Label Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train_nn.csv\")\n",
    "\n",
    "data = data.drop(labels = \"Unnamed: 0\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame(data.mean())\n",
    "\n",
    "results.reset_index(inplace = True)\n",
    "\n",
    "results.columns = ['Column', 'Mean']\n",
    "\n",
    "results['rater'] = results['Column'].str[-1:]\n",
    "\n",
    "results['category'] = results['Column'].str[0:-1]\n",
    "\n",
    "results[['rater', 'category', 'Mean']].pivot(index = 'category', columns = 'rater').to_csv('pct_by_rater.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = ['CAPS', 'Obscenity', 'Threat', 'hatespeech', 'namecalling', 'negprejudice', 'noneng', 'porn', 'stereotypes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#calculate the percent of raters who gave it that label\n",
    "\n",
    "for label in labels:\n",
    "    cols = [label + str(x) for x in range(1,8)]\n",
    "    data[label + '_pct'] = data[cols].sum(axis = 1)/(7 - data[cols].isnull().sum(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#calculate the number of raters who gave it that label\n",
    "\n",
    "for label in labels:\n",
    "    cols = [label + str(x) for x in range(1,8)]\n",
    "    data[label + '_num_yes'] = data[cols].sum(axis = 1)\n",
    "    data[label + '_num_raters'] = 7 - data[cols].isnull().sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "label_pct = [label + '_pct' for label in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAPS_pct</th>\n",
       "      <th>Obscenity_pct</th>\n",
       "      <th>Threat_pct</th>\n",
       "      <th>hatespeech_pct</th>\n",
       "      <th>namecalling_pct</th>\n",
       "      <th>negprejudice_pct</th>\n",
       "      <th>noneng_pct</th>\n",
       "      <th>porn_pct</th>\n",
       "      <th>stereotypes_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5585.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "      <td>5584.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.014446</td>\n",
       "      <td>0.054441</td>\n",
       "      <td>0.033721</td>\n",
       "      <td>0.032772</td>\n",
       "      <td>0.082423</td>\n",
       "      <td>0.142550</td>\n",
       "      <td>0.081528</td>\n",
       "      <td>0.010387</td>\n",
       "      <td>0.093675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.100945</td>\n",
       "      <td>0.188191</td>\n",
       "      <td>0.132906</td>\n",
       "      <td>0.132772</td>\n",
       "      <td>0.208419</td>\n",
       "      <td>0.254525</td>\n",
       "      <td>0.268300</td>\n",
       "      <td>0.090642</td>\n",
       "      <td>0.206472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CAPS_pct  Obscenity_pct   Threat_pct  hatespeech_pct  \\\n",
       "count  5584.000000    5584.000000  5585.000000     5584.000000   \n",
       "mean      0.014446       0.054441     0.033721        0.032772   \n",
       "std       0.100945       0.188191     0.132906        0.132772   \n",
       "min       0.000000       0.000000     0.000000        0.000000   \n",
       "25%       0.000000       0.000000     0.000000        0.000000   \n",
       "50%       0.000000       0.000000     0.000000        0.000000   \n",
       "75%       0.000000       0.000000     0.000000        0.000000   \n",
       "max       1.000000       1.000000     1.000000        1.000000   \n",
       "\n",
       "       namecalling_pct  negprejudice_pct   noneng_pct     porn_pct  \\\n",
       "count      5584.000000       5584.000000  5584.000000  5584.000000   \n",
       "mean          0.082423          0.142550     0.081528     0.010387   \n",
       "std           0.208419          0.254525     0.268300     0.090642   \n",
       "min           0.000000          0.000000     0.000000     0.000000   \n",
       "25%           0.000000          0.000000     0.000000     0.000000   \n",
       "50%           0.000000          0.000000     0.000000     0.000000   \n",
       "75%           0.000000          0.250000     0.000000     0.000000   \n",
       "max           3.000000          1.000000     1.000000     1.000000   \n",
       "\n",
       "       stereotypes_pct  \n",
       "count      5584.000000  \n",
       "mean          0.093675  \n",
       "std           0.206472  \n",
       "min           0.000000  \n",
       "25%           0.000000  \n",
       "50%           0.000000  \n",
       "75%           0.000000  \n",
       "max           1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[label_pct].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_imbalance = data[labels[0]].value_counts()/data.shape[0]\n",
    "\n",
    "for label in labels[1:len(labels)]:\n",
    "    series = data[label].value_counts()/data.shape[0]\n",
    "    class_imbalance = pd.concat([class_imbalance, series], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CAPS</th>\n",
       "      <th>Obscenity</th>\n",
       "      <th>Threat</th>\n",
       "      <th>hatespeech</th>\n",
       "      <th>namecalling</th>\n",
       "      <th>negprejudice</th>\n",
       "      <th>noneng</th>\n",
       "      <th>porn</th>\n",
       "      <th>stereotypes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.985318</td>\n",
       "      <td>0.951835</td>\n",
       "      <td>0.97171</td>\n",
       "      <td>0.976007</td>\n",
       "      <td>0.927126</td>\n",
       "      <td>0.866607</td>\n",
       "      <td>0.917278</td>\n",
       "      <td>0.990868</td>\n",
       "      <td>0.920322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.014682</td>\n",
       "      <td>0.048165</td>\n",
       "      <td>0.02829</td>\n",
       "      <td>0.023993</td>\n",
       "      <td>0.072874</td>\n",
       "      <td>0.133393</td>\n",
       "      <td>0.082722</td>\n",
       "      <td>0.009132</td>\n",
       "      <td>0.079678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CAPS  Obscenity   Threat  hatespeech  namecalling  negprejudice  \\\n",
       "0  0.985318   0.951835  0.97171    0.976007     0.927126      0.866607   \n",
       "1  0.014682   0.048165  0.02829    0.023993     0.072874      0.133393   \n",
       "\n",
       "     noneng      porn  stereotypes  \n",
       "0  0.917278  0.990868     0.920322  \n",
       "1  0.082722  0.009132     0.079678  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class_imbalance.to_csv(\"class_imbalance.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45846153846153848"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#46% of negative prejudice labels are corrobarated by another rater\n",
    "\n",
    "sum(data['negprejudice_num_yes'] > 1)/sum(data['negprejudice_num_yes'] > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x102aceef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgJlZxhwCZmYZcwiYmWXMIWBmljGHgJlZxhwCZmYZcwiYmWXMIWBmljGHgJlZxhwCZmYZ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\nAX7PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ec6bc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['negprejudice'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10efca828>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef93f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Threat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10effdb70>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cuHxo+bLW9/9U1W5g92IPluRQVU0vdj8riTX3obeae6sXxlfzuK8O+jtgU5IrkrwZ2A7s\nH/McJEnNWF8JVNXpJO8F/gxYA3ygqo6Mcw6SpC8a9+kgqupPgD8Z0+EWfUppBbLmPvRWc2/1wphq\nHusbw5KkNxa/NkKSOrYqQmCur6LIrHvb+k8l+Y5JzHOpjFDvTa3Ow0n+JsnbJjHPpTTq140k+c4k\np9vlyCvaKDUnmUnyZJIjSf5y3HNcaiP8bX91kj9O8slW809NYp5LJckHkpxM8unzrF/+x66qWtE3\nZt9g/mfgG4A3A58ENp+1zbuAR4EAW4BPTHrey1zvdwMXt/Y7V3K9o9Y8tN3HmH3P6d2TnvcYfs9v\nYfbT9l/Xlr920vMeQ82/CPxaa78V+DfgzZOe+yJq/l7gO4BPn2f9sj92rYZXAl/4Koqq+h/gzFdR\nDNsGPFSzDgJvSbJ+3BNdInPWW1V/U1X/3hYPMvt5jJVslN8xwM8CfwicHOfklskoNf848KGq+ixA\nVa30ukepuYCvShLgK5kNgdPjnebSqaqPM1vD+Sz7Y9dqCIFzfRXFhgVss1LMt5ZbmH0msZLNWXOS\nDcCPAg+McV7LaZTf8zcBFycZJHkiyc1jm93yGKXm3wS+FfgX4DDwvqr6/HimNxHL/tg19ktENT5J\nvo/ZEHjHpOcyBr8B/EJVfX72SWIX1gLXANcB64C/TXKwqv5pstNaVtcDTwLfD3wj8FiSv6qqVyY7\nrZVrNYTAKF9FMdLXVawQI9WS5NuA9wPvrKqXxzS35TJKzdPAIy0ALgXeleR0Vf3ReKa45Eap+Rjw\nclW9Crya5OPA24CVGgKj1PxTwN01e8L8aJLngG8BHh/PFMdu2R+7VsPpoFG+imI/cHN7p30L8J9V\ndWLcE10ic9ab5OuADwHvWSXPCuesuaquqKqNVbUR+APgthUcADDa3/U+4B1J1ib5Cma/kffpMc9z\nKY1S82eZfeVDkilmv2DyM2Od5Xgt+2PXin8lUOf5KookP9PW/zazV4u8CzgK/DezzyZWpBHr/SXg\na4D72zPj07WCv3xrxJpXlVFqrqqnk/wp8Cng88D7q+qclxquBCP+nn8VeDDJYWavmPmFqlqx3y6a\n5IPADHBpkmPALwNvgvE9dvmJYUnq2Go4HSRJWiBDQJI6ZghIUscMAUnqmCEgSR0zBCSpY4aAJHXM\nEJCkjv0fiVSdeKEpLCoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f086e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['porn'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3793691389599318"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#38% of stereotype labels are corroborated by another rater\n",
    "\n",
    "sum(data['stereotypes_num_yes'] > 1)/sum(data['stereotypes_num_yes'] > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ef31630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f14a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['stereotypes'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f243160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wvS1+G/DkAnd5EfCvCxy7UllzH3qrubd6YfE1f2NVvWG+jcb9SeAYcMnQ8sWt78tU1U5g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\n4Zv2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2670f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Obscenity'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32289156626506021"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#32% of hatespeech labels are corroborated by another rater\n",
    "\n",
    "sum(data['hatespeech_num_yes'] > 1)/sum(data['hatespeech_num_yes'] > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f3e7400>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11002e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['hatespeech'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11014ae10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110162588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['namecalling'].hist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}