Book Recommendation based on - Count and TFidf.ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [
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     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>goodreads_book_id</th>\n",
       "      <th>best_book_id</th>\n",
       "      <th>work_id</th>\n",
       "      <th>books_count</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>authors</th>\n",
       "      <th>original_publication_year</th>\n",
       "      <th>original_title</th>\n",
       "      <th>...</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>work_ratings_count</th>\n",
       "      <th>work_text_reviews_count</th>\n",
       "      <th>ratings_1</th>\n",
       "      <th>ratings_2</th>\n",
       "      <th>ratings_3</th>\n",
       "      <th>ratings_4</th>\n",
       "      <th>ratings_5</th>\n",
       "      <th>image_url</th>\n",
       "      <th>small_image_url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2767052</td>\n",
       "      <td>2792775</td>\n",
       "      <td>272</td>\n",
       "      <td>439023483</td>\n",
       "      <td>9.780439e+12</td>\n",
       "      <td>Suzanne Collins</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>The Hunger Games</td>\n",
       "      <td>...</td>\n",
       "      <td>4780653</td>\n",
       "      <td>4942365</td>\n",
       "      <td>155254</td>\n",
       "      <td>66715</td>\n",
       "      <td>127936</td>\n",
       "      <td>560092</td>\n",
       "      <td>1481305</td>\n",
       "      <td>2706317</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1447303603s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4640799</td>\n",
       "      <td>491</td>\n",
       "      <td>439554934</td>\n",
       "      <td>9.780440e+12</td>\n",
       "      <td>J.K. Rowling, Mary GrandPré</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "      <td>...</td>\n",
       "      <td>4602479</td>\n",
       "      <td>4800065</td>\n",
       "      <td>75867</td>\n",
       "      <td>75504</td>\n",
       "      <td>101676</td>\n",
       "      <td>455024</td>\n",
       "      <td>1156318</td>\n",
       "      <td>3011543</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1474154022s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>41865</td>\n",
       "      <td>41865</td>\n",
       "      <td>3212258</td>\n",
       "      <td>226</td>\n",
       "      <td>316015849</td>\n",
       "      <td>9.780316e+12</td>\n",
       "      <td>Stephenie Meyer</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Twilight</td>\n",
       "      <td>...</td>\n",
       "      <td>3866839</td>\n",
       "      <td>3916824</td>\n",
       "      <td>95009</td>\n",
       "      <td>456191</td>\n",
       "      <td>436802</td>\n",
       "      <td>793319</td>\n",
       "      <td>875073</td>\n",
       "      <td>1355439</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361039443s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2657</td>\n",
       "      <td>2657</td>\n",
       "      <td>3275794</td>\n",
       "      <td>487</td>\n",
       "      <td>61120081</td>\n",
       "      <td>9.780061e+12</td>\n",
       "      <td>Harper Lee</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "      <td>...</td>\n",
       "      <td>3198671</td>\n",
       "      <td>3340896</td>\n",
       "      <td>72586</td>\n",
       "      <td>60427</td>\n",
       "      <td>117415</td>\n",
       "      <td>446835</td>\n",
       "      <td>1001952</td>\n",
       "      <td>1714267</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1361975680s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4671</td>\n",
       "      <td>4671</td>\n",
       "      <td>245494</td>\n",
       "      <td>1356</td>\n",
       "      <td>743273567</td>\n",
       "      <td>9.780743e+12</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>The Great Gatsby</td>\n",
       "      <td>...</td>\n",
       "      <td>2683664</td>\n",
       "      <td>2773745</td>\n",
       "      <td>51992</td>\n",
       "      <td>86236</td>\n",
       "      <td>197621</td>\n",
       "      <td>606158</td>\n",
       "      <td>936012</td>\n",
       "      <td>947718</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560m...</td>\n",
       "      <td>https://images.gr-assets.com/books/1490528560s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id  goodreads_book_id  best_book_id  work_id  books_count       isbn  \\\n",
       "0        1            2767052       2767052  2792775          272  439023483   \n",
       "1        2                  3             3  4640799          491  439554934   \n",
       "2        3              41865         41865  3212258          226  316015849   \n",
       "3        4               2657          2657  3275794          487   61120081   \n",
       "4        5               4671          4671   245494         1356  743273567   \n",
       "\n",
       "         isbn13                      authors  original_publication_year  \\\n",
       "0  9.780439e+12              Suzanne Collins                     2008.0   \n",
       "1  9.780440e+12  J.K. Rowling, Mary GrandPré                     1997.0   \n",
       "2  9.780316e+12              Stephenie Meyer                     2005.0   \n",
       "3  9.780061e+12                   Harper Lee                     1960.0   \n",
       "4  9.780743e+12          F. Scott Fitzgerald                     1925.0   \n",
       "\n",
       "                             original_title  ... ratings_count  \\\n",
       "0                          The Hunger Games  ...       4780653   \n",
       "1  Harry Potter and the Philosopher's Stone  ...       4602479   \n",
       "2                                  Twilight  ...       3866839   \n",
       "3                     To Kill a Mockingbird  ...       3198671   \n",
       "4                          The Great Gatsby  ...       2683664   \n",
       "\n",
       "  work_ratings_count  work_text_reviews_count  ratings_1  ratings_2  \\\n",
       "0            4942365                   155254      66715     127936   \n",
       "1            4800065                    75867      75504     101676   \n",
       "2            3916824                    95009     456191     436802   \n",
       "3            3340896                    72586      60427     117415   \n",
       "4            2773745                    51992      86236     197621   \n",
       "\n",
       "   ratings_3  ratings_4  ratings_5  \\\n",
       "0     560092    1481305    2706317   \n",
       "1     455024    1156318    3011543   \n",
       "2     793319     875073    1355439   \n",
       "3     446835    1001952    1714267   \n",
       "4     606158     936012     947718   \n",
       "\n",
       "                                           image_url  \\\n",
       "0  https://images.gr-assets.com/books/1447303603m...   \n",
       "1  https://images.gr-assets.com/books/1474154022m...   \n",
       "2  https://images.gr-assets.com/books/1361039443m...   \n",
       "3  https://images.gr-assets.com/books/1361975680m...   \n",
       "4  https://images.gr-assets.com/books/1490528560m...   \n",
       "\n",
       "                                     small_image_url  \n",
       "0  https://images.gr-assets.com/books/1447303603s...  \n",
       "1  https://images.gr-assets.com/books/1474154022s...  \n",
       "2  https://images.gr-assets.com/books/1361039443s...  \n",
       "3  https://images.gr-assets.com/books/1361975680s...  \n",
       "4  https://images.gr-assets.com/books/1490528560s...  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "data = pd.read_csv('data/books.csv')\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "book_id                         0\n",
       "goodreads_book_id               0\n",
       "best_book_id                    0\n",
       "work_id                         0\n",
       "books_count                     0\n",
       "isbn                          700\n",
       "isbn13                        585\n",
       "authors                         0\n",
       "original_publication_year      21\n",
       "original_title                585\n",
       "title                           0\n",
       "language_code                1084\n",
       "average_rating                  0\n",
       "ratings_count                   0\n",
       "work_ratings_count              0\n",
       "work_text_reviews_count         0\n",
       "ratings_1                       0\n",
       "ratings_2                       0\n",
       "ratings_3                       0\n",
       "ratings_4                       0\n",
       "ratings_5                       0\n",
       "image_url                       0\n",
       "small_image_url                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_id</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>The Hunger Games (The Hunger Games, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Sorcerer's Stone (Harry P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Twilight (Twilight, #1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>To Kill a Mockingbird</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>The Great Gatsby</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   book_id                                              title\n",
       "0        1            The Hunger Games (The Hunger Games, #1)\n",
       "1        2  Harry Potter and the Sorcerer's Stone (Harry P...\n",
       "2        3                            Twilight (Twilight, #1)\n",
       "3        4                              To Kill a Mockingbird\n",
       "4        5                                   The Great Gatsby"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting relevant columns that would influence a book's rating based on book title. \n",
    "books_title = data[['book_id', 'title']]\n",
    "books_title.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 261)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets vectorize all these titles\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "#initialize vectorizer\n",
    "vect = CountVectorizer(analyzer = 'word', ngram_range = (1,2), stop_words = 'english', min_df = 0.002) #min_df = rare words, max_df = most used words\n",
    "#ngram_range = (1,2) - if used more than  1(value), lots of features or noise\n",
    "\n",
    "#Fit into the title\n",
    "vect.fit(books_title['title'])\n",
    "title_matrix = vect.transform(books_title['title'])\n",
    "title_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01',\n",
       " '10',\n",
       " '11',\n",
       " '12',\n",
       " '13',\n",
       " '14',\n",
       " '15',\n",
       " '16',\n",
       " '39',\n",
       " 'adventures',\n",
       " 'alex',\n",
       " 'alex cross',\n",
       " 'america',\n",
       " 'american',\n",
       " 'angel',\n",
       " 'angels',\n",
       " 'anita',\n",
       " 'anita blake',\n",
       " 'apprentice',\n",
       " 'art',\n",
       " 'bad',\n",
       " 'batman',\n",
       " 'beautiful',\n",
       " 'beauty',\n",
       " 'best',\n",
       " 'big',\n",
       " 'black',\n",
       " 'blake',\n",
       " 'blake vampire',\n",
       " 'blood',\n",
       " 'blue',\n",
       " 'body',\n",
       " 'bone',\n",
       " 'bones',\n",
       " 'book',\n",
       " 'books',\n",
       " 'born',\n",
       " 'bosch',\n",
       " 'bosch universe',\n",
       " 'boy',\n",
       " 'boys',\n",
       " 'broken',\n",
       " 'brothers',\n",
       " 'case',\n",
       " 'cat',\n",
       " 'child',\n",
       " 'children',\n",
       " 'chronicles',\n",
       " 'circle',\n",
       " 'city',\n",
       " 'club',\n",
       " 'complete',\n",
       " 'confessions',\n",
       " 'cross',\n",
       " 'cycle',\n",
       " 'dance',\n",
       " 'dark',\n",
       " 'dark hunter',\n",
       " 'darkest',\n",
       " 'darkness',\n",
       " 'daughter',\n",
       " 'davenport',\n",
       " 'dawn',\n",
       " 'day',\n",
       " 'days',\n",
       " 'dead',\n",
       " 'death',\n",
       " 'death death',\n",
       " 'detective',\n",
       " 'devil',\n",
       " 'diaries',\n",
       " 'diary',\n",
       " 'die',\n",
       " 'discworld',\n",
       " 'dog',\n",
       " 'don',\n",
       " 'dragon',\n",
       " 'dream',\n",
       " 'dreams',\n",
       " 'earth',\n",
       " 'empire',\n",
       " 'end',\n",
       " 'fall',\n",
       " 'fallen',\n",
       " 'family',\n",
       " 'fear',\n",
       " 'files',\n",
       " 'food',\n",
       " 'forever',\n",
       " 'forgotten',\n",
       " 'forgotten realms',\n",
       " 'game',\n",
       " 'garden',\n",
       " 'girl',\n",
       " 'girls',\n",
       " 'glass',\n",
       " 'god',\n",
       " 'gods',\n",
       " 'golden',\n",
       " 'gone',\n",
       " 'good',\n",
       " 'grave',\n",
       " 'great',\n",
       " 'green',\n",
       " 'guide',\n",
       " 'hard',\n",
       " 'harry',\n",
       " 'harry bosch',\n",
       " 'harry potter',\n",
       " 'heart',\n",
       " 'heaven',\n",
       " 'hercule',\n",
       " 'hercule poirot',\n",
       " 'hidden',\n",
       " 'high',\n",
       " 'history',\n",
       " 'home',\n",
       " 'house',\n",
       " 'human',\n",
       " 'hunter',\n",
       " 'ice',\n",
       " 'immortals',\n",
       " 'inside',\n",
       " 'island',\n",
       " 'jack',\n",
       " 'jack reacher',\n",
       " 'john',\n",
       " 'journey',\n",
       " 'just',\n",
       " 'kay',\n",
       " 'kay scarpetta',\n",
       " 'key',\n",
       " 'king',\n",
       " 'kinsey',\n",
       " 'kinsey millhone',\n",
       " 'kiss',\n",
       " 'knight',\n",
       " 'know',\n",
       " 'lady',\n",
       " 'left',\n",
       " 'legacy',\n",
       " 'legend',\n",
       " 'life',\n",
       " 'light',\n",
       " 'like',\n",
       " 'little',\n",
       " 'lives',\n",
       " 'living',\n",
       " 'long',\n",
       " 'lord',\n",
       " 'lost',\n",
       " 'love',\n",
       " 'lucas',\n",
       " 'lucas davenport',\n",
       " 'magic',\n",
       " 'magician',\n",
       " 'man',\n",
       " 'memoir',\n",
       " 'men',\n",
       " 'mercy',\n",
       " 'midnight',\n",
       " 'millhone',\n",
       " 'mind',\n",
       " 'miss',\n",
       " 'moon',\n",
       " 'mr',\n",
       " 'murder',\n",
       " 'new',\n",
       " 'night',\n",
       " 'novel',\n",
       " 'novels',\n",
       " 'old',\n",
       " 'people',\n",
       " 'perfect',\n",
       " 'plum',\n",
       " 'poems',\n",
       " 'poirot',\n",
       " 'potter',\n",
       " 'power',\n",
       " 'pretty',\n",
       " 'prey',\n",
       " 'prey lucas',\n",
       " 'prince',\n",
       " 'princess',\n",
       " 'queen',\n",
       " 'reacher',\n",
       " 'realms',\n",
       " 'red',\n",
       " 'rising',\n",
       " 'river',\n",
       " 'road',\n",
       " 'rock',\n",
       " 'rose',\n",
       " 'saga',\n",
       " 'scarpetta',\n",
       " 'school',\n",
       " 'science',\n",
       " 'sea',\n",
       " 'second',\n",
       " 'secret',\n",
       " 'secrets',\n",
       " 'series',\n",
       " 'set',\n",
       " 'seven',\n",
       " 'shadow',\n",
       " 'shadows',\n",
       " 'short',\n",
       " 'sisters',\n",
       " 'small',\n",
       " 'snow',\n",
       " 'son',\n",
       " 'song',\n",
       " 'soul',\n",
       " 'star',\n",
       " 'stars',\n",
       " 'stephanie',\n",
       " 'stephanie plum',\n",
       " 'stone',\n",
       " 'stories',\n",
       " 'storm',\n",
       " 'story',\n",
       " 'strange',\n",
       " 'street',\n",
       " 'summer',\n",
       " 'sun',\n",
       " 'sword',\n",
       " 'tale',\n",
       " 'tales',\n",
       " 'tell',\n",
       " 'things',\n",
       " 'time',\n",
       " 'tree',\n",
       " 'trilogy',\n",
       " 'true',\n",
       " 'true story',\n",
       " 'truth',\n",
       " 'twilight',\n",
       " 'universe',\n",
       " 'vampire',\n",
       " 'vampire hunter',\n",
       " 'vampires',\n",
       " 'vol',\n",
       " 'volume',\n",
       " 'walking',\n",
       " 'walking dead',\n",
       " 'war',\n",
       " 'wars',\n",
       " 'water',\n",
       " 'way',\n",
       " 'wheel',\n",
       " 'white',\n",
       " 'wicked',\n",
       " 'wife',\n",
       " 'wild',\n",
       " 'winter',\n",
       " 'witch',\n",
       " 'woman',\n",
       " 'women',\n",
       " 'world',\n",
       " 'year',\n",
       " 'years']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets find vocabulary/features\n",
    "features = vect.get_feature_names()\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cosine Similarity between titles\n",
    "\n",
    "# Initializing Cosine similarity into title matrix\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "cosine_sim_titles = cosine_similarity(title_matrix, title_matrix)\n",
    "cosine_sim_titles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The Glass Castle'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get books which are similar to a given title\n",
    "title_id = 80\n",
    "books_title['title'].iloc[title_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['glass']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find out what features have been considered  by the vectorizer for a given title ?\n",
    "feature_array = np.squeeze(title_matrix[title_id].toarray())\n",
    "idx = np.where(feature_array > 0)\n",
    "idx[0]\n",
    "[features[x] for x in idx[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find index of feature\n",
    "idx[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        , 1.        , 0.89442719, 0.89442719, 0.70710678])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cosine similarity with other similar titles\n",
    "n = 15 #how many books to be recommended\n",
    "top_n_idx = np.flip(np.argsort(cosine_sim_titles[title_id,]), axis = 0)[0:n]\n",
    "top_n_sim_values = cosine_sim_titles[title_id, top_n_idx]\n",
    "top_n_sim_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392                 Throne of Glass (Throne of Glass, #1)\n",
       "6382                                       The Glass Lake\n",
       "7620    The Assassin and the Desert (Throne of Glass, ...\n",
       "1185                                  The Glass Menagerie\n",
       "5036                                     The Glass Palace\n",
       "80                                       The Glass Castle\n",
       "1485    Through the Looking-Glass, and What Alice Foun...\n",
       "1021                   Heir of Fire (Throne of Glass, #3)\n",
       "8667    The Assassin and the Underworld (Throne of Gla...\n",
       "1851                                    Glass (Crank, #2)\n",
       "2628     The Assassin's Blade (Throne of Glass, #0.1-0.5)\n",
       "8433                                   The Sound of Glass\n",
       "9975                                Sea Glass (Glass, #2)\n",
       "8506                              Storm Glass (Glass, #1)\n",
       "4633                                  The Glass Bead Game\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find top n with values > 0\n",
    "top_n_idx = top_n_idx[top_n_sim_values > 0]\n",
    "\n",
    "\n",
    "\n",
    "#Matching books\n",
    "books_title['title'].iloc[top_n_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_sim_books(title_id, title_matrix, vectorizer, top_n = 10):\n",
    "    \n",
    "    # generate sim matrix\n",
    "    sim_matrix = cosine_similarity(title_matrix, title_matrix)\n",
    "    features = vectorizer.get_feature_names()\n",
    "\n",
    "    top_n_idx = np.flip(np.argsort(sim_matrix[title_id,]),axis=0)[0:top_n]\n",
    "    top_n_sim_values = sim_matrix[title_id, top_n_idx]\n",
    "    \n",
    "    # find top n with values > 0\n",
    "    top_n_idx = top_n_idx[top_n_sim_values > 0]\n",
    "    scores = top_n_sim_values[top_n_sim_values > 0]\n",
    "    \n",
    "    \n",
    "    # find features from the vectorized matrix\n",
    "    sim_books_idx = books_title['title'].iloc[top_n_idx].index\n",
    "    words = []\n",
    "    for book_idx in sim_books_idx:\n",
    "        try:\n",
    "            feature_array = np.squeeze(title_matrix[book_idx,].toarray())\n",
    "        except:\n",
    "            feature_array = np.squeeze(title_matrix[book_idx,])\n",
    "        idx = np.where(feature_array > 0)\n",
    "        words.append([\" , \".join([features[i] for i in idx[0]])])\n",
    "        \n",
    "    # collate results\n",
    "    res = pd.DataFrame({\"book_title\" : books_title['title'].iloc[title_id],\n",
    "           \"sim_books\": books_title['title'].iloc[top_n_idx].values,\"words\":words,\n",
    "           \"scores\":scores}, columns = [\"book_title\",\"sim_books\",\"scores\",\"words\"])\n",
    "    \n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Kite Runner\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>book_title</th>\n",
       "      <th>sim_books</th>\n",
       "      <th>scores</th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [book_title, sim_books, scores, words]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vect = CountVectorizer(analyzer='word',ngram_range=(1,2),stop_words='english', min_df = 0.001)\n",
    "vect.fit(books_title['title'])\n",
    "title_matrix = vect.transform(books_title['title'])\n",
    "print(books_title['title'][10])\n",
    "return_sim_books(10,title_matrix,vect,top_n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Tf-Idf Vectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "\n",
    "tf = TfidfVectorizer(analyzer = 'word', ngram_range = (1,2), min_df = 0, stop_words = 'english')\n",
    "tfidf_matrix = tf.fit_transform(books_title['title'])\n",
    "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
    "cosine_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "titles = books_title['title']\n",
    "indices = pd.Series(books_title.index, index = books_title['title']) #converting all titles into a Series\n",
    "\n",
    "#Function that gets book recommendations based on the cosine similarity score of book titles\n",
    "def book_recommendations(title, n):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key = lambda x:x[1], reverse = True)\n",
    "    sim_scores = sim_scores[1:n+1]\n",
    "    book_indices = [i[0] for i in sim_scores]\n",
    "    return titles.iloc[book_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Kite Runner\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8946                                        Once a Runner\n",
       "90                      The Maze Runner (Maze Runner, #1)\n",
       "375                      The Death Cure (Maze Runner, #3)\n",
       "945                    The Kill Order (Maze Runner, #0.5)\n",
       "258                   The Scorch Trials (Maze Runner, #2)\n",
       "6711    Ultramarathon Man: Confessions of an All-Night...\n",
       "0                 The Hunger Games (The Hunger Games, #1)\n",
       "1       Harry Potter and the Sorcerer's Stone (Harry P...\n",
       "2                                 Twilight (Twilight, #1)\n",
       "3                                   To Kill a Mockingbird\n",
       "4                                        The Great Gatsby\n",
       "5                                  The Fault in Our Stars\n",
       "6                                              The Hobbit\n",
       "7                                  The Catcher in the Rye\n",
       "8                   Angels & Demons  (Robert Langdon, #1)\n",
       "9                                     Pride and Prejudice\n",
       "11                              Divergent (Divergent, #1)\n",
       "12                                                   1984\n",
       "13                                            Animal Farm\n",
       "14                              The Diary of a Young Girl\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recommend n books for a book having index 1\n",
    "book_index = 10\n",
    "n = 20\n",
    "\n",
    "print(books_title['title'][book_index])\n",
    "book_recommendations(books_title.title[book_index],n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5871    A Tale of Two Cities / Great Expectations\n",
       "2697                             Invisible Cities\n",
       "1699                    A Tale for the Time Being\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recommend similar books based on a list of books read\n",
    "\n",
    "book_recommendations('A Tale of Two Cities',3)"
   ]
  }
 ],
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