Macro_NLU_Entity_Refinement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "import pandas as pd\n",
    "import nltk\n",
    "\n",
    "from nlu_engine import NLUEngine\n",
    "from nlu_engine import MacroDataRefinement\n",
    "from nlu_engine import MacroEntityRefinement\n",
    "\n",
    "from nlu_engine import DataUtils\n",
    "from nlu_engine import RenderJSON\n",
    "\n",
    "from nlu_engine import Analytics\n",
    "\n",
    "from nlu_engine import EntityExtractor, crf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Macro NLU Data Refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's a bit like the TV show [Serverance](https://www.imdb.com/title/tt11280740/) .\n",
    "\n",
    "![Helly R and Mark S](https://media.npr.org/assets/img/2022/02/15/atv_severance_photo_010103-5f8033cc2b219ba64fe265ce893eae4c90e83896-s1100-c50.jpg \"Helly R and Mark G\")\n",
    "\n",
    "*Helly R*: `My job is to scroll through the spreadsheet and look for the numbers that feel scary?`\n",
    "\n",
    "*Mark S*: `I told you, you’ll understand when you see it, so just be patient.`\n",
    "\n",
    "![MDR](https://www.imore.com/sites/imore.com/files/styles/large/public/field/image/2022/03/refinement-software-severance-apple-tv.jpg \"serverance micro data refinement\")\n",
    "\n",
    "*Helly R*: `That was scary. The numbers were scary.`\n",
    "\n",
    "Hopefully the intents and entities that are wrong aren't scary, just a bit frustrating. Let's see if we can find the right ones.\n",
    "\n",
    "NOTE: We will use Logistic Regression with TFIDF features to train our intent models and CRFs for entity exraction. Why? Well, they are very fast and both methods aren't state-of-the-art. This is good, because it is easier to find problems we will need to refine in the dataset than if we were to use a proper NLU engine like Snips or something SOTA like BERT. It is very important to note that some of the the problems we will pick up on, might not be an actual issue, but might be due to the limitations of the models. Refining the real problems and ignoring the limitations of the models is a good way to improve the models. Then when the dataset is ready, we can use some more advanced NLU engine and get the best performance possible.\n",
    "\n",
    "* Macro NLU Data Refinement: Intent\n",
    "* Macro NLU Data Refinement: Entity\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded nlu_data_intent_and_entity_refined_df.csv\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    nlu_data_refined_df = pd.read_csv(\n",
    "        'data/refined/nlu_data_intent_and_entity_refined_df.csv', index_col=0)\n",
    "    print('Successfully loaded nlu_data_intent_and_entity_refined_df.csv')\n",
    "    removed_nlu_data_refined_df = nlu_data_refined_df[nlu_data_refined_df['remove'] != True]\n",
    "except:\n",
    "    nlu_data_refined_df = pd.read_csv(\n",
    "        'data/refined/nlu_data_refined_df.csv', sep=',', index_col=0)\n",
    "    print('Successfully loaded nlu_data_refined_df.csv')\n",
    "    \n",
    "    nlu_data_refined_df = DataUtils.convert_annotated_utterances_to_normalised_utterances(\n",
    "        nlu_data_refined_df)\n",
    "\n",
    "    removed_nlu_data_refined_df = nlu_data_refined_df[nlu_data_refined_df['remove'] != True]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: does this NEED to be here, LOL!\n",
    "from matplotlib.pyplot import axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should remove the unwanted entries for the next few steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entity extraction reports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entity extraction could be greatly improved by improving the features it uses. It would be great if someone would take a look at this. Perhaps the CRF features similar to what Snips uses would be better such as Brown clustering (probably)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: implement brown clustering to improve entity extraction (see entity_extractor.py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to have the NLTK tokenizer to be able to extract entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    nltk.data.find('tokenizers/punkt')\n",
    "except LookupError:\n",
    "        nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to this error featured in [this git issue](https://github.com/TeamHG-Memex/sklearn-crfsuite/issues/60) we have to use an older version of scikit learn (sklearn<0.24), otherwise the latest version would work. Hopefully this gets fixed one day.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded entity_report.csv\n"
     ]
    }
   ],
   "source": [
    "# try to load entity_report.csv\n",
    "try:\n",
    "    entity_report_df = pd.read_csv(\n",
    "        'data/reports/entity_report.csv')\n",
    "    print('Successfully loaded entity_report.csv')\n",
    "except:\n",
    "    entity_report_df = NLUEngine.evaluate_entity_classifier(\n",
    "        data_df=removed_nlu_data_refined_df, cv=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "entity_report_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export entity_report_df to csv\n",
    "entity_report_df.to_csv(\n",
    "    'data/reports/entity_report.csv',\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the report plotted for us"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Analytics.plot_report(entity_report_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to extract out the entity types from the dataset. We need this to calculate the precentage of entities, to filter out any domains that have too few entities for benchmarking by domain instead of all domains together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df['entities'] = nlu_data_refined_df['answer_annotation'].apply(\n",
    "    EntityExtractor.extract_entities)\n",
    "\n",
    "nlu_data_refined_df['entity_types'] = nlu_data_refined_df['entities'].apply(\n",
    "    EntityExtractor.extract_entity_types)\n",
    "\n",
    "import numpy as np\n",
    "nlu_data_refined_df['entity_types'] = nlu_data_refined_df['entity_types'].apply(lambda y: np.nan if len(y)==0 else y)\n",
    "nlu_data_refined_df['entities'] = nlu_data_refined_df['entities'].apply(lambda y: np.nan if len(y)==0 else y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_nlu_data_refined_df = nlu_data_refined_df[nlu_data_refined_df['remove'] != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_entity_reports_df = NLUEngine.get_entity_reports_for_domains(removed_nlu_data_refined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_entity_reports_df = domain_entity_reports_df[domain_entity_reports_df['entity-type'].str.contains(\n",
    "    'weighted avg')].sort_values(by='f1-score', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_entity_reports_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_entity_reports_df.to_csv('data/reports/domain_entity_reports.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_entity_reports_df = pd.read_csv('data/reports/domain_entity_reports.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = domain_entity_reports_df.columns[5]\n",
    "Analytics.plot_entity_report(domain_entity_reports_df, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## entity classification \n",
    "\n",
    "We can get the `predicted_tagging` and find all of the entities that are being incorrectly classified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you don't already have a saved model (for the default data set there is already one), you can train a model and save it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crf_model = NLUEngine.train_entity_classifier(removed_nlu_data_refined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = 'models/analytics/entity_tagger.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataUtils.pickle_model(classifier=crf_model, model_path=model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can open it from here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = 'models/analytics/entity_tagger.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "crf_model = DataUtils.import_pickled_model(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run it across our data set to get the `predicted_tagging`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df['predicted_tagging'] = nlu_data_refined_df['answer_normalised'].apply(\n",
    "    lambda x: NLUEngine.create_entity_tagged_utterance(x, crf_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_nlu_data_refined_df = nlu_data_refined_df[nlu_data_refined_df['remove'] != True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now load a dataset of all of the incorrectly predicted entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: I might move this out since it is only being done after the overlapping entities have been removed\n",
    "incorrect_predicted_entities_df = removed_nlu_data_refined_df[removed_nlu_data_refined_df['answer_annotation']\n",
    "                            != removed_nlu_data_refined_df['predicted_tagging']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incorrect_predicted_entities_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Domain specific entity analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a new domain start here!\n",
    "\n",
    "Pick a domain to start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_selection = MacroDataRefinement.list_and_select_domain(\n",
    "    removed_nlu_data_refined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded dataframe\n"
     ]
    }
   ],
   "source": [
    "domain_df = DataUtils.get_domain_df(\n",
    "    removed_nlu_data_refined_df, domain_selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incorrectly predicted entities report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity type: player_setting\n",
      "Correct entities: 1\n",
      "Incorrect entities: 0\n",
      "Total entities: 1\n",
      "Entity type: relation\n",
      "Correct entities: 0\n",
      "Incorrect entities: 1\n",
      "Total entities: 1\n",
      "Entity type: business_name\n",
      "Correct entities: 0\n",
      "Incorrect entities: 1\n",
      "Total entities: 1\n",
      "Entity type: date\n",
      "Correct entities: 4\n",
      "Incorrect entities: 1\n",
      "Total entities: 5\n",
      "Entity type: timeofday\n",
      "Correct entities: 1\n",
      "Incorrect entities: 0\n",
      "Total entities: 1\n",
      "Entity type: house_place\n",
      "Correct entities: 261\n",
      "Incorrect entities: 39\n",
      "Total entities: 300\n",
      "Entity type: place_name\n",
      "Correct entities: 0\n",
      "Incorrect entities: 1\n",
      "Total entities: 1\n",
      "Entity type: time\n",
      "Correct entities: 28\n",
      "Incorrect entities: 14\n",
      "Total entities: 42\n",
      "Entity type: business_type\n",
      "Correct entities: 0\n",
      "Incorrect entities: 2\n",
      "Total entities: 2\n",
      "Entity type: color_type\n",
      "Correct entities: 126\n",
      "Incorrect entities: 19\n",
      "Total entities: 145\n",
      "Entity type: person\n",
      "Correct entities: 1\n",
      "Incorrect entities: 0\n",
      "Total entities: 1\n",
      "Entity type: device_type\n",
      "Correct entities: 305\n",
      "Incorrect entities: 47\n",
      "Total entities: 352\n",
      "Entity type: change_amount\n",
      "Correct entities: 26\n",
      "Incorrect entities: 2\n",
      "Total entities: 28\n",
      "Entity type: general_frequency\n",
      "Correct entities: 0\n",
      "Incorrect entities: 3\n",
      "Total entities: 3\n",
      "Entity type: coffee_type\n",
      "Correct entities: 24\n",
      "Incorrect entities: 8\n",
      "Total entities: 32\n"
     ]
    }
   ],
   "source": [
    "incorrect_predicted_entities_report = MacroEntityRefinement.get_incorrect_predicted_entities_report(\n",
    "    domain_df, entity_report_df, domain_entity_reports_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a better look at this report of incorrectly predicted entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RenderJSON(incorrect_predicted_entities_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incorrect_entity_types, incorrect_entity_counts = Analytics.plot_incorrect_entities_by_count_and_f1_score(\n",
    "    incorrect_predicted_entities_report)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refine sparse entity types\n",
    "\n",
    "In this step, we will look at the sparse entity types that are labeled incorrect in the domain and select the ones we want to remove or untag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: add this to a method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "entity_types_and_counts = zip(incorrect_entity_types, incorrect_entity_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# arrange the entities in entity_types by total count in ascending order\n",
    "entity_types_and_counts = sorted(entity_types_and_counts, key=lambda x: x[1])\n",
    "\n",
    "# create a list of the entities in entity_types in ascending order of total count\n",
    "entity_types = [entity_type for entity_type, count in entity_types_and_counts]\n",
    "\n",
    "# create a list of the total counts of the entities in entity_types in ascending order of total count\n",
    "entity_counts = [count for entity_type, count in entity_types_and_counts]\n",
    "\n",
    "# refactor above code to be more compact\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_entity_types = [entity_type for entity_type, count in sorted(\n",
    "    zip(entity_types, entity_counts), key=lambda x: x[1])]\n",
    "sorted_entity_counts = [count for entity_type, count in sorted(\n",
    "    zip(entity_types, entity_counts), key=lambda x: x[1])]\n",
    "sorted_entity_types_and_counts = zip(\n",
    "    sorted_entity_types, sorted_entity_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From sorted_entity_types get all entities with less than 5 total count and output a dataframe of entries in domain_df with those entities\n",
    "\n",
    "def get_entries_with_sparse_total(domain_df, sorted_entity_types_and_counts):\n",
    "    entity_types_with_sparse_total = []\n",
    "    entity_counts_with_sparse_total = []\n",
    "    for entity_type, entity_count in sorted_entity_types_and_counts:\n",
    "        if entity_count < 5:\n",
    "            print(f'Entity type: {entity_type}, Total count: {entity_count}')\n",
    "            entity_types_with_sparse_total.append(entity_type)\n",
    "            entity_counts_with_sparse_total.append(entity_count)\n",
    "    if len(entity_types_with_sparse_total) > 0:\n",
    "        sparse_total_df = pd.DataFrame(\n",
    "            {\n",
    "                'entity_type': entity_types_with_sparse_total,\n",
    "                'total_count': entity_counts_with_sparse_total\n",
    "                })\n",
    "        sparse_entity_entries_df = domain_df[domain_df['entity_types'].str.contains(\n",
    "            ('|').join(entity_types_with_sparse_total), na=False)]\n",
    "        return sparse_total_df, sparse_entity_entries_df\n",
    "    else: \n",
    "        return None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_total_df, sparse_entries_df = get_entries_with_sparse_total(\n",
    "    domain_df, sorted_entity_types_and_counts)\n",
    "# create a ipysheet for sparse_total_df with boolean buttons for merge, remove, untag\n",
    "# Should the merge be multiple buttons, one for each entity type to merge with?\n",
    "sparse_entries_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entity type: player_setting, Total count: 1\n",
      "Entity type: relation, Total count: 1\n",
      "Entity type: business_name, Total count: 1\n",
      "Entity type: timeofday, Total count: 1\n",
      "Entity type: place_name, Total count: 1\n",
      "Entity type: person, Total count: 1\n",
      "Entity type: business_type, Total count: 2\n",
      "Entity type: general_frequency, Total count: 3\n"
     ]
    }
   ],
   "source": [
    "sparse_total_df, sparse_entries_df = MacroEntityRefinement.get_entries_with_sparse_total(\n",
    "    domain_df, incorrect_entity_types, incorrect_entity_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipysheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sparse_entity_review_sheet(sparse_total_df):\n",
    "    '''\n",
    "    Create a sheet with the entity types and total counts of entities with boolean columns for whether the entity should be removed, merged, or untagged\n",
    "    :param sparse_total_df: a dataframe with the entity types and total counts of entities with less than 5 total count\n",
    "    :return: an ipysheet with the entity types and total counts of entities with boolean columns for whether the entity should be removed, merged, or untagged\n",
    "    '''\n",
    "\n",
    "    to_review_df = sparse_total_df.copy()\n",
    "    to_review_df = to_review_df.assign(remove=None)\n",
    "    to_review_df['remove'] = to_review_df['remove'].astype('bool')\n",
    "    to_review_df = to_review_df.assign(merge=None)\n",
    "    to_review_df['merge'] = to_review_df['merge'].astype('bool')\n",
    "    to_review_df = to_review_df.assign(untag_entity=None)\n",
    "    to_review_df['untag_entity'] = to_review_df['untag_entity'].astype('bool')\n",
    "    return ipysheet.from_dataframe(to_review_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "704f7c1921644f7bb04a2bb208bd98d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sheet(cells=(Cell(column_end=0, column_start=0, numeric_format=None, row_end=7, row_start=0, squeeze_row=False…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sparse_entity_review_sheet = create_sparse_entity_review_sheet(sparse_total_df)\n",
    "sparse_entity_review_sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FROM HERE\n",
    "#TODO: Clean up notebook!\n",
    "def get_sparse_entries_by_type(sparse_entries_df):\n",
    "    entity_types = sparse_total_df['entity_type'].tolist()\n",
    "    entity_type_index_dict = {\n",
    "        entity_type: index for index, entity_type in enumerate(entity_types)}\n",
    "    number_choice = input(\n",
    "        'Enter the number of the entity type you want to look at: ')\n",
    "    entity_type = entity_types[int(number_choice)]\n",
    "\n",
    "    return sparse_entries_df[sparse_entries_df['entity_types'].str.contains(entity_type, na=False)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>question</th>\n",
       "      <th>answer_normalised</th>\n",
       "      <th>scenario</th>\n",
       "      <th>intent</th>\n",
       "      <th>predicted_label</th>\n",
       "      <th>answer_annotation</th>\n",
       "      <th>entities</th>\n",
       "      <th>entity_types</th>\n",
       "      <th>predicted_tagging</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2291</th>\n",
       "      <td>Write what you would tell your PDA in the foll...</td>\n",
       "      <td>turn on my son's room plug socket on off</td>\n",
       "      <td>iot</td>\n",
       "      <td>wemo_on</td>\n",
       "      <td>wemo_on</td>\n",
       "      <td>turn on my [relation : son's] [house_place : r...</td>\n",
       "      <td>[{'type': 'relation', 'words': ['son's']}, {'t...</td>\n",
       "      <td>relation,house_place,device_type</td>\n",
       "      <td>turn on my son's [house_place : room] [device_...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               question  \\\n",
       "2291  Write what you would tell your PDA in the foll...   \n",
       "\n",
       "                             answer_normalised scenario   intent  \\\n",
       "2291  turn on my son's room plug socket on off      iot  wemo_on   \n",
       "\n",
       "     predicted_label                                  answer_annotation  \\\n",
       "2291         wemo_on  turn on my [relation : son's] [house_place : r...   \n",
       "\n",
       "                                               entities  \\\n",
       "2291  [{'type': 'relation', 'words': ['son's']}, {'t...   \n",
       "\n",
       "                          entity_types  \\\n",
       "2291  relation,house_place,device_type   \n",
       "\n",
       "                                      predicted_tagging  \n",
       "2291  turn on my son's [house_place : room] [device_...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_sparse_entries_by_type(sparse_entries_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: add a function to remove entries marked for removal\n",
    "#TODO: add a function to untag entities marked for untagging\n",
    "#TODO: add a function to merge entities?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am not sure if this part is necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_entity_types(domain_df):\n",
    "    '''\n",
    "    Split entity types by , and group entries by split entity types\n",
    "    :param domain_df: a dataframe with entries in the entity_types column\n",
    "    :return: a dataframe with entries grouped by entity type\n",
    "    '''\n",
    "    split_entity_types_df = domain_df.copy()\n",
    "    split_entity_types_df['entity_types'] = split_entity_types_df['entity_types'].str.split(\n",
    "        ',')\n",
    "    split_entity_types_df = split_entity_types_df.explode(\n",
    "        'entity_types').reset_index(drop=True)\n",
    "    return split_entity_types_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create multi index from sparse_entries_df with frame with the split entity_types and the answer_annotation as the sub column\n",
    "\n",
    "split_entity_types_df = split_entity_types(sparse_entries_df)\n",
    "\n",
    "split_entity_types_df = split_entity_types_df[['intent', 'entity_types', 'answer_annotation', 'predicted_tagging']]\n",
    "split_entity_types_df\n",
    "\n",
    "# TODO: make the entity_type the index and then group all of the answer_annotations by entity_type and then count the number of each answer_annotation\n",
    "# Do I need to use a multi index for this?\n",
    "pd.MultiIndex.from_frame(split_entity_types_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "entity_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_entity_types_df = create_entity_type_df(sparse_entries_df)\n",
    "split_entity_types_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: the entries should be arranged in the same order as the entity types in sparse_total_df\n",
    "# TODO: add use predicted tagging column as boolean?\n",
    "# TODO: make df that only has intent, predicted label, answer annotation, predicted tagging\n",
    "df = sparse_entries_df[['intent', 'predicted_label', 'answer_annotation', 'predicted_tagging']]\n",
    "\n",
    "# display the entire columns for answer_annotation and predicted_tagging\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: From here we will want to create an ipysheet of the above, and allow the user to select entries to remove or if there is an entity from the list to be untagged or merged:\n",
    "# Option 1: Remove entry\n",
    "# Option 2: Merge entry (how to get which one?)\n",
    "# Option 3: Untag entry (how to get which one?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: place_name -> house_place\n",
    "# Add in ability to change a whole entity type to another. e.g. place_name -> house_place"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entity overlap refinement\n",
    "We want to find the entities that have overlapping entity types. Entities (the words themselves that get tagged), should very rarely have more than one entity type in a domain. If they do, then we need to refine the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "take the `domain_df['entities']` and get a list of dictionaries for:\n",
    "* `id`: the original index of the row\n",
    "* `entity_type`: the entity type\n",
    "* `entity_words`: the joined words with spaces that make up the entity\n",
    "\n",
    "and then drop it into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_entity_types, incorrect_entity_types, overlapping_entity_words = MacroEntityRefinement.pick_correct_entity_type_for_overlapping_entities(\n",
    "    domain_df)\n",
    "\n",
    "    #TODO: add option to just press enter to skip picking a correct entity type (i.e. coffee type black but color black)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have the `correct_entity_types`. We will need those to know what to change the `entity_type` to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_entity_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should probably get a list of the `incorrect_entity_types` to go along with our `correct_entity_types`, so we know what to replace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "incorrect_entity_types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about we for loop over the zip of `incorrect_entity_types` and `correct_entity_types` and replace the `entity_type` in `overlapping_domain_df` with the `correct_entity_type`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_overlapping_domain_df = MacroEntityRefinement.refine_overlapping_entity_types(\n",
    "    domain_df, correct_entity_types, incorrect_entity_types, overlapping_entity_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entity refinement\n",
    "We will want to refine all of the entries that have the incorrect entity types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_incorrect_predicted_entities_df = refined_overlapping_domain_df[refined_overlapping_domain_df['answer_annotation']\n",
    "                                                              != refined_overlapping_domain_df['predicted_tagging']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_review_sheet = MacroDataRefinement.create_sheet(\n",
    "    domain_incorrect_predicted_entities_df)\n",
    "to_review_sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_review_sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: start from here, refine then save to CSV the changes. Repeat until all domains have been refined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_domain_entities_df = MacroDataRefinement.convert_sheet_to_dataframe(to_review_sheet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_domain_entities_df = reviewed_domain_entities_df.apply(\n",
    "    MacroDataRefinement.replace_annotated_utterance_with_predicted_tagging, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_domain_entities_df.to_csv(\n",
    "    f'data/refined/entities/{domain_selection}_refined_entities_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_domain_entities_df = MacroDataRefinement.mark_entries_as_refined(\n",
    "    refined_dataframe=refined_domain_entities_df, refined_type='entity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df.update(refined_domain_entities_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_nlu_data_refined_df = nlu_data_refined_df[nlu_data_refined_df['remove'] != True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_refined_df.to_csv(\n",
    "    f'data/refined/nlu_data_intent_and_entity_refined_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FROM HERE: loop back for each domain to refine the entities until done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: once all of the refinements of each domain have been completed the nlu_data_refined_df should be updated with the refined data from removed_nlu_data_refined_df!\n",
    "# Don't forget: The removed entries are no longer in the removed_nlu_data_refined_df so they need to be added back into the nlu_data_refined_df from the files!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_df = pd.read_csv(\n",
    "    'data/refined/nlu_data_refined_df.csv', sep=',', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_df = DataUtils.convert_annotated_utterances_to_normalised_utterances(\n",
    "    nlu_data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and concat all csv files in the data/refined/entities folder\n",
    "path = 'data/refined/entities'\n",
    "all_files = glob.glob(path + \"/*.csv\")\n",
    "\n",
    "df = pd.concat((pd.read_csv(f, index_col=0)\n",
    "               for f in all_files))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_df = MacroDataRefinement.mark_entries_as_refined(\n",
    "    refined_dataframe=df, refined_type='entity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "refined_df = refined_df[refined_df['scenario'] != 'iot']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_df.update(refined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_df.to_csv('data/refined/nlu_data_intent_and_entity_refined_df.csv')\n",
    "#FROM HERE!\n",
    "#TODO use this for the flow from the start instead of the removed_nlu_data_refined_updated_df!\n",
    "#TODO: test this on the flow and make sure it works before going to refinement\n",
    "#TODO: change after the report on entities with JSON to get a simple output of all the entities in the domain and examples like in the report but have the user see if any entities need to be totally removed as labels or merged with others before continuing to the refinement stage!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: refactor intent refinement to dump the csvs into a sub dir called intents!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: It is probably a good idea to drop all of the ones that lack a good support.\n",
    "#NOTE: But it didn't work to fix the problem.\n",
    "remove_entities = [\n",
    "    'music_album',\n",
    "    'game_type',\n",
    "    \n",
    "]\n",
    "removed_nlu_data_refined__entities_cleaned_df = removed_nlu_data_refined_df[~removed_nlu_data_refined_df['answer_annotation'].str.contains('|'.join(remove_entities))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Analytics.plot_report(entity_report_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: Remove/replace worst: add in state features like here: https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html#let-s-check-what-classifier-learned\n",
    "# Specifically, we want print_state_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_state_features(state_features):\n",
    "    for (attr, label), weight in state_features:\n",
    "        print(\"%0.6f %-8s %s\" % (weight, label, attr))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nlu_engine import crf\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crf_model.state_features_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_state_features(Counter(crf.state_features_).most_common(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: review the most common features, none of them are the word parts (chunks) or POS tags, are these even needed or helpful?\n",
    "# Can we remove them and speed up the process?\n",
    "# What other features could be used? Word2vec? Brown clustering?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "domain_selection = MacroDataRefinement.list_and_select_domain(nlu_data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have seen from the entity extraction report, the entity extraction is not working for the alarm_type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO: review all scoring 0, see if they can be completely dropped or what\n",
    "entity_to_refine = 'alarm_type'\n",
    "nlu_scenario_df = removed_nlu_data_refined_df[removed_nlu_data_refined_df['answer_annotation'].str.contains(\n",
    "    entity_to_refine)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_scenario_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_entity(df, entity_to_remove):\n",
    "    \"\"\"\n",
    "        Remove all entries of an entity type from the dataframe.\n",
    "        :param df: pandas dataframe\n",
    "        :return: pandas dataframe\n",
    "        \"\"\"\n",
    "    updated_df = df.copy()\n",
    "    updated_df.loc[updated_df['answer_annotation'].str.contains(\n",
    "        entity_to_remove), 'remove'] = True\n",
    "    return updated_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "updated_df = remove_entity(removed_nlu_data_refined_df, entity_to_refine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_nlu_data_refined_df[removed_nlu_data_refined_df['answer_annotation'].str.contains(\n",
    "    entity_to_refine)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entity Convert to ipysheet and review\n",
    "TODO: add in description of the types of fixes we can do to the NLU data for entity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# TODO: same as above for intents but with predicted entities: report on them, break them down into a dictionary of dataframes and refine them.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the example with 'alarm' and the alarm_type:\n",
    "* We see that the alarm_type entities are really event_name(ie wake up, soccer practice) except for ID 5879, we will need to change them to event_name and remove ID 5879.\n",
    "* The last one(ID 6320) is a mistake. Someone got confused with the prompt and assumed alarm is a security system. This is out of scope for the alarm domain, as the alarms are ones set on a phone or other device. We will drop this utterance.\n",
    "Once you are done reviewing, you convert it back to a dataframe and check to make sure it looks okay.\n",
    "Let's change all alarm_type entities to event_name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "reviewed_scenario_df['answer_annotation'] = reviewed_scenario_df['answer_annotation'].str.replace(\n",
    "    'alarm_type', 'event_name')\n",
    "reviewed_scenario_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay dokey, now we can merge this with the original data set and see if it made a difference already(well of course it did!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlu_data_df.drop(\n",
    "    reviewed_scenario_df[reviewed_scenario_df['remove'] == True].index, inplace=True)\n",
    "\n",
    "reviewed_scenario_df = reviewed_scenario_df[~reviewed_scenario_df['remove'] == True]\n",
    "\n",
    "nlu_data_df.loc[nlu_data_df.index.intersection(\n",
    "    reviewed_scenario_df.index), 'answer_annotation'] = reviewed_scenario_df['answer_annotation']\n",
    "\n",
    "nlu_data_df[(nlu_data_df['scenario'].str.contains('alarm')) & (nlu_data_df['answer_annotation'].str.contains(\n",
    "    'event_name'))]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark changed data set\n",
    "TODO: repeat reports for the changed data set for domain and entities and compare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "entity_reviewed_report_df = NLUEngine.evaluate_entity_classifier(\n",
    "    data_df=nlu_data_df)\n",
    "entity_reviewed_report_df.sort_values(by=['f1-score'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are sure it is okay, you can save it as a csv file, make sure to name it correctly(i.e. `alarm_domain_first_review.csv`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviewed_scenario_df.to_csv('alarm_domain_first_review.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load it back up and check to make sure it looks okay. Make sure to give it the right name!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_domain_first_review_df = pd.read_csv(\n",
    "    'alarm_domain_first_review.csv', index_col=0)\n",
    "audio_domain_first_review_df.tail(50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: implement the evaluate_classifier in the NLU engine to check f1 score for intents and entities in the domain vs original NLU data of domain!\n",
    "# Value: benchmark!\n",
    "#TODO: implement a flow for getting the domains with the lowest f1 scores by intent/domain and entities and cleaning them by the order of the lowest f1 scores\n",
    "# TODO: concat all reviewed dfs and save to csv\n",
    "# TODO: add benchmark for whole NLU data set before and after cleaning! (by intents and domains!)\n",
    "# TODO: review the review marked entries\n",
    "# TODO: add new column for notes\n",
    "# TODO: change flow of review for only ones that should be reviewed, not all of the ones that have been changed (track changes by comparing against the original data set)\n",
    "# TODO: do the changed utterances have to be changed in other fields too or is it just enough for the tagged utterancve field?\n",
    "# TODO: add visualizations of domains, their intents, keywords in utterances, and entities to top\n"
   ]
  }
 ],
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