specific_metric.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03647e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e131b006",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mglearn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f8df443",
   "metadata": {},
   "source": [
    "# Valid1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51d39490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>output</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3244</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3246</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3247 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      output  target\n",
       "0          0       0\n",
       "1          4       4\n",
       "2         15      15\n",
       "3          1       1\n",
       "4          0       0\n",
       "...      ...     ...\n",
       "3242       0       0\n",
       "3243       0       0\n",
       "3244       1       1\n",
       "3245       1       1\n",
       "3246       1       1\n",
       "\n",
       "[3247 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_name = 'submission_bert_metric_valid2.0'\n",
    "save_path = os.path.join(\"../dataset/metric\", f'{save_name}.csv')\n",
    "dataformetric1 = pd.read_csv(save_path)\n",
    "dataformetric1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d0b3027",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.77      0.76       906\n",
      "           1       0.95      0.94      0.95       433\n",
      "           2       0.57      0.60      0.59        40\n",
      "           3       0.74      0.82      0.78        34\n",
      "           4       0.90      0.90      0.90       209\n",
      "           5       0.91      0.86      0.89       139\n",
      "           6       0.87      0.85      0.86       368\n",
      "           7       0.76      0.76      0.76       119\n",
      "           8       0.50      0.50      0.50        14\n",
      "           9       0.60      1.00      0.75         3\n",
      "          10       0.73      0.79      0.76        28\n",
      "          11       0.53      0.32      0.40        31\n",
      "          12       0.87      0.91      0.89        96\n",
      "          13       0.58      0.58      0.58        19\n",
      "          14       0.55      0.62      0.58        47\n",
      "          15       0.78      0.76      0.77       126\n",
      "          16       0.79      0.85      0.81        13\n",
      "          17       0.86      0.87      0.87        79\n",
      "          18       0.91      0.80      0.85        51\n",
      "          19       0.70      0.50      0.58        14\n",
      "          20       0.75      0.69      0.72       205\n",
      "          21       0.90      0.81      0.85        58\n",
      "          22       0.71      0.83      0.77         6\n",
      "          23       0.88      0.88      0.88         8\n",
      "          24       0.93      0.95      0.94        41\n",
      "          25       0.99      0.98      0.99       114\n",
      "          26       0.65      0.79      0.71        14\n",
      "          27       0.75      0.75      0.75         4\n",
      "          28       0.93      0.82      0.87        17\n",
      "          29       0.90      0.82      0.86        11\n",
      "\n",
      "    accuracy                           0.82      3247\n",
      "   macro avg       0.77      0.78      0.77      3247\n",
      "weighted avg       0.82      0.82      0.82      3247\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(dataformetric1['output'], dataformetric1['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede524b2",
   "metadata": {},
   "source": [
    "print(Confusion_matrix_fromfile(dataformetric2['output'], dataformetric2['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6143a3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 1, figsize=(12, 12))\n",
    "scores_image = mglearn.tools.heatmap(confusion_matrix(dataformetric1['output'], dataformetric1['target']),\n",
    "                                    xlabel='pred_labels', ylabel='target_labels', xticklabels=np.arange(30),\n",
    "                                    yticklabels=np.arange(30), cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title('Confusion Matrix')\n",
    "# axes[0].gca().invert_yaxis()\n",
    "# plt.show()\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de4075a0",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}