SVM_TFIDF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split Data Into Train and Test Data\n",
    "def splitIntoTestAndTrain(dataFrame,splitBy):\n",
    "    rand = list(range(len(dataFrame)))\n",
    "    div = int(len(dataFrame) * splitBy)\n",
    "    rd.shuffle(rand)\n",
    "    train_df_index = rand[:div]\n",
    "    test_df_index = rand[div:]\n",
    "    train_df = dataFrame.iloc[train_df_index, :]\n",
    "    test_df = dataFrame.iloc[test_df_index, :]\n",
    "    return train_df,test_df;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: 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",
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/sklearn/grid_search.py:43: 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. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#Best SVM Classifier\n",
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import svm, grid_search\n",
    "def svc_param_selection(X, y, nfolds):\n",
    "    param_grid = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [10.**i for i in np.arange(-2,2)]},\n",
    "                    {'kernel': ['linear'], 'C': [10.**i for i in np.arange(-2,2)]}]\n",
    "    grid_search = GridSearchCV(svm.SVC(probability=True), param_grid, cv=nfolds)\n",
    "    grid_search.fit(X, y)\n",
    "    return grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reportCard(y_true, y_pred, y_proba,name):\n",
    "    from sklearn import metrics\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "    print('\\nReport for:',name)\n",
    "    target_names = ['low', 'high']\n",
    "    print(metrics.classification_report(y_true, y_pred, target_names=target_names))\n",
    "    print(\"Confusion Matrix\\n\",metrics.confusion_matrix(y_true,y_pred))\n",
    "    print(\"F1 Score:\",metrics.f1_score(y_true,y_pred))\n",
    "    print(\"Accuracy:\",metrics.accuracy_score(y_true,y_pred))\n",
    "    print(\"Log Loss:\",metrics.log_loss(y_true,y_pred))\n",
    "    print(\"AUC Score:\",metrics.roc_auc_score(y_true,y_proba[:,1]))\n",
    "    fpr,tpr,thresholds = metrics.roc_curve(y_true,y_proba[:,1])\n",
    "    plt.figure(1)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.plot(fpr, tpr)\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.title('ROC curve for {}'.format(name))\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svm_run(filename):\n",
    "    print(\"\\nRunning for\",filename)\n",
    "    df = pd.read_csv(filename+'.csv', sep=\",\")\n",
    "    if df.shape[0]>5000:\n",
    "        df = df[:5000]\n",
    "    print(\"dataset size is:\",df.shape)\n",
    "    print(\"columns:\",df.columns)\n",
    "    print(\"\\nSVM\")\n",
    "    #vectorization\n",
    "    df = df.ix[:,['Clean_Text']+['ScoreLabel']]\n",
    "    df = df.dropna()\n",
    "    outc = df.ix[:,'ScoreLabel']\n",
    "    row = df.shape[0]\n",
    "    istest = np.repeat(np.repeat([False,True],[9,1]), 1+row/10)[:row]\n",
    "    np.random.shuffle(istest)\n",
    "    vect = TfidfVectorizer()\n",
    "    fitted = vect.fit_transform(df.ix[~istest,'Clean_Text'])\n",
    "    test = vect.transform(df.ix[istest,'Clean_Text'])\n",
    "    best_svm = svc_param_selection(fitted, outc[~istest],5)#check validation\n",
    "    grid_svm_pred = best_svm.predict(test)\n",
    "    grid_svm_pred_prob = best_svm.predict_proba(test)\n",
    "    print(\"*****BOF: {}*****\".format(filename))\n",
    "    reportCard(outc[istest], grid_svm_pred, grid_svm_pred_prob,'GridSearched SVM With TFIDF')\n",
    "    print(\"*****EOF: {}*****\".format(filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running for iot_posts_with_readibility_measures_score_adj\n",
      "dataset size is: (1140, 31)\n",
      "columns: Index(['AcceptedAnswerId', 'AnswerCount', 'Body', 'ClosedDate', 'CommentCount',\n",
      "       'CommunityOwnedDate', 'CreationDate', 'FavoriteCount', 'Id',\n",
      "       'LastActivityDate', 'LastEditDate', 'LastEditorUserId',\n",
      "       'OwnerDisplayName', 'OwnerUserId', 'ParentId', 'PostTypeId', 'Score',\n",
      "       'Tags', 'Title', 'ViewCount', 'Flesch_Reading_Ease_Value',\n",
      "       'Coleman_Liau_Index_Value', 'Dale_Chall_Readability_Score',\n",
      "       'Code_Count', 'Latex_Count', 'Clean_Text', 'Text_Length', 'Punc_Rate',\n",
      "       'Polarity', 'Subjectivity', 'ScoreLabel'],\n",
      "      dtype='object')\n",
      "\n",
      "SVM\n",
      "*****BOF: iot_posts_with_readibility_measures_score_adj*****\n",
      "\n",
      "Report for: GridSearched SVM With TFIDF\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        low       1.00      0.66      0.79        29\n",
      "       high       0.88      1.00      0.94        74\n",
      "\n",
      "avg / total       0.91      0.90      0.90       103\n",
      "\n",
      "Confusion Matrix\n",
      " [[19 10]\n",
      " [ 0 74]]\n",
      "F1 Score: 0.936708860759\n",
      "Accuracy: 0.902912621359\n",
      "Log Loss: 3.35335689246\n",
      "AUC Score: 0.852749301025\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
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18fHxJCQk0K5dO0aPHk1MTIzXYRlzVjJNCqoa7/69InThGBMeEhMTady4MaVL\nl2bKlCncdtttNoCdiQiBjH10i4iUcJ/3F5EPRaR+8EMzJu/Zu3cvAD6fjxdeeIF169Zx++23W0Iw\nESOQU1IHqepBEbkMaAO8h915zUSZw4cP07dvX2rUqMHmzZsREfr06WMD2JmIE0hSSD3b6EbgDVWd\nDhQKXkjG5C1ff/01l1xyCS+//DL33nsv5557rtchGRM0gYySmiQio4DWQGMRKUhgycSYsHbq1Cm6\nd+/O+PHjqVmzJvPnz6dFCzu/wkS2QA7utwPzgTaquh9nLKT+Wb/EmPCXP39+UlJS6NevHz/88IMl\nBBMVsk0KqnoISARaikgPoLSqzg56ZMZ44Ndff+Wuu+5i9erVAIwfP57nn3+eIkWKeByZMaERyNlH\nvYCPgKru40MReTDYgRkTSqrKu+++i8/nY9q0aaxcuRLAzioyUSfQO681cWsMiMhQYBEwOpiBGRMq\n27dvp0ePHsyePZu4uDjGjx/PxRdf7HVYxngikD4FAU74TSe784yJCCNGjODbb79l5MiRLFiwwBKC\niWqB1BTeAb4XkWk4yeBmYFJQozImyDZu3MiRI0do2LAhgwcP5qGHHqJGjRpeh2WM5wLpaB4GdAeO\nAIeAHqo6PNiBGRMMJ0+e5Pnnn6d+/fr06tULgBIlSlhCMMYV6PUGx4Djfn+NCTurVq2iadOmDBgw\ngBtuuIGpU6d6HZIxeU4gZx89AbwPVAAqA5NFZECwAzMmN82dO5dLL72UXbt2MXXqVKZNm0aFChW8\nDsuYPCeQPoWOQENVPQIgIkOAlTg33zEmTzt8+DDFihWjefPmPPbYY/zrX/+iTJkyXodlTJ4VSPNR\nEqcnjwLuvGyJSCsR2Sgim0Uk06ugReRSETkpIh0CWa8x2Tl06BAPP/wwl1xyCQcPHqRw4cL85z//\nsYRgTDYCqSnsAxJF5Eucm+tcBywTkZcAVLVvRi8Skfw4N+i5FtjpvmaGqq7LoNx/gTk53gtj/MyZ\nM4du3bqxfft2evbsaRegGXMGAkkKM91HqiUBrrsJsFlVtwCIyAdAO2BdunIPAdOASwNcrzEZOnz4\nML169WLixInUrl2bb7/9lubNm3sdljFhJdukoKrjc7juSsAOv+mdQFP/AiJSCWgPXEUWSUFEuuFc\nWU3VqlVzGI6JdIUKFWLDhg0MGDCAgQMHUrhwYa9DMibseD0E9gign6qmZFVIVceqaqyqxpYvXz5E\noZlw8MvjC99eAAAUUklEQVQvv9ClSxd+//13ChQowIIFCxg6dKglBGNyKJhJYRdQxW+6sjvPXyzw\ngYhsAzoAo0Xk5iDGZCKEqjJx4kR8Ph/vvvsuS5Y4rZoFCgTSImqMyUzA/0EiUkhVz+TCtWVATRGp\ngZMM7gTu8i+gqmmXkYrIROBzVf30DLbhqcnfb2f6qvR5LmPrkg7gq1AyyBFFh23bttG9e3fmzJlD\n8+bNGTduHLVr1/Y6LGMiQiAXrzURkTXAj+50fRF5NbvXqepJoBfwJbAe+FBVE0Wkh3tfhrA3fdUu\n1iUdCKisr0JJ2jWoFOSIosNDDz3EokWLGDVqFPPnz7eEYEwuElXNuoDIEuAO4FNVbejOW6uqdUMQ\n39/ExsZqQkKCF5v+mzveWAzAlO5xHkcS+TZs2EDJkiWpWLEi27ZtQ0SoVq2a12EZEzZEZLmqxmZX\nLpDmo3yq+nO6c71P5TiyMBBos5A1CQVfcnIyL7zwAoMHD+b222/nnXfeoXr16l6HZUzECqSjeYeI\nNAFURPKLSB9gU5Dj8lSgzULWJBRcK1asoEmTJjzxxBO0bduW4cNtcF5jgi2QmsIDwEicW3H+Csx1\n50U0X4WS1izkoffee49OnTpRvnx5pk2bxi233OJ1SMZEhUAuXvsN58whY4IuOTmZmJgYWrZsSbdu\n3RgyZAilS5f2Oixjoka2SUFE3sQZ8+g0qtotKBGZqHTw4EH69+/Phg0bmDt3LpUqVWL0aLsNuDGh\nFkifwlzga/exEDgXu9GOyUWzZ8+mTp06vP7669SrV4/k5GSvQzImagXSfDTFf1pE3gG+C1pEJmrs\n37+f3r17884773DxxRezcOFC4uKsH8cYL+VkmIsawHm5HYiJPikpKcybN4+nnnqKlStXWkIwJg8I\npE9hP3/1KeTDub9CpjfMMSYrSUlJjBgxgiFDhlC2bFk2bdpE0aJFvQ7LGOPKsqYgzhVr9YHy7qO0\nqv5DVT8MRXAmcqgqEyZMwOfzMXLkSFasWAFgCcGYPCbLpKDOGBizVPWU+8h6TAxjMrB161auu+46\n4uPjqVevHj/88ANNmjTxOixjTAYCuXhtlYg0VNWVQY/GRJyUlBRuvPFGduzYweuvv063bt3Il8/r\n23gYYzKTaVIQkQLuSKcNce6v/BNwGBCcSkSjEMVowtCGDRuoXr06hQsX5q233qJChQpUqVIl+xca\nYzyV1U+2pe7ftkBtoA1wG87NcG4LclwmTJ04cYJnn32W+vXrM2zYMACaNGliCcGYMJFV85EAqOpP\nIYrFhLmEhATi4+NZvXo1d955Jw88EPFDZBkTcbJKCuVFpG9mC1X1pSDEY8LUyJEjeeSRRzj//POZ\nPn06bdu29TokY0wOZJUU8gPFcWsMxmREVRERLr30UuLj4xk2bBilSpXyOixjTA5llRSSVPWZkEVi\nwsqBAwfo168fMTExjBw5kri4OLsi2ZgIkFVHs9UQTIZmzpxJnTp1GDt2LAULFsQuXzEmcmSVFK4J\nWRQmLOzdu5d77rmHG2+8kXPOOYdFixYxfPhw0t2q1RgTxjJNCqq6L5SBmLxv9+7dfPLJJzz99NOs\nWLGCpk2beh2SMSaXBXJFs4liu3btYtq0aTz88MPUq1eP7du3U7ZsWa/DMsYEiY03YDKkqrz55pv4\nfD769+/P9u3bASwhGBPhLCmYv/npp5+45ppr6NatG40bN2bNmjVUrVrV67CMMSFgzUfmNEeOHKFZ\ns2acOHGCsWPH0qVLF+tINiaKWFIwAGzbto1q1apRtGhRxo0bR+PGjalcubLXYRljQsyaj6LciRMn\nGDx4MLVq1WLq1KkAtGvXzhKCMVHKagpRbOnSpcTHx7N27VruuusurrrqKq9DMsZ4zGoKUWrw4MHE\nxcWxf/9+PvvsM9577z3KlSvndVjGGI9ZUohSlStXpmvXriQmJnLjjTd6HY4xJo8IalIQkVYislFE\nNotI/wyW3y0iq0VkjYgsEpH6wYwnmv35559069aNsWPHAhAfH8+YMWM455xzPI7MGJOXBK1PQUTy\nA6OAa4GdOLf0nKGq6/yKbQWuVNX9ItIaGAvY2Am57LPPPqNHjx788ssv1oFsjMlSMGsKTYDNqrpF\nVU8AHwDt/Auo6iJV3e9OLgHsiJWLfvvtN/75z3/Stm1bypYty/fff8/AgQO9DssYk4cFMylUAnb4\nTe9052UmHpid0QIR6SYiCSKSsGfPnlwMMbItWLCAadOm8cwzz5CQkEBsbKzXIRlj8rg8cUqqiFyF\nkxSaZ7RcVcfiNC0RGxtrg/dnYceOHSQkJNC+fXtuueUWfvzxR6pVq+Z1WMaYMBHMmsIuoIrfdGV3\n3mlEpB4wDminqr8HMZ6IlpKSwhtvvEGdOnXo2rUrhw8fRkQsIRhjzkgwk8IyoKaI1BCRgsCdwAz/\nAiJSFfgYuFdVNwUxloj2448/cvXVV9OjRw+aNGnC0qVLKVasmNdhGWPCUNCaj1T1pIj0Ar4E8gMT\nVDVRRHq4y8cAA4GywGh30LWTqmoN32dgx44d1K9fn4IFCzJ+/Hg6d+5sA9gZY3IsqH0KqjoLmJVu\n3hi/512ALsGMIVLt3buXcuXKUaVKFV544QXat29PxYoVvQ7LGBPm7IrmMHP8+HEGDhxI1apVWbly\nJQA9e/a0hGCMyRV54uwjE5jFixcTHx/P+vXr6dixo934xhiT66ymEAZUlb59+3L55Zdz6NAhZs2a\nxaRJk+zWmMaYXGdJIQyICKrKgw8+SGJiIq1bt/Y6JGNMhLLmozzqjz/+4LHHHuO+++6jefPmvPTS\nS3ZWkTEm6KymkAd9+umn+Hw+Jk6cyIoVKwAsIRhjQiJqagqTv9/O9FV/u6A6Q+uSDuCrUDLIEf3d\nr7/+ykMPPcRHH31E/fr1+eyzz2jcuHHI4zDGRK+oqSlMX7WLdUkHAirrq1CSdg2yGrsvON58802m\nT5/OkCFDWLZsmSUEY0zIRU1NAZyD/ZTucV6HcZrt27eze/dumjVrxr/+9S9uu+02ateu7XVYxpgo\nFTU1hbwmJSWFUaNGUadOHe6//35SUlIoVKiQJQRjjKcsKXhg48aNXHnllfTq1Yu4uDhmzZpFvnz2\nURhjvBdVzUd5wbJly7jiiisoWrQoEydOpGPHjnZmkTEmz7CfpyFy+PBhABo1akTfvn1Zt24dnTp1\nsoRgjMlTLCkE2bFjx3jiiSeoVasWe/fuJX/+/AwdOpTzzz/f69CMMeZvrPkoiBYtWkR8fDwbNmyg\nU6dO5M+f3+uQjDEmS1ZTCIITJ07w8MMP07x5c44cOcIXX3zBxIkTKV26tNehGWNMliwpBEFMTAwb\nNmygZ8+erF27luuvv97rkIwxJiCWFHLJvn376NGjBzt37kREmDVrFq+++iolSpTwOjRjjAmYJYVc\nMG3aNHw+H+PGjWP+/PkAFChg3TXGmPBjSeEsJCUlceutt9KhQwcqVqxIQkICd999t9dhGWNMjllS\nOAv9+/dn5syZPP/88yxdupQGDRp4HZIxxpwVa+M4Q9u2bUNVqVGjBs8//zyPP/64jVdkjIkYVlMI\nUEpKCq+++ip169alV69eAFS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b086f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****EOF: iot_posts_with_readibility_measures_score_adj*****\n"
     ]
    }
   ],
   "source": [
    "svm_run(\"iot_posts_with_readibility_measures_score_adj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running for ai_posts_with_readibility_measures_score_adj\n",
      "dataset size is: (1735, 31)\n",
      "columns: Index(['AcceptedAnswerId', 'AnswerCount', 'Body', 'ClosedDate', 'CommentCount',\n",
      "       'CreationDate', 'FavoriteCount', 'Id', 'LastActivityDate',\n",
      "       'LastEditDate', 'LastEditorDisplayName', 'LastEditorUserId',\n",
      "       'OwnerDisplayName', 'OwnerUserId', 'ParentId', 'PostTypeId', 'Score',\n",
      "       'Tags', 'Title', 'ViewCount', 'Flesch_Reading_Ease_Value',\n",
      "       'Coleman_Liau_Index_Value', 'Dale_Chall_Readability_Score',\n",
      "       'Code_Count', 'Latex_Count', 'Clean_Text', 'Text_Length', 'Punc_Rate',\n",
      "       'Polarity', 'Subjectivity', 'ScoreLabel'],\n",
      "      dtype='object')\n",
      "\n",
      "SVM\n",
      "*****BOF: ai_posts_with_readibility_measures_score_adj*****\n",
      "\n",
      "Report for: GridSearched SVM With TFIDF\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        low       0.64      1.00      0.78       101\n",
      "       high       0.00      0.00      0.00        58\n",
      "\n",
      "avg / total       0.40      0.64      0.49       159\n",
      "\n",
      "Confusion Matrix\n",
      " [[101   0]\n",
      " [ 58   0]]\n",
      "F1 Score: 0.0\n",
      "Accuracy: 0.635220125786\n",
      "Log Loss: 12.5990505088\n",
      "AUC Score: 0.625981563674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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9GJMNwYaeOBuR2o6gqsycOZO+ffty8OBBmjRp4ndIxnjipUtq3cAX7sVrV4Qm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\nE+puqEuXLuWqq66iWLFiTJ8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf16320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****EOF: ai_posts_with_readibility_measures_score_adj*****\n"
     ]
    }
   ],
   "source": [
    "svm_run(\"ai_posts_with_readibility_measures_score_adj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running for stats_posts_with_readibility_measures_score_adj\n",
      "dataset size is: (5000, 32)\n",
      "columns: Index(['AcceptedAnswerId', 'AnswerCount', 'Body', 'ClosedDate', 'CommentCount',\n",
      "       'CommunityOwnedDate', 'CreationDate', 'FavoriteCount', 'Id',\n",
      "       'LastActivityDate', 'LastEditDate', 'LastEditorDisplayName',\n",
      "       'LastEditorUserId', 'OwnerDisplayName', 'OwnerUserId', 'ParentId',\n",
      "       'PostTypeId', 'Score', 'Tags', 'Title', 'ViewCount',\n",
      "       'Flesch_Reading_Ease_Value', 'Coleman_Liau_Index_Value',\n",
      "       'Dale_Chall_Readability_Score', 'Code_Count', 'Latex_Count',\n",
      "       'Clean_Text', 'Text_Length', 'Punc_Rate', 'Polarity', 'Subjectivity',\n",
      "       'ScoreLabel'],\n",
      "      dtype='object')\n",
      "\n",
      "SVM\n",
      "*****BOF: stats_posts_with_readibility_measures_score_adj*****\n",
      "\n",
      "Report for: GridSearched SVM With TFIDF\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        low       0.00      0.00      0.00       126\n",
      "       high       0.75      1.00      0.85       369\n",
      "\n",
      "avg / total       0.56      0.75      0.64       495\n",
      "\n",
      "Confusion Matrix\n",
      " [[  0 126]\n",
      " [  0 369]]\n",
      "F1 Score: 0.854166666667\n",
      "Accuracy: 0.745454545455\n",
      "Log Loss: 8.79189207078\n",
      "AUC Score: 0.531014754592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/Users/amanmahato/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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y5ghhk4KIfK2q/xSR7YB3ELrAzGvlYx6dMfmc36kxA2rUqEH//v0ZMmQIJUuW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\nsllSMMYYk82SgjHGmGz/DzG/fUgMpgYvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11baace10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****EOF: stats_posts_with_readibility_measures_score_adj*****\n"
     ]
    }
   ],
   "source": [
    "svm_run(\"stats_posts_with_readibility_measures_score_adj\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}