llm_recs_clean.ipynb

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        "<a href=\"https://colab.research.google.com/github/simon19891101/ML_experiments/blob/main/llm_recs_clean.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "## Dataset"
      ],
      "metadata": {
        "id": "6QxrqIcgVObn"
      }
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    {
      "cell_type": "code",
      "source": [
        "!unzip sample_data/ml-100k.zip -d sample_data"
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        },
        "id": "HGr4mgXxVS3n",
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      "execution_count": 1,
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        {
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          "text": [
            "Archive:  sample_data/ml-100k.zip\n",
            "   creating: sample_data/ml-100k/\n",
            "  inflating: sample_data/ml-100k/allbut.pl  \n",
            "  inflating: sample_data/ml-100k/mku.sh  \n",
            "  inflating: sample_data/ml-100k/README  \n",
            "  inflating: sample_data/ml-100k/u.data  \n",
            "  inflating: sample_data/ml-100k/u.genre  \n",
            "  inflating: sample_data/ml-100k/u.info  \n",
            "  inflating: sample_data/ml-100k/u.item  \n",
            "  inflating: sample_data/ml-100k/u.occupation  \n",
            "  inflating: sample_data/ml-100k/u.user  \n",
            "  inflating: sample_data/ml-100k/u1.base  \n",
            "  inflating: sample_data/ml-100k/u1.test  \n",
            "  inflating: sample_data/ml-100k/u2.base  \n",
            "  inflating: sample_data/ml-100k/u2.test  \n",
            "  inflating: sample_data/ml-100k/u3.base  \n",
            "  inflating: sample_data/ml-100k/u3.test  \n",
            "  inflating: sample_data/ml-100k/u4.base  \n",
            "  inflating: sample_data/ml-100k/u4.test  \n",
            "  inflating: sample_data/ml-100k/u5.base  \n",
            "  inflating: sample_data/ml-100k/u5.test  \n",
            "  inflating: sample_data/ml-100k/ua.base  \n",
            "  inflating: sample_data/ml-100k/ua.test  \n",
            "  inflating: sample_data/ml-100k/ub.base  \n",
            "  inflating: sample_data/ml-100k/ub.test  \n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import time\n",
        "from datetime import datetime\n",
        "import json\n",
        "\n",
        "dir = 'sample_data/ml-100k'\n",
        "col_names = ['user id', 'item id', 'rating', 'timestamp']\n",
        "data = pd.read_csv(os.path.join(dir, 'u.data'), delimiter='\\t', names=col_names, header=None)\n",
        "data['timestamp'] = data['timestamp'].apply(lambda x: datetime.fromtimestamp(x))\n",
        "\n",
        "with open(os.path.join(dir, 'u.item'), encoding = \"ISO-8859-1\") as f:\n",
        "  movie = pd.read_csv(f, delimiter='|', header=None)\n",
        "\n",
        "movie.columns = ['item id', 'title' ,'release date','video release date', 'IMDb URL', 'unknown', 'Action', 'Adventure',\n",
        " 'Animation', 'Children\\'s', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
        " 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n",
        "\n",
        "with open(os.path.join(dir, 'u.user'), encoding = \"ISO-8859-1\") as f:\n",
        "  user = pd.read_csv(f, delimiter='|', header=None)\n",
        "\n",
        "user.columns = ['user id', 'age', 'gender', 'occupation', 'zip code']\n",
        "\n",
        "ratings = data.merge(movie[['item id', 'title']], on='item id')\n",
        "\n",
        "ratings['like'] = ratings['rating'] > 3"
      ],
      "metadata": {
        "id": "FV_PPmINVXRT"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.sort_values(by=['user id'], ascending=[True]).head(10)"
      ],
      "metadata": {
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       user id  item id  rating           timestamp  \\\n",
              "43606        1       12       5 1997-11-03 07:42:40   \n",
              "79998        1      254       1 1997-11-03 07:16:32   \n",
              "35906        1      189       3 1998-03-01 06:15:28   \n",
              "69127        1       87       5 1997-11-03 07:52:21   \n",
              "51383        1      187       4 1997-09-22 22:01:18   \n",
              "71026        1      180       3 1997-09-24 03:42:53   \n",
              "97387        1       46       4 1997-10-15 05:27:10   \n",
              "6611         1      201       3 1997-11-03 07:42:40   \n",
              "36126        1       64       5 1997-09-24 03:40:04   \n",
              "6751         1      241       4 1997-11-03 07:45:33   \n",
              "\n",
              "                                    title   like  \n",
              "43606          Usual Suspects, The (1995)   True  \n",
              "79998               Batman & Robin (1997)  False  \n",
              "35906             Grand Day Out, A (1992)  False  \n",
              "69127  Searching for Bobby Fischer (1993)   True  \n",
              "51383      Godfather: Part II, The (1974)   True  \n",
              "71026               Apocalypse Now (1979)  False  \n",
              "97387                      Exotica (1994)   True  \n",
              "6611                  Evil Dead II (1987)  False  \n",
              "36126    Shawshank Redemption, The (1994)   True  \n",
              "6751     Last of the Mohicans, The (1992)   True  "
            ],
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              "      <th></th>\n",
              "      <th>user id</th>\n",
              "      <th>item id</th>\n",
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              "      <td>1997-11-03 07:16:32</td>\n",
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              "      <td>1997-11-03 07:52:21</td>\n",
              "      <td>Searching for Bobby Fischer (1993)</td>\n",
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              "      <td>Exotica (1994)</td>\n",
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              "      <td>1997-11-03 07:42:40</td>\n",
              "      <td>Evil Dead II (1987)</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36126</th>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>5</td>\n",
              "      <td>1997-09-24 03:40:04</td>\n",
              "      <td>Shawshank Redemption, The (1994)</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6751</th>\n",
              "      <td>1</td>\n",
              "      <td>241</td>\n",
              "      <td>4</td>\n",
              "      <td>1997-11-03 07:45:33</td>\n",
              "      <td>Last of the Mohicans, The (1992)</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c56a56c9-de06-415a-bdf5-c7b381a4edb1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c56a56c9-de06-415a-bdf5-c7b381a4edb1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c56a56c9-de06-415a-bdf5-c7b381a4edb1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aaeb6c56-cef0-4943-80f6-3408bea40365\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aaeb6c56-cef0-4943-80f6-3408bea40365')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aaeb6c56-cef0-4943-80f6-3408bea40365 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ratio = 0.9\n",
        "train_size = int(len(ratings)*train_ratio)\n",
        "ratings_train = ratings.sample(train_size, random_state=42)\n",
        "ratings_test = ratings[~ratings.index.isin(ratings_train.index)]"
      ],
      "metadata": {
        "id": "F0v6Tyi5WICX"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Recall (MF)"
      ],
      "metadata": {
        "id": "3hjWwPwdVmMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install implicit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCxohtVUitDh",
        "outputId": "0c013c72-e926-4246-c737-9f5e3f0c5698"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting implicit\n",
            "  Downloading implicit-0.7.2-cp310-cp310-manylinux2014_x86_64.whl (8.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from implicit) (1.23.5)\n",
            "Requirement already satisfied: scipy>=0.16 in /usr/local/lib/python3.10/dist-packages (from implicit) (1.11.4)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from implicit) (4.66.1)\n",
            "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.10/dist-packages (from implicit) (3.2.0)\n",
            "Installing collected packages: implicit\n",
            "Successfully installed implicit-0.7.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.sparse import csr_matrix\n",
        "\n",
        "n_users = ratings_train['user id'].max()\n",
        "n_item = ratings_train['item id'].max()\n",
        "ratings_train_pos = ratings_train[ratings_train['like']]\n",
        "ratings_test_pos = ratings_test[ratings_test['like']]\n",
        "\n",
        "\n",
        "row=ratings_train_pos['user id'].values - 1\n",
        "col=ratings_train_pos['item id'].values - 1\n",
        "data=np.ones(len(ratings_train_pos))\n",
        "user_item_data = csr_matrix((data, (row, col)), shape=(n_users, n_item))"
      ],
      "metadata": {
        "id": "h_Xx-DbQWSki"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import implicit\n",
        "\n",
        "# initialize a model\n",
        "model = implicit.als.AlternatingLeastSquares(factors=50, random_state=42)\n",
        "\n",
        "# train the model on a sparse matrix of user/item/confidence weights\n",
        "model.fit(user_item_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9d11a8d77f1e44b79149ad325c545279",
            "031d4a224a314a349ca37fdf0cc9bc2b",
            "7eae6146ea024a3cac50aad99ad11cca",
            "a2de628dfe1747f8afe6a18ce56fa9ae",
            "24fd56290bd645449edd683b30c1d34e",
            "332084c4550f48d880feec8a5df48fee",
            "d6b2937d57d44580b608497cea3b447a",
            "5371c327a64e4163a63a26ef29d06e79",
            "eae0488a82ff4b97ab6eb5cb97e8dad7",
            "a18bf94c6fd047ccadfeb943738d21c7",
            "807108f4244b4789a4bf6c0d894ca252"
          ]
        },
        "id": "4QzvrrLcWUw3",
        "outputId": "21a50d57-8a3a-43d6-f1a2-78724190d409"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d11a8d77f1e44b79149ad325c545279"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import dcg_score, ndcg_score\n",
        "\n",
        "def precision_k(actuals, recs, k=5):\n",
        "  return len(set(recs[0:k]).intersection(set(actuals)))/k\n",
        "\n",
        "def recall_k(actuals, recs, k=5):\n",
        "  return len(set(recs[0:k]).intersection(set(actuals)))/len(actuals)\n",
        "\n",
        "def dcg_k(actuals, recs, k=5):\n",
        "  relevance = np.array([[float(i in actuals) for i in recs[0:k]]])\n",
        "  score = k - np.arange(k)\n",
        "  return dcg_score(relevance, score.reshape(1,-1), k=k)\n",
        "\n",
        "def ndcg_k(actuals, recs, k=5):\n",
        "  relevance = np.array([[float(i in actuals) for i in recs[0:k]]])\n",
        "  score = k - np.arange(k)\n",
        "  return ndcg_score(relevance, score.reshape(1,-1), k=k)\n",
        "\n",
        "def recall_stage(model, user_id, user_item_data, ratings_train, N):\n",
        "  filter_items = ratings_train[ratings_train['user id']==user_id]['item id'].values\n",
        "  filter_items = filter_items - 1\n",
        "  user_id = user_id - 1\n",
        "\n",
        "  recs, scores = model.recommend(user_id,\n",
        "                                 user_item_data[user_id],\n",
        "                                 filter_items=filter_items,\n",
        "                                 N=N_recall)\n",
        "  recs = recs.flatten() + 1\n",
        "  return recs\n",
        "\n",
        "def evaluate(user_id, ratings_test_pos, recs, k=5):\n",
        "  actuals = ratings_test_pos[ratings_test_pos['user id']==user_id]['item id'].values\n",
        "  return precision_k(actuals, recs, k), recall_k(actuals, recs, k), dcg_k(actuals, recs, k)"
      ],
      "metadata": {
        "id": "XXhwcGzWXfgb"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# recommend items for a user\n",
        "N_recall=30\n",
        "user_id=1\n",
        "recs = recall_stage(model, user_id, user_item_data, ratings_train, N_recall)\n",
        "evaluate(user_id, ratings_test_pos, recs, 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ga92nwEiyjD",
        "outputId": "d73982f0-ed18-4c72-e4a3-5babaac1a2c3"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.2, 0.26666666666666666, 1.8389804011272912)"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ranking (GPT)"
      ],
      "metadata": {
        "id": "5hvrISseWa2S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install langchain openai==0.27.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGDIpsc8mhXV",
        "outputId": "6f477b5c-bfe6-4fdd-aaed-d96df64ed2f1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting langchain\n",
            "  Downloading langchain-0.1.0-py3-none-any.whl (797 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m798.0/798.0 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting openai==0.27.0\n",
            "  Downloading openai-0.27.0-py3-none-any.whl (70 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.1/70.1 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: async-timeout<5.0.0,>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from langchain) (4.0.3)\n",
            "Collecting dataclasses-json<0.7,>=0.5.7 (from langchain)\n",
            "  Downloading dataclasses_json-0.6.3-py3-none-any.whl (28 kB)\n",
            "Collecting jsonpatch<2.0,>=1.33 (from langchain)\n",
            "  Downloading jsonpatch-1.33-py2.py3-none-any.whl (12 kB)\n",
            "Collecting langchain-community<0.1,>=0.0.9 (from langchain)\n",
            "  Downloading langchain_community-0.0.11-py3-none-any.whl (1.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting langchain-core<0.2,>=0.1.7 (from langchain)\n",
            "  Downloading langchain_core-0.1.8-py3-none-any.whl (215 kB)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, jsonpointer, typing-inspect, langsmith, jsonpatch, openai, langchain-core, dataclasses-json, langchain-community, langchain\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
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            "\u001b[0mSuccessfully installed dataclasses-json-0.6.3 jsonpatch-1.33 jsonpointer-2.4 langchain-0.1.0 langchain-community-0.0.11 langchain-core-0.1.8 langsmith-0.0.78 marshmallow-3.20.1 mypy-extensions-1.0.0 openai-0.27.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "86zGXBbaVCAI"
      },
      "outputs": [],
      "source": [
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.prompts import ChatPromptTemplate\n",
        "from langchain.chains import LLMChain\n",
        "import openai\n",
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "if \"OPENAI_API_KEY\" not in os.environ:\n",
        "    os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_model = \"gpt-3.5-turbo\"\n",
        "llm = ChatOpenAI(temperature=0.0, model=llm_model)\n",
        "\n",
        "prompt = ChatPromptTemplate.from_template(\n",
        "\"\"\"The person has a list of liked movies: {movies_liked}. \\\n",
        "The person has a list of disliked movies: {movies_disliked}. \\\n",
        "Tell me if this person likes each of the candidate movies: {movies_candidates}.\\\n",
        "Return a list of boolean values and explain why the person likes or dislikes.\n",
        "\n",
        "<< FORMATTING >>\n",
        "Return a markdown code snippet with a list of JSON object formatted to look like:\n",
        "{{\n",
        "    \"title\": string \\ the name of the movie in candidate movies\n",
        "    \"like\": boolean \\ true or false\n",
        "    \"explanation\": string \\ explain why the person likes or dislikes the candidate movie\n",
        "}}\n",
        "\n",
        "\n",
        "REMEMBER: Each boolean and explanation for each element in candidate movies.\n",
        "REMEMBER: The explanation must relate to the person's liked and disliked movies.\n",
        "\"\"\"\n",
        ")\n",
        "\n",
        "chain = LLMChain(llm=llm, prompt=prompt)"
      ],
      "metadata": {
        "id": "KdDwrE09XKU3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df1ba647-e49b-4a5b-8bd7-9570524ba965"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:115: LangChainDeprecationWarning: The class `ChatOpenAI` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use langchain_openai.ChatOpenAI instead.\n",
            "  warn_deprecated(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def ranking_stage(chain, user_id, ratings_train, pre_recs, movie, batch_size=10):\n",
        "\n",
        "  few_shot = ratings_train[(ratings_train['user id']==user_id)]\n",
        "  if len(few_shot) >= 300:\n",
        "    few_shot = few_shot.sample(300, random_state=42)\n",
        "  recall_recs = movie.set_index('item id').loc[pre_recs].reset_index()\n",
        "\n",
        "  movies_liked = ','.join(few_shot[few_shot['like']]['title'].values.tolist())\n",
        "  movies_disliked = ','.join(few_shot[~few_shot['like']]['title'].values.tolist())\n",
        "\n",
        "  n_batch = int(np.ceil(len(recall_recs)/batch_size))\n",
        "  candidates = recall_recs[['item id', 'title']]\n",
        "  result_json = []\n",
        "\n",
        "  for i in range(n_batch):\n",
        "    candidates_batch = candidates.iloc[i*batch_size: (i+1)*batch_size]\n",
        "    movies_candidates = ','.join(candidates_batch['title'].values.tolist())\n",
        "    result = chain.run(movies_liked=movies_liked, movies_disliked=movies_disliked, movies_candidates=movies_candidates)\n",
        "    result_list = result.replace('\\n', '').replace('},', '}\\n,').split('\\n,')\n",
        "    result_json_batch = [json.loads(i) for i in result_list]\n",
        "    result_json = result_json + result_json_batch\n",
        "\n",
        "  result_rank = pd.DataFrame.from_dict(result_json)\n",
        "  result_rank['item id'] = recall_recs['item id'].values\n",
        "  result_rank = pd.concat([result_rank[result_rank['like']], result_rank[~result_rank['like']]])\n",
        "\n",
        "  return result_rank"
      ],
      "metadata": {
        "id": "vJiP_zDAXOJg"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank_result = ranking_stage(chain, user_id, ratings_train, recs, movie)\n",
        "rank_recs = rank_result['item id'].values\n",
        "\n",
        "p, r, ndcg = evaluate(user_id, ratings_test_pos, rank_recs, k=5)"
      ],
      "metadata": {
        "id": "vXCRKl0wnJN7"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "k=20\n",
        "evaluate(user_id, ratings_test_pos, recs, k), evaluate(user_id, ratings_test_pos, rank_recs, k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F94myY11qXd1",
        "outputId": "4d555439-176e-4e29-d4ee-922cdc68b80f"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.2, 0.26666666666666666, 1.8389804011272912),\n",
              " (0.25, 0.3333333333333333, 2.083630943245517))"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation\n"
      ],
      "metadata": {
        "id": "3uOQilr6gt4x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "Wi_vCeCrkCRD"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_train_pos['user id'].nunique(), ratings_test_pos['user id'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0__vxQrhG89",
        "outputId": "7e666f6d-efd4-463d-980d-53d62337f19a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(942, 854)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(ratings_train_pos['user id'].unique()).issuperset(set(ratings_test_pos['user id'].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33zh0CRWhhwG",
        "outputId": "0e7b0987-3bba-451b-9a47-734a67897280"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generate recs for sample users"
      ],
      "metadata": {
        "id": "7OHLsDRDLzgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)\n",
        "eval_users = np.random.choice(ratings_test_pos['user id'].sort_values().unique(), 20, replace=False)\n",
        "eval_users"
      ],
      "metadata": {
        "id": "xijAAn0qh6aN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3eea7ba3-1058-456e-f9f1-803ed7a1e118"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 73, 474, 221, 236, 875, 591, 308, 325, 399, 891, 776, 695, 771,\n",
              "       345, 342, 568, 483, 628, 450, 929])"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get recall recs..."
      ],
      "metadata": {
        "id": "voEGrujwigZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "N_recall = 30\n",
        "recall_results_dict = {}\n",
        "for user_id in eval_users:\n",
        "  recs = recall_stage(model, user_id, user_item_data, ratings_train, N_recall)\n",
        "  recall_results_dict.update({user_id: recs})\n",
        "\n",
        "path = 'drive/MyDrive/colab/'\n",
        "joblib.dump(recall_results_dict, path+'recall_results_dict.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmvvO25Vijow",
        "outputId": "22770938-65b9-4c05-d970-095f533d2ba6"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['drive/MyDrive/colab/recall_results_dict.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get ranking recs..."
      ],
      "metadata": {
        "id": "UA9lIZyllcta"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rank_results_dict = {}\n",
        "rank_results_raw = pd.DataFrame()\n",
        "\n",
        "eval_recall = []\n",
        "eval_rank = []\n",
        "\n",
        "for user_id in eval_users:\n",
        "  print(np.where(eval_users==user_id)[0][0], user_id)\n",
        "  recs = recall_results_dict.get(user_id)\n",
        "  rank_result = ranking_stage(chain, user_id, ratings_train, recs, movie)\n",
        "  rank_result['user id'] = user_id\n",
        "  rank_result['rank'] = list(range(len(rank_result)))\n",
        "\n",
        "  rank_results_dict.update({user_id: rank_result['item id'].values})\n",
        "  rank_results_raw = pd.concat([rank_results_raw, rank_result])\n",
        "\n",
        "  k=20\n",
        "  p, r, n = evaluate(user_id, ratings_test_pos, recs, k)\n",
        "  eval_recall.append({'p': p, 'r': r, 'n': n})\n",
        "  rank_recs = rank_results_dict.get(user_id)\n",
        "  p_rank, r_rank, n_rank = evaluate(user_id, ratings_test_pos, rank_result['item id'].values, k)\n",
        "  eval_rank.append({'p': p_rank, 'r': r_rank, 'n': n_rank})\n",
        "\n",
        "  eval_recall_df = pd.DataFrame.from_records(eval_recall)\n",
        "  eval_rank_df = pd.DataFrame.from_records(eval_rank)\n",
        "\n",
        "  print(eval_recall_df.mean().values, eval_rank_df.mean().values)\n",
        "\n",
        "joblib.dump(rank_results_dict, path+'rank_results_dict.pkl')\n",
        "rank_results_raw.to_csv(path+'rank_results_raw.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iebdzZHulf60",
        "outputId": "caf68e9d-05fb-4551-8141-8387f69f4537"
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 73\n",
            "[0.05       0.2        0.23981247] [0.05 0.2  0.25]\n",
            "1 474\n",
            "[0.15       0.225      1.21504605] [0.15       0.225      1.21599661]\n",
            "2 221\n",
            "[0.1       0.15      0.8100307] [0.1        0.15       0.81066441]\n",
            "3 236\n",
            "[0.075      0.1125     0.60752302] [0.075      0.1125     0.60799831]\n",
            "4 875\n",
            "[0.08       0.19       0.60027205] [0.07       0.14       0.54949162]\n",
            "5 591\n",
            "[0.075      0.21388889 0.56470217] [0.06666667 0.17222222 0.52968911]\n",
            "6 308\n",
            "[0.07142857 0.19285714 0.53491718] [0.07142857 0.16666667 0.54980338]\n",
            "7 325\n",
            "[0.0875     0.24017857 0.67777413] [0.08125    0.19940476 0.69116253]\n",
            "8 399\n",
            "[0.07777778 0.21349206 0.6024659 ] [0.07222222 0.17724868 0.61436669]\n",
            "9 891\n",
            "[0.08       0.25880952 0.68090459] [0.075      0.22619048 0.69599768]\n",
            "10 776\n",
            "[0.08181818 0.26125541 0.68515601] [0.08181818 0.24458874 0.73722856]\n",
            "11 695\n",
            "[0.07916667 0.26031746 0.65057952] [0.08333333 0.26587302 0.71986595]\n",
            "12 771\n",
            "[0.07692308 0.25567766 0.6236911 ] [0.08846154 0.29157509 0.73627154]\n",
            "13 345\n",
            "[0.08214286 0.25272109 0.65306323] [0.09642857 0.29115646 0.77672688]\n",
            "14 342\n",
            "[0.09       0.25492063 0.75625333] [0.1        0.28603175 0.84421608]\n",
            "15 568\n",
            "[0.0875    0.2546131 0.7246125] [0.096875   0.28377976 0.80951912]\n",
            "16 483\n",
            "[0.08529412 0.26904762 0.70732215] [0.09411765 0.2964986  0.79131212]\n",
            "17 628\n",
            "[0.08888889 0.30965608 0.73288029] [0.09444444 0.31706349 0.77978797]\n",
            "18 450\n",
            "[0.09736842 0.29920635 0.76908237] [0.10526316 0.30739348 0.84316793]\n",
            "19 929\n",
            "[0.095      0.30924603 0.74729492] [0.1025     0.31702381 0.82254336]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Consolidate results"
      ],
      "metadata": {
        "id": "s8byCVkEboe8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "path = 'drive/MyDrive/colab/'\n",
        "\n",
        "recall_results_dict = joblib.load(path+'recall_results_dict.pkl')\n",
        "rank_results_dict = joblib.load(path+'rank_results_dict.pkl')\n",
        "rank_results_raw = pd.read_csv(path+'rank_results_raw.csv')\n",
        "\n",
        "eval_users = list(rank_results_dict.keys())"
      ],
      "metadata": {
        "id": "e3BjctOsGbuN"
      },
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option(\"display.precision\", 4)\n",
        "\n",
        "k_val=[5, 10, 15, 20]\n",
        "eval_results = pd.DataFrame({'Method': ['MF', 'MF+GPT']})\n",
        "\n",
        "for k in k_val:\n",
        "\n",
        "  eval_recall = []\n",
        "  eval_rank = []\n",
        "  eval_all = []\n",
        "\n",
        "  for user_id in eval_users:\n",
        "\n",
        "    recall_recs = recall_results_dict.get(user_id)\n",
        "    p, r, d = evaluate(user_id, ratings_test_pos, recall_recs, k)\n",
        "    eval_recall.append({'p': p, 'r': r, 'd': d})\n",
        "\n",
        "    rank_recs = rank_results_dict.get(user_id)\n",
        "    p_rank, r_rank, d_rank = evaluate(user_id, ratings_test_pos, rank_recs, k)\n",
        "    eval_rank.append({'p': p_rank, 'r': r_rank, 'd': d_rank})\n",
        "\n",
        "  eval_recall = pd.DataFrame.from_records(eval_recall).mean().round(4)\n",
        "  eval_rank = pd.DataFrame.from_records(eval_rank).mean().round(4)\n",
        "  eval_all = pd.DataFrame({'P@'+str(k): [eval_recall['p'], eval_rank['p']],\n",
        "                'R@'+str(k): [eval_recall['r'], eval_rank['r']],\n",
        "                'DCG@'+str(k): [eval_recall['d'], eval_rank['d']]})\n",
        "\n",
        "  eval_results = pd.concat([eval_results, eval_all], axis=1)\n",
        "\n",
        "eval_results.to_csv(path+'eval.csv', index=False)\n",
        "eval_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "tAwFiNm4Yxhl",
        "outputId": "e1eccf24-1107-4e88-ec39-c758bafedfe5"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Method   P@5     R@5   DCG@5   P@10    R@10  DCG@10    P@15    R@15  \\\n",
              "0      MF  0.13  0.1299  0.3948  0.115  0.2117  0.5583  0.1067  0.2661   \n",
              "1  MF+GPT  0.16  0.1502  0.4823  0.125  0.2176  0.6223  0.1133  0.2673   \n",
              "\n",
              "   DCG@15    P@20    R@20  DCG@20  \n",
              "0  0.6768  0.0950  0.3092  0.7473  \n",
              "1  0.7405  0.1025  0.3170  0.8225  "
            ],
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              "      <th></th>\n",
              "      <th>Method</th>\n",
              "      <th>P@5</th>\n",
              "      <th>R@5</th>\n",
              "      <th>DCG@5</th>\n",
              "      <th>P@10</th>\n",
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              "      <th>DCG@20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MF</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.1299</td>\n",
              "      <td>0.3948</td>\n",
              "      <td>0.115</td>\n",
              "      <td>0.2117</td>\n",
              "      <td>0.5583</td>\n",
              "      <td>0.1067</td>\n",
              "      <td>0.2661</td>\n",
              "      <td>0.6768</td>\n",
              "      <td>0.0950</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>0.7473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MF+GPT</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.1502</td>\n",
              "      <td>0.4823</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.2176</td>\n",
              "      <td>0.6223</td>\n",
              "      <td>0.1133</td>\n",
              "      <td>0.2673</td>\n",
              "      <td>0.7405</td>\n",
              "      <td>0.1025</td>\n",
              "      <td>0.3170</td>\n",
              "      <td>0.8225</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explanation Example"
      ],
      "metadata": {
        "id": "x7uqmVPkWiNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "user_id = eval_users[1]\n",
        "recall_results = pd.DataFrame({'item id':recall_results_dict.get(user_id),\n",
        "                               'MF rank': np.arange(len(recall_results_dict.get(user_id)))})\n",
        "actuals = ratings_test[ratings_test['user id']==user_id]\n",
        "actuals = actuals[actuals['like']]['item id'].values"
      ],
      "metadata": {
        "id": "Y3LJ7ZemYrPU"
      },
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rank_result = rank_results_raw[rank_results_raw['user id']==user_id].drop(columns=['user id'])\n",
        "rank_result.rename(columns={'rank': 'MF+GPT rank', 'like': 'GPT like'}, inplace=True)\n",
        "rank_result = rank_result.merge(recall_results, on='item id')\n",
        "rank_result['actual like'] = rank_result['item id'].isin(actuals)\n",
        "cols = ['title', 'explanation', 'MF+GPT rank']\n",
        "rank_result[cols].to_csv(path+'user_1_demo.csv', index=False)\n",
        "rank_result[cols]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "zumufn7mWket",
        "outputId": "8e36475d-ae54-4769-db5f-03385e5100f4"
      },
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          title  \\\n",
              "0                       Right Stuff, The (1983)   \n",
              "1                        His Girl Friday (1940)   \n",
              "2                       Harold and Maude (1971)   \n",
              "3                            Sling Blade (1996)   \n",
              "4                      Good Will Hunting (1997)   \n",
              "5   My Life as a Dog (Mitt liv som hund) (1985)   \n",
              "6                             Birds, The (1963)   \n",
              "7                     Mr. Holland's Opus (1995)   \n",
              "8                        Full Monty, The (1997)   \n",
              "9                 Much Ado About Nothing (1993)   \n",
              "10            E.T. the Extra-Terrestrial (1982)   \n",
              "11                                  Dave (1993)   \n",
              "12                             Ninotchka (1939)   \n",
              "13                            Local Hero (1983)   \n",
              "14                                  Heat (1995)   \n",
              "15                                Casino (1995)   \n",
              "16                       Beautiful Girls (1996)   \n",
              "17                     Circle of Friends (1995)   \n",
              "18                         Seven (Se7en) (1995)   \n",
              "19                          Pretty Woman (1990)   \n",
              "20                   Legends of the Fall (1994)   \n",
              "21             Last of the Mohicans, The (1992)   \n",
              "22                   Great Dictator, The (1940)   \n",
              "23                         Red Rock West (1992)   \n",
              "24                      Crying Game, The (1992)   \n",
              "25                           Hoop Dreams (1994)   \n",
              "26                 Devil in a Blue Dress (1995)   \n",
              "27                            Highlander (1986)   \n",
              "28                                  Kids (1995)   \n",
              "29              Long Kiss Goodnight, The (1996)   \n",
              "\n",
              "                                          explanation  MF+GPT rank  \n",
              "0   The person likes this movie because they enjoy...            0  \n",
              "1   The person likes this movie because they appre...            1  \n",
              "2   The person likes this movie because they enjoy...            2  \n",
              "3   The person likes this movie because they appre...            3  \n",
              "4   The person likes this movie because they enjoy...            4  \n",
              "5   The person likes this movie because they appre...            5  \n",
              "6   The person likes this movie because they enjoy...            6  \n",
              "7   The person likes this movie because they appre...            7  \n",
              "8   The person likes 'Full Monty, The (1997)' beca...            8  \n",
              "9   The person likes 'Much Ado About Nothing (1993...            9  \n",
              "10  The person likes 'E.T. the Extra-Terrestrial (...           10  \n",
              "11  The person likes 'Dave (1993)' because it is a...           11  \n",
              "12  The person likes 'Ninotchka (1939)' because it...           12  \n",
              "13  The person likes 'Local Hero (1983)' because i...           13  \n",
              "14  The person likes 'Heat (1995)' because it is a...           14  \n",
              "15  The person likes 'Casino (1995)' because it is...           15  \n",
              "16  The person likes Beautiful Girls (1996) becaus...           16  \n",
              "17  The person likes Circle of Friends (1995) beca...           17  \n",
              "18  The person likes Seven (Se7en) (1995) because ...           18  \n",
              "19  The person likes Pretty Woman (1990) because i...           19  \n",
              "20  The person likes Legends of the Fall (1994) be...           20  \n",
              "21  The person likes Last of the Mohicans, The (19...           21  \n",
              "22  The person likes Great Dictator, The (1940) be...           22  \n",
              "23  The person dislikes this movie because they pr...           23  \n",
              "24  The person dislikes this movie because they ma...           24  \n",
              "25  The person dislikes 'Hoop Dreams (1994)' becau...           25  \n",
              "26  The person dislikes 'Devil in a Blue Dress (19...           26  \n",
              "27  The person dislikes Highlander (1986) because ...           27  \n",
              "28  The person dislikes Kids (1995) because it is ...           28  \n",
              "29  The person dislikes Long Kiss Goodnight, The (...           29  "
            ],
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              "    <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>title</th>\n",
              "      <th>explanation</th>\n",
              "      <th>MF+GPT rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Right Stuff, The (1983)</td>\n",
              "      <td>The person likes this movie because they enjoy...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>His Girl Friday (1940)</td>\n",
              "      <td>The person likes this movie because they appre...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Harold and Maude (1971)</td>\n",
              "      <td>The person likes this movie because they enjoy...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sling Blade (1996)</td>\n",
              "      <td>The person likes this movie because they appre...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Good Will Hunting (1997)</td>\n",
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              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>My Life as a Dog (Mitt liv som hund) (1985)</td>\n",
              "      <td>The person likes this movie because they appre...</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Birds, The (1963)</td>\n",
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              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Mr. Holland's Opus (1995)</td>\n",
              "      <td>The person likes this movie because they appre...</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Full Monty, The (1997)</td>\n",
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              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Much Ado About Nothing (1993)</td>\n",
              "      <td>The person likes 'Much Ado About Nothing (1993...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>E.T. the Extra-Terrestrial (1982)</td>\n",
              "      <td>The person likes 'E.T. the Extra-Terrestrial (...</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Dave (1993)</td>\n",
              "      <td>The person likes 'Dave (1993)' because it is a...</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Ninotchka (1939)</td>\n",
              "      <td>The person likes 'Ninotchka (1939)' because it...</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Local Hero (1983)</td>\n",
              "      <td>The person likes 'Local Hero (1983)' because i...</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Heat (1995)</td>\n",
              "      <td>The person likes 'Heat (1995)' because it is a...</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Casino (1995)</td>\n",
              "      <td>The person likes 'Casino (1995)' because it is...</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Beautiful Girls (1996)</td>\n",
              "      <td>The person likes Beautiful Girls (1996) becaus...</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Circle of Friends (1995)</td>\n",
              "      <td>The person likes Circle of Friends (1995) beca...</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Seven (Se7en) (1995)</td>\n",
              "      <td>The person likes Seven (Se7en) (1995) because ...</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Pretty Woman (1990)</td>\n",
              "      <td>The person likes Pretty Woman (1990) because i...</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Legends of the Fall (1994)</td>\n",
              "      <td>The person likes Legends of the Fall (1994) be...</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Last of the Mohicans, The (1992)</td>\n",
              "      <td>The person likes Last of the Mohicans, The (19...</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Great Dictator, The (1940)</td>\n",
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              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Red Rock West (1992)</td>\n",
              "      <td>The person dislikes this movie because they pr...</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Crying Game, The (1992)</td>\n",
              "      <td>The person dislikes this movie because they ma...</td>\n",
              "      <td>24</td>\n",
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              "      <td>26</td>\n",
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              "      <th>27</th>\n",
              "      <td>Highlander (1986)</td>\n",
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              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Kids (1995)</td>\n",
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              "      <td>28</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-514342a1-8417-4a7f-aeee-51a1755111fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    }
  ]
}