feature_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from typing import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 17})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>1476662123</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>1477959850</td>\n",
       "      <td>The blazers and Mavericks (The wests 5 and 6 s...</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",
       "\n",
       "  subreddit  score  ups  downs     date  created_utc  \\\n",
       "0  politics      2   -1     -1  2016-10   1476662123   \n",
       "1       nba     -4   -1     -1  2016-11   1477959850   \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...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load train set\n",
    "train = pd.read_csv('dataset/train-balanced.csv', sep='\\t', names=['label', 'comment', 'author', 'subreddit', 'score', 'ups', 'downs', 'date', 'created_utc', 'parent_comment'])\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>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>Actually most of her supporters and sane peopl...</td>\n",
       "      <td>Quinnjester</td>\n",
       "      <td>politics</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-09</td>\n",
       "      <td>1473569605</td>\n",
       "      <td>Hillary's Surrogotes Told to Blame Media for '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>They can't survive without an echo chamber whi...</td>\n",
       "      <td>TheGettysburgAddress</td>\n",
       "      <td>The_Donald</td>\n",
       "      <td>13</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2016-11</td>\n",
       "      <td>1478788413</td>\n",
       "      <td>Thank God Liberals like to live in concentrate...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            comment  \\\n",
       "0      0  Actually most of her supporters and sane peopl...   \n",
       "1      0  They can't survive without an echo chamber whi...   \n",
       "\n",
       "                 author   subreddit  score  ups  downs     date  created_utc  \\\n",
       "0           Quinnjester    politics      3    3      0  2016-09   1473569605   \n",
       "1  TheGettysburgAddress  The_Donald     13   -1     -1  2016-11   1478788413   \n",
       "\n",
       "                                      parent_comment  \n",
       "0  Hillary's Surrogotes Told to Blame Media for '...  \n",
       "1  Thank God Liberals like to live in concentrate...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load test set\n",
    "test = pd.read_csv('dataset/test-balanced.csv', sep='\\t', names=['label', 'comment', 'author', 'subreddit', 'score', 'ups', 'downs', 'date', 'created_utc', 'parent_comment'])\n",
    "test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows without comment text\n",
    "train['comment'] = train['comment'].dropna(axis=0)\n",
    "test['comment'] = test['comment'].dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cast comments to string\n",
    "train['comment'] = train['comment'].astype(str)\n",
    "train['parent_comment'] = train['parent_comment'].astype(str)\n",
    "test['comment'] = test['comment'].astype(str)\n",
    "test['parent_comment'] = test['parent_comment'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# classify subreddits, string to int (only run once)\n",
    "comment_series = pd.concat((train[\"subreddit\"], test[\"subreddit\"]))\n",
    "unique_values = comment_series.unique()\n",
    "value_dict = dict(enumerate(unique_values))\n",
    "lookup = {v: k for k, v in value_dict.items()}\n",
    "train[\"subreddit\"] = train[\"subreddit\"].map(lookup)\n",
    "test[\"subreddit\"] = test[\"subreddit\"].map(lookup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop author\n",
    "train = train.drop(\"author\", axis=1)\n",
    "test = test.drop(\"author\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reduce dates to year (temporary)\n",
    "train['date'] = pd.to_datetime(train['date'],yearfirst=True)\n",
    "train['year'] = train['date'].apply(lambda d: d.year)\n",
    "\n",
    "# plt sarcasm hist over years\n",
    "comments_by_year = train.groupby('year')['label'].agg([np.sum, np.mean])\n",
    "fig = plt.figure(figsize=(15,10), facecolor='#ffffff')\n",
    "comments_by_year['mean'].plot(kind='line')\n",
    "plt.ylabel('Mean Sarcasm')\n",
    "plt.title('Rate of Sarcasm on Reddit')\n",
    "fig.savefig('graphs/sarcasm_timeline.png')\n",
    "\n",
    "# drop year and date\n",
    "train = train.drop(\"year\", axis=1)\n",
    "train = train.drop(\"date\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving time as seconds of day (starting midnight)\n",
    "# might cause problems because of time zone differences\n",
    "train['time'] = train['created_utc'].map(lambda x: x%86400)\n",
    "test['time'] = test['created_utc'].map(lambda x: x%86400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add day of week column (monday=0, ...,  sunday=6)\n",
    "train['created_utc'] = pd.to_datetime(train['created_utc'], unit='s')\n",
    "train['day_of_week'] = train['created_utc'].dt.day_of_week\n",
    "test['created_utc'] = pd.to_datetime(test['created_utc'], unit='s')\n",
    "test['day_of_week'] = test['created_utc'].dt.day_of_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop original created_utc column. no longer needed.\n",
    "train = train.drop(\"created_utc\", axis=1)\n",
    "test = test.drop(\"created_utc\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create column with comment text length\n",
    "train['comment_length'] = train['comment'].str.len()\n",
    "train['parent_comment_length'] = train['parent_comment'].str.len()\n",
    "test['comment_length'] = test['comment'].str.len()\n",
    "test['parent_comment_length'] = test['parent_comment'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped 55676 comments with >400 chars in training\n",
      "dropped 13848 comments with >400 chars in testing\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop rows with comments > 400 chars\n",
    "# spam and edge cases\n",
    "number_comments_before_drop = train.shape[0]\n",
    "train = train.drop(train[(train.comment_length > 400)].index)\n",
    "train = train.drop(train[(train.parent_comment_length > 400)].index)\n",
    "number_comments_after_drop = train.shape[0]\n",
    "print('dropped', number_comments_before_drop - number_comments_after_drop, 'comments with >400 chars in training')\n",
    "\n",
    "number_comments_before_drop = test.shape[0]\n",
    "test = test.drop(test[(test.comment_length > 400)].index)\n",
    "test = test.drop(test[(test.parent_comment_length > 400)].index)\n",
    "number_comments_after_drop = test.shape[0]\n",
    "\n",
    "print('dropped', number_comments_before_drop - number_comments_after_drop, 'comments with >400 chars in testing')\n",
    "\n",
    "# plot number comments with letter counts\n",
    "fig = plt.figure(figsize=(15,10), facecolor='#ffffff')\n",
    "data = train['comment_length'].value_counts().to_numpy()\n",
    "plt.plot(data)\n",
    "plt.xlabel('comment length')\n",
    "fig.savefig('graphs/comment_length.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detect presence of smileys in comment text\n",
    "def count_smileys(smileys: list, comment: str) -> float:\n",
    "    count = 0\n",
    "    for smiley in smileys: count += comment.count(smiley)\n",
    "    return count/len(comment)\n",
    "    \n",
    "smileys = [':P', '\\\"', 'xD', 'XD', ':D', ':)',':‑)', ':>', '8)', ':o', ':^)', '=)', ':/'] # todo: add more possible smileys\n",
    "train['smileys'] = train['comment'].map(lambda comment: count_smileys(smileys, comment))\n",
    "train['smileys_parent'] = train['parent_comment'].map(lambda comment: count_smileys(smileys, comment))\n",
    "test['smileys'] = test['comment'].map(lambda comment: count_smileys(smileys, comment))\n",
    "test['smileys_parent'] = test['parent_comment'].map(lambda comment: count_smileys(smileys, comment))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detect presence of sarcasm indicators in comment text\n",
    "def count_indiccators(indicators: list, comment: str) -> float:\n",
    "    count = 0\n",
    "    for indicator in indicators: count += comment.lower().count(indicator)\n",
    "    return count/len(comment)\n",
    "    \n",
    "indicators = ['...', 'duh', 'lol', 'rofl', 'hehe', 'psh'] # todo: add more possible indicators\n",
    "train['sarcasm_indicators'] = train['comment'].map(lambda comment: count_indiccators(indicators, comment))\n",
    "train['sarcasm_indicators_parent'] = train['parent_comment'].map(lambda comment: count_indiccators(indicators, comment))\n",
    "test['sarcasm_indicators'] = test['comment'].map(lambda comment: count_indiccators(indicators, comment))\n",
    "test['sarcasm_indicators_parent'] = test['parent_comment'].map(lambda comment: count_indiccators(indicators, comment))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detect presence of caps lock in comment text\n",
    "def count_capslock(comment: str) -> float:\n",
    "    count = 0\n",
    "    comment_padded = ' ' + comment + ' ' # pad the comment text with whitespaces\n",
    "    for pos, char in enumerate(comment):\n",
    "        #check if previous and current char is upper\n",
    "        if (comment_padded[pos].isupper() and (comment_padded[pos-1].isupper() or comment_padded[pos+1].isupper())):\n",
    "            count += 1\n",
    "    return count/len(comment)\n",
    "\n",
    "train['caps_lock'] = train['comment'].map(lambda comment: count_capslock(comment))\n",
    "train['caps_lock_parent'] = train['parent_comment'].map(lambda comment: count_capslock(comment))\n",
    "test['caps_lock'] = test['comment'].map(lambda comment: count_capslock(comment))\n",
    "test['caps_lock_parent'] = test['parent_comment'].map(lambda comment: count_capslock(comment))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detect presence of letter duplication in comment text\n",
    "def count_letter_dupes(comment: str) -> float:\n",
    "    count = 0\n",
    "    comment_padded = ' ' + comment + ' ' # pad the comment text with whitespaces\n",
    "    for pos, char in enumerate(comment):\n",
    "        # check if previous and next char is the same as current\n",
    "        # counts only of n>2 dupes\n",
    "        if ((comment_padded[pos] == comment_padded[pos-1]) and (comment_padded[pos] == comment_padded[pos+1])):\n",
    "            count += 1\n",
    "    return count/len(comment)\n",
    "\n",
    "train['letter_duplication'] = train['comment'].map(lambda comment: count_letter_dupes(comment))\n",
    "train['letter_duplication_parent'] = train['parent_comment'].map(lambda comment: count_letter_dupes(comment))\n",
    "test['letter_duplication'] = test['comment'].map(lambda comment: count_letter_dupes(comment))\n",
    "test['letter_duplication_parent'] = test['parent_comment'].map(lambda comment: count_letter_dupes(comment))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x1224 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plots correlation matrix\n",
    "# comment and parent_comment not shown\n",
    "\n",
    "fig = plt.figure(figsize=(18, 17), facecolor='#ffffff')\n",
    "plt.matshow(train.corr(), fignum=fig.number)\n",
    "plt.xticks(range(train.select_dtypes(['number']).shape[1]), train.select_dtypes(['number']).columns, fontsize=14, rotation=90)\n",
    "plt.yticks(range(train.select_dtypes(['number']).shape[1]), train.select_dtypes(['number']).columns, fontsize=14)\n",
    "cb = plt.colorbar()\n",
    "cb.ax.tick_params(labelsize=14)\n",
    "plt.title('Correlation Matrix', fontsize=16);\n",
    "fig.savefig('graphs/correlation_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>subreddit</th>\n",
       "      <th>score</th>\n",
       "      <th>ups</th>\n",
       "      <th>downs</th>\n",
       "      <th>parent_comment</th>\n",
       "      <th>time</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>comment_length</th>\n",
       "      <th>parent_comment_length</th>\n",
       "      <th>smileys</th>\n",
       "      <th>smileys_parent</th>\n",
       "      <th>sarcasm_indicators</th>\n",
       "      <th>sarcasm_indicators_parent</th>\n",
       "      <th>caps_lock</th>\n",
       "      <th>caps_lock_parent</th>\n",
       "      <th>letter_duplication</th>\n",
       "      <th>letter_duplication_parent</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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>Yeah, I get that argument. At this point, I'd ...</td>\n",
       "      <td>86123</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>The blazers and Mavericks (The wests 5 and 6 s...</td>\n",
       "      <td>1450</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   label                                            comment  subreddit  score  \\\n",
       "0      0                                         NC and NH.          0      2   \n",
       "1      0  You do know west teams play against west teams...          1     -4   \n",
       "\n",
       "   ups  downs                                     parent_comment   time  \\\n",
       "0   -1     -1  Yeah, I get that argument. At this point, I'd ...  86123   \n",
       "1   -1     -1  The blazers and Mavericks (The wests 5 and 6 s...   1450   \n",
       "\n",
       "   day_of_week  comment_length  parent_comment_length  smileys  \\\n",
       "0            6              10                     80      0.0   \n",
       "1            1              74                    134      0.0   \n",
       "\n",
       "   smileys_parent  sarcasm_indicators  sarcasm_indicators_parent  caps_lock  \\\n",
       "0             0.0                 0.0                        0.0        0.4   \n",
       "1             0.0                 0.0                        0.0        0.0   \n",
       "\n",
       "   caps_lock_parent  letter_duplication  letter_duplication_parent  \n",
       "0             0.025                 0.0                        0.0  \n",
       "1             0.000                 0.0                        0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# have a look at finished dataset\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save dataset for machine_leaning.ipynb to use\n",
    "train.to_csv(path_or_buf='dataset/final_train.csv', sep='\\t', index=False)\n",
    "test.to_csv(path_or_buf='dataset/final_test.csv', sep='\\t', index=False)"
   ]
  }
 ],
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