model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "import pickle\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r'data\\answer\\answer_data_annotations.json', 'r') as f:\n",
    "    answer_annotations = json.load(f)\n",
    "\n",
    "with open(r'data\\wrong\\wrong_data_annotations.json', 'r') as f:\n",
    "    wrong_annotations = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_data = 'answer_annotated_data.csv'\n",
    "wrong_data = 'wrong_annotated_data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_data = pd.read_csv(answer_data)\n",
    "wrong_data = pd.read_csv(wrong_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_data['Label'] = 1  # 정답 데이터 ==> label 1\n",
    "wrong_data['Label'] = 0  # 오답 데이터 ==> label 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_data(data, annotations):\n",
    "    video_data = []\n",
    "    for video in annotations['videos']:\n",
    "        frames = [int(frame['frame'].split('_')[-1].split('.')[0]) for frame in video['annotations']]\n",
    "        video_data.append(data.loc[frames].reset_index(drop=True))  # 인덱스 리셋\n",
    "    return video_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer_videos = get_video_data(answer_data, answer_annotations)\n",
    "wrong_videos = get_video_data(wrong_data, wrong_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_videos = answer_videos + wrong_videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-16 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 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-16 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-16 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-16 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-16 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-16 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-16 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-16 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-16 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-16 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 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-16 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 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-16 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-16 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-16 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-16 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-16 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-16 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-16 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-16 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-16 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-16 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-16 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-16 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-16 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-16 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-16 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-16 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: 1ex;\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-16 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-16 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-16 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-16 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</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-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(pd.concat(all_videos).select_dtypes(include = [np.number]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_videos(videos, scaler):\n",
    "    scaled_videos = []\n",
    "    for video in videos:\n",
    "        features = video.select_dtypes(include=[np.number]).columns.difference(['Label'])  # 숫자형 데이터만 select\n",
    "        scaled_data = scaler.fit_transform(video[features])\n",
    "        scaled_video = pd.DataFrame(scaled_data, columns=features)\n",
    "        scaled_video['Label'] = video['Label'].values\n",
    "        scaled_videos.append(scaled_video)\n",
    "    return scaled_videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_videos = scale_videos(all_videos, scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_rf(videos, time_step=10):\n",
    "    dataX, dataY = [], [] # dataX가 flatten된 시계열 데이터, dataY가 레이블\n",
    "    for video in videos:\n",
    "        dataset = video.drop(columns=['Label']).values\n",
    "        labels = video['Label'].values\n",
    "        for i in range(len(dataset) - time_step):\n",
    "            a = dataset[i:(i + time_step)].flatten() ## i에서 time_step 까지의 데이터 가져오고 flatten\n",
    "            dataX.append(a)\n",
    "            dataY.append(labels[i + time_step]) # 레이블은 바로 다음 레이블 입력. 시계열 데이터 특성 반영\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_step = 3 # 10개 연속된 데이터 설정.\n",
    "X_rf, y_rf = create_dataset_rf(scaled_videos, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 기존 trian, test, vali 셋 구분지어 놓은 것, train, vali 셋으로만 분리 수정\n",
    "\n",
    "# X_train_rf, X_temp_rf, y_train_rf, y_temp_rf = train_test_split(X_rf, y_rf, test_size=0.4, random_state=42, stratify=y_rf) # stratify로 각 클래스 비율 유지.\n",
    "# X_val_rf, X_test_rf, y_val_rf, y_test_rf = train_test_split(X_temp_rf, y_temp_rf, test_size=0.5, random_state=42, stratify=y_temp_rf)\n",
    "\n",
    "X_train_rf, X_val_rf, y_train_rf, y_val_rf = train_test_split(X_rf, y_rf, test_size=0.4, random_state=42, stratify=y_rf)\n",
    "\n",
    "oversample = RandomOverSampler(sampling_strategy='minority') # 적은 클래스(Label => 0), 많은 클래스(Label => 1) 동일 수 맞추기\n",
    "\n",
    "X_train_oversampled, y_train_oversampled = oversample.fit_resample(X_train_rf, y_train_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\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-17 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 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-17 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-17 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-17 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-17 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-17 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-17 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-17 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-17 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-17 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 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-17 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 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-17 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-17 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-17 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-17 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-17 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-17 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-17 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-17 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-17 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-17 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-17 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-17 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-17 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-17 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-17 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-17 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: 1ex;\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-17 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-17 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-17 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-17 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=10,\n",
       "                       min_samples_split=5, random_state=42)</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-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=10,\n",
       "                       min_samples_split=5, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_depth=10,\n",
       "                       min_samples_split=5, random_state=42)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf = RandomForestClassifier(n_estimators=100, random_state=42, max_depth = 10, min_samples_split = 5, class_weight = 'balanced')\n",
    "model_rf.fit(X_train_oversampled, y_train_oversampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set accuracy: 1.0\n",
      "trainint set report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       138\n",
      "           1       1.00      1.00      1.00       138\n",
      "\n",
      "    accuracy                           1.00       276\n",
      "   macro avg       1.00      1.00      1.00       276\n",
      "weighted avg       1.00      1.00      1.00       276\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 훈련셋 성능\n",
    "\n",
    "y_train_pred_rf = model_rf.predict(X_train_oversampled)\n",
    "\n",
    "print(f'training set accuracy: {accuracy_score(y_train_oversampled, y_train_pred_rf)}')\n",
    "print(f'trainint set report: \\n {classification_report(y_train_oversampled, y_train_pred_rf)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainint_set C_M_: \n",
      "[[138   0]\n",
      " [  0 138]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion maxtrix\n",
    "\n",
    "cm_train = confusion_matrix(y_train_oversampled, y_train_pred_rf)\n",
    "\n",
    "print(f'trainint_set C_M_: \\n{cm_train}')\n",
    "\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_train, display_labels=['label => 0', 'label => 1']).plot(cmap=plt.cm.Blues)\n",
    "plt.title('confusion matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vali set\n",
    "y_val_pred_rf = model_rf.predict(X_val_rf)\n",
    "accuracy_val_rf = accuracy_score(y_val_rf, y_val_pred_rf)\n",
    "report_val_rf = classification_report(y_val_rf, y_val_pred_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vali set Accuracy: 1.0\n",
      "Vali set Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        59\n",
      "           1       1.00      1.00      1.00        93\n",
      "\n",
      "    accuracy                           1.00       152\n",
      "   macro avg       1.00      1.00      1.00       152\n",
      "weighted avg       1.00      1.00      1.00       152\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Vali set Accuracy: {accuracy_val_rf}')\n",
    "print(f'Vali set Report:\\n{report_val_rf}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vali set C_M_: \n",
      "[[59  0]\n",
      " [ 0 93]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_val = confusion_matrix(y_val_rf, y_val_pred_rf)\n",
    "print(f'vali set C_M_: \\n{cm_val}')\n",
    "ConfusionMatrixDisplay(confusion_matrix=cm_val, display_labels=['label => 0', 'label => 1']).plot(cmap=plt.cm.Blues)\n",
    "plt.title('C_M_ vali set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_answer_annotations = json.load(open(r'data\\test\\answer\\test_answer_data_annotations.json', 'r'))\n",
    "test_wrong_annotations = json.load(open(r'data\\test\\wrong\\test_wrong_data_annotations.json', 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_answer_data = pd.read_csv('test_answer_annotated_data.csv')\n",
    "test_wrong_data = pd.read_csv('test_wrong_annotated_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_answer_data['Label'] = 1  # 정답 데이터 ==> label 1\n",
    "test_wrong_data['Label'] = 0  # 오답 데이터 ==> label 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_answer_videos = get_video_data(test_answer_data, test_answer_annotations)\n",
    "test_wrong_videos = get_video_data(test_wrong_data, test_wrong_annotations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_all_videos = test_answer_videos + test_wrong_videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_test_videos = scale_videos(test_all_videos, scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_rf, y_test_rf = create_dataset_rf(scaled_test_videos, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.5\n",
      "Test Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.36      0.43        88\n",
      "           1       0.49      0.64      0.56        84\n",
      "\n",
      "    accuracy                           0.50       172\n",
      "   macro avg       0.50      0.50      0.49       172\n",
      "weighted avg       0.50      0.50      0.49       172\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_test_pred_rf = model_rf.predict(X_test_rf)\n",
    "accuracy_test_rf = accuracy_score(y_test_rf, y_test_pred_rf)\n",
    "report_test_rf = classification_report(y_test_rf, y_test_pred_rf)\n",
    "print(f'Test Accuracy: {accuracy_test_rf}')\n",
    "print(f'Test Report:\\n{report_test_rf}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('running_posture_model_edit_frame.pkl', 'wb') as model_file:\n",
    "#    pickle.dump(model_rf, model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('data_scaler_edit_frame.pkl', 'wb') as scaler_file:\n",
    "#    pickle.dump(scaler, scaler_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}