05_OpenSet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pandas as pd\n",
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.607</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.680</td>\n",
       "      <td>4212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.838</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.769</td>\n",
       "      <td>10153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.639</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.673</td>\n",
       "      <td>785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.733</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.711</td>\n",
       "      <td>2295.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.915</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.457</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.599</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.646</td>\n",
       "      <td>1222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.286</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.150</td>\n",
       "      <td>0.301</td>\n",
       "      <td>0.200</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.864</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.866</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.361</td>\n",
       "      <td>0.938</td>\n",
       "      <td>0.522</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>micro avg</th>\n",
       "      <td>0.718</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.718</td>\n",
       "      <td>19265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.596</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.581</td>\n",
       "      <td>19265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.744</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.724</td>\n",
       "      <td>19265.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "1                 0.607   0.774     0.680   4212.0\n",
       "2                 0.838   0.711     0.769  10153.0\n",
       "3                 0.639   0.711     0.673    785.0\n",
       "4                 0.733   0.690     0.711   2295.0\n",
       "5                 0.915   0.305     0.457    141.0\n",
       "6                 0.599   0.700     0.646   1222.0\n",
       "7                 0.250   0.333     0.286      3.0\n",
       "8                 0.150   0.301     0.200    153.0\n",
       "10                0.864   0.868     0.866    205.0\n",
       "12                0.361   0.938     0.522     96.0\n",
       "micro avg         0.718   0.718     0.718  19265.0\n",
       "macro avg         0.596   0.633     0.581  19265.0\n",
       "weighted avg      0.744   0.718     0.724  19265.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deepsub_result = pd.read_csv(\"./output/groundtruth_and_labels.csv\")\n",
    "deepsub_report = pd.DataFrame(classification_report(deepsub_result[\"GroundTruth\"], deepsub_result[\"PredictedLabels\"], zero_division=0, output_dict=True)).T.round(3)\n",
    "queen_result = pd.read_csv(\"./output/queen_report.csv\")\n",
    "queen_result.index = queen_result[\"Unnamed: 0\"]\n",
    "queen_result = queen_result.drop(\"Unnamed: 0\", axis=1).rename_axis(None).round(3)\n",
    "queen_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = ['mACC', 'mRecall', 'mPrecision', 'mF1']\n",
    "deepsub_scores = deepsub_report.T['accuracy']['precision'],deepsub_report.T['macro avg']['recall'],deepsub_report.T['macro avg']['precision'],deepsub_report.T['macro avg']['f1-score']\n",
    "queen_scores = queen_result.T['micro avg']['precision'],queen_result.T['macro avg']['recall'],queen_result.T['macro avg']['precision'],queen_result.T['macro avg']['f1-score']\n",
    "\n",
    "x = np.arange(len(metrics))  \n",
    "width = 0.35  \n",
    "\n",
    "deepsub_color = (72/255, 116/255, 203/255)  # Normalized blue color\n",
    "queen_color = (242/255, 186/255, 2/255)     # Normalized orange color\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "rects1 = ax.bar(x - width/2, deepsub_scores, width, label='DeepSub', color=deepsub_color)\n",
    "rects2 = ax.bar(x + width/2, queen_scores, width, label='QUEEN', color=queen_color)\n",
    "\n",
    "def add_value_labels(rects):\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  \n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "add_value_labels(rects1)\n",
    "add_value_labels(rects2)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(metrics)\n",
    "ax.legend(loc='upper center',frameon=False, bbox_to_anchor=(0.5, 1.1), ncol=len(metrics))\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.yaxis.set_ticks([])\n",
    "plt.show()\n"
   ]
  }
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