sentimental_analysis_BenchMarkModel_ASTD.html

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  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 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 */
    </style>
<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>


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<h2 id="1--Prerequisite-and-setting-up-the-environment.">1- Prerequisite and setting up the environment.<a class="anchor-link" href="#1--Prerequisite-and-setting-up-the-environment.">&#182;</a></h2>
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<p>Data Folder:
Data/tweet.txt
Data/tweet_x.csv</p>
<p>Libraries:
sklearn
scipy</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>

<span class="kn">from</span> <span class="nn">Definations</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">Utilities</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_selection.univariate_selection</span> <span class="kn">import</span> <span class="n">SelectPercentile</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">TruncatedSVD</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.manifold</span> <span class="kn">import</span> <span class="n">isomap</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">hstack</span>
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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\ensemble\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  &#34;This module will be removed in 0.20.&#34;, DeprecationWarning)
C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
  DeprecationWarning)
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<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">,</span> <span class="n">f1_score</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;GridSearchCV&#39;, &#39;make_scorer&#39;, and any other necessary libraries</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">fbeta_score</span>
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<h1 id="First-Stage">First Stage<a class="anchor-link" href="#First-Stage">&#182;</a></h1><h2 id="Dataset-Exploration">Dataset Exploration<a class="anchor-link" href="#Dataset-Exploration">&#182;</a></h2>
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<h2 id="Benchmark-Experiment-Replication-using-ASTD">Benchmark Experiment Replication using ASTD<a class="anchor-link" href="#Benchmark-Experiment-Replication-using-ASTD">&#182;</a></h2>
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<ol>
<li>It stands for Arabic Sentemental Tweet Dataset which is used for arabic social sentimental analysis .</li>
<li>Consist of 10000 tweets gathered from twitter.</li>
<li>Each tweet is Classified as: objective, subjective positive, subjective negative or subjective mixed.</li>
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<h4 id="Loading-the-data-set-in-jupyter-for-exploration.">Loading the data set in jupyter for exploration.<a class="anchor-link" href="#Loading-the-data-set-in-jupyter-for-exploration.">&#182;</a></h4>
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<h3 id="Text-File">Text File<a class="anchor-link" href="#Text-File">&#182;</a></h3>
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<span class="n">filename</span> <span class="o">=</span> <span class="n">codecs</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">&#39;data\Tweets.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
<span class="n">outputfile</span> <span class="o">=</span> <span class="n">filename</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
<span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">outputfile</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]:</span>
    <span class="k">print</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
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<pre>بعد استقالة رئيس #المحكمة_الدستورية ننتظر استقالة #رئيس_القضاء #السودان	OBJ

أهنئ الدكتور أحمد جمال الدين، القيادي بحزب مصر، بمناسبة صدور أولى روايته	POS

البرادعي يستقوى بامريكا مرةاخرى و يرسل عصام العريان الي واشنطن شئ مقرف	NEG

#الحرية_والعدالة | شاهد الآن: #ليلة_الاتحادية أول فيلم استقصائي يتناول أسرار و كواليس تعرض لأول مرة حول حقيقة	OBJ

الوالدة لو اقولها بخاطري حشيشة تضحك بس من اقولها ملل الله وكيلك تعطيني محاضرة عن الفسق والفجور بجنوب الشيشان #ليه كذا يانبع الحنان	NEUTRAL

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<h3 id="CSV-File">CSV File<a class="anchor-link" href="#CSV-File">&#182;</a></h3>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;data\Tweets_x.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">names</span><span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Tweet&quot;</span><span class="p">,</span> <span class="s2">&quot;Classification&quot;</span><span class="p">])</span>

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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        vertical-align: top;
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<table border="1" class="dataframe">
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      <th></th>
      <th>Tweet</th>
      <th>Classification</th>
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      <th>0</th>
      <td>بعد استقالة رئيس #المحكمة_الدستورية ننتظر استق...</td>
      <td>OBJ</td>
    </tr>
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      <th>1</th>
      <td>أهنئ الدكتور أحمد جمال الدين، القيادي بحزب مصر...</td>
      <td>POS</td>
    </tr>
    <tr>
      <th>2</th>
      <td>البرادعي يستقوى بامريكا مرةاخرى و يرسل عصام ال...</td>
      <td>NEG</td>
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      <th>3</th>
      <td>#الحرية_والعدالة | شاهد الآن: #ليلة_الاتحادية ...</td>
      <td>OBJ</td>
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    <tr>
      <th>4</th>
      <td>الوالدة لو اقولها بخاطري حشيشة تضحك بس من اقول...</td>
      <td>NEUTRAL</td>
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      <th>count</th>
      <td>10006</td>
      <td>9986</td>
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      <th>unique</th>
      <td>10002</td>
      <td>4</td>
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      <th>top</th>
      <td>يوسف الحسيني بالفيديو يفضح كذب قناة الجزيرة وي...</td>
      <td>OBJ</td>
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      <th>freq</th>
      <td>2</td>
      <td>6675</td>
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<p>AS it can be clearly seen, I lost some data in this process since i converted the txt into csv using excel. I removed them to keep dataset consistant but I will fix this issue later</p>

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<h2 id="Show-Count-of-All-Categories">Show Count of All Categories<a class="anchor-link" href="#Show-Count-of-All-Categories">&#182;</a></h2>
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      <th>count</th>
      <td>9986</td>
      <td>9986</td>
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      <td>9982</td>
      <td>4</td>
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      <th>top</th>
      <td>الإخوان يطلقون طفايات الحريق للإيحاء بإلقاء ال...</td>
      <td>OBJ</td>
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      <td>2</td>
      <td>6675</td>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">s2</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Classification</span>
<span class="k">print</span><span class="p">(</span><span class="n">s2</span><span class="o">.</span><span class="n">value_counts</span><span class="p">())</span>
<span class="c1"># s2.value_counts().plot(kind=&#39;hist&#39;) </span>
<span class="n">s2</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span> <span class="n">kind</span><span class="o">=</span><span class="s2">&quot;bar&quot;</span><span class="p">)</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<pre>OBJ        6675
NEG        1682
NEUTRAL     831
POS         798
Name: Classification, dtype: int64
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># import pandas as pd</span>
<span class="c1"># import matplotlib.pyplot as plt</span>
<span class="c1"># import numpy as np</span>

<span class="c1"># y = np.random.rand(10,4)</span>
<span class="c1"># y[:,0]= np.arange(10)</span>
<span class="c1"># df = pd.DataFrame(y, columns=[&quot;X&quot;, &quot;A&quot;, &quot;B&quot;, &quot;C&quot;])</span>

<span class="c1"># ax = df.plot(x=&quot;X&quot;, y=&quot;A&quot;, kind=&quot;bar&quot;)</span>
<span class="c1"># df.plot(x=&quot;X&quot;, y=&quot;B&quot;, kind=&quot;bar&quot;, ax=ax, color=&quot;C2&quot;)</span>
<span class="c1"># df.plot(x=&quot;X&quot;, y=&quot;C&quot;, kind=&quot;bar&quot;, ax=ax, color=&quot;C3&quot;)</span>

<span class="c1"># plt.show()</span>
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<h2 id="Benchmark-Model">Benchmark Model<a class="anchor-link" href="#Benchmark-Model">&#182;</a></h2>
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<p>Experments conducted is about applying wide range of standard classifiers on balanced and unbalanced data using token count with/without Tf-Idf to preprocess the dataset.</p>
<p>These Classifiers are:</p>

<pre><code>- Logistic Regression
- Passive Aggresive
- SVM
- Perceptron
- bnb
- mnb
- sgd
- KNN</code></pre>

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<h3 id="ASTD-paper-Experiment-Replication">ASTD paper Experiment Replication<a class="anchor-link" href="#ASTD-paper-Experiment-Replication">&#182;</a></h3>
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<h4 id="String-Token-Count:">String Token Count:<a class="anchor-link" href="#String-Token-Count:">&#182;</a></h4><p>It takes each sentence (all the words) present in the data set in the review section and then splits each of the words present in the form of tokens. The occurrence of these tokens in the whole data set are counted in such a way that the count of the occurrence of each token in a positive and negative feedback (in balanced dataset) or positive, negative and neutral (in unbalanced dataset) are collected separately. Finally, the word frequency of the tokens is calculated.
Tokens count is calculated by LinkedHashMap method.</p>
<p>Tools: LinkedHashMap is it contains only the unique elements implemented using JAVA or OrderedDict in Python</p>
<p>code:  <a href="https://stackoverflow.com/questions/653887/equivalent-for-linkedhashmap-in-python">https://stackoverflow.com/questions/653887/equivalent-for-linkedhashmap-in-python</a></p>
<p>Ref: <a href="https://acadpubl.eu/hub/2018-119-12/articles/5/1211.pdf">https://acadpubl.eu/hub/2018-119-12/articles/5/1211.pdf</a></p>

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<h4 id="Frequency&#8211;inverse-document-frequency(Tfidf):">Frequency&#8211;inverse document frequency(Tfidf):<a class="anchor-link" href="#Frequency&#8211;inverse-document-frequency(Tfidf):">&#182;</a></h4><p>It measures how important a word is to differentiate each category. It reduce the weightage of more common words like (stop words or common words) which occurs in all tweets.</p>
<p>Code: <a href="https://towardsdatascience.com/another-twitter-sentiment-analysis-with-python-part-5-50b4e87d9bdd">https://towardsdatascience.com/another-twitter-sentiment-analysis-with-python-part-5-50b4e87d9bdd</a></p>
<p>Ref: <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.121.1424&amp;rep=rep1&amp;type=pdf">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.121.1424&amp;rep=rep1&amp;type=pdf</a></p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#this function to retrive sentimental count in each class</span>
<span class="k">def</span> <span class="nf">GetCount</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">array</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Class&quot;</span><span class="p">])</span>
    <span class="n">counts</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Class</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
    <span class="n">OBJ</span> <span class="o">=</span> <span class="n">counts</span><span class="p">[</span><span class="s1">&#39;OBJ&#39;</span><span class="p">]</span>
    <span class="n">NEG</span> <span class="o">=</span> <span class="n">counts</span><span class="p">[</span><span class="s1">&#39;NEG&#39;</span><span class="p">]</span>
    <span class="n">NEUTRAL</span> <span class="o">=</span> <span class="n">counts</span><span class="p">[</span><span class="s1">&#39;NEUTRAL&#39;</span><span class="p">]</span>
    <span class="n">POS</span> <span class="o">=</span> <span class="n">counts</span><span class="p">[</span><span class="s1">&#39;POS&#39;</span><span class="p">]</span>
    <span class="k">return</span> <span class="p">[</span><span class="n">OBJ</span><span class="p">,</span> <span class="n">NEG</span><span class="p">,</span> <span class="n">NEUTRAL</span> <span class="p">,</span><span class="n">POS</span><span class="p">]</span>
</pre></div>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># -*- coding: utf-8 -*-</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Created on Sun Apr 14 19:05:12 2013</span>

<span class="sd">@author1: Mohamed Aly &lt;mohamed@mohamedaly.info&gt;</span>
<span class="sd">@author2: Mahmoud Nabil &lt;mah.nabil@yahoo.com&gt;</span>


<span class="sd">moved to jupyter notebook and edited by Rabab Alkhalifa &lt;raalkhalifa@iau.edu.sa&gt;</span>
<span class="sd">&quot;&quot;&quot;</span>


<span class="n">gr</span> <span class="o">=</span> <span class="n">AraTweet</span><span class="p">()</span>


<span class="n">classifiers_collection</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">scores_collection</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">dataframe_collection</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">TweetCount_collection</span> <span class="o">=</span> <span class="p">{}</span>

<span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">FeatuerDataSet</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">datas</span><span class="p">:</span>
    <span class="n">scores</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
    
    <span class="c1">###################################load the data####################################</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">40</span><span class="p">)</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Loading data:&quot;</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">])</span>
    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">40</span><span class="p">)</span>
    <span class="k">if</span> <span class="p">(</span><span class="n">LoadValidation</span><span class="p">):</span>
        <span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">d_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">d_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span> <span class="o">=</span> <span class="n">gr</span><span class="o">.</span><span class="n">get_train_test_validation</span><span class="p">(</span><span class="o">**</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])</span>
        <span class="k">if</span> <span class="p">(</span><span class="n">Evaluate_On_TestSet</span><span class="p">):</span>
            <span class="n">d_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">d_train</span><span class="p">,</span> <span class="n">d_valid</span><span class="p">))</span>
            <span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">))</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">d_test</span> <span class="o">=</span> <span class="n">d_valid</span>
            <span class="n">y_test</span> <span class="o">=</span> <span class="n">y_valid</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="p">(</span><span class="n">d_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">d_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">gr</span><span class="o">.</span><span class="n">get_train_test</span><span class="p">(</span><span class="o">**</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])</span>
    

<span class="c1">#     OBJ, NEG, NEUTRAL ,POS = GetCount(y_train) </span>
<span class="c1">#     OBJ, NEG, NEUTRAL ,POS = GetCount(y_test) </span>
<span class="c1">#     OBJ, NEG, NEUTRAL ,POS = GetCount(y_valid) </span>
    <span class="c1">#---------------------------------------------------------------------------------</span>
    <span class="n">TweetCount</span> <span class="o">=</span> <span class="p">[</span><span class="n">GetCount</span><span class="p">(</span><span class="n">y_train</span> <span class="p">),</span> <span class="n">GetCount</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span> <span class="p">]</span>
    <span class="n">df_tweetCount</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">TweetCount</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span> <span class="p">[</span><span class="s1">&#39;OBJ&#39;</span><span class="p">,</span> <span class="s1">&#39;NEG&#39;</span><span class="p">,</span> <span class="s1">&#39;NEUTRAL&#39;</span> <span class="p">,</span><span class="s1">&#39;POS&#39;</span><span class="p">])</span> 
    <span class="n">df_tweetCount</span><span class="p">[</span><span class="s1">&#39;Dataset&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Train Set&#39;</span><span class="p">,</span><span class="s1">&#39;Test Set&#39;</span><span class="p">]</span>
    <span class="n">df_tweetCount</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span>
    <span class="n">TweetCount_collection</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df_tweetCount</span>
    <span class="c1">#---------------------------------------------------------------------------------</span>
    <span class="c1">####################################################################################</span>
    
    <span class="k">for</span> <span class="n">feat_generator</span> <span class="ow">in</span> <span class="n">Features_Generators</span><span class="p">:</span>
        <span class="c1">####################################Features Generation#############################</span>
<span class="c1">#         print(&quot;Features Generation:&quot;, feat_generator[&#39;name&#39;])</span>
        <span class="n">X_train</span> <span class="o">=</span> <span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;feat_generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">d_train</span><span class="p">)</span>
        <span class="n">X_test</span> <span class="o">=</span> <span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;feat_generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">d_test</span><span class="p">)</span>
        <span class="c1">####################################################################################</span>
        
        
        <span class="n">FeatuerData</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span> <span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
        <span class="n">FeatuerDataSet</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">FeatuerData</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">clf</span><span class="p">[</span><span class="s1">&#39;parameter_tunning&#39;</span><span class="p">]:</span>
                        <span class="k">print</span><span class="p">(</span><span class="s2">&quot; tuning:&quot;</span><span class="p">,</span> <span class="n">clf</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">])</span>
                        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span><span class="s2">&quot;-&quot;</span><span class="o">*</span><span class="mi">40</span><span class="p">)</span>

                        <span class="c1"># region parameter tunning</span>
                        
                        <span class="n">clf</span><span class="p">[</span><span class="s1">&#39;tune_clf&#39;</span><span class="p">]</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">best_predictions</span> <span class="o">=</span> <span class="n">clf</span><span class="p">[</span><span class="s1">&#39;tune_clf&#39;</span><span class="p">]</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="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">clf</span><span class="p">[</span><span class="s2">&quot;clf&quot;</span><span class="p">]</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="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
                        
                
                        <span class="c1"># Report the before-and-afterscores</span>
                        <span class="n">Clf_name</span> <span class="o">=</span> <span class="n">clf</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> 

                        <span class="n">UnOpt_accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
                        <span class="n">UnOpt_fScore</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span><span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
                        <span class="n">Opt_accuracy</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">)</span>
                        <span class="n">Opt_fScore</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span><span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span>
                        <span class="n">Best_Settings</span> <span class="o">=</span> <span class="n">clf</span><span class="p">[</span><span class="s2">&quot;tune_clf&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">best_params_</span>
                        <span class="n">Settings</span> <span class="o">=</span> <span class="n">clf</span><span class="p">[</span><span class="s2">&quot;clf&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
                        <span class="n">datarow</span> <span class="o">=</span> <span class="p">[</span><span class="n">Clf_name</span><span class="p">,</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span><span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span> <span class="n">Best_Settings</span><span class="p">,</span><span class="n">Settings</span><span class="p">,</span><span class="n">UnOpt_accuracy</span> <span class="p">,</span> <span class="n">UnOpt_fScore</span> <span class="p">,</span> <span class="n">Opt_accuracy</span> <span class="p">,</span> <span class="n">Opt_fScore</span><span class="p">]</span>

                        <span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">datarow</span><span class="p">)</span>
                        
                        <span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">tacc</span><span class="p">,</span> <span class="n">support</span><span class="p">,</span> <span class="n">f1</span> <span class="p">,</span> <span class="n">df</span><span class="p">)</span> <span class="o">=</span> <span class="n">Evaluate_Result</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

                        <span class="n">score</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span>
                                         <span class="n">feat_generator</span><span class="o">=</span><span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span>
                                         <span class="n">clf</span><span class="o">=</span><span class="n">clf</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">],</span>
                                         <span class="c1"># feat_ext=feat_ext[&#39;name&#39;],</span>
                                         <span class="n">f1</span><span class="o">=</span><span class="n">f1</span><span class="p">,</span>
                                         <span class="n">acc</span><span class="o">=</span><span class="n">acc</span><span class="p">,</span>
                                         <span class="n">tacc</span><span class="o">=</span><span class="n">tacc</span><span class="p">)</span>

                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Classifier&#39;</span><span class="p">]</span> <span class="o">=</span>  <span class="n">clf</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span>
                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;feauter_generator&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span>

                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;acc&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">acc</span>
                        <span class="n">df</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">f1</span>

                        <span class="n">dataframe_collection</span><span class="p">[</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;-&#39;</span> <span class="o">+</span> <span class="n">clf</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;-&#39;</span> <span class="o">+</span> <span class="n">feat_generator</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="p">]</span>  <span class="o">=</span> <span class="n">df</span>
<span class="c1">#                         print(clf[&quot;name&quot;],df[&#39;feauter_generator&#39;] )</span>
                        <span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
    
    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dataframe_collection</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span><span class="s2">&quot;=&quot;</span><span class="o">*</span><span class="mi">40</span><span class="p">)</span>
        <span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span><span class="o">*</span><span class="mi">40</span><span class="p">)</span>
        <span class="k">print</span><span class="p">(</span><span class="n">dataframe_collection</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>

        <span class="n">df_Classifiers</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">classifiers</span><span class="p">)</span>
        <span class="n">classifiers_collection</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df_Classifiers</span>

    <span class="c1">#     print(&quot;-&quot;*40)</span>
    <span class="c1">#     print(df_Classifiers.head())</span>

        <span class="n">df_s</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">scores</span><span class="p">)</span>
        <span class="n">scores_collection</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">df_s</span>
    
                        
<span class="n">df_feauterCount</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">FeatuerDataSet</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;DataSet_Name&#39;</span><span class="p">,</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">,</span><span class="s1">&#39;Feauters_Count&#39;</span><span class="p">])</span>
    
<span class="c1"># ####################################Testing##############################################</span>
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========================================
(&#39;Loading data:&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in &lt;class &#39;sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier&#39;&gt; in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
  &#34;and default tol will be 1e-3.&#34; % type(self), FutureWarning)
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<pre>(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in &lt;class &#39;sklearn.linear_model.perceptron.Perceptron&#39;&gt; in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
  &#34;and default tol will be 1e-3.&#34; % type(self), FutureWarning)
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<pre>(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\linear_model\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in &lt;class &#39;sklearn.linear_model.stochastic_gradient.SGDClassifier&#39;&gt; in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.
  &#34;and default tol will be 1e-3.&#34; % type(self), FutureWarning)
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<pre>(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\naive_bayes.py:472: UserWarning: alpha too small will result in numeric errors, setting alpha = 1.0e-10
  &#39;setting alpha = %.1e&#39; % _ALPHA_MIN)
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<pre>(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Logistic Regression&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Passive Aggresive&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;SVM&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;Perceptron&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;bnb&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;KNN&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;RandomForest&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;DecisionTree&#39;, &#39;4-balanced&#39;)

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(&#39; tuning:&#39;, &#39;mnb&#39;, &#39;4-balanced&#39;)

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========================================
4-balanced-Perceptron-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  69.0   82.0   90.0      NEG   0.456954  0.433962  Perceptron   
1  57.0   91.0  102.0  NEUTRAL   0.385135  0.358491  Perceptron   
2  82.0  101.0   77.0      OBJ   0.448087  0.515723  Perceptron   
3  73.0   81.0   86.0      POS   0.474026  0.459119  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.441824  0.440621  
1         tfidf_ng3  0.441824  0.440621  
2         tfidf_ng3  0.441824  0.440621  
3         tfidf_ng3  0.441824  0.440621  

========================================
4-balanced-Perceptron-tfidf_ng2
----------------------------------------
     TP    FP     FN        L  Percision    Recall  Classifier  \
0  71.0  95.0   88.0      NEG   0.427711  0.446541  Perceptron   
1  52.0  83.0  107.0  NEUTRAL   0.385185  0.327044  Perceptron   
2  77.0  82.0   82.0      OBJ   0.484277  0.484277  Perceptron   
3  85.0  91.0   74.0      POS   0.482955  0.534591  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.448113  0.445601  
1         tfidf_ng2  0.448113  0.445601  
2         tfidf_ng2  0.448113  0.445601  
3         tfidf_ng2  0.448113  0.445601  

========================================
4-balanced-Perceptron-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  73.0   96.0   86.0      NEG   0.431953  0.459119  Perceptron   
1  46.0   91.0  113.0  NEUTRAL   0.335766  0.289308  Perceptron   
2  90.0  114.0   69.0      OBJ   0.441176  0.566038  Perceptron   
3  64.0   62.0   95.0      POS   0.507937  0.402516  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.429245  0.425231  
1         tfidf_ng1  0.429245  0.425231  
2         tfidf_ng1  0.429245  0.425231  
3         tfidf_ng1  0.429245  0.425231  

========================================
4-balanced-SVM-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  63.0   89.0   96.0      NEG   0.414474  0.396226        SVM   
1  54.0   70.0  105.0  NEUTRAL   0.435484  0.339623        SVM   
2  75.0   77.0   84.0      OBJ   0.493421  0.471698        SVM   
3  93.0  115.0   66.0      POS   0.447115  0.584906        SVM   

  feauter_generator       acc        f1  
0         count_ng2  0.448113  0.443974  
1         count_ng2  0.448113  0.443974  
2         count_ng2  0.448113  0.443974  
3         count_ng2  0.448113  0.443974  

========================================
4-balanced-mnb-count_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  68.0   72.0  91.0      NEG   0.485714  0.427673        mnb   
1  77.0  131.0  82.0  NEUTRAL   0.370192  0.484277        mnb   
2  89.0   73.0  70.0      OBJ   0.549383  0.559748        mnb   
3  76.0   50.0  83.0      POS   0.603175  0.477987        mnb   

  feauter_generator       acc       f1  
0         count_ng2  0.487421  0.49058  
1         count_ng2  0.487421  0.49058  
2         count_ng2  0.487421  0.49058  
3         count_ng2  0.487421  0.49058  

========================================
4-balanced-mnb-count_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  65.0   78.0  94.0      NEG   0.454545  0.408805        mnb   
1  73.0  131.0  86.0  NEUTRAL   0.357843  0.459119        mnb   
2  87.0   78.0  72.0      OBJ   0.527273  0.547170        mnb   
3  73.0   51.0  86.0      POS   0.588710  0.459119        mnb   

  feauter_generator       acc        f1  
0         count_ng1  0.468553  0.471401  
1         count_ng1  0.468553  0.471401  
2         count_ng1  0.468553  0.471401  
3         count_ng1  0.468553  0.471401  

========================================
4-balanced-SVM-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  65.0   89.0   94.0      NEG   0.422078  0.408805        SVM   
1  47.0   77.0  112.0  NEUTRAL   0.379032  0.295597        SVM   
2  79.0   94.0   80.0      OBJ   0.456647  0.496855        SVM   
3  82.0  103.0   77.0      POS   0.443243  0.515723        SVM   

  feauter_generator       acc        f1  
0         count_ng1  0.429245  0.425035  
1         count_ng1  0.429245  0.425035  
2         count_ng1  0.429245  0.425035  
3         count_ng1  0.429245  0.425035  

========================================
4-balanced-Logistic Regression-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  56.0   65.0  103.0      NEG   0.462810  0.352201  Logistic Regression   
1  74.0  114.0   85.0  NEUTRAL   0.393617  0.465409  Logistic Regression   
2  89.0  113.0   70.0      OBJ   0.440594  0.559748  Logistic Regression   
3  65.0   60.0   94.0      POS   0.520000  0.408805  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.446541  0.444334  
1         tfidf_ng3  0.446541  0.444334  
2         tfidf_ng3  0.446541  0.444334  
3         tfidf_ng3  0.446541  0.444334  

========================================
4-balanced-Logistic Regression-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  58.0   78.0  101.0      NEG   0.426471  0.364780  Logistic Regression   
1  71.0  110.0   88.0  NEUTRAL   0.392265  0.446541  Logistic Regression   
2  85.0   98.0   74.0      OBJ   0.464481  0.534591  Logistic Regression   
3  73.0   63.0   86.0      POS   0.536765  0.459119  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.451258  0.450715  
1         tfidf_ng2  0.451258  0.450715  
2         tfidf_ng2  0.451258  0.450715  
3         tfidf_ng2  0.451258  0.450715  

========================================
4-balanced-Logistic Regression-tfidf_ng1
----------------------------------------
     TP    FP    FN        L  Percision    Recall           Classifier  \
0  65.0  92.0  94.0      NEG   0.414013  0.408805  Logistic Regression   
1  64.0  97.0  95.0  NEUTRAL   0.397516  0.402516  Logistic Regression   
2  85.0  92.0  74.0      OBJ   0.480226  0.534591  Logistic Regression   
3  74.0  67.0  85.0      POS   0.524823  0.465409  Logistic Regression   

  feauter_generator      acc       f1  
0         tfidf_ng1  0.45283  0.45267  
1         tfidf_ng1  0.45283  0.45267  
2         tfidf_ng1  0.45283  0.45267  
3         tfidf_ng1  0.45283  0.45267  

========================================
4-balanced-Perceptron-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  53.0   73.0  106.0      NEG   0.420635  0.333333  Perceptron   
1  65.0  115.0   94.0  NEUTRAL   0.361111  0.408805  Perceptron   
2  60.0   56.0   99.0      OBJ   0.517241  0.377358  Perceptron   
3  95.0  119.0   64.0      POS   0.443925  0.597484  Perceptron   

  feauter_generator       acc        f1  
0         count_ng3  0.429245  0.425289  
1         count_ng3  0.429245  0.425289  
2         count_ng3  0.429245  0.425289  
3         count_ng3  0.429245  0.425289  

========================================
4-balanced-Perceptron-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  61.0   73.0   98.0      NEG   0.455224  0.383648  Perceptron   
1  56.0   88.0  103.0  NEUTRAL   0.388889  0.352201  Perceptron   
2  67.0   73.0   92.0      OBJ   0.478571  0.421384  Perceptron   
3  94.0  124.0   65.0      POS   0.431193  0.591195  Perceptron   

  feauter_generator       acc        f1  
0         count_ng2  0.437107  0.433213  
1         count_ng2  0.437107  0.433213  
2         count_ng2  0.437107  0.433213  
3         count_ng2  0.437107  0.433213  

========================================
4-balanced-Perceptron-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  60.0   56.0   99.0      NEG   0.517241  0.377358  Perceptron   
1  76.0  145.0   83.0  NEUTRAL   0.343891  0.477987  Perceptron   
2  84.0  110.0   75.0      OBJ   0.432990  0.528302  Perceptron   
3  53.0   52.0  106.0      POS   0.504762  0.333333  Perceptron   

  feauter_generator       acc       f1  
0         count_ng1  0.429245  0.42845  
1         count_ng1  0.429245  0.42845  
2         count_ng1  0.429245  0.42845  
3         count_ng1  0.429245  0.42845  

========================================
4-balanced-KNN-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  62.0  101.0   97.0      NEG   0.380368  0.389937        KNN   
1  59.0  119.0  100.0  NEUTRAL   0.331461  0.371069        KNN   
2  80.0   88.0   79.0      OBJ   0.476190  0.503145        KNN   
3  58.0   69.0  101.0      POS   0.456693  0.364780        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.407233  0.407533  
1         tfidf_ng2  0.407233  0.407533  
2         tfidf_ng2  0.407233  0.407533  
3         tfidf_ng2  0.407233  0.407533  

========================================
4-balanced-sgd-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  58.0   76.0  101.0      NEG   0.432836  0.364780        sgd   
1  73.0  112.0   86.0  NEUTRAL   0.394595  0.459119        sgd   
2  87.0   81.0   72.0      OBJ   0.517857  0.547170        sgd   
3  80.0   69.0   79.0      POS   0.536913  0.503145        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.468553  0.467978  
1         tfidf_ng2  0.468553  0.467978  
2         tfidf_ng2  0.468553  0.467978  
3         tfidf_ng2  0.468553  0.467978  

========================================
4-balanced-sgd-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  65.0   89.0   94.0      NEG   0.422078  0.408805        sgd   
1  55.0  100.0  104.0  NEUTRAL   0.354839  0.345912        sgd   
2  87.0  103.0   72.0      OBJ   0.457895  0.547170        sgd   
3  69.0   68.0   90.0      POS   0.503650  0.433962        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.433962  0.432609  
1         tfidf_ng1  0.433962  0.432609  
2         tfidf_ng1  0.433962  0.432609  
3         tfidf_ng1  0.433962  0.432609  

========================================
4-balanced-mnb-count_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  69.0   73.0  90.0      NEG   0.485915  0.433962        mnb   
1  80.0  131.0  79.0  NEUTRAL   0.379147  0.503145        mnb   
2  86.0   72.0  73.0      OBJ   0.544304  0.540881        mnb   
3  75.0   50.0  84.0      POS   0.600000  0.471698        mnb   

  feauter_generator       acc        f1  
0         count_ng3  0.487421  0.490415  
1         count_ng3  0.487421  0.490415  
2         count_ng3  0.487421  0.490415  
3         count_ng3  0.487421  0.490415  

========================================
4-balanced-sgd-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  62.0   84.0  97.0      NEG   0.424658  0.389937        sgd   
1  73.0  117.0  86.0  NEUTRAL   0.384211  0.459119        sgd   
2  83.0   81.0  76.0      OBJ   0.506098  0.522013        sgd   
3  72.0   64.0  87.0      POS   0.529412  0.452830        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.455975  0.456741  
1         tfidf_ng3  0.455975  0.456741  
2         tfidf_ng3  0.455975  0.456741  
3         tfidf_ng3  0.455975  0.456741  

========================================
4-balanced-KNN-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  89.0  225.0   70.0      NEG   0.283439  0.559748        KNN   
1   3.0    4.0  156.0  NEUTRAL   0.428571  0.018868        KNN   
2  50.0  132.0  109.0      OBJ   0.274725  0.314465        KNN   
3  33.0  100.0  126.0      POS   0.248120  0.207547        KNN   

  feauter_generator       acc        f1  
0         count_ng3  0.275157  0.232937  
1         count_ng3  0.275157  0.232937  
2         count_ng3  0.275157  0.232937  
3         count_ng3  0.275157  0.232937  

========================================
4-balanced-sgd-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  87.0  109.0   72.0      NEG   0.443878  0.547170        sgd   
1  41.0   80.0  118.0  NEUTRAL   0.338843  0.257862        sgd   
2  61.0   82.0   98.0      OBJ   0.426573  0.383648        sgd   
3  80.0   96.0   79.0      POS   0.454545  0.503145        sgd   

  feauter_generator       acc        f1  
0         count_ng1  0.422956  0.416146  
1         count_ng1  0.422956  0.416146  
2         count_ng1  0.422956  0.416146  
3         count_ng1  0.422956  0.416146  

========================================
4-balanced-sgd-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  56.0   92.0  103.0      NEG   0.378378  0.352201        sgd   
1  54.0  100.0  105.0  NEUTRAL   0.350649  0.339623        sgd   
2  75.0   71.0   84.0      OBJ   0.513699  0.471698        sgd   
3  92.0   96.0   67.0      POS   0.489362  0.578616        sgd   

  feauter_generator       acc        f1  
0         count_ng2  0.435535  0.432983  
1         count_ng2  0.435535  0.432983  
2         count_ng2  0.435535  0.432983  
3         count_ng2  0.435535  0.432983  

========================================
4-balanced-sgd-count_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  65.0   86.0  94.0      NEG   0.430464  0.408805        sgd   
1  64.0  108.0  95.0  NEUTRAL   0.372093  0.402516        sgd   
2  73.0   75.0  86.0      OBJ   0.493243  0.459119        sgd   
3  85.0   80.0  74.0      POS   0.515152  0.534591        sgd   

  feauter_generator       acc        f1  
0         count_ng3  0.451258  0.451581  
1         count_ng3  0.451258  0.451581  
2         count_ng3  0.451258  0.451581  
3         count_ng3  0.451258  0.451581  

========================================
4-balanced-SVM-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   82.0  99.0      NEG   0.422535  0.377358        SVM   
1  71.0  113.0  88.0  NEUTRAL   0.385870  0.446541        SVM   
2  89.0   83.0  70.0      OBJ   0.517442  0.559748        SVM   
3  75.0   63.0  84.0      POS   0.543478  0.471698        SVM   

  feauter_generator       acc       f1  
0         tfidf_ng2  0.463836  0.46387  
1         tfidf_ng2  0.463836  0.46387  
2         tfidf_ng2  0.463836  0.46387  
3         tfidf_ng2  0.463836  0.46387  

========================================
4-balanced-Passive Aggresive-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  71.0   90.0   88.0      NEG   0.440994  0.446541  Passive Aggresive   
1  57.0   90.0  102.0  NEUTRAL   0.387755  0.358491  Passive Aggresive   
2  86.0  102.0   73.0      OBJ   0.457447  0.540881  Passive Aggresive   
3  70.0   70.0   89.0      POS   0.500000  0.440252  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.446541  0.445051  
1         tfidf_ng1  0.446541  0.445051  
2         tfidf_ng1  0.446541  0.445051  
3         tfidf_ng1  0.446541  0.445051  

========================================
4-balanced-Passive Aggresive-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall         Classifier  \
0  64.0   85.0  95.0      NEG   0.429530  0.402516  Passive Aggresive   
1  67.0  109.0  92.0  NEUTRAL   0.380682  0.421384  Passive Aggresive   
2  90.0   83.0  69.0      OBJ   0.520231  0.566038  Passive Aggresive   
3  74.0   64.0  85.0      POS   0.536232  0.465409  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.463836  0.464017  
1         tfidf_ng2  0.463836  0.464017  
2         tfidf_ng2  0.463836  0.464017  
3         tfidf_ng2  0.463836  0.464017  

========================================
4-balanced-Passive Aggresive-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall         Classifier  \
0  60.0   82.0  99.0      NEG   0.422535  0.377358  Passive Aggresive   
1  71.0  112.0  88.0  NEUTRAL   0.387978  0.446541  Passive Aggresive   
2  85.0   91.0  74.0      OBJ   0.482955  0.534591  Passive Aggresive   
3  70.0   65.0  89.0      POS   0.518519  0.440252  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.449686  0.449382  
1         tfidf_ng3  0.449686  0.449382  
2         tfidf_ng3  0.449686  0.449382  
3         tfidf_ng3  0.449686  0.449382  

========================================
4-balanced-DecisionTree-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  44.0  110.0  115.0      NEG   0.285714  0.276730  DecisionTree   
1  53.0   85.0  106.0  NEUTRAL   0.384058  0.333333  DecisionTree   
2  58.0  110.0  101.0      OBJ   0.345238  0.364780  DecisionTree   
3  63.0  113.0   96.0      POS   0.357955  0.396226  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.342767  0.342228  
1         tfidf_ng1  0.342767  0.342228  
2         tfidf_ng1  0.342767  0.342228  
3         tfidf_ng1  0.342767  0.342228  

========================================
4-balanced-DecisionTree-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  52.0   90.0  107.0      NEG   0.366197  0.327044  DecisionTree   
1  43.0   76.0  116.0  NEUTRAL   0.361345  0.270440  DecisionTree   
2  76.0  140.0   83.0      OBJ   0.351852  0.477987  DecisionTree   
3  58.0  101.0  101.0      POS   0.364780  0.364780  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.360063  0.356245  
1         tfidf_ng3  0.360063  0.356245  
2         tfidf_ng3  0.360063  0.356245  
3         tfidf_ng3  0.360063  0.356245  

========================================
4-balanced-DecisionTree-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  54.0  110.0  105.0      NEG   0.329268  0.339623  DecisionTree   
1  46.0   95.0  113.0  NEUTRAL   0.326241  0.289308  DecisionTree   
2  64.0  115.0   95.0      OBJ   0.357542  0.402516  DecisionTree   
3  55.0   97.0  104.0      POS   0.361842  0.345912  DecisionTree   

  feauter_generator      acc        f1  
0         tfidf_ng2  0.34434  0.343357  
1         tfidf_ng2  0.34434  0.343357  
2         tfidf_ng2  0.34434  0.343357  
3         tfidf_ng2  0.34434  0.343357  

========================================
4-balanced-RandomForest-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  55.0   97.0  104.0      NEG   0.361842  0.345912  RandomForest   
1  49.0   79.0  110.0  NEUTRAL   0.382812  0.308176  RandomForest   
2  74.0   99.0   85.0      OBJ   0.427746  0.465409  RandomForest   
3  68.0  115.0   91.0      POS   0.371585  0.427673  RandomForest   

  feauter_generator       acc        f1  
0         count_ng1  0.386792  0.384651  
1         count_ng1  0.386792  0.384651  
2         count_ng1  0.386792  0.384651  
3         count_ng1  0.386792  0.384651  

========================================
4-balanced-RandomForest-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  69.0  156.0   90.0      NEG   0.306667  0.433962  RandomForest   
1  24.0   48.0  135.0  NEUTRAL   0.333333  0.150943  RandomForest   
2  68.0   81.0   91.0      OBJ   0.456376  0.427673  RandomForest   
3  71.0  119.0   88.0      POS   0.373684  0.446541  RandomForest   

  feauter_generator      acc        f1  
0         count_ng3  0.36478  0.353901  
1         count_ng3  0.36478  0.353901  
2         count_ng3  0.36478  0.353901  
3         count_ng3  0.36478  0.353901  

========================================
4-balanced-RandomForest-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  75.0  158.0   84.0      NEG   0.321888  0.471698  RandomForest   
1  36.0   57.0  123.0  NEUTRAL   0.387097  0.226415  RandomForest   
2  61.0   79.0   98.0      OBJ   0.435714  0.383648  RandomForest   
3  70.0  100.0   89.0      POS   0.411765  0.440252  RandomForest   

  feauter_generator       acc        f1  
0         count_ng2  0.380503  0.375482  
1         count_ng2  0.380503  0.375482  
2         count_ng2  0.380503  0.375482  
3         count_ng2  0.380503  0.375482  

========================================
4-balanced-SVM-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  60.0   93.0   99.0      NEG   0.392157  0.377358        SVM   
1  51.0   67.0  108.0  NEUTRAL   0.432203  0.320755        SVM   
2  71.0   79.0   88.0      OBJ   0.473333  0.446541        SVM   
3  94.0  121.0   65.0      POS   0.437209  0.591195        SVM   

  feauter_generator       acc        f1  
0         count_ng3  0.433962  0.428767  
1         count_ng3  0.433962  0.428767  
2         count_ng3  0.433962  0.428767  
3         count_ng3  0.433962  0.428767  

========================================
4-balanced-Logistic Regression-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  59.0   92.0  100.0      NEG   0.390728  0.371069  Logistic Regression   
1  53.0   74.0  106.0  NEUTRAL   0.417323  0.333333  Logistic Regression   
2  71.0   74.0   88.0      OBJ   0.489655  0.446541  Logistic Regression   
3  96.0  117.0   63.0      POS   0.450704  0.603774  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng3  0.438679  0.433627  
1         count_ng3  0.438679  0.433627  
2         count_ng3  0.438679  0.433627  
3         count_ng3  0.438679  0.433627  

========================================
4-balanced-SVM-tfidf_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  67.0   84.0  92.0      NEG   0.443709  0.421384        SVM   
1  64.0  105.0  95.0  NEUTRAL   0.378698  0.402516        SVM   
2  86.0   88.0  73.0      OBJ   0.494253  0.540881        SVM   
3  75.0   67.0  84.0      POS   0.528169  0.471698        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.459119  0.459339  
1         tfidf_ng1  0.459119  0.459339  
2         tfidf_ng1  0.459119  0.459339  
3         tfidf_ng1  0.459119  0.459339  

========================================
4-balanced-KNN-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  62.0  175.0   97.0      NEG   0.261603  0.389937        KNN   
1   6.0   20.0  153.0  NEUTRAL   0.230769  0.037736        KNN   
2  59.0  124.0  100.0      OBJ   0.322404  0.371069        KNN   
3  56.0  134.0  103.0      POS   0.294737  0.352201        KNN   

  feauter_generator       acc        f1  
0         count_ng1  0.287736  0.260986  
1         count_ng1  0.287736  0.260986  
2         count_ng1  0.287736  0.260986  
3         count_ng1  0.287736  0.260986  

========================================
4-balanced-bnb-count_ng1
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   66.0   75.0   93.0      NEG   0.468085  0.415094        bnb   
1   32.0   37.0  127.0  NEUTRAL   0.463768  0.201258        bnb   
2   90.0   99.0   69.0      OBJ   0.476190  0.566038        bnb   
3  109.0  128.0   50.0      POS   0.459916  0.685535        bnb   

  feauter_generator       acc        f1  
0         count_ng1  0.466981  0.447112  
1         count_ng1  0.466981  0.447112  
2         count_ng1  0.466981  0.447112  
3         count_ng1  0.466981  0.447112  

========================================
4-balanced-bnb-count_ng2
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   48.0   46.0  111.0      NEG   0.510638  0.301887        bnb   
1   16.0    6.0  143.0  NEUTRAL   0.727273  0.100629        bnb   
2   86.0   91.0   73.0      OBJ   0.485876  0.540881        bnb   
3  124.0  219.0   35.0      POS   0.361516  0.779874        bnb   

  feauter_generator       acc        f1  
0         count_ng2  0.430818  0.390543  
1         count_ng2  0.430818  0.390543  
2         count_ng2  0.430818  0.390543  
3         count_ng2  0.430818  0.390543  

========================================
4-balanced-Logistic Regression-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  62.0   97.0   97.0      NEG   0.389937  0.389937  Logistic Regression   
1  53.0   74.0  106.0  NEUTRAL   0.417323  0.333333  Logistic Regression   
2  72.0   73.0   87.0      OBJ   0.496552  0.452830  Logistic Regression   
3  96.0  109.0   63.0      POS   0.468293  0.603774  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng2  0.444969  0.440431  
1         count_ng2  0.444969  0.440431  
2         count_ng2  0.444969  0.440431  
3         count_ng2  0.444969  0.440431  

========================================
4-balanced-Logistic Regression-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  65.0   92.0   94.0      NEG   0.414013  0.408805  Logistic Regression   
1  52.0   76.0  107.0  NEUTRAL   0.406250  0.327044  Logistic Regression   
2  80.0   79.0   79.0      OBJ   0.503145  0.503145  Logistic Regression   
3  91.0  101.0   68.0      POS   0.473958  0.572327  Logistic Regression   

  feauter_generator      acc        f1  
0         count_ng1  0.45283  0.448856  
1         count_ng1  0.45283  0.448856  
2         count_ng1  0.45283  0.448856  
3         count_ng1  0.45283  0.448856  

========================================
4-balanced-Passive Aggresive-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  64.0   92.0   95.0      NEG   0.410256  0.402516  Passive Aggresive   
1  51.0   65.0  108.0  NEUTRAL   0.439655  0.320755  Passive Aggresive   
2  81.0   98.0   78.0      OBJ   0.452514  0.509434  Passive Aggresive   
3  83.0  102.0   76.0      POS   0.448649  0.522013  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng2  0.438679  0.434777  
1         count_ng2  0.438679  0.434777  
2         count_ng2  0.438679  0.434777  
3         count_ng2  0.438679  0.434777  

========================================
4-balanced-Passive Aggresive-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  65.0   90.0   94.0      NEG   0.419355  0.408805  Passive Aggresive   
1  52.0   61.0  107.0  NEUTRAL   0.460177  0.327044  Passive Aggresive   
2  82.0   99.0   77.0      OBJ   0.453039  0.515723  Passive Aggresive   
3  85.0  102.0   74.0      POS   0.454545  0.534591  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng3  0.446541  0.442512  
1         count_ng3  0.446541  0.442512  
2         count_ng3  0.446541  0.442512  
3         count_ng3  0.446541  0.442512  

========================================
4-balanced-KNN-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  63.0  111.0   96.0      NEG   0.362069  0.396226        KNN   
1  48.0  117.0  111.0  NEUTRAL   0.290909  0.301887        KNN   
2  77.0  104.0   82.0      OBJ   0.425414  0.484277        KNN   
3  49.0   67.0  110.0      POS   0.422414  0.308176        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.372642  0.370995  
1         tfidf_ng1  0.372642  0.370995  
2         tfidf_ng1  0.372642  0.370995  
3         tfidf_ng1  0.372642  0.370995  

========================================
4-balanced-bnb-count_ng3
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   36.0   23.0  123.0      NEG   0.610169  0.226415        bnb   
1    6.0    5.0  153.0  NEUTRAL   0.545455  0.037736        bnb   
2   70.0   69.0   89.0      OBJ   0.503597  0.440252        bnb   
3  139.0  288.0   20.0      POS   0.325527  0.874214        bnb   

  feauter_generator       acc        f1  
0         count_ng3  0.394654  0.336266  
1         count_ng3  0.394654  0.336266  
2         count_ng3  0.394654  0.336266  
3         count_ng3  0.394654  0.336266  

========================================
4-balanced-KNN-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  76.0  212.0   83.0      NEG   0.263889  0.477987        KNN   
1   5.0   12.0  154.0  NEUTRAL   0.294118  0.031447        KNN   
2  53.0  102.0  106.0      OBJ   0.341935  0.333333        KNN   
3  44.0  132.0  115.0      POS   0.250000  0.276730        KNN   

  feauter_generator       acc        f1  
0         count_ng2  0.279874  0.249282  
1         count_ng2  0.279874  0.249282  
2         count_ng2  0.279874  0.249282  
3         count_ng2  0.279874  0.249282  

========================================
4-balanced-mnb-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   68.0  99.0      NEG   0.468750  0.377358        mnb   
1  82.0  132.0  77.0  NEUTRAL   0.383178  0.515723        mnb   
2  89.0   72.0  70.0      OBJ   0.552795  0.559748        mnb   
3  77.0   56.0  82.0      POS   0.578947  0.484277        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.484277  0.485361  
1         tfidf_ng3  0.484277  0.485361  
2         tfidf_ng3  0.484277  0.485361  
3         tfidf_ng3  0.484277  0.485361  

========================================
4-balanced-mnb-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   69.0  99.0      NEG   0.465116  0.377358        mnb   
1  83.0  130.0  76.0  NEUTRAL   0.389671  0.522013        mnb   
2  90.0   72.0  69.0      OBJ   0.555556  0.566038        mnb   
3  79.0   53.0  80.0      POS   0.598485  0.496855        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.490566  0.491652  
1         tfidf_ng2  0.490566  0.491652  
2         tfidf_ng2  0.490566  0.491652  
3         tfidf_ng2  0.490566  0.491652  

========================================
4-balanced-mnb-tfidf_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  67.0   80.0  92.0      NEG   0.455782  0.421384        mnb   
1  79.0  128.0  80.0  NEUTRAL   0.381643  0.496855        mnb   
2  86.0   68.0  73.0      OBJ   0.558442  0.540881        mnb   
3  75.0   53.0  84.0      POS   0.585938  0.471698        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.482704  0.485443  
1         tfidf_ng1  0.482704  0.485443  
2         tfidf_ng1  0.482704  0.485443  
3         tfidf_ng1  0.482704  0.485443  

========================================
4-balanced-RandomForest-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  65.0  128.0   94.0      NEG   0.336788  0.408805  RandomForest   
1  45.0   76.0  114.0  NEUTRAL   0.371901  0.283019  RandomForest   
2  69.0   95.0   90.0      OBJ   0.420732  0.433962  RandomForest   
3  63.0   95.0   96.0      POS   0.398734  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.380503  0.378867  
1         tfidf_ng1  0.380503  0.378867  
2         tfidf_ng1  0.380503  0.378867  
3         tfidf_ng1  0.380503  0.378867  

========================================
4-balanced-RandomForest-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  58.0   88.0  101.0      NEG   0.397260  0.364780  RandomForest   
1  32.0   53.0  127.0  NEUTRAL   0.376471  0.201258  RandomForest   
2  82.0  138.0   77.0      OBJ   0.372727  0.515723  RandomForest   
3  63.0  122.0   96.0      POS   0.340541  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.369497  0.360405  
1         tfidf_ng3  0.369497  0.360405  
2         tfidf_ng3  0.369497  0.360405  
3         tfidf_ng3  0.369497  0.360405  

========================================
4-balanced-RandomForest-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  77.0  155.0   82.0      NEG   0.331897  0.484277  RandomForest   
1  43.0   61.0  116.0  NEUTRAL   0.413462  0.270440  RandomForest   
2  71.0   79.0   88.0      OBJ   0.473333  0.446541  RandomForest   
3  63.0   87.0   96.0      POS   0.420000  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.399371  0.397043  
1         tfidf_ng2  0.399371  0.397043  
2         tfidf_ng2  0.399371  0.397043  
3         tfidf_ng2  0.399371  0.397043  

========================================
4-balanced-DecisionTree-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  46.0  104.0  113.0      NEG   0.306667  0.289308  DecisionTree   
1  54.0  106.0  105.0  NEUTRAL   0.337500  0.339623  DecisionTree   
2  70.0  112.0   89.0      OBJ   0.384615  0.440252  DecisionTree   
3  62.0   82.0   97.0      POS   0.430556  0.389937  DecisionTree   

  feauter_generator      acc        f1  
0         count_ng1  0.36478  0.364023  
1         count_ng1  0.36478  0.364023  
2         count_ng1  0.36478  0.364023  
3         count_ng1  0.36478  0.364023  

========================================
4-balanced-SVM-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  53.0   75.0  106.0      NEG   0.414062  0.333333        SVM   
1  74.0  123.0   85.0  NEUTRAL   0.375635  0.465409        SVM   
2  88.0   91.0   71.0      OBJ   0.491620  0.553459        SVM   
3  72.0   60.0   87.0      POS   0.545455  0.452830        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.451258  0.450156  
1         tfidf_ng3  0.451258  0.450156  
2         tfidf_ng3  0.451258  0.450156  
3         tfidf_ng3  0.451258  0.450156  

========================================
4-balanced-DecisionTree-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  48.0   95.0  111.0      NEG   0.335664  0.301887  DecisionTree   
1  50.0  109.0  109.0  NEUTRAL   0.314465  0.314465  DecisionTree   
2  65.0  109.0   94.0      OBJ   0.373563  0.408805  DecisionTree   
3  63.0   97.0   96.0      POS   0.393750  0.396226  DecisionTree   

  feauter_generator       acc       f1  
0         count_ng3  0.355346  0.35443  
1         count_ng3  0.355346  0.35443  
2         count_ng3  0.355346  0.35443  
3         count_ng3  0.355346  0.35443  

========================================
4-balanced-DecisionTree-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  57.0  111.0  102.0      NEG   0.339286  0.358491  DecisionTree   
1  49.0   95.0  110.0  NEUTRAL   0.340278  0.308176  DecisionTree   
2  62.0  105.0   97.0      OBJ   0.371257  0.389937  DecisionTree   
3  59.0   98.0  100.0      POS   0.375796  0.371069  DecisionTree   

  feauter_generator       acc        f1  
0         count_ng2  0.356918  0.356461  
1         count_ng2  0.356918  0.356461  
2         count_ng2  0.356918  0.356461  
3         count_ng2  0.356918  0.356461  

========================================
4-balanced-bnb-tfidf_ng1
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   66.0   75.0   93.0      NEG   0.468085  0.415094        bnb   
1   32.0   37.0  127.0  NEUTRAL   0.463768  0.201258        bnb   
2   90.0   99.0   69.0      OBJ   0.476190  0.566038        bnb   
3  109.0  128.0   50.0      POS   0.459916  0.685535        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.466981  0.447112  
1         tfidf_ng1  0.466981  0.447112  
2         tfidf_ng1  0.466981  0.447112  
3         tfidf_ng1  0.466981  0.447112  

========================================
4-balanced-bnb-tfidf_ng2
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   48.0   46.0  111.0      NEG   0.510638  0.301887        bnb   
1   16.0    6.0  143.0  NEUTRAL   0.727273  0.100629        bnb   
2   86.0   91.0   73.0      OBJ   0.485876  0.540881        bnb   
3  124.0  219.0   35.0      POS   0.361516  0.779874        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.430818  0.390543  
1         tfidf_ng2  0.430818  0.390543  
2         tfidf_ng2  0.430818  0.390543  
3         tfidf_ng2  0.430818  0.390543  

========================================
4-balanced-bnb-tfidf_ng3
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   36.0   23.0  123.0      NEG   0.610169  0.226415        bnb   
1    6.0    5.0  153.0  NEUTRAL   0.545455  0.037736        bnb   
2   70.0   69.0   89.0      OBJ   0.503597  0.440252        bnb   
3  139.0  288.0   20.0      POS   0.325527  0.874214        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.394654  0.336266  
1         tfidf_ng3  0.394654  0.336266  
2         tfidf_ng3  0.394654  0.336266  
3         tfidf_ng3  0.394654  0.336266  

========================================
4-balanced-KNN-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  64.0  105.0   95.0      NEG   0.378698  0.402516        KNN   
1  58.0  109.0  101.0  NEUTRAL   0.347305  0.364780        KNN   
2  82.0   87.0   77.0      OBJ   0.485207  0.515723        KNN   
3  58.0   73.0  101.0      POS   0.442748  0.364780        KNN   

  feauter_generator      acc        f1  
0         tfidf_ng3  0.41195  0.411518  
1         tfidf_ng3  0.41195  0.411518  
2         tfidf_ng3  0.41195  0.411518  
3         tfidf_ng3  0.41195  0.411518  

========================================
4-balanced-Passive Aggresive-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  65.0   83.0   94.0      NEG   0.439189  0.408805  Passive Aggresive   
1  49.0   82.0  110.0  NEUTRAL   0.374046  0.308176  Passive Aggresive   
2  88.0  119.0   71.0      OBJ   0.425121  0.553459  Passive Aggresive   
3  72.0   78.0   87.0      POS   0.480000  0.452830  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng1  0.430818  0.427069  
1         count_ng1  0.430818  0.427069  
2         count_ng1  0.430818  0.427069  
3         count_ng1  0.430818  0.427069  

========================================
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<pre>C:\Users\rababalkhalifa\AppData\Local\conda\conda\envs\py27\lib\site-packages\sklearn\metrics\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  &#39;precision&#39;, &#39;predicted&#39;, average, warn_for)
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<pre>(&#39; tuning:&#39;, &#39;sgd&#39;, &#39;4-unbalanced&#39;)

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========================================
4-unbalanced-SVM-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0   100.0  135.0  236.0      NEG   0.425532  0.297619        SVM   
1    11.0   49.0  155.0  NEUTRAL   0.183333  0.066265        SVM   
2  1202.0  446.0  136.0      OBJ   0.729369  0.898356        SVM   
3    29.0   27.0  130.0      POS   0.517857  0.182390        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.671336  0.627289  
1         tfidf_ng1  0.671336  0.627289  
2         tfidf_ng1  0.671336  0.627289  
3         tfidf_ng1  0.671336  0.627289  

========================================
4-unbalanced-Logistic Regression-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    38.0   36.0  298.0      NEG   0.513514  0.113095  Logistic Regression   
1     2.0    3.0  164.0  NEUTRAL   0.400000  0.012048  Logistic Regression   
2  1317.0  601.0   21.0      OBJ   0.686653  0.984305  Logistic Regression   
3     2.0    0.0  157.0      POS   1.000000  0.012579  Logistic Regression   

  feauter_generator      acc        f1  
0         tfidf_ng1  0.67984  0.576546  
1         tfidf_ng1  0.67984  0.576546  
2         tfidf_ng1  0.67984  0.576546  
3         tfidf_ng1  0.67984  0.576546  

========================================
4-unbalanced-Logistic Regression-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    21.0   21.0  315.0      NEG   0.500000  0.062500  Logistic Regression   
1     1.0    2.0  165.0  NEUTRAL   0.333333  0.006024  Logistic Regression   
2  1328.0  624.0   10.0      OBJ   0.680328  0.992526  Logistic Regression   
3     2.0    0.0  157.0      POS   1.000000  0.012579  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.676338  0.561985  
1         tfidf_ng2  0.676338  0.561985  
2         tfidf_ng2  0.676338  0.561985  
3         tfidf_ng2  0.676338  0.561985  

========================================
4-unbalanced-SVM-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0   101.0  112.0  235.0      NEG   0.474178  0.300595        SVM   
1    12.0   35.0  154.0  NEUTRAL   0.255319  0.072289        SVM   
2  1238.0  458.0  100.0      OBJ   0.729953  0.925262        SVM   
3    23.0   20.0  136.0      POS   0.534884  0.144654        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.687344  0.635549  
1         tfidf_ng2  0.687344  0.635549  
2         tfidf_ng2  0.687344  0.635549  
3         tfidf_ng2  0.687344  0.635549  

========================================
4-balanced-mnb-count_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  69.0   73.0  90.0      NEG   0.485915  0.433962        mnb   
1  80.0  131.0  79.0  NEUTRAL   0.379147  0.503145        mnb   
2  86.0   72.0  73.0      OBJ   0.544304  0.540881        mnb   
3  75.0   50.0  84.0      POS   0.600000  0.471698        mnb   

  feauter_generator       acc        f1  
0         count_ng3  0.487421  0.490415  
1         count_ng3  0.487421  0.490415  
2         count_ng3  0.487421  0.490415  
3         count_ng3  0.487421  0.490415  

========================================
4-balanced-mnb-count_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  68.0   72.0  91.0      NEG   0.485714  0.427673        mnb   
1  77.0  131.0  82.0  NEUTRAL   0.370192  0.484277        mnb   
2  89.0   73.0  70.0      OBJ   0.549383  0.559748        mnb   
3  76.0   50.0  83.0      POS   0.603175  0.477987        mnb   

  feauter_generator       acc       f1  
0         count_ng2  0.487421  0.49058  
1         count_ng2  0.487421  0.49058  
2         count_ng2  0.487421  0.49058  
3         count_ng2  0.487421  0.49058  

========================================
4-balanced-mnb-count_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  65.0   78.0  94.0      NEG   0.454545  0.408805        mnb   
1  73.0  131.0  86.0  NEUTRAL   0.357843  0.459119        mnb   
2  87.0   78.0  72.0      OBJ   0.527273  0.547170        mnb   
3  73.0   51.0  86.0      POS   0.588710  0.459119        mnb   

  feauter_generator       acc        f1  
0         count_ng1  0.468553  0.471401  
1         count_ng1  0.468553  0.471401  
2         count_ng1  0.468553  0.471401  
3         count_ng1  0.468553  0.471401  

========================================
4-balanced-Logistic Regression-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  56.0   65.0  103.0      NEG   0.462810  0.352201  Logistic Regression   
1  74.0  114.0   85.0  NEUTRAL   0.393617  0.465409  Logistic Regression   
2  89.0  113.0   70.0      OBJ   0.440594  0.559748  Logistic Regression   
3  65.0   60.0   94.0      POS   0.520000  0.408805  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.446541  0.444334  
1         tfidf_ng3  0.446541  0.444334  
2         tfidf_ng3  0.446541  0.444334  
3         tfidf_ng3  0.446541  0.444334  

========================================
4-balanced-Logistic Regression-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  58.0   78.0  101.0      NEG   0.426471  0.364780  Logistic Regression   
1  71.0  110.0   88.0  NEUTRAL   0.392265  0.446541  Logistic Regression   
2  85.0   98.0   74.0      OBJ   0.464481  0.534591  Logistic Regression   
3  73.0   63.0   86.0      POS   0.536765  0.459119  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.451258  0.450715  
1         tfidf_ng2  0.451258  0.450715  
2         tfidf_ng2  0.451258  0.450715  
3         tfidf_ng2  0.451258  0.450715  

========================================
4-balanced-Logistic Regression-tfidf_ng1
----------------------------------------
     TP    FP    FN        L  Percision    Recall           Classifier  \
0  65.0  92.0  94.0      NEG   0.414013  0.408805  Logistic Regression   
1  64.0  97.0  95.0  NEUTRAL   0.397516  0.402516  Logistic Regression   
2  85.0  92.0  74.0      OBJ   0.480226  0.534591  Logistic Regression   
3  74.0  67.0  85.0      POS   0.524823  0.465409  Logistic Regression   

  feauter_generator      acc       f1  
0         tfidf_ng1  0.45283  0.45267  
1         tfidf_ng1  0.45283  0.45267  
2         tfidf_ng1  0.45283  0.45267  
3         tfidf_ng1  0.45283  0.45267  

========================================
4-unbalanced-mnb-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision  Recall Classifier  \
0     0.0    1.0  336.0      NEG    0.00000     1.0        mnb   
1     0.0    0.0  166.0  NEUTRAL    0.00000     0.0        mnb   
2  1338.0  660.0    0.0      OBJ    0.66967     1.0        mnb   
3     0.0    0.0  159.0      POS    0.00000     0.0        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.669335  0.536912  
1         tfidf_ng1  0.669335  0.536912  
2         tfidf_ng1  0.669335  0.536912  
3         tfidf_ng1  0.669335  0.536912  

========================================
4-unbalanced-bnb-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision  Recall Classifier  \
0     0.0    1.0  336.0      NEG    0.00000     1.0        bnb   
1     0.0    0.0  166.0  NEUTRAL    0.00000     0.0        bnb   
2  1338.0  660.0    0.0      OBJ    0.66967     1.0        bnb   
3     0.0    0.0  159.0      POS    0.00000     0.0        bnb   

  feauter_generator       acc        f1  
0         count_ng3  0.669335  0.536912  
1         count_ng3  0.669335  0.536912  
2         count_ng3  0.669335  0.536912  
3         count_ng3  0.669335  0.536912  

========================================
4-unbalanced-bnb-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision  Recall Classifier  \
0     0.0    1.0  336.0      NEG    0.00000     1.0        bnb   
1     0.0    0.0  166.0  NEUTRAL    0.00000     0.0        bnb   
2  1338.0  660.0    0.0      OBJ    0.66967     1.0        bnb   
3     0.0    0.0  159.0      POS    0.00000     0.0        bnb   

  feauter_generator       acc        f1  
0         count_ng2  0.669335  0.536912  
1         count_ng2  0.669335  0.536912  
2         count_ng2  0.669335  0.536912  
3         count_ng2  0.669335  0.536912  

========================================
4-unbalanced-bnb-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0     3.0    4.0  333.0      NEG   0.428571  0.008929        bnb   
1     0.0    0.0  166.0  NEUTRAL   0.000000  0.000000        bnb   
2  1336.0  656.0    2.0      OBJ   0.670683  0.998505        bnb   
3     0.0    0.0  159.0      POS   0.000000  0.000000        bnb   

  feauter_generator       acc        f1  
0         count_ng1  0.669835  0.540016  
1         count_ng1  0.669835  0.540016  
2         count_ng1  0.669835  0.540016  
3         count_ng1  0.669835  0.540016  

========================================
4-unbalanced-Logistic Regression-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    15.0   17.0  321.0      NEG   0.468750  0.044643  Logistic Regression   
1     1.0    1.0  165.0  NEUTRAL   0.500000  0.006024  Logistic Regression   
2  1332.0  631.0    6.0      OBJ   0.678553  0.995516  Logistic Regression   
3     2.0    0.0  157.0      POS   1.000000  0.012579  Logistic Regression   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.675338  0.556839  
1         tfidf_ng3  0.675338  0.556839  
2         tfidf_ng3  0.675338  0.556839  
3         tfidf_ng3  0.675338  0.556839  

========================================
4-unbalanced-KNN-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    18.0   51.0  318.0      NEG   0.260870  0.053571        KNN   
1     3.0   10.0  163.0  NEUTRAL   0.230769  0.018072        KNN   
2  1287.0  621.0   51.0      OBJ   0.674528  0.961883        KNN   
3     2.0    7.0  157.0      POS   0.222222  0.012579        KNN   

  feauter_generator       acc        f1  
0         count_ng2  0.655328  0.550384  
1         count_ng2  0.655328  0.550384  
2         count_ng2  0.655328  0.550384  
3         count_ng2  0.655328  0.550384  

========================================
4-balanced-Passive Aggresive-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  71.0   90.0   88.0      NEG   0.440994  0.446541  Passive Aggresive   
1  57.0   90.0  102.0  NEUTRAL   0.387755  0.358491  Passive Aggresive   
2  86.0  102.0   73.0      OBJ   0.457447  0.540881  Passive Aggresive   
3  70.0   70.0   89.0      POS   0.500000  0.440252  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.446541  0.445051  
1         tfidf_ng1  0.446541  0.445051  
2         tfidf_ng1  0.446541  0.445051  
3         tfidf_ng1  0.446541  0.445051  

========================================
4-balanced-Passive Aggresive-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall         Classifier  \
0  64.0   85.0  95.0      NEG   0.429530  0.402516  Passive Aggresive   
1  67.0  109.0  92.0  NEUTRAL   0.380682  0.421384  Passive Aggresive   
2  90.0   83.0  69.0      OBJ   0.520231  0.566038  Passive Aggresive   
3  74.0   64.0  85.0      POS   0.536232  0.465409  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.463836  0.464017  
1         tfidf_ng2  0.463836  0.464017  
2         tfidf_ng2  0.463836  0.464017  
3         tfidf_ng2  0.463836  0.464017  

========================================
4-unbalanced-KNN-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    14.0   37.0  322.0      NEG   0.274510  0.041667        KNN   
1     3.0   16.0  163.0  NEUTRAL   0.157895  0.018072        KNN   
2  1284.0  625.0   54.0      OBJ   0.672603  0.959641        KNN   
3     4.0   16.0  155.0      POS   0.200000  0.025157        KNN   

  feauter_generator       acc        f1  
0         count_ng1  0.652826  0.547775  
1         count_ng1  0.652826  0.547775  
2         count_ng1  0.652826  0.547775  
3         count_ng1  0.652826  0.547775  

========================================
4-unbalanced-bnb-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision  Recall Classifier  \
0     0.0    1.0  336.0      NEG    0.00000     1.0        bnb   
1     0.0    0.0  166.0  NEUTRAL    0.00000     0.0        bnb   
2  1338.0  660.0    0.0      OBJ    0.66967     1.0        bnb   
3     0.0    0.0  159.0      POS    0.00000     0.0        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.669335  0.536912  
1         tfidf_ng3  0.669335  0.536912  
2         tfidf_ng3  0.669335  0.536912  
3         tfidf_ng3  0.669335  0.536912  

========================================
4-unbalanced-bnb-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision  Recall Classifier  \
0     0.0    1.0  336.0      NEG    0.00000     1.0        bnb   
1     0.0    0.0  166.0  NEUTRAL    0.00000     0.0        bnb   
2  1338.0  660.0    0.0      OBJ    0.66967     1.0        bnb   
3     0.0    0.0  159.0      POS    0.00000     0.0        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.669335  0.536912  
1         tfidf_ng2  0.669335  0.536912  
2         tfidf_ng2  0.669335  0.536912  
3         tfidf_ng2  0.669335  0.536912  

========================================
4-unbalanced-bnb-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0     3.0    4.0  333.0      NEG   0.428571  0.008929        bnb   
1     0.0    0.0  166.0  NEUTRAL   0.000000  0.000000        bnb   
2  1336.0  656.0    2.0      OBJ   0.670683  0.998505        bnb   
3     0.0    0.0  159.0      POS   0.000000  0.000000        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.669835  0.540016  
1         tfidf_ng1  0.669835  0.540016  
2         tfidf_ng1  0.669835  0.540016  
3         tfidf_ng1  0.669835  0.540016  

========================================
4-unbalanced-Perceptron-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0   102.0  120.0  234.0      NEG   0.459459  0.303571  Perceptron   
1    11.0   48.0  155.0  NEUTRAL   0.186441  0.066265  Perceptron   
2  1201.0  445.0  137.0      OBJ   0.729648  0.897608  Perceptron   
3    29.0   43.0  130.0      POS   0.402778  0.182390  Perceptron   

  feauter_generator       acc        f1  
0         count_ng2  0.671836  0.628328  
1         count_ng2  0.671836  0.628328  
2         count_ng2  0.671836  0.628328  
3         count_ng2  0.671836  0.628328  

========================================
4-unbalanced-Perceptron-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0   106.0  123.0  230.0      NEG   0.462882  0.315476  Perceptron   
1    19.0   63.0  147.0  NEUTRAL   0.231707  0.114458  Perceptron   
2  1183.0  427.0  155.0      OBJ   0.734783  0.884155  Perceptron   
3    31.0   47.0  128.0      POS   0.397436  0.194969  Perceptron   

  feauter_generator       acc        f1  
0         count_ng3  0.669835  0.633794  
1         count_ng3  0.669835  0.633794  
2         count_ng3  0.669835  0.633794  
3         count_ng3  0.669835  0.633794  

========================================
4-unbalanced-Perceptron-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0   115.0  180.0  221.0      NEG   0.389831  0.342262  Perceptron   
1    23.0  110.0  143.0  NEUTRAL   0.172932  0.138554  Perceptron   
2  1121.0  385.0  217.0      OBJ   0.744356  0.837818  Perceptron   
3    20.0   45.0  139.0      POS   0.307692  0.125786  Perceptron   

  feauter_generator      acc      f1  
0         count_ng1  0.63982  0.6159  
1         count_ng1  0.63982  0.6159  
2         count_ng1  0.63982  0.6159  
3         count_ng1  0.63982  0.6159  

========================================
4-unbalanced-mnb-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0     1.0    1.0  335.0      NEG   0.500000  0.002976        mnb   
1     1.0    0.0  165.0  NEUTRAL   1.000000  0.006024        mnb   
2  1338.0  658.0    0.0      OBJ   0.670341  1.000000        mnb   
3     0.0    0.0  159.0      POS   0.000000  0.000000        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.670335  0.539224  
1         tfidf_ng2  0.670335  0.539224  
2         tfidf_ng2  0.670335  0.539224  
3         tfidf_ng2  0.670335  0.539224  

========================================
4-balanced-bnb-count_ng1
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   66.0   75.0   93.0      NEG   0.468085  0.415094        bnb   
1   32.0   37.0  127.0  NEUTRAL   0.463768  0.201258        bnb   
2   90.0   99.0   69.0      OBJ   0.476190  0.566038        bnb   
3  109.0  128.0   50.0      POS   0.459916  0.685535        bnb   

  feauter_generator       acc        f1  
0         count_ng1  0.466981  0.447112  
1         count_ng1  0.466981  0.447112  
2         count_ng1  0.466981  0.447112  
3         count_ng1  0.466981  0.447112  

========================================
4-balanced-bnb-count_ng2
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   48.0   46.0  111.0      NEG   0.510638  0.301887        bnb   
1   16.0    6.0  143.0  NEUTRAL   0.727273  0.100629        bnb   
2   86.0   91.0   73.0      OBJ   0.485876  0.540881        bnb   
3  124.0  219.0   35.0      POS   0.361516  0.779874        bnb   

  feauter_generator       acc        f1  
0         count_ng2  0.430818  0.390543  
1         count_ng2  0.430818  0.390543  
2         count_ng2  0.430818  0.390543  
3         count_ng2  0.430818  0.390543  

========================================
4-balanced-bnb-count_ng3
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   36.0   23.0  123.0      NEG   0.610169  0.226415        bnb   
1    6.0    5.0  153.0  NEUTRAL   0.545455  0.037736        bnb   
2   70.0   69.0   89.0      OBJ   0.503597  0.440252        bnb   
3  139.0  288.0   20.0      POS   0.325527  0.874214        bnb   

  feauter_generator       acc        f1  
0         count_ng3  0.394654  0.336266  
1         count_ng3  0.394654  0.336266  
2         count_ng3  0.394654  0.336266  
3         count_ng3  0.394654  0.336266  

========================================
4-unbalanced-DecisionTree-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    71.0  182.0  265.0      NEG   0.280632  0.211310  DecisionTree   
1    19.0   92.0  147.0  NEUTRAL   0.171171  0.114458  DecisionTree   
2  1082.0  446.0  256.0      OBJ   0.708115  0.808670  DecisionTree   
3    19.0   88.0  140.0      POS   0.177570  0.119497  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.595798  0.568665  
1         tfidf_ng1  0.595798  0.568665  
2         tfidf_ng1  0.595798  0.568665  
3         tfidf_ng1  0.595798  0.568665  

========================================
4-unbalanced-DecisionTree-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    77.0  181.0  259.0      NEG   0.298450  0.229167  DecisionTree   
1    13.0   70.0  153.0  NEUTRAL   0.156627  0.078313  DecisionTree   
2  1106.0  442.0  232.0      OBJ   0.714470  0.826607  DecisionTree   
3    19.0   91.0  140.0      POS   0.172727  0.119497  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.607804  0.576502  
1         tfidf_ng2  0.607804  0.576502  
2         tfidf_ng2  0.607804  0.576502  
3         tfidf_ng2  0.607804  0.576502  

========================================
4-unbalanced-DecisionTree-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    77.0  197.0  259.0      NEG   0.281022  0.229167  DecisionTree   
1    13.0  100.0  153.0  NEUTRAL   0.115044  0.078313  DecisionTree   
2  1063.0  432.0  275.0      OBJ   0.711037  0.794469  DecisionTree   
3    22.0   95.0  137.0      POS   0.188034  0.138365  DecisionTree   

  feauter_generator       acc       f1  
0         tfidf_ng3  0.587794  0.56515  
1         tfidf_ng3  0.587794  0.56515  
2         tfidf_ng3  0.587794  0.56515  
3         tfidf_ng3  0.587794  0.56515  

========================================
4-balanced-mnb-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   68.0  99.0      NEG   0.468750  0.377358        mnb   
1  82.0  132.0  77.0  NEUTRAL   0.383178  0.515723        mnb   
2  89.0   72.0  70.0      OBJ   0.552795  0.559748        mnb   
3  77.0   56.0  82.0      POS   0.578947  0.484277        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.484277  0.485361  
1         tfidf_ng3  0.484277  0.485361  
2         tfidf_ng3  0.484277  0.485361  
3         tfidf_ng3  0.484277  0.485361  

========================================
4-balanced-mnb-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   69.0  99.0      NEG   0.465116  0.377358        mnb   
1  83.0  130.0  76.0  NEUTRAL   0.389671  0.522013        mnb   
2  90.0   72.0  69.0      OBJ   0.555556  0.566038        mnb   
3  79.0   53.0  80.0      POS   0.598485  0.496855        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.490566  0.491652  
1         tfidf_ng2  0.490566  0.491652  
2         tfidf_ng2  0.490566  0.491652  
3         tfidf_ng2  0.490566  0.491652  

========================================
4-balanced-mnb-tfidf_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  67.0   80.0  92.0      NEG   0.455782  0.421384        mnb   
1  79.0  128.0  80.0  NEUTRAL   0.381643  0.496855        mnb   
2  86.0   68.0  73.0      OBJ   0.558442  0.540881        mnb   
3  75.0   53.0  84.0      POS   0.585938  0.471698        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.482704  0.485443  
1         tfidf_ng1  0.482704  0.485443  
2         tfidf_ng1  0.482704  0.485443  
3         tfidf_ng1  0.482704  0.485443  

========================================
4-balanced-RandomForest-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  65.0  128.0   94.0      NEG   0.336788  0.408805  RandomForest   
1  45.0   76.0  114.0  NEUTRAL   0.371901  0.283019  RandomForest   
2  69.0   95.0   90.0      OBJ   0.420732  0.433962  RandomForest   
3  63.0   95.0   96.0      POS   0.398734  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.380503  0.378867  
1         tfidf_ng1  0.380503  0.378867  
2         tfidf_ng1  0.380503  0.378867  
3         tfidf_ng1  0.380503  0.378867  

========================================
4-balanced-RandomForest-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  58.0   88.0  101.0      NEG   0.397260  0.364780  RandomForest   
1  32.0   53.0  127.0  NEUTRAL   0.376471  0.201258  RandomForest   
2  82.0  138.0   77.0      OBJ   0.372727  0.515723  RandomForest   
3  63.0  122.0   96.0      POS   0.340541  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.369497  0.360405  
1         tfidf_ng3  0.369497  0.360405  
2         tfidf_ng3  0.369497  0.360405  
3         tfidf_ng3  0.369497  0.360405  

========================================
4-balanced-RandomForest-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  77.0  155.0   82.0      NEG   0.331897  0.484277  RandomForest   
1  43.0   61.0  116.0  NEUTRAL   0.413462  0.270440  RandomForest   
2  71.0   79.0   88.0      OBJ   0.473333  0.446541  RandomForest   
3  63.0   87.0   96.0      POS   0.420000  0.396226  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.399371  0.397043  
1         tfidf_ng2  0.399371  0.397043  
2         tfidf_ng2  0.399371  0.397043  
3         tfidf_ng2  0.399371  0.397043  

========================================
4-balanced-DecisionTree-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  46.0  104.0  113.0      NEG   0.306667  0.289308  DecisionTree   
1  54.0  106.0  105.0  NEUTRAL   0.337500  0.339623  DecisionTree   
2  70.0  112.0   89.0      OBJ   0.384615  0.440252  DecisionTree   
3  62.0   82.0   97.0      POS   0.430556  0.389937  DecisionTree   

  feauter_generator      acc        f1  
0         count_ng1  0.36478  0.364023  
1         count_ng1  0.36478  0.364023  
2         count_ng1  0.36478  0.364023  
3         count_ng1  0.36478  0.364023  

========================================
4-balanced-DecisionTree-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  48.0   95.0  111.0      NEG   0.335664  0.301887  DecisionTree   
1  50.0  109.0  109.0  NEUTRAL   0.314465  0.314465  DecisionTree   
2  65.0  109.0   94.0      OBJ   0.373563  0.408805  DecisionTree   
3  63.0   97.0   96.0      POS   0.393750  0.396226  DecisionTree   

  feauter_generator       acc       f1  
0         count_ng3  0.355346  0.35443  
1         count_ng3  0.355346  0.35443  
2         count_ng3  0.355346  0.35443  
3         count_ng3  0.355346  0.35443  

========================================
4-balanced-DecisionTree-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  57.0  111.0  102.0      NEG   0.339286  0.358491  DecisionTree   
1  49.0   95.0  110.0  NEUTRAL   0.340278  0.308176  DecisionTree   
2  62.0  105.0   97.0      OBJ   0.371257  0.389937  DecisionTree   
3  59.0   98.0  100.0      POS   0.375796  0.371069  DecisionTree   

  feauter_generator       acc        f1  
0         count_ng2  0.356918  0.356461  
1         count_ng2  0.356918  0.356461  
2         count_ng2  0.356918  0.356461  
3         count_ng2  0.356918  0.356461  

========================================
4-unbalanced-RandomForest-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    46.0   75.0  290.0      NEG   0.380165  0.136905  RandomForest   
1     5.0    8.0  161.0  NEUTRAL   0.384615  0.030120  RandomForest   
2  1273.0  572.0   65.0      OBJ   0.689973  0.951420  RandomForest   
3    10.0   10.0  149.0      POS   0.500000  0.062893  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.667334  0.582747  
1         tfidf_ng1  0.667334  0.582747  
2         tfidf_ng1  0.667334  0.582747  
3         tfidf_ng1  0.667334  0.582747  

========================================
4-unbalanced-RandomForest-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    55.0   55.0  281.0      NEG   0.500000  0.163690  RandomForest   
1     1.0    9.0  165.0  NEUTRAL   0.100000  0.006024  RandomForest   
2  1294.0  567.0   44.0      OBJ   0.695325  0.967115  RandomForest   
3     4.0   14.0  155.0      POS   0.222222  0.025157  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.677339  0.587488  
1         tfidf_ng2  0.677339  0.587488  
2         tfidf_ng2  0.677339  0.587488  
3         tfidf_ng2  0.677339  0.587488  

========================================
4-unbalanced-RandomForest-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    46.0   57.0  290.0      NEG   0.446602  0.136905  RandomForest   
1     4.0   12.0  162.0  NEUTRAL   0.250000  0.024096  RandomForest   
2  1284.0  578.0   54.0      OBJ   0.689581  0.959641  RandomForest   
3     6.0   12.0  153.0      POS   0.333333  0.037736  RandomForest   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.670335  0.581409  
1         tfidf_ng3  0.670335  0.581409  
2         tfidf_ng3  0.670335  0.581409  
3         tfidf_ng3  0.670335  0.581409  

========================================
4-unbalanced-SVM-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    98.0  156.0  238.0      NEG   0.385827  0.291667        SVM   
1    17.0   66.0  149.0  NEUTRAL   0.204819  0.102410        SVM   
2  1161.0  423.0  177.0      OBJ   0.732955  0.867713        SVM   
3    25.0   53.0  134.0      POS   0.320513  0.157233        SVM   

  feauter_generator       acc        f1  
0         count_ng1  0.650825  0.615852  
1         count_ng1  0.650825  0.615852  
2         count_ng1  0.650825  0.615852  
3         count_ng1  0.650825  0.615852  

========================================
4-unbalanced-SVM-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    79.0   85.0  257.0      NEG   0.481707  0.235119        SVM   
1     8.0   23.0  158.0  NEUTRAL   0.258065  0.048193        SVM   
2  1261.0  505.0   77.0      OBJ   0.714043  0.942451        SVM   
3    16.0   22.0  143.0      POS   0.421053  0.100629        SVM   

  feauter_generator       acc        f1  
0         count_ng3  0.682341  0.616614  
1         count_ng3  0.682341  0.616614  
2         count_ng3  0.682341  0.616614  
3         count_ng3  0.682341  0.616614  

========================================
4-unbalanced-SVM-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    90.0  102.0  246.0      NEG   0.468750  0.267857        SVM   
1    10.0   39.0  156.0  NEUTRAL   0.204082  0.060241        SVM   
2  1243.0  468.0   95.0      OBJ   0.726476  0.928999        SVM   
3    20.0   27.0  139.0      POS   0.425532  0.125786        SVM   

  feauter_generator       acc        f1  
0         count_ng2  0.681841  0.626212  
1         count_ng2  0.681841  0.626212  
2         count_ng2  0.681841  0.626212  
3         count_ng2  0.681841  0.626212  

========================================
4-balanced-sgd-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  87.0  109.0   72.0      NEG   0.443878  0.547170        sgd   
1  41.0   80.0  118.0  NEUTRAL   0.338843  0.257862        sgd   
2  61.0   82.0   98.0      OBJ   0.426573  0.383648        sgd   
3  80.0   96.0   79.0      POS   0.454545  0.503145        sgd   

  feauter_generator       acc        f1  
0         count_ng1  0.422956  0.416146  
1         count_ng1  0.422956  0.416146  
2         count_ng1  0.422956  0.416146  
3         count_ng1  0.422956  0.416146  

========================================
4-balanced-sgd-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  56.0   92.0  103.0      NEG   0.378378  0.352201        sgd   
1  54.0  100.0  105.0  NEUTRAL   0.350649  0.339623        sgd   
2  75.0   71.0   84.0      OBJ   0.513699  0.471698        sgd   
3  92.0   96.0   67.0      POS   0.489362  0.578616        sgd   

  feauter_generator       acc        f1  
0         count_ng2  0.435535  0.432983  
1         count_ng2  0.435535  0.432983  
2         count_ng2  0.435535  0.432983  
3         count_ng2  0.435535  0.432983  

========================================
4-balanced-sgd-count_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  65.0   86.0  94.0      NEG   0.430464  0.408805        sgd   
1  64.0  108.0  95.0  NEUTRAL   0.372093  0.402516        sgd   
2  73.0   75.0  86.0      OBJ   0.493243  0.459119        sgd   
3  85.0   80.0  74.0      POS   0.515152  0.534591        sgd   

  feauter_generator       acc        f1  
0         count_ng3  0.451258  0.451581  
1         count_ng3  0.451258  0.451581  
2         count_ng3  0.451258  0.451581  
3         count_ng3  0.451258  0.451581  

========================================
4-balanced-RandomForest-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  55.0   97.0  104.0      NEG   0.361842  0.345912  RandomForest   
1  49.0   79.0  110.0  NEUTRAL   0.382812  0.308176  RandomForest   
2  74.0   99.0   85.0      OBJ   0.427746  0.465409  RandomForest   
3  68.0  115.0   91.0      POS   0.371585  0.427673  RandomForest   

  feauter_generator       acc        f1  
0         count_ng1  0.386792  0.384651  
1         count_ng1  0.386792  0.384651  
2         count_ng1  0.386792  0.384651  
3         count_ng1  0.386792  0.384651  

========================================
4-balanced-RandomForest-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  69.0  156.0   90.0      NEG   0.306667  0.433962  RandomForest   
1  24.0   48.0  135.0  NEUTRAL   0.333333  0.150943  RandomForest   
2  68.0   81.0   91.0      OBJ   0.456376  0.427673  RandomForest   
3  71.0  119.0   88.0      POS   0.373684  0.446541  RandomForest   

  feauter_generator      acc        f1  
0         count_ng3  0.36478  0.353901  
1         count_ng3  0.36478  0.353901  
2         count_ng3  0.36478  0.353901  
3         count_ng3  0.36478  0.353901  

========================================
4-balanced-RandomForest-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  75.0  158.0   84.0      NEG   0.321888  0.471698  RandomForest   
1  36.0   57.0  123.0  NEUTRAL   0.387097  0.226415  RandomForest   
2  61.0   79.0   98.0      OBJ   0.435714  0.383648  RandomForest   
3  70.0  100.0   89.0      POS   0.411765  0.440252  RandomForest   

  feauter_generator       acc        f1  
0         count_ng2  0.380503  0.375482  
1         count_ng2  0.380503  0.375482  
2         count_ng2  0.380503  0.375482  
3         count_ng2  0.380503  0.375482  

========================================
4-unbalanced-Perceptron-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0   117.0  203.0  219.0      NEG   0.365625  0.348214  Perceptron   
1    18.0   78.0  148.0  NEUTRAL   0.187500  0.108434  Perceptron   
2  1112.0  356.0  226.0      OBJ   0.757493  0.831091  Perceptron   
3    35.0   80.0  124.0      POS   0.304348  0.220126  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.641321  0.622194  
1         tfidf_ng2  0.641321  0.622194  
2         tfidf_ng2  0.641321  0.622194  
3         tfidf_ng2  0.641321  0.622194  

========================================
4-unbalanced-Perceptron-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0   114.0  191.0  222.0      NEG   0.373770  0.339286  Perceptron   
1    17.0   81.0  149.0  NEUTRAL   0.173469  0.102410  Perceptron   
2  1103.0  369.0  235.0      OBJ   0.749321  0.824365  Perceptron   
3    34.0   90.0  125.0      POS   0.274194  0.213836  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.634317  0.615057  
1         tfidf_ng3  0.634317  0.615057  
2         tfidf_ng3  0.634317  0.615057  
3         tfidf_ng3  0.634317  0.615057  

========================================
4-unbalanced-Perceptron-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall  Classifier  \
0    95.0  179.0  241.0      NEG   0.346715  0.282738  Perceptron   
1    20.0   95.0  146.0  NEUTRAL   0.173913  0.120482  Perceptron   
2  1110.0  393.0  228.0      OBJ   0.738523  0.829596  Perceptron   
3    34.0   73.0  125.0      POS   0.317757  0.213836  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.629815  0.607537  
1         tfidf_ng1  0.629815  0.607537  
2         tfidf_ng1  0.629815  0.607537  
3         tfidf_ng1  0.629815  0.607537  

========================================
4-balanced-bnb-tfidf_ng3
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   36.0   23.0  123.0      NEG   0.610169  0.226415        bnb   
1    6.0    5.0  153.0  NEUTRAL   0.545455  0.037736        bnb   
2   70.0   69.0   89.0      OBJ   0.503597  0.440252        bnb   
3  139.0  288.0   20.0      POS   0.325527  0.874214        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.394654  0.336266  
1         tfidf_ng3  0.394654  0.336266  
2         tfidf_ng3  0.394654  0.336266  
3         tfidf_ng3  0.394654  0.336266  

========================================
4-unbalanced-RandomForest-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    48.0   59.0  288.0      NEG   0.448598  0.142857  RandomForest   
1     5.0   15.0  161.0  NEUTRAL   0.250000  0.030120  RandomForest   
2  1290.0  573.0   48.0      OBJ   0.692432  0.964126  RandomForest   
3     6.0    3.0  153.0      POS   0.666667  0.037736  RandomForest   

  feauter_generator       acc        f1  
0         count_ng1  0.674837  0.586053  
1         count_ng1  0.674837  0.586053  
2         count_ng1  0.674837  0.586053  
3         count_ng1  0.674837  0.586053  

========================================
4-unbalanced-RandomForest-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    44.0   50.0  292.0      NEG   0.468085  0.130952  RandomForest   
1     4.0    9.0  162.0  NEUTRAL   0.307692  0.024096  RandomForest   
2  1299.0  584.0   39.0      OBJ   0.689857  0.970852  RandomForest   
3     7.0    2.0  152.0      POS   0.777778  0.044025  RandomForest   

  feauter_generator       acc        f1  
0         count_ng2  0.677339  0.584611  
1         count_ng2  0.677339  0.584611  
2         count_ng2  0.677339  0.584611  
3         count_ng2  0.677339  0.584611  

========================================
4-unbalanced-RandomForest-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    26.0   25.0  310.0      NEG   0.509804  0.077381  RandomForest   
1     2.0    5.0  164.0  NEUTRAL   0.285714  0.012048  RandomForest   
2  1316.0  619.0   22.0      OBJ   0.680103  0.983558  RandomForest   
3     5.0    1.0  154.0      POS   0.833333  0.031447  RandomForest   

  feauter_generator       acc        f1  
0         count_ng3  0.674837  0.567575  
1         count_ng3  0.674837  0.567575  
2         count_ng3  0.674837  0.567575  
3         count_ng3  0.674837  0.567575  

========================================
4-unbalanced-sgd-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    87.0  106.0  249.0      NEG   0.450777  0.258929        sgd   
1    11.0   41.0  155.0  NEUTRAL   0.211538  0.066265        sgd   
2  1235.0  477.0  103.0      OBJ   0.721379  0.923019        sgd   
3    22.0   20.0  137.0      POS   0.523810  0.138365        sgd   

  feauter_generator       acc       f1  
0         count_ng3  0.677839  0.62313  
1         count_ng3  0.677839  0.62313  
2         count_ng3  0.677839  0.62313  
3         count_ng3  0.677839  0.62313  

========================================
4-unbalanced-sgd-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    88.0  102.0  248.0      NEG   0.463158  0.261905        sgd   
1    16.0   54.0  150.0  NEUTRAL   0.228571  0.096386        sgd   
2  1226.0  466.0  112.0      OBJ   0.724586  0.916293        sgd   
3    23.0   24.0  136.0      POS   0.489362  0.144654        sgd   

  feauter_generator       acc        f1  
0         count_ng2  0.676838  0.626915  
1         count_ng2  0.676838  0.626915  
2         count_ng2  0.676838  0.626915  
3         count_ng2  0.676838  0.626915  

========================================
4-unbalanced-sgd-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    81.0  132.0  255.0      NEG   0.380282  0.241071        sgd   
1    14.0   73.0  152.0  NEUTRAL   0.160920  0.084337        sgd   
2  1197.0  451.0  141.0      OBJ   0.726335  0.894619        sgd   
3    21.0   30.0  138.0      POS   0.411765  0.132075        sgd   

  feauter_generator       acc       f1  
0         count_ng1  0.656828  0.61133  
1         count_ng1  0.656828  0.61133  
2         count_ng1  0.656828  0.61133  
3         count_ng1  0.656828  0.61133  

========================================
4-unbalanced-Passive Aggresive-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0   119.0  144.0  217.0      NEG   0.452471  0.354167  Passive Aggresive   
1    15.0   60.0  151.0  NEUTRAL   0.200000  0.090361  Passive Aggresive   
2  1190.0  412.0  148.0      OBJ   0.742821  0.889387  Passive Aggresive   
3    29.0   30.0  130.0      POS   0.491525  0.182390  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.676838  0.640126  
1         tfidf_ng3  0.676838  0.640126  
2         tfidf_ng3  0.676838  0.640126  
3         tfidf_ng3  0.676838  0.640126  

========================================
4-unbalanced-Passive Aggresive-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0   121.0  155.0  215.0      NEG   0.438406  0.360119  Passive Aggresive   
1    16.0   65.0  150.0  NEUTRAL   0.197531  0.096386  Passive Aggresive   
2  1180.0  401.0  158.0      OBJ   0.746363  0.881913  Passive Aggresive   
3    30.0   31.0  129.0      POS   0.491803  0.188679  Passive Aggresive   

  feauter_generator       acc       f1  
0         tfidf_ng2  0.673837  0.64007  
1         tfidf_ng2  0.673837  0.64007  
2         tfidf_ng2  0.673837  0.64007  
3         tfidf_ng2  0.673837  0.64007  

========================================
4-unbalanced-Passive Aggresive-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0   107.0  184.0  229.0      NEG   0.367698  0.318452  Passive Aggresive   
1    16.0   83.0  150.0  NEUTRAL   0.161616  0.096386  Passive Aggresive   
2  1128.0  403.0  210.0      OBJ   0.736773  0.843049  Passive Aggresive   
3    31.0   47.0  128.0      POS   0.397436  0.194969  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.641321  0.614526  
1         tfidf_ng1  0.641321  0.614526  
2         tfidf_ng1  0.641321  0.614526  
3         tfidf_ng1  0.641321  0.614526  

========================================
4-unbalanced-KNN-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    89.0  124.0  247.0      NEG   0.417840  0.264881        KNN   
1    16.0   41.0  150.0  NEUTRAL   0.280702  0.096386        KNN   
2  1212.0  478.0  126.0      OBJ   0.717160  0.905830        KNN   
3    17.0   22.0  142.0      POS   0.435897  0.106918        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.667334  0.615893  
1         tfidf_ng3  0.667334  0.615893  
2         tfidf_ng3  0.667334  0.615893  
3         tfidf_ng3  0.667334  0.615893  

========================================
4-unbalanced-mnb-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    83.0   75.0  253.0      NEG   0.525316  0.247024        mnb   
1     1.0    8.0  165.0  NEUTRAL   0.111111  0.006024        mnb   
2  1285.0  539.0   53.0      OBJ   0.704496  0.960389        mnb   
3     3.0    5.0  156.0      POS   0.375000  0.018868        mnb   

  feauter_generator       acc        f1  
0         count_ng1  0.686343  0.604308  
1         count_ng1  0.686343  0.604308  
2         count_ng1  0.686343  0.604308  
3         count_ng1  0.686343  0.604308  

========================================
4-unbalanced-mnb-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    50.0   40.0  286.0      NEG   0.555556  0.148810        mnb   
1     3.0    8.0  163.0  NEUTRAL   0.272727  0.018072        mnb   
2  1311.0  579.0   27.0      OBJ   0.693651  0.979821        mnb   
3     5.0    3.0  154.0      POS   0.625000  0.031447        mnb   

  feauter_generator       acc        f1  
0         count_ng2  0.684842  0.590713  
1         count_ng2  0.684842  0.590713  
2         count_ng2  0.684842  0.590713  
3         count_ng2  0.684842  0.590713  

========================================
4-unbalanced-mnb-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    41.0   32.0  295.0      NEG   0.561644  0.122024        mnb   
1     3.0    8.0  163.0  NEUTRAL   0.272727  0.018072        mnb   
2  1315.0  592.0   23.0      OBJ   0.689565  0.982810        mnb   
3     5.0    3.0  154.0      POS   0.625000  0.031447        mnb   

  feauter_generator       acc        f1  
0         count_ng3  0.682341  0.583758  
1         count_ng3  0.682341  0.583758  
2         count_ng3  0.682341  0.583758  
3         count_ng3  0.682341  0.583758  

========================================
4-balanced-DecisionTree-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  44.0  110.0  115.0      NEG   0.285714  0.276730  DecisionTree   
1  53.0   85.0  106.0  NEUTRAL   0.384058  0.333333  DecisionTree   
2  58.0  110.0  101.0      OBJ   0.345238  0.364780  DecisionTree   
3  63.0  113.0   96.0      POS   0.357955  0.396226  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.342767  0.342228  
1         tfidf_ng1  0.342767  0.342228  
2         tfidf_ng1  0.342767  0.342228  
3         tfidf_ng1  0.342767  0.342228  

========================================
4-balanced-DecisionTree-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  52.0   90.0  107.0      NEG   0.366197  0.327044  DecisionTree   
1  43.0   76.0  116.0  NEUTRAL   0.361345  0.270440  DecisionTree   
2  76.0  140.0   83.0      OBJ   0.351852  0.477987  DecisionTree   
3  58.0  101.0  101.0      POS   0.364780  0.364780  DecisionTree   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.360063  0.356245  
1         tfidf_ng3  0.360063  0.356245  
2         tfidf_ng3  0.360063  0.356245  
3         tfidf_ng3  0.360063  0.356245  

========================================
4-balanced-DecisionTree-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall    Classifier  \
0  54.0  110.0  105.0      NEG   0.329268  0.339623  DecisionTree   
1  46.0   95.0  113.0  NEUTRAL   0.326241  0.289308  DecisionTree   
2  64.0  115.0   95.0      OBJ   0.357542  0.402516  DecisionTree   
3  55.0   97.0  104.0      POS   0.361842  0.345912  DecisionTree   

  feauter_generator      acc        f1  
0         tfidf_ng2  0.34434  0.343357  
1         tfidf_ng2  0.34434  0.343357  
2         tfidf_ng2  0.34434  0.343357  
3         tfidf_ng2  0.34434  0.343357  

========================================
4-balanced-Logistic Regression-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  59.0   92.0  100.0      NEG   0.390728  0.371069  Logistic Regression   
1  53.0   74.0  106.0  NEUTRAL   0.417323  0.333333  Logistic Regression   
2  71.0   74.0   88.0      OBJ   0.489655  0.446541  Logistic Regression   
3  96.0  117.0   63.0      POS   0.450704  0.603774  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng3  0.438679  0.433627  
1         count_ng3  0.438679  0.433627  
2         count_ng3  0.438679  0.433627  
3         count_ng3  0.438679  0.433627  

========================================
4-balanced-KNN-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  62.0  175.0   97.0      NEG   0.261603  0.389937        KNN   
1   6.0   20.0  153.0  NEUTRAL   0.230769  0.037736        KNN   
2  59.0  124.0  100.0      OBJ   0.322404  0.371069        KNN   
3  56.0  134.0  103.0      POS   0.294737  0.352201        KNN   

  feauter_generator       acc        f1  
0         count_ng1  0.287736  0.260986  
1         count_ng1  0.287736  0.260986  
2         count_ng1  0.287736  0.260986  
3         count_ng1  0.287736  0.260986  

========================================
4-unbalanced-sgd-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    96.0  107.0  240.0      NEG   0.472906  0.285714        sgd   
1     8.0   27.0  158.0  NEUTRAL   0.228571  0.048193        sgd   
2  1258.0  467.0   80.0      OBJ   0.729275  0.940209        sgd   
3    20.0   16.0  139.0      POS   0.555556  0.125786        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.691346  0.632603  
1         tfidf_ng2  0.691346  0.632603  
2         tfidf_ng2  0.691346  0.632603  
3         tfidf_ng2  0.691346  0.632603  

========================================
4-balanced-KNN-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  89.0  225.0   70.0      NEG   0.283439  0.559748        KNN   
1   3.0    4.0  156.0  NEUTRAL   0.428571  0.018868        KNN   
2  50.0  132.0  109.0      OBJ   0.274725  0.314465        KNN   
3  33.0  100.0  126.0      POS   0.248120  0.207547        KNN   

  feauter_generator       acc        f1  
0         count_ng3  0.275157  0.232937  
1         count_ng3  0.275157  0.232937  
2         count_ng3  0.275157  0.232937  
3         count_ng3  0.275157  0.232937  

========================================
4-balanced-Logistic Regression-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  62.0   97.0   97.0      NEG   0.389937  0.389937  Logistic Regression   
1  53.0   74.0  106.0  NEUTRAL   0.417323  0.333333  Logistic Regression   
2  72.0   73.0   87.0      OBJ   0.496552  0.452830  Logistic Regression   
3  96.0  109.0   63.0      POS   0.468293  0.603774  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng2  0.444969  0.440431  
1         count_ng2  0.444969  0.440431  
2         count_ng2  0.444969  0.440431  
3         count_ng2  0.444969  0.440431  

========================================
4-balanced-Passive Aggresive-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  65.0   83.0   94.0      NEG   0.439189  0.408805  Passive Aggresive   
1  49.0   82.0  110.0  NEUTRAL   0.374046  0.308176  Passive Aggresive   
2  88.0  119.0   71.0      OBJ   0.425121  0.553459  Passive Aggresive   
3  72.0   78.0   87.0      POS   0.480000  0.452830  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng1  0.430818  0.427069  
1         count_ng1  0.430818  0.427069  
2         count_ng1  0.430818  0.427069  
3         count_ng1  0.430818  0.427069  

========================================
4-balanced-Passive Aggresive-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  64.0   92.0   95.0      NEG   0.410256  0.402516  Passive Aggresive   
1  51.0   65.0  108.0  NEUTRAL   0.439655  0.320755  Passive Aggresive   
2  81.0   98.0   78.0      OBJ   0.452514  0.509434  Passive Aggresive   
3  83.0  102.0   76.0      POS   0.448649  0.522013  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng2  0.438679  0.434777  
1         count_ng2  0.438679  0.434777  
2         count_ng2  0.438679  0.434777  
3         count_ng2  0.438679  0.434777  

========================================
4-balanced-Passive Aggresive-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall         Classifier  \
0  65.0   90.0   94.0      NEG   0.419355  0.408805  Passive Aggresive   
1  52.0   61.0  107.0  NEUTRAL   0.460177  0.327044  Passive Aggresive   
2  82.0   99.0   77.0      OBJ   0.453039  0.515723  Passive Aggresive   
3  85.0  102.0   74.0      POS   0.454545  0.534591  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng3  0.446541  0.442512  
1         count_ng3  0.446541  0.442512  
2         count_ng3  0.446541  0.442512  
3         count_ng3  0.446541  0.442512  

========================================
4-balanced-KNN-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  63.0  111.0   96.0      NEG   0.362069  0.396226        KNN   
1  48.0  117.0  111.0  NEUTRAL   0.290909  0.301887        KNN   
2  77.0  104.0   82.0      OBJ   0.425414  0.484277        KNN   
3  49.0   67.0  110.0      POS   0.422414  0.308176        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.372642  0.370995  
1         tfidf_ng1  0.372642  0.370995  
2         tfidf_ng1  0.372642  0.370995  
3         tfidf_ng1  0.372642  0.370995  

========================================
4-balanced-KNN-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  64.0  105.0   95.0      NEG   0.378698  0.402516        KNN   
1  58.0  109.0  101.0  NEUTRAL   0.347305  0.364780        KNN   
2  82.0   87.0   77.0      OBJ   0.485207  0.515723        KNN   
3  58.0   73.0  101.0      POS   0.442748  0.364780        KNN   

  feauter_generator      acc        f1  
0         tfidf_ng3  0.41195  0.411518  
1         tfidf_ng3  0.41195  0.411518  
2         tfidf_ng3  0.41195  0.411518  
3         tfidf_ng3  0.41195  0.411518  

========================================
4-balanced-KNN-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  62.0  101.0   97.0      NEG   0.380368  0.389937        KNN   
1  59.0  119.0  100.0  NEUTRAL   0.331461  0.371069        KNN   
2  80.0   88.0   79.0      OBJ   0.476190  0.503145        KNN   
3  58.0   69.0  101.0      POS   0.456693  0.364780        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.407233  0.407533  
1         tfidf_ng2  0.407233  0.407533  
2         tfidf_ng2  0.407233  0.407533  
3         tfidf_ng2  0.407233  0.407533  

========================================
4-unbalanced-KNN-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    84.0  103.0  252.0      NEG   0.449198  0.250000        KNN   
1     7.0   43.0  159.0  NEUTRAL   0.140000  0.042169        KNN   
2  1229.0  495.0  109.0      OBJ   0.712877  0.918535        KNN   
3    13.0   25.0  146.0      POS   0.342105  0.081761        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.666833  0.607176  
1         tfidf_ng1  0.666833  0.607176  
2         tfidf_ng1  0.666833  0.607176  
3         tfidf_ng1  0.666833  0.607176  

========================================
4-unbalanced-KNN-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    15.0   39.0  321.0      NEG   0.277778  0.044643        KNN   
1     2.0    5.0  164.0  NEUTRAL   0.285714  0.012048        KNN   
2  1298.0  633.0   40.0      OBJ   0.672191  0.970105        KNN   
3     0.0    7.0  159.0      POS   0.000000  1.000000        KNN   

  feauter_generator       acc        f1  
0         count_ng3  0.657829  0.546386  
1         count_ng3  0.657829  0.546386  
2         count_ng3  0.657829  0.546386  
3         count_ng3  0.657829  0.546386  

========================================
4-balanced-bnb-tfidf_ng1
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   66.0   75.0   93.0      NEG   0.468085  0.415094        bnb   
1   32.0   37.0  127.0  NEUTRAL   0.463768  0.201258        bnb   
2   90.0   99.0   69.0      OBJ   0.476190  0.566038        bnb   
3  109.0  128.0   50.0      POS   0.459916  0.685535        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.466981  0.447112  
1         tfidf_ng1  0.466981  0.447112  
2         tfidf_ng1  0.466981  0.447112  
3         tfidf_ng1  0.466981  0.447112  

========================================
4-balanced-bnb-tfidf_ng2
----------------------------------------
      TP     FP     FN        L  Percision    Recall Classifier  \
0   48.0   46.0  111.0      NEG   0.510638  0.301887        bnb   
1   16.0    6.0  143.0  NEUTRAL   0.727273  0.100629        bnb   
2   86.0   91.0   73.0      OBJ   0.485876  0.540881        bnb   
3  124.0  219.0   35.0      POS   0.361516  0.779874        bnb   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.430818  0.390543  
1         tfidf_ng2  0.430818  0.390543  
2         tfidf_ng2  0.430818  0.390543  
3         tfidf_ng2  0.430818  0.390543  

========================================
4-balanced-Passive Aggresive-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall         Classifier  \
0  60.0   82.0  99.0      NEG   0.422535  0.377358  Passive Aggresive   
1  71.0  112.0  88.0  NEUTRAL   0.387978  0.446541  Passive Aggresive   
2  85.0   91.0  74.0      OBJ   0.482955  0.534591  Passive Aggresive   
3  70.0   65.0  89.0      POS   0.518519  0.440252  Passive Aggresive   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.449686  0.449382  
1         tfidf_ng3  0.449686  0.449382  
2         tfidf_ng3  0.449686  0.449382  
3         tfidf_ng3  0.449686  0.449382  

========================================
4-balanced-Perceptron-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  69.0   82.0   90.0      NEG   0.456954  0.433962  Perceptron   
1  57.0   91.0  102.0  NEUTRAL   0.385135  0.358491  Perceptron   
2  82.0  101.0   77.0      OBJ   0.448087  0.515723  Perceptron   
3  73.0   81.0   86.0      POS   0.474026  0.459119  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.441824  0.440621  
1         tfidf_ng3  0.441824  0.440621  
2         tfidf_ng3  0.441824  0.440621  
3         tfidf_ng3  0.441824  0.440621  

========================================
4-balanced-Perceptron-tfidf_ng2
----------------------------------------
     TP    FP     FN        L  Percision    Recall  Classifier  \
0  71.0  95.0   88.0      NEG   0.427711  0.446541  Perceptron   
1  52.0  83.0  107.0  NEUTRAL   0.385185  0.327044  Perceptron   
2  77.0  82.0   82.0      OBJ   0.484277  0.484277  Perceptron   
3  85.0  91.0   74.0      POS   0.482955  0.534591  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.448113  0.445601  
1         tfidf_ng2  0.448113  0.445601  
2         tfidf_ng2  0.448113  0.445601  
3         tfidf_ng2  0.448113  0.445601  

========================================
4-balanced-Perceptron-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  73.0   96.0   86.0      NEG   0.431953  0.459119  Perceptron   
1  46.0   91.0  113.0  NEUTRAL   0.335766  0.289308  Perceptron   
2  90.0  114.0   69.0      OBJ   0.441176  0.566038  Perceptron   
3  64.0   62.0   95.0      POS   0.507937  0.402516  Perceptron   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.429245  0.425231  
1         tfidf_ng1  0.429245  0.425231  
2         tfidf_ng1  0.429245  0.425231  
3         tfidf_ng1  0.429245  0.425231  

========================================
4-unbalanced-KNN-tfidf_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    88.0  123.0  248.0      NEG   0.417062  0.261905        KNN   
1    14.0   42.0  152.0  NEUTRAL   0.250000  0.084337        KNN   
2  1215.0  481.0  123.0      OBJ   0.716392  0.908072        KNN   
3    16.0   20.0  143.0      POS   0.444444  0.100629        KNN   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.666833  0.613694  
1         tfidf_ng2  0.666833  0.613694  
2         tfidf_ng2  0.666833  0.613694  
3         tfidf_ng2  0.666833  0.613694  

========================================
4-unbalanced-DecisionTree-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    81.0  186.0  255.0      NEG   0.303371  0.241071  DecisionTree   
1    14.0   92.0  152.0  NEUTRAL   0.132075  0.084337  DecisionTree   
2  1116.0  439.0  222.0      OBJ   0.717685  0.834081  DecisionTree   
3    18.0   53.0  141.0      POS   0.253521  0.113208  DecisionTree   

  feauter_generator       acc        f1  
0         count_ng1  0.614807  0.582558  
1         count_ng1  0.614807  0.582558  
2         count_ng1  0.614807  0.582558  
3         count_ng1  0.614807  0.582558  

========================================
4-unbalanced-DecisionTree-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    84.0  180.0  252.0      NEG   0.318182  0.250000  DecisionTree   
1    10.0   72.0  156.0  NEUTRAL   0.121951  0.060241  DecisionTree   
2  1129.0  452.0  209.0      OBJ   0.714105  0.843797  DecisionTree   
3    16.0   56.0  143.0      POS   0.222222  0.100629  DecisionTree   

  feauter_generator      acc        f1  
0         count_ng2  0.61981  0.582544  
1         count_ng2  0.61981  0.582544  
2         count_ng2  0.61981  0.582544  
3         count_ng2  0.61981  0.582544  

========================================
4-unbalanced-DecisionTree-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall    Classifier  \
0    88.0  184.0  248.0      NEG   0.323529  0.261905  DecisionTree   
1    11.0   72.0  155.0  NEUTRAL   0.132530  0.066265  DecisionTree   
2  1134.0  446.0  204.0      OBJ   0.717722  0.847534  DecisionTree   
3    18.0   46.0  141.0      POS   0.281250  0.113208  DecisionTree   

  feauter_generator       acc       f1  
0         count_ng3  0.625813  0.58907  
1         count_ng3  0.625813  0.58907  
2         count_ng3  0.625813  0.58907  
3         count_ng3  0.625813  0.58907  

========================================
4-balanced-SVM-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  63.0   89.0   96.0      NEG   0.414474  0.396226        SVM   
1  54.0   70.0  105.0  NEUTRAL   0.435484  0.339623        SVM   
2  75.0   77.0   84.0      OBJ   0.493421  0.471698        SVM   
3  93.0  115.0   66.0      POS   0.447115  0.584906        SVM   

  feauter_generator       acc        f1  
0         count_ng2  0.448113  0.443974  
1         count_ng2  0.448113  0.443974  
2         count_ng2  0.448113  0.443974  
3         count_ng2  0.448113  0.443974  

========================================
4-balanced-SVM-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  60.0   93.0   99.0      NEG   0.392157  0.377358        SVM   
1  51.0   67.0  108.0  NEUTRAL   0.432203  0.320755        SVM   
2  71.0   79.0   88.0      OBJ   0.473333  0.446541        SVM   
3  94.0  121.0   65.0      POS   0.437209  0.591195        SVM   

  feauter_generator       acc        f1  
0         count_ng3  0.433962  0.428767  
1         count_ng3  0.433962  0.428767  
2         count_ng3  0.433962  0.428767  
3         count_ng3  0.433962  0.428767  

========================================
4-balanced-Logistic Regression-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall           Classifier  \
0  65.0   92.0   94.0      NEG   0.414013  0.408805  Logistic Regression   
1  52.0   76.0  107.0  NEUTRAL   0.406250  0.327044  Logistic Regression   
2  80.0   79.0   79.0      OBJ   0.503145  0.503145  Logistic Regression   
3  91.0  101.0   68.0      POS   0.473958  0.572327  Logistic Regression   

  feauter_generator      acc        f1  
0         count_ng1  0.45283  0.448856  
1         count_ng1  0.45283  0.448856  
2         count_ng1  0.45283  0.448856  
3         count_ng1  0.45283  0.448856  

========================================
4-balanced-SVM-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  65.0   89.0   94.0      NEG   0.422078  0.408805        SVM   
1  47.0   77.0  112.0  NEUTRAL   0.379032  0.295597        SVM   
2  79.0   94.0   80.0      OBJ   0.456647  0.496855        SVM   
3  82.0  103.0   77.0      POS   0.443243  0.515723        SVM   

  feauter_generator       acc        f1  
0         count_ng1  0.429245  0.425035  
1         count_ng1  0.429245  0.425035  
2         count_ng1  0.429245  0.425035  
3         count_ng1  0.429245  0.425035  

========================================
4-balanced-Perceptron-count_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  53.0   73.0  106.0      NEG   0.420635  0.333333  Perceptron   
1  65.0  115.0   94.0  NEUTRAL   0.361111  0.408805  Perceptron   
2  60.0   56.0   99.0      OBJ   0.517241  0.377358  Perceptron   
3  95.0  119.0   64.0      POS   0.443925  0.597484  Perceptron   

  feauter_generator       acc        f1  
0         count_ng3  0.429245  0.425289  
1         count_ng3  0.429245  0.425289  
2         count_ng3  0.429245  0.425289  
3         count_ng3  0.429245  0.425289  

========================================
4-balanced-Perceptron-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  61.0   73.0   98.0      NEG   0.455224  0.383648  Perceptron   
1  56.0   88.0  103.0  NEUTRAL   0.388889  0.352201  Perceptron   
2  67.0   73.0   92.0      OBJ   0.478571  0.421384  Perceptron   
3  94.0  124.0   65.0      POS   0.431193  0.591195  Perceptron   

  feauter_generator       acc        f1  
0         count_ng2  0.437107  0.433213  
1         count_ng2  0.437107  0.433213  
2         count_ng2  0.437107  0.433213  
3         count_ng2  0.437107  0.433213  

========================================
4-balanced-Perceptron-count_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall  Classifier  \
0  60.0   56.0   99.0      NEG   0.517241  0.377358  Perceptron   
1  76.0  145.0   83.0  NEUTRAL   0.343891  0.477987  Perceptron   
2  84.0  110.0   75.0      OBJ   0.432990  0.528302  Perceptron   
3  53.0   52.0  106.0      POS   0.504762  0.333333  Perceptron   

  feauter_generator       acc       f1  
0         count_ng1  0.429245  0.42845  
1         count_ng1  0.429245  0.42845  
2         count_ng1  0.429245  0.42845  
3         count_ng1  0.429245  0.42845  

========================================
4-unbalanced-Passive Aggresive-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0    80.0   81.0  256.0      NEG   0.496894  0.238095  Passive Aggresive   
1     8.0   24.0  158.0  NEUTRAL   0.250000  0.048193  Passive Aggresive   
2  1263.0  502.0   75.0      OBJ   0.715581  0.943946  Passive Aggresive   
3    17.0   24.0  142.0      POS   0.414634  0.106918  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng3  0.684342  0.619216  
1         count_ng3  0.684342  0.619216  
2         count_ng3  0.684342  0.619216  
3         count_ng3  0.684342  0.619216  

========================================
4-unbalanced-Passive Aggresive-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0    88.0   85.0  248.0      NEG   0.508671  0.261905  Passive Aggresive   
1    10.0   33.0  156.0  NEUTRAL   0.232558  0.060241  Passive Aggresive   
2  1251.0  481.0   87.0      OBJ   0.722286  0.934978  Passive Aggresive   
3    21.0   30.0  138.0      POS   0.411765  0.132075  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng2  0.685343  0.627471  
1         count_ng2  0.685343  0.627471  
2         count_ng2  0.685343  0.627471  
3         count_ng2  0.685343  0.627471  

========================================
4-unbalanced-Passive Aggresive-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall         Classifier  \
0    86.0  142.0  250.0      NEG   0.377193  0.255952  Passive Aggresive   
1    12.0   62.0  154.0  NEUTRAL   0.162162  0.072289  Passive Aggresive   
2  1182.0  449.0  156.0      OBJ   0.724709  0.883408  Passive Aggresive   
3    23.0   43.0  136.0      POS   0.348485  0.144654  Passive Aggresive   

  feauter_generator       acc        f1  
0         count_ng1  0.651826  0.608768  
1         count_ng1  0.651826  0.608768  
2         count_ng1  0.651826  0.608768  
3         count_ng1  0.651826  0.608768  

========================================
4-unbalanced-mnb-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0     0.0    2.0  336.0      NEG    0.00000  1.000000        mnb   
1     1.0    0.0  165.0  NEUTRAL    1.00000  0.006024        mnb   
2  1337.0  659.0    1.0      OBJ    0.66984  0.999253        mnb   
3     0.0    0.0  159.0      POS    0.00000  0.000000        mnb   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.669335  0.537827  
1         tfidf_ng3  0.669335  0.537827  
2         tfidf_ng3  0.669335  0.537827  
3         tfidf_ng3  0.669335  0.537827  

========================================
4-balanced-sgd-tfidf_ng1
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  65.0   89.0   94.0      NEG   0.422078  0.408805        sgd   
1  55.0  100.0  104.0  NEUTRAL   0.354839  0.345912        sgd   
2  87.0  103.0   72.0      OBJ   0.457895  0.547170        sgd   
3  69.0   68.0   90.0      POS   0.503650  0.433962        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.433962  0.432609  
1         tfidf_ng1  0.433962  0.432609  
2         tfidf_ng1  0.433962  0.432609  
3         tfidf_ng1  0.433962  0.432609  

========================================
4-balanced-sgd-tfidf_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  58.0   76.0  101.0      NEG   0.432836  0.364780        sgd   
1  73.0  112.0   86.0  NEUTRAL   0.394595  0.459119        sgd   
2  87.0   81.0   72.0      OBJ   0.517857  0.547170        sgd   
3  80.0   69.0   79.0      POS   0.536913  0.503145        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng2  0.468553  0.467978  
1         tfidf_ng2  0.468553  0.467978  
2         tfidf_ng2  0.468553  0.467978  
3         tfidf_ng2  0.468553  0.467978  

========================================
4-balanced-sgd-tfidf_ng3
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  62.0   84.0  97.0      NEG   0.424658  0.389937        sgd   
1  73.0  117.0  86.0  NEUTRAL   0.384211  0.459119        sgd   
2  83.0   81.0  76.0      OBJ   0.506098  0.522013        sgd   
3  72.0   64.0  87.0      POS   0.529412  0.452830        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.455975  0.456741  
1         tfidf_ng3  0.455975  0.456741  
2         tfidf_ng3  0.455975  0.456741  
3         tfidf_ng3  0.455975  0.456741  

========================================
4-unbalanced-sgd-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    93.0  110.0  243.0      NEG   0.458128  0.276786        sgd   
1     9.0   24.0  157.0  NEUTRAL   0.272727  0.054217        sgd   
2  1253.0  475.0   85.0      OBJ   0.725116  0.936472        sgd   
3    20.0   15.0  139.0      POS   0.571429  0.125786        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.687844  0.628996  
1         tfidf_ng3  0.687844  0.628996  
2         tfidf_ng3  0.687844  0.628996  
3         tfidf_ng3  0.687844  0.628996  

========================================
4-unbalanced-sgd-tfidf_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    86.0  101.0  250.0      NEG   0.459893  0.255952        sgd   
1     3.0   25.0  163.0  NEUTRAL   0.107143  0.018072        sgd   
2  1255.0  494.0   83.0      OBJ   0.717553  0.937967        sgd   
3    20.0   15.0  139.0      POS   0.571429  0.125786        sgd   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.682341  0.618474  
1         tfidf_ng1  0.682341  0.618474  
2         tfidf_ng1  0.682341  0.618474  
3         tfidf_ng1  0.682341  0.618474  

========================================
4-balanced-KNN-count_ng2
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  76.0  212.0   83.0      NEG   0.263889  0.477987        KNN   
1   5.0   12.0  154.0  NEUTRAL   0.294118  0.031447        KNN   
2  53.0  102.0  106.0      OBJ   0.341935  0.333333        KNN   
3  44.0  132.0  115.0      POS   0.250000  0.276730        KNN   

  feauter_generator       acc        f1  
0         count_ng2  0.279874  0.249282  
1         count_ng2  0.279874  0.249282  
2         count_ng2  0.279874  0.249282  
3         count_ng2  0.279874  0.249282  

========================================
4-unbalanced-SVM-tfidf_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall Classifier  \
0    97.0  110.0  239.0      NEG   0.468599  0.288690        SVM   
1     9.0   32.0  157.0  NEUTRAL   0.219512  0.054217        SVM   
2  1242.0  467.0   96.0      OBJ   0.726741  0.928251        SVM   
3    23.0   19.0  136.0      POS   0.547619  0.144654        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.685843  0.631137  
1         tfidf_ng3  0.685843  0.631137  
2         tfidf_ng3  0.685843  0.631137  
3         tfidf_ng3  0.685843  0.631137  

========================================
4-balanced-SVM-tfidf_ng2
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  60.0   82.0  99.0      NEG   0.422535  0.377358        SVM   
1  71.0  113.0  88.0  NEUTRAL   0.385870  0.446541        SVM   
2  89.0   83.0  70.0      OBJ   0.517442  0.559748        SVM   
3  75.0   63.0  84.0      POS   0.543478  0.471698        SVM   

  feauter_generator       acc       f1  
0         tfidf_ng2  0.463836  0.46387  
1         tfidf_ng2  0.463836  0.46387  
2         tfidf_ng2  0.463836  0.46387  
3         tfidf_ng2  0.463836  0.46387  

========================================
4-balanced-SVM-tfidf_ng3
----------------------------------------
     TP     FP     FN        L  Percision    Recall Classifier  \
0  53.0   75.0  106.0      NEG   0.414062  0.333333        SVM   
1  74.0  123.0   85.0  NEUTRAL   0.375635  0.465409        SVM   
2  88.0   91.0   71.0      OBJ   0.491620  0.553459        SVM   
3  72.0   60.0   87.0      POS   0.545455  0.452830        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng3  0.451258  0.450156  
1         tfidf_ng3  0.451258  0.450156  
2         tfidf_ng3  0.451258  0.450156  
3         tfidf_ng3  0.451258  0.450156  

========================================
4-balanced-SVM-tfidf_ng1
----------------------------------------
     TP     FP    FN        L  Percision    Recall Classifier  \
0  67.0   84.0  92.0      NEG   0.443709  0.421384        SVM   
1  64.0  105.0  95.0  NEUTRAL   0.378698  0.402516        SVM   
2  86.0   88.0  73.0      OBJ   0.494253  0.540881        SVM   
3  75.0   67.0  84.0      POS   0.528169  0.471698        SVM   

  feauter_generator       acc        f1  
0         tfidf_ng1  0.459119  0.459339  
1         tfidf_ng1  0.459119  0.459339  
2         tfidf_ng1  0.459119  0.459339  
3         tfidf_ng1  0.459119  0.459339  

========================================
4-unbalanced-Logistic Regression-count_ng1
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    85.0  100.0  251.0      NEG   0.459459  0.252976  Logistic Regression   
1     8.0   42.0  158.0  NEUTRAL   0.160000  0.048193  Logistic Regression   
2  1252.0  480.0   86.0      OBJ   0.722864  0.935725  Logistic Regression   
3    16.0   16.0  143.0      POS   0.500000  0.100629  Logistic Regression   

  feauter_generator      acc        f1  
0         count_ng1  0.68084  0.620255  
1         count_ng1  0.68084  0.620255  
2         count_ng1  0.68084  0.620255  
3         count_ng1  0.68084  0.620255  

========================================
4-unbalanced-Logistic Regression-count_ng2
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    80.0   67.0  256.0      NEG   0.544218  0.238095  Logistic Regression   
1     7.0   21.0  159.0  NEUTRAL   0.250000  0.042169  Logistic Regression   
2  1289.0  515.0   49.0      OBJ   0.714523  0.963378  Logistic Regression   
3    10.0   10.0  149.0      POS   0.500000  0.062893  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng2  0.693347  0.619747  
1         count_ng2  0.693347  0.619747  
2         count_ng2  0.693347  0.619747  
3         count_ng2  0.693347  0.619747  

========================================
4-unbalanced-Logistic Regression-count_ng3
----------------------------------------
       TP     FP     FN        L  Percision    Recall           Classifier  \
0    70.0   59.0  266.0      NEG   0.542636  0.208333  Logistic Regression   
1     6.0   12.0  160.0  NEUTRAL   0.333333  0.036145  Logistic Regression   
2  1297.0  537.0   41.0      OBJ   0.707197  0.969357  Logistic Regression   
3    10.0    8.0  149.0      POS   0.555556  0.062893  Logistic Regression   

  feauter_generator       acc        f1  
0         count_ng3  0.691846  0.612378  
1         count_ng3  0.691846  0.612378  
2         count_ng3  0.691846  0.612378  
3         count_ng3  0.691846  0.612378  
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<h3 id="Data-Preperation-Statistics">Data Preperation Statistics<a class="anchor-link" href="#Data-Preperation-Statistics">&#182;</a></h3>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanceFeauters</span> <span class="o">=</span> <span class="n">df_feauterCount</span><span class="p">[</span><span class="n">df_feauterCount</span><span class="p">[</span><span class="s1">&#39;DataSet_Name&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-balanced&#39;</span><span class="p">]</span>
<span class="n">balanceFeauters</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">balanceFeauters</span><span class="o">.</span><span class="n">Feauter_Generator</span>

<span class="n">balanceFeauters</span><span class="p">[</span><span class="s1">&#39;Feauters_Count&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;bar&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[16]:</div>
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    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanceFeauters</span>
</pre></div>

    </div>
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    <div class="prompt output_prompt">Out[16]:</div>


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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>DataSet_Name</th>
      <th>Feauter_Generator</th>
      <th>Feauters_Count</th>
    </tr>
    <tr>
      <th>Feauter_Generator</th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count_ng1</th>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>16457</td>
    </tr>
    <tr>
      <th>count_ng2</th>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>52036</td>
    </tr>
    <tr>
      <th>count_ng3</th>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>88672</td>
    </tr>
    <tr>
      <th>tfidf_ng1</th>
      <td>4-balanced</td>
      <td>tfidf_ng1</td>
      <td>16457</td>
    </tr>
    <tr>
      <th>tfidf_ng2</th>
      <td>4-balanced</td>
      <td>tfidf_ng2</td>
      <td>52036</td>
    </tr>
    <tr>
      <th>tfidf_ng3</th>
      <td>4-balanced</td>
      <td>tfidf_ng3</td>
      <td>88672</td>
    </tr>
  </tbody>
</table>
</div>
</div>

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<div class="prompt input_prompt">In&nbsp;[17]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">unbalanceFeauters</span> <span class="o">=</span> <span class="n">df_feauterCount</span><span class="p">[</span><span class="n">df_feauterCount</span><span class="p">[</span><span class="s1">&#39;DataSet_Name&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
<span class="n">unbalanceFeauters</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">unbalanceFeauters</span><span class="o">.</span><span class="n">Feauter_Generator</span>

<span class="n">unbalanceFeauters</span><span class="p">[</span><span class="s1">&#39;Feauters_Count&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;bar&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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
"
>
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</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="n">unbalanceFeauters</span>
</pre></div>

    </div>
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<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[18]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>DataSet_Name</th>
      <th>Feauter_Generator</th>
      <th>Feauters_Count</th>
    </tr>
    <tr>
      <th>Feauter_Generator</th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count_ng1</th>
      <td>4-unbalanced</td>
      <td>count_ng1</td>
      <td>33365</td>
    </tr>
    <tr>
      <th>count_ng2</th>
      <td>4-unbalanced</td>
      <td>count_ng2</td>
      <td>124760</td>
    </tr>
    <tr>
      <th>count_ng3</th>
      <td>4-unbalanced</td>
      <td>count_ng3</td>
      <td>225111</td>
    </tr>
    <tr>
      <th>tfidf_ng1</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>33365</td>
    </tr>
    <tr>
      <th>tfidf_ng2</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>124760</td>
    </tr>
    <tr>
      <th>tfidf_ng3</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>225111</td>
    </tr>
  </tbody>
</table>
</div>
</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>
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    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># df_feauterCount[df_feauterCount[&#39;Feauter_Generator&#39;].isin([&#39;tfidf_ng1&#39;, &#39;tfidf_ng2&#39;,&#39;tfidf_ng3&#39;])]</span>
<span class="n">df_feauterCount</span><span class="p">[</span><span class="n">df_feauterCount</span><span class="p">[</span><span class="s1">&#39;DataSet_Name&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
</pre></div>

    </div>
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<div class="output_area">

    <div class="prompt output_prompt">Out[19]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

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

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>DataSet_Name</th>
      <th>Feauter_Generator</th>
      <th>Feauters_Count</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>6</th>
      <td>4-unbalanced</td>
      <td>count_ng1</td>
      <td>33365</td>
    </tr>
    <tr>
      <th>7</th>
      <td>4-unbalanced</td>
      <td>count_ng2</td>
      <td>124760</td>
    </tr>
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      <th>8</th>
      <td>4-unbalanced</td>
      <td>count_ng3</td>
      <td>225111</td>
    </tr>
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      <th>9</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>33365</td>
    </tr>
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      <th>10</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>124760</td>
    </tr>
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      <th>11</th>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>225111</td>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">TweetCount_collection</span><span class="p">[</span><span class="s1">&#39;4-balanced&#39;</span><span class="p">]</span>
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      <th>POS</th>
      <th>Dataset</th>
      <th>name</th>
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      <th>0</th>
      <td>640</td>
      <td>640</td>
      <td>640</td>
      <td>640</td>
      <td>Train Set</td>
      <td>4-balanced</td>
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      <th>1</th>
      <td>159</td>
      <td>159</td>
      <td>159</td>
      <td>159</td>
      <td>Test Set</td>
      <td>4-balanced</td>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">TweetCount_collection</span><span class="p">[</span><span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
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      <th>POS</th>
      <th>Dataset</th>
      <th>name</th>
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      <th>0</th>
      <td>5353</td>
      <td>1348</td>
      <td>666</td>
      <td>640</td>
      <td>Train Set</td>
      <td>4-unbalanced</td>
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      <th>1</th>
      <td>1338</td>
      <td>336</td>
      <td>166</td>
      <td>159</td>
      <td>Test Set</td>
      <td>4-unbalanced</td>
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<h2 id="Benchmark-model-experiments-settings">Benchmark model experiments settings<a class="anchor-link" href="#Benchmark-model-experiments-settings">&#182;</a></h2>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">df_resutlts</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">results</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;clasifiers&#39;</span><span class="p">,</span><span class="s1">&#39;Data&#39;</span><span class="p">,</span><span class="s1">&#39;Feauter_Generator&#39;</span> <span class="p">,</span><span class="s1">&#39;Best_Settings&#39;</span><span class="p">,</span><span class="s1">&#39;Settings&#39;</span> <span class="p">,</span> <span class="s1">&#39;UnOpt_accuracys&#39;</span> <span class="p">,</span> <span class="s1">&#39;UnOpt_fScores&#39;</span> <span class="p">,</span> <span class="s1">&#39;Opt_accuracys&#39;</span> <span class="p">,</span> <span class="s1">&#39;Opt_fScores&#39;</span><span class="p">])</span>
<span class="c1"># df_resutlts.index = df_resutlts.clasifiers.get_values()</span>
<span class="n">df_resutlts</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
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    <tr>
      <th>0</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'C': 1.0}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.452830</td>
      <td>0.448856</td>
      <td>0.452830</td>
      <td>0.448856</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.430818</td>
      <td>0.427069</td>
      <td>0.437107</td>
      <td>0.432830</td>
    </tr>
    <tr>
      <th>2</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'loss': u'hinge', u'C': 0.1, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.429245</td>
      <td>0.425035</td>
      <td>0.449686</td>
      <td>0.446250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.429245</td>
      <td>0.428450</td>
      <td>0.408805</td>
      <td>0.408631</td>
    </tr>
    <tr>
      <th>4</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.466981</td>
      <td>0.447112</td>
      <td>0.468553</td>
      <td>0.462307</td>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Opt_accuracys&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="o">.</span><span class="mi">69</span><span class="p">]</span>
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<table border="1" class="dataframe">
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      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
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    <tr>
      <th>70</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>count_ng2</td>
      <td>{u'C': 1.0}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.693347</td>
      <td>0.333914</td>
      <td>0.693347</td>
      <td>0.333914</td>
    </tr>
    <tr>
      <th>75</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.676838</td>
      <td>0.375684</td>
      <td>0.691846</td>
      <td>0.357411</td>
    </tr>
    <tr>
      <th>80</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>count_ng3</td>
      <td>{u'C': 2.7825594}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.691846</td>
      <td>0.324267</td>
      <td>0.691846</td>
      <td>0.327991</td>
    </tr>
    <tr>
      <th>105</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.691346</td>
      <td>0.365591</td>
      <td>0.691846</td>
      <td>0.362187</td>
    </tr>
    <tr>
      <th>115</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.687844</td>
      <td>0.364768</td>
      <td>0.690345</td>
      <td>0.361492</td>
    </tr>
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<h2 id="Standard-Classifiers-Performance">Standard Classifiers Performance<a class="anchor-link" href="#Standard-Classifiers-Performance">&#182;</a></h2>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">ax</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span> <span class="n">df_resutlts</span><span class="o">.</span><span class="n">clasifiers</span><span class="o">.</span><span class="n">get_values</span><span class="p">())</span>
<span class="c1"># ax.set_xticks([1,2,3,4,5,6,7,8,9])</span>
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<pre>[Text(0,0,&#39;Logistic Regression&#39;),
 Text(0,0,&#39;Passive Aggresive&#39;),
 Text(0,0,&#39;SVM&#39;),
 Text(0,0,&#39;Perceptron&#39;),
 Text(0,0,&#39;bnb&#39;),
 Text(0,0,&#39;sgd&#39;),
 Text(0,0,&#39;KNN&#39;),
 Text(0,0,&#39;RandomForest&#39;),
 Text(0,0,&#39;DecisionTree&#39;)]</pre>
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<p>It can be clearly seen how the performance increase after using the unbalanced data. The one with higherst performance is BNB.</p>

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<h3 id="String-Token-Count-Classification-results-and-evaluation-using-accuracy-and-F1-measure-in-the-Balanced-and-Un-Balanced-datasets">String Token Count Classification results and evaluation using accuracy and F1-measure in the Balanced and Un-Balanced datasets<a class="anchor-link" href="#String-Token-Count-Classification-results-and-evaluation-using-accuracy-and-F1-measure-in-the-Balanced-and-Un-Balanced-datasets">&#182;</a></h3>
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<div class="prompt input_prompt">In&nbsp;[27]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanced_scores</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Data&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-balanced&#39;</span><span class="p">]</span>
<span class="n">subsetDataFrame</span> <span class="o">=</span> <span class="n">balanced_scores</span><span class="p">[</span><span class="n">balanced_scores</span><span class="p">[</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">&#39;count_ng1&#39;</span><span class="p">,</span> <span class="s1">&#39;count_ng2&#39;</span><span class="p">,</span><span class="s1">&#39;count_ng3&#39;</span><span class="p">])]</span>
<span class="n">subsetDataFrame</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'C': 1.0}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.452830</td>
      <td>0.448856</td>
      <td>0.452830</td>
      <td>0.448856</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.430818</td>
      <td>0.427069</td>
      <td>0.437107</td>
      <td>0.432830</td>
    </tr>
    <tr>
      <th>2</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'loss': u'hinge', u'C': 0.1, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.429245</td>
      <td>0.425035</td>
      <td>0.449686</td>
      <td>0.446250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.429245</td>
      <td>0.428450</td>
      <td>0.408805</td>
      <td>0.408631</td>
    </tr>
    <tr>
      <th>4</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.466981</td>
      <td>0.447112</td>
      <td>0.468553</td>
      <td>0.462307</td>
    </tr>
    <tr>
      <th>5</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.422956</td>
      <td>0.416146</td>
      <td>0.427673</td>
      <td>0.426888</td>
    </tr>
    <tr>
      <th>6</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.287736</td>
      <td>0.260986</td>
      <td>0.295597</td>
      <td>0.267405</td>
    </tr>
    <tr>
      <th>7</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.386792</td>
      <td>0.384651</td>
      <td>0.421384</td>
      <td>0.413204</td>
    </tr>
    <tr>
      <th>8</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.364780</td>
      <td>0.364023</td>
      <td>0.303459</td>
      <td>0.256532</td>
    </tr>
    <tr>
      <th>9</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'alpha': 0.6, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.468553</td>
      <td>0.471401</td>
      <td>0.471698</td>
      <td>0.473933</td>
    </tr>
    <tr>
      <th>10</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'C': 1291.54967}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.444969</td>
      <td>0.440431</td>
      <td>0.437107</td>
      <td>0.433404</td>
    </tr>
    <tr>
      <th>11</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.438679</td>
      <td>0.434777</td>
      <td>0.440252</td>
      <td>0.436034</td>
    </tr>
    <tr>
      <th>12</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'loss': u'squared_hinge', u'C': 0.1, u'inter...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.448113</td>
      <td>0.443974</td>
      <td>0.437107</td>
      <td>0.433087</td>
    </tr>
    <tr>
      <th>13</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.437107</td>
      <td>0.433213</td>
      <td>0.397799</td>
      <td>0.394887</td>
    </tr>
    <tr>
      <th>14</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.430818</td>
      <td>0.390543</td>
      <td>0.462264</td>
      <td>0.440915</td>
    </tr>
    <tr>
      <th>15</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.435535</td>
      <td>0.432983</td>
      <td>0.440252</td>
      <td>0.440570</td>
    </tr>
    <tr>
      <th>16</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.279874</td>
      <td>0.249282</td>
      <td>0.279874</td>
      <td>0.246131</td>
    </tr>
    <tr>
      <th>17</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.380503</td>
      <td>0.375482</td>
      <td>0.402516</td>
      <td>0.383214</td>
    </tr>
    <tr>
      <th>18</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.356918</td>
      <td>0.356461</td>
      <td>0.305031</td>
      <td>0.255784</td>
    </tr>
    <tr>
      <th>19</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'alpha': 1.0, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.487421</td>
      <td>0.490580</td>
      <td>0.487421</td>
      <td>0.490580</td>
    </tr>
    <tr>
      <th>20</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'C': 2.7825594}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.438679</td>
      <td>0.433627</td>
      <td>0.437107</td>
      <td>0.432645</td>
    </tr>
    <tr>
      <th>21</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.446541</td>
      <td>0.442512</td>
      <td>0.446541</td>
      <td>0.442338</td>
    </tr>
    <tr>
      <th>22</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'loss': u'squared_hinge', u'C': 0.1, u'inter...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.433962</td>
      <td>0.428767</td>
      <td>0.441824</td>
      <td>0.436675</td>
    </tr>
    <tr>
      <th>23</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.429245</td>
      <td>0.425289</td>
      <td>0.415094</td>
      <td>0.413750</td>
    </tr>
    <tr>
      <th>24</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.394654</td>
      <td>0.336266</td>
      <td>0.448113</td>
      <td>0.411186</td>
    </tr>
    <tr>
      <th>25</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.451258</td>
      <td>0.451581</td>
      <td>0.430818</td>
      <td>0.425462</td>
    </tr>
    <tr>
      <th>26</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.275157</td>
      <td>0.232937</td>
      <td>0.276730</td>
      <td>0.238596</td>
    </tr>
    <tr>
      <th>27</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.364780</td>
      <td>0.353901</td>
      <td>0.408805</td>
      <td>0.401336</td>
    </tr>
    <tr>
      <th>28</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.355346</td>
      <td>0.354430</td>
      <td>0.301887</td>
      <td>0.253779</td>
    </tr>
    <tr>
      <th>29</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'alpha': 0.8, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.487421</td>
      <td>0.490415</td>
      <td>0.487421</td>
      <td>0.490497</td>
    </tr>
  </tbody>
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<div class="prompt input_prompt">In&nbsp;[31]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanced_scores</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Data&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
<span class="n">subsetDataFrame</span> <span class="o">=</span> <span class="n">balanced_scores</span><span class="p">[</span><span class="n">balanced_scores</span><span class="p">[</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">&#39;tfidf_ng1&#39;</span><span class="p">])]</span>
<span class="n">subsetDataFrame</span><span class="p">[</span><span class="n">subsetDataFrame</span><span class="p">[</span><span class="s1">&#39;Opt_accuracys&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="o">.</span><span class="mi">680</span><span class="p">]</span>
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    <div class="prompt output_prompt">Out[31]:</div>


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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>94</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.6, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669835</td>
      <td>0.204974</td>
      <td>0.680840</td>
      <td>0.257156</td>
    </tr>
    <tr>
      <th>95</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.682341</td>
      <td>0.344768</td>
      <td>0.682841</td>
      <td>0.356516</td>
    </tr>
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<div class="cell border-box-sizing code_cell rendered">
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<div class="prompt input_prompt">In&nbsp;[33]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># for key in scores_collection.keys():</span>
<span class="c1">#     print(&quot;\n&quot; +&quot;=&quot;*40)</span>
<span class="c1">#     print(key)</span>
<span class="c1">#     print(&quot;-&quot;*40)</span>
<span class="c1">#     print(scores_collection[key])</span>
<span class="n">balanced_scores</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Data&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-balanced&#39;</span><span class="p">]</span>
<span class="n">subsetDataFrame</span> <span class="o">=</span> <span class="n">balanced_scores</span><span class="p">[</span><span class="n">balanced_scores</span><span class="p">[</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">&#39;count_ng1&#39;</span><span class="p">,</span> <span class="s1">&#39;count_ng2&#39;</span><span class="p">,</span><span class="s1">&#39;count_ng3&#39;</span><span class="p">])]</span>
<span class="n">subsetDataFrame</span>
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    <div class="prompt output_prompt">Out[33]:</div>


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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'C': 1.0}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.452830</td>
      <td>0.448856</td>
      <td>0.452830</td>
      <td>0.448856</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.430818</td>
      <td>0.427069</td>
      <td>0.437107</td>
      <td>0.432830</td>
    </tr>
    <tr>
      <th>2</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'loss': u'hinge', u'C': 0.1, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.429245</td>
      <td>0.425035</td>
      <td>0.449686</td>
      <td>0.446250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.429245</td>
      <td>0.428450</td>
      <td>0.408805</td>
      <td>0.408631</td>
    </tr>
    <tr>
      <th>4</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.466981</td>
      <td>0.447112</td>
      <td>0.468553</td>
      <td>0.462307</td>
    </tr>
    <tr>
      <th>5</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.422956</td>
      <td>0.416146</td>
      <td>0.427673</td>
      <td>0.426888</td>
    </tr>
    <tr>
      <th>6</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.287736</td>
      <td>0.260986</td>
      <td>0.295597</td>
      <td>0.267405</td>
    </tr>
    <tr>
      <th>7</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.386792</td>
      <td>0.384651</td>
      <td>0.421384</td>
      <td>0.413204</td>
    </tr>
    <tr>
      <th>8</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.364780</td>
      <td>0.364023</td>
      <td>0.303459</td>
      <td>0.256532</td>
    </tr>
    <tr>
      <th>9</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng1</td>
      <td>{u'alpha': 0.6, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.468553</td>
      <td>0.471401</td>
      <td>0.471698</td>
      <td>0.473933</td>
    </tr>
    <tr>
      <th>10</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'C': 1291.54967}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.444969</td>
      <td>0.440431</td>
      <td>0.437107</td>
      <td>0.433404</td>
    </tr>
    <tr>
      <th>11</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.438679</td>
      <td>0.434777</td>
      <td>0.440252</td>
      <td>0.436034</td>
    </tr>
    <tr>
      <th>12</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'loss': u'squared_hinge', u'C': 0.1, u'inter...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.448113</td>
      <td>0.443974</td>
      <td>0.437107</td>
      <td>0.433087</td>
    </tr>
    <tr>
      <th>13</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.437107</td>
      <td>0.433213</td>
      <td>0.397799</td>
      <td>0.394887</td>
    </tr>
    <tr>
      <th>14</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.430818</td>
      <td>0.390543</td>
      <td>0.462264</td>
      <td>0.440915</td>
    </tr>
    <tr>
      <th>15</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.435535</td>
      <td>0.432983</td>
      <td>0.440252</td>
      <td>0.440570</td>
    </tr>
    <tr>
      <th>16</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.279874</td>
      <td>0.249282</td>
      <td>0.279874</td>
      <td>0.246131</td>
    </tr>
    <tr>
      <th>17</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.380503</td>
      <td>0.375482</td>
      <td>0.402516</td>
      <td>0.383214</td>
    </tr>
    <tr>
      <th>18</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.356918</td>
      <td>0.356461</td>
      <td>0.305031</td>
      <td>0.255784</td>
    </tr>
    <tr>
      <th>19</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng2</td>
      <td>{u'alpha': 1.0, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.487421</td>
      <td>0.490580</td>
      <td>0.487421</td>
      <td>0.490580</td>
    </tr>
    <tr>
      <th>20</th>
      <td>Logistic Regression</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'C': 2.7825594}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.438679</td>
      <td>0.433627</td>
      <td>0.437107</td>
      <td>0.432645</td>
    </tr>
    <tr>
      <th>21</th>
      <td>Passive Aggresive</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.446541</td>
      <td>0.442512</td>
      <td>0.446541</td>
      <td>0.442338</td>
    </tr>
    <tr>
      <th>22</th>
      <td>SVM</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'loss': u'squared_hinge', u'C': 0.1, u'inter...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.433962</td>
      <td>0.428767</td>
      <td>0.441824</td>
      <td>0.436675</td>
    </tr>
    <tr>
      <th>23</th>
      <td>Perceptron</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.429245</td>
      <td>0.425289</td>
      <td>0.415094</td>
      <td>0.413750</td>
    </tr>
    <tr>
      <th>24</th>
      <td>bnb</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'binarize': 0.0, u'alpha': 0.2, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.394654</td>
      <td>0.336266</td>
      <td>0.448113</td>
      <td>0.411186</td>
    </tr>
    <tr>
      <th>25</th>
      <td>sgd</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.451258</td>
      <td>0.451581</td>
      <td>0.430818</td>
      <td>0.425462</td>
    </tr>
    <tr>
      <th>26</th>
      <td>KNN</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.275157</td>
      <td>0.232937</td>
      <td>0.276730</td>
      <td>0.238596</td>
    </tr>
    <tr>
      <th>27</th>
      <td>RandomForest</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.364780</td>
      <td>0.353901</td>
      <td>0.408805</td>
      <td>0.401336</td>
    </tr>
    <tr>
      <th>28</th>
      <td>DecisionTree</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.355346</td>
      <td>0.354430</td>
      <td>0.301887</td>
      <td>0.253779</td>
    </tr>
    <tr>
      <th>29</th>
      <td>mnb</td>
      <td>4-balanced</td>
      <td>count_ng3</td>
      <td>{u'alpha': 0.8, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.487421</td>
      <td>0.490415</td>
      <td>0.487421</td>
      <td>0.490497</td>
    </tr>
  </tbody>
</table>
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<h3 id="Tf-Idf-Classification-results-and-evaluation-using-accuracy-and-F1-measure-in-the-Balanced-and-Un-Balanced-datasets">Tf-Idf Classification results and evaluation using accuracy and F1-measure in the Balanced and Un-Balanced datasets<a class="anchor-link" href="#Tf-Idf-Classification-results-and-evaluation-using-accuracy-and-F1-measure-in-the-Balanced-and-Un-Balanced-datasets">&#182;</a></h3>
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<div class="prompt input_prompt">In&nbsp;[35]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanced_scores</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Data&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
<span class="n">subsetDataFrame</span> <span class="o">=</span> <span class="n">balanced_scores</span><span class="p">[</span><span class="n">balanced_scores</span><span class="p">[</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">&#39;tfidf_ng1&#39;</span><span class="p">,</span> <span class="s1">&#39;tfidf_ng2&#39;</span><span class="p">,</span><span class="s1">&#39;tfidf_ng3&#39;</span><span class="p">])]</span>
<span class="n">subsetDataFrame</span>
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    <div class="prompt output_prompt">Out[35]:</div>


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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>90</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'C': 21.5443469}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.679840</td>
      <td>0.260643</td>
      <td>0.671336</td>
      <td>0.382344</td>
    </tr>
    <tr>
      <th>91</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.641321</td>
      <td>0.377501</td>
      <td>0.672836</td>
      <td>0.383046</td>
    </tr>
    <tr>
      <th>92</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.671336</td>
      <td>0.380616</td>
      <td>0.675338</td>
      <td>0.375602</td>
    </tr>
    <tr>
      <th>93</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.629815</td>
      <td>0.372720</td>
      <td>0.613307</td>
      <td>0.359975</td>
    </tr>
    <tr>
      <th>94</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.6, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669835</td>
      <td>0.204974</td>
      <td>0.680840</td>
      <td>0.257156</td>
    </tr>
    <tr>
      <th>95</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.682341</td>
      <td>0.344768</td>
      <td>0.682841</td>
      <td>0.356516</td>
    </tr>
    <tr>
      <th>96</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.666833</td>
      <td>0.330190</td>
      <td>0.677839</td>
      <td>0.309907</td>
    </tr>
    <tr>
      <th>97</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.667334</td>
      <td>0.292196</td>
      <td>0.678339</td>
      <td>0.277080</td>
    </tr>
    <tr>
      <th>98</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.595798</td>
      <td>0.319047</td>
      <td>0.675338</td>
      <td>0.241380</td>
    </tr>
    <tr>
      <th>99</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.677339</td>
      <td>0.236297</td>
    </tr>
    <tr>
      <th>100</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'C': 3593.81366}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.676338</td>
      <td>0.238771</td>
      <td>0.679340</td>
      <td>0.398504</td>
    </tr>
    <tr>
      <th>101</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.673837</td>
      <td>0.401551</td>
      <td>0.687344</td>
      <td>0.396603</td>
    </tr>
    <tr>
      <th>102</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.687344</td>
      <td>0.381106</td>
      <td>0.688344</td>
      <td>0.386825</td>
    </tr>
    <tr>
      <th>103</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.641321</td>
      <td>0.385543</td>
      <td>0.635818</td>
      <td>0.355513</td>
    </tr>
    <tr>
      <th>104</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'binarize': 0.0, u'alpha': 0.4, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.670835</td>
      <td>0.218285</td>
    </tr>
    <tr>
      <th>105</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.691346</td>
      <td>0.365591</td>
      <td>0.691846</td>
      <td>0.362187</td>
    </tr>
    <tr>
      <th>106</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.666833</td>
      <td>0.353227</td>
      <td>0.677339</td>
      <td>0.315188</td>
    </tr>
    <tr>
      <th>107</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.677339</td>
      <td>0.278050</td>
      <td>0.670835</td>
      <td>0.267045</td>
    </tr>
    <tr>
      <th>108</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.607804</td>
      <td>0.317850</td>
      <td>0.673837</td>
      <td>0.248463</td>
    </tr>
    <tr>
      <th>109</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.670335</td>
      <td>0.205133</td>
      <td>0.674337</td>
      <td>0.230197</td>
    </tr>
    <tr>
      <th>110</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'C': 3593.81366}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.675338</td>
      <td>0.231325</td>
      <td>0.677339</td>
      <td>0.393118</td>
    </tr>
    <tr>
      <th>111</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.676838</td>
      <td>0.399347</td>
      <td>0.682341</td>
      <td>0.386897</td>
    </tr>
    <tr>
      <th>112</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.685843</td>
      <td>0.372079</td>
      <td>0.686843</td>
      <td>0.384658</td>
    </tr>
    <tr>
      <th>113</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.634317</td>
      <td>0.377455</td>
      <td>0.643322</td>
      <td>0.381698</td>
    </tr>
    <tr>
      <th>114</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'binarize': 0.0, u'alpha': 0.4, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.670335</td>
      <td>0.215383</td>
    </tr>
    <tr>
      <th>115</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.687844</td>
      <td>0.364768</td>
      <td>0.690345</td>
      <td>0.361492</td>
    </tr>
    <tr>
      <th>116</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.667334</td>
      <td>0.359992</td>
      <td>0.677839</td>
      <td>0.314923</td>
    </tr>
    <tr>
      <th>117</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.670335</td>
      <td>0.280955</td>
      <td>0.676338</td>
      <td>0.277015</td>
    </tr>
    <tr>
      <th>118</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.587794</td>
      <td>0.313878</td>
      <td>0.673837</td>
      <td>0.248463</td>
    </tr>
    <tr>
      <th>119</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.669335</td>
      <td>0.203504</td>
      <td>0.674337</td>
      <td>0.234698</td>
    </tr>
  </tbody>
</table>
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<div class="prompt input_prompt">In&nbsp;[36]:</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">balanced_scores</span> <span class="o">=</span> <span class="n">df_resutlts</span><span class="p">[</span><span class="n">df_resutlts</span><span class="p">[</span><span class="s1">&#39;Data&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;4-unbalanced&#39;</span><span class="p">]</span>
<span class="n">subsetDataFrame</span> <span class="o">=</span> <span class="n">balanced_scores</span><span class="p">[</span><span class="n">balanced_scores</span><span class="p">[</span><span class="s1">&#39;Feauter_Generator&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="s1">&#39;tfidf_ng1&#39;</span><span class="p">,</span> <span class="s1">&#39;tfidf_ng2&#39;</span><span class="p">,</span><span class="s1">&#39;tfidf_ng3&#39;</span><span class="p">])]</span>
<span class="n">subsetDataFrame</span>
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    <div class="prompt output_prompt">Out[36]:</div>


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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>clasifiers</th>
      <th>Data</th>
      <th>Feauter_Generator</th>
      <th>Best_Settings</th>
      <th>Settings</th>
      <th>UnOpt_accuracys</th>
      <th>UnOpt_fScores</th>
      <th>Opt_accuracys</th>
      <th>Opt_fScores</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>90</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'C': 21.5443469}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.679840</td>
      <td>0.260643</td>
      <td>0.671336</td>
      <td>0.382344</td>
    </tr>
    <tr>
      <th>91</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.641321</td>
      <td>0.377501</td>
      <td>0.672836</td>
      <td>0.383046</td>
    </tr>
    <tr>
      <th>92</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.671336</td>
      <td>0.380616</td>
      <td>0.675338</td>
      <td>0.375602</td>
    </tr>
    <tr>
      <th>93</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.629815</td>
      <td>0.372720</td>
      <td>0.613307</td>
      <td>0.359975</td>
    </tr>
    <tr>
      <th>94</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'binarize': 0.0, u'alpha': 0.6, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669835</td>
      <td>0.204974</td>
      <td>0.680840</td>
      <td>0.257156</td>
    </tr>
    <tr>
      <th>95</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.682341</td>
      <td>0.344768</td>
      <td>0.682841</td>
      <td>0.356516</td>
    </tr>
    <tr>
      <th>96</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.666833</td>
      <td>0.330190</td>
      <td>0.677839</td>
      <td>0.309907</td>
    </tr>
    <tr>
      <th>97</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.667334</td>
      <td>0.292196</td>
      <td>0.678339</td>
      <td>0.277080</td>
    </tr>
    <tr>
      <th>98</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.595798</td>
      <td>0.319047</td>
      <td>0.675338</td>
      <td>0.241380</td>
    </tr>
    <tr>
      <th>99</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng1</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.677339</td>
      <td>0.236297</td>
    </tr>
    <tr>
      <th>100</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'C': 3593.81366}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.676338</td>
      <td>0.238771</td>
      <td>0.679340</td>
      <td>0.398504</td>
    </tr>
    <tr>
      <th>101</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.673837</td>
      <td>0.401551</td>
      <td>0.687344</td>
      <td>0.396603</td>
    </tr>
    <tr>
      <th>102</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.687344</td>
      <td>0.381106</td>
      <td>0.688344</td>
      <td>0.386825</td>
    </tr>
    <tr>
      <th>103</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.641321</td>
      <td>0.385543</td>
      <td>0.635818</td>
      <td>0.355513</td>
    </tr>
    <tr>
      <th>104</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'binarize': 0.0, u'alpha': 0.4, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.670835</td>
      <td>0.218285</td>
    </tr>
    <tr>
      <th>105</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.691346</td>
      <td>0.365591</td>
      <td>0.691846</td>
      <td>0.362187</td>
    </tr>
    <tr>
      <th>106</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.666833</td>
      <td>0.353227</td>
      <td>0.677339</td>
      <td>0.315188</td>
    </tr>
    <tr>
      <th>107</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.677339</td>
      <td>0.278050</td>
      <td>0.670835</td>
      <td>0.267045</td>
    </tr>
    <tr>
      <th>108</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.607804</td>
      <td>0.317850</td>
      <td>0.673837</td>
      <td>0.248463</td>
    </tr>
    <tr>
      <th>109</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng2</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.670335</td>
      <td>0.205133</td>
      <td>0.674337</td>
      <td>0.230197</td>
    </tr>
    <tr>
      <th>110</th>
      <td>Logistic Regression</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'C': 3593.81366}</td>
      <td>{u'warm_start': False, u'C': 1.0, u'n_jobs': 1...</td>
      <td>0.675338</td>
      <td>0.231325</td>
      <td>0.677339</td>
      <td>0.393118</td>
    </tr>
    <tr>
      <th>111</th>
      <td>Passive Aggresive</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'C'...</td>
      <td>0.676838</td>
      <td>0.399347</td>
      <td>0.682341</td>
      <td>0.386897</td>
    </tr>
    <tr>
      <th>112</th>
      <td>SVM</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'loss': u'hinge', u'C': 0.7, u'intercept_sca...</td>
      <td>{u'loss': u'squared_hinge', u'C': 1.0, u'verbo...</td>
      <td>0.685843</td>
      <td>0.372079</td>
      <td>0.686843</td>
      <td>0.384658</td>
    </tr>
    <tr>
      <th>113</th>
      <td>Perceptron</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': True, u'eta0': 0.5, u'shuffle'...</td>
      <td>{u'warm_start': False, u'n_iter': None, u'n_jo...</td>
      <td>0.634317</td>
      <td>0.377455</td>
      <td>0.643322</td>
      <td>0.381698</td>
    </tr>
    <tr>
      <th>114</th>
      <td>bnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'binarize': 0.0, u'alpha': 0.4, u'fit_prior'...</td>
      <td>{u'binarize': 0.0, u'alpha': 1.0, u'fit_prior'...</td>
      <td>0.669335</td>
      <td>0.200540</td>
      <td>0.670335</td>
      <td>0.215383</td>
    </tr>
    <tr>
      <th>115</th>
      <td>sgd</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>{u'warm_start': False, u'loss': u'hinge', u'n_...</td>
      <td>0.687844</td>
      <td>0.364768</td>
      <td>0.690345</td>
      <td>0.361492</td>
    </tr>
    <tr>
      <th>116</th>
      <td>KNN</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'n_neighbors': 8, u'n_jobs': 1, u'algorithm'...</td>
      <td>{u'n_neighbors': 5, u'n_jobs': 1, u'algorithm'...</td>
      <td>0.667334</td>
      <td>0.359992</td>
      <td>0.677839</td>
      <td>0.314923</td>
    </tr>
    <tr>
      <th>117</th>
      <td>RandomForest</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>{u'warm_start': False, u'oob_score': False, u'...</td>
      <td>0.670335</td>
      <td>0.280955</td>
      <td>0.676338</td>
      <td>0.277015</td>
    </tr>
    <tr>
      <th>118</th>
      <td>DecisionTree</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'presort': False, u'splitter': u'best', u'ra...</td>
      <td>{u'presort': False, u'splitter': u'best', u'mi...</td>
      <td>0.587794</td>
      <td>0.313878</td>
      <td>0.673837</td>
      <td>0.248463</td>
    </tr>
    <tr>
      <th>119</th>
      <td>mnb</td>
      <td>4-unbalanced</td>
      <td>tfidf_ng3</td>
      <td>{u'alpha': 0.3, u'fit_prior': True}</td>
      <td>{u'alpha': 1.0, u'fit_prior': True, u'class_pr...</td>
      <td>0.669335</td>
      <td>0.203504</td>
      <td>0.674337</td>
      <td>0.234698</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># from sklearn.linear_model import LogisticRegression</span>
<span class="c1"># from sklearn.ensemble import RandomForestClassifier</span>
<span class="c1"># from sklearn.svm import LinearSVC</span>
<span class="c1"># from sklearn.cross_validation import cross_val_score</span>
<span class="c1"># models = [</span>
<span class="c1">#     LogisticRegression(random_state=0),</span>
<span class="c1">#     PassiveAggressiveClassifier(random_state=0),</span>
<span class="c1">#     LinearSVC(tol=1e-3, random_state=0),</span>
<span class="c1">#     Perceptron(n_iter=100, random_state=0),</span>
<span class="c1">#     BernoulliNB(binarize=0.5),</span>
<span class="c1">#     SGDClassifier(loss=&quot;hinge&quot;, penalty=&quot;l2&quot;, random_state=0),</span>
<span class="c1">#     KNeighborsClassifier(n_neighbors=5, metric=&#39;euclidean&#39;, random_state=0),</span>
<span class="c1">#     RandomForestClassifier(n_estimators=200, max_depth=3, random_state=0),</span>
<span class="c1">#     DecisionTreeClassifier(),</span>
<span class="c1">#     MultinomialNB()</span>
<span class="c1"># ]</span>
<span class="c1"># CV = 5</span>
<span class="c1"># cv_df = pd.DataFrame(index=range(CV * len(models)))</span>
<span class="c1"># entries = []</span>
<span class="c1"># for model in models:</span>
<span class="c1">#     model_name = model.__class__.__name__</span>
<span class="c1">#     accuracies = cross_val_score(model, X_train, y_train, scoring=&#39;accuracy&#39;, cv=CV)</span>
<span class="c1">#     for fold_idx, accuracy in enumerate(accuracies):</span>
<span class="c1">#         entries.append((model_name, fold_idx, accuracy))</span>
        
        
<span class="c1"># cv_df = pd.DataFrame(entries, columns=[&#39;model_name&#39;, &#39;fold_idx&#39;, &#39;accuracy&#39;])</span>
<span class="c1"># cv_df.head()</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># cv_df[cv_df[&#39;accuracy&#39;] &gt; .68]</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># import seaborn as sns</span>
<span class="c1"># sns.boxplot(x=&#39;model_name&#39;, y=&#39;accuracy&#39;, data=cv_df)</span>
<span class="c1"># sns.stripplot(x=&#39;model_name&#39;, y=&#39;accuracy&#39;, data=cv_df, </span>
<span class="c1">#               size=8, jitter=True, edgecolor=&quot;gray&quot;, linewidth=2)</span>
<span class="c1"># # plt.show()</span>
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