Compute Performance metrics from scratch.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s0Ej_bXyQvnV"
   },
   "source": [
    "# Compute performance metrics for the given Y and Y_score from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Performance metrics for the dataset in which positive positive points >> number of negative points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "4CHb6NE7Qvnc"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
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    "id": "s9B1734xY8nL",
    "outputId": "3885e4b3-c677-409e-98a4-3f6bae8bd814"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
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    "id": "WaFLW7oBQvnt",
    "outputId": "522b4718-03a3-45fe-bcbc-bb68e8cc6a93"
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   "outputs": [
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      "text/plain": [
       "         y     proba\n",
       "0      1.0  0.637387\n",
       "1      1.0  0.635165\n",
       "2      1.0  0.766586\n",
       "3      1.0  0.724564\n",
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       "\n",
       "[10100 rows x 2 columns]"
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     "execution_count": 3,
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   "source": [
    "df_a=pd.read_csv('/content/drive/MyDrive/aaic/5_a.csv')\n",
    "df_a"
   ]
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  {
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   "execution_count": 5,
   "metadata": {
    "id": "XZp3sGqHFPub"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "5309PhVIPi5j"
   },
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   "source": [
    "df_a['y_proba']=0\n",
    "def ap(df):\n",
    "  if df['proba']>0.5:\n",
    "    return 1\n",
    "  else:\n",
    "    return 0  \n",
    "df_a['y_proba']=df_a.apply(ap,axis=1)"
   ]
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      "text/plain": [
       "         y     proba  y_proba\n",
       "0      1.0  0.637387        1\n",
       "1      1.0  0.635165        1\n",
       "2      1.0  0.766586        1\n",
       "3      1.0  0.724564        1\n",
       "4      1.0  0.889199        1\n",
       "...    ...       ...      ...\n",
       "10095  1.0  0.665371        1\n",
       "10096  1.0  0.607961        1\n",
       "10097  1.0  0.777724        1\n",
       "10098  1.0  0.846036        1\n",
       "10099  1.0  0.679507        1\n",
       "\n",
       "[10100 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#added a column with y_proba which says wheather the output is o or 1 given threshold =0.5\n",
    "df_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "dL-vymoJVk5k"
   },
   "outputs": [],
   "source": [
    "def confusion_matrix(df_a):\n",
    "  cm=np.empty(shape=(2,2))\n",
    "  #True positive \n",
    "  cm[1][1]=len(df_a[(df_a['y']==1) & (df_a['y_proba']==1)])\n",
    "  #True Negative\n",
    "  cm[0][0]=len(df_a[(df_a['y']==0) & (df_a['y_proba']==0)])\n",
    "  #False Positive\n",
    "  cm[0][1]=len(df_a[(df_a['y']==1) & (df_a['y_proba']==0)])\n",
    "  #False Negative\n",
    "  cm[1][0]=len(df_a[(df_a['y']==0) & (df_a['y_proba']==1)])\n",
    "  return cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "L7GoH8Z3WBlR"
   },
   "outputs": [],
   "source": [
    "#confusion matrix \n",
    "cm=confusion_matrix(df_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "array([[    0.,     0.],\n",
       "       [  100., 10000.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "H3HNassXYg-l"
   },
   "outputs": [],
   "source": [
    "def F1_score(cm):\n",
    "  return((cm[1][1])/(cm[1][1]+0.5*(cm[1][0]+cm[0][1])))"
   ]
  },
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    },
    "id": "G73RFSR-UwVu",
    "outputId": "60a21d5b-6373-4b84-fca8-749a8549514b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score is : 0.9950248756218906\n"
     ]
    }
   ],
   "source": [
    "#F1 score \n",
    "print(\"F1 score is : \" ,end='')\n",
    "print(F1_score(cm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "rFBrGrIaizMI"
   },
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "91_wybLIxhtk",
    "outputId": "16918dd8-cea3-4f7c-ace3-fc7769d9bc14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.637387\n",
       "1        0.635165\n",
       "2        0.766586\n",
       "3        0.724564\n",
       "4        0.889199\n",
       "           ...   \n",
       "10095    0.665371\n",
       "10096    0.607961\n",
       "10097    0.777724\n",
       "10098    0.846036\n",
       "10099    0.679507\n",
       "Name: proba, Length: 10100, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a['proba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "TEvc3_v8hkxU",
    "outputId": "aa6cf406-5754-494f-8349-ba2d7ef8d8ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.,     0.],\n",
       "       [  100., 10000.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "vp-mUchgYlkX"
   },
   "outputs": [],
   "source": [
    "def roc(df_a):\n",
    "  uuu=sorted(list(set(df_a['proba'])),reverse=True)\n",
    "  tpr_list=[]\n",
    "  fpr_list=[]\n",
    "  #no_of_points=len(df_a)\n",
    "  for i in tqdm(range(len(uuu))):\n",
    "    thre=uuu[i]\n",
    "    def ap(df):\n",
    "      if df['proba']>=thre:\n",
    "        return 1\n",
    "      else:\n",
    "        return 0  \n",
    "    df_a['y_proba']=df_a.apply(ap,axis=1)\n",
    "    cm=confusion_matrix(df_a)\n",
    "    no_of_p_points=cm[0][1]+cm[1][1]\n",
    "    no_of_n_points=cm[0][0]+cm[1][0]\n",
    "    tpr=cm[1][1]/no_of_p_points\n",
    "    fpr=cm[1][0]/no_of_n_points\n",
    "    tpr_list.append(tpr)\n",
    "    fpr_list.append(fpr)\n",
    "  return tpr_list,fpr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 48,
     "referenced_widgets": [
      "e16ef503cdc749da91a8800d6b9f3064",
      "e0ad301790fe4c789e1c44f9c46f95e0",
      "1081639256b14e279ba3d73ae9997d70",
      "a0debbb26d424eb5a6e167f900635800",
      "b5d43fbf15054deb8ff74c53a91cf5c4",
      "ff4de27f789e4382a0c83f41c7bb3733",
      "4fcf3cf23ffb46fc8d4f53558c7ebf32",
      "058a49b742464d43afe7d99969875a01",
      "49587b1eb54946d89d1e25628fbcbf6a",
      "1b1f20e3114c47dcb7e1d13ad572f68d",
      "39213bd3fe5140e1b96589261eb5225f"
     ]
    },
    "id": "QyZFXfpVf4xv",
    "outputId": "7a395695-00cd-4a9b-bcdd-dd37eaad1342"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e16ef503cdc749da91a8800d6b9f3064",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpr,fpr=roc(df_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "Ow6WVxdoiFt1"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "yw-QvLgigbyZ",
    "outputId": "0083ed96-4408-4580-da23-82514e45d762"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC CURVE')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr,tpr)\n",
    "plt.legend()\n",
    "plt.xlabel('fpr')\n",
    "plt.ylabel('tpr')\n",
    "plt.title('ROC CURVE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "Bl0eqemCkTqo",
    "outputId": "ef055b2b-4f6c-4e3d-e733-1cc3cbf34070"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-49e145ba-843f-4fff-afe4-f642b9a00c08\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tpr</th>\n",
       "      <th>fpr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0001</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0002</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10095</th>\n",
       "      <td>0.9996</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>10097</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>10099</th>\n",
       "      <td>1.0000</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10100 rows × 2 columns</p>\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "          const element = document.querySelector('#df-49e145ba-843f-4fff-afe4-f642b9a00c08');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "          tpr  fpr\n",
       "0      0.0001  0.0\n",
       "1      0.0002  0.0\n",
       "2      0.0003  0.0\n",
       "3      0.0004  0.0\n",
       "4      0.0005  0.0\n",
       "...       ...  ...\n",
       "10095  0.9996  1.0\n",
       "10096  0.9997  1.0\n",
       "10097  0.9998  1.0\n",
       "10098  0.9999  1.0\n",
       "10099  1.0000  1.0\n",
       "\n",
       "[10100 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'tpr':tpr,'fpr':fpr})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "qHrNsAmj3qZs",
    "outputId": "fc12dd87-9bd5-4e05-9f85-411bfd68ed81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC is  0.48829900000000004\n"
     ]
    }
   ],
   "source": [
    "#auc \n",
    "print('AUC is ',np.trapz(tpr,fpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "mn_cNvTZo9Rp"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "_IjuwNKB4CIH",
    "outputId": "b55f61ec-6e64-4990-e0b1-44d71a9cb28c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is : "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9900990099009901"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy is \n",
    "print('accuracy is : ',end='')\n",
    "len(df_a[df_a['y']==df_a['y_proba']])/len(df_a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Performance metrics for the dataset in which positive positive points << number of negative points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "U2sKlq0YQvn5",
    "outputId": "ff0b7321-0e44-4672-d6e2-f292a9ac943d"
   },
   "outputs": [
    {
     "data": {
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       "      <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>y</th>\n",
       "      <th>proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.281035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.465152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.352793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.276648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79e68279-c5d5-4c57-951c-7831256744ea')\"\n",
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       "        \n",
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       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
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       "          document.querySelector('#df-79e68279-c5d5-4c57-951c-7831256744ea button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
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       "          const element = document.querySelector('#df-79e68279-c5d5-4c57-951c-7831256744ea');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "  "
      ],
      "text/plain": [
       "     y     proba\n",
       "0  0.0  0.281035\n",
       "1  0.0  0.465152\n",
       "2  0.0  0.352793\n",
       "3  0.0  0.157818\n",
       "4  0.0  0.276648"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_b=pd.read_csv('/content/drive/MyDrive/aaic/5_b.csv')\n",
    "df_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "buDo1QNM7K6v"
   },
   "outputs": [],
   "source": [
    "df_b['y_proba']=0\n",
    "def ap(df):\n",
    "  if df['proba']>0.5:\n",
    "    return 1\n",
    "  else:\n",
    "    return 0  \n",
    "df_b['y_proba']=df_b.apply(ap,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "xlLVa-cVAfCS"
   },
   "outputs": [],
   "source": [
    "cm=confusion_matrix(df_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "SAy7_Pej7YE1",
    "outputId": "7c310309-913c-45ed-b0ae-17305be6008d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9761.,   45.],\n",
       "       [ 239.,   55.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "frh_IbQ5_of3"
   },
   "outputs": [],
   "source": [
    "def roc(df_a):\n",
    "  uuu=sorted(list(set(df_a['proba'])),reverse=True)\n",
    "  tpr_list=[]\n",
    "  fpr_list=[]\n",
    "  #no_of_points=len(df_a)\n",
    "  for i in tqdm(range(len(uuu))):\n",
    "    thre=uuu[i]\n",
    "    def ap(df):\n",
    "      if df['proba']>=thre:\n",
    "        return 1\n",
    "      else:\n",
    "        return 0  \n",
    "    df_a['y_proba']=df_a.apply(ap,axis=1)\n",
    "    cm=confusion_matrix(df_a)\n",
    "    no_of_p_points=cm[0][1]+cm[1][1]\n",
    "    no_of_n_points=cm[0][0]+cm[1][0]\n",
    "    tpr=cm[1][1]/no_of_p_points\n",
    "    fpr=cm[1][0]/no_of_n_points\n",
    "    tpr_list.append(tpr)\n",
    "    fpr_list.append(fpr)\n",
    "  return tpr_list,fpr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 48,
     "referenced_widgets": [
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      "3770912d383d41d2afea0db3f8baee75",
      "ce09245fdd4146dc959bb9fd81c9bc08",
      "ed9fd5f7e3794ccc80c7b325a9f02401",
      "4817e0db5ce345cc8957403446c6481b",
      "5b4f89e9ada24f83980cf914699e4b87",
      "d1812a777f9b4ad69baa42f53d7f521f",
      "1400605899284193b59cc04f81acaec0"
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    },
    "id": "h_t-Y_MmMuxv",
    "outputId": "fdb2fc66-80bb-4277-9f94-e7c8446e0ad7"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a51ff0b03bb44a1e91095c24758235d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpr,fpr=roc(df_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "yYkRZO947YJI",
    "outputId": "0a53c490-0775-49b6-d07c-0264f9fd91f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27918781725888325"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F1_score(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "PsvnsmCU7iZF",
    "outputId": "4ddf1ea0-2150-4ad0-d90f-c5a6f0101398"
   },
   "outputs": [
    {
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       "      <div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>tpr</th>\n",
       "      <th>fpr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0000</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.0000</td>\n",
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       "    <tr>\n",
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       "<p>10100 rows × 2 columns</p>\n",
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       "      background-color: #434B5C;\n",
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       "          document.querySelector('#df-72380524-b344-47af-9394-d246d70dd2c8 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-72380524-b344-47af-9394-d246d70dd2c8');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        tpr     fpr\n",
       "0      0.01  0.0000\n",
       "1      0.02  0.0000\n",
       "2      0.03  0.0000\n",
       "3      0.04  0.0000\n",
       "4      0.05  0.0000\n",
       "...     ...     ...\n",
       "10095  1.00  0.9996\n",
       "10096  1.00  0.9997\n",
       "10097  1.00  0.9998\n",
       "10098  1.00  0.9999\n",
       "10099  1.00  1.0000\n",
       "\n",
       "[10100 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'tpr':tpr,'fpr':fpr})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "Dm35xMvyNN99",
    "outputId": "582717bf-11b1-4075-da81-6db48ddfa02e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC is  0.9377570000000001\n"
     ]
    }
   ],
   "source": [
    "#auc \n",
    "print('AUC is ',np.trapz(tpr,fpr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "b0_MmqFdNODd",
    "outputId": "d1ff9cab-b7d8-438e-e244-2b012596786a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is : "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9900990099009901"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy is \n",
    "print('accuracy is : ',end='')\n",
    "len(df_a[df_a['y']==df_a['y_proba']])/len(df_a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "id": "kHFULXjmNOIh",
    "outputId": "41d01b97-a98e-4b88-c5c0-bc9e1e8b90f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ROC CURVE')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(fpr,tpr)\n",
    "plt.legend()\n",
    "plt.xlabel('fpr')\n",
    "plt.ylabel('tpr')\n",
    "plt.title('ROC CURVE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrutEs5kNOWz"
   },
   "source": [
    "### computing best threshold of probablity which gi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GiPGonTzQvoB"
   },
   "source": [
    "### C. Compute the best threshold (similarly to ROC curve computation) of probability which gives lowest values of metric <b>A</b> for the given data \n",
    "<br>\n",
    "\n",
    "you will be predicting label of a data points like this: $y^{pred}= \\text{[0 if y_score < threshold  else 1]}$\n",
    "\n",
    "$ A = 500 \\times \\text{number of false negative} + 100 \\times \\text{numebr of false positive}$\n",
    "\n",
    "<pre>\n",
    "   <b>Note 1:</b> in this data you can see number of negative points > number of positive points\n",
    "   <b>Note 2:</b> use pandas or numpy to read the data from <b>5_c.csv</b>\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "x5HIJzq1QvoE",
    "outputId": "20293a41-326f-4556-dea4-f74d861061e8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-f420b3d2-6baf-4f83-a2a9-45dce78b8439\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>y</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.458521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.418652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.412057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.375579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "        \n",
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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       "      \n",
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       "\n",
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       "\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-f420b3d2-6baf-4f83-a2a9-45dce78b8439 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-f420b3d2-6baf-4f83-a2a9-45dce78b8439');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   y      prob\n",
       "0  0  0.458521\n",
       "1  0  0.505037\n",
       "2  0  0.418652\n",
       "3  0  0.412057\n",
       "4  0  0.375579"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c=pd.read_csv('/content/drive/MyDrive/aaic/5_c.csv')\n",
    "df_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "eAPjewjzAfCa"
   },
   "outputs": [],
   "source": [
    "df_c['y_proba']=0\n",
    "def ap(df):\n",
    "  if df['prob']>0.5:\n",
    "    return 1\n",
    "  else:\n",
    "    return 0  \n",
    "df_c['y_proba']=df_c.apply(ap,axis=1)\n",
    "def roc(df_a):\n",
    "  uuu=sorted(list(set(df_a['prob'])),reverse=True)\n",
    "  tpr_list=[]\n",
    "  fpr_list=[]\n",
    "  aaa=[]\n",
    "  ttt=[]\n",
    "  #no_of_points=len(df_a)\n",
    "  for i in tqdm(range(len(uuu))):\n",
    "    thre=uuu[i]\n",
    "    def ap(df):\n",
    "      if df['prob']>=thre:\n",
    "        return 1\n",
    "      else:\n",
    "        return 0  \n",
    "    df_a['y_proba']=df_a.apply(ap,axis=1)\n",
    "    cm=confusion_matrix(df_a)\n",
    "    no_of_points=cm[1][1]+cm[0][1]\n",
    "    tpr=cm[1][1]/no_of_points\n",
    "    fpr=cm[1][0]/no_of_points\n",
    "    tpr_list.append(tpr)\n",
    "    fpr_list.append(fpr)\n",
    "    aaa.append(500*cm[0][1]+100*cm[1][0])\n",
    "    ttt.append(uuu[i])\n",
    "  return tpr_list,fpr_list,aaa,ttt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 48,
     "referenced_widgets": [
      "65867cc783374b809cc8ebda5a680034",
      "41a44290a8ec42bebfa9af67b6622799",
      "47650a069c29452a819671c46eba490d",
      "9928c7dc713745d2b2214e367d5432e9",
      "9d4be1e6c10740d698fc5f75c6a6866b",
      "3582a1bec6614d639429ccf7940e6cfb",
      "8f1c65ea72ae41fcafe3902c4690da6f",
      "e917e44e9d9e4875a00f955a819d129b",
      "c5d9eacb41314d009104a639dae754dd",
      "3eefacc64cf74b658827603c1d0de0d3",
      "4c81ffa989f44832a795a7b2488648c9"
     ]
    },
    "id": "aKP2kTlr_Ftw",
    "outputId": "c5410d94-d996-4030-e17a-c16d2c5698ea"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65867cc783374b809cc8ebda5a680034",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2791 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tpr,fpr,aaa,ttt=roc(df_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "_imuUZjSBgM8",
    "outputId": "093b08be-d67d-4a29-daaa-a5beaa1ce026"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141000.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# minimum A value \n",
    "min(aaa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "LuOzQfEAC4td",
    "outputId": "5acab1f0-630b-4312-f715-f7172c8f4377"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2300390278970873"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#threshold value satisfying minimum A value\n",
    "ttt[aaa.index(min(aaa))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing Performance metrics for Regrssion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "sVOj-bF9AfCd",
    "outputId": "5c9cde10-da29-44bc-f331-2a895ebd5162"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    <div class=\"colab-df-container\">\n",
       "      <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>y</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>164.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154.0</td>\n",
       "      <td>152.0</td>\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
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       "        const buttonEl =\n",
       "          document.querySelector('#df-56c0c8bc-b556-4455-ad32-ba50439e7617 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-56c0c8bc-b556-4455-ad32-ba50439e7617');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "        }\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       y   pred\n",
       "0  101.0  100.0\n",
       "1  120.0  100.0\n",
       "2  131.0  113.0\n",
       "3  164.0  125.0\n",
       "4  154.0  152.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_d=pd.read_csv('/content/drive/MyDrive/aaic/5_d.csv')\n",
    "df_d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ycbDApeQEuEh",
    "outputId": "af484eb2-2bdc-420d-ab77-a0b142e55119"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177.16569974554707"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mean squared error\n",
    "((df_d['y']-df_d['pred'])**2).sum()/len(df_d['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "uRhL1pheAfCe",
    "outputId": "c39b6f1c-daf3-4d43-f3d8-80e7fda40abb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12912029940096867"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#modified mean absolute percentage error \n",
    "# modified MAPE to avoid zero division error \n",
    "# taken mean of actual values instead of actual values to avoid zero division error\n",
    "(1/len(df_d['y']))*(abs((df_d['y']-df_d['pred']))/(df_d['y']).mean()).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "SG0WwwbYGA4d",
    "outputId": "4bbf5c65-6702-44d7-deee-7d9d02f4dfd0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9563582786990937"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#r^2 \n",
    "1-((df_d['y']-df_d['pred'])**2).sum()/((df_d['y']-df_d['y'].mean())**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "QH7e_lBZHnYz"
   },
   "outputs": [],
   "source": []
  }
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