</span><span class="p">(</span><span class="n">negativeUserProducts</span><span class="p">)</span><span class="o">.</span><span class="n">groupBy</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">r</span><span class="o">.</span><span class="n">user</span><span class="p">)</span>
    
    <span class="k">return</span> <span class="p">(</span>
            <span class="n">positivePredictions</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">negativePredictions</span><span class="p">)</span>
                <span class="o">.</span><span class="n">values</span><span class="p">()</span>
                <span class="o">.</span><span class="n">map</span><span class="p">(</span>
                    <span class="k">lambda</span> <span class="n">positive_negativeRatings</span><span class="p">:</span> <span class="n">ratioOfCorrectRanks</span><span class="p">(</span><span class="n">positive_negativeRatings</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">positive_negativeRatings</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
                <span class="p">)</span>
                <span class="o">.</span><span class="n">mean</span><span class="p">()</span>
            <span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">validateModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">all_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;* Model validation (Area under the ROC curve)&#39;</span><span class="p">)</span>
    <span class="k">global</span> <span class="n">bAllItemIDs</span>
    
    <span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="c1"># we need to pass the data we kept out for training the model</span>
    <span class="n">auc</span> <span class="o">=</span> <span class="n">calculateAUC</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">bAllItemIDs</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predictAll</span><span class="p">)</span>
    <span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;auc=&quot;</span><span class="p">,</span><span class="n">auc</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;finish in </span><span class="si">%f</span><span class="s2"> seconds&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">t1</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">auc</span>
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This function does not work if we try to compute the <b>bAllItemIDs</b> variable inside: this is the reason why we decided to implement it as a <b>global</b> one
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At the moment, it is necessary to manually unpersist the RDDs inside the model when you are done with it. The following function can be used to make sure models are promptly uncached.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">unpersist</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
    <span class="n">model</span><span class="o">.</span><span class="n">userFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>
    <span class="n">model</span><span class="o">.</span><span class="n">productFeatures</span><span class="p">()</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>

<span class="c1"># uncache data and model when they are no longer used  </span>
<span class="n">unpersist</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
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<p><hr></p>
<h2 id="Experiments---Other-models">Experiments - Other models<a class="anchor-link" href="#Experiments---Other-models">&#182;</a></h2><p>We are able now to create a reccomandation system, given a rating matrix. In order to explore the possibilities and compare different techniques, we are going to instantiate several other models using the functions we have previously defined</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># reccomandations, users and n are the same variables used before</span>

<span class="c1"># this flag is used to indicate if the models have already been saved on disk</span>
<span class="c1"># alreadySaved = False</span>

<span class="k">def</span> <span class="nf">all_training_steps</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">showRec</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">numbRec</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    With this function, we create the model from skratch (or load it from the disk, if available) and </span>
<span class="sd">    we use it to make predictions</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="k">global</span> <span class="n">users</span>
    <span class="k">global</span> <span class="n">n</span>
    <span class="k">global</span> <span class="n">recommendations</span>
    
    <span class="n">auc</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">modelName</span> <span class="o">=</span> <span class="n">currentRDD</span><span class="p">[</span><span class="s1">&#39;filename&#39;</span><span class="p">]</span>
    <span class="n">alreadySaved</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="c1">#alreadySaved = not &#39;No such file or directory&#39; in str(run_cmd([&#39;hdfs&#39;, &#39;dfs&#39;, &#39;-ls&#39;, modelName + &#39;.spark&#39;])[2])</span>
    
    <span class="k">if</span> <span class="n">split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="c1"># Get all unique artistId, and broadcast them</span>
        <span class="n">allItemIDs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">[</span><span class="s1">&#39;dataset&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">())</span>
        <span class="n">bAllItemIDs</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">allItemIDs</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">*** &#39;</span> <span class="o">+</span> <span class="n">modelName</span> <span class="o">+</span> <span class="s1">&#39; ***&#39;</span><span class="p">)</span>
    <span class="c1"># create/load the model</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">alreadySaved</span><span class="p">:</span>
        <span class="c1"># map to Ratings</span>
        <span class="n">allData</span> <span class="o">=</span> <span class="n">ratingDefinitionRDD</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">)</span>
        <span class="c1"># compute the model and the validation set</span>
        <span class="n">model</span><span class="p">,</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">test_set</span> <span class="o">=</span> <span class="n">trainTheModel</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="n">iterations</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="n">split</span><span class="p">)</span>
        <span class="n">saveModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">modelName</span><span class="p">,</span> <span class="n">lsShow</span><span class="o">=</span><span class="n">lsShow</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1">#allData = sc.textFile(modelName + str(&#39;/ratings.txt&#39;))</span>
        <span class="c1">#allData = sc.textFile(modelName + str(&#39;/validationSet.txt&#39;))</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">loadModel</span><span class="p">(</span><span class="n">modelName</span><span class="p">)</span>
    <span class="c1"># model validation</span>
    <span class="k">if</span> <span class="n">split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">auc</span> <span class="o">=</span> <span class="n">validateModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">allData</span><span class="p">,</span> <span class="n">test_set</span><span class="p">)</span>
        
    <span class="c1"># user reccomandations</span>
    <span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">users</span><span class="p">:</span>
        <span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">topN_recommendations</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">user</span><span class="p">,</span> <span class="n">numbRec</span><span class="p">,</span> <span class="n">showRec</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
    <span class="n">recommendations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
    <span class="c1"># uncache the model</span>
    <span class="n">unpersist</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
    <span class="c1"># result: how valid is the model?</span>
    <span class="k">return</span> <span class="n">auc</span><span class="p">,</span> <span class="n">res</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">auc</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="n">all_training_steps</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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*** normalized_model ***
* Map to Rating
* Training the model
finish training model in 77.938318 secs
* Save the model on disk
model saved on disk in 2.323449 secs

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
Let us explain <u>why</u> we have created the method above in this way
<br><br>
First of all, the reason why it exists is to have an unique method able to:
<ul>
    <li>create the <b>Rating</b> matrix</li>
    <li><b>train</b> the model</li>
    <li>eventually <b>validate</b> it</li>
    <li><b>recommend</b> some artists to a bunch of users</li>
    <li><b>load</b> from and <b>save</b> to disk the model, in order not to waste time recomputing it in the future 
</ul>
<br>
Now: the code may look complicated, but it is actually an execution flow composed by the functions previously defined in this section of the notebook.
<h3>The problem</h3>
Our result is not as powerful as we expected because the save/load process <b>is not effective</b> at all. The reason is: Python APIs does not allow us to save on disk RDDs as <u>objects</u> (we are forced to save them as text files)
<h3>Consequences</h3>
Because of the operation the method performs, if we want to save computational time we need to save on disk both the model, the rating matrix (RDD of "Ratings") and the validation data (DoubleRDD). Altough we are able to save the model on disk, the rating matrix cannot be saved in a proper format.<br>
We opted then to create a dictionary (*<b>userArtistDataRDDs</b>*), able to maintain the pointers to all the created RDDs we need to operate
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<h2 id="3.5-Evaluating-Recommendation-Quality">3.5 Evaluating Recommendation Quality<a class="anchor-link" href="#3.5-Evaluating-Recommendation-Quality">&#182;</a></h2><p>In this section, we study how to evaluate the quality of our model. It's hard to say how good the recommendations are.
One of serveral methods approach to evaluate  a recommender based on its ability to rank good items (artists) high in a list of recommendations. The problem is how to define "good artists". Currently, by training all data, "good artists" is defined as "artists the user has listened to", and the recommender system has already received all of this information as input. It could trivially return the users previously-listened artists as top recommendations and score perfectly. Indeed, this is not useful, because the recommender's is used to recommend artists that the user has <strong>never</strong> listened to.</p>
<p>To overcome that problem, we can hide the some of the artist play data and only use the rest to train model. Then, this held-out data can be interpreted as a collection of "good" recommendations for each user. The recommender is asked to rank all items in the model, and the rank of the held-out artists are examined. Ideally the recommender places all of them at or near the top of the list.</p>
<p>The recommender's score can then be computed by comparing all held-out artists' ranks to the rest.  The fraction of pairs where the held-out artist is ranked higher is its score. 1.0 is perfect, 0.0 is the worst possible score, and 0.5 is the expected value achieved from randomly ranking artists.</p>
<p>AUC(Area Under the Curve) can be used as a metric to evaluate model. It is also viewed as the probability that a randomly-chosen "good" artist ranks above a randomly-chosen "bad" artist.</p>
<p>Next, we split the training data into 2 parts: <code>trainData</code> and <code>cvData</code> with ratio 0.9:0.1 respectively, where <code>trainData</code> is the dataset that will be used to train model. Then we write a function to calculate AUC to evaluate the quality of our model.</p>

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<h3 id="Question-9">Question 9<a class="anchor-link" href="#Question-9">&#182;</a></h3><h4 id="Question-9.1">Question 9.1<a class="anchor-link" href="#Question-9.1">&#182;</a></h4><div class="alert alert-info">
Split the data into `trainData` and `cvData` with ratio 0.9:0.1 and use the first part to train a statistic model with:
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<li>`rank`=10</li>
<li>`iterations`=5</li>
<li>`lambda_`=0.01</li>
<li>`alpha`=1.0</li>
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<h4 id="Reminder">Reminder<a class="anchor-link" href="#Reminder">&#182;</a></h4><p>The code required for this question has been moved above into the function trainTheModel(dataset, split)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># re-train the model, using only training data (90 % of the dataset)</span>
<span class="n">allData</span> <span class="o">=</span> <span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;ratings&#39;</span><span class="p">]</span>
<span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">cvData</span> <span class="o">=</span> <span class="n">trainTheModel</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span>
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<pre>* Training the model
finish training model in 114.764969 secs
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<h4 id="Area-under-the-ROC-curve:-a-function-to-compute-it">Area under the ROC curve: a function to compute it<a class="anchor-link" href="#Area-under-the-ROC-curve:-a-function-to-compute-it">&#182;</a></h4>
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These functions have been moved in the previous section.
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<h4 id="Question-9.2">Question 9.2<a class="anchor-link" href="#Question-9.2">&#182;</a></h4><div class="alert alert-info">
Using part `cvData` and function `calculateAUC` to compute the AUC of the trained model.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Get all unique artistId, and broadcast them</span>
<span class="n">allItemIDs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">allData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">distinct</span><span class="p">()</span><span class="o">.</span><span class="n">collect</span><span class="p">())</span>
<span class="n">bAllItemIDs</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">allItemIDs</span><span class="p">)</span>

<span class="n">validateModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">allData</span><span class="p">,</span> <span class="n">cvData</span><span class="p">)</span>
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<pre>* Model validation (Area under the ROC curve)
auc= 0.9663505905456732
finish in 88.829242 seconds
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<pre>&#39;\nt0 = time()\n#\xa0we need to pass the data we kept out for training the model\nauc = calculateAUC(cvData , bAllItemIDs, model.predictAll)\nt1 = time()\nprint(&#34;auc=&#34;,auc)\nprint(&#34;finish in %f seconds&#34; % (t1 - t0))\n&#39;</pre>
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<h2 id="Experiment:-pre-processed-data">Experiment: pre-processed data<a class="anchor-link" href="#Experiment:-pre-processed-data">&#182;</a></h2><p>What if we consider now to analyze dataset that have been pre-processed? (data standardization)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">auc_test</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">rec_test</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># test using the non base dataSet and the normalized one</span>
<span class="k">for</span> <span class="n">currentRDD</span> <span class="ow">in</span> <span class="p">[</span> <span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="p">]:</span>
    <span class="n">auc</span><span class="p">,</span> <span class="n">rec</span> <span class="o">=</span> <span class="n">all_training_steps</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="n">showRec</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
    <span class="n">auc_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">auc</span><span class="p">)</span>
    <span class="n">rec_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rec</span><span class="p">)</span>
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*** basic_model ***
* Map to Rating
* Training the model
finish training model in 125.487013 secs
* Save the model on disk
model saved on disk in 2.731073 secs
* Model validation (Area under the ROC curve)
auc= 0.9667654446584664
finish in 94.021075 seconds


*** normalized_model ***
* Map to Rating
* Training the model
finish training model in 87.756866 secs
* Save the model on disk
model saved on disk in 2.697860 secs
* Model validation (Area under the ROC curve)
auc= 0.8621208216257236
finish in 67.951473 seconds

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
As we can see from the value of AUC, the non-normalized model seems to give us better results.
<br><br>
One possible reason is that the standardization of the rows may change the distribution of the data but we cannot prove it here. What we can argue is that, based on what is described in the paper [Collaborative Filtering for Implicit Feedback Datasets] concerning the implementation of ALS in Mllib, the presence of a confidence factor which is increased by the value of the rating may be influencd by the rescalig of some of those retings. That is, we lower the confidence for each rating and we introduce also negative ratings. 

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<h2 id="Experiment:-filter-least-popular-artists">Experiment: filter least popular artists<a class="anchor-link" href="#Experiment:-filter-least-popular-artists">&#182;</a></h2><p>Now, exploititng the funtion we defined before we want to run a simple test to understand if not including the artist which we have defined as <em>fewListenedArtists</em> in the dataset would have some performance improvement. Our idea is that those features, which are associated with a low number of plays may present two situations:</p>
<ul>
<li>At least one play per user: so we have only $x$ potential  entries of the type <em>(userID, artistID, 1)</em></li>
<li>One single user with all the plays: in this case there is only one user with that artist associated with</li>
</ul>
<p>Considering this, we may arguee about the usefulness of those features in identifying a profile of a user which can be used to suggest new artists to other users. This is mainly due to te fact that few plays of an artist may not indiicate that a user likes that artist (maybe he/she miss clicked a song or was directed there by a predefined playlist). The only useful case is when a single user has assosciated all the <em>Play Counts</em> of artists which we defined as <em>fewListenedArtists</em>. In this case, due to the user may have assosciated other artists, this can represent a potential candiadate to suggest the <em>fewListenedArtist</em> to other user which are similar to him/her.</p>

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<div class="highlight"><pre><span></span><span class="n">thresholds</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span>
<span class="n">simulations</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;result_models_cleaned.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">thr</span> <span class="ow">in</span> <span class="n">thresholds</span><span class="p">:</span>
    <span class="n">cleanedRDD</span> <span class="o">=</span> <span class="n">filterByArtistTotalPlays</span><span class="p">(</span><span class="n">userArtistDataRDD</span><span class="p">,</span> <span class="n">thr</span><span class="p">)</span>
    <span class="n">currentRDD</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">cleanedRDD</span><span class="p">,</span>
            <span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="s1">&#39;cleaned_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">thr</span><span class="p">),</span>
            <span class="s1">&#39;ratings&#39;</span><span class="p">:</span> <span class="bp">None</span><span class="p">,</span>
            <span class="s1">&#39;validation&#39;</span><span class="p">:</span> <span class="bp">None</span>
        <span class="p">}</span>
    <span class="n">userArtistDataRDDs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">)</span>
    <span class="n">current_auc</span><span class="p">,</span> <span class="n">rec</span> <span class="o">=</span> <span class="n">all_training_steps</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">])</span>
    <span class="n">simulations</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">thr</span><span class="p">,</span> <span class="n">current_auc</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="s2">&quot;Threshold: {} --- AUC: {}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">thr</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_auc</span><span class="p">)))</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Threshold: {} --- AUC: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">thr</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_auc</span><span class="p">)))</span>    

<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s2">&quot;result_models_cleaned.txt&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">record</span> <span class="ow">in</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">():</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>

<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
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<pre>Threshold: 0 --- AUC: 0.9649052943863901

Threshold: 1 --- AUC: 0.9649305891958085

Threshold: 2 --- AUC: 0.9639512096356811

Threshold: 3 --- AUC: 0.9634565042024438

Threshold: 4 --- AUC: 0.9635476872572675

Threshold: 5 --- AUC: 0.9637559708869771

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
This is different from what we expected: removing very-low popularity artist seems not to improve the effectiveness of the reccomender system. 
<br><br>
Is it reue that the ALS algorithm implemneted in Spark Mllib it's modified in order to assign a confidence level based on the value of the rating, but we expected that the removal of features could have improved the AUC. We came to the conclusion that there might be  cases of some users which are quite new in the system and have few <i>playcounts</i> for the few artists. Under hypohtesis that the few entries for some of those users are concerning artsits that we defined as <i>fewListenedArtsits</i>, this appeoach may leave those users with few entries in the Rating matrix or worse it eliminates the users.

<br><br>
Then when considering the trainig and the validation, some of those users are completly absent in the training but present with  few left entries in the validation. That is, for the Collaborative Fealtering those are new users and we know that there is a <b>cold start</b> problem for which the ALS is not able to provide user features when applied.
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<h4 id="Question-9.3">Question 9.3<a class="anchor-link" href="#Question-9.3">&#182;</a></h4><div class="alert alert-info">
Now we have the UAC of our model, it’s helpful to benchmark this against a simpler approach. For example, consider recommending the globally most-played artists to every user. This is not personalized, but is simple and may be effective.   
<ul></ul>
Implement this simple pupolarity-based prediction algorithm, evaluate its AUC score, and compare to the results achieved by the more sophisticated ALS algorithm.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">trainData</span> <span class="o">=</span> <span class="n">training</span>

<span class="n">bListenCount</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">trainData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">reduceByKey</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">collectAsMap</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">predictMostListened</span><span class="p">(</span><span class="n">allData</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">allData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="n">Rating</span><span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">bListenCount</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">get</span><span class="p">(</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mf">0.0</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">auc</span> <span class="o">=</span> <span class="n">calculateAUC</span><span class="p">(</span><span class="n">cvData</span><span class="p">,</span> <span class="n">bListenCount</span><span class="p">,</span> <span class="n">predictMostListened</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">auc</span><span class="p">)</span>
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<pre>0.937308569676979
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<h4 id="Comments">Comments<a class="anchor-link" href="#Comments">&#182;</a></h4><div class="alert alert-warning">
The popularity-based algorithm is  performing almost 3% worst than the ALS one. For some extent, the fact that the popularity-based is doing a considerable good job is due to the ALS is just considering the number of times a given artist is listened by a given user. That is, reading the paper [Collaborative Filtering for Implicit Feedback Datasets], the rating is considered as a composiiton of preference $p_{ui}$ with the respect of the user $u$ and the item $i$, and a confidence level $c_{ui}$ which is weighted according to the $alfa$ parameter. So, being a popular artist  defined as the one which is listened the majority of the times, the confidence level in general is expect to be quite high.
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<h2 id="3.6-Personalized-recommendations-with-ALS">3.6 Personalized recommendations with ALS<a class="anchor-link" href="#3.6-Personalized-recommendations-with-ALS">&#182;</a></h2><p>In the previous section, we build our models with some given paramters without any knowledge about them. Actually, choosing the best parameters' values is very important. It can significantly affect the quality of models. Especially, with the current implementation of ALS in MLLIB, these parameters are not learned by the algorithm, and must be chosen by the caller. The following parameters should get consideration before training models:</p>
<ul>
<li><p><code>rank = 10</code>: the number of latent factors in the model, or equivalently, the number of columns $k$ in the user-feature and product-feature matrices. In non-trivial cases, this is also their rank.</p>
</li>
<li><p><code>iterations = 5</code>: the number of iterations that the factorization runs. Instead of runing the algorithm until RMSE converged which actually takes very long time to finish with large datasets, we only let it run in a given number of iterations. More iterations take more time but may produce a better factorization.</p>
</li>
<li><p><code>lambda_ = 0.01</code>: a standard overfitting parameter. Higher values resist overfitting, but values that are too high hurt the factorization's accuracy.</p>
</li>
<li><p><code>alpha = 1.0</code>: controls the relative weight of observed versus unobserved userproduct interactions in the factorization.</p>
</li>
</ul>
<p>Although all of them have impact on the models' quality, <code>iterations</code> is more of a constraint on resources used in the factorization. So, <code>rank</code>, <code>lambda_</code> and <code>alpha</code> can be considered hyperparameters to the model. 
We will try to find "good" values for them. Indeed, the values of hyperparameter are not necessarily optimal. Choosing good hyperparameter values is a common problem in machine learning. The most basic way to choose values is to simply try combinations of values and evaluate a metric for each of them, and choose the combination that produces the best value of the metric.</p>

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<h3 id="Question-10">Question 10<a class="anchor-link" href="#Question-10">&#182;</a></h3><h4 id="Question-10.1">Question 10.1<a class="anchor-link" href="#Question-10.1">&#182;</a></h4><div class="alert alert-info">
For simplicity, assume that we want to explore the following parameter space: $ rank \in \{10, 50\}$, $lambda\_ \in \{1.0, 0.0001\}$ and $alpha \in \{1.0, 40.0\}$.

Find the best combination of them in terms of the highest AUC value.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">evaluations</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">rank</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">50</span><span class="p">]:</span>
    <span class="k">for</span> <span class="n">lambda_</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0001</span><span class="p">]:</span>
        <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">40.0</span><span class="p">]:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train model with rank=</span><span class="si">%d</span><span class="s2"> lambda_=</span><span class="si">%f</span><span class="s2"> alpha=</span><span class="si">%f</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="p">))</span>
            <span class="c1"># with each combination of params, we should run multiple times and get avg</span>
            <span class="c1"># for simple, we only run one time.</span>
            <span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span> <span class="n">trainData</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span> <span class="p">)</span>
            
            <span class="n">auc</span> <span class="o">=</span> <span class="n">calculateAUC</span><span class="p">(</span> <span class="n">cvData</span><span class="p">,</span> <span class="n">bAllItemIDs</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">predictAll</span> <span class="p">)</span>
            
            <span class="n">evaluations</span><span class="o">.</span><span class="n">append</span><span class="p">(((</span><span class="n">rank</span><span class="p">,</span> <span class="n">lambda_</span><span class="p">,</span> <span class="n">alpha</span><span class="p">),</span> <span class="n">auc</span><span class="p">))</span>
            
            <span class="n">unpersist</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
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<pre>Train model with rank=10 lambda_=1.000000 alpha=1.000000
Train model with rank=10 lambda_=1.000000 alpha=40.000000
Train model with rank=10 lambda_=0.000100 alpha=1.000000
Train model with rank=10 lambda_=0.000100 alpha=40.000000
Train model with rank=50 lambda_=1.000000 alpha=1.000000
Train model with rank=50 lambda_=1.000000 alpha=40.000000
Train model with rank=50 lambda_=0.000100 alpha=1.000000
Train model with rank=50 lambda_=0.000100 alpha=40.000000
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># sort based on the highest AUC value</span>
<span class="n">evaluations</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span> <span class="p">)</span>
                 
<span class="n">evalDataFrame</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">data</span><span class="o">=</span><span class="n">evaluations</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">evalDataFrame</span><span class="p">)</span>

<span class="n">trainData</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>
<span class="n">cvData</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>
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<pre>                    0         1
0  (10, 0.0001, 40.0)  0.975972
1     (50, 1.0, 40.0)  0.975827
2     (10, 1.0, 40.0)  0.975789
3  (50, 0.0001, 40.0)  0.975012
4      (10, 1.0, 1.0)  0.968950
5      (50, 1.0, 1.0)  0.966317
6   (10, 0.0001, 1.0)  0.963890
7   (50, 0.0001, 1.0)  0.953247
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<pre>PythonRDD[2343] at RDD at PythonRDD.scala:48</pre>
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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
The search for <b>hyperparameters</b> resulted into the following paramters: 
<ul>
    <li>rank: 10</li>
    <li>lambda: 1</li>
    <li>alpha: 40</li>
</ul>
<br>
For the alpha parameter this is the same value which is suggested in the paper (Collaborative Filtering for Implicit Feedback Datasets) as the value which leades to better results. Just to remind, the alpha parameter is used for weigth more the confience values for which the associated rating is high. Indeed, as the paper suggest, we would like to have higher confidence values $c_{ui}$ for thoses ratings for which the value is high $r_{ui}$ (in our case the number of plays for the artist). That is intuitive, if an artist has been listen a lot we are more confident that the rating for that artist is correlated with his popularity. 
<br>
<br>
Also, the second best result is close to the best one. If we observe the values the difference is related to the higer value of the rank.
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<h4 id="Question-10.2">Question 10.2<a class="anchor-link" href="#Question-10.2">&#182;</a></h4><div class="alert alert-info">
Using "optimal" hyper-parameters in question 10.1, re-train the model and show top-5 artist names recommendated for user `2093760`.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span> <span class="n">trainData</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">lambda_</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">40.0</span> <span class="p">)</span>
<span class="n">allData</span><span class="o">.</span><span class="n">unpersist</span><span class="p">()</span>

<span class="n">userID</span> <span class="o">=</span> <span class="mi">2093760</span>
<span class="c1"># use the function defined before</span>
<span class="n">topN_recommendations</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">userID</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>

<span class="n">unpersist</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
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<pre>Recommendations for userID: 2093760
[&#39;50 Cent&#39;, &#39;Maroon 5&#39;, &#39;Eminem&#39;, &#39;Green Day&#39;]

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<h1 id="EXTRA">EXTRA<a class="anchor-link" href="#EXTRA">&#182;</a></h1><h2 id="Experiment:-test-a-simple-improving-method">Experiment: test a simple improving method<a class="anchor-link" href="#Experiment:-test-a-simple-improving-method">&#182;</a></h2><p>Here we have tried a method we wanted to test for pure practice, reasoning then about the results. The initial idea was about applying a Bagging (bootstap aggregating) but it requires to sample the dataSet with replacement. This is not our case, where for Collaborative Feltering we want to avoid duplicate records as we did previously. What came out is a custom (or better to say "partially") implementation of what is called in the literature <em>Subbaging</em> (subsample aggregating).</p>
<p><br>
As said, we borrow partially this idea, because then we proceded creating up to five model starting from the subsampled dataSet and then produce a vote from each one to rank the recommendations for a given user. 
What we have tryied is coded below:</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># starts with the original dataset normalized</span>
<span class="n">dataSet</span> <span class="o">=</span> <span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;dataset&#39;</span><span class="p">]</span>

<span class="c1"># variables used to store the sampledDataSets and the (AUC, Recommendation) tuples</span>
<span class="n">sampledDataSets</span><span class="o">=</span> <span class="p">[]</span>
<span class="n">modelsAUCandRec</span> <span class="o">=</span> <span class="p">[]</span>

<span class="c1"># generate some datasets sampled from the original one</span>
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
    <span class="c1"># sampling without replacement</span>
    <span class="n">new_RDD</span> <span class="o">=</span> <span class="n">dataSet</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="kc">False</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">)</span>
    <span class="n">sampledDataSets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_RDD</span><span class="p">)</span>
    <span class="n">currentRDD</span> <span class="o">=</span> <span class="p">{</span>
            <span class="s1">&#39;dataset&#39;</span><span class="p">:</span> <span class="n">new_RDD</span><span class="p">,</span>
            <span class="s1">&#39;filename&#39;</span><span class="p">:</span> <span class="s1">&#39;bagging_&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">),</span>
            <span class="s1">&#39;ratings&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
            <span class="s1">&#39;validation&#39;</span><span class="p">:</span> <span class="kc">None</span>
        <span class="p">}</span>
    <span class="n">userArtistDataRDDs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">)</span>
    <span class="c1"># executing all the functions to generate the Rating and the model. This also returns recommendations</span>
    <span class="n">current_auc</span><span class="p">,</span> <span class="n">rec</span> <span class="o">=</span> <span class="n">all_training_steps</span><span class="p">(</span><span class="n">currentRDD</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="n">showRec</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">lsShow</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">numbRec</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
    <span class="n">modelsAUCandRec</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">current_auc</span><span class="p">,</span> <span class="n">rec</span><span class="p">))</span>
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*** bagging_0 ***
* Map to Rating
* Training the model
finish training model in 74.926031 secs
* Save the model on disk
model saved on disk in 1.718022 secs
* Model validation (Area under the ROC curve)
auc= 0.8529861294550576
finish in 59.847561 seconds


*** bagging_1 ***
* Map to Rating
* Training the model
finish training model in 84.448639 secs
* Save the model on disk
model saved on disk in 3.001408 secs
* Model validation (Area under the ROC curve)
auc= 0.8532908072675169
finish in 63.754844 seconds


*** bagging_2 ***
* Map to Rating
* Training the model
finish training model in 76.319346 secs
* Save the model on disk
model saved on disk in 2.051286 secs
* Model validation (Area under the ROC curve)
auc= 0.8503333806364423
finish in 58.944925 seconds


*** bagging_3 ***
* Map to Rating
* Training the model
finish training model in 78.326096 secs
* Save the model on disk
model saved on disk in 2.347808 secs
* Model validation (Area under the ROC curve)
auc= 0.850564492617121
finish in 57.907561 seconds


*** bagging_4 ***
* Map to Rating
* Training the model
finish training model in 80.108215 secs
* Save the model on disk
model saved on disk in 2.267891 secs
* Model validation (Area under the ROC curve)
auc= 0.8523925137177619
finish in 56.663781 seconds

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># define a dictionary to rank the recommendations, the user global variables contains the users to recommend</span>
<span class="n">referenceUser</span> <span class="o">=</span> <span class="n">users</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="c1"># define a dictionary (hasTable) to map the artist&#39;s names to the occurencies (votes from the models)</span>
<span class="n">rankingUserReference</span> <span class="o">=</span> <span class="p">{}</span>

<span class="c1"># extract the recommendation for the first user (userReference). Note, the recommendation are saved in a vector </span>
<span class="c1"># (one tuple for each model containing the AUC and the recommendations)</span>
<span class="n">userRec</span> <span class="o">=</span> <span class="p">[</span> <span class="n">rec_list</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">rec_list</span> <span class="ow">in</span> <span class="n">modelsAUCandRec</span> <span class="p">]</span>

<span class="k">for</span> <span class="n">listRec</span> <span class="ow">in</span> <span class="n">userRec</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">rec</span> <span class="ow">in</span> <span class="n">listRec</span><span class="p">:</span>
        <span class="c1"># if the key already exists, increment the counter</span>
        <span class="k">if</span> <span class="n">rankingUserReference</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="n">rec</span><span class="p">):</span>
            <span class="n">rankingUserReference</span><span class="p">[</span><span class="n">rec</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
        <span class="c1"># if not set it to zero</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">rankingUserReference</span><span class="p">[</span><span class="n">rec</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>

<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rankingUserReference</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">rankingUserReference</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="p">]</span>    
<span class="n">tmp</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># printing for testing the results</span>
<span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
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<pre>[(&#39;Massive Attack&#39;, 3),
 (&#39;Dredg&#39;, 2),
 (&#39;System of a Down&#39;, 2),
 (&#39;A Perfect Circle&#39;, 2),
 (&#39;Tool&#39;, 2)]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we recover the model for the original dataSet (the one obtained after cleaning the artistIDs)</span>
<span class="n">modelTest</span> <span class="o">=</span> <span class="n">loadModel</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;filename&#39;</span><span class="p">])</span>
<span class="n">topN_recommendations</span><span class="p">(</span><span class="n">modelTest</span><span class="p">,</span> <span class="n">referenceUser</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
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<pre>* Load the model from disk
finish loading model in 1.037837 secs
Recommendations for userID: 2400812
[&#39;A Perfect Circle&#39;, &#39;System of a Down&#39;, &#39;Tool&#39;, &#39;Rammstein&#39;, &#39;Nine Inch Nails&#39;]

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<pre>[&#39;A Perfect Circle&#39;,
 &#39;System of a Down&#39;,
 &#39;Tool&#39;,
 &#39;Rammstein&#39;,
 &#39;Nine Inch Nails&#39;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from the original RDD we construct the DataFrame</span>
<span class="n">schema</span> <span class="o">=</span> <span class="n">StructType</span><span class="p">([</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;userID&quot;</span><span class="p">,</span> <span class="n">IntegerType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;artistID&quot;</span><span class="p">,</span> <span class="n">IntegerType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">),</span> \
    <span class="n">StructField</span><span class="p">(</span><span class="s2">&quot;playCount&quot;</span><span class="p">,</span> <span class="n">FloatType</span><span class="p">(),</span> <span class="kc">True</span><span class="p">)])</span>
            
<span class="n">df</span> <span class="o">=</span> <span class="n">sqlContext</span><span class="o">.</span><span class="n">createDataFrame</span><span class="p">(</span><span class="n">userArtistDataRDDs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">&#39;dataset&#39;</span><span class="p">],</span> <span class="n">schema</span><span class="p">)</span>
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<pre>+-------+--------+---------+
| userID|artistID|playCount|
+-------+--------+---------+
|1000002|       1|     55.0|
|1000002| 1000006|     33.0|
|1000002| 1000007|      8.0|
|1000002| 1000009|    144.0|
|1000002| 1000010|    314.0|
|1000002| 1000013|      8.0|
|1000002| 1000014|     42.0|
|1000002| 1000017|     69.0|
|1000002| 1000024|    329.0|
|1000002| 1000025|      1.0|
+-------+--------+---------+
only showing top 10 rows

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we perform a join between the artistCleanedDF (which containes the name of the artists) with the constructed DF</span>
<span class="n">userArtistNamedDF</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">artistAliasCleanedDF</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;artistID&quot;</span><span class="p">)[</span><span class="n">df</span><span class="o">.</span><span class="n">userID</span> <span class="o">==</span> <span class="n">referenceUser</span><span class="p">]</span><span class="o">.</span><span class="n">cache</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistNamedDF</span><span class="p">[</span><span class="n">artistAliasCleanedDF</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;Massive Attack&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+-------+---------+--------------+
|artistID| userID|playCount|          name|
+--------+-------+---------+--------------+
|      59|2400812|    221.0|Massive Attack|
+--------+-------+---------+--------------+

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">userArtistNamedDF</span><span class="p">[</span><span class="n">artistAliasCleanedDF</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;A Perfect Circle&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>+--------+-------+---------+----------------+
|artistID| userID|playCount|            name|
+--------+-------+---------+----------------+
| 1000009|2400812|     16.0|A Perfect Circle|
+--------+-------+---------+----------------+

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<h3 id="Comment">Comment<a class="anchor-link" href="#Comment">&#182;</a></h3><div class="alert alert-warning">
With the final test we verify if the recoomendation given by our combination of models by raking is better that the recommendations given by the basic one. But this affermation is based on inference from the single result obtained here. The result can be refined increasing the number of model included in the subboosting procedure.
<br>
<br>
We want to give in any case our interpretation here. The fact that we random sample the original dataSet, which is an RDD of entries (userID, artistID, playCount) may lead to escluding, randomly, some of those entries for a user. We have observed that the majority of the population is based on entries for which the total plays, given an artist, are low. The sampling add randomness which may lead to the exclusion (considering those not selected) of entries with those artists (the one we defined as *fewListenedArtists*).
<br>
<br>
Given the hypotesis above, we think that the sampling may produce more focused profiles of users (defined by their features, in this case the artists) which can lead to more accurate matching with other users and so recommendation.

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<h1 id="Summary">Summary<a class="anchor-link" href="#Summary">&#182;</a></h1><p>In this notebook, we introduce an algorithm to do matrix factorization and the way of using it to make recommendation. Further more, we studied how to build a large-scale recommender system on SPARK using ALS algorithm and evaluate its quality. Finally, a simple approach to choose good parameters is mentioned.</p>

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<h1 id="References">References<a class="anchor-link" href="#References">&#182;</a></h1><ul>
<li>The example in section 2 is taken from <a href="infolab.stanford.edu/~ullman/mmds/ch9.pdf">Recommender system</a></li>
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