hipom_data_mapping/post_process/tfidf_class/2z.plot_distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# 그룹 번호 리스트 설정 (1부터 5까지)\n",
    "group_numbers = range(1, 6)\n",
    "\n",
    "# 빈 데이터프레임 생성\n",
    "combined_data = pd.DataFrame()\n",
    "\n",
    "# 그룹별 CSV 파일 읽고 데이터 합치기\n",
    "for group_number in group_numbers:\n",
    "    file_path = f'0.class_document/knn_tfidf/{group_number}/test_p_c.csv'\n",
    "    data = pd.read_csv(file_path)\n",
    "    combined_data = pd.concat([combined_data, data], ignore_index=True)\n",
    "\n",
    "# p_MDM이 True인 항목 필터링\n",
    "filtered_data = combined_data\n",
    "\n",
    "# ctp_correct가 True인 경우와 False인 경우의 c_score 추출\n",
    "correct_scores = filtered_data[filtered_data['ctp_correct'] == True]['c_score']\n",
    "incorrect_scores = filtered_data[filtered_data['ctp_correct'] == False]['c_score']\n",
    "\n",
    "# 시각화 스타일 설정\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 0.0에서 1.0까지 0.2 간격으로 bin을 설정\n",
    "bins = np.arange(0, 1.1, 0.05)\n",
    "\n",
    "# 플롯 크기 설정\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# alpha 값을 낮추고, 두 분포가 겹치도록 설정\n",
    "# Correct: 초록색, Incorrect: 빨간색, 테두리 추가\n",
    "sns.histplot(correct_scores, bins=bins, kde=False, color='green', alpha=0.5, label='Correct', edgecolor='black')\n",
    "sns.histplot(incorrect_scores, bins=bins, kde=False, color='red', alpha=0.5, label='Incorrect', edgecolor='black')\n",
    "\n",
    "# 그래프 꾸미기\n",
    "plt.xlabel('Score', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 범례 폰트 크기를 키우고 위치 설정\n",
    "plt.legend(fontsize=20, loc='upper left')\n",
    "\n",
    "# 레이아웃 조정 및 출력\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# 그룹 번호 리스트 설정 (1부터 5까지)\n",
    "group_numbers = range(1, 6)\n",
    "\n",
    "# 빈 데이터프레임 생성\n",
    "combined_data = pd.DataFrame()\n",
    "\n",
    "# 그룹별 CSV 파일 읽고 데이터 합치기\n",
    "for group_number in group_numbers:\n",
    "    file_path = f'0.class_document/distilbert/{group_number}/test_p_c.csv'\n",
    "    data = pd.read_csv(file_path)\n",
    "    combined_data = pd.concat([combined_data, data], ignore_index=True)\n",
    "\n",
    "# p_MDM이 True인 항목 필터링\n",
    "filtered_data = combined_data\n",
    "\n",
    "# ctp_correct가 True인 경우와 False인 경우의 c_score 추출\n",
    "correct_scores = filtered_data[filtered_data['ctp_correct'] == True]['c_score']\n",
    "incorrect_scores = filtered_data[filtered_data['ctp_correct'] == False]['c_score']\n",
    "\n",
    "# 시각화 스타일 설정\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 0.0에서 1.0까지 0.2 간격으로 bin을 설정\n",
    "bins = np.arange(0, 1.1, 0.05)\n",
    "\n",
    "# 플롯 크기 설정\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# alpha 값을 낮추고, 두 분포가 겹치도록 설정\n",
    "# Correct: 초록색, Incorrect: 빨간색, 테두리 추가\n",
    "sns.histplot(correct_scores, bins=bins, kde=False, color='green', alpha=0.5, label='Correct', edgecolor='black')\n",
    "sns.histplot(incorrect_scores, bins=bins, kde=False, color='red', alpha=0.5, label='Incorrect', edgecolor='black')\n",
    "\n",
    "# 그래프 꾸미기\n",
    "plt.xlabel('Score', fontsize=18)\n",
    "plt.ylabel('Frequency', fontsize=18)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# 범례 폰트 크기를 키우고 위치 설정\n",
    "plt.legend(fontsize=20, loc='upper left')\n",
    "\n",
    "# 레이아웃 조정 및 출력\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# 그룹 번호 리스트 설정 (1부터 5까지)\n",
    "group_numbers = range(1, 6)\n",
    "\n",
    "# 빈 데이터프레임 생성 (TF-IDF와 DistilBERT)\n",
    "combined_data_tfidf = pd.DataFrame()\n",
    "combined_data_bert = pd.DataFrame()\n",
    "\n",
    "# TF-IDF 데이터 읽기\n",
    "for group_number in group_numbers:\n",
    "    file_path_tfidf = f'0.class_document/knn_tfidf/{group_number}/test_p_c.csv'\n",
    "    data_tfidf = pd.read_csv(file_path_tfidf)\n",
    "    combined_data_tfidf = pd.concat([combined_data_tfidf, data_tfidf], ignore_index=True)\n",
    "\n",
    "# DistilBERT 데이터 읽기\n",
    "for group_number in group_numbers:\n",
    "    file_path_bert = f'0.class_document/distilbert/{group_number}/test_p_c.csv'\n",
    "    data_bert = pd.read_csv(file_path_bert)\n",
    "    combined_data_bert = pd.concat([combined_data_bert, data_bert], ignore_index=True)\n",
    "\n",
    "# 데이터 필터링 및 점수 추출\n",
    "filtered_data_tfidf = combined_data_tfidf\n",
    "filtered_data_bert = combined_data_bert\n",
    "\n",
    "correct_scores_tfidf = filtered_data_tfidf[filtered_data_tfidf['ctp_correct'] == True]['c_score']\n",
    "incorrect_scores_tfidf = filtered_data_tfidf[filtered_data_tfidf['ctp_correct'] == False]['c_score']\n",
    "\n",
    "correct_scores_bert = filtered_data_bert[filtered_data_bert['ctp_correct'] == True]['c_score']\n",
    "incorrect_scores_bert = filtered_data_bert[filtered_data_bert['ctp_correct'] == False]['c_score']\n",
    "\n",
    "# 시각화 스타일 설정\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# 0.0에서 1.0까지 0.05 간격으로 bin을 설정\n",
    "bins = np.arange(0, 1.05, 0.05)\n",
    "\n",
    "# TF-IDF와 DistilBERT 점수 분포 계산\n",
    "tfidf_correct_hist, _ = np.histogram(correct_scores_tfidf, bins=bins)\n",
    "tfidf_incorrect_hist, _ = np.histogram(incorrect_scores_tfidf, bins=bins)\n",
    "\n",
    "bert_correct_hist, _ = np.histogram(correct_scores_bert, bins=bins)\n",
    "bert_incorrect_hist, _ = np.histogram(incorrect_scores_bert, bins=bins)\n",
    "\n",
    "# 나비 차트 그리기\n",
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "\n",
    "bin_centers = 0.5 * (bins[1:] + bins[:-1])\n",
    "\n",
    "# 왼쪽: DistilBERT 점수 분포\n",
    "ax.barh(bin_centers, -bert_correct_hist, height=0.05, color='green', alpha=0.7, label='DistilBERT Correct', edgecolor='black')\n",
    "ax.barh(bin_centers, -bert_incorrect_hist, height=0.05, color='red', alpha=0.4, label='DistilBERT Incorrect', edgecolor='black')\n",
    "\n",
    "# 오른쪽: TF-IDF 점수 분포\n",
    "ax.barh(bin_centers, tfidf_correct_hist, height=0.05, color='green', alpha=0.7, label='TF-IDF Correct', edgecolor='black', left=0)\n",
    "ax.barh(bin_centers, tfidf_incorrect_hist, height=0.05, color='red', alpha=0.4, label='TF-IDF Incorrect', edgecolor='black', left=0)\n",
    "\n",
    "\n",
    "ax.set_xlabel('Frequency', fontsize=18)  # x축 폰트 크기를 18로 설정\n",
    "ax.set_ylabel('Score', fontsize=18)      # y축 폰트 크기를 18로 설정\n",
    "\n",
    "# x축과 y축의 눈금 폰트 크기 조정\n",
    "ax.tick_params(axis='x', labelsize=16)   # x축 눈금 폰트 크기를 16으로 설정\n",
    "ax.tick_params(axis='y', labelsize=16)   # y축 눈금 폰트 크기를 16으로 설정\n",
    "\n",
    "ax.set_yticks(np.arange(0, 1.1, 0.1))\n",
    "# 기존의 x축 눈금을 설정한 부분을 수정\n",
    "ax.set_xticks(np.arange(-10000, 10001, 2000))  # x축 단위를 2000으로 설정\n",
    "\n",
    "# 음수 눈금을 양수로 표시하도록 라벨을 변경\n",
    "ax.set_xticklabels([str(abs(x)) for x in np.arange(-10000, 10001, 2000)])\n",
    "\n",
    "# 범례 폰트 크기를 키우고 위치 설정\n",
    "# DistilBERT는 좌측 하단에, TF-IDF는 우측 하단에 범례 표시\n",
    "legend_bert = ax.legend(['DistilBERT Correct', 'DistilBERT Incorrect'], fontsize=20, loc='lower left')\n",
    "legend_tfidf = ax.legend(['TF-IDF Correct', 'TF-IDF Incorrect'], fontsize=20, loc='lower right')\n",
    "\n",
    "# 범례 두 개 모두 추가\n",
    "ax.add_artist(legend_bert)\n",
    "\n",
    "# 레이아웃 조정 및 출력\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}