fcc_book_recommendation_knn.ipynb

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        "# import libraries (you may add additional imports but you may not have to)\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy.sparse import csr_matrix\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "import matplotlib.pyplot as plt"
      ]
    },
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            "--2023-05-27 16:35:33--  https://cdn.freecodecamp.org/project-data/books/book-crossings.zip\n",
            "Resolving cdn.freecodecamp.org (cdn.freecodecamp.org)... 172.67.70.149, 104.26.2.33, 104.26.3.33, ...\n",
            "Connecting to cdn.freecodecamp.org (cdn.freecodecamp.org)|172.67.70.149|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 26085508 (25M) [application/zip]\n",
            "Saving to: ‘book-crossings.zip’\n",
            "\n",
            "book-crossings.zip  100%[===================>]  24.88M  29.8MB/s    in 0.8s    \n",
            "\n",
            "2023-05-27 16:35:34 (29.8 MB/s) - ‘book-crossings.zip’ saved [26085508/26085508]\n",
            "\n",
            "Archive:  book-crossings.zip\n",
            "  inflating: BX-Book-Ratings.csv     \n",
            "  inflating: BX-Books.csv            \n",
            "  inflating: BX-Users.csv            \n"
          ]
        }
      ],
      "source": [
        "# get data files\n",
        "!wget https://cdn.freecodecamp.org/project-data/books/book-crossings.zip\n",
        "\n",
        "!unzip book-crossings.zip\n",
        "\n",
        "books_filename = 'BX-Books.csv'\n",
        "ratings_filename = 'BX-Book-Ratings.csv'"
      ]
    },
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        "# import csv data into dataframes\n",
        "df_books = pd.read_csv(\n",
        "    books_filename,\n",
        "    encoding = \"ISO-8859-1\",\n",
        "    sep=\";\",\n",
        "    header=0,\n",
        "    names=['isbn', 'title', 'author'],\n",
        "    usecols=['isbn', 'title', 'author'],\n",
        "    dtype={'isbn': 'str', 'title': 'str', 'author': 'str'})\n",
        "\n",
        "df_ratings = pd.read_csv(\n",
        "    ratings_filename,\n",
        "    encoding = \"ISO-8859-1\",\n",
        "    sep=\";\",\n",
        "    header=0,\n",
        "    names=['user', 'isbn', 'rating'],\n",
        "    usecols=['user', 'isbn', 'rating'],\n",
        "    dtype={'user': 'int32', 'isbn': 'str', 'rating': 'float32'})"
      ]
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              "         isbn                                              title  \\\n",
              "0  0195153448                                Classical Mythology   \n",
              "1  0002005018                                       Clara Callan   \n",
              "2  0060973129                               Decision in Normandy   \n",
              "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
              "4  0393045218                             The Mummies of Urumchi   \n",
              "\n",
              "                 author  \n",
              "0    Mark P. O. Morford  \n",
              "1  Richard Bruce Wright  \n",
              "2          Carlo D'Este  \n",
              "3      Gina Bari Kolata  \n",
              "4       E. J. W. Barber  "
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        "df_books.head()"
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              "     user        isbn  rating\n",
              "0  276725  034545104X     0.0\n",
              "1  276726  0155061224     5.0\n",
              "2  276727  0446520802     0.0\n",
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        "df = df_ratings[[\"user\", \"rating\"]]\\\n",
        "    .groupby([\"user\"])\\\n",
        "    .count()\\\n",
        "    .reset_index()\n",
        "\n",
        "df['rating_log10'] = np.log10(df['rating'])\n",
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              "<Axes: xlabel='user', ylabel='rating_log10'>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfb = df_ratings.groupby([\"isbn\"]).count().reset_index()\n",
        "good_books = dfb.loc[dfb[\"rating\"] >= 100][\"isbn\"]\n",
        "\n",
        "# books contains those have no less than 100 ratings\n",
        "good_books = df_books.loc[df_books[\"isbn\"].isin(good_books)]"
      ],
      "metadata": {
        "id": "c1itUqLvGihg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfu = df_ratings[[\"user\", \"rating\"]]\\\n",
        "    .groupby([\"user\"])\\\n",
        "    .count()\\\n",
        "    .reset_index()\n",
        "\n",
        "good_users = dfu.loc[dfu[\"rating\"] >= 200][\"user\"]\n",
        "\n",
        "df = df_ratings.loc[df_ratings[\"user\"].isin(good_users)]\n",
        "df = df.loc[df[\"isbn\"].isin(good_books[\"isbn\"])]\n"
      ],
      "metadata": {
        "id": "r0uc5pOKGm0n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_book_features.head()\n"
      ],
      "metadata": {
        "id": "gfeGGgRKG49T",
        "outputId": "eb1f03e7-61f8-4901-9e1d-973ef3453c7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user        254     2276    2766    2977    3363    4017    4385    6242    \\\n",
              "isbn                                                                         \n",
              "002542730X     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "0060008032     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "0060096195     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "006016848X     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "0060173289     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "user        6251    6323    ...  274004  274061  274301  274308  274808  \\\n",
              "isbn                        ...                                           \n",
              "002542730X     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
              "0060008032     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
              "0060096195     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
              "006016848X     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
              "0060173289     0.0     0.0  ...     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "user        275970  277427  277478  277639  278418  \n",
              "isbn                                                \n",
              "002542730X     0.0    10.0     0.0     0.0     0.0  \n",
              "0060008032     0.0     0.0     0.0     0.0     0.0  \n",
              "0060096195     0.0     0.0     0.0     0.0     0.0  \n",
              "006016848X     0.0     0.0     0.0     0.0     0.0  \n",
              "0060173289     0.0     0.0     0.0     0.0     0.0  \n",
              "\n",
              "[5 rows x 888 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-572c265a-3edf-48fd-91fb-aef3b961373d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>user</th>\n",
              "      <th>254</th>\n",
              "      <th>2276</th>\n",
              "      <th>2766</th>\n",
              "      <th>2977</th>\n",
              "      <th>3363</th>\n",
              "      <th>4017</th>\n",
              "      <th>4385</th>\n",
              "      <th>6242</th>\n",
              "      <th>6251</th>\n",
              "      <th>6323</th>\n",
              "      <th>...</th>\n",
              "      <th>274004</th>\n",
              "      <th>274061</th>\n",
              "      <th>274301</th>\n",
              "      <th>274308</th>\n",
              "      <th>274808</th>\n",
              "      <th>275970</th>\n",
              "      <th>277427</th>\n",
              "      <th>277478</th>\n",
              "      <th>277639</th>\n",
              "      <th>278418</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>isbn</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = NearestNeighbors(metric='cosine')\n",
        "model.fit(mat_book_features)\n"
      ],
      "metadata": {
        "id": "1hVK4UBuG6_v",
        "outputId": "79b8c38e-fb27-45bb-eb87-d137466a4bd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NearestNeighbors(metric='cosine')"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pivot ratings into book features\n",
        "df_book_features = df.pivot(\n",
        "    index='isbn',\n",
        "    columns='user',\n",
        "    values='rating'\n",
        ").fillna(0)\n",
        "\n",
        "# convert dataframe of book features to scipy sparse matrix\n",
        "# this part requires a lot of memory!\n",
        "mat_book_features = csr_matrix(df_book_features.values)\n"
      ],
      "metadata": {
        "id": "dzvPPL4QG0YC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f5ZUd-L1SQz7",
        "outputId": "f8d9361a-eb75-43c7-8179-b7dbcb0dea33",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['The Queen of the Damned (Vampire Chronicles (Paperback))',\n",
              " [['Catch 22', 0.7939835],\n",
              "  ['The Witching Hour (Lives of the Mayfair Witches)', 0.74486566],\n",
              "  ['Interview with the Vampire', 0.73450685],\n",
              "  ['The Tale of the Body Thief (Vampire Chronicles (Paperback))', 0.53763384],\n",
              "  ['The Vampire Lestat (Vampire Chronicles, Book II)', 0.51784116]]]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# function to return recommended books - this will be tested\n",
        "def get_recommends(title = \"\"):\n",
        "    try:\n",
        "        book = good_books.loc[good_books[\"title\"] == title]\n",
        "    except KeyError as e:\n",
        "        print('Book', e, 'does not exist')\n",
        "        return\n",
        "\n",
        "    b = df_book_features.loc[df_book_features.index.isin(book[\"isbn\"])]\n",
        "    distance, indice = model.kneighbors([x for x in b.values], n_neighbors=6)\n",
        "\n",
        "    distance =  distance[0][1:]\n",
        "    indice = indice[0][1:]\n",
        "\n",
        "    titles = [\n",
        "        df_books.loc[df_books['isbn'] == df_book_features.iloc[i].name][\"title\"].values[0]\\\n",
        "        for i in indice\n",
        "    ]\n",
        "\n",
        "    recommended = [list(z) for z in zip(titles, distance)][::-1]\n",
        "    return [title, recommended]\n",
        "\n",
        "get_recommends(\"The Queen of the Damned (Vampire Chronicles (Paperback))\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jd2SLCh8oxMh",
        "outputId": "4dd1a2f2-d6b0-4be8-ccae-559720762171",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[\"Where the Heart Is (Oprah's Book Club (Paperback))\", [[\"I'll Be Seeing You\", 0.8016211], ['The Weight of Water', 0.77085835], ['The Surgeon', 0.7699411], ['I Know This Much Is True', 0.7677075], ['The Lovely Bones: A Novel', 0.7234864]]]\n",
            "You passed the challenge! 🎉🎉🎉🎉🎉\n"
          ]
        }
      ],
      "source": [
        "books = get_recommends(\"Where the Heart Is (Oprah's Book Club (Paperback))\")\n",
        "print(books)\n",
        "\n",
        "def test_book_recommendation():\n",
        "  test_pass = True\n",
        "  recommends = get_recommends(\"Where the Heart Is (Oprah's Book Club (Paperback))\")\n",
        "  if recommends[0] != \"Where the Heart Is (Oprah's Book Club (Paperback))\":\n",
        "    test_pass = False\n",
        "  recommended_books = [\"I'll Be Seeing You\", 'The Weight of Water', 'The Surgeon', 'I Know This Much Is True']\n",
        "  recommended_books_dist = [0.8, 0.77, 0.77, 0.77]\n",
        "  for i in range(2):\n",
        "    if recommends[1][i][0] not in recommended_books:\n",
        "      test_pass = False\n",
        "    if abs(recommends[1][i][1] - recommended_books_dist[i]) >= 0.05:\n",
        "      test_pass = False\n",
        "  if test_pass:\n",
        "    print(\"You passed the challenge! 🎉🎉🎉🎉🎉\")\n",
        "  else:\n",
        "    print(\"You haven't passed yet. Keep trying!\")\n",
        "\n",
        "test_book_recommendation()"
      ]
    }
  ],
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