SVM- open or close.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Classifiers for classification of open/close questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "openCloseData = pd.read_csv('openCloseData9.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'AboutMe', 'Body', 'Comment', 'Downvotes', 'Location',\n",
       "       'PostId', 'ProfileImageUrl', 'Reputation', 'SOUrlCount', 'Tags',\n",
       "       'Title', 'Unnamed: 0.1', 'Upvotes', 'UserId', 'Views', 'WebsiteUrl',\n",
       "       'aboutMeFilled', 'bodyWordCount', 'closed', 'codeLength',\n",
       "       'colemanLiauIndex', 'countOfInterrogativeSent',\n",
       "       'daleChallReadabilityScore', 'experienceInTime', 'firstLineLength',\n",
       "       'fleschReadingEase', 'gunningFog', 'locationFilled', 'lowerUpperRatio',\n",
       "       'numAns', 'numPR', 'numPeopleReached', 'numQues',\n",
       "       'profileImageUrlFilled', 'punctuationCount', 'sentenceCount',\n",
       "       'sentencesStartWithICount', 'sentencesStartWithYouCount',\n",
       "       'shortWordCount', 'smogIndex', 'tagCount', 'tagWeightSum',\n",
       "       'titleLengthInChars', 'urlCount', 'userProfileFilled',\n",
       "       'websiteUrlFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openCloseData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 47)\n",
      "(159996, 46)\n"
     ]
    }
   ],
   "source": [
    "print(openCloseData.shape)\n",
    "openCloseData.drop(['Unnamed: 0'],inplace=True,axis=1)\n",
    "print(openCloseData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 29)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "X = openCloseData.loc[:,['Reputation',\n",
    "       'experienceInTime', 'Views',\n",
    "       'Upvotes', 'Downvotes', 'tagCount', 'closed',\n",
    "       'lowerUpperRatio', 'sentenceCount', 'firstLineLength', 'codeLength',\n",
    "       'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
    "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
    "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
    "       'punctuationCount', 'tagWeightSum', 'gunningFog',\n",
    "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
    "       'colemanLiauIndex', 'userProfileFilled', 'numAns', 'numQues'] ]\n",
    "\n",
    "\n",
    "X.replace(np.nan,0,inplace=True)  #replacing NaN to 0\n",
    "X.lowerUpperRatio.replace(np.inf,0,inplace=True)  #replacing infinity to 0\n",
    "print(X.shape)\n",
    "print(type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996,)\n",
      "(159996, 28)\n"
     ]
    }
   ],
   "source": [
    "y = X.closed\n",
    "X.drop(['closed'],inplace=True,axis=1)\n",
    "print(y.shape)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# x=(x-u)/std\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "#print(scaler.fit(X))\n",
    "#print(scaler.mean_)\n",
    "X=scaler.fit_transform(X)\n",
    "print(type(X))\n",
    "X = pd.DataFrame(X, columns=['Reputation', 'experienceInTime', 'Views', 'Upvotes', 'Downvotes',\n",
    "       'tagCount', 'lowerUpperRatio', 'sentenceCount', 'firstLineLength',\n",
    "       'codeLength', 'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
    "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
    "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
    "       'punctuationCount', 'tagWeightSum', 'gunningFog',\n",
    "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
    "       'colemanLiauIndex', 'userProfileFilled', 'numAns', 'numQues'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  2.01807416e+03   5.10066908e+02   3.01059583e+02   2.26216980e+02\n",
      "   2.18902723e+01   2.90577889e+00   2.99731747e+01   5.96079902e+00\n",
      "   9.98289770e+01   5.68528057e+02   1.91942299e-01   1.54941374e-02\n",
      "   5.12481187e+01   1.19541739e+00   8.63771594e-03   2.02549439e+00\n",
      "   4.09886685e+01   8.53872034e+01   4.33703530e+01  -2.44966593e+02\n",
      "   1.89378949e+01   5.42152991e+01   7.71907479e+00   5.76047464e+00\n",
      "   9.82247931e+00   1.54041351e+00   3.25230693e+01   4.77225618e+01]\n"
     ]
    }
   ],
   "source": [
    "print(scaler.mean_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.17173655e+08   3.42615432e+05   2.94724961e+06   6.03714126e+05\n",
      "   1.11587341e+05   1.50461647e+00   6.64480756e+02   1.22872198e+02\n",
      "   8.75204965e+03   2.07884824e+06   8.66455736e-01   2.93294209e-02\n",
      "   4.03492388e+02   4.61051576e+00   1.12006717e-02   2.81215161e+00\n",
      "   1.21652922e+03   4.70156550e+03   2.03530533e+04   4.59393389e+04\n",
      "   1.23125524e+02   1.37349155e+03   3.37276468e+00   3.21043352e+01\n",
      "   3.34948687e+01   1.23490391e+00   3.60008314e+04   1.52441460e+04]\n"
     ]
    }
   ],
   "source": [
    "print(scaler.var_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting Data to Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Train/Test Split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
      "29416    -0.186063         -0.804784 -0.168376 -0.291145  -0.065531  1.707299   \n",
      "58063    -0.186340         -0.871412 -0.175365 -0.291145  -0.065531  0.076813   \n",
      "62206    -0.170266         -0.364009 -0.166046 -0.249961  -0.059543  0.892056   \n",
      "14790    -0.182922         -0.796241 -0.170123 -0.273127  -0.065531  1.707299   \n",
      "152752   -0.182737         -0.866287 -0.168376 -0.284710  -0.065531  1.707299   \n",
      "\n",
      "        lowerUpperRatio  sentenceCount  firstLineLength  codeLength    ...     \\\n",
      "29416         -0.146375      -0.086677        -0.511253    2.121950    ...      \n",
      "58063          0.421303      -0.176891        -0.350915   -0.385296    ...      \n",
      "62206         -0.925224       5.687009        -0.201267    0.739670    ...      \n",
      "14790          0.004567      -0.357319         1.476937    0.218801    ...      \n",
      "152752         0.094962       0.003536         0.354572   -0.394312    ...      \n",
      "\n",
      "        punctuationCount  tagWeightSum  gunningFog  fleschReadingEase  \\\n",
      "29416          -0.247927     -0.265550   -0.469341           0.465850   \n",
      "58063          -0.275965     -0.479731   -0.495476           0.302334   \n",
      "62206           0.572181     -0.017494   -0.215199          -0.489341   \n",
      "14790          -0.268956     -0.384663   -0.293605          -0.037379   \n",
      "152752         -0.191852     -0.231941    0.229998          -0.235433   \n",
      "\n",
      "        daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
      "29416                   -0.745477   0.695284         -0.993953   \n",
      "58063                   -0.179185   0.659986         -0.375376   \n",
      "62206                    0.261869   1.224751          1.551198   \n",
      "14790                   -0.429660   1.277698         -0.342546   \n",
      "152752                   0.888057   1.507134          0.498926   \n",
      "\n",
      "        userProfileFilled    numAns   numQues  \n",
      "29416           -0.486306 -0.166139 -0.354123  \n",
      "58063           -0.486306 -0.171410 -0.378421  \n",
      "62206           -0.486306 -0.108165 -0.305527  \n",
      "14790           -0.486306 -0.155598 -0.297428  \n",
      "152752           1.313449 -0.160869 -0.329825  \n",
      "\n",
      "[5 rows x 28 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(111997, 28)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training LinearSVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tuning value of C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for C = 0.5 is : 0.8794\n",
      "Accuracy for C = 1.5 is : 0.8832\n",
      "Accuracy for C = 2.5 is : 0.8822\n",
      "Accuracy for C = 3.5 is : 0.8850\n",
      "Accuracy for C = 4.5 is : 0.8850\n",
      "Accuracy for C = 5.5 is : 0.8866\n",
      "Accuracy for C = 6.5 is : 0.8857\n",
      "Accuracy for C = 7.5 is : 0.8861\n",
      "Accuracy for C = 8.5 is : 0.8874\n",
      "Accuracy for C = 9.5 is : 0.8845\n"
     ]
    }
   ],
   "source": [
    "c = list(np.arange(0.5, 10.0, 1.0))\n",
    "accuracy = []\n",
    "y_predicted = np.zeros((y_test.shape))\n",
    "max_accuracy = 0.0\n",
    "linear_svm_classifier = LinearSVC()\n",
    "\n",
    "for i in c:\n",
    "    classifier = LinearSVC(C = i)\n",
    "    classifier.fit(X_train, y_train)\n",
    "    predicted_y = classifier.predict(X_test)\n",
    "    accuracy.append(np.mean(y_test==predicted_y))\n",
    "    print('Accuracy for C = {} is : {:.4f}'.format(i, accuracy[-1]))\n",
    "    if(accuracy[-1] > max_accuracy):\n",
    "        y_predicted = predicted_y\n",
    "        max_accuracy = accuracy[-1]\n",
    "        linear_svm_classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(c, accuracy)\n",
    "plt.scatter(c, accuracy)\n",
    "plt.title('Accuracy vs C')\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.89      0.89     23916\n",
      "          1       0.89      0.89      0.89     24083\n",
      "\n",
      "avg / total       0.89      0.89      0.89     47999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "k_fold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "val_score = cross_val_score(linear_svm_classifier,X,y,cv=k_fold,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.8116875   0.80752524  0.8927154   0.83749492  0.72621019]\n"
     ]
    }
   ],
   "source": [
    "print(val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iEantc6+G4Ti9woLT6+x5IlLDPZF/HjDZ7aY6XZz7uAtOr6JfhXAbjDHGHIWQJRf3niC34SSKpTi3GF0sIo+JSD93tp7AchFZgdNt+r/dZXcBj+MkqJnAY+44cO4H8RbO/a9XA9+FahuMMcYcnZD+iVJVJ+HciMl33MM+w5/j3MOgoGXf4a8jGd/xszj8bmrGlDlfzk3lmcnLGdAonQeemsKw3q3p36nAa1OMKZMqxD/0jQknX85NZfj4hWRm50IjSE3LZPj4hQCWYEy5YR1XGlPKnpm83EkswKztzgWQmdm5PDN5uZdhGRNUllyMKWWb0jIPDf+8JbLA8caUdZZcjCll9RIqHRo+pW7eoeG6VWO9CMeYkLDkYkwpa98w4dBwj8S/ksu+g7nM2bDbi5CMCTpLLsaUol9WbOeHJVs5uWkN6rtHMA2qxzH8/BOoVSWGAaN/5+v59r9gU/bZ1WLGlJIte7K485N5tKxbhff/0ZXKMVEkJydz+6CeAFye1IibPpzF7WPnsn7nPm7t1QK7o4Qpq+zIxZhSkJ2bx20fzyErO5dRg7pQOebI33U142P46IZuXNKpAc/+sIJ7PlvAwZy8AkozJvzZkYsxpeCZycuZtX43Lw3oSIu6VQqdLzYqkuev6EDTWvG88L8VpOzez+tXd6FGfEwpRmvMsbMjF2NC7IfFWxj9yxquPqUxF3cs/k+SIsId57TkpQEdmbshjUtfm87aHftKIVJjgseSizEhtHHXfu75bD7tGyTwUF//G7EW7eKODfj4xm7syczmklHT+GPNzhBFaUzwWXIxJkQO5OQydMwcFBh5VWdioyKLXcZfUtOaTBh6KrXiY7j67T/4YnZK8QsZEwYsuRgTIk98s5SFqXt47vIONK5V+ajLaVIrnvG39ODkpjW5+7P5PP/Dcpy7fBsTviy5GBMCE+dv4sPf1zPkjOac1+64Yy4voXI07/+jK1cmNeLlKav457h5ZLn9kxkTjuxqMWOCbNW2DO77YgFJTWowrHfroJUbHRnBU39rT7M68Tz13TJSd+9n9LVJ1K5i3caY8GNHLsYEUebBXG4dM4dK0ZG8clUnoiOD+xETEW4+83hev7ozSzbv5ZJR01i5NT2o6zAmGCy5GBMkqsqDXy5ixbZ0XryyI/US4kK2rj4n1uOTId3Jys7j0tem89vKHSFblzFHw5KLMUHy2awUvpiTwu1nteSMVnVCvr4Ojarz5a09aFA9juve/ZOxf24I+TqNCZQlF2OCYMmmvTz01SJ6tKjFHWe3LLX1Nqgex2c3d+f0lrUZPn4h/5m0lLw8u5LMeC+kyUVE+ojIchFZJSL3FTC9sYhMFZG5IrJARC5wxw8SkXk+jzwR6ehOS3bLzJ9WN5TbYExx0rOyufXjOSTERfPilZ2IjCjdziarVormrWuTuLZ7E0b/soabP5rN/oM5pRqDMf5CllxEJBIYCZwPtAUGioj/X5QfBD5V1U7AAGAUgKqOUdWOqtoRuAZYp6rzfJYblD9dVbeFahuMKY6qct8XC9mwaz+vXtWZOh7d8CsqMoLHLj6RRy5qy/+WbuXKN35n694sT2IxBkJ75NIVWKWqa1T1IDAOuNhvHgWqucMJQEE3shgIjA1ZlMYcg/enr+PbhZsZ1rs1XZvV9Doc/t6jGW9em8Tq7Rn0HzmNJZv2eh2SqaBCmVwaABt9Xqe443yNAK4WkRRgEnB7AeVcyZHJ5V23SewhsRteGI/M25jGvyct5ewT6jLk9OZeh3PI2W0S+ezm7qjC5a9PZ8qyrV6HZCogCVU3EiJyOdBbVW9wX18DdFXV233m+T83hudEpDvwNnCiqua507sBb6lqe59lGqhqqohUBb4APlLVDwpY/xBgCEBiYmKXcePGHfM2ZWRkUKVK4d2lm7+U97rKOKg8Mj0TgEdPjaNKzNH9xgllPe3OyuPFOQfYsDePq9rEcG6T6JCsp7SU930qWIJZT7169ZqtqklHs2wo/6GfAjTyed2QI5u9rgf6AKjqDBGpBNQG8s+jDMDvqEVVU93ndBH5GKf57YjkoqqjgdEASUlJ2rNnz2PcHEhOTiYY5VQE5bmu8vKUGz6Yxd7sTD67+VQ6Nqp+1GWFup56n5XDHePmMWbJVqKq1+Ohvm2JCvIfO0tLed6ngilc6imUe9lMoKWINBORGJxEMdFvng3A2QAi0gaoBGx3X0cAl+Ocq8EdFyUitd3haKAvsCiE22DMEd74ZQ1Tlm3jwQvbHlNiKQ2VY6J4/eou3Hh6M96fsZ4bP5hFxgG7ksyEXsiSi6rmALcBk4GlOFeFLRaRx0Sknzvb3cCNIjIf5whlsP7VTncGkKKqa3yKjQUmi8gCYB6QCrwZqm0wxt8fa3by7A/LufCkelzbvYnX4QQkMkJ44MK2/PuSE/ll5Q4ue206qWmZXodlyrmQdlypqpNwTtT7jnvYZ3gJ0KOQZZOBU/zG7QO6BD1QYwKwPf0At4+dS+OalXnq0vaUtWtJBnVrQuOalRn60Rz6j5zG29clcVLD8D7yMmVX2Wx8NaaU5eYpd4yby57MbEYN6kzVSmXz5PjpLeswfuipxEZFcMUbM/h+0RavQzLllCUXYwLw0v9WMH31Th6/+ETa1KtW/AJhrGViVSYM7UGbetW4Zcxs3vh5td18zASdJRdjivHziu28MnUVl3VpyBUnNyp+gTKgTtVYxt54Che0r8eT3y1j+PiFZOfmeR2WKUfsZmHGFGHznkzu+mQerepW5fGLT/Q6nKCqFB3JKwM60bx2PK9MWcXG3fsZNagLCXFls8nPhBc7cjGmENm5edz28VwOZOcy6urOxMVEeh1S0EVECHef15pnL+/An2t3cemoaWzYud/rsEw5YMnFmEI8/f0yZq/fzVN/O4nj65Tvf4Zf1qUhH17fjR0ZB+k/ahqz1+/yOiRTxllyMaYAkxdv4c1f13Jt9yZc1KG+1+GUilOa12LC0FOpVimKgW/+wVfzUr0OyZRhllyM8bNh537u+Ww+JzVM4IEL23gdTqlqXqcKE4b2oGOj6twxbh4v/7TSriQzR8WSizE+srJzGfrxbAQYeVVnYqPK33mW4tSIj+HD67tyaecGPP/jCu7+dD4HcnK9DsuUMXa1mDE+Hv9mCYtS9/LmtUk0qlnZ63A8ExsVyXOXd6B57Xie/WEFKbszef2aLtSMj/E6NFNG2JGLMa6v5qUy5o8N3HRGc85tm+h1OJ4TEW47qyUvD+zEvJQ0Lhk1jdXbM7wOy5QRllyMAVZtS2f4+IWc3LQG9/Ru7XU4YaVfh/qMvfEUMrJyuHTUdGas3ul1SKYMsORiKrz9B3O45aM5xEVH8srAzkSX0fudhFKXJjX48tYe1Kkay7Xv/MFnszYWv5Cp0OxTZCo0VeXBCYtYtT2DlwZ04riESl6HFLYa1azMF7ecSrdmtRj2+QKembyMvDy7kswUzJKLqdA+mbmR8XNTuePslpzWsrbX4YS9hLho3v37yQzs2oiRU1dz+9i5ZGXblWTmSHa1mKmwFm/aw8MTF3Nai9rcflZLr8MpM6IjI/jPJe1pVjueJ79bRmpaJm9em0SdqrFeh2bCiB25mAppb1Y2t46ZQ43K0bw4oCOREWXrxl9eExGGnHE8rw3qwrIte+k/chortqZ7HZYJI5ZcTIWjqtz7+QI27s7k1as6U7uK/eI+Wn1OPI7PbjqV7Nw8/jZqOj+v2O51SCZMWHIxFc5709fx3aIt/Kt3a05uWtPrcMq89g0T+PLWHjSoEcc/3pvJR7+v9zokEwYsuZgKZe6G3fxn0lLOaVOXIWc09zqccqN+9Tg+v+VUzmhZmwe/XMTj3ywh164kq9BCmlxEpI+ILBeRVSJyXwHTG4vIVBGZKyILROQCd3xTEckUkXnu43WfZbqIyEK3zJdFxBrLTUB27zvIbR/PJbFaJZ67vCO26wRXldgo3rw2icGnNuXt39Zy04ez2Xcgx+uwjEdCllxEJBIYCZwPtAUGikhbv9keBD5V1U7AAGCUz7TVqtrRfdzsM/41YAjQ0n30CdU2mPIjL0/5v0/nsT39AKMGdSahst1tMRSiIiMY0a8dj/Zrx5RlW7nijRls2ZPldVjGA6E8cukKrFLVNap6EBgHXOw3jwLV3OEEYFNRBYpIPaCaqs5Qpx/wD4D+wQ3blEev/byaqcu382DfNpzUsLrX4ZR7153alLevO5l1O/Zx8cjfWJS6x+uQTCmTUN2rQUQuA/qo6g3u62uAbqp6m8889YAfgBpAPHCOqs4WkabAYmAFsBd4UFV/FZEk4ClVPcdd/nTgXlXtW8D6h+Ac4ZCYmNhl3Lhxx7xNGRkZVKlSvu9IGCzhVFfLduXy3z+z6HpcJDd3iA2r5rBwqqdQ2Jiexwuzs9iXrdzcIZZOdY/+r3Xlva6CJZj11KtXr9mqmnQ0y4byT5QFfYL9M9lA4D1VfU5EugMfisiJwGagsaruFJEuwJci0i7AMp2RqqOB0QBJSUnas2fPo9yMvyQnJxOMciqCcKmrbelZDHv5N5rVjuftW06jSmx4/W84XOoplHqfmcUNH8zi5bl7ePDC4/lHj6ZHleArQl0FQ7jUUyibxVKARj6vG3Jks9f1wKcAqjoDqATUVtUDqrrTHT8bWA20cstsWEyZxgCQm6fcMXYe6VnZjLq6c9glloqibrVKfDKkO73bHsfj3yzh4a8Wk5Ob53VYJsRCmVxmAi1FpJmIxOCcsJ/oN88G4GwAEWmDk1y2i0gd94IARKQ5zon7Naq6GUgXkVPcq8SuBb4K4TaYMuzF/61gxpqdPH7xiZxwXLXiFzAhExcTyahBnbnpzOZ8+Pt6rn9/FulZ2V6HZUIoZMlFVXOA24DJwFKcq8IWi8hjItLPne1u4EYRmQ+MBQa7J+rPABa44z8HblbVXe4ytwBvAatwjmi+C9U2mLIrefk2XpmyiiuSGnJ5UqPiFzAhFxEhDD+/DU9e2p5pq3Zw2WszSNm93+uwTIiEtJ1AVScBk/zGPewzvAToUcByXwBfFFLmLODE4EZqypNNaZnc9ck8TjiuKo/2s10l3Azs2phGNSpzy5jZ9B85nbeuS6JjI7uCr7yxf+ibciU7N4/bPp7DwZw8Rg3qTFxMpNchmQKc1rI2E4aeSlxMBFe+MYNJCzd7HZIJMksuplx56rtlzNmQxn8vO4nmdeyy1XDWom5VvhzagxMbJDB0zBxGJa8iVH+NMKXPkospN75ftIW3f1vLdd2b0Pek+l6HYwJQq0osY27oxkUd6vP098u594sFHMyxK8nKg2KTi4i0EpGfRGSR+/okEXkw9KEZE7j1O/cx7LP5dGiYwP0XtvE6HFMClaIjeXlAR/55dks+nZXCde/8yZ79diVZWRfIkcubwHAgG0BVF+BcVmxMWMjKzmXomDlERAivXtWZ2Cg7z1LWiAj/d24rnr+iA7PX7+aS16axfuc+r8MyxyCQ5FJZVf/0G2ddnZqw8dg3S1i8aS/PX9GBRjUrex2OOQaXdm7IRzd0Y/e+g/QfOY2Z63YVv5AJS4Eklx0icjxuNytun2F2aYcJC1/OTeXjPzZw85nHc3abRK/DMUHQtVlNJgztQY3KMQx68w8enLCQHk9NYWHqHno8NYUv56Z6HaIJQCDJ5VbgDeAEEUkF7gRuLnoRY0Jv5dZ0ho9fSNemNbnnvFZeh2OCqGnteMYPPZXGtSrz0R8bSE3LRBVS0zIZPn6hJZgyoMjkIiIRQJLbC3Ed4ARVPU1V7T6mxlP7D+YwdMwcKsdE8spVnYiKtAsfy5vqlWPY73Ozsd+2Ou9xZnYuz0xe7lVYJkBFfiJVNQ+nCxdUdZ+qppdKVMYUQVV5YMIiVm3P4KUBnUisVsnrkEyIbPa50dis7X/1pLwpLdOLcEwJBPJz70cRuUdEGolIzfxHyCMzphDjZm5kwtxU7jy7Fae1rO11OCaE6lePOzQcFVHweBOeAkku/8A57/ILMNt9zAplUMYUZlHqHh6ZuJjTW9bm9rNaeB2OCbFhvVsTF+1cWt69rvPnypjICIb1bu1lWCYAxXZcqarNSiMQY4qzNyubWz+eQ83KMbx4ZUciIsLnjpImNPp3agDAM5OX06lWOtO2CQlx0VyngVpAAAAgAElEQVTQvp7HkZniBPIP/WgR+aeIfO4+bhOR6NIIzph8qsq/PltAyu5MXr2qE7WqxHodkikl/Ts1YNp9Z9GxUQJvXZfE9owDvDttrddhmWIE0iz2GtAFGOU+urjjjCk170xbx/eLt3BfnxNIamqn/Cqqnq3rcvYJdXllyiq2pWcVv4DxTCDJ5WRVvU5Vp7iPvwMnhzowY/LN2bCbJyct5dy2idxwurXSVnQP9m3LgZxcnv7eLkcOZ4Ekl1z3H/rAodsO54YuJGP+snvfQW4bM4d61Svx7GUdcO5ubSqyZrXj+cdpzfh8dgrzNqZ5HY4pRCDJZRgwVUSSReRnYArO7YmNCam8POWuT+exI+Mgo67qQkJlO9VnHLef1ZI6VWMZMXExeXl2D5hwVGxyUdWfgJbAP91Ha1WdGurAjHnt59UkL9/OQxe1pX3DBK/DMWGkSmwU9/Y5gXkb05hgXcGEpUCuFrsViFPVBao6H6gsIkNDH5qpyKav3sFzPyynX4f6XN2tsdfhmDB0aacGdGhUnae+X0bGAeuoPdwE0ix2o6oeathU1d3AjYEULiJ9RGS5iKwSkfsKmN5YRKaKyFwRWSAiF7jjzxWR2SKy0H0+y2eZZLfMee6jbiCxmLJj294s/jl2Hk1rx/OfS9vbeRZToIgIYcRFbdmefoBXp6zyOhzjJ5DkEiE+n24RiQRiilvInW8kcD7QFhgoIm39ZnsQ+FRVO+HcgGyUO34HcJGqtgeuAz70W26QqnZ0H9sC2AZTRuTk5nH72LlkHMjmtUFdqBJb7P98TQXWqXEN/ta5Ie/8tpZ1O+zmYuEkkOQyGfhURM52jyDGAt8HsFxXYJWqrlHVg8A44GK/eRSo5g4nAJsAVHWuqm5yxy8GKomI/WuuAnjhfyv4Y+0unujfntbHVfU6HFMG3NunNdGRwhPfLvE6FONDVIu+0sLtdn8IcA4gwA/AW6pa5OXI7k3F+qjqDe7ra4Buqnqbzzz13PJqAPHAOao6u4Bybna7/UdEkoFaOJdDfwE8oQVshIgMceMmMTGxy7hx44rczkBkZGRQpUqVYy6nIjiaulqwPYfnZx/gjIZR/OPEivFbwvapwBVVV5PWHOTTFdnc3SWW9nUq9tFuMPepXr16zVbVpKNaWFUDeuA0hXUC6gY4/+U4SSj/9TXAK37z/B9wtzvcHVgCRPhMbwesBo73GdfAfa6Kk5iuLS6WLl26aDBMnTo1KOVUBCWtq5Td+7XDo5O19ws/a+bBnNAEFYZsnwpcUXWVlZ2jZz49Rc96dqoezMktvaDCUDD3KWCWBpgj/B+FNouJyOsi0s4dTgDmAR8Ac0VkYAB5KwVo5PO6IW6zl4/rgU/dJDcDqATUdtfZEJjgJo/VPskw1X1OBz7GaX4zZdjBnDxuHTOHnFzltau7UMntBdeYQMVGRfJQ37as3r6P96ev8zocQ9HnXE5X1cXu8N+BFeqcYO8C/CuAsmcCLUWkmYjE4Jywn+g3zwbgbAARaYOTXLaLSHXgW2C4qk7Ln1lEokQkP/lEA32BRQHEYsLYU98tY97GNJ6+7CSa1Y73OhxTRp11Ql3ObFWHl/63kh0ZB7wOp8IrKrkc9Bk+F/gSQFW3BFKwqubg3MVyMrAU56qwxSLymIj0c2e7G7hRRObjXCgw2D0Uuw1oATzkd8lxLDBZRBbgHEmlAm8GuK0mDH23cDPvTFvL4FObWjfq5piICA/1bUtmdi7P2m2QPVfUma80EemL8wXeA6cJCxGJAgK6DZyqTgIm+Y172Gd4iVu2/3JPAE8UUmyXQNZtwt+6Hfv41+cL6NCoOvdf0MbrcEw50KJuFQaf2pS3p61lULcm1rODh4o6crkJ5wjiXeBOnyOWs3GarIw5alnZuQwdM4eICGHkVZ2IiQrkqnhjivfPc1pSKz6GR79enH8RkPFAoZ9oVV2hqn3U+aPiez7jJ6uqdVxpjsmjXy9myea9vHBlBxrWqOx1OKYcqVYpmmG9WzNr/W4mzve/hsiUFvu5aErd+DkpjP1zI7f0PJ6zTkj0OhxTDl3epRHtGyTw5KRl7D9o/Y55wZKLKVUrtqbzwIRFdG1Wk7vPbeV1OKaciogQRvRry5a9WYyaurr4BUzQWXIxpWbfgRyGjplDfGwkrw7sRFSk7X4mdLo0qUn/jvUZ/esaNuzc73U4FU4gXe7HishVInK/iDyc/yiN4Ez5oarcP2Eha7Zn8PKATtStVsnrkEwFcN/5bYiKEP49yfodK22B/HT8CqfDyRxgn8/DmIB9/OcGvpq3ibvOacWpLWp7HY6pII5LqMStvVowefFWpq3a4XU4FUogyaWhql6pqk+r6nP5j5BHFka+nJtKj6emsDB1Dz2emsKXdue7ElmUuodHJy7hjFZ1uLVXC6/DMRXM9ac1o3HNyjz69WJycvO8DqfCCCS5TBeR9iGPJEx9OTeV4eMXkpqWyZb9kJqWyX1fLOCL2Sleh1Ym7MnMZuiYOdSMj+HFKzsSEWE3/jKlq1J0JA9c2IYVWzP46Pf1XodTYQTSN/VpwGARWQscwOl2X1X1pJBGFiaembyczGzn7gJjVjvVlZWTx92fzefhrxaREBdNtfxHpWiqxUU54ypFH5rmvI4iofJf4yvHRJb7OyyqKsM+m8+mtEw+uekUasYXe485Y0LivLaJnNaiNs//uIJ+HRvYvlgKAkku54c8ijC2KS3z0HD/Jrl8uf6vHnuvPLkxezKz2ZuVzd7MbFJ27yd9cw57MrOLvad3ZIQ4CeewBJSfqApPUPnD0WXgSqu3f1vLD0u28uCFbejSpKbX4ZgKTER45KK29HnpV577YTn/vqTCNsaUmmKTi6quF5EOwOnuqF9VdX5owwof9avHkeommOOr/dWVRIPqcTx8kf9dm/+Sk5tHelYOe7OynQSUmXNYIsof3pOZc+h1alrmoeHs3KK7rYiLjnQTz5GJqFr+kdIRicsZVyU2KmRHTV/OTeWZycs5vWY649YspX2DBK4/rVlI1mVMSbRMrMo1pzThgxnrGNStCW3rVyt2GXP0ik0uInIHcCMw3h31kYiMVtVXQhpZmBjWuzXDxy881DQGzhf7sN6ti1wuKjKCGvEx1DiKw29VJSs7r4BE5JOk/MZv3pPF8q3p7MnMJj2r6KOmCOFQM15xCco3OeXPGxtV8P1W8s9PZWbn8s0+Z56VW9P5at4m+ndqUOJ6MCbY7jqnFV/NS2XE14v5ZMgp5b5p2kuBNItdj3N74n0AIvJfYAZQIZJL/pfiM5OXA+k0qB7HsN6tQ/plKSLExUQSFxNJ4lH8HyQ3T8k47Kgp2+eoKccvWTnPW/ceOPT6QE7RV9RUio4osMnuxyVbDyXhTDe/ZeXk8czk5ZZcTFhIqBzNPb1b88CERXy7cDN9T6rvdUjlViDJRXDuV58v1x1XYfTv1ID+nRqQnJzM7YN6eh1OsSIjhITK0SRUjj7sVqCBysrOPSIR7T0sSeWwZ/9fCWpbehartuWw7+Bfu8lZ9fP4MdU5evE9b2WM1wac3Jgxv2/gP98u5ewTEomLsTufhkIgyeVd4A8RmeC+7g+8HbqQjNcqRUdSKTqSulVLtlyPp6YcOj91Uk3lR/fvQPWrB3T7H2NKRWSEMKJfO654Ywav/7yau6yPu5Ao9pIjVX0e5zbHu4DdwN9V9cVQB2bKnmG9WxMXffivwEDOTxlT2ro2q0nfk+rx+s+rSdlt/Y6FQqHJRUSquc81gXXAR8CHwHp3nDGH6d+pAU9e2p4G7pFKg+pxPHlpezvfYsLS/Re0QQSenLTM61DKpaKaxT4G+gKzAd/rYsV93TyEcZkyqqydnzIVV/3qcdxyZgte+N8Krl69k+7H1/I6pHKlqDtR9nWfm6lqc59HM1W1xGKMKfNuOrM5DarHWb9jIRBIl/s/BTKukGX7iMhyEVklIvcVML2xiEwVkbkiskBELvCZNtxdbrmI9A60TGOMCVR+v2PLtqQzduZGr8MpV4o651LJPbdSW0RqiEhN99EUKPbicBGJBEbidB/TFhgoIv5/aX8Q+FRVOwEDgFHusm3d1+2APsAoEYkMsExjjAnY+ScexynNa/LcD8tJ23/Q63DKjaKOXG7COd9ygvuc//gK5wu+OF2BVaq6RlUPAuNw7gvjS4H8PhgSgE3u8MXAOFU9oKprgVVueYGUaYwxAXP6HWvH3sxsXvhxhdfhlBuFntBX1ZeAl0Tk9qPs6qUB4HucmQJ085tnBPCDiNwOxAPn+Cz7u9+y+ZccFVcmACIyBBgCkJiYSHJycok3wF9GRkZQyqkIrK4CY/UUuFDXVc9GUXz4+3paRmyjYdXw7xi2MOGyTwXSceUrInIiTjNUJZ/xHxSzaEH/4vfvjXEg8J6qPici3YEP3XUVtmxB73iBPTyq6mhgNEBSUpL27NmzmHCLl5ycTDDKqQisrgJj9RS4UNdVh5MP0vPZZCZtqcyYvt3KbL9j4bJPBXJC/xGcfsReAXoBTwP9Aig7BQ7rfaQhfzV75bse+BRAVWfgJK/aRSwbSJnGGFNiNeJj+L9zWzF99U4mL97idThlXiDHfpcBZwNbVPXvQAcgNoDlZgItRaSZiMTgnKCf6DfPBrdsRKQNTnLZ7s43QERiRaQZ0BL4M8AyjTHmqAzq1pjWiVV54tulZGXnFr+AKVQgySVTVfOAHPdf+9sI4A+UqpoD3AZMBpbiXBW2WEQeE5H8I5+7gRtFZD4wFhisjsU4RzRLgO+BW1U1t7AyS7LBxhhTmKjICB65qC0puzN585c1XodTpgXSceUsEakOvIlztVgGzlFEsVR1EjDJb9zDPsNLgB6FLPtv4N+BlGmMMcFyaova9Gl3HKOSV3NZUkPqJVjHq0cjkI4rh6pqmqq+DpwLXOc2jxljTLn0wIVtyFW1fseOQVF/ouzs/wBqAlHusDHGlEuNalbmpjOaM3H+Jmau2+V1OGVSUc1iz7nPlYAkYD7OJcInAX8Ap4U2NGOM8c4tPY/n89kpjJi4mIm3nUZkRNm8NNkrRXVc2UtVewHrgc6qmqSqXYBOOP+YN8aYcqtyTBT3nX8Cizft5dNZ1u9YSQVytdgJqrow/4WqLgI6hi4kY4wJD/061OfkpjV4ZvJy9mRmex1OmRJIclkqIm+JSE8ROVNE3sS5DNgYY8q1/H7Hdu8/yEv/W+l1OGVKIMnl78Bi4A7gTpz/ntjVYsaYCuHEBgkMOLkRH8xYx6pt6V6HU2YEcilylqq+oKqXuI8XVDWrNIIzxphwcM95rYmLieTRr5egWmB3hsZPUZcif+o+L3Rv5HXYo/RCNMYYb9WqEsud57Ti15U7+GnpNq/DKROKuhT5Dve5b2kEYowx4eza7k0Y++cGHv92Cae3qk1sVKTXIYW1oi5F3uw+ry/oUXohGmOM96IjI3i4b1vW79zPO7+t8zqcsFdUs1i6iOwt4JEuIntLM0hjjAkHZ7SqwzltEnl1ykq27bVTz0Up6silqqpWK+BRVVWrFbacMcaUZw/1bUN2rvLU99bvWFECvpeniNQVkcb5j1AGZYwx4apJrXiuP70Z4+ekMnfDbq/DCVuB3Imyn4isBNYCPwPrgO9CHJcxxoStW3u1oG7VWEZMXExenl2aXJBAjlweB04BVqhqM5w7R04LaVTGGBPGqsQ6/Y7NT9nDF3NSvA4nLAWSXLJVdScQISIRqjoV61vMGFPB9e/YgE6Nq/Pf75eTnmX9jvkLJLmkiUgV4BdgjIi8BOSENixjjAlvERHCiIvasSPjAK9OsY7i/QWSXC4GMoG7cO5nvxq4KJRBGWNMWdChUXUu79KQd6atZc32DK/DCStF/c/lVRE5VVX3qWququao6vuq+rLbTGaMMRXesD6tiY2K5IlvrbN4X0UduawEnhORdSLyXxEp8XkWEekjIstFZJWI3FfA9BdEZJ77WCEiae74Xj7j54lIloj0d6e9JyJrfabZ+R9jjGfqVq3EP89uwZRl25i63Pody1fUnyhfUtXuwJnALuBdEVkqIg+LSKviChaRSGAkcD7QFhgoIm391nGXqnZU1Y7AK8B4d/xUn/FnAfuBH3wWHZY/XVXnlWiLjTEmyAaf2ozmteN5/OslHMzJ8zqcsBBIl/vrVfW/qtoJuAq4hMBuFtYVWKWqa1T1IDAO5/xNYQYCYwsYfxnwnaruD2CdxhhT6mKiIniob1vW7NjH+9PXeR1OWCiqV2QARCQa6AMMwPmPy8/AowGU3QDwvfF0CtCtkHU0AZoBUwqYPAB43m/cv0XkYeAn4D5VPVBAmUOAIQCJiYkkJycHEHLRMjIyglJORWB1FRirp8CFe10JcFKdSJ7/YSl1M9eTECuexBEu9VRochGRc3GOJi4E/sQ58hiiqvsCLLugmi3sr6wDgM9VNdcvhnpAe2Cyz+jhwBYgBhgN3As8dsSKVEe700lKStKePXsGGHbhkpOTCUY5FYHVVWCsngJXFuqqcbsMer/4C9PSa/J07w6exBAu9VRUs9j9wAygjapepKpjSpBYwDlSaeTzuiGwqZB5B1Bwk9gVwARVPfQPJVXdrI4DwLs4zW/GGOO55nWq8PcezfhsdgoLUtK8DsdTRZ3Q76Wqb6rqrqMseybQUkSaiUgMTgKZ6D+TiLQGauAkMn9HnIdxj2YQEQH6A4uOMj5jjAm6289qQa14p9+xinxL5IB7RS4pVc0BbsNp0loKfKqqi0XkMRHp5zPrQGCc+r0LItIU58jnZ7+ix4jIQmAhUBt4IjRbYIwxJVe1UjT/6tOaORvS+HJeqtfheKbYE/rHQlUnAZP8xj3s93pEIcuuw7kowH/8WcGL0Bhjgu+yzg0Z8/t6nvpuGee1PY742JB+1YalkB25GGNMRRURITzSrx1b9x5g5NSK2e+YJRdjjAmBzo1rcGmnBrz161rW7yzJtVDlgyUXY4wJkXvPP4GoSKmQ/Y5ZcjHGmBBJrFaJ285qwY9LtvLryu1eh1OqLLkYY0wIXX9aM5rUqsyjXy8hO7fi9DtmycUYY0IoNiqSBy9sy6ptGXw4Y73X4ZQaSy7GGBNi57Spy+kta/PC/1awM+OIrhDLJUsuxhgTYiLCIxe1JfNgLs/+sMLrcEqFJRdjjCkFLepW5druTRk3cwOLUvd4HU7IWXIxxphScsc5LalZOYZHvy7//Y5ZcjHGmFKSEBfNPb1bM3Pdbr5esNnrcELKkosxxpSiK5Ia0a5+NZ6ctJT9B3O8DidkLLkYY0wpiowQRvRrx+Y9WbyevNrrcELGkosxxpSyk5vWpF+H+rzxyxo27trvdTghYcnFGGM8MPyCE4gQ4T+Tyme/Y5ZcjDHGA/US4hja83i+W7SF6at3eB1O0FlyMcYYj9x4RnMa1ojj0YlLyCln/Y5ZcjHGGI9Uio7kwQvbsHxrOh//ucHrcILKkosxxniod7vj6N68Fs/9sILd+w56HU7QhDS5iEgfEVkuIqtE5L4Cpr8gIvPcxwoRSfOZluszbaLP+GYi8oeIrBSRT0QkJpTbYIwxoSQiPNKvLelZ2Tz/Y/npdyxkyUVEIoGRwPlAW2CgiLT1nUdV71LVjqraEXgFGO8zOTN/mqr28xn/X+AFVW0J7AauD9U2GGNMaTjhuGpcfUoTxvyxnqWb93odTlCE8silK7BKVdeo6kFgHHBxEfMPBMYWVaCICHAW8Lk76n2gfxBiNcYYT/3fua2oFhddbvodC2VyaQBs9Hmd4o47gog0AZoBU3xGVxKRWSLyu4jkJ5BaQJqq5veZUGiZxhhTllSvHMPd57bi9zW7+G7RFq/DOWZRISxbChhXWDoeAHyuqrk+4xqr6iYRaQ5MEZGFQEHHiwWWKSJDgCEAiYmJJCcnBxx4YTIyMoJSTkVgdRUYq6fAVYS6qp+nNKwiPPTFXKK2LSMmsqCv0aKFSz2FMrmkAI18XjcENhUy7wDgVt8RqrrJfV4jIslAJ+ALoLqIRLlHL4WWqaqjgdEASUlJ2rNnz6PekHzJyckEo5yKwOoqMFZPgasodVW5yU4Gvvk7S7Uhd/RsWeLlw6WeQtksNhNo6V7dFYOTQCb6zyQirYEawAyfcTVEJNYdrg30AJao0xA5FbjMnfU64KsQboMxxpSq7sfX4oL2x/Haz6tITcv0OpyjFrLk4h5Z3AZMBpYCn6rqYhF5TER8r/4aCIzTw89gtQFmich8nGTylKoucafdC/yfiKzCOQfzdqi2wRhjvHD/BW1QhSfLcL9joWwWQ1UnAZP8xj3s93pEActNB9oXUuYanCvRjDGmXGpYozI3nXk8L/+0kmtO2Um35rW8DqnE7B/6xhgThm4583jqJ1RixNdLyM0re5cmW3IxxpgwFBcTyfAL2rB0817GzSx7/Y5ZcjHGmDDV96R6dG1Wk2cnL2fP/myvwykRSy7GGBOmRIRHLmrLnsxsXvhf2ep3zJKLMcaEsXb1ExjQtTEf/r6elVvTvQ4nYJZcjDEmzN1zXmviYyJ57JslZabfMUsuxhgT5mrGx3DXua34deUOflyy1etwAmLJxRhjyoCrT2lCy7pVeOLbpWRl5xa/gMcsuRhjTBkQHRnBIxe1Y8Ou/bz921qvwymWJRdjjCkjTmtZm/PaJjJy6iq27MnyOpwiWXIxxpgy5MEL25KTp/z3+2Veh1IkSy7GGFOGNK5VmRtPb8aEuanMXr/b63AKZcnFGGPKmKE9W5BYLZZHv15MXpj2O2bJxRhjypj42CiGn9+GBSl7+Hx2itfhFMiSizHGlEEXd6xPlyY1eHryMvZmhV+/Y5ZcjDGmDBIRRlzUjp37DvLKTyu9DucIllyMMaaMat8wgSu6NOLdaetYvT3D63AOY8nFGGPKsGF9WhMXHcnj3ywpfuZSZMnFGGPKsNpVYrnjnJYkL9/OlGXh0++YJRdjjCnjru3elLpVY7nxg9nM27iHHk9N4cu5qZ7GFNLkIiJ9RGS5iKwSkfsKmP6CiMxzHytEJM0d31FEZojIYhFZICJX+izznois9VmuYyi3wRhjwt2khZtJ259Nbp4yZ6eQmpbJ8PELPU0wUaEqWEQigZHAuUAKMFNEJqrqoYZBVb3LZ/7bgU7uy/3Ataq6UkTqA7NFZLKqprnTh6nq56GK3RhjypJnJi/nYG4eAL9vc44ZMrNzeWbycvp3auBJTKE8cukKrFLVNap6EBgHXFzE/AOBsQCqukJVV7rDm4BtQJ0QxmqMMWXWprTMQ8MJMQWPL20SqruaichlQB9VvcF9fQ3QTVVvK2DeJsDvQENVzfWb1hV4H2inqnki8h7QHTgA/ATcp6oHCihzCDAEIDExscu4ceOOeZsyMjKoUqXKMZdTEVhdBcbqKXBWV4VbviX90JFL3Uqwze0wOSYygtbHVT3qcnv16jVbVZOOZtmQNYsBUsC4wjLZAODzAhJLPeBD4DpVzXNHDwe2ADHAaOBe4LEjVqQ62p1OUlKS9uzZ8yg24XDJyckEo5yKwOoqMFZPgbO6Klza3FSGj19IZnYud7fP4bmFUcRFR/Lkpe3p6VGzWCiTSwrQyOd1Q2BTIfMOAG71HSEi1YBvgQdV9ff88aq62R08ICLvAvcELWJjjCmD8s+rPDN5OZBOg+pxDOvd2rPzLRDa5DITaCkizYBUnARylf9MItIaqAHM8BkXA0wAPlDVz/zmr6eqm0VEgP7AotBtgjHGlA39OzWgf6cGJCcnc/ugnl6HE7rkoqo5InIbMBmIBN5R1cUi8hgwS1UnurMOBMbp4Sd/rgDOAGqJyGB33GBVnQeMEZE6OM1u84CbQ7UNxhhjjk4oj1xQ1UnAJL9xD/u9HlHAch8BHxVS5llBDNEYY0wI2D/0jTHGBJ0lF2OMMUFnycUYY0zQWXIxxhgTdJZcjDHGBJ0lF2OMMUEXsr7FwomIbAfWB6Go2sCOIJRTEVhdBcbqKXBWV4EJZj01UdWj6jS4QiSXYBGRWUfbiVtFY3UVGKunwFldBSZc6smaxYwxxgSdJRdjjDFBZ8mlZEZ7HUAZYnUVGKunwFldBSYs6snOuRhjjAk6O3IxxhgTdJZcjDHGBJ0llwCIyDsisk1E7MZkRRCRRiIyVUSWishiEbnD65jClYhUEpE/RWS+W1ePeh1TOBORSBGZKyLfeB1LOBORdSKyUETmicgsT2Oxcy7FE5EzgAycO2Oe6HU84UpE6gH1VHWOiFQFZgP9VXWJx6GFHfdOqvGqmiEi0cBvwB2+t/Q2fxGR/wOSgGqq2tfreMKViKwDklTV8z+b2pFLAFT1F2CX13GEO1XdrKpz3OF0YCng3U28w5g6MtyX0e7DfukVQEQaAhcCb3kdiwmcJRcTEiLSFOgE/OFtJOHLbeqZB2wDflRVq6uCvQj8C8jzOpAyQIEfRGS2iAzxMhBLLiboRKQK8AVwp6ru9TqecKWquaraEWgIdBURa3L1IyJ9gW2qOtvrWMqIHqraGTgfuNVt0veEJRcTVO75gy+AMao63ut4ygJVTQOSgT4ehxKOegD93HMJ44CzROQjb0MKX6q6yX3eBkwAunoViyUXEzTuSeq3gaWq+rzX8YQzEakjItXd4TjgHGCZt1GFH1UdrqoNVbUpMACYoqpXexxWWBKRePdCGkQkHjgP8OwKV0suARCRscAMoLWIpIjI9V7HFKZ6ANfg/Lqc5z4u8DqoMFUPmCoiC4CZOOdc7DJbcywSgd9EZD7wJ/Ctqn7vVTB2KbIxxpigsyMXY4wxQWfJxRhjTNBZcjHGGBN0llyMMcYEnSUXY4wxQWfJxZgSEpEH3J6MF7iXW3crZv4RInKPO/yYiJzjDt8pIpVLI2ZjSluU1wEYU5aISHegL9BZVQ+ISG0gJtDlVfVhn5d3Ah8B+4MbpTHes+RiTMnUA3ao6gEA367N3S5KPgF6uaOuUtVVvguLyHvAN0B99zFVRHaoai+/+dYB7wMX4fGn/xYAAAFnSURBVPSYfLmqLhORmsA7QHOcpDREVRcEeRuNOWbWLGZMyfwANBKRFSIySkTO9Ju+V1W7Aq/i9OZbIFV9GdgE9PJPLD52uJ0Qvgbc4457FJirqicB9wMfHMO2GBMyllyMKQH3HixdgCHAduATERnsM8tYn+fux7i6/I4/ZwNN3eHTgA/dWKYAtUQk4RjXY0zQWbOYMSWkqrk4vRgni8hC4DrgvfzJvrMe46oOuM+5/PVZlYJCOsb1GBN0duRiTAmISGsRaekzqiOw3uf1lT7PM4opLh2oWsIQfgEGubH0xGk6s3vmmLBjRy7GlEwV4BW3u/wcYBVOE1m+WBH5A+eH28BiyhoNfCcim4s47+JvBPCu25vyfpyjJkQkCbhZVW8IeEuMCSHrFdmYIHGv8EryvYLMmIrKmsWMMcYEnR25GGOMCTo7cjHGGBN0llyMMcYEnSUXY4wxQWfJxRhjTNBZcjHGGBN0/w/ko5uk+qK3fAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.plot(range(1, len(val_score) + 1), val_score)\n",
    "plt.scatter(range(1, len(val_score) + 1), val_score)\n",
    "plt.title('Validation Scores for 5-fold cross validation on Linear SVM')\n",
    "plt.xlabel('Split no.')\n",
    "plt.ylabel('Validation Score')\n",
    "plt.xticks(range(1, len(val_score) + 1))\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = linear_svm_classifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  0.885702295597\n"
     ]
    }
   ],
   "source": [
    "print('Train Accuracy: ', np.mean(y_train == y_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training RBF kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_svm_classifier = SVC(kernel = 'rbf')\n",
    "rbf_svm_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.906893893623\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.90      0.91     23916\n",
      "          1       0.90      0.91      0.91     24083\n",
      "\n",
      "avg / total       0.91      0.91      0.91     47999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predicted_y = rbf_svm_classifier.predict(X_test)\n",
    "print('Accuracy: ' + str(np.mean(y_test==predicted_y)))\n",
    "print(classification_report(y_test,predicted_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "val_score = cross_val_score(rbf_svm_classifier,X,y,cv=k_fold,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.plot(range(1, len(val_score) + 1), val_score)\n",
    "plt.scatter(range(1, len(val_score) + 1), val_score)\n",
    "plt.title('Validation Scores for 5-fold cross validation on SVM RBF kernel')\n",
    "plt.xlabel('Split no.')\n",
    "plt.ylabel('Validation Score')\n",
    "plt.xticks(range(1, len(val_score) + 1))\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# This is not included as better results are obtained after tuning C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trying different values of C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for C = 5.00 is : 0.9120\n",
      "Accuracy for C = 6.00 is : 0.9121\n",
      "Accuracy for C = 7.00 is : 0.9122\n",
      "Accuracy for C = 8.00 is : 0.9127\n",
      "Accuracy for C = 9.00 is : 0.9130\n",
      "Accuracy for C = 10.00 is : 0.9130\n",
      "Accuracy for C = 11.00 is : 0.9131\n",
      "Accuracy for C = 12.00 is : 0.9132\n",
      "Accuracy for C = 13.00 is : 0.9131\n"
     ]
    }
   ],
   "source": [
    "c_rbf = list(np.arange(5, 14))\n",
    "accuracy_rbf = []\n",
    "y_predicted = np.zeros((y_test.shape))\n",
    "max_accuracy_rbf = 0.0\n",
    "rbf_svm_classifier = SVC(kernel = 'rbf')\n",
    "\n",
    "for i in c_rbf:\n",
    "    classifier = SVC(kernel = 'rbf', C = i)\n",
    "    classifier.fit(X_train, y_train)\n",
    "    predicted_y = classifier.predict(X_test)\n",
    "    accuracy_rbf.append(np.mean(y_test==predicted_y))\n",
    "    print('Accuracy for C = {:.2f} is : {:.4f}'.format(i, accuracy_rbf[-1]))\n",
    "    if(accuracy_rbf[-1] > max_accuracy_rbf):\n",
    "        y_predicted = predicted_y\n",
    "        max_accuracy_rbf = accuracy_rbf[-1]\n",
    "        rbf_svm_classifier = classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(c_rbf, accuracy_rbf)\n",
    "plt.scatter(c_rbf, accuracy_rbf)\n",
    "plt.title('Accuracy vs C')\n",
    "plt.xlabel('C')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.92      0.91      0.91     23916\n",
      "          1       0.91      0.92      0.91     24083\n",
      "\n",
      "avg / total       0.91      0.91      0.91     47999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.85321875  0.8542767   0.91096597  0.88293384  0.81402544]\n"
     ]
    }
   ],
   "source": [
    "val_score = cross_val_score(rbf_svm_classifier,X,y,cv=k_fold,scoring='accuracy',n_jobs=-1)\n",
    "print(val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "plt.plot(range(1, len(val_score) + 1), val_score)\n",
    "plt.scatter(range(1, len(val_score) + 1), val_score)\n",
    "plt.title('Validation Scores for 5-fold cross validation on SVM with RBF Kernel')\n",
    "plt.xlabel('Split no.')\n",
    "plt.ylabel('Validation Score')\n",
    "plt.xticks(range(1, len(val_score) + 1))\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[21658  2258]\n",
      " [ 1907 22176]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = rbf_svm_classifier.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  0.928024857809\n"
     ]
    }
   ],
   "source": [
    "print('Train Accuracy: ', np.mean(y_train == y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rbf_svm_open_close.joblib']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump, load\n",
    "dump(rbf_svm_classifier, 'rbf_svm_open_close.joblib') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training poly kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='poly',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly_svm_classifier = SVC(kernel = 'poly')\n",
    "poly_svm_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.880997520782\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.91      0.88     23916\n",
      "          1       0.91      0.85      0.88     24083\n",
      "\n",
      "avg / total       0.88      0.88      0.88     47999\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predicted_y = poly_svm_classifier.predict(X_test)\n",
    "print('Accuracy: ' + str(np.mean(y_test==predicted_y)))\n",
    "print(classification_report(y_test,predicted_y))\n",
    "# Accuracy: 0.878101627117\n",
    "#              precision    recall  f1-score   support\n",
    "\n",
    "#           0       0.86      0.91      0.88     24085\n",
    "#           1       0.90      0.85      0.87     23914\n",
    "\n",
    "# avg / total       0.88      0.88      0.88     47999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.78315625  0.77499297  0.88437139  0.74380449  0.63542611]\n"
     ]
    }
   ],
   "source": [
    "val_score = cross_val_score(poly_svm_classifier,X,y,cv=k_fold,scoring='accuracy',n_jobs=-1)\n",
    "print(val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy:  0.884014750395\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = poly_svm_classifier.predict(X_train)\n",
    "print('Train Accuracy: ', np.mean(y_train == y_train_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using sigmoid kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sigmoid_svm_classifier = SVC(kernel = 'sigmoid')\n",
    "# sigmoid_svm_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# predicted_y = sigmoid_svm_classifier.predict(X_test)\n",
    "# print('Accuracy: ' + str(np.mean(y_test==predicted_y)))\n",
    "# print(classification_report(y_test,predicted_y))"
   ]
  }
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