diaries_classification.ipynb 375 KB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
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    "# Howto classify articles with scikit-learn\n",
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    "\n",
    "This notebook aims to demonstrate the ability of scikit-learn to process articles published on the french website linuxfr.org. The data are collected with a short script that convert the atom feed of the journals into a csv file. In order to minimize the lenght of this scrit, it will not fetches the score of the article.\n",
    "\n",
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    "## Useful resources\n",
    "\n",
    "\n",
    "\n",
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    "## Read the data\n",
    "\n",
    "This section describes the convertion of article fromp CSV to dµpanda dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "title               object\n",
       "author              object\n",
       "url                 object\n",
       "score              float64\n",
       "content             object\n",
       "quality_content     object\n",
       "count              float64\n",
       "dtype: object"
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      ]
     },
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     "execution_count": 3,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
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    "import numpy as np\n",
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    "import sys\n",
    "import matplotlib\n",
    "# Enable inline plotting\n",
    "%matplotlib inline\n",
    "filename = r'linuxfr.csv'\n",
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    "lf_data = pd.read_csv(filename, encoding=\"UTF-8\", sep='£', engine='python', quotechar='µ')\n",
    "lf_data.dtypes"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Average Troll        2614\n",
       "Quality Troll        2545\n",
       "Great Troll           460\n",
       "Magnificent Troll     302\n",
       "Name: quality_content, dtype: int64"
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      ]
     },
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     "execution_count": 4,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "lf_data.quality_content.value_counts()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
   "outputs": [
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       "      <th rowspan=\"8\" valign=\"top\">Magnificent Troll</th>\n",
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       "      <th>count</th>\n",
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       "      <th rowspan=\"8\" valign=\"top\">Quality Troll</th>\n",
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       "      <th rowspan=\"8\" valign=\"top\">Magnificent Troll</th>\n",
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       "      <th>count</th>\n",
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      ]
     },
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     "execution_count": 5,
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     "metadata": {},
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    }
   ],
   "source": [
    "lf_data.groupby('quality_content').describe()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
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       "      <td>Intuition\\n\\n</td>\n",
       "      <td>thamieu</td>\n",
       "      <td>https://linuxfr.org/users/thamieu/journaux/int...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Il y a quelques jours j'ai pris le train. Des ...</td>\n",
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       "      <td>#REM on saura peut-être faire le café et pas v...</td>\n",
       "      <td>Patrick Trauquesègues</td>\n",
       "      <td>https://linuxfr.org/users/patrick32/journaux/r...</td>\n",
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       "      <td>-12.0</td>\n",
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       "      <td>Considérant, _ la nécessité de rendre le WEB a...</td>\n",
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       "      <td>L'équipe Ubuntu Desktop aimerait avoir vos com...</td>\n",
       "      <td>Lawless</td>\n",
       "      <td>https://linuxfr.org/users/lawless/journaux/l-e...</td>\n",
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       "      <td>Vu sur l'article The Ubuntu Desktop Team Wants...</td>\n",
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       "      <th>3</th>\n",
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       "      <td>RyDroid</td>\n",
       "      <td>https://linuxfr.org/users/rydroid/journaux/sor...</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>Sommaire C'est quoi Replicant ? La version 6.0...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>Retour d'expérience Yunohost\\n\\n</td>\n",
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       "      <td>EauFroide</td>\n",
       "      <td>https://linuxfr.org/users/eaufroide/journaux/r...</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>Sommaire Qu'est-ce que Yunohost Installation A...</td>\n",
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       "      <td>NaN</td>\n",
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       "<div>\n",
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       "      <td>Intuition\\n\\n</td>\n",
       "      <td>thamieu</td>\n",
       "      <td>https://linuxfr.org/users/thamieu/journaux/int...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Il y a quelques jours j'ai pris le train. Des ...</td>\n",
       "      <td>Average Troll</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>#REM on saura peut-être faire le café et pas v...</td>\n",
       "      <td>Patrick Trauquesègues</td>\n",
       "      <td>https://linuxfr.org/users/patrick32/journaux/r...</td>\n",
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       "      <td>-12.0</td>\n",
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       "      <td>Considérant, _ la nécessité de rendre le WEB a...</td>\n",
       "      <td>Great Troll</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2</th>\n",
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       "      <td>L'équipe Ubuntu Desktop aimerait avoir vos com...</td>\n",
       "      <td>Lawless</td>\n",
       "      <td>https://linuxfr.org/users/lawless/journaux/l-e...</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>Vu sur l'article The Ubuntu Desktop Team Wants...</td>\n",
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       "      <td>Quality Troll</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>3</th>\n",
       "      <td>Sortie de Replicant 6.0\\n\\n</td>\n",
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       "      <td>RyDroid</td>\n",
       "      <td>https://linuxfr.org/users/rydroid/journaux/sor...</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>Sommaire C'est quoi Replicant ? La version 6.0...</td>\n",
       "      <td>Quality Troll</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>4</th>\n",
       "      <td>Retour d'expérience Yunohost\\n\\n</td>\n",
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       "      <td>EauFroide</td>\n",
       "      <td>https://linuxfr.org/users/eaufroide/journaux/r...</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>Sommaire Qu'est-ce que Yunohost Installation A...</td>\n",
       "      <td>Quality Troll</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
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     "execution_count": 6,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf_data.head()"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to plot the confusion matrix"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
    "lf_data = lf_data.reindex(np.random.permutation(lf_data.index))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>url</th>\n",
       "      <th>score</th>\n",
       "      <th>content</th>\n",
       "      <th>quality_content</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>5639</th>\n",
       "      <td>Les motards, cette engeance du diable.\\n\\n</td>\n",
       "      <td>Fopossum</td>\n",
       "      <td>/users/o_possum/journaux/les-motards-cette-eng...</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>Lectrice, lecteur, toi qui t'aventure sur ce j...</td>\n",
       "      <td>Great Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>3725</th>\n",
       "      <td>[Filesystem] Benchmark SSD vs HDD\\n\\n</td>\n",
       "      <td>xunfr</td>\n",
       "      <td>/users/xunfr/journaux/filesystem-benchmark-ssd...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Salut tout le monde, Je voulais partager avec ...</td>\n",
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       "      <td>Average Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>1485</th>\n",
       "      <td>FreeGLUT : premier port Wayland disponible !\\n\\n</td>\n",
       "      <td>Tarnyko</td>\n",
       "      <td>/users/tarnyko/journaux/freeglut-premier-port-...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Salut nal' ! Il y a des moments où il faut pos...</td>\n",
       "      <td>Quality Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>4154</th>\n",
       "      <td>HP recommande passer à la Windows® 7 Professio...</td>\n",
       "      <td>Samuel Pajilewski</td>\n",
       "      <td>/users/sam_from_ms/journaux/hp-recommande-pass...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Il n'est pas facile de nos jours de trouver un...</td>\n",
       "      <td>Average Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>1322</th>\n",
       "      <td>Talking about a Revolution\\n\\n</td>\n",
       "      <td>Stein Straßenbahn-Hohe</td>\n",
       "      <td>https://linuxfr.org/users/chrisix/journaux/tal...</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>… Et comme nous sommes sur un site dédié au hi...</td>\n",
       "      <td>Great Troll</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>url</th>\n",
       "      <th>score</th>\n",
       "      <th>content</th>\n",
       "      <th>quality_content</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <th>5639</th>\n",
       "      <td>Les motards, cette engeance du diable.\\n\\n</td>\n",
       "      <td>Fopossum</td>\n",
       "      <td>/users/o_possum/journaux/les-motards-cette-eng...</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>Lectrice, lecteur, toi qui t'aventure sur ce j...</td>\n",
       "      <td>Great Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>3725</th>\n",
       "      <td>[Filesystem] Benchmark SSD vs HDD\\n\\n</td>\n",
       "      <td>xunfr</td>\n",
       "      <td>/users/xunfr/journaux/filesystem-benchmark-ssd...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Salut tout le monde, Je voulais partager avec ...</td>\n",
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       "      <td>Average Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>1485</th>\n",
       "      <td>FreeGLUT : premier port Wayland disponible !\\n\\n</td>\n",
       "      <td>Tarnyko</td>\n",
       "      <td>/users/tarnyko/journaux/freeglut-premier-port-...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Salut nal' ! Il y a des moments où il faut pos...</td>\n",
       "      <td>Quality Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>4154</th>\n",
       "      <td>HP recommande passer à la Windows® 7 Professio...</td>\n",
       "      <td>Samuel Pajilewski</td>\n",
       "      <td>/users/sam_from_ms/journaux/hp-recommande-pass...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Il n'est pas facile de nos jours de trouver un...</td>\n",
       "      <td>Average Troll</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <th>1322</th>\n",
       "      <td>Talking about a Revolution\\n\\n</td>\n",
       "      <td>Stein Straßenbahn-Hohe</td>\n",
       "      <td>https://linuxfr.org/users/chrisix/journaux/tal...</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>… Et comme nous sommes sur un site dédié au hi...</td>\n",
       "      <td>Great Troll</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
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     "execution_count": 8,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf_data.tail()"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definitions\n",
    "Define a tabular with the targets properties (type of journal)"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 10,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['title', 'author', 'url', 'score', 'content', 'quality_content', 'count']"
      ]
     },
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     "execution_count": 9,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties = [entry for entry in lf_data.keys()]\n",
    "properties"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the available targets names (e.x. the several available categories of an article)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "targets_names = []\n",
    "for index, row in lf_data.iterrows():\n",
    "    if row['quality_content'] not in targets_names:\n",
    "        targets_names.append(row['quality_content'])\n",
    "        #print(row)\n",
    "    if row['quality_content'] == 'quality_content':\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 12,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "['Quality Troll', 'Average Troll', 'Great Troll', 'Magnificent Troll']"
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      ]
     },
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     "execution_count": 11,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets_names"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
    "# Correction of the order for the matrix\n",
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    "targets_names = ['Average Troll', 'Great Troll',  'Magnificent Troll', 'Quality Troll']"
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   ]
  },
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  {
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   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
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   "source": [
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    "import numpy as np\n",
    "targets=lf_data.quality_content"
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   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 15,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)"
      ]
     },
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     "execution_count": 14,
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     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "image/png": 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Bn1U979P0kn0oKw/PbTj2PWB5yiKMofMuBewF3E+55hURETXpdHXfNsBxtq+t9pNqdhvw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\nAAAASUVORK5CYII=\n",
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      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27cd74a6d8>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
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   "source": [
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    "lf_data.quality_content.value_counts().plot(kind='bar')\n",
    "plt.ylabel('Occurences', fontsize='xx-large')\n",
    "plt.yticks(fontsize='xx-large')\n",
    "plt.xlabel('Trolls', fontsize='xx-large')\n",
    "plt.xticks(fontsize='xx-large')"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 16,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "Average Troll        2614\n",
       "Quality Troll        2545\n",
       "Great Troll           460\n",
       "Magnificent Troll     302\n",
       "Name: quality_content, dtype: int64"
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      ]
     },
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     "execution_count": 15,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lf_data.quality_content.value_counts()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 17,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "5921"
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      ]
     },
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     "execution_count": 16,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lf_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to plot the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 18,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.0\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, round(cm[i, j],2),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.figure(figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 19,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5921"
      ]
     },
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     "execution_count": 18,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lf_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text analysis\n",
    "## Bag of words\n",
    "This section aimsto present the `baf of word` approach on linuxfr data. I follow here the [official documentation](http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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   "source": []
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  },
  {
   "cell_type": "code",
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   "execution_count": 20,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "(5921, 78879)"
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      ]
     },
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     "execution_count": 19,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer()\n",
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    "X_train_counts = count_vect.fit_transform(lf_data['content'].values)\n",
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    "X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 21,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "42616"
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      ]
     },
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     "execution_count": 20,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "count_vect.vocabulary_.get(u'linux')"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 22,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "(5921, 78879)"
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      ]
     },
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     "execution_count": 21,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)\n",
    "X_train_tf = tf_transformer.transform(X_train_counts)\n",
    "X_train_tf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From occurences to frequencies\n",
    "#### tf_transformer (Term Frequencies)\n",
    "The frequency of word is computated"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 23,
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   "metadata": {},
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "(5921, 78879)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)\n",
    "X_train_tf = tf_transformer.transform(X_train_counts)\n",
    "X_train_tf.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### tfidf_transformer (Term Frequency times Inverse Document Frequency)\n",
    "In this approach, only words that differs from document matters)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 24,
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   "metadata": {},
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "(5921, 78879)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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    }
   ],
   "source": [
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "X_train_tfidf.shape"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Buiding a Pipeline"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a classifier\n",
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    "### Bayes  approach : MultinomialNB\n",
    "\n",
    "MultinomialNB is the simpliest approach."
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 25,
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   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "'Sécuriser son serveur avec la commande sudo rm -rf /*' => Quality Troll\n",
      "'Debian is dying' => Quality Troll\n",
      "'Windows Millenium est meilleur que Linux sur calculatrice graphique' => Quality Troll\n",
      "'MultiDeskOS est 42% plus performant que Redhat 3.0.3 (Picasso)' => Average Troll\n",
      "'Pierre Tramo président !' => Average Troll\n",
      "'Des chocolatines au menu des cantines situées dans les DOM-TOM' => Quality Troll\n",
      "'1515, l’année du Desktop Linux!' => Average Troll\n"
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     ]
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    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
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    "classifier = MultinomialNB()\n",
    "classifier.fit(X_train_tfidf, targets)\n",
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    "training_journals = ['Sécuriser son serveur avec la commande sudo rm -rf /*', \n",
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    "                     'Debian is dying', \n",
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    "                     'Windows Millenium est meilleur que Linux sur calculatrice graphique',\n",
    "                     \"MultiDeskOS est 42% plus performant que Redhat 3.0.3 (Picasso)\",\n",
    "                     \"Pierre Tramo président !\",\n",
    "                     \"Des chocolatines au menu des cantines situées dans les DOM-TOM\", \n",
    "                     \"1515, l’année du Desktop Linux!\"]\n",
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    "X_new_counts = count_vect.transform(training_journals)\n",
    "X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
    "predicted = classifier.predict(X_new_tfidf)\n",
    "for doc, category in zip(training_journals, predicted):\n",
    "    print('%r => %s' % (doc, category))"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 26,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "['Average Troll', 'Great Troll', 'Magnificent Troll', 'Quality Troll']\n"
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     ]
    },
    {
     "data": {
      "text/plain": [
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       "array([[ 0.38146407,  0.01242555,  0.00699732,  0.59911306],\n",
       "       [ 0.45180296,  0.03300345,  0.01880854,  0.49638505],\n",
       "       [ 0.37809693,  0.0190014 ,  0.00917897,  0.5937227 ],\n",
       "       [ 0.47083803,  0.0629247 ,  0.02837355,  0.43786371],\n",
       "       [ 0.54130358,  0.04642992,  0.03861831,  0.37364818],\n",
       "       [ 0.45172753,  0.03297976,  0.01805764,  0.49723507],\n",
       "       [ 0.59237292,  0.01164186,  0.00420374,  0.39178148]])"
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      ]
     },
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     "execution_count": 25,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_proba = classifier.predict_proba(X_new_tfidf)\n",
    "print(targets_names)\n",
    "predicted_proba"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Buiding a Pipeline"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 27,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
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    "from sklearn.pipeline import Pipeline\n",
    "text_clf = Pipeline([('vect', CountVectorizer()),\n",
    "                     ('tfidf', TfidfTransformer()),\n",
    "                     ('clf', MultinomialNB()),])"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 28,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
    "text_clf = text_clf.fit(lf_data.content, targets)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the model on its own data"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 29,
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   "metadata": {
    "collapsed": true
   },
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   "outputs": [],
   "source": [
    "diaries_test = lf_data.sample(frac=0.2)\n",
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    "\n",
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    "predicted = text_clf.predict(diaries_test['quality_content'])\n",
    "#predicted"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 30,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Diaries: 5921\n",
      "Score: 0.255963872035\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "/usr/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
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     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, f1_score\n",
    "score = f1_score(diaries_test['quality_content'], predicted, average='weighted')\n",
    "print('Diaries:', len(lf_data))\n",
    "print('Score:', score)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 31,
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   "metadata": {},
   "outputs": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Confusion matrix, without normalization\n",
      "[[  0   0   0 538]\n",
      " [  0   0   0  89]\n",
      " [ 62   0   0   0]\n",
      " [  0   0   0 495]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.  0.  0.  1.]\n",
      " [ 0.  0.  0.  1.]\n",
      " [ 1.  0.  0.  0.]\n",
      " [ 0.  0.  0.  1.]]\n"
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     ]
    },
    {
     "data": {
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      "image/png": 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g7JMXIcHFtw7j1zl/5vMjBKpBwJtCV7qHArfE9SFxezzwMnBbVKw9gXfMbIGkPYAtknqO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\naq90c0XsrWbWP8txnIxTKLb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ERgIT40DafEIoxGwr3B2Ao6NsEBzzWxH8cDGz4YT4D/cB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\n",
1390
      "text/plain": [
1391
       "<matplotlib.figure.Figure at 0x7f27c3a85b38>"
1392 1393 1394 1395 1396
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
1397 1398 1399
    {
     "data": {
      "text/plain": [
1400
       "<matplotlib.figure.Figure at 0x7f27c3a3e4a8>"
1401 1402
      ]
     },
1403 1404 1405 1406 1407 1408
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
1409
      "image/png": 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MPjCzPmbWB3gSODtu90+ul/Qwy3vKK1m2ZZUs32zTqVNnZsxYHiI+c+YMOnbs\nuGKd6aHOkiVL+HnuXNq2bZtVOSvIUkDyLpOnwGSuTImUckmDJcCZZrY+sAVwoqQNgPOAF81sHeDF\nuA2wO7BOXI4FbqtRvmr2CSAqrhWWdKQHngH2jOuHAA8tazzk6X0zWqRvSlovlq8k6ZH41HhY0tuS\n+sV98yRdLul9SW9Jah/LL5Z0lqQ9gNOAoyW9nDgm6ZznSPogHn9lLOsh6VlJ4yW9JqlXLB8an2Bv\nSvpC0gGxmSuBbaMVe3o6N0FSf0kvSBoOvJsky4dxOTnN+5lVFixaSstmpQA0LRNLjWp9wJmm36ab\nMmXKZ0ybOpVFixbx6MPD2XPAwAp19hwwkAeG3QfA44+NYLsddsyZFVZo8kJhypxMffPpmtnXZjYh\nrv8CfAR0AvYG7ovV7iMYd8Ty+y3wFtBaUofqzlGd1bW6pDOqEe66Gq8AhgMXRZfCRsA9wLZx38fA\nH8xsiaT+wN+B/YE/Az+Z2UaSNgSSX7FXBt4yswskXQUcA1yWJNPTkm4H5pnZNcmCSNqdcKM2N7Nf\nJSUezXcCx5vZZ5I2B/4J7Bj3dSC4V3oRrNcRhCfcWWY2II3rT2YLYAMz+ypmbTsM2AwoJbhvXgEm\nV9dA0rUcS3iq0inJ/1Zb2rVsQrMmJZQKOrVuxtwFS5btm7ewnAWLl9KiaQkdWzfFDGbPW1znczUE\nZWVlXH/jLey1566Ul5czaPAQNujdm0suvohN+vZjwF4DGTzkKIYMPoLevXrSpk1bhj0w3OUtcpkr\n01D6X1JX4PfA20B7M/sagmKWtEas1omKI3RnxLKvU7VbndItBVoSLd66YGYTo+CHAE9X2t0KuE/S\nOoTcDol4pG2AG+PxH0qamHTMIiDhEx5PcFmkS3/gXjP7Nbb9o6SWwFbAo0lP6mZJxzxhZkuByQmr\nuh78z8y+iuvbAo8lZJH0BOG601K6ZnYn4WHBRn361tn2/CENJfrj/CU11skmu+2+B7vtvkeFsosu\nvmTZevPmzXlw+KPZFislhSYvFKbMCaQahwG3k/RO0vad8fdUqR21BB4DTjOzn6ux5KvaUe1vsjql\n+7WZXVLN/nR5ErgG2B5YLan8UuBlM9s3KuYxsby6O7Y4KWytnNqNqBMr3owSYE70w1ZF8pxw9X1+\nzm/AthzHSUENP64fzKxftcdLTQgK9wEzezwWfyupQ7RyOwDfxfIZQPLrZmdgVnXt1+jTbQDuAS4x\nsw8qlbePoxSHAAAgAElEQVRiecfa4KTy14E/AkQH9u8aSI7ngSGSVopttzWzn4Gpkg6MZZK0cQ3t\n/AKsUk9ZXgX2ldQiPlH3Bl6rZ5uO0+hJJLypa8iYgkl7N/BRJRfqk8CguD4IGJlUfmTUHVsAcxNu\niFRUp3R3qlHCNDCzGWZ2YxW7rgKukPQGwZWR4J8Ef/JE4FxgIjC3AeR4lnCD3omhWGfFXYcBR0l6\nH5hEUIDVMRFYEjvj0upIq0KWsYROxXHAW8BtVTyUHMepA/UcHLE1cASwo5aHgO5B6EDfWdJnBLdm\nYnz008AXwBTgLkKfVPXy5dNIEgCFKYGamNlvknoQwjPWNbPU41UbMRv16WujXnwj12KkTftWzXMt\ngpNnbL15P8aPf6dB3qx7bLCx/f2Byt1Hyzl4k87ja3IvZJp8jBldCXg5+lUEnOAK13GcdMmX8LVU\n5J3SjbFxOX0SOY5TuOS3ys1Dpes4jlNX0ggZyzmudB3HKSrcveA4jpNF8iRtbkpc6TqOUzQIKMlz\nr64rXcdxioi0s4nlDFe6juMUFXmuc13pOo5TPHj0guM4TpbJc53rStdxnOJC3pHmOI6THRJZxvIZ\nV7qO4xQVea5zXek6jlM8uKXrOI6TVeQ+XcdxnKwhHwbsZJgmpfLE4E4F2ux+Va5FqBULP/umwdoS\n+Ig0x3GcbJLnOteVruM4xYX7dB3HcbKIW7qO4zhZxJWu4zhOlhDuXnAcx8keHjLmOI6TZfJc6Zbk\nWgDHcZyGI8wckWpJqwXpHknfSfowqaytpP9K+ix+tonlknSTpCmSJkrapKb2Xek6jlM0qIYlTYYC\nu1UqOw940czWAV6M2wC7A+vE5Vjgtpoad6XrOE5RISnlkg5m9irwY6XivYH74vp9wD5J5fdb4C2g\ntaQO1bXvStdxnKJCSr3Ug/Zm9jVA/FwjlncCpifVmxHLUuIdaY7jFA81K9d2kt5J2r7TzO6s3xlX\nwKo7wJWu4zhFRQ1xuj+YWb86NPutpA5m9nV0H3wXy2cAXZLqdQZmVdeQuxccxykaRMbcC08Cg+L6\nIGBkUvmRMYphC2Buwg2RCle6jZznn3uWjXqvR+9ePbn6qitX2L9w4UIOP/QgevfqybZbbc6X06Zl\nX8gkXN7McvuZu/HlIyfyzp1/Slnn2j/vxIdDj2HsHYPp07N9FqVLj/oqXUkPAf8D1pM0Q9JRwJXA\nzpI+A3aO2wBPA18AU4C7gD/X1L4r3UZMeXk5p51yIiNHPcO7Eyfz6PCH+Gjy5Ap1ht5zN21at2HS\nx1M4+dTTueD8c3MkrcubDYY9/yF7nz8i5f5dN+tOj05t2HDwXZx0w3PcdMrOWZQuPVTNXzqY2SFm\n1sHMmphZZzO728xmm9lOZrZO/Pwx1jUzO9HMepjZ78zsnZrad6XbiBk3diw9evSkW/fuNG3alAMP\nOpjRo0ZWqDN61EgOOyK8Ve23/wGMeelFzKrtJ8gYLm/meeODGfz4y4KU+wds2ZMHX5gEwNiPvqZV\ny+as2XblbImXFiVKveQDrnQbMbNmzaRz5+V9AJ06dWbmzJkr1ukS6pSVlbFqq1bMnj07q3JWkMXl\nzSkd263CjO9+XrY984df6NhulRxKVAUNMDoikxSk0pXUWdLIOCTvC0m3SGpWj/bGSOoX15+W1Dou\nNfpnktr4naT34vKjpKlx/YVatFEmaU5c7ynpvdpfTfpUZVFVDiBPp062cHlzT1Wi5dIyr4xEvYcB\nZ5qCU7oK38jHgSfikLx1gBZAg0wMZWZ7mNkcoDVpOMWTjvvAzPqYWR9Cj+bZcbt/JfnzJkyvU6fO\nzJixPK575swZdOzYccU600OdJUuW8PPcubRt2zarclaQxeXNKTO//4XOa6y6bLtTu1X4eva8HEq0\nInlu6Bae0gV2BH4zs3sBzKwcOJ0QttFS0mBJtyQqSxotafu4fpukdyRNkvS3qhqXNE1SO0LvZI9o\nrV4taZikvZPqPSBpYDoCS+ov6QVJw4F3Y9k5kj6My8l1uhP1pN+mmzJlymdMmzqVRYsW8ejDw9lz\nQMVL2nPAQB4YFkY/Pv7YCLbbYcecWWIub+556n9TOLR/bwA2W78DP89fyDc/zs+xVMmkHgKcL/c1\nb6yuWtAbGJ9cYGY/S5oG9Kzh2AvM7EdJpcCLkjYys4kp6p4HbBgtVyRtR1DuIyW1ArZiedxeOmwB\nbGBmX0naDDgM2AwoBcZKegWYXF0DCSQdS0iuQZe11qqFCBUpKyvj+htvYa89d6W8vJxBg4ewQe/e\nXHLxRWzStx8D9hrI4CFHMWTwEfTu1ZM2bdoy7IHhdT5ffXF5M8995+/Ftht1oV2rFkx58AQuvf91\nmpSVAvCv0e/x7Ngv2HXz7ky67xh+XbiE4655JqfyVkWe6NaUKJ/8Mekg6VRgbTM7o1L5e8BgoA/Q\nz8xOiuWjgWvMbIyk4wnKqgzoAJxsZsMljQHOMrN3ovLuB7QERpvZhknn+JBgae8H9DSzs1LIODQe\nOyJu9wfONbOd4/aZwMpmdkncvoIwfvtOwoiZ1pJ6AiMSSj8Vffv2szferjFKxWlEFNwU7G/fxNKf\nZzSIqtyoT1978oU3Uu7vtnqL8XUckdZgFKJ7YRJBKS5D0qpAe+ATYAkVr6t5rNMNOAvYycw2Ap5K\n7KsFwwgW6p+Ae2t5bPI7WJ4/ix2ncMl390IhKt0XgZUkHQkQXQXXAreY2QJgGtBHUomkLoRXeIBV\nCYpvrqT2hDyY1fELUDkWZihwGoCZTarHNbwK7CuphaSWhPRwr9WjPcdxIhkaBtxgFJzSteAP2Rc4\nIA7Jmw0sNbPLY5U3gKnAB8A1wIR43PuETqxJwD2xXnXnmQ28ETu6ro5l3wIfUXsrt3LbY4GHgHHA\nW8BtZvZBfdp0HIdlc6Tl8+CIQuxIw8ymAwMBJG0FPCSpr5mNj0r5sBTHDU5Rvn3Setek9UOT60la\niRCi9lAN8g2utP0C8EKlsquoFOZmZksIoWqY2RSCf9pxnFqRJ9o1BQWpdJMxszeBtTN9ntgZdg9w\nnZnNzfT5HMepPSJ/LNpUFLzSzRbRWq17fJbjOFkhX3y3qXCl6zhOUZEvUQqpcKXrOE5Rkd8q15Wu\n4zhFRD6FhqXCla7jOEWFuxccx3GySH6rXFe6juMUFfmTNzcVrnQdxykaErMB5zOudB3HKSpc6TqO\n42SRdGf9zRWudB3HKRqUR4ltUuFK13Gc4iLPlW7BpXZ0HMepDlXzl9bx0m6SPpE0RdJ5DS2fK13H\ncYqK+uTTjZMi3EqY5GAD4BBJGzSofA3ZmOM4Ts6p3xzsmwFTzOwLM1sEDCfM7NJguNJ1HKdoCPl0\nlXJJg06ESWITzIhlDYZ3pBU4EyaM/6FFE32ZgabbAT9koN1MUWjyQuHJnCl5G2wSggkTxj/Xoona\nVVOluaTk6bPvNLM7k7ar0swNOmW6K90Cx8xWz0S7kt7J9VTVtaHQ5IXCk7kQ5DWz3erZxAygS9J2\nZ2BWPdusgLsXHMdxljMOWEdSN0lNgYOBJxvyBG7pOo7jRMxsiaSTgOeAUuAeM5vUkOdwpeuk4s6a\nq+QVhSYvFJ7MhSZvnTCzp4GnM9W+wozljuM4TjZwn67jOE4WcaXrZBRJq0vK2++ZpFXjZ56P2C98\nJK2Saxnygbz9MTiFj6QmwAXAHfmmeBXoAkyUtIWZWbEpXkmdJDXPAzkkaWXgSUl/yrU8uSavfghO\n8SBpNTNbDNwNLAWuzTPFu7KZTQduAO6TtGkRKt6zgP9KapFjOZqa2XxCToMTJB2SY3lySj79CJwi\nIVq4/5J0nZl9QFBsq5AnildST2CEpN+b2Q3AjcDDxaJ4JXWIq2cCk4BHc6V4JbUCPpC0lZmNAC4D\nzm7MijfnPwCn+IgW7gXAhpIuNbOPgGvJE8VrZlOAicDFkjYys38C11A8ineopGfNbCnwZ+BrcqR4\nzWwu8E/CQ25TM3sSuJhGrHg9ZMzJGJLWA24H3jCzCyWtD5xGcDecGJVCNuUR4Tu/NG5fBvQFzjOz\n9yX9GTgV+JOZvZlN2RqSeJ2vAbPM7I/xIXcH0AE40MwWZFEO4kPsBOBSYE8ze1vSXsD/Abea2X3Z\nkCdfcEvXaTASPzJJbSStbmafAMcBm0m6LFq8NwPNgfWyLZsFlkrqDGBmFwIvAVdJ2jhavHcAt+ZD\nB1RdkFRiwZLaFlhb0qPxIXMcIa/AU9mweBP3G2gpqdTMbgPOjuff3MxGAVcAZ0rqWOBvFrXCLV2n\nQZG0D8GabUkY1fMAIUvTzcD7ZnaOpJVjx0ou5DsB2A/4BvgIuAo4BdgRuMjMJkhqY2Y/5UK+upJQ\ncvGB0tLMPo6KbAzwvZkdEC3efwH/NLN3qmuvgWTaCzgEaE3oUB0JHERw5exvZm9KWsPMvsu0LPmE\nK12nwYjuhAeAI4FFwBnAd8DlQE+CFXm0mX2aI/l2JnTq7Q2sD2wFrGRmp0q6ipBi8EhgkRXgDyM+\n8M4jpCd8l/C/eAN4GfjFzAZkUZaNgYcJ93Nzwr39xsyukXQywdWwdpQrq26mXOPuBadBiIMMfgbm\nAFNjZ9XfgYHAIdG1sFsOFW4rQkfeqCjb88AwYA1JPczsHOAkM1tYoAq3E+H1fRCwC/AxsC+wOrA9\n0EHS7zMsQ7KLoC3hzWasmd1MSCCzh6R14vZGZja3sSlccKXrNACSdiK8OrYFvgR2ktTazGYA9wLN\nAMzs1xzJN4TQQTYL2E9S/6hcJwNNgF5Rvu9zIV9dSFZwktoTkouXAQtixMAwoBswKPqy+5rZuxmU\npzS6NwZIuoPwPVg1vl1gZv8lzMjwu3jIjEzJku+40nVqjaSeiXAfSb2Ak4DTYwq894E9gL9IOowQ\nK/pZluXbUlK3uH4wwbXxoJm9RXitPU/SMZIOBboCH2RTvoYgYY1L2g54BWhDiFjYV1InM5sN/AdY\nWVJppsL0JHWX1NPMyqMlfSghHeIXBLfGjpJOkbQpsCXwRZS/0Vm4CVzpOrUi+m0fBMpj0W7AJkAf\nADO7CXgG+An4A3CMmY3Jony7AP8GVotFpwCDWZ79/wlCzPCuhNfuIWb2Vbbkqy+Sekk6Pa5vSHAp\n/MnMviEo3U7ALTH87RLgNTMrz4SSi9+Fx4GNJDUDDgD6A4n7OQIYT/genAmcbWbvNbQchYZ3pDlp\nE39kTwI3m9ktsWwt4I+EELBH4mtkon6TOFAiW/LtCgwFjoo5URPlbwFfm9m+SWWlBIOxYCyueP+H\nAbeY2f2x4+zOuH1JrNMb2IKgfF8zs5czKMvjhEiIW6O7oyfwV0K0yqlm9mNS/VXM7JekULJGi1u6\nTlpI2oDQG94UaKKQPUzRSnyMMNx0b0nJc1QtyaJ8uxLC0t4GNpDUNrHPzLYgdJg9klSWEesvU0Ql\nNxp4zszuj8X/BU4ANpU0GMDMJpnZ3cClGVS46wOPEt52vogx2WZmnxHcN98A/6j0P/glfjZqhQuu\ndJ00iJEJVwL/IHQ69QdOJr7Cm9lUwmv7V4SOqkR5Vn5g0br7J3A0cCHQEThDUutEHTPbGugt6f6q\nW8lf4gNvGCH8bqGkzeIgiPmEKIy7gT0lHZ04JlP3XlI7YDghh8KZBB/ubrGcOCDmX8BvwHWSfHaa\nSrh7wakRhbR87WPnCJK6EjJGjSO82v4Qy7sDS81sWg5kXNfMPo1ug+2APYFfgWvNbE5Sva65kK+u\nxJFx9xJC3R6UdAPhDeJhMxsX66xC8K0fAZxgZjMzKM+qwDpmNj5uH0LoOH0OeDbpu7A+Qfd/nClZ\nChVXuk6tkFRmYfK+tYDbCK/zt+Uq3CqGKpXH9RJbnldhB2AA8AtwY6GNMEsmvr5/H9fbEaz5xQQf\nekLxrgq0MLNvsyTTMt+spIMI9/oZ4L+FFHqXC1zpOrUmodwUkoDfD7wOXJLNTrMqZEoMg01WBtsR\nhqF+BVxR6P7EpAdeW+Aiwiv8EzEULhfyJN/rPxI6VEcCw3P5Xch3XOk6VVJVL3MlSzKheNcC1rAs\njOVPkqMD0M7MPpA0EBhnZl9XJbukbYBPrYDG91e6zxX+D0n3vS1hxN98wgNvbhbkWgv4NeFCqCxf\ndDVMNrP3My1LIeNK11mBJKtxB2ANoImZ/buKesuUQ5bl60roQBoLrAkcVvm1ulBDk2K8a3/gRaAH\n0Bt4NIXiXY3ga5+cYZlEGHxxO8FH/nYlZVuQ9zpXePSCswJR4e4K3ETwid4n6fgq6uUk5Cp2hA0j\nBOOPMrNvJTWJyiFRp1CVQBNCVq7XCBEh4ytfS1S4JWY2O9MKN57PYsztBELi95bJMhXwvc4JrnSd\nCigMGV0JGELw0S0k/NhG5ViuyvlWRwGHA5dIGmRmi+PDYuUciNdgmNk8Ql6CbsBMINGBVpqoEy3L\njD7wFIcNS1pLyxPlXAV8Qni7WFbHqR0eQ+dURmb2q6RPCblPdwQON7OZkg4nzEbwUlYFqvgqux/B\nEhxnZiMk/QCMlPQjIaXhLpJOM7OsDcxoCJKv0cxekfQHQhjYUEnnW8iP25GQGzdjnVTxodXCzH6Q\ntAlwOrAghq6dR0jHeBjwt1y96RQ6/qRykmd8WB+4KAa0/0QITRpkZp9Ea+ccQo95VklSuCcScg2s\nCjwv6VALeR32IYyEOgu4o9AULixz6QyUdL+k24GpwC2EudyulHQkIRdwh+raaQDWB/4p6SSCwr2W\nkKHtZ8LsE22AwxXy5Tp1wC1dJ/GD356Qi3UzYK6ZXStpbeDfkj4ANgX+z3I0d5hClqp9CZ1Mg4DZ\nwLGSWpjZ3VF+kgdCFBKSNiLkLbiM8BAZB/QjJK05jTDB5GWWoeQ8sXNynpm9I2kOwZVwki1PUHOS\npDUJnZenEFI0epRCHfDoBQdJmxMyhw0hJEvpQnAj/D3ua0IIFZqQrZ7qFCFrqwNbAyeb2U6STiEo\nqSPN7IlMy5QpFLKFnQZMN7O/xbJ7CNnbtoruntZmNidT918hc9mbBGV/OEHhbw4cmwgBUxyIEsPw\nLgUGWsyp4KSPuxccCJ02o83sFcLr5CiCb/RcQtzl62Y2AbLTU13Jh7ujpD0kdbYw0mkVlifA/oqQ\n9GVspmXKMOWEOeV6K+SRwMyGEOZwmxg7rDKSMEbSmpI6mNn1hMTjrwIvmtkphERGd8c6XQnJdQBa\nEFw87tOtA650HQiJpbeX9AczW2JmzwHTCAm+d4UqowcyRpLCPYsw8movQtjaZoRX2lUkjSJM4X2u\nmc1K2VgekuRD30LS1oQ3iaMJAx32ir51zOwQYF8zW2pxqHMGOA+4WSEnxTeEbHGPSupoZlcRsom9\nTphkNJGM/ifCFEw5mVy00HH3QiMjaeDDloTg+8nRbfBnQiLyZwk/rhuA94AlZnZuNmWL672AK81s\nH0nnA9uY2R4KyV06AdsAr1hIJ1hwSNqDkLXtBpYnVf+F0Hk1i5BXYVI23DkK0+s0B/5iZrMkXUdw\n4+wbt7ckTAPU6BOQNwRu6TYyosLdnZDsuy3wtMLwzTeBlwgRCtcTOkteBjpJapZpS7eSwl2P8Kr7\nlaQ7CQp2n1h1e+BLM/tXISpcSSUKI8lOJ1jwPxIectPjQIerCW8Yv0F23Dlmdhwhgc4/FKb6OYPg\nZnghbv8voXCz+cZTrLjSbWTEHui/ErJCfUgY/HAssLmZDScotb0IOWmvBv5hWZghN0nhHg7cBbQn\nfD97AX82s0UKE0xeCrTKpCwZxizMX/Yuwbo9kxAHPUvSgcBc4Dgz+zxTAiS5N9ZJhH6Z2dHAPJYr\n3rMJ6Rq7VhY+U3I1Fty90IiQ1J/QO92SMKroDkKI2P6EYbXHE+YXawqcAYywDOdDVcXkLlsTHghH\nmdl0SXsRcrWuQeg86w/80cIEmAVDkktnW2B9M7tT0r8JGdBWN7Mfo7/6dsJ8ZxkPxZK0NyEOezrw\nNXCDmX0m6WbCA/dUC7M5Ow2MW7qNhDi66HLCj34mIcj+q6jwPgXGAO/HjrRfCakQM61wtwEGR9kg\nBOZ3JcThYmajCPkf/gW8BexVgAq3JCrcPQgPuS/iriMJ/vMHJV1ImOvsb1lSuJsR3Ei7ERLr7AOc\nopAI/mTCdO7tMi1HY8Ut3UaApM6E+c3GmNlf4+tld0JHTjlB2Z1iZmOyGIe7G3AFwX883cxeltSK\n4PbYEXjVzO7LtByZQtJq0Y2ApJYEhXu7mb0mqamZLYr7jiF0oM2M+7LRcdaLEHrXhvAgPpHwhgFw\nfjYUf2PGR6Q1DuYDbwBHShoVRx19BVwMbEWY+WEMZC0OdzvCENfDzOztpF3tzOyBGJe6bVROd2Va\nnoZGIU/Bg5KONbMvzWyepKbAuoQZehMKtztwryUNW87k/VcYyt2BMLlluaRLCNMtjZU0muVTHDkZ\nxN0LRUhSR0m/6LtrA1xHsCr/T1JfC1m5PjSzOxMKN4v8njCN+zKFK+lqYKyko8xsGGHAQ2+FaWgK\nBkm/I8TcHga0kHRp3PUCIRLkd7FeP4LrZO0sirc1YeRb/7j9BXBhtLaPILiUCi4ipNBwS7cISfIh\nXksY3nszYTTRI4AReqjPsyzO9gAVwsJ6EHrpE+W7A6sDA4GHJE0HHgJKzeznbMpYH6Ib4S6CD/on\noBQ4W9L3BPfCPwipKH8hdGCek8kohSS5OprZLDO7RVI5cEJ8MD9GGJixCyGvwxuZlsVxpVuUxDjQ\nowghSWsRUjS+bSFd38NAs1zIlfTq/ARwnqRNLAwvfoEw9HRRjMttV4ijnaIb4XHCIJNPzKxnDMl6\nHSg3s9MU8iysC1xvZu9m2oerMLXRZZLeim81tynk5r0KuNDM7pJ0X7z3PgNEFnClW2RIamNmsyWN\nI8SAbkHo9f9BUiJ71XWW21yobxF8zAcrTLY4FpbNsbU74VW3YJDUHtjEzJ4hDDI4CrgrXtsnMRRu\njMKsvhcT4qOBjORS6E7ojJxPyL37pKTXgb6SBpvZ0GjxDgCOlvSmxTnPXOFmB1e6RYSkbsDpMQSp\nObADMMTMpiqkRvwHISPXzFzKaWbzJd1FUE5XSXoXWECYfmcfM/ui2gbyj22ASdH/PBE4ieBCOVnS\nCDP7VGG+uTclPQB8nomHnqQNCMmKngIWESzcPgS30mJgm+gCeY2QH/daS5pk0skOHjJWRMQf/Rjg\nVuBxQuznbMJgh00J4UA5nXYnGUktCOkLdyZMTTOmUDtyokvnQkKs81CFOeYGEGKgn4iDPVaKMdCZ\nOH8rgsK918zujWWdgXsJ7o1LCNEJxxBSd15kZqMzIYtTPa50i4D4gzMz+zlatCcBJxNiMXsSRnRN\njaFi7rdrIJLvpcIsvkcTYp5fNbNH4gjAgwgpGm8lJA/KSLawGKZ2F3B8fJNobma/SepCsGxPNLOn\nYt3OPtosd7h7ocCR1JOQj+BLSc8Q/KWLCD7GMQQLchmucBuOGCWyLWH01sdmdqukQYQ0mUstzOFW\nShj8sTDD4rQA+hJyZzwVFW7TaGHfSYgOScjtCjeHuNItMBQycP0eeN3MZpjZFIXUfGsD9xFGFrUB\nrpG0i4Wps50GJGHhxreKYYQMbYslvRxdC0uBPWNH2vBsyGRmP0m6Edhf0kwLWcESVvVCoLOkLQgT\nemYqN6+TBq50C4gYW3ksIe3iozH+8yzgDQtDeD8mRCv8BmxImDXXlW4DExVuf2A/Qsffe5IGAvtJ\nIireMuCDLIv2OCFE8LjYgfeipK2ACwjJydcws7eyLJNTCffpFhiSdiHMpjAIuIYww8NvhDCw72Od\nEqBfIhTLaXgknQzcCOxtZqMktSFEMRwBPG9m/8qRXO0JfuQTCeGB6wKXm9nIXMjjrIgr3QJE0hPA\neDO7VGFq7n8SOmseIvSev5hU1zvOGhCFOcyaxoENZwN/IeQi/iwq3u0InZY5TRqjkDcZoEUMGUy4\nRPz7kGPcvVBAaHnu2b8T5tLamJCi7xRCXtRtCcNPl+E/sPqTpLC2IEyvs6akfczs6ujyeUVSfzOb\nLGm0JSWwyRUW5jtL3rbkTyd3uKVbgEhag5CqcRvgNDO7I5a3MLMFORWuSImDG64guHaOInRWnhst\n3v8jJH3vTJhLzGfJdVLiSrdAUUhEfRNh8sCvk6xgJwNIugJYbGYXJW3vAxxsZu9LWqdQB3Y42cVT\nOxYu7xJ6pLd1hZsVJgErx5FnmNlfCENpL5DUMvp0fdJGp0Zc6RYoZraYkC5whivchiWhPBXyEfdR\nmGnhJcIkmXtJWlfSRoTOy1UIw3/dX+qkhbsXHCcJSaUWZlXYk5D+8F+E+cwSs+WeS4h/XpcQmvU7\noLuZ/T1HIjsFhlu6jgNIagsQFe4GhKmMBhAGlzQlJPzuYGZDCANUdgK6AecBT+ZCZqcwcaXrNHok\ndQXGS7oyFn1JsG47AacDmxNioUdKOiCmQ5wH/BEYZGYfrtCo46TAla7jwBLCb2F7SVeb2Xwz+wjY\nAHjAzOYRRv6NIA6rtjCzxZ9yPQjCKTx8cITT6DGzGZJuJsxptpqkW83sREJ0wp6SlgBDgMNjeJgs\n4IljnFrjlq7TKJHUTdJBSUXvExLYPAuUS7oyZgh7HGhLSAD/PniUglM/PHrBaXRIakqY0WEt4ErC\ndO9jCB1nrQlJv08F5prZ6UnHed4Cp964pes0OsxsEbA3ocNsa0DAaGBLoG+0aG8AWsdIhsRxrnCd\neuNK12mURMW6NyHv8Cosn4G4g6R1CblwTzGzyTkS0SlS3L3gNGri7A8vACeY2YOSWgPz8iFTmFOc\nePSC06gxs3FxFoinJLUzs5tyLZNT3Lil6ziApM0JFm9vPJ+Fk0Fc6TpORNKqZvZzruVwihvvSHOc\n5fwCy7OMOU4mcEvXcRwni7il6ziOk0Vc6TqO42QRV7qO4zhZxJWuk1dIKpf0nqQPJT0qaaV6tLW9\npNFxfaCk86qp21rSn+twjoslnZVueaU6QyUdUItzdZXkuXsLHFe6Tr6xwMz6mNmGwCLg+OSdCtT6\ne9a/0AoAAAMCSURBVGtmT5rZldVUaQ3UWuk6Tm1xpevkM68BPaOF95GkfwITgC6SdpH0P0kTokXc\nEkDSbpI+lvQ6IVUjsXywpFvientJ/5H0fly2ImQb6xGt7KtjvbMljZM0UdLfktq6QNInkl4A1qvp\nIiQdE9t5X9Jjlaz3/pJek/SppAGxfqmkq5POfVx9b6STP7jSdfISSWXA7oTEMxCU2/1m9ntgPmEG\n3v5mtgnwDnCGpObAXcBewLbAmimavwl4xcw2BjYhTK9+HvB5tLLPlrQLsA6wGdAH6CvpD5L6AgcD\nvyco9U3TuJzHzWzTeL6P4P/bu3vWKKIojOP/R1GJZhUbBW2MiiCIpBEEC8UijU0sLIKNuCimEP0A\n2gl+BRUFC0G0sZQgFhrDxiYGbEwCipVFbATfGjkW9wjjgDiIDDvw/GBh986dObNbHIbD3nvoV47t\nAo4CJ4Ab+R36lG0lD+X1z0kaaxDHOsB7L9iwGZG0mO9ngTvADuB9RMzn+GFKK525XMewHhhQWqS/\ni4gVAEn3KE0k645TeqCR3R8+SdpamzORr1f5eZSShHvAo4j4mjGaNKU8IOkapYQxCsxUjj3MJccr\nkt7md5gADlbqvVsy9nKDWDbknHRt2HyLiPHqQCbWL9Uh4ElETNXmjQP/a7WPgOsRcbMW4/I/xLgL\nTGarnzPAscqx+rUiY1+MiGpy/tVA0zrO5QXronngiKS9AJI25h64b4AxSXty3tQfzn8KTOe5ayVt\npiwB7lXmzABnK7XinZK2Ac+Bk5JGJPUopYy/6QEfJK0DTteOnZK0Ju95N7CUsadzPpL2SdrUII51\ngJ90rXMiYjWfGO9L2pDDVyJiWdJ5yjaNH4EXlE3K6y4BtyT1gR+UvXQHkubyL1mPs667Hxjkk/Zn\nSmPKBUkPgEVK54nZBrd8FXiZ81/ze3JfAp4B24ELEfFd0m1KrXch94FYBSab/To27Lz3gplZi1xe\nMDNrkZOumVmLnHTNzFrkpGtm1iInXTOzFjnpmpm1yEnXzKxFPwGcaj6Yemj9RgAAAABJRU5ErkJg\ngg==\n",
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      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3a95208>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c378b0b8>"
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      ]
     },
     "execution_count": 0,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "# Compute confusion matrix\n",
    "import itertools\n",
    "cnf_matrix = confusion_matrix(diaries_test['quality_content'], predicted)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=targets_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=targets_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 32,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "5921"
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      ]
     },
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     "execution_count": 31,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "len(lf_data)"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "# Support vector machine (SVM)\n",
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    "It is widely regarded as one of the best text classification algorithms (http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html)\n",
    "\n",
    "From the doc:\n",
    " ‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates. \n",
    "\n",
    "* ‘f1’ \tmetrics.f1_score \tfor binary targets  \n",
    "* ‘f1_micro’ \tmetrics.f1_score \tmicro-averaged  \n",
    "* ‘f1_macro’ \tmetrics.f1_score \tmacro-averaged  \n",
    "* ‘f1_weighted’ \tmetrics.f1_score \tweighted average  \n",
    "* ‘f1_samples’ \tmetrics.f1_score \tby multilabel sample  \n",
    "\n",
    "SGDClassifier: loss = The loss function to be used. Defaults to ‘hinge’, which gives a linear SVM. The ‘log’ loss gives logistic regression, a probabilistic classifier. __‘modified_huber’ is another smooth loss that brings tolerance to outliers as well as probability estimates.__ ‘squared_hinge’ is like hinge but is quadratically penalized. ‘perceptron’ is the linear loss used by the perceptron algorithm. The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description.\n"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 33,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "text/plain": [
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       "0.96452702702702697"
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      ]
     },
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     "execution_count": 32,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "from sklearn.linear_model import SGDClassifier\n",
    "text_clf = Pipeline([('vect', CountVectorizer()), \n",
    "                     ('tfidf', TfidfTransformer()), \n",
jnanar's avatar
jnanar committed
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    "                     ('clf', SGDClassifier()),])\n",
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    "_ = text_clf.fit(lf_data.content, lf_data.quality_content)\n",
    "predicted = text_clf.predict(diaries_test.content)\n",
    "np.mean(predicted == diaries_test.quality_content)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 34,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "                   precision    recall  f1-score   support\n",
      "\n",
      "    Average Troll       0.97      0.96      0.96       538\n",
      "      Great Troll       0.99      0.96      0.97        89\n",
      "Magnificent Troll       1.00      0.94      0.97        62\n",
      "    Quality Troll       0.95      0.98      0.96       495\n",
      "\n",
      "      avg / total       0.96      0.96      0.96      1184\n",
      "\n"
1530 1531 1532 1533
     ]
    }
   ],
   "source": [
1534 1535 1536 1537 1538 1539
    "from sklearn import metrics\n",
    "print(metrics.classification_report(diaries_test.quality_content, predicted, target_names=targets_names))"
   ]
  },
  {
   "cell_type": "code",
1540
   "execution_count": 35,
1541 1542
   "metadata": {},
   "outputs": [
1543 1544 1545 1546
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
1547 1548 1549 1550 1551 1552 1553 1554 1555 1556
      "Confusion matrix, without normalization\n",
      "[[514   1   0  23]\n",
      " [  2  85   0   2]\n",
      " [  3   0  58   1]\n",
      " [ 10   0   0 485]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.96  0.    0.    0.04]\n",
      " [ 0.02  0.96  0.    0.02]\n",
      " [ 0.05  0.    0.94  0.02]\n",
      " [ 0.02  0.    0.    0.98]]\n"
1557 1558 1559 1560
     ]
    },
    {
     "data": {
1561
      "image/png": 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JHMdxSkllGQZ8J3Ag8DuAmX2ODwN2HCdPqVLMkg+k416oYmY/FeoRLMiQPI7j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\n6QgsqaukdyUNBD6LZRdI+jI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QOex4QrKURgQ3wvVxX3VCqNC4bPVUpwhZ2wLYHTjTzPaV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\nBpmboCuxG5cAAAAASUVORK5CYII=\n",
1562
      "text/plain": [
1563
       "<matplotlib.figure.Figure at 0x7f27c3c58358>"
1564 1565 1566 1567 1568
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
1569 1570 1571
    {
     "data": {
      "text/plain": [
1572
       "<matplotlib.figure.Figure at 0x7f27c3a41240>"
1573 1574
      ]
     },
1575 1576 1577 1578 1579 1580
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
1581
      "image/png": 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vujSJBtcTWro14lDgdTP7VtKvknqb2TRgDHAM8KqkLNyc6DNxxnm5mfWTVB/4SNKbvq5d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\nAM5zE9uG24vtAAAAAElFTkSuQmCC\n",
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      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3c67c88>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3ed0b70>"
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      ]
     },
     "execution_count": 0,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "metrics.confusion_matrix(diaries_test.quality_content, predicted)\n",
    "# Compute confusion matrix\n",
    "import itertools\n",
    "cnf_matrix = confusion_matrix(diaries_test['quality_content'], predicted)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=targets_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=targets_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 36,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[ 0.56  0.54  0.55  0.52  0.54  0.53  0.53  0.57  0.56  0.56]\n",
      "Total diaries classified: 5921\n",
      "Score: 0.546367446963\n"
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     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(text_clf,  # steps to convert raw messages into models\n",
    "                         lf_data.content,  # training data\n",
    "                         lf_data.quality_content,  # training labels\n",
    "                         cv=10,  # split data randomly into 10 parts: 9 for training, 1 for scoring\n",
    "                         scoring='accuracy',  # which scoring metric?\n",
    "                         n_jobs=-1,  # -1 = use all cores = faster\n",
    "                         )\n",
    "print(scores)\n",
    "\n",
    "print('Total diaries classified:', len(lf_data))\n",
    "print('Score:', sum(scores)/len(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si on préfère afficher la matrice de confusion, "
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 37,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Total diaries classified: 5921\n",
      "Score: 0.517031844562\n",
      "Confusion matrix:\n",
      "[[1429   25   10 1150]\n",
      " [ 254   16   14  176]\n",
      " [ 163   12   28   99]\n",
      " [ 771    8    6 1760]]\n"
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     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import confusion_matrix, f1_score,precision_score\n",
    "\n",
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    "k_fold = KFold(n_splits=10)\n",
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    "scores = []\n",
    "confusion = np.array([[0, 0,0,0], [0, 0,0,0], [0, 0,0,0], [0, 0,0,0]])\n",
    "for train_indices, test_indices in k_fold.split(lf_data):\n",
    "    train_text = lf_data.iloc[train_indices]['content'].values\n",
    "    train_y = lf_data.iloc[train_indices]['quality_content'].values\n",
    "    test_text = lf_data.iloc[test_indices]['content'].values\n",
    "    test_y = lf_data.iloc[test_indices]['quality_content'].values\n",
    "    text_clf.fit(train_text, train_y)\n",
    "    predictions = text_clf.predict(test_text)\n",
    "    confusion += confusion_matrix(test_y, predictions)\n",
    "    score = f1_score(test_y, predictions, average='weighted')\n",
    "    ps = precision_score(test_y, predictions, average='weighted')\n",
    "    scores.append(score)\n",
    "\n",
    "print('Total diaries classified:', len(lf_data))\n",
    "print('Score:', sum(scores)/len(scores))\n",
    "print('Confusion matrix:')\n",
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    "print(confusion)"
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   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 38,
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "[0.52674193930832813,\n",
       " 0.48318684704311976,\n",
       " 0.5117023333113857,\n",
       " 0.50029344305792656,\n",
       " 0.52179134297296403,\n",
       " 0.52858002802931348,\n",
       " 0.52104195538055964,\n",
       " 0.50287334894704838,\n",
       " 0.55752176025840749,\n",
       " 0.51658544730711664]"
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      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
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  {
   "cell_type": "code",
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   "execution_count": 39,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Confusion matrix, without normalization\n",
      "[[1429   25   10 1150]\n",
      " [ 254   16   14  176]\n",
      " [ 163   12   28   99]\n",
      " [ 771    8    6 1760]]\n",
      "Normalized confusion matrix\n",
      "[[ 0.55  0.01  0.    0.44]\n",
      " [ 0.55  0.03  0.03  0.38]\n",
      " [ 0.54  0.04  0.09  0.33]\n",
      " [ 0.3   0.    0.    0.69]]\n"
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     ]
    },
    {
     "data": {
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      "image/png": 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kRZMTKFS4MC+/OjpmPkvTkrdf/4EZn5iHXHt5H2bNnM7mTRtp0aAWN99xD5f0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\nXQTzDt6TNBD4g0OhliYReFIsJ3Bt608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      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3b7b710>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c0c14160>"
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      ]
     },
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "image/png": 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7lxwwuzIz8KqTMvlMJZ0KdDCzOyVlAS3NbHb1ilZh5gMn5h+Y2UWSmgGzwsps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\nr7i6O5mm6EZIGkjslvcmuqguIUrZvk60F+9HzI/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      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3c79b00>"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       "<matplotlib.figure.Figure at 0x7f27c3b29668>"
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      ]
     },
     "execution_count": 0,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(confusion, classes=targets_names,\n",
    "                      title='Confusion matrix, without normalization')\n",
    "\n",
    "# Plot normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(confusion, classes=targets_names, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
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  {
   "c