SegFault_FinalNotebook.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "LiI0t_Jx_17g"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "GLWiGRbEAY8V"
      },
      "outputs": [],
      "source": [
        "places_original = pd.read_csv(\"data/preProcessed.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IjBNuDyhAw59",
        "outputId": "1109bef4-a2e7-45c4-a824-2af93271badc"
      },
      "outputs": [
        {
          "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>Unnamed: 0</th>\n",
              "      <th>name</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>formatted_address</th>\n",
              "      <th>rating</th>\n",
              "      <th>user_ratings_total</th>\n",
              "      <th>latest_reviews</th>\n",
              "      <th>filtered_place</th>\n",
              "      <th>emotion_scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Arugam Bay Beach</td>\n",
              "      <td>6.840408</td>\n",
              "      <td>81.836848</td>\n",
              "      <td>Arugam Bay Beach, Sri Lanka</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1591.0</td>\n",
              "      <td>['Arugam Bay Beach is a surfer's paradise! I s...</td>\n",
              "      <td>arugam bay beach</td>\n",
              "      <td>0.998887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Mirissa Beach</td>\n",
              "      <td>5.944703</td>\n",
              "      <td>80.459161</td>\n",
              "      <td>Mirissa, Sri Lanka</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1748.0</td>\n",
              "      <td>['Mirissa Beach is truly a gem on Sri Lanka\\'s...</td>\n",
              "      <td>mirissa beach</td>\n",
              "      <td>0.998933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Weligama Beach (surf and stay)</td>\n",
              "      <td>5.972486</td>\n",
              "      <td>80.435714</td>\n",
              "      <td>Weligama, Sri Lanka</td>\n",
              "      <td>4.4</td>\n",
              "      <td>325.0</td>\n",
              "      <td>['Weligama Beach is a fantastic spot for both ...</td>\n",
              "      <td>weligama beach</td>\n",
              "      <td>0.998928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Hikkaduwa Beach</td>\n",
              "      <td>6.137727</td>\n",
              "      <td>80.099060</td>\n",
              "      <td>Hikkaduwa Beach, Sri Lanka</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1438.0</td>\n",
              "      <td>['Hikkaduwa Beach is a delightful escape for s...</td>\n",
              "      <td>hikkaduwa beach</td>\n",
              "      <td>0.998933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Pigeon Island</td>\n",
              "      <td>8.721837</td>\n",
              "      <td>81.204071</td>\n",
              "      <td>Pigeon Island, Sri Lanka</td>\n",
              "      <td>4.5</td>\n",
              "      <td>174.0</td>\n",
              "      <td>['Pigeon Island is a gem! Snorkeling here was ...</td>\n",
              "      <td>pigeon island</td>\n",
              "      <td>0.998702</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0                            name       lat        lng  \\\n",
              "0           0                Arugam Bay Beach  6.840408  81.836848   \n",
              "1           1                   Mirissa Beach  5.944703  80.459161   \n",
              "2           2  Weligama Beach (surf and stay)  5.972486  80.435714   \n",
              "3           3                 Hikkaduwa Beach  6.137727  80.099060   \n",
              "4           4                   Pigeon Island  8.721837  81.204071   \n",
              "\n",
              "             formatted_address  rating  user_ratings_total  \\\n",
              "0  Arugam Bay Beach, Sri Lanka     4.8              1591.0   \n",
              "1           Mirissa, Sri Lanka     4.6              1748.0   \n",
              "2          Weligama, Sri Lanka     4.4               325.0   \n",
              "3   Hikkaduwa Beach, Sri Lanka     4.7              1438.0   \n",
              "4     Pigeon Island, Sri Lanka     4.5               174.0   \n",
              "\n",
              "                                      latest_reviews    filtered_place  \\\n",
              "0  ['Arugam Bay Beach is a surfer's paradise! I s...  arugam bay beach   \n",
              "1  ['Mirissa Beach is truly a gem on Sri Lanka\\'s...     mirissa beach   \n",
              "2  ['Weligama Beach is a fantastic spot for both ...    weligama beach   \n",
              "3  ['Hikkaduwa Beach is a delightful escape for s...   hikkaduwa beach   \n",
              "4  ['Pigeon Island is a gem! Snorkeling here was ...     pigeon island   \n",
              "\n",
              "   emotion_scores  \n",
              "0        0.998887  \n",
              "1        0.998933  \n",
              "2        0.998928  \n",
              "3        0.998933  \n",
              "4        0.998702  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "places_original.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "HZI9TuUBAqdi"
      },
      "outputs": [],
      "source": [
        "visitors_original = pd.read_excel(\"data/Visitors Preference Dataset.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K4kSun0dH4sX",
        "outputId": "c1c89bc2-d62d-4324-f6fa-2198989f0feb"
      },
      "outputs": [
        {
          "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>User ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>Email</th>\n",
              "      <th>Preferred Activities</th>\n",
              "      <th>Bucket list destinations Sri Lanka</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Jennifer Quinn</td>\n",
              "      <td>jennifer.quinn@example.com</td>\n",
              "      <td>['cycling', 'historical monuments', 'village h...</td>\n",
              "      <td>['Polonnaruwa', 'Hatton', 'Anuradhapura', 'Ell...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Emily Perry</td>\n",
              "      <td>emily.perry@example.com</td>\n",
              "      <td>['butterfly watching', 'hot springs', 'wildlif...</td>\n",
              "      <td>['Madunagala Hot Water Spring', 'Wilpattu Nati...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Danielle Mcbride</td>\n",
              "      <td>danielle.mcbride@example.com</td>\n",
              "      <td>['sea cruises', 'themed parks', 'craft worksho...</td>\n",
              "      <td>['Mirissa Beach', 'Negombo Lagoon', 'Batadomba...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Angelica Wilson</td>\n",
              "      <td>angelica.wilson@example.com</td>\n",
              "      <td>['fishing', 'hot springs', 'sailing']</td>\n",
              "      <td>['Maha Oya Hot Water Springs', 'Colombo Port C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Laurie Powers</td>\n",
              "      <td>laurie.powers@example.com</td>\n",
              "      <td>['history tours', 'sailing', 'literary tours']</td>\n",
              "      <td>['Negombo Lagoon', 'Colombo Port City', 'Galle...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   User ID              Name                         Email  \\\n",
              "0        1    Jennifer Quinn    jennifer.quinn@example.com   \n",
              "1        2       Emily Perry       emily.perry@example.com   \n",
              "2        3  Danielle Mcbride  danielle.mcbride@example.com   \n",
              "3        4   Angelica Wilson   angelica.wilson@example.com   \n",
              "4        5     Laurie Powers     laurie.powers@example.com   \n",
              "\n",
              "                                Preferred Activities  \\\n",
              "0  ['cycling', 'historical monuments', 'village h...   \n",
              "1  ['butterfly watching', 'hot springs', 'wildlif...   \n",
              "2  ['sea cruises', 'themed parks', 'craft worksho...   \n",
              "3              ['fishing', 'hot springs', 'sailing']   \n",
              "4     ['history tours', 'sailing', 'literary tours']   \n",
              "\n",
              "                  Bucket list destinations Sri Lanka  \n",
              "0  ['Polonnaruwa', 'Hatton', 'Anuradhapura', 'Ell...  \n",
              "1  ['Madunagala Hot Water Spring', 'Wilpattu Nati...  \n",
              "2  ['Mirissa Beach', 'Negombo Lagoon', 'Batadomba...  \n",
              "3  ['Maha Oya Hot Water Springs', 'Colombo Port C...  \n",
              "4  ['Negombo Lagoon', 'Colombo Port City', 'Galle...  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "visitors_original.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "mbRcZX7SAvfX"
      },
      "outputs": [],
      "source": [
        "# places_original[\"latest_reviews\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "720gI4eBA_Ln"
      },
      "outputs": [],
      "source": [
        "# pattern = r'\\'\\s*,\\s*\\'|\\\"\\s*,\\s*\\\"'\n",
        "\n",
        "# for review_set in places_original[\"latest_reviews\"].unique():\n",
        "#   for review in re.split(pattern, review_set):\n",
        "#     print(re.sub(\"Ã¢Â€Â™\", \"'\", review.strip(\"[\").strip(\"]\").strip('\"').strip(\"'\")))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "5wDfUZhfBULz",
        "outputId": "e78cffbb-f416-4254-e8a0-dd97fa505048"
      },
      "outputs": [
        {
          "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>Preferred Activities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>['cycling', 'historical monuments', 'village h...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>['butterfly watching', 'hot springs', 'wildlif...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>['sea cruises', 'themed parks', 'craft worksho...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>['fishing', 'hot springs', 'sailing']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>['history tours', 'sailing', 'literary tours']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>['paddleboarding', 'river cruises', 'kayaking']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>['theater', 'scuba diving', 'yoga retreats']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>['sea cruises', 'zip-lining', 'outdoor adventu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>['cycling', 'amusement parks', 'paddleboarding']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>['camping', 'hiking', 'safaris']</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ],
            "text/plain": [
              "0       ['cycling', 'historical monuments', 'village h...\n",
              "1       ['butterfly watching', 'hot springs', 'wildlif...\n",
              "2       ['sea cruises', 'themed parks', 'craft worksho...\n",
              "3                   ['fishing', 'hot springs', 'sailing']\n",
              "4          ['history tours', 'sailing', 'literary tours']\n",
              "                              ...                        \n",
              "9995      ['paddleboarding', 'river cruises', 'kayaking']\n",
              "9996         ['theater', 'scuba diving', 'yoga retreats']\n",
              "9997    ['sea cruises', 'zip-lining', 'outdoor adventu...\n",
              "9998     ['cycling', 'amusement parks', 'paddleboarding']\n",
              "9999                     ['camping', 'hiking', 'safaris']\n",
              "Name: Preferred Activities, Length: 10000, dtype: object"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "visitors_original[\"Preferred Activities\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5UNRE54H7tG",
        "outputId": "0fdcb6bd-629d-46dd-fba2-d6f30ae09e5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'historic walks', 'photography', 'butterfly watching', 'sailing lessons', 'spiritual retreats', 'yoga retreats', 'sailing', 'architecture photography', 'beach visits', 'local crafts', 'city tours', 'bird watching', 'archaeological sites', 'themed parks', 'cultural festivals', 'sea cruises', 'sightseeing', 'cultural experiences', 'rock climbing', 'hiking', 'turtle watching', 'village homestays', 'public art installations', 'hot air ballooning', 'kayaking', 'tea tasting', 'botanical gardens', 'historic sites', 'art classes', 'horseback riding', 'museum visits', 'cycling', 'beachfront dining', 'animal encounters', 'historical monuments', 'craft workshops', 'zip-lining', 'caving', 'architecture tours', 'temple pilgrimages', 'horse shows', 'amusement parks', 'fishing', 'arts and culture', 'whale watching', 'river cruises', 'traditional ceremonies', 'history tours', 'water parks', 'waterfalls', 'golfing', 'mountain biking', 'paddleboarding', 'camping', 'hot springs', 'safaris', 'planetarium visits', 'boat safaris', 'landscape photography', 'ayurvedic spa treatments', 'elephant rides', 'wildlife viewing', 'literary tours', 'surfing', 'outdoor adventures', 'theater', 'scuba diving', 'snorkeling'}\n",
            "Number of activities is  68\n"
          ]
        }
      ],
      "source": [
        "activity_list = set()\n",
        "for activities in visitors_original[\"Preferred Activities\"]:\n",
        "  for activity in activities.strip('[').strip(']').split(\"', '\"):\n",
        "    activity_list.add(activity.strip(\"'\"))\n",
        "print(activity_list)\n",
        "print(\"Number of activities is \", len(activity_list))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbbdnWTXIAwP",
        "outputId": "a3145b50-14a4-46b4-da06-8d1f9e7f3d19"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence_transformers-3.1.0-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.44.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.66.5)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (2.4.0+cu121)\n",
            "Requirement already satisfied: numpy<2.0.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.26.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.3.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (1.13.1)\n",
            "Requirement already satisfied: huggingface-hub>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (0.24.6)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (9.4.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (3.16.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (2.32.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->sentence-transformers) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (1.13.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers) (3.1.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.38.0->sentence-transformers) (2024.5.15)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.38.0->sentence-transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.38.0->sentence-transformers) (0.19.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers) (3.5.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->sentence-transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->sentence-transformers) (3.8)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->sentence-transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->sentence-transformers) (2024.8.30)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\n",
            "Downloading sentence_transformers-3.1.0-py3-none-any.whl (249 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m249.1/249.1 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentence-transformers\n",
            "Successfully installed sentence-transformers-3.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqvS2BGCPIS9",
        "outputId": "39257a1d-1ac8-4f60-ce83-923f10a6567b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n"
          ]
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer"
      ]
    },
    {
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      "execution_count": 10,
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          ]
        },
        "id": "Rep6J7KYPP_4",
        "outputId": "292fb595-8775-4320-f81f-f73c268ac1c4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68ed0b935e874e368e6e5dd2e373007d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f65cfe12dd8c4e239b507fb9c0dff685",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60a7837e6cc5401ba37440ca7525e2dc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ef4a945663b4bf5beab7b21e78ad285",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ff822a403294a8c8d75a8bb9617b1e7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "60ca1395a1eb49b4b69ef571b06e6a5d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1940a1f22c3c403c94fbec31b4a4a4dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c559e1377d544974ae85323449bdef45",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21c2bb1b42284abc8568343cb7a8c327",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3042bc8c08f94863b945a8f6e8e6ff58",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a777f4521c6e4eb089e1c58c785ba655",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = SentenceTransformer(\"all-mpnet-base-v2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8sl716lRPYt5"
      },
      "outputs": [],
      "source": [
        "embedding = model.encode(eval(visitors_original[\"Preferred Activities\"].tolist()[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ7f6ktVPpj-",
        "outputId": "8c7913d3-c6c9-42d8-bc15-09d71bdc021a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3, 768)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "embedding.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "l6mIBCpnPuOG"
      },
      "outputs": [],
      "source": [
        "embeddings = dict()\n",
        "\n",
        "for activity in activity_list:\n",
        "  embeddings[activity] = model.encode(activity)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "I4535JMVQAJa"
      },
      "outputs": [],
      "source": [
        "# embeddings[\"surfing\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WtV86fY6QEf7"
      },
      "outputs": [],
      "source": [
        "review = eval(visitors_original[\"Preferred Activities\"].tolist()[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "TQKMtP6kVyPC"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "\n",
        "review_embeddings = []\n",
        "\n",
        "\n",
        "try:\n",
        "  with open('data/review_embeddings.pkl', 'rb') as f:\n",
        "      review_embeddings = pickle.load(f)\n",
        "\n",
        "\n",
        "except:\n",
        "  pattern = r'\\'\\s*,\\s*\\'|\\\"\\s*,\\s*\\\"'\n",
        "\n",
        "  for review_set in places_original[\"latest_reviews\"].unique():\n",
        "    reviews = \"\"\n",
        "    for review in re.split(pattern, review_set):\n",
        "      print(re.sub(\"Ã¢Â€Â™\", \"'\", review.strip(\"[\").strip(\"]\").strip('\"').strip(\"'\")))\n",
        "      reviews += \" \" + re.sub(\"Ã¢Â€Â™\", \"'\", review.strip(\"[\").strip(\"]\").strip('\"').strip(\"'\"))\n",
        "    review_embeddings.append(model.encode(reviews))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d34Aw6k9fnSk",
        "outputId": "f1267a11-7591-480d-cc2c-b03c937af055"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "341"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(review_embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0BmMaS-xHGnj"
      },
      "outputs": [],
      "source": [
        "def find_most_similar(vector, embeddings, similarity_function):\n",
        "    \"\"\"\n",
        "    Find the most similar vector to a given vector from a list of embeddings.\n",
        "\n",
        "    Args:\n",
        "        vector (list or np.array): The vector to compare against.\n",
        "        embeddings (list of lists or np.array): The list of vectors (embeddings) to search through.\n",
        "        similarity_function (function): The function used to compute similarity or distance.\n",
        "\n",
        "    Returns:\n",
        "        tuple: The most similar vector and its similarity score.\n",
        "    \"\"\"\n",
        "    vector = np.array(vector)\n",
        "    embeddings = np.array(embeddings)\n",
        "\n",
        "    # Initialize variables to track the most similar vector and its score\n",
        "    max_similarity = -float('inf')\n",
        "    most_similar_vector = None\n",
        "\n",
        "    for i, emb in enumerate(embeddings):\n",
        "        similarity = similarity_function(vector, emb)\n",
        "        if similarity > max_similarity:\n",
        "            max_similarity = similarity\n",
        "            most_similar_vector = i\n",
        "\n",
        "    return most_similar_vector, max_similarity\n",
        "\n",
        "# Example similarity functions\n",
        "def cosine_similarity(vec1, vec2):\n",
        "    vec1 = np.array(vec1)\n",
        "    vec2 = np.array(vec2)\n",
        "    dot_product = np.dot(vec1, vec2)\n",
        "    norm_vec1 = np.linalg.norm(vec1)\n",
        "    norm_vec2 = np.linalg.norm(vec2)\n",
        "    return dot_product / (norm_vec1 * norm_vec2)\n",
        "\n",
        "def euclidean_distance(vec1, vec2):\n",
        "    vec1 = np.array(vec1)\n",
        "    vec2 = np.array(vec2)\n",
        "    return -np.linalg.norm(vec1 - vec2)  # Negative because we want the largest value for similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fQtj7_WJwt2",
        "outputId": "e60af0d0-87aa-42a6-dab6-6f5166210dfb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Most similar vector (cosine similarity): SrÃƒÂ„Ã‚Â« VijayÃƒÂ„Ã‚Â_x0081_rÃƒÂ„Ã‚Â_x0081_ma Ancient Temple, Similarity: 0.4831258952617645\n",
            "Most similar vector (Euclidean distance): SrÃƒÂ„Ã‚Â« VijayÃƒÂ„Ã‚Â_x0081_rÃƒÂ„Ã‚Â_x0081_ma Ancient Temple, Distance: 1.016734004020691\n"
          ]
        }
      ],
      "source": [
        "vector = embeddings[\"sightseeing\"]\n",
        "\n",
        "\n",
        "# Find the most similar vector using cosine similarity\n",
        "most_similar_vector, similarity = find_most_similar(vector, review_embeddings, cosine_similarity)\n",
        "print(f\"Most similar vector (cosine similarity): {places_original.iloc[most_similar_vector]['name']}, Similarity: {similarity}\")\n",
        "\n",
        "# Find the most similar vector using Euclidean distance\n",
        "most_similar_vector, similarity = find_most_similar(vector, review_embeddings, euclidean_distance)\n",
        "print(f\"Most similar vector (Euclidean distance): {places_original.iloc[most_similar_vector]['name']}, Distance: {-similarity}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "8lR6lVQIJ-gu",
        "outputId": "df0ec352-131f-460f-dab6-cce8fc18d8a6"
      },
      "outputs": [
        {
          "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>227</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>Arthur's Seat View Point, Kandy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lat</th>\n",
              "      <td>7.288817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lng</th>\n",
              "      <td>80.639805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>formatted_address</th>\n",
              "      <td>Kandy, Sri Lanka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user_ratings_total</th>\n",
              "      <td>1432.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latest_reviews</th>\n",
              "      <td>['Arthur\\'s Seat View Point is a hidden gem in...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ],
            "text/plain": [
              "name                                    Arthur's Seat View Point, Kandy\n",
              "lat                                                            7.288817\n",
              "lng                                                           80.639805\n",
              "formatted_address                                      Kandy, Sri Lanka\n",
              "rating                                                              4.5\n",
              "user_ratings_total                                               1432.0\n",
              "latest_reviews        ['Arthur\\'s Seat View Point is a hidden gem in...\n",
              "Name: 227, dtype: object"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "places_original.iloc[most_similar_vector]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "A31g908gKPYc",
        "outputId": "e1b3d347-67f4-49f8-f0b3-4a298e96b20b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Arthur's Seat View Point, Kandy\""
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "places_original.iloc[most_similar_vector][\"name\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "870XZ0fHLaKa"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import heapq\n",
        "\n",
        "def find_top_k_similar(vector, embeddings, similarity_function, k):\n",
        "    \"\"\"\n",
        "    Find the top K most similar vectors to a given vector from a list of embeddings.\n",
        "\n",
        "    Args:\n",
        "        vector (list or np.array): The vector to compare against.\n",
        "        embeddings (list of lists or np.array): The list of vectors (embeddings) to search through.\n",
        "        similarity_function (function): The function used to compute similarity or distance.\n",
        "        k (int): The number of top similar vectors to return.\n",
        "\n",
        "    Returns:\n",
        "        list of tuples: Each tuple contains an index of the similar vector and its similarity score.\n",
        "    \"\"\"\n",
        "    vector = np.array(vector)\n",
        "    embeddings = np.array(embeddings)\n",
        "\n",
        "    # Initialize a min-heap to store the top K similarities and their indices\n",
        "    min_heap = []\n",
        "\n",
        "    for i, emb in enumerate(embeddings):\n",
        "        similarity = similarity_function(vector, emb)\n",
        "\n",
        "        if len(min_heap) < k:\n",
        "            heapq.heappush(min_heap, (similarity, i))\n",
        "        else:\n",
        "            if similarity > min_heap[0][0]:\n",
        "                heapq.heappushpop(min_heap, (similarity, i))\n",
        "\n",
        "    # Convert the min-heap to a sorted list of tuples\n",
        "    top_k_similarities = sorted(min_heap, reverse=True, key=lambda x: x[0])\n",
        "\n",
        "    return top_k_similarities\n",
        "\n",
        "# Example similarity functions\n",
        "def cosine_similarity_custom(vec1, vec2):\n",
        "    vec1 = np.array(vec1)\n",
        "    vec2 = np.array(vec2)\n",
        "    dot_product = np.dot(vec1, vec2)\n",
        "    norm_vec1 = np.linalg.norm(vec1)\n",
        "    norm_vec2 = np.linalg.norm(vec2)\n",
        "    return dot_product / (norm_vec1 * norm_vec2)\n",
        "\n",
        "def euclidean_distance(vec1, vec2):\n",
        "    vec1 = np.array(vec1)\n",
        "    vec2 = np.array(vec2)\n",
        "    return -np.linalg.norm(vec1 - vec2)  # Negative because we want the largest value for similarity\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "7zqo4l-B_QlU"
      },
      "outputs": [],
      "source": [
        "# Example usage\n",
        "vector = embeddings[\"surfing\"]\n",
        "k = 3\n",
        "top_k_cosine = find_top_k_similar(vector, review_embeddings, cosine_similarity, k)\n",
        "top_k_distance = find_top_k_similar(vector, review_embeddings, euclidean_distance, k)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQ5tZgIUgrdX",
        "outputId": "ef980f55-63b8-40b1-be52-a80995a9712c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(0.33151555, 2), (0.29400927, 0), (0.2760561, 338)]\n"
          ]
        }
      ],
      "source": [
        "print(top_k_cosine)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "iCp_xZ5nCy54"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "class DestinationSearch():\n",
        "  \"\"\"\n",
        "  Used to find the closest match to destination in bucket list column\n",
        "  to the destinations in the destination dataset\n",
        "  \"\"\"\n",
        "  def __init__(self, destination_list):\n",
        "    self.tfidf_vectorizer = TfidfVectorizer()\n",
        "    self.tfidf_matrix = self.tfidf_vectorizer.fit_transform(destination_list)\n",
        "\n",
        "  def find_similar(self, search_term):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      search_term (string) : The term to find similar phrase for\n",
        "\n",
        "    Returns:\n",
        "      most_similar_document_index (int) : The index of the most similar item from the list\n",
        "      similarity (float) : The cosime similarity between the most similar and search_term\n",
        "    \"\"\"\n",
        "    search_vector = self.tfidf_vectorizer.transform([search_term])\n",
        "    # Compute cosine similarity between the search vector and the corpus TF-IDF matrix\n",
        "    cosine_similarities = cosine_similarity(search_vector, self.tfidf_matrix)\n",
        "    most_similar_document_index = np.argmax(cosine_similarities)\n",
        "    similarity = np.max(cosine_similarities)\n",
        "\n",
        "    # print(\"Similarity: \", similarity)\n",
        "    # print(\"Search string: \", search_term)\n",
        "    # print(\"Result: \", name)\n",
        "\n",
        "    return most_similar_document_index, similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "OgyGjpimjAPv"
      },
      "outputs": [],
      "source": [
        "class CollaborativeFiltering():\n",
        "  def __init__(self, dataset, debug=False):\n",
        "    \"\"\"\n",
        "    Args:\n",
        "      dataset (pd.Dataframe) : Contains columns 'Preferred Activities' and\n",
        "                              'Bucket list destinations Sri Lanka'\n",
        "    \"\"\"\n",
        "\n",
        "    self.debug = debug # Set to False by default, used only to debug the class\n",
        "    self.activity_list = self.get_activity_list(dataset[\"Preferred Activities\"])\n",
        "    self.destination_list = self.get_destination_list(dataset[\"Bucket list destinations Sri Lanka\"])\n",
        "\n",
        "\n",
        "    self.user_count = len(dataset)\n",
        "    self.activity_count = len(self.activity_list)\n",
        "    self.destination_count = len(self.destination_list)\n",
        "\n",
        "\n",
        "    self.activity_index = {activity: idx for idx, activity in enumerate(self.activity_list)}\n",
        "    self.user_activity_matrix = self.get_activity_matrix(dataset[\"Preferred Activities\"])\n",
        "\n",
        "    self.destination_index = {place: idx for idx, place in enumerate(self.destination_list)}\n",
        "    self.user_destination_matrix = self.get_destination_matrix(dataset[\"Bucket list destinations Sri Lanka\"])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  def get_activity_list(self, activity_df):\n",
        "    \"\"\"\n",
        "    Returns the list of activities\n",
        "    \"\"\"\n",
        "    activity_set = set()\n",
        "    for activities in activity_df:\n",
        "      for activity in activities.strip('[').strip(']').split(\"', '\"):\n",
        "        activity_set.add(activity.strip(\"'\"))\n",
        "\n",
        "    if self.debug:\n",
        "      print(\"Printing from get_activity_list()\")\n",
        "      print(activity_set)\n",
        "      print(\"Number of activities is \", len(activity_set))\n",
        "    activity_list = sorted(activity_set)\n",
        "\n",
        "    return activity_list\n",
        "\n",
        "\n",
        "  def get_destination_list(self, bucket_list_df):\n",
        "    \"\"\"\n",
        "    Returns the set of destinations appearing in bucket lists\n",
        "    \"\"\"\n",
        "    bucket_list_set = set()\n",
        "\n",
        "    for destinations in bucket_list_df:\n",
        "      for destination in eval(destinations):\n",
        "        bucket_list_set.add(destination)\n",
        "\n",
        "    bucket_list = sorted(bucket_list_set)\n",
        "    if self.debug:\n",
        "      print(\"Printing from get_destination_list()\")\n",
        "      print(\"Number of unique places in the bucket list: \", len(bucket_list_set))\n",
        "      print(bucket_list_set)\n",
        "      print(bucket_list)\n",
        "\n",
        "    return bucket_list\n",
        "\n",
        "\n",
        "  def get_destination_matrix(self, bucket_list_df):\n",
        "    user_place_matrix = np.zeros((self.user_count, self.destination_count))\n",
        "\n",
        "    # Fill the matrix\n",
        "    for user_id, places in enumerate(bucket_list_df):\n",
        "        for destination in eval(places): # Places are given in the form ['place1', 'place2']\n",
        "            if destination in self.destination_index:\n",
        "                place_id = self.destination_index[destination]\n",
        "                user_place_matrix[user_id, place_id] = 1\n",
        "\n",
        "    # Create DataFrame for easier manipulation\n",
        "    df_user_place_matrix = pd.DataFrame(user_place_matrix)\n",
        "\n",
        "    return df_user_place_matrix\n",
        "\n",
        "\n",
        "\n",
        "  def get_activity_matrix(self, activity_df):\n",
        "\n",
        "    user_activity_matrix = np.zeros((self.user_count, self.activity_count))\n",
        "    # Fill the matrix\n",
        "    for user_id, activities in enumerate(visitors_original[\"Preferred Activities\"].iloc[:9990]):\n",
        "        for activity in eval(activities): # Places are given in the form ['place1', 'place2']\n",
        "            if activity in self.activity_index:\n",
        "                activity_id = self.activity_index[activity]\n",
        "                user_activity_matrix[user_id, activity_id] = 1\n",
        "\n",
        "    # Create DataFrame for easier manipulation\n",
        "    df_user_activity_matrix = pd.DataFrame(user_activity_matrix)\n",
        "\n",
        "    return df_user_activity_matrix\n",
        "\n",
        "\n",
        "\n",
        "  def activity_based_collaborative_filtering(self, new_user_activities: list,\n",
        "                                           top_n=5,\n",
        "                                           min_similarity=0.2):\n",
        "      # Convert new user purchases to a vector\n",
        "      new_user_vector = np.zeros(self.user_activity_matrix.shape[1])\n",
        "      for activity in new_user_activities:\n",
        "          if activity in self.activity_index:\n",
        "              new_user_vector[self.activity_index[activity]] = 1\n",
        "\n",
        "      # Calculate similarities between the new user and all existing users\n",
        "      user_similarity = cosine_similarity([new_user_vector], self.user_activity_matrix)\n",
        "\n",
        "      # Compute weighted scores\n",
        "      weighted_scores = np.dot(user_similarity, self.user_destination_matrix)\n",
        "      sum_of_weights = np.sum(user_similarity)\n",
        "\n",
        "      if sum_of_weights == 0:\n",
        "          return \"No recommendations available.\"\n",
        "\n",
        "      # Calculate average score for each activity\n",
        "      recommendations = weighted_scores.flatten() / sum_of_weights\n",
        "\n",
        "\n",
        "      # Get indices of top N items\n",
        "      # recommended_places_indices = np.argsort(recommendations)[::-1][:top_n]\n",
        "      # Filter recommendations to keep only those with a\n",
        "      # score higher than minimum similarity (default is set to 0.2)\n",
        "      filtered_indices = np.where(recommendations > min_similarity)[0]\n",
        "\n",
        "      # Sort the filtered indices based on the recommendation scores in descending order\n",
        "      sorted_filtered_indices = filtered_indices[np.argsort(recommendations[filtered_indices])[::-1]]\n",
        "\n",
        "      # Select the top_n items from the sorted filtered indices\n",
        "      if top_n <= len(filtered_indices):\n",
        "        top_indices = sorted_filtered_indices\n",
        "      else:\n",
        "        top_indices = sorted_filtered_indices[:top_n]\n",
        "      recommended_places = [self.destination_list[index] for index in top_indices]\n",
        "\n",
        "      return recommended_places\n",
        "\n",
        "\n",
        "\n",
        "  def bucket_list_based_collaborative_filtering(self, new_user_places: list, top_n=5):\n",
        "    ###################\n",
        "    ### NOTE\n",
        "    ### Simply looking at the recommendations we can see that this is not as effective\n",
        "    ###################\n",
        "\n",
        "\n",
        "    # Convert new user purchases to a vector\n",
        "    new_user_vector = np.zeros(self.user_destination_matrix.shape[1])\n",
        "    for item in new_user_places:\n",
        "        if item in self.destination_index:\n",
        "            new_user_vector[self.destination_index[item]] = 1\n",
        "\n",
        "    # Calculate similarities between the new user and all existing users\n",
        "    user_similarity = cosine_similarity([new_user_vector], self.user_destination_matrix)\n",
        "\n",
        "    # Compute weighted scores\n",
        "    weighted_scores = np.dot(user_similarity, self.user_destination_matrix)\n",
        "    sum_of_weights = np.sum(user_similarity)\n",
        "\n",
        "    if sum_of_weights == 0:\n",
        "        return \"No recommendations available.\"\n",
        "\n",
        "    # Calculate average score for each item\n",
        "    recommendations = weighted_scores.flatten() / sum_of_weights\n",
        "\n",
        "    # Get indices of top N items\n",
        "    recommended_places_indices = np.argsort(recommendations)[::-1][:top_n]\n",
        "    recommneded_places = [self.destination_list[index] for index in recommended_places_indices]\n",
        "\n",
        "    return recommneded_places"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "JTMT6B7xjZGO",
        "outputId": "f3e62dcb-7680-434a-9f02-3ad682392812"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"initial_recommendations[[\\\"name\\\", \\\"lat\\\", \\\"lng\\\", \\\"rating\\\", \\\"similarity\\\"]]\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Nuwara Eliya Golf Club\",\n          \"Kandy\",\n          \"rumassala\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 6.0328948,\n        \"max\": 9.6743625,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          6.971737,\n          7.2905715,\n          6.0328948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lng\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 79.7596142,\n        \"max\": 81.2152121,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          80.76573069999999,\n          80.6337262,\n          80.2167912\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 4.1,\n        \"max\": 4.7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4.7,\n          4.6,\n          4.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.3712201714515686,\n        \"max\": 0.5875799059867859,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.47339141368865967,\n          0.5875799059867859,\n          0.5219296216964722\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-157296e6-4acb-44a8-b340-90693161b516\" 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></th>\n",
              "      <th>name</th>\n",
              "      <th>lat</th>\n",
              "      <th>lng</th>\n",
              "      <th>rating</th>\n",
              "      <th>similarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nallur Kandaswamy Devasthanam</td>\n",
              "      <td>9.674363</td>\n",
              "      <td>80.029828</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0.37122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kandy</td>\n",
              "      <td>7.290572</td>\n",
              "      <td>80.633726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.37122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Lionel Wendt Art Centre</td>\n",
              "      <td>6.906871</td>\n",
              "      <td>79.860626</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.58758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kalpitiya</td>\n",
              "      <td>8.229528</td>\n",
              "      <td>79.759614</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.530624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Trincomalee</td>\n",
              "      <td>8.587364</td>\n",
              "      <td>81.215212</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.509612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kandy</td>\n",
              "      <td>7.290572</td>\n",
              "      <td>80.633726</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.524189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>rumassala</td>\n",
              "      <td>6.032895</td>\n",
              "      <td>80.216791</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.52193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Viharamahadevi Park</td>\n",
              "      <td>6.913391</td>\n",
              "      <td>79.86174</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.519806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Archaeology Museum Kotte</td>\n",
              "      <td>6.893248</td>\n",
              "      <td>79.905084</td>\n",
              "      <td>4.7</td>\n",
              "      <td>0.473391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuwara Eliya Golf Club</td>\n",
              "      <td>6.971737</td>\n",
              "      <td>80.765731</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.379016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Madu Ganga</td>\n",
              "      <td>6.314099</td>\n",
              "      <td>80.091646</td>\n",
              "      <td>4.6</td>\n",
              "      <td>0.37122</td>\n",
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            "text/plain": [
              "                            name       lat        lng rating similarity\n",
              "0  Nallur Kandaswamy Devasthanam  9.674363  80.029828    4.4    0.37122\n",
              "0                          Kandy  7.290572  80.633726    NaN    0.37122\n",
              "0        Lionel Wendt Art Centre  6.906871  79.860626    4.7    0.58758\n",
              "0                      Kalpitiya  8.229528  79.759614    NaN   0.530624\n",
              "0                    Trincomalee  8.587364  81.215212    NaN   0.509612\n",
              "0                          Kandy  7.290572  80.633726    NaN   0.524189\n",
              "0                      rumassala  6.032895  80.216791    NaN    0.52193\n",
              "0            Viharamahadevi Park  6.913391   79.86174    4.1   0.519806\n",
              "0       Archaeology Museum Kotte  6.893248  79.905084    4.7   0.473391\n",
              "0         Nuwara Eliya Golf Club  6.971737  80.765731    4.1   0.379016\n",
              "0                     Madu Ganga  6.314099  80.091646    4.6    0.37122"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# CHANGE THIS\n",
        "userid = 9991\n",
        "\n",
        "\n",
        "new_user = visitors_original.iloc[userid]\n",
        "new_user_activities = eval(new_user[\"Preferred Activities\"]) # New user's purchases\n",
        "new_user_bucket_list = eval(new_user[\"Bucket list destinations Sri Lanka\"])\n",
        "\n",
        "\n",
        "initial_recommendations = pd.DataFrame({})\n",
        "\n",
        "for activity in new_user_activities:\n",
        "  vector = embeddings[activity]\n",
        "  top_k_cosine = find_top_k_similar(vector, review_embeddings, cosine_similarity_custom, k)\n",
        "  for place in top_k_cosine:\n",
        "    similarity, index = place\n",
        "    new_record = pd.concat([places_original.iloc[index], pd.Series({\"similarity\": similarity})])\n",
        "    initial_recommendations = pd.concat([initial_recommendations, new_record], axis=1 )\n",
        "\n",
        "\n",
        "place_list = places_original[\"name\"].unique()\n",
        "CFModel = CollaborativeFiltering(visitors_original)\n",
        "Search = DestinationSearch(place_list)\n",
        "\n",
        "places_activity_collaborative = CFModel.activity_based_collaborative_filtering(new_user_activities)\n",
        "\n",
        "\n",
        "for place in places_activity_collaborative:\n",
        "  if place not in places_original[\"name\"]:\n",
        "    index, simlarity = Search.find_similar(place)\n",
        "    place = place_list[index]\n",
        "\n",
        "  # new_record = pd.concat([places_original[places_original[\"name\"] == place], pd.Series({\"similarity\": 0.9})], axis=1)\n",
        "  new_record = pd.concat([places_original.iloc[index], pd.Series({\"similarity\": similarity})])\n",
        "  initial_recommendations = pd.concat([new_record, initial_recommendations], axis=1 )\n",
        "\n",
        "\n",
        "\n",
        "initial_recommendations = initial_recommendations.T\n",
        "initial_recommendations[[\"name\", \"lat\", \"lng\", \"rating\", \"similarity\"]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TbB1YOhemJJf"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import OPTICS\n",
        "import numpy as np\n",
        "\n",
        "X = initial_recommendations[[\"lat\", \"lng\", \"rating\", \"similarity\"]].values\n",
        "\n",
        "\n",
        "optics = OPTICS(min_samples=5, xi=0.05, min_cluster_size=0.1)\n",
        "clusters = optics.fit_predict(X)\n",
        "\n",
        "initial_recommendations[\"cluster\"] = clusters\n",
        "\n",
        "cluster_ratings = initial_recommendations.groupby(\"cluster\")[\"rating\"].mean()\n",
        "\n",
        "\n",
        "best_cluster = cluster_ratings.idxmax()\n",
        "\n",
        "\n",
        "best_locations = initial_recommendations[initial_recommendations[\"cluster\"] == best_cluster]\n",
        "\n",
        "\n",
        "best_locations = best_locations.sort_values(by=\"rating\", ascending=False)\n",
        "\n",
        "\n",
        "top_5_locations = best_locations.head(5)\n",
        "\n",
        "top_5_locations"
      ]
    }
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