Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['label', 'comment', 'author', 'subreddit', 'score', 'ups', 'downs',\n",
       "       'date', 'created_utc', 'parent_comment'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import nltk\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = pd.read_csv(\"data/data-filtered.csv\")\n",
    "df = pd.DataFrame(data=data)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf79192da0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['label'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2]), <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df[['score','ups','downs']]\n",
    "y = df['label']\n",
    "data_norm = (X - X.mean()) / (X.max() - X.min())             #normalization\n",
    "data = pd.concat([y,data_norm],axis=1)\n",
    "data = pd.melt(data, id_vars='label', var_name='features', value_name='value')\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.violinplot(x=\"features\", y=\"value\", hue=\"label\", data=data,split=True, inner=\"quart\",scale='width',palette='Blues')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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>label</th>\n",
       "      <th>comment</th>\n",
       "      <th>author</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>score</th>\n",
       "      <th>ups</th>\n",
       "      <th>downs</th>\n",
       "      <th>date</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NC and NH.</td>\n",
       "      <td>Trumpbart</td>\n",
       "      <td>politics</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>2016-10-16 23:55:23</td>\n",
       "      <td>Yeah, I get that argument. At this point, I'd ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>You do know west teams play against west teams...</td>\n",
       "      <td>Shbshb906</td>\n",
       "      <td>nba</td>\n",
       "      <td>-4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>2016-11-01 00:24:10</td>\n",
       "      <td>The blazers and Mavericks (The wests 5 and 6 s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>They were underdogs earlier today, but since G...</td>\n",
       "      <td>Creepeth</td>\n",
       "      <td>nfl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>2016-09-22 21:45:37</td>\n",
       "      <td>They're favored to win.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>This meme isn't funny none of the \"new york ni...</td>\n",
       "      <td>icebrotha</td>\n",
       "      <td>BlackPeopleTwitter</td>\n",
       "      <td>-8</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2016-10</td>\n",
       "      <td>2016-10-18 21:03:47</td>\n",
       "      <td>deadass don't kill my buzz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>I could use one of those tools.</td>\n",
       "      <td>cush2push</td>\n",
       "      <td>MaddenUltimateTeam</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2016-12</td>\n",
       "      <td>2016-12-30 17:00:13</td>\n",
       "      <td>Yep can confirm I saw the tool they use for th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            comment     author  \\\n",
       "0      0                                         NC and NH.  Trumpbart   \n",
       "1      0  You do know west teams play against west teams...  Shbshb906   \n",
       "2      0  They were underdogs earlier today, but since G...   Creepeth   \n",
       "3      0  This meme isn't funny none of the \"new york ni...  icebrotha   \n",
       "4      0                    I could use one of those tools.  cush2push   \n",
       "\n",
       "            subreddit  score  ups  downs     date          created_utc  \\\n",
       "0            politics      2   -1     -1  2016-10  2016-10-16 23:55:23   \n",
       "1                 nba     -4   -1     -1  2016-11  2016-11-01 00:24:10   \n",
       "2                 nfl      3    3      0  2016-09  2016-09-22 21:45:37   \n",
       "3  BlackPeopleTwitter     -8   -1     -1  2016-10  2016-10-18 21:03:47   \n",
       "4  MaddenUltimateTeam      6   -1     -1  2016-12  2016-12-30 17:00:13   \n",
       "\n",
       "                                      parent_comment  \n",
       "0  Yeah, I get that argument. At this point, I'd ...  \n",
       "1  The blazers and Mavericks (The wests 5 and 6 s...  \n",
       "2                            They're favored to win.  \n",
       "3                         deadass don't kill my buzz  \n",
       "4  Yep can confirm I saw the tool they use for th...  "
      ]
     },
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