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   "execution_count": 50,
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "from sklearn import metrics, model_selection\n",
    "import plotly.figure_factory as ff \n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "def plot_confusion_matrix(label, pred, LABELS):\n",
    "    c_matrix = metrics.confusion_matrix(label, pred, labels=range(len(LABELS)), normalize='true')\n",
    "    df = pd.DataFrame(c_matrix, index=LABELS, columns=LABELS)\n",
    "    df_text = np.around(df.values, decimals=2)\n",
    "    fig = ff.create_annotated_heatmap(df.values, annotation_text=df_text, x=LABELS, y=LABELS, colorscale='PuBu' )\n",
    "    fig.update_layout(font_size=9, height=400, margin=dict(r=5, t=50, b=50, l=5)) \n",
    "    fig.show()  "
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "LABELS_STATUS = ['정상', '불량']\n",
    "\n",
    "LABELS_TYPE = [\n",
    "    'COS',\n",
    "    'GS',\n",
    "    'LA',\n",
    "    'LP자기재',\n",
    "    'LP폴리머',\n",
    "    '전주',\n",
    "    '현수애자자기',\n",
    "    '현수애자폴리머'\n",
    "]\n",
    "\n",
    "LABELS_FAULT= ['정상', '부식', '석면노출', '크랙', '아크흔적', '파손', '휴즈링크 석면 노출', '박리',\n",
    "       '휴즈링크 석면노출', '홀더불량', '설치불량', '균열', '침식', '탄화', '정상차측탈락',\n",
    "       '설치불량 (정상)', '불순물', '탈락정상', '탈락', '커버없음', '박리 (정상)', '커버탈락',\n",
    "       '스프리트핀유실']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "DIR = '/media/daitran/Data_SSD/code/SmartInsideAI/github/2021aichamp/src/ensemble_submission/submission_results/ensemble/STATUS_TTA_INFERENCE'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "folders = os.listdir(DIR)\n",
    "# folders"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "for i in range(len(folders)-8, len(folders)):\n",
    "    folder_path = os.path.join(DIR, folders[i])\n",
    "    try:\n",
    "        oof_path = os.path.join(folder_path, 'oof_df.csv')\n",
    "        oof_type = pd.read_csv(oof_path)\n",
    "        plot_confusion_matrix(oof_type['label'], oof_type['preds'], LABELS= LABELS_STATUS)\n",
    "        print(folders[i])\n",
    "        \n",
    "    except: \n",
    "        continue\n",
    "    \n",
    "    break\n",
    "\n",
    "    "
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   "cell_type": "code",
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   "source": [
    "DIR = '/media/daitran/Data_SSD/code/SmartInsideAI/github/2021aichamp/src/ensemble_submission/submission_results/ensemble/TYPE_TTA_INFERENCE'"
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   "metadata": {}
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   "cell_type": "code",
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    "# folders"
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    "for i in range(len(folders)-8, len(folders)):\n",
    "    folder_path = os.path.join(DIR, folders[i])\n",
    "    try:\n",
    "        oof_path = os.path.join(folder_path, 'oof_df.csv')\n",
    "        oof_type = pd.read_csv(oof_path)\n",
    "        plot_confusion_matrix(oof_type['label'], oof_type['preds'], LABELS= LABELS_TYPE)\n",
    "        print(folders[i])\n",
    "        \n",
    "    except: \n",
    "        continue\n",
    "    \n",
    "    break\n",
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    "DIR = '/media/daitran/Data_SSD/code/SmartInsideAI/github/2021aichamp/src/ensemble_submission/submission_results/ensemble/FAULT_TTA_INFERENCE'"
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    "    try:\n",
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    "        plot_confusion_matrix(oof_type['label'], oof_type['preds'], LABELS= LABELS_TYPE)\n",
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