confusion_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.models as models\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import copy\n",
    "from collections import namedtuple\n",
    "import os,glob\n",
    "import random\n",
    "import time\n",
    "\n",
    "import cv2 \n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"classification_results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Predicted Class</th>\n",
       "      <th>Predicted Color</th>\n",
       "      <th>Original Class</th>\n",
       "      <th>Original Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pre_filtered</td>\n",
       "      <td>red</td>\n",
       "      <td>AUR</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pre_filtered</td>\n",
       "      <td>red</td>\n",
       "      <td>AUR</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pre_filtered</td>\n",
       "      <td>red</td>\n",
       "      <td>AUR</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pre_filtered</td>\n",
       "      <td>red</td>\n",
       "      <td>AUR</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pre_filtered</td>\n",
       "      <td>red</td>\n",
       "      <td>AUR</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>BPH</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3558</th>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3560</th>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "      <td>Normal</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3561 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predicted Class Predicted Color Original Class Original Category\n",
       "0       pre_filtered             red            AUR               red\n",
       "1       pre_filtered             red            AUR               red\n",
       "2       pre_filtered             red            AUR               red\n",
       "3       pre_filtered             red            AUR               red\n",
       "4       pre_filtered             red            AUR               red\n",
       "...              ...             ...            ...               ...\n",
       "3556          Normal           green         Normal             green\n",
       "3557             BPH          yellow         Normal             green\n",
       "3558          Normal           green         Normal             green\n",
       "3559          Normal           green         Normal             green\n",
       "3560          Normal           green         Normal             green\n",
       "\n",
       "[3561 rows x 4 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(df['Original Category'], df['Predicted Color'], labels=df['Original Category'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=df['Original Category'].unique(), yticklabels=df['Original Category'].unique())\n",
    "plt.xlabel('Predicted Color')\n",
    "plt.ylabel('Original Category')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "task3_hs2",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}