notebooks/Chapter_05.ipynb

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   "source": [
    "import pandas as pd\n",
    "\n",
    "tweet_df = pd.read_csv('../data/cleaned_airline_tweets.csv')"
   ]
  },
  {
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   "id": "extensive-medicaid",
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       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@VirginAmerica I &amp;lt;3 pretty graphics. so muc...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@VirginAmerica So excited for my first cross c...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I ❤️ flying @VirginAmerica. ☺️👍</td>\n",
       "      <td>positive</td>\n",
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       "                                                text sentiment\n",
       "0                @VirginAmerica What @dhepburn said.   neutral\n",
       "1  @VirginAmerica it was amazing, and arrived an ...  positive\n",
       "2  @VirginAmerica I &lt;3 pretty graphics. so muc...  positive\n",
       "3  @VirginAmerica So excited for my first cross c...  positive\n",
       "4                    I ❤️ flying @VirginAmerica. ☺️👍  positive"
      ]
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   "source": [
    "tweet_df.head()"
   ]
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.navbar-brand,.navbar-inverse .navbar-nav&gt;li&gt;a{text-shadow:0 -1px 0 rgba(0,0,0,.25)}.navbar-fixed-bottom,.navbar-fixed-top,.navbar-static-top{border-radius:0}@media (max-width:767px){.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a,.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a:focus,.navbar .navbar-nav .open .dropdown-menu&gt;.active&gt;a:hover{color:#fff;background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2e6da4&#x27;, GradientType=0);background-repeat:repeat-x}}.alert{text-shadow:0 1px 0 rgba(255,255,255,.2);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.25),0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 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endColorstr=&#x27;#ffe8e8e8&#x27;, GradientType=0);background-repeat:repeat-x}.panel-primary&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-o-linear-gradient(top,#337ab7 0,#2e6da4 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#337ab7),to(#2e6da4));background-image:linear-gradient(to bottom,#337ab7 0,#2e6da4 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ff337ab7&#x27;, endColorstr=&#x27;#ff2e6da4&#x27;, GradientType=0);background-repeat:repeat-x}.panel-success&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-o-linear-gradient(top,#dff0d8 0,#d0e9c6 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#dff0d8),to(#d0e9c6));background-image:linear-gradient(to bottom,#dff0d8 0,#d0e9c6 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffdff0d8&#x27;, endColorstr=&#x27;#ffd0e9c6&#x27;, GradientType=0);background-repeat:repeat-x}.panel-info&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-o-linear-gradient(top,#d9edf7 0,#c4e3f3 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#d9edf7),to(#c4e3f3));background-image:linear-gradient(to bottom,#d9edf7 0,#c4e3f3 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffd9edf7&#x27;, endColorstr=&#x27;#ffc4e3f3&#x27;, GradientType=0);background-repeat:repeat-x}.panel-warning&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-o-linear-gradient(top,#fcf8e3 0,#faf2cc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#fcf8e3),to(#faf2cc));background-image:linear-gradient(to bottom,#fcf8e3 0,#faf2cc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fffcf8e3&#x27;, endColorstr=&#x27;#fffaf2cc&#x27;, GradientType=0);background-repeat:repeat-x}.panel-danger&gt;.panel-heading{background-image:-webkit-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-o-linear-gradient(top,#f2dede 0,#ebcccc 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#f2dede),to(#ebcccc));background-image:linear-gradient(to bottom,#f2dede 0,#ebcccc 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#fff2dede&#x27;, endColorstr=&#x27;#ffebcccc&#x27;, GradientType=0);background-repeat:repeat-x}.well{background-image:-webkit-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-o-linear-gradient(top,#e8e8e8 0,#f5f5f5 100%);background-image:-webkit-gradient(linear,left top,left bottom,from(#e8e8e8),to(#f5f5f5));background-image:linear-gradient(to bottom,#e8e8e8 0,#f5f5f5 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr=&#x27;#ffe8e8e8&#x27;, endColorstr=&#x27;#fff5f5f5&#x27;, GradientType=0);background-repeat:repeat-x;border-color:#dcdcdc;-webkit-box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 3px rgba(0,0,0,.05),0 1px 0 rgba(255,255,255,.1)}\n",
       "/*# sourceMappingURL=bootstrap-theme.min.css.map */        &lt;/style&gt;&lt;style&gt;body {\n",
       "  padding-top: 80px;\n",
       "}\n",
       ".content .container-fluid{\n",
       "    margin-left: 20px !important;\n",
       "    margin-right: 20px !important;\n",
       "    margin-bottom: 20px;\n",
       "}\n",
       "\n",
       ".page-header{\n",
       "    border:0 !important;\n",
       "}\n",
       "\n",
       ".row.variable, .section-items &gt; .row {\n",
       "    border: 1px solid #e1e1e8;\n",
       "    border-top: hidden;\n",
       "}\n",
       "\n",
       ".row.spacing{\n",
       "    padding: 2em 1em;\n",
       "}\n",
       "\n",
       ".row.header {\n",
       "    border-bottom: 1px solid #e1e1e8;\n",
       "    /*background-color: #f5f5f5;*/\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       "\n",
       ".dl-horizontal dt {\n",
       "    text-align: left;\n",
       "    padding-right: 1em;\n",
       "    white-space: normal;\n",
       "}\n",
       "\n",
       ".dl-horizontal dd {\n",
       "    margin-left: 0;\n",
       "}\n",
       "\n",
       ".col-md-12 {\n",
       "    padding-left: 2em;\n",
       "}\n",
       "\n",
       ".indent {\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".center-img {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       "/* Table example_values */\n",
       "table.example_values {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".example_values th {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".example_values tr, .example_values td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "    color: #555;\n",
       "}\n",
       "\n",
       "/* STATS */\n",
       "table.stats, table.sample, table.duplicate{\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats tr, .sample tr, .duplicate tr {\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th, .stats td{\n",
       "    color: #555;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       ".stats th {\n",
       "    padding: 0 2em 0 0;\n",
       "    font-weight: 600;\n",
       "}\n",
       "\n",
       ".stats td {\n",
       "    padding: 1px;\n",
       "}\n",
       "\n",
       "\n",
       "/* Sample table */\n",
       "table.sample, table.duplicate{\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 1em;\n",
       "}\n",
       "\n",
       ".sample td, .sample th, .duplicate td, .duplicate th {\n",
       "    padding: 0.5em;\n",
       "    white-space: nowrap;\n",
       "    border: 0;\n",
       "\n",
       "}\n",
       "\n",
       ".sample thead, .duplicate thead {\n",
       "    border-top: 0;\n",
       "    border-bottom: 2px solid #ddd;\n",
       "}\n",
       "\n",
       ".sample td, .duplicate td {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       "\n",
       "/* There is no good solution available to make the divs equal height and then center ... */\n",
       ".histogram {\n",
       "    margin-top: 3em;\n",
       "}\n",
       "\n",
       "/* Freq table */\n",
       "table.freq {\n",
       "    margin-bottom: 2em;\n",
       "    border: 0;\n",
       "}\n",
       "\n",
       "table.freq th, table.freq tr, table.freq td {\n",
       "    border: 0;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".freq thead {\n",
       "    font-weight: 600;\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "\n",
       "}\n",
       "\n",
       "/* Freq mini */\n",
       ".freq.mini td {\n",
       "    width: 50%;\n",
       "    padding: 1px;\n",
       "    font-size: 12px;\n",
       "\n",
       "}\n",
       "\n",
       "table.freq.mini {\n",
       "    width: 100%;\n",
       "}\n",
       "\n",
       ".freq.mini th {\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "    white-space: nowrap;\n",
       "    max-width: 5em;\n",
       "    font-weight: 400;\n",
       "    text-align: right;\n",
       "    padding-right: 0.5em;\n",
       "}\n",
       "\n",
       "/* Message classes */\n",
       ".missing {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".alert, .alert &gt; th, .alert &gt; td {\n",
       "    color: #a94442;\n",
       "}\n",
       "\n",
       ".ignore {\n",
       "    opacity: 0.4;\n",
       "}\n",
       "\n",
       "/* Bars in tables */\n",
       ".freq.table{\n",
       "    table-layout: fixed;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(1), .freq:not(.mini) tr th:nth-child(1){\n",
       "    width: auto;\n",
       "    max-width: none;\n",
       "\n",
       "    white-space: nowrap;\n",
       "    overflow: hidden;\n",
       "    text-overflow: ellipsis;\n",
       "}\n",
       "\n",
       ".freq:not(.mini) tr td:nth-child(2), .freq:not(.mini) tr td:nth-child(3), .freq:not(.mini) tr th:nth-child(2), .freq:not(.mini) tr th:nth-child(3){\n",
       "    width: 100px;\n",
       "    text-align: right;\n",
       "}\n",
       ".freq:not(.mini) tr td:nth-child(4), .freq:not(.mini) tr th:nth-child(4){\n",
       "    width:200px;\n",
       "}\n",
       "\n",
       ".freq .bar {\n",
       "    float: left;\n",
       "    width: 0;\n",
       "    height: 100%;\n",
       "    line-height: 20px;\n",
       "    color: #fff;\n",
       "    text-align: center;\n",
       "    background-color: #337ab7;\n",
       "    border-radius: 3px;\n",
       "    margin-right: 4px;\n",
       "}\n",
       "\n",
       ".other .bar {\n",
       "    background-color: #999;\n",
       "}\n",
       "\n",
       ".missing .bar {\n",
       "    background-color: #a94442;\n",
       "}\n",
       "\n",
       ".tooltip-inner {\n",
       "    width: 100%;\n",
       "    white-space: nowrap;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".extrapadding {\n",
       "    padding: 2em;\n",
       "}\n",
       "\n",
       ".variable .h4 {\n",
       "    text-overflow: ellipsis;\n",
       "    display:inline-block;\n",
       "    width: calc(90%); /* The trick is here! */\n",
       "    overflow:hidden;\n",
       "}\n",
       "\n",
       ".variable.ignore .h4{\n",
       "    text-decoration: line-through;\n",
       "}\n",
       "\n",
       ".table-responsive{\n",
       "    overflow: scroll;\n",
       "    width: 100%;\n",
       "    overflow-y: hidden;\n",
       "}\n",
       ".img-responsive{\n",
       "    max-width: 99%;\n",
       "}\n",
       ".footer-text{\n",
       "    padding:20px;\n",
       "}\n",
       "\n",
       "table.list-warnings td{\n",
       "    padding-right:10px;\n",
       "}\n",
       "\n",
       "a.anchor-pos {\n",
       "    display: block;\n",
       "    position: relative;\n",
       "    top: -70px;\n",
       "    visibility: hidden;\n",
       "}\n",
       "\n",
       "a.anchor-pos-variable{\n",
       "    /*top: -70px;*/\n",
       "}\n",
       "\n",
       "#sample-container, #duplicate-container{\n",
       "    overflow: auto;\n",
       "    width: 100%;\n",
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       "}&lt;/style&gt;&lt;/head&gt;&lt;body&gt;&lt;a class=anchor-pos id=top&gt;&lt;/a&gt;&lt;nav class=&quot;navbar navbar-default navbar-fixed-top&quot;&gt;&lt;div class=container-fluid&gt;&lt;div class=navbar-header&gt;&lt;button type=button class=&quot;navbar-toggle collapsed&quot; data-toggle=collapse data-target=#navbar aria-expanded=false aria-controls=navbar&gt;&lt;span class=sr-only&gt;Toggle navigation&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;span class=icon-bar&gt;&lt;/span&gt;&lt;/button&gt;&lt;a class=&quot;navbar-brand anchor&quot; href=#top&gt;Tweets Report&lt;/a&gt;&lt;/div&gt;&lt;div id=navbar class=&quot;navbar-collapse collapse&quot;&gt;&lt;ul class=&quot;nav navbar-nav navbar-right&quot;&gt;&lt;li&gt;&lt;a class=anchor href=#overview&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#variables&gt;Variables&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#missing&gt;Missing values&lt;/a&gt;&lt;/li&gt;&lt;li&gt;&lt;a class=anchor href=#sample&gt;Sample&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;/div&gt;&lt;/nav&gt;&lt;div class=content&gt;&lt;div class=container&gt;&lt;div class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=overview&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Overview&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-pills&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#overview-dataset_overview aria-controls=overview-dataset_overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-alerts aria-controls=overview-alerts role=tab data-toggle=tab&gt;Alerts &lt;span class=badge&gt;3&lt;/span&gt;&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#overview-reproduction aria-controls=overview-reproduction role=tab data-toggle=tab&gt;Reproduction&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content style=&quot;padding-top: 10px;&quot;&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=overview-dataset_overview&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Dataset statistics&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Number of variables&lt;/th&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Number of observations&lt;/th&gt;&lt;td&gt;3860&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing cells (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duplicate rows (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Total size in memory&lt;/th&gt;&lt;td&gt;880.4 KiB&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Average record size in memory&lt;/th&gt;&lt;td&gt;233.6 B&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;p class=h4&gt;Variable types&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Categorical&lt;/th&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-alerts&gt;&lt;table class=&quot;table table-condensed list-warnings&quot;&gt;&lt;p class=h4&gt;Alerts&lt;/p&gt;&lt;tr style=border-top:0&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_-4708681615919706112&gt;&lt;code&gt;text&lt;/code&gt;&lt;/a&gt; has a high cardinality: 3860 distinct values &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-primary&quot;&gt;High cardinality&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_-4708681615919706112&gt;&lt;code&gt;text&lt;/code&gt;&lt;/a&gt; is uniformly distributed &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-primary&quot;&gt;Uniform&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;a class=anchor href=#pp_var_-4708681615919706112&gt;&lt;code&gt;text&lt;/code&gt;&lt;/a&gt; has unique values &lt;/td&gt;&lt;td&gt;&lt;span class=&quot;label label-primary&quot;&gt;Unique&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=overview-reproduction&gt;&lt;div class=col-sm-12&gt;&lt;p class=h4&gt;Reproduction&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Analysis started&lt;/th&gt;&lt;td&gt;2022-04-04 14:56:29.169230&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Analysis finished&lt;/th&gt;&lt;td&gt;2022-04-04 14:56:29.546355&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Duration&lt;/th&gt;&lt;td&gt;0.38 seconds&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Software version&lt;/th&gt;&lt;td&gt;&lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling v3.1.0&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Download configuration&lt;/th&gt;&lt;td&gt;&lt;a download=config.json 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class=&quot;row header&quot;&gt;&lt;a class=anchor-pos id=variables&gt;&lt;/a&gt;&lt;h1 class=page-header&gt;Variables&lt;/h1&gt;&lt;/div&gt;&lt;div class=section-items&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-4708681615919706112&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=text&gt;&lt;a href=#pp_var_-4708681615919706112&gt;text&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;code&gt;HIGH CARDINALITY&lt;/code&gt;&lt;br&gt;&lt;code&gt;UNIFORM&lt;/code&gt;&lt;br&gt;&lt;code&gt;UNIQUE&lt;/code&gt;&lt;br&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr class=alert&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;3860&lt;/td&gt;&lt;/tr&gt;&lt;tr class=alert&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;100.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;636.7 KiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; @VirginAmerica What @dhepburn said. &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 1 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; @JetBlue I would fly to Washington DC to see the actual Constitution and the Declaration of Independence, in honor of a US Navy friend. &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 1 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; @JetBlue CEO weighs profits, flyers - @ChronicleHerald (registration) http://t.co/9vKj9S7jrM &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 1 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; @JetBlue A flight delay due to pilots oversleeping is apparently an uncontrollable irregularity that is not eligible for delay compensation. &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 1 &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; @JetBlue&amp;#39;s new CEO seeks the right balance to please passengers and Wall ... - Greenfield Daily Reporter http://t.co/LM3opxkxch &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:0.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; &amp;nbsp; &lt;/div&gt; 1 &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;th width=50%&gt; Other values (3855) &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 3855&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom--4708681615919706112, #minifreqtable-4708681615919706112&quot; aria-expanded=true aria-controls=collapseExample&gt;Toggle details&lt;/button&gt;&lt;/div&gt;&lt;div id=bottom--4708681615919706112 class=collapse&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-4708681615919706112bottom--4708681615919706112overview aria-controls=-4708681615919706112bottom--4708681615919706112overview role=tab data-toggle=tab&gt;Overview&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112bottom--4708681615919706112string aria-controls=-4708681615919706112bottom--4708681615919706112string role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112bottom--4708681615919706112word aria-controls=-4708681615919706112bottom--4708681615919706112word role=tab data-toggle=tab&gt;Words&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112bottom--4708681615919706112characters aria-controls=-4708681615919706112bottom--4708681615919706112characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-4708681615919706112bottom--4708681615919706112overview&gt;&lt;div class=row&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Length&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Max length&lt;/th&gt;&lt;td&gt;170&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Median length&lt;/th&gt;&lt;td&gt;100&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Mean length&lt;/th&gt;&lt;td&gt;95.9992228&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Min length&lt;/th&gt;&lt;td&gt;12&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Characters and Unicode&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Total characters&lt;/th&gt;&lt;td&gt;73&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct characters&lt;/th&gt;&lt;td&gt;1&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct categories&lt;/th&gt;&lt;td&gt;1 &lt;a title=&quot;Unicode categories (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td&gt;1 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td&gt;1 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr class=alert&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;3860 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class=alert&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;100.0%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;@VirginAmerica What @dhepburn said.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;@VirginAmerica it was amazing, and arrived an hour early. You&amp;#39;re too good to me.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;@VirginAmerica I &amp;amp;lt;3 pretty graphics. so much better than minimal iconography. :D&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;@VirginAmerica So excited for my first cross country flight LAX to MCO I&amp;#39;ve heard nothing but great things about Virgin America. #29DaysToGo&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;I ❤️ flying @VirginAmerica. ☺️👍&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112bottom--4708681615919706112string&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;h4&gt;Common Values&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;@VirginAmerica What @dhepburn said.&quot;&gt;@VirginAmerica What @dhepburn said.&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@JetBlue I would fly to Washington  DC to see the actual Constitution  and the Declaration of Independence, in honor of a US Navy friend.&quot;&gt;@JetBlue I would fly to Washington DC to see the actual Constitution and the Declaration of Independence, in honor of a US Navy friend.&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@JetBlue CEO weighs profits, flyers - @ChronicleHerald (registration) http://t.co/9vKj9S7jrM&quot;&gt;@JetBlue CEO weighs profits, flyers - @ChronicleHerald (registration) http://t.co/9vKj9S7jrM&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@JetBlue A flight delay due to pilots oversleeping is apparently an uncontrollable irregularity that is not eligible for delay compensation.&quot;&gt;@JetBlue A flight delay due to pilots oversleeping is apparently an uncontrollable irregularity that is not eligible for delay compensation.&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@JetBlue&#x27;s new CEO seeks the right balance to please passengers and Wall ... - Greenfield Daily Reporter http://t.co/LM3opxkxch&quot;&gt;@JetBlue&amp;#39;s new CEO seeks the right balance to please passengers and Wall ... - Greenfield Daily Reporter http://t.co/LM3opxkxch&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@USAirways and when will one of these agents be available to speak?&quot;&gt;@USAirways and when will one of these agents be available to speak?&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@USAirways is a DM possible if you aren&#x27;t following me?&quot;&gt;@USAirways is a DM possible if you aren&amp;#39;t following me?&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@USAirways Fortunately you have staff like Lynn S. and DeeDee who actually understand customer service and simply being NICE.&quot;&gt;@USAirways Fortunately you have staff like Lynn S. and DeeDee who actually understand customer service and simply being NICE.&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@USAirways you&#x27;re killing me from the inside&quot;&gt;@USAirways you&amp;#39;re killing me from the inside&lt;/td&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:0.0%&gt; &amp;nbsp; &lt;/div&gt;&lt; 0.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=&quot;@USAirways we were moved to a delta direct. 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       "L 52.12 10.8 \n",
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       "L 421.2 248.75363 \n",
       "&quot; style=&quot;fill: none&quot;/&gt;&lt;/g&gt;&lt;g id=patch_56&gt;&lt;path d=&quot;M 52.12 10.8 \n",
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       "&quot; style=&quot;fill: none&quot;/&gt;&lt;/g&gt;&lt;/g&gt;&lt;/g&gt;&lt;defs&gt;&lt;clippath id=pe0b7bcc8f4&gt;&lt;rect x=52.12 y=10.8 width=369.08 height=237.95363 /&gt;&lt;/clippath&gt;&lt;/defs&gt;&lt;/svg&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; Histogram of lengths of the category &lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112bottom--4708681615919706112word&gt;&lt;div class=col-sm-12&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=to&gt;to&lt;/td&gt;&lt;td&gt;2043&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:4.1%&gt; &amp;nbsp; &lt;/div&gt; 3.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=the&gt;the&lt;/td&gt;&lt;td&gt;1452&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.9%&gt; &amp;nbsp; &lt;/div&gt; 2.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=i&gt;i&lt;/td&gt;&lt;td&gt;1276&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.5%&gt; &amp;nbsp; &lt;/div&gt; 2.1% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=you&gt;you&lt;/td&gt;&lt;td&gt;1181&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.4%&gt; &amp;nbsp; &lt;/div&gt; 1.9% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=a&gt;a&lt;/td&gt;&lt;td&gt;1078&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.2%&gt; &amp;nbsp; &lt;/div&gt; 1.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=for&gt;for&lt;/td&gt;&lt;td&gt;1051&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:2.1%&gt; &amp;nbsp; &lt;/div&gt; 1.7% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=united&gt;united&lt;/td&gt;&lt;td&gt;936&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.9%&gt; &amp;nbsp; &lt;/div&gt; 1.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=flight&gt;flight&lt;/td&gt;&lt;td&gt;932&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.9%&gt; &amp;nbsp; &lt;/div&gt; 1.5% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=on&gt;on&lt;/td&gt;&lt;td&gt;884&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.8%&gt; &amp;nbsp; &lt;/div&gt; 1.4% &lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;td title=and&gt;and&lt;/td&gt;&lt;td&gt;821&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:1.6%&gt; &amp;nbsp; &lt;/div&gt; 1.3% &lt;/td&gt;&lt;/tr&gt;&lt;tr class=other&gt;&lt;td title=&quot;Other values (7635)&quot;&gt;Other values (7635)&lt;/td&gt;&lt;td&gt;50084&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 81.1% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112bottom--4708681615919706112characters&gt;&lt;div class=col-sm-12&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;ul class=&quot;nav nav-tabs&quot; role=tablist&gt;&lt;li role=presentation class=active&gt;&lt;a href=#-4708681615919706112unicode--4708681615919706112characters aria-controls=-4708681615919706112unicode--4708681615919706112characters role=tab data-toggle=tab&gt;Characters&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112unicode--4708681615919706112categories aria-controls=-4708681615919706112unicode--4708681615919706112categories role=tab data-toggle=tab&gt;Categories&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112unicode--4708681615919706112scripts aria-controls=-4708681615919706112unicode--4708681615919706112scripts role=tab data-toggle=tab&gt;Scripts&lt;/a&gt;&lt;/li&gt;&lt;li role=presentation&gt;&lt;a href=#-4708681615919706112unicode--4708681615919706112blocks aria-controls=-4708681615919706112unicode--4708681615919706112blocks role=tab data-toggle=tab&gt;Blocks&lt;/a&gt;&lt;/li&gt;&lt;/ul&gt;&lt;div class=tab-content&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12 active&quot; id=-4708681615919706112unicode--4708681615919706112characters&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring characters&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;\n",
       "&quot;&gt;&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112unicode--4708681615919706112categories&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring categories&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=Control&gt;Control&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per category&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;h5&gt;&lt;em&gt;Control&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;\n",
       "&quot;&gt;&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112unicode--4708681615919706112scripts&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring scripts&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=Common&gt;Common&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per script&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;h5&gt;&lt;em&gt;Common&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;\n",
       "&quot;&gt;&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; id=-4708681615919706112unicode--4708681615919706112blocks&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most occurring blocks&lt;/h4&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=ASCII&gt;ASCII&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=&quot;row named-list-item&quot;&gt;&lt;h4&gt;Most frequent character per block&lt;/h4&gt;&lt;div class=row&gt;&lt;div class=col-sm-6&gt;&lt;h5&gt;&lt;em&gt;ASCII&lt;/em&gt;&lt;/h5&gt;&lt;table class=&quot;freq table table-hover table-striped&quot;&gt;&lt;thead&gt;&lt;tr&gt;&lt;td&gt;Value&lt;/td&gt;&lt;td&gt;Count&lt;/td&gt;&lt;td&gt;Frequency (%)&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr class&gt;&lt;td title=&quot;\n",
       "&quot;&gt;&lt;/td&gt;&lt;td&gt;73&lt;/td&gt;&lt;td&gt;&lt;div class=bar style=width:100.0%&gt; 100.0% &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;a class=&quot;anchor-pos anchor-pos-variable&quot; id=pp_var_-3139347003416232073&gt;&lt;/a&gt;&lt;div class=variable&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4 title=sentiment&gt;&lt;a href=#pp_var_-3139347003416232073&gt;sentiment&lt;/a&gt;&lt;br&gt;&lt;small&gt;Categorical&lt;/small&gt;&lt;/p&gt;&lt;p class=variable-description&gt;&lt;/p&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Distinct&lt;/th&gt;&lt;td&gt;3&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct (%)&lt;/th&gt;&lt;td&gt;0.1%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing&lt;/th&gt;&lt;td&gt;0&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Missing (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Memory size&lt;/th&gt;&lt;td&gt;243.9 KiB&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-6&gt;&lt;div class=&quot;col-sm- collapse in&quot; id=minifreqtable&gt;&lt;table class=&quot;mini freq&quot;&gt;&lt;tr class&gt;&lt;th width=50%&gt; positive &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:100.0% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 1346&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; neutral &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:96.5% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 1299&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr class&gt;&lt;th width=50%&gt; negative &lt;/th&gt;&lt;td width=50%&gt;&lt;div class=bar style=width:90.3% data-toggle=tooltip data-placement=right data-html=true data-delay=500&gt; 1215&amp;nbsp; &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;col-sm-12 text-right&quot;&gt;&lt;button class=&quot;btn btn-default btn-sm&quot; data-toggle=collapse data-target=&quot;#bottom--3139347003416232073, #minifreqtable-3139347003416232073&quot; 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href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode categories (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct scripts&lt;/th&gt;&lt;td&gt;0 &lt;a title=&quot;Unicode scripts (click for more information)&quot; href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode scripts (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Distinct blocks&lt;/th&gt;&lt;td&gt;0 &lt;a title=&quot;Unicode blocks (click for more information)&quot; href=https://en.wikipedia.org/wiki/Unicode_block target=_blank&gt;&lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;Unicode blocks (click for more information)&quot;&gt;?&lt;/span&gt;&lt;/a&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;div class=&quot;caption text-center text-muted&quot;&gt; The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables. &lt;/div&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Unique&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;Unique&lt;/th&gt;&lt;td&gt;0 &lt;span class=&quot;badge pull-right&quot; style=color:#fff;background-color:#337ab7; title=&quot;The number of unique values (all values that occur exactly once in the dataset).&quot;&gt;?&lt;/span&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;Unique (%)&lt;/th&gt;&lt;td&gt;0.0%&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;div class=col-sm-3&gt;&lt;p class=h4&gt;Sample&lt;/p&gt;&lt;table class=&quot;table table-condensed stats&quot;&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;1st row&lt;/th&gt;&lt;td&gt;neutral&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2nd row&lt;/th&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3rd row&lt;/th&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4th row&lt;/th&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5th row&lt;/th&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;div role=tabpanel class=&quot;tab-pane col-sm-12&quot; 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You&#x27;re too good to me.&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;2&lt;/th&gt;&lt;td&gt;@VirginAmerica I &amp;amp;lt;3 pretty graphics. so much better than minimal iconography. :D&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3&lt;/th&gt;&lt;td&gt;@VirginAmerica So excited for my first cross country flight LAX to MCO I&#x27;ve heard nothing but great things about Virgin America. #29DaysToGo&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;4&lt;/th&gt;&lt;td&gt;I ❤️ flying @VirginAmerica. ☺️👍&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;5&lt;/th&gt;&lt;td&gt;@VirginAmerica you know what would be amazingly awesome? BOS-FLL PLEASE!!!!!!! I want to fly with only you.&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;6&lt;/th&gt;&lt;td&gt;@VirginAmerica I love this graphic. http://t.co/UT5GrRwAaA&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;7&lt;/th&gt;&lt;td&gt;@VirginAmerica I love the hipster innovation. You are a feel good brand.&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;8&lt;/th&gt;&lt;td&gt;@VirginAmerica will you be making BOS&amp;amp;gt;LAS non stop permanently anytime soon?&lt;/td&gt;&lt;td&gt;neutral&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;9&lt;/th&gt;&lt;td&gt;@VirginAmerica this is great news! America could start flights to Hawaii by end of year http://t.co/r8p2Zy3fe4 via @Pacificbiznews&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;div class=&quot;row spacing&quot;&gt;&lt;h2 class=indent&gt;Last rows&lt;/h2&gt;&lt;div id=sample-container class=col-sm-12&gt;&lt;table border=1 class=&quot;dataframe sample table table-striped&quot;&gt;&lt;thead&gt;&lt;tr style=&quot;text-align: right;&quot;&gt;&lt;th&gt;&lt;/th&gt;&lt;th&gt;text&lt;/th&gt;&lt;th&gt;sentiment&lt;/th&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;th&gt;3850&lt;/th&gt;&lt;td&gt;@AmericanAir Cancelled Flights flights arbitrarily on same itinerary. Weekend ruined for no good reason! No crew = missed Monday am mtg.&lt;/td&gt;&lt;td&gt;negative&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3851&lt;/th&gt;&lt;td&gt;@AmericanAir i need someone to help me out&lt;/td&gt;&lt;td&gt;neutral&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3852&lt;/th&gt;&lt;td&gt;@AmericanAir guarantee no retribution? If so, I&#x27;d be glad to share.&lt;/td&gt;&lt;td&gt;neutral&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3853&lt;/th&gt;&lt;td&gt;@AmericanAir I love your company and your staff is amazing. They just made an uncomfortable situation comfortable&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3854&lt;/th&gt;&lt;td&gt;@AmericanAir I wait 2+ hrs for CS to call me back re why flt is cxld/protection &amp;amp;amp; they hang up the minute I answer on 1st ring?&lt;/td&gt;&lt;td&gt;negative&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3855&lt;/th&gt;&lt;td&gt;@AmericanAir Flight 236 was great. Fantastic cabin crew. A+ landing. #thankyou #JFK http://t.co/dRW08djHAI&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3856&lt;/th&gt;&lt;td&gt;@AmericanAir Flight Cancelled Flightled, can&#x27;t go home until tomorrow. I could use dinner and a play, @AmericanAir! It&#x27;s my first time in NYC.&lt;/td&gt;&lt;td&gt;negative&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3857&lt;/th&gt;&lt;td&gt;Thank you. “@AmericanAir: @jlhalldc Customer Relations will review your concerns and contact you back directly, John.”&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3858&lt;/th&gt;&lt;td&gt;@AmericanAir Thanks! He is.&lt;/td&gt;&lt;td&gt;positive&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;th&gt;3859&lt;/th&gt;&lt;td&gt;@AmericanAir thx for nothing on getting us out of the country and back to US. Broken plane? Come on. Get another one.&lt;/td&gt;&lt;td&gt;negative&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;footer&gt;&lt;div class=container-fluid&gt;&lt;div class=&quot;row center-block footer-text&quot;&gt;&lt;p class=&quot;text-muted text-center&quot;&gt;Report generated with &lt;a href=https://github.com/pandas-profiling/pandas-profiling&gt;pandas-profiling&lt;/a&gt;.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/footer&gt;&lt;script&gt;\n",
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h.notifyWith(a,[j,f,c]),1&gt;f&amp;&amp;i?c:(h.resolveWith(a,[j]),!1)},j=h.promise({elem:a,props:n.extend({},b),opts:n.extend(!0,{specialEasing:{},easing:n.easing._default},c),originalProperties:b,originalOptions:c,startTime:hb||lb(),duration:c.duration,tweens:[],createTween:function(b,c){var d=n.Tween(a,j.opts,b,c,j.opts.specialEasing[b]||j.opts.easing);return j.tweens.push(d),d},stop:function(b){var c=0,d=b?j.tweens.length:0;if(e)return this;for(e=!0;d&gt;c;c++)j.tweens[c].run(1);return b?(h.notifyWith(a,[j,1,0]),h.resolveWith(a,[j,b])):h.rejectWith(a,[j,b]),this}}),k=j.props;for(pb(k,j.opts.specialEasing);g&gt;f;f++)if(d=qb.prefilters[f].call(j,a,k,j.opts))return n.isFunction(d.stop)&amp;&amp;(n._queueHooks(j.elem,j.opts.queue).stop=n.proxy(d.stop,d)),d;return n.map(k,nb,j),n.isFunction(j.opts.start)&amp;&amp;j.opts.start.call(a,j),n.fx.timer(n.extend(i,{elem:a,anim:j,queue:j.opts.queue})),j.progress(j.opts.progress).done(j.opts.done,j.opts.complete).fail(j.opts.fail).always(j.opts.always)}n.Animation=n.extend(qb,{tweeners:{&quot;*&quot;:[function(a,b){var c=this.createTween(a,b);return X(c.elem,a,U.exec(b),c),c}]},tweener:function(a,b){n.isFunction(a)?(b=a,a=[&quot;*&quot;]):a=a.match(G);for(var c,d=0,e=a.length;e&gt;d;d++)c=a[d],qb.tweeners[c]=qb.tweeners[c]||[],qb.tweeners[c].unshift(b)},prefilters:[ob],prefilter:function(a,b){b?qb.prefilters.unshift(a):qb.prefilters.push(a)}}),n.speed=function(a,b,c){var d=a&amp;&amp;&quot;object&quot;==typeof a?n.extend({},a):{complete:c||!c&amp;&amp;b||n.isFunction(a)&amp;&amp;a,duration:a,easing:c&amp;&amp;b||b&amp;&amp;!n.isFunction(b)&amp;&amp;b};return d.duration=n.fx.off?0:&quot;number&quot;==typeof d.duration?d.duration:d.duration in n.fx.speeds?n.fx.speeds[d.duration]:n.fx.speeds._default,null!=d.queue&amp;&amp;d.queue!==!0||(d.queue=&quot;fx&quot;),d.old=d.complete,d.complete=function(){n.isFunction(d.old)&amp;&amp;d.old.call(this),d.queue&amp;&amp;n.dequeue(this,d.queue)},d},n.fn.extend({fadeTo:function(a,b,c,d){return this.filter(W).css(&quot;opacity&quot;,0).show().end().animate({opacity:b},a,c,d)},animate:function(a,b,c,d){var e=n.isEmptyObject(a),f=n.speed(b,c,d),g=function(){var b=qb(this,n.extend({},a),f);(e||n._data(this,&quot;finish&quot;))&amp;&amp;b.stop(!0)};return g.finish=g,e||f.queue===!1?this.each(g):this.queue(f.queue,g)},stop:function(a,b,c){var d=function(a){var b=a.stop;delete a.stop,b(c)};return&quot;string&quot;!=typeof a&amp;&amp;(c=b,b=a,a=void 0),b&amp;&amp;a!==!1&amp;&amp;this.queue(a||&quot;fx&quot;,[]),this.each(function(){var b=!0,e=null!=a&amp;&amp;a+&quot;queueHooks&quot;,f=n.timers,g=n._data(this);if(e)g[e]&amp;&amp;g[e].stop&amp;&amp;d(g[e]);else for(e in g)g[e]&amp;&amp;g[e].stop&amp;&amp;kb.test(e)&amp;&amp;d(g[e]);for(e=f.length;e--;)f[e].elem!==this||null!=a&amp;&amp;f[e].queue!==a||(f[e].anim.stop(c),b=!1,f.splice(e,1));!b&amp;&amp;c||n.dequeue(this,a)})},finish:function(a){return a!==!1&amp;&amp;(a=a||&quot;fx&quot;),this.each(function(){var b,c=n._data(this),d=c[a+&quot;queue&quot;],e=c[a+&quot;queueHooks&quot;],f=n.timers,g=d?d.length:0;for(c.finish=!0,n.queue(this,a,[]),e&amp;&amp;e.stop&amp;&amp;e.stop.call(this,!0),b=f.length;b--;)f[b].elem===this&amp;&amp;f[b].queue===a&amp;&amp;(f[b].anim.stop(!0),f.splice(b,1));for(b=0;g&gt;b;b++)d[b]&amp;&amp;d[b].finish&amp;&amp;d[b].finish.call(this);delete c.finish})}}),n.each([&quot;toggle&quot;,&quot;show&quot;,&quot;hide&quot;],function(a,b){var c=n.fn[b];n.fn[b]=function(a,d,e){return null==a||&quot;boolean&quot;==typeof 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a,b=d.createElement(&quot;input&quot;),c=d.createElement(&quot;div&quot;),e=d.createElement(&quot;select&quot;),f=e.appendChild(d.createElement(&quot;option&quot;));c=d.createElement(&quot;div&quot;),c.setAttribute(&quot;className&quot;,&quot;t&quot;),c.innerHTML=&quot;  &lt;link/&gt;&lt;table&gt;&lt;/table&gt;&lt;a href=&#x27;/a&#x27;&gt;a&lt;/a&gt;&lt;input type=&#x27;checkbox&#x27;/&gt;&quot;,a=c.getElementsByTagName(&quot;a&quot;)[0],b.setAttribute(&quot;type&quot;,&quot;checkbox&quot;),c.appendChild(b),a=c.getElementsByTagName(&quot;a&quot;)[0],a.style.cssText=&quot;top:1px&quot;,l.getSetAttribute=&quot;t&quot;!==c.className,l.style=/top/.test(a.getAttribute(&quot;style&quot;)),l.hrefNormalized=&quot;/a&quot;===a.getAttribute(&quot;href&quot;),l.checkOn=!!b.value,l.optSelected=f.selected,l.enctype=!!d.createElement(&quot;form&quot;).enctype,e.disabled=!0,l.optDisabled=!f.disabled,b=d.createElement(&quot;input&quot;),b.setAttribute(&quot;value&quot;,&quot;&quot;),l.input=&quot;&quot;===b.getAttribute(&quot;value&quot;),b.value=&quot;t&quot;,b.setAttribute(&quot;type&quot;,&quot;radio&quot;),l.radioValue=&quot;t&quot;===b.value}();var rb=/\\r/g,sb=/[\\x20\\t\\r\\n\\f]+/g;n.fn.extend({val:function(a){var b,c,d,e=this[0];{if(arguments.length)return d=n.isFunction(a),this.each(function(c){var e;1===this.nodeType&amp;&amp;(e=d?a.call(this,c,n(this).val()):a,null==e?e=&quot;&quot;:&quot;number&quot;==typeof e?e+=&quot;&quot;:n.isArray(e)&amp;&amp;(e=n.map(e,function(a){return null==a?&quot;&quot;:a+&quot;&quot;})),b=n.valHooks[this.type]||n.valHooks[this.nodeName.toLowerCase()],b&amp;&amp;&quot;set&quot;in b&amp;&amp;void 0!==b.set(this,e,&quot;value&quot;)||(this.value=e))});if(e)return b=n.valHooks[e.type]||n.valHooks[e.nodeName.toLowerCase()],b&amp;&amp;&quot;get&quot;in b&amp;&amp;void 0!==(c=b.get(e,&quot;value&quot;))?c:(c=e.value,&quot;string&quot;==typeof c?c.replace(rb,&quot;&quot;):null==c?&quot;&quot;:c)}}}),n.extend({valHooks:{option:{get:function(a){var b=n.find.attr(a,&quot;value&quot;);return null!=b?b:n.trim(n.text(a)).replace(sb,&quot; &quot;)}},select:{get:function(a){for(var b,c,d=a.options,e=a.selectedIndex,f=&quot;select-one&quot;===a.type||0&gt;e,g=f?null:[],h=f?e+1:d.length,i=0&gt;e?h:f?e:0;h&gt;i;i++)if(c=d[i],(c.selected||i===e)&amp;&amp;(l.optDisabled?!c.disabled:null===c.getAttribute(&quot;disabled&quot;))&amp;&amp;(!c.parentNode.disabled||!n.nodeName(c.parentNode,&quot;optgroup&quot;))){if(b=n(c).val(),f)return b;g.push(b)}return g},set:function(a,b){var c,d,e=a.options,f=n.makeArray(b),g=e.length;while(g--)if(d=e[g],n.inArray(n.valHooks.option.get(d),f)&gt;-1)try{d.selected=c=!0}catch(h){d.scrollHeight}else d.selected=!1;return c||(a.selectedIndex=-1),e}}}}),n.each([&quot;radio&quot;,&quot;checkbox&quot;],function(){n.valHooks[this]={set:function(a,b){return n.isArray(b)?a.checked=n.inArray(n(a).val(),b)&gt;-1:void 0}},l.checkOn||(n.valHooks[this].get=function(a){return null===a.getAttribute(&quot;value&quot;)?&quot;on&quot;:a.value})});var tb,ub,vb=n.expr.attrHandle,wb=/^(?:checked|selected)$/i,xb=l.getSetAttribute,yb=l.input;n.fn.extend({attr:function(a,b){return Y(this,n.attr,a,b,arguments.length&gt;1)},removeAttr:function(a){return this.each(function(){n.removeAttr(this,a)})}}),n.extend({attr:function(a,b,c){var d,e,f=a.nodeType;if(3!==f&amp;&amp;8!==f&amp;&amp;2!==f)return&quot;undefined&quot;==typeof a.getAttribute?n.prop(a,b,c):(1===f&amp;&amp;n.isXMLDoc(a)||(b=b.toLowerCase(),e=n.attrHooks[b]||(n.expr.match.bool.test(b)?ub:tb)),void 0!==c?null===c?void n.removeAttr(a,b):e&amp;&amp;&quot;set&quot;in e&amp;&amp;void 0!==(d=e.set(a,c,b))?d:(a.setAttribute(b,c+&quot;&quot;),c):e&amp;&amp;&quot;get&quot;in e&amp;&amp;null!==(d=e.get(a,b))?d:(d=n.find.attr(a,b),null==d?void 0:d))},attrHooks:{type:{set:function(a,b){if(!l.radioValue&amp;&amp;&quot;radio&quot;===b&amp;&amp;n.nodeName(a,&quot;input&quot;)){var c=a.value;return a.setAttribute(&quot;type&quot;,b),c&amp;&amp;(a.value=c),b}}}},removeAttr:function(a,b){var c,d,e=0,f=b&amp;&amp;b.match(G);if(f&amp;&amp;1===a.nodeType)while(c=f[e++])d=n.propFix[c]||c,n.expr.match.bool.test(c)?yb&amp;&amp;xb||!wb.test(c)?a[d]=!1:a[n.camelCase(&quot;default-&quot;+c)]=a[d]=!1:n.attr(a,c,&quot;&quot;),a.removeAttribute(xb?c:d)}}),ub={set:function(a,b,c){return b===!1?n.removeAttr(a,c):yb&amp;&amp;xb||!wb.test(c)?a.setAttribute(!xb&amp;&amp;n.propFix[c]||c,c):a[n.camelCase(&quot;default-&quot;+c)]=a[c]=!0,c}},n.each(n.expr.match.bool.source.match(/\\w+/g),function(a,b){var c=vb[b]||n.find.attr;yb&amp;&amp;xb||!wb.test(b)?vb[b]=function(a,b,d){var e,f;return d||(f=vb[b],vb[b]=e,e=null!=c(a,b,d)?b.toLowerCase():null,vb[b]=f),e}:vb[b]=function(a,b,c){return c?void 0:a[n.camelCase(&quot;default-&quot;+b)]?b.toLowerCase():null}}),yb&amp;&amp;xb||(n.attrHooks.value={set:function(a,b,c){return n.nodeName(a,&quot;input&quot;)?void(a.defaultValue=b):tb&amp;&amp;tb.set(a,b,c)}}),xb||(tb={set:function(a,b,c){var d=a.getAttributeNode(c);return d||a.setAttributeNode(d=a.ownerDocument.createAttribute(c)),d.value=b+=&quot;&quot;,&quot;value&quot;===c||b===a.getAttribute(c)?b:void 0}},vb.id=vb.name=vb.coords=function(a,b,c){var d;return c?void 0:(d=a.getAttributeNode(b))&amp;&amp;&quot;&quot;!==d.value?d.value:null},n.valHooks.button={get:function(a,b){var c=a.getAttributeNode(b);return c&amp;&amp;c.specified?c.value:void 0},set:tb.set},n.attrHooks.contenteditable={set:function(a,b,c){tb.set(a,&quot;&quot;===b?!1:b,c)}},n.each([&quot;width&quot;,&quot;height&quot;],function(a,b){n.attrHooks[b]={set:function(a,c){return&quot;&quot;===c?(a.setAttribute(b,&quot;auto&quot;),c):void 0}}})),l.style||(n.attrHooks.style={get:function(a){return a.style.cssText||void 0},set:function(a,b){return a.style.cssText=b+&quot;&quot;}});var zb=/^(?:input|select|textarea|button|object)$/i,Ab=/^(?:a|area)$/i;n.fn.extend({prop:function(a,b){return Y(this,n.prop,a,b,arguments.length&gt;1)},removeProp:function(a){return 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       "$(function () {\n",
       "    $(&#x27;[data-toggle=&quot;tooltip&quot;]&#x27;).tooltip();\n",
       "});\n",
       "\n",
       "$(&quot;a[href^=&#x27;#&#x27;].anchor&quot;).on(&#x27;click&#x27;, function (e) {\n",
       "\n",
       "    // prevent default anchor click behavior\n",
       "    e.preventDefault();\n",
       "\n",
       "    // store hash\n",
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       "\n",
       "    // animate\n",
       "    $(&#x27;html, body&#x27;).animate({\n",
       "        scrollTop: $(hash).offset().top\n",
       "    }, 300, function () {\n",
       "\n",
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       "\n",
       "});        &lt;/script&gt;&lt;/body&gt;&lt;/html&gt;\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "profile = ProfileReport(tweet_df, title=\"Tweets Report\", explorative=True)\n",
    "\n",
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "constant-treasure",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only consider positive vs negative\n",
    "# tweet_df = tweet_df[tweet_df['sentiment'].isin(['negative', 'positive'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "worth-testing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    0.348705\n",
       "neutral     0.336528\n",
       "negative    0.314767\n",
       "Name: sentiment, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_df['sentiment'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "listed-blackjack",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# tweet_df['sentiment'] = tweet_df['sentiment'].map({'negative': 0, 'neutral': 1, 'positive': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "psychological-blank",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of tweets in training set: 3,088\n",
      "Count of tweets in testing set: 772\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(tweet_df, test_size=0.2, random_state=0, stratify=tweet_df['sentiment'])\n",
    "\n",
    "print(f'Count of tweets in training set: {train.shape[0]:,}')\n",
    "print(f'Count of tweets in testing set: {test.shape[0]:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "thrown-charleston",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO show words in common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "colonial-ticket",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3088, 6018)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer()  # A\n",
    "single_word = cv.fit_transform(train['text'])  #  B\n",
    "\n",
    "print(single_word.shape)  # 6,018 features!\n",
    "        \n",
    "# A Instantiate the CountVectorizer\n",
    "# B In one line, fit the CountVectorizer to our training set and transform it into a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "based-advisory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token: jetblue Index 3126\n",
      "Token: maybe Index 3547\n",
      "Token: ll Index 3382\n",
      "Token: just Index 3177\n",
      "Token: go Index 2595\n",
      "Token: to Index 5401\n",
      "Token: cleveland Index 1437\n",
      "Token: instead Index 3014\n",
      "Token: smh Index 4944\n",
      "Token: rt Index 4686\n",
      "Token: our Index 3993\n"
     ]
    }
   ],
   "source": [
    "for i, (token, token_index) in enumerate(cv.vocabulary_.items()):\n",
    "    print(f'Token: {token} Index {token_index}')\n",
    "    if i == 10:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "abroad-action",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
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      ],
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       "      00  000  000114  000ft  00pm  0167560070877  02  0200  03  0400  ...  \\\n",
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       "\n",
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       "\n",
       "      zsdgzydnde  zukes  zv2pt6trk9  \n",
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       "\n",
       "[3088 rows x 6018 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count Vectorizer feature matrix\n",
    "pd.DataFrame(single_word.todense(), columns=cv.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "premium-catalyst",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<3088x6018 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 45222 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "advanced-complexity",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
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       "      <th>@JetBlue Maybe I'll just go to Cleveland instead.</th>\n",
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       "      <th>“@JetBlue: Our fleet's on fleek. http://t.co/b5ttno68xu” I just 🙈</th>\n",
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       "      <th>@united caught earlier flight to ORD. Gate checked bag, and you've lost it at O'Hare. original flight lands in 20minutes. #frustrating!</th>\n",
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       "      <th>@AmericanAir hi when will your next set of flights be out for next year from Dublin???</th>\n",
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       "      <th>@SouthwestAir Finally! Integration w/ passbook is a great Valentine gift - better then chocoLate Flight. You do heart me.</th>\n",
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       "      <th>@JetBlue @cflanagian she's on to something</th>\n",
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      ],
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       "                                                    americanair  and  flight  \\\n",
       "text                                                                           \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.             0    0       0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...            0    0       0   \n",
       "@SouthwestAir I would.                                        0    0       0   \n",
       "@USAirways trying to Cancelled Flight a flight ...            0    0       2   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...            1    0       0   \n",
       "...                                                         ...  ...     ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...            0    0       0   \n",
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       "@AmericanAir hi when will your next set of flig...            1    0       0   \n",
       "@SouthwestAir Finally! Integration w/ passbook ...            0    0       1   \n",
       "@JetBlue @cflanagian she's on to something                    0    0       0   \n",
       "\n",
       "                                                    for  in  is  it  jetblue  \\\n",
       "text                                                                           \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.     0   0   0   0        1   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...    0   0   0   0        1   \n",
       "@SouthwestAir I would.                                0   0   0   0        0   \n",
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       "@AmericanAir you are beyond redemption. Jfk. Ba...    0   0   0   0        0   \n",
       "...                                                 ...  ..  ..  ..      ...   \n",
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       "@SouthwestAir Finally! Integration w/ passbook ...    0   0   1   0        0   \n",
       "@JetBlue @cflanagian she's on to something            0   0   0   0        1   \n",
       "\n",
       "                                                    me  my  of  on  \\\n",
       "text                                                                 \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.    0   0   0   0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...   0   0   0   1   \n",
       "@SouthwestAir I would.                               0   0   0   0   \n",
       "@USAirways trying to Cancelled Flight a flight ...   0   0   0   1   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...   0   0   0   0   \n",
       "...                                                 ..  ..  ..  ..   \n",
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       "@JetBlue @cflanagian she's on to something           0   0   0   1   \n",
       "\n",
       "                                                    southwestair  thanks  the  \\\n",
       "text                                                                            \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.              0       0    0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...             0       0    0   \n",
       "@SouthwestAir I would.                                         1       0    0   \n",
       "@USAirways trying to Cancelled Flight a flight ...             0       0    0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...             0       0    0   \n",
       "...                                                          ...     ...  ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...             0       0    0   \n",
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       "@AmericanAir hi when will your next set of flig...             0       0    0   \n",
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       "@JetBlue @cflanagian she's on to something                     0       0    0   \n",
       "\n",
       "                                                    to  united  usairways  \\\n",
       "text                                                                        \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.    1       0          0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...   0       0          0   \n",
       "@SouthwestAir I would.                               0       0          0   \n",
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       "...                                                 ..     ...        ...   \n",
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       "\n",
       "                                                    you  your  \n",
       "text                                                           \n",
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       "@SouthwestAir I would.                                0     0  \n",
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       "@JetBlue @cflanagian she's on to something            0     0  \n",
       "\n",
       "[3088 rows x 20 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer(max_features=20)  # A \n",
    "\n",
    "limited_vocab = cv.fit_transform(train['text'])\n",
    "\n",
    "pd.DataFrame(limited_vocab.toarray(), index = train['text'], columns = cv.get_feature_names())\n",
    "\n",
    "# A setting max_features chooses most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "whole-swift",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3088, 70613)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
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       "                                                    00  00 phone  \\\n",
       "text                                                               \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.    0         0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...   0         0   \n",
       "@SouthwestAir I would.                               0         0   \n",
       "@USAirways trying to Cancelled Flight a flight ...   0         0   \n",
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       "\n",
       "                                                    00 phone hold  00 pm  \\\n",
       "text                                                                       \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.               0      0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...              0      0   \n",
       "@SouthwestAir I would.                                          0      0   \n",
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       "                                                    00 pm that  000  000 air  \\\n",
       "text                                                                           \n",
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       "@SouthwestAir I would.                                       0    0        0   \n",
       "@USAirways trying to Cancelled Flight a flight ...           0    0        0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...           0    0        0   \n",
       "\n",
       "                                                    000 air miles  \\\n",
       "text                                                                \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.               0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...              0   \n",
       "@SouthwestAir I would.                                          0   \n",
       "@USAirways trying to Cancelled Flight a flight ...              0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...              0   \n",
       "\n",
       "                                                    000 crewmembers  \\\n",
       "text                                                                  \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.                 0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...                0   \n",
       "@SouthwestAir I would.                                            0   \n",
       "@USAirways trying to Cancelled Flight a flight ...                0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...                0   \n",
       "\n",
       "                                                    000 crewmembers embody  \\\n",
       "text                                                                         \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.                        0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...                       0   \n",
       "@SouthwestAir I would.                                                   0   \n",
       "@USAirways trying to Cancelled Flight a flight ...                       0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...                       0   \n",
       "\n",
       "                                                    ...  zj76 how  \\\n",
       "text                                                ...             \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.   ...         0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...  ...         0   \n",
       "@SouthwestAir I would.                              ...         0   \n",
       "@USAirways trying to Cancelled Flight a flight ...  ...         0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...  ...         0   \n",
       "\n",
       "                                                    zj76 how did  zone  \\\n",
       "text                                                                     \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.              0     0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...             0     0   \n",
       "@SouthwestAir I would.                                         0     0   \n",
       "@USAirways trying to Cancelled Flight a flight ...             0     0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...             0     0   \n",
       "\n",
       "                                                    zone was  zone was after  \\\n",
       "text                                                                           \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.          0               0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...         0               0   \n",
       "@SouthwestAir I would.                                     0               0   \n",
       "@USAirways trying to Cancelled Flight a flight ...         0               0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...         0               0   \n",
       "\n",
       "                                                    zsdgzydnde  zukes  \\\n",
       "text                                                                    \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.            0      0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...           0      0   \n",
       "@SouthwestAir I would.                                       0      0   \n",
       "@USAirways trying to Cancelled Flight a flight ...           0      0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...           0      0   \n",
       "\n",
       "                                                    zukes non  \\\n",
       "text                                                            \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.           0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...          0   \n",
       "@SouthwestAir I would.                                      0   \n",
       "@USAirways trying to Cancelled Flight a flight ...          0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...          0   \n",
       "\n",
       "                                                    zukes non vegan  \\\n",
       "text                                                                  \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.                 0   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...                0   \n",
       "@SouthwestAir I would.                                            0   \n",
       "@USAirways trying to Cancelled Flight a flight ...                0   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...                0   \n",
       "\n",
       "                                                    zv2pt6trk9  \n",
       "text                                                            \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.            0  \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...           0  \n",
       "@SouthwestAir I would.                                       0  \n",
       "@USAirways trying to Cancelled Flight a flight ...           0  \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...           0  \n",
       "\n",
       "[5 rows x 70613 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer(ngram_range=(1, 3))  # A\n",
    "more_ngrams = cv.fit_transform(train['text'])\n",
    "\n",
    "print(more_ngrams.shape)  # 70,613 features!\n",
    "\n",
    "pd.DataFrame(more_ngrams.toarray(), index = train['text'], columns = cv.get_feature_names()).head()\n",
    "\n",
    "# A Consider unigrams bigrams, and trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "experimental-strengthening",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[6017, 4607, 2527, ..., 5983, 5316, 5401]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_word.sum(axis=0).argsort()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "previous-highlight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['jetblue', 'maybe', 'll', 'just', 'go', 'to', 'cleveland',\n",
       "        'instead'], dtype='<U12'),\n",
       " array(['jetblue', 'smh', 'rt', 'our', 'fleet', 'on', 'fleek', 'http',\n",
       "        'co', 'irixaifjjx'], dtype='<U12'),\n",
       " array(['southwestair', 'would'], dtype='<U12'),\n",
       " array(['to', 'on', 'usairways', 'trying', 'cancelled', 'flight',\n",
       "        'urgently', 'get', 'hung', 'up', 'twice', 'sweet', 'refund',\n",
       "        'policy'], dtype='<U12'),\n",
       " array(['americanair', 'you', 'are', 'beyond', 'redemption', 'jfk',\n",
       "        'baggage', 'claim', 'looks', 'like', 'luggage', 'warehouse'],\n",
       "       dtype='<U12')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer()\n",
    "\n",
    "cv.inverse_transform(cv.fit_transform(train['text'].head()))  # bag of words, order doesn't matter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "special-domain",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['and',\n",
       " 'flight',\n",
       " 'for',\n",
       " 'jetblue',\n",
       " 'on',\n",
       " 'southwestair',\n",
       " 'the',\n",
       " 'to',\n",
       " 'united',\n",
       " 'you']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most common words in our training set\n",
    "cv = CountVectorizer(max_features=10)\n",
    "cv.fit(train['text'])\n",
    "\n",
    "cv.get_feature_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "vital-handbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['americanair',\n",
       " 'flight',\n",
       " 'http',\n",
       " 'jetblue',\n",
       " 'service',\n",
       " 'southwestair',\n",
       " 'thank',\n",
       " 'thanks',\n",
       " 'united',\n",
       " 'usairways']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most common words non stopwords in our training set\n",
    "cv = CountVectorizer(stop_words='english', max_features=10)  # A\n",
    "cv.fit(train['text'])\n",
    "\n",
    "cv.get_feature_names()\n",
    "\n",
    "# A Don't consider common words as tokens like \"A\", \"the\", \"an\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "unsigned-impact",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run ./Base.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "posted-germany",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "otherwise-missile",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count Vectorizer + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.77      0.78       243\n",
      "     neutral       0.75      0.78      0.77       260\n",
      "    positive       0.84      0.83      0.84       269\n",
      "\n",
      "    accuracy                           0.79       772\n",
      "   macro avg       0.79      0.79      0.79       772\n",
      "weighted avg       0.79      0.79      0.79       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0, 'vectorizer__lowercase': True, 'vectorizer__max_features': 5000, 'vectorizer__ngram_range': (1, 1), 'vectorizer__stop_words': None}\n",
      "Overall took 27.30 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('vectorizer', CountVectorizer(max_features=5000)),\n",
       "                 ('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'negative', 'negative', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'negative', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'positive',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
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       "        'negative', 'neutral', 'positive', 'positive', 'neutral',\n",
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       "        'neutral', 'positive', 'negative', 'negative', 'negative',\n",
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       "        'positive', 'positive', 'positive', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'neutral'], dtype=object))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression(max_iter=10000)  # A\n",
    "\n",
    "ml_pipeline = Pipeline([\n",
    "    ('vectorizer', CountVectorizer()),  # B\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'vectorizer__lowercase': [True, False],  # C\n",
    "    'vectorizer__stop_words': [None, 'english'],\n",
    "    'vectorizer__max_features': [100, 1000, 5000],\n",
    "    'vectorizer__ngram_range': [(1, 1), (1, 3)],\n",
    "    \n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "    \n",
    "}\n",
    "\n",
    "print(\"Count Vectorizer + Log Reg\\n=====================\")\n",
    "advanced_grid_search(  # D\n",
    "    train['text'], train['sentiment'], test['text'], test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")\n",
    "\n",
    "# A Very simple classifier\n",
    "# B our count vectorizer\n",
    "# C lowercase is another parameter that if true, will lowercase all text before tokenizing\n",
    "# D Function from our base notebook that will train our pipeline on the training set and calculate\n",
    "## a classification report from the testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specific-therapist",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "acting-measure",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>americanair</th>\n",
       "      <th>flight</th>\n",
       "      <th>http</th>\n",
       "      <th>jetblue</th>\n",
       "      <th>service</th>\n",
       "      <th>southwestair</th>\n",
       "      <th>thank</th>\n",
       "      <th>thanks</th>\n",
       "      <th>united</th>\n",
       "      <th>usairways</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>@JetBlue Maybe I'll just go to Cleveland instead.</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smh RT @JetBlue: Our fleet's on fleek. http://t.co/IRiXaIfJJX</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.78863</td>\n",
       "      <td>0.614869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@SouthwestAir I would.</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@USAirways trying to Cancelled Flight a flight urgently...get hung up on twice??? Sweet refund policy</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.877741</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.479136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@AmericanAir you are beyond redemption. Jfk. Baggage claim looks like a luggage warehouse</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>“@JetBlue: Our fleet's on fleek. http://t.co/b5ttno68xu” I just 🙈</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.78863</td>\n",
       "      <td>0.614869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@united caught earlier flight to ORD. Gate checked bag, and you've lost it at O'Hare. original flight lands in 20minutes. #frustrating!</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.898683</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.438599</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@AmericanAir hi when will your next set of flights be out for next year from Dublin???</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@SouthwestAir Finally! Integration w/ passbook is a great Valentine gift - better then chocoLate Flight. You do heart me.</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.703237</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.710956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@JetBlue @cflanagian she's on to something</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3088 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    americanair    flight  \\\n",
       "text                                                                        \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.           0.0  0.000000   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...          0.0  0.000000   \n",
       "@SouthwestAir I would.                                      0.0  0.000000   \n",
       "@USAirways trying to Cancelled Flight a flight ...          0.0  0.877741   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...          1.0  0.000000   \n",
       "...                                                         ...       ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...          0.0  0.000000   \n",
       "@united caught earlier flight to ORD. Gate chec...          0.0  0.898683   \n",
       "@AmericanAir hi when will your next set of flig...          1.0  0.000000   \n",
       "@SouthwestAir Finally! Integration w/ passbook ...          0.0  0.703237   \n",
       "@JetBlue @cflanagian she's on to something                  0.0  0.000000   \n",
       "\n",
       "                                                       http   jetblue  \\\n",
       "text                                                                    \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.   0.00000  1.000000   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...  0.78863  0.614869   \n",
       "@SouthwestAir I would.                              0.00000  0.000000   \n",
       "@USAirways trying to Cancelled Flight a flight ...  0.00000  0.000000   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...  0.00000  0.000000   \n",
       "...                                                     ...       ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...  0.78863  0.614869   \n",
       "@united caught earlier flight to ORD. Gate chec...  0.00000  0.000000   \n",
       "@AmericanAir hi when will your next set of flig...  0.00000  0.000000   \n",
       "@SouthwestAir Finally! Integration w/ passbook ...  0.00000  0.000000   \n",
       "@JetBlue @cflanagian she's on to something          0.00000  1.000000   \n",
       "\n",
       "                                                    service  southwestair  \\\n",
       "text                                                                        \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.       0.0      0.000000   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...      0.0      0.000000   \n",
       "@SouthwestAir I would.                                  0.0      1.000000   \n",
       "@USAirways trying to Cancelled Flight a flight ...      0.0      0.000000   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...      0.0      0.000000   \n",
       "...                                                     ...           ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...      0.0      0.000000   \n",
       "@united caught earlier flight to ORD. Gate chec...      0.0      0.000000   \n",
       "@AmericanAir hi when will your next set of flig...      0.0      0.000000   \n",
       "@SouthwestAir Finally! Integration w/ passbook ...      0.0      0.710956   \n",
       "@JetBlue @cflanagian she's on to something              0.0      0.000000   \n",
       "\n",
       "                                                    thank  thanks    united  \\\n",
       "text                                                                          \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.     0.0     0.0  0.000000   \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...    0.0     0.0  0.000000   \n",
       "@SouthwestAir I would.                                0.0     0.0  0.000000   \n",
       "@USAirways trying to Cancelled Flight a flight ...    0.0     0.0  0.000000   \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...    0.0     0.0  0.000000   \n",
       "...                                                   ...     ...       ...   \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...    0.0     0.0  0.000000   \n",
       "@united caught earlier flight to ORD. Gate chec...    0.0     0.0  0.438599   \n",
       "@AmericanAir hi when will your next set of flig...    0.0     0.0  0.000000   \n",
       "@SouthwestAir Finally! Integration w/ passbook ...    0.0     0.0  0.000000   \n",
       "@JetBlue @cflanagian she's on to something            0.0     0.0  0.000000   \n",
       "\n",
       "                                                    usairways  \n",
       "text                                                           \n",
       "@JetBlue Maybe I'll just go to Cleveland instead.    0.000000  \n",
       "smh RT @JetBlue: Our fleet's on fleek. http://t...   0.000000  \n",
       "@SouthwestAir I would.                               0.000000  \n",
       "@USAirways trying to Cancelled Flight a flight ...   0.479136  \n",
       "@AmericanAir you are beyond redemption. Jfk. Ba...   0.000000  \n",
       "...                                                       ...  \n",
       "“@JetBlue: Our fleet's on fleek. http://t.co/b5...   0.000000  \n",
       "@united caught earlier flight to ORD. Gate chec...   0.000000  \n",
       "@AmericanAir hi when will your next set of flig...   0.000000  \n",
       "@SouthwestAir Finally! Integration w/ passbook ...   0.000000  \n",
       "@JetBlue @cflanagian she's on to something           0.000000  \n",
       "\n",
       "[3088 rows x 10 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_features=10)\n",
    "\n",
    "tfdf_text = tfidf_vectorizer.fit_transform(train['text'])\n",
    "pd.DataFrame(tfdf_text.toarray(), index = train['text'], columns = tfidf_vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "imported-authorization",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_name</th>\n",
       "      <th>idf_weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5401</th>\n",
       "      <td>to</td>\n",
       "      <td>1.932281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5316</th>\n",
       "      <td>the</td>\n",
       "      <td>2.163475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5983</th>\n",
       "      <td>you</td>\n",
       "      <td>2.288016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2419</th>\n",
       "      <td>for</td>\n",
       "      <td>2.375028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5608</th>\n",
       "      <td>united</td>\n",
       "      <td>2.497463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3460</th>\n",
       "      <td>lucas</td>\n",
       "      <td>8.342456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3461</th>\n",
       "      <td>lucia</td>\n",
       "      <td>8.342456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>cbv7f3kbkx</td>\n",
       "      <td>8.342456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3450</th>\n",
       "      <td>lowstandards</td>\n",
       "      <td>8.342456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6017</th>\n",
       "      <td>zv2pt6trk9</td>\n",
       "      <td>8.342456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6018 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature_name  idf_weights\n",
       "5401            to     1.932281\n",
       "5316           the     2.163475\n",
       "5983           you     2.288016\n",
       "2419           for     2.375028\n",
       "5608        united     2.497463\n",
       "...            ...          ...\n",
       "3460         lucas     8.342456\n",
       "3461         lucia     8.342456\n",
       "1320    cbv7f3kbkx     8.342456\n",
       "3450  lowstandards     8.342456\n",
       "6017    zv2pt6trk9     8.342456\n",
       "\n",
       "[6018 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer()  # A\n",
    "\n",
    "tfidf_vectorizer.fit(train['text'])\n",
    "\n",
    "idf = pd.DataFrame({'feature_name':tfidf_vectorizer.get_feature_names(), 'idf_weights':tfidf_vectorizer.idf_})\n",
    "idf.sort_values('idf_weights', ascending=True)\n",
    "\n",
    "# A vanilla tf-idf vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "square-anthropology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.342455512358637"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IDF for a term that appears once in a given tweet and once overall in the entire corpus\n",
    "np.log((1 + train.shape[0]) / (1 + 1)) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "accurate-mexico",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Vectorizer + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.80      0.84      0.82       243\n",
      "     neutral       0.82      0.80      0.81       260\n",
      "    positive       0.89      0.87      0.88       269\n",
      "\n",
      "    accuracy                           0.84       772\n",
      "   macro avg       0.84      0.84      0.84       772\n",
      "weighted avg       0.84      0.84      0.84       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0, 'vectorizer__lowercase': True, 'vectorizer__max_features': 5000, 'vectorizer__ngram_range': (1, 3), 'vectorizer__stop_words': None}\n",
      "Overall took 26.48 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('vectorizer',\n",
       "                  TfidfVectorizer(max_features=5000, ngram_range=(1, 3))),\n",
       "                 ('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'negative', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'positive', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'negative', 'negative',\n",
       "        'negative', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_pipeline = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer()),  # A\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "print(\"TF-IDF Vectorizer + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    train['text'], train['sentiment'], test['text'], test['sentiment'], \n",
    "    ml_pipeline, params  # B\n",
    ")\n",
    "\n",
    "# A our tfidf-vectorizer\n",
    "# B same parameters as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "accompanied-accessory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'@United is #awesome 👍 %'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean tweets https://pypi.org/project/tweet-preprocessor\n",
    "\n",
    "import preprocessor as tweet_preprocessor\n",
    "\n",
    "# remove urls and mentions\n",
    "tweet_preprocessor.set_options(\n",
    "    tweet_preprocessor.OPT.URL, tweet_preprocessor.OPT.NUMBER\n",
    ")\n",
    "\n",
    "tweet_preprocessor.clean(\n",
    "    '@United is #awesome 👍 https://a.link/s/redirect 100%'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "better-chair",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet Cleaning + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.81      0.80       243\n",
      "     neutral       0.78      0.78      0.78       260\n",
      "    positive       0.86      0.85      0.85       269\n",
      "\n",
      "    accuracy                           0.81       772\n",
      "   macro avg       0.81      0.81      0.81       772\n",
      "weighted avg       0.81      0.81      0.81       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0, 'vectorizer__lowercase': True, 'vectorizer__max_features': 5000, 'vectorizer__ngram_range': (1, 1), 'vectorizer__stop_words': None}\n",
      "Overall took 25.71 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('vectorizer', TfidfVectorizer(max_features=5000)),\n",
       "                 ('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'negative', 'positive', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'positive', 'negative',\n",
       "        'positive', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'positive', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'positive',\n",
       "        'negative', 'positive', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet_preprocessor.set_options(\n",
    "    tweet_preprocessor.OPT.URL, tweet_preprocessor.OPT.NUMBER\n",
    ")\n",
    "\n",
    "ml_pipeline = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer()),  # TfidfVectorizer gave us better results\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'vectorizer__lowercase': [True, False],\n",
    "    'vectorizer__stop_words': [None, 'english'],\n",
    "    'vectorizer__max_features': [100, 1000, 5000],\n",
    "    'vectorizer__ngram_range': [(1, 1), (1, 3)],\n",
    "    \n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "\n",
    "}\n",
    "\n",
    "print(\"Tweet Cleaning + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    # apply cleaning here because it does not change given the training data\n",
    "    train['text'].apply(tweet_preprocessor.clean), train['sentiment'], \n",
    "    test['text'].apply(tweet_preprocessor.clean), test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "following-cannon",
   "metadata": {},
   "outputs": [],
   "source": [
    "# looks like we need those cleaned tokens to make better predictions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "polished-immunology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'wait'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.stem import SnowballStemmer  # A\n",
    "\n",
    "snowball_stemmer = SnowballStemmer(language='english')  # B\n",
    "\n",
    "snowball_stemmer.stem('waiting')\n",
    "\n",
    "# A import our stemmer\n",
    "# B instantiate our stemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eastern-balloon",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "billion-training",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/sinanozdemir/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['wait', 'plane']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk  # A\n",
    "\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "stemmed_stopwords = list(map(snowball_stemmer.stem, stopwords.words('english')))  # B\n",
    "\n",
    "import re\n",
    "\n",
    "def stem_tokenizer(_input):  #  C\n",
    "    tokenized_words = re.sub(r\"[^A-Za-z0-9\\-]\", \" \", _input).lower().split()\n",
    "    return [snowball_stemmer.stem(word) for word in tokenized_words if snowball_stemmer.stem(word) not in stemmed_stopwords]\n",
    "\n",
    "stem_tokenizer('waiting for the plane')  # D\n",
    "\n",
    "# A import nltk\n",
    "# B stem stop words from nltk\n",
    "# C custom tokenizer that stems words and filters out stopwords\n",
    "# D lowercases the string, stems the words and removes stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "naughty-conducting",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "usual-counter",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stemming + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.80      0.81      0.80       243\n",
      "     neutral       0.77      0.78      0.78       260\n",
      "    positive       0.86      0.84      0.85       269\n",
      "\n",
      "    accuracy                           0.81       772\n",
      "   macro avg       0.81      0.81      0.81       772\n",
      "weighted avg       0.81      0.81      0.81       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0, 'vectorizer__max_features': 5000, 'vectorizer__ngram_range': (1, 1)}\n",
      "Overall took 36.28 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('vectorizer',\n",
       "                  TfidfVectorizer(max_features=5000,\n",
       "                                  tokenizer=<function stem_tokenizer at 0x1687049d0>)),\n",
       "                 ('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'negative', 'negative', 'positive',\n",
       "        'positive', 'negative', 'negative', 'negative', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'positive',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'negative', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'neutral', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'positive', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'negative',\n",
       "        'positive', 'negative', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'positive', 'positive', 'positive',\n",
       "        'positive', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'neutral'], dtype=object))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml_pipeline = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer(tokenizer=stem_tokenizer)),  # A\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "#     'vectorizer__lowercase': [True, False],\n",
    "#     'vectorizer__stop_words': [],  # B\n",
    "    \n",
    "    'vectorizer__max_features': [100, 1000, 5000],\n",
    "    'vectorizer__ngram_range': [(1, 1), (1, 3)],\n",
    "    \n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "\n",
    "}\n",
    "\n",
    "print(\"Stemming + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    # remove cleaning\n",
    "    train['text'], train['sentiment'], \n",
    "    test['text'], test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")\n",
    "\n",
    "# A Using a custom tokenizer\n",
    "# B not needed anymore as our tokenizer is removing stop words and lower casing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "polar-cable",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "infectious-mobile",
   "metadata": {},
   "outputs": [],
   "source": [
    "# when stemming/lemmatization and cleaning don't seem to help and our gridsearch keeps picking ngram range of (1, 3), \n",
    "# it is an indication that the grammar / semantic structure of the text is meaningful. \n",
    "# We should turn to some extraction / learning methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "creative-berkeley",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVD + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.84      0.82       243\n",
      "     neutral       0.81      0.79      0.80       260\n",
      "    positive       0.89      0.86      0.87       269\n",
      "\n",
      "    accuracy                           0.83       772\n",
      "   macro avg       0.83      0.83      0.83       772\n",
      "weighted avg       0.83      0.83      0.83       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0, 'reducer__n_components': 2000, 'vectorizer__lowercase': True, 'vectorizer__max_features': 5000, 'vectorizer__ngram_range': (1, 3), 'vectorizer__stop_words': None}\n",
      "Overall took 3169.20 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('vectorizer',\n",
       "                  TfidfVectorizer(max_features=5000, ngram_range=(1, 3))),\n",
       "                 ('reducer', TruncatedSVD(n_components=2000)),\n",
       "                 ('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'positive',\n",
       "        'positive', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'negative', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'positive', 'negative', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'negative', 'neutral', 'negative',\n",
       "        'neutral', 'negative', 'positive', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'positive',\n",
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       "        'neutral', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'positive', 'positive', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'neutral', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'positive', 'neutral',\n",
       "        'positive', 'positive', 'negative', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'negative', 'neutral', 'neutral',\n",
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       "        'negative', 'positive', 'positive', 'negative', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'negative', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
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       "        'positive', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'positive', 'negative',\n",
       "        'positive', 'negative', 'negative', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'neutral', 'neutral', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'positive', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'negative',\n",
       "        'positive', 'positive', 'negative', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'negative',\n",
       "        'negative', 'negative', 'negative', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'negative', 'negative',\n",
       "        'neutral', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'negative', 'negative', 'positive', 'negative', 'negative',\n",
       "        'negative', 'negative', 'positive', 'neutral', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'positive',\n",
       "        'positive', 'neutral', 'negative', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'positive', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'neutral',\n",
       "        'neutral', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'neutral', 'positive', 'negative', 'positive',\n",
       "        'neutral', 'negative', 'positive', 'positive', 'negative',\n",
       "        'positive', 'positive', 'positive', 'positive', 'positive',\n",
       "        'neutral', 'neutral', 'negative', 'negative', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
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       "        'negative', 'neutral', 'positive', 'neutral', 'negative',\n",
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       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'negative', 'negative', 'neutral',\n",
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       "        'neutral', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'neutral', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'positive',\n",
       "        'neutral', 'positive', 'positive', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD  # A\n",
    "\n",
    "ml_pipeline = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer()),  # B\n",
    "    ('reducer', TruncatedSVD()),\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'vectorizer__lowercase': [True, False],\n",
    "    'vectorizer__stop_words': [None, 'english'],\n",
    "    'vectorizer__max_features': [5000],\n",
    "    'vectorizer__ngram_range': [(1, 3)],\n",
    "    \n",
    "    'reducer__n_components': [500, 1000, 1500, 2000],  # number of components to reduce to\n",
    "    \n",
    "    'classifier__C': [1e-1, 1e0, 1e1]\n",
    "\n",
    "}\n",
    "\n",
    "print(\"SVD + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    train['text'], train['sentiment'], \n",
    "    test['text'], test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")\n",
    "\n",
    "# A feature extraction / dimension reduction with SVD\n",
    "# B our custom tokenizer didn't work out so well so we will remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "apparent-prisoner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TruncatedSVD(n_components=10)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = TfidfVectorizer(max_features=1000)\n",
    "X = t.fit_transform(train['text']).toarray()\n",
    "\n",
    "svd = TruncatedSVD(n_components=10)\n",
    "\n",
    "svd.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "small-society",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 1000)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svd.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "grave-diana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.matmul(X, svd.components_.T) == svd.transform(X)).mean()  # A\n",
    "\n",
    "# A Multiplying X by the components is the same thing as transforming the matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cloudy-pregnancy",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "brief-elements",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(**{\n",
    "    'lowercase': True, 'max_features': 5000, 'ngram_range': (1, 3), 'stop_words': None\n",
    "})\n",
    "\n",
    "vectorized_X_train = vectorizer.fit_transform(train['text']).toarray()  # A\n",
    "vectorized_X_test = vectorizer.transform(test['text']).toarray()  # A\n",
    "\n",
    "# A fit a vectorizer on training data and transform training and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "assumed-china",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3088, 5000), (772, 5000))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorized_X_train.shape, vectorized_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "surface-grave",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense      # A\n",
    "from keras.models import Model, Sequential # A\n",
    "import tensorflow as tf                    # A\n",
    "\n",
    "n_inputs = vectorized_X_train.shape[1]\n",
    "n_bottleneck = 2000  # B\n",
    "\n",
    "# encoder\n",
    "visible = Input(shape=(n_inputs,), name='input')\n",
    "e = Dense(n_inputs//2, activation='relu', name='encoder')(visible)\n",
    "# code/bottleneck\n",
    "bottleneck = Dense(n_bottleneck, name='bottleneck')(e)\n",
    "\n",
    "# decoder\n",
    "d = Dense(n_inputs//2, activation='relu', name='decoder')(bottleneck)\n",
    "# output layer\n",
    "output = Dense(n_inputs, activation='relu', name='output')(d)\n",
    "\n",
    "# define autoencoder model\n",
    "autoencoder = Model(inputs=visible, outputs=output)\n",
    "\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='mse')  # C\n",
    "\n",
    "# A imports for the autoencoder\n",
    "# B We will attempt to compress 5000 tokens into a latent dimension of size 2000\n",
    "# C Compile autoencoder model to minimize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "norman-month",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "wooden-mainstream",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !brew install graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "amended-planner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n"
     ]
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "\n",
    "plot_model(autoencoder, to_file='autoencoder.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "naughty-elder",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-04 08:52:57.776181: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 2s 312ms/step - loss: 2.0867e-04 - val_loss: 2.0060e-04\n",
      "Epoch 2/100\n",
      "6/6 [==============================] - 2s 308ms/step - loss: 1.9904e-04 - val_loss: 1.9768e-04\n",
      "Epoch 3/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.9707e-04 - val_loss: 1.9689e-04\n",
      "Epoch 4/100\n",
      "6/6 [==============================] - 2s 300ms/step - loss: 1.9650e-04 - val_loss: 1.9670e-04\n",
      "Epoch 5/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.9630e-04 - val_loss: 1.9653e-04\n",
      "Epoch 6/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.9611e-04 - val_loss: 1.9636e-04\n",
      "Epoch 7/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.9593e-04 - val_loss: 1.9624e-04\n",
      "Epoch 8/100\n",
      "6/6 [==============================] - 2s 308ms/step - loss: 1.9576e-04 - val_loss: 1.9613e-04\n",
      "Epoch 9/100\n",
      "6/6 [==============================] - 2s 300ms/step - loss: 1.9559e-04 - val_loss: 1.9602e-04\n",
      "Epoch 10/100\n",
      "6/6 [==============================] - 2s 296ms/step - loss: 1.9542e-04 - val_loss: 1.9590e-04\n",
      "Epoch 11/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.9524e-04 - val_loss: 1.9576e-04\n",
      "Epoch 12/100\n",
      "6/6 [==============================] - 2s 285ms/step - loss: 1.9505e-04 - val_loss: 1.9559e-04\n",
      "Epoch 13/100\n",
      "6/6 [==============================] - 2s 298ms/step - loss: 1.9483e-04 - val_loss: 1.9543e-04\n",
      "Epoch 14/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.9459e-04 - val_loss: 1.9525e-04\n",
      "Epoch 15/100\n",
      "6/6 [==============================] - 2s 302ms/step - loss: 1.9433e-04 - val_loss: 1.9500e-04\n",
      "Epoch 16/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.9403e-04 - val_loss: 1.9476e-04\n",
      "Epoch 17/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.9371e-04 - val_loss: 1.9448e-04\n",
      "Epoch 18/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.9334e-04 - val_loss: 1.9417e-04\n",
      "Epoch 19/100\n",
      "6/6 [==============================] - 2s 292ms/step - loss: 1.9294e-04 - val_loss: 1.9379e-04\n",
      "Epoch 20/100\n",
      "6/6 [==============================] - 2s 300ms/step - loss: 1.9248e-04 - val_loss: 1.9341e-04\n",
      "Epoch 21/100\n",
      "6/6 [==============================] - 2s 304ms/step - loss: 1.9197e-04 - val_loss: 1.9295e-04\n",
      "Epoch 22/100\n",
      "6/6 [==============================] - 2s 299ms/step - loss: 1.9142e-04 - val_loss: 1.9247e-04\n",
      "Epoch 23/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.9082e-04 - val_loss: 1.9196e-04\n",
      "Epoch 24/100\n",
      "6/6 [==============================] - 2s 306ms/step - loss: 1.9015e-04 - val_loss: 1.9138e-04\n",
      "Epoch 25/100\n",
      "6/6 [==============================] - 2s 296ms/step - loss: 1.8946e-04 - val_loss: 1.9076e-04\n",
      "Epoch 26/100\n",
      "6/6 [==============================] - 2s 284ms/step - loss: 1.8870e-04 - val_loss: 1.9013e-04\n",
      "Epoch 27/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.8790e-04 - val_loss: 1.8947e-04\n",
      "Epoch 28/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.8705e-04 - val_loss: 1.8877e-04\n",
      "Epoch 29/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.8616e-04 - val_loss: 1.8803e-04\n",
      "Epoch 30/100\n",
      "6/6 [==============================] - 2s 286ms/step - loss: 1.8522e-04 - val_loss: 1.8726e-04\n",
      "Epoch 31/100\n",
      "6/6 [==============================] - 2s 302ms/step - loss: 1.8421e-04 - val_loss: 1.8647e-04\n",
      "Epoch 32/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.8315e-04 - val_loss: 1.8559e-04\n",
      "Epoch 33/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.8205e-04 - val_loss: 1.8474e-04\n",
      "Epoch 34/100\n",
      "6/6 [==============================] - 2s 286ms/step - loss: 1.8090e-04 - val_loss: 1.8381e-04\n",
      "Epoch 35/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.7969e-04 - val_loss: 1.8284e-04\n",
      "Epoch 36/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.7845e-04 - val_loss: 1.8186e-04\n",
      "Epoch 37/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.7715e-04 - val_loss: 1.8087e-04\n",
      "Epoch 38/100\n",
      "6/6 [==============================] - 2s 282ms/step - loss: 1.7578e-04 - val_loss: 1.7979e-04\n",
      "Epoch 39/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.7439e-04 - val_loss: 1.7876e-04\n",
      "Epoch 40/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.7299e-04 - val_loss: 1.7764e-04\n",
      "Epoch 41/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.7151e-04 - val_loss: 1.7654e-04\n",
      "Epoch 42/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.7003e-04 - val_loss: 1.7540e-04\n",
      "Epoch 43/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.6854e-04 - val_loss: 1.7429e-04\n",
      "Epoch 44/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.6700e-04 - val_loss: 1.7312e-04\n",
      "Epoch 45/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.6548e-04 - val_loss: 1.7203e-04\n",
      "Epoch 46/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.6393e-04 - val_loss: 1.7085e-04\n",
      "Epoch 47/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.6241e-04 - val_loss: 1.6982e-04\n",
      "Epoch 48/100\n",
      "6/6 [==============================] - 2s 297ms/step - loss: 1.6090e-04 - val_loss: 1.6868e-04\n",
      "Epoch 49/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.5939e-04 - val_loss: 1.6762e-04\n",
      "Epoch 50/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.5791e-04 - val_loss: 1.6652e-04\n",
      "Epoch 51/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.5640e-04 - val_loss: 1.6556e-04\n",
      "Epoch 52/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.5492e-04 - val_loss: 1.6451e-04\n",
      "Epoch 53/100\n",
      "6/6 [==============================] - 2s 298ms/step - loss: 1.5351e-04 - val_loss: 1.6354e-04\n",
      "Epoch 54/100\n",
      "6/6 [==============================] - 2s 298ms/step - loss: 1.5205e-04 - val_loss: 1.6257e-04\n",
      "Epoch 55/100\n",
      "6/6 [==============================] - 2s 297ms/step - loss: 1.5063e-04 - val_loss: 1.6158e-04\n",
      "Epoch 56/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.4924e-04 - val_loss: 1.6062e-04\n",
      "Epoch 57/100\n",
      "6/6 [==============================] - 2s 284ms/step - loss: 1.4782e-04 - val_loss: 1.5967e-04\n",
      "Epoch 58/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.4651e-04 - val_loss: 1.5883e-04\n",
      "Epoch 59/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.4518e-04 - val_loss: 1.5792e-04\n",
      "Epoch 60/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.4387e-04 - val_loss: 1.5699e-04\n",
      "Epoch 61/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.4261e-04 - val_loss: 1.5616e-04\n",
      "Epoch 62/100\n",
      "6/6 [==============================] - 2s 292ms/step - loss: 1.4136e-04 - val_loss: 1.5543e-04\n",
      "Epoch 63/100\n",
      "6/6 [==============================] - 2s 285ms/step - loss: 1.4018e-04 - val_loss: 1.5457e-04\n",
      "Epoch 64/100\n",
      "6/6 [==============================] - 2s 297ms/step - loss: 1.3895e-04 - val_loss: 1.5375e-04\n",
      "Epoch 65/100\n",
      "6/6 [==============================] - 2s 297ms/step - loss: 1.3773e-04 - val_loss: 1.5301e-04\n",
      "Epoch 66/100\n",
      "6/6 [==============================] - 2s 295ms/step - loss: 1.3661e-04 - val_loss: 1.5230e-04\n",
      "Epoch 67/100\n",
      "6/6 [==============================] - 2s 296ms/step - loss: 1.3545e-04 - val_loss: 1.5150e-04\n",
      "Epoch 68/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.3440e-04 - val_loss: 1.5080e-04\n",
      "Epoch 69/100\n",
      "6/6 [==============================] - 2s 288ms/step - loss: 1.3328e-04 - val_loss: 1.5008e-04\n",
      "Epoch 70/100\n",
      "6/6 [==============================] - 2s 303ms/step - loss: 1.3222e-04 - val_loss: 1.4951e-04\n",
      "Epoch 71/100\n",
      "6/6 [==============================] - 2s 286ms/step - loss: 1.3123e-04 - val_loss: 1.4873e-04\n",
      "Epoch 72/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.3016e-04 - val_loss: 1.4804e-04\n",
      "Epoch 73/100\n",
      "6/6 [==============================] - 2s 290ms/step - loss: 1.2921e-04 - val_loss: 1.4752e-04\n",
      "Epoch 74/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.2829e-04 - val_loss: 1.4696e-04\n",
      "Epoch 75/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.2736e-04 - val_loss: 1.4634e-04\n",
      "Epoch 76/100\n",
      "6/6 [==============================] - 2s 302ms/step - loss: 1.2647e-04 - val_loss: 1.4584e-04\n",
      "Epoch 77/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 2s 289ms/step - loss: 1.2558e-04 - val_loss: 1.4522e-04\n",
      "Epoch 78/100\n",
      "6/6 [==============================] - 2s 300ms/step - loss: 1.2468e-04 - val_loss: 1.4462e-04\n",
      "Epoch 79/100\n",
      "6/6 [==============================] - 2s 306ms/step - loss: 1.2387e-04 - val_loss: 1.4414e-04\n",
      "Epoch 80/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.2304e-04 - val_loss: 1.4355e-04\n",
      "Epoch 81/100\n",
      "6/6 [==============================] - 2s 285ms/step - loss: 1.2222e-04 - val_loss: 1.4317e-04\n",
      "Epoch 82/100\n",
      "6/6 [==============================] - 2s 297ms/step - loss: 1.2148e-04 - val_loss: 1.4261e-04\n",
      "Epoch 83/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.2066e-04 - val_loss: 1.4214e-04\n",
      "Epoch 84/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.1993e-04 - val_loss: 1.4168e-04\n",
      "Epoch 85/100\n",
      "6/6 [==============================] - 2s 299ms/step - loss: 1.1925e-04 - val_loss: 1.4126e-04\n",
      "Epoch 86/100\n",
      "6/6 [==============================] - 2s 291ms/step - loss: 1.1853e-04 - val_loss: 1.4074e-04\n",
      "Epoch 87/100\n",
      "6/6 [==============================] - 2s 303ms/step - loss: 1.1788e-04 - val_loss: 1.4036e-04\n",
      "Epoch 88/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.1724e-04 - val_loss: 1.4001e-04\n",
      "Epoch 89/100\n",
      "6/6 [==============================] - 2s 294ms/step - loss: 1.1661e-04 - val_loss: 1.3956e-04\n",
      "Epoch 90/100\n",
      "6/6 [==============================] - 2s 287ms/step - loss: 1.1601e-04 - val_loss: 1.3919e-04\n",
      "Epoch 91/100\n",
      "6/6 [==============================] - 2s 292ms/step - loss: 1.1544e-04 - val_loss: 1.3884e-04\n",
      "Epoch 92/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.1484e-04 - val_loss: 1.3849e-04\n",
      "Epoch 93/100\n",
      "6/6 [==============================] - 2s 298ms/step - loss: 1.1426e-04 - val_loss: 1.3817e-04\n",
      "Epoch 94/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.1369e-04 - val_loss: 1.3772e-04\n",
      "Epoch 95/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.1320e-04 - val_loss: 1.3739e-04\n",
      "Epoch 96/100\n",
      "6/6 [==============================] - 2s 309ms/step - loss: 1.1266e-04 - val_loss: 1.3718e-04\n",
      "Epoch 97/100\n",
      "6/6 [==============================] - 2s 292ms/step - loss: 1.1216e-04 - val_loss: 1.3677e-04\n",
      "Epoch 98/100\n",
      "6/6 [==============================] - 2s 295ms/step - loss: 1.1165e-04 - val_loss: 1.3651e-04\n",
      "Epoch 99/100\n",
      "6/6 [==============================] - 2s 293ms/step - loss: 1.1117e-04 - val_loss: 1.3621e-04\n",
      "Epoch 100/100\n",
      "6/6 [==============================] - 2s 289ms/step - loss: 1.1071e-04 - val_loss: 1.3592e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x332084430>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "early_stopping_callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)  # A\n",
    "\n",
    "# B\n",
    "autoencoder_history = autoencoder.fit(vectorized_X_train, vectorized_X_train, \n",
    "                batch_size = 512, epochs = 100,  callbacks=[early_stopping_callback],\n",
    "                shuffle = True, validation_split = 0.10)\n",
    "\n",
    "\n",
    "plt.plot(autoencoder_history.history['loss'], label='Loss')\n",
    "plt.plot(autoencoder_history.history['val_loss'], label='Val Loss')\n",
    "\n",
    "plt.title('Autoencoder Loss')\n",
    "plt.legend()\n",
    "\n",
    "# A Stop training when the loss stops decreasing\n",
    "# B Training our Auto-encoder network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "surgical-christopher",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autoencoder + Log Reg\n",
      "=====================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.79      0.84      0.82       243\n",
      "     neutral       0.80      0.79      0.79       260\n",
      "    positive       0.88      0.84      0.86       269\n",
      "\n",
      "    accuracy                           0.82       772\n",
      "   macro avg       0.82      0.82      0.82       772\n",
      "weighted avg       0.82      0.82      0.82       772\n",
      "\n",
      "Best params: {'classifier__C': 1.0}\n",
      "Overall took 86.95 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Pipeline(steps=[('classifier', LogisticRegression(max_iter=10000))]),\n",
       " array(['negative', 'neutral', 'positive', 'neutral', 'neutral',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'negative',\n",
       "        'neutral', 'negative', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'negative', 'positive', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'positive', 'positive',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'positive', 'neutral', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'positive', 'negative', 'positive',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'positive', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'positive', 'negative',\n",
       "        'negative', 'positive', 'positive', 'negative', 'negative',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'neutral',\n",
       "        'negative', 'neutral', 'positive', 'neutral', 'positive',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'negative',\n",
       "        'positive', 'positive', 'neutral', 'negative', 'positive',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'positive',\n",
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       "        'neutral', 'positive', 'negative', 'positive', 'negative',\n",
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       "        'neutral', 'neutral', 'positive', 'positive', 'positive',\n",
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       "        'negative', 'positive', 'neutral', 'negative', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'negative', 'neutral',\n",
       "        'neutral', 'positive', 'positive', 'negative', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'positive', 'negative', 'negative', 'neutral', 'negative',\n",
       "        'positive', 'neutral', 'negative', 'positive', 'neutral',\n",
       "        'negative', 'negative', 'neutral', 'neutral', 'negative',\n",
       "        'positive', 'negative', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'negative', 'negative', 'negative', 'positive',\n",
       "        'negative', 'positive', 'neutral', 'neutral', 'negative',\n",
       "        'negative', 'positive', 'negative', 'positive', 'positive',\n",
       "        'positive', 'positive', 'neutral', 'neutral', 'neutral',\n",
       "        'negative', 'neutral', 'neutral', 'neutral', 'positive', 'neutral',\n",
       "        'negative', 'positive', 'negative', 'positive', 'negative',\n",
       "        'positive', 'neutral', 'positive', 'positive', 'negative',\n",
       "        'neutral', 'positive', 'negative', 'negative', 'neutral',\n",
       "        'neutral', 'neutral', 'neutral', 'neutral', 'neutral'],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latent_representation = Model(inputs=visible, outputs=bottleneck)  # A\n",
    "\n",
    "encoded_X_train = latent_representation.predict(vectorized_X_train)  # B\n",
    "encoded_X_test = latent_representation.predict(vectorized_X_test)  # B\n",
    "\n",
    "\n",
    "ml_pipeline = Pipeline([\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "}\n",
    "\n",
    "print(\"Autoencoder + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    encoded_X_train, train['sentiment'], encoded_X_test, test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")\n",
    "\n",
    "# A create our latent representation encoder\n",
    "# B encode our training and testing corpus into our latent representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "prospective-there",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "respected-ocean",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transfer Learning with BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "serial-divorce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "champion-newark",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 768])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import BertTokenizer, BertModel  # A\n",
    "import torch\n",
    "\n",
    "bert_model = BertModel.from_pretrained('bert-base-uncased')  # B\n",
    "\n",
    "bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')  # C\n",
    "\n",
    "tweet = 'I hate this airline'\n",
    "\n",
    "token_ids = torch.tensor(bert_tokenizer.encode(tweet)).unsqueeze(0)  # D\n",
    "\n",
    "bert_model(token_ids)[1].shape  # E\n",
    "\n",
    "# A Imports for BERT\n",
    "# B Load a vanilla BERT-base uncased (all lowercased) model\n",
    "# C We also need to load the BERT tokenizer\n",
    "# D Run a tokenized input through BERT\n",
    "# E the base BERT model outputs fixed-length vectors of length 768"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "disabled-ebony",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|██████████████████████████████████████████████▌                                                      | 6/13 [2:28:40<2:53:27, 1486.77s/it]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [50]\u001b[0m, in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m             embedded \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack([embedded, batch_bert])\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m embedded\n\u001b[0;32m---> 28\u001b[0m bert_X_train \u001b[38;5;241m=\u001b[39m \u001b[43mbatch_embed_text\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbert_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbert_tokenizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m bert_X_test \u001b[38;5;241m=\u001b[39m batch_embed_text(bert_model, bert_tokenizer, test[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "Input \u001b[0;32mIn [50]\u001b[0m, in \u001b[0;36mbatch_embed_text\u001b[0;34m(bert_model, tokenizer, text_iterable, batch_size)\u001b[0m\n\u001b[1;32m     14\u001b[0m embedded \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m start_idx, end_idx \u001b[38;5;129;01min\u001b[39;00m batch_array_idx(input_ids, batch_size\u001b[38;5;241m=\u001b[39mbatch_size):\n\u001b[0;32m---> 17\u001b[0m     batch_bert \u001b[38;5;241m=\u001b[39m \u001b[43mbert_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstart_idx\u001b[49m\u001b[43m:\u001b[49m\u001b[43mend_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstart_idx\u001b[49m\u001b[43m:\u001b[49m\u001b[43mend_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m embedded \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     22\u001b[0m         embedded \u001b[38;5;241m=\u001b[39m batch_bert\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/transformers/models/bert/modeling_bert.py:996\u001b[0m, in \u001b[0;36mBertModel.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    987\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[1;32m    989\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m    990\u001b[0m     input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m    991\u001b[0m     position_ids\u001b[38;5;241m=\u001b[39mposition_ids,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    994\u001b[0m     past_key_values_length\u001b[38;5;241m=\u001b[39mpast_key_values_length,\n\u001b[1;32m    995\u001b[0m )\n\u001b[0;32m--> 996\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    997\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    998\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    999\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1000\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1001\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1002\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1003\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1004\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1005\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1006\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1007\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1008\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1009\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/transformers/models/bert/modeling_bert.py:585\u001b[0m, in \u001b[0;36mBertEncoder.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    576\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mcheckpoint\u001b[38;5;241m.\u001b[39mcheckpoint(\n\u001b[1;32m    577\u001b[0m         create_custom_forward(layer_module),\n\u001b[1;32m    578\u001b[0m         hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    582\u001b[0m         encoder_attention_mask,\n\u001b[1;32m    583\u001b[0m     )\n\u001b[1;32m    584\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 585\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    586\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    587\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    588\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    589\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    590\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    592\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    593\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    595\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    596\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/transformers/models/bert/modeling_bert.py:472\u001b[0m, in \u001b[0;36mBertLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    460\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    461\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    462\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    469\u001b[0m ):\n\u001b[1;32m    470\u001b[0m     \u001b[38;5;66;03m# decoder uni-directional self-attention cached key/values tuple is at positions 1,2\u001b[39;00m\n\u001b[1;32m    471\u001b[0m     self_attn_past_key_value \u001b[38;5;241m=\u001b[39m past_key_value[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m past_key_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 472\u001b[0m     self_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    473\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    474\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    475\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    476\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    477\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mself_attn_past_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    478\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    479\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    481\u001b[0m     \u001b[38;5;66;03m# if decoder, the last output is tuple of self-attn cache\u001b[39;00m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/transformers/models/bert/modeling_bert.py:402\u001b[0m, in \u001b[0;36mBertAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    392\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    393\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    394\u001b[0m     hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    400\u001b[0m     output_attentions\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    401\u001b[0m ):\n\u001b[0;32m--> 402\u001b[0m     self_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    403\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    404\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    405\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    406\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    407\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    408\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    409\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    410\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    411\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(self_outputs[\u001b[38;5;241m0\u001b[39m], hidden_states)\n\u001b[1;32m    412\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (attention_output,) \u001b[38;5;241m+\u001b[39m self_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/torch/nn/modules/module.py:1102\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1098\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1099\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/pytorch_env/lib/python3.9/site-packages/transformers/models/bert/modeling_bert.py:306\u001b[0m, in \u001b[0;36mBertSelfAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    303\u001b[0m     past_key_value \u001b[38;5;241m=\u001b[39m (key_layer, value_layer)\n\u001b[1;32m    305\u001b[0m \u001b[38;5;66;03m# Take the dot product between \"query\" and \"key\" to get the raw attention scores.\u001b[39;00m\n\u001b[0;32m--> 306\u001b[0m attention_scores \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_layer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey_layer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtranspose\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposition_embedding_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelative_key\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mposition_embedding_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelative_key_query\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    309\u001b[0m     seq_length \u001b[38;5;241m=\u001b[39m hidden_states\u001b[38;5;241m.\u001b[39msize()[\u001b[38;5;241m1\u001b[39m]\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "def batch_embed_text(bert_model, tokenizer, text_iterable, batch_size=256):\n",
    "    ''' This helper method will batch embed an iterable of text using a given tokenizer and bert model '''\n",
    "    encoding = tokenizer.batch_encode_plus(text_iterable, padding=True)\n",
    "    input_ids = np.vstack(encoding['input_ids'])\n",
    "    attention_mask = np.vstack(encoding['attention_mask'])\n",
    "    \n",
    "    def batch_array_idx(np_array, batch_size):\n",
    "        for i in tqdm(range(0, np_array.shape[0], batch_size)):\n",
    "            yield i, i + batch_size\n",
    "            \n",
    "    embedded = None\n",
    "\n",
    "    for start_idx, end_idx in batch_array_idx(input_ids, batch_size=batch_size):\n",
    "        batch_bert = bert_model(\n",
    "            torch.tensor(input_ids[start_idx:end_idx]), \n",
    "            attention_mask=torch.tensor(attention_mask[start_idx:end_idx])\n",
    "        )[1].detach().numpy()\n",
    "        if embedded is None:\n",
    "            embedded = batch_bert\n",
    "        else:\n",
    "            embedded = np.vstack([embedded, batch_bert])\n",
    "\n",
    "    return embedded\n",
    "\n",
    "bert_X_train = batch_embed_text(bert_model, bert_tokenizer, train['text'])\n",
    "\n",
    "bert_X_test = batch_embed_text(bert_model, bert_tokenizer, test['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "criminal-publisher",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_pipeline = Pipeline([\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "}\n",
    "\n",
    "print(\"BERT + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    bert_X_train, train['sentiment'], bert_X_test, test['sentiment'], \n",
    "    ml_pipeline, params\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rising-reynolds",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "crucial-amendment",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlp import load_dataset, Dataset\n",
    "\n",
    "\n",
    "sample_tweets, _ = train_test_split(train, test_size=0.9, random_state=0, stratify=train['sentiment'])\n",
    "\n",
    "# Let's map our sentiment to a numerical classes. negative -> 0, neutral -> 1, and positive -> 2\n",
    "sample_tweets['sentiment'] = sample_tweets['sentiment'].map({'negative': 0, 'neutral': 1, 'positive': 2})\n",
    "\n",
    "# the trainer is expecting a 'label' (see the forward method in the docs)\n",
    "sample_tweets['label'] = sample_tweets['sentiment']\n",
    "\n",
    "print(sample_tweets['label'].value_counts())\n",
    "\n",
    "sample_dataset = Dataset.from_pandas(sample_tweets)\n",
    "\n",
    "# Dataset has a built in train test split method\n",
    "sample_dataset = sample_dataset.train_test_split(test_size=0.3)\n",
    "\n",
    "train_set = sample_dataset['train']\n",
    "test_set = sample_dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sticky-hawaiian",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "further-requirement",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will pad our dataset so that our input matrices are the same length and truncate anything longer than 512 tokens\n",
    "def preprocess(data):\n",
    "    return bert_tokenizer(data['text'], padding=True, truncation=True)\n",
    "\n",
    "train_set = train_set.map(preprocess, batched=True, batch_size=len(train_set))\n",
    "test_set = test_set.map(preprocess, batched=True, batch_size=len(test_set))\n",
    "\n",
    "train_set.set_format('torch', \n",
    "                      columns=['input_ids', 'attention_mask', 'label'])\n",
    "test_set.set_format('torch', \n",
    "                     columns=['input_ids', 'attention_mask', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "legitimate-enterprise",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertForSequenceClassification, BertTokenizer, Trainer, TrainingArguments\n",
    "\n",
    "sequence_classification_model = BertForSequenceClassification.from_pretrained(\n",
    "    'bert-base-uncased', num_labels=3\n",
    ")\n",
    "\n",
    "batch_size = 128\n",
    "epochs = 10\n",
    "\n",
    "warmup_steps = 500\n",
    "weight_decay = 0.01\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='./results',\n",
    "    num_train_epochs=epochs,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    warmup_steps=warmup_steps,\n",
    "    weight_decay=weight_decay,\n",
    "    logging_dir='./logs',\n",
    ")\n",
    "# Define the trainer: \n",
    "\n",
    "trainer = Trainer(\n",
    "    model=sequence_classification_model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_set,\n",
    "    eval_dataset=test_set\n",
    ")\n",
    "\n",
    "# Get initial metrics\n",
    "trainer.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rubber-virus",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nominated-printing",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tuned_bert_model = sequence_classification_model.bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "measured-productivity",
   "metadata": {},
   "outputs": [],
   "source": [
    "fine_tuned_bert_X_train = batch_embed_text(fine_tuned_bert_model, bert_tokenizer, train['text'])\n",
    "\n",
    "fine_tuned_bert_X_test = batch_embed_text(fine_tuned_bert_model, bert_tokenizer, test['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sublime-completion",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ml_pipeline = Pipeline([\n",
    "    ('classifier', clf)\n",
    "])\n",
    "\n",
    "params = {\n",
    "    'classifier__C': [1e-1, 1e0, 1e1]  \n",
    "}\n",
    "\n",
    "print(\"Fine-tuned BERT + Log Reg\\n=====================\")\n",
    "advanced_grid_search(\n",
    "    fine_tuned_bert_X_train, train['sentiment'],\n",
    "    fine_tuned_bert_X_test, test['sentiment'],\n",
    "    ml_pipeline, params\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "congressional-region",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d9ecb67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a11c906e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38c6c7a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0b154c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8595340d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06482306",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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