Naive Bayes Classifier for Text Classification.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#importing required packages\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load train files as .csv files\n",
    "train_label = pd.read_csv('train_label.csv', header = None)\n",
    "train_data = pd.read_csv('train_data.csv', header = None)\n",
    "\n",
    "#load vocabulary and map files\n",
    "voc = pd.read_csv('vocabulary.txt', header= None)\n",
    "map_data = pd.read_csv('map.csv')\n",
    "\n",
    "#cleaning vocabulary dataframe\n",
    "voc['wordID'] = np.arange(1, len(voc) + 1)\n",
    "voc.index.names = ['wordID']\n",
    "voc.index = voc.index + 1\n",
    "voc['wordID'] = voc.index\n",
    "\n",
    "#cleaning training files dataframes\n",
    "train_data.columns = ['docID', 'wordID', 'count']\n",
    "train_label.columns = ['categoryID_true']\n",
    "map_data.columns = ['categoryID', 'categoryName']\n",
    "train_label['docID'] = train_label.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#obtaining the range for documents ID for each class\n",
    "y = []\n",
    "for i in range(1,21):\n",
    "       y.append(train_label[train_label['categoryID_true'] == i].iloc[0,1])\n",
    "        \n",
    "y[0] = 0\n",
    "y.insert(20, len(train_label))\n",
    "y = pd.Series(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#assigning a new columns for train data which indicates corresponding categories\n",
    "x = []\n",
    "l = np.array(range(0,20))\n",
    "train_data['cat'] = pd.cut(train_data['docID'], bins = y, labels = l)\n",
    "\n",
    "#total number of words in all documents for each category\n",
    "n = train_data.groupby(['cat']).sum()['count']\n",
    "\n",
    "#wordsID occuring in each category for all documents\n",
    "train_data4 = train_data.iloc[:,1:].groupby(['cat','wordID']).sum().dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train_data4.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#appending words to train data which were previously absent\n",
    "#assigning zero count to previously absent words \n",
    "pp = []\n",
    "df = pd.DataFrame([])\n",
    "for i in range (0,20):\n",
    "    t = train[train['cat'] == i]\n",
    "    p = pd.merge(t,voc, how = 'outer')\n",
    "    p['count'].fillna(0, inplace=True)\n",
    "    p['cat'].fillna(i,inplace =True)\n",
    "    p = p.sort_values(by='wordID')\n",
    "    p = p.reset_index(drop=True)\n",
    "    p.drop(0, axis = 1, inplace =True)\n",
    "    df = pd.DataFrame(p)\n",
    "    pp.append(df)\n",
    "p.sort_values(by = 'wordID')\n",
    "master_df = pd.concat(pp)\n",
    "master_df = master_df.groupby(['cat','wordID']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># docs in cat i</th>\n",
       "      <th>prior</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>480</td>\n",
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       "      <td>-2.965060</td>\n",
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       "      <td>-2.963340</td>\n",
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       "      <th>7</th>\n",
       "      <td>592</td>\n",
       "      <td>-2.946304</td>\n",
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       "      <th>8</th>\n",
       "      <td>596</td>\n",
       "      <td>-2.939570</td>\n",
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       "      <th>11</th>\n",
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       "      <td>-2.942932</td>\n",
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       "      <th>12</th>\n",
       "      <td>591</td>\n",
       "      <td>-2.947995</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>594</td>\n",
       "      <td>-2.942932</td>\n",
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       "      <td>564</td>\n",
       "      <td>-2.994757</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>464</td>\n",
       "      <td>-3.189926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>376</td>\n",
       "      <td>-3.400222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "    # docs in cat i     prior\n",
       "0               480 -3.156025\n",
       "1               581 -2.965060\n",
       "2               572 -2.980672\n",
       "3               587 -2.954786\n",
       "4               575 -2.975441\n",
       "5               592 -2.946304\n",
       "6               582 -2.963340\n",
       "7               592 -2.946304\n",
       "8               596 -2.939570\n",
       "9               594 -2.942932\n",
       "10              598 -2.936220\n",
       "11              594 -2.942932\n",
       "12              591 -2.947995\n",
       "13              594 -2.942932\n",
       "14              593 -2.944616\n",
       "15              599 -2.934549\n",
       "16              545 -3.029025\n",
       "17              564 -2.994757\n",
       "18              464 -3.189926\n",
       "19              376 -3.400222"
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     "execution_count": 17,
     "metadata": {},
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    }
   ],
   "source": [
    "#total number of documents in each categories and calculating prior probabilities\n",
    "totdoc = len(train_label)\n",
    "doc_j = []\n",
    "for item in range(1,21):\n",
    "     x = doc_j.append(len(train_label[train_label['categoryID_true'] == item]))\n",
    "        \n",
    "doc_j = pd.DataFrame(doc_j)\n",
    "doc_j['prior'] = np.log(doc_j/totdoc)\n",
    "doc_j['# docs in cat i'] = doc_j[0]\n",
    "doc_j.drop(0, axis=1, inplace=True)\n",
    "doc_j = doc_j[['# docs in cat i', 'prior']]\n",
    "train_label['docID'] = train_label['docID'] + 1\n",
    "train_label =train_label.set_index('docID')\n",
    "\n",
    "doc_j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "td = master_df.groupby(level = [0]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/farshad/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in log\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "#calculating Bayesian estimator and Maximum likelihood estimator \n",
    "z_BE = []\n",
    "z_MLE = []\n",
    "for i in range (0,20):\n",
    "    BE = (1+master_df[master_df.index.get_level_values('cat') == i])/(td[td.index.get_level_values('cat') == i] + len(voc))\n",
    "    MLE = master_df[master_df.index.get_level_values('cat') == i]/td[td.index.get_level_values('cat') == i]\n",
    "    z_BE.append(BE)\n",
    "    z_MLE.append(MLE)\n",
    "    master_dfBE = np.log(pd.concat(z_BE))\n",
    "    master_dfMLE = np.log(pd.concat(z_MLE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_dfBE = master_dfBE.reset_index()\n",
    "master_dfBE.columns = [['cat', 'wordID', 'BE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "master_dfMLE = master_dfMLE.reset_index()\n",
    "master_dfMLE.columns = [['cat', 'wordID', 'MLE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#training the model on training data\n",
    "z = pd.DataFrame()\n",
    "for i in range (0,20):\n",
    "    m = master_dfBE[master_dfBE['cat'] == i]\n",
    "    common = pd.merge(m, train_data, on=['wordID']).dropna()\n",
    "    common = common.groupby(['docID', 'wordID']).sum()\n",
    "    c = pd.DataFrame(common.groupby(level = 'docID')['BE'].sum())\n",
    "    c = c.reset_index(['docID'])\n",
    "    c = c.drop_duplicates(subset= ['docID'], keep = 'first')\n",
    "    c = c.reset_index(drop = True)\n",
    "    c[str(i + 1)] = (c['BE'] + doc_j.iloc[i,1])\n",
    "    c.drop('BE', axis = 1, inplace = True)\n",
    "    z = pd.DataFrame(pd.concat([c,z], axis =1))\n",
    "z = z.T.drop_duplicates().T\n",
    "z = z.set_index('docID')\n",
    "final_df = pd.DataFrame(z.apply(lambda x: x.argmax(), axis=1))\n",
    "final_df.columns = ['categoryID_predict']\n",
    "final_df = final_df.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "#overall accuracy for performance evaluation on training data\n",
    "f = pd.concat([final_df,train_label], axis = 1)\n",
    "(len(train_label)- len(f[f['categoryID_true'] != f['categoryID_predict']]))/len(train_label)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#constructing the required matrix for confusion matrix (1)\n",
    "true = []\n",
    "false = []\n",
    "for i in range (1,21):\n",
    "    x = len(f[(f['categoryID_predict'] == f['categoryID_true']) & (f['categoryID_true'] == i)])\n",
    "    true.append(x)\n",
    "\n",
    "ConfusionMatrix = pd.DataFrame(np.diag(true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#constructing the required matrix for confusion matrix (2)\n",
    "for i in range(1,21):\n",
    "    for j in range (1,21):\n",
    "        z = f[(f['categoryID_predict'] != f['categoryID_true']) & (f['categoryID_true'] == i) & (f['categoryID_predict'] == j)]\n",
    "        if not z.empty: \n",
    "            false.append(z)\n",
    "            ConfusionMatrix.iloc[i-1, j-1] = len(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ConfusionMatrix.columns = ConfusionMatrix.columns + 1\n",
    "ConfusionMatrix.index = ConfusionMatrix.index + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>473</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>570</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>579</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>579</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>585</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>583</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>551</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>580</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>582</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>590</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>539</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>548</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     1    2    3    4    5    6    7    8    9    10   11   12   13   14   15  \\\n",
       "1   471    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "2     0  537    6   15    1   11    2    1    1    0    0    2    1    0    3   \n",
       "3     1   10  510   23    0   18    2    0    0    0    0    3    1    1    0   \n",
       "4     0   12    4  547    3    5    6    0    0    0    0    2    3    0    1   \n",
       "5     1    4    2    5  549    2    0    0    2    0    0    2    1    3    1   \n",
       "6     1   12    8    4    2  557    0    0    1    1    0    1    0    0    2   \n",
       "7     1    4    0   30    6    1  473   20    1    3    3   10   13    3    1   \n",
       "8     1    0    0    2    1    2    3  570    1    1    0    1    1    1    0   \n",
       "9     1    1    0    1    1    0    4    2  579    0    0    0    0    2    0   \n",
       "10    0    3    0    1    0    1    1    2    0  579    4    0    1    1    0   \n",
       "11    1    0    1    2    0    0    0    2    0    0  585    1    1    0    0   \n",
       "12    0    2    0    0    0    0    0    0    0    0    0  583    0    1    0   \n",
       "13    0    4    1   14    3    0    3    1    0    0    1    4  551    2    2   \n",
       "14    0    1    0    0    0    1    0    1    1    0    0    1    2  580    1   \n",
       "15    1    1    0    1    0    2    0    1    0    0    0    1    1    1  582   \n",
       "16    0    2    0    1    0    0    0    0    0    0    0    0    0    1    0   \n",
       "17    0    0    0    0    0    0    0    1    0    0    0    0    1    0    0   \n",
       "18    0    1    0    0    0    0    0    1    0    0    0    1    0    2    0   \n",
       "19    2    2    0    0    0    1    0    0    0    0    0    3    0    1    0   \n",
       "20   19    1    0    0    0    1    0    0    1    0    0    0    0    0    1   \n",
       "\n",
       "     16   17   18   19   20  \n",
       "1     5    0    1    1    2  \n",
       "2     1    0    0    0    0  \n",
       "3     2    0    0    1    0  \n",
       "4     1    1    1    1    0  \n",
       "5     1    0    0    2    0  \n",
       "6     1    1    0    1    0  \n",
       "7     3    5    1    4    0  \n",
       "8     1    2    0    4    1  \n",
       "9     2    2    0    1    0  \n",
       "10    0    1    0    0    0  \n",
       "11    1    0    1    3    0  \n",
       "12    0    2    0    6    0  \n",
       "13    1    2    0    2    0  \n",
       "14    4    2    0    0    0  \n",
       "15    1    0    0    1    0  \n",
       "16  590    2    2    1    0  \n",
       "17    2  539    0    2    0  \n",
       "18    7    0  548    4    0  \n",
       "19    0    4    2  449    0  \n",
       "20   28   13    4    2  306  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfusionMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.981250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.924269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.891608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.931857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.954783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.940878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.812715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.962838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.971477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.974747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.978261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.981481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.932318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.976431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.981450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.984975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.988991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.971631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.967672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.813830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Accuracy\n",
       "1   0.981250\n",
       "2   0.924269\n",
       "3   0.891608\n",
       "4   0.931857\n",
       "5   0.954783\n",
       "6   0.940878\n",
       "7   0.812715\n",
       "8   0.962838\n",
       "9   0.971477\n",
       "10  0.974747\n",
       "11  0.978261\n",
       "12  0.981481\n",
       "13  0.932318\n",
       "14  0.976431\n",
       "15  0.981450\n",
       "16  0.984975\n",
       "17  0.988991\n",
       "18  0.971631\n",
       "19  0.967672\n",
       "20  0.813830"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#class accuracies for trained model\n",
    "ConfusionMatrix2 = pd.DataFrame(ConfusionMatrix.max(axis = 1)/ConfusionMatrix.sum(axis =1))\n",
    "ConfusionMatrix2.columns = ['Accuracy']\n",
    "ConfusionMatrix2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#loading and cleaning test data\n",
    "test_label = pd.read_csv('test_label.csv', header = None)\n",
    "test_data = pd.read_csv('test_data.csv', header = None)\n",
    "test_label['docID'] = test_label.index + 1\n",
    "test_label.columns = [['categoryID_true', 'docID']]\n",
    "test_data.columns = ['docID', 'wordID', 'count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#predicting the categories for each document using BE\n",
    "z2 = pd.DataFrame()\n",
    "for i in range (0,20):\n",
    "    m2 = master_dfBE[master_dfBE['cat'] == i]\n",
    "    common2 = pd.merge(m2, test_data, on=['wordID'])\n",
    "    c2 = common2.groupby(['docID'])['BE'].sum()\n",
    "    c2 = c2.reset_index(['docID'])\n",
    "    c3 = pd.DataFrame((c2['BE'] + doc_j.iloc[i,1]))\n",
    "    z2 = pd.DataFrame(pd.concat([z2,c3], axis =1))\n",
    "z2.columns = np.array(range(1,21))\n",
    "final_df2 = pd.DataFrame(z2.apply(lambda x: x.argmax(), axis=1))\n",
    "final_df2.columns = ['categoryID_predict']\n",
    "final_df2 = final_df2.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.73417721518987"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluate the performance on test data\n",
    "f2 = pd.concat([final_df2,test_label], axis = 1)\n",
    "(len(test_label)- len(f2[f2['categoryID_true'] != f2['categoryID_predict']]))/len(test_label)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#confusion matrix construction\n",
    "true = []\n",
    "false = []\n",
    "for i in range (1,21):\n",
    "    x = len(f2[(f2['categoryID_predict'] == f2['categoryID_true']) & (f2['categoryID_true'] == i)])\n",
    "    true.append(x)\n",
    "\n",
    "ConfusionMatrix_test = pd.DataFrame(np.diag(true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#confusion matrix construction\n",
    "for i in range(1,21):\n",
    "    for j in range (1,21):\n",
    "        z = f[(f['categoryID_predict'] != f['categoryID_true']) & (f['categoryID_true'] == i) & (f['categoryID_predict'] == j)]\n",
    "        if not z.empty: \n",
    "            false.append(z)\n",
    "            ConfusionMatrix_test.iloc[i-1, j-1] = len(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6    7    8    9    10   11   12   13   14  \\\n",
       "0   246    0    0    0    0    0    0    0    0    0    0    0    0    0    0   \n",
       "1     0  296    6   15    1   11    2    1    1    0    0    2    1    0    3   \n",
       "2     1   10  201   23    0   18    2    0    0    0    0    3    1    1    0   \n",
       "3     0   12    4  305    3    5    6    0    0    0    0    2    3    0    1   \n",
       "4     1    4    2    5  275    2    0    0    2    0    0    2    1    3    1   \n",
       "5     1   12    8    4    2  302    0    0    1    1    0    1    0    0    2   \n",
       "6     1    4    0   30    6    1  255   20    1    3    3   10   13    3    1   \n",
       "7     1    0    0    2    1    2    3  350    1    1    0    1    1    1    0   \n",
       "8     1    1    0    1    1    0    4    2  359    0    0    0    0    2    0   \n",
       "9     0    3    0    1    0    1    1    2    0  358    4    0    1    1    0   \n",
       "10    1    0    1    2    0    0    0    2    0    0  380    1    1    0    0   \n",
       "11    0    2    0    0    0    0    0    0    0    0    0  359    0    1    0   \n",
       "12    0    4    1   14    3    0    3    1    0    0    1    4  261    2    2   \n",
       "13    0    1    0    0    0    1    0    1    1    0    0    1    2  327    1   \n",
       "14    1    1    0    1    0    2    0    1    0    0    0    1    1    1  343   \n",
       "15    0    2    0    1    0    0    0    0    0    0    0    0    0    1    0   \n",
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       "19   19    1    0    0    0    1    0    0    1    0    0    0    0    0    1   \n",
       "\n",
       "     15   16   17   18  19  \n",
       "0     5    0    1    1   2  \n",
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       "5     1    1    0    1   0  \n",
       "6     3    5    1    4   0  \n",
       "7     1    2    0    4   1  \n",
       "8     2    2    0    1   0  \n",
       "9     0    1    0    0   0  \n",
       "10    1    0    1    3   0  \n",
       "11    0    2    0    6   0  \n",
       "12    1    2    0    2   0  \n",
       "13    4    2    0    0   0  \n",
       "14    1    0    0    1   0  \n",
       "15  372    2    2    1   0  \n",
       "16    2  331    0    2   0  \n",
       "17    7    0  314    4   0  \n",
       "18    0    4    2  182   0  \n",
       "19   28   13    4    2  93  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfusionMatrix_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.964706</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Accuracy\n",
       "0   0.964706\n",
       "1   0.870588\n",
       "2   0.764259\n",
       "3   0.884058\n",
       "4   0.913621\n",
       "5   0.896142\n",
       "6   0.700549\n",
       "7   0.940860\n",
       "8   0.954787\n",
       "9   0.959786\n",
       "10  0.966921\n",
       "11  0.970270\n",
       "12  0.867110\n",
       "13  0.958944\n",
       "14  0.968927\n",
       "15  0.976378\n",
       "16  0.982196\n",
       "17  0.951515\n",
       "18  0.923858\n",
       "19  0.570552"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#class accuracies for test data\n",
    "ConfusionMatrix_test2 = pd.DataFrame(ConfusionMatrix_test.max(axis = 1)/ConfusionMatrix_test.sum(axis =1))\n",
    "ConfusionMatrix_test2.columns = ['Accuracy']\n",
    "ConfusionMatrix_test2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#evaluation of the model performance on test data using \n",
    "z3 = pd.DataFrame()\n",
    "for i in range (0,20):\n",
    "    m3 = master_dfMLE[master_dfMLE['cat'] == i]\n",
    "    common3 = pd.merge(m3, test_data, on=['wordID'])\n",
    "    c3 = common3.groupby(['docID'])['MLE'].sum()\n",
    "    c3 = c3.reset_index(['docID'])\n",
    "    c3 = pd.DataFrame((c3['MLE'] + doc_j.iloc[i,1]))\n",
    "    z3 = pd.DataFrame(pd.concat([z3,c3], axis =1))\n",
    "z3.columns = np.array(range(1,21))\n",
    "final_df3 = pd.DataFrame(z3.apply(lambda x: x.argmax(), axis=1)).dropna()\n",
    "final_df3.columns = ['categoryID_predict']\n",
    "final_df3 = final_df3.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.463024650233178"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f3 = pd.concat([final_df3,test_label], axis = 1)\n",
    "(len(test_label)- len(f3[f3['categoryID_true'] != f3['categoryID_predict']]))/len(test_label)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true = []\n",
    "false = []\n",
    "for i in range (1,21):\n",
    "    x = len(f3[(f3['categoryID_predict'] == f3['categoryID_true']) & (f3['categoryID_true'] == i)])\n",
    "    true.append(x)\n",
    "\n",
    "ConfusionMatrix_testMLE = pd.DataFrame(np.diag(true))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(1,21):\n",
    "    for j in range (1,21):\n",
    "        z = f3[(f3['categoryID_predict'] != f3['categoryID_true']) & (f3['categoryID_true'] == i) & (f3['categoryID_predict'] == j)]\n",
    "        if not z.empty: \n",
    "            false.append(z)\n",
    "            ConfusionMatrix_testMLE.iloc[i-1, j-1] = len(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  \\\n",
       "0   14   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1    0  26   2   1   1   4   0   0   0   0   0   0   1   0   2   0   0   0   \n",
       "2    0   3  19   6   3   1   0   0   0   0   0   1   1   2   0   0   1   0   \n",
       "3    0   3   5  28   2   0   4   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4    0   2   0   2  22   0   1   0   0   0   0   0   4   1   0   0   0   0   \n",
       "5    0   3   0   1   1  33   0   0   0   0   0   0   0   0   0   0   1   0   \n",
       "6    0   4   2   3   1   1  47   2   2   0   0   0   0   0   1   0   1   0   \n",
       "7    0   0   0   0   0   0   1  19   2   0   0   1   2   0   0   0   0   0   \n",
       "8    0   0   0   0   0   0   0   1  20   0   0   0   0   0   0   0   0   0   \n",
       "9    0   0   0   0   0   0   1   0   0  21   3   0   0   0   0   0   0   0   \n",
       "10   0   0   0   0   0   0   0   0   0   0  36   0   0   0   0   0   0   0   \n",
       "11   0   1   0   0   1   0   0   0   0   0   0  17   1   0   0   0   0   0   \n",
       "12   0   1   0   0   0   0   3   1   0   0   0   3  10   1   1   0   0   0   \n",
       "13   0   0   0   0   0   0   1   0   0   0   0   1   1  14   0   0   1   0   \n",
       "14   0   0   0   0   0   0   0   0   0   0   0   0   0   0  17   0   0   0   \n",
       "15   3   0   0   0   0   0   0   0   0   0   0   0   0   1   0  26   0   1   \n",
       "16   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  12   1   \n",
       "17   2   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   1  15   \n",
       "18   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   2   0   \n",
       "19   2   0   0   0   0   0   0   0   0   0   0   0   0   1   1   4   0   0   \n",
       "\n",
       "    18  19  \n",
       "0    1   1  \n",
       "1    0   0  \n",
       "2    0   0  \n",
       "3    0   0  \n",
       "4    0   0  \n",
       "5    0   0  \n",
       "6    0   1  \n",
       "7    0   1  \n",
       "8    0   0  \n",
       "9    0   0  \n",
       "10   0   0  \n",
       "11   0   0  \n",
       "12   0   0  \n",
       "13   0   0  \n",
       "14   0   0  \n",
       "15   1   1  \n",
       "16   0   1  \n",
       "17   0   0  \n",
       "18   8   0  \n",
       "19   2   6  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfusionMatrix_testMLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.702703</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.513514</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.723077</td>\n",
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       "      <th>7</th>\n",
       "      <td>0.730769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.787879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Accuracy\n",
       "0   0.875000\n",
       "1   0.702703\n",
       "2   0.513514\n",
       "3   0.666667\n",
       "4   0.687500\n",
       "5   0.846154\n",
       "6   0.723077\n",
       "7   0.730769\n",
       "8   0.952381\n",
       "9   0.840000\n",
       "10  1.000000\n",
       "11  0.850000\n",
       "12  0.500000\n",
       "13  0.777778\n",
       "14  1.000000\n",
       "15  0.787879\n",
       "16  0.857143\n",
       "17  0.750000\n",
       "18  0.615385\n",
       "19  0.375000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ConfusionMatrix_testMLE2 = pd.DataFrame(ConfusionMatrix_testMLE.max(axis = 1)/ConfusionMatrix_testMLE.sum(axis =1))\n",
    "ConfusionMatrix_testMLE2.columns = ['Accuracy']\n",
    "ConfusionMatrix_testMLE2"
   ]
  }
 ],
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