Impact+Assessment+of+PRESENCES-BRACED+Resilience+Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Make sure to address all inter-dependencies and packages installations\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfCSI = pd.read_csv(\"C:/Users/Nicola/Desktop/BRACED Final Evaluation/Monitoring Tools and Data/Processing Data/CSI_reviewed1.csv\",encoding='latin-1')\n",
    "# Dataset Uploaded from Coping Strategy Index dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#What is CSI: Check this online \n",
    "#https://www.wfp.org/content/coping-strategies-index-field-methods-manual-2nd-edition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 746)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCSI.rename(columns={'CSI score assets':'CSIAssets'},inplace=True)\n",
    "Base=dfCSI.loc[dfCSI['Study'] == 'Baseline']\n",
    "Base = Base.apply(pd.to_numeric, errors='coerce')\n",
    "End=dfCSI.loc[dfCSI['Study'] == 'Endline']\n",
    "End = End.apply(pd.to_numeric, errors='coerce')\n",
    "len(Base),len(End)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6947973161402035"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-(Base.CSI_food.mean()/End.CSI_food.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "CSIAssets=dfCSI.CSIAssets.apply(pd.to_numeric, errors='coerce')\n",
    "CSIfood=dfCSI.CSI_food.apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2969372693726937"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-(Base.CSIAssets.mean()/End.CSIAssets.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Defining new variables\n",
    "Year=dfCSI.Year.apply(pd.to_numeric, errors='coerce')\n",
    "Commune=dfCSI.commune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define font specifics for graphical visualisation\n",
    "import matplotlib.font_manager as font_manager\n",
    "title_font = {'fontname':'Futura', 'size':'18', 'color':'black', 'weight':'normal',\n",
    "              'verticalalignment':'bottom'}\n",
    "axis_font = {'fontname':'Futura', 'size':'15'}\n",
    "# Set the font properties\n",
    "font_path = 'C:\\Windows\\Fonts\\Futura.ttf'\n",
    "font_prop = font_manager.FontProperties(fname=font_path, size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jx+EVQqKqc4A5AAMHDtRJ55/dYfp7r70xrENv0djYyIwZMzjnnHOorKxk2rRp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\nlgCfB76q3D+hnKQxkrYgVZM/nJseuseNJ/0QDfRH+RHgPZI2Llv2xsB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f2b9573e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot seaborn library parameters\n",
    "DsgImpF=sns.boxplot(CSIfood,Commune, Year, palette=\"BuGn_r\")\n",
    "#Disaggregated CSI value for Food per Commune\n",
    "plt.xlim([0,200])\n",
    "plt.title('Coping Strategy Index for food security',**title_font)\n",
    "plt.ylabel('Commune',**axis_font)\n",
    "plt.xlabel('CSI Food- Higher value equals to greater insecurity',**axis_font)\n",
    "plt.tight_layout()\n",
    "#Graph Into Figure\n",
    "figure = DsgImpF.get_figure()\n",
    "figure.set_size_inches(8, 7)\n",
    "# Save in png format and adjust resolution\n",
    "#figure.savefig('CSIFood.png', dpi=1000)\n",
    "\n",
    "#Interpretation- Significant drop in CSI for food, less food insecurity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JxIkTufXWWzn++OOpqqqiurqa6upqZsyYQWlpKddcc82OX8dNOuigg1iyZAkA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\nas4JiJmZmdXc/weyMvvcBHRUxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f2bb768f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Assets=sns.boxplot(CSIAssets,Commune, Year, palette=\"Blues\")\n",
    "plt.title('Coping Strategy Index for asset security',**title_font)\n",
    "plt.xlabel('CSI Assets- Higher value equals to greater asset insecurity',**axis_font)\n",
    "figure = Assets.get_figure()\n",
    "figure.set_size_inches(8, 7)\n",
    "#Drop in CSI for asset as well, less asset insecurity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicola\\Anaconda3\\envs\\r\\lib\\site-packages\\gensim\\utils.py:860: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "#import packages to visualise words processing\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.corpus import stopwords\n",
    "from stop_words import get_stop_words\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from gensim import corpora, models\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfCSIQual = pd.read_csv(\"C:/Users/Nicola/Desktop/BRACED Final Evaluation/Final Evaluation Process and CSI/CSI Evaluation_2018.csv\",encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# one-review-per-line of the new file with all qualitative evidence\n",
    "def parse(dfCSIQual):\n",
    "    g = gzip.open(dfCSI, 'rb')\n",
    "    for l in g:\n",
    "        yield eval(l)\n",
    "def getDF(dfCSIQual):\n",
    "    i = 0\n",
    "    df = {}\n",
    "    for d in parse(dfCSIQual):\n",
    "        df[i] = d\n",
    "        i += 1\n",
    "    return pd.DataFrame.from_dict(df, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  variable                                              value\n",
      "0      Q_3     2 membres comité et 12bénéficiaires warrantage\n",
      "1      Q_3  Warrantage, BC, jardins maraîchers et PFLN, VS...\n"
     ]
    }
   ],
   "source": [
    "#Select and shape columns with text to be processed\n",
    "analysis=dfCSIQual[['Q_3','Q_5','Q_7','Q_9','Q_11','Q_13','Q_15','Q_17','Q_19','Q_21','Q_23','Q_25','Q_27','Q_29']].dropna()\n",
    "', '.join(analysis)\n",
    "analysis.shape\n",
    "analysis=pd.melt(analysis)\n",
    "print(analysis.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "588\n"
     ]
    }
   ],
   "source": [
    "print(analysis.ndim)\n",
    "print(analysis.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#remove punctuation from melted text\n",
    "import string\n",
    "exclude = set(string.punctuation)\n",
    "def remove_punctuation(x):\n",
    "    try:\n",
    "        x = ''.join(ch for ch in x if ch not in exclude)\n",
    "    except:\n",
    "        pass\n",
    "    return x\n",
    "analysis1 = analysis.apply(remove_punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the clean list as set of word to be tokenized\n",
    "words=pd.DataFrame(analysis1)\n",
    "raw=words.as_matrix().flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#tokenize words\n",
    "import nltk\n",
    "def tokenize(word):\n",
    "    return word\n",
    "tokenized = [tokenize(word) for sentence in raw for word in sentence.split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clean the bag of words form stop words, set the language in French for this dataset\n",
    "from stop_words import get_stop_words\n",
    "fr_stop = get_stop_words('fr')\n",
    "stopped_tokens = [i for i in tokenized if not i in fr_stop]\n",
    "nltk_stpwd = stopwords.words('french')\n",
    "stop_words_stpwd = get_stop_words('fr')\n",
    "merged_stopwords = list(set(nltk_stpwd + fr_stop))\n",
    "sb_stemmer = SnowballStemmer('french')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "#Shape the text in a way to create a cleaner dictionary\n",
    "num_reviews = analysis.shape[0]\n",
    "doc_set = [analysis1.value for i in range(num_reviews)]\n",
    "texts = []\n",
    "for doc in doc_set:\n",
    "    stopped_tokens = [token for token in tokenized if not token in merged_stopwords]\n",
    "    stemmed_tokens = [sb_stemmer.stem(token) for token in stopped_tokens]\n",
    "    # add tokens to list\n",
    "    texts.append(stopped_tokens)\n",
    "print (texts[1:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary(1011 unique tokens: ['(', '(15000fcfa),', '(CFW)Cash', '(CFW)agriculteurs', '(CFW,']...)\n"
     ]
    }
   ],
   "source": [
    "#Creat dictionary\n",
    "texts_dict = corpora.Dictionary(texts)\n",
    "texts_dict.save('auto_review.dict') # lets save to disk for later use\n",
    "# Examine each token’s unique id\n",
    "print(texts_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "id2word = gensim.corpora.Dictionary()\n",
    "corpus = [texts_dict.doc2bow(text) for text in texts]\n",
    "len(corpus)\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore', category=UserWarning, module='gensim')\n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel\n",
    "lsimodel = LsiModel(corpus=corpus, num_topics=10, id2word=texts_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lsimodel = LsiModel(corpus=corpus, num_topics=5, id2word=texts_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lsimodel.show_topics(num_topics=5)\n",
    "lsitopics = lsimodel.show_topics(formatted=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ldamodel = LdaModel(corpus=corpus, num_topics=5, id2word=texts_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pyLDAvis.enable_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nicola\\Anaconda3\\envs\\r\\lib\\site-packages\\pyLDAvis\\_prepare.py:387: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  topic_term_dists = topic_term_dists.ix[topic_order]\n"
     ]
    },
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el571632416228756162173026544\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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701.4998243742212, 2739.528590087516, 119.27937596028437, 58.25768569758424, 237.56004202625493, 59.69418964042355, 58.37412483431137, 58.92969838127602, 399.15397343737624, 57.399049984853136, 526.9462802338513, 57.79773100208641, 116.71400023554277, 57.51315306259642, 57.92029750663999, 223.2097976380928, 680.0154345643175, 56.389974626632885, 57.15753595236847, 115.86640655024893, 2115.311571082236, 1009.0063232094027, 789.2906570743467, 162.155223834724, 617.9643465067727, 1828.3599914190831, 116.37859067379249, 728.0765674618957, 516.2412069656481, 608.7653802503123, 366.2998186035951, 758.1342171503816, 456.5488491528976, 677.5566750397562, 1260.0070018699823, 719.5424206146059, 611.4900164771433, 446.2796359827496, 1055.5891264960512, 1311.019075112782, 393.3961369522538, 626.6521881637094, 1435.3415301012326, 1402.5071104979925, 763.5772601134198, 733.8056053182338, 917.1769934362585, 813.6336171594879, 2054.0566834123406, 1203.3011741972452, 1484.6817510325836, 550.7312413555967, 640.8355639013977, 815.3083289092556, 1026.0226364002544], \"Term\": [\"am\\u00e9lior\\u00e9es\", \"activit\\u00e9s\", \"vivres\", \"warrantage\", \"production\", \"permis\", \"pratique\", \"mara\\u00eechage,\", \"BRACED\", \"plus\", \"AGR\", \"permet\", \"semences\", \"vivre\", \"diversit\\u00e9\", \"projet\", \"cr\\u00e9dits\", \"revenus\", \"warrantage,\", \"toujours\", \"contribu\\u00e9\", \"acheter\", \"BC,\", \"caisse\", \"diversification\", \"m\\u00e9nages\", \"transformation\", \"groupement\", \"permettent\", \"petit\", \"activit\\u00e9s\", \"majorit\\u00e9\", \"g\\u00e8re\", \"m\\u00e9nagenotre\", \"ach\\u00e8tent\", \"rendements\", \"d'\\u00e9pargneon\", \"rupture\", \"lait\", \"existe\", \"zai\", \"comptant\", \"avantage,(\", \"disponible\", \"suffisante\", \"menages\", \"strat\\u00e9gies\", \"VSLA\", \"PFLN\", \"b\\u00e9n\\u00e9fici\\u00e9\", \"commencer\", \"Pr\\u00e9sences.Warrantage,\", \"clientsMieux,\", \"d'am\\u00e9liorer\", \"b\\u00e9tail\", \"brader\", \"mais,\", \"garanti\", \"manger,le\", \"pr\\u00e9par\\u00e9\", \"hommes\", \"r\\u00e9duit\", \"l'embouche,\", \"cr\\u00e9dits\", \"d'\\u00e9viter\", \"pratique\", \"VSLA,\", \"initi\\u00e9es\", \"petits\", \"gr\\u00e2ce\", \"tir\\u00e9s\", \"d'une\", \"semences\", \"situation\", \"revenus\", \"mara\\u00eechage\", \"vivres\", \"assez\", \"BC\", \"permettent\", \"PFLN,\", \"disponibilit\\u00e9\", \"cr\\u00e9dit\", \"travers\", \"BC,\", \"(\", \"cause\", \"permis\", \"am\\u00e9lior\\u00e9es\", \"appuis\", \"diversification\", \",\", \"production\", \"AGR\", \"warrantage\", \"BRACED\", \"transformation\", \"m\\u00e9nages\", \"warrantage,\", \"petit\", \"mara\\u00eechage,\", \"projet\", \"d'AGR\", \"beaucoup\", \"contre\", \"productionnous\", \"g\\u00e9n\\u00e9r\\u00e9s\", \"warrantge,\", \"permet\", \"connaissons\", \"r\\u00e9sultat\", \"Mara\\u00eechage\", \"parentsen\", \"nourriture\", \"projet.cette\", \"bc,il\", \"groupement),\", \"utilis\\u00e9e\", \"pendant\", \"(CFW)Cash\", \"appuy\\u00e9s\", \"lesquels\", \"\\u00e9vit\\u00e9A\", \"diminuer\", \"autour\", \"nouvelles\", \"semences\", \"ruminants\", \"projetla\", \"enrichi\", \"casia\", \"...\", \"issues\", \"bonne\", \"gr\\u00e2ce\", \"assez\", \"initi\\u00e9es\", \"BRACED\", \"vivres\", \"warrantage,\", \"cause\", \"b\\u00e9n\\u00e9fices\", \"s\\u00e9curiser\", \"warrantage\", \"femmes\", \"productions\", \"CFW\", \"vente\", \"appuis\", \"revenus\", \"p\\u00e9riode\", \"petit\", \"am\\u00e9lior\\u00e9es\", \"permis\", \"transformation\", \"cr\\u00e9dit\", \"projet\", \"mara\\u00eechage\", \"AGR\", \"aliments\", \"BC\", \"production\", \"pratique\", \"activit\\u00e9s\", \"acheter\", \"cr\\u00e9dits\", \"BC,\", \"disponibilit\\u00e9\", \"situation\", \"mara\\u00eechage,\", \"termeil\", \"menages.aliments\", \"(ma\\u00efs,\", \"disparue,\", \"SCAP/RU)\", \"unis\", \"mendier.Avec\", \"vivreles\", \"caisse\", \"rembourser\", \"hauteur\", \"alimentsles\", \"Habanael'appui\", \"PHASE,\", \"cr\\u00e9dits\", \"(mara\\u00eechage,\", \"CFWApr\\u00e8s\", \"ans\", \"d\\u00e9placer\", \"d\\u00e9gager\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"permis\", \"warrantageWarrantage,\", \"situation\\u00e0\", \"urgent\", \"warrantage.\", \"production,avec\", \"conserver\", \"pratiquele\", \"dettesle\", \"r\\u00e9coltes\", \"embouche,\", \"r\\u00e9duire\", \"connu\", \"plus\", \"prolong\\u00e9e\", \"mara\\u00eechage,\", \"BRACED\", \"quoi\", \"am\\u00e9lior\\u00e9es,\", \"groupements\", \"gagner\", \"hommes\", \"AGR\", \"appui\", \"acheter\", \"gr\\u00e2ce\", \"am\\u00e9lior\\u00e9es\", \"m\\u00e9nages\", \"situation\", \"l'usage\", \"strat\\u00e9gie\", \"issues\", \"production\", \"diversification\", \"pratique\", \"BC,\", \"\\u00e9levageA\", \"disponibilit\\u00e9\", \"warrantage,\", \"semences\", \"mara\\u00eechage\", \"warrantage\", \"revenus\", \"cr\\u00e9dit\", \"activit\\u00e9s\", \"petit\", \"tir\\u00e9s\", \"BC\", \"vivres\", \"permettent\", \"transformation\", \"cultuver\", \"alimentaire,\", \"AGRpar\", \"suffisamment\", \"baobab,les\", \"pay\\u00e9\", \"met\", \"vivres\", \"vivre\", \"d\\u00e9placement,\", \"oignon,)jardin\", \"l'appui\", \"l'adoption\", \"Habanae,\", \"locales.\", \"p\\u00e9nurie.Par\", \"detrimernt\", \"s'il\", \"AGR)gr\\u00e2ce\", \"objet\", \"niveau\", \"meilleursc'est\", \"conforme\", \"s'agit\", \"vendu\", \"consommation\", \"production\", \"petits.\", \"amener\", \"pratiquel'usage\", \"groupement\", \"mauvaise\", \"plus\", \"pratique\", \"initi\\u00e9\", \"argent\", \"permettent\", \"comit\\u00e9\", \"projet\", \"diversit\\u00e9\", \"activit\\u00e9s\", \"diversification\", \"nourriture\", \"travers\", \"semences\", \"warrantage\", \"caisse\", \"PFLN,\", \"AGR\", \"revenus\", \"mara\\u00eechage,\", \"warrantage,\", \"BC,\", \"am\\u00e9lior\\u00e9es\", \"d'\\u00e9viter\", \"acheter\", \"issues\", \"BC\", \"BRACED\", \"gr\\u00e2ce\", \"mara\\u00eechage\", \"permis\", \"(\", \"cr\\u00e9dit\", \"cr\\u00e9dits\", \"petit\", \"m\\u00e9nages\", \"bcles\", \"foyer,c'est\", \"warrantagenous\", \"l'embouche\", \"jardins\", \"source\", \"mangentnous\", \"disparu\", \"poids\", \"attentes,\", \",on\", \"permet\", \"am\\u00e9lior\\u00e9es\", \"\\u00e9conomies\", \"BC,la\", \"mara\\u00eechagela\", \"diff\\u00e9rentes\", \"Pr\\u00e9sences.CS/DRS,petit\", \"rations\", \"pu\", \"mis\", \"toujours\", \"warrantage)nous\", \"rares\", \"collectif.On\", \"\\u00e9levagegr\\u00e2ce\", \"caisses\", \"contribu\\u00e9\", \"mara\\u00eechers)la\", \"semences.\", \"zai,\", \"warrantage\", \"mara\\u00eechage,\", \"transformation\", \"plut\\u00f4t\", \"CFW\", \"permis\", \"magasin\", \"disponibilit\\u00e9\", \"vivre\", \"diversit\\u00e9\", \"appui\", \"petit\", \"agr\", \"m\\u00e9nages\", \"production\", \"projet\", \"p\\u00e9riode\", \"banque\", \"AGR\", \"revenus\", \"l,argent\", \"plus\", \"BRACED\", \"pratique\", \"cr\\u00e9dit\", \"BC,\", \"warrantage,\", \"mara\\u00eechage\", \"semences\", \"vivres\", \"activit\\u00e9s\", \"strat\\u00e9gie\", \"BC\", \"cr\\u00e9dits\", \"gr\\u00e2ce\"], \"Total\": [13864.0, 14471.0, 10478.0, 11627.0, 8279.0, 10974.0, 10661.0, 5545.0, 10797.0, 4044.0, 7160.0, 3546.0, 21010.0, 3052.0, 3651.0, 4471.0, 7466.0, 9279.0, 6717.0, 2743.0, 3564.0, 4185.0, 5038.0, 2161.0, 4175.0, 4162.0, 4381.0, 2156.0, 4412.0, 4690.0, 14471.682206121073, 281.252362639422, 287.0662863385876, 290.7574838192661, 567.5552228842156, 2551.633542622484, 289.878536773944, 295.0331392033553, 282.9567079899899, 578.4743949924252, 281.6019770484315, 575.7350691590354, 289.0744102826921, 858.0783174616531, 293.4348402054164, 290.0681209439158, 290.0643200481049, 289.89542024750153, 577.7767224048498, 581.2047261417333, 286.19926497404884, 286.5671178226658, 283.91595665568207, 846.1717732108739, 576.0072366156171, 289.97403099947223, 285.4723697150607, 285.7762111240313, 286.09544680372716, 286.0692388804119, 1435.9933868680732, 874.4319867039642, 601.05782265618, 7466.530557488717, 3211.7756887637715, 10661.013636484333, 1739.0105491917295, 4453.692440407767, 1144.354536511206, 11956.618672335671, 3736.3076508234267, 1120.971986363676, 21010.74852176729, 4293.846629010821, 9279.550436526322, 5791.816030954158, 10478.733017468274, 2227.370092771645, 4656.101483894674, 4412.958349733164, 2390.8875424152816, 4365.148621482459, 5231.286010733279, 3841.7068319590394, 5038.004756073733, 3513.2676326127107, 3411.1522042380507, 10974.528080359096, 13864.600539093579, 3436.8095988274354, 4175.389991532111, 2618.424913165424, 8279.11841870308, 7160.809801812656, 11627.546549087634, 10797.103485968517, 4381.148524608093, 4162.655250896489, 6717.484082432003, 4690.15494785112, 5545.200821234892, 4471.646906490139, 289.3304988321397, 3118.5445634588327, 293.0886045542872, 291.21979698589314, 286.6246750222619, 282.1948617124009, 3546.091559878425, 874.3202900965894, 282.50906168893806, 288.0332200233663, 288.9520578586757, 1762.911751324683, 297.5061173649873, 288.17166271587024, 291.65995560765225, 568.0003663202326, 862.0102411959459, 289.8171509599621, 1485.1923211274006, 585.0458362260629, 282.4602488237343, 859.7275279516443, 297.61261047089044, 1463.2543904133674, 21010.74852176729, 288.53333723130964, 280.3820666099808, 288.6673655534597, 293.7369542460937, 579.6666986241202, 3811.292200424542, 3170.945917049932, 11956.618672335671, 2227.370092771645, 4453.692440407767, 10797.103485968517, 10478.733017468274, 6717.484082432003, 3411.1522042380507, 1736.713315938826, 1409.7790280174195, 11627.546549087634, 3426.843730356149, 1748.8106286344027, 3477.526331297301, 2034.9351686562138, 3436.8095988274354, 9279.550436526322, 3815.80462150295, 4690.15494785112, 13864.600539093579, 10974.528080359096, 4381.148524608093, 5231.286010733279, 4471.646906490139, 5791.816030954158, 7160.809801812656, 2655.5549490824415, 4656.101483894674, 8279.11841870308, 10661.013636484333, 14471.682206121073, 4185.18543460861, 7466.530557488717, 5038.004756073733, 4365.148621482459, 4293.846629010821, 5545.200821234892, 309.5998305602445, 294.76636694167865, 303.64323047111185, 598.7348016708513, 314.56088956378744, 297.5877297620545, 288.7591117899127, 299.9552336451982, 2161.342170302422, 606.8665913217005, 297.3115377253109, 298.68058650715307, 298.8715343788709, 296.1456722468994, 7466.530557488717, 891.0629452835286, 298.9398252923891, 601.6330927750729, 305.18405079986053, 599.4376921951628, 296.6062490891162, 10974.528080359096, 302.03117209532275, 297.6893240486581, 299.48390433645824, 295.3283972056568, 301.6849992499976, 297.3120887292345, 301.51707758690947, 307.65464118111674, 2426.7460718429493, 901.0014500685417, 903.2156192892459, 611.0681391636783, 4044.224908055619, 1209.8172306419885, 5545.200821234892, 10797.103485968517, 1730.5293078267239, 2943.3480126899, 2666.0094878936206, 1825.382570455519, 1435.9933868680732, 7160.809801812656, 2123.5332650658097, 4185.18543460861, 11956.618672335671, 13864.600539093579, 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\"caisse\", \"caisse\", \"caisses\", \"caisses\", \"caisses\", \"caisses\", \"caisses\", \"casia\", \"casia\", \"casia\", \"casia\", \"casia\", \"cause\", \"cause\", \"cause\", \"cause\", \"cause\", \"clientsMieux,\", \"clientsMieux,\", \"clientsMieux,\", \"clientsMieux,\", \"clientsMieux,\", \"collectif.On\", \"collectif.On\", \"collectif.On\", \"collectif.On\", \"collectif.On\", \"comit\\u00e9\", \"comit\\u00e9\", \"comit\\u00e9\", \"comit\\u00e9\", \"comit\\u00e9\", \"commencer\", \"commencer\", \"commencer\", \"commencer\", \"commencer\", \"comptant\", \"comptant\", \"comptant\", \"comptant\", \"comptant\", \"conforme\", \"conforme\", \"conforme\", \"conforme\", \"conforme\", \"connaissons\", \"connaissons\", \"connaissons\", \"connaissons\", \"connaissons\", \"connu\", \"connu\", \"connu\", \"connu\", \"connu\", \"conserver\", \"conserver\", \"conserver\", \"conserver\", \"conserver\", \"consommation\", \"consommation\", \"consommation\", \"consommation\", \"consommation\", \"contre\", \"contre\", \"contre\", \"contre\", \"contre\", \"contribu\\u00e9\", \"contribu\\u00e9\", \"contribu\\u00e9\", \"contribu\\u00e9\", \"contribu\\u00e9\", \"cr\\u00e9dit\", \"cr\\u00e9dit\", \"cr\\u00e9dit\", \"cr\\u00e9dit\", \"cr\\u00e9dit\", \"cr\\u00e9dits\", \"cr\\u00e9dits\", \"cr\\u00e9dits\", \"cr\\u00e9dits\", \"cr\\u00e9dits\", \"cultuver\", \"cultuver\", \"cultuver\", \"cultuver\", \"cultuver\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"c\\u00e9r\\u00e9ali\\u00e8reau\", \"d'AGR\", \"d'AGR\", \"d'AGR\", \"d'AGR\", \"d'AGR\", \"d'am\\u00e9liorer\", \"d'am\\u00e9liorer\", \"d'am\\u00e9liorer\", \"d'am\\u00e9liorer\", \"d'am\\u00e9liorer\", \"d'une\", \"d'une\", \"d'une\", \"d'une\", \"d'une\", \"d'\\u00e9pargneon\", \"d'\\u00e9pargneon\", \"d'\\u00e9pargneon\", \"d'\\u00e9pargneon\", \"d'\\u00e9pargneon\", \"d'\\u00e9viter\", \"d'\\u00e9viter\", \"d'\\u00e9viter\", 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\"l'embouche,\", \"l'embouche,\", \"l'embouche,\", \"l'embouche,\", \"l'usage\", \"l'usage\", \"l'usage\", \"l'usage\", \"l'usage\", \"l,argent\", \"l,argent\", \"l,argent\", \"l,argent\", \"l,argent\", \"lait\", \"lait\", \"lait\", \"lait\", \"lait\", \"lesquels\", \"lesquels\", \"lesquels\", \"lesquels\", \"lesquels\", \"locales.\", \"locales.\", \"locales.\", \"locales.\", \"locales.\", \"magasin\", \"magasin\", \"magasin\", \"magasin\", \"magasin\", \"mais,\", \"mais,\", \"mais,\", \"mais,\", \"mais,\", \"majorit\\u00e9\", \"majorit\\u00e9\", \"majorit\\u00e9\", \"majorit\\u00e9\", \"majorit\\u00e9\", \"mangentnous\", \"mangentnous\", \"mangentnous\", \"mangentnous\", \"mangentnous\", \"manger,le\", \"manger,le\", \"manger,le\", \"manger,le\", \"manger,le\", \"mara\\u00eechage\", \"mara\\u00eechage\", \"mara\\u00eechage\", \"mara\\u00eechage\", \"mara\\u00eechage\", \"mara\\u00eechage,\", \"mara\\u00eechage,\", \"mara\\u00eechage,\", \"mara\\u00eechage,\", \"mara\\u00eechage,\", \"mara\\u00eechagela\", \"mara\\u00eechagela\", \"mara\\u00eechagela\", \"mara\\u00eechagela\", \"mara\\u00eechagela\", \"mara\\u00eechers)la\", \"mara\\u00eechers)la\", \"mara\\u00eechers)la\", \"mara\\u00eechers)la\", \"mara\\u00eechers)la\", \"mauvaise\", \"mauvaise\", \"mauvaise\", \"mauvaise\", \"mauvaise\", \"meilleursc'est\", \"meilleursc'est\", \"meilleursc'est\", \"meilleursc'est\", \"meilleursc'est\", \"menages\", \"menages\", \"menages\", \"menages\", \"menages\", \"menages.aliments\", \"menages.aliments\", \"menages.aliments\", \"menages.aliments\", \"menages.aliments\", \"mendier.Avec\", \"mendier.Avec\", \"mendier.Avec\", \"mendier.Avec\", \"mendier.Avec\", \"met\", \"met\", \"met\", \"met\", \"met\", \"mis\", \"mis\", \"mis\", \"mis\", \"mis\", \"m\\u00e9nagenotre\", \"m\\u00e9nagenotre\", \"m\\u00e9nagenotre\", \"m\\u00e9nagenotre\", \"m\\u00e9nagenotre\", \"m\\u00e9nages\", \"m\\u00e9nages\", \"m\\u00e9nages\", \"m\\u00e9nages\", \"m\\u00e9nages\", \"niveau\", 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\"prolong\\u00e9e\", \"prolong\\u00e9e\", \"prolong\\u00e9e\", \"prolong\\u00e9e\", \"prolong\\u00e9e\", \"pr\\u00e9par\\u00e9\", \"pr\\u00e9par\\u00e9\", \"pr\\u00e9par\\u00e9\", \"pr\\u00e9par\\u00e9\", \"pr\\u00e9par\\u00e9\", \"pu\", \"pu\", \"pu\", \"pu\", \"pu\", \"p\\u00e9nurie.Par\", \"p\\u00e9nurie.Par\", \"p\\u00e9nurie.Par\", \"p\\u00e9nurie.Par\", \"p\\u00e9nurie.Par\", \"p\\u00e9riode\", \"p\\u00e9riode\", \"p\\u00e9riode\", \"p\\u00e9riode\", \"p\\u00e9riode\", \"quoi\", \"quoi\", \"quoi\", \"quoi\", \"quoi\", \"rares\", \"rares\", \"rares\", \"rares\", \"rares\", \"rations\", \"rations\", \"rations\", \"rations\", \"rations\", \"rembourser\", \"rembourser\", \"rembourser\", \"rembourser\", \"rembourser\", \"rendements\", \"rendements\", \"rendements\", \"rendements\", \"rendements\", \"revenus\", \"revenus\", \"revenus\", \"revenus\", \"revenus\", \"ruminants\", \"ruminants\", \"ruminants\", \"ruminants\", \"ruminants\", \"rupture\", \"rupture\", \"rupture\", 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\"zai\", \"zai,\", \"zai,\", \"zai,\", \"zai,\", \"zai,\", \"\\u00e9conomies\", \"\\u00e9conomies\", \"\\u00e9conomies\", \"\\u00e9conomies\", \"\\u00e9conomies\", \"\\u00e9levageA\", \"\\u00e9levageA\", \"\\u00e9levageA\", \"\\u00e9levageA\", \"\\u00e9levageA\", \"\\u00e9levagegr\\u00e2ce\", \"\\u00e9levagegr\\u00e2ce\", \"\\u00e9levagegr\\u00e2ce\", \"\\u00e9levagegr\\u00e2ce\", \"\\u00e9levagegr\\u00e2ce\", \"\\u00e9vit\\u00e9A\", \"\\u00e9vit\\u00e9A\", \"\\u00e9vit\\u00e9A\", \"\\u00e9vit\\u00e9A\", \"\\u00e9vit\\u00e9A\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 4, 1, 2, 3]};\n",
       "\n",
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       "   }(LDAvis);\n",
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       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
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       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
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      ],
      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "4      26.989890        1       1 -0.004483 -0.001881\n",
       "3      25.280531        1       2 -0.000624  0.004630\n",
       "0      18.737388        1       3  0.002149 -0.002526\n",
       "1      14.586198        1       4 -0.001761 -0.000145\n",
       "2      14.405993        1       5  0.004719 -0.000079, topic_info=     Category          Freq             Term         Total  loglift  logprob\n",
       "term                                                                        \n",
       "164   Default  13864.000000       améliorées  13864.000000  30.0000  30.0000\n",
       "121   Default  14471.000000        activités  14471.000000  29.0000  29.0000\n",
       "955   Default  10478.000000           vivres  10478.000000  28.0000  28.0000\n",
       "966   Default  11627.000000       warrantage  11627.000000  27.0000  27.0000\n",
       "725   Default   8279.000000       production   8279.000000  26.0000  26.0000\n",
       "687   Default  10974.000000           permis  10974.000000  25.0000  25.0000\n",
       "702   Default  10661.000000         pratique  10661.000000  24.0000  24.0000\n",
       "574   Default   5545.000000      maraîchage,   5545.000000  23.0000  23.0000\n",
       "48    Default  10797.000000           BRACED  10797.000000  22.0000  22.0000\n",
       "691   Default   4044.000000             plus   4044.000000  21.0000  21.0000\n",
       "32    Default   7160.000000              AGR   7160.000000  20.0000  20.0000\n",
       "684   Default   3546.000000           permet   3546.000000  19.0000  19.0000\n",
       "836   Default  21010.000000         semences  21010.000000  18.0000  18.0000\n",
       "952   Default   3052.000000            vivre   3052.000000  17.0000  17.0000\n",
       "405   Default   3651.000000        diversité   3651.000000  16.0000  16.0000\n",
       "740   Default   4471.000000           projet   4471.000000  15.0000  15.0000\n",
       "324   Default   7466.000000          crédits   7466.000000  14.0000  14.0000\n",
       "799   Default   9279.000000          revenus   9279.000000  13.0000  13.0000\n",
       "970   Default   6717.000000      warrantage,   6717.000000  12.0000  12.0000\n",
       "905   Default   2743.000000         toujours   2743.000000  11.0000  11.0000\n",
       "315   Default   3564.000000        contribué   3564.000000  10.0000  10.0000\n",
       "116   Default   4185.000000          acheter   4185.000000   9.0000   9.0000\n",
       "43    Default   5038.000000              BC,   5038.000000   8.0000   8.0000\n",
       "254   Default   2161.000000           caisse   2161.000000   7.0000   7.0000\n",
       "401   Default   4175.000000  diversification   4175.000000   6.0000   6.0000\n",
       "625   Default   4162.000000          ménages   4162.000000   5.0000   5.0000\n",
       "911   Default   4381.000000   transformation   4381.000000   4.0000   4.0000\n",
       "476   Default   2156.000000       groupement   2156.000000   3.0000   3.0000\n",
       "685   Default   4412.000000       permettent   4412.000000   2.0000   2.0000\n",
       "688   Default   4690.000000            petit   4690.000000   1.0000   1.0000\n",
       "...       ...           ...              ...           ...      ...      ...\n",
       "687    Topic5   1828.359991           permis  10974.528080   0.1454  -4.0939\n",
       "549    Topic5    116.378591          magasin    623.369263   0.2592  -6.8482\n",
       "393    Topic5    728.076567    disponibilité   4365.148621   0.1465  -5.0147\n",
       "952    Topic5    516.241207            vivre   3052.165537   0.1605  -5.3585\n",
       "405    Topic5    608.765380        diversité   3651.082407   0.1462  -5.1936\n",
       "188    Topic5    366.299819            appui   2123.533265   0.1801  -5.7016\n",
       "688    Topic5    758.134217            petit   4690.154948   0.1152  -4.9742\n",
       "126    Topic5    456.548849              agr   2702.649664   0.1592  -5.4814\n",
       "625    Topic5    677.556675          ménages   4162.655251   0.1221  -5.0866\n",
       "725    Topic5   1260.007002       production   8279.118419   0.0549  -4.4662\n",
       "740    Topic5    719.542421           projet   4471.646906   0.1106  -5.0265\n",
       "765    Topic5    611.490016          période   3815.804622   0.1065  -5.1892\n",
       "216    Topic5    446.279636           banque   2688.306933   0.1418  -5.5041\n",
       "32     Topic5   1055.589126              AGR   7160.809802   0.0230  -4.6432\n",
       "799    Topic5   1311.019075          revenus   9279.550437  -0.0195  -4.4265\n",
       "534    Topic5    393.396137         l,argent   2333.355788   0.1573  -5.6303\n",
       "691    Topic5    626.652188             plus   4044.224908   0.0729  -5.1647\n",
       "48     Topic5   1435.341530           BRACED  10797.103486  -0.0803  -4.3359\n",
       "702    Topic5   1402.507110         pratique  10661.013636  -0.0908  -4.3591\n",
       "320    Topic5    763.577260           crédit   5231.286011   0.0131  -4.9671\n",
       "43     Topic5    733.805605              BC,   5038.004756   0.0110  -5.0068\n",
       "970    Topic5    917.176993      warrantage,   6717.484082  -0.0536  -4.7838\n",
       "571    Topic5    813.633617       maraîchage   5791.816031  -0.0252  -4.9036\n",
       "836    Topic5   2054.056683         semences  21010.748522  -0.3877  -3.9775\n",
       "955    Topic5   1203.301174           vivres  10478.733017  -0.2268  -4.5123\n",
       "121    Topic5   1484.681751        activités  14471.682206  -0.3395  -4.3021\n",
       "875    Topic5    550.731241        stratégie   3560.274860   0.0712  -5.2938\n",
       "42     Topic5    640.835564               BC   4656.101484  -0.0456  -5.1423\n",
       "324    Topic5    815.308329          crédits   7466.530557  -0.2771  -4.9015\n",
       "481    Topic5   1026.022636            grâce  11956.618672  -0.5181  -4.6716\n",
       "\n",
       "[375 rows x 6 columns], token_table=      Topic      Freq          Term\n",
       "term                               \n",
       "0         1  0.286058             (\n",
       "0         2  0.250764             (\n",
       "0         3  0.161957             (\n",
       "0         4  0.151141             (\n",
       "0         5  0.150287             (\n",
       "2         1  0.231180     (CFW)Cash\n",
       "2         2  0.327793     (CFW)Cash\n",
       "2         3  0.158721     (CFW)Cash\n",
       "2         4  0.127667     (CFW)Cash\n",
       "2         5  0.158721     (CFW)Cash\n",
       "10        1  0.269341  (maraîchage,\n",
       "10        2  0.214351  (maraîchage,\n",
       "10        3  0.251385  (maraîchage,\n",
       "10        4  0.135793  (maraîchage,\n",
       "10        5  0.130182  (maraîchage,\n",
       "11        1  0.240414        (maïs,\n",
       "11        2  0.210774        (maïs,\n",
       "11        3  0.270054        (maïs,\n",
       "11        4  0.158080        (maïs,\n",
       "11        5  0.125147        (maïs,\n",
       "18        1  0.293306             ,\n",
       "18        2  0.244040             ,\n",
       "18        3  0.189809             ,\n",
       "18        4  0.149326             ,\n",
       "18        5  0.123739             ,\n",
       "21        1  0.261271           ,on\n",
       "21        2  0.189868           ,on\n",
       "21        3  0.189868           ,on\n",
       "21        4  0.160658           ,on\n",
       "21        5  0.197982           ,on\n",
       "...     ...       ...           ...\n",
       "984       1  0.348009           zai\n",
       "984       2  0.241476           zai\n",
       "984       3  0.195311           zai\n",
       "984       4  0.113636           zai\n",
       "984       5  0.102982           zai\n",
       "985       1  0.204495          zai,\n",
       "985       2  0.240486          zai,\n",
       "985       3  0.235578          zai,\n",
       "985       4  0.129241          zai,\n",
       "985       5  0.189771          zai,\n",
       "988       1  0.248176     économies\n",
       "988       2  0.241558     économies\n",
       "988       3  0.162142     économies\n",
       "988       4  0.150560     économies\n",
       "988       5  0.196887     économies\n",
       "992       1  0.203603      élevageA\n",
       "992       2  0.287157      élevageA\n",
       "992       3  0.218489      élevageA\n",
       "992       4  0.127252      élevageA\n",
       "992       5  0.163267      élevageA\n",
       "995       1  0.258286  élevagegrâce\n",
       "995       2  0.231795  élevagegrâce\n",
       "995       3  0.172191  élevagegrâce\n",
       "995       4  0.145700  élevagegrâce\n",
       "995       5  0.192059  élevagegrâce\n",
       "1009      1  0.272605        évitéA\n",
       "1009      2  0.322169        évitéA\n",
       "1009      3  0.166395        évitéA\n",
       "1009      4  0.123911        évitéA\n",
       "1009      5  0.113290        évitéA\n",
       "\n",
       "[1110 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[5, 4, 1, 2, 3])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.gensim.prepare(ldamodel, corpus, texts_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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