main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# main.ipynb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file includes a series of attempts to construct an effective text classification model, accompanied by code and comments demonstrating our thought process as well as output of different variables to show how we attempted to verify things were working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegressionCV as LogReg\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_score\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from scipy.sparse import csr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red;font-size: larger\">Part 1: Developing Our Multinomial Naive Bayes Model (Chapter Classification) </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
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       "          label  foolishness  hath  wholesome  takest  feelings  anger  \\\n",
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     "execution_count": 239,
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   "source": [
    "# Load the data\n",
    "labelled_data = pd.read_csv('AllBooks_baseline_DTM_Labelled.csv')\n",
    "labelled_data.rename(columns={'Unnamed: 0': 'label'}, inplace=True)\n",
    "\n",
    "#testing\n",
    "labelled_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
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       "          label  foolishness  hath  wholesome  takest  feelings  anger  \\\n",
       "0  Buddhism_Ch1            0     0          0       0         0      0   \n",
       "1  Buddhism_Ch2            0     0          0       0         0      0   \n",
       "2  Buddhism_Ch3            0     0          0       0         0      0   \n",
       "3  Buddhism_Ch4            0     0          0       0         0      0   \n",
       "4  Buddhism_Ch5            0     0          0       0         0      0   \n",
       "\n",
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       "\n",
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     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "labelled_data.head()"
   ]
  },
  {
   "cell_type": "code",
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       "<p>5 rows × 8266 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   # foolishness  hath  wholesome  takest  feelings  anger  vaivaswata  \\\n",
       "0              0     0          0       0         0      0           0   \n",
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       "4              0     0          0       0         0      0           0   \n",
       "\n",
       "   matrix  kindled  convict  ...  erred  thinkest  modern  reigned  sparingly  \\\n",
       "0       0        0        0  ...      0         0       0        0          0   \n",
       "1       0        0        0  ...      0         0       0        0          0   \n",
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       "3       0        0        0  ...      0         0       0        0          0   \n",
       "4       0        0        0  ...      0         0       0        0          0   \n",
       "\n",
       "   visual  thoughts  illumines  attire  explains  \n",
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       "2       0         0          0       0         0  \n",
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       "4       0         0          0       0         0  \n",
       "\n",
       "[5 rows x 8266 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unlabelled_data = pd.read_csv('AllBooks_baseline_DTM_Unlabelled_duplicate.csv')\n",
    "labelled_data.rename(columns={'# foolishness': 'foolishness'}, inplace=True)\n",
    "\n",
    "unlabelled_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     foolishness  hath  wholesome  takest  feelings  anger  vaivaswata  \\\n",
       "0              0     0          0       0         0      0           0   \n",
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       "..           ...   ...        ...     ...       ...    ...         ...   \n",
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       "\n",
       "     matrix  kindled  convict  ...  erred  thinkest  modern  reigned  \\\n",
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       "588       0        0        0  ...      0         0       0        0   \n",
       "589       0        0        0  ...      0         0       0        0   \n",
       "\n",
       "     sparingly  visual  thoughts  illumines  attire  explains  \n",
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       "\n",
       "[590 rows x 8266 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The below is for using the already vectorized BoW (bag of words) data\n",
    "# and doing a TF-IDF transform on it and then feeding it into the model\n",
    "\n",
    "# Assuming 'labelled_data' is your original dataframe\n",
    "# 'labels' column is the one with text labels like \"Buddhism_Ch1\"\n",
    "\n",
    "# Step 1: Separate labels from features\n",
    "labels = labelled_data['label'].values  # This creates a separate array of labels\n",
    "labelled_data = labelled_data.drop('label', axis=1)  # This drops the label column from the dataframe\n",
    "\n",
    "# Step 2: Encode labels if necessary\n",
    "label_encoder = LabelEncoder()\n",
    "encoded_labels = label_encoder.fit_transform(labels)  # This encodes text labels to numbers\n",
    "\n",
    "# Finally, view output\n",
    "labelled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "foolishness    int64\n",
       "hath           int64\n",
       "wholesome      int64\n",
       "takest         int64\n",
       "feelings       int64\n",
       "               ...  \n",
       "visual         int64\n",
       "thoughts       int64\n",
       "illumines      int64\n",
       "attire         int64\n",
       "explains       int64\n",
       "Length: 8266, dtype: object"
      ]
     },
     "execution_count": 243,
     "metadata": {},
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   ],
   "source": [
    "labelled_data.dtypes #testing"
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  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
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       "         4,   5,   6,   7,   8,   9,  10,  12,  13,  14,  15,  16,  17,\n",
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       "        99, 100, 101, 102, 103])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = labelled_data\n",
    "y_train = encoded_labels\n",
    "y_train # testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    labelled_data, encoded_labels, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
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       "        99, 100, 101, 102, 103])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so that training set is based on entire 'labelled' dataset\n",
    "X_train = labelled_data\n",
    "y_train = encoded_labels\n",
    "\n",
    "#testing\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
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       "     foolishness  hath  wholesome  takest  feelings  anger  vaivaswata  \\\n",
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       "\n",
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       "\n",
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       "[590 rows x 8266 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking output of train test split\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
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       "      <th>...</th>\n",
       "      <th>erred</th>\n",
       "      <th>thinkest</th>\n",
       "      <th>modern</th>\n",
       "      <th>reigned</th>\n",
       "      <th>sparingly</th>\n",
       "      <th>visual</th>\n",
       "      <th>thoughts</th>\n",
       "      <th>illumines</th>\n",
       "      <th>attire</th>\n",
       "      <th>explains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</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",
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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>165</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>232</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",
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       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</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",
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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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\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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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</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",
       "      <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>261</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",
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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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1</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",
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       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</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>51</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>383</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>295 rows × 8266 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     foolishness  hath  wholesome  takest  feelings  anger  vaivaswata  \\\n",
       "114            0     0          0       0         0      0           0   \n",
       "165            0     0          0       0         0      0           0   \n",
       "232            0     0          0       0         0      0           0   \n",
       "429            0     0          0       0         0      0           0   \n",
       "408            0     0          0       0         0      0           0   \n",
       "..           ...   ...        ...     ...       ...    ...         ...   \n",
       "261            0     0          0       0         0      0           0   \n",
       "493            1     2          0       0         0      0           0   \n",
       "44             0     0          0       0         0      0           0   \n",
       "51             0     0          0       0         0      0           0   \n",
       "383            0     0          0       0         0      0           0   \n",
       "\n",
       "     matrix  kindled  convict  ...  erred  thinkest  modern  reigned  \\\n",
       "114       0        0        0  ...      0         0       0        0   \n",
       "165       0        0        0  ...      0         0       0        0   \n",
       "232       0        0        0  ...      0         0       0        0   \n",
       "429       0        0        0  ...      0         0       0        0   \n",
       "408       0        0        0  ...      0         0       0        0   \n",
       "..      ...      ...      ...  ...    ...       ...     ...      ...   \n",
       "261       0        0        0  ...      0         0       0        0   \n",
       "493       0        0        0  ...      0         0       0        0   \n",
       "44        0        0        0  ...      0         0       0        0   \n",
       "51        0        0        0  ...      0         0       0        0   \n",
       "383       0        0        0  ...      0         0       0        0   \n",
       "\n",
       "     sparingly  visual  thoughts  illumines  attire  explains  \n",
       "114          0       0         0          0       0         0  \n",
       "165          0       0         0          0       0         0  \n",
       "232          0       0         1          0       0         0  \n",
       "429          0       0         0          0       0         0  \n",
       "408          0       0         0          0       0         0  \n",
       "..         ...     ...       ...        ...     ...       ...  \n",
       "261          0       0         0          0       0         0  \n",
       "493          0       0         1          0       0         0  \n",
       "44           0       0         0          0       0         0  \n",
       "51           0       0         0          0       0         0  \n",
       "383          0       0         0          0       0         0  \n",
       "\n",
       "[295 rows x 8266 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([112, 123, 134, 145, 153, 154, 155, 156, 157, 113, 114, 115, 116,\n",
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       "       521, 522, 523, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534,\n",
       "       536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 547, 548, 549,\n",
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       "        99, 100, 101, 102, 103])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       138,  14,  15, 574, 279,  69, 151, 213, 585])"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Trying to skip the CountVectorizer step and directly feed the data into the TfidfTransformer\n",
    "# since the spreadsheet already has the word counts, so\n",
    "# effectively the spreadsheet is already the output of the CountVectorizer\n",
    "\n",
    "# Ensure the data is all numeric / float\n",
    "X_train = X_train.astype(np.float64)\n",
    "X_test = X_test.astype(np.float64)\n",
    "\n",
    "#transform X_train into sparse matrix\n",
    "X_train_sparse = csr_matrix(X_train.values)\n",
    "\n",
    "#apply the tfidf_transformer\n",
    "tfidf_transformer = TfidfTransformer(use_idf=True)\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_transformer.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foolishness</th>\n",
       "      <th>hath</th>\n",
       "      <th>wholesome</th>\n",
       "      <th>takest</th>\n",
       "      <th>feelings</th>\n",
       "      <th>anger</th>\n",
       "      <th>vaivaswata</th>\n",
       "      <th>matrix</th>\n",
       "      <th>kindled</th>\n",
       "      <th>convict</th>\n",
       "      <th>...</th>\n",
       "      <th>erred</th>\n",
       "      <th>thinkest</th>\n",
       "      <th>modern</th>\n",
       "      <th>reigned</th>\n",
       "      <th>sparingly</th>\n",
       "      <th>visual</th>\n",
       "      <th>thoughts</th>\n",
       "      <th>illumines</th>\n",
       "      <th>attire</th>\n",
       "      <th>explains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>585</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>590 rows × 8266 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     foolishness  hath  wholesome  takest  feelings  anger  vaivaswata  \\\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",
       "585          0.0   2.0        0.0     0.0       0.0    0.0         0.0   \n",
       "586          0.0   0.0        0.0     0.0       0.0    0.0         0.0   \n",
       "587          0.0   0.0        0.0     0.0       0.0    0.0         0.0   \n",
       "588          0.0   0.0        0.0     0.0       0.0    0.0         0.0   \n",
       "589          0.0   0.0        0.0     0.0       0.0    0.0         0.0   \n",
       "\n",
       "     matrix  kindled  convict  ...  erred  thinkest  modern  reigned  \\\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",
       "585     0.0      0.0      0.0  ...    0.0       0.0     0.0      0.0   \n",
       "586     0.0      0.0      0.0  ...    0.0       0.0     0.0      0.0   \n",
       "587     0.0      0.0      0.0  ...    1.0       0.0     0.0      0.0   \n",
       "588     0.0      0.0      0.0  ...    0.0       0.0     0.0      0.0   \n",
       "589     0.0      0.0      0.0  ...    0.0       0.0     0.0      0.0   \n",
       "\n",
       "     sparingly  visual  thoughts  illumines  attire  explains  \n",
       "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  \n",
       "2          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  \n",
       "4          0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "..         ...     ...       ...        ...     ...       ...  \n",
       "585        0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "586        0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "587        0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "588        0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "589        0.0     0.0       0.0        0.0     0.0       0.0  \n",
       "\n",
       "[590 rows x 8266 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.9966101694915255\n"
     ]
    }
   ],
   "source": [
    "# Assuming 'X' is your frequency matrix and 'y' is your target array\n",
    "model = MultinomialNB()\n",
    "model.fit(X_train_tfidf, y_train)\n",
    "print(\"Model accuracy:\", model.score(X_test_tfidf, y_test))  # Evaluating the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PREDICTIONS ##\n",
    "\n",
    "# Ensure unlabelled_data has correct data types (float) and correct any label issues.\n",
    "unlabelled_data = unlabelled_data.astype(np.float64)\n",
    "\n",
    "unlabelled_data_sparse = csr_matrix(unlabelled_data.values)\n",
    "\n",
    "# Apply the same TF-IDF transformation on unlabelled data\n",
    "transformer = TfidfTransformer(use_idf=False)\n",
    "unlabelled_data_tfidf = transformer.transform(X=unlabelled_data_sparse)\n",
    "\n",
    "# Predict using the trained model\n",
    "unlabelled_predictions = model.predict(unlabelled_data_tfidf)\n",
    "\n",
    "# transform the numerical predictions back to label names using the inverse transform of LabelEncoder\n",
    "predicted_labels = label_encoder.inverse_transform(unlabelled_predictions)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Buddhism_Ch1', 'Buddhism_Ch3', 'Buddhism_Ch4', 'Buddhism_Ch5',\n",
       "       'Buddhism_Ch5', 'Buddhism_Ch6', 'Buddhism_Ch7', 'Buddhism_Ch8',\n",
       "       'Buddhism_Ch9', 'Buddhism_Ch10', 'Buddhism_Ch11', 'Buddhism_Ch12',\n",
       "       'Buddhism_Ch13', 'BookOfEccleasiasticus_Ch1', 'Buddhism_Ch15',\n",
       "       'Buddhism_Ch16', 'Buddhism_Ch17', 'Buddhism_Ch18', 'Buddhism_Ch19',\n",
       "       'Buddhism_Ch20', 'Buddhism_Ch21', 'Buddhism_Ch22', 'Buddhism_Ch23',\n",
       "       'Buddhism_Ch24', 'Buddhism_Ch25', 'Buddhism_Ch26', 'Buddhism_Ch27',\n",
       "       'Buddhism_Ch28', 'Buddhism_Ch29', 'Buddhism_Ch30', 'Buddhism_Ch31',\n",
       "       'Buddhism_Ch32', 'Buddhism_Ch33', 'Buddhism_Ch34', 'Buddhism_Ch35',\n",
       "       'Buddhism_Ch36', 'Buddhism_Ch37', 'Buddhism_Ch38', 'Buddhism_Ch39',\n",
       "       'Buddhism_Ch40', 'Buddhism_Ch41', 'Buddhism_Ch42', 'Buddhism_Ch43',\n",
       "       'Buddhism_Ch44', 'Buddhism_Ch45', 'Buddhism_Ch46',\n",
       "       'TaoTeChing_Ch1', 'TaoTeChing_Ch2', 'TaoTeChing_Ch3',\n",
       "       'TaoTeChing_Ch4', 'TaoTeChing_Ch5', 'TaoTeChing_Ch6',\n",
       "       'TaoTeChing_Ch7', 'TaoTeChing_Ch8', 'TaoTeChing_Ch9',\n",
       "       'TaoTeChing_Ch10', 'TaoTeChing_Ch11', 'TaoTeChing_Ch12',\n",
       "       'TaoTeChing_Ch13', 'TaoTeChing_Ch14', 'TaoTeChing_Ch15',\n",
       "       'TaoTeChing_Ch16', 'TaoTeChing_Ch17', 'TaoTeChing_Ch18',\n",
       "       'TaoTeChing_Ch19', 'TaoTeChing_Ch20', 'TaoTeChing_Ch21',\n",
       "       'TaoTeChing_Ch22', 'TaoTeChing_Ch23', 'TaoTeChing_Ch24',\n",
       "       'TaoTeChing_Ch25', 'TaoTeChing_Ch26', 'TaoTeChing_Ch27',\n",
       "       'TaoTeChing_Ch28', 'TaoTeChing_Ch29', 'TaoTeChing_Ch30',\n",
       "       'TaoTeChing_Ch31', 'TaoTeChing_Ch32', 'TaoTeChing_Ch33',\n",
       "       'TaoTeChing_Ch34', 'TaoTeChing_Ch35', 'TaoTeChing_Ch36',\n",
       "       'TaoTeChing_Ch37', 'TaoTeChing_Ch38', 'TaoTeChing_Ch39',\n",
       "       'TaoTeChing_Ch40', 'TaoTeChing_Ch41', 'TaoTeChing_Ch42',\n",
       "       'TaoTeChing_Ch43', 'TaoTeChing_Ch44', 'TaoTeChing_Ch45',\n",
       "       'TaoTeChing_Ch46', 'TaoTeChing_Ch47', 'TaoTeChing_Ch48',\n",
       "       'TaoTeChing_Ch49', 'TaoTeChing_Ch50', 'TaoTeChing_Ch51',\n",
       "       'TaoTeChing_Ch52', 'TaoTeChing_Ch53', 'TaoTeChing_Ch54',\n",
       "       'TaoTeChing_Ch55', 'TaoTeChing_Ch56', 'TaoTeChing_Ch57',\n",
       "       'TaoTeChing_Ch58', 'TaoTeChing_Ch59', 'TaoTeChing_Ch60',\n",
       "       'TaoTeChing_Ch61', 'TaoTeChing_Ch62', 'TaoTeChing_Ch63',\n",
       "       'TaoTeChing_Ch64', 'TaoTeChing_Ch65', 'TaoTeChing_Ch66',\n",
       "       'TaoTeChing_Ch67', 'TaoTeChing_Ch68', 'TaoTeChing_Ch69',\n",
       "       'TaoTeChing_Ch70', 'TaoTeChing_Ch71', 'TaoTeChing_Ch72',\n",
       "       'TaoTeChing_Ch73', 'TaoTeChing_Ch74', 'TaoTeChing_Ch75',\n",
       "       'TaoTeChing_Ch76', 'TaoTeChing_Ch77', 'TaoTeChing_Ch78',\n",
       "       'TaoTeChing_Ch79', 'TaoTeChing_Ch80', 'TaoTeChing_Ch81',\n",
       "       'Upanishad_Ch1', 'Upanishad_Ch2', 'Upanishad_Ch3', 'Upanishad_Ch4',\n",
       "       'Upanishad_Ch5', 'Upanishad_Ch6', 'Upanishad_Ch7', 'Upanishad_Ch8',\n",
       "       'Upanishad_Ch9', 'Upanishad_Ch10', 'Upanishad_Ch11',\n",
       "       'Upanishad_Ch12', 'Upanishad_Ch13', 'Upanishad_Ch14',\n",
       "       'Upanishad_Ch15', 'Upanishad_Ch16', 'Upanishad_Ch17',\n",
       "       'Upanishad_Ch18', 'Upanishad_Ch19', 'Upanishad_Ch20',\n",
       "       'Upanishad_Ch21', 'Upanishad_Ch22', 'Upanishad_Ch23',\n",
       "       'Upanishad_Ch24', 'Upanishad_Ch25', 'Upanishad_Ch26',\n",
       "       'Upanishad_Ch27', 'Upanishad_Ch28', 'Upanishad_Ch29',\n",
       "       'Upanishad_Ch30', 'Upanishad_Ch31', 'Upanishad_Ch32',\n",
       "       'Upanishad_Ch33', 'Upanishad_Ch34', 'Upanishad_Ch35',\n",
       "       'Upanishad_Ch36', 'Upanishad_Ch37', 'Upanishad_Ch38',\n",
       "       'Upanishad_Ch39', 'Upanishad_Ch40', 'Upanishad_Ch41',\n",
       "       'Upanishad_Ch42', 'Upanishad_Ch43', 'Upanishad_Ch44',\n",
       "       'Upanishad_Ch45', 'Upanishad_Ch46', 'Upanishad_Ch47',\n",
       "       'Upanishad_Ch48', 'Upanishad_Ch49', 'Upanishad_Ch50',\n",
       "       'Upanishad_Ch51', 'Upanishad_Ch52', 'Upanishad_Ch53',\n",
       "       'Upanishad_Ch54', 'Upanishad_Ch55', 'Upanishad_Ch56',\n",
       "       'Upanishad_Ch57', 'Upanishad_Ch58', 'Upanishad_Ch59',\n",
       "       'Upanishad_Ch60', 'Upanishad_Ch61', 'Upanishad_Ch62',\n",
       "       'Upanishad_Ch63', 'Upanishad_Ch64', 'Upanishad_Ch65',\n",
       "       'Upanishad_Ch66', 'Upanishad_Ch67', 'Upanishad_Ch68',\n",
       "       'Upanishad_Ch69', 'Upanishad_Ch70', 'Upanishad_Ch71',\n",
       "       'Upanishad_Ch72', 'Upanishad_Ch73', 'Upanishad_Ch74',\n",
       "       'Upanishad_Ch75', 'Upanishad_Ch76', 'Upanishad_Ch77',\n",
       "       'Upanishad_Ch78', 'Upanishad_Ch79', 'Upanishad_Ch80',\n",
       "       'Upanishad_Ch81', 'Upanishad_Ch82', 'Upanishad_Ch83',\n",
       "       'Upanishad_Ch84', 'Upanishad_Ch85', 'Upanishad_Ch86',\n",
       "       'Upanishad_Ch87', 'Upanishad_Ch88', 'Upanishad_Ch89',\n",
       "       'Upanishad_Ch90', 'Upanishad_Ch91', 'Upanishad_Ch92',\n",
       "       'Upanishad_Ch93', 'Upanishad_Ch94', 'Upanishad_Ch95',\n",
       "       'Upanishad_Ch96', 'Upanishad_Ch97', 'Upanishad_Ch98',\n",
       "       'Upanishad_Ch99', 'Upanishad_Ch100', 'Upanishad_Ch101',\n",
       "       'Upanishad_Ch102', 'Upanishad_Ch103', 'Upanishad_Ch104',\n",
       "       'Upanishad_Ch105', 'Upanishad_Ch106', 'Upanishad_Ch107',\n",
       "       'Upanishad_Ch108', 'Upanishad_Ch109', 'Upanishad_Ch110',\n",
       "       'Upanishad_Ch111', 'Upanishad_Ch112', 'Upanishad_Ch113',\n",
       "       'Upanishad_Ch114', 'Upanishad_Ch115', 'Upanishad_Ch116',\n",
       "       'Upanishad_Ch117', 'Upanishad_Ch118', 'Upanishad_Ch119',\n",
       "       'Upanishad_Ch120', 'Upanishad_Ch121', 'Upanishad_Ch122',\n",
       "       'Upanishad_Ch123', 'Upanishad_Ch124', 'Upanishad_Ch125',\n",
       "       'Upanishad_Ch126', 'Upanishad_Ch127', 'Upanishad_Ch128',\n",
       "       'Upanishad_Ch129', 'Upanishad_Ch130', 'Upanishad_Ch131',\n",
       "       'Upanishad_Ch132', 'Upanishad_Ch133', 'Upanishad_Ch134',\n",
       "       'Upanishad_Ch135', 'Upanishad_Ch136', 'Upanishad_Ch137',\n",
       "       'Upanishad_Ch138', 'Upanishad_Ch139', 'Upanishad_Ch140',\n",
       "       'Upanishad_Ch141', 'Upanishad_Ch142', 'Upanishad_Ch143',\n",
       "       'Upanishad_Ch144', 'Upanishad_Ch145', 'Upanishad_Ch146',\n",
       "       'Upanishad_Ch147', 'Upanishad_Ch148', 'Upanishad_Ch149',\n",
       "       'Upanishad_Ch150', 'Upanishad_Ch151', 'Upanishad_Ch152',\n",
       "       'Upanishad_Ch153', 'Upanishad_Ch154', 'Upanishad_Ch155',\n",
       "       'Upanishad_Ch156', 'Upanishad_Ch157', 'Upanishad_Ch158',\n",
       "       'Upanishad_Ch159', 'Upanishad_Ch160', 'Upanishad_Ch161',\n",
       "       'Upanishad_Ch162', 'YogaSutra_Ch1', 'YogaSutra_Ch2',\n",
       "       'YogaSutra_Ch3', 'YogaSutra_Ch4', 'YogaSutra_Ch5', 'YogaSutra_Ch6',\n",
       "       'YogaSutra_Ch7', 'YogaSutra_Ch8', 'YogaSutra_Ch9',\n",
       "       'YogaSutra_Ch10', 'YogaSutra_Ch11', 'YogaSutra_Ch12',\n",
       "       'YogaSutra_Ch13', 'YogaSutra_Ch14', 'YogaSutra_Ch15',\n",
       "       'YogaSutra_Ch16', 'YogaSutra_Ch17', 'YogaSutra_Ch18',\n",
       "       'YogaSutra_Ch19', 'YogaSutra_Ch20', 'YogaSutra_Ch21',\n",
       "       'YogaSutra_Ch22', 'YogaSutra_Ch23', 'YogaSutra_Ch24',\n",
       "       'YogaSutra_Ch25', 'YogaSutra_Ch26', 'YogaSutra_Ch27',\n",
       "       'YogaSutra_Ch28', 'YogaSutra_Ch29', 'YogaSutra_Ch30',\n",
       "       'YogaSutra_Ch31', 'YogaSutra_Ch32', 'YogaSutra_Ch33',\n",
       "       'YogaSutra_Ch34', 'YogaSutra_Ch35', 'YogaSutra_Ch36',\n",
       "       'YogaSutra_Ch37', 'YogaSutra_Ch38', 'YogaSutra_Ch39',\n",
       "       'YogaSutra_Ch40', 'YogaSutra_Ch41', 'YogaSutra_Ch42',\n",
       "       'YogaSutra_Ch43', 'YogaSutra_Ch44', 'YogaSutra_Ch45',\n",
       "       'YogaSutra_Ch46', 'YogaSutra_Ch47', 'YogaSutra_Ch48',\n",
       "       'YogaSutra_Ch49', 'YogaSutra_Ch50', 'YogaSutra_Ch51',\n",
       "       'YogaSutra_Ch52', 'YogaSutra_Ch53', 'YogaSutra_Ch54',\n",
       "       'YogaSutra_Ch55', 'YogaSutra_Ch56', 'YogaSutra_Ch57',\n",
       "       'YogaSutra_Ch58', 'YogaSutra_Ch59', 'YogaSutra_Ch60',\n",
       "       'YogaSutra_Ch61', 'YogaSutra_Ch62', 'YogaSutra_Ch63',\n",
       "       'YogaSutra_Ch64', 'YogaSutra_Ch65', 'YogaSutra_Ch66',\n",
       "       'YogaSutra_Ch67', 'YogaSutra_Ch68', 'YogaSutra_Ch69',\n",
       "       'YogaSutra_Ch70', 'YogaSutra_Ch71', 'YogaSutra_Ch72',\n",
       "       'YogaSutra_Ch73', 'YogaSutra_Ch74', 'YogaSutra_Ch75',\n",
       "       'YogaSutra_Ch76', 'YogaSutra_Ch77', 'YogaSutra_Ch78',\n",
       "       'YogaSutra_Ch79', 'YogaSutra_Ch80', 'YogaSutra_Ch81',\n",
       "       'YogaSutra_Ch82', 'YogaSutra_Ch83', 'YogaSutra_Ch84',\n",
       "       'YogaSutra_Ch85', 'YogaSutra_Ch86', 'YogaSutra_Ch87',\n",
       "       'YogaSutra_Ch88', 'YogaSutra_Ch89', 'YogaSutra_Ch90',\n",
       "       'YogaSutra_Ch91', 'YogaSutra_Ch92', 'YogaSutra_Ch93',\n",
       "       'YogaSutra_Ch94', 'YogaSutra_Ch95', 'YogaSutra_Ch96',\n",
       "       'YogaSutra_Ch97', 'YogaSutra_Ch98', 'YogaSutra_Ch99',\n",
       "       'YogaSutra_Ch100', 'YogaSutra_Ch101', 'YogaSutra_Ch102',\n",
       "       'YogaSutra_Ch103', 'YogaSutra_Ch104', 'YogaSutra_Ch105',\n",
       "       'YogaSutra_Ch106', 'YogaSutra_Ch107', 'YogaSutra_Ch108',\n",
       "       'YogaSutra_Ch109', 'YogaSutra_Ch110', 'YogaSutra_Ch111',\n",
       "       'YogaSutra_Ch112', 'YogaSutra_Ch113', 'YogaSutra_Ch114',\n",
       "       'YogaSutra_Ch115', 'YogaSutra_Ch116', 'YogaSutra_Ch117',\n",
       "       'YogaSutra_Ch118', 'YogaSutra_Ch119', 'YogaSutra_Ch120',\n",
       "       'YogaSutra_Ch121', 'YogaSutra_Ch122', 'YogaSutra_Ch123',\n",
       "       'YogaSutra_Ch124', 'YogaSutra_Ch125', 'YogaSutra_Ch126',\n",
       "       'YogaSutra_Ch127', 'YogaSutra_Ch128', 'YogaSutra_Ch129',\n",
       "       'YogaSutra_Ch130', 'YogaSutra_Ch131', 'YogaSutra_Ch132',\n",
       "       'YogaSutra_Ch133', 'YogaSutra_Ch134', 'YogaSutra_Ch135',\n",
       "       'YogaSutra_Ch136', 'YogaSutra_Ch137', 'YogaSutra_Ch138',\n",
       "       'YogaSutra_Ch139', 'YogaSutra_Ch140', 'YogaSutra_Ch141',\n",
       "       'YogaSutra_Ch142', 'YogaSutra_Ch143', 'YogaSutra_Ch144',\n",
       "       'YogaSutra_Ch145', 'YogaSutra_Ch146', 'YogaSutra_Ch147',\n",
       "       'YogaSutra_Ch148', 'YogaSutra_Ch149', 'YogaSutra_Ch150',\n",
       "       'YogaSutra_Ch151', 'YogaSutra_Ch152', 'YogaSutra_Ch153',\n",
       "       'YogaSutra_Ch154', 'YogaSutra_Ch155', 'YogaSutra_Ch156',\n",
       "       'YogaSutra_Ch157', 'YogaSutra_Ch158', 'YogaSutra_Ch159',\n",
       "       'YogaSutra_Ch160', 'YogaSutra_Ch161', 'YogaSutra_Ch162',\n",
       "       'YogaSutra_Ch163', 'YogaSutra_Ch164', 'YogaSutra_Ch165',\n",
       "       'YogaSutra_Ch166', 'YogaSutra_Ch167', 'YogaSutra_Ch168',\n",
       "       'YogaSutra_Ch169', 'YogaSutra_Ch170', 'YogaSutra_Ch171',\n",
       "       'YogaSutra_Ch172', 'YogaSutra_Ch173', 'YogaSutra_Ch174',\n",
       "       'YogaSutra_Ch175', 'YogaSutra_Ch176', 'YogaSutra_Ch177',\n",
       "       'YogaSutra_Ch178', 'YogaSutra_Ch179', 'YogaSutra_Ch180',\n",
       "       'YogaSutra_Ch181', 'YogaSutra_Ch182', 'YogaSutra_Ch183',\n",
       "       'YogaSutra_Ch184', 'YogaSutra_Ch185', 'YogaSutra_Ch186',\n",
       "       'YogaSutra_Ch187', 'YogaSutra_Ch188', 'YogaSutra_Ch189',\n",
       "       'BookOfProverb_Ch1', 'BookOfProverb_Ch2', 'BookOfProverb_Ch3',\n",
       "       'BookOfProverb_Ch4', 'BookOfProverb_Ch5', 'BookOfProverb_Ch6',\n",
       "       'BookOfProverb_Ch7', 'BookOfProverb_Ch8', 'BookOfProverb_Ch9',\n",
       "       'BookOfProverb_Ch10', 'BookOfProverb_Ch11', 'BookOfProverb_Ch12',\n",
       "       'BookOfProverb_Ch13', 'BookOfProverb_Ch14', 'BookOfProverb_Ch15',\n",
       "       'BookOfProverb_Ch16', 'BookOfProverb_Ch17', 'BookOfProverb_Ch18',\n",
       "       'BookOfProverb_Ch19', 'BookOfProverb_Ch20', 'BookOfProverb_Ch21',\n",
       "       'BookOfProverb_Ch22', 'BookOfProverb_Ch23', 'BookOfProverb_Ch24',\n",
       "       'BookOfProverb_Ch25', 'BookOfProverb_Ch26', 'BookOfProverb_Ch27',\n",
       "       'BookOfProverb_Ch28', 'BookOfProverb_Ch29', 'BookOfProverb_Ch30',\n",
       "       'BookOfProverb_Ch31', 'BookOfEcclesiastes_Ch1',\n",
       "       'BookOfEcclesiastes_Ch2', 'BookOfEcclesiastes_Ch3',\n",
       "       'BookOfEcclesiastes_Ch4', 'BookOfEcclesiastes_Ch5',\n",
       "       'BookOfEcclesiastes_Ch6', 'BookOfEcclesiastes_Ch7',\n",
       "       'BookOfEcclesiastes_Ch8', 'BookOfEcclesiastes_Ch9',\n",
       "       'BookOfEcclesiastes_Ch10', 'BookOfEcclesiastes_Ch11',\n",
       "       'BookOfEcclesiastes_Ch12', 'BookOfEccleasiasticus_Ch1',\n",
       "       'BookOfEccleasiasticus_Ch2', 'BookOfEccleasiasticus_Ch3',\n",
       "       'BookOfEccleasiasticus_Ch4', 'BookOfEccleasiasticus_Ch5',\n",
       "       'BookOfEccleasiasticus_Ch6', 'BookOfEccleasiasticus_Ch7',\n",
       "       'BookOfEccleasiasticus_Ch8', 'BookOfEccleasiasticus_Ch9',\n",
       "       'BookOfEccleasiasticus_Ch10', 'BookOfEccleasiasticus_Ch11',\n",
       "       'BookOfEccleasiasticus_Ch12', 'BookOfEccleasiasticus_Ch13',\n",
       "       'BookOfEccleasiasticus_Ch14', 'BookOfEccleasiasticus_Ch15',\n",
       "       'BookOfEccleasiasticus_Ch16', 'BookOfEccleasiasticus_Ch17',\n",
       "       'BookOfEccleasiasticus_Ch18', 'BookOfEccleasiasticus_Ch19',\n",
       "       'BookOfEccleasiasticus_Ch20', 'BookOfEccleasiasticus_Ch21',\n",
       "       'BookOfEccleasiasticus_Ch22', 'BookOfEccleasiasticus_Ch23',\n",
       "       'BookOfEccleasiasticus_Ch24', 'BookOfEccleasiasticus_Ch25',\n",
       "       'BookOfEccleasiasticus_Ch26', 'BookOfEccleasiasticus_Ch27',\n",
       "       'BookOfEccleasiasticus_Ch28', 'BookOfEccleasiasticus_Ch29',\n",
       "       'BookOfEccleasiasticus_Ch30', 'BookOfEccleasiasticus_Ch31',\n",
       "       'BookOfEccleasiasticus_Ch32', 'BookOfEccleasiasticus_Ch33',\n",
       "       'BookOfEccleasiasticus_Ch34', 'BookOfEccleasiasticus_Ch35',\n",
       "       'BookOfEccleasiasticus_Ch36', 'BookOfEccleasiasticus_Ch37',\n",
       "       'BookOfEccleasiasticus_Ch38', 'BookOfEccleasiasticus_Ch39',\n",
       "       'BookOfEccleasiasticus_Ch40', 'BookOfEccleasiasticus_Ch41',\n",
       "       'BookOfEccleasiasticus_Ch42', 'BookOfEccleasiasticus_Ch43',\n",
       "       'BookOfEccleasiasticus_Ch44', 'BookOfEccleasiasticus_Ch45',\n",
       "       'BookOfEccleasiasticus_Ch46', 'BookOfEccleasiasticus_Ch47',\n",
       "       'BookOfEccleasiasticus_Ch48', 'BookOfEccleasiasticus_Ch49',\n",
       "       'BookOfEccleasiasticus_Ch50', 'BookOfWisdom_Ch1',\n",
       "       'BookOfWisdom_Ch2', 'BookOfWisdom_Ch3', 'BookOfWisdom_Ch4',\n",
       "       'BookOfWisdom_Ch5', 'BookOfWisdom_Ch6', 'BookOfWisdom_Ch7',\n",
       "       'BookOfWisdom_Ch8', 'BookOfWisdom_Ch9', 'BookOfWisdom_Ch10',\n",
       "       'BookOfWisdom_Ch11', 'BookOfWisdom_Ch12', 'BookOfWisdom_Ch13',\n",
       "       'BookOfWisdom_Ch14', 'BookOfWisdom_Ch15', 'BookOfWisdom_Ch16',\n",
       "       'BookOfWisdom_Ch17', 'BookOfWisdom_Ch18', 'BookOfWisdom_Ch19'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_labels # testing to view predicted labels (chapters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix (subset):\n",
      "[[1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0]\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x2000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize performance using confusion matrices to get summary of\n",
    "#predictions made by our classifier model compared to the actual labels.\n",
    "\n",
    "# confusion matrix based on smaller, random subset of labels (because we have 590 classes/chapters\n",
    "# which is difficult to visualize)\n",
    "\n",
    "predict_test = model.predict(X_test_tfidf) #get the model predictions based on the testing data\n",
    "\n",
    "subset_size = 30\n",
    "\n",
    "# Get a random subset of indices\n",
    "subset_indices = np.random.choice(len(y_test), size=subset_size, replace=False)\n",
    "\n",
    "# Get the subset of true labels and predicted labels\n",
    "y_test_subset = y_test[subset_indices]\n",
    "predict_test_subset = predict_test[subset_indices]\n",
    "\n",
    "# Create confusion matrix based on the subset\n",
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test_subset, predict_test_subset)\n",
    "\n",
    "# Visualize the confusion matrix\n",
    "disp.figure_.suptitle(\"Confusion Matrix (Subset)\")\n",
    "print(f\"Confusion matrix (subset):\\n{disp.confusion_matrix}\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.xticks(fontsize=8)# make size smaller so they don't overlap\n",
    "plt.yticks(fontsize=8)\n",
    "plt.figure(figsize=(10, 20))# Adjust the size as needed\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"color:red;font-size: larger\">PART 2: Updating the Multinomial Bayes Model (Book Classification) </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy: 0.847457627118644\n"
     ]
    }
   ],
   "source": [
    "#load data\n",
    "\n",
    "labelled_data = pd.read_csv('AllBooks_baseline_DTM_Labelled.csv')\n",
    "labelled_data.rename(columns={'Unnamed: 0': 'label'}, inplace=True)\n",
    "\n",
    "# Extract labels (book names) and features (bag of words)\n",
    "labels = labelled_data['label'].str.split('_').str[0].values\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "#instead of encoding with chapters, we now encode with book name\n",
    "encoded_labels = label_encoder.fit_transform(labels) \n",
    "labelled_data = labelled_data.drop('label', axis=1)\n",
    "\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    labelled_data, encoded_labels, test_size=0.5)\n",
    "#train on whole dataset\n",
    "X_train = labelled_data\n",
    "y_train = encoded_labels\n",
    "\n",
    "# Convert to sparse matrices and apply TF-IDF transformation\n",
    "X_train_sparse = csr_matrix(X_train.values.astype(np.float64))\n",
    "X_test_sparse = csr_matrix(X_test.values.astype(np.float64))\n",
    "\n",
    "tfidf_transformer = TfidfTransformer(use_idf=True)\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_sparse)\n",
    "X_test_tfidf = tfidf_transformer.transform(X_test_sparse)\n",
    "\n",
    "# Train the model\n",
    "model = MultinomialNB()\n",
    "model.fit(X_train_tfidf, y_train)\n",
    "\n",
    "print(\"Model accuracy:\", model.score(X_test_tfidf, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load unlabelled data\n",
    "unlabelled_data = pd.read_csv('AllBooks_baseline_DTM_Unlabelled_duplicate.csv')\n",
    "labelled_data.rename(columns={'# foolishness': 'foolishness'}, inplace=True)\n",
    "unlabelled_data_sparse = csr_matrix(unlabelled_data.values.astype(np.float64))\n",
    "\n",
    "# Apply TF-IDF transformation on unlabelled data\n",
    "unlabelled_data_tfidf = tfidf_transformer.transform(unlabelled_data_sparse)\n",
    "\n",
    "# Predict book names\n",
    "unlabelled_predictions = model.predict(unlabelled_data_tfidf)\n",
    "predicted_book_names = label_encoder.inverse_transform(unlabelled_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['YogaSutra', 'Buddhism', 'Buddhism', 'YogaSutra', 'YogaSutra',\n",
       "       'Buddhism', 'YogaSutra', 'Buddhism', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'Buddhism', 'Buddhism', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'Buddhism', 'Buddhism', 'Upanishad', 'YogaSutra',\n",
       "       'Buddhism', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'Upanishad', 'YogaSutra', 'Buddhism', 'Buddhism',\n",
       "       'YogaSutra', 'YogaSutra', 'Buddhism', 'YogaSutra', 'YogaSutra',\n",
       "       'Upanishad', 'Buddhism', 'Buddhism', 'Buddhism', 'Buddhism',\n",
       "       'Buddhism', 'Buddhism', 'YogaSutra', 'Buddhism', 'Buddhism',\n",
       "       'YogaSutra', 'TaoTeChing', 'YogaSutra', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'YogaSutra', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'YogaSutra',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'Upanishad', 'YogaSutra',\n",
       "       'Upanishad', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'YogaSutra',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'Upanishad', 'YogaSutra', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'YogaSutra',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'Upanishad', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'TaoTeChing',\n",
       "       'TaoTeChing', 'TaoTeChing', 'TaoTeChing', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad',\n",
       "       'Upanishad', 'Upanishad', 'Upanishad', 'Upanishad', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'YogaSutra', 'YogaSutra', 'YogaSutra', 'YogaSutra',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfProverb', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfProverb', 'BookOfEccleasiasticus',\n",
       "       'BookOfProverb', 'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus', 'BookOfProverb',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'YogaSutra', 'BookOfEccleasiasticus',\n",
       "       'YogaSutra', 'YogaSutra', 'BookOfEccleasiasticus', 'YogaSutra',\n",
       "       'BookOfEccleasiasticus', 'YogaSutra', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus', 'YogaSutra',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'YogaSutra', 'YogaSutra',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'YogaSutra', 'BookOfEccleasiasticus',\n",
       "       'BookOfEccleasiasticus', 'BookOfEccleasiasticus', 'YogaSutra',\n",
       "       'BookOfEccleasiasticus', 'YogaSutra'], dtype=object)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_book_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[30  0  0  0  0  0  0  0]\n",
      " [ 5  0  0  0  0  0  0  1]\n",
      " [13  0  2  0  0  0  0  0]\n",
      " [ 6  0  0  0  0  0  0  0]\n",
      " [ 0  0  0  0 10  0  3 11]\n",
      " [ 0  0  0  0  0 28  2  4]\n",
      " [ 0  0  0  0  0  0 88  0]\n",
      " [ 0  0  0  0  0  0  0 92]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix with book as labels #\n",
    "\n",
    "predict_test = model.predict(X_test_tfidf) #get the model predictions based on the testing data\n",
    "\n",
    "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predict_test)\n",
    "disp.figure_.suptitle(\"Confusion Matrix\")\n",
    "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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