ChatGPTNoHumor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "import json\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI(api_key='')\n",
    "MODEL = 'gpt-3.5-turbo-1106'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1: Pun Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>het_857</td>\n",
       "      <td>Those whose child is going through a'phase'may...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>het_368</td>\n",
       "      <td>Bureaucrats cut red tape-- lengthwise.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>het_1204</td>\n",
       "      <td>If they say you look good in glasses that woul...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>het_1043</td>\n",
       "      <td>Nearing the end of his sentence, the convicted...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>het_1098</td>\n",
       "      <td>A logger went to his doctor because of a probl...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                               text  label\n",
       "0   het_857  Those whose child is going through a'phase'may...      1\n",
       "1   het_368             Bureaucrats cut red tape-- lengthwise.      0\n",
       "2  het_1204  If they say you look good in glasses that woul...      1\n",
       "3  het_1043  Nearing the end of his sentence, the convicted...      1\n",
       "4  het_1098  A logger went to his doctor because of a probl...      1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json('data/llm_task1_hetero.json', orient=\"records\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df\n",
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_prompt_nohumor(sentence):\n",
    "  messages=[\n",
    "  { \"role\": \"system\",\n",
    "    \"content\": f'''\n",
    "You are to classify whether a given sentence is a pun:\n",
    "Here are examples of the cases you will encounter:\n",
    "1. An example of a pun - \n",
    "The magician got so mad that he pulled his hare out.\n",
    "{{\n",
    "  \"output\": true,\n",
    "  \"explanation\": \"The pun word 'hare' supports two plausible interpretations of 'hare' meaning a rabbit and 'hair' meaning human hair. In the given sentence, the words 'magician' relates to 'hare' while 'pulled' refers to the second interpretation of 'hair'. Both of these are distinct where one refers to a magician's animal while the other refers to an action done in anger which is pulling your hair.\"\n",
    "}}\n",
    "2. An example of no pun -\n",
    "I went to the bank.\n",
    "{{\n",
    "  \"output\": false,\n",
    "  \"explanation\": \"The word 'bank' does have ambiguity here where it supports two plausible interpretations of bank as in a financial institution and bank as in the banks of a river. There are no other words in the sentence that realize both interpretations, hence the sentence is not a pun.\"\n",
    "}}\n",
    "3. An example of no pun -\n",
    "Let us go home.\n",
    "{{\n",
    "  \"output\": false,\n",
    "  \"explanation\": \"There is no pun word in the sentence.\"\n",
    "}}\n",
    "Identify whether the input sentence is a pun and explain the result in valid JSON format. Generate a response in the form of a valid JSON object with two keys: output and explanation.\\n\n",
    "'''\n",
    "  },\n",
    "  {\n",
    "    \"role\": \"user\", \"content\": f'''{sentence}'''\n",
    "  }\n",
    "]\n",
    "  return messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "i = 0\n",
    "results = []\n",
    "for _, row in test.iterrows():\n",
    "  prompt = prepare_prompt_nohumor(row['text'])\n",
    "  response = client.chat.completions.create(\n",
    "  model=MODEL,\n",
    "  response_format={ \"type\": \"json_object\" },\n",
    "  temperature=0.2,\n",
    "  messages=prompt,\n",
    "  timeout=5,\n",
    "  )\n",
    "  out = json.loads(response.choices[0].message.content)\n",
    "  out['text'] = row['text']\n",
    "  results.append(out)\n",
    "  print(i)\n",
    "  i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "445"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results, columns=['text', 'output', 'explanation'])\n",
    "results_df.to_json('outputs-task1-hetero-chatgpt-nohumor.json', orient=\"records\")\n",
    "len(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df['output'] = results_df['output'].replace({True: 1, False: 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = [int(x) for x in list(test['label'])]\n",
    "y_pred = [int(x) for x in list(results_df['output'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.7257    0.6457    0.6833       127\n",
      "           1     0.8645    0.9025    0.8831       318\n",
      "\n",
      "    accuracy                         0.8292       445\n",
      "   macro avg     0.7951    0.7741    0.7832       445\n",
      "weighted avg     0.8248    0.8292    0.8261       445\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2 and 3: Pun Location and Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_sentence(text):\n",
    "  res = text.replace(\" ' ' \", '\" ')\n",
    "  res = res.replace(\" ' \", \"'\")\n",
    "  res = res.replace(\" , \", \", \")\n",
    "  res = res.replace(\" . \", \". \")\n",
    "  res = res.replace(\" .\", \". \")\n",
    "  res = res.replace(\" ! \", \"! \")\n",
    "  res = res.replace(\" !\", \"! \")\n",
    "  res = res.replace(\" ? \", \"? \")\n",
    "  res = res.replace(\" ?\", \"? \")\n",
    "  res = res.replace(\" - \", \"-\")\n",
    "  return res.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>pun_word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>In some countries there is a lot of Manuel labor.</td>\n",
       "      <td>Manuel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Someone who attempts long distance fraud is kn...</td>\n",
       "      <td>phony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>In the delivery room, the unborn infant expect...</td>\n",
       "      <td>berth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beautifully manicured lawns are highly sod after.</td>\n",
       "      <td>sod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I saw something similar to moss the other day,...</td>\n",
       "      <td>lichen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text pun_word\n",
       "0  In some countries there is a lot of Manuel labor.   Manuel\n",
       "1  Someone who attempts long distance fraud is kn...    phony\n",
       "2  In the delivery room, the unborn infant expect...    berth\n",
       "3  Beautifully manicured lawns are highly sod after.      sod\n",
       "4  I saw something similar to moss the other day,...   lichen"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json('data/llm_task2_hetero.json', orient=\"records\")\n",
    "df['sentence'] = df['sentence'].apply(clean_sentence)\n",
    "df = df.rename(columns={'sentence': 'text'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = df\n",
    "len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_prompt_humor(sentence):\n",
    "  messages=[\n",
    "  { \"role\": \"system\",\n",
    "    \"content\": f'''\n",
    "You are to identify the pun word in a given sentence. It is guaranteed that the sentence has a pun word.\\n\n",
    "Also, list all the words that realize each interpretation of the pun word. If there are multiple words that support an interpretation, separate them with a semicolon.\\n\n",
    "Here are two examples of the cases you will encounter:\n",
    "1. An example with heterographic puns (words that sound similar) - \n",
    "The magician got so mad that he pulled his hare out.\n",
    "{{\n",
    "  \"pun_word\": \"hare\",\n",
    "  \"target_word\": \"hair\",\n",
    "  \"source_sense\": \"magician\",\n",
    "  \"target_sense\": \"angry;pulled\"\n",
    "}}\n",
    "2. An example with homographic puns (same words with different senses) -\n",
    "I used to be a banker but I lost interest.\n",
    "{{\n",
    "  \"pun_word\": \"interest\",\n",
    "  \"target_word\": \"interest\",\n",
    "  \"source_sense\": \"banker\",\n",
    "  \"target_sense\": \"used;to;be\"\n",
    "}}\n",
    "Identify the pun word in the input sentence and predict the target word. Also, pick the supporting words for each sense. Generate a response in the form of a valid JSON object with four keys: pun_word, target_word, source_sense and target_sense.\\n\n",
    "'''\n",
    "  },\n",
    "  {\n",
    "    \"role\": \"user\", \"content\": f'''{sentence}'''\n",
    "  }\n",
    "]\n",
    "  return messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "i = 0\n",
    "results = []\n",
    "for _, row in test.iterrows():\n",
    "  prompt = prepare_prompt_humor(row['text'])\n",
    "  response = client.chat.completions.create(\n",
    "  model=MODEL,\n",
    "  response_format={ \"type\": \"json_object\" },\n",
    "  temperature=0.2,\n",
    "  messages=prompt,\n",
    "  timeout=5,\n",
    "  )\n",
    "  out = json.loads(response.choices[0].message.content)\n",
    "  out['text'] = row['text']\n",
    "  results.append(out)\n",
    "  print(i)\n",
    "  i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "318"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results, columns=['text', 'pun_word', 'target_word', 'source_sense', 'target_sense'])\n",
    "results_df.to_json('outputs-task2-hetero-chatgpt-nohumor.json', orient=\"records\")\n",
    "len(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cprf(true, pred):\n",
    "  correct = (true['pun_word'].str.strip().str.lower() == pred['pun_word'].str.strip().str.lower()).sum()\n",
    "  coverage = len(pred) / len(true)\n",
    "  precision = correct / len(pred)\n",
    "  recall = correct / len(true)\n",
    "  f1 = (2 * precision * recall) / (precision + recall)\n",
    "  return coverage, precision, recall, f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coverage: 1.0000\n",
      "Precision: 0.8648\n",
      "Recall: 0.8648\n",
      "F1-Score: 0.8648\n"
     ]
    }
   ],
   "source": [
    "c, p, r, f1 = cprf(test, results_df)\n",
    "print(f\"Coverage: {c:.4f}\")\n",
    "print(f\"Precision: {p:.4f}\")\n",
    "print(f\"Recall: {r:.4f}\")\n",
    "print(f\"F1-Score: {f1:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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