{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import os, re, warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Id_Number</th>\n",
       "      <th>Patient Question</th>\n",
       "      <th>Distorted part</th>\n",
       "      <th>Dominant Distortion</th>\n",
       "      <th>Secondary Distortion (Optional)</th>\n",
       "      <th>gen_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4511</td>\n",
       "      <td>My husband and I have been married for over a ...</td>\n",
       "      <td>I thought that he displayed traits of honor, l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no distortion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4523</td>\n",
       "      <td>I’ve been having problems for a while. I am hi...</td>\n",
       "      <td>I am highly disorganized, I’m concerned with b...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>I keep burying the issue in fear that treatmen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4512</td>\n",
       "      <td>I don’t know how to recover from my husband’s ...</td>\n",
       "      <td>I attributed his behavior to stress – he owned...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I don’t know how to recover from my husband’s ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4521</td>\n",
       "      <td>I have always suffered from performance anxiet...</td>\n",
       "      <td>During this time I was recruited to many great...</td>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>but was scared to take them and instead starte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4510</td>\n",
       "      <td>I have been with my fiancé for two years now....</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have my future mother in-law blatantly not ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id_Number                                   Patient Question  \\\n",
       "0       4511  My husband and I have been married for over a ...   \n",
       "1       4523  I’ve been having problems for a while. I am hi...   \n",
       "2       4512  I don’t know how to recover from my husband’s ...   \n",
       "3       4521  I have always suffered from performance anxiet...   \n",
       "4       4510   I have been with my fiancé for two years now....   \n",
       "\n",
       "                                      Distorted part Dominant Distortion  \\\n",
       "0  I thought that he displayed traits of honor, l...            Labeling   \n",
       "1  I am highly disorganized, I’m concerned with b...            Labeling   \n",
       "2  I attributed his behavior to stress – he owned...        Mind Reading   \n",
       "3  During this time I was recruited to many great...     Fortune-telling   \n",
       "4  I felt like the response was totally irrationa...       Magnification   \n",
       "\n",
       "  Secondary Distortion (Optional)  \\\n",
       "0                             NaN   \n",
       "1                   Mental filter   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "                                        gen_response  \n",
       "0                                      no distortion  \n",
       "1  I keep burying the issue in fear that treatmen...  \n",
       "2  I don’t know how to recover from my husband’s ...  \n",
       "3  but was scared to take them and instead starte...  \n",
       "4  to have my future mother in-law blatantly not ...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CDs-LLMs-infer-gpt-4.1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dominant Distortion\n",
       "Overgeneralization         239\n",
       "Mind Reading               239\n",
       "Magnification              195\n",
       "Labeling                   165\n",
       "Personalization            153\n",
       "Fortune-telling            143\n",
       "Emotional Reasoning        134\n",
       "Mental filter              122\n",
       "Should statements          107\n",
       "All-or-nothing thinking    100\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Dominant Distortion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('distorted_vietnamese.csv', encoding='utf-8-sig')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Id_Number                                      Longest Match  \\\n",
      "0          4500  The voice are always fimilar (someone she know...   \n",
      "1          4501  I’m just afraid sometimes that since I’m not r...   \n",
      "2          4502                                                NaN   \n",
      "3          4503                                                NaN   \n",
      "4          4504  I refused to go because I didn’t know if it wa...   \n",
      "...         ...                                                ...   \n",
      "2525       2562                                                NaN   \n",
      "2526       2563  Now I am at university my peers around me all ...   \n",
      "2527       2564  He claims he’s severely depressed and has outb...   \n",
      "2528       2565                                                NaN   \n",
      "2529       2568                                                NaN   \n",
      "\n",
      "      Answer Start  \n",
      "0            547.0  \n",
      "1           1457.0  \n",
      "2             -1.0  \n",
      "3             -1.0  \n",
      "4            427.0  \n",
      "...            ...  \n",
      "2525          -1.0  \n",
      "2526         357.0  \n",
      "2527         162.0  \n",
      "2528          -1.0  \n",
      "2529          -1.0  \n",
      "\n",
      "[2530 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def longest_common_substrings(s1, s2):\n",
    "    \"\"\"Tìm tất cả chuỗi con chung dài nhất giữa s1 (Distorted part) và s2 (Patient Question).\"\"\"\n",
    "    m, n = len(s1), len(s2)\n",
    "    dp = [[0] * (n + 1) for _ in range(m + 1)]\n",
    "    \n",
    "    max_length = 0\n",
    "    substrings = []  # Danh sách lưu các chuỗi con chung dài nhất\n",
    "    \n",
    "    for i in range(1, m + 1):\n",
    "        for j in range(1, n + 1):\n",
    "            if s1[i - 1] == s2[j - 1]:\n",
    "                dp[i][j] = dp[i - 1][j - 1] + 1\n",
    "                if dp[i][j] > max_length:\n",
    "                    max_length = dp[i][j]\n",
    "                    substrings = [s1[i - max_length:i]]  # Lưu chuỗi con mới dài nhất\n",
    "                elif dp[i][j] == max_length:\n",
    "                    substrings.append(s1[i - max_length:i])  # Lưu chuỗi con dài nhất có độ dài bằng nhau\n",
    "            else:\n",
    "                dp[i][j] = 0\n",
    "\n",
    "    return substrings  # Trả về danh sách tất cả chuỗi con chung dài nhất\n",
    "\n",
    "def get_first_longest_match(row):\n",
    "    \"\"\"Tìm đoạn text dài nhất đầu tiên và vị trí bắt đầu trong Patient Question.\"\"\"\n",
    "    question = row[\"Patient Question\"]\n",
    "    distorted = row[\"Distorted part\"]\n",
    "\n",
    "    if pd.isna(distorted) or pd.isna(question):\n",
    "        return pd.Series([None, -1], index=[\"Longest Match\", \"Answer Start\"])\n",
    "\n",
    "    lcs_list = longest_common_substrings(distorted, question)\n",
    "    if not lcs_list:\n",
    "        return pd.Series([None, -1], index=[\"Longest Match\", \"Answer Start\"])\n",
    "    # Tìm chuỗi con dài nhất đầu tiên\n",
    "    lcs = lcs_list[0]\n",
    "    start_idx = question.find(lcs) if lcs else -1\n",
    "\n",
    "    return pd.Series([lcs, start_idx], index=[\"Longest Match\", \"Answer Start\"])\n",
    "\n",
    "# Áp dụng hàm vào DataFrame\n",
    "df[[\"Longest Match\", \"Answer Start\"]] = df.apply(get_first_longest_match, axis=1)\n",
    "\n",
    "# In thử kết quả\n",
    "print(df[[\"Id_Number\", \"Longest Match\", \"Answer Start\"]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_d_part(s1, s2):\n",
    "    \"\"\"Xử lý phần biến dạng giữa s1 và s2.\"\"\"\n",
    "    if pd.isna(s1) or pd.isna(s2):  # Kiểm tra nếu một trong hai giá trị là NaN\n",
    "        return None  # Hoặc có thể trả về giá trị mặc định khác như \"\"\n",
    "    \n",
    "    start = s1.find(s2)\n",
    "    if start != 0:\n",
    "        return s1[:start]  # Loại bỏ s2 khỏi s1 nếu không bắt đầu bằng s2\n",
    "    else:\n",
    "        return s2  # Nếu bắt đầu bằng s2, trả về s2 không thay đổi\n",
    "    \n",
    "df['processed_substrings'] = df.apply(\n",
    "    lambda row: process_d_part(row['Distorted part'], row['Longest Match']),\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('distortion_processed.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bỏ các dòng không có Distorted part\n",
    "df_filtered = df[~df[\"Distorted part\"].isna()].copy()\n",
    "\n",
    "# Tách theo dấu chấm, bỏ khoảng trắng thừa, lọc bỏ rỗng\n",
    "df_filtered[\"Distorted part split\"] = df_filtered[\"Distorted part\"].apply(\n",
    "    lambda x: [s.strip() for s in x.split(\".\") if s.strip()]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I feel trapped inside my disgusting self and like I’m willing to do anything to escape it.I’m just afraid sometimes that since I’m not really thinking during these times that I might do something to myself–especially since I think during these times that I don’t deserve to get help or that I’m not worth disturbing people by calling them.'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Đếm số phần tử sau khi split\n",
    "df_filtered[\"num_parts\"] = df_filtered[\"Distorted part\"].apply(\n",
    "    lambda x: len([s.strip() for s in x.split(\".\") if s.strip()])\n",
    ")\n",
    "\n",
    "# Xem kết quả\n",
    "df_filtered[\"Distorted part\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Ive been depressed for 4 years now and Ive noticed that I have changed dramatically as a person. Right now though, the thing that bothers me the most is that I seem to find it hard to maintain a friendships. And Ive been told its because I don’t trust. Before I was depressed, I never had issues. I never doubted my friends. I took their yes as a yes. Now my mind analyses everything to an extent where I get terribly drained and tired from all the thinking. I was such a simple person before depression. And I dont like being like this because Im making things hard for my friends..because Im always doubting them OR guessing things that I think they might be thinking..which 99% of the times are wrong. Why do I have doubts? If I love my friends like I know I do, why do I analyse everything, and say “if this, if that” etc. I was never an insecure person…does this mean I am now? I want things to be simple..but depression makes it so hard. I feel like I should isolate myself from others..because I cant be a true friend if I cant truly trust them. I make life hard for them. And I dont want too. Maybe Im too over protective.Maybe Im too attached to the extent where I go over board. I dont know. Im sure its all depression related..but how can I trust my friends more.. or at least..not show them my doubts. Please help.. thank you for reading..\n",
      "Before I was depressed, I never had issues. I never doubted my friends. I took their yes as a yes. Now my mind analyses everything to an extent where I get terribly drained and tired from all the thinking. I was such a simple person before depression. And I dont like being like this because Im making things hard for my friends..because Im always doubting them OR guessing things that I think they might be thinking..which 99% of the times are wrong. I was never an insecure person…does this mean I am now? I want things to be simple..but depression makes it so hard. I feel like I should isolate myself from others..because I cant be a true friend if I cant truly trust them. I make life hard for them. And I dont want too. Maybe Im too over protective.Maybe Im too attached to the extent where I go over board. I dont know.\n"
     ]
    }
   ],
   "source": [
    "print(df_filtered[df_filtered['num_parts']==17]['Patient Question'][130])\n",
    "print(df_filtered[df_filtered['num_parts']==17]['Distorted part'][130])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I don’t really know how to explain the situation, but I deal with a lot of family drama, my parents have kind of high expectations, with the stress of school on top of that I became extremely unhappy and kept all of my emotions in and I wore that fake smile everyday until I finally reached my breaking point. I started self harming for about six months until my mom found out and suggested an inpatient program at a hospital. I refused to go because I didn’t know if it was going to go on my record which could possibly affect my dreams of becoming a psychiatrist. So, instead she sent me to a councelor who I’ve been seeing for about a year now. She sent me to a psychiatrist when I first started going to her and I was diagnosed with depression and put on medication but I stopped taking it a month after it was perscribed. I have a major fear of choking and drowning As well and can’t swallow pills. I’m still having major problems with the depression And My anxiety has been very bad, I’ve been considering talking to my parents about finishing the year by taking online classes. Because I need to get away. But what I really want to know is, how can I deal with depression and dig myself out of this hole? It seems like everytime I get so close of beating in, I get nocked right back down to the bottom. I’m still seeing my therapist but it’s not enough, my mom is still pushing me to go back on medication but doesn’t understand my fears. Is it even possible to beat this without medication? I also apologize if a lot of this doesn’t make sense.'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Patient Question'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I refused to go because I didn’t know if it was going to go on my record which could possibly affect my dreams of becoming a psychiatrist.I have a major fear of choking and drowningIt seems like everytime I get so close of beating in, I get nocked right back down to the bottom.'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Distorted part'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def find_answer_start(context, answer):\n",
    "    match = re.search(re.escape(answer.strip()), context)\n",
    "    return match.start() if match else -1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Question']=\"What is the distorted part?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Id_Number</th>\n",
       "      <th>Patient Question</th>\n",
       "      <th>Distorted part</th>\n",
       "      <th>Dominant Distortion</th>\n",
       "      <th>Secondary Distortion (Optional)</th>\n",
       "      <th>Question</th>\n",
       "      <th>tmp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2530.000000</td>\n",
       "      <td>2530</td>\n",
       "      <td>1597</td>\n",
       "      <td>2530</td>\n",
       "      <td>416</td>\n",
       "      <td>2530</td>\n",
       "      <td>1597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2529</td>\n",
       "      <td>1590</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>759.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>I grew up in a upper middle class family. I wa...</td>\n",
       "      <td>I feel as if moving on to higher education wil...</td>\n",
       "      <td>No Distortion</td>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>933</td>\n",
       "      <td>67</td>\n",
       "      <td>2530</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1496.877075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1153.563554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>632.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1281.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4701.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id_Number                                   Patient Question  \\\n",
       "count   2530.000000                                               2530   \n",
       "unique          NaN                                               2529   \n",
       "top             NaN  I grew up in a upper middle class family. I wa...   \n",
       "freq            NaN                                                  2   \n",
       "mean    1496.877075                                                NaN   \n",
       "std     1153.563554                                                NaN   \n",
       "min        0.000000                                                NaN   \n",
       "25%      632.250000                                                NaN   \n",
       "50%     1281.500000                                                NaN   \n",
       "75%     2016.750000                                                NaN   \n",
       "max     4701.000000                                                NaN   \n",
       "\n",
       "                                           Distorted part Dominant Distortion  \\\n",
       "count                                                1597                2530   \n",
       "unique                                               1590                  11   \n",
       "top     I feel as if moving on to higher education wil...       No Distortion   \n",
       "freq                                                    2                 933   \n",
       "mean                                                  NaN                 NaN   \n",
       "std                                                   NaN                 NaN   \n",
       "min                                                   NaN                 NaN   \n",
       "25%                                                   NaN                 NaN   \n",
       "50%                                                   NaN                 NaN   \n",
       "75%                                                   NaN                 NaN   \n",
       "max                                                   NaN                 NaN   \n",
       "\n",
       "       Secondary Distortion (Optional)                     Question     tmp  \n",
       "count                              416                         2530  1597.0  \n",
       "unique                              10                            1   759.0  \n",
       "top                    Fortune-telling  What is the distorted part?    -1.0  \n",
       "freq                                67                         2530   242.0  \n",
       "mean                               NaN                          NaN     NaN  \n",
       "std                                NaN                          NaN     NaN  \n",
       "min                                NaN                          NaN     NaN  \n",
       "25%                                NaN                          NaN     NaN  \n",
       "50%                                NaN                          NaN     NaN  \n",
       "75%                                NaN                          NaN     NaN  \n",
       "max                                NaN                          NaN     NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2530 entries, 0 to 2529\n",
      "Data columns (total 7 columns):\n",
      " #   Column                           Non-Null Count  Dtype \n",
      "---  ------                           --------------  ----- \n",
      " 0   Id_Number                        2530 non-null   int64 \n",
      " 1   Patient Question                 2530 non-null   object\n",
      " 2   Distorted part                   1597 non-null   object\n",
      " 3   Dominant Distortion              2530 non-null   object\n",
      " 4   Secondary Distortion (Optional)  416 non-null    object\n",
      " 5   Question                         2530 non-null   object\n",
      " 6   tmp                              1597 non-null   object\n",
      "dtypes: int64(1), object(6)\n",
      "memory usage: 138.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tmp']=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import re\n",
    "\n",
    "# def find_answer_start(context, answer):\n",
    "#     match = re.search(re.escape(answer.strip()), context)\n",
    "#     return match.start() if match else -1\n",
    "\n",
    "# # Chuyển đổi DataFrame thành định dạng cho mô hình QA\n",
    "# questions = df[\"Question\"].tolist()\n",
    "# contexts = df[\"Patient Question\"].tolist()\n",
    "# answer_ = df[\"Distorted part\"].tolist()\n",
    "\n",
    "# answers = []\n",
    "# for i, row in df.iterrows():\n",
    "#     if pd.isna(row[\"Distorted part\"]):  # Không có câu trả lời\n",
    "#         answer = {\"text\": [\"\"], \"answer_start\": [-1]}  \n",
    "#     else:\n",
    "#         start_idx = find_answer_start(row[\"Patient Question\"], answer_[i])\n",
    "#         answer = {\n",
    "#             \"text\": [row[\"Distorted part\"]],\n",
    "#             \"answer_start\": [start_idx]\n",
    "#         }\n",
    "#     answers.append(answer)\n",
    "\n",
    "\n",
    "\n",
    "# # Tạo dataset theo định dạng của Hugging Face\n",
    "# qa_data = {\n",
    "#     \"question\": questions,\n",
    "#     \"context\": df[\"Patient Question\"],\n",
    "#     \"answers\": answers\n",
    "# }\n",
    "\n",
    "# #Chuyển sang Dataset của Hugging Face\n",
    "# from datasets import Dataset\n",
    "# dataset = Dataset.from_dict(qa_data)\n",
    "\n",
    "import re\n",
    "import pandas as pd\n",
    "from datasets import Dataset\n",
    "\n",
    "def find_answer_start(context, answer):\n",
    "    match = re.search(re.escape(answer.strip()), context)\n",
    "    return match.start() if match else -1\n",
    "\n",
    "questions = []\n",
    "contexts = []\n",
    "answers = []\n",
    "\n",
    "for i, row in df.iterrows():\n",
    "    context = row[\"Patient Question\"]\n",
    "    question = row[\"Question\"]\n",
    "    distorted = row[\"Distorted part\"]\n",
    "\n",
    "    if pd.isna(distorted):\n",
    "        questions.append(question)\n",
    "        contexts.append(context)\n",
    "        answers.append({\"text\": [\"\"], \"answer_start\": [-1]})\n",
    "    else:\n",
    "        # Tách theo dấu chấm, lọc các đoạn không rỗng\n",
    "        parts = [part.strip() for part in distorted.split(\".\") if part.strip()]\n",
    "        for part in parts:\n",
    "            start_idx = find_answer_start(context, part)\n",
    "            questions.append(question)\n",
    "            contexts.append(context)\n",
    "            answers.append({\n",
    "                \"text\": [part],\n",
    "                \"answer_start\": [start_idx]\n",
    "            })\n",
    "\n",
    "# Tạo Dataset\n",
    "qa_data = {\n",
    "    \"question\": questions,\n",
    "    \"context\": contexts,\n",
    "    \"answers\": answers\n",
    "}\n",
    "\n",
    "dataset = Dataset.from_dict(qa_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3748"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(qa_data[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'What is the distorted part?'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_data[\"question\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, I have a beautiful,smart,outgoing and amazing five year old little girl. Yesterday she came to me and said mom can you take me to the doctor. I ask her what was wrong and she replied: I hear voices in my ears but I dont see the people saying it. She says it happened during school doing a reading circle. She thought someone called her stupid and let the teacher know. The teacher said no one said anything. It happened again when my husband was talking to my other children, she said I heard daddy say shut up, but he didnt really say it. The voice are always fimilar (someone she knows) Im very concerned about this and hope it has nothing to do with my pregnancy while on active duty.'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_data[\"context\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': ['I’m just afraid sometimes that since I’m not really thinking during these times that I might do something to myself–especially since I think during these times that I don’t deserve to get help or that I’m not worth disturbing people by calling them'],\n",
       " 'answer_start': [1457]}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_data[\"answers\"][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello, I have a beautiful,smart,outgoing and amazing five year old little girl. Yesterday she came to me and said mom can you take me to the doctor. I ask her what was wrong and she replied: I hear voices in my ears but I dont see the people saying it. She says it happened during school doing a reading circle. She thought someone called her stupid and let the teacher know. The teacher said no one said anything. It happened again when my husband was talking to my other children, she said I heard daddy say shut up, but he didnt really say it. The voice are always fimilar (someone she knows) Im very concerned about this and hope it has nothing to do with my pregnancy while on active duty.'"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Patient Question'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I feel trapped inside my disgusting self and like I’m willing to do anything to escape it.I’m just afraid sometimes that since I’m not really thinking during these times that I might do something to myself–especially since I think during these times that I don’t deserve to get help or that I’m not worth disturbing people by calling them.'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Distorted part'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>distortion</th>\n",
       "      <th>question</th>\n",
       "      <th>context</th>\n",
       "      <th>answers</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Personalization</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
       "      <td>{'text': ['The voice are always fimilar (someo...</td>\n",
       "      <td>The voice are always fimilar (someone she know...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Labeling</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
       "      <td>{'text': ['I feel trapped inside my disgusting...</td>\n",
       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>So I’ve been dating on and off this guy for a...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>My parents got divorced in 2004. My mother has...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I don’t really know how to explain the situati...</td>\n",
       "      <td>{'text': ['I refused to go because I didn’t kn...</td>\n",
       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I’m a 21 year old female. I spent most of my l...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>Overgeneralization</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I am 21 female and have not had any friends fo...</td>\n",
       "      <td>{'text': ['Now I am at university my peers aro...</td>\n",
       "      <td>Now I am at university my peers around me all ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>Mental filter</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
       "      <td>{'text': ['He claims he’s severely depressed a...</td>\n",
       "      <td>He claims he’s severely depressed and has outb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2528</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>From the U.S.: I am a 21 year old woman who ha...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I recently moved out on my ex-roommate because...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2530 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              distortion                     question  \\\n",
       "0        Personalization  What is the distorted part?   \n",
       "1               Labeling  What is the distorted part?   \n",
       "2          No Distortion  What is the distorted part?   \n",
       "3          No Distortion  What is the distorted part?   \n",
       "4        Fortune-telling  What is the distorted part?   \n",
       "...                  ...                          ...   \n",
       "2525       No Distortion  What is the distorted part?   \n",
       "2526  Overgeneralization  What is the distorted part?   \n",
       "2527       Mental filter  What is the distorted part?   \n",
       "2528       No Distortion  What is the distorted part?   \n",
       "2529       No Distortion  What is the distorted part?   \n",
       "\n",
       "                                                context  \\\n",
       "0     Hello, I have a beautiful,smart,outgoing and a...   \n",
       "1     Since I was about 16 years old I’ve had these ...   \n",
       "2      So I’ve been dating on and off this guy for a...   \n",
       "3     My parents got divorced in 2004. My mother has...   \n",
       "4     I don’t really know how to explain the situati...   \n",
       "...                                                 ...   \n",
       "2525  I’m a 21 year old female. I spent most of my l...   \n",
       "2526  I am 21 female and have not had any friends fo...   \n",
       "2527  From the U.S.: My brother is 19 years old and ...   \n",
       "2528  From the U.S.: I am a 21 year old woman who ha...   \n",
       "2529  I recently moved out on my ex-roommate because...   \n",
       "\n",
       "                                                answers  \\\n",
       "0     {'text': ['The voice are always fimilar (someo...   \n",
       "1     {'text': ['I feel trapped inside my disgusting...   \n",
       "2                  {'text': [''], 'answer_start': [-1]}   \n",
       "3                  {'text': [''], 'answer_start': [-1]}   \n",
       "4     {'text': ['I refused to go because I didn’t kn...   \n",
       "...                                                 ...   \n",
       "2525               {'text': [''], 'answer_start': [-1]}   \n",
       "2526  {'text': ['Now I am at university my peers aro...   \n",
       "2527  {'text': ['He claims he’s severely depressed a...   \n",
       "2528               {'text': [''], 'answer_start': [-1]}   \n",
       "2529               {'text': [''], 'answer_start': [-1]}   \n",
       "\n",
       "                                                predict  \n",
       "0     The voice are always fimilar (someone she know...  \n",
       "1     I feel trapped inside my disgusting self and l...  \n",
       "2                                                   NaN  \n",
       "3                                                   NaN  \n",
       "4     I refused to go because I didn’t know if it wa...  \n",
       "...                                                 ...  \n",
       "2525                                                NaN  \n",
       "2526  Now I am at university my peers around me all ...  \n",
       "2527  He claims he’s severely depressed and has outb...  \n",
       "2528                                                NaN  \n",
       "2529                                                NaN  \n",
       "\n",
       "[2530 rows x 5 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Personalization</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
       "      <td>{'text': ['The voice are always fimilar (someo...</td>\n",
       "      <td>The voice are always fimilar (someone she know...</td>\n",
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       "      <th>1</th>\n",
       "      <td>Labeling</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
       "      <td>{'text': ['I feel trapped inside my disgusting...</td>\n",
       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
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       "      <th>2</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>So I’ve been dating on and off this guy for a...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>My parents got divorced in 2004. My mother has...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I don’t really know how to explain the situati...</td>\n",
       "      <td>{'text': ['I refused to go because I didn’t kn...</td>\n",
       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I’m a 21 year old female. I spent most of my l...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>Overgeneralization</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I am 21 female and have not had any friends fo...</td>\n",
       "      <td>{'text': ['Now I am at university my peers aro...</td>\n",
       "      <td>Now I am at university my peers around me all ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>Mental filter</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
       "      <td>{'text': ['He claims he’s severely depressed a...</td>\n",
       "      <td>He claims he’s severely depressed and has outb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2528</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>From the U.S.: I am a 21 year old woman who ha...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>No Distortion</td>\n",
       "      <td>What is the distorted part?</td>\n",
       "      <td>I recently moved out on my ex-roommate because...</td>\n",
       "      <td>{'text': [''], 'answer_start': [-1]}</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2530 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              distortion  ...                                            predict\n",
       "0        Personalization  ...  The voice are always fimilar (someone she know...\n",
       "1               Labeling  ...  I feel trapped inside my disgusting self and l...\n",
       "2          No Distortion  ...                                                NaN\n",
       "3          No Distortion  ...                                                NaN\n",
       "4        Fortune-telling  ...  I refused to go because I didn’t know if it wa...\n",
       "...                  ...  ...                                                ...\n",
       "2525       No Distortion  ...                                                NaN\n",
       "2526  Overgeneralization  ...  Now I am at university my peers around me all ...\n",
       "2527       Mental filter  ...  He claims he’s severely depressed and has outb...\n",
       "2528       No Distortion  ...                                                NaN\n",
       "2529       No Distortion  ...                                                NaN\n",
       "\n",
       "[2530 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4502</td>\n",
       "      <td>So I’ve been dating on and off this guy for a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Distortion</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4503</td>\n",
       "      <td>My parents got divorced in 2004. My mother has...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No Distortion</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4504</td>\n",
       "      <td>I don’t really know how to explain the situati...</td>\n",
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       "      <td>Fortune-telling</td>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id_Number                                   Patient Question  \\\n",
       "0       4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
       "1       4501  Since I was about 16 years old I’ve had these ...   \n",
       "2       4502   So I’ve been dating on and off this guy for a...   \n",
       "3       4503  My parents got divorced in 2004. My mother has...   \n",
       "4       4504  I don’t really know how to explain the situati...   \n",
       "\n",
       "                                      Distorted part Dominant Distortion  \\\n",
       "0  The voice are always fimilar (someone she know...     Personalization   \n",
       "1  I feel trapped inside my disgusting self and l...            Labeling   \n",
       "2                                                NaN       No Distortion   \n",
       "3                                                NaN       No Distortion   \n",
       "4  I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
       "\n",
       "  Secondary Distortion (Optional)  \n",
       "0                             NaN  \n",
       "1             Emotional Reasoning  \n",
       "2                             NaN  \n",
       "3                             NaN  \n",
       "4             Emotional Reasoning  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4500</td>\n",
       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
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       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
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       "      <td>I don’t really know how to explain the situati...</td>\n",
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       "      <th>9</th>\n",
       "      <td>4510</td>\n",
       "      <td>I have been with my fiancé for two years now....</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4511</td>\n",
       "      <td>My husband and I have been married for over a ...</td>\n",
       "      <td>I thought that he displayed traits of honor, l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2553</td>\n",
       "      <td>I feel overprotective about my mother because ...</td>\n",
       "      <td>Since then, whenever my mother is out alone, I...</td>\n",
       "      <td>All-or-nothing thinking</td>\n",
       "      <td>Overgeneralization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>2557</td>\n",
       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
       "      <td>My family hate him but they didn’t met him at ...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>2559</td>\n",
       "      <td>From the U.S.: I am a junior in high school, a...</td>\n",
       "      <td>However, I am not happy, at the least only hal...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>All-or-nothing thinking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>2563</td>\n",
       "      <td>I am 21 female and have not had any friends fo...</td>\n",
       "      <td>Now I am at university my peers around me all ...</td>\n",
       "      <td>Overgeneralization</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>2564</td>\n",
       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
       "      <td>He claims he’s severely depressed and has outb...</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>Mind Reading</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1597 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_Number                                   Patient Question  \\\n",
       "0          4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
       "1          4501  Since I was about 16 years old I’ve had these ...   \n",
       "4          4504  I don’t really know how to explain the situati...   \n",
       "9          4510   I have been with my fiancé for two years now....   \n",
       "10         4511  My husband and I have been married for over a ...   \n",
       "...         ...                                                ...   \n",
       "2519       2553  I feel overprotective about my mother because ...   \n",
       "2522       2557  From Lebanon: I am dealing with a big problem!...   \n",
       "2523       2559  From the U.S.: I am a junior in high school, a...   \n",
       "2526       2563  I am 21 female and have not had any friends fo...   \n",
       "2527       2564  From the U.S.: My brother is 19 years old and ...   \n",
       "\n",
       "                                         Distorted part  \\\n",
       "0     The voice are always fimilar (someone she know...   \n",
       "1     I feel trapped inside my disgusting self and l...   \n",
       "4     I refused to go because I didn’t know if it wa...   \n",
       "9     I felt like the response was totally irrationa...   \n",
       "10    I thought that he displayed traits of honor, l...   \n",
       "...                                                 ...   \n",
       "2519  Since then, whenever my mother is out alone, I...   \n",
       "2522  My family hate him but they didn’t met him at ...   \n",
       "2523  However, I am not happy, at the least only hal...   \n",
       "2526  Now I am at university my peers around me all ...   \n",
       "2527  He claims he’s severely depressed and has outb...   \n",
       "\n",
       "          Dominant Distortion Secondary Distortion (Optional)  \n",
       "0             Personalization                             NaN  \n",
       "1                    Labeling             Emotional Reasoning  \n",
       "4             Fortune-telling             Emotional Reasoning  \n",
       "9               Magnification                             NaN  \n",
       "10                   Labeling                             NaN  \n",
       "...                       ...                             ...  \n",
       "2519  All-or-nothing thinking              Overgeneralization  \n",
       "2522            Magnification                             NaN  \n",
       "2523                 Labeling         All-or-nothing thinking  \n",
       "2526       Overgeneralization                             NaN  \n",
       "2527            Mental filter                    Mind Reading  \n",
       "\n",
       "[1597 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df = df[df['Dominant Distortion'] != 'No Distortion']\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
       "      <td>The voice are always familiar (someone she kno...</td>\n",
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       "      <td>The voice are always familiar (someone she kno...</td>\n",
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       "      <td>Emotional Reasoning</td>\n",
       "      <td>I refused to go because I didnt know if it was...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4510</td>\n",
       "      <td>I have been with my fiancé for two years now. ...</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4511</td>\n",
       "      <td>My husband and I have been married for over a ...</td>\n",
       "      <td>I thought that he displayed traits of honor, l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>While we were dating, I thought that he displa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id_Number                                   Patient Question  \\\n",
       "0       4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
       "1       4501  Since I was about 16 years old I’ve had these ...   \n",
       "2       4504  I don’t really know how to explain the situati...   \n",
       "3       4510  I have been with my fiancé for two years now. ...   \n",
       "4       4511  My husband and I have been married for over a ...   \n",
       "\n",
       "                                      Distorted part Dominant Distortion  \\\n",
       "0  The voice are always familiar (someone she kno...     Personalization   \n",
       "1  I feel trapped inside my disgusting self and l...            Labeling   \n",
       "2  I refused to go because I didn’t know if it wa...     Fortune-telling   \n",
       "3  I felt like the response was totally irrationa...       Magnification   \n",
       "4  I thought that he displayed traits of honor, l...            Labeling   \n",
       "\n",
       "  Secondary Distortion (Optional)  \\\n",
       "0                             NaN   \n",
       "1             Emotional Reasoning   \n",
       "2             Emotional Reasoning   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "                            Predicted_Distorted_Part  \n",
       "0  The voice are always familiar (someone she kno...  \n",
       "1  I feel trapped inside my disgusting self and l...  \n",
       "2  I refused to go because I didnt know if it was...  \n",
       "3  I felt like the response was totally irrationa...  \n",
       "4  While we were dating, I thought that he displa...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "# Tạo LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# Mã hóa cột 'Dominant Distortion', 'No Distortion' thành 0, các loại còn lại thành 1\n",
    "df['Dominant Distortion Encoded'] = encoder.fit_transform(df['Dominant Distortion'].apply(lambda x: 0 if x == 'No Distortion' else 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4525</td>\n",
       "      <td>I was born with Attention Deficit Hyperactivit...</td>\n",
       "      <td>I was a very difficult child to raise. There w...</td>\n",
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       "      <th>1</th>\n",
       "      <td>4523</td>\n",
       "      <td>I’ve been having problems for a while. I am hi...</td>\n",
       "      <td>I am highly disorganized, I’m concerned with b...</td>\n",
       "      <td>Labeling</td>\n",
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       "      <td>I’m concerned with being diagnosed with schizo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4575</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4540</td>\n",
       "      <td>Plz help. Ok so some times I hear people whist...</td>\n",
       "      <td>I always feel like people talk about me and ha...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I always feel like people talk about me</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4559</td>\n",
       "      <td>Although im sure I know the answer to this, I ...</td>\n",
       "      <td>im sure she has a disorder of some sort….she l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>she lies constantly, tells the same story 3 ti...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id_Number                                   Patient Question  \\\n",
       "0       4525  I was born with Attention Deficit Hyperactivit...   \n",
       "1       4523  I’ve been having problems for a while. I am hi...   \n",
       "2       4575  I am incredibly jealous in my current relation...   \n",
       "3       4540  Plz help. Ok so some times I hear people whist...   \n",
       "4       4559  Although im sure I know the answer to this, I ...   \n",
       "\n",
       "                                      Distorted part Dominant Distortion  \\\n",
       "0  I was a very difficult child to raise. There w...  Overgeneralization   \n",
       "1  I am highly disorganized, I’m concerned with b...            Labeling   \n",
       "2  I am incredibly jealous in my current relation...            Labeling   \n",
       "3  I always feel like people talk about me and ha...        Mind Reading   \n",
       "4  im sure she has a disorder of some sort….she l...            Labeling   \n",
       "\n",
       "  Secondary Distortion (Optional)  \\\n",
       "0                 Personalization   \n",
       "1                   Mental filter   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                    Mind Reading   \n",
       "\n",
       "                                        gen_response  \\\n",
       "0  I felt no love whatsoever and would not until ...   \n",
       "1  I’m concerned with being diagnosed with schizo...   \n",
       "2  I am incredibly jealous in my current relation...   \n",
       "3            I always feel like people talk about me   \n",
       "4  she lies constantly, tells the same story 3 ti...   \n",
       "\n",
       "   Dominant Distortion Encoded  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Id_Number</th>\n",
       "      <th>Patient Question</th>\n",
       "      <th>Distorted part</th>\n",
       "      <th>Dominant Distortion</th>\n",
       "      <th>Secondary Distortion (Optional)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2530.000000</td>\n",
       "      <td>2530</td>\n",
       "      <td>1597</td>\n",
       "      <td>2530</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2529</td>\n",
       "      <td>1590</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>I grew up in a upper middle class family. I wa...</td>\n",
       "      <td>I feel as if moving on to higher education wil...</td>\n",
       "      <td>No Distortion</td>\n",
       "      <td>Fortune-telling</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>933</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1496.877075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1153.563554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>632.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1281.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4701.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id_Number                                   Patient Question  \\\n",
       "count   2530.000000                                               2530   \n",
       "unique          NaN                                               2529   \n",
       "top             NaN  I grew up in a upper middle class family. I wa...   \n",
       "freq            NaN                                                  2   \n",
       "mean    1496.877075                                                NaN   \n",
       "std     1153.563554                                                NaN   \n",
       "min        0.000000                                                NaN   \n",
       "25%      632.250000                                                NaN   \n",
       "50%     1281.500000                                                NaN   \n",
       "75%     2016.750000                                                NaN   \n",
       "max     4701.000000                                                NaN   \n",
       "\n",
       "                                           Distorted part Dominant Distortion  \\\n",
       "count                                                1597                2530   \n",
       "unique                                               1590                  11   \n",
       "top     I feel as if moving on to higher education wil...       No Distortion   \n",
       "freq                                                    2                 933   \n",
       "mean                                                  NaN                 NaN   \n",
       "std                                                   NaN                 NaN   \n",
       "min                                                   NaN                 NaN   \n",
       "25%                                                   NaN                 NaN   \n",
       "50%                                                   NaN                 NaN   \n",
       "75%                                                   NaN                 NaN   \n",
       "max                                                   NaN                 NaN   \n",
       "\n",
       "       Secondary Distortion (Optional)  \n",
       "count                              416  \n",
       "unique                              10  \n",
       "top                    Fortune-telling  \n",
       "freq                                67  \n",
       "mean                               NaN  \n",
       "std                                NaN  \n",
       "min                                NaN  \n",
       "25%                                NaN  \n",
       "50%                                NaN  \n",
       "75%                                NaN  \n",
       "max                                NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2530 entries, 0 to 2529\n",
      "Data columns (total 5 columns):\n",
      " #   Column                           Non-Null Count  Dtype \n",
      "---  ------                           --------------  ----- \n",
      " 0   Id_Number                        2530 non-null   int64 \n",
      " 1   Patient Question                 2530 non-null   object\n",
      " 2   Distorted part                   1597 non-null   object\n",
      " 3   Dominant Distortion              2530 non-null   object\n",
      " 4   Secondary Distortion (Optional)  416 non-null    object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 99.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lengths = df['Patient Question'].apply(lambda x: len(x.split(' ')))\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.hist(lengths, bins=30, edgecolor='k', alpha=0.7)\n",
    "plt.title('Distribution of sentence lengths')\n",
    "plt.xlabel('Sentence Length')\n",
    "plt.ylabel('Number of Sentences')\n",
    "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def create_corpus():\n",
    "    corpus = []\n",
    "    for x in df['Patient Question'].str.split():\n",
    "        for i in x:\n",
    "            corpus.append(i)\n",
    "    return corpus\n",
    "\n",
    "corpus = create_corpus()\n",
    "\n",
    "counter = Counter(corpus)\n",
    "most_common_words = counter.most_common(40)\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "for word, count in most_common_words:\n",
    "    x.append(word)\n",
    "    y.append(count)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x=y, y=x, palette='viridis')\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Words')\n",
    "plt.title('Top 40 Most Common Words in content')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/luftmensch/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/luftmensch/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /home/luftmensch/nltk_data...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i luv programmng! u r gr8. i can't believe it's 123 times better!\n"
     ]
    }
   ],
   "source": [
    "# !pip install spellchecker\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "# from spellchecker import SpellChecker\n",
    "\n",
    "# Tải các tài nguyên cần thiết\n",
    "nltk.download(\"punkt\")\n",
    "nltk.download(\"stopwords\")\n",
    "nltk.download(\"wordnet\")\n",
    "\n",
    "# Khởi tạo các công cụ\n",
    "stemmer = PorterStemmer()\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "# spell = SpellChecker()\n",
    "stop_words = set(stopwords.words(\"english\"))\n",
    "\n",
    "def preprocess_text(text, use_stemming=False, use_lemmatization=True, correct_spelling=False):\n",
    "    text = text.lower()\n",
    "    \n",
    "    # text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
    "    \n",
    "    return text#\n",
    "\n",
    "# Ví dụ sử dụng\n",
    "text = \"I luv programmng! U r gr8. I can't believe it's 123 times better!\"\n",
    "processed_text = preprocess_text(text)\n",
    "print(processed_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Id_Number</th>\n",
       "      <th>Patient Question</th>\n",
       "      <th>Distorted part</th>\n",
       "      <th>Dominant Distortion</th>\n",
       "      <th>Secondary Distortion (Optional)</th>\n",
       "      <th>gen_response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4525</td>\n",
       "      <td>I was born with Attention Deficit Hyperactivit...</td>\n",
       "      <td>I was a very difficult child to raise. There w...</td>\n",
       "      <td>Overgeneralization</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>I felt no love whatsoever and would not until ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4523</td>\n",
       "      <td>I’ve been having problems for a while. I am hi...</td>\n",
       "      <td>I am highly disorganized, I’m concerned with b...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>I’m concerned with being diagnosed with schizo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4575</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I am incredibly jealous in my current relation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4540</td>\n",
       "      <td>Plz help. Ok so some times I hear people whist...</td>\n",
       "      <td>I always feel like people talk about me and ha...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I always feel like people talk about me</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4559</td>\n",
       "      <td>Although im sure I know the answer to this, I ...</td>\n",
       "      <td>im sure she has a disorder of some sort….she l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>she lies constantly, tells the same story 3 ti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>2536</td>\n",
       "      <td>I met my bipolar boyfriend 6 years ago…we fell...</td>\n",
       "      <td>He has been experiencing some mood swings duri...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "      <td>he broke up with me few days ago saying we don...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>2557</td>\n",
       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
       "      <td>My family hate him but they didn’t met him at ...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I tried to talk to them but they think that I’...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>2519</td>\n",
       "      <td>Back in 6th grade, my best friend and I (both ...</td>\n",
       "      <td>But I want this weird arousal to go away, and ...</td>\n",
       "      <td>Should statements</td>\n",
       "      <td>NaN</td>\n",
       "      <td>my future looks pretty good if it wasn’t for t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>2549</td>\n",
       "      <td>From the U.S.: I have had anxiety almost all o...</td>\n",
       "      <td>I have had anxiety almost all of my life but l...</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I am to the point where I am constantly thinki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>2351</td>\n",
       "      <td>I rented out a beautiful flat, but the moment ...</td>\n",
       "      <td>I rented out a beautiful flat, but the moment ...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>she will buy flat in the place where I am curr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1597 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_Number                                   Patient Question  \\\n",
       "0          4525  I was born with Attention Deficit Hyperactivit...   \n",
       "1          4523  I’ve been having problems for a while. I am hi...   \n",
       "2          4575  I am incredibly jealous in my current relation...   \n",
       "3          4540  Plz help. Ok so some times I hear people whist...   \n",
       "4          4559  Although im sure I know the answer to this, I ...   \n",
       "...         ...                                                ...   \n",
       "1592       2536  I met my bipolar boyfriend 6 years ago…we fell...   \n",
       "1593       2557  From Lebanon: I am dealing with a big problem!...   \n",
       "1594       2519  Back in 6th grade, my best friend and I (both ...   \n",
       "1595       2549  From the U.S.: I have had anxiety almost all o...   \n",
       "1596       2351  I rented out a beautiful flat, but the moment ...   \n",
       "\n",
       "                                         Distorted part Dominant Distortion  \\\n",
       "0     I was a very difficult child to raise. There w...  Overgeneralization   \n",
       "1     I am highly disorganized, I’m concerned with b...            Labeling   \n",
       "2     I am incredibly jealous in my current relation...            Labeling   \n",
       "3     I always feel like people talk about me and ha...        Mind Reading   \n",
       "4     im sure she has a disorder of some sort….she l...            Labeling   \n",
       "...                                                 ...                 ...   \n",
       "1592  He has been experiencing some mood swings duri...       Magnification   \n",
       "1593  My family hate him but they didn’t met him at ...       Magnification   \n",
       "1594  But I want this weird arousal to go away, and ...   Should statements   \n",
       "1595  I have had anxiety almost all of my life but l...       Mental filter   \n",
       "1596  I rented out a beautiful flat, but the moment ...        Mind Reading   \n",
       "\n",
       "     Secondary Distortion (Optional)  \\\n",
       "0                    Personalization   \n",
       "1                      Mental filter   \n",
       "2                                NaN   \n",
       "3                                NaN   \n",
       "4                       Mind Reading   \n",
       "...                              ...   \n",
       "1592                             NaN   \n",
       "1593                             NaN   \n",
       "1594                             NaN   \n",
       "1595                             NaN   \n",
       "1596                             NaN   \n",
       "\n",
       "                                           gen_response  \n",
       "0     I felt no love whatsoever and would not until ...  \n",
       "1     I’m concerned with being diagnosed with schizo...  \n",
       "2     I am incredibly jealous in my current relation...  \n",
       "3               I always feel like people talk about me  \n",
       "4     she lies constantly, tells the same story 3 ti...  \n",
       "...                                                 ...  \n",
       "1592  he broke up with me few days ago saying we don...  \n",
       "1593  I tried to talk to them but they think that I’...  \n",
       "1594  my future looks pretty good if it wasn’t for t...  \n",
       "1595  I am to the point where I am constantly thinki...  \n",
       "1596  she will buy flat in the place where I am curr...  \n",
       "\n",
       "[1597 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'filtered_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m filtered_df = \u001b[43mfiltered_df\u001b[49m.dropna()\n",
      "\u001b[31mNameError\u001b[39m: name 'filtered_df' is not defined"
     ]
    }
   ],
   "source": [
    "filtered_df = filtered_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Id_Number</th>\n",
       "      <th>Patient Question</th>\n",
       "      <th>Distorted part</th>\n",
       "      <th>Dominant Distortion</th>\n",
       "      <th>Secondary Distortion (Optional)</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500</td>\n",
       "      <td>Hello, I have a beautiful,smart,outgoing and a...</td>\n",
       "      <td>The voice are always fimilar (someone she know...</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4501</td>\n",
       "      <td>Since I was about 16 years old I’ve had these ...</td>\n",
       "      <td>I feel trapped inside my disgusting self and l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Emotional Reasoning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4504</td>\n",
       "      <td>I don’t really know how to explain the situati...</td>\n",
       "      <td>I refused to go because I didn’t know if it wa...</td>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>Emotional Reasoning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4510</td>\n",
       "      <td>I have been with my fiancé for two years now....</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4511</td>\n",
       "      <td>My husband and I have been married for over a ...</td>\n",
       "      <td>I thought that he displayed traits of honor, l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2553</td>\n",
       "      <td>I feel overprotective about my mother because ...</td>\n",
       "      <td>Since then, whenever my mother is out alone, I...</td>\n",
       "      <td>All-or-nothing thinking</td>\n",
       "      <td>Overgeneralization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>2557</td>\n",
       "      <td>From Lebanon: I am dealing with a big problem!...</td>\n",
       "      <td>My family hate him but they didn’t met him at ...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>2559</td>\n",
       "      <td>From the U.S.: I am a junior in high school, a...</td>\n",
       "      <td>However, I am not happy, at the least only hal...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>All-or-nothing thinking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>2563</td>\n",
       "      <td>I am 21 female and have not had any friends fo...</td>\n",
       "      <td>Now I am at university my peers around me all ...</td>\n",
       "      <td>Overgeneralization</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>2564</td>\n",
       "      <td>From the U.S.: My brother is 19 years old and ...</td>\n",
       "      <td>He claims he’s severely depressed and has outb...</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>Mind Reading</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1597 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_Number                                   Patient Question  \\\n",
       "0          4500  Hello, I have a beautiful,smart,outgoing and a...   \n",
       "1          4501  Since I was about 16 years old I’ve had these ...   \n",
       "4          4504  I don’t really know how to explain the situati...   \n",
       "9          4510   I have been with my fiancé for two years now....   \n",
       "10         4511  My husband and I have been married for over a ...   \n",
       "...         ...                                                ...   \n",
       "2519       2553  I feel overprotective about my mother because ...   \n",
       "2522       2557  From Lebanon: I am dealing with a big problem!...   \n",
       "2523       2559  From the U.S.: I am a junior in high school, a...   \n",
       "2526       2563  I am 21 female and have not had any friends fo...   \n",
       "2527       2564  From the U.S.: My brother is 19 years old and ...   \n",
       "\n",
       "                                         Distorted part  \\\n",
       "0     The voice are always fimilar (someone she know...   \n",
       "1     I feel trapped inside my disgusting self and l...   \n",
       "4     I refused to go because I didn’t know if it wa...   \n",
       "9     I felt like the response was totally irrationa...   \n",
       "10    I thought that he displayed traits of honor, l...   \n",
       "...                                                 ...   \n",
       "2519  Since then, whenever my mother is out alone, I...   \n",
       "2522  My family hate him but they didn’t met him at ...   \n",
       "2523  However, I am not happy, at the least only hal...   \n",
       "2526  Now I am at university my peers around me all ...   \n",
       "2527  He claims he’s severely depressed and has outb...   \n",
       "\n",
       "          Dominant Distortion Secondary Distortion (Optional)  \n",
       "0             Personalization                             NaN  \n",
       "1                    Labeling             Emotional Reasoning  \n",
       "4             Fortune-telling             Emotional Reasoning  \n",
       "9               Magnification                             NaN  \n",
       "10                   Labeling                             NaN  \n",
       "...                       ...                             ...  \n",
       "2519  All-or-nothing thinking              Overgeneralization  \n",
       "2522            Magnification                             NaN  \n",
       "2523                 Labeling         All-or-nothing thinking  \n",
       "2526       Overgeneralization                             NaN  \n",
       "2527            Mental filter                    Mind Reading  \n",
       "\n",
       "[1597 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cleaned'] = df['gen_response'].map(lambda text: preprocess_text(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Id_Number</th>\n",
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       "      <th>cleaned</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4511</td>\n",
       "      <td>My husband and I have been married for over a ...</td>\n",
       "      <td>I thought that he displayed traits of honor, l...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no distortion</td>\n",
       "      <td>no distortion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4523</td>\n",
       "      <td>I’ve been having problems for a while. I am hi...</td>\n",
       "      <td>I am highly disorganized, I’m concerned with b...</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>I keep burying the issue in fear that treatmen...</td>\n",
       "      <td>i keep burying the issue in fear that treatmen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4512</td>\n",
       "      <td>I don’t know how to recover from my husband’s ...</td>\n",
       "      <td>I attributed his behavior to stress – he owned...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I don’t know how to recover from my husband’s ...</td>\n",
       "      <td>i don’t know how to recover from my husband’s ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4521</td>\n",
       "      <td>I have always suffered from performance anxiet...</td>\n",
       "      <td>During this time I was recruited to many great...</td>\n",
       "      <td>Fortune-telling</td>\n",
       "      <td>NaN</td>\n",
       "      <td>but was scared to take them and instead starte...</td>\n",
       "      <td>but was scared to take them and instead starte...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4510</td>\n",
       "      <td>I have been with my fiancé for two years now....</td>\n",
       "      <td>I felt like the response was totally irrationa...</td>\n",
       "      <td>Magnification</td>\n",
       "      <td>NaN</td>\n",
       "      <td>to have my future mother in-law blatantly not ...</td>\n",
       "      <td>to have my future mother in-law blatantly not ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1592</th>\n",
       "      <td>2513</td>\n",
       "      <td>My spouse and I have been living apart since d...</td>\n",
       "      <td>I don’t know what to do, how to start dealing ...</td>\n",
       "      <td>Personalization</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I’m dealing with his addiction, childish fear ...</td>\n",
       "      <td>i’m dealing with his addiction, childish fear ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593</th>\n",
       "      <td>2530</td>\n",
       "      <td>I’ve been dealing with this problem on and off...</td>\n",
       "      <td>And at times my derealization gets so bad I fe...</td>\n",
       "      <td>Mental filter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I’m finally in that “it’s hopeless” stage.</td>\n",
       "      <td>i’m finally in that “it’s hopeless” stage.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1594</th>\n",
       "      <td>2022</td>\n",
       "      <td>Hello. I am a 15 year old girl. Since I was a ...</td>\n",
       "      <td>I was terrified of what people thought about m...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>I was terrified of what people thought about m...</td>\n",
       "      <td>i was terrified of what people thought about m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>2532</td>\n",
       "      <td>I experienced three consecutive traumas during...</td>\n",
       "      <td>I’m not depressed, I am very happy in general,...</td>\n",
       "      <td>Should statements</td>\n",
       "      <td>Labeling</td>\n",
       "      <td>I feel I am stuck with the emotional maturity ...</td>\n",
       "      <td>i feel i am stuck with the emotional maturity ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>2531</td>\n",
       "      <td>I am trying to figure out if certain problem I...</td>\n",
       "      <td>I thought I could hear the whole bus talking a...</td>\n",
       "      <td>Mind Reading</td>\n",
       "      <td>Emotional Reasoning</td>\n",
       "      <td>I thought I could hear the whole bus talking a...</td>\n",
       "      <td>i thought i could hear the whole bus talking a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1597 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_Number                                   Patient Question  \\\n",
       "0          4511  My husband and I have been married for over a ...   \n",
       "1          4523  I’ve been having problems for a while. I am hi...   \n",
       "2          4512  I don’t know how to recover from my husband’s ...   \n",
       "3          4521  I have always suffered from performance anxiet...   \n",
       "4          4510   I have been with my fiancé for two years now....   \n",
       "...         ...                                                ...   \n",
       "1592       2513  My spouse and I have been living apart since d...   \n",
       "1593       2530  I’ve been dealing with this problem on and off...   \n",
       "1594       2022  Hello. I am a 15 year old girl. Since I was a ...   \n",
       "1595       2532  I experienced three consecutive traumas during...   \n",
       "1596       2531  I am trying to figure out if certain problem I...   \n",
       "\n",
       "                                         Distorted part Dominant Distortion  \\\n",
       "0     I thought that he displayed traits of honor, l...            Labeling   \n",
       "1     I am highly disorganized, I’m concerned with b...            Labeling   \n",
       "2     I attributed his behavior to stress – he owned...        Mind Reading   \n",
       "3     During this time I was recruited to many great...     Fortune-telling   \n",
       "4     I felt like the response was totally irrationa...       Magnification   \n",
       "...                                                 ...                 ...   \n",
       "1592  I don’t know what to do, how to start dealing ...     Personalization   \n",
       "1593  And at times my derealization gets so bad I fe...       Mental filter   \n",
       "1594  I was terrified of what people thought about m...        Mind Reading   \n",
       "1595  I’m not depressed, I am very happy in general,...   Should statements   \n",
       "1596  I thought I could hear the whole bus talking a...        Mind Reading   \n",
       "\n",
       "     Secondary Distortion (Optional)  \\\n",
       "0                                NaN   \n",
       "1                      Mental filter   \n",
       "2                                NaN   \n",
       "3                                NaN   \n",
       "4                                NaN   \n",
       "...                              ...   \n",
       "1592                             NaN   \n",
       "1593                             NaN   \n",
       "1594                             NaN   \n",
       "1595                        Labeling   \n",
       "1596             Emotional Reasoning   \n",
       "\n",
       "                                           gen_response  \\\n",
       "0                                         no distortion   \n",
       "1     I keep burying the issue in fear that treatmen...   \n",
       "2     I don’t know how to recover from my husband’s ...   \n",
       "3     but was scared to take them and instead starte...   \n",
       "4     to have my future mother in-law blatantly not ...   \n",
       "...                                                 ...   \n",
       "1592  I’m dealing with his addiction, childish fear ...   \n",
       "1593         I’m finally in that “it’s hopeless” stage.   \n",
       "1594  I was terrified of what people thought about m...   \n",
       "1595  I feel I am stuck with the emotional maturity ...   \n",
       "1596  I thought I could hear the whole bus talking a...   \n",
       "\n",
       "                                                cleaned  \n",
       "0                                         no distortion  \n",
       "1     i keep burying the issue in fear that treatmen...  \n",
       "2     i don’t know how to recover from my husband’s ...  \n",
       "3     but was scared to take them and instead starte...  \n",
       "4     to have my future mother in-law blatantly not ...  \n",
       "...                                                 ...  \n",
       "1592  i’m dealing with his addiction, childish fear ...  \n",
       "1593         i’m finally in that “it’s hopeless” stage.  \n",
       "1594  i was terrified of what people thought about m...  \n",
       "1595  i feel i am stuck with the emotional maturity ...  \n",
       "1596  i thought i could hear the whole bus talking a...  \n",
       "\n",
       "[1597 rows x 7 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = df[df['Dominant Distortion'] != 'No Distortion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df['cleaned'].values,\n",
    "                                                    df['Dominant Distortion'].values,\n",
    "                                                    test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "import pandas as pd\n",
    "\n",
    "df_train = pd.DataFrame({'text': X_train, 'label': y_train})\n",
    "\n",
    "max_count = df_train['label'].value_counts().max()\n",
    "\n",
    "resampled_dfs = []\n",
    "\n",
    "for label, group in df_train.groupby('label'):\n",
    "    resampled_group = resample(\n",
    "        group,\n",
    "        replace=True,\n",
    "        n_samples=max_count,\n",
    "        random_state=42\n",
    "    )\n",
    "    resampled_dfs.append(resampled_group)\n",
    "\n",
    "df_train_balanced = pd.concat(resampled_dfs).sample(frac=1, random_state=42)\n",
    "\n",
    "X_train_balanced = df_train_balanced['text'].tolist()\n",
    "y_train_balanced = df_train_balanced['label'].tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[13]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mdf_train\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mlabel\u001b[39m\u001b[33m'\u001b[39m].value_counts()\n",
      "\u001b[31mNameError\u001b[39m: name 'df_train' is not defined"
     ]
    }
   ],
   "source": [
    "df_train['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "y_train_encoded = encoder.fit_transform(y_train)\n",
    "y_test_encoded = encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# from imblearn.over_sampling import SMOTE\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)\n",
    "\n",
    "# X_train_dense = X_train_tfidf.toarray()\n",
    "# X_test_dense = X_test_tfidf.toarray()\n",
    "\n",
    "# scaler = StandardScaler()\n",
    "# X_train_scaled = scaler.fit_transform(X_train_dense)\n",
    "# X_test_scaled = scaler.transform(X_test_dense)\n",
    "\n",
    "# smote = SMOTE(random_state=42)\n",
    "# X_resampled, y_resampled = smote.fit_resample(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "bow_vectorizer = CountVectorizer()\n",
    "X_train_bow = bow_vectorizer.fit_transform(X_train)\n",
    "X_test_bow = bow_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1277, 2883)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_bow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "# from xgboost import XGBClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>MLPClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('hidden_layer_sizes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">hidden_layer_sizes&nbsp;</td>\n",
       "            <td class=\"value\">(100,)</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('activation',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">activation&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;relu&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">solver&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;adam&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('batch_size',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">batch_size&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;constant&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('learning_rate_init',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">learning_rate_init&nbsp;</td>\n",
       "            <td class=\"value\">0.001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('power_t',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">power_t&nbsp;</td>\n",
       "            <td class=\"value\">0.5</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">200</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('shuffle',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">shuffle&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">momentum&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('nesterovs_momentum',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">nesterovs_momentum&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('early_stopping',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">early_stopping&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('validation_fraction',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">validation_fraction&nbsp;</td>\n",
       "            <td class=\"value\">0.1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_1',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_1&nbsp;</td>\n",
       "            <td class=\"value\">0.9</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('beta_2',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">beta_2&nbsp;</td>\n",
       "            <td class=\"value\">0.999</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('epsilon',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">epsilon&nbsp;</td>\n",
       "            <td class=\"value\">1e-08</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_iter_no_change',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_iter_no_change&nbsp;</td>\n",
       "            <td class=\"value\">10</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_fun',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_fun&nbsp;</td>\n",
       "            <td class=\"value\">15000</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
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       "document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {\n",
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       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "MLPClassifier()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MLPClassifier()\n",
    "\n",
    "model.fit(X_train_tfidf,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, classification_report\n",
    "def calculate_results(y_true, y_pred, y_pred_proba=None):\n",
    "    \"\"\"\n",
    "    Tính toán các chỉ số đánh giá, bao gồm accuracy, precision, recall, f1 và roc-auc.\n",
    "    Nếu `y_pred_proba` được cung cấp, tính thêm ROC-AUC cho từng nhãn trong bài toán đa nhãn.\n",
    "    \"\"\"\n",
    "    results = {\n",
    "        \"accuracy\": accuracy_score(y_true, y_pred) * 100,\n",
    "        \"precision\": precision_score(y_true, y_pred, average='weighted'),\n",
    "        \"recall\": recall_score(y_true, y_pred, average='weighted'),\n",
    "        \"f1\": f1_score(y_true, y_pred, average='weighted')\n",
    "    }\n",
    "    \n",
    "    # if y_pred_proba is not None:\n",
    "    #     # Chuyển đổi nhãn thành dạng nhị phân\n",
    "    #     y_true_bin = label_binarize(y_true, classes=np.unique(y_true))\n",
    "        \n",
    "    #     # Tính ROC-AUC cho từng nhãn (macro-average)\n",
    "    #     roc_auc_macro = []\n",
    "    #     for i in range(y_true_bin.shape[1]):\n",
    "    #         roc_auc = roc_auc_score(y_true_bin[:, i], y_pred_proba[:, i])\n",
    "    #         roc_auc_macro.append(roc_auc)\n",
    "        \n",
    "    #     # Tính ROC-AUC macro trung bình\n",
    "    #     results[\"roc_auc_macro\"] = np.mean(roc_auc_macro)\n",
    "        \n",
    "    #     # Tính ROC-AUC micro-average\n",
    "    #     roc_auc_micro = roc_auc_score(y_true_bin, y_pred_proba, average=\"micro\")\n",
    "    #     results[\"roc_auc_micro\"] = roc_auc_micro\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'accuracy': 16.25, 'precision': 0.16483342705340182, 'recall': 0.1625, 'f1': 0.15820475546466675}\n",
      "                         precision    recall  f1-score   support\n",
      "\n",
      "All-or-nothing thinking       0.13      0.10      0.11        20\n",
      "    Emotional Reasoning       0.10      0.10      0.10        21\n",
      "        Fortune-telling       0.17      0.11      0.13        37\n",
      "               Labeling       0.09      0.10      0.09        30\n",
      "          Magnification       0.10      0.11      0.11        35\n",
      "          Mental filter       0.12      0.19      0.15        16\n",
      "           Mind Reading       0.21      0.33      0.26        45\n",
      "     Overgeneralization       0.21      0.22      0.21        51\n",
      "        Personalization       0.24      0.15      0.18        41\n",
      "      Should statements       0.14      0.08      0.11        24\n",
      "\n",
      "               accuracy                           0.16       320\n",
      "              macro avg       0.15      0.15      0.14       320\n",
      "           weighted avg       0.16      0.16      0.16       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_tfidf)\n",
    "print(calculate_results(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "Best parameters from RandomizedSearch: {'subsample': 0.8, 'n_estimators': 200, 'min_child_weight': 5, 'max_depth': 7, 'learning_rate': 0.01, 'gamma': 0.2, 'colsample_bytree': 0.8}\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'My Dad and I usually get on quite well. We have a lot in common, but whenever he cooks or cleans he doesn’t do the job properly. For instance, he won’t wash his hands before cooking or clean the sides down properly. I’m generally not that bothered by germs but everyone knows the basics of hygiene in the kitchen. I try to tell him that he should wash his hands, but he just get annoyed, and makes some excuse like he’s ‘thinking about other things’. 9 times out of 10 he doesn’t do the washing up properly either eg. there will still be oil on the frying pan. So when I try to do it or I tell him that it’s unclean he just gets irritated and says “your being rather annoying now” when I’m just trying to makes sure my sister and I get a healthy meal in a clean kitchen. My mum usually makes sure that the kitchen and everything else is clean but she’s away seeing her parents because my grandma’s going into surgery soon. Now my dad’s left with us he just seems incapable of doing even basic chores. I can’t keep pestering him but he doesn’t learn. It could be that he listens to my mum or that she does it usually and dad hasn’t had to do the washing up more than once every few days. I think it’s a bit of both.'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[19], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m best_model_random \u001b[38;5;241m=\u001b[39m random_search\u001b[38;5;241m.\u001b[39mbest_estimator_\n\u001b[0;32m     28\u001b[0m \u001b[38;5;66;03m# Dự đoán và đánh giá mô hình tốt nhất\u001b[39;00m\n\u001b[1;32m---> 29\u001b[0m y_pred_random \u001b[38;5;241m=\u001b[39m \u001b[43mbest_model_random\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     30\u001b[0m accuracy_random \u001b[38;5;241m=\u001b[39m accuracy_score(y_test, y_pred_random)\n\u001b[0;32m     31\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAccuracy with best parameters from RandomizedSearch: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy_random\u001b[38;5;250m \u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\core.py:729\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    728\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\sklearn.py:1718\u001b[0m, in \u001b[0;36mXGBClassifier.predict\u001b[1;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1707\u001b[0m \u001b[38;5;129m@_deprecate_positional_args\u001b[39m\n\u001b[0;32m   1708\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\n\u001b[0;32m   1709\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1715\u001b[0m     iteration_range: Optional[IterationRange] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1716\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ArrayLike:\n\u001b[0;32m   1717\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(verbosity\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbosity):\n\u001b[1;32m-> 1718\u001b[0m         class_probs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1719\u001b[0m \u001b[43m            \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1720\u001b[0m \u001b[43m            \u001b[49m\u001b[43moutput_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1721\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalidate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1722\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1723\u001b[0m \u001b[43m            \u001b[49m\u001b[43miteration_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miteration_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1724\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1725\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m output_margin:\n\u001b[0;32m   1726\u001b[0m             \u001b[38;5;66;03m# If output_margin is active, simply return the scores\u001b[39;00m\n\u001b[0;32m   1727\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m class_probs\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\core.py:729\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    728\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\sklearn.py:1327\u001b[0m, in \u001b[0;36mXGBModel.predict\u001b[1;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[0;32m   1325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_can_use_inplace_predict():\n\u001b[0;32m   1326\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1327\u001b[0m         predts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_booster\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minplace_predict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1328\u001b[0m \u001b[43m            \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1329\u001b[0m \u001b[43m            \u001b[49m\u001b[43miteration_range\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miteration_range\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1330\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpredict_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmargin\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43moutput_margin\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvalue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1331\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmissing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmissing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1332\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbase_margin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbase_margin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1333\u001b[0m \u001b[43m            \u001b[49m\u001b[43mvalidate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1334\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1335\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m _is_cupy_alike(predts):\n\u001b[0;32m   1336\u001b[0m             cp \u001b[38;5;241m=\u001b[39m import_cupy()\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\core.py:729\u001b[0m, in \u001b[0;36mrequire_keyword_args.<locals>.throw_if.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args):\n\u001b[0;32m    728\u001b[0m     kwargs[k] \u001b[38;5;241m=\u001b[39m arg\n\u001b[1;32m--> 729\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\core.py:2685\u001b[0m, in \u001b[0;36mBooster.inplace_predict\u001b[1;34m(self, data, iteration_range, predict_type, missing, validate_features, base_margin, strict_shape)\u001b[0m\n\u001b[0;32m   2682\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_np_array_like(data):\n\u001b[0;32m   2683\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _ensure_np_dtype\n\u001b[1;32m-> 2685\u001b[0m     data, _ \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_np_dtype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2686\u001b[0m     _check_call(\n\u001b[0;32m   2687\u001b[0m         _LIB\u001b[38;5;241m.\u001b[39mXGBoosterPredictFromDense(\n\u001b[0;32m   2688\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2695\u001b[0m         )\n\u001b[0;32m   2696\u001b[0m     )\n\u001b[0;32m   2697\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _prediction_output(shape, dims, preds, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\xgboost\\data.py:239\u001b[0m, in \u001b[0;36m_ensure_np_dtype\u001b[1;34m(data, dtype)\u001b[0m\n\u001b[0;32m    237\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array_hasobject(data) \u001b[38;5;129;01mor\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;129;01min\u001b[39;00m [np\u001b[38;5;241m.\u001b[39mfloat16, np\u001b[38;5;241m.\u001b[39mbool_]:\n\u001b[0;32m    238\u001b[0m     dtype \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfloat32\n\u001b[1;32m--> 239\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data\u001b[38;5;241m.\u001b[39mflags\u001b[38;5;241m.\u001b[39maligned:\n\u001b[0;32m    241\u001b[0m     data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrequire(data, requirements\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'My Dad and I usually get on quite well. We have a lot in common, but whenever he cooks or cleans he doesn’t do the job properly. For instance, he won’t wash his hands before cooking or clean the sides down properly. I’m generally not that bothered by germs but everyone knows the basics of hygiene in the kitchen. I try to tell him that he should wash his hands, but he just get annoyed, and makes some excuse like he’s ‘thinking about other things’. 9 times out of 10 he doesn’t do the washing up properly either eg. there will still be oil on the frying pan. So when I try to do it or I tell him that it’s unclean he just gets irritated and says “your being rather annoying now” when I’m just trying to makes sure my sister and I get a healthy meal in a clean kitchen. My mum usually makes sure that the kitchen and everything else is clean but she’s away seeing her parents because my grandma’s going into surgery soon. Now my dad’s left with us he just seems incapable of doing even basic chores. I can’t keep pestering him but he doesn’t learn. It could be that he listens to my mum or that she does it usually and dad hasn’t had to do the washing up more than once every few days. I think it’s a bit of both.'"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# Các tham số cần tìm kiếm ngẫu nhiên\n",
    "param_dist = {\n",
    "    'learning_rate': [0.01, 0.1, 0.2],\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [3, 5, 7],\n",
    "    'min_child_weight': [1, 3, 5],\n",
    "    'subsample': [0.6, 0.8, 1.0],\n",
    "    'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "    'gamma': [0, 0.1, 0.2]\n",
    "}\n",
    "\n",
    "# Khởi tạo mô hình XGBoost\n",
    "model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# RandomizedSearchCV để tìm bộ siêu tham số tối ưu\n",
    "random_search = RandomizedSearchCV(estimator=model, param_distributions=param_dist, n_iter=10, cv=3, verbose=1, random_state=42)\n",
    "\n",
    "# Huấn luyện với RandomizedSearchCV\n",
    "random_search.fit(X_train_tfidf,y_train_encoded)\n",
    "\n",
    "# In ra bộ tham số tối ưu\n",
    "print(f\"Best parameters from RandomizedSearch: {random_search.best_params_}\")\n",
    "best_model_random = random_search.best_estimator_\n",
    "\n",
    "# Dự đoán và đánh giá mô hình tốt nhất\n",
    "y_pred_random = best_model_random.predict(X_test)\n",
    "accuracy_random = accuracy_score(y_test, y_pred_random)\n",
    "print(f'Accuracy with best parameters from RandomizedSearch: {accuracy_random * 100:.2f}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with best parameters from RandomizedSearch: 37.94%\n"
     ]
    }
   ],
   "source": [
    "# Dự đoán và đánh giá mô hình tốt nhất\n",
    "y_pred_random = best_model_random.predict(X_test_tfidf)\n",
    "accuracy_random = accuracy_score(y_test_encoded, y_pred_random)\n",
    "print(f'Accuracy with best parameters from RandomizedSearch: {accuracy_random * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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