removeNonEncoded.ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "import unicodedata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentencetokenizer = nltk.data.load('tokenizers/punkt/english.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "trainpath=\"/home/mamitiana/imageclef/train/CaptionPredictionTraining2017-Captions.csv\"\n",
    "valpath = \"/home/mamitiana/imageclef/val/CaptionPredictionValidation2017-Captions.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "def removeParanthes(x):\n",
    "        \"\"\" enleve l'indication de l'annee precedent de courses\"\"\"\n",
    "        regex = re.compile(\".*?\\((.*?)\\)\")\n",
    "        \n",
    "        temp= re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\",x)\n",
    "        return temp"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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    "deletable": true,
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   "outputs": [],
   "source": [
    "train=pd.read_csv(trainpath,sep=\"\\t\",names=['imageName','caption'])\n",
    "val = pd.read_csv(valpath,sep=\"\\t\",names=['imageName','caption'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "train['caption']=train['caption'].apply(removeParanthes)\n",
    "train['captions']=train['caption'].apply(lambda x: [ str(i)for i in sentencetokenizer.tokenize(x)])\n",
    "val['caption']=val['caption'].apply(removeParanthes)\n",
    "val['captions']=val['caption'].apply(lambda x: [ str(i)for i in sentencetokenizer.tokenize(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import jsonLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "traindet =jsonLoader.TrainLoader()\n",
    "valdet =jsonLoader.ValLoader()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<jsonLoader.TrainLoader at 0x7f6c0a281240>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
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    "deletable": true,
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   "outputs": [],
   "source": [
    "traindet=pd.DataFrame( traindet.filtrer())\n",
    "valdet = pd.DataFrame( valdet.filtrer())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traindet=traindet.drop(['path','pathfc7','classindex'],axis=1)\n",
    "valdet=valdet.drop(['path','pathfc7','classindex'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "val=valdet.merge(val,left_on=\"imageName\",right_on='imageName')\n",
    "train =traindet.merge(train,left_on=\"imageName\",right_on='imageName')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "train.to_json('train.json',orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "val.to_json('val.json',orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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