1nn_for_glcmpca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(nn.Module):\n",
    "    def __init__(self,insize,outsize):\n",
    "        super().__init__()\n",
    "        self.hidden1=nn.Linear(insize,320)\n",
    "        self.act1=nn.ReLU()\n",
    "        self.hidden2=nn.Linear(320,160)\n",
    "        self.act2=nn.ReLU()\n",
    "        self.hidden3=nn.Linear(160,40)\n",
    "        self.act3=nn.ReLU()\n",
    "        self.hidden4=nn.Linear(40,32)\n",
    "        self.act4=nn.ReLU()\n",
    "        self.hidden5=nn.Linear(32,8)\n",
    "        self.act5=nn.ReLU()\n",
    "        self.hidden6=nn.Linear(8,outsize)\n",
    "        self.act6=nn.Sigmoid()\n",
    "    def forward(self,x):\n",
    "        x=self.act1(self.hidden1(x))\n",
    "        x=self.act2(self.hidden2(x))\n",
    "        x=self.act3(self.hidden3(x))\n",
    "        x=self.act4(self.hidden4(x))\n",
    "        x=self.act5(self.hidden5(x))\n",
    "        x=self.act6(self.hidden6(x))\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(\n",
       "  (hidden1): Linear(in_features=200, out_features=320, bias=True)\n",
       "  (act1): ReLU()\n",
       "  (hidden2): Linear(in_features=320, out_features=160, bias=True)\n",
       "  (act2): ReLU()\n",
       "  (hidden3): Linear(in_features=160, out_features=40, bias=True)\n",
       "  (act3): ReLU()\n",
       "  (hidden4): Linear(in_features=40, out_features=32, bias=True)\n",
       "  (act4): ReLU()\n",
       "  (hidden5): Linear(in_features=32, out_features=8, bias=True)\n",
       "  (act5): ReLU()\n",
       "  (hidden6): Linear(in_features=8, out_features=2, bias=True)\n",
       "  (act6): Sigmoid()\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=Model(200,2)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   Unnamed: 0           0          1          2          3          4   \n",
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       "[5 rows x 202 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('./Output/glcmpca1000pca200.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,1:201].values\n",
    "y=df.iloc[:,-1].values\n",
    "x=torch.FloatTensor(x)\n",
    "y=torch.LongTensor(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.2)\n",
    "sc=StandardScaler()\n",
    "xtrain=sc.fit_transform(xtrain)\n",
    "xtest=sc.transform(xtest)\n",
    "xtrain=torch.FloatTensor(xtrain)\n",
    "xtest=torch.FloatTensor(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer=optim.Adam(model.parameters(),lr=0.001)\n",
    "loss=nn.CrossEntropyLoss()\n",
    "nepoch=50\n",
    "batch_size=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50, train loss : 0.6791\n",
      "Epoch 2/50, train loss : 0.5800\n",
      "Epoch 3/50, train loss : 0.5103\n",
      "Epoch 4/50, train loss : 0.3134\n",
      "Epoch 5/50, train loss : 0.3149\n",
      "Epoch 6/50, train loss : 0.3139\n",
      "Epoch 7/50, train loss : 0.4137\n",
      "Epoch 8/50, train loss : 0.4135\n",
      "Epoch 9/50, train loss : 0.3147\n",
      "Epoch 10/50, train loss : 0.3135\n",
      "Epoch 11/50, train loss : 0.3136\n",
      "Epoch 12/50, train loss : 0.3481\n",
      "Epoch 13/50, train loss : 0.3133\n",
      "Epoch 14/50, train loss : 0.3136\n",
      "Epoch 15/50, train loss : 0.3133\n",
      "Epoch 16/50, train loss : 0.3133\n",
      "Epoch 17/50, train loss : 0.3142\n",
      "Epoch 18/50, train loss : 0.3133\n",
      "Epoch 19/50, train loss : 0.3133\n",
      "Epoch 20/50, train loss : 0.3133\n",
      "Epoch 21/50, train loss : 0.3133\n",
      "Epoch 22/50, train loss : 0.3133\n",
      "Epoch 23/50, train loss : 0.3133\n",
      "Epoch 24/50, train loss : 0.3134\n",
      "Epoch 25/50, train loss : 0.3133\n",
      "Epoch 26/50, train loss : 0.3133\n",
      "Epoch 27/50, train loss : 0.3133\n",
      "Epoch 28/50, train loss : 0.3133\n",
      "Epoch 29/50, train loss : 0.3133\n",
      "Epoch 30/50, train loss : 0.3133\n",
      "Epoch 31/50, train loss : 0.3133\n",
      "Epoch 32/50, train loss : 0.3133\n",
      "Epoch 33/50, train loss : 0.3133\n",
      "Epoch 34/50, train loss : 0.3133\n",
      "Epoch 35/50, train loss : 0.3133\n",
      "Epoch 36/50, train loss : 0.3133\n",
      "Epoch 37/50, train loss : 0.3133\n",
      "Epoch 38/50, train loss : 0.3133\n",
      "Epoch 39/50, train loss : 0.3133\n",
      "Epoch 40/50, train loss : 0.3133\n",
      "Epoch 41/50, train loss : 0.3133\n",
      "Epoch 42/50, train loss : 0.3133\n",
      "Epoch 43/50, train loss : 0.3133\n",
      "Epoch 44/50, train loss : 0.3133\n",
      "Epoch 45/50, train loss : 0.3133\n",
      "Epoch 46/50, train loss : 0.3133\n",
      "Epoch 47/50, train loss : 0.3133\n",
      "Epoch 48/50, train loss : 0.3133\n",
      "Epoch 49/50, train loss : 0.3133\n",
      "Epoch 50/50, train loss : 0.3133\n"
     ]
    }
   ],
   "source": [
    "trainlosses=np.zeros(nepoch)\n",
    "for epoch in range(nepoch):\n",
    "    resloss=0\n",
    "    for i in range(0,len(xtrain),batch_size):\n",
    "        optimizer.zero_grad()\n",
    "        xbatch=xtrain[i:i+batch_size]\n",
    "        ypred=model(xbatch)\n",
    "        trainloss=loss(ypred,ytrain[i:i+batch_size])\n",
    "        trainloss.backward()\n",
    "        optimizer.step()\n",
    "        resloss+=trainloss.item()\n",
    "    trainlosses[epoch]=resloss\n",
    "    print(f\"Epoch {epoch+1}/{nepoch}, train loss : {trainloss.item():.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.plot(trainlosses,label='train loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.8000)\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_pred=model(xtest)\n",
    "y_pred=np.argmax(y_pred,axis=1)\n",
    "accuracy=(y_pred==ytest).float().mean()\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[174,  51],\n",
       "       [ 29, 146]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf=confusion_matrix(y_pred,ytest)\n",
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22666666666666666"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp=cf[0][1]/(cf[0][1]+cf[0][0])\n",
    "fp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAAGdCAYAAAB3v4sOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfqUlEQVR4nO3df3BU9f3v8dfGhCUTmo0B2c12jObbokFrQwoYoxSlZCb8uEg0VtNJaaqMqS0/CmlBM9egba0LaBUDSCrTSu1gfziVXKHTUBp+pI5rgERqaxGlZsBiNxkmDTGxrJE99w+ve93zWSgLJ+zaPh+dM9Occ/ZzPmSG8cX7/fmcdVmWZQkAAOBj0pI9AQAAkHoICAAAwEBAAAAABgICAAAwEBAAAICBgAAAAAwEBAAAYCAgAAAAAwEBAAAY0pM9gY8MHX8r2VMAUs7UovnJngKQkoLHdg3r+E7+NyljzP84NtaFlDIBAQCAlBE5lewZJB0tBgAAYKCCAACAnRVJ9gySjoAAAIBdhIBAQAAAwMaigsAaBAAAYKKCAACAHS0GAgIAAAZaDLQYAACAiQoCAAB2vCiJgAAAgIEWAy0GAABgooIAAIAduxgICAAA2PGiJFoMAAAgDioIAADY0WIgIAAAYKDFQEAAAMDAexBYgwAAAExUEAAAsKPFQEAAAMDAIkVaDAAAwEQFAQAAO1oMBAQAAAy0GGgxAAAAExUEAABsLIv3IBAQAACwYw0CLQYAAGCiggAAgB2LFAkIAAAYaDHQYgAAwBA55dyRgLa2Ns2ZM0d+v18ul0vNzc3GPQcPHtTNN98sj8ejrKwsTZ48WUePHo1eP3nypBYsWKDRo0dr1KhRqqysVHd3d8K/AgICAAApYnBwUEVFRVq/fn3c63/72980ZcoUFRYWavfu3Xr11VfV0NCgkSNHRu9ZunSptm7dqueee0579uzRO++8o1tvvTXhubgsy7LO+U/ioKHjbyV7CkDKmVo0P9lTAFJS8NiuYR3/5N7nHBtr5LVfPqfPuVwubdmyRRUVFdFzVVVVysjI0M9//vO4nzlx4oQuueQSPfvss7rtttskSa+//rrGjx+vYDCo66677qyfTwUBAAC7SMSxIxwOq7+/P+YIh8PnMKWIfvvb3+qKK65QeXm5xo4dq5KSkpg2REdHh4aGhlRWVhY9V1hYqPz8fAWDwYSeR0AAAGAYBQIBeTyemCMQCCQ8Tk9PjwYGBrRy5UrNmDFDv//973XLLbfo1ltv1Z49eyRJoVBII0aMUE5OTsxnvV6vQqFQQs9jFwMAAHYO7mKor69XXV1dzDm3253wOJH/t/Vy7ty5Wrp0qSRpwoQJeumll9TU1KQbb7zx/Cf7MQQEAADsHHwPgtvtPqdAYDdmzBilp6frqquuijk/fvx4vfjii5Ikn8+n999/X319fTFVhO7ubvl8voSeR4sBAIBPgBEjRmjy5Mk6dOhQzPk33nhDl112mSRp4sSJysjIUGtra/T6oUOHdPToUZWWlib0PCoIAADYJelNigMDAzp8+HD0566uLh04cEC5ubnKz8/XsmXLdMcdd2jq1KmaNm2aWlpatHXrVu3evVuS5PF4NH/+fNXV1Sk3N1fZ2dlatGiRSktLE9rBIBEQAAAwJOvbHPfv369p06ZFf/5o7UJNTY02bdqkW265RU1NTQoEAlq8eLGuvPJK/eY3v9GUKVOin3n88ceVlpamyspKhcNhlZeX68knn0x4LrwHAUhhvAcBiG+434Pwr7ZNjo2VOfXrjo11IVFBAADAji9rIiAAAGDgy5oICAAAGKggsM0RAACYqCAAAGBHi4GAAACAgRYDLQYAAGCiggAAgB0tBgICAAAGWgy0GAAAgIkKAgAAdlQQCAgAABhYg0CLAQAAmKggAABgR4uBgAAAgIEWAwEBAAADFQTWIAAAABMVBAAA7GgxEBAAADDQYqDFAAAATFQQAACwo4JAQAAAwGBZyZ5B0tFiAAAABioIAADY0WIgIAAAYCAg0GIAAAAmKggAANjxoiQCAgAABloMBAQAAAxsc2QNAgAAMFFBAADAjhYDAQEAAAMBgRYDAAAwUUEAAMCObY4EBAAA7KwIuxhoMQAAAAMVBAAA7FikSEAAAMDAGgRaDAAAwEQFAQAAOxYpUkEAAMAQiTh3JKCtrU1z5syR3++Xy+VSc3Pzae+955575HK5tGbNmpjzvb29qq6uVnZ2tnJycjR//nwNDAwk/CsgIAAAYJekgDA4OKiioiKtX7/+jPdt2bJFL7/8svx+v3Gturpar732mnbs2KFt27apra1NtbW1Cc1DosUAAEDKmDlzpmbOnHnGe44dO6ZFixZp+/btmj17dsy1gwcPqqWlRfv27dOkSZMkSWvXrtWsWbP06KOPxg0Up0MFAQAAO8ty7AiHw+rv7485wuHwOU0rEolo3rx5WrZsma6++mrjejAYVE5OTjQcSFJZWZnS0tLU3t6e0LMICAAA2DnYYggEAvJ4PDFHIBA4p2mtWrVK6enpWrx4cdzroVBIY8eOjTmXnp6u3NxchUKhhJ5FiwEAgGFUX1+vurq6mHNutzvhcTo6OvTEE0+os7NTLpfLqemdFhUEAADsIpZjh9vtVnZ2dsxxLgHhj3/8o3p6epSfn6/09HSlp6fryJEj+s53vqPLL79ckuTz+dTT0xPzuQ8++EC9vb3y+XwJPY8KAgAAdin4JsV58+aprKws5lx5ebnmzZunO++8U5JUWlqqvr4+dXR0aOLEiZKknTt3KhKJqKSkJKHnJRwQjh8/rp/+9KcKBoPRfobP59P111+vr3/967rkkksSHRIAAEgaGBjQ4cOHoz93dXXpwIEDys3NVX5+vkaPHh1zf0ZGhnw+n6688kpJ0vjx4zVjxgzdfffdampq0tDQkBYuXKiqqqqEdjBICbYY9u3bpyuuuEKNjY3yeDyaOnWqpk6dKo/Ho8bGRhUWFmr//v3/dhwnV3QCAOA4B1sMidi/f7+Ki4tVXFwsSaqrq1NxcbFWrFhx1mNs3rxZhYWFmj59umbNmqUpU6boqaeeSmgekuSyLOusZ3/dddepqKhITU1NxgIJy7J0zz336NVXX1UwGDzjOA8++KC+973vxZy7f9lirVj+7QSmDvznm1o0P9lTAFJS8NiuYR1/MFDj2FhZ9T9zbKwLKaGAkJmZqVdeeUWFhYVxr7/++usqLi7Wv/71rzOOEw6HjYpB2rvHzmnRBvCfjIAAxEdAGH4JrUHw+Xzau3fvaQPC3r175fV6/+04brfbCAND7x9PZCoAAAwfvqwpsYDw3e9+V7W1tero6ND06dOjYaC7u1utra3auHGjHn300WGZKAAAF0wK7mK40BIKCAsWLNCYMWP0+OOP68knn9SpU6ckSRdddJEmTpyoTZs26fbbbx+WiQIAcMFQQUh8m+Mdd9yhO+64Q0NDQzp+/MO2wJgxY5SRkeH45AAAQHKc84uSMjIylJeX5+RcAABIDQl+TfN/It6kCACAHS0GvosBAACYqCAAAGDHLgYCAgAABloMtBgAAICJCgIAADYWuxgICAAAGGgx0GIAAAAmKggAANhRQSAgAABgYJsjAQEAAAMVBNYgAAAAExUEAABsLCoIBAQAAAwEBFoMAADARAUBAAA73qRIQAAAwECLgRYDAAAwUUEAAMCOCgIBAQAAO8siINBiAAAABioIAADY0WIgIAAAYCAgEBAAALDjVcusQQAAAHFQQQAAwI4KAgEBAAADb1qmxQAAAExUEAAAsGGRIgEBAAATAYEWAwAAMFFBAADAjkWKBAQAAOxYg0CLAQAAxEFAAADALuLgkYC2tjbNmTNHfr9fLpdLzc3N0WtDQ0O69957dc011ygrK0t+v19f+9rX9M4778SM0dvbq+rqamVnZysnJ0fz58/XwMBAwr8CAgIAADZWxHLsSMTg4KCKioq0fv1649p7772nzs5ONTQ0qLOzU88//7wOHTqkm2++Oea+6upqvfbaa9qxY4e2bdumtrY21dbWJvw7cFmWlRKNlqHjbyV7CkDKmVo0P9lTAFJS8NiuYR2/d+6Njo2V+3/2nNPnXC6XtmzZooqKitPes2/fPl177bU6cuSI8vPzdfDgQV111VXat2+fJk2aJElqaWnRrFmz9Pe//11+v/+sn08FAQCAYRQOh9Xf3x9zhMNhR8Y+ceKEXC6XcnJyJEnBYFA5OTnRcCBJZWVlSktLU3t7e0JjExAAALCxIs4dgUBAHo8n5ggEAuc9x5MnT+ree+/VV77yFWVnZ0uSQqGQxo4dG3Nfenq6cnNzFQqFEhqfbY4AANg5+B6E+vp61dXVxZxzu93nNebQ0JBuv/12WZalDRs2nNdYp0NAAABgGLnd7vMOBB/3UTg4cuSIdu7cGa0eSJLP51NPT0/M/R988IF6e3vl8/kSeg4tBgAAbJxsMTjpo3Dw5ptv6g9/+INGjx4dc720tFR9fX3q6OiIntu5c6cikYhKSkoSehYVBAAA7JL0quWBgQEdPnw4+nNXV5cOHDig3Nxc5eXl6bbbblNnZ6e2bdumU6dORdcV5ObmasSIERo/frxmzJihu+++W01NTRoaGtLChQtVVVWV0A4GiW2OQEpjmyMQ33Bvczxe7tw2xzHbz36b4+7duzVt2jTjfE1NjR588EEVFBTE/dyuXbt00003SfrwRUkLFy7U1q1blZaWpsrKSjU2NmrUqFEJzZsKAgAANk63Bs7WTTfdpDP9u/1s/k2fm5urZ5999rznQkAAAMAmWQEhlRAQAACwISCwiwEAAMRBBQEAADvLlewZJB0BAQAAG1oMtBgAAEAcVBAAALCxIrQYCAgAANjQYqDFAAAA4qCCAACAjcUuBgICAAB2tBhoMQAAgDioIAAAYMMuBgICAACGs/jSxP94BAQAAGyoILAGAQAAxEEFAQAAGyoIBAQAAAysQaDFAAAA4qCCAACADS0GAgIAAAZetUyLAQAAxEEFAQAAG76LgYAAAIAhQouBFgMAADBRQQAAwIZFigQEAAAMbHMkIAAAYOBNiqxBAAAAcVBBAADAhhYDAQEAAAPbHGkxAACAOKggAABgwzZHAgIAAAZ2MdBiAAAAcVBBAADAhkWKBAQAAAysQaDFAAAA4qCCAACADYsUCQgAABhYg5BCASHT/8VkTwFIOe9unJfsKQD/lZK1BqGtrU2PPPKIOjo69I9//ENbtmxRRUXFx+Zl6YEHHtDGjRvV19enG264QRs2bNC4ceOi9/T29mrRokXaunWr0tLSVFlZqSeeeEKjRo1KaC6sQQAAIEUMDg6qqKhI69evj3t99erVamxsVFNTk9rb25WVlaXy8nKdPHkyek91dbVee+017dixQ9u2bVNbW5tqa2sTnkvKVBAAAEgVyWoxzJw5UzNnzox7zbIsrVmzRvfff7/mzp0rSXrmmWfk9XrV3NysqqoqHTx4UC0tLdq3b58mTZokSVq7dq1mzZqlRx99VH6//6znQgUBAAAby8EjHA6rv78/5giHwwnPqaurS6FQSGVlZdFzHo9HJSUlCgaDkqRgMKicnJxoOJCksrIypaWlqb29PaHnERAAABhGgUBAHo8n5ggEAgmPEwqFJElerzfmvNfrjV4LhUIaO3ZszPX09HTl5uZG7zlbtBgAALBxssVQX1+vurq6mHNut9ux8YcLAQEAABsndzG43W5HAoHP55MkdXd3Ky8vL3q+u7tbEyZMiN7T09MT87kPPvhAvb290c+fLVoMAAB8AhQUFMjn86m1tTV6rr+/X+3t7SotLZUklZaWqq+vTx0dHdF7du7cqUgkopKSkoSeRwUBAACbSJKeOzAwoMOHD0d/7urq0oEDB5Sbm6v8/HwtWbJEDz30kMaNG6eCggI1NDTI7/dH35Uwfvx4zZgxQ3fffbeampo0NDSkhQsXqqqqKqEdDBIBAQAAg6XkbHPcv3+/pk2bFv35o7ULNTU12rRpk5YvX67BwUHV1taqr69PU6ZMUUtLi0aOHBn9zObNm7Vw4UJNnz49+qKkxsbGhOfisqzUeON0+ohPJ3sKQMrhTYpAfJk1K4d1/Dbflx0ba2roOcfGupCoIAAAYBNJiX86JxcBAQAAm0iSWgyphIAAAIBNstYgpBK2OQIAAAMVBAAAbJK1zTGVEBAAALChxUCLAQAAxEEFAQAAG1oMBAQAAAwEBFoMAAAgDioIAADYsEiRgAAAgCFCPqDFAAAATFQQAACw4bsYCAgAABj4MkcCAgAABrY5sgYBAADEQQUBAACbiIs1CAQEAABsWINAiwEAAMRBBQEAABsWKRIQAAAw8CZFWgwAACAOKggAANjwJkUCAgAABnYx0GIAAABxUEEAAMCGRYoEBAAADGxzJCAAAGBgDQJrEAAAQBxUEAAAsGENAgEBAAADaxBoMQAAgDioIAAAYEMFgYAAAIDBYg0CLQYAAGCiggAAgA0tBgICAAAGAgItBgAAEAcVBAAAbHjVMhUEAAAMEZdzRyJOnTqlhoYGFRQUKDMzU5/5zGf0gx/8QJb1/yOLZVlasWKF8vLylJmZqbKyMr355psO/wYICAAAGCIOHolYtWqVNmzYoHXr1ungwYNatWqVVq9erbVr10bvWb16tRobG9XU1KT29nZlZWWpvLxcJ0+ePJ8/soEWAwAAKeKll17S3LlzNXv2bEnS5Zdfrl/84hfau3evpA+rB2vWrNH999+vuXPnSpKeeeYZeb1eNTc3q6qqyrG5UEEAAMDGyQpCOBxWf39/zBEOh+M+9/rrr1dra6veeOMNSdKf/vQnvfjii5o5c6YkqaurS6FQSGVlZdHPeDwelZSUKBgMOvo7ICAAAGBjOXgEAgF5PJ6YIxAIxH3ufffdp6qqKhUWFiojI0PFxcVasmSJqqurJUmhUEiS5PV6Yz7n9Xqj15xCiwEAgGFUX1+vurq6mHNutzvuvb/+9a+1efNmPfvss7r66qt14MABLVmyRH6/XzU1NRdiulEEBAAAbBLdfXAmbrf7tIHAbtmyZdEqgiRdc801OnLkiAKBgGpqauTz+SRJ3d3dysvLi36uu7tbEyZMcG7SosUAAIAhWbsY3nvvPaWlxf6n+aKLLlIk8uFIBQUF8vl8am1tjV7v7+9Xe3u7SktLE3zamVFBAAAgRcyZM0c//OEPlZ+fr6uvvlqvvPKKHnvsMd11112SJJfLpSVLluihhx7SuHHjVFBQoIaGBvn9flVUVDg6FwICAAA2yXqT4tq1a9XQ0KBvfetb6unpkd/v1ze+8Q2tWLEies/y5cs1ODio2tpa9fX1acqUKWppadHIkSMdnYvL+vjrmZIofcSnkz0FIOW8u3FesqcApKTMmpXDOv4PL6t2bKz/fWSzY2NdSKxBAAAABloMAADY8HXPBAQAAAwp0XtPMgICAAA2VBBYgwAAAOKgggAAgI2Tb1L8pCIgAABgE2EVAi0GAABgooIAAIAN9QMCAgAABnYx0GIAAABxUEEAAMCGRYoEBAAADMQDWgwAACAOKggAANiwSJGAAACAgTUIBAQAAAzEA9YgAACAOKggAABgwxoEAgIAAAaLJgMtBgAAYKKCAACADS0GAgIAAAa2OdJiAAAAcVBBAADAhvoBAQEAAAMtBloMAAAgDioIAADYsIuBgAAAgIEXJREQAAAwUEEYhjUIb7/9tu66664z3hMOh9Xf3x9zWBZpDQCAVOF4QOjt7dXPfvazM94TCATk8XhiDivyrtNTAQDgnFgO/u+TKuEWwwsvvHDG62+99da/HaO+vl51dXUx5y4eXZjoVAAAGBa0GM4hIFRUVMjlcp2xJeByuc44htvtltvtTugzAADgwkm4xZCXl6fnn39ekUgk7tHZ2Tkc8wQA4IKJWJZjxydVwgFh4sSJ6ujoOO31f1ddAAAg1VkOHp9UCbcYli1bpsHBwdNe/+xnP6tdu3ad16QAAEByJRwQvvjFL57xelZWlm688cZznhAAAMnGdzHwoiQAAAyf5O2JTuHLmgAAgIGAAACATcTBI1HHjh3TV7/6VY0ePVqZmZm65pprtH///uh1y7K0YsUK5eXlKTMzU2VlZXrzzTfP9Y96WgQEAABsIrIcOxLxz3/+UzfccIMyMjL0u9/9Tn/961/1ox/9SBdffHH0ntWrV6uxsVFNTU1qb29XVlaWysvLdfLkSUd/B6xBAADAJllrEFatWqVLL71UTz/9dPRcQUFB9P9blqU1a9bo/vvv19y5cyVJzzzzjLxer5qbm1VVVeXYXKggAAAwjOJ9QWE4HI577wsvvKBJkybpy1/+ssaOHavi4mJt3Lgxer2rq0uhUEhlZWXRcx6PRyUlJQoGg47Om4AAAICNk2sQ4n1BYSAQiPvct956Sxs2bNC4ceO0fft2ffOb39TixYujX4IYCoUkSV6vN+ZzXq83es0ptBgAALBx8o3A8b6g0P59RB+JRCKaNGmSHn74YUlScXGx/vKXv6ipqUk1NTWOzelsUEEAAGAYud1uZWdnxxynCwh5eXm66qqrYs6NHz9eR48elST5fD5JUnd3d8w93d3d0WtOISAAAGCTrF0MN9xwgw4dOhRz7o033tBll10m6cMFiz6fT62trdHr/f39am9vV2lp6fn/wT+GFgMAADbn8v4CJyxdulTXX3+9Hn74Yd1+++3au3evnnrqKT311FOSPvxCxCVLluihhx7SuHHjVFBQoIaGBvn9flVUVDg6FwICAAApYvLkydqyZYvq6+v1/e9/XwUFBVqzZo2qq6uj9yxfvlyDg4Oqra1VX1+fpkyZopaWFo0cOdLRubisFPlu5vQRn072FICU8+7GecmeApCSMmtWDuv4/yt/tmNjbTv6W8fGupCoIAAAYMO3ObJIEQAAxEEFAQAAmxTpvicVAQEAAJtk7WJIJQQEAABskvVlTamENQgAAMBABQEAABt2MRAQAAAwsEiRFgMAAIiDCgIAADa0GAgIAAAY2MVAiwEAAMRBBQEAAJsIixQJCAAA2BEPaDEAAIA4qCAAAGDDLgYCAgAABgICAQEAAANvUmQNAgAAiIMKAgAANrQYCAgAABh4kyItBgAAEAcVBAAAbFikSEAAAMDAGgRaDAAAIA4qCAAA2NBiICAAAGCgxUCLAQAAxEEFAQAAG96DQEAAAMAQYQ0CAQEAADsqCKxBAAAAcVBBAADAhhYDAQEAAAMtBloMAAAgDioIAADY0GIgIAAAYKDFQIsBAADEQQUBAAAbWgwEBAAADLQYaDEAAJCSVq5cKZfLpSVLlkTPnTx5UgsWLNDo0aM1atQoVVZWqru7e1ieT0AAAMDGsiKOHedi3759+vGPf6zPf/7zMeeXLl2qrVu36rnnntOePXv0zjvv6NZbb3Xij2wgIAAAYBOR5diRqIGBAVVXV2vjxo26+OKLo+dPnDihn/zkJ3rsscf0pS99SRMnTtTTTz+tl156SS+//LKTf3xJBAQAAAyWZTl2hMNh9ff3xxzhcPi0z16wYIFmz56tsrKymPMdHR0aGhqKOV9YWKj8/HwFg0HHfwcEBAAAhlEgEJDH44k5AoFA3Ht/+ctfqrOzM+71UCikESNGKCcnJ+a81+tVKBRyfN7sYgAAwOZcWgOnU19fr7q6uphzbrfbuO/tt9/Wt7/9be3YsUMjR4507PnnioAAAICN5eB7ENxud9xAYNfR0aGenh594QtfiJ47deqU2tratG7dOm3fvl3vv/+++vr6YqoI3d3d8vl8js33IwQEAABSwPTp0/XnP/855tydd96pwsJC3Xvvvbr00kuVkZGh1tZWVVZWSpIOHTqko0ePqrS01PH5EBAAALBJxpsUP/WpT+lzn/tczLmsrCyNHj06en7+/Pmqq6tTbm6usrOztWjRIpWWluq6665zfD4EBAAAbFL1TYqPP/640tLSVFlZqXA4rPLycj355JPD8iyX5WSj5Tykj/h0sqcApJx3N85L9hSAlJRZs3JYx/fljHdsrFDfQcfGupCoIAAAYJMi/3ZOKgICAAA2Tm5z/KTiRUkAAMBABQEAABtaDAQEAAAMydjmmGoICAAA2FBBYA0CAACIgwoCAAA27GIgIAAAYKDFQIsBAADEQQUBAAAbdjEQEAAAMKTqlzVdSLQYAACAgQoCAAA2tBgICAAAGNjFQIsBAADEQQUBAAAbFikSEAAAMNBiICAAAGAgILAGAQAAxEEFAQAAG+oHksuijoKPCYfDCgQCqq+vl9vtTvZ0gJTA3wv8NyIgIEZ/f788Ho9OnDih7OzsZE8HSAn8vcB/I9YgAAAAAwEBAAAYCAgAAMBAQEAMt9utBx54gIVYwMfw9wL/jVikCAAADFQQAACAgYAAAAAMBAQAAGAgIAAAAAMBAVHr16/X5ZdfrpEjR6qkpER79+5N9pSApGpra9OcOXPk9/vlcrnU3Nyc7CkBFwwBAZKkX/3qV6qrq9MDDzygzs5OFRUVqby8XD09PcmeGpA0g4ODKioq0vr165M9FeCCY5sjJEklJSWaPHmy1q1bJ0mKRCK69NJLtWjRIt13331Jnh2QfC6XS1u2bFFFRUWypwJcEFQQoPfff18dHR0qKyuLnktLS1NZWZmCwWASZwYASBYCAnT8+HGdOnVKXq835rzX61UoFErSrAAAyURAAAAABgICNGbMGF100UXq7u6OOd/d3S2fz5ekWQEAkomAAI0YMUITJ05Ua2tr9FwkElFra6tKS0uTODMAQLKkJ3sCSA11dXWqqanRpEmTdO2112rNmjUaHBzUnXfemeypAUkzMDCgw4cPR3/u6urSgQMHlJubq/z8/CTODBh+bHNE1Lp16/TII48oFAppwoQJamxsVElJSbKnBSTN7t27NW3aNON8TU2NNm3adOEnBFxABAQAAGBgDQIAADAQEAAAgIGAAAAADAQEAABgICAAAAADAQEAABgICAAAwEBAAAAABgICAAAwEBAAAICBgAAAAAwEBAAAYPi/CiSv+7qCPuIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=[[172,57],[20,151]]\n",
    "plt.title(\"ConfusionMatrix\")\n",
    "plt.xticks([0,1],[\"Actual Cover\",\"Actual Stego\"])\n",
    "plt.yticks([0,1],[\"Predicted Cover\",\"Predicted Stego\"])\n",
    "sns.heatmap(cm,annot=True,fmt=\".0f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TP = 151\n",
    "FP = 57\n",
    "TN = 172\n",
    "FN = 20\n",
    "\n",
    "# Calculate true positive rate (sensitivity) and false positive rate\n",
    "tpr = TP / (TP + FN)\n",
    "fpr = FP / (FP + TN)\n",
    "x=[0,fpr,1]\n",
    "y=[0,tpr,1]\n",
    "# Plot ROC curve\n",
    "plt.plot(x, y, marker='o', label='ROC curve')\n",
    "\n",
    "# Plot diagonal line (random classifier)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', label='Random classifier')\n",
    "\n",
    "# Set labels and title\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}