Confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c8c7c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5648cfc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61507d32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred, title):\n",
    "    # Generate the confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    # Rearrange the confusion matrix (TP, FP, FN, TN)\n",
    "    cm_rearranged = np.array([[cm[1, 1], cm[0, 1]], \n",
    "                              [cm[1, 0], cm[0, 0]]])\n",
    "\n",
    "    # Plot the rearranged confusion matrix\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    sns.heatmap(cm_rearranged, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False,\n",
    "                xticklabels=['Actual Positive', 'Actual Negative'],\n",
    "                yticklabels=['Predicted Positive', 'Predicted Negative'])\n",
    "\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('Actual')\n",
    "    plt.title(f'Confusion Matrix: {title}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e915753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAIjCAYAAABS7iKKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABO0klEQVR4nO3dd3gUVf/+8Xsp2YRUWqghQcAAShERpUhoSpemVCWhKYiCQBTQ5xEEAUFpohCaNIEHDYgUUbqAIKBUpZcQaSqdEFJ3fn/wY78sSSCDwCz4fl0X15U9c3bmM5sMuXPmzIzNMAxDAAAAJmSxugAAAPDgIUAAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNA4F/h4MGDev755+Xv7y+bzaaFCxfe1fXHxMTIZrNp+vTpd3W9D7IaNWqoRo0aVpcBi61du1Y2m03R0dFWl4K7jACB++bw4cN67bXX9Mgjj8jT01N+fn6qWrWqxo4dq6tXr97TbYeHh2v37t0aMmSIZs2apYoVK97T7d1PERERstls8vPzS/dzPHjwoGw2m2w2mz755BPT6z958qQGDhyoHTt23IVq767M/nKy2Wx64403btmnRo0astlsKlGiRLrLV6xY4fwc+WUISNmsLgD/DkuXLtVLL70ku92u9u3b6/HHH1dSUpI2bNigt99+W7///rsmTZp0T7Z99epVbdq0Se+9995tf4ncqeDgYF29elXZs2e/J+u/nWzZsik+Pl6LFy9Wy5YtXZbNnj1bnp6eSkhIuKN1nzx5Uh988IFCQkJUvnz5TL9v+fLld7Q9K3l6eurQoUPasmWLKlWq5LLsn36OwMOGEQjcc0ePHlXr1q0VHBysPXv2aOzYserSpYu6d++uuXPnas+ePXrsscfu2fb//vtvSVJAQMA924bNZpOnp6eyZs16z7ZxK3a7XbVr19bcuXPTLJszZ44aNmx432qJj4+XJHl4eMjDw+O+bfduKFasmEJDQ9N8jgkJCfrmm2/u6+d4N6WkpCgpKcnqMvCQIUDgnhsxYoTi4uI0depUFShQIM3y4sWLq2fPns7XKSkpGjx4sIoVKya73a6QkBC9++67SkxMdHlfSEiIGjVqpA0bNqhSpUry9PTUI488opkzZzr7DBw4UMHBwZKkt99+WzabTSEhIZKuDf1f//pGAwcOlM1mc2lbsWKFqlWrpoCAAPn4+Cg0NFTvvvuuc3lGcyBWr16tZ599Vt7e3goICFCTJk20d+/edLd36NAhRUREKCAgQP7+/urQoYPzl3FmtG3bVsuWLdOFCxecbVu3btXBgwfVtm3bNP3PnTunyMhIlSlTRj4+PvLz81P9+vW1c+dOZ5+1a9fqqaeekiR16NDBOYR/fT9r1Kihxx9/XL/++quqV6+uHDlyOD+Xm+dAhIeHy9PTM83+161bVzlz5tTJkyedbYcPH9bhw4czve93U5s2bTRv3jw5HA5n2+LFixUfH59mdCcjSUlJev/99/Xkk0/K399f3t7eevbZZ7VmzRqXftd/bj755BONHj1awcHB8vLyUlhYmH777TeXvhEREfLx8dGRI0dUt25deXt7q2DBgho0aJBufKjyjescM2aM8zjas2ePpMz9TB47dkyvv/66QkND5eXlpdy5c+ull15STExMmn29cOGCevXqpZCQENntdhUuXFjt27fXmTNnXPo5HA4NGTJEhQsXlqenp2rXrq1Dhw5l6vOEe+IUBu65xYsX65FHHlGVKlUy1b9z586aMWOGXnzxRfXp00ebN2/WsGHDtHfvXn3zzTcufQ8dOqQXX3xRnTp1Unh4uL744gtFREToySef1GOPPabmzZsrICBAvXr1Ups2bdSgQQP5+PiYqv/3339Xo0aNVLZsWQ0aNEh2u12HDh3STz/9dMv3rVy5UvXr19cjjzyigQMH6urVqxo3bpyqVq2qbdu2pQkvLVu2VNGiRTVs2DBt27ZNU6ZMUWBgoIYPH56pOps3b66uXbtqwYIF6tixo6Rrow8lS5ZUhQoV0vQ/cuSIFi5cqJdeeklFixbVn3/+qYkTJyosLEx79uxRwYIFVapUKQ0aNEjvv/++Xn31VT377LOS5PK9PHv2rOrXr6/WrVvr5ZdfVr58+dKtb+zYsVq9erXCw8O1adMmZc2aVRMnTtTy5cs1a9YsFSxY0Nm3du3akpTuL6x7rW3btho4cKDWrl2rWrVqSbr2OdauXVuBgYGZWselS5c0ZcoUtWnTRl26dNHly5c1depU1a1bV1u2bElzKmjmzJm6fPmyunfvroSEBI0dO1a1atXS7t27XT7P1NRU1atXT88884xGjBih77//XgMGDFBKSooGDRrkss5p06YpISFBr776qux2u3LlypXpn8mtW7dq48aNat26tQoXLqyYmBhNmDBBNWrU0J49e5QjRw5JUlxcnJ599lnt3btXHTt2VIUKFXTmzBktWrRIx48fV548eZz1fPTRR8qSJYsiIyN18eJFjRgxQu3atdPmzZvNfovgLgzgHrp48aIhyWjSpEmm+u/YscOQZHTu3NmlPTIy0pBkrF692tkWHBxsSDLWrVvnbPvrr78Mu91u9OnTx9l29OhRQ5Lx8ccfu6wzPDzcCA4OTlPDgAEDjBsPjdGjRxuSjL///jvDuq9vY9q0ac628uXLG4GBgcbZs2edbTt37jSyZMlitG/fPs32Onbs6LLOZs2aGblz585wmzfuh7e3t2EYhvHiiy8atWvXNgzDMFJTU438+fMbH3zwQbqfQUJCgpGamppmP+x2uzFo0CBn29atW9Ps23VhYWGGJCMqKirdZWFhYS5tP/zwgyHJ+PDDD40jR44YPj4+RtOmTdO8Nzg4ON3vzc3WrFljSDK+/vrrW/aTZHTv3v2WfcLCwozHHnvMMAzDqFixotGpUyfDMAzj/PnzhoeHhzFjxoxMby8lJcVITEx0aTt//ryRL18+l+/z9e+Ll5eXcfz4cWf75s2bDUlGr169nG3h4eGGJOPNN990tjkcDqNhw4aGh4eH8+fz+jr9/PyMv/76y6WGzP5MxsfHp9mnTZs2GZKMmTNnOtvef/99Q5KxYMGCNP0dDodhGP/3PSpVqpTLZzJ27FhDkrF79+4078WDgVMYuKcuXbokSfL19c1U/++++06S1Lt3b5f2Pn36SLo2GfNGpUuXdv5VLEl58+ZVaGiojhw5csc13+z63Ilvv/3WZVj7Vk6dOqUdO3YoIiJCuXLlcraXLVtWzz33nHM/b9S1a1eX188++6zOnj3r/Awzo23btlq7dq1Onz6t1atX6/Tp0+mevpCuzZvIkuXafwGpqak6e/as8/TMtm3bMr1Nu92uDh06ZKrv888/r9dee02DBg1S8+bN5enpqYkTJ6bpFxMTY8now3Vt27bVggULlJSUpOjoaGXNmlXNmjXL9PuzZs3qnP/hcDh07tw5paSkqGLFiul+tk2bNlWhQoWcrytVqqSnn3463Z+TGycCX7+6JCkpSStXrnTp16JFC+XNm9f52szPpJeXl/Pr5ORknT17VsWLF1dAQIBL/fPnz1e5cuXS/WxuPg3YoUMHlzkx14/bu3ms4v4iQOCe8vPzkyRdvnw5U/2PHTumLFmyqHjx4i7t+fPnV0BAgI4dO+bSXqRIkTTryJkzp86fP3+HFafVqlUrVa1aVZ07d1a+fPnUunVrffXVV7cME9frDA0NTbOsVKlSOnPmjK5cueLSfvO+5MyZU5JM7UuDBg3k6+urefPmafbs2XrqqafSfJbXORwOjR49WiVKlJDdbleePHmUN29e7dq1SxcvXsz0NgsVKmRqsuQnn3yiXLlyaceOHfr0008zfVrgfmrdurUuXryoZcuWafbs2WrUqFGmQ/B1M2bMUNmyZeXp6ancuXMrb968Wrp0abqfbXqXjj766KNpQlSWLFn0yCOPpOknpT3dU7RoUZfXZn4mr169qvfff19BQUEuPxsXLlxwqf/w4cN6/PHHM/gEXN2Nn2+4FwIE7ik/Pz8VLFgwzYSw27n5r5eMZHTVg3HDpDKz20hNTXV57eXlpXXr1mnlypV65ZVXtGvXLrVq1UrPPfdcmr7/xD/Zl+vsdruaN2+uGTNm6Jtvvslw9EGShg4dqt69e6t69er68ssv9cMPP2jFihV67LHHMj3SIrn+tZoZ27dv119//SVJ2r17t6n33i8FChRQjRo1NHLkSK1bt+6Wn2N6vvzyS0VERKhYsWKaOnWqvv/+e61YsUK1atUy9dn+E2a/Lzd68803NWTIELVs2VJfffWVli9frhUrVih37tx3XP/d+PmGe2ESJe65Ro0aadKkSdq0aZMqV658y77BwcFyOBw6ePCgSpUq5Wz/888/deHCBecVFXdDzpw5Xa5YuO7mUQ7p2l9+tWvXVu3atTVq1CgNHTpU7733ntasWaM6deqkux+StH///jTL9u3bpzx58sjb2/uf70Q62rZtqy+++EJZsmRR69atM+wXHR2tmjVraurUqS7tFy5ccJn8ltkwlxlXrlxRhw4dVLp0aVWpUkUjRoxQs2bNnFd6uJO2bduqc+fOCggIUIMGDUy9Nzo6Wo888ogWLFjg8vkNGDAg3f4HDx5M03bgwIE0E20dDoeOHDniHHW43k9SulcU3cjMz2R0dLTCw8M1cuRIZ5+EhIQ0x0uxYsVM/3GAhwcjELjn3nnnHXl7e6tz5876888/0yw/fPiwxo4dK0nO/6jHjBnj0mfUqFGSdFevwy9WrJguXryoXbt2OdtOnTqV5kqPc+fOpXnv9Vn0N19ael2BAgVUvnx5zZgxw+U/3d9++03Lly83/QvJjJo1a2rw4MH67LPPlD9//gz7Zc2aNc1ff19//bVOnDjh0nb9l0p6Ycusvn37KjY2VjNmzNCoUaMUEhKi8PDwNJ+jlZdxXvfiiy9qwIABGj9+vOn7WVz/a/vGz3fz5s3atGlTuv0XLlzo8rlv2bJFmzdvVv369dP0/eyzz5xfG4ahzz77TNmzZ3deuZIRMz+T6f1sjBs3Ls2IW4sWLbRz5840x8z12vBwYwQC91yxYsU0Z84ctWrVSqVKlXK5E+XGjRv19ddfKyIiQpJUrlw5hYeHa9KkSbpw4YLCwsK0ZcsWzZgxQ02bNlXNmjXvWl2tW7dW37591axZM/Xo0UPx8fGaMGGCHn30UZeJYoMGDdK6devUsGFDBQcH66+//tL48eNVuHBhVatWLcP1f/zxx6pfv74qV66sTp06OS+Z8/f318CBA+/aftwsS5Ys+s9//nPbfo0aNdKgQYPUoUMHValSRbt379bs2bPTnGMvVqyYAgICFBUVJV9fX3l7e+vpp59Oc479dlavXq3x48drwIABzstKp02bpho1aui///2vRowY4exr9jLO+fPna9++fWnaw8PDFRQUJEn65Zdf9OGHH6bpU6NGjXS/j//k+9SoUSMtWLBAzZo1U8OGDXX06FFFRUWpdOnSiouLS9O/ePHiqlatmrp166bExESNGTNGuXPn1jvvvOPSz9PTU99//73Cw8P19NNPa9myZVq6dKneffddlwmTGcnsz2SjRo00a9Ys+fv7q3Tp0tq0aZNWrlyp3Llzu6zv7bffVnR0tF566SV17NhRTz75pM6dO6dFixYpKipK5cqVu6PPDw8I6y4Awb/NgQMHjC5duhghISGGh4eH4evra1StWtUYN26ckZCQ4OyXnJxsfPDBB0bRokWN7NmzG0FBQUb//v1d+hjGtUv9GjZsmGY7N18+mNFlnIZhGMuXLzcef/xxw8PDwwgNDTW+/PLLNJdxrlq1ymjSpIlRsGBBw8PDwyhYsKDRpk0b48CBA2m2cfOljitXrjSqVq1qeHl5GX5+fkbjxo2NPXv2uPS5vr2bLxOdNm2aIck4evRohp+pYbhexpmRjC7j7NOnj1GgQAHDy8vLqFq1qrFp06Z0L7/89ttvjdKlSxvZsmVz2c8bL3282Y3ruXTpkhEcHGxUqFDBSE5OdunXq1cvI0uWLMamTZucbWYv48zo3/r16w3DMG7ZZ/Dgwbfdl5u3d7vLOB0OhzF06FAjODjYsNvtxhNPPGEsWbIkzaXDN35fRo4caQQFBRl2u9149tlnjZ07d7qs8/r3+fDhw8bzzz9v5MiRw8iXL58xYMAAl8txb/XzbhiZ+5k8f/680aFDByNPnjyGj4+PUbduXWPfvn1GcHCwER4e7tL37NmzxhtvvGEUKlTI8PDwMAoXLmyEh4cbZ86cueVnltExgweHzTAYZwIAK8TExKho0aL6+OOPFRkZecu+ERERio6OTncEA7ACcyAAAIBpBAgAAGAaAQIAAJjGHAgAAGAaIxAAAMA0AgQAADCNAAEAAEx7KO9EOenntM8yAOA+2le8e880AXB3eWYyGTACAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADT3CJAXLhwQVOmTFH//v117tw5SdK2bdt04sQJiysDAADpyWZ1Abt27VKdOnXk7++vmJgYdenSRbly5dKCBQsUGxurmTNnWl0iAAC4ieUjEL1791ZERIQOHjwoT09PZ3uDBg20bt06CysDAAAZsTxAbN26Va+99lqa9kKFCun06dMWVAQAAG7H8gBht9t16dKlNO0HDhxQ3rx5LagIAADcjuUB4oUXXtCgQYOUnJwsSbLZbIqNjVXfvn3VokULi6sDAADpsTxAjBw5UnFxcQoMDNTVq1cVFham4sWLy9fXV0OGDLG6PAAAkA7Lr8Lw9/fXihUrtGHDBu3atUtxcXGqUKGC6tSpY3VpAAAgA5YHiD/++ENBQUGqVq2aqlWrZnU5AAAgEyw/hRESEqKwsDBNnjxZ58+ft7ocAACQCZYHiF9++UWVKlXSoEGDVKBAATVt2lTR0dFKTEy0ujQAAJABywPEE088oY8//lixsbFatmyZ8ubNq1dffVX58uVTx44drS4PAACkw2YYhmF1ETfbtm2bOnXqpF27dik1NdX0+yf9fOweVAXgbmlfMdjqEgBkwDOTsyMtH4G47vjx4xoxYoTKly+vSpUqycfHR59//rnVZQEAgHRYfhXGxIkTNWfOHP30008qWbKk2rVrp2+//VbBwfyFAgCAu7I8QHz44Ydq06aNPv30U5UrV87qcgAAQCZYHiBiY2Nls9msLgMAAJhgSYDYtWuXHn/8cWXJkkW7d+++Zd+yZcvep6oAAEBmWRIgypcvr9OnTyswMFDly5eXzWbTjReDXH9ts9nu6CoMAABwb1kSII4ePep8VPfRo0etKAEAAPwDlgSIG6+wOHbsmKpUqaJs2VxLSUlJ0caNG7kaAwAAN2T5fSBq1qypc+fOpWm/ePGiatasaUFFAADgdiwPENfnOtzs7Nmz8vb2tqAiAABwO5Zdxtm8eXNJ1yZMRkREyG63O5elpqZq165dqlKlilXlAQCAW7AsQPj7+0u6NgLh6+srLy8v5zIPDw8988wz6tKli1XlAQCAW7AsQEybNk2SFBISosjISE5XAADwALH8TpQDBgywugQAAGCSJQGiQoUKWrVqlXLmzKknnnjilrey3rZt232sDAAAZIYlAaJJkybOSZNNmza1ogQAAPAP2Iwb7yH9kJj08zGrSwBwC+0rcoM4wF15ZnJowfL7QPzxxx86fvy48/WWLVv01ltvadKkSRZWBQAAbsXyANG2bVutWbNGknT69GnVqVNHW7Zs0XvvvadBgwZZXB0AAEiP5QHit99+U6VKlSRJX331lcqUKaONGzdq9uzZmj59urXFAQCAdFkeIJKTk50TKleuXKkXXnhBklSyZEmdOnXKytIAAEAGLA8Qjz32mKKiorR+/XqtWLFC9erVkySdPHlSuXPntrg6AACQHssDxPDhwzVx4kTVqFFDbdq0Ubly5SRJixYtcp7aAAAA7sUtLuNMTU3VpUuXlDNnTmdbTEyMcuTIocDAQNPr4zJOwL1xGSfgvjJ7Gaflt7KWpKxZsyolJUUbNmyQJIWGhiokJMTaogAAQIYsP4Vx5coVdezYUQUKFFD16tVVvXp1FSxYUJ06dVJ8fLzV5QEAgHRYHiB69+6tH3/8UYsXL9aFCxd04cIFffvtt/rxxx/Vp08fq8sDAADpsHwORJ48eRQdHa0aNWq4tK9Zs0YtW7bU33//bXqdzIEA3BtzIAD39cDcyjo+Pl758uVL0x4YGMgpDAAA3JTlAaJy5coaMGCAEhISnG1Xr17VBx98oMqVK1tYGQAAyIjlV2GMGTNGdevWVeHChZ33gNi5c6c8PT31ww8/WFwdAABIj+UBokyZMjp06JDmzJmjvXv3SpLatGmjdu3aycvLy+LqAABAeiwNED///LMWL16spKQk1apVS507d7ayHAAAkEmWBYjo6Gi1atVKXl5eyp49u0aNGqXhw4crMjLSqpIAAEAmWTaJctiwYerSpYsuXryo8+fP68MPP9TQoUOtKgcAAJhg2X0gfHx8tGPHDhUvXlySlJSUJG9vb504ceKOnn9xI+4DAbg37gMBuC+3vw9EfHy8/Pz8nK89PDzk6empuLg4q0oCAACZZOkkyilTpsjHx8f5OiUlRdOnT1eePHmcbT169LCiNAAAcAuWncIICQmRzWa7ZR+bzaYjR46YXjenMAD3xikMwH25/eO8Y2JirNo0AAD4hyy/lTUAAHjwECAAAIBplt/KGpCkyX1e0aUzf6ZpL1e7seq0f1O71izV3p/X6K+YQ0pKiFf38Qvk6e2TzpoA3A+//rJV07+Yqr17ftPff/+t0Z9+rlq161hdFu4jAgTcQrsB42Q4HM7XZ07EKHpEP4U+VV2SlJyUqJAyFRVSpqI2fP2FVWUC+P+uXo1XaGiomjZvod4937C6HFiAAAG3kMMvwOX1lqXzFBBYUIVLlpUkPVm3uSTpj70773dpANJR7dkwVXs2zOoyYCFLAsSlS5cy3ffGm03h3yE1JVl7Nq5SxXotbnupLwDAGpYEiICAgEz/YkhNTb3l8sTERCUmJrq0JSclKruH/Y7rg7UO/bpRifFxeqza81aXAgDIgCUBYs2aNc6vY2Ji1K9fP0VERKhy5cqSpE2bNmnGjBkaNmzYbdc1bNgwffDBBy5tjTr1VOPOve5u0bhvdq/7XkXLPiWfnLmtLgUAkAFLAkRY2P+dNxs0aJBGjRqlNm3aONteeOEFlSlTRpMmTVJ4ePgt19W/f3/17t3bpW3WjtN3t2DcN5fO/KnY37frhR7vW10KAOAWLL8PxKZNm1SxYsU07RUrVtSWLVtu+3673S4/Pz+Xf5y+eHD9tv4H5fAL0CPlnra6FADALVgeIIKCgjR58uQ07VOmTFFQUJAFFcEqhsOh39YvV+lqzylL1qwuy65cOKe/jh3W+T9PSpLOHD+qv44d1tW4zE/IBXD3xF+5on1792rf3r2SpBPHj2vf3r06dfKkxZXhfrH8Ms7Ro0erRYsWWrZsmZ5++tpfnVu2bNHBgwc1f/58i6vD/XTs9226fPYvPV69bpplO9cs0aaFXzpfzxvaR5JUt3OkHn+WyZbA/fb777+pc4f2ztefjLg2Z+2FJs00eOhHVpWF+8iyp3He6I8//tCECRO0b98+SVKpUqXUtWvXOx6B4GmcgHvjaZyA+8rs0zjdIkDcbQQIwL0RIAD3ldkAYfkcCElav369Xn75ZVWpUkUnTpyQJM2aNUsbNmywuDIAAJAeywPE/PnzVbduXXl5eWnbtm3Om0JdvHhRQ4cOtbg6AACQHssDxIcffqioqChNnjxZ2bNnd7ZXrVpV27Zts7AyAACQEcsDxP79+1W9evU07f7+/rpw4cL9LwgAANyW5QEif/78OnToUJr2DRs26JFHHrGgIgAAcDuWB4guXbqoZ8+e2rx5s2w2m06ePKnZs2crMjJS3bp1s7o8AACQDstvJNWvXz85HA7Vrl1b8fHxql69uux2uyIjI/Xmm29aXR4AAEiH29wHIikpSYcOHVJcXJxKly4tHx+fO14X94EA3Bv3gQDc1wNzH4iOHTvq8uXL8vDwUOnSpVWpUiX5+PjoypUr6tixo9XlAQCAdFgeIGbMmKGrV6+mab969apmzpxpQUUAAOB2LJsDcenSJRmGIcMwdPnyZXl6ejqXpaam6rvvvlNgYKBV5QEAgFuwLEAEBATIZrPJZrPp0UcfTbPcZrPpgw8+sKAyAABwO5YFiDVr1sgwDNWqVUvz589Xrly5nMs8PDwUHBysggULWlUeAAC4BcsCRFhYmCTp6NGjKlKkiGw2m1WlAAAAkyyfRLl69WpFR0enaf/66681Y8YMCyoCAAC3Y3mAGDZsmPLkyZOmPTAwkKdxAgDgpiwPELGxsSpatGia9uDgYMXGxlpQEQAAuB3LA0RgYKB27dqVpn3nzp3KnTu3BRUBAIDbsTxAtGnTRj169NCaNWuUmpqq1NRUrV69Wj179lTr1q2tLg8AAKTD8odpDR48WDExMapdu7ayZbtWjsPhUPv27ZkDAQCAm3Kbh2kdOHBAO3fulJeXl8qUKaPg4Dt/2A4P0wLcGw/TAtxXZh+mZfkIxHWPPvpounekBAAA7seSANG7d28NHjxY3t7e6t279y37jho16j5VBQAAMsuSALF9+3YlJyc7v84Id6cEAMA9uc0ciLuJORCAe2MOBOC+MjsHwvLLOAEAwIPHklMYzZs3z3TfBQsW3MNKAADAnbBkBMLf39/5z8/PT6tWrdIvv/ziXP7rr79q1apV8vf3t6I8AABwG5aMQEybNs35dd++fdWyZUtFRUUpa9askqTU1FS9/vrr8vPzs6I8AABwG5ZPosybN682bNig0NBQl/b9+/erSpUqOnv2rOl1MokScG9MogTc1wMziTIlJUX79u1L075v3z45HA4LKgIAALdj+Z0oO3TooE6dOunw4cOqVKmSJGnz5s366KOP1KFDB4urAwAA6bE8QHzyySfKnz+/Ro4cqVOnTkmSChQooLffflt9+vSxuDoAAJAey+dA3OjSpUuS9I8nTzIHAnBvzIEA3NcDMwdCujYPYuXKlZo7d67z9tUnT55UXFycxZUBAID0WH4K49ixY6pXr55iY2OVmJio5557Tr6+vho+fLgSExMVFRVldYkAAOAmlo9A9OzZUxUrVtT58+fl5eXlbG/WrJlWrVplYWUAACAjlo9ArF+/Xhs3bpSHh4dLe0hIiE6cOGFRVQAA4FYsH4FwOBxKTU1N0378+HH5+vpaUBEAALgdywPE888/rzFjxjhf22w2xcXFacCAAWrQoIF1hQEAgAxZfhnnH3/8oXr16skwDB08eFAVK1bUwYMHlSdPHq1bt06BgYGm18llnIB74zJOwH1l9jJOy+dABAUFaefOnZo3b5527typuLg4derUSe3atXOZVAkAANyHpSMQycnJKlmypJYsWaJSpUrdtfUyAgG4N0YgAPf1QNxIKnv27EpISLCyBAAAcAcsn0TZvXt3DR8+XCkpKVaXAgAAMsnyORBbt27VqlWrtHz5cpUpU0be3t4uyxcsWGBRZQAAICOWB4iAgAC1aNHC6jIAAIAJlgeIadOmWV0CAAAwybI5EA6HQ8OHD1fVqlX11FNPqV+/frp69apV5QAAABMsCxBDhgzRu+++Kx8fHxUqVEhjx45V9+7drSoHAACYYFmAmDlzpsaPH68ffvhBCxcu1OLFizV79mw5HA6rSgIAAJlkWYCIjY11edZFnTp1ZLPZdPLkSatKAgAAmWRZgEhJSZGnp6dLW/bs2ZWcnGxRRQAAILMsuwrDMAxFRETIbrc72xISEtS1a1eXe0FwHwgAANyPZQEiPDw8TdvLL79sQSUAAMAsywIE938AAODBZfmzMAAAwIOHAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMy5aZTosWLcr0Cl944YU7LgYAADwYMhUgmjZtmqmV2Ww2paam/pN6AADAAyBTAcLhcNzrOgAAwAOEORAAAMC0TI1A3OzKlSv68ccfFRsbq6SkJJdlPXr0uCuFAQAA92U6QGzfvl0NGjRQfHy8rly5oly5cunMmTPKkSOHAgMDCRAAAPwLmD6F0atXLzVu3Fjnz5+Xl5eXfv75Zx07dkxPPvmkPvnkk3tRIwAAcDOmA8SOHTvUp08fZcmSRVmzZlViYqKCgoI0YsQIvfvuu/eiRgAA4GZMB4js2bMrS5ZrbwsMDFRsbKwkyd/fX3/88cfdrQ4AALgl03MgnnjiCW3dulUlSpRQWFiY3n//fZ05c0azZs3S448/fi9qBAAAbsb0CMTQoUNVoEABSdKQIUOUM2dOdevWTX///bcmTZp01wsEAADux2YYhmF1EXfbpJ+PWV0CgFtoXzHY6hIAZMAzk+cmuJEUAAAwzfQciKJFi8pms2W4/MiRI/+oIAAA4P5MB4i33nrL5XVycrK2b9+u77//Xm+//fbdqgsAALgx0wGiZ8+e6bZ//vnn+uWXX/5xQQAAwP3dtTkQ9evX1/z58+/W6gAAgBu7awEiOjpauXLlulurAwAAbuyObiR14yRKwzB0+vRp/f333xo/fvxdLQ4AALgn0wGiSZMmLgEiS5Ysyps3r2rUqKGSJUve1eLuVMX8Oa0uAcAt5HzqDatLAJCBq9s/y1Q/0wFi4MCBZt8CAAAeMqbnQGTNmlV//fVXmvazZ88qa9asd6UoAADg3kwHiIzufJ2YmCgPD49/XBAAAHB/mT6F8emnn0qSbDabpkyZIh8fH+ey1NRUrVu3zm3mQAAAgHsr0wFi9OjRkq6NQERFRbmcrvDw8FBISIiioqLufoUAAMDtZDpAHD16VJJUs2ZNLViwQDlzcqUDAAD/VqavwlizZs29qAMAADxATE+ibNGihYYPH56mfcSIEXrppZfuSlEAAMC9mQ4Q69atU4MGDdK0169fX+vWrbsrRQEAAPdmOkDExcWle7lm9uzZdenSpbtSFAAAcG+mA0SZMmU0b968NO3/+9//VLp06btSFAAAcG+mJ1H+97//VfPmzXX48GHVqlVLkrRq1SrNmTNH0dHRd71AAADgfkwHiMaNG2vhwoUaOnSooqOj5eXlpXLlymn16tU8zhsAgH8J0wFCkho2bKiGDRtKki5duqS5c+cqMjJSv/76q1JTU+9qgQAAwP2YngNx3bp16xQeHq6CBQtq5MiRqlWrln7++ee7WRsAAHBTpkYgTp8+renTp2vq1Km6dOmSWrZsqcTERC1cuJAJlAAA/ItkegSicePGCg0N1a5duzRmzBidPHlS48aNu5e1AQAAN5XpEYhly5apR48e6tatm0qUKHEvawIAAG4u0yMQGzZs0OXLl/Xkk0/q6aef1meffaYzZ87cy9oAAICbynSAeOaZZzR58mSdOnVKr732mv73v/+pYMGCcjgcWrFihS5fvnwv6wQAAG7E9FUY3t7e6tixozZs2KDdu3erT58++uijjxQYGKgXXnjhXtQIAADczB1fxilJoaGhGjFihI4fP665c+ferZoAAICb+0cB4rqsWbOqadOmWrRo0d1YHQAAcHN3JUAAAIB/FwIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMc4sAceHCBU2ZMkX9+/fXuXPnJEnbtm3TiRMnLK4MAACkJ5vVBezatUt16tSRv7+/YmJi1KVLF+XKlUsLFixQbGysZs6caXWJAADgJpaPQPTu3VsRERE6ePCgPD09ne0NGjTQunXrLKwMAABkxPIAsXXrVr322mtp2gsVKqTTp09bUBEAALgdywOE3W7XpUuX0rQfOHBAefPmtaAiAABwO5YHiBdeeEGDBg1ScnKyJMlmsyk2NlZ9+/ZVixYtLK4OAACkx/IAMXLkSMXFxSkwMFBXr15VWFiYihcvLl9fXw0ZMsTq8gAAQDosvwrD399fK1as0IYNG7Rr1y7FxcWpQoUKqlOnjtWlAQCADFgeIP744w8FBQWpWrVqqlatmtXlAACATLD8FEZISIjCwsI0efJknT9/3upyAABAJlgeIH755RdVqlRJgwYNUoECBdS0aVNFR0crMTHR6tIAAEAGLA8QTzzxhD7++GPFxsZq2bJlyps3r1599VXly5dPHTt2tLo8AACQDssDxHU2m001a9bU5MmTtXLlShUtWlQzZsywuiwAAJAOtwkQx48f14gRI1S+fHlVqlRJPj4++vzzz60uCwAApMPyqzAmTpyoOXPm6KefflLJkiXVrl07ffvttwoODra6NAAAkAHLA8SHH36oNm3a6NNPP1W5cuWsLgcAAGSC5QEiNjZWNpvN6jIAAIAJlgSIXbt26fHHH1eWLFm0e/fuW/YtW7bsfaoKAABkliUBonz58jp9+rQCAwNVvnx52Ww2GYbhXH79tc1mU2pqqhUlAgCAW7AkQBw9etT5qO6jR49aUQIAAPgHLAkQN15hcezYMVWpUkXZsrmWkpKSoo0bN3I1BgAAbsjy+0DUrFlT586dS9N+8eJF1axZ04KKAADA7VgeIK7PdbjZ2bNn5e3tbUFFAADgdiy7jLN58+aSrk2YjIiIkN1udy5LTU3Vrl27VKVKFavKAwAAt2BZgPD395d0bQTC19dXXl5ezmUeHh565pln1KVLF6vKAwAAt2BZgJg2bZokKSQkRJGRkZyuAADgAWL5nSgHDBhgdQkAAMAkywOEJEVHR+urr75SbGyskpKSXJZt27bNoqoAAEBGLL8K49NPP1WHDh2UL18+bd++XZUqVVLu3Ll15MgR1a9f3+ryAABAOiwPEOPHj9ekSZM0btw4eXh46J133tGKFSvUo0cPXbx40eryAABAOiwPELGxsc7LNb28vHT58mVJ0iuvvKK5c+daWRoAAMiA5QEif/78zjtRFilSRD///LOka8/IuPEBWwAAwH1YHiBq1aqlRYsWSZI6dOigXr166bnnnlOrVq3UrFkzi6sDAADpsfwqjEmTJsnhcEiSunfvrty5c2vjxo164YUX9Nprr1lcHQAASI/NeAjPE2yLuWR1CQBuoWqzd60uAUAGrm7/LFP9LB+B2LVrV7rtNptNnp6eKlKkiMtzMgAAgPUsDxDly5d3eRrnzU/nzJ49u1q1aqWJEyfK09PTihIBAMBNLJ9E+c0336hEiRKaNGmSduzYoZ07d2rSpEkKDQ3VnDlzNHXqVK1evVr/+c9/rC4VAAD8f5aPQAwZMkRjx45V3bp1nW1lypRR4cKF9d///ldbtmyRt7e3+vTpo08++cTCSgEAwHWWj0Ds3r1bwcHBadqDg4O1e/duSddOc5w6dep+lwYAADJgeYAoWbKkPvroI5eHaCUnJ+ujjz5SyZIlJUknTpxQvnz5rCoRAADcxPJTGJ9//rleeOEFFS5cWGXLlpV0bVQiNTVVS5YskSQdOXJEr7/+upVlAgCAG7jFfSAuX76s2bNn68CBA5Kk0NBQtW3bVr6+vne0Pu4DAbg37gMBuK8H5j4QkuTr66uuXbtaXQYAAMgky+dASNKsWbNUrVo1FSxYUMeOHZMkjR49Wt9++63FlQEAgPRYHiAmTJig3r17q379+jp//rxSU1MlSTlz5tSYMWOsLQ4AAKTL8gAxbtw4TZ48We+9956yZfu/MyoVK1Z0XsYJAADci+UB4ujRo3riiSfStNvtdl25csWCigAAwO1YHiCKFi2qHTt2pGn//vvvVapUqftfEAAAuC3Lr8Lo3bu3unfvroSEBBmGoS1btmju3LkaNmyYpkyZYnV5AAAgHZYHiM6dO8vLy0v/+c9/FB8fr7Zt26pgwYIaO3asWrdubXV5AAAgHW5xI6nr4uPjFRcXp8DAwH+0Hm4kBbg3biQFuK8H6kZS1+XIkUM5cuSwugwAAHAblgWImjVrymaz3bKPzWbTqlWr7lNFAAAgsywLEOXLl89w2eXLlzVnzhwlJibev4IAAECmWRYgRo8enaYtJSVFn3/+uYYMGaJChQpp8ODBFlQGAABux23mQMyePVvvv/++rl69qoEDB+rVV191uTMlAABwH5b/hv7+++/Vr18/HT16VJGRkerdu7e8vb2tLgsAANyCZQFiy5Yt6tu3r37++Wd17dpVK1euVJ48eawqBwAAmGBZgHjmmWfk5eWlrl27qmjRopozZ066/Xr06HGfK4PVvp03Xf/74nPVa9pa4d36SJKSkhL15aQx2rR2hZKTk1TuyWfU4c2+CsiZ2+JqgYdP1QrF1Kt9HVUoXUQF8vqrZa9JWrx2l3O5t5eHPuzRRI1rllUuf2/FnDyr8XN/1JToDZKknH459N9uDVX7mZIKyp9TZ87HafHaXfpg/BJdikuwardwl1kWIIoUKSKbzaaFCxdm2MdmsxEg/mUO7/9dq5Z+oyJFS7i0z4oare1bNqjnf4Yph7ePpn/+sUYPekcfjJ5qUaXAw8vby67dB05o5rebNG/Uq2mWD+/TQjWeelQd3pupYyfPqk7lUhrbv6VO/X1RS3/crQJ5/VUgr7/6j/5Ge4+cVpECuTTuvdYqkNdfbd/mmH1YWBYgYmJirNo03FTC1Xh9Nvx9dXnrXX0z9wtne/yVOK354Vu92e9DPV7+KUnSa73fV2SXl3Rw726VKFXGqpKBh9Lyn/Zo+U97Mlz+TLmi+nLJZq3/9aAk6YsFP6lTi6qq+Fiwlv64W3sOn1KbyP97ltHR42c08LPF+mJIe2XNmkWpqY57vg+49yx/Gidw3RefjdATlaqqTIWnXdqPHNyr1JQUPf5EJWdboSIhyhOYXwf37r7fZQL/ej/vPKpGYWVUMK+/JKl6xRIqERyolT/vzfA9fr6eunQlgfDwELH8Kox/KjExMc0Np5ISE+Vht1tUEe7ExrXLFXNonz4cNyPNsovnzipb9uzy9vF1afcPyKUL587erxIB/H+9h3+tz//bRoeXD1FycqochkOvD56rn7YdTrd/7gBv9e9SX1/M33ifK8W99MCPQAwbNkz+/v4u/6ZNGGV1WTDh7F+nNWPCSHXvO1geHgQ/wN293jpMlcqEqEXPKFVpN1z9Rn2jMf1aqubToWn6+np76ptPu2nvkVP6cOJSC6rFvfLAj0D0799fvXv3dmnbc4pbYD9Ijhzap0sXzund7q842xyOVO3bvV3LF32t/kM/VUpysq7EXXYZhbh44ZwCcnEVBnA/edqz64M3G6tV78n6fsPvkqTfDp5U2dDCeuuV2lqzeb+zr08OuxZ9/rouxyeoVe/JSknh9MXD5IEPEHa7XfabTld4nONx3g+Sx8s/pRET57q0RY0cpIJBIXqhZXvlzptfWbNl02/bt+rpZ2tJkk7+EaMzf51mAiVwn2XPllUe2bPJYRgu7ampDmXJ8n8PSPT19tTi8d2VmJSiF9+aqMSklPtdKu4xSwLEpUuZ/wXv5+d3DyuBO/DK4a2gkOIubXZPL/n4+jvba9Ztoi8njZaPr5+8vL01/fOPVaJUGQIEcA94e3moWFBe5+uQQrlV9tFCOn8pXn+cPq91vxzU0Lea6mpCsmJPndOzTxZXu0aV1HfUAknXwsOS8d3l5emhDu/NkJ+3p/y8PSVJf5+Pk8NhpLtdPFgsCRABAQG3fZT3dampqfe4GjwIXunaS7YsNo0e3FcpyUkqW/EZdXyjr9VlAQ+lCqWDtXxKT+frEZEtJEmzFv2sVwd8qfb9vtCgN5to+tBw5fTLodhT5zTw8yWa/PW1G0mVLxmkSmWLSpL2LB7osu7QBu8r9tS5+7MjuKdshmHc9yj4448/Or+OiYlRv379FBERocqVK0uSNm3apBkzZmjYsGEKDw83vf5tMZzCANxZ1WbvWl0CgAxc3f5ZpvpZEiBuVLt2bXXu3Flt2rRxaZ8zZ44mTZqktWvXml4nAQJwbwQIwH1lNkBYfhnnpk2bVLFixTTtFStW1JYtWyyoCAAA3I7lASIoKEiTJ09O0z5lyhQFBQVZUBEAALgdyy/jHD16tFq0aKFly5bp6aev3cJ4y5YtOnjwoObPn29xdQAAID2Wj0A0aNBABw4cUOPGjXXu3DmdO3dOjRs31oEDB9SgQQOrywMAAOmwfARCunYaY+jQoVaXAQAAMsnyEQhJWr9+vV5++WVVqVJFJ06ckCTNmjVLGzZssLgyAACQHssDxPz581W3bl15eXlp27ZtzidrXrx4kVEJAADclOUB4sMPP1RUVJQmT56s7NmzO9urVq2qbdu2WVgZAADIiOUBYv/+/apevXqadn9/f124cOH+FwQAAG7L8gCRP39+HTp0KE37hg0b9Mgjj1hQEQAAuB3LA0SXLl3Us2dPbd68WTabTSdPntTs2bMVGRmpbt26WV0eAABIh+WXcfbr108Oh0O1a9dWfHy8qlevLrvdrsjISL355ptWlwcAANJh+cO0rktKStKhQ4cUFxen0qVLy8fH547XxcO0APfGw7QA9/XAPEyrY8eOunz5sjw8PFS6dGlVqlRJPj4+unLlijp27Gh1eQAAIB2WB4gZM2bo6tWradqvXr2qmTNnWlARAAC4HcvmQFy6dEmGYcgwDF2+fFmenp7OZampqfruu+8UGBhoVXkAAOAWLAsQAQEBstlsstlsevTRR9Mst9ls+uCDDyyoDAAA3I5lAWLNmjUyDEO1atXS/PnzlStXLucyDw8PBQcHq2DBglaVBwAAbsGyABEWFiZJOnr0qIoUKSKbzWZVKQAAwCTLJ1GuXr1a0dHRadq//vprzZgxw4KKAADA7VgeIIYNG6Y8efKkaQ8MDORpnAAAuCnLA0RsbKyKFi2apj04OFixsbEWVAQAAG7H8gARGBioXbt2pWnfuXOncufObUFFAADgdiwPEG3atFGPHj20Zs0apaamKjU1VatXr1bPnj3VunVrq8sDAADpsPxhWoMHD1ZMTIxq166tbNmuleNwONS+fXvmQAAA4Kbc5mFaBw4c0M6dO+Xl5aUyZcooODj4jtfFw7QA98bDtAD3ldmHaVk+AnHdo48+mu4dKQEAgPuxJED07t1bgwcPlre3t3r37n3LvqNGjbpPVQEAgMyyJEBs375dycnJzq8zwt0pAQBwT5YEiDVr1qT7NQAAeDBYfhknAAB48FgyAtG8efNM912wYME9rAQAANwJS0Yg/P39nf/8/Py0atUq/fLLL87lv/76q1atWiV/f38rygMAALdhyQjEtGnTnF/37dtXLVu2VFRUlLJmzSpJSk1N1euvvy4/Pz8rygMAALdh+Y2k8ubNqw0bNig0NNSlff/+/apSpYrOnj1rep3cSApwb9xICnBfmb2RlOWTKFNSUrRv37407fv27ZPD4bCgIgAAcDuW34myQ4cO6tSpkw4fPqxKlSpJkjZv3qyPPvpIHTp0sLg6AACQHssDxCeffKL8+fNr5MiROnXqlCSpQIECevvtt9WnTx+LqwMAAOmxfA7EjS5dujZ34Z9OnmQOBODemAMBuK8HZg6EdG0exMqVKzV37lzn7atPnjypuLg4iysDAADpsfwUxrFjx1SvXj3FxsYqMTFRzz33nHx9fTV8+HAlJiYqKirK6hIBAMBNLB+B6NmzpypWrKjz58/Ly8vL2d6sWTOtWrXKwsoAAEBGLB+BWL9+vTZu3CgPDw+X9pCQEJ04ccKiqgAAwK1YPgLhcDiUmpqapv348ePy9fW1oCIAAHA7lgeI559/XmPGjHG+ttlsiouL04ABA9SgQQPrCgMAABmy/BTGJ598onr16ql06dJKSEhQ27ZtdfDgQeXJk0dz5861ujwAAJAOywNEUFCQdu7cqXnz5mnnzp2Ki4tTp06d1K5dO5dJlQAAwH1YGiCSk5NVsmRJLVmyRO3atVO7du2sLAcAAGSSpXMgsmfProSEBCtLAAAAd8DySZTdu3fX8OHDlZKSYnUpAAAgkyyfA7F161atWrVKy5cvV5kyZeTt7e2yfMGCBRZVBgAAMmJ5gAgICFCLFi2sLgMAAJhgeYCYNm2a1SUAAACTLJsD4XA4NHz4cFWtWlVPPfWU+vXrp6tXr1pVDgAAMMGyADFkyBC9++678vHxUaFChTR27Fh1797dqnIAAIAJlgWImTNnavz48frhhx+0cOFCLV68WLNnz5bD4bCqJAAAkEmWBYjY2FiXZ13UqVNHNptNJ0+etKokAACQSZYFiJSUFHl6erq0Zc+eXcnJyRZVBAAAMsuyqzAMw1BERITsdruzLSEhQV27dnW5FwT3gQAAwP1YFiDCw8PTtL388ssWVAIAAMyyLEBw/wcAAB5clj8LAwAAPHgIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEwjQAAAANMIEAAAwDQCBAAAMI0AAQAATCNAAAAA0wgQAADANAIEAAAwjQABAABMI0AAAADTCBAAAMA0AgQAADCNAAEAAEyzGYZhWF0EcCuJiYkaNmyY+vfvL7vdbnU5AG7A8fnvRYCA27t06ZL8/f118eJF+fn5WV0OgBtwfP57cQoDAACYRoAAAACmESAAAIBpBAi4PbvdrgEDBjBBC3BDHJ//XkyiBAAApjECAQAATCNAAAAA0wgQAADANAIELGWz2bRw4UJLtr127VrZbDZduHDhlv1CQkI0ZsyY+1ITYCUrj8e7iWP2/iBA/Ets2rRJWbNmVcOGDU2/18qDMSIiQjabTTabTR4eHipevLgGDRqklJSUf7zuKlWq6NSpU/L395ckTZ8+XQEBAWn6bd26Va+++uo/3h5w3YN+PH700Ucu7QsXLpTNZrvv9XDMWosA8S8xdepUvfnmm1q3bp1OnjxpdTmm1KtXT6dOndLBgwfVp08fDRw4UB9//PE/Xq+Hh4fy589/2//48ubNqxw5cvzj7QHXPcjHo6enp4YPH67z589bXUqGOGbvDwLEv0BcXJzmzZunbt26qWHDhpo+fXqaPosXL9ZTTz0lT09P5cmTR82aNZMk1ahRQ8eOHVOvXr2cIwGSNHDgQJUvX95lHWPGjFFISIjz9datW/Xcc88pT5488vf3V1hYmLZt22a6frvdrvz58ys4OFjdunVTnTp1tGjRIknS+fPn1b59e+XMmVM5cuRQ/fr1dfDgQed7jx07psaNGytnzpzy9vbWY489pu+++06S6ymMtWvXqkOHDrp48aJzPwcOHCjJ9S++tm3bqlWrVi71JScnK0+ePJo5c6YkyeFwaNiwYSpatKi8vLxUrlw5RUdHm95vPJwe9OOxTp06yp8/v4YNG3bLfhs2bNCzzz4rLy8vBQUFqUePHrpy5Ypz+alTp9SwYUN5eXmpaNGimjNnTprRlVGjRqlMmTLy9vZWUFCQXn/9dcXFxUkSx6wbIED8C3z11VcqWbKkQkND9fLLL+uLL77Qjbf/WLp0qZo1a6YGDRpo+/btWrVqlSpVqiRJWrBggQoXLqxBgwbp1KlTOnXqVKa3e/nyZYWHh2vDhg36+eefVaJECTVo0ECXL1/+R/vj5eWlpKQkSdeGVH/55RctWrRImzZtkmEYatCggZKTkyVJ3bt3V2JiotatW6fdu3dr+PDh8vHxSbPOKlWqaMyYMfLz83PuZ2RkZJp+7dq10+LFi53/iUnSDz/8oPj4eOd/8sOGDdPMmTMVFRWl33//Xb169dLLL7+sH3/88R/tNx4OD/rxmDVrVg0dOlTjxo3T8ePH0+1z+PBh1atXTy1atNCuXbs0b948bdiwQW+88YazT/v27XXy5EmtXbtW8+fP16RJk/TXX3+5rCdLliz69NNP9fvvv2vGjBlavXq13nnnHUkcs27BwEOvSpUqxpgxYwzDMIzk5GQjT548xpo1a5zLK1eubLRr1y7D9wcHBxujR492aRswYIBRrlw5l7bRo0cbwcHBGa4nNTXV8PX1NRYvXuxsk2R88803Gb4nPDzcaNKkiWEYhuFwOIwVK1YYdrvdiIyMNA4cOGBIMn766Sdn/zNnzhheXl7GV199ZRiGYZQpU8YYOHBguutes2aNIck4f/68YRiGMW3aNMPf3z9Nvxv3//rnN3PmTOfyNm3aGK1atTIMwzASEhKMHDlyGBs3bnRZR6dOnYw2bdpkuJ/493hYjsdnnnnG6Nixo2EYhvHNN98YN/466dSpk/Hqq6+6vHf9+vVGlixZjKtXrxp79+41JBlbt251Lj948KAhKc2+3ejrr782cufO7XzNMWstRiAecvv379eWLVvUpk0bSVK2bNnUqlUrTZ061dlnx44dql279l3f9p9//qkuXbqoRIkS8vf3l5+fn+Li4hQbG2tqPUuWLJGPj488PT1Vv359tWrVSgMHDtTevXuVLVs2Pf30086+uXPnVmhoqPbu3StJ6tGjhz788ENVrVpVAwYM0K5du/7RPmXLlk0tW7bU7NmzJUlXrlzRt99+q3bt2kmSDh06pPj4eD333HPy8fFx/ps5c6YOHz78j7aNB9/DcDxeN3z4cM2YMcN5rN1o586dmj59ussxULduXTkcDh09elT79+9XtmzZVKFCBed7ihcvrpw5c7qsZ+XKlapdu7YKFSokX19fvfLKKzp79qzi4+MzXSfH7L2TzeoCcG9NnTpVKSkpKliwoLPNMAzZ7XZ99tln8vf3l5eXl+n1ZsmSxWXYVZLztMF14eHhOnv2rMaOHavg4GDZ7XZVrlzZefohs2rWrKkJEybIw8NDBQsWVLZsmf+x7dy5s+rWraulS5dq+fLlGjZsmEaOHKk333zTVA03ateuncLCwvTXX39pxYoV8vLyUr169STJOUy6dOlSFSpUyOV9PCsAD8PxeF316tVVt25d9e/fXxERES7L4uLi9Nprr6lHjx5p3lekSBEdOHDgtuuPiYlRo0aN1K1bNw0ZMkS5cuXShg0b1KlTJyUlJZmaJMkxe28QIB5iKSkpmjlzpkaOHKnnn3/eZVnTpk01d+5cde3aVWXLltWqVavUoUOHdNfj4eGh1NRUl7a8efPq9OnTMgzDOZFrx44dLn1++uknjR8/Xg0aNJAk/fHHHzpz5ozp/fD29lbx4sXTtJcqVUopKSnavHmzqlSpIkk6e/as9u/fr9KlSzv7BQUFqWvXruratav69++vyZMnpxsg0tvP9FSpUkVBQUGaN2+eli1bppdeeknZs2eXJJUuXVp2u12xsbEKCwszva94eD0sx+ONPvroI5UvX16hoaEu7RUqVNCePXvSPW4lKTQ0VCkpKdq+fbuefPJJSddGAm68suPXX3+Vw+HQyJEjlSXLtcHyr776ymU9HLPWIkA8xJYsWaLz58+rU6dOznsdXNeiRQtNnTpVXbt21YABA1S7dm0VK1ZMrVu3VkpKir777jv17dtX0rUZzevWrVPr1q1lt9uVJ08e1ahRQ3///bdGjBihF198Ud9//72WLVsmPz8/5zZKlCihWbNmqWLFirp06ZLefvvtO/rrKiMlSpRQkyZN1KVLF02cOFG+vr7q16+fChUqpCZNmkiS3nrrLdWvX1+PPvqozp8/rzVr1qhUqVLpri8kJERxcXFatWqVypUrpxw5cmT4V07btm0VFRWlAwcOaM2aNc52X19fRUZGqlevXnI4HKpWrZouXryon376SX5+fgoPD79r+48Hy8N4PJYpU0bt2rXTp59+6tLet29fPfPMM3rjjTfUuXNneXt7a8+ePVqxYoU+++wzlSxZUnXq1NGrr76qCRMmKHv27OrTp4+8vLycAah48eJKTk7WuHHj1LhxY/3000+Kiopy2Q7HrMWsnICBe6tRo0ZGgwYN0l22efNmQ5Kxc+dOwzAMY/78+Ub58uUNDw8PI0+ePEbz5s2dfTdt2mSULVvWsNvtLhOlJkyYYAQFBRne3t5G+/btjSFDhrhM2tq2bZtRsWJFw9PT0yhRooTx9ddfp5kAJhOTttJz7tw545VXXjH8/f0NLy8vo27dusaBAwecy9944w2jWLFiht1uN/LmzWu88sorxpkzZwzDSDuJ0jAMo2vXrkbu3LkNScaAAQMMw0h/0tqePXsMSUZwcLDhcDhcljkcDmPMmDFGaGiokT17diNv3rxG3bp1jR9//DHD/cDD72E9Ho8ePWp4eHgYN/862bJli/Hcc88ZPj4+hre3t1G2bFljyJAhzuUnT5406tevb9jtdiM4ONiYM2eOERgYaERFRTn7jBo1yihQoIDz2J45cybHrBvhcd4AAMsdP35cQUFBzomTcH8ECADAfbd69WrFxcWpTJkyOnXqlN555x2dOHFCBw4ccM5PgHtjDgQA4L5LTk7Wu+++qyNHjsjX11dVqlTR7NmzCQ8PEEYgAACAadxICgAAmEaAAAAAphEgAACAaQQIAABgGgECAACYRoAAcM9ERESoadOmztc1atTQW2+9dd/rWLt2rWw2my5cuHDftw08rAgQwL9QRESEbDabbDabPDw8VLx4cQ0aNEgpKSn3dLsLFizQ4MGDM9WXX/qAe+NGUsC/VL169TRt2jQlJibqu+++U/fu3ZU9e3b179/fpV9SUpI8PDzuyjZz5cp1V9YDwHqMQAD/Una7Xfnz51dwcLC6deumOnXqaNGiRc7TDkOGDFHBggWdj2r+448/1LJlSwUEBChXrlxq0qSJYmJinOtLTU1V7969FRAQoNy5c+udd97Rzfepu/kURmJiovr27augoCDZ7XYVL15cU6dOVUxMjGrWrClJypkzp2w2myIiIiRJDodDw4YNU9GiReXl5aVy5copOjraZTvfffedHn30UXl5ealmzZoudQK4OwgQACRJXl5eSkpKkiStWrVK+/fv14oVK7RkyRIlJyerbt268vX11fr16/XTTz/Jx8dH9erVc75n5MiRmj59ur744gtt2LBB586d0zfffHPLbbZv315z587Vp59+qr1792rixIny8fFRUFCQ5s+fL0nav3+/Tp06pbFjx0qShg0bppkzZyoqKkq///67evXqpZdfflk//vijpGtBp3nz5mrcuLF27Nihzp07q1+/fvfqYwP+vSx8EigAi9z4WGaHw2GsWLHCsNvtRmRkpBEeHm7ky5fPSExMdPafNWuWERoa6vIY5MTERMPLy8v44YcfDMMwjAIFChgjRoxwLk9OTjYKFy7s8vjnsLAwo2fPnoZhGMb+/fsNScaKFSvSrTG9x60nJCQYOXLkMDZu3OjSt1OnTkabNm0MwzCM/v37G6VLl3ZZ3rdv3zTrAvDPMAcC+JdasmSJfHx8lJycLIfDobZt22rgwIHq3r27ypQp4zLvYefOnTp06JB8fX1d1pGQkKDDhw/r4sWLOnXqlJ5++mnnsmzZsqlixYppTmNct2PHDmXNmlVhYWGZrvnQoUOKj4/Xc88959KelJSkJ554QpK0d+9elzokqXLlypneBoDMIUAA/1I1a9bUhAkT5OHhoYIFCypbtv/778Db29ulb1xcnJ588knNnj07zXry5s17R9v38vIy/Z64uDhJ0tKlS1WoUCGXZXa7/Y7qAHBnCBDAv5S3t7eKFy+eqb4VKlTQvHnzFBgYKD8/v3T7FChQQJs3b1b16tUlSSkpKfr1119VoUKFdPuXKVNGDodDP/74o+rUqZNm+fURkNTUVGdb6dKlZbfbFRsbm+HIRalSpbRo0SKXtp9//vn2OwnAFCZRAritdu3aKU+ePGrSpInWr1+vo0ePau3aterRo4eOHz8uSerZs6c++ugjLVy4UPv27dPrr79+y3s4hISEKDw8XB07dtTChQud6/zqq68kScHBwbLZbFqyZIn+/vtvxcXFydfXV5GRkerVq5dmzJihw4cPa9u2bRo3bpxmzJghSeratasOHjyot99+W/v379ecOXM0ffr0e/0RAf86BAgAt5UjRw6tW7dORYoUUfPmzVWqVCl16tRJCQkJzhGJPn366JVXXlF4eLgqV64sX19fNWvW7JbrnTBhgl588UW9/vrrKlmypLp06aIrV65IkgoVKqQPPvhA/fr1U758+fTGG29IkgYPHqz//ve/GjZsmEqVKqV69epp6dKlKlq0qCSpSJEimj9/vhYuXKhy5copKipKQ4cOvYefDvDvZDMymuEEAACQAUYgAACAaQQIAABgGgECAACYRoAAAACmESAAAIBpBAgAAGAaAQIAAJhGgAAAAKYRIAAAgGkECAAAYBoBAgAAmPb/AJ3T52OwSGgkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %% Confusion Matrix: label vs rule\n",
    "y_true = df['label'].apply(lambda x: 1 if x != 0 else 0)\n",
    "\n",
    "y_pred_rule = df['rule'].apply(lambda x: 1 if x != 0 else 0)\n",
    "plot_confusion_matrix(y_true, y_pred_rule, 'Rulebased')\n",
    "\n",
    "# %% Confusion Matrix: label vs ml\n",
    "y_pred_ml = df['ml'].apply(lambda x: 1 if x != 0 else 0)\n",
    "plot_confusion_matrix(y_true, y_pred_ml, 'ML classifier')\n",
    "\n",
    "# %% Confusion Matrix: label vs llm\n",
    "y_pred_llm = df['llm'].apply(lambda x: 1 if x != 0 else 0)\n",
    "plot_confusion_matrix(y_true, y_pred_llm, 'LLM approach')\n",
    "\n",
    "# %% Confusion Matrix: label vs combined\n",
    "y_pred_combined = df['combined'].apply(lambda x: 1 if x != 0 else 0)\n",
    "plot_confusion_matrix(y_true, y_pred_combined, 'Combined')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a74b2980",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'score'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\prkna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3803\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3802\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\prkna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\_libs\\index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\prkna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\_libs\\index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'score'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [9], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mscore\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mvalue_counts()\n\u001b[0;32m      2\u001b[0m frequency\u001b[38;5;241m=\u001b[39mfrequency\u001b[38;5;241m.\u001b[39msort_index()\n\u001b[0;32m      3\u001b[0m frequency\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\prkna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\frame.py:3805\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3803\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   3804\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3805\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   3807\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[1;32mc:\\Users\\prkna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3803\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3808\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3809\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3810\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: 'score'"
     ]
    }
   ],
   "source": [
    "frequency = df['score'].value_counts()\n",
    "frequency=frequency.sort_index()\n",
    "frequency.plot(kind='bar')\n",
    "plt.xlabel('Integer')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Frequency of Each Integer')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac45e9ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}