Food_Items_Embeddings.ipynb

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 },
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Fine-Tuning and Clustering Sentence Embeddings for Food Items"
   ],
   "metadata": {
    "id": "k1CrG8pZZmVo",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_ikcesBCqwYl",
    "outputId": "9d497094-6091-4f61-93fd-d23fc5c07f04",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "# External package installations\n",
    "!pip3 install sentence_transformers hdbscan umap-learn umap-learn[plot]\n",
    "\n",
    "# Imports\n",
    "import re\n",
    "import nltk\n",
    "import hdbscan\n",
    "import umap.plot\n",
    "import pandas                       as pd\n",
    "import matplotlib.pyplot            as plt\n",
    "from   nltk.corpus              import stopwords\n",
    "from   nltk.tokenize            import WhitespaceTokenizer\n",
    "from   nltk.stem                import WordNetLemmatizer\n",
    "from   nltk.probability         import FreqDist\n",
    "from   umap                     import UMAP\n",
    "from   sklearn.cluster          import KMeans\n",
    "from   torch.utils.data         import DataLoader\n",
    "from   sklearn.metrics.cluster  import calinski_harabasz_score, silhouette_score\n",
    "from   sentence_transformers    import models, datasets, losses, SentenceTransformer\n",
    "\n",
    "# NLTK Downloads for Pre-processing\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Preprocessing"
   ],
   "metadata": {
    "id": "Z1Gb9It6roGw",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "data_path                = '/content/drive/MyDrive/Colab Notebooks/'\n",
    "df                       = pd.read_csv(f\"{data_path}MenuItem.csv\")\n",
    "\n",
    "# Remove all commas from individual food items\n",
    "df['Original food Item'] = df['Original food Item'].replace(',', '', regex=True)\n",
    "df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "5SuVD4eGFsvF",
    "outputId": "b6028626-2d0c-4c92-986b-b97865e003a8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 59,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             Restaurant Name                         Original food Item\n",
       "0                Coffee Time                  Garden Vegetable Sandwich\n",
       "1                      HoSan                      Roasted Seaweed Snack\n",
       "2            Weight Watchers                                    Muffins\n",
       "3                Natural Sea                  Albacore Tuna Solid White\n",
       "4              World of Beer  Caesar Salad with Grilled Flat Iron Steak\n",
       "...                      ...                                        ...\n",
       "4519   Moxie's Classic Grill                   Spring Mix Starter Salad\n",
       "4520       Thundercloud Subs             Genoa Salami Low-Fat Sub Large\n",
       "4521             Chick-fil-A            Cookies & Cream Milkshake Small\n",
       "4522               Cereality                                    2% Milk\n",
       "4523  The Habit Burger Grill                 Kids Charburger with Fries\n",
       "\n",
       "[4524 rows x 2 columns]"
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       "      <td>HoSan</td>\n",
       "      <td>Roasted Seaweed Snack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Weight Watchers</td>\n",
       "      <td>Muffins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Natural Sea</td>\n",
       "      <td>Albacore Tuna Solid White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>World of Beer</td>\n",
       "      <td>Caesar Salad with Grilled Flat Iron Steak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4519</th>\n",
       "      <td>Moxie's Classic Grill</td>\n",
       "      <td>Spring Mix Starter Salad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4520</th>\n",
       "      <td>Thundercloud Subs</td>\n",
       "      <td>Genoa Salami Low-Fat Sub Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4521</th>\n",
       "      <td>Chick-fil-A</td>\n",
       "      <td>Cookies &amp; Cream Milkshake Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4522</th>\n",
       "      <td>Cereality</td>\n",
       "      <td>2% Milk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4523</th>\n",
       "      <td>The Habit Burger Grill</td>\n",
       "      <td>Kids Charburger with Fries</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4524 rows × 2 columns</p>\n",
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   "cell_type": "code",
   "source": [
    "# Combine food items from all restaurants to create a Single Menu per row for each Restaurant\n",
    "combined_df                       = df.groupby(df['Restaurant Name']).aggregate({'Original food Item': ', '.join}).reset_index()\n",
    "combined_df['Original food Item'] = combined_df['Original food Item'].astype(str)\n",
    "print(f'Total Restaurants = {len(combined_df)}')\n",
    "combined_df.head(15)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
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    "id": "kCqaxBg2rOFy",
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 60,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Total Restaurants = 341\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 Restaurant Name  \\\n",
       "0                      241 Pizza   \n",
       "1                   A&W (Canada)   \n",
       "2                         Amay's   \n",
       "3            Ameci Pizza & Pasta   \n",
       "4            America's Taco Shop   \n",
       "5     American Flatbread Grocery   \n",
       "6                         Angy's   \n",
       "7                          Arbys   \n",
       "8                     Arcor Saic   \n",
       "9              Back Yard Burgers   \n",
       "10                Baskin Robbins   \n",
       "11  Baton Rouge Steakhouse & Bar   \n",
       "12                       Benny's   \n",
       "13                 Betty Crocker   \n",
       "14                         Bimbo   \n",
       "\n",
       "                                   Original food Item  \n",
       "0   Bacon Cheeseburger Pizza Medium Slice, Meat Lo...  \n",
       "1   Apple Juice 12 oz, Bacon & Egger on Sesame See...  \n",
       "2                                      Almond Cookies  \n",
       "3   Large Ameci Deluxe Pizza, Chicken Caesar Salad...  \n",
       "4   Jack Cheese, Carne Asada Quesadilla Regular, T...  \n",
       "5   Thin & Crispy Pizza Sliced Tomato & 5 Cheese, ...  \n",
       "6   Tri Color Cheese Tortellini, Tortellini, Torte...  \n",
       "7                                Seasoned Curly Fries  \n",
       "8                          Precooked Yellow Corn Meal  \n",
       "9   Classic Turkey Burger, Chili Cheese Fries - La...  \n",
       "10  Pink Bubblegum Ice Cream 2.5 oz Scoop, Vanilla...  \n",
       "11  Caesar Salad with garlic cheese toast, Chicken...  \n",
       "12  Bagel Dogs Polish Sausage, Straws, Smoked Chor...  \n",
       "13  Cookie Mix Chocolate Chip, Hamburger Helper Sa...  \n",
       "14  Bread Soft White Small, Cookies Marshmallow, B...  "
      ],
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       "      <th>2</th>\n",
       "      <td>Amay's</td>\n",
       "      <td>Almond Cookies</td>\n",
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       "      <th>3</th>\n",
       "      <td>Ameci Pizza &amp; Pasta</td>\n",
       "      <td>Large Ameci Deluxe Pizza, Chicken Caesar Salad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>America's Taco Shop</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>American Flatbread Grocery</td>\n",
       "      <td>Thin &amp; Crispy Pizza Sliced Tomato &amp; 5 Cheese, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Angy's</td>\n",
       "      <td>Tri Color Cheese Tortellini, Tortellini, Torte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Arcor Saic</td>\n",
       "      <td>Precooked Yellow Corn Meal</td>\n",
       "    </tr>\n",
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       "      <th>9</th>\n",
       "      <td>Back Yard Burgers</td>\n",
       "      <td>Classic Turkey Burger, Chili Cheese Fries - La...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Baskin Robbins</td>\n",
       "      <td>Pink Bubblegum Ice Cream 2.5 oz Scoop, Vanilla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Baton Rouge Steakhouse &amp; Bar</td>\n",
       "      <td>Caesar Salad with garlic cheese toast, Chicken...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Benny's</td>\n",
       "      <td>Bagel Dogs Polish Sausage, Straws, Smoked Chor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>Cookie Mix Chocolate Chip, Hamburger Helper Sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bimbo</td>\n",
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       "                                                     [key], {});\n",
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       "          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",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now we have a total of **341** restaurants with their entire Menu."
   ],
   "metadata": {
    "id": "rU-WN-gSaJtv",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Cleaning "
   ],
   "metadata": {
    "id": "fxfBT_TQaT9u",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "stop_words       = set(stopwords.words('english'))\n",
    "def clean(menu):\n",
    "\n",
    "    # Remove punctuations\n",
    "    clean_menu   = re.sub(r'[^\\w\\s]', '', menu)\n",
    "    clean_menu   = clean_menu.lower()\n",
    "\n",
    "    # Tokenize based on Whitespaces\n",
    "    words        = WhitespaceTokenizer().tokenize(clean_menu)\n",
    "\n",
    "    # Remove stop words\n",
    "    cleaned_items = [w for w in words if w not in stop_words]\n",
    "\n",
    "    # Lemmatize words\n",
    "    wnl           = WordNetLemmatizer()\n",
    "    return ' '.join( [ wnl.lemmatize(token) for token in cleaned_items ] )"
   ],
   "metadata": {
    "id": "fxsUFMjrrR1e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 64,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "combined_df['Cleaned Items'] = combined_df['Original food Item'].apply(lambda x: clean(x))\n",
    "combined_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "ZpC5Q4bLrWsp",
    "outputId": "d12cb78c-eff4-4e58-f6c2-b0f32cd486e0",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 65,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       Restaurant Name                                 Original food Item  \\\n",
       "0            241 Pizza  Bacon Cheeseburger Pizza Medium Slice, Meat Lo...   \n",
       "1         A&W (Canada)  Apple Juice 12 oz, Bacon & Egger on Sesame See...   \n",
       "2               Amay's                                     Almond Cookies   \n",
       "3  Ameci Pizza & Pasta  Large Ameci Deluxe Pizza, Chicken Caesar Salad...   \n",
       "4  America's Taco Shop  Jack Cheese, Carne Asada Quesadilla Regular, T...   \n",
       "\n",
       "                                       Cleaned Items  \n",
       "0  bacon cheeseburger pizza medium slice meat lov...  \n",
       "1  apple juice 12 oz bacon egger sesame seed bun ...  \n",
       "2                                       almond cooky  \n",
       "3  large ameci deluxe pizza chicken caesar salad ...  \n",
       "4  jack cheese carne asada quesadilla regular tom...  "
      ],
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       "      <th></th>\n",
       "      <th>Restaurant Name</th>\n",
       "      <th>Original food Item</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>241 Pizza</td>\n",
       "      <td>Bacon Cheeseburger Pizza Medium Slice, Meat Lo...</td>\n",
       "      <td>bacon cheeseburger pizza medium slice meat lov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A&amp;W (Canada)</td>\n",
       "      <td>Apple Juice 12 oz, Bacon &amp; Egger on Sesame See...</td>\n",
       "      <td>apple juice 12 oz bacon egger sesame seed bun ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amay's</td>\n",
       "      <td>Almond Cookies</td>\n",
       "      <td>almond cooky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ameci Pizza &amp; Pasta</td>\n",
       "      <td>Large Ameci Deluxe Pizza, Chicken Caesar Salad...</td>\n",
       "      <td>large ameci deluxe pizza chicken caesar salad ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>America's Taco Shop</td>\n",
       "      <td>Jack Cheese, Carne Asada Quesadilla Regular, T...</td>\n",
       "      <td>jack cheese carne asada quesadilla regular tom...</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
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     "metadata": {},
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   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's look at the frequency of words in the Restaurant Menus"
   ],
   "metadata": {
    "id": "y44-07owadrc",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Calculate Frequency Distribution\n",
    "num_words          = 20\n",
    "wt                 = WhitespaceTokenizer()\n",
    "clean_tokens       = wt.tokenize(''.join([d for d in combined_df['Cleaned Items']]))\n",
    "freq_dist          = FreqDist(clean_tokens)\n",
    "common_x, common_y = [t[0] for t in freq_dist.most_common(num_words)], [t[1] for t in freq_dist.most_common(num_words)]\n",
    "\n",
    "print(freq_dist.most_common(num_words))\n",
    "plt.title(f'{num_words} Most Frequent Words')\n",
    "plt.bar(common_x, common_y)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "id": "6FjiWI0jrY4J",
    "outputId": "08729ce7-4cd2-43a0-cbed-18ce107ba258",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 66,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[('chicken', 435), ('oz', 339), ('cheese', 248), ('pizza', 238), ('large', 221), ('cream', 208), ('chocolate', 198), ('salad', 180), ('sauce', 177), ('small', 154), ('ice', 137), ('sandwich', 122), ('rice', 122), ('milk', 114), ('smoothie', 110), ('regular', 103), ('beef', 103), ('dressing', 102), ('vanilla', 97), ('strawberry', 97)]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "As we can see in the plot, **chicken** occurs most frequently in the corpus followed by *cheese, pizza, cream* and *choclate*"
   ],
   "metadata": {
    "id": "UhF7LZ3DamG8",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Fine-Tune Sentence Embeddings Model"
   ],
   "metadata": {
    "id": "oncLqXKusGqG",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We implement a class to fine-tune a Transformer model with the an addition pooling layer the embedding layer. This enables us to extract sentence embeddings for this task.\n",
    "\n",
    "\n",
    "The learning is performed in an unsupervised fashion to learn embeddings from the given food items dataset.\n",
    "\n",
    "The class `FineTune` performs this training and returns the sentence embeddings for the given data."
   ],
   "metadata": {
    "id": "ChnRItsUbGav",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "class FineTune:\n",
    "\n",
    "    def __init__(self, model_name, max_epochs = 2, data_path = '/content/drive/MyDrive/Colab Notebooks/'):\n",
    "        self.model_name = model_name\n",
    "        self.max_epochs = max_epochs\n",
    "        self.data_path  = data_path\n",
    "        self.dataframe  = combined_df\n",
    "\n",
    "\n",
    "    def prepare_data(self):\n",
    "        train_dataset    = datasets.DenoisingAutoEncoderDataset(self.dataframe['Cleaned Items'])\n",
    "        train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
    "        return train_dataloader\n",
    "\n",
    "\n",
    "    def fine_tune(self,\n",
    "                  wdecay     = 0,\n",
    "                  lr         = 3e-5):\n",
    "\n",
    "        word_embedding_model = models.Transformer(self.model_name)\n",
    "        pooling_model        = models.Pooling( word_embedding_model.get_word_embedding_dimension(), 'cls' )\n",
    "        model                = SentenceTransformer( modules = [ word_embedding_model, pooling_model ] )\n",
    "\n",
    "        train_loss           = losses.DenoisingAutoEncoderLoss( model, decoder_name_or_path = self.model_name, tie_encoder_decoder=True )\n",
    "\n",
    "        # Data prep\n",
    "        sentences            = [ items for items in self.dataframe['Cleaned Items'] ]\n",
    "        train_dataset        = datasets.DenoisingAutoEncoderDataset(sentences)\n",
    "        train_dataloader     = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
    "\n",
    "        model.fit(\n",
    "            train_objectives   = [ ( train_dataloader, train_loss ) ],\n",
    "            epochs             = self.max_epochs,\n",
    "            weight_decay       = wdecay,\n",
    "            scheduler          = 'constantlr',\n",
    "            optimizer_params   = {'lr': lr },\n",
    "            show_progress_bar  = True\n",
    "        )\n",
    "        return model\n",
    "\n",
    "\n",
    "    def get_embeddings(self, model):\n",
    "        model.eval()\n",
    "        sentences = [ items for items in self.dataframe['Cleaned Items'] ]\n",
    "        return model.encode( sentences, batch_size=16, show_progress_bar=True, convert_to_tensor=True )\n",
    "\n",
    "\n",
    "    def visualize(self, embeddings):\n",
    "\n",
    "        # Reduce to 2 dimensions for visualization\n",
    "        umap_data        = UMAP(\n",
    "         n_components    = 2,\n",
    "         random_state    = 42).fit_transform(embeddings.cpu())\n",
    "\n",
    "        # Store 2D co-ordinates in dataframe for plotting\n",
    "        result           = pd.DataFrame(umap_data, columns=['x', 'y'])\n",
    "        result['labels'] = self.dataframe['Restaurant Name']\n",
    "\n",
    "        # Plot embeddings as scatter plot\n",
    "        plt.title(f'Sentence Embeddings after Fine-Tuning')\n",
    "        plt.scatter(result.x, result.y)\n",
    "        plt.savefig(f'{self.data_path}embeddings_viz_{self.model_name}.png')\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "    def run(self):\n",
    "        # Fine-tune the model and extract embeddings\n",
    "        model           = self.fine_tune()\n",
    "        embeddings      = self.get_embeddings(model)\n",
    "        self.visualize(embeddings)\n",
    "        return embeddings"
   ],
   "metadata": {
    "id": "wkT17Fys5T3g",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 67,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dimensionality Reduction"
   ],
   "metadata": {
    "id": "uPo3aR39daKW",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Dimensionality Reduction is performed to reduce the 784 dimensional sentence embedding vector to 50.\n",
    "\n",
    "Uniform Manifold Approximation and Projection (UMAP) was chosen since it [claims to](https://arxiv.org/abs/1802.03426) __preserve more of the global structure with superior run time performance__ as compared to t-SNE"
   ],
   "metadata": {
    "id": "MRSD_HQSeVvZ",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def reduce_dims( embeddings,\n",
    "                 n_components = 50,\n",
    "                 n_neighbors  = 15,\n",
    "                 min_dist     = 0.1,\n",
    "                 metric       = 'cosine'):\n",
    "\n",
    "    return UMAP( n_neighbors  = n_neighbors,\n",
    "                 n_components = n_components,\n",
    "                 min_dist     = min_dist,\n",
    "                 metric       = metric,\n",
    "                 random_state = 42\n",
    "                ).fit_transform(embeddings)"
   ],
   "metadata": {
    "id": "mCyhXM3jWbcN",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 68,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Clustering the Sentence Embeddings"
   ],
   "metadata": {
    "id": "25q5EV0BdCeg",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We compare cluster outputs from two clustering methods on the 50 dimensional sentence embeddings."
   ],
   "metadata": {
    "id": "bF_IkQo4fOcX",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### K-Means Clustering"
   ],
   "metadata": {
    "id": "UHd5iPobdmjE",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def k_means_clustering(reduced_emb):\n",
    "    \"\"\"\n",
    "    K-means clustering on the dim reduced embeddings.\n",
    "    \"\"\"\n",
    "    return KMeans(n_clusters   = 10,\n",
    "                  random_state = 42\n",
    "                  ).fit(reduced_emb).labels_"
   ],
   "metadata": {
    "id": "bvPwKTbIdCE9",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 69,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### HDB Scan Clustering"
   ],
   "metadata": {
    "id": "XnlcE6S6c_Th",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def hdb_clustering(reduced_emb):\n",
    "    \"\"\"\n",
    "    HDB clustering on the dim reduced embeddings.\n",
    "    \"\"\"\n",
    "    return hdbscan.HDBSCAN(min_cluster_size         = 5,\n",
    "                           min_samples              = 4,\n",
    "                           metric                   = 'euclidean',\n",
    "                           cluster_selection_method = 'eom'\n",
    "                          ).fit(reduced_emb).labels_"
   ],
   "metadata": {
    "id": "MK-ngXP6c_2C",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 133,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cluster Visualization"
   ],
   "metadata": {
    "id": "oJBQzI4NdEfG",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def plot_clusters(reduced_emb, labels):\n",
    "    mapper = UMAP( n_neighbors  = 15,\n",
    "                   n_components = 2,\n",
    "                   min_dist     = 0.1,\n",
    "                   metric       = 'cosine',\n",
    "                   random_state = 42\n",
    "                   ).fit(reduced_emb)\n",
    "\n",
    "    umap.plot.points( mapper,\n",
    "                      labels = labels,\n",
    "                      theme  = 'fire')\n",
    "    plt.show()"
   ],
   "metadata": {
    "id": "Bun0RFktdE0Q",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 71,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Cluster Analysis"
   ],
   "metadata": {
    "id": "smN3EHKxfiW2",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The quantitative evaluation of the clustering done was done using two unsupervised clustering metrics:\n",
    "\n",
    "- Silhouette score\n",
    "- Calinski-Harabasz score"
   ],
   "metadata": {
    "id": "Ba5pBUFtmVie",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "def cluster_analysis(reduced_emb, labels):\n",
    "    \"\"\"\n",
    "    Cluster analysis on the embeddings.\n",
    "    \"\"\"\n",
    "    plot_clusters(reduced_emb, labels)\n",
    "\n",
    "    sil_score = silhouette_score(reduced_emb, labels)\n",
    "    cal_score = calinski_harabasz_score(reduced_emb, labels)\n",
    "\n",
    "    print(f'Silhouette score: {sil_score}')\n",
    "    print(f'Calinski-Harabasz score: {cal_score}')"
   ],
   "metadata": {
    "id": "Vi-foUZxW7pd",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 31,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "def analyse_labels(label_df, num_words = 5):\n",
    "    for cluster in range(label_df.shape[0]):\n",
    "        clean_tokens       = wt.tokenize(''.join([d for d in hdb_df['Cleaned Items'][cluster]]))\n",
    "        freq_dist          = FreqDist(clean_tokens)\n",
    "        common_x, common_y = [ t[0] for t in freq_dist.most_common(num_words) ], [ t[1] for t in freq_dist.most_common(num_words) ]\n",
    "\n",
    "        plt.title(f'Most Frequent Words for Label {cluster+1}')\n",
    "        plt.bar(common_x, common_y)\n",
    "        plt.xticks(rotation=90)\n",
    "        plt.show()"
   ],
   "metadata": {
    "id": "xeOzhHWapit7",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 155,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Results"
   ],
   "metadata": {
    "id": "afkprC0MDY9e",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the base Transformer to build the Sentence Transformer model, we use the `BERT BASE UNCASED` model. You can experiment with different models using the list [here](https://huggingface.co/models) and supply the name to the `FineTune` class. "
   ],
   "metadata": {
    "id": "FyGtKiQQfqxJ",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "task       = FineTune( 'bert-base-uncased', max_epochs = 2 )\n",
    "embeddings = task.run()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 603,
     "referenced_widgets": [
      "a82ddec0f4b3430b9599eeece89e364f",
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      "066b5eb37f594b708c42867d58782e73",
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    },
    "id": "ekdWbXp8B8MO",
    "outputId": "077f5475-53b4-4f98-93ed-6f383c20ad78",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 97,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "When tie_encoder_decoder=True, the decoder_name_or_path will be invalid.\n",
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertLMHeadModel: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']\n",
      "- This IS expected if you are initializing BertLMHeadModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertLMHeadModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of BertLMHeadModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.6.crossattention.output.dense.bias', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.query.weight', 'bert.encoder.layer.8.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.query.bias', 'bert.encoder.layer.6.crossattention.output.dense.weight', 'bert.encoder.layer.3.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.self.value.bias', 'bert.encoder.layer.7.crossattention.self.query.weight', 'bert.encoder.layer.6.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.self.value.bias', 'bert.encoder.layer.8.crossattention.output.dense.bias', 'bert.encoder.layer.3.crossattention.self.key.bias', 'bert.encoder.layer.2.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.5.crossattention.output.dense.bias', 'bert.encoder.layer.8.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.output.dense.weight', 'bert.encoder.layer.7.crossattention.self.key.bias', 'bert.encoder.layer.7.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.output.dense.bias', 'bert.encoder.layer.4.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.self.query.weight', 'bert.encoder.layer.10.crossattention.self.value.weight', 'bert.encoder.layer.8.crossattention.self.query.bias', 'bert.encoder.layer.9.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.11.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.3.crossattention.output.dense.weight', 'bert.encoder.layer.8.crossattention.self.value.weight', 'bert.encoder.layer.2.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.query.bias', 'bert.encoder.layer.2.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.self.query.weight', 'bert.encoder.layer.3.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.self.value.weight', 'bert.encoder.layer.4.crossattention.self.value.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.self.value.bias', 'bert.encoder.layer.5.crossattention.self.query.weight', 'bert.encoder.layer.5.crossattention.output.dense.weight', 'bert.encoder.layer.4.crossattention.output.dense.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.2.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.self.key.weight', 'bert.encoder.layer.8.crossattention.self.value.bias', 'bert.encoder.layer.10.crossattention.output.dense.bias', 'bert.encoder.layer.6.crossattention.self.value.bias', 'bert.encoder.layer.2.crossattention.self.key.bias', 'bert.encoder.layer.9.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.7.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.7.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.query.bias', 'bert.encoder.layer.3.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.query.weight', 'bert.encoder.layer.9.crossattention.self.key.weight', 'bert.encoder.layer.2.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.key.bias', 'bert.encoder.layer.11.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.7.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.crossattention.self.query.bias', 'bert.encoder.layer.10.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.key.bias', 'bert.encoder.layer.4.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.11.crossattention.output.dense.weight', 'bert.encoder.layer.10.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.6.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.9.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.8.crossattention.self.key.weight', 'bert.encoder.layer.11.crossattention.output.dense.bias', 'bert.encoder.layer.2.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.9.crossattention.output.dense.weight', 'bert.encoder.layer.11.crossattention.self.query.bias', 'bert.encoder.layer.8.crossattention.self.key.bias', 'bert.encoder.layer.6.crossattention.self.query.bias', 'bert.encoder.layer.5.crossattention.self.key.weight', 'bert.encoder.layer.9.crossattention.output.dense.bias', 'bert.encoder.layer.5.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.6.crossattention.self.key.bias', 'bert.encoder.layer.5.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.9.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.7.crossattention.self.value.weight', 'bert.encoder.layer.10.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.4.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.11.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.dense.weight', 'bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.3.crossattention.self.value.bias', 'bert.encoder.layer.4.crossattention.output.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "The following encoder weights were not tied to the decoder ['bert/pooler']\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Epoch:   0%|          | 0/2 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "a82ddec0f4b3430b9599eeece89e364f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Iteration:   0%|          | 0/43 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "19f086bcefb64849bb48b0870f425558"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Iteration:   0%|          | 0/43 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c57ad620219547e0b58dd668d9835d90"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Batches:   0%|          | 0/22 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1720eed28a844b8faac86df36084dd5b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Examples of other transformer models\n",
    "```\n",
    "'bert-base-cased'\n",
    "'bert-large-uncased'\n",
    "'bert-large-cased'\n",
    "'xlnet-base-cased'\n",
    "'xlnet-large-cased'\n",
    "'roberta-base'\n",
    "'roberta-large'\n",
    "'roberta-large-mnli'\n",
    "'roberta-large-mnli-mm'\n",
    "```\n",
    "\n"
   ],
   "metadata": {
    "id": "BgOkLJ0Zi7BA",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Reduce Dimensionality for Clustering\n",
    "reduced_emb = reduce_dims(embeddings.cpu(), n_components = 20)"
   ],
   "metadata": {
    "id": "EgVK4MOzWpPM",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 101,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# K-Means Clustering\n",
    "print(f'K-Means Clustering')\n",
    "kmeans_labels = k_means_clustering(reduced_emb)\n",
    "cluster_analysis(reduced_emb, kmeans_labels)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 690
    },
    "id": "LEDBSrNvYk7p",
    "outputId": "fc2ccbd4-5340-4ec6-d859-fcd60e9bcc54",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 102,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "K-Means Clustering\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Silhouette score: 0.3659208416938782\n",
      "Calinski-Harabasz score: 391.9122885061257\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# HDB Scan Clustering\n",
    "print(f'HDB Scan Clustering')\n",
    "reduced_emb  = reduce_dims(embeddings.cpu(), n_components = 30)\n",
    "hdb_labels   = hdb_clustering(reduced_emb)\n",
    "cluster_analysis(reduced_emb, hdb_labels)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 690
    },
    "id": "Rv44ho25YibO",
    "outputId": "1d337f08-a92d-4f18-b64f-379ffebf16c2",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 127,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "HDB Scan Clustering\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Silhouette score: 0.06848572194576263\n",
      "Calinski-Harabasz score: 65.16148660598063\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Comparison of Index Scores"
   ],
   "metadata": {
    "id": "t_RDeL3BmIit",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We reduce the dimensionality to **30** for HDB Scan Clustering. The Silhouette Score is **significantly lower** than that of K-Means. \n",
    "Also, the Variance Ratio Criterion index is lower, denoting poor clustering of the embeddings."
   ],
   "metadata": {
    "id": "VYCnLVYulXFY",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Analyse Labels"
   ],
   "metadata": {
    "id": "glfDDdW0mPb2",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can group the menu items for restaurants from the same cluster and calculate the frequency distribution. This will help us analyse the semantic relationship within the cluster."
   ],
   "metadata": {
    "id": "VN1rIQBjqLmg",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "combined_df['HDB Labels']     = hdb_labels\n",
    "combined_df['K Means Labels'] = kmeans_labels\n",
    "combined_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "GP-doTq6k4wJ",
    "outputId": "541ff843-67c8-4867-884b-2cafad0bb7e8",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 132,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Restaurant Name                                 Original food Item  \\\n",
       "0              241 Pizza  Bacon Cheeseburger Pizza Medium Slice, Meat Lo...   \n",
       "1           A&W (Canada)  Apple Juice 12 oz, Bacon & Egger on Sesame See...   \n",
       "2                 Amay's                                     Almond Cookies   \n",
       "3    Ameci Pizza & Pasta  Large Ameci Deluxe Pizza, Chicken Caesar Salad...   \n",
       "4    America's Taco Shop  Jack Cheese, Carne Asada Quesadilla Regular, T...   \n",
       "..                   ...                                                ...   \n",
       "336          Yellow Tail                              Moscato, Pinot Grigio   \n",
       "337              YoCream  Plain, Very Strawberry, Pistachio Gelato, Silk...   \n",
       "338          Yogli Mogli  Carrot Cake, Pumpkin, Cookies N Cream, Mango T...   \n",
       "339            Yoshinoya  No Calorie Brisk Green Tea with Peach, Grilled...   \n",
       "340               Zapata                    Refried Beans Spicy, Dried Beef   \n",
       "\n",
       "                                         Cleaned Items  HDB Labels  \\\n",
       "0    bacon cheeseburger pizza medium slice meat lov...           3   \n",
       "1    apple juice 12 oz bacon egger sesame seed bun ...           3   \n",
       "2                                         almond cooky          12   \n",
       "3    large ameci deluxe pizza chicken caesar salad ...           4   \n",
       "4    jack cheese carne asada quesadilla regular tom...           3   \n",
       "..                                                 ...         ...   \n",
       "336                               moscato pinot grigio          13   \n",
       "337  plain strawberry pistachio gelato silk almondm...          10   \n",
       "338  carrot cake pumpkin cooky n cream mango tango ...          10   \n",
       "339  calorie brisk green tea peach grilled steak ki...           3   \n",
       "340                      refried bean spicy dried beef          -1   \n",
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       "     K Means Labels  \n",
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    "hdb_df = combined_df.sort_values('HDB Labels')\n",
    "hdb_df = hdb_df.groupby(hdb_df['HDB Labels']).aggregate({'Cleaned Items': ', '.join}).reset_index()\n",
    "hdb_df"
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      "text/plain": [
       "    HDB Labels                                      Cleaned Items\n",
       "0           -1  refried bean spicy dried beef, parmesan caesar...\n",
       "1            0  shrimp calamari ring crab cheese bite stuffed ...\n",
       "2            1  100 grated parmesan cheese bread crumb plain g...\n",
       "3            2  cranberry nut mix, dark chocolate pear cinnamo...\n",
       "4            3  margherita calzone t garden veggie pizza famil...\n",
       "5            4  extra meaty baby back pork rib sweet smoky bab...\n",
       "6            5  powerup fresh fruit yogurt cup americano small...\n",
       "7            6  planet pro veg 32g strawberry acai smoothie 32...\n",
       "8            7  sliced bamboo shoot baby corn small whole cock...\n",
       "9            8  raisin, steak mushroom cheddar flatbread melt,...\n",
       "10           9  pudding pie filling instant vanilla pie fillin...\n",
       "11          10  cookie mix chocolate chip hamburger helper sal...\n",
       "12          11  pistachio, red kidney bean, pecan praline, bab...\n",
       "13          12  parmesan cheese freshly grated gnocchi potato,...\n",
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    "analyse_labels(hdb_df)"
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   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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2DFgGsGjRojkoLSK6YMkhp7RdwpxZedi+I7nfoXcmStoM+CLwats3DS63fYTtpbaXTk1NzWWNERGdNlRQS9qQJqRPsP2l0ZYUERH9hhn1IeAo4CLbHxh9SRER0W+YFvVjgOcBe0k6t9yeMuK6IiKimHFnou0fABpDLRERMY0cmRgRUbkEdURE5RLUERGVS1BHRFQuQR0RUbkEdURE5RLUERGVS1BHRFQuQR0RUbkEdURE5RLUERGVS1BHRFQuQR0RUbkEdURE5RLUERGVS1BHRFQuQR0RUbkEdURE5RLUERGVS1BHRFQuQR0RUbkEdURE5RLUERGVS1BHRFQuQR0RUbkZg1rS0ZKulXT+OAqKiIjVDdOi/jSwz4jriIiINZgxqG2fBtwwhloiImIac9ZHLWmZpLMlnX3dddfN1d1GRHTenAW17SNsL7W9dGpqaq7uNiKi8zLqIyKicgnqiIjKDTM877PAGcDOkn4l6cDRlxURET0LZ1rB9v7jKCQiIqaXro+IiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKpegjoioXII6IqJyCeqIiMolqCMiKjdUUEvaR9Ilki6VdMioi4qIiFVmDGpJGwAfAZ4MPBDYX9IDR11YREQ0hmlRPwq41PZltv8MfA54+mjLioiIHtle+wrSM4F9bL+kTD8PeLTtVwystwxYViZ3Bi6Z+3LnzDbA9W0X0aIub3+2vbtq3/7FtqemW7Bwrh7B9hHAEXN1f6Mk6WzbS9uuoy1d3v5seze3Heb39g/T9XEVcO++6R3KvIiIGINhgvp/gftK2lHSRsBzgK+MtqyIiOiZsevD9u2SXgF8A9gAONr2BSOvbLTmRRfNCHV5+7Pt3TVvt3/GnYkREdGuHJkYEVG5BHVEROUS1BERlUtQR0RUbs4OeJkPJN0LWEzfdts+rb2KxkPStsC7ge1tP7mcq2V320e1XForJP0r8Fvgi7Zvb7ueUZD0VWCNIwVsP22M5bRK0i7AElZ/33+ptYLWQWeCWtJ7gWcDFwJ3lNkGJj6ogU8DnwLeUqZ/BpwIdDKoAQF7AAcAkxpY/1l+/hNwT+D4Mr0/8JtWKmqBpKOBXYALgDvLbAPzKqg7MzxP0iXALrZva7uWcZP0v7YfKenHth9W5p1re9e2a4vRmu6w6fl8KPVsSbrQ9rw/22eX+qgvAzZsu4iW/EHS31K+CkvaDfh9uyWNj6RtJR0l6Wtl+oGSDmy7rjHZVNLf9SYk7Qhs2mI943bGJJyWuTNdH8CtwLmSvgPc1aq2/ar2Shqb19Ic9r+TpB8CU8Az2y1prD5Nd7t+XgMsl3QZTZfPYuCl7ZY0VsfShPU1NO97Aba9S7tlzU6Xuj5eMN1828eMu5Y2SFpIc/pZAZfY/kvLJY1N17t+JN0NuH+ZvLhL3X+SLqVpqPyUVX3U2L6itaLWQWda1LaPkbQJsMh2zefKnnOSXg6c0DtHi6StJe1v+6MtlzYune36kXR3mqBabPsgSfeVtLPtk9uubUyusz3vTyLXpRb1U2n2hG9ke0dJuwL/0YVhStO1Hvtbl5NO0sOBw4EHA+dTun5s/6TVwsZA0onACuD5th9cgvv0Dn2b+CiwFfBVVu/ynFejPjrTogYOpbms2HIA2+f272SZcBtIksuncrkO5kYt1zQ2ts+R9Di62fWzk+1nS9ofwPatktR2UWO0CU1A/33fvHk3PK9LQf0X278feI3euaaVJ8zXgRMlfaJMv7TM64SOd/38uXT59T6kd6KvZTnpbL+o7RrmQpeG510g6Z9pWpf3lXQ4cHrbRY3JG4FTgZeV23eAN7Ra0XgdZPvG3oTt3wEHtVjPOB1K86F8b0kn0Pzv39hqRWMkaQdJJ0m6tty+KGmHtuuarS71Ud+dZnhW7yvQN2n6qDvTuugqST+lOdipv+vnJ7Yf1G5l41F2pO5G0+1zpu2aL/A6pyR9C/gMcFyZ9VzgANt7t1fV7HUpqA8cPLeFpMNsH9JWTeMi6XKmOe+D7U700Ut6H8344f6unyttv669qsZD0ndsP3GmeZNqDTvS593QzC71Uf8fSX+yfQKApA/T7Gjogv7DhTcG9gP+pqVa2vBGmnB+WZn+FvDJ9soZPUkbA3cHtpG0NU1rGmAL4F6tFTZ+v5X0XOCzZXp/mhNyzStdalFvQnN03tHAPsCNtg9ut6r2SFph+xFt1xGjIelg4NXA9sBVrArqm4AjbX+4rdrGSdJimqGZu5dZPwReZfuX7VU1exMf1JL6W46bA/9N88/6dwDbN7RR1ziVccQ9C2ha2C+z/dCWShqrLnf9SHql7cPbriPWTxe6PlbQvEnV93PfcjMw8W9W4P19v98OrASe1U4prehy1881kja3fbOktwIPB95p+5y2CxuHMsLjcOAxZdb3gYNt/6q9qmZv4lvUEdPpStePpJ/Y3kXSHsA7gfcB/2770S2XNhaTMuqjCy1qYLWDHm4s01sDXTnoAUn7Ag+iaVECYPs/2qtofNbQ9dOV137vIhn7AkfYPkXSO9ssaMymbH+qb/rTkl7dWjXrqCsvVmgOevhIb8L27yQdBEx8UEv6OM0IgCfQjHZ4JnBWq0WNV5e7fq4qR6TuDby3nEmvSwe6ZdTHfNLlgx76vv72fm4GfM32Y9uuLUarHOi1D/BT2z+XtB3wENvfbLm0sRgY9WGao5Hn3aiPLrWou3y+iz+Vn7dK2h64AdiuxXrGrqtdP7ZvBb4k6R6SFpXZF7dZ07iUxti7J+EMmV0K6s4d9NDnq5K2otmRdA5Ny+LIdksany53/Uh6Gk3Xz/bAtcAimqCe+G+Stu+QtFjSRrb/3HY966MzXR9dJmk/4OtliNbbaIZovaNDQ7Q62/Uj6TxgL+Dbth8m6QnAc2134pqRko4FHkBzsNsfevNtf6C1otbBxO9UkPT58vOnkn4yeGu7vjF5WwnpPWjetJ8EPtZyTeM02PVzO93p+vmL7d8CCyQtsH0qq48rn3S/AE6mybrN+27zShe6PnqHif9jq1W0q3+I1pEdHKLV5a6fG8s3iO8DJ0i6lr6W5aSz/fa2a5gL6froAEkn05zvYW+abo8/Amd16BDyznb9SNqU5huFgAOALWmOJ5h3Q9TWhaT7Af8GLKGvYWp7r7ZqWhedCWpJ/wS8F7gHzYu2d9n4LVotbAwyRGu1o/PeQXPtzC4dnbct8MgyeZbta9usZ5xKH/3HaU4l0ftmie0VrRW1DroU1JcCT7V9Udu1xHj1LuQr6T00H1afUUcu7ivpWTRdPstpGiePBV5v+wtt1jUuk3KqgC4F9Q9tP2bmNWPSdLnrp7Qo9+61oiVN0YwAmeht7ztr5qtohiWexOpXIZ9XZ82c+KAuXR4AjwPuSXOa03l72fiYvS53/Uj6qe2H9E0vAM7rnzeJ+k5tO90V1z3fTnHbhaDunZBlun+abb94zCVFjIUkAUfRXNGld66LZ9OcOqEzF7idBBMf1D2SjqE5D23/2fPen6COSSbpfJqLZOxRZn3f9kktljRWAyN+eufjfoftH7dc2qx0YRx1zy69kIa7zp438TuTovNW0FzI97VtF9KSt9n+rzLi50k0O1Y/DsyrET8Tf2RinwWlFQ3ctbOhSx9U0U2PBs6Q9IsOHpEL05yPG9ioxXrWSZeC6v00L9j/KtP7Ae9qsZ6IcfiHtgto2UScj7szfdQAkh5Ic64LgO/avrDNeiJitCZlxE+ngjoiuqXvHNyrmW8XDkhQR8TEKld26g3N3RjYEbhkvl3ZqUt91BHRMYMH9pQLHf9rS+Wss7SoI6JTBo/WnA/Soo6IiSWpf/z4AuARwK9bKmedJagjYpJtTtNHDc2Vfb4KfLG9ctZNuj4iYmJJeiTwZla/cIBt79JaUesgQR0RE0vSJTRXeDkfuLM33/YVrRW1DtL1ERGT7DrbX227iPWVFnVETCxJTwT2B77DPD4PfVrUETHJXgTcH9iQVV0fBuZVUKdFHRETS9Iltnduu471Ne/OIhURMQunl5OxzWtpUUfExJJ0EbATcDlNH7XI8LyIiHpIWjzd/Pk2PC9BHRFRufRRR0RULkEdEVG5BHVEROUS1BERlfv/XPYZcvnHggYAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "kmeans_df = combined_df.sort_values('K Means Labels')\n",
    "kmeans_df = kmeans_df.groupby(kmeans_df['K Means Labels']).aggregate({'Cleaned Items': ', '.join}).reset_index()\n",
    "kmeans_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "RuwuB_TFmvOP",
    "outputId": "90816836-deeb-410f-dd04-0aff33101f29",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 158,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   K Means Labels                                      Cleaned Items\n",
       "0               0  refried bean spicy dried beef, peanut salted s...\n",
       "1               1  bagel blueberry bagel cream cheese bagel egg c...\n",
       "2               2  creme brulee, original snappy beer cheese, toc...\n",
       "3               3  teriyaki salmon burger wild alaskan pink salmo...\n",
       "4               4  cheese tortellini stuffed shell ricotta cavate...\n",
       "5               5  hard candy butterscotch rich chocolate cookie ...\n",
       "6               6  junior jax pork rice bowl kid mandarin orange ...\n",
       "7               7  pecan praline, blueberry pomegranate chia dres...\n",
       "8               8  steak tip natural ranch steak natural cube ste...\n",
       "9               9  bagel dog polish sausage straw smoked chorizo ..."
      ],
      "text/html": [
       "\n",
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>K Means Labels</th>\n",
       "      <th>Cleaned Items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>refried bean spicy dried beef, peanut salted s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>bagel blueberry bagel cream cheese bagel egg c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>creme brulee, original snappy beer cheese, toc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>teriyaki salmon burger wild alaskan pink salmo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>cheese tortellini stuffed shell ricotta cavate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>hard candy butterscotch rich chocolate cookie ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>junior jax pork rice bowl kid mandarin orange ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>pecan praline, blueberry pomegranate chia dres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>steak tip natural ranch steak natural cube ste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>bagel dog polish sausage straw smoked chorizo ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-493cfd40-77e8-4602-ad1c-5c3e10f1e9c1')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "        \n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-493cfd40-77e8-4602-ad1c-5c3e10f1e9c1 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-493cfd40-77e8-4602-ad1c-5c3e10f1e9c1');\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",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 158
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "analyse_labels(kmeans_df)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "kiyUE2Z0qA4V",
    "outputId": "bd941154-7c3a-4439-a058-d2a8d21222ad",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 159,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Group Restaurants from same clusters"
   ],
   "metadata": {
    "id": "k9XAWRPHs5nY",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "grouped = combined_df.sort_values('K Means Labels')\n",
    "grouped = grouped.groupby( grouped['K Means Labels'] ).aggregate( { 'Restaurant Name': ', '.join } ).reset_index()\n",
    "grouped.T"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 243
    },
    "id": "FrDfhfe1tNkj",
    "outputId": "e7ac978d-25ff-482c-e9d8-f470e9656ec3",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 165,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                                                 0  \\\n",
       "K Means Labels                                                   0   \n",
       "Restaurant Name  Zapata, Texas Roadhouse, Martel, Marie Callend...   \n",
       "\n",
       "                                                                 1  \\\n",
       "K Means Labels                                                   1   \n",
       "Restaurant Name  Duck Donuts, Harris Teeter, Cracker Barrel, Du...   \n",
       "\n",
       "                                                                 2  \\\n",
       "K Means Labels                                                   2   \n",
       "Restaurant Name  Emeril, Hall's, PFC, Paone, Henny's, Frey's, Y...   \n",
       "\n",
       "                                                                 3  \\\n",
       "K Means Labels                                                   3   \n",
       "Restaurant Name  Henry & Lisa's, Rushing Waters, Mrs. Friday's,...   \n",
       "\n",
       "                                                                 4  \\\n",
       "K Means Labels                                                   4   \n",
       "Restaurant Name  Rizzo's, Maninis, Ivar's, Tom and Chee, Delmon...   \n",
       "\n",
       "                                                                 5  \\\n",
       "K Means Labels                                                   5   \n",
       "Restaurant Name  Mandy's, Maizena, Capri Sun, Gail Ambrosius, M...   \n",
       "\n",
       "                                                                 6  \\\n",
       "K Means Labels                                                   6   \n",
       "Restaurant Name  Pepperjax Grill, Roy Rogers Restaurants, Silve...   \n",
       "\n",
       "                                                                 7  \\\n",
       "K Means Labels                                                   7   \n",
       "Restaurant Name  Nanny's, Organic Drew's, Molina, Emily Kate's,...   \n",
       "\n",
       "                                                                 8  \\\n",
       "K Means Labels                                                   8   \n",
       "Restaurant Name  Tender Bison, Tom's, Tubby's, Tyson, Triple O'...   \n",
       "\n",
       "                                                                 9  \n",
       "K Means Labels                                                   9  \n",
       "Restaurant Name  Benny's, Burger King, Whitey's Ice Cream, Wild...  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-888b7f83-f91a-4c8f-bef9-ba2e06f54bce\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K Means Labels</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant Name</th>\n",
       "      <td>Zapata, Texas Roadhouse, Martel, Marie Callend...</td>\n",
       "      <td>Duck Donuts, Harris Teeter, Cracker Barrel, Du...</td>\n",
       "      <td>Emeril, Hall's, PFC, Paone, Henny's, Frey's, Y...</td>\n",
       "      <td>Henry &amp; Lisa's, Rushing Waters, Mrs. Friday's,...</td>\n",
       "      <td>Rizzo's, Maninis, Ivar's, Tom and Chee, Delmon...</td>\n",
       "      <td>Mandy's, Maizena, Capri Sun, Gail Ambrosius, M...</td>\n",
       "      <td>Pepperjax Grill, Roy Rogers Restaurants, Silve...</td>\n",
       "      <td>Nanny's, Organic Drew's, Molina, Emily Kate's,...</td>\n",
       "      <td>Tender Bison, Tom's, Tubby's, Tyson, Triple O'...</td>\n",
       "      <td>Benny's, Burger King, Whitey's Ice Cream, Wild...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-888b7f83-f91a-4c8f-bef9-ba2e06f54bce')\"\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 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\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",
       "    [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-888b7f83-f91a-4c8f-bef9-ba2e06f54bce 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-888b7f83-f91a-4c8f-bef9-ba2e06f54bce');\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",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 165
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Final Thoughts"
   ],
   "metadata": {
    "id": "ngLVi9QquqAP",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For the given set of paramters, K-Means clustering seemingly outperforms HDB Scan clustering for the given dataset.\n",
    "\n",
    "However, there is plenty of hyper-parameter tuning for the dimensions and clustering parameters that can be done for the `bert-base-uncased` and other listed models.\n",
    "\n",
    "\n",
    "This notebook present reproducible code for quick experimentation and hyper-parameter tuning with the `MenuItems.csv` dataset."
   ],
   "metadata": {
    "id": "6nX7CnrBuPkU",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ]
}