confusion_matrix.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import confusion_matrix,f1_score\n",
        "class Evaluation():\n",
        "    def __init__(self,ground_truth,predicted):\n",
        "        \n",
        "        self.ground_truth = ground_truth\n",
        "        self.predicted    = predicted\n",
        "        ip_matrix = np.column_stack((self.ground_truth, self.predicted))\n",
        "        c_matrix = confusion_matrix(ground_truth, predicted)\n",
        "        \n",
        "        recall = self.getRecall(c_matrix)\n",
        "        precision = self.getPrecision(c_matrix)\n",
        "        self.f1_score = f1_score(ground_truth, predicted, average='macro')\n",
        "        Accuracy = self.getAccuracy(self.ground_truth, self.predicted,c_matrix)\n",
        "\n",
        "        \n",
        "    \n",
        "    def getAccuracy(self,ground_truth, predicted,c_matrix):\n",
        "        tp = np.diag(c_matrix)\n",
        "        accuracy =tp/len(ground_truth)\n",
        "        return accuracy \n",
        "        print(accuracy) \n",
        "    \n",
        "\n",
        "    \n",
        "    def getPrecision(self,c_matrix):\n",
        "        j = 1\n",
        "        precision = []\n",
        "        for i in range(len(c_matrix)):\n",
        "            precision_val = c_matrix[i][i] / (c_matrix[i][i] + c_matrix[j][i])\n",
        "            precision  +=precision_val\n",
        "            j -= 1\n",
        "        return precision\n",
        "        print(precision)\n",
        "    \n",
        "    def getRecall(self,c_matrix):\n",
        "        j = 1\n",
        "        recall = []\n",
        "        for i in range(len(c_matrix)):\n",
        "\n",
        "            recall_val = c_matrix[i][i] / (c_matrix[i][i] + c_matrix[i][j])\n",
        "            recall  += recall_val\n",
        "            j -= 1\n",
        "        return recall\n",
        "        print(recall)\n",
        "    \n",
        "    def getF1Score(self):\n",
        "        return self.f1_score\n",
        "        print(self.f1_score)\n",
        "    \n",
        "    \n",
        "\n",
        "\n",
        "    \n",
        "ground_truth = [0,1,0,0,0,1,1,0,0,1]\n",
        "predicted    = [0,0,0,1,0,1,0,1,0,0]\n",
        "        \n",
        "obj = Evaluation(ground_truth,predicted)\n"
      ]
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