SentimentAnalysisClassification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:23:32.002420Z",
     "start_time": "2018-03-22T10:22:04.529667Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import gensim, logging\n",
    "from gensim.models.word2vec import Word2Vec # the word2vec model gensim class\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:36:59.158928Z",
     "start_time": "2018-03-22T10:36:56.971504Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_values(pc, fmt=\"%.2f\", **kw):\n",
    "    '''\n",
    "    Heatmap with text in each cell with matpl otlib's pyplot\n",
    "    Source: https://stackoverflow.com/a/25074150/395857 \n",
    "    By HYRY\n",
    "    '''\n",
    "    pc.update_scalarmappable()\n",
    "    ax = pc.get_axes()\n",
    "    for p, color, value in zip(pc.get_paths(), pc.get_facecolors(), pc.get_array()):\n",
    "        x, y = p.vertices[:-2, :].mean(0)\n",
    "        if np.all(color[:3] > 0.5):\n",
    "            color = (0.0, 0.0, 0.0)\n",
    "        else:\n",
    "            color = (1.0, 1.0, 1.0)\n",
    "        ax.text(x, y, fmt % value, ha=\"center\", va=\"center\", color=color, **kw)\n",
    "\n",
    "\n",
    "def cm2inch(*tupl):\n",
    "    '''\n",
    "    Specify figure size in centimeter in matplotlib\n",
    "    Source: https://stackoverflow.com/a/22787457/395857\n",
    "    By gns-ank\n",
    "    '''\n",
    "    inch = 2.54\n",
    "    if type(tupl[0]) == tuple:\n",
    "        return tuple(i/inch for i in tupl[0])\n",
    "    else:\n",
    "        return tuple(i/inch for i in tupl)\n",
    "\n",
    "\n",
    "def heatmap(AUC, title, xlabel, ylabel, xticklabels, yticklabels, figure_width=40, figure_height=20, correct_orientation=False, cmap='RdBu'):\n",
    "    '''\n",
    "    Inspired by:\n",
    "    - https://stackoverflow.com/a/16124677/395857 \n",
    "    - https://stackoverflow.com/a/25074150/395857\n",
    "    '''\n",
    "\n",
    "    # Plot it out\n",
    "    fig, ax = plt.subplots()\n",
    "    #c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap='RdBu', vmin=0.0, vmax=1.0)\n",
    "    c = ax.pcolor(AUC, edgecolors='k', linestyle= 'dashed', linewidths=0.2, cmap=cmap)\n",
    "\n",
    "    # put the major ticks at the middle of each cell\n",
    "    ax.set_yticks(np.arange(AUC.shape[0]) + 0.5, minor=False)\n",
    "    ax.set_xticks(np.arange(AUC.shape[1]) + 0.5, minor=False)\n",
    "\n",
    "    # set tick labels\n",
    "    #ax.set_xticklabels(np.arange(1,AUC.shape[1]+1), minor=False)\n",
    "    ax.set_xticklabels(xticklabels, minor=False)\n",
    "    ax.set_yticklabels(yticklabels, minor=False)\n",
    "\n",
    "    # set title and x/y labels\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)      \n",
    "\n",
    "    # Remove last blank column\n",
    "    plt.xlim( (0, AUC.shape[1]) )\n",
    "\n",
    "    # Turn off all the ticks\n",
    "    ax = plt.gca()    \n",
    "    for t in ax.xaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "    for t in ax.yaxis.get_major_ticks():\n",
    "        t.tick1On = False\n",
    "        t.tick2On = False\n",
    "\n",
    "    # Add color bar\n",
    "    plt.colorbar(c)\n",
    "\n",
    "    # Add text in each cell \n",
    "    show_values(c)\n",
    "\n",
    "    # Proper orientation (origin at the top left instead of bottom left)\n",
    "    if correct_orientation:\n",
    "        ax.invert_yaxis()\n",
    "        ax.xaxis.tick_top()       \n",
    "\n",
    "    # resize \n",
    "    fig = plt.gcf()\n",
    "    #fig.set_size_inches(cm2inch(40, 20))\n",
    "    #fig.set_size_inches(cm2inch(40*4, 20*4))\n",
    "    fig.set_size_inches(cm2inch(figure_width, figure_height))\n",
    "    \n",
    "def plot_classification_report(classification_report, title='Classification report ', cmap='RdBu'):\n",
    "    '''\n",
    "    Plot scikit-learn classification report.\n",
    "    Extension based on https://stackoverflow.com/a/31689645/395857 \n",
    "    '''\n",
    "    lines = classification_report.split('\\n')\n",
    "\n",
    "    classes = []\n",
    "    plotMat = []\n",
    "    support = []\n",
    "    class_names = []\n",
    "    for line in lines[2 : (len(lines) - 2)]:\n",
    "        t = line.strip().split()\n",
    "        if len(t) < 2: continue\n",
    "        classes.append(t[0])\n",
    "        v = [float(x) for x in t[1: len(t) - 1]]\n",
    "        support.append(int(t[-1]))\n",
    "        class_names.append(t[0])\n",
    "        #print(v)\n",
    "        plotMat.append(v)\n",
    "\n",
    "    #print('plotMat: {0}'.format(plotMat))\n",
    "    #print('support: {0}'.format(support))\n",
    "\n",
    "    xlabel = 'Metrics'\n",
    "    ylabel = 'Classes'\n",
    "    xticklabels = ['Precision', 'Recall', 'F1-score']\n",
    "    yticklabels = ['{0} ({1})'.format(class_names[idx], sup) for idx, sup  in enumerate(support)]\n",
    "    figure_width = 25\n",
    "    figure_height = len(class_names) + 7\n",
    "    correct_orientation = False\n",
    "    heatmap(np.array(plotMat), title, xlabel, ylabel, xticklabels, yticklabels, figure_width, figure_height, correct_orientation, cmap=cmap)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:03.854762Z",
     "start_time": "2018-03-22T10:26:02.799206Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('Documents.pickle', 'rb') as f:\n",
    "     Documents = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:04.417858Z",
     "start_time": "2018-03-22T10:26:04.402233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(['This', 'cd', 'storage', 'unit', 'isnt', 'greatest', 'cd', 'rack', 'want', 'something', 'job', 'isnt', 'costly', 'thing', 'made', 'plasti'], 'neg')\n"
     ]
    }
   ],
   "source": [
    "print(Documents[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:05.003317Z",
     "start_time": "2018-03-22T10:26:04.987690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38252"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load model Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:09.678057Z",
     "start_time": "2018-03-22T10:26:07.575036Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Word2Vec.load('model.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:11.650413Z",
     "start_time": "2018-03-22T10:26:11.629778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loooooove', 'unravelling', 'va', 'ranked', 'Hank', 'financial', 'learnt', 'Ginsberg', 'conditioning', 'Ormandy', 'transducer', 'Fluke', 'abode', 'Pendergast', 'unburgerlike', 'hylexin', 'Louris', 'First', 'BIOSAFE', 'HR', 'transferred', 'Brilliance', 'Gorton', 'miso', 'Take', 'variet', 'seagate', 'Gut', 'impressively', 'nodding', 'Kaspersky', 'aboslutely', 'critisize', 'See', 'STRAITS', 'Spyder', 'omission', 'Monte', 'catchiest', 'battery', 'G7', 'obituary', 'Holding', 'prominent', 'rancid', 'Sidekick', 'Promote', 'offputting', 'Schacter', 'orchestra', 'infustion', 'flyin', 'OVERLY', 'CS70', 'discontinued', 'licking', 'cupboard', 'puerile', 'Nonce', 'Lillard', 'slogan', 'RC60i', 'Vengeance', 'oprah', 'agreeed', 'constructing', 'Bisset', 'Girard', 'HDV', 'EXTREAMLY', 'verbena', 'Ryan', 'TerrorKing', 'diffusing', 'recomiendo', 'mocked', 'fiesty', 'XCR', 'Dentist', 'Adjani', 'unneccesary', 'Tosca', 'Prodigal', 'quoted', 'Itch', 'showy', 'gestalt', 'June', 'disablity', 'overcoming', 'PVR', 'Speer', 'paticulsr', '22f', 'Razor', 'loner', 'nutcracker', 'Morissey', '26th', 'Limp']\n",
      "61782\n"
     ]
    }
   ],
   "source": [
    "words = list(model.wv.vocab)\n",
    "print(words[:100])\n",
    "print(len(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:14.605994Z",
     "start_time": "2018-03-22T10:26:14.420117Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.7942369  -0.33382452  0.53393573 -1.49024665  2.13324285 -1.07749557\n",
      " -0.21262288  0.34546664  0.18661968  0.5688681  -0.42535612 -1.46868777\n",
      "  0.93791354 -2.37185121 -0.70141095 -0.97950858 -0.20312025 -1.05897725\n",
      "  0.24206859 -1.13369942  1.64162016 -0.62616527  0.84927803 -0.47696629\n",
      "  0.9985494   2.15098667 -1.19898152 -0.41598585 -0.09528159 -0.71344221\n",
      "  1.71227407  0.55450243  1.27753496  1.19460034  0.40057173  0.09237026\n",
      "  0.4424268   1.08676672  0.90509695 -0.19481269  0.91103834  1.15113425\n",
      " -0.06785039 -1.40121436 -1.46531594  2.50178266 -0.34803048  0.43969256\n",
      " -0.88642263  0.0435985   0.38556442 -0.51324308  0.47357002  0.97148091\n",
      " -1.61388481  1.60577309 -1.04554963 -0.12987329 -0.04974201  1.37973344\n",
      " -0.5297085  -1.21039498 -0.49806824  2.03258801  1.18041825 -0.10756348\n",
      " -0.62049466  0.31814304  0.93198586 -0.41446197 -0.50262237  0.63203847\n",
      "  0.2493732   1.07827461  0.12296637 -0.33538577 -0.52173108 -1.23262429\n",
      " -0.45114192 -1.53980911 -0.46345225  2.11707377  0.02449238 -0.39949843\n",
      "  0.27405429  0.42163128 -0.11416981  1.00852191 -0.14881191 -0.61544049\n",
      " -0.96436018  1.54028845  1.01998723  0.38207138 -0.2976515  -0.10284822\n",
      "  0.42682004  0.0925106  -0.1790923  -0.11463826]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "print(model['good'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:17.970548Z",
     "start_time": "2018-03-22T10:26:17.954918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61782"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First approche : Word Average embedding model\n",
    "-  a naive approche is to take the average of all words vectors from sentence to get a single 100 dimension vector that represent the sentiment of a hole sentence we feed it to the model and try to get some fast results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:20.438066Z",
     "start_time": "2018-03-22T10:26:20.391187Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Sent_Embeding(words,Documents):\n",
    "    X_SentsEmb = []\n",
    "    y_SentsEmb = []\n",
    "    #on Va récuperer l'embeding de chaque sentence avec l'average des vecteur qui le compose\n",
    "    for S,t in Documents: #S est la phrase , t est la class\n",
    "        #Average of embeding of all words in sentence\n",
    "        Sent = []\n",
    "        Sent = [model[m] for m in S]\n",
    "        \n",
    "        #Add Sentence Vector to list\n",
    "        X_SentsEmb.append(np.mean(Sent, axis=0).tolist())\n",
    "        #Add label to y_SentEmb\n",
    "        if t == 'neg':\n",
    "            y_SentsEmb.append(0)\n",
    "        elif t == 'pos':\n",
    "            y_SentsEmb.append(1)\n",
    "        else:\n",
    "            print('No match label !!! ',t)\n",
    "    return X_SentsEmb,y_SentsEmb\n",
    "    \n",
    "    \n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\ipykernel_launcher.py:8: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "X,Y = Sent_Embeding(words,Documents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's Test our sentence embeddings with the cosin similarity\n",
    "- We take 2 positif sentences "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence 7 :  (['Jeff', 'Beck', 'best', 'guitarist', 'planet', 'long', 'time'], 'pos')\n",
      "Sentence 2 :  (['bought', 'skate', 'christmas', 'present', 'daughter', 'skated', 'around', 'house', 'morning', 'good', 'buy'], 'pos')\n",
      "Sentence embedding 7 :  [0.047813210636377335, -0.30950334668159485, -0.10223495960235596, 0.3831125795841217, 0.3887883722782135, -0.41005679965019226, -0.4837762415409088, 0.25184711813926697, 0.27380043268203735, 0.28150010108947754, 0.639875054359436, 0.20240937173366547, 0.7023287415504456, -0.5865568518638611, -0.2365250289440155, -0.011564970016479492, 0.03291887417435646, 0.2969110608100891, -0.23104561865329742, -0.5682569742202759, 1.0255542993545532, -0.5468829870223999, 0.12040659040212631, -0.19912053644657135, -0.2024727165699005, 0.51146000623703, -0.032202303409576416, 0.060952942818403244, 0.12817616760730743, -0.0744028240442276, 0.44108375906944275, -0.4889131486415863, 0.19122996926307678, 0.5238741040229797, 0.4133130609989166, -0.2780018448829651, 0.36904558539390564, 0.5438994765281677, 0.1469743400812149, 0.03692752122879028, 0.2145051211118698, 0.670513927936554, -0.3368745446205139, -0.2797633707523346, -0.6940759420394897, 0.6909577250480652, 0.05142110586166382, -0.0942484512925148, -0.4781200587749481, -0.17519010603427887, 0.3660601079463959, -0.1746463030576706, 0.513908326625824, 0.4436669647693634, -0.5999895334243774, 0.6894833445549011, -0.5521361231803894, 0.6822255253791809, -0.13669072091579437, 0.37894222140312195, -0.5034905672073364, -0.17562292516231537, -0.4778960645198822, 0.7871719002723694, 0.5908733010292053, 0.11410616338253021, 0.29059362411499023, 0.4373432695865631, 0.4464690387248993, -0.1867877095937729, 0.03023848868906498, 0.8404511213302612, 0.4660767912864685, 0.6720147728919983, 0.30514654517173767, 0.1425720453262329, 0.06911782920360565, -0.7122936248779297, 0.5427897572517395, 0.014590604230761528, 0.36407747864723206, 0.36014336347579956, 0.06884588301181793, 0.14275969564914703, 0.019497470930218697, -0.27886873483657837, 0.1862374246120453, 0.1539682149887085, 0.34715989232063293, 0.09538456052541733, -0.05962490290403366, 0.23228637874126434, -0.04431472346186638, -0.17333801090717316, -0.0735805407166481, -0.20245906710624695, 0.2053077220916748, 0.2382720559835434, -0.20246608555316925, -0.4306011199951172]\n",
      "Sentence embedding 2 :  [-0.0450625978410244, -0.4996716380119324, -0.41935253143310547, 0.2911669909954071, 0.27122762799263, -0.4946122467517853, -0.08288067579269409, 0.21103601157665253, 1.0275012254714966, -0.026848221197724342, -0.026732338592410088, 0.017801739275455475, 0.07605958729982376, -0.292155921459198, 0.06415048986673355, -0.04277163743972778, 0.6278260946273804, 0.4297197461128235, 0.33615824580192566, -0.8399210572242737, 1.2687745094299316, -0.054546717554330826, -0.0825323686003685, -0.03626839816570282, 0.047694768756628036, 0.5426226258277893, -0.16245125234127045, 0.06458175927400589, -0.2971886098384857, 0.4417322278022766, 1.1324665546417236, -0.1982385516166687, 0.3283715546131134, 0.20259538292884827, 0.2764051854610443, -0.09639987349510193, 0.5401071906089783, 0.5778460502624512, 0.5626225471496582, -0.3410617709159851, -0.14833040535449982, 0.45333245396614075, 0.37243539094924927, -0.141792893409729, -0.6007025837898254, 1.3902134895324707, 0.046049777418375015, 0.305479496717453, -0.5691192150115967, -0.30370885133743286, 1.000930666923523, -0.08788266032934189, -0.05092815309762955, 1.0682545900344849, -0.4928675889968872, 0.7404206991195679, -0.19136714935302734, -0.16685207188129425, 0.2592238187789917, 0.22942385077476501, -0.6304343938827515, -0.4973055124282837, -1.1565816402435303, 0.9571824669837952, 1.1228281259536743, 0.43641796708106995, -0.5770981311798096, 0.9553932547569275, 0.59529709815979, -0.5447241067886353, -0.5593380331993103, 0.17205609381198883, 0.18622465431690216, 0.9682502746582031, -0.44185057282447815, 0.3172159790992737, -0.3790391683578491, -0.8320122361183167, 0.23355785012245178, 0.00547832390293479, 0.3798008859157562, 0.18617743253707886, -0.031815171241760254, 0.29549339413642883, -0.3600611686706543, -0.6360162496566772, -0.10873808711767197, 0.06405434757471085, 0.2347903847694397, -0.016075633466243744, -0.512406587600708, 0.4901304543018341, -0.1776692122220993, 0.3098953068256378, -0.028282664716243744, -0.36305806040763855, 0.6140505075454712, 0.49393120408058167, -0.049445632845163345, -0.23882311582565308]\n"
     ]
    }
   ],
   "source": [
    "print(\"Sentence 7 : \",Documents[7])\n",
    "print(\"Sentence 2 : \",Documents[2])\n",
    "print(\"Sentence embedding 7 : \",X[7])\n",
    "print(\"Sentence embedding 2 : \",X[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Cosine similarity\n",
    "\n",
    "To measure how similar two words are, we need a way to measure the degree of similarity between two embedding vectors for the two words. Given two vectors $u$ and $v$, cosine similarity is defined as follows: \n",
    "\n",
    "$$\\text{CosineSimilarity(u, v)} = \\frac {u . v} {||u||_2 ||v||_2} = cos(\\theta) \\tag{1}$$\n",
    "\n",
    "where $u.v$ is the dot product (or inner product) of two vectors, $||u||_2$ is the norm (or length) of the vector $u$, and $\\theta$ is the angle between $u$ and $v$. This similarity depends on the angle between $u$ and $v$. If $u$ and $v$ are very similar, their cosine similarity will be close to 1; if they are dissimilar, the cosine similarity will take a smaller value. \n",
    "\n",
    "**Exercise**: Implement the function `cosine_similarity()` to evaluate similarity between word vectors.\n",
    "\n",
    "**Reminder**: The norm of $u$ is defined as $ ||u||_2 = \\sqrt{\\sum_{i=1}^{n} u_i^2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: cosine_similarity\n",
    "\n",
    "def cosine_similarity(u, v):\n",
    "    \"\"\"\n",
    "    Cosine similarity reflects the degree of similariy between u and v\n",
    "        \n",
    "    Arguments:\n",
    "        u -- a word vector of shape (n,)          \n",
    "        v -- a word vector of shape (n,)\n",
    "\n",
    "    Returns:\n",
    "        cosine_similarity -- the cosine similarity between u and v defined by the formula above.\n",
    "    \"\"\"\n",
    "    \n",
    "    distance = 0.0\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    # Compute the dot product between u and v (≈1 line)\n",
    "    dot = np.dot(u,v)\n",
    "    # Compute the L2 norm of u (≈1 line)\n",
    "    norm_u = np.sqrt(sum(u**2))\n",
    "    \n",
    "    # Compute the L2 norm of v (≈1 line)\n",
    "    norm_v = np.sqrt(sum(v**2))\n",
    "    # Compute the cosine similarity defined by formula (1) (≈1 line)\n",
    "    cosine_similarity = dot/(norm_u*norm_v)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return cosine_similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Very good we got 0.67 which means that this sentence are close to each other as they are positives comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine_similarity =  0.68969735523\n"
     ]
    }
   ],
   "source": [
    "print(\"cosine_similarity = \", cosine_similarity(np.array(X[2]),np.array(X[7])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's See with different class we take this time two negatif\n",
    "- This example is tricky ['This', 'cd', 'storage', 'unit', 'isnt', 'greatest', 'cd', 'rack', 'want', 'something', 'job', 'isnt', 'costly', 'thing', 'made', 'plasti'] because we see that we a naive approche the model will catch the word greatest which is a positif word so it will consider this statment as positive were the truth is this comment containt words befor and after greatest like isnt that help the model to distingush the class this probleme will be delt with when we use the ** RNN ** because it takes into account past and future words in a sequence\n",
    "-  For know we focus on the similarity to see the average vectors how they handle this evalution test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence 9 :  (['Bought', 'camera', 'wife', 'Takes', 'good', 'picture', 'handle', 'camera', 'extreme', 'caution', 'If', 'drop', 'prepared', 'large', 'repair', 'bill', 'Fuji', 'customer', 'relation', 'sub', 'par', 'Warranty', 'cover', 'impact', 'internal', 'damage', 'I', 'better', 'luck', 'Nikon', 'Kodak', 'steer', 'clear', 'Fuji', 'future'], 'neg')\n",
      "Sentence 1 :  (['This', 'cd', 'storage', 'unit', 'isnt', 'greatest', 'cd', 'rack', 'want', 'something', 'job', 'isnt', 'costly', 'thing', 'made', 'plasti'], 'neg')\n",
      "Sentence embedding 9 :  [-0.14247314631938934, -0.29889851808547974, -0.1033640205860138, -0.0217785332351923, 0.02472921460866928, 0.005216314923018217, 0.01827956922352314, 0.1828407198190689, 0.2856401801109314, -0.04774235561490059, 0.4215581715106964, -0.11381033807992935, 0.3692322373390198, -0.44062796235084534, 0.2630363702774048, -0.0026879191864281893, 0.2243727445602417, 0.35658395290374756, -0.010400648228824139, -0.4465780556201935, 0.6662558913230896, -0.24486801028251648, 0.36716020107269287, -0.34338685870170593, 0.1490878015756607, 0.6531403064727783, -0.550736665725708, 0.1082208976149559, -0.07293032109737396, 0.2844158411026001, 0.8442422747612, -0.45727670192718506, 0.47561079263687134, 0.40833407640457153, 0.3359449505805969, -0.2728375196456909, 0.2584378123283386, 0.37024936079978943, 0.3444395959377289, -0.009393899701535702, 0.2826041877269745, 0.5026003122329712, -0.12522871792316437, -0.09896870702505112, -0.44476497173309326, 0.5008035898208618, -0.06090758368372917, 0.10924786329269409, -0.3055243194103241, 0.1395842432975769, 0.6542723774909973, -0.12357830256223679, 0.048531170934438705, 0.642069935798645, -0.524350643157959, 0.6336342692375183, -0.19029320776462555, -0.028729824349284172, 0.10735995322465897, 0.16146120429039001, -0.5693987011909485, -0.3521404266357422, -0.5921061635017395, 0.7787351012229919, 0.7146305441856384, -0.10890629887580872, 0.11465068906545639, 0.6216538548469543, 0.35174113512039185, -0.04135739058256149, -0.4307359457015991, 0.6912196278572083, 0.4354032576084137, 0.6882712244987488, -0.023397013545036316, -0.0724002867937088, -0.2922082245349884, -0.2603185176849365, -0.155300110578537, -0.036666952073574066, 0.477861613035202, 0.4070908725261688, -0.1890622079372406, -0.0797903910279274, 0.013472634367644787, -0.08073623478412628, -0.014939690008759499, 0.30907106399536133, 0.13265977799892426, 0.20827867090702057, -0.1431005895137787, 0.39676421880722046, 0.15170970559120178, 0.31495824456214905, 0.05263134837150574, -0.14013630151748657, 0.13737799227237701, 0.16264648735523224, -0.0745682492852211, -0.18387053906917572]\n",
      "Sentence embedding 1 :  [-0.20820209383964539, -0.33093202114105225, 0.1396114081144333, 0.1351734697818756, 0.18619304895401, -0.1103290468454361, -0.49652010202407837, 0.2256840020418167, 0.462761253118515, -0.2043735235929489, 0.4390527904033661, -0.2692364454269409, 0.45562177896499634, -0.4926413297653198, 0.032634712755680084, 0.10385735332965851, 0.10804049670696259, 0.6497535109519958, 0.00024077750276774168, -0.2919554114341736, 0.9799926280975342, -0.192856565117836, 0.10696961730718613, -0.08980102837085724, 0.2618483006954193, 0.7641797661781311, -0.6571890115737915, -0.22985929250717163, -0.050732627511024475, 0.2372552752494812, 0.7367695569992065, -0.6043192148208618, 0.5821031928062439, 0.5727090835571289, 0.17997899651527405, -0.5205521583557129, 0.38272416591644287, 0.1811026632785797, 0.38982513546943665, -0.01953463815152645, 0.6171772480010986, 0.4121771454811096, -0.30076828598976135, -0.07538434118032455, -0.759520947933197, 0.7564665675163269, -0.004655822180211544, 0.031802963465452194, -0.5652034282684326, -0.013662468641996384, 0.4522526264190674, -0.09771695733070374, 0.0858507975935936, 0.7892341017723083, -0.12444577366113663, 0.915560245513916, -0.2448483258485794, 0.17789335548877716, -0.20002995431423187, 0.23367628455162048, -0.7739582061767578, -0.29662251472473145, -0.595093846321106, 0.9216963648796082, 0.4440295994281769, 0.025675009936094284, 0.23041148483753204, 1.0171809196472168, 0.36347734928131104, -0.005402050446718931, -0.14672811329364777, 0.7849027514457703, 0.6427292227745056, 0.8613337278366089, -0.2037935107946396, -0.041300851851701736, 0.13787242770195007, -0.05908200517296791, -0.06114635244011879, -0.21094653010368347, 0.4560874104499817, 0.29427242279052734, -0.001273320522159338, 0.1923712193965912, -0.10185879468917847, -0.176519513130188, 0.23569351434707642, 0.48777681589126587, 0.2044093757867813, -0.04765467718243599, -0.07696796953678131, 0.1199665516614914, 0.20999684929847717, 0.35472267866134644, 0.1148872897028923, -0.21314512193202972, 0.1595637947320938, 0.3601706027984619, -0.014448290690779686, -0.3312567472457886]\n"
     ]
    }
   ],
   "source": [
    "print(\"Sentence 9 : \",Documents[9])\n",
    "print(\"Sentence 1 : \",Documents[1])\n",
    "print(\"Sentence embedding 9 : \",X[9])\n",
    "print(\"Sentence embedding 1 : \",X[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0.89 these two vecotors are highly correlated so far so good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine_similarity =  0.895176503045\n"
     ]
    }
   ],
   "source": [
    "print(\"cosine_similarity = \", cosine_similarity(np.array(X[1]),np.array(X[9])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see different classes\n",
    "- We see that this average embeding still got some problems separating different classes with cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence 22 :  (['I', 'got', 'device', 'help', 'clean', 'around', 'place', 'I', 'thought', 'would', 'easy', 'use', 'The', 'pad', 'stay', 'When', 'trying', 'put', 'solution', 'leak', 'After', 'working', 'shower', 'I', 'saw', 'difference', 'I', 'wish', 'could', 'negative', 'star', 'piece', 'junk', 'worth'], 'neg')\n",
      "Sentence 5 :  (['Barbershop', 'rare', 'event', 'African', 'American', 'filmmaking', 'It', 'one', 'actually', 'tell', 'fairly', 'accurate', 'story', 'life', 'like', 'African', 'Americans', 'I', 'love', 'fact', 'movie', 'showed', 'diverse', 'African', 'American', 'community', 'see', 'black', 'people', 'different', 'walk', 'life', 'different', 'lifestyle', 'shown', 'onscreen', 'I', 'really', 'liked', 'movie', 'We', 'definitely', 'need', 'film', 'like'], 'pos')\n",
      "Sentence embedding 22 :  [-0.15051762759685516, -0.2947107255458832, -0.2443128526210785, -0.04557662829756737, 0.32981249690055847, -0.3590693175792694, -0.5598522424697876, 0.5571661591529846, 0.28510579466819763, -0.332745760679245, 0.3333064019680023, -0.362984836101532, 0.42181849479675293, -0.35911402106285095, 0.5584141612052917, 0.20102554559707642, 0.6705875396728516, 1.0499683618545532, 0.2289166897535324, -0.48263633251190186, 1.4154187440872192, -0.48113560676574707, 0.6376106142997742, -0.2860214412212372, 0.01658267341554165, 0.8967753052711487, -0.7111605405807495, 0.24248720705509186, -0.43400534987449646, 0.43616217374801636, 0.8931437730789185, -0.6111222505569458, 0.7645235657691956, 0.6019487380981445, 0.6322337985038757, -0.12800048291683197, 0.30901455879211426, 0.4056442975997925, 0.5406558513641357, -0.012767777778208256, 0.3995150625705719, 0.5939273238182068, 0.2697601914405823, -0.24328383803367615, -0.6844292879104614, 0.9520127773284912, 0.1955859214067459, 0.11480424553155899, -0.7200925946235657, 0.1896751970052719, 0.943270742893219, -0.2942589819431305, 0.16776365041732788, 1.045133352279663, -0.4251861274242401, 1.3026095628738403, -0.49103349447250366, 0.04406026750802994, 0.31416577100753784, 0.49747511744499207, -0.4716312289237976, -0.8456060290336609, -0.8968442678451538, 1.388582706451416, 1.2258760929107666, 0.11558938026428223, -0.06357547640800476, 1.3474743366241455, 0.4148685932159424, -0.3307422399520874, -0.5766873955726624, 1.0313776731491089, 0.7365756034851074, 1.1923784017562866, -0.33256348967552185, 0.29733559489250183, -0.0805138498544693, -0.790876030921936, -0.03674956411123276, 0.1661779135465622, 0.6021570563316345, 0.3389822244644165, -0.0074612535536289215, 0.23583479225635529, -0.17040057480335236, -0.15553009510040283, 0.2790822386741638, 0.061557967215776443, 0.08031493425369263, 0.010895762592554092, 0.16333900392055511, 0.5577701926231384, 0.3025491535663605, 0.3829961121082306, -0.20164577662944794, -0.2159450203180313, 0.24776706099510193, 0.6668261289596558, 0.05483398586511612, -0.3858972489833832]\n",
      "Sentence embedding 5 :  [-0.16980506479740143, -0.9258888363838196, 0.055883973836898804, 0.4457511007785797, 0.29242944717407227, -0.27550008893013, -0.5875261425971985, 0.41965562105178833, 0.44554927945137024, -0.16962486505508423, 0.4940526783466339, -0.2998471260070801, 0.3488621413707733, -0.19312407076358795, -0.18383030593395233, -0.22074729204177856, 0.14946316182613373, 0.32793599367141724, 0.05396062880754471, -0.3271178603172302, 0.7451677322387695, -0.1787717193365097, 0.060318052768707275, -0.026752905920147896, 0.091870978474617, 0.9742462038993835, -0.41204342246055603, -0.13485215604305267, -0.49914199113845825, 0.28404900431632996, 1.0259126424789429, -0.6592890024185181, 0.525575578212738, 0.5218905806541443, 0.4400580823421478, -0.30444809794425964, 0.09127627313137054, 0.6104084253311157, 0.3586696684360504, -0.04104429855942726, 0.9021968245506287, 0.525955855846405, -0.4209790825843811, -0.18536733090877533, -0.5566452145576477, 0.6217740774154663, -0.26797327399253845, 0.24251492321491241, -0.48096469044685364, 0.44482284784317017, 0.5851040482521057, -0.20286248624324799, 0.39275529980659485, 0.6905145645141602, 0.01802997663617134, 0.6579126715660095, -0.3165096640586853, 0.392888605594635, -0.1626032441854477, 0.11832031607627869, -0.737663209438324, -0.3384764790534973, -0.33472469449043274, 0.7705230712890625, 0.7297833561897278, 0.09962411224842072, 0.08402170240879059, 0.8509987592697144, 0.6956608295440674, 0.017960963770747185, -0.026577085256576538, 0.6587194204330444, 0.15292483568191528, 0.8433278203010559, -0.27727803587913513, 0.07998646795749664, -0.20126082003116608, -0.35100582242012024, 0.04794057086110115, -0.2802985608577728, 0.15713956952095032, 0.3508976399898529, -0.17212939262390137, 0.07667528092861176, 0.039390306919813156, -0.010836001485586166, 0.23001238703727722, 0.18380607664585114, 0.30962926149368286, 0.16092808544635773, -0.2621845602989197, 0.4462422728538513, -0.10303844511508942, 0.19287194311618805, 0.13445927202701569, -0.44633978605270386, 0.21105223894119263, 0.09023421257734299, -0.1721189022064209, -0.5447885990142822]\n"
     ]
    }
   ],
   "source": [
    "print(\"Sentence 22 : \",Documents[22])\n",
    "print(\"Sentence 5 : \",Documents[85])\n",
    "print(\"Sentence embedding 22 : \",X[22])\n",
    "print(\"Sentence embedding 5 : \",X[85])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cosine_similarity =  0.812476665008\n"
     ]
    }
   ],
   "source": [
    "print(\"cosine_similarity = \", cosine_similarity(np.array(X[22]),np.array(X[85])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38252"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X) # embeding for Sentence one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split corpus to train ,test and dev sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#train test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
    "#train dev\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.15, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length X_train : 27636\n",
      "length y_train : 27636\n",
      "length X_test : 5738\n",
      "length y_test : 5738\n",
      "length X_val : 4878\n",
      "length y_val : 4878\n"
     ]
    }
   ],
   "source": [
    "print('length X_train :',len(X_train))\n",
    "print('length y_train :',len(y_train))\n",
    "print('length X_test :',len(X_test))\n",
    "print('length y_test :',len(y_test))\n",
    "print('length X_val :',len(X_val))\n",
    "print('length y_val :',len(y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27636, 100)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X_train).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN\n",
    "#### First Model is the knn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors (3) Result 1:\n",
      "0.99978289188\n",
      "0.656849076333\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "from sklearn import cross_validation\n",
    "#for i in range (1,25):\n",
    "#learned i = 5 has best result here\n",
    "knn = neighbors.KNeighborsClassifier(3,'distance')\n",
    "#train the classifier on partial dataset\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "#print(heart_test_results)\n",
    "print(\"Nearest Neighbors (3) Result 1:\")\n",
    "print(knn.score(X_train,y_train))\n",
    "print(knn.score(X_test,y_test))\n",
    "#print(\"Nearest Neighbors (5) Cross-Validation  Class binaire:\")\n",
    "#scores1 = cross_validation.cross_val_score(knn,X,Y, cv=5)\n",
    "#print(scores1)\n",
    "#print(np.mean(scores1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Classification KNN:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.60      0.58      0.59      2448\n",
      "          1       0.70      0.71      0.70      3290\n",
      "\n",
      "avg / total       0.66      0.66      0.66      5738\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print('\\n*Classification KNN:\\n', classification_report(y_test, knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Br0bENEmbA89KGhsRSxo7n4MjMzMzK5+WeVptODA9ImYASLoFOAKYksnzTeDy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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce154ba5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classification_report(classification_report(y_test, knn.predict(X_test)), title='Classification report KNN ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** logistic Regression ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6868246775880098"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Classification LogReg:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.65      0.57      0.61      2448\n",
      "          1       0.71      0.77      0.74      3290\n",
      "\n",
      "avg / total       0.68      0.69      0.68      5738\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\n*Classification LogReg:\\n', classification_report(y_test, logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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W4Chgcq7MV4BrImIRQETMLVLPMcB9EfHe2jSi3Kvn/kdSd+A94EpJz0s6dG12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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0f1da8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classification_report(classification_report(y_test, logreg.predict(X_test)), title='Classification report logestic Regression ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "clf_R = RandomForestClassifier(n_estimators=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.965335070198\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.66277448588358312"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_R.fit(X_train,y_train)\n",
    "print(clf_R.score(X_train,y_train))\n",
    "clf_R.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "*Classification RandomForest down sample:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.61      0.56      0.59      2448\n",
      "          1       0.69      0.73      0.71      3290\n",
      "\n",
      "avg / total       0.66      0.66      0.66      5738\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\n*Classification RandomForest down sample:\\n', classification_report(y_test, clf_R.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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1UWX25WK2npJmtUJLW1Ps02rfTqcPORzYGvgWcFnJSmVmZmbtQm2zWnt8Wq22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\njszMzMwyHByZmZmZZTg4MjMzM8v4/+aE1AkTXEqfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0f1c56dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classification_report(classification_report(y_test, clf_R.predict(X_test)), title='Classification report RandomForest ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fprU, tprU, thresholdsU = metrics.roc_curve(y_test, knn.predict_proba(X_test)[:,1])\n",
    "fprD, tprD, thresholdsD = metrics.roc_curve(y_test, clf_R.predict_proba(X_test)[:,1])\n",
    "fprl, tprl, thresholdsl = metrics.roc_curve(y_test, logreg.predict_proba(X_test)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VDm6EA79B6pbicFe0lF6vipY0EwRhjcsPWUUBLPQw+0sEMt/gFl68Pdj7Oii0alq8pHz5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce1eee12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "lw = 2\n",
    "plt.plot(fprD, tprD, color='darkorange',\n",
    "         lw=lw, label='ROC curve  Random Forest (area = %0.2f)' % auc(fprD, tprD))\n",
    "plt.plot(fprU, tprU, color='red',\n",
    "         lw=lw, label='ROC curve knn (area = %0.2f)' % auc(fprU, tprU))\n",
    "plt.plot(fprl, tprl, color='black',\n",
    "         lw=lw, label='ROC curve regression logistic (area = %0.2f)' % auc(fprl, tprl))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "27636/27636 [==============================] - 1s - loss: 0.2166 - binary_accuracy: 0.6486      \n",
      "Epoch 2/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.2047 - binary_accuracy: 0.6775     \n",
      "Epoch 3/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.2008 - binary_accuracy: 0.6843     \n",
      "Epoch 4/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1983 - binary_accuracy: 0.6914     \n",
      "Epoch 5/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1966 - binary_accuracy: 0.6924     \n",
      "Epoch 6/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1955 - binary_accuracy: 0.6966     \n",
      "Epoch 7/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1942 - binary_accuracy: 0.6970     \n",
      "Epoch 8/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1931 - binary_accuracy: 0.6990     \n",
      "Epoch 9/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1902 - binary_accuracy: 0.7059     \n",
      "Epoch 10/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1901 - binary_accuracy: 0.7041     \n",
      "Epoch 11/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1887 - binary_accuracy: 0.7081     \n",
      "Epoch 12/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1875 - binary_accuracy: 0.7114     \n",
      "Epoch 13/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1865 - binary_accuracy: 0.7128     \n",
      "Epoch 14/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1853 - binary_accuracy: 0.7144     \n",
      "Epoch 15/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1829 - binary_accuracy: 0.7228     \n",
      "Epoch 16/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1820 - binary_accuracy: 0.7220     \n",
      "Epoch 17/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1807 - binary_accuracy: 0.7234     \n",
      "Epoch 18/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1800 - binary_accuracy: 0.7277     \n",
      "Epoch 19/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1799 - binary_accuracy: 0.7262     \n",
      "Epoch 20/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1778 - binary_accuracy: 0.7310     \n",
      "Epoch 21/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1763 - binary_accuracy: 0.7346     \n",
      "Epoch 22/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1754 - binary_accuracy: 0.7376     \n",
      "Epoch 23/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1733 - binary_accuracy: 0.7387     \n",
      "Epoch 24/100\n",
      "27636/27636 [==============================] - 1s - loss: 0.1736 - binary_accuracy: 0.7395     \n",
      "Epoch 25/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1725 - binary_accuracy: 0.7421     \n",
      "Epoch 26/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1703 - binary_accuracy: 0.7477     \n",
      "Epoch 27/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1696 - binary_accuracy: 0.7470     \n",
      "Epoch 28/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1680 - binary_accuracy: 0.7484     \n",
      "Epoch 29/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1677 - binary_accuracy: 0.7528     \n",
      "Epoch 30/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1669 - binary_accuracy: 0.7520     \n",
      "Epoch 31/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1651 - binary_accuracy: 0.7565     \n",
      "Epoch 32/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1630 - binary_accuracy: 0.7609     \n",
      "Epoch 33/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1635 - binary_accuracy: 0.7620     \n",
      "Epoch 34/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1611 - binary_accuracy: 0.7647     \n",
      "Epoch 35/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1610 - binary_accuracy: 0.7669     \n",
      "Epoch 36/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1599 - binary_accuracy: 0.7667     \n",
      "Epoch 37/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1581 - binary_accuracy: 0.7691     \n",
      "Epoch 38/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1568 - binary_accuracy: 0.7717     \n",
      "Epoch 39/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1554 - binary_accuracy: 0.7741     \n",
      "Epoch 40/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1553 - binary_accuracy: 0.7744     \n",
      "Epoch 41/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1539 - binary_accuracy: 0.7770     \n",
      "Epoch 42/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1522 - binary_accuracy: 0.7817     \n",
      "Epoch 43/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1518 - binary_accuracy: 0.7829     \n",
      "Epoch 44/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1505 - binary_accuracy: 0.7852     \n",
      "Epoch 45/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1489 - binary_accuracy: 0.7883     \n",
      "Epoch 46/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1480 - binary_accuracy: 0.7878     \n",
      "Epoch 47/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1460 - binary_accuracy: 0.7913     \n",
      "Epoch 48/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1459 - binary_accuracy: 0.7927     \n",
      "Epoch 49/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1450 - binary_accuracy: 0.7942     \n",
      "Epoch 50/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1436 - binary_accuracy: 0.7963     \n",
      "Epoch 51/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1413 - binary_accuracy: 0.8004     \n",
      "Epoch 52/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1410 - binary_accuracy: 0.7990     \n",
      "Epoch 53/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1406 - binary_accuracy: 0.8013     \n",
      "Epoch 54/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1383 - binary_accuracy: 0.8058     \n",
      "Epoch 55/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1373 - binary_accuracy: 0.8072     \n",
      "Epoch 56/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1368 - binary_accuracy: 0.8089     \n",
      "Epoch 57/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1357 - binary_accuracy: 0.8110     \n",
      "Epoch 58/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1339 - binary_accuracy: 0.8151     \n",
      "Epoch 59/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1335 - binary_accuracy: 0.8148     \n",
      "Epoch 60/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1322 - binary_accuracy: 0.8157     \n",
      "Epoch 61/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1300 - binary_accuracy: 0.8193     \n",
      "Epoch 62/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1300 - binary_accuracy: 0.8201     \n",
      "Epoch 63/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1289 - binary_accuracy: 0.8213     \n",
      "Epoch 64/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1286 - binary_accuracy: 0.8221     \n",
      "Epoch 65/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1270 - binary_accuracy: 0.8260     \n",
      "Epoch 66/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1248 - binary_accuracy: 0.8284     \n",
      "Epoch 67/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1246 - binary_accuracy: 0.8309     \n",
      "Epoch 68/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1226 - binary_accuracy: 0.8349     \n",
      "Epoch 69/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1231 - binary_accuracy: 0.8316     \n",
      "Epoch 70/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1202 - binary_accuracy: 0.8377     \n",
      "Epoch 71/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1200 - binary_accuracy: 0.8377     \n",
      "Epoch 72/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1182 - binary_accuracy: 0.8407     \n",
      "Epoch 73/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1184 - binary_accuracy: 0.8388     \n",
      "Epoch 74/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1175 - binary_accuracy: 0.8416     \n",
      "Epoch 75/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1162 - binary_accuracy: 0.8460     \n",
      "Epoch 76/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1147 - binary_accuracy: 0.8472     \n",
      "Epoch 77/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1130 - binary_accuracy: 0.8508     \n",
      "Epoch 78/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1149 - binary_accuracy: 0.8473     \n",
      "Epoch 79/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1121 - binary_accuracy: 0.8523     \n",
      "Epoch 80/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1108 - binary_accuracy: 0.8539     \n",
      "Epoch 81/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1111 - binary_accuracy: 0.8519     \n",
      "Epoch 82/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1096 - binary_accuracy: 0.8544     \n",
      "Epoch 83/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1073 - binary_accuracy: 0.8604     \n",
      "Epoch 84/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1064 - binary_accuracy: 0.8596     \n",
      "Epoch 85/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1064 - binary_accuracy: 0.8593     \n",
      "Epoch 86/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1061 - binary_accuracy: 0.8603     \n",
      "Epoch 87/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1046 - binary_accuracy: 0.8635     \n",
      "Epoch 88/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1039 - binary_accuracy: 0.8639     \n",
      "Epoch 89/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1035 - binary_accuracy: 0.8653     \n",
      "Epoch 90/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1028 - binary_accuracy: 0.8669     \n",
      "Epoch 91/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1010 - binary_accuracy: 0.8696     \n",
      "Epoch 92/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.1014 - binary_accuracy: 0.8669     \n",
      "Epoch 93/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0983 - binary_accuracy: 0.8727     \n",
      "Epoch 94/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0994 - binary_accuracy: 0.8729     \n",
      "Epoch 95/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0983 - binary_accuracy: 0.8729     \n",
      "Epoch 96/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0968 - binary_accuracy: 0.8766     \n",
      "Epoch 97/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0957 - binary_accuracy: 0.8780     \n",
      "Epoch 98/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0946 - binary_accuracy: 0.8802     \n",
      "Epoch 99/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0948 - binary_accuracy: 0.8791     \n",
      "Epoch 100/100\n",
      "27636/27636 [==============================] - 0s - loss: 0.0943 - binary_accuracy: 0.8804     \n",
      "27232/27636 [============================>.] - ETA: 0strain accuracy :  [0.09959383260683552, 0.86872195685922637]\n",
      "dev accuracy :   [0.24187473904504889, 0.67363673644067812]\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense,Dropout\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from keras.optimizers import SGD,Adam,Nadam\n",
    "from keras import callbacks\n",
    "model = Sequential()\n",
    "# Dense(64) is a fully-connected layer with 64 hidden units.\n",
    "# in the first layer, you must specify the expected input data shape:\n",
    "# here, 20-dimensional vectors.\n",
    "model.add(Dense(50, activation='relu', input_dim=np.array(X_train).shape[1])) # dimmension of each example\n",
    "model.add(Dropout(0))\n",
    "model.add(Dense(150, activation='relu'))\n",
    "model.add(Dropout(0))\n",
    "model.add(Dense(60, activation='relu'))\n",
    "model.add(Dropout(0))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "#nadam = Nadam(lr=0.001,beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.001)\n",
    "model.compile(loss='mean_squared_error',\n",
    "              optimizer='adam',\n",
    "              metrics=['binary_accuracy'])\n",
    "#for tensorboard viz\n",
    "tbCallBack = callbacks.TensorBoard(log_dir='./SentimentAnalysis', histogram_freq=1, write_graph=True, write_images=True,embeddings_metadata='model.bin')\n",
    "\n",
    "history = model.fit(np.array(X_train),y_train,epochs=100,callbacks=[tbCallBack],batch_size=128)\n",
    "score = model.evaluate(np.array(X_val),y_val)\n",
    "#back propagation\n",
    "score1 = model.evaluate(np.array(X_train),y_train)\n",
    "print(\"train accuracy : \",score1)\n",
    "print(\"dev accuracy :  \",score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4736/5738 [=======================>......] - ETA: 0s\n",
      "*Classification KNN:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.61      0.69      0.65      2448\n",
      "          1       0.74      0.67      0.70      3290\n",
      "\n",
      "avg / total       0.69      0.68      0.68      5738\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\n*Classification KNN:\\n', classification_report(y_test, model.predict_classes(np.array(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4928/5738 [========================>.....] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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7DN+euQuXMv/Nt1m7rop7J/6Now9seBmaMYftxR/+8rf127sN3Y5e3TszafKMlqiuFbGp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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0e3600eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classification_report(classification_report(y_test, model.predict_classes(np.array(X_test))), title='Classification report MLP ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4800/5738 [========================>.....] - ETA: 0s"
     ]
    }
   ],
   "source": [
    "fprM, tprM, thresholdsM = metrics.roc_curve(y_test, model.predict_classes(np.array(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8O5zYCWUOJu7wCrS7d28C9Bwjk7WUldUMd46GZqanm3vzTicszHGPnX248/I6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\njoWYGPDzgxtvhHHjWu7FiWY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4ca17/XasRkmGNckc3yUlGUT7ceJEIc8//xOvvLKJfGuo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\nyoj/B98USyEHBgS/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ce1011eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "lw = 2\n",
    "plt.plot(fprD, tprD, color='darkorange',\n",
    "         lw=lw, label='ROC curve  Random Forest (area = %0.2f)' % auc(fprD, tprD))\n",
    "plt.plot(fprU, tprU, color='red',\n",
    "         lw=lw, label='ROC curve knn (area = %0.2f)' % auc(fprU, tprU))\n",
    "plt.plot(fprl, tprl, color='black',\n",
    "         lw=lw, label='ROC curve regression logistic (area = %0.2f)' % auc(fprl, tprl))\n",
    "plt.plot(fprM, tprM, color='purple',\n",
    "         lw=lw, label='ROC curve  MLP (area = %0.2f)' % auc(fprM, tprM))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Random forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RandomForestAvgModel.pickle', 'wb') as f:\n",
    "    pickle.dump(clf_R, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RandomForestAvgModel.pickle', 'rb') as f:\n",
    "     clf_R = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7202858138724294"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_R.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For our first approche with average word embeddings \n",
    "-  Best Model So far is the random forest with AUCROC ** 0.79 ** on positif reviews\n",
    "-  RAndom forest  with ** 0.72 **  accuracy\n",
    "For next with will try the **LSTM** neural network for sequence models that take in a sequence of words and remebers the order of the words we will try **LSTM**,**GRU** for it's gates to handle the vanishing gradient problem with deep **RNN** ,next we will combine **CNN + LSTM** ,**LSTM + CNN** and see wihch one gives the better results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare our input this time :\n",
    "-  the Training shape will be (M,100,n) where M is number of **exemple** ,100 each word dimension and n is the number of words in each sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:51.956358Z",
     "start_time": "2018-03-22T10:26:51.824834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max length sentence is : 506 Min length sentence is : 2\n",
      "Sentence with more than 50 word  8366\n",
      "Sentence with more than 100 word  1373\n",
      "Sentence with more than 200 word  92\n",
      "Sentence with more than 300 word  18\n",
      "Sentence with more than 400 word  6\n"
     ]
    }
   ],
   "source": [
    "def get_Max_Min_length_Sent():\n",
    "    Max = 0\n",
    "    Min = 10\n",
    "    CountSup50 = 0\n",
    "    CountSup100 = 0\n",
    "    CountSup200 = 0\n",
    "    CountSup300 = 0\n",
    "    CountSup400 = 0\n",
    "    for S,t in Documents:\n",
    "        if len(S) > Max:\n",
    "            Max = len(S)\n",
    "        if len(S) < Min :\n",
    "            Min = len(S)\n",
    "        if len(S) > 50:\n",
    "            CountSup50 +=1\n",
    "        if len(S) > 100:\n",
    "            CountSup100 +=1\n",
    "        if len(S) >200 :\n",
    "            CountSup200 +=1\n",
    "        if len(S) > 300:\n",
    "            CountSup300 +=1\n",
    "        if len(S) >400 :\n",
    "            CountSup400 +=1\n",
    "            \n",
    "    return Max,Min,CountSup50,CountSup100,CountSup200,CountSup300,CountSup400\n",
    "\n",
    "Max,Min,CountSup50,CountSup100,CountSup200,CountSup300,CountSup400 = get_Max_Min_length_Sent()\n",
    "print(\"Max length sentence is :\",Max,\"Min length sentence is :\",Min)\n",
    "print(\"Sentence with more than 50 word \",CountSup50)\n",
    "print(\"Sentence with more than 100 word \",CountSup100)\n",
    "print(\"Sentence with more than 200 word \",CountSup200)\n",
    "print(\"Sentence with more than 300 word \",CountSup300)\n",
    "print(\"Sentence with more than 400 word \",CountSup400)\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fix_sentence_length is a function to fix the size of sentences to a fixed size, to feed them into the neural net with specific length.\n",
    "- Sentences with a length less than 100 characters will be extended with zeros this process doesn't affect the learning of the model.\n",
    "- Sentences with length more that the specified length will be truncated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:54.768689Z",
     "start_time": "2018-03-22T10:26:54.737433Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 5, 4, 8, 7, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n"
     ]
    }
   ],
   "source": [
    "def fix_sentence_length(length,exemple):\n",
    "    if len(exemple) > length:\n",
    "        exemple = exemple[:length]\n",
    "    elif len(exemple) < length:\n",
    "        for i in range(length - len(exemple)):\n",
    "            listzeros = [0] * 100\n",
    "            exemple.append(listzeros)\n",
    "    return exemple\n",
    "ex = [2,5,4,8,7]\n",
    "ex = fix_sentence_length(7,ex)\n",
    "print(ex)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:26:56.490025Z",
     "start_time": "2018-03-22T10:26:56.439139Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Sent_Embeding_sequence(words,Documents):\n",
    "    X_SentsEmb = []\n",
    "    y_SentsEmb = []\n",
    "    #on Va récuperer l'embeding de chaque sentence avec l'average des vecteur qui le compose\n",
    "    for S,t in Documents: #S est la phrase , t est la class\n",
    "        #Average of embeding of all words in sentence\n",
    "        Sent = []\n",
    "        Sent = [list(model[m]) for m in S]\n",
    "#        if len(Sent) < 100:\n",
    "#            for i in range(100 - len(Sent)):\n",
    "#                Sent.append(0) # pour avoir une longeur standart de 100 mots par Sent\n",
    "#        else :\n",
    "#            Sent = Sent[:100]\n",
    "        #Add Sentence Vector to list\n",
    "        Sent = fix_sentence_length(100,Sent)\n",
    "        X_SentsEmb.append(Sent)\n",
    "        #Add label to y_SentEmb\n",
    "        if t == 'neg':\n",
    "            y_SentsEmb.append(0)\n",
    "        elif t == 'pos':\n",
    "            y_SentsEmb.append(1)\n",
    "        else:\n",
    "            print('No match label !!! ',t)\n",
    "    return X_SentsEmb,y_SentsEmb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = list(model.wv.vocab)\n",
    "X,Y = Sent_Embeding_sequence(words,Documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "16\n",
      "[[[0.57772708, -2.6966903, 1.3096311, 1.7993178, -0.45872158, -0.44108117, -1.3963939, 1.4934766, 1.6493213, 0.44445017, -1.2355235, 0.33959857, -0.34104031, -2.3669698, 2.1460216, 0.45897022, 0.62695801, 3.946667, 1.397765, -2.0303574, 1.5760499, -0.72018641, -0.037402246, 0.54166126, -0.62807137, -1.6776373, -1.8471816, 0.21798298, -0.66202945, -0.53047299, 1.2584944, 1.056882, 0.78776073, 0.12464927, -0.64093775, 1.2785107, -0.29681087, 0.76776183, -0.87678987, -1.5992565, 0.10518414, 0.88694847, -0.48308522, -0.094921567, -0.24480118, -0.70208395, 1.2938561, 1.6348791, -0.24320202, -1.0535762, 0.60283428, -0.29820505, -1.03376, 2.2614436, 0.24393557, 1.0993811, 0.83535999, -0.71629745, 0.73035371, -0.27199832, -1.4130625, -3.0202918, -2.0102763, 1.0955864, 0.26779354, 0.60060024, -3.1038978, 3.7645278, 1.6210555, 0.49657267, 0.040597435, -1.1487242, 0.59847349, 0.85542178, -2.083113, -0.14881144, 0.5724619, -0.28277364, -1.5713739, 0.49018058, 0.24886343, -0.59172142, 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     ]
    }
   ],
   "source": [
    "print(len(X[1]))\n",
    "print(len(Documents[1][0]))\n",
    "print(X[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train Shape (38252, 100, 100)\n"
     ]
    }
   ],
   "source": [
    "X_a = np.array(X)\n",
    "#lista = np.reshape(lista, (len(lista),2, 3))\n",
    "print(\"X_train Shape\",X_a.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Numpy Array for X_train and y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('D://content/datalab/X_train.npy',X_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('D://content/datalab/y_train.npy',Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### load Numpy Array for X_train and y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:28:44.084026Z",
     "start_time": "2018-03-22T10:27:28.437492Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.load('D://content/datalab/X_train.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:28:48.212354Z",
     "start_time": "2018-03-22T10:28:46.586721Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = np.load('D://content/datalab/y_train.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:28:49.076158Z",
     "start_time": "2018-03-22T10:28:48.914197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:33:53.176139Z",
     "start_time": "2018-03-22T10:30:28.581174Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#train test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
    "#train dev\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.15, random_state=1)\n",
    "del X\n",
    "del Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:36:06.671510Z",
     "start_time": "2018-03-22T10:36:05.570051Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del X_train\n",
    "del y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next we will continue on google colabratory for better performence on GPU we will use X and Y\n",
    "- In this file you will find the LSTM neural networks architecture :\n",
    "https://github.com/alaBay94/Sentiment-analysis-amazon-Products-Reviews/blob/master/SentimentAnalysisClassificationWithLSTM_GoogleColab.ipynb\n",
    "- Load LSTM model with on layer We will compare 5 RNN models:\n",
    "- LSTM_Model with mini batch\n",
    "- LSTMBatch_Model with batch learning\n",
    "- LSTM3L_Model with 3 stacked layers\n",
    "- BILSTM_Model with Biderctional LSTM\n",
    "- CNNLSTM_Model with one convultional layer and LSTM layer\n",
    "- **We also load our test set that these models nerver saw before**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:39:35.910763Z",
     "start_time": "2018-03-22T10:38:20.218893Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "modelMini = load_model('LSTM_Model.h5')\n",
    "model3L = load_model('LSTM3L_Model.h5')\n",
    "modelBatch = load_model('LSTMBatch_Model.h5')\n",
    "modelBI = load_model('BILSTM_Model.h5')\n",
    "modelCNNLSTM = load_model('CNNLSTM_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:40:56.330900Z",
     "start_time": "2018-03-22T10:40:47.035938Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model LSTM mini Batch Classification report\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_13 (LSTM)               (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 80,501\n",
      "Trainable params: 80,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "*Classification LSTM mini Batch:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.75      0.77      0.76      2448\n",
      "          1       0.83      0.81      0.82      3290\n",
      "\n",
      "avg / total       0.79      0.79      0.79      5738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
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VPDTvdXbo05NJH9qJH941h/W1wR2zXmNIv2qG9K1uv4uwdpcT/OA/38eJv32cJW+u4aZT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\nWBkRzZYnIo5vIv0a4JrM+m/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26148641908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(\"Model LSTM mini Batch Classification report\")\n",
    "MiniPrediction = modelMini.predict_classes(X_test)\n",
    "print(modelMini.summary())\n",
    "print('\\n*Classification LSTM mini Batch:\\n', classification_report(y_test, MiniPrediction))\n",
    "plot_classification_report(classification_report(y_test, MiniPrediction), title='Classification LSTM mini Batch ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:43:43.010141Z",
     "start_time": "2018-03-22T10:43:37.052666Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model LSTM Batch Classification report\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_13 (LSTM)               (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 80,501\n",
      "Trainable params: 80,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "*Classification LSTM Batch:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.75      0.77      0.76      2448\n",
      "          1       0.83      0.81      0.82      3290\n",
      "\n",
      "avg / total       0.79      0.79      0.79      5738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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TJKHn0cAlEXEJ0LfEfc3MzKwLU+2moksJFgEjMuvDgSWN5J3I5iY1ACLivIjY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\nMnVrPY1Nu2LW4QVETWXdvw6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2615c398e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model LSTM Batch Classification report\")\n",
    "BatchPrediction = modelMini.predict_classes(X_test)\n",
    "print(modelMini.summary())\n",
    "print('\\n*Classification LSTM Batch:\\n', classification_report(y_test, BatchPrediction))\n",
    "plot_classification_report(classification_report(y_test, BatchPrediction), title='Classification LSTM Batch ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:42:33.896366Z",
     "start_time": "2018-03-22T10:42:22.299310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 3 layer LSTM Classification report\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_2 (LSTM)                (None, 100, 100)          80400     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 100, 32)           17024     \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 32)                8320      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 105,777\n",
      "Trainable params: 105,777\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "*Classification 3 layer LSTM:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.72      0.73      2448\n",
      "          1       0.79      0.80      0.80      3290\n",
      "\n",
      "avg / total       0.77      0.77      0.77      5738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MpB+X56w7hzHD+/LSyjXUvPo26+uCaU8s4eO7bVcw/6f3HMK0x5cA8L6BvZn5wkrq6oO1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\nU4E909W3geMj4nlJg4HHJE2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2615c491e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model 3 layer LSTM Classification report\")\n",
    "model3LPrediction = model3L.predict_classes(X_test)\n",
    "print(model3L.summary())\n",
    "print('\\n*Classification 3 layer LSTM:\\n', classification_report(y_test, model3LPrediction))\n",
    "plot_classification_report(classification_report(y_test, model3LPrediction), title='Classification 3 layer LSTM ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:45:11.498887Z",
     "start_time": "2018-03-22T10:45:06.223457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model CNN-LSTM Classification report\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_3 (Conv1D)            (None, 100, 100)          30100     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 50, 100)           0         \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 100)               80400     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 110,601\n",
      "Trainable params: 110,601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "*Classification CNN-LSTM:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.76      0.72      0.74      2448\n",
      "          1       0.80      0.83      0.81      3290\n",
      "\n",
      "avg / total       0.78      0.78      0.78      5738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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LM9s2l3SfpJckzWhsrEbXHJkVWXiUI2sB0Q4fdzYrREQQ65rXrCapDLgM2BeY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\nYH0lry5aWbOey+WaNEhtVVU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2615d65d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model CNN-LSTM Classification report\")\n",
    "CNNLSTMPrediction = modelCNNLSTM.predict_classes(X_test)\n",
    "print(modelCNNLSTM.summary())\n",
    "print('\\n*Classification CNN-LSTM:\\n', classification_report(y_test, CNNLSTMPrediction))\n",
    "plot_classification_report(classification_report(y_test, CNNLSTMPrediction), title='Classification CNN-LSTM ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:46:51.914298Z",
     "start_time": "2018-03-22T10:46:44.321444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model BILSTM Classification report\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "bidirectional_4 (Bidirection (None, 128)               84480     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 84,609\n",
      "Trainable params: 84,609\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "\n",
      "*Classification BILSTM:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.73      0.72      0.73      2448\n",
      "          1       0.79      0.81      0.80      3290\n",
      "\n",
      "avg / total       0.77      0.77      0.77      5738\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Users\\ala94\\Anaconda3\\envs\\DS\\lib\\site-packages\\matplotlib\\artist.py:233: MatplotlibDeprecationWarning: get_axes has been deprecated in mpl 1.5, please use the\n",
      "axes property.  A removal date has not been set.\n",
      "  stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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yv4gETMrk2zEiTkzT8w6IFREXAScBXYCn0tqhrHwzJX6QeV2Fg/Zy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\nluHgyMzMzCzj/wMz65hPfW2YXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2615d65d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Model BILSTM Classification report\")\n",
    "BILSTMPrediction = modelBI.predict_classes(X_test)\n",
    "print(modelBI.summary())\n",
    "print('\\n*Classification BILSTM:\\n', classification_report(y_test, BILSTMPrediction))\n",
    "plot_classification_report(classification_report(y_test, BILSTMPrediction), title='Classification BILSTM ', cmap='RdBu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  We can't tell what is the best model from these result because they are too close to each other ,but what we can tell is that the model succeded to predict the positive reviews more than the negatif ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:52:20.915171Z",
     "start_time": "2018-03-22T10:51:44.647322Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "fprB, tprB, thresholdsB = metrics.roc_curve(y_test, modelBatch.predict_proba(X_test))\n",
    "fprM, tprM, thresholdsM = metrics.roc_curve(y_test, modelMini.predict_proba(X_test))\n",
    "fpr3l, tpr3l, thresholds3l = metrics.roc_curve(y_test, model3L.predict_proba(X_test))\n",
    "fprBI, tprBI, thresholdsBI = metrics.roc_curve(y_test, modelBI.predict_proba(X_test))\n",
    "fprCNN, tprCNN, thresholdsCNN = metrics.roc_curve(y_test, modelCNNLSTM.predict_proba(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-22T10:54:03.097890Z",
     "start_time": "2018-03-22T10:54:02.465692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L20VBqgXwDQsluidEdIFuk+pdh9piSBwKXrdpg4bQJFP8xB7a0pdRiA7nd7/7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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2615d5d17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "lw = 2\n",
    "plt.plot(fprB, tprB, color='darkorange',\n",
    "         lw=lw, label='ROC curve  LSTM Batch (area = %0.2f)' % auc(fprB, tprB))\n",
    "plt.plot(fprM, tprM, color='red',\n",
    "         lw=lw, label='ROC curve LSTM Mini Batch (area = %0.2f)' % auc(fprM, tprM))\n",
    "plt.plot(fpr3l, tpr3l, color='black',\n",
    "         lw=lw, label='ROC curve LSTM 3L (area = %0.2f)' % auc(fpr3l, tpr3l))\n",
    "plt.plot(fprBI, tprBI, color='purple',\n",
    "         lw=lw, label='ROC curve  LSTM BI (area = %0.2f)' % auc(fprBI, tprBI))\n",
    "plt.plot(fprCNN, tprCNN, color='green',\n",
    "         lw=lw, label='ROC curve  CNN LSTM (area = %0.2f)' % auc(fprCNN, tprCNN))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best One LSTM Mini Batch with 0.88 auc"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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