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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b1f5378",
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   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "# Load data files\n",
    "annotator_df = pd.read_csv(\"./trac4_PER_train.csv\")\n",
    "df = pd.read_csv(\"./trac3_EMP_train.csv\", on_bad_lines='skip')\n",
    "article_df = pd.read_csv(\"./articles_adobe_AMT.csv\")\n",
    "\n",
    "# Mappings for structured user profile\n",
    "gender_map = {1: 'Male', 2: 'Female', 5: 'Other'}\n",
    "race_map = {1: 'White', 2: 'Hispanic / Latino', 3: 'Black / African American', 4: 'Native American / American Indian', 5: 'Asian / Pacific Islander', 6: 'Other'}\n",
    "education_map = {\n",
    "    1: 'a diploma less than a high school',\n",
    "    2: 'High school degree or diploma',\n",
    "    3: 'went to Technical / Vocational School',\n",
    "    4: 'went to college but did not get a degree',\n",
    "    5: 'Two year associate degree',\n",
    "    6: 'College or university degree',\n",
    "    7: 'Postgraduate / professional degree'\n",
    "}\n",
    "\n",
    "# Build annotator dicts\n",
    "annotator_text_dict = {}\n",
    "annotator_structured_dict = {}\n",
    "for idx, row in annotator_df.iterrows():\n",
    "    persona = []\n",
    "    structured = {}\n",
    "    gender = gender_map.get(row['gender'], 'Unknown')\n",
    "    race = race_map.get(row['race'], 'Unknown')\n",
    "    age = row['age']\n",
    "    education = education_map.get(row['education'], 'Unknown')\n",
    "    income = row['income']\n",
    "    personality = {\n",
    "        \"openness\": row['personality_openess'],\n",
    "        \"conscientiousness\": row['personality_conscientiousness'],\n",
    "        \"extraversion\": row['personality_extraversion'],\n",
    "        \"agreeableness\": row['personality_agreeableness'],\n",
    "        \"stability\": row['personality_stability']\n",
    "    }\n",
    "    persona.append(f\"The person is {gender.lower()}.\")\n",
    "    persona.append(f\"Racially, the person is {race.lower()}.\")\n",
    "    persona.append(f\"The person is {age} years old.\")\n",
    "    persona.append(f\"The person has a {education.lower()}.\")\n",
    "    persona.append(f\"The person earns {income} dollar per year.\")\n",
    "    persona.append(\n",
    "        f\"According to the Big Five personality test, on a scale of 10, the person has scored {personality['openness']} in openness, {personality['conscientiousness']} in conscientiousness, {personality['extraversion']} in extraversion, {personality['agreeableness']} in agreeableness, and {personality['stability']} in stability.\"\n",
    "    )\n",
    "    persona = [p for p in persona if \"nan\" not in p]\n",
    "    persona_text = \" \".join(persona)\n",
    "    annotator_text_dict[row['person_id']] = persona_text\n",
    "    structured = {\n",
    "        \"gender\": gender,\n",
    "        \"race\": race,\n",
    "        \"age\": age,\n",
    "        \"education\": education,\n",
    "        \"income\": income,\n",
    "        \"personality\": personality\n",
    "    }\n",
    "    annotator_structured_dict[row['person_id']] = structured\n",
    "\n",
    "# Prepare dataset\n",
    "dataset = []\n",
    "for idx, row in df.iterrows():\n",
    "    person_id = row['person_id']\n",
    "    article_id = row['article_id']\n",
    "    # Get user profile\n",
    "    user_profile_text = annotator_text_dict.get(person_id, \"\")\n",
    "    user_profile_structured = annotator_structured_dict.get(person_id, {})\n",
    "    # Get article\n",
    "    article_row = article_df[article_df['article_id'] == article_id]\n",
    "    if article_row.empty:\n",
    "        continue\n",
    "    article_text = article_row['text'].values[0]\n",
    "    # Input prompt (as in ec.py, with_persona)\n",
    "    prompt = (\n",
    "        f\"{article_text}\"\n",
    "    )\n",
    "    # Output: the user's essay/response\n",
    "    output = row['person_essay']\n",
    "    # Compose item\n",
    "    item = {\n",
    "        \"user_id\": f\"ec_{person_id}\",\n",
    "        \"profile_text\": user_profile_text,\n",
    "        \"profile\": user_profile_structured,\n",
    "        \"article\": article_text,\n",
    "        # \"input\": prompt,\n",
    "        \"essay\": output\n",
    "    }\n",
    "    dataset.append(item)\n",
    "\n",
    "# Save to JSON\n",
    "# with open(\"ec_dataset.json\", \"w\", encoding=\"utf-8\") as f:\n",
    "#     json.dump(dataset, f, ensure_ascii=False, indent=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb69aa41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "974"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13f8cca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/home/zxtan/text-to-lora/data_p13n/EC/ec_dataset.jsonl', 'w') as f:\n",
    "    for line in dataset:\n",
    "        f.write(json.dumps(line) + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46944ebd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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