notebooks/chatintents_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f8bcc5e-f730-43a6-9084-5e2338b0c244",
   "metadata": {},
   "source": [
    "# Chat Intents Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e97ed57-8074-4b1d-9ce4-3980f9ec60df",
   "metadata": {},
   "source": [
    "The following provides an example of using the `chatintents` package on a dataset of chat message intents to automatically extract the number of clusters and apply descriptive labels. The associated [Medium blog post](https://towardsdatascience.com/clustering-sentence-embeddings-to-identify-intents-in-short-text-48d22d3bf02e) provides additional detail about the `chatintents` package and this example.\n",
    "\n",
    "The [bank77 dataset](https://github.com/PolyAI-LDN/task-specific-datasets) is used here, which contains messages representing 77 different intents from users. Four models are compared: Universal Sentence Encoder and three different Sentence Transformer models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03ce00fb-fc41-4299-8b86-5312822fdf07",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from hyperopt import hp\n",
    "\n",
    "# for USE model\n",
    "import tensorflow as tf\n",
    "import tensorflow_hub as hub\n",
    "\n",
    "# for Sentence Transformer models\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "import chatintents\n",
    "from chatintents import ChatIntents\n",
    "\n",
    "pd.set_option(\"display.max_rows\", 600)\n",
    "pd.set_option(\"display.max_columns\", 500)\n",
    "pd.set_option(\"max_colwidth\", 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd4839b-afa4-4714-8b84-16056ec8a284",
   "metadata": {},
   "source": [
    "## Load data and pre-trained models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81eee19-2fc7-4a04-a39e-8cca25117390",
   "metadata": {},
   "source": [
    "Load a sample of the bank77 dataset to use as an example. Larger datasets can be used but will take longer to process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a97675ac-dcb0-4bf2-83a9-48ef9249d9da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I'm worried my card might be lost in the mail? How long does it usually take to arrive?</td>\n",
       "      <td>card_delivery_estimate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I got charged a fee that shouldn't be there from my cash</td>\n",
       "      <td>cash_withdrawal_charge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Do you charge for making a withdrawal? I took some money out of my account earlier and I was charged for this.</td>\n",
       "      <td>cash_withdrawal_charge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Is there an issue with my account?  I don't see a cheque deposit that I made yesterday.  Please assist.</td>\n",
       "      <td>balance_not_updated_after_cheque_or_cash_deposit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Are there ways for other people to send me money?</td>\n",
       "      <td>receiving_money</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                             text  \\\n",
       "0                         I'm worried my card might be lost in the mail? How long does it usually take to arrive?   \n",
       "1                                                        I got charged a fee that shouldn't be there from my cash   \n",
       "2  Do you charge for making a withdrawal? I took some money out of my account earlier and I was charged for this.   \n",
       "3         Is there an issue with my account?  I don't see a cheque deposit that I made yesterday.  Please assist.   \n",
       "4                                                               Are there ways for other people to send me money?   \n",
       "\n",
       "                                           category  \n",
       "0                            card_delivery_estimate  \n",
       "1                            cash_withdrawal_charge  \n",
       "2                            cash_withdrawal_charge  \n",
       "3  balance_not_updated_after_cheque_or_cash_deposit  \n",
       "4                                   receiving_money  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sample = pd.read_csv('../data/processed/data_sample.csv')[['text', 'category']]\n",
    "data_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddc170db-1e83-4217-98a5-1d97b4543558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_intents = list(data_sample['text'])\n",
    "len(all_intents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7cd3c824-2a63-4b01-9a6b-7aae1d132d5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "module https://tfhub.dev/google/universal-sentence-encoder/4 loaded\n"
     ]
    }
   ],
   "source": [
    "module_url = \"https://tfhub.dev/google/universal-sentence-encoder/4\"\n",
    "model_use = hub.load(module_url)\n",
    "\n",
    "print(f\"module {module_url} loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97016d39-1ffd-4efa-aa25-4cc53fe71ff0",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_st1 = SentenceTransformer('all-mpnet-base-v2')\n",
    "model_st2 = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "model_st3 = SentenceTransformer('all-distilroberta-v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b53dbf49-2707-4f49-900c-6de896900818",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed(model, model_type, sentences):\n",
    "    if model_type == 'use':\n",
    "        embeddings = model(sentences)\n",
    "    elif model_type == 'sentence transformer':\n",
    "        embeddings = model.encode(sentences)\n",
    "    \n",
    "    return embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3993860a-50a0-445e-bb46-d640b711f77b",
   "metadata": {},
   "source": [
    "Create document embeddings for the four different models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25f438c7-1e73-479e-ba94-5de20a6062ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1000, 512])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_use = embed(model_use, 'use', all_intents)\n",
    "embeddings_use.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "03963e44-cffb-49fd-afb4-e6989d8b6514",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 768)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_st1 = embed(model_st1, 'sentence transformer', all_intents)\n",
    "embeddings_st1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "78b3d55e-5b74-416b-a8a1-74b47d9188f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 384)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_st2 = embed(model_st2, 'sentence transformer', all_intents)\n",
    "embeddings_st2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba64e992-9f92-40fd-82dd-71dbf74e1d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 768)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_st3 = embed(model_st3, 'sentence transformer', all_intents)\n",
    "embeddings_st3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "073e377c-6e68-44e6-8595-35dc527dfb1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_use = ChatIntents(embeddings_use, 'use')\n",
    "model_st1 = ChatIntents(embeddings_st1, 'st1')\n",
    "model_st2 = ChatIntents(embeddings_st2, 'st2')\n",
    "model_st3 = ChatIntents(embeddings_st3, 'st3')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee38fbbe-69ff-49c9-a14b-b0e05c60e949",
   "metadata": {},
   "source": [
    "## Results with user-supplied hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0a3c7846-4317-4011-aa80-04d439da3d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters_manual = model_st1.generate_clusters(n_neighbors = 15, \n",
    "                                              n_components = 5, \n",
    "                                              min_cluster_size = 5, \n",
    "                                              min_samples = None,\n",
    "                                              random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94a026dd-5504-4853-a1c4-6740e792363c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n",
      "0.146\n"
     ]
    }
   ],
   "source": [
    "labels_manual, cost_manual = model_st1.score_clusters(clusters_manual)\n",
    "print(labels_manual)\n",
    "print(cost_manual)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692e5dc0-4ed0-4b18-8d80-8fb4668bb96c",
   "metadata": {},
   "source": [
    "## Tuning hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff16945-e71a-4e42-be59-9c2d2074bbc4",
   "metadata": {},
   "source": [
    "### Random hyperparameter search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80fd926c-3715-4226-8fa5-116be78a6304",
   "metadata": {},
   "source": [
    "Randomly evalute 100 hyperparameter combinations within user-supplied ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e56e95a2-8db7-4750-a43c-aa68acdc7c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70c53e2747fd4887a0ff3cabcc7cd6a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "space = {\n",
    "        \"n_neighbors\": range(12,16),\n",
    "        \"n_components\": range(3,7),\n",
    "        \"min_cluster_size\": range(2,15),\n",
    "        \"min_samples\": [None]\n",
    "    }\n",
    "\n",
    "random_st1 = model_st1.random_search(space, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aee0b214-96d2-4e48-84c2-30384e7148a2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run_id</th>\n",
       "      <th>n_neighbors</th>\n",
       "      <th>n_components</th>\n",
       "      <th>min_cluster_size</th>\n",
       "      <th>min_samples</th>\n",
       "      <th>label_count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>0.043</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>0.043</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>82</td>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>112</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>117</td>\n",
       "      <td>0.106</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>87</td>\n",
       "      <td>0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>80</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>80</td>\n",
       "      <td>0.109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>68</td>\n",
       "      <td>0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>41</td>\n",
       "      <td>0.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>93</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>65</td>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>40</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>88</td>\n",
       "      <td>0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>91</td>\n",
       "      <td>0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>42</td>\n",
       "      <td>0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>46</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>46</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>None</td>\n",
       "      <td>46</td>\n",
       "      <td>0.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>59</td>\n",
       "      <td>0.124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    run_id  n_neighbors  n_components  min_cluster_size min_samples  \\\n",
       "81      81           14             5                14        None   \n",
       "6        6           14             5                14        None   \n",
       "51      51           14             6                 3        None   \n",
       "69      69           14             5                 2        None   \n",
       "64      64           12             5                 2        None   \n",
       "46      46           12             4                 3        None   \n",
       "22      22           15             6                 3        None   \n",
       "11      11           15             6                 3        None   \n",
       "42      42           12             6                 4        None   \n",
       "95      95           15             6                 8        None   \n",
       "59      59           13             3                 3        None   \n",
       "24      24           12             5                 5        None   \n",
       "41      41           12             6                 8        None   \n",
       "30      30           12             5                 3        None   \n",
       "52      52           12             6                 3        None   \n",
       "53      53           12             3                 8        None   \n",
       "67      67           12             4                 7        None   \n",
       "78      78           12             4                 7        None   \n",
       "3        3           12             4                 7        None   \n",
       "13      13           13             4                 5        None   \n",
       "\n",
       "    label_count   cost  \n",
       "81            9  0.043  \n",
       "6             9  0.043  \n",
       "51           82  0.097  \n",
       "69          112  0.106  \n",
       "64          117  0.106  \n",
       "46           87  0.106  \n",
       "22           80  0.109  \n",
       "11           80  0.109  \n",
       "42           68  0.110  \n",
       "95           41  0.111  \n",
       "59           93  0.112  \n",
       "24           65  0.112  \n",
       "41           40  0.118  \n",
       "30           88  0.118  \n",
       "52           91  0.120  \n",
       "53           42  0.121  \n",
       "67           46  0.123  \n",
       "78           46  0.123  \n",
       "3            46  0.123  \n",
       "13           59  0.124  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_st1.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f2e153-e9e6-40ac-b086-4c7265dca52c",
   "metadata": {},
   "source": [
    "As seen above, we could manually inspect the random search results and apply our domain knowledge to decide which model is best. For this problem, we'd expect there to be between 30 and 100 clusters, so the third configuration in the above table seems reasonable (82 clusters and 9.7% of the data is labeled as noise)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89502189-0e3f-4b1b-af25-9df568582846",
   "metadata": {},
   "source": [
    "### Bayesian optimization with Hyperopt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df18477-040d-4c02-b75c-2d6a7eaaca4d",
   "metadata": {},
   "source": [
    "Rather than selecting the parameters at random, here we use hyperopt to perform a Bayesian search of the hyperparameter space. Note that the hspace dictionary must use hyperopt functions to define the hyperparameter search space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b4cbeee3-d676-4849-8718-7fa77ba8d771",
   "metadata": {},
   "outputs": [],
   "source": [
    "hspace = {\n",
    "    \"n_neighbors\": hp.choice('n_neighbors', range(3,16)),\n",
    "    \"n_components\": hp.choice('n_components', range(3,16)),\n",
    "    \"min_cluster_size\": hp.choice('min_cluster_size', range(2,16)),\n",
    "    \"min_samples\": None,\n",
    "    \"random_state\": 42\n",
    "}\n",
    "\n",
    "label_lower = 30\n",
    "label_upper = 100\n",
    "max_evals = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94120a91-187d-410d-a40f-3dbf028cc8b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████| 100/100 [05:48<00:00,  3.48s/trial, best loss: 0.113]\n",
      "best:\n",
      "{'min_cluster_size': 5, 'min_samples': None, 'n_components': 10, 'n_neighbors': 6, 'random_state': 42}\n",
      "label count: 55\n"
     ]
    }
   ],
   "source": [
    "model_use.bayesian_search(space=hspace,\n",
    "                          label_lower=label_lower, \n",
    "                          label_upper=label_upper, \n",
    "                          max_evals=max_evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f385ce66-d739-4844-af11-8720aebd737b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_cluster_size': 5,\n",
       " 'min_samples': None,\n",
       " 'n_components': 10,\n",
       " 'n_neighbors': 6,\n",
       " 'random_state': 42}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_use.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "879cf926-d9e5-4149-9b17-b7e4ce5b27dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'state': 2,\n",
       " 'tid': 80,\n",
       " 'spec': None,\n",
       " 'result': {'loss': 0.113, 'label_count': 55, 'status': 'ok'},\n",
       " 'misc': {'tid': 80,\n",
       "  'cmd': ('domain_attachment', 'FMinIter_Domain'),\n",
       "  'workdir': None,\n",
       "  'idxs': {'min_cluster_size': [80],\n",
       "   'n_components': [80],\n",
       "   'n_neighbors': [80]},\n",
       "  'vals': {'min_cluster_size': [3], 'n_components': [7], 'n_neighbors': [3]}},\n",
       " 'exp_key': None,\n",
       " 'owner': None,\n",
       " 'version': 0,\n",
       " 'book_time': datetime.datetime(2021, 10, 18, 0, 20, 0, 608000),\n",
       " 'refresh_time': datetime.datetime(2021, 10, 18, 0, 20, 3, 400000)}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_use.trials.best_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e09d78a5-d84f-4783-809a-ca51c1ca2b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████| 100/100 [05:38<00:00,  3.38s/trial, best loss: 0.056]\n",
      "best:\n",
      "{'min_cluster_size': 6, 'min_samples': None, 'n_components': 9, 'n_neighbors': 6, 'random_state': 42}\n",
      "label count: 55\n"
     ]
    }
   ],
   "source": [
    "model_st1.bayesian_search(space=hspace,\n",
    "                          label_lower=label_lower, \n",
    "                          label_upper=label_upper, \n",
    "                          max_evals=max_evals)                                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5641fe18-da7b-4a7f-ab35-e20c7e7b13d4",
   "metadata": {},
   "source": [
    "Here we see that the bayesian search resulted in a lower loss (0.056) than the previous random search above (0.097 loss)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ab15f34-04cd-41df-9f9a-655a7a02a397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████| 100/100 [04:58<00:00,  2.98s/trial, best loss: 0.079]\n",
      "best:\n",
      "{'min_cluster_size': 7, 'min_samples': None, 'n_components': 13, 'n_neighbors': 3, 'random_state': 42}\n",
      "label count: 41\n"
     ]
    }
   ],
   "source": [
    "model_st2.bayesian_search(space=hspace,\n",
    "                          label_lower=label_lower, \n",
    "                          label_upper=label_upper, \n",
    "                          max_evals=max_evals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb5af658-29fe-46ba-aecb-6428eca0708e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|████████████████████| 100/100 [05:31<00:00,  3.32s/trial, best loss: 0.082]\n",
      "best:\n",
      "{'min_cluster_size': 3, 'min_samples': None, 'n_components': 14, 'n_neighbors': 9, 'random_state': 42}\n",
      "label count: 88\n"
     ]
    }
   ],
   "source": [
    "model_st3.bayesian_search(space=hspace,\n",
    "                          label_lower=label_lower, \n",
    "                          label_upper=label_upper, \n",
    "                          max_evals=max_evals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33768528-9a03-4216-9f99-3fe13d3a7053",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visually inspect clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8ee772-cb22-494b-9099-fe276b3f5636",
   "metadata": {},
   "source": [
    "We can visualize the best clusters by using UMAP to reduce the dimensionality down to two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "28a69d46-421d-4f82-ace1-07e4f67317b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_use.plot_best_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f3e397f0-29e2-446a-a47b-78d898b457de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_st1.plot_best_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05bbd0db-bf74-46f5-873e-b1e526d872de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_st2.plot_best_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "913ad59b-0cfe-4844-aa04-0153b287ba24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_st3.plot_best_clusters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58aab97a-e888-42cc-8c9b-4d1a509d139e",
   "metadata": {},
   "source": [
    "## Apply labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f4c854-b49c-42a0-ad5a-240e75cda25c",
   "metadata": {},
   "source": [
    "Since sentence transformer 1 achieved the lowest loss (after hyperparameter tuning), we'll select that as our best clusters. Then, we can get our final result below of clusters and applied descriptive labels, and then apply those to each document in the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a137cfd-7611-4af8-b033-327e072c9ae3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.3 s, sys: 1.63 s, total: 27 s\n",
      "Wall time: 28.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "df_summary, labeled_docs = model_st1.apply_and_summarize_labels(data_sample[['text']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed6e9494-d765-4aed-ad65-a5f3ac72d1f2",
   "metadata": {},
   "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>label_st1</th>\n",
       "      <th>count</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>56</td>\n",
       "      <td>add_card_app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>use_account_card_auto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>pending_money_account</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>help_refund_statement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>help_card_app</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label_st1  count                  label\n",
       "0          -1     56           add_card_app\n",
       "27         26     49  use_account_card_auto\n",
       "14         13     45  pending_money_account\n",
       "4           3     44  help_refund_statement\n",
       "15         14     38          help_card_app"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e5c6774a-17f3-4428-9676-374c9ca861cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label_st1</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I'm worried my card might be lost in the mail? How long does it usually take to arrive?</td>\n",
       "      <td>30</td>\n",
       "      <td>expect_card_week</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I got charged a fee that shouldn't be there from my cash</td>\n",
       "      <td>32</td>\n",
       "      <td>charged_fee_withdrawal_cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Do you charge for making a withdrawal? I took some money out of my account earlier and I was charged for this.</td>\n",
       "      <td>32</td>\n",
       "      <td>charged_fee_withdrawal_cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Is there an issue with my account?  I don't see a cheque deposit that I made yesterday.  Please assist.</td>\n",
       "      <td>53</td>\n",
       "      <td>deposited_cheque_balance_yesterday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Are there ways for other people to send me money?</td>\n",
       "      <td>51</td>\n",
       "      <td>sent_money_friend_hour</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                             text  \\\n",
       "0                         I'm worried my card might be lost in the mail? How long does it usually take to arrive?   \n",
       "1                                                        I got charged a fee that shouldn't be there from my cash   \n",
       "2  Do you charge for making a withdrawal? I took some money out of my account earlier and I was charged for this.   \n",
       "3         Is there an issue with my account?  I don't see a cheque deposit that I made yesterday.  Please assist.   \n",
       "4                                                               Are there ways for other people to send me money?   \n",
       "\n",
       "   label_st1                               label  \n",
       "0         30                    expect_card_week  \n",
       "1         32         charged_fee_withdrawal_cash  \n",
       "2         32         charged_fee_withdrawal_cash  \n",
       "3         53  deposited_cheque_balance_yesterday  \n",
       "4         51              sent_money_friend_hour  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_docs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f6dc99-c0eb-4fa7-8e46-abd96cc6be0e",
   "metadata": {},
   "source": [
    "For most applications, these above two dataframes would be the final results we'd be trying to obtain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f84e273-7c7c-453f-ac43-cdbe45be42c6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Evaluate clustering performance using ground truth labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28db657-3df4-4701-bb3b-4343073167f1",
   "metadata": {},
   "source": [
    "If we know the ground truth labels, then we can evaluate how well our tuned models actually did. In this case we do know the ground truth labels of the bank77 dataset so we can compare the four models evaluated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcd96ee4-a82b-45bc-afc1-5192cf0ff618",
   "metadata": {},
   "source": [
    "### Comparing multiple models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "db411b96-9608-422f-b031-2fc23abeea6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [model_use, model_st1, model_st2, model_st3]\n",
    "\n",
    "df_comparison, labeled_docs_all_models = chatintents.evaluate_models(data_sample[['text', 'category']], models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "443a9a09-b828-4b54-b781-c4d539c69917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>ARI</th>\n",
       "      <th>NMI</th>\n",
       "      <th>loss</th>\n",
       "      <th>label_count</th>\n",
       "      <th>n_neighbors</th>\n",
       "      <th>n_components</th>\n",
       "      <th>min_cluster_size</th>\n",
       "      <th>random_state</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>st1</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.056</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>st3</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.795</td>\n",
       "      <td>0.082</td>\n",
       "      <td>88</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>st2</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.079</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>use</td>\n",
       "      <td>0.253</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.113</td>\n",
       "      <td>55</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model    ARI    NMI   loss  label_count  n_neighbors  n_components  \\\n",
       "1   st1  0.455  0.806  0.056           55            6             9   \n",
       "3   st3  0.398  0.795  0.082           88            9            14   \n",
       "2   st2  0.247  0.709  0.079           41            3            13   \n",
       "0   use  0.253  0.708  0.113           55            6            10   \n",
       "\n",
       "   min_cluster_size  random_state  \n",
       "1                 6            42  \n",
       "3                 3            42  \n",
       "2                 7            42  \n",
       "0                 5            42  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763287bf-0788-4544-97c1-7c1078377e33",
   "metadata": {},
   "source": [
    "In agreement with our above conclusion that sentence transformer 1 would be best since it has the lowest cost, here we see that it does in fact have the highest [Adjusted Rand Index (ARI)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html) and [Normalized Mutual Info (NMI)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html). Sentence transformers 2 and 3 are swithced in order in actual performance from what would be expected from their loss, but the loss measure is still a helpful metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e38d4b1-8fa9-4bcd-a4a9-b3926550e955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>It has been forever ago that I made an order online. The product simply is not coming. Just give me back my money because I'm not paying these people.</td>\n",
       "      <td>request_refund</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>940</th>\n",
       "      <td>It does not appear that my message went through.</td>\n",
       "      <td>top_up_failed</td>\n",
       "      <td>34</td>\n",
       "      <td>-1</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>How long do I have to wait until the transferred money shows up?</td>\n",
       "      <td>balance_not_updated_after_bank_transfer</td>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "      <td>26</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Verify my source of funds?</td>\n",
       "      <td>verify_source_of_funds</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>I'm travelling abroad but I've run into a situation where I need to change my PIN immediately. Can I do this from here?</td>\n",
       "      <td>change_pin</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                       text  \\\n",
       "228  It has been forever ago that I made an order online. The product simply is not coming. Just give me back my money because I'm not paying these people.   \n",
       "940                                                                                                        It does not appear that my message went through.   \n",
       "18                                                                                         How long do I have to wait until the transferred money shows up?   \n",
       "98                                                                                                                               Verify my source of funds?   \n",
       "340                                 I'm travelling abroad but I've run into a situation where I need to change my PIN immediately. Can I do this from here?   \n",
       "\n",
       "                                    category  label_use  label_st1  label_st2  \\\n",
       "228                           request_refund         -1          3         18   \n",
       "940                            top_up_failed         34         -1         15   \n",
       "18   balance_not_updated_after_bank_transfer         15         50         26   \n",
       "98                    verify_source_of_funds          3         19         17   \n",
       "340                               change_pin         31         20         19   \n",
       "\n",
       "     label_st3  \n",
       "228         12  \n",
       "940         76  \n",
       "18          81  \n",
       "98          11  \n",
       "340          2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_docs_all_models.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad5da3f5-21a7-4c83-886e-8d0996f6cca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
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       "      <td>My passcode won't work.</td>\n",
       "      <td>passcode_forgotten</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>What can I do if my passcode won't work?</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <th>359</th>\n",
       "      <td>I've forgotten my app passcode completely.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>I would like to better understand why my pass-code is not working.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>I've forgotten my passcode. Can I reset?</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>I am entering my passcode but getting an error.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>Help me.  The passcode doesn't work.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>I can't remember my password.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>I think my passcode was changed</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>I don't know what my passcode is, can you help?</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>Need a new passcode.</td>\n",
       "      <td>passcode_forgotten</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   text  \\\n",
       "84                                        I don't remember my passcode?   \n",
       "171                                             My passcode won't work.   \n",
       "323                            What can I do if my passcode won't work?   \n",
       "359                          I've forgotten my app passcode completely.   \n",
       "425  I would like to better understand why my pass-code is not working.   \n",
       "572                            I've forgotten my passcode. Can I reset?   \n",
       "778                     I am entering my passcode but getting an error.   \n",
       "809                                Help me.  The passcode doesn't work.   \n",
       "810                                       I can't remember my password.   \n",
       "920                                     I think my passcode was changed   \n",
       "966                     I don't know what my passcode is, can you help?   \n",
       "974                                                Need a new passcode.   \n",
       "\n",
       "               category  label_use  label_st1  label_st2  label_st3  \n",
       "84   passcode_forgotten          2          2          3          3  \n",
       "171  passcode_forgotten          2          2          3          3  \n",
       "323  passcode_forgotten          2          2          3          3  \n",
       "359  passcode_forgotten          2          2          3          3  \n",
       "425  passcode_forgotten          2          2          3          3  \n",
       "572  passcode_forgotten          2          2          3          3  \n",
       "778  passcode_forgotten          2          2          3          3  \n",
       "809  passcode_forgotten          2          2          3          3  \n",
       "810  passcode_forgotten          2          2          3          3  \n",
       "920  passcode_forgotten          2          2          3          3  \n",
       "966  passcode_forgotten          2          2          3          3  \n",
       "974  passcode_forgotten          2          2          3          3  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_docs_all_models[labeled_docs_all_models['label_st1']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b2b5aa57-e020-4681-a3d3-71402fc83fbd",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>text</th>\n",
       "      <th>category</th>\n",
       "      <th>label_use</th>\n",
       "      <th>label_st1</th>\n",
       "      <th>label_st2</th>\n",
       "      <th>label_st3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>I am overseas in China, can I get a replacement card?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>If I am in China, can I still order a new card and if so, how?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>My card is almost expired.  How fast will I get a new one and what is the cost?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>My card is about to expire. Do I have to go to a branch to order a new card?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>My card expires soon, will you send me one automatically?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>My card expires very soon.  What is the cost and delivery time to get a new one?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Where can I have a new bank card sent to China?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>-1</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>How do I get a new expiration date?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Are there any express fees if i want my new card faster?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>-1</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>My card is almost going to expire.</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>My card expires soon</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>What is the cost and time frame of getting a new card?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>Help me order a new card that's going to be expired soon.</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>What happens if my card expires?</td>\n",
       "      <td>card_about_to_expire</td>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                 text  \\\n",
       "27                              I am overseas in China, can I get a replacement card?   \n",
       "370                    If I am in China, can I still order a new card and if so, how?   \n",
       "441   My card is almost expired.  How fast will I get a new one and what is the cost?   \n",
       "477      My card is about to expire. Do I have to go to a branch to order a new card?   \n",
       "521                         My card expires soon, will you send me one automatically?   \n",
       "524  My card expires very soon.  What is the cost and delivery time to get a new one?   \n",
       "551                                   Where can I have a new bank card sent to China?   \n",
       "584                                               How do I get a new expiration date?   \n",
       "605                          Are there any express fees if i want my new card faster?   \n",
       "646                                                My card is almost going to expire.   \n",
       "765                                                              My card expires soon   \n",
       "812                            What is the cost and time frame of getting a new card?   \n",
       "833                         Help me order a new card that's going to be expired soon.   \n",
       "888                                                  What happens if my card expires?   \n",
       "\n",
       "                 category  label_use  label_st1  label_st2  label_st3  \n",
       "27   card_about_to_expire         31         31         -1         -1  \n",
       "370  card_about_to_expire         31         31         -1         -1  \n",
       "441  card_about_to_expire         11         31          9         67  \n",
       "477  card_about_to_expire         11         31          9         67  \n",
       "521  card_about_to_expire         11         31          9         67  \n",
       "524  card_about_to_expire         11         31          9         67  \n",
       "551  card_about_to_expire         31         31         -1         37  \n",
       "584  card_about_to_expire         11         31          9         67  \n",
       "605  card_about_to_expire         -1         31          9         -1  \n",
       "646  card_about_to_expire         11         31          9         67  \n",
       "765  card_about_to_expire         11         31          9         67  \n",
       "812  card_about_to_expire         11         31          9         67  \n",
       "833  card_about_to_expire         11         31          9         67  \n",
       "888  card_about_to_expire         11         31          9         67  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labeled_docs_all_models[labeled_docs_all_models['category']=='card_about_to_expire']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3cd9ccc-e415-4664-9604-f9b427310c11",
   "metadata": {},
   "source": [
    "### Evaluating labels from a single model to ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cd6d493b-3c05-4213-bd1e-d3fc6e05a09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_st1</th>\n",
       "      <th>count</th>\n",
       "      <th>label</th>\n",
       "      <th>top_ground_category</th>\n",
       "      <th>top_cat_count</th>\n",
       "      <th>perc_top_cat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>56</td>\n",
       "      <td>add_card_app</td>\n",
       "      <td>beneficiary_not_allowed</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>49</td>\n",
       "      <td>use_account_card_auto</td>\n",
       "      <td>automatic_top_up</td>\n",
       "      <td>14</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>45</td>\n",
       "      <td>pending_money_account</td>\n",
       "      <td>pending_top_up</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>44</td>\n",
       "      <td>help_refund_statement</td>\n",
       "      <td>Refund_not_showing_up</td>\n",
       "      <td>26</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>38</td>\n",
       "      <td>help_card_app</td>\n",
       "      <td>lost_or_stolen_card</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>like_card_cost</td>\n",
       "      <td>order_physical_card</td>\n",
       "      <td>15</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>think_rate_exchange</td>\n",
       "      <td>wrong_exchange_rate_for_cash_withdrawal</td>\n",
       "      <td>14</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33</td>\n",
       "      <td>31</td>\n",
       "      <td>tried_money_card_today</td>\n",
       "      <td>declined_cash_withdrawal</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>charged_fee_withdrawal_cash</td>\n",
       "      <td>cash_withdrawal_charge</td>\n",
       "      <td>23</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>use_currency_app</td>\n",
       "      <td>fiat_currency_support</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>explain_card_limit</td>\n",
       "      <td>getting_virtual_card</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>waiting_payment_card</td>\n",
       "      <td>pending_card_payment</td>\n",
       "      <td>21</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>charged_card_fee</td>\n",
       "      <td>card_payment_fee_charged</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>cancel_transfer_account_tomorrow</td>\n",
       "      <td>cancel_transfer</td>\n",
       "      <td>22</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>24</td>\n",
       "      <td>waiting_transfer_account</td>\n",
       "      <td>pending_transfer</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>want_pin_card</td>\n",
       "      <td>get_physical_card</td>\n",
       "      <td>12</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>charged_fee_transfer_bank</td>\n",
       "      <td>transfer_fee_charged</td>\n",
       "      <td>12</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>45</td>\n",
       "      <td>19</td>\n",
       "      <td>tried_transfer_account</td>\n",
       "      <td>failed_transfer</td>\n",
       "      <td>7</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>cancelled_payment_card</td>\n",
       "      <td>reverted_card_payment?</td>\n",
       "      <td>12</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>find_source_fund_money</td>\n",
       "      <td>verify_source_of_funds</td>\n",
       "      <td>17</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label_st1  count                             label  \\\n",
       "0          -1     56                      add_card_app   \n",
       "1          26     49             use_account_card_auto   \n",
       "2          13     45             pending_money_account   \n",
       "3           3     44             help_refund_statement   \n",
       "4          14     38                     help_card_app   \n",
       "5          31     37                    like_card_cost   \n",
       "6           4     36               think_rate_exchange   \n",
       "7          33     31            tried_money_card_today   \n",
       "8          32     31       charged_fee_withdrawal_cash   \n",
       "9          29     29                  use_currency_app   \n",
       "10         18     28                explain_card_limit   \n",
       "11         12     28              waiting_payment_card   \n",
       "12         35     25                  charged_card_fee   \n",
       "13          9     25  cancel_transfer_account_tomorrow   \n",
       "14         49     24          waiting_transfer_account   \n",
       "15         21     22                     want_pin_card   \n",
       "16         15     22         charged_fee_transfer_bank   \n",
       "17         45     19            tried_transfer_account   \n",
       "18         38     19            cancelled_payment_card   \n",
       "19         19     18            find_source_fund_money   \n",
       "\n",
       "                        top_ground_category  top_cat_count  perc_top_cat  \n",
       "0                   beneficiary_not_allowed              8            14  \n",
       "1                          automatic_top_up             14            29  \n",
       "2                            pending_top_up             14            31  \n",
       "3                     Refund_not_showing_up             26            59  \n",
       "4                       lost_or_stolen_card             10            26  \n",
       "5                       order_physical_card             15            41  \n",
       "6   wrong_exchange_rate_for_cash_withdrawal             14            39  \n",
       "7                  declined_cash_withdrawal             14            45  \n",
       "8                    cash_withdrawal_charge             23            74  \n",
       "9                     fiat_currency_support             10            34  \n",
       "10                     getting_virtual_card              9            32  \n",
       "11                     pending_card_payment             21            75  \n",
       "12                 card_payment_fee_charged             11            44  \n",
       "13                          cancel_transfer             22            88  \n",
       "14                         pending_transfer             11            46  \n",
       "15                        get_physical_card             12            55  \n",
       "16                     transfer_fee_charged             12            55  \n",
       "17                          failed_transfer              7            37  \n",
       "18                   reverted_card_payment?             12            63  \n",
       "19                   verify_source_of_funds             17            94  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chatintents.top_cluster_category(labeled_docs, \n",
    "                                 data_sample[['text', 'category']], \n",
    "                                 'text',\n",
    "                                 df_summary).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df5071e5-2150-4c18-9fd4-329c9cd9463a",
   "metadata": {},
   "source": [
    "Most of the extracted labels match the ground labels quite well. Understandably, the labels match the most when the derived clusters contain a larger percentage of the dominant ground truth category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a73d2d4-52ba-4fe5-b388-e7ec4fd3d395",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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