chapter10/model_calibration_on_Kaggle_HR_data.ipynb

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    "<a href=\"https://colab.research.google.com/github/packtpublishing/Machine-Learning-for-Imbalanced-Data/blob/main/chapter10/model_calibration_on_Kaggle_HR_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    "### [Brief about Data](https://www.kaggle.com/datasets/jacksonchou/hr-data-for-analytics#Breif-about-Data-:)\n",
    "\n",
    "Data is about Human resources in a specific company each record represents an employee\n",
    "\n",
    "#### [Variables description](https://www.kaggle.com/code/ahmedhas93/hr-data-cleaning-outliers-analysis-eda-lr-rf/notebook#Variables-description-:)\n",
    "\n",
    "Dependent variable\n",
    "\n",
    "-   Left : 0 if employee did not leave , 1 if left company\n",
    "\n",
    "Independent variables\n",
    "\n",
    "-   satisfaction_level : means how much employee satisfied (0 less satisfied , 1 most satisfied)\n",
    "-   last_evaluation : means employees' evaluation for last month (0 bad , 1 Excellent)\n",
    "-   number_project : number of projects the employee worked on\n",
    "-   average_montly_hours : average months employee spends at work per month\n",
    "-   time_spend_company : years the employee spent in a company\n",
    "-   Work_accident : 0 if he did not have an accident , 1 if had at least one\n",
    "-   promotion_last_5years : 0 if he did not have any promotion in last 5 years , 1 if had at least one\n",
    "-   sales : department in which employee works\n"
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  {
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   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5tMgySj_yr1b",
   "metadata": {
    "id": "5tMgySj_yr1b"
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV, calibration_curve\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import brier_score_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "Tv5QLmxaFM-o",
   "metadata": {
    "colab": {
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    },
    "id": "Tv5QLmxaFM-o",
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     "text": [
      "--2023-10-15 01:27:52--  https://raw.githubusercontent.com/PacktPublishing/Machine-Learning-for-Imbalanced-Data/main/chapter10/HR_comma_sep.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 566778 (553K) [text/plain]\n",
      "Saving to: ‘HR_comma_sep.csv’\n",
      "\n",
      "HR_comma_sep.csv    100%[===================>] 553.49K  --.-KB/s    in 0.1s    \n",
      "\n",
      "2023-10-15 01:27:53 (3.72 MB/s) - ‘HR_comma_sep.csv’ saved [566778/566778]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/PacktPublishing/Machine-Learning-for-Imbalanced-Data/main/chapter10/HR_comma_sep.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bNilJV9hyuiO",
   "metadata": {
    "id": "bNilJV9hyuiO"
   },
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6FuRIIe8yv8Q",
   "metadata": {
    "id": "6FuRIIe8yv8Q"
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   "source": [
    "# Load the dataset\n",
    "df = pd.read_csv(\"HR_comma_sep.csv\")"
   ]
  },
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       "      [theme=dark] .colab-df-generate: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",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_d84cf2ae-47a9-4319-a80e-5d0c60e897cb button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('df');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years    sales  salary  \n",
       "0                          0    sales     low  \n",
       "1                          0    sales  medium  \n",
       "2                          0    sales  medium  \n",
       "3                          0    sales     low  \n",
       "4                          0    sales     low  \n",
       "...                      ...      ...     ...  \n",
       "14994                      0  support     low  \n",
       "14995                      0  support     low  \n",
       "14996                      0  support     low  \n",
       "14997                      0  support     low  \n",
       "14998                      0  support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iQVVb3dlzcf8",
   "metadata": {
    "id": "iQVVb3dlzcf8"
   },
   "source": [
    "# Check Data imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "BiYYsa9gzb2r",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 494
    },
    "id": "BiYYsa9gzb2r",
    "outputId": "d88500bf-cb76-4939-95c9-5ae62e925e97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    11428\n",
      "1     3571\n",
      "Name: left, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "print(df[\"left\"].value_counts())\n",
    "df[\"left\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8SFicXIV02AN",
   "metadata": {
    "id": "8SFicXIV02AN"
   },
   "source": [
    "# Convert categorical columns to label encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ZtdOP3wT03Xq",
   "metadata": {
    "id": "ZtdOP3wT03Xq"
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == object:\n",
    "        df[column] = le.fit_transform(df[column])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bqul0tNuFY4P",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "bqul0tNuFY4P",
    "outputId": "cc3753ce-d6fe-41fe-e23f-7bae3f7c30e1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
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      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years  sales  salary  \n",
       "0                          0      7       1  \n",
       "1                          0      7       2  \n",
       "2                          0      7       2  \n",
       "3                          0      7       1  \n",
       "4                          0      7       1  \n",
       "...                      ...    ...     ...  \n",
       "14994                      0      8       1  \n",
       "14995                      0      8       1  \n",
       "14996                      0      8       1  \n",
       "14997                      0      8       1  \n",
       "14998                      0      8       1  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ln9ZGkBmcd1w",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "ln9ZGkBmcd1w",
    "outputId": "c8836177-ad24-4312-a377-340e4606d1cb"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.936495</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.882040</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.393725</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.752814</td>\n",
       "      <td>0.840707</td>\n",
       "      <td>0.971113</td>\n",
       "      <td>1.220423</td>\n",
       "      <td>1.713436</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.393725</td>\n",
       "      <td>1.043004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.022479</td>\n",
       "      <td>0.957554</td>\n",
       "      <td>2.593763</td>\n",
       "      <td>1.420657</td>\n",
       "      <td>0.343655</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.393725</td>\n",
       "      <td>1.043004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431041</td>\n",
       "      <td>0.899131</td>\n",
       "      <td>0.971113</td>\n",
       "      <td>0.439508</td>\n",
       "      <td>1.028546</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.393725</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.841993</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.393725</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>-0.856051</td>\n",
       "      <td>-0.853580</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-1.002181</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.742316</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.379394</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.821970</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.742316</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.087275</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-1.162368</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.742316</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>-2.022479</td>\n",
       "      <td>1.424944</td>\n",
       "      <td>1.782438</td>\n",
       "      <td>1.580845</td>\n",
       "      <td>0.343655</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.742316</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-1.145699</td>\n",
       "      <td>-1.462863</td>\n",
       "      <td>-0.862016</td>\n",
       "      <td>-0.341235</td>\n",
       "      <td>-0.411165</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>0.742316</td>\n",
       "      <td>-0.554955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 9 columns</p>\n",
       "</div>\n",
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       "\n",
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       "            style=\"display:none;\">\n",
       "\n",
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       "      display:flex;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "        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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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
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       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
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       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "        document.querySelector('#df-de3ef521-7508-4843-9452-4f7ca882381d button');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "</div>\n",
       "\n",
       "  <div id=\"id_0e0c4f9d-f154-4a6e-94bc-16f1640571ea\">\n",
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       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "        fill: #174EA6;\n",
       "      }\n",
       "\n",
       "      [theme=dark] .colab-df-generate {\n",
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       "\n",
       "      [theme=dark] .colab-df-generate: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",
       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_scaled')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
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       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_0e0c4f9d-f154-4a6e-94bc-16f1640571ea button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('df_scaled');\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "0     -0.936495 -1.087275 -1.462863 -0.882040 -0.341235 -0.411165 -0.147412   \n",
       "1      0.752814  0.840707  0.971113  1.220423  1.713436 -0.411165 -0.147412   \n",
       "2     -2.022479  0.957554  2.593763  1.420657  0.343655 -0.411165 -0.147412   \n",
       "3      0.431041  0.899131  0.971113  0.439508  1.028546 -0.411165 -0.147412   \n",
       "4     -0.976716 -1.145699 -1.462863 -0.841993 -0.341235 -0.411165 -0.147412   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "14994 -0.856051 -0.853580 -1.462863 -1.002181 -0.341235 -0.411165 -0.147412   \n",
       "14995 -0.976716 -1.379394 -1.462863 -0.821970 -0.341235 -0.411165 -0.147412   \n",
       "14996 -0.976716 -1.087275 -1.462863 -1.162368 -0.341235 -0.411165 -0.147412   \n",
       "14997 -2.022479  1.424944  1.782438  1.580845  0.343655 -0.411165 -0.147412   \n",
       "14998 -0.976716 -1.145699 -1.462863 -0.862016 -0.341235 -0.411165 -0.147412   \n",
       "\n",
       "              7         8  \n",
       "0      0.393725 -0.554955  \n",
       "1      0.393725  1.043004  \n",
       "2      0.393725  1.043004  \n",
       "3      0.393725 -0.554955  \n",
       "4      0.393725 -0.554955  \n",
       "...         ...       ...  \n",
       "14994  0.742316 -0.554955  \n",
       "14995  0.742316 -0.554955  \n",
       "14996  0.742316 -0.554955  \n",
       "14997  0.742316 -0.554955  \n",
       "14998  0.742316 -0.554955  \n",
       "\n",
       "[14999 rows x 9 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_scaler = StandardScaler()\n",
    "\n",
    "df_scaled = std_scaler.fit_transform(df.drop(\"left\", axis=1).to_numpy())\n",
    "df_scaled = pd.DataFrame(df_scaled)\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "Ss_VuUnQ079g",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ss_VuUnQ079g",
    "outputId": "067f63cc-11d2-416e-b2e3-0d09b4b9741d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11999\n",
      "1500\n",
      "1500\n"
     ]
    }
   ],
   "source": [
    "X = df_scaled\n",
    "y = df[\"left\"]\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "X_val, X_test, y_val, y_test = train_test_split(\n",
    "    X_temp, y_temp, test_size=0.5, random_state=42\n",
    ")\n",
    "print(len(X_train))\n",
    "print(len(X_val))\n",
    "print(len(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "AsxcAgY_06fE",
   "metadata": {
    "id": "AsxcAgY_06fE"
   },
   "source": [
    "\n",
    "\n",
    "# Split the data into training, testing, and validation sets\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "y_8A4J-v0_kK",
   "metadata": {
    "id": "y_8A4J-v0_kK"
   },
   "source": [
    "# Train a random forest model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4B2lQNbK1Bek",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4B2lQNbK1Bek",
    "outputId": "560de171-31a0-42b3-f1c0-6cdac0a37f8c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9446666666666667"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=49, max_depth=4)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_9-d8Nnm3nqs",
   "metadata": {
    "id": "_9-d8Nnm3nqs"
   },
   "source": [
    "# Plot calibration curve for raw model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bcTGas4n3tAP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bcTGas4n3tAP",
    "outputId": "5361a481-eb46-4952-d0d3-8a58731f992c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brier Score for Uncalibrated Model: 0.0447\n"
     ]
    }
   ],
   "source": [
    "# Get the model probabilities on the test set (uncalibrated model)\n",
    "probs_uncalibrated = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate Brier score for the uncalibrated model\n",
    "brier_uncalibrated = brier_score_loss(y_test, probs_uncalibrated)\n",
    "print(f\"Brier Score for Uncalibrated Model: {round(brier_uncalibrated, 4)}\")\n",
    "\n",
    "# Compute the calibration curve for the uncalibrated model\n",
    "(\n",
    "    fraction_of_positives_uncalibrated,\n",
    "    mean_predicted_value_uncalibrated,\n",
    ") = calibration_curve(y_test, probs_uncalibrated, n_bins=5, strategy=\"uniform\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39BRJ11CEJL9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "39BRJ11CEJL9",
    "outputId": "fd25565d-e283-460d-e92a-ecddbc629894"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "plt.plot(\n",
    "    mean_predicted_value_uncalibrated,\n",
    "    fraction_of_positives_uncalibrated,\n",
    "    label=\"Uncalibrated\",\n",
    ")\n",
    "plt.xlabel(\"Mean predicted value\")\n",
    "plt.ylabel(\"Fraction of positives\")\n",
    "plt.title(\"Calibration curve (Test set)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roE3jrk23yKJ",
   "metadata": {
    "id": "roE3jrk23yKJ"
   },
   "source": [
    "# Calibration using Platt's scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1tO7W5cK33R2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1tO7W5cK33R2",
    "outputId": "c20ef895-0942-4af4-8718-a139ceee9942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brier Score for Platt's Scaling Calibrated Model: 0.032\n"
     ]
    }
   ],
   "source": [
    "# Calibrate the model on the validation data using Platt's scaling\n",
    "platt_scaling = CalibratedClassifierCV(model, method=\"sigmoid\", cv=\"prefit\")\n",
    "platt_scaling.fit(X_val, y_val)\n",
    "\n",
    "# Get the model probabilities on the test set (calibrated model - Platt's scaling)\n",
    "probs_ps = platt_scaling.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Calculate Brier score for the Platt's scaling calibrated model\n",
    "brier_platt = brier_score_loss(y_test, probs_ps, pos_label=1)\n",
    "print(f\"Brier Score for Platt's Scaling Calibrated Model: {round(brier_platt, 4)}\")\n",
    "\n",
    "# Compute the calibration curve for Platt's scaling\n",
    "fraction_of_positives_ps, mean_predicted_value_ps = calibration_curve(\n",
    "    y_test, probs_ps, n_bins=5, strategy=\"uniform\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hfJKjTta38S1",
   "metadata": {
    "id": "hfJKjTta38S1"
   },
   "source": [
    "# Calibration using Platt's scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8Izu3qj388G",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e8Izu3qj388G",
    "outputId": "779115c9-6ab0-4f97-95bb-faa292cd2bf4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brier Score for Isotonic Regression Calibrated Model: 0.0317\n"
     ]
    }
   ],
   "source": [
    "# Calibrate the model on the validation data using isotonic regression\n",
    "isotonic_regression = CalibratedClassifierCV(model, method=\"isotonic\", cv=\"prefit\")\n",
    "isotonic_regression.fit(X_val, y_val)\n",
    "\n",
    "# Get the model probabilities on the test set (calibrated model - Isotonic regression)\n",
    "probs_ir = isotonic_regression.predict_proba(X_test)[:, 1]\n",
    "# print(probs_ir)\n",
    "\n",
    "# Calculate Brier score for the isotonic regression calibrated model\n",
    "brier_isotonic = brier_score_loss(y_test, probs_ir)\n",
    "print(\n",
    "    f\"Brier Score for Isotonic Regression Calibrated Model: {round(brier_isotonic, 4)}\"\n",
    ")\n",
    "# print(X_test)\n",
    "\n",
    "# Compute the calibration curve for isotonic regression\n",
    "fraction_of_positives_ir, mean_predicted_value_ir = calibration_curve(\n",
    "    y_test, probs_ir, n_bins=5, strategy=\"uniform\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Z0LZ8qMH4DMz",
   "metadata": {
    "id": "Z0LZ8qMH4DMz"
   },
   "source": [
    "# Create a calibration plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "VjDCd3odtsXx",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "VjDCd3odtsXx",
    "outputId": "d2c1a494-7f22-4cf8-dd46-0c74001cff1d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "plt.plot(\n",
    "    mean_predicted_value_uncalibrated,\n",
    "    fraction_of_positives_uncalibrated,\n",
    "    label=\"Uncalibrated %s\" % round(brier_uncalibrated, 4),\n",
    ")\n",
    "plt.plot(\n",
    "    mean_predicted_value_ps,\n",
    "    fraction_of_positives_ps,\n",
    "    label=\"Platt's scaling %s\" % round(brier_platt, 4),\n",
    "    linestyle=\"-.\",\n",
    ")\n",
    "plt.plot(\n",
    "    mean_predicted_value_ir,\n",
    "    fraction_of_positives_ir,\n",
    "    label=\"Isotonic regression %s\" % round(brier_isotonic, 4),\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "plt.xlabel(\"Mean predicted value\")\n",
    "plt.ylabel(\"Fraction of positives\")\n",
    "plt.title(\"Calibration curves (Test set)\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9m7yTRg8-Jx",
   "metadata": {
    "id": "b9m7yTRg8-Jx"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "provenance": []
  },
  "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": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 49.575869,
   "end_time": "2022-07-16T09:31:04.392023",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-07-16T09:30:14.816154",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}