07 Bayes Classifier - Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_DATA_FILE = \"SpamData/02_Training/train-data.txt\"\n",
    "TEST_DATA_FILE = \"SpamData/02_Training/test-data.txt\"\n",
    "\n",
    "TOKEN_SPAM_PROB_FILE = \"SpamData/03_Testing/prob_spam.txt\"\n",
    "TOKEN_HAM_PROB_FILE = \"SpamData/03_Testing/prob_nonspam.txt\"\n",
    "TOKEN_ALL_PROB_FILE = \"SpamData/03_Testing/prob_all_token.txt\"\n",
    "\n",
    "TEST_FEATURE_MATRIX = \"SpamData/03_Testing/test-features.txt\"\n",
    "TEST_TARGET_FILE = \"SpamData/03_Testing/test-target.txt\"\n",
    "\n",
    "VOCAB_SIZE = 2500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read and Load features from .txt file into NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_train_data = np.loadtxt(TRAINING_DATA_FILE, delimiter=' ', dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    2,    1,    1],\n",
       "       [   0,    3,    1,    2],\n",
       "       [   0,    4,    1,    1],\n",
       "       ...,\n",
       "       [5795, 2266,    0,    1],\n",
       "       [5795, 2332,    0,    1],\n",
       "       [5795, 2360,    0,    1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_test_data = np.loadtxt(TEST_DATA_FILE, delimiter=' ', dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   8,    2,    1,    1],\n",
       "       [   8,    3,    1,    4],\n",
       "       [   8,    4,    1,    2],\n",
       "       ...,\n",
       "       [5793, 2383,    0,    4],\n",
       "       [5793, 2386,    0,    3],\n",
       "       [5793, 2481,    0,    1]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of rows in training dataset: 258351\n",
      "Nr of rows in testing dataset: 117770\n"
     ]
    }
   ],
   "source": [
    "print('Nr of rows in training dataset:', sparse_train_data.shape[0])\n",
    "print('Nr of rows in testing dataset:', sparse_test_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of emails in training set 4013\n"
     ]
    }
   ],
   "source": [
    "print('Nr of emails in training set', len(np.unique(sparse_train_data[:,0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of emails in testing set 1724\n"
     ]
    }
   ],
   "source": [
    "print('Nr of emails in testing set', len(np.unique(sparse_test_data[:,0])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to create an empty DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['DOC_ID'] + ['CATEGORY'] + list(range(0, VOCAB_SIZE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DOC_ID', 'CATEGORY', 0, 1, 2]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2502"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 5791, 5794, 5795])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_names = np.unique(sparse_train_data[ : , 0])\n",
    "index_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<p>4013 rows × 2502 columns</p>\n",
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      ],
      "text/plain": [
       "      DOC_ID  CATEGORY  0  1  2  3  4  5  6  7  ...  2490  2491  2492  2493  \\\n",
       "0          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "1          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "2          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "3          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "4          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "...      ...       ... .. .. .. .. .. .. .. ..  ...   ...   ...   ...   ...   \n",
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       "5795       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "\n",
       "      2494  2495  2496  2497  2498  2499  \n",
       "0        0     0     0     0     0     0  \n",
       "1        0     0     0     0     0     0  \n",
       "2        0     0     0     0     0     0  \n",
       "3        0     0     0     0     0     0  \n",
       "4        0     0     0     0     0     0  \n",
       "...    ...   ...   ...   ...   ...   ...  \n",
       "5789     0     0     0     0     0     0  \n",
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       "5791     0     0     0     0     0     0  \n",
       "5794     0     0     0     0     0     0  \n",
       "5795     0     0     0     0     0     0  \n",
       "\n",
       "[4013 rows x 2502 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data = pd.DataFrame(columns=column_names, index=index_names)\n",
    "full_train_data = full_train_data.fillna(value=0)\n",
    "full_train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Full Matrix from Sparse Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_full_matrix(sparse_matrix, nr_words, doc_idx=0, word_idx=1, cat_idx=2, freq_idx=3):\n",
    "    \"\"\"\n",
    "    Form a full matrix from Sparse Matrix. Return Pandas DataFrame\n",
    "    Keyword arguments: \n",
    "        sparse matrix --- numpy array\n",
    "        nr_words --- size of words, Total number of tokens\n",
    "        doc_idx --- position of the document id in sparse matrix. Default = 1st column\n",
    "        word_idx --- position of the word id in sparse matrix. Default = 2nd column\n",
    "        cat_idx --- position of the category(spam or non-spam). Default = 3d column\n",
    "        freq_idx --- psition of the frequency of the word. Default = 4th column\n",
    "    \"\"\"\n",
    "    column_names = ['DOC_ID'] + ['CATEGORY'] + list(range(0, VOCAB_SIZE))\n",
    "    doc_id_names = np.unique(sparse_matrix[:,0])\n",
    "    full_matrix = pd.DataFrame(columns=column_names, index=doc_id_names)\n",
    "    full_matrix.fillna(value=0, inplace=True)\n",
    "    \n",
    "    for i in range(sparse_matrix.shape[0]):\n",
    "        doc_nr = sparse_matrix[i][doc_idx]\n",
    "        label = sparse_matrix[i][cat_idx]\n",
    "        word_id = sparse_matrix[i][word_idx]\n",
    "        occurance = sparse_matrix[i][freq_idx]\n",
    "        \n",
    "        full_matrix.at[doc_nr, 'DOC_ID'] = doc_nr\n",
    "        full_matrix.at[doc_nr, 'CATEGORY'] = label\n",
    "        full_matrix.at[doc_nr, word_id] = occurance\n",
    "    \n",
    "    full_matrix.set_index('DOC_ID', inplace=True)\n",
    "    return full_matrix\n",
    "    "
   ]
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     "execution_count": 16,
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   "source": [
    "%%time\n",
    "make_full_matrix(sparse_train_data, VOCAB_SIZE).head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
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    {
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       "5791           0  3  1  0  1  1  0  0   0  1  ...     0     0     0     0   \n",
       "5794           0  1  1  1  0  0  1  2   0  0  ...     0     0     0     0   \n",
       "5795           0  3  4  2  0  5  0  3   0  0  ...     0     0     0     0   \n",
       "\n",
       "        2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                      \n",
       "0          0     0     0     0     0     0  \n",
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       "4          0     0     0     0     0     0  \n",
       "...      ...   ...   ...   ...   ...   ...  \n",
       "5789       0     0     0     0     0     0  \n",
       "5790       0     0     0     0     0     0  \n",
       "5791       0     0     0     0     0     0  \n",
       "5794       0     0     0     0     0     0  \n",
       "5795       0     0     0     0     0     0  \n",
       "\n",
       "[4013 rows x 2501 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "full_training_matrix = make_full_matrix(sparse_train_data, VOCAB_SIZE)\n",
    "full_training_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training te Naive Bayes Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the probability of Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1248"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_training_matrix.CATEGORY.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of spam is: 0.310989284824321\n"
     ]
    }
   ],
   "source": [
    "prob_spam = full_training_matrix.CATEGORY.sum()/len(full_training_matrix['CATEGORY'])\n",
    "print('Probability of spam is:', prob_spam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total number of Words/Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
       "0          0     0     1     2     1     0     0     3     0     0  ...     0   \n",
       "1          7     1     2     0     1     0     0     1     0     0  ...     0   \n",
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       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
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       "\n",
       "        2491  2492  2493  2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                                        \n",
       "0          0     0     0     0     0     0     0     0     0  \n",
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       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
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       "5794       0     0     0     0     0     0     0     0     0  \n",
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       "\n",
       "[4013 rows x 2500 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_features = full_training_matrix.loc[:, full_training_matrix.columns != 'CATEGORY']\n",
    "full_train_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DOC_ID\n",
       "0     87\n",
       "1     53\n",
       "2     40\n",
       "3    183\n",
       "4     43\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_lengths = full_train_features.sum(axis=1)\n",
    "email_lengths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "429252"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_wc = email_lengths.sum()\n",
    "total_wc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nr of tokens in Spam and Ham emails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "176314"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_lengths = email_lengths[full_training_matrix['CATEGORY'] == 1]\n",
    "spam_wc = spam_lengths.sum()\n",
    "spam_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252938"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_lengths = email_lengths[full_training_matrix['CATEGORY'] == 0]\n",
    "ham_wc = ham_lengths.sum()\n",
    "ham_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1248"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(spam_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2765"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ham_lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average words per email spam 141\n",
      "average words per email non-spam 91\n"
     ]
    }
   ],
   "source": [
    "print('average words per email spam', round(spam_wc/len(spam_lengths)))\n",
    "print('average words per email non-spam', round(ham_wc/len(ham_lengths)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>2490</th>\n",
       "      <th>2491</th>\n",
       "      <th>2492</th>\n",
       "      <th>2493</th>\n",
       "      <th>2494</th>\n",
       "      <th>2495</th>\n",
       "      <th>2496</th>\n",
       "      <th>2497</th>\n",
       "      <th>2498</th>\n",
       "      <th>2499</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOC_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1895</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1248 rows × 2500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
       "0          0     0     1     2     1     0     0     3     0     0  ...     0   \n",
       "1          7     1     2     0     1     0     0     1     0     0  ...     0   \n",
       "2          6     1     1     0     1     0     0     1     0     0  ...     0   \n",
       "3          6     0     0     2     4     0     3    14     0     0  ...     0   \n",
       "4          5     1     2     0     1     0     0     1     0     0  ...     0   \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "1885       1     0     0     2     0     1     0     0     0     0  ...     0   \n",
       "1887       2     0     0     0     0     0     0     0     0     0  ...     0   \n",
       "1889       0     0     0     0     0     0     0     0     1     0  ...     0   \n",
       "1890       2     0     0     0     1     0     0     2     0     0  ...     0   \n",
       "1895       1     1     1     0     0     0     0     0     0     0  ...     0   \n",
       "\n",
       "        2491  2492  2493  2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                                        \n",
       "0          0     0     0     0     0     0     0     0     0  \n",
       "1          0     0     0     0     0     0     0     0     0  \n",
       "2          0     0     0     0     0     0     0     0     0  \n",
       "3          0     0     0     0     0     0     0     0     0  \n",
       "4          0     0     0     0     0     0     0     0     0  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "1885       0     0     0     0     0     0     0     0     0  \n",
       "1887       0     0     0     0     0     0     0     0     0  \n",
       "1889       0     0     0     1     0     0     0     0     0  \n",
       "1890       0     0     0     0     0     0     0     0     0  \n",
       "1895       0     0     0     0     0     0     0     0     0  \n",
       "\n",
       "[1248 rows x 2500 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_spam_tokens = full_train_features.loc[full_training_matrix.CATEGORY == 1]\n",
    "train_spam_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_spam_tokens = train_spam_tokens.sum(axis=0) + 1\n",
    "summed_spam_tokens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2179\n",
       "1     935\n",
       "2    1217\n",
       "3    2022\n",
       "4    1219\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_spam_tokens.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summing the tokens occuring in ham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>2491</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1896</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5789</th>\n",
       "      <td>3</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5790</th>\n",
       "      <td>1</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2765 rows × 2500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
       "1896       1     2     1     0     1     1     2     0     1     1  ...     0   \n",
       "1898       2     1     0     1     1     0     2     1     0     0  ...     0   \n",
       "1899       2     1     1     0     0     1     1     0     1     0  ...     0   \n",
       "1900       3     4     0     1     0     0     1     1     1     1  ...     0   \n",
       "1901       2     2     0     1     0     0     1     1     0     0  ...     0   \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "5789       3     1     0     1     0     0     0     1     0     0  ...     0   \n",
       "5790       1     1     1     0     0     0     1     0     0     0  ...     0   \n",
       "5791       3     1     0     1     1     0     0     0     1     0  ...     0   \n",
       "5794       1     1     1     0     0     1     2     0     0     1  ...     0   \n",
       "5795       3     4     2     0     5     0     3     0     0     4  ...     0   \n",
       "\n",
       "        2491  2492  2493  2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                                        \n",
       "1896       0     0     0     0     0     0     0     0     0  \n",
       "1898       0     0     0     0     0     0     0     0     0  \n",
       "1899       0     0     0     0     0     0     0     0     0  \n",
       "1900       0     0     0     0     0     0     0     0     0  \n",
       "1901       0     0     0     0     0     0     0     0     0  \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "5789       0     0     0     0     0     0     0     0     0  \n",
       "5790       0     0     0     0     0     0     0     0     0  \n",
       "5791       0     0     0     0     0     0     0     0     0  \n",
       "5794       0     0     0     0     0     0     0     0     0  \n",
       "5795       0     0     0     0     0     0     0     0     0  \n",
       "\n",
       "[2765 rows x 2500 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ham_tokens = full_train_features.loc[full_training_matrix.CATEGORY == 0]\n",
    "train_ham_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding 1 to all the tokens due to Laplace Smoothing\n",
    "\n",
    "summed_ham_tokens = train_ham_tokens.sum(axis=0) + 1\n",
    "summed_ham_tokens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5484\n",
       "1    2590\n",
       "2    2045\n",
       "3     938\n",
       "4    1612\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_ham_tokens.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2495     7\n",
       "2496    25\n",
       "2497    36\n",
       "2498    23\n",
       "2499    33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_ham_tokens.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ham_tokens[2499].sum() + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(Token| Spam) - Probablity that a Token occurs given the Email is Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding 2500 to spam_wc due Laplace Smoothing at summed_spam_tokens\n",
    "\n",
    "prob_spam_tokens = summed_spam_tokens/ (spam_wc + 2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.012186\n",
       "1       0.005229\n",
       "2       0.006806\n",
       "3       0.011308\n",
       "4       0.006817\n",
       "          ...   \n",
       "2495    0.000123\n",
       "2496    0.000034\n",
       "2497    0.000022\n",
       "2498    0.000034\n",
       "2499    0.000006\n",
       "Length: 2500, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_spam_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_spam_tokens.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(Token| Ham) - Probablity that a Token occurs given the Email is Non-Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_ham_tokens = summed_ham_tokens/ (ham_wc + 2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.021469\n",
       "1       0.010139\n",
       "2       0.008006\n",
       "3       0.003672\n",
       "4       0.006311\n",
       "          ...   \n",
       "2495    0.000027\n",
       "2496    0.000098\n",
       "2497    0.000141\n",
       "2498    0.000090\n",
       "2499    0.000129\n",
       "Length: 2500, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ham_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_ham_tokens.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P(Token) - Probaility that a Token Occurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_tokens_all = full_train_features.sum(axis=0)/total_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.017847\n",
       "1       0.008207\n",
       "2       0.007595\n",
       "3       0.006891\n",
       "4       0.006591\n",
       "          ...   \n",
       "2495    0.000063\n",
       "2496    0.000068\n",
       "2497    0.000089\n",
       "2498    0.000063\n",
       "2499    0.000075\n",
       "Length: 2500, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_all.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(TOKEN_ALL_PROB_FILE, prob_tokens_all)\n",
    "np.savetxt(TOKEN_SPAM_PROB_FILE, prob_spam_tokens)\n",
    "np.savetxt(TOKEN_HAM_PROB_FILE, prob_ham_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOC_ID</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>2490</th>\n",
       "      <th>2491</th>\n",
       "      <th>2492</th>\n",
       "      <th>2493</th>\n",
       "      <th>2494</th>\n",
       "      <th>2495</th>\n",
       "      <th>2496</th>\n",
       "      <th>2497</th>\n",
       "      <th>2498</th>\n",
       "      <th>2499</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5789</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",
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       "    <tr>\n",
       "      <th>5790</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5791</th>\n",
       "      <td>0</td>\n",
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       "      <th>5794</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4013 rows × 2502 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      DOC_ID  CATEGORY  0  1  2  3  4  5  6  7  ...  2490  2491  2492  2493  \\\n",
       "0          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
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       "3          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "4          0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "...      ...       ... .. .. .. .. .. .. .. ..  ...   ...   ...   ...   ...   \n",
       "5789       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "5790       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "5791       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "5794       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "5795       0         0  0  0  0  0  0  0  0  0  ...     0     0     0     0   \n",
       "\n",
       "      2494  2495  2496  2497  2498  2499  \n",
       "0        0     0     0     0     0     0  \n",
       "1        0     0     0     0     0     0  \n",
       "2        0     0     0     0     0     0  \n",
       "3        0     0     0     0     0     0  \n",
       "4        0     0     0     0     0     0  \n",
       "...    ...   ...   ...   ...   ...   ...  \n",
       "5789     0     0     0     0     0     0  \n",
       "5790     0     0     0     0     0     0  \n",
       "5791     0     0     0     0     0     0  \n",
       "5794     0     0     0     0     0     0  \n",
       "5795     0     0     0     0     0     0  \n",
       "\n",
       "[4013 rows x 2502 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_test_data = pd.DataFrame(columns=column_names, index=index_names)\n",
    "full_test_data = full_train_data.fillna(value=0)\n",
    "full_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(117770, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4.13 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "full_test_data = make_full_matrix(sparse_test_data, nr_words=VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1724 rows × 2501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CATEGORY  0  1  2  3  4  5  6  7  8  ...  2490  2491  2492  2493  \\\n",
       "DOC_ID                                       ...                           \n",
       "8              1  0  0  1  4  2  1  2  4  1  ...     0     0     0     0   \n",
       "12             1  6  1  1  0  1  0  0  1  0  ...     0     0     0     0   \n",
       "14             1  0  0  1  1  0  0  0  0  0  ...     0     0     0     0   \n",
       "15             1  0  2  1  1  2  0  0  3  0  ...     0     0     0     0   \n",
       "17             1  0  0  0  0  1  0  1  0  0  ...     0     0     0     1   \n",
       "...          ... .. .. .. .. .. .. .. .. ..  ...   ...   ...   ...   ...   \n",
       "5783           0  2  1  0  2  0  0  0  0  0  ...     0     0     0     0   \n",
       "5786           0  5  5  2  2  1  2  0  0  1  ...     0     0     0     0   \n",
       "5788           0  0  4  0  2  4  3  3  1  4  ...     0     0     0     0   \n",
       "5792           0  2  2  0  1  0  0  2  1  0  ...     0     0     0     0   \n",
       "5793           0  1  9  1  0  0  1  2  1  1  ...     0     0     0     0   \n",
       "\n",
       "        2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                      \n",
       "8          0     0     0     0     0     0  \n",
       "12         0     0     0     0     0     0  \n",
       "14         0     0     0     0     0     0  \n",
       "15         0     0     0     0     0     0  \n",
       "17         0     0     0     0     0     0  \n",
       "...      ...   ...   ...   ...   ...   ...  \n",
       "5783       2     0     0     0     0     0  \n",
       "5786       0     0     0     0     0     0  \n",
       "5788       0     0     0     0     0     0  \n",
       "5792       0     0     0     0     0     0  \n",
       "5793       0     0     0     0     0     0  \n",
       "\n",
       "[1724 rows x 2501 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = full_test_data.loc[:,full_test_data.columns != 'CATEGORY']\n",
    "y_test = full_test_data.CATEGORY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(TEST_TARGET_FILE, y_test)\n",
    "np.savetxt(TEST_FEATURE_MATRIX, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1724, 2500)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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