Naive Bayes Spam Email 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": 65,
   "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-tokens.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 Files 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": [],
   "source": [
    "sparse_test_data = np.loadtxt(TEST_DATA_FILE, delimiter = ' ', dtype = int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  1,  2],\n",
       "       [ 0,  7,  1,  1],\n",
       "       [ 0, 15,  1,  1],\n",
       "       [ 0, 18,  1,  1],\n",
       "       [ 0, 24,  1,  1]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_train_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5795, 1896,    0,    1],\n",
       "       [5795, 1908,    0,    2],\n",
       "       [5795, 2128,    0,    1],\n",
       "       [5795, 2300,    0,    1],\n",
       "       [5795, 2478,    0,    1]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_train_data[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in training file:  265379\n",
      "Number of rows in test file:  110495\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows in training file: ', sparse_train_data.shape[0])\n",
    "print('Number of rows in test file: ', sparse_test_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of emails in training file: 4015\n"
     ]
    }
   ],
   "source": [
    "print('Number of emails in training file:', np.unique(sparse_train_data[:,0]).size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of emails in test file: 1724\n"
     ]
    }
   ],
   "source": [
    "print('Number of emails in test file:', np.unique(sparse_test_data[:,0]).size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Empty DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DOC_ID', 'CATEGORY', 0, 1, 2]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['DOC_ID'] + ['CATEGORY'] + list(range(0, VOCAB_SIZE))\n",
    "column_names[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2502"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    1,    2, ..., 5791, 5794, 5795])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_names = np.unique(sparse_train_data[:, 0])\n",
    "index_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_train_data = pd.DataFrame(index = index_names, columns = column_names)\n",
    "full_train_data.fillna(value=0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "   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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       "\n",
       "[5 rows x 2502 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Full Matrix from a Sparse Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 a sparse matrix. Return a pandas dataframe.\n",
    "    Keyword arguments:\n",
    "    sparse_matrix -- numpy array\n",
    "    nr_words -- size of the vocabulary. Total number of tokens.\n",
    "    doc_idx -- position of the document id in the sparse matrix. Default: 1st column\n",
    "    word_idx -- position of the word id in the sparse matrix. Default: 2nd column\n",
    "    cat_idx -- position of the label (spam is 1, nonspam is 0). Default: 3rd column\n",
    "    freq_idx -- position of occurrence of word in sparse matrix. Default: 4th column\n",
    "    \"\"\"\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(index = doc_id_names, columns = column_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",
    "        word_id = sparse_matrix[i][word_idx]\n",
    "        label = sparse_matrix[i][cat_idx]\n",
    "        occurrence = 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] = occurrence\n",
    "    \n",
    "    full_matrix.set_index('DOC_ID', inplace = True)\n",
    "    return full_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.54 s, sys: 39.7 ms, total: 7.58 s\n",
      "Wall time: 6.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "full_train_data = make_full_matrix(sparse_train_data, VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "0              1  2  0  0  0  0  0  0  1  0  ...     0     0     0     0   \n",
       "1              1  2  0  1  2  1  1  0  0  0  ...     0     0     0     0   \n",
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       "3              1  3  0  0  1  0  1  1  0  0  ...     0     0     0     0   \n",
       "4              1  0  0  0  1  2  4  2  3  1  ...     0     0     0     0   \n",
       "\n",
       "        2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                      \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",
       "[5 rows x 2501 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 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",
       "5789           0  0  0  0  0  0  5  0  0  0  ...     0     0     0     0   \n",
       "5790           0  2  2  1  0  0  0  1  0  1  ...     0     0     0     0   \n",
       "5791           0  2  0  1  1  0  1  2  0  1  ...     0     0     0     0   \n",
       "5794           0  2  2  1  0  1  1  1  0  1  ...     0     0     0     0   \n",
       "5795           0  1  1  1  0  1  1  0  0  0  ...     0     0     0     0   \n",
       "\n",
       "        2494  2495  2496  2497  2498  2499  \n",
       "DOC_ID                                      \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",
       "[5 rows x 2501 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Naive Bayes Model\n",
    "\n",
    "## Calculating the Probability of Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of spam is:  0.31133250311332505\n"
     ]
    }
   ],
   "source": [
    "prob_spam = full_train_data.CATEGORY.sum() / full_train_data.CATEGORY.size\n",
    "print('Probability of spam is: ', prob_spam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total Number of Words/Tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
       "0          2     0     0     0     0     0     0     1     0     0  ...     0   \n",
       "1          2     0     1     2     1     1     0     0     0     0  ...     0   \n",
       "2          2     0     2     0     0     3     0     0     0     0  ...     0   \n",
       "3          3     0     0     1     0     1     1     0     0     0  ...     0   \n",
       "4          0     0     0     1     2     4     2     3     1     1  ...     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",
       "[5 rows x 2500 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_features = full_train_data.loc[:, full_train_data.columns != 'CATEGORY']\n",
    "full_train_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4015,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_lengths = full_train_features.sum(axis=1)\n",
    "email_lengths.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DOC_ID\n",
       "0     50\n",
       "1     76\n",
       "2     87\n",
       "3     76\n",
       "4    137\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_lengths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445868"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_wc = email_lengths.sum()\n",
    "total_wc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of Tokens in Spam and Ham Emails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1250,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_lengths = email_lengths[full_train_data.CATEGORY == 1]\n",
    "spam_lengths.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "195697"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_wc = spam_lengths.sum()\n",
    "spam_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2765,)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham_lengths = email_lengths[full_train_data.CATEGORY == 0]\n",
    "ham_lengths.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250171"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonspam_wc = ham_lengths.sum()\n",
    "nonspam_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "email_lengths.shape[0] - spam_lengths.shape[0] - ham_lengths.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_wc + nonspam_wc - total_wc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summing the Tokens occurring in Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4015, 2500)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_train_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
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       "\n",
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       "DOC_ID                                                        \n",
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      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_spam_tokens = full_train_features.loc[full_train_data.CATEGORY == 1]\n",
    "train_spam_tokens.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       "        0     1     2     3     4     5     6     7     8     9     ...  2490  \\\n",
       "DOC_ID                                                              ...         \n",
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       "\n",
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       "DOC_ID                                                        \n",
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       "\n",
       "[5 rows x 2500 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_spam_tokens.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1250, 2500)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_spam_tokens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 51,
     "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": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2495     2\n",
       "2496     1\n",
       "2497    17\n",
       "2498    33\n",
       "2499     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "summed_spam_tokens.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summing the Tokens occurring in Ham"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ham_tokens = full_train_features.loc[full_train_data.CATEGORY == 0]\n",
    "summed_ham_tokens = train_ham_tokens.sum(axis=0) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_ham_tokens.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2495    19\n",
       "2496    27\n",
       "2497    14\n",
       "2498     2\n",
       "2499    34\n",
       "dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summed_ham_tokens.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ham_tokens[2499].sum() + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# P(Token | Spam) - Probability that a Token occurs given that the Email is Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.009047\n",
       "1    0.004884\n",
       "2    0.006837\n",
       "3    0.010590\n",
       "4    0.006801\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_spam = summed_spam_tokens / (spam_wc + VOCAB_SIZE)\n",
    "prob_tokens_spam[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_spam.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# P(Token | Ham) - Probability that a Token occurs given that the Email is Non-Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_nonspam = summed_ham_tokens / (nonspam_wc + VOCAB_SIZE)\n",
    "prob_tokens_nonspam.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# P(Token) - Probability that Token occurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_tokens_all = full_train_features.sum(axis=0) / total_wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_tokens_all.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(TOKEN_SPAM_PROB_FILE, prob_tokens_spam)\n",
    "np.savetxt(TOKEN_HAM_PROB_FILE, prob_tokens_nonspam)\n",
    "np.savetxt(TOKEN_ALL_PROB_FILE, prob_tokens_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110495, 4)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sparse_test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.26 s, sys: 68 ms, total: 4.33 s\n",
      "Wall time: 2.93 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "full_test_data = make_full_matrix(sparse_test_data, nr_words=VOCAB_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt(TEST_TARGET_FILE, y_test)\n",
    "np.savetxt(TEST_FEATURE_MATRIX, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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