analysis_interns_groups1.html

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<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">statsmodels.stats.multicomp</span> <span class="k">import</span> <span class="n">pairwise_tukeyhsd</span>
<span class="kn">from</span> <span class="nn">statsmodels.stats.multicomp</span> <span class="k">import</span> <span class="n">MultiComparison</span>
<span class="kn">from</span> <span class="nn">statsmodels.formula.api</span> <span class="k">import</span> <span class="n">ols</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">stats</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;playlists.csv&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">encoding</span> <span class="o">=</span> <span class="s2">&quot;ISO-8859-1&quot;</span><span class="p">)</span> 
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<h3 id="Negatives-and-positives">Negatives and positives<a class="anchor-link" href="#Negatives-and-positives">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">companies</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;company&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
<span class="n">by_company</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">company</span> <span class="o">==</span> <span class="n">company</span><span class="p">]</span> <span class="k">for</span> <span class="n">company</span> <span class="ow">in</span> <span class="n">companies</span><span class="p">]</span>
<span class="n">positives</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">negatives</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">data_com</span> <span class="ow">in</span> <span class="n">by_company</span><span class="p">:</span>
    <span class="n">data_com</span> <span class="o">=</span> <span class="n">data_com</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">&#39;playlist_sample&#39;</span><span class="p">)</span>
    <span class="n">last_pl</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">data_com</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">playlist_sample</span><span class="p">)</span>
    <span class="c1">#pls = pd.DataFrame({&#39;pl&#39;:range (1,last_pl+1), &#39;old&#39;:[0]*last_pl, &#39;new&#39;:[0]*last_pl})</span>
    <span class="c1">#curr_pl = data_com.query(&#39;playlist_sample == &#39;+str(1))</span>
    <span class="c1">#pls.new[0]=(curr_pl.shape[0])/3</span>
    <span class="c1">#sum_pls = [curr_pl.shape[0]/3]</span>
    <span class="c1">#for i in range(2,last_pl+1):</span>
    <span class="c1">#    curr_pl = data_com.query(&#39;playlist_sample == &#39;+str(i))</span>
    <span class="c1">#    pre_pl = data_com.query(&#39;playlist_sample == &#39;+str(i-1))</span>
    <span class="c1">#    olds = curr_pl[&#39;song&#39;].map(pre_pl[&#39;song&#39;].value_counts()).sum(axis = 0)/3</span>
    <span class="c1">#    pls.old[i-1]= olds/3 </span>
    <span class="c1">#    pls.new[i-1]=(curr_pl.shape[0]-olds)/3</span>
    <span class="c1">#    sum_pls.append(curr_pl.shape[0]/3)</span>
    <span class="c1">#sum_pls = pd.DataFrame(sum_pls)</span>
    <span class="c1">#pls_rel = (pls[[&#39;old&#39;]].div(sum_pls.values, axis=0)*100).join(pls[[&#39;new&#39;]].div(sum_pls.values, axis=0)*100)</span>
    <span class="c1">#pls[[&#39;old&#39;,&#39;new&#39;]].plot(kind=&#39;bar&#39;, stacked=True, title=data_com.iloc[0,0])</span>
    <span class="c1">#for n in pls_rel:</span>
    <span class="c1">#    for i, (cs, ab, pc) in enumerate(zip(pls.iloc[:, 1:].cumsum(1)[n].values, pls[n].values, pls_rel[n].values)):</span>
    <span class="c1">#        if(pc&gt;0):</span>
    <span class="c1">#            plt.text(i, cs - ab/2, str(np.round(pc, 1)) + &#39;%&#39;, va=&#39;center&#39;, ha=&#39;center&#39;)</span>
    <span class="n">df_last_pl</span><span class="o">=</span> <span class="n">data_com</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">&#39;playlist_sample == &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">last_pl</span><span class="p">))</span>
    <span class="n">positives</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_last_pl</span><span class="p">)</span>
    <span class="n">pos_loc</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({},</span> <span class="n">columns</span><span class="o">=</span><span class="n">data_com</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
    <span class="n">rep</span><span class="o">=</span><span class="mi">0</span>
    <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data_com</span><span class="p">[</span><span class="n">data_com</span><span class="o">.</span><span class="n">playlist_sample</span><span class="o">&lt;</span><span class="n">last_pl</span><span class="p">]</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span> 
        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">df_last_pl</span><span class="p">[</span><span class="s1">&#39;artist&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;artist&#39;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_last_pl</span><span class="p">[</span><span class="s1">&#39;song&#39;</span><span class="p">]</span> <span class="o">==</span>  <span class="n">row</span><span class="p">[</span><span class="s1">&#39;song&#39;</span><span class="p">]))</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">and</span> <span class="n">pos_loc</span><span class="p">[(</span><span class="n">pos_loc</span><span class="p">[</span><span class="s1">&#39;artist&#39;</span><span class="p">]</span><span class="o">==</span> <span class="n">row</span><span class="p">[</span><span class="s1">&#39;artist&#39;</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">pos_loc</span><span class="p">[</span><span class="s1">&#39;song&#39;</span><span class="p">]</span> <span class="o">==</span>  <span class="n">row</span><span class="p">[</span><span class="s1">&#39;song&#39;</span><span class="p">])]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">&lt;</span><span class="mi">3</span><span class="p">:</span>
            <span class="n">pos_loc</span><span class="o">=</span> <span class="n">pos_loc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">rep</span><span class="o">+=</span><span class="mi">1</span>
    <span class="c1">#n_vs_p = pd.DataFrame({&#39;sam&#39;:[&#39;pos&#39;, &#39;neg&#39;],&#39;num&#39;:[df_last_pl.shape[0]/3,pos_loc.shape[0]/3]})</span>
    <span class="c1"># n_vs_p.plot.bar(x=&#39;sam&#39;, y=&#39;num&#39;, rot=0, title=data_com.iloc[0,0])</span>
    <span class="n">negatives</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pos_loc</span><span class="p">)</span>
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<p>Count of positives negatives and total by company.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">companies</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Negatives: &quot;</span><span class="p">,</span><span class="n">negatives</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Positives: &quot;</span><span class="p">,</span><span class="n">positives</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total: &quot;</span><span class="p">,</span> <span class="n">by_company</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
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<pre>Arte Francés
Negatives:  76.0
Positives:  29.0
Total:  147.0
Club De Banqueros y Empresarios
Negatives:  61.0
Positives:  24.0
Total:  88.0
Gramma
Negatives:  51.0
Positives:  21.0
Total:  103.0
Hotel Marrakech
Negatives:  28.0
Positives:  20.0
Total:  68.0
Specialized
Negatives:  38.0
Positives:  35.0
Total:  103.0
Urban Place
Negatives:  48.0
Positives:  24.0
Total:  85.0
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<p>Append the positives and negatives records with a new column "chosen" that takes value of 1 if is chosen.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">negatives</span><span class="p">)):</span>
    <span class="n">negatives</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">=</span><span class="mi">0</span>
    <span class="n">positives</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">=</span><span class="mi">1</span>
    <span class="n">df_n_ps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">negatives</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">positives</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\ipykernel_launcher.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  after removing the cwd from sys.path.
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<h3 id="Preprocessing">Preprocessing<a class="anchor-link" href="#Preprocessing">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>


<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span><span class="p">,</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">silhouette_samples</span><span class="p">,</span> <span class="n">silhouette_score</span><span class="p">,</span> <span class="n">calinski_harabaz_score</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bitratefiles</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bitratefiles</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float64&#39;</span><span class="p">)</span>
    <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">pitchfiles</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">pitchfiles</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float64&#39;</span><span class="p">)</span>
    <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bestkeyfiles</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">bestkeyfiles</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float64&#39;</span><span class="p">)</span>
<span class="n">df_n_ps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
Int64Index: 315 entries, 0 to 179
Data columns (total 65 columns):
company                 315 non-null object
playlist_sample         315 non-null object
namesfiles              315 non-null object
no                      315 non-null object
artist                  315 non-null object
song                    315 non-null object
sampleratefiles         315 non-null object
totalsamplesfiles       315 non-null object
durationfiles           315 non-null float64
bitratefiles            315 non-null float64
rmsfiles                315 non-null float64
rmsmedianfiles          315 non-null float64
lowenergyfiles          315 non-null float64
ASRfiles                315 non-null float64
beatspectrumfiles       315 non-null float64
eventdensityfiles       315 non-null float64
tempofiles              315 non-null float64
pulseclarityfiles       315 non-null float64
zerocrossfiles          315 non-null float64
rolloffsfiles           315 non-null float64
brightnessfiles         315 non-null float64
spreadfiles             315 non-null float64
centroidfiles           314 non-null float64
kurtosisfiles           315 non-null float64
flatnessfiles           315 non-null float64
entropyfiles            315 non-null float64
mfccfiles_1             315 non-null float64
mfccfiles_2             315 non-null float64
mfccfiles_3             315 non-null float64
mfccfiles_4             315 non-null float64
mfccfiles_5             315 non-null float64
mfccfiles_6             315 non-null float64
mfccfiles_7             315 non-null float64
mfccfiles_8             315 non-null float64
mfccfiles_9             315 non-null float64
mfccfiles_10            315 non-null float64
mfccfiles_11            315 non-null float64
mfccfiles_12            315 non-null float64
mfccfiles_13            315 non-null float64
pitchfiles              315 non-null float64
inharmonicityfiles      315 non-null float64
bestkeyfiles            315 non-null float64
keyclarityfiles         315 non-null float64
modalityfiles           315 non-null float64
tonalcentroidfiles_1    315 non-null float64
tonalcentroidfiles_2    315 non-null float64
tonalcentroidfiles_3    315 non-null float64
tonalcentroidfiles_4    315 non-null float64
tonalcentroidfiles_5    315 non-null float64
tonalcentroidfiles_6    315 non-null float64
chromagramfiles_1       315 non-null float64
chromagramfiles_2       315 non-null float64
chromagramfiles_3       315 non-null float64
chromagramfiles_4       315 non-null float64
chromagramfiles_5       315 non-null float64
chromagramfiles_6       315 non-null float64
chromagramfiles_7       315 non-null float64
chromagramfiles_8       315 non-null float64
chromagramfiles_9       315 non-null float64
chromagramfiles_10      315 non-null float64
chromagramfiles_11      315 non-null float64
chromagramfiles_12      315 non-null float64
attackslopefiles        315 non-null float64
attackleapfiles         315 non-null float64
chosen                  315 non-null int64
dtypes: float64(56), int64(1), object(8)
memory usage: 162.4+ KB
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<p>Vamos a reemplazar los NaN y entonces a normalizar los datos para que todas las variables tengan la misma importancia. Solo vamos a considerar los datos numéricos.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</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">preprocessing</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">8</span><span class="p">:]))</span>
    <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">8</span><span class="p">:]</span>
<span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>durationfiles          -1.889141e-16
bitratefiles            0.000000e+00
rmsfiles                3.559763e-16
rmsmedianfiles         -2.396672e-16
lowenergyfiles          1.543739e-16
ASRfiles                1.226532e-16
beatspectrumfiles       2.269789e-16
eventdensityfiles      -6.132661e-17
tempofiles              4.103860e-16
pulseclarityfiles      -6.696583e-17
zerocrossfiles         -1.092600e-16
rolloffsfiles           2.661011e-16
brightnessfiles         1.092600e-16
spreadfiles             1.519067e-16
centroidfiles           1.501444e-16
kurtosisfiles           1.875043e-16
flatnessfiles          -4.017950e-17
entropyfiles            6.012827e-16
mfccfiles_1            -4.398598e-16
mfccfiles_2            -2.326182e-17
mfccfiles_3             6.308886e-17
mfccfiles_4             1.718202e-17
mfccfiles_5             1.832749e-17
mfccfiles_6            -3.172066e-17
mfccfiles_7            -1.400996e-16
mfccfiles_8             5.110550e-17
mfccfiles_9             3.101575e-17
mfccfiles_10           -6.485112e-17
mfccfiles_11           -4.229421e-18
mfccfiles_12           -1.304071e-17
mfccfiles_13           -1.233581e-17
pitchfiles              0.000000e+00
inharmonicityfiles     -1.009422e-15
bestkeyfiles            2.424868e-16
keyclarityfiles        -3.972131e-16
modalityfiles          -3.771234e-17
tonalcentroidfiles_1   -1.517745e-17
tonalcentroidfiles_2   -5.921189e-17
tonalcentroidfiles_3    2.326182e-17
tonalcentroidfiles_4    2.502407e-17
tonalcentroidfiles_5    3.260179e-17
tonalcentroidfiles_6   -2.361427e-17
chromagramfiles_1       6.414622e-17
chromagramfiles_2      -2.061843e-17
chromagramfiles_3      -3.489272e-17
chromagramfiles_4      -1.755210e-16
chromagramfiles_5       1.797504e-17
chromagramfiles_6      -3.101575e-17
chromagramfiles_7      -8.776049e-17
chromagramfiles_8       7.471977e-17
chromagramfiles_9      -4.194176e-17
chromagramfiles_10      3.630253e-17
chromagramfiles_11      5.057683e-17
chromagramfiles_12     -5.894756e-17
attackslopefiles       -6.626093e-17
attackleapfiles        -1.423905e-16
chosen                  7.218212e-16
dtype: float64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>durationfiles           1.001591
bitratefiles            0.000000
rmsfiles                1.001591
rmsmedianfiles          1.001591
lowenergyfiles          1.001591
ASRfiles                1.001591
beatspectrumfiles       1.001591
eventdensityfiles       1.001591
tempofiles              1.001591
pulseclarityfiles       1.001591
zerocrossfiles          1.001591
rolloffsfiles           1.001591
brightnessfiles         1.001591
spreadfiles             1.001591
centroidfiles           1.001591
kurtosisfiles           1.001591
flatnessfiles           1.001591
entropyfiles            1.001591
mfccfiles_1             1.001591
mfccfiles_2             1.001591
mfccfiles_3             1.001591
mfccfiles_4             1.001591
mfccfiles_5             1.001591
mfccfiles_6             1.001591
mfccfiles_7             1.001591
mfccfiles_8             1.001591
mfccfiles_9             1.001591
mfccfiles_10            1.001591
mfccfiles_11            1.001591
mfccfiles_12            1.001591
mfccfiles_13            1.001591
pitchfiles              0.000000
inharmonicityfiles      1.001591
bestkeyfiles            1.001591
keyclarityfiles         1.001591
modalityfiles           1.001591
tonalcentroidfiles_1    1.001591
tonalcentroidfiles_2    1.001591
tonalcentroidfiles_3    1.001591
tonalcentroidfiles_4    1.001591
tonalcentroidfiles_5    1.001591
tonalcentroidfiles_6    1.001591
chromagramfiles_1       1.001591
chromagramfiles_2       1.001591
chromagramfiles_3       1.001591
chromagramfiles_4       1.001591
chromagramfiles_5       1.001591
chromagramfiles_6       1.001591
chromagramfiles_7       1.001591
chromagramfiles_8       1.001591
chromagramfiles_9       1.001591
chromagramfiles_10      1.001591
chromagramfiles_11      1.001591
chromagramfiles_12      1.001591
attackslopefiles        1.001591
attackleapfiles         1.001591
chosen                  1.001591
dtype: float64</pre>
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<p>Borramos pitch y bitrate porque todos sus valores son 0.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="s2">&quot;pitchfiles&quot;</span><span class="p">)</span>
    <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="s2">&quot;bitratefiles&quot;</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">keras</span>
<span class="n">keras</span><span class="o">.</span><span class="n">__version__</span>
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<pre>Using TensorFlow backend.
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<pre>&#39;2.3.1&#39;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span><span class="c1">#, Lambda</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">layers</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">models</span><span class="p">,</span> <span class="n">optimizers</span>

<span class="kn">from</span> <span class="nn">sklearn.neural_network</span> <span class="k">import</span> <span class="n">MLPClassifier</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span> <span class="c1">#permite buscar la mejor configuración de parámetros con C-V</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">make_scorer</span> <span class="c1"># permite crear una clase scorer a partir de una función de score (necesario para el kappa)</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">cohen_kappa_score</span><span class="p">,</span> <span class="n">classification_report</span><span class="p">,</span> <span class="n">roc_auc_score</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span> <span class="c1">#metodo de particionamiento de datasets para evaluación</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
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<h1 id="MFCC">MFCC<a class="anchor-link" href="#MFCC">&#182;</a></h1>
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<pre>Index([&#39;mfccfiles_1&#39;, &#39;mfccfiles_2&#39;, &#39;mfccfiles_3&#39;, &#39;mfccfiles_4&#39;,
       &#39;mfccfiles_5&#39;, &#39;mfccfiles_6&#39;, &#39;mfccfiles_7&#39;, &#39;mfccfiles_8&#39;,
       &#39;mfccfiles_9&#39;, &#39;mfccfiles_10&#39;, &#39;mfccfiles_11&#39;, &#39;mfccfiles_12&#39;,
       &#39;mfccfiles_13&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std_mfcc</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">i</span><span class="p">]</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">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">17</span><span class="p">:</span><span class="mi">30</span><span class="p">])</span>
    <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">17</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span>
<span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 315 entries, 0 to 314
Data columns (total 13 columns):
mfccfiles_1     315 non-null float64
mfccfiles_2     315 non-null float64
mfccfiles_3     315 non-null float64
mfccfiles_4     315 non-null float64
mfccfiles_5     315 non-null float64
mfccfiles_6     315 non-null float64
mfccfiles_7     315 non-null float64
mfccfiles_8     315 non-null float64
mfccfiles_9     315 non-null float64
mfccfiles_10    315 non-null float64
mfccfiles_11    315 non-null float64
mfccfiles_12    315 non-null float64
mfccfiles_13    315 non-null float64
dtypes: float64(13)
memory usage: 32.1 KB
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<h2 id="2-comp-groups-combinations">2-comp groups combinations<a class="anchor-link" href="#2-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">two_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">two_combs</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;## &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39; y &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span><span class="p">())</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    
    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="n">two_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">two_combs</span><span class="p">[</span><span class="mi">2</span><span class="p">:]:</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;## &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">+</span><span class="s1">&#39; y &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="n">two_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">two_combs</span><span class="p">:</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;## &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">+</span><span class="s1">&#39; y &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

    </div>
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<div class="output_markdown rendered_html output_subarea ">
<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">Arte Franc&#233;s y Club De Banqueros y Empresarios<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 75.41% y un Kappa del 16.04
Model: &#34;model_1&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                140       
_________________________________________________________________
dense_2 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_3 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 11        
=================================================================
Total params: 371
Trainable params: 371
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
143/143 [==============================] - 0s 68us/step
test loss: 0.620383562324764, test accuracy: 0.6783216595649719
AUC ROC:  0.5883720930232559
Kappa:  -0.004581551618815016
[[95  5]
 [41  2]]
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<h2 id="Arte-Franc&#233;s-y-Gramma">Arte Franc&#233;s y Gramma<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
</pre>
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 74.37% y un Kappa del 17.35
Model: &#34;model_2&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_5 (Dense)              (None, 20)                280       
_________________________________________________________________
dense_6 (Dense)              (None, 10)                210       
_________________________________________________________________
dense_7 (Dense)              (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
133/133 [==============================] - 0s 0us/step
test loss: 0.7802316792925498, test accuracy: 0.6992481350898743
AUC ROC:  0.600268817204301
Kappa:  0.13748378728923472
[[85  8]
 [32  8]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech">Arte Franc&#233;s y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 76.16% y un Kappa del 42.68
Model: &#34;model_3&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_8 (Dense)              (None, 30)                420       
_________________________________________________________________
dense_9 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_10 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_11 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
115/115 [==============================] - 0s 0us/step
test loss: 2.585697315050208, test accuracy: 0.695652186870575
AUC ROC:  0.6950000000000001
Kappa:  0.3006081668114683
[[61 14]
 [21 19]]
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<h2 id="Arte-Franc&#233;s-y-Specialized">Arte Franc&#233;s y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 68.25% y un Kappa del 31.87
Model: &#34;model_4&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_12 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_13 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_14 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_15 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
134/134 [==============================] - 0s 133us/step
test loss: 2.059194803237915, test accuracy: 0.6716417670249939
AUC ROC:  0.6780274656679152
Kappa:  0.25574349911638483
[[68 21]
 [23 22]]
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<h2 id="Arte-Franc&#233;s-y-Urban-Place">Arte Franc&#233;s y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 2000}, que permiten obtener un Accuracy de 73.12% y un Kappa del 36.34
Model: &#34;model_5&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_16 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_17 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_18 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_19 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
133/133 [==============================] - 0s 0us/step
test loss: 1.5871249448536033, test accuracy: 0.6766917109489441
AUC ROC:  0.61399416909621
Kappa:  0.17391304347826086
[[76 22]
 [21 14]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma">Club De Banqueros y Empresarios y Gramma<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
</pre>
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 75.64% y un Kappa del 37.68
Model: &#34;model_6&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_20 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_21 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
118/118 [==============================] - 0s 0us/step
test loss: 0.48742022201166313, test accuracy: 0.7288135886192322
AUC ROC:  0.7760557923285549
Kappa:  0.19350704826997012
[[77 12]
 [20  9]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">Club De Banqueros y Empresarios y Hotel Marrakech<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
</pre>
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 79.26% y un Kappa del 49.78
Model: &#34;model_7&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_22 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_23 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_24 (Dense)             (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
100/100 [==============================] - 0s 0us/step
test loss: 0.8159343588352204, test accuracy: 0.5699999928474426
AUC ROC:  0.6104166666666666
Kappa:  0.06113537117903922
[[44 16]
 [27 13]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Specialized">Club De Banqueros y Empresarios y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 1000}, que permiten obtener un Accuracy de 74.65% y un Kappa del 43.35
Model: &#34;model_8&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_25 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_26 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_27 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
119/119 [==============================] - 0s 0us/step
test loss: 1.1444906747641683, test accuracy: 0.7310924530029297
AUC ROC:  0.7417391304347827
Kappa:  0.4511386566733929
[[52 17]
 [15 35]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Urban-Place">Club De Banqueros y Empresarios y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
</pre>
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 73.65% y un Kappa del 26.31
Model: &#34;model_9&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_9 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_28 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_29 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
118/118 [==============================] - 0s 90us/step
test loss: 0.6000517404685586, test accuracy: 0.694915235042572
AUC ROC:  0.5983893557422969
Kappa:  0.03717135086128731
[[79  5]
 [31  3]]
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<h2 id="Gramma-y-Hotel-Marrakech">Gramma y Hotel Marrakech<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 76.30% y un Kappa del 45.85
Model: &#34;model_10&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_10 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_30 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_31 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_32 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_33 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
90/90 [==============================] - 0s 0us/step
test loss: 2.297124226888021, test accuracy: 0.699999988079071
AUC ROC:  0.7206273258904838
Kappa:  0.37403400309119006
[[41 16]
 [11 22]]
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<h2 id="Gramma-y-Specialized">Gramma y Specialized<a class="anchor-link" href="#Gramma-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 69.94% y un Kappa del 35.97
Model: &#34;model_11&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_11 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_34 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_35 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
109/109 [==============================] - 0s 0us/step
test loss: 0.6649873464479359, test accuracy: 0.60550457239151
AUC ROC:  0.5998498498498499
Kappa:  0.10262301359372006
[[52 20]
 [23 14]]
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<h2 id="Gramma-y-Urban-Place">Gramma y Urban Place<a class="anchor-link" href="#Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 71.60% y un Kappa del 30.30
Model: &#34;model_12&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_12 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_36 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_37 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
108/108 [==============================] - 0s 0us/step
test loss: 0.7955479599811413, test accuracy: 0.5462962985038757
AUC ROC:  0.5060745705906996
Kappa:  -0.164612676056338
[[55 22]
 [27  4]]
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<h2 id="Hotel-Marrakech-y-Specialized">Hotel Marrakech y Specialized<a class="anchor-link" href="#Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.221235     1.617887     0.929874    -0.231486    -0.525862   
1     0.836735    -0.529605    -1.268139    -0.791053     0.815880   
2    -0.190995     1.202756     0.050028    -2.631154     3.701544   
3     0.521202     1.354284     1.423683    -0.634173     0.934734   
4     0.250234     1.586078    -1.791096     0.127156     1.573000   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.384826     0.709441     0.512679    -2.231286     -2.278872   
1    -1.992230    -0.371430    -0.356669     1.323871      0.946394   
2    -1.158173     0.439586     2.317548    -2.282526     -1.571775   
3     0.214772    -0.349135     1.009101    -2.193012     -0.301254   
4     0.288525     1.962471     1.500627     1.352853     -1.921935   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.728806     -2.187766     -1.206544  
1     -1.085097      0.673490     -1.496313  
2     -2.541951     -2.587380     -2.132445  
3     -0.356046     -0.668937     -0.421263  
4      0.705405     -0.230103     -0.803009  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 2000}, que permiten obtener un Accuracy de 67.28% y un Kappa del 34.25
Model: &#34;model_13&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_13 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_38 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_39 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_40 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
91/91 [==============================] - 0s 0us/step
test loss: 1.5946678905696658, test accuracy: 0.593406617641449
AUC ROC:  0.6231380337636544
Kappa:  0.16720257234726688
[[34 19]
 [18 20]]
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<h2 id="Hotel-Marrakech-y-Urban-Place">Hotel Marrakech y Urban Place<a class="anchor-link" href="#Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.221235     1.617887     0.929874    -0.231486    -0.525862   
1     0.836735    -0.529605    -1.268139    -0.791053     0.815880   
2    -0.190995     1.202756     0.050028    -2.631154     3.701544   
3     0.521202     1.354284     1.423683    -0.634173     0.934734   
4     0.250234     1.586078    -1.791096     0.127156     1.573000   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.384826     0.709441     0.512679    -2.231286     -2.278872   
1    -1.992230    -0.371430    -0.356669     1.323871      0.946394   
2    -1.158173     0.439586     2.317548    -2.282526     -1.571775   
3     0.214772    -0.349135     1.009101    -2.193012     -0.301254   
4     0.288525     1.962471     1.500627     1.352853     -1.921935   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.728806     -2.187766     -1.206544  
1     -1.085097      0.673490     -1.496313  
2     -2.541951     -2.587380     -2.132445  
3     -0.356046     -0.668937     -0.421263  
4      0.705405     -0.230103     -0.803009  
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 69.63% y un Kappa del 33.84
Model: &#34;model_14&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_14 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_41 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_42 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
90/90 [==============================] - 0s 0us/step
test loss: 0.6443683491812812, test accuracy: 0.6222222447395325
AUC ROC:  0.6266666666666667
Kappa:  0.13559322033898313
[[44 16]
 [18 12]]
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<h2 id="Specialized-y-Urban-Place">Specialized y Urban Place<a class="anchor-link" href="#Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.992062    -0.477172    -1.079451    -2.369470    -1.705431   
1     0.843575    -0.507672    -0.731713    -0.334904     1.442336   
2     0.816922    -0.263544     0.639646    -0.865417     1.276602   
3     4.368525     0.851784    -0.671158    -0.128467     2.141169   
4     0.001312     0.535305    -0.648296     0.221414     0.549478   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -0.098594    -0.281836    -1.432001    -0.898623      0.130446   
1    -0.491141    -0.266416    -0.511246     1.004414      0.558777   
2    -0.245238     0.106722    -0.761365    -0.170481     -1.443667   
3    -0.472725    -1.437233    -1.858760     1.581800     -0.145852   
4     0.736878    -0.439538    -0.138787     0.584258      0.095671   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.024683     -0.312128      0.020392  
1      0.127114     -1.667555      0.835458  
2     -0.451102      1.196430     -0.037846  
3      0.107228      1.458238      1.666081  
4      1.901833      2.909252      1.802578  
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<pre>C:\Users\nata\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 69.63% y un Kappa del 33.01
Model: &#34;model_15&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_15 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_43 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_44 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_45 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_46 (Dense)             (None, 1)                 21        
=================================================================
Total params: 1,141
Trainable params: 1,141
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
109/109 [==============================] - 0s 143us/step
test loss: 1.9546940950078702, test accuracy: 0.5596330165863037
AUC ROC:  0.6398247978436657
Kappa:  0.11141304347826086
[[40 16]
 [32 21]]
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<h2 id="3-comp-groups-combinations">3-comp groups combinations<a class="anchor-link" href="#3-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">three_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">three_combs</span><span class="p">:</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;## &#39;</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">no</span><span class="p">]</span>
        <span class="n">title</span><span class="o">+=</span><span class="s1">&#39; y &#39;</span>
    <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">title</span><span class="p">))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
        <span class="n">X</span><span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">no</span><span class="p">])</span>
        <span class="n">y</span><span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">no</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>
    

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(786, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 74.70% y un Kappa del 26.53
Model: &#34;model_1&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 30)                420       
_________________________________________________________________
dense_2 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_3 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_4 (Dense)              (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
197/197 [==============================] - 0s 30us/step
test loss: 0.6007471054338561, test accuracy: 0.6903553009033203
AUC ROC:  0.6249078850405305
Kappa:  0.11110289222575631
[[125  13]
 [ 48  11]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(714, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 74.39% y un Kappa del 37.61
Model: &#34;model_2&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_5 (Dense)              (None, 30)                420       
_________________________________________________________________
dense_6 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_7 (Dense)              (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
179/179 [==============================] - 0s 45us/step
test loss: 0.8997574944735905, test accuracy: 0.6759776473045349
AUC ROC:  0.6919279907084785
Kappa:  0.2158610271903324
[[98 25]
 [33 23]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(789, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 69.54% y un Kappa del 32.02
Model: &#34;model_3&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_8 (Dense)              (None, 30)                420       
_________________________________________________________________
dense_9 (Dense)              (None, 30)                930       
_________________________________________________________________
dense_10 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
198/198 [==============================] - 0s 30us/step
test loss: 0.9417000592808531, test accuracy: 0.6717171669006348
AUC ROC:  0.7123521521765639
Kappa:  0.24833547482770701
[[102  37]
 [ 28  31]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(786, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 70.80% y un Kappa del 7.30
Model: &#34;model_4&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_11 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_12 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
197/197 [==============================] - 0s 41us/step
test loss: 0.5736924785042777, test accuracy: 0.720812201499939
AUC ROC:  0.579973474801061
Kappa:  0.03834206088577252
[[138   7]
 [ 48   4]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech">Arte Franc&#233;s y Gramma y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(675, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 69.96% y un Kappa del 15.14
Model: &#34;model_5&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_13 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_14 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_15 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
169/169 [==============================] - 0s 35us/step
test loss: 1.5889130231191422, test accuracy: 0.668639063835144
AUC ROC:  0.6099159663865547
Kappa:  0.21382289416846645
[[90 29]
 [27 23]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Specialized">Arte Franc&#233;s y Gramma y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(750, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 69.22% y un Kappa del 28.64
Model: &#34;model_6&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_6 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_16 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_17 (Dense)             (None, 20)                620       
_________________________________________________________________
dense_18 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_19 (Dense)             (None, 1)                 11        
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
188/188 [==============================] - 0s 37us/step
test loss: 0.9825812349928186, test accuracy: 0.5797872543334961
AUC ROC:  0.5978638847491307
Kappa:  0.054735234215886
[[86 36]
 [43 23]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Urban-Place">Arte Franc&#233;s y Gramma y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(747, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 68.57% y un Kappa del 9.44
Model: &#34;model_7&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_20 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_21 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_22 (Dense)             (None, 1)                 21        
=================================================================
Total params: 721
Trainable params: 721
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
187/187 [==============================] - 0s 48us/step
test loss: 0.5900288157284579, test accuracy: 0.7272727489471436
AUC ROC:  0.6224523889074508
Kappa:  0.15128593040847205
[[124  22]
 [ 29  12]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(678, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 64.57% y un Kappa del 12.16
Model: &#34;model_8&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_8 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_23 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_24 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
170/170 [==============================] - 0s 71us/step
test loss: 0.6898417111705332, test accuracy: 0.6529411673545837
AUC ROC:  0.6288325471698113
Kappa:  0.2145653876272514
[[86 20]
 [39 25]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(675, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.003, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 70.95% y un Kappa del 16.81
Model: &#34;model_9&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_9 (InputLayer)         (None, 13)                0         
_________________________________________________________________
dense_25 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_26 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_27 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
169/169 [==============================] - 0s 53us/step
test loss: 0.7179625766164452, test accuracy: 0.6568047404289246
AUC ROC:  0.6463187325256291
Kappa:  0.1395716292134832
[[96 15]
 [43 15]]
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<h2 id="Arte-Franc&#233;s-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(750, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 1000}, que permiten obtener un Accuracy de 71.89% y un Kappa del 36.07
Model: &#34;model_10&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_10 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_28 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_29 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
188/188 [==============================] - 0s 37us/step
test loss: 0.6679085125314429, test accuracy: 0.6329787373542786
AUC ROC:  0.543974343160232
Kappa:  0.05890887986070803
[[109  12]
 [ 57  10]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(615, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 74.62% y un Kappa del 38.11
Model: &#34;model_11&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_11 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_30 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_31 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_32 (Dense)             (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
154/154 [==============================] - 0s 58us/step
test loss: 0.7654741104547079, test accuracy: 0.6948052048683167
AUC ROC:  0.7051208833047782
Kappa:  0.3076334417447867
[[80 23]
 [24 27]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">Club De Banqueros y Empresarios y Gramma y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(690, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 74.08% y un Kappa del 38.81
Model: &#34;model_12&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_12 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_33 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_34 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_35 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
173/173 [==============================] - 0s 35us/step
test loss: 1.4033675400507932, test accuracy: 0.676300585269928
AUC ROC:  0.6981255448997384
Kappa:  0.3108550291648883
[[80 31]
 [25 37]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(687, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (400) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 73.01% y un Kappa del 21.67
Model: &#34;model_13&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_13 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_36 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_37 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
172/172 [==============================] - 0s 41us/step
test loss: 0.6511809784312581, test accuracy: 0.6569767594337463
AUC ROC:  0.5520833333333333
Kappa:  0.034259611724400396
[[105  15]
 [ 44   8]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">Club De Banqueros y Empresarios y Hotel Marrakech y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(618, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 72.79% y un Kappa del 42.30
Model: &#34;model_14&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_14 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_38 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_39 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_40 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
155/155 [==============================] - 0s 32us/step
test loss: 1.1106072802697458, test accuracy: 0.6580645442008972
AUC ROC:  0.6626377532883042
Kappa:  0.28234471914038617
[[68 29]
 [24 34]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">Club De Banqueros y Empresarios y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(615, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 68.11% y un Kappa del 20.47
Model: &#34;model_15&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_15 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_41 (Dense)             (None, 10)                140       
_________________________________________________________________
dense_42 (Dense)             (None, 1)                 11        
=================================================================
Total params: 151
Trainable params: 151
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
154/154 [==============================] - 0s 39us/step
test loss: 0.5610680889773678, test accuracy: 0.7272727489471436
AUC ROC:  0.6909276248725789
Kappa:  0.25294525294525294
[[97 12]
 [30 15]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(690, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 69.44% y un Kappa del 26.20
Model: &#34;model_16&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_16 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_43 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_44 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_45 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,381
Trainable params: 1,381
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
173/173 [==============================] - 0s 58us/step
test loss: 1.0460927052304925, test accuracy: 0.6878612637519836
AUC ROC:  0.7035953177257525
Kappa:  0.3157046586580722
[[87 17]
 [37 32]]
</pre>
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<h2 id="Gramma-y-Hotel-Marrakech-y-Specialized">Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
(579, 13)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 65.67% y un Kappa del 28.05
Model: &#34;model_17&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_46 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_47 (Dense)             (None, 20)                620       
_________________________________________________________________
dense_48 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_49 (Dense)             (None, 1)                 11        
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
145/145 [==============================] - 0s 62us/step
test loss: 1.071592510979751, test accuracy: 0.6413792967796326
AUC ROC:  0.6563275434243176
Kappa:  0.23976608187134507
[[64 29]
 [23 29]]
</pre>
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<h2 id="Gramma-y-Hotel-Marrakech-y-Urban-Place">Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
(576, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 67.82% y un Kappa del 7.15
Model: &#34;model_18&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_18 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_50 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_51 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
144/144 [==============================] - 0s 42us/step
test loss: 0.6109174754884508, test accuracy: 0.6666666865348816
AUC ROC:  0.6595744680851063
Kappa:  0.10559006211180133
[[89  5]
 [43  7]]
</pre>
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<h2 id="Gramma-y-Specialized-y-Urban-Place">Gramma y Specialized y Urban Place<a class="anchor-link" href="#Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
(651, 13)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 68.44% y un Kappa del 27.07
Model: &#34;model_19&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_19 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_52 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_53 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
163/163 [==============================] - 0s 43us/step
test loss: 0.6811560271707781, test accuracy: 0.5828220844268799
AUC ROC:  0.5981424148606811
Kappa:  0.03751302535602652
[[87  8]
 [60  8]]
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<h2 id="Hotel-Marrakech-y-Specialized-y-Urban-Place">Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.221235     1.617887     0.929874    -0.231486    -0.525862   
1     0.836735    -0.529605    -1.268139    -0.791053     0.815880   
2    -0.190995     1.202756     0.050028    -2.631154     3.701544   
3     0.521202     1.354284     1.423683    -0.634173     0.934734   
4     0.250234     1.586078    -1.791096     0.127156     1.573000   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.384826     0.709441     0.512679    -2.231286     -2.278872   
1    -1.992230    -0.371430    -0.356669     1.323871      0.946394   
2    -1.158173     0.439586     2.317548    -2.282526     -1.571775   
3     0.214772    -0.349135     1.009101    -2.193012     -0.301254   
4     0.288525     1.962471     1.500627     1.352853     -1.921935   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.728806     -2.187766     -1.206544  
1     -1.085097      0.673490     -1.496313  
2     -2.541951     -2.587380     -2.132445  
3     -0.356046     -0.668937     -0.421263  
4      0.705405     -0.230103     -0.803009  
(579, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 66.13% y un Kappa del 28.79
Model: &#34;model_20&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_20 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_54 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_55 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_56 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_57 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
145/145 [==============================] - 0s 34us/step
test loss: 1.6027446672834198, test accuracy: 0.6758620738983154
AUC ROC:  0.7168021680216802
Kappa:  0.33918355473674
[[59 23]
 [24 39]]
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<h2 id="4-comp-groups-combinations">4-comp groups combinations<a class="anchor-link" href="#4-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">three_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">4</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">three_combs</span><span class="p">:</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;## &#39;</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">no</span><span class="p">]</span>
        <span class="n">title</span><span class="o">+=</span><span class="s1">&#39; y &#39;</span>
    <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">title</span><span class="p">))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
        <span class="n">X</span><span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_mfcc</span><span class="p">[</span><span class="n">no</span><span class="p">])</span>
        <span class="n">y</span><span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">no</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>
    

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(930, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 71.88% y un Kappa del 22.47
Model: &#34;model_16&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_16 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_47 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_48 (Dense)             (None, 20)                620       
_________________________________________________________________
dense_49 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_50 (Dense)             (None, 1)                 11        
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
233/233 [==============================] - 0s 54us/step
test loss: 0.6579493527248693, test accuracy: 0.7038626670837402
AUC ROC:  0.6565538048944015
Kappa:  0.22352088867423336
[[142  15]
 [ 54  22]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(1005, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 72.11% y un Kappa del 21.68
Model: &#34;model_17&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_17 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_51 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_52 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
252/252 [==============================] - 0s 73us/step
test loss: 0.6859365020479474, test accuracy: 0.6428571343421936
AUC ROC:  0.5917405764966741
Kappa:  0.08959537572254339
[[145  19]
 [ 71  17]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(1002, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 71.50% y un Kappa del 4.46
Model: &#34;model_18&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_18 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_53 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_54 (Dense)             (None, 20)                620       
_________________________________________________________________
dense_55 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_56 (Dense)             (None, 1)                 11        
=================================================================
Total params: 1,261
Trainable params: 1,261
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
251/251 [==============================] - 0s 61us/step
test loss: 0.7648932287892497, test accuracy: 0.6733067631721497
AUC ROC:  0.5741666666666667
Kappa:  0.1627206899357253
[[144  32]
 [ 50  25]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(933, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 69.24% y un Kappa del 31.72
Model: &#34;model_19&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_19 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_57 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_58 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
234/234 [==============================] - 0s 36us/step
test loss: 0.7985203103122548, test accuracy: 0.6452991366386414
AUC ROC:  0.6020153231179214
Kappa:  0.1537254901960785
[[123  35]
 [ 48  28]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(930, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 1000}, que permiten obtener un Accuracy de 70.73% y un Kappa del 28.90
Model: &#34;model_20&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_20 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_59 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_60 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
233/233 [==============================] - 0s 49us/step
test loss: 0.5833681647869651, test accuracy: 0.7081544995307922
AUC ROC:  0.5945054945054945
Kappa:  0.026661752057992483
[[161   7]
 [ 61   4]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(1005, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 67.99% y un Kappa del 11.76
Model: &#34;model_21&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_21 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_61 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_62 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_63 (Dense)             (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
252/252 [==============================] - 0s 70us/step
test loss: 0.6661094039205521, test accuracy: 0.6309523582458496
AUC ROC:  0.6046628407460546
Kappa:  0.12342908438061051
[[130  40]
 [ 53  29]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(894, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 66.27% y un Kappa del 13.31
Model: &#34;model_22&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_22 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_64 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_65 (Dense)             (None, 1)                 21        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
224/224 [==============================] - 0s 0us/step
test loss: 0.6703500917979649, test accuracy: 0.6205357313156128
AUC ROC:  0.5141358463726884
Kappa:  -0.01927194860813719
[[132  16]
 [ 69   7]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(891, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 70.21% y un Kappa del 14.66
Model: &#34;model_23&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_23 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_66 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_67 (Dense)             (None, 1)                 31        
=================================================================
Total params: 451
Trainable params: 451
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
223/223 [==============================] - 0s 60us/step
test loss: 0.6408152198043104, test accuracy: 0.681614339351654
AUC ROC:  0.5058868944219262
Kappa:  -0.007252369743622422
[[149   8]
 [ 63   3]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Gramma y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(966, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 68.65% y un Kappa del 11.38
Model: &#34;model_24&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_24 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_68 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_69 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_70 (Dense)             (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
242/242 [==============================] - 0s 57us/step
test loss: 0.7815157445994291, test accuracy: 0.5909090638160706
AUC ROC:  0.560233918128655
Kappa:  0.06274939363117127
[[117  35]
 [ 64  26]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0     0.297583     1.225637    -0.367641     0.606499     0.072373   
1     0.637676    -1.507256    -1.572737    -0.954161    -0.857425   
2     2.236730    -0.319414     0.669910    -1.918119    -0.820882   
3     0.662077    -0.381499     0.111981    -1.743808    -1.317593   
4     0.736502     0.112932    -0.065024    -1.049458    -0.408043   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0    -2.029620     0.791469     0.752018     2.268802     -1.383289   
1     0.327005     0.816764     0.214245     0.241703      0.637066   
2    -2.379333    -1.570021    -2.755344    -2.150610     -2.528577   
3    -1.348534    -0.627198    -1.629882    -2.075974     -1.248765   
4    -0.437499     0.090831    -0.852983    -1.922491     -0.284365   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0      0.548279      1.903211     -1.011470  
1      1.601538      0.300317     -0.466779  
2     -0.877081     -0.522248     -1.429911  
3     -1.126014     -1.316359     -1.126174  
4      0.210624     -0.032122     -0.700183  
(894, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 66.27% y un Kappa del 26.22
Model: &#34;model_25&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_25 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_71 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_72 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_73 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_74 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
224/224 [==============================] - 0s 73us/step
test loss: 1.4138764142990112, test accuracy: 0.6339285969734192
AUC ROC:  0.6976938502673797
Kappa:  0.20718232044198892
[[103  33]
 [ 49  39]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Specialized">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(834, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 69.60% y un Kappa del 31.32
Model: &#34;model_26&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_26 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_75 (Dense)             (None, 30)                420       
_________________________________________________________________
dense_76 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_77 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_78 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,311
Trainable params: 2,311
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
209/209 [==============================] - 0s 24us/step
test loss: 0.9195588627500397, test accuracy: 0.6411483287811279
AUC ROC:  0.6434509326845093
Kappa:  0.23323386978427818
[[94 43]
 [32 40]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(831, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 71.27% y un Kappa del 14.58
Model: &#34;model_27&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_27 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_79 (Dense)             (None, 10)                140       
_________________________________________________________________
dense_80 (Dense)             (None, 1)                 11        
=================================================================
Total params: 151
Trainable params: 151
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
208/208 [==============================] - 0s 29us/step
test loss: 0.6857350468635559, test accuracy: 0.6634615659713745
AUC ROC:  0.6406900050735668
Kappa:  0.1363151026219005
[[123  12]
 [ 58  15]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(906, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 69.22% y un Kappa del 13.61
Model: &#34;model_28&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_28 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_81 (Dense)             (None, 10)                140       
_________________________________________________________________
dense_82 (Dense)             (None, 10)                110       
_________________________________________________________________
dense_83 (Dense)             (None, 10)                110       
_________________________________________________________________
dense_84 (Dense)             (None, 1)                 11        
=================================================================
Total params: 371
Trainable params: 371
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
227/227 [==============================] - 0s 86us/step
test loss: 0.8942637527566649, test accuracy: 0.6035242080688477
AUC ROC:  0.5946248600223963
Kappa:  0.12272414977670909
[[110  23]
 [ 67  27]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.339415     0.847773     0.497198    -0.389310     1.225458   
1     0.587658    -1.195426     0.636375     0.199876     0.765321   
2     1.465595    -2.307943     0.354567    -0.058273    -1.298853   
3     0.749403    -1.690498    -0.125200    -1.016135     0.825845   
4    -0.280577     0.393332     0.744917     2.411400    -0.777421   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     1.947033    -0.736267     0.492219     0.576682      1.504697   
1     0.061181     0.379367    -0.440867     0.232893      1.339920   
2    -0.811453    -1.551580    -3.934320    -1.079432      2.546130   
3     0.271444    -0.104786    -0.992141     0.049182      1.425948   
4    -0.420018     1.258355    -1.544565    -0.498071      0.421527   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -1.796460      0.724954      0.958600  
1      0.110001      0.807525      0.815678  
2      1.421407      0.639359      0.199094  
3     -0.343269     -0.789558     -0.411898  
4     -0.632908     -0.056846     -0.072348  
(834, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 68.32% y un Kappa del 33.23
Model: &#34;model_29&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_29 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_85 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_86 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_87 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_88 (Dense)             (None, 1)                 21        
=================================================================
Total params: 1,141
Trainable params: 1,141
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
209/209 [==============================] - 0s 24us/step
test loss: 2.0945880855099435, test accuracy: 0.6172248721122742
AUC ROC:  0.6314959773863883
Kappa:  0.159376571141277
[[97 49]
 [31 32]]
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<h2 id="Gramma-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Gramma y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   mfccfiles_1  mfccfiles_2  mfccfiles_3  mfccfiles_4  mfccfiles_5  \
0    -0.674917     0.169246     0.673543     1.157142    -0.633186   
1     0.277269     0.514176     0.200398     0.988939    -1.756594   
2     1.483921     0.724793     0.473099     0.439577    -0.358096   
3    -0.734008    -0.683844    -0.764866    -0.225060    -0.261235   
4    -0.834815    -0.735908    -1.177596    -0.093532     0.508050   

   mfccfiles_6  mfccfiles_7  mfccfiles_8  mfccfiles_9  mfccfiles_10  \
0     0.688145     0.215883    -0.452048     1.101066      0.064017   
1    -0.022788    -0.235704     0.523508    -0.604231      1.188209   
2    -0.452581    -0.213173    -0.596057    -0.767473      0.696227   
3    -0.243429     0.588768     0.874148     1.302526      0.091256   
4     0.503458     1.380798     1.847226     1.227896      0.017729   

   mfccfiles_11  mfccfiles_12  mfccfiles_13  
0     -0.153703      1.751289      0.812723  
1      0.863617     -0.801768      0.229305  
2     -0.111259     -0.370649     -1.325817  
3     -0.600323     -0.827452      0.390838  
4     -0.329325     -0.953249     -0.125917  
(795, 13)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 65.60% y un Kappa del 18.39
Model: &#34;model_30&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_30 (InputLayer)        (None, 13)                0         
_________________________________________________________________
dense_89 (Dense)             (None, 20)                280       
_________________________________________________________________
dense_90 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_91 (Dense)             (None, 1)                 11        
=================================================================
Total params: 501
Trainable params: 501
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
199/199 [==============================] - 0s 34us/step
test loss: 0.7093380165459523, test accuracy: 0.572864294052124
AUC ROC:  0.5327342747111681
Kappa:  0.01879459365392422
[[96 27]
 [58 18]]
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<h1 id="Tonal-Centroid">Tonal Centroid<a class="anchor-link" href="#Tonal-Centroid">&#182;</a></h1>
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<h1 id="Tonal-Centroid">Tonal Centroid<a class="anchor-link" href="#Tonal-Centroid">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span>
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<pre>Index([&#39;durationfiles&#39;, &#39;rmsfiles&#39;, &#39;rmsmedianfiles&#39;, &#39;lowenergyfiles&#39;,
       &#39;ASRfiles&#39;, &#39;beatspectrumfiles&#39;, &#39;eventdensityfiles&#39;, &#39;tempofiles&#39;,
       &#39;pulseclarityfiles&#39;, &#39;zerocrossfiles&#39;, &#39;rolloffsfiles&#39;,
       &#39;brightnessfiles&#39;, &#39;spreadfiles&#39;, &#39;centroidfiles&#39;, &#39;kurtosisfiles&#39;,
       &#39;flatnessfiles&#39;, &#39;entropyfiles&#39;, &#39;mfccfiles_1&#39;, &#39;mfccfiles_2&#39;,
       &#39;mfccfiles_3&#39;, &#39;mfccfiles_4&#39;, &#39;mfccfiles_5&#39;, &#39;mfccfiles_6&#39;,
       &#39;mfccfiles_7&#39;, &#39;mfccfiles_8&#39;, &#39;mfccfiles_9&#39;, &#39;mfccfiles_10&#39;,
       &#39;mfccfiles_11&#39;, &#39;mfccfiles_12&#39;, &#39;mfccfiles_13&#39;, &#39;inharmonicityfiles&#39;,
       &#39;bestkeyfiles&#39;, &#39;keyclarityfiles&#39;, &#39;modalityfiles&#39;,
       &#39;tonalcentroidfiles_1&#39;, &#39;tonalcentroidfiles_2&#39;, &#39;tonalcentroidfiles_3&#39;,
       &#39;tonalcentroidfiles_4&#39;, &#39;tonalcentroidfiles_5&#39;, &#39;tonalcentroidfiles_6&#39;,
       &#39;chromagramfiles_1&#39;, &#39;chromagramfiles_2&#39;, &#39;chromagramfiles_3&#39;,
       &#39;chromagramfiles_4&#39;, &#39;chromagramfiles_5&#39;, &#39;chromagramfiles_6&#39;,
       &#39;chromagramfiles_7&#39;, &#39;chromagramfiles_8&#39;, &#39;chromagramfiles_9&#39;,
       &#39;chromagramfiles_10&#39;, &#39;chromagramfiles_11&#39;, &#39;chromagramfiles_12&#39;,
       &#39;attackslopefiles&#39;, &#39;attackleapfiles&#39;, &#39;chosen&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">34</span><span class="p">:</span><span class="mi">40</span><span class="p">]</span>
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<pre>Index([&#39;tonalcentroidfiles_1&#39;, &#39;tonalcentroidfiles_2&#39;, &#39;tonalcentroidfiles_3&#39;,
       &#39;tonalcentroidfiles_4&#39;, &#39;tonalcentroidfiles_5&#39;, &#39;tonalcentroidfiles_6&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std_tc</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">i</span><span class="p">]</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">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">34</span><span class="p">:</span><span class="mi">40</span><span class="p">])</span>
    <span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">34</span><span class="p">:</span><span class="mi">40</span><span class="p">]</span>
<span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 315 entries, 0 to 314
Data columns (total 6 columns):
tonalcentroidfiles_1    315 non-null float64
tonalcentroidfiles_2    315 non-null float64
tonalcentroidfiles_3    315 non-null float64
tonalcentroidfiles_4    315 non-null float64
tonalcentroidfiles_5    315 non-null float64
tonalcentroidfiles_6    315 non-null float64
dtypes: float64(6)
memory usage: 14.8 KB
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<h2 id="2-comp-groups-combinations">2-comp groups combinations<a class="anchor-link" href="#2-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">two_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">two_combs</span><span class="p">:</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;## &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">+</span><span class="s1">&#39; y &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">Arte Franc&#233;s y Club De Banqueros y Empresarios<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 74.24% y un Kappa del 17.05
Model: &#34;model_31&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_31 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_92 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_93 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_94 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_95 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
143/143 [==============================] - 0s 49us/step
test loss: 0.5952307015032201, test accuracy: 0.7132866978645325
AUC ROC:  0.46566416040100245
Kappa:  0.00677621548365237
[[100   5]
 [ 36   2]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma">Arte Franc&#233;s y Gramma<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 74.12% y un Kappa del 10.34
Model: &#34;model_32&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_32 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_96 (Dense)             (None, 20)                140       
_________________________________________________________________
dense_97 (Dense)             (None, 1)                 21        
=================================================================
Total params: 161
Trainable params: 161
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
133/133 [==============================] - 0s 106us/step
test loss: 0.6227084559605534, test accuracy: 0.7142857313156128
AUC ROC:  0.5494623655913978
Kappa:  0.06855879100626605
[[93  0]
 [38  2]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech">Arte Franc&#233;s y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 2000}, que permiten obtener un Accuracy de 68.60% y un Kappa del 9.31
Model: &#34;model_33&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_33 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_98 (Dense)             (None, 20)                140       
_________________________________________________________________
dense_99 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_100 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_101 (Dense)            (None, 1)                 21        
=================================================================
Total params: 1,001
Trainable params: 1,001
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
115/115 [==============================] - 0s 43us/step
test loss: 0.647810119131337, test accuracy: 0.695652186870575
AUC ROC:  0.4128571428571429
Kappa:  0.0
[[80  0]
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<h2 id="Arte-Franc&#233;s-y-Specialized">Arte Franc&#233;s y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 66.00% y un Kappa del 15.34
Model: &#34;model_34&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_34 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_102 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_103 (Dense)            (None, 20)                620       
_________________________________________________________________
dense_104 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_105 (Dense)            (None, 1)                 11        
=================================================================
Total params: 1,051
Trainable params: 1,051
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
134/134 [==============================] - 0s 40us/step
test loss: 0.6884028511260872, test accuracy: 0.611940324306488
AUC ROC:  0.4812382739212008
Kappa:  0.0
[[82  0]
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<h2 id="Arte-Franc&#233;s-y-Urban-Place">Arte Franc&#233;s y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 72.36% y un Kappa del 19.04
Model: &#34;model_35&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_35 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_106 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_107 (Dense)            (None, 1)                 11        
=================================================================
Total params: 81
Trainable params: 81
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
133/133 [==============================] - 0s 0us/step
test loss: 0.584130020518052, test accuracy: 0.7368420958518982
AUC ROC:  0.550728862973761
Kappa:  0.051355206847360835
[[96  2]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma">Club De Banqueros y Empresarios y Gramma<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 2000}, que permiten obtener un Accuracy de 71.95% y un Kappa del 8.25
Model: &#34;model_36&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_36 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_108 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_109 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_110 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_111 (Dense)            (None, 1)                 31        
=================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
118/118 [==============================] - 0s 0us/step
test loss: 0.5803299677574029, test accuracy: 0.7372881174087524
AUC ROC:  0.42862439747868
Kappa:  0.0
[[87  0]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">Club De Banqueros y Empresarios y Hotel Marrakech<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 71.91% y un Kappa del 27.01
Model: &#34;model_37&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_37 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_112 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_113 (Dense)            (None, 20)                620       
_________________________________________________________________
dense_114 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_115 (Dense)            (None, 1)                 11        
=================================================================
Total params: 1,051
Trainable params: 1,051
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
100/100 [==============================] - 0s 0us/step
test loss: 0.7094994020462037, test accuracy: 0.6100000143051147
AUC ROC:  0.5689338235294117
Kappa:  -0.023084994753410415
[[56 12]
 [27  5]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Specialized">Club De Banqueros y Empresarios y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
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<pre>
<span class="ansi-red-intense-fg ansi-bold">---------------------------------------------------------------------------</span>
<span class="ansi-red-intense-fg ansi-bold">KeyboardInterrupt</span>                         Traceback (most recent call last)
<span class="ansi-green-intense-fg ansi-bold">&lt;ipython-input-24-aeeb6d5db628&gt;</span> in <span class="ansi-cyan-fg">&lt;module&gt;</span>
<span class="ansi-green-fg">     30</span>     grid <span class="ansi-yellow-intense-fg ansi-bold">=</span> GridSearchCV<span class="ansi-yellow-intense-fg ansi-bold">(</span>mlp<span class="ansi-yellow-intense-fg ansi-bold">,</span> param_grid<span class="ansi-yellow-intense-fg ansi-bold">=</span>parametros<span class="ansi-yellow-intense-fg ansi-bold">,</span> cv<span class="ansi-yellow-intense-fg ansi-bold">=</span><span class="ansi-cyan-intense-fg ansi-bold">5</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> scoring<span class="ansi-yellow-intense-fg ansi-bold">=</span>scoring<span class="ansi-yellow-intense-fg ansi-bold">,</span> refit<span class="ansi-yellow-intense-fg ansi-bold">=</span><span class="ansi-blue-intense-fg ansi-bold">&#39;accuracy&#39;</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> n_jobs<span class="ansi-yellow-intense-fg ansi-bold">=</span><span class="ansi-yellow-intense-fg ansi-bold">-</span><span class="ansi-cyan-intense-fg ansi-bold">1</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> iid<span class="ansi-yellow-intense-fg ansi-bold">=</span><span class="ansi-green-intense-fg ansi-bold">True</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">     31</span> 
<span class="ansi-green-intense-fg ansi-bold">---&gt; 32</span><span class="ansi-yellow-intense-fg ansi-bold">     </span>grid<span class="ansi-yellow-intense-fg ansi-bold">.</span>fit<span class="ansi-yellow-intense-fg ansi-bold">(</span>X_train<span class="ansi-yellow-intense-fg ansi-bold">,</span> y_train<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">     33</span> 
<span class="ansi-green-fg">     34</span>     print(&#34;Los parámetros del mejor modelo fueron {0}, que permiten obtener un Accuracy de {1:.2f}% y un Kappa del {2:.2f}&#34;.format(

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py</span> in <span class="ansi-cyan-fg">fit</span><span class="ansi-blue-intense-fg ansi-bold">(self, X, y, groups, **fit_params)</span>
<span class="ansi-green-fg">    685</span>                 <span class="ansi-green-intense-fg ansi-bold">return</span> results
<span class="ansi-green-fg">    686</span> 
<span class="ansi-green-intense-fg ansi-bold">--&gt; 687</span><span class="ansi-yellow-intense-fg ansi-bold">             </span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_run_search<span class="ansi-yellow-intense-fg ansi-bold">(</span>evaluate_candidates<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    688</span> 
<span class="ansi-green-fg">    689</span>         <span class="ansi-red-intense-fg ansi-bold"># For multi-metric evaluation, store the best_index_, best_params_ and</span>

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py</span> in <span class="ansi-cyan-fg">_run_search</span><span class="ansi-blue-intense-fg ansi-bold">(self, evaluate_candidates)</span>
<span class="ansi-green-fg">   1146</span>     <span class="ansi-green-intense-fg ansi-bold">def</span> _run_search<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">,</span> evaluate_candidates<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">   1147</span>         <span class="ansi-blue-intense-fg ansi-bold">&#34;&#34;&#34;Search all candidates in param_grid&#34;&#34;&#34;</span>
<span class="ansi-green-intense-fg ansi-bold">-&gt; 1148</span><span class="ansi-yellow-intense-fg ansi-bold">         </span>evaluate_candidates<span class="ansi-yellow-intense-fg ansi-bold">(</span>ParameterGrid<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>param_grid<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">   1149</span> 
<span class="ansi-green-fg">   1150</span> 

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\sklearn\model_selection\_search.py</span> in <span class="ansi-cyan-fg">evaluate_candidates</span><span class="ansi-blue-intense-fg ansi-bold">(candidate_params)</span>
<span class="ansi-green-fg">    664</span>                                <span class="ansi-green-intense-fg ansi-bold">for</span> parameters<span class="ansi-yellow-intense-fg ansi-bold">,</span> <span class="ansi-yellow-intense-fg ansi-bold">(</span>train<span class="ansi-yellow-intense-fg ansi-bold">,</span> test<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    665</span>                                in product(candidate_params,
<span class="ansi-green-intense-fg ansi-bold">--&gt; 666</span><span class="ansi-yellow-intense-fg ansi-bold">                                           cv.split(X, y, groups)))
</span><span class="ansi-green-fg">    667</span> 
<span class="ansi-green-fg">    668</span>                 <span class="ansi-green-intense-fg ansi-bold">if</span> len<span class="ansi-yellow-intense-fg ansi-bold">(</span>out<span class="ansi-yellow-intense-fg ansi-bold">)</span> <span class="ansi-yellow-intense-fg ansi-bold">&lt;</span> <span class="ansi-cyan-intense-fg ansi-bold">1</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\joblib\parallel.py</span> in <span class="ansi-cyan-fg">__call__</span><span class="ansi-blue-intense-fg ansi-bold">(self, iterable)</span>
<span class="ansi-green-fg">    932</span> 
<span class="ansi-green-fg">    933</span>             <span class="ansi-green-intense-fg ansi-bold">with</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_backend<span class="ansi-yellow-intense-fg ansi-bold">.</span>retrieval_context<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-intense-fg ansi-bold">--&gt; 934</span><span class="ansi-yellow-intense-fg ansi-bold">                 </span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>retrieve<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    935</span>             <span class="ansi-red-intense-fg ansi-bold"># Make sure that we get a last message telling us we are done</span>
<span class="ansi-green-fg">    936</span>             elapsed_time <span class="ansi-yellow-intense-fg ansi-bold">=</span> time<span class="ansi-yellow-intense-fg ansi-bold">.</span>time<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span> <span class="ansi-yellow-intense-fg ansi-bold">-</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_start_time

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\joblib\parallel.py</span> in <span class="ansi-cyan-fg">retrieve</span><span class="ansi-blue-intense-fg ansi-bold">(self)</span>
<span class="ansi-green-fg">    831</span>             <span class="ansi-green-intense-fg ansi-bold">try</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">    832</span>                 <span class="ansi-green-intense-fg ansi-bold">if</span> getattr<span class="ansi-yellow-intense-fg ansi-bold">(</span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_backend<span class="ansi-yellow-intense-fg ansi-bold">,</span> <span class="ansi-blue-intense-fg ansi-bold">&#39;supports_timeout&#39;</span><span class="ansi-yellow-intense-fg ansi-bold">,</span> <span class="ansi-green-intense-fg ansi-bold">False</span><span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-intense-fg ansi-bold">--&gt; 833</span><span class="ansi-yellow-intense-fg ansi-bold">                     </span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_output<span class="ansi-yellow-intense-fg ansi-bold">.</span>extend<span class="ansi-yellow-intense-fg ansi-bold">(</span>job<span class="ansi-yellow-intense-fg ansi-bold">.</span>get<span class="ansi-yellow-intense-fg ansi-bold">(</span>timeout<span class="ansi-yellow-intense-fg ansi-bold">=</span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>timeout<span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    834</span>                 <span class="ansi-green-intense-fg ansi-bold">else</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">    835</span>                     self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_output<span class="ansi-yellow-intense-fg ansi-bold">.</span>extend<span class="ansi-yellow-intense-fg ansi-bold">(</span>job<span class="ansi-yellow-intense-fg ansi-bold">.</span>get<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\site-packages\joblib\_parallel_backends.py</span> in <span class="ansi-cyan-fg">wrap_future_result</span><span class="ansi-blue-intense-fg ansi-bold">(future, timeout)</span>
<span class="ansi-green-fg">    519</span>         AsyncResults.get from multiprocessing.&#34;&#34;&#34;
<span class="ansi-green-fg">    520</span>         <span class="ansi-green-intense-fg ansi-bold">try</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-intense-fg ansi-bold">--&gt; 521</span><span class="ansi-yellow-intense-fg ansi-bold">             </span><span class="ansi-green-intense-fg ansi-bold">return</span> future<span class="ansi-yellow-intense-fg ansi-bold">.</span>result<span class="ansi-yellow-intense-fg ansi-bold">(</span>timeout<span class="ansi-yellow-intense-fg ansi-bold">=</span>timeout<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    522</span>         <span class="ansi-green-intense-fg ansi-bold">except</span> LokyTimeoutError<span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-fg">    523</span>             <span class="ansi-green-intense-fg ansi-bold">raise</span> TimeoutError<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\concurrent\futures\_base.py</span> in <span class="ansi-cyan-fg">result</span><span class="ansi-blue-intense-fg ansi-bold">(self, timeout)</span>
<span class="ansi-green-fg">    425</span>                 <span class="ansi-green-intense-fg ansi-bold">return</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>__get_result<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    426</span> 
<span class="ansi-green-intense-fg ansi-bold">--&gt; 427</span><span class="ansi-yellow-intense-fg ansi-bold">             </span>self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_condition<span class="ansi-yellow-intense-fg ansi-bold">.</span>wait<span class="ansi-yellow-intense-fg ansi-bold">(</span>timeout<span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    428</span> 
<span class="ansi-green-fg">    429</span>             <span class="ansi-green-intense-fg ansi-bold">if</span> self<span class="ansi-yellow-intense-fg ansi-bold">.</span>_state <span class="ansi-green-intense-fg ansi-bold">in</span> <span class="ansi-yellow-intense-fg ansi-bold">[</span>CANCELLED<span class="ansi-yellow-intense-fg ansi-bold">,</span> CANCELLED_AND_NOTIFIED<span class="ansi-yellow-intense-fg ansi-bold">]</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-green-intense-fg ansi-bold">~\Anaconda3\lib\threading.py</span> in <span class="ansi-cyan-fg">wait</span><span class="ansi-blue-intense-fg ansi-bold">(self, timeout)</span>
<span class="ansi-green-fg">    294</span>         <span class="ansi-green-intense-fg ansi-bold">try</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>    <span class="ansi-red-intense-fg ansi-bold"># restore state no matter what (e.g., KeyboardInterrupt)</span>
<span class="ansi-green-fg">    295</span>             <span class="ansi-green-intense-fg ansi-bold">if</span> timeout <span class="ansi-green-intense-fg ansi-bold">is</span> <span class="ansi-green-intense-fg ansi-bold">None</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>
<span class="ansi-green-intense-fg ansi-bold">--&gt; 296</span><span class="ansi-yellow-intense-fg ansi-bold">                 </span>waiter<span class="ansi-yellow-intense-fg ansi-bold">.</span>acquire<span class="ansi-yellow-intense-fg ansi-bold">(</span><span class="ansi-yellow-intense-fg ansi-bold">)</span>
<span class="ansi-green-fg">    297</span>                 gotit <span class="ansi-yellow-intense-fg ansi-bold">=</span> <span class="ansi-green-intense-fg ansi-bold">True</span>
<span class="ansi-green-fg">    298</span>             <span class="ansi-green-intense-fg ansi-bold">else</span><span class="ansi-yellow-intense-fg ansi-bold">:</span>

<span class="ansi-red-intense-fg ansi-bold">KeyboardInterrupt</span>: </pre>
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<h2 id="3-comp-groups-combinations">3-comp groups combinations<a class="anchor-link" href="#3-comp-groups-combinations">&#182;</a></h2>
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<div class="prompt input_prompt">In&nbsp;[22]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">three_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">three_combs</span><span class="p">:</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;## &#39;</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">no</span><span class="p">]</span>
        <span class="n">title</span><span class="o">+=</span><span class="s1">&#39; y &#39;</span>
    <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">title</span><span class="p">))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
        <span class="n">X</span><span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">no</span><span class="p">])</span>
        <span class="n">y</span><span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">no</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>
    

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(786, 6)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 73.34% y un Kappa del 7.33
Model: &#34;model_21&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_21 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_58 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_59 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_60 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,171
Trainable params: 1,171
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
197/197 [==============================] - 0s 41us/step
test loss: 0.6227250885842416, test accuracy: 0.6700507402420044
AUC ROC:  0.5804136253041362
Kappa:  -0.02333573083992646
[[130   7]
 [ 58   2]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(714, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 72.15% y un Kappa del 12.71
Model: &#34;model_22&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_22 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_61 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_62 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_63 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_64 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
179/179 [==============================] - 0s 28us/step
test loss: 0.6578460099310849, test accuracy: 0.659217894077301
AUC ROC:  0.5349883855981417
Kappa:  0.033460210675400504
[[111  12]
 [ 49   7]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(789, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 68.19% y un Kappa del 26.70
Model: &#34;model_23&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_23 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_65 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_66 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_67 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,171
Trainable params: 1,171
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
198/198 [==============================] - 0s 40us/step
test loss: 0.7369338743614428, test accuracy: 0.6464646458625793
AUC ROC:  0.5769809138230192
Kappa:  0.13730860201668127
[[107  26]
 [ 44  21]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(786, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 73.85% y un Kappa del 28.45
Model: &#34;model_24&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_24 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_68 (Dense)             (None, 20)                140       
_________________________________________________________________
dense_69 (Dense)             (None, 1)                 21        
=================================================================
Total params: 161
Trainable params: 161
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
197/197 [==============================] - 0s 25us/step
test loss: 0.6419816567813079, test accuracy: 0.6446700692176819
AUC ROC:  0.6370370370370371
Kappa:  0.05223367697594494
[[115  20]
 [ 50  12]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech">Arte Franc&#233;s y Gramma y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(675, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 69.96% y un Kappa del 5.23
Model: &#34;model_25&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_25 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_70 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_71 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_72 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_73 (Dense)             (None, 1)                 31        
=================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
169/169 [==============================] - 0s 30us/step
test loss: 0.6389501479250439, test accuracy: 0.6804733872413635
AUC ROC:  0.4647039687703318
Kappa:  -0.011751662971175136
[[115   1]
 [ 53   0]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Specialized">Arte Franc&#233;s y Gramma y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(750, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 67.26% y un Kappa del 3.98
Model: &#34;model_26&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_26 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_74 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_75 (Dense)             (None, 1)                 31        
=================================================================
Total params: 241
Trainable params: 241
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
188/188 [==============================] - 0s 32us/step
test loss: 0.6565095820325486, test accuracy: 0.6436170339584351
AUC ROC:  0.4995032290114257
Kappa:  -0.010590500641848521
[[121   1]
 [ 66   0]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Urban-Place">Arte Franc&#233;s y Gramma y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(747, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 70.71% y un Kappa del 9.67
Model: &#34;model_27&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_27 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_76 (Dense)             (None, 10)                70        
_________________________________________________________________
dense_77 (Dense)             (None, 10)                110       
_________________________________________________________________
dense_78 (Dense)             (None, 10)                110       
_________________________________________________________________
dense_79 (Dense)             (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
187/187 [==============================] - 0s 32us/step
test loss: 0.6191649401889128, test accuracy: 0.6844919919967651
AUC ROC:  0.5633903133903134
Kappa:  0.009160305343511421
[[123  12]
 [ 47   5]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(678, 6)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 67.32% y un Kappa del 10.31
Model: &#34;model_28&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_28 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_80 (Dense)             (None, 20)                140       
_________________________________________________________________
dense_81 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_82 (Dense)             (None, 20)                420       
_________________________________________________________________
dense_83 (Dense)             (None, 1)                 21        
=================================================================
Total params: 1,001
Trainable params: 1,001
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
170/170 [==============================] - 0s 29us/step
test loss: 1.1758464588838464, test accuracy: 0.5529412031173706
AUC ROC:  0.5280327637339359
Kappa:  0.07197241775606944
[[64 33]
 [43 30]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(675, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 69.57% y un Kappa del 10.97
Model: &#34;model_29&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_29 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_84 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_85 (Dense)             (None, 20)                620       
_________________________________________________________________
dense_86 (Dense)             (None, 10)                210       
_________________________________________________________________
dense_87 (Dense)             (None, 1)                 11        
=================================================================
Total params: 1,051
Trainable params: 1,051
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
169/169 [==============================] - 0s 35us/step
test loss: 0.8209835008756649, test accuracy: 0.6863905191421509
AUC ROC:  0.6336125158027813
Kappa:  0.1859492865582114
[[101  12]
 [ 41  15]]
</pre>
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<div class="output_markdown rendered_html output_subarea ">
<h2 id="Arte-Franc&#233;s-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(750, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 67.97% y un Kappa del 23.62
Model: &#34;model_30&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_30 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_88 (Dense)             (None, 10)                70        
_________________________________________________________________
dense_89 (Dense)             (None, 10)                110       
_________________________________________________________________
dense_90 (Dense)             (None, 1)                 11        
=================================================================
Total params: 191
Trainable params: 191
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
188/188 [==============================] - 0s 32us/step
test loss: 0.6406431362984029, test accuracy: 0.6489361524581909
AUC ROC:  0.6210131332082551
Kappa:  0.1633175994605529
[[100  23]
 [ 43  22]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(615, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 70.50% y un Kappa del 7.47
Model: &#34;model_31&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_31 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_91 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_92 (Dense)             (None, 1)                 31        
=================================================================
Total params: 241
Trainable params: 241
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
154/154 [==============================] - 0s 39us/step
test loss: 0.645158732866312, test accuracy: 0.6623376607894897
AUC ROC:  0.5677190360545488
Kappa:  0.02460414129110844
[[101   0]
 [ 52   1]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">Club De Banqueros y Empresarios y Gramma y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(690, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 66.15% y un Kappa del 3.87
Model: &#34;model_32&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_32 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_93 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_94 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_95 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,171
Trainable params: 1,171
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
173/173 [==============================] - 0s 23us/step
test loss: 0.6482885759000834, test accuracy: 0.6416184902191162
AUC ROC:  0.5390855457227139
Kappa:  -0.012651057401812826
[[110   3]
 [ 59   1]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(687, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 71.26% y un Kappa del 6.69
Model: &#34;model_33&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_33 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_96 (Dense)             (None, 30)                210       
_________________________________________________________________
dense_97 (Dense)             (None, 30)                930       
_________________________________________________________________
dense_98 (Dense)             (None, 1)                 31        
=================================================================
Total params: 1,171
Trainable params: 1,171
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
172/172 [==============================] - 0s 41us/step
test loss: 0.6352972180344337, test accuracy: 0.680232584476471
AUC ROC:  0.5673061677501189
Kappa:  0.09352242238405528
[[108  11]
 [ 44   9]]
</pre>
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<div class="output_markdown rendered_html output_subarea ">
<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">Club De Banqueros y Empresarios y Hotel Marrakech y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(618, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 300}, que permiten obtener un Accuracy de 64.15% y un Kappa del 23.85
Model: &#34;model_34&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_34 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_99 (Dense)             (None, 10)                70        
_________________________________________________________________
dense_100 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_101 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_102 (Dense)            (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 300
155/155 [==============================] - 0s 32us/step
test loss: 0.6952421103754352, test accuracy: 0.6451612710952759
AUC ROC:  0.6549422799422799
Kappa:  0.19033146547630342
[[78 21]
 [34 22]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">Club De Banqueros y Empresarios y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(615, 6)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 69.20% y un Kappa del 16.25
Model: &#34;model_35&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_35 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_103 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_104 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_105 (Dense)            (None, 1)                 11        
=================================================================
Total params: 191
Trainable params: 191
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
154/154 [==============================] - 0s 52us/step
test loss: 0.6325509857821774, test accuracy: 0.649350643157959
AUC ROC:  0.6517722473604827
Kappa:  0.14338689740420263
[[84 18]
 [36 16]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(690, 6)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 65.96% y un Kappa del 7.09
Model: &#34;model_36&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_36 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_106 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_107 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_108 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_109 (Dense)            (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
173/173 [==============================] - 0s 35us/step
test loss: 0.6230642020357826, test accuracy: 0.6242774724960327
AUC ROC:  0.6555332568807339
Kappa:  0.07119848021805564
[[97 12]
 [53 11]]
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<h2 id="Gramma-y-Hotel-Marrakech-y-Specialized">Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.618349              0.564005              0.091611   
1             -0.149103             -1.303101             -0.498811   
2             -1.141294             -1.317570              0.575363   
3             -0.988346             -0.540855              1.006800   
4             -0.640925             -0.228256              0.461986   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.885208              0.563756              1.803892  
1              0.416525             -0.660751             -1.179350  
2             -1.560836              0.336340              1.197709  
3             -0.214650             -0.364940              0.068869  
4             -1.274446             -1.494581             -0.125833  
(579, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 63.13% y un Kappa del 16.42
Model: &#34;model_37&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_37 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_110 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_111 (Dense)            (None, 1)                 11        
=================================================================
Total params: 81
Trainable params: 81
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
145/145 [==============================] - 0s 48us/step
test loss: 0.7010458753026765, test accuracy: 0.5793103575706482
AUC ROC:  0.5068686868686869
Kappa:  0.012283640424343933
[[73 17]
 [44 11]]
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<h2 id="Gramma-y-Hotel-Marrakech-y-Urban-Place">Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.618349              0.564005              0.091611   
1             -0.149103             -1.303101             -0.498811   
2             -1.141294             -1.317570              0.575363   
3             -0.988346             -0.540855              1.006800   
4             -0.640925             -0.228256              0.461986   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.885208              0.563756              1.803892  
1              0.416525             -0.660751             -1.179350  
2             -1.560836              0.336340              1.197709  
3             -0.214650             -0.364940              0.068869  
4             -1.274446             -1.494581             -0.125833  
(576, 6)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20), &#39;learning_rate_init&#39;: 0.005, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 68.75% y un Kappa del 21.36
Model: &#34;model_38&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_38 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_112 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_113 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_114 (Dense)            (None, 1)                 21        
=================================================================
Total params: 581
Trainable params: 581
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
144/144 [==============================] - 0s 49us/step
test loss: 0.6971434023645189, test accuracy: 0.6041666865348816
AUC ROC:  0.5875402792696026
Kappa:  0.03752345215759856
[[75 20]
 [37 12]]
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<h2 id="Gramma-y-Specialized-y-Urban-Place">Gramma y Specialized y Urban Place<a class="anchor-link" href="#Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.618349              0.564005              0.091611   
1             -0.149103             -1.303101             -0.498811   
2             -1.141294             -1.317570              0.575363   
3             -0.988346             -0.540855              1.006800   
4             -0.640925             -0.228256              0.461986   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.885208              0.563756              1.803892  
1              0.416525             -0.660751             -1.179350  
2             -1.560836              0.336340              1.197709  
3             -0.214650             -0.364940              0.068869  
4             -1.274446             -1.494581             -0.125833  
(651, 6)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 65.37% y un Kappa del 7.82
Model: &#34;model_39&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_39 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_115 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_116 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_117 (Dense)            (None, 1)                 11        
=================================================================
Total params: 191
Trainable params: 191
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
163/163 [==============================] - 0s 43us/step
test loss: 0.6838659391812751, test accuracy: 0.6073619723320007
AUC ROC:  0.5291982323232323
Kappa:  0.044863578099249346
[[92  7]
 [57  7]]
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<h2 id="Hotel-Marrakech-y-Specialized-y-Urban-Place">Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.297447              0.231356              0.546295   
1              0.766614             -1.227377             -0.605576   
2             -0.431734             -1.183099              1.287522   
3              0.078607             -1.572049              1.188424   
4              0.007655             -1.466562              1.294323   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.007345              0.269632              0.236051  
1             -1.216250              0.473081             -0.446139  
2              0.927452             -0.309857             -1.666006  
3              0.092330             -0.244414             -0.490911  
4             -0.410616             -0.763440             -1.007284  
(579, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.003, &#39;max_iter&#39;: 400}, que permiten obtener un Accuracy de 67.05% y un Kappa del 31.01
Model: &#34;model_40&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_40 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_118 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_119 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_120 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_121 (Dense)            (None, 1)                 21        
=================================================================
Total params: 1,001
Trainable params: 1,001
Non-trainable params: 0
_________________________________________________________________
None
epochs: 400
145/145 [==============================] - 0s 28us/step
test loss: 0.8860082655117429, test accuracy: 0.6137930750846863
AUC ROC:  0.6517647058823529
Kappa:  0.1960396039603961
[[59 26]
 [30 30]]
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<h2 id="4-comp-groups-combinations">4-comp groups combinations<a class="anchor-link" href="#4-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s1">&#39;ignore&#39;</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">three_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">4</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">three_combs</span><span class="p">:</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;## &#39;</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">no</span><span class="p">]</span>
        <span class="n">title</span><span class="o">+=</span><span class="s1">&#39; y &#39;</span>
    <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">title</span><span class="p">))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
        <span class="n">X</span><span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_tc</span><span class="p">[</span><span class="n">no</span><span class="p">])</span>
        <span class="n">y</span><span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">no</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>
    

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(930, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 69.58% y un Kappa del 4.72
Model: &#34;model_38&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_38 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_116 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_117 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_118 (Dense)            (None, 1)                 21        
=================================================================
Total params: 581
Trainable params: 581
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
233/233 [==============================] - 0s 34us/step
test loss: 0.5936217755718805, test accuracy: 0.721030056476593
AUC ROC:  0.5517399267399268
Kappa:  0.0
[[168   0]
 [ 65   0]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(1005, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 68.92% y un Kappa del 5.84
Model: &#34;model_39&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_39 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_119 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_120 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_121 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_122 (Dense)            (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
252/252 [==============================] - 0s 58us/step
test loss: 0.7797506934120542, test accuracy: 0.6190476417541504
AUC ROC:  0.43629535147392295
Kappa:  0.0
[[144  24]
 [ 72  12]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Gramma y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Gramma-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(1002, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 71.90% y un Kappa del 4.29
Model: &#34;model_40&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_40 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_123 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_124 (Dense)            (None, 1)                 11        
=================================================================
Total params: 81
Trainable params: 81
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
251/251 [==============================] - 0s 54us/step
test loss: 0.6206853342246249, test accuracy: 0.6932271122932434
AUC ROC:  0.5412034978508967
Kappa:  0.01758755654958588
[[173   0]
 [ 77   1]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(933, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 20, 20), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 50}, que permiten obtener un Accuracy de 64.81% y un Kappa del 4.13
Model: &#34;model_41&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_41 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_125 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_126 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_127 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_128 (Dense)            (None, 1)                 21        
=================================================================
Total params: 1,001
Trainable params: 1,001
Non-trainable params: 0
_________________________________________________________________
None
epochs: 50
234/234 [==============================] - 0s 71us/step
test loss: 0.6240878869325687, test accuracy: 0.6794871687889099
AUC ROC:  0.5591216216216216
Kappa:  0.020538006473936776
[[156   4]
 [ 71   3]]
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(930, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (10, 10), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 69.30% y un Kappa del 12.33
Model: &#34;model_42&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_42 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_129 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_130 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_131 (Dense)            (None, 1)                 11        
=================================================================
Total params: 191
Trainable params: 191
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
233/233 [==============================] - 0s 58us/step
test loss: 0.6098034921633839, test accuracy: 0.7038626670837402
AUC ROC:  0.5531113058720422
Kappa:  0.030863825426487246
[[162   1]
 [ 68   2]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Club De Banqueros y Empresarios y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(1005, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30, 30), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 1000}, que permiten obtener un Accuracy de 68.13% y un Kappa del 10.87
Model: &#34;model_43&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_43 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_132 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_133 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_134 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_135 (Dense)            (None, 1)                 31        
=================================================================
Total params: 2,101
Trainable params: 2,101
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
252/252 [==============================] - 0s 47us/step
test loss: 0.6843863460752699, test accuracy: 0.6309523582458496
AUC ROC:  0.5911236350827757
Kappa:  0.11575611228493798
[[132  35]
 [ 58  27]]
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Specialized">Arte Franc&#233;s y Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(894, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 65.22% y un Kappa del 3.87
Model: &#34;model_44&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_44 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_136 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_137 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_138 (Dense)            (None, 1)                 11        
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
224/224 [==============================] - 0s 101us/step
test loss: 0.6527888178825378, test accuracy: 0.65625
AUC ROC:  0.5048897581792319
Kappa:  -0.00889096864763661
[[147   1]
 [ 76   0]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">Arte Franc&#233;s y Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(891, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.004, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 69.01% y un Kappa del 3.78
Model: &#34;model_45&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_45 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_139 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_140 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_141 (Dense)            (None, 1)                 31        
=================================================================
Total params: 1,171
Trainable params: 1,171
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
223/223 [==============================] - 0s 93us/step
test loss: 0.5914497979553291, test accuracy: 0.6905829310417175
AUC ROC:  0.6395633352155091
Kappa:  0.03208152481600279
[[151   3]
 [ 66   3]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Gramma y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(966, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.007, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 65.88% y un Kappa del 9.32
Model: &#34;model_46&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_46 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_142 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_143 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_144 (Dense)            (None, 1)                 11        
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
242/242 [==============================] - 0s 57us/step
test loss: 0.6673690504279018, test accuracy: 0.6487603187561035
AUC ROC:  0.5285429141716567
Kappa:  0.03327380392894075
[[145  22]
 [ 63  12]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Arte Franc&#233;s y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              1.585484              0.923309             -0.748807   
1              1.129768              0.963814              0.209096   
2             -0.066076              1.857866              1.921193   
3              0.119831              1.429286              1.472808   
4             -0.123292              0.197415              0.503797   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.209820             -1.073924              0.283035  
1             -0.143471              0.115184              0.020905  
2              1.093619              2.089353              1.984310  
3              1.282029              1.096897              2.122871  
4              1.431215              1.715761              0.683611  
(894, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 63.88% y un Kappa del 19.52
Model: &#34;model_47&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_47 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_145 (Dense)            (None, 30)                210       
_________________________________________________________________
dense_146 (Dense)            (None, 20)                620       
_________________________________________________________________
dense_147 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_148 (Dense)            (None, 1)                 11        
=================================================================
Total params: 1,051
Trainable params: 1,051
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
224/224 [==============================] - 0s 45us/step
test loss: 0.6094303982598441, test accuracy: 0.6473214030265808
AUC ROC:  0.6060462021104668
Kappa:  0.01073345259391778
[[135  22]
 [ 57  10]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Specialized">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Specialized">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(834, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 20), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 66.24% y un Kappa del 5.90
Model: &#34;model_48&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_48 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_149 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_150 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_151 (Dense)            (None, 1)                 21        
=================================================================
Total params: 581
Trainable params: 581
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
209/209 [==============================] - 0s 72us/step
test loss: 0.6779475947886563, test accuracy: 0.5885167717933655
AUC ROC:  0.5332364341085272
Kappa:  -0.02113396204976703
[[117  12]
 [ 74   6]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Hotel Marrakech y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Hotel-Marrakech-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(831, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 68.54% y un Kappa del 6.77
Model: &#34;model_49&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_49 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_152 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_153 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_154 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_155 (Dense)            (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
208/208 [==============================] - 0s 95us/step
test loss: 0.6604985640599177, test accuracy: 0.6538461446762085
AUC ROC:  0.5425497579343733
Kappa:  0.0017331022530329143
[[130  13]
 [ 59   6]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Gramma y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(906, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.008, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 67.16% y un Kappa del 5.61
Model: &#34;model_50&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_50 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_156 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_157 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_158 (Dense)            (None, 1)                 11        
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
227/227 [==============================] - 0s 66us/step
test loss: 0.6587515289562914, test accuracy: 0.6475771069526672
AUC ROC:  0.5773092369477911
Kappa:  0.08078558412634151
[[138   6]
 [ 74   9]]
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Club De Banqueros y Empresarios y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.898091              0.151819             -1.172713   
1              0.618513             -0.762588              0.061946   
2              0.685649              0.002933              0.719805   
3              1.175209             -0.552349              0.336427   
4              1.350337             -1.407757              0.258917   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              0.474387             -0.020230              1.228657  
1              0.944076              0.697880              0.021150  
2             -1.251700             -0.952424              1.444556  
3              0.482978             -0.212146             -0.144225  
4             -0.523670              0.099306              1.706064  
(834, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 20}, que permiten obtener un Accuracy de 65.28% y un Kappa del 5.98
Model: &#34;model_51&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_51 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_159 (Dense)            (None, 10)                70        
_________________________________________________________________
dense_160 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_161 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_162 (Dense)            (None, 1)                 11        
=================================================================
Total params: 301
Trainable params: 301
Non-trainable params: 0
_________________________________________________________________
None
epochs: 20
209/209 [==============================] - 0s 96us/step
test loss: 0.6804209633877403, test accuracy: 0.5980861186981201
AUC ROC:  0.5416508538899432
Kappa:  0.04326975476839234
[[117   7]
 [ 77   8]]
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<h2 id="Gramma-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">Gramma y Hotel Marrakech y Specialized y Urban Place<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech-y-Specialized-y-Urban-Place">&#182;</a></h2>
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<pre>   tonalcentroidfiles_1  tonalcentroidfiles_2  tonalcentroidfiles_3  \
0              0.618349              0.564005              0.091611   
1             -0.149103             -1.303101             -0.498811   
2             -1.141294             -1.317570              0.575363   
3             -0.988346             -0.540855              1.006800   
4             -0.640925             -0.228256              0.461986   

   tonalcentroidfiles_4  tonalcentroidfiles_5  tonalcentroidfiles_6  
0              1.885208              0.563756              1.803892  
1              0.416525             -0.660751             -1.179350  
2             -1.560836              0.336340              1.197709  
3             -0.214650             -0.364940              0.068869  
4             -1.274446             -1.494581             -0.125833  
(795, 6)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 10), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 1000}, que permiten obtener un Accuracy de 64.93% y un Kappa del 24.52
Model: &#34;model_52&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_52 (InputLayer)        (None, 6)                 0         
_________________________________________________________________
dense_163 (Dense)            (None, 20)                140       
_________________________________________________________________
dense_164 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_165 (Dense)            (None, 1)                 11        
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
epochs: 1000
199/199 [==============================] - 0s 0us/step
test loss: 0.6784647730127651, test accuracy: 0.5678392052650452
AUC ROC:  0.5518372703412073
Kappa:  -0.04239249604093054
[[101  26]
 [ 60  12]]
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<h1 id="Chromagram">Chromagram<a class="anchor-link" href="#Chromagram">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span>
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<pre>Index([&#39;durationfiles&#39;, &#39;rmsfiles&#39;, &#39;rmsmedianfiles&#39;, &#39;lowenergyfiles&#39;,
       &#39;ASRfiles&#39;, &#39;beatspectrumfiles&#39;, &#39;eventdensityfiles&#39;, &#39;tempofiles&#39;,
       &#39;pulseclarityfiles&#39;, &#39;zerocrossfiles&#39;, &#39;rolloffsfiles&#39;,
       &#39;brightnessfiles&#39;, &#39;spreadfiles&#39;, &#39;centroidfiles&#39;, &#39;kurtosisfiles&#39;,
       &#39;flatnessfiles&#39;, &#39;entropyfiles&#39;, &#39;mfccfiles_1&#39;, &#39;mfccfiles_2&#39;,
       &#39;mfccfiles_3&#39;, &#39;mfccfiles_4&#39;, &#39;mfccfiles_5&#39;, &#39;mfccfiles_6&#39;,
       &#39;mfccfiles_7&#39;, &#39;mfccfiles_8&#39;, &#39;mfccfiles_9&#39;, &#39;mfccfiles_10&#39;,
       &#39;mfccfiles_11&#39;, &#39;mfccfiles_12&#39;, &#39;mfccfiles_13&#39;, &#39;inharmonicityfiles&#39;,
       &#39;bestkeyfiles&#39;, &#39;keyclarityfiles&#39;, &#39;modalityfiles&#39;,
       &#39;tonalcentroidfiles_1&#39;, &#39;tonalcentroidfiles_2&#39;, &#39;tonalcentroidfiles_3&#39;,
       &#39;tonalcentroidfiles_4&#39;, &#39;tonalcentroidfiles_5&#39;, &#39;tonalcentroidfiles_6&#39;,
       &#39;chromagramfiles_1&#39;, &#39;chromagramfiles_2&#39;, &#39;chromagramfiles_3&#39;,
       &#39;chromagramfiles_4&#39;, &#39;chromagramfiles_5&#39;, &#39;chromagramfiles_6&#39;,
       &#39;chromagramfiles_7&#39;, &#39;chromagramfiles_8&#39;, &#39;chromagramfiles_9&#39;,
       &#39;chromagramfiles_10&#39;, &#39;chromagramfiles_11&#39;, &#39;chromagramfiles_12&#39;,
       &#39;attackslopefiles&#39;, &#39;attackleapfiles&#39;, &#39;chosen&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">40</span><span class="p">:</span><span class="mi">52</span><span class="p">]</span>
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<pre>Index([&#39;chromagramfiles_1&#39;, &#39;chromagramfiles_2&#39;, &#39;chromagramfiles_3&#39;,
       &#39;chromagramfiles_4&#39;, &#39;chromagramfiles_5&#39;, &#39;chromagramfiles_6&#39;,
       &#39;chromagramfiles_7&#39;, &#39;chromagramfiles_8&#39;, &#39;chromagramfiles_9&#39;,
       &#39;chromagramfiles_10&#39;, &#39;chromagramfiles_11&#39;, &#39;chromagramfiles_12&#39;],
      dtype=&#39;object&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_n_ps_std_ch</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">companies</span><span class="p">)):</span>
    <span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">i</span><span class="p">]</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">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span><span class="mi">40</span><span class="p">:</span><span class="mi">52</span><span class="p">])</span>
    <span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="o">=</span><span class="n">df_n_ps_std</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">40</span><span class="p">:</span><span class="mi">52</span><span class="p">]</span>
<span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">()</span>
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<pre>&lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;
RangeIndex: 315 entries, 0 to 314
Data columns (total 12 columns):
chromagramfiles_1     315 non-null float64
chromagramfiles_2     315 non-null float64
chromagramfiles_3     315 non-null float64
chromagramfiles_4     315 non-null float64
chromagramfiles_5     315 non-null float64
chromagramfiles_6     315 non-null float64
chromagramfiles_7     315 non-null float64
chromagramfiles_8     315 non-null float64
chromagramfiles_9     315 non-null float64
chromagramfiles_10    315 non-null float64
chromagramfiles_11    315 non-null float64
chromagramfiles_12    315 non-null float64
dtypes: float64(12)
memory usage: 29.6 KB
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<h2 id="2-comp-groups-combinations">2-comp groups combinations<a class="anchor-link" href="#2-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">two_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">two_combs</span><span class="p">:</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="s1">&#39;## &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">+</span><span class="s1">&#39; y &#39;</span><span class="o">+</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
</pre></div>

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<h2 id="Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">Arte Franc&#233;s y Club De Banqueros y Empresarios<a class="anchor-link" href="#Arte-Franc&#233;s-y-Club-De-Banqueros-y-Empresarios">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           1.713160           0.038243           2.143431           0.466379   
1           1.315247           0.387677           1.394548          -0.542923   
2           1.737505          -1.440944          -1.114255           0.326790   
3           1.737506          -1.127088          -0.672464           0.505231   
4           1.737506           1.455410           0.333584           1.122628   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           2.480688           1.533552          -0.564788          -0.236267   
1          -0.194326           0.100084           0.191568           1.691930   
2          -1.157732          -1.261521          -1.130503           1.585017   
3          -0.830959          -1.046716          -0.912922           1.082754   
4           1.139134          -0.481280           0.129896           0.915597   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.737672            0.470152           -0.935468   
1          -0.164059            0.599296           -0.175780   
2          -1.367743           -1.287569           -0.805298   
3          -0.919242           -0.761758           -1.023346   
4           0.333032           -0.289806           -0.236639   

   chromagramfiles_12  
0            0.074931  
1            0.267042  
2           -1.676436  
3           -1.220716  
4           -0.281891  
(570, 12)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 500}, que permiten obtener un Accuracy de 73.54% y un Kappa del 7.92
Model: &#34;model_41&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_41 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_122 (Dense)            (None, 20)                260       
_________________________________________________________________
dense_123 (Dense)            (None, 1)                 21        
=================================================================
Total params: 281
Trainable params: 281
Non-trainable params: 0
_________________________________________________________________
None
epochs: 500
143/143 [==============================] - 0s 49us/step
test loss: 0.6013401425801791, test accuracy: 0.7132866978645325
AUC ROC:  0.5564323290291727
Kappa:  0.058757424947824655
[[99  3]
 [38  3]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Gramma">Arte Franc&#233;s y Gramma<a class="anchor-link" href="#Arte-Franc&#233;s-y-Gramma">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           1.713160           0.038243           2.143431           0.466379   
1           1.315247           0.387677           1.394548          -0.542923   
2           1.737505          -1.440944          -1.114255           0.326790   
3           1.737506          -1.127088          -0.672464           0.505231   
4           1.737506           1.455410           0.333584           1.122628   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           2.480688           1.533552          -0.564788          -0.236267   
1          -0.194326           0.100084           0.191568           1.691930   
2          -1.157732          -1.261521          -1.130503           1.585017   
3          -0.830959          -1.046716          -0.912922           1.082754   
4           1.139134          -0.481280           0.129896           0.915597   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.737672            0.470152           -0.935468   
1          -0.164059            0.599296           -0.175780   
2          -1.367743           -1.287569           -0.805298   
3          -0.919242           -0.761758           -1.023346   
4           0.333032           -0.289806           -0.236639   

   chromagramfiles_12  
0            0.074931  
1            0.267042  
2           -1.676436  
3           -1.220716  
4           -0.281891  
(531, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (10, 10), &#39;learning_rate_init&#39;: 0.02, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 71.61% y un Kappa del 8.27
Model: &#34;model_42&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_42 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_124 (Dense)            (None, 10)                130       
_________________________________________________________________
dense_125 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_126 (Dense)            (None, 1)                 11        
=================================================================
Total params: 251
Trainable params: 251
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
133/133 [==============================] - 0s 53us/step
test loss: 0.6136606084673029, test accuracy: 0.7293233275413513
AUC ROC:  0.5542424242424242
Kappa:  0.06593835349200161
[[93  7]
 [29  4]]
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<h2 id="Arte-Franc&#233;s-y-Hotel-Marrakech">Arte Franc&#233;s y Hotel Marrakech<a class="anchor-link" href="#Arte-Franc&#233;s-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           1.713160           0.038243           2.143431           0.466379   
1           1.315247           0.387677           1.394548          -0.542923   
2           1.737505          -1.440944          -1.114255           0.326790   
3           1.737506          -1.127088          -0.672464           0.505231   
4           1.737506           1.455410           0.333584           1.122628   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           2.480688           1.533552          -0.564788          -0.236267   
1          -0.194326           0.100084           0.191568           1.691930   
2          -1.157732          -1.261521          -1.130503           1.585017   
3          -0.830959          -1.046716          -0.912922           1.082754   
4           1.139134          -0.481280           0.129896           0.915597   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.737672            0.470152           -0.935468   
1          -0.164059            0.599296           -0.175780   
2          -1.367743           -1.287569           -0.805298   
3          -0.919242           -0.761758           -1.023346   
4           0.333032           -0.289806           -0.236639   

   chromagramfiles_12  
0            0.074931  
1            0.267042  
2           -1.676436  
3           -1.220716  
4           -0.281891  
(459, 12)
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20,), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 72.97% y un Kappa del 30.60
Model: &#34;model_43&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_43 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_127 (Dense)            (None, 20)                260       
_________________________________________________________________
dense_128 (Dense)            (None, 1)                 21        
=================================================================
Total params: 281
Trainable params: 281
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
115/115 [==============================] - 0s 52us/step
test loss: 0.6349654368732287, test accuracy: 0.6608695387840271
AUC ROC:  0.5981012658227849
Kappa:  0.11064842355740634
[[67 12]
 [27  9]]
</pre>
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<h2 id="Arte-Franc&#233;s-y-Specialized">Arte Franc&#233;s y Specialized<a class="anchor-link" href="#Arte-Franc&#233;s-y-Specialized">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           1.713160           0.038243           2.143431           0.466379   
1           1.315247           0.387677           1.394548          -0.542923   
2           1.737505          -1.440944          -1.114255           0.326790   
3           1.737506          -1.127088          -0.672464           0.505231   
4           1.737506           1.455410           0.333584           1.122628   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           2.480688           1.533552          -0.564788          -0.236267   
1          -0.194326           0.100084           0.191568           1.691930   
2          -1.157732          -1.261521          -1.130503           1.585017   
3          -0.830959          -1.046716          -0.912922           1.082754   
4           1.139134          -0.481280           0.129896           0.915597   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.737672            0.470152           -0.935468   
1          -0.164059            0.599296           -0.175780   
2          -1.367743           -1.287569           -0.805298   
3          -0.919242           -0.761758           -1.023346   
4           0.333032           -0.289806           -0.236639   

   chromagramfiles_12  
0            0.074931  
1            0.267042  
2           -1.676436  
3           -1.220716  
4           -0.281891  
(534, 12)
Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (20, 20), &#39;learning_rate_init&#39;: 0.01, &#39;max_iter&#39;: 2000}, que permiten obtener un Accuracy de 68.25% y un Kappa del 31.03
Model: &#34;model_44&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_44 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_129 (Dense)            (None, 20)                260       
_________________________________________________________________
dense_130 (Dense)            (None, 20)                420       
_________________________________________________________________
dense_131 (Dense)            (None, 1)                 21        
=================================================================
Total params: 701
Trainable params: 701
Non-trainable params: 0
_________________________________________________________________
None
epochs: 2000
134/134 [==============================] - 0s 52us/step
test loss: 0.8510398740199074, test accuracy: 0.611940324306488
AUC ROC:  0.6521739130434783
Kappa:  0.0821917808219178
[[68 20]
 [32 14]]
</pre>
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<div class="output_markdown rendered_html output_subarea ">
<h2 id="Arte-Franc&#233;s-y-Urban-Place">Arte Franc&#233;s y Urban Place<a class="anchor-link" href="#Arte-Franc&#233;s-y-Urban-Place">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           1.713160           0.038243           2.143431           0.466379   
1           1.315247           0.387677           1.394548          -0.542923   
2           1.737505          -1.440944          -1.114255           0.326790   
3           1.737506          -1.127088          -0.672464           0.505231   
4           1.737506           1.455410           0.333584           1.122628   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           2.480688           1.533552          -0.564788          -0.236267   
1          -0.194326           0.100084           0.191568           1.691930   
2          -1.157732          -1.261521          -1.130503           1.585017   
3          -0.830959          -1.046716          -0.912922           1.082754   
4           1.139134          -0.481280           0.129896           0.915597   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.737672            0.470152           -0.935468   
1          -0.164059            0.599296           -0.175780   
2          -1.367743           -1.287569           -0.805298   
3          -0.919242           -0.761758           -1.023346   
4           0.333032           -0.289806           -0.236639   

   chromagramfiles_12  
0            0.074931  
1            0.267042  
2           -1.676436  
3           -1.220716  
4           -0.281891  
(531, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;logistic&#39;, &#39;hidden_layer_sizes&#39;: (10,), &#39;learning_rate_init&#39;: 0.009, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 71.61% y un Kappa del 23.98
Model: &#34;model_45&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_45 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_132 (Dense)            (None, 10)                130       
_________________________________________________________________
dense_133 (Dense)            (None, 1)                 11        
=================================================================
Total params: 141
Trainable params: 141
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
133/133 [==============================] - 0s 53us/step
test loss: 0.5838545421908673, test accuracy: 0.7218044996261597
AUC ROC:  0.588180693069307
Kappa:  0.09357156013998891
[[90 11]
 [26  6]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Gramma">Club De Banqueros y Empresarios y Gramma<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Gramma">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0          -0.369691          -0.881824          -0.095656          -0.923999   
1          -0.175875          -0.403800          -0.657709          -0.201259   
2           0.894452          -0.189794           1.959063           0.169276   
3           0.060782          -0.392075           0.826233          -0.048480   
4          -1.116536          -0.923742          -1.238971          -0.919598   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0          -0.030645          -0.834931          -1.031650          -0.840942   
1           1.691433          -0.672783          -0.119944          -0.440080   
2          -0.403611          -1.036954           1.447615          -0.340767   
3           1.789786          -0.552163           0.121028          -0.111355   
4           0.313068          -1.160111           1.316032          -0.700013   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.677716            1.084098           -1.064999   
1           0.339906            1.084098            0.504608   
2          -0.846170           -0.515065           -0.699878   
3           0.220614            1.084098            0.073241   
4          -1.600210            1.084098           -1.072155   

   chromagramfiles_12  
0           -1.156623  
1            0.931676  
2            0.032355  
3            1.176257  
4            1.270095  
(471, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 30), &#39;learning_rate_init&#39;: 0.002, &#39;max_iter&#39;: 200}, que permiten obtener un Accuracy de 71.95% y un Kappa del 27.04
Model: &#34;model_46&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_46 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_134 (Dense)            (None, 30)                390       
_________________________________________________________________
dense_135 (Dense)            (None, 30)                930       
_________________________________________________________________
dense_136 (Dense)            (None, 1)                 31        
=================================================================
Total params: 1,351
Trainable params: 1,351
Non-trainable params: 0
_________________________________________________________________
None
epochs: 200
118/118 [==============================] - 0s 51us/step
test loss: 0.6019874669737735, test accuracy: 0.7372881174087524
AUC ROC:  0.5298479792361883
Kappa:  0.13276434329065911
[[82  5]
 [26  5]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">Club De Banqueros y Empresarios y Hotel Marrakech<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0          -0.369691          -0.881824          -0.095656          -0.923999   
1          -0.175875          -0.403800          -0.657709          -0.201259   
2           0.894452          -0.189794           1.959063           0.169276   
3           0.060782          -0.392075           0.826233          -0.048480   
4          -1.116536          -0.923742          -1.238971          -0.919598   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0          -0.030645          -0.834931          -1.031650          -0.840942   
1           1.691433          -0.672783          -0.119944          -0.440080   
2          -0.403611          -1.036954           1.447615          -0.340767   
3           1.789786          -0.552163           0.121028          -0.111355   
4           0.313068          -1.160111           1.316032          -0.700013   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.677716            1.084098           -1.064999   
1           0.339906            1.084098            0.504608   
2          -0.846170           -0.515065           -0.699878   
3           0.220614            1.084098            0.073241   
4          -1.600210            1.084098           -1.072155   

   chromagramfiles_12  
0           -1.156623  
1            0.931676  
2            0.032355  
3            1.176257  
4            1.270095  
(399, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (75) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;relu&#39;, &#39;hidden_layer_sizes&#39;: (30, 20, 10), &#39;learning_rate_init&#39;: 0.001, &#39;max_iter&#39;: 75}, que permiten obtener un Accuracy de 72.24% y un Kappa del 20.51
Model: &#34;model_47&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_47 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_137 (Dense)            (None, 30)                390       
_________________________________________________________________
dense_138 (Dense)            (None, 20)                620       
_________________________________________________________________
dense_139 (Dense)            (None, 10)                210       
_________________________________________________________________
dense_140 (Dense)            (None, 1)                 11        
=================================================================
Total params: 1,231
Trainable params: 1,231
Non-trainable params: 0
_________________________________________________________________
None
epochs: 75
100/100 [==============================] - 0s 60us/step
test loss: 0.6944141483306885, test accuracy: 0.5699999928474426
AUC ROC:  0.5492359932088284
Kappa:  -0.08366935483870952
[[56  6]
 [37  1]]
</pre>
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<div class="output_markdown rendered_html output_subarea ">
<h2 id="Club-De-Banqueros-y-Empresarios-y-Specialized">Club De Banqueros y Empresarios y Specialized<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Specialized">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0          -0.369691          -0.881824          -0.095656          -0.923999   
1          -0.175875          -0.403800          -0.657709          -0.201259   
2           0.894452          -0.189794           1.959063           0.169276   
3           0.060782          -0.392075           0.826233          -0.048480   
4          -1.116536          -0.923742          -1.238971          -0.919598   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0          -0.030645          -0.834931          -1.031650          -0.840942   
1           1.691433          -0.672783          -0.119944          -0.440080   
2          -0.403611          -1.036954           1.447615          -0.340767   
3           1.789786          -0.552163           0.121028          -0.111355   
4           0.313068          -1.160111           1.316032          -0.700013   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.677716            1.084098           -1.064999   
1           0.339906            1.084098            0.504608   
2          -0.846170           -0.515065           -0.699878   
3           0.220614            1.084098            0.073241   
4          -1.600210            1.084098           -1.072155   

   chromagramfiles_12  
0           -1.156623  
1            0.931676  
2            0.032355  
3            1.176257  
4            1.270095  
(474, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (30,), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 100}, que permiten obtener un Accuracy de 69.01% y un Kappa del 30.47
Model: &#34;model_48&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_48 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_141 (Dense)            (None, 30)                390       
_________________________________________________________________
dense_142 (Dense)            (None, 1)                 31        
=================================================================
Total params: 421
Trainable params: 421
Non-trainable params: 0
_________________________________________________________________
None
epochs: 100
119/119 [==============================] - 0s 59us/step
test loss: 0.6398004749241997, test accuracy: 0.6218487620353699
AUC ROC:  0.626777983920841
Kappa:  0.14905450500556172
[[57 20]
 [25 17]]
</pre>
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<h2 id="Club-De-Banqueros-y-Empresarios-y-Urban-Place">Club De Banqueros y Empresarios y Urban Place<a class="anchor-link" href="#Club-De-Banqueros-y-Empresarios-y-Urban-Place">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0          -0.369691          -0.881824          -0.095656          -0.923999   
1          -0.175875          -0.403800          -0.657709          -0.201259   
2           0.894452          -0.189794           1.959063           0.169276   
3           0.060782          -0.392075           0.826233          -0.048480   
4          -1.116536          -0.923742          -1.238971          -0.919598   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0          -0.030645          -0.834931          -1.031650          -0.840942   
1           1.691433          -0.672783          -0.119944          -0.440080   
2          -0.403611          -1.036954           1.447615          -0.340767   
3           1.789786          -0.552163           0.121028          -0.111355   
4           0.313068          -1.160111           1.316032          -0.700013   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.677716            1.084098           -1.064999   
1           0.339906            1.084098            0.504608   
2          -0.846170           -0.515065           -0.699878   
3           0.220614            1.084098            0.073241   
4          -1.600210            1.084098           -1.072155   

   chromagramfiles_12  
0           -1.156623  
1            0.931676  
2            0.032355  
3            1.176257  
4            1.270095  
(471, 12)
</pre>
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<pre>C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (10) reached and the optimization hasn&#39;t converged yet.
  % self.max_iter, ConvergenceWarning)
</pre>
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<pre>Los parámetros del mejor modelo fueron {&#39;activation&#39;: &#39;tanh&#39;, &#39;hidden_layer_sizes&#39;: (10, 10, 10), &#39;learning_rate_init&#39;: 0.006, &#39;max_iter&#39;: 10}, que permiten obtener un Accuracy de 69.97% y un Kappa del 6.67
Model: &#34;model_49&#34;
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_49 (InputLayer)        (None, 12)                0         
_________________________________________________________________
dense_143 (Dense)            (None, 10)                130       
_________________________________________________________________
dense_144 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_145 (Dense)            (None, 10)                110       
_________________________________________________________________
dense_146 (Dense)            (None, 1)                 11        
=================================================================
Total params: 361
Trainable params: 361
Non-trainable params: 0
_________________________________________________________________
None
epochs: 10
118/118 [==============================] - 0s 51us/step
test loss: 0.5701536152322414, test accuracy: 0.7118644118309021
AUC ROC:  0.6657487091222032
Kappa:  0.17278350515463914
[[76  7]
 [27  8]]
</pre>
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<h2 id="Gramma-y-Hotel-Marrakech">Gramma y Hotel Marrakech<a class="anchor-link" href="#Gramma-y-Hotel-Marrakech">&#182;</a></h2>
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<pre>   chromagramfiles_1  chromagramfiles_2  chromagramfiles_3  chromagramfiles_4  \
0           2.134448          -0.644769          -0.741425          -0.871269   
1          -0.027862           1.690489           0.055411          -0.561004   
2          -0.377201           2.125071          -0.304151           2.056775   
3          -0.590231           0.143657          -0.601072           1.282246   
4          -0.233782           0.378000           1.366350           0.941326   

   chromagramfiles_5  chromagramfiles_6  chromagramfiles_7  chromagramfiles_8  \
0           0.189298          -0.623423          -0.529336           0.357204   
1           0.495091          -0.306711           0.886175          -0.561042   
2          -0.998633          -0.428416           1.821160          -1.229826   
3          -0.578812          -0.045810           1.094766           0.365693   
4          -0.898589          -0.324641           1.117103           0.022453   

   chromagramfiles_9  chromagramfiles_10  chromagramfiles_11  \
0          -0.325100            0.746582           -0.476439   
1           1.431598            0.158038            0.235945   
2          -0.732612           -1.686385           -0.297109   
3           1.431598           -0.618841            0.417565   
4           1.431598           -0.507270            0.477771   

   chromagramfiles_12  
0            0.217416  
1            1.435645  
2            0.515440  
3            0.135593  
4           -0.297988  
(360, 12)
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<h2 id="3-comp-groups-combinations">3-comp groups combinations<a class="anchor-link" href="#3-comp-groups-combinations">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">Markdown</span><span class="p">,</span> <span class="n">Latex</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">three_combs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</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">companies</span><span class="p">)),</span> <span class="mi">3</span><span class="p">))</span>
<span class="k">for</span> <span class="n">comb</span> <span class="ow">in</span> <span class="n">three_combs</span><span class="p">:</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;## &#39;</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
        <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">no</span><span class="p">]</span>
        <span class="n">title</span><span class="o">+=</span><span class="s1">&#39; y &#39;</span>
    <span class="n">title</span><span class="o">+=</span><span class="n">companies</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">Markdown</span><span class="p">(</span><span class="n">title</span><span class="p">))</span>
    <span class="n">X</span> <span class="o">=</span> <span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
    <span class="n">y</span> <span class="o">=</span> <span class="n">df_n_ps</span><span class="p">[</span><span class="n">comb</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="s1">&#39;chosen&#39;</span><span class="p">]</span>
    <span class="k">for</span> <span class="n">no</span> <span class="ow">in</span> <span class="n">comb</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
        <span class="n">X</span><span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps_std_ch</span><span class="p">[</span><span class="n">no</span><span class="p">])</span>
        <span class="n">y</span><span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_n_ps</span><span class="p">[</span><span class="n">no</span><span class="p">][</span><span class="s1">&#39;chosen&#39;</span><span class="p">])</span>
    

    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

    <span class="n">mlp</span> <span class="o">=</span> <span class="n">MLPClassifier</span><span class="p">(</span><span class="n">hidden_layer_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">30</span><span class="p">))</span>

    <span class="n">activation_vec</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;logistic&#39;</span><span class="p">,</span> <span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="s1">&#39;tanh&#39;</span><span class="p">]</span>
    <span class="n">max_iter_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">200</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">400</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">2000</span><span class="p">]</span>
    <span class="n">hidden_layer_sizes_vec</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">10</span><span class="p">,),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,),</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> 
                              <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="p">(</span><span class="mi">30</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">)]</span>
    <span class="n">learning_rate_init_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.002</span><span class="p">,</span> <span class="mf">0.003</span><span class="p">,</span> <span class="mf">0.004</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.006</span><span class="p">,</span> <span class="mf">0.007</span><span class="p">,</span> <span class="mf">0.008</span><span class="p">,</span> <span class="mf">0.009</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">]</span>
    <span class="n">batch_size_vec</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">40</span><span class="p">,</span> <span class="mi">60</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">150</span><span class="p">]</span>


    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1234</span><span class="p">)</span>
    <span class="n">parametros</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;activation&#39;</span><span class="p">:</span> <span class="n">activation_vec</span><span class="p">,</span>
                  <span class="s1">&#39;max_iter&#39;</span><span class="p">:</span><span class="n">max_iter_vec</span><span class="p">,</span>
                  <span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">:</span> <span class="n">hidden_layer_sizes_vec</span><span class="p">,</span>
                  <span class="s1">&#39;learning_rate_init&#39;</span><span class="p">:</span> <span class="n">learning_rate_init_vec</span><span class="c1">#,</span>
                  <span class="c1">#&#39;batch_size&#39;: batch_size_vec</span>
                  <span class="p">}</span>
    <span class="n">scoring</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;kappa&#39;</span><span class="p">:</span><span class="n">make_scorer</span><span class="p">(</span><span class="n">cohen_kappa_score</span><span class="p">),</span> <span class="s1">&#39;accuracy&#39;</span><span class="p">:</span><span class="s1">&#39;accuracy&#39;</span><span class="p">}</span>
    <span class="n">grid</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">mlp</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">parametros</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">iid</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">grid</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Los parámetros del mejor modelo fueron </span><span class="si">{0}</span><span class="s2">, que permiten obtener un Accuracy de </span><span class="si">{1:.2f}</span><span class="s2">% y un Kappa del </span><span class="si">{2:.2f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_score_</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="n">grid</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;mean_test_kappa&#39;</span><span class="p">][</span><span class="n">grid</span><span class="o">.</span><span class="n">best_index_</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>

    <span class="n">n0</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
    <span class="c1">### hidden_layer_sizes</span>
    <span class="n">ns</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">])):</span>
        <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;hidden_layer_sizes&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span>

    <span class="n">ns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">lr</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;learning_rate_init&#39;</span><span class="p">]</span>
    <span class="n">epochs</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;max_iter&#39;</span><span class="p">]</span>

    <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">Input</span><span class="p">(</span><span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">n0</span><span class="p">,))</span>
    <span class="n">hidden_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_tensor</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;logistic&#39;</span><span class="p">:</span>
        <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ns</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
        <span class="n">hidden_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;activation&#39;</span><span class="p">])(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
    <span class="n">classification_output</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">ns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">activation</span> <span class="o">=</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">)(</span><span class="n">hidden_outputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>

    <span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">([</span><span class="n">input_tensor</span><span class="p">],</span> <span class="p">[</span><span class="n">classification_output</span><span class="p">])</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_weights</span><span class="p">()</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>

    <span class="n">model</span><span class="o">.</span><span class="n">set_weights</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
    <span class="n">adam</span> <span class="o">=</span> <span class="n">keras</span><span class="o">.</span><span class="n">optimizers</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="n">adam</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
    <span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> 
                <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
                    <span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span>
                        <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span>
                    <span class="p">)</span>
                <span class="p">]</span>
             <span class="p">)</span>

    <span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">]</span>
    <span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_accuracy&#39;</span><span class="p">]</span>
    <span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
    <span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

    <span class="n">epochs</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">acc</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;epochs: &quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">acc</span><span class="p">)))</span>

    <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;test loss: </span><span class="si">{}</span><span class="s2">, test accuracy: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span><span class="p">))</span>

    <span class="n">y_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;AUC ROC: &quot;</span><span class="p">,</span><span class="n">roc_auc_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="n">y_pred</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">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">&gt;=</span><span class="mf">0.5</span><span class="p">),</span> <span class="n">y_pred</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Kappa: &quot;</span><span class="p">,</span><span class="n">cohen_kappa_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>

    <span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">))</span>
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